{"id":18108,"artifact_id":17152,"version":1,"data":{"version":1,"artifact":{"chain":"tezos","title":"Nodevember 2021-08","artist":"tz1djshvCnhi5p5wAebiBb2XQLcvVKBqyH7F","tokenId":"527817","description":"An interactive 3D artwork, created by @neoyume - neoyume.com","contractAddress":"KT1RJ6PbjHpwc3M5rw5s2Nbmefwbuwbdxton"},"snapshot":{"net":[{"data":{"url":"https://ipfs.arkivo.art/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8?creator=tz1djshvCnhi5p5wAebiBb2XQLcvVKBqyH7F&viewer=&objkt=527817","host":"ipfs.arkivo.art","path":"/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8","type":"http","query":"?creator=tz1djshvCnhi5p5wAebiBb2XQLcvVKBqyH7F&viewer=&objkt=527817","method":"GET","headers":{"sec-ch-ua":"\"HeadlessChrome\";v=\"119\", \"Chromium\";v=\"119\", \"Not?A_Brand\";v=\"24\"","user-agent":"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) HeadlessChrome/119.0.6045.9 Safari/537.36","sec-ch-ua-mobile":"?0","sec-ch-ua-platform":"\"Linux\"","upgrade-insecure-requests":"1"},"fragment":"","postData":null,"protocol":"https:"},"type":1,"external":false,"timestamp":1723910781710},{"data":{"url":"https://ipfs.arkivo.art/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8?creator=tz1djshvCnhi5p5wAebiBb2XQLcvVKBqyH7F&viewer=&objkt=527817","body":"","status":301,"headers":{"date":"Sat, 17 Aug 2024 16:06:21 GMT","server":"nginx/1.27.0","location":"/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8/?creator=tz1djshvCnhi5p5wAebiBb2XQLcvVKBqyH7F&viewer=&objkt=527817","connection":"keep-alive","x-ipfs-path":"/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8","content-type":"text/html; charset=utf-8","x-ipfs-roots":"QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8","content-length":"162","access-control-allow-origin":"*","access-control-allow-headers":"Content-Type\nRange\nUser-Agent\nX-Requested-With","access-control-allow-methods":"GET\nHEAD\nOPTIONS","access-control-expose-headers":"Content-Length\nContent-Range\nX-Chunked-Output\nX-Ipfs-Path\nX-Ipfs-Roots\nX-Stream-Output"}},"type":2,"external":false,"timestamp":1723910781757},{"data":{"url":"https://ipfs.arkivo.art/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8/?creator=tz1djshvCnhi5p5wAebiBb2XQLcvVKBqyH7F&viewer=&objkt=527817","host":"ipfs.arkivo.art","path":"/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8/","type":"http","query":"?creator=tz1djshvCnhi5p5wAebiBb2XQLcvVKBqyH7F&viewer=&objkt=527817","method":"GET","headers":{"sec-ch-ua":"\"HeadlessChrome\";v=\"119\", \"Chromium\";v=\"119\", \"Not?A_Brand\";v=\"24\"","user-agent":"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) HeadlessChrome/119.0.6045.9 Safari/537.36","sec-ch-ua-mobile":"?0","sec-ch-ua-platform":"\"Linux\"","upgrade-insecure-requests":"1"},"fragment":"","postData":null,"protocol":"https:"},"type":1,"external":false,"timestamp":1723910781757},{"data":{"url":"https://ipfs.arkivo.art/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8/?creator=tz1djshvCnhi5p5wAebiBb2XQLcvVKBqyH7F&viewer=&objkt=527817","body":"","status":200,"headers":{"date":"Sat, 17 Aug 2024 16:06:21 GMT","etag":"\"QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8\"","server":"nginx/1.27.0","connection":"keep-alive","x-ipfs-path":"/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8/","content-type":"text/html","x-ipfs-roots":"QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8","accept-ranges":"bytes","cache-control":"public, max-age=29030400, immutable","content-length":"2675","access-control-allow-origin":"*","access-control-allow-headers":"Content-Type\nRange\nUser-Agent\nX-Requested-With","access-control-allow-methods":"GET\nHEAD\nOPTIONS","access-control-expose-headers":"Content-Length\nContent-Range\nX-Chunked-Output\nX-Ipfs-Path\nX-Ipfs-Roots\nX-Stream-Output"}},"type":2,"external":false,"timestamp":1723910781767},{"data":{"url":"https://ipfs.arkivo.art/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8/style.css","host":"ipfs.arkivo.art","path":"/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8/style.css","type":"http","query":"","method":"GET","headers":{"referer":"https://ipfs.arkivo.art/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8/?creator=tz1djshvCnhi5p5wAebiBb2XQLcvVKBqyH7F&viewer=&objkt=527817","sec-ch-ua":"\"HeadlessChrome\";v=\"119\", \"Chromium\";v=\"119\", \"Not?A_Brand\";v=\"24\"","user-agent":"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) HeadlessChrome/119.0.6045.9 Safari/537.36","sec-ch-ua-mobile":"?0","sec-ch-ua-platform":"\"Linux\""},"fragment":"","postData":null,"protocol":"https:"},"type":1,"external":false,"timestamp":1723910781787},{"data":{"url":"https://ipfs.arkivo.art/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8/viewer.js","host":"ipfs.arkivo.art","path":"/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8/viewer.js","type":"http","query":"","method":"GET","headers":{"referer":"https://ipfs.arkivo.art/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8/?creator=tz1djshvCnhi5p5wAebiBb2XQLcvVKBqyH7F&viewer=&objkt=527817","sec-ch-ua":"\"HeadlessChrome\";v=\"119\", \"Chromium\";v=\"119\", \"Not?A_Brand\";v=\"24\"","user-agent":"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) HeadlessChrome/119.0.6045.9 Safari/537.36","sec-ch-ua-mobile":"?0","sec-ch-ua-platform":"\"Linux\""},"fragment":"","postData":null,"protocol":"https:"},"type":1,"external":false,"timestamp":1723910781787},{"data":{"url":"https://ipfs.arkivo.art/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8/viewer.js","body":"","status":200,"headers":{"date":"Sat, 17 Aug 2024 16:06:21 GMT","etag":"\"QmQSx7jXH1MrpU7mJeihUwX5P6M4ri8KK7zmFDozLo9c55\"","server":"nginx/1.27.0","connection":"keep-alive","x-ipfs-path":"/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8/viewer.js","content-type":"text/javascript; charset=utf-8","x-ipfs-roots":"QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8,QmQSx7jXH1MrpU7mJeihUwX5P6M4ri8KK7zmFDozLo9c55","accept-ranges":"bytes","cache-control":"public, max-age=29030400, immutable","content-length":"11766","access-control-allow-origin":"*","access-control-allow-headers":"Content-Type, Range, User-Agent, X-Requested-With","access-control-allow-methods":"GET, HEAD, OPTIONS","access-control-expose-headers":"Content-Length, Content-Range, X-Chunked-Output, X-Ipfs-Path, X-Ipfs-Roots, X-Stream-Output"}},"type":2,"external":false,"timestamp":1723910781803},{"data":{"url":"https://ipfs.arkivo.art/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8/style.css","body":"","status":200,"headers":{"date":"Sat, 17 Aug 2024 16:06:21 GMT","etag":"\"QmPbsQLLXNhFAwKHqFy7nJfyFfmhwqU4gypXMEAr9LWc7Y\"","server":"nginx/1.27.0","connection":"keep-alive","x-ipfs-path":"/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8/style.css","content-type":"text/css; charset=utf-8","x-ipfs-roots":"QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8,QmPbsQLLXNhFAwKHqFy7nJfyFfmhwqU4gypXMEAr9LWc7Y","accept-ranges":"bytes","cache-control":"public, max-age=29030400, immutable","content-length":"243","access-control-allow-origin":"*","access-control-allow-headers":"Content-Type, Range, User-Agent, X-Requested-With","access-control-allow-methods":"GET, HEAD, OPTIONS","access-control-expose-headers":"Content-Length, Content-Range, X-Chunked-Output, X-Ipfs-Path, X-Ipfs-Roots, X-Stream-Output"}},"type":2,"external":false,"timestamp":1723910781804},{"data":{"url":"https://ipfs.arkivo.art/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8/nodevember08.polygonjs","host":"ipfs.arkivo.art","path":"/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8/nodevember08.polygonjs","type":"http","query":"","method":"GET","headers":{"referer":"https://ipfs.arkivo.art/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8/?creator=tz1djshvCnhi5p5wAebiBb2XQLcvVKBqyH7F&viewer=&objkt=527817","sec-ch-ua":"\"HeadlessChrome\";v=\"119\", \"Chromium\";v=\"119\", \"Not?A_Brand\";v=\"24\"","user-agent":"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) HeadlessChrome/119.0.6045.9 Safari/537.36","sec-ch-ua-mobile":"?0","sec-ch-ua-platform":"\"Linux\""},"fragment":"","postData":null,"protocol":"https:"},"type":1,"external":false,"timestamp":1723910781824},{"data":{"url":"https://ipfs.arkivo.art/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8/poster.jpg","host":"ipfs.arkivo.art","path":"/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8/poster.jpg","type":"http","query":"","method":"GET","headers":{"referer":"https://ipfs.arkivo.art/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8/?creator=tz1djshvCnhi5p5wAebiBb2XQLcvVKBqyH7F&viewer=&objkt=527817","sec-ch-ua":"\"HeadlessChrome\";v=\"119\", \"Chromium\";v=\"119\", \"Not?A_Brand\";v=\"24\"","user-agent":"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) HeadlessChrome/119.0.6045.9 Safari/537.36","sec-ch-ua-mobile":"?0","sec-ch-ua-platform":"\"Linux\""},"fragment":"","postData":null,"protocol":"https:"},"type":1,"external":false,"timestamp":1723910781825},{"data":{"url":"https://ipfs.arkivo.art/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8/poster.jpg","body":"","status":200,"headers":{"date":"Sat, 17 Aug 2024 16:06:21 GMT","etag":"\"QmX7BrgyEbrw3vf2kQzvtgUfUHN6iTrcUBu7w3LM4EHDNj\"","server":"nginx/1.27.0","connection":"keep-alive","x-ipfs-path":"/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8/poster.jpg","content-type":"image/jpeg","x-ipfs-roots":"QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8,QmX7BrgyEbrw3vf2kQzvtgUfUHN6iTrcUBu7w3LM4EHDNj","accept-ranges":"bytes","cache-control":"public, max-age=29030400, immutable","content-length":"29277","access-control-allow-origin":"*","access-control-allow-headers":"Content-Type, Range, User-Agent, X-Requested-With","access-control-allow-methods":"GET, HEAD, OPTIONS","access-control-expose-headers":"Content-Length, Content-Range, X-Chunked-Output, X-Ipfs-Path, X-Ipfs-Roots, X-Stream-Output"}},"type":2,"external":false,"timestamp":1723910781839},{"data":{"url":"https://ipfs.arkivo.art/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8/nodevember08.polygonjs","body":"","status":200,"headers":{"date":"Sat, 17 Aug 2024 16:06:21 GMT","etag":"\"QmNzsSNko5cJnzeeh2dFPwYuM7PVsvisbZS5HN9tJm1Eik\"","server":"nginx/1.27.0","connection":"keep-alive","x-ipfs-path":"/ipfs/QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8/nodevember08.polygonjs","content-type":"application/zip","x-ipfs-roots":"QmQUyTywHfYdhMJatfJChKbc6T6n6XxZ1MCJaHVW7suHW8,QmNzsSNko5cJnzeeh2dFPwYuM7PVsvisbZS5HN9tJm1Eik","accept-ranges":"bytes","cache-control":"public, max-age=29030400, immutable","content-length":"769224","access-control-allow-origin":"*","access-control-allow-headers":"Content-Type, Range, User-Agent, X-Requested-With","access-control-allow-methods":"GET, HEAD, OPTIONS","access-control-expose-headers":"Content-Length, Content-Range, X-Chunked-Output, X-Ipfs-Path, X-Ipfs-Roots, X-Stream-Output"}},"type":2,"external":false,"timestamp":1723910781854},{"data":{"url":"blob:https://ipfs.arkivo.art/463aa7c0-2dfa-4074-b06c-5f72e85eb1f0","host":"","path":"https://ipfs.arkivo.art/463aa7c0-2dfa-4074-b06c-5f72e85eb1f0","type":"http","query":"","method":"GET","headers":{"origin":"https://ipfs.arkivo.art","referer":"","user-agent":"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) HeadlessChrome/119.0.6045.9 Safari/537.36"},"fragment":"","postData":null,"protocol":"blob:"},"type":1,"external":false,"timestamp":1723910782015},{"data":{"url":"blob:https://ipfs.arkivo.art/463aa7c0-2dfa-4074-b06c-5f72e85eb1f0","body":"\"/*! For license information please see all.js.LICENSE.txt */\\nvar POLY=function(t){var e={};function n(i){if(e[i])return e[i].exports;var r=e[i]={i:i,l:!1,exports:{}};return t[i].call(r.exports,r,r.exports,n),r.l=!0,r.exports}return n.m=t,n.c=e,n.d=function(t,e,i){n.o(t,e)||Object.defineProperty(t,e,{enumerable:!0,get:i})},n.r=function(t){\\\\\\\"undefined\\\\\\\"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(t,Symbol.toStringTag,{value:\\\\\\\"Module\\\\\\\"}),Object.defineProperty(t,\\\\\\\"__esModule\\\\\\\",{value:!0})},n.t=function(t,e){if(1&e&&(t=n(t)),8&e)return t;if(4&e&&\\\\\\\"object\\\\\\\"==typeof t&&t&&t.__esModule)return t;var i=Object.create(null);if(n.r(i),Object.defineProperty(i,\\\\\\\"default\\\\\\\",{enumerable:!0,value:t}),2&e&&\\\\\\\"string\\\\\\\"!=typeof t)for(var r in t)n.d(i,r,function(e){return t[e]}.bind(null,r));return i},n.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return n.d(e,\\\\\\\"a\\\\\\\",e),e},n.o=function(t,e){return Object.prototype.hasOwnProperty.call(t,e)},n.p=\\\\\\\"https://unpkg.com/@polygonjs/polygonjs@1.1.189/dist/\\\\\\\",n(n.s=217)}([function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return s}));var i=n(3),r=n(8);class s{constructor(t=0,e=0,n=0){this.x=t,this.y=e,this.z=n}set(t,e,n){return void 0===n&&(n=this.z),this.x=t,this.y=e,this.z=n,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y,this.z)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this}sub(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .multiply() now only accepts one argument. Use .multiplyVectors( a, b ) instead.\\\\\\\"),this.multiplyVectors(t,e)):(this.x*=t.x,this.y*=t.y,this.z*=t.z,this)}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this}multiplyVectors(t,e){return this.x=t.x*e.x,this.y=t.y*e.y,this.z=t.z*e.z,this}applyEuler(t){return t&&t.isEuler||console.error(\\\\\\\"THREE.Vector3: .applyEuler() now expects an Euler rotation rather than a Vector3 and order.\\\\\\\"),this.applyQuaternion(a.setFromEuler(t))}applyAxisAngle(t,e){return this.applyQuaternion(a.setFromAxisAngle(t,e))}applyMatrix3(t){const e=this.x,n=this.y,i=this.z,r=t.elements;return this.x=r[0]*e+r[3]*n+r[6]*i,this.y=r[1]*e+r[4]*n+r[7]*i,this.z=r[2]*e+r[5]*n+r[8]*i,this}applyNormalMatrix(t){return this.applyMatrix3(t).normalize()}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,r=t.elements,s=1/(r[3]*e+r[7]*n+r[11]*i+r[15]);return this.x=(r[0]*e+r[4]*n+r[8]*i+r[12])*s,this.y=(r[1]*e+r[5]*n+r[9]*i+r[13])*s,this.z=(r[2]*e+r[6]*n+r[10]*i+r[14])*s,this}applyQuaternion(t){const e=this.x,n=this.y,i=this.z,r=t.x,s=t.y,o=t.z,a=t.w,l=a*e+s*i-o*n,c=a*n+o*e-r*i,u=a*i+r*n-s*e,h=-r*e-s*n-o*i;return this.x=l*a+h*-r+c*-o-u*-s,this.y=c*a+h*-s+u*-r-l*-o,this.z=u*a+h*-o+l*-s-c*-r,this}project(t){return this.applyMatrix4(t.matrixWorldInverse).applyMatrix4(t.projectionMatrix)}unproject(t){return this.applyMatrix4(t.projectionMatrixInverse).applyMatrix4(t.matrixWorld)}transformDirection(t){const e=this.x,n=this.y,i=this.z,r=t.elements;return this.x=r[0]*e+r[4]*n+r[8]*i,this.y=r[1]*e+r[5]*n+r[9]*i,this.z=r[2]*e+r[6]*n+r[10]*i,this.normalize()}divide(t){return this.x/=t.x,this.y/=t.y,this.z/=t.z,this}divideScalar(t){return this.multiplyScalar(1/t)}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this.z=Math.min(this.z,t.z),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this.z=Math.max(this.z,t.z),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this.z=Math.max(t.z,Math.min(e.z,this.z)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this.z=Math.max(t,Math.min(e,this.z)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this.z=Math.floor(this.z),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this.z=this.z<0?Math.ceil(this.z):Math.floor(this.z),this}negate(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z}lengthSq(){return this.x*this.x+this.y*this.y+this.z*this.z}length(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)}normalize(){return this.divideScalar(this.length()||1)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this.z+=(t.z-this.z)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this.z=t.z+(e.z-t.z)*n,this}cross(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .cross() now only accepts one argument. Use .crossVectors( a, b ) instead.\\\\\\\"),this.crossVectors(t,e)):this.crossVectors(this,t)}crossVectors(t,e){const n=t.x,i=t.y,r=t.z,s=e.x,o=e.y,a=e.z;return this.x=i*a-r*o,this.y=r*s-n*a,this.z=n*o-i*s,this}projectOnVector(t){const e=t.lengthSq();if(0===e)return this.set(0,0,0);const n=t.dot(this)/e;return this.copy(t).multiplyScalar(n)}projectOnPlane(t){return o.copy(this).projectOnVector(t),this.sub(o)}reflect(t){return this.sub(o.copy(t).multiplyScalar(2*this.dot(t)))}angleTo(t){const e=Math.sqrt(this.lengthSq()*t.lengthSq());if(0===e)return Math.PI/2;const n=this.dot(t)/e;return Math.acos(i.d(n,-1,1))}distanceTo(t){return Math.sqrt(this.distanceToSquared(t))}distanceToSquared(t){const e=this.x-t.x,n=this.y-t.y,i=this.z-t.z;return e*e+n*n+i*i}manhattanDistanceTo(t){return Math.abs(this.x-t.x)+Math.abs(this.y-t.y)+Math.abs(this.z-t.z)}setFromSpherical(t){return this.setFromSphericalCoords(t.radius,t.phi,t.theta)}setFromSphericalCoords(t,e,n){const i=Math.sin(e)*t;return this.x=i*Math.sin(n),this.y=Math.cos(e)*t,this.z=i*Math.cos(n),this}setFromCylindrical(t){return this.setFromCylindricalCoords(t.radius,t.theta,t.y)}setFromCylindricalCoords(t,e,n){return this.x=t*Math.sin(e),this.y=n,this.z=t*Math.cos(e),this}setFromMatrixPosition(t){const e=t.elements;return this.x=e[12],this.y=e[13],this.z=e[14],this}setFromMatrixScale(t){const e=this.setFromMatrixColumn(t,0).length(),n=this.setFromMatrixColumn(t,1).length(),i=this.setFromMatrixColumn(t,2).length();return this.x=e,this.y=n,this.z=i,this}setFromMatrixColumn(t,e){return this.fromArray(t.elements,4*e)}setFromMatrix3Column(t,e){return this.fromArray(t.elements,3*e)}equals(t){return t.x===this.x&&t.y===this.y&&t.z===this.z}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this.z=t[e+2],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t[e+2]=this.z,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn(\\\\\\\"THREE.Vector3: offset has been removed from .fromBufferAttribute().\\\\\\\"),this.x=t.getX(e),this.y=t.getY(e),this.z=t.getZ(e),this}random(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this}randomDirection(){const t=2*(Math.random()-.5),e=Math.random()*Math.PI*2,n=Math.sqrt(1-t**2);return this.x=n*Math.cos(e),this.y=n*Math.sin(e),this.z=t,this}*[Symbol.iterator](){yield this.x,yield this.y,yield this.z}}s.prototype.isVector3=!0;const o=new s,a=new r.a},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"hb\\\\\\\",(function(){return i})),n.d(e,\\\\\\\"Tc\\\\\\\",(function(){return r})),n.d(e,\\\\\\\"u\\\\\\\",(function(){return s})),n.d(e,\\\\\\\"s\\\\\\\",(function(){return o})),n.d(e,\\\\\\\"t\\\\\\\",(function(){return a})),n.d(e,\\\\\\\"k\\\\\\\",(function(){return l})),n.d(e,\\\\\\\"Fb\\\\\\\",(function(){return c})),n.d(e,\\\\\\\"Gb\\\\\\\",(function(){return u})),n.d(e,\\\\\\\"gd\\\\\\\",(function(){return h})),n.d(e,\\\\\\\"H\\\\\\\",(function(){return d})),n.d(e,\\\\\\\"i\\\\\\\",(function(){return p})),n.d(e,\\\\\\\"z\\\\\\\",(function(){return _})),n.d(e,\\\\\\\"F\\\\\\\",(function(){return m})),n.d(e,\\\\\\\"ub\\\\\\\",(function(){return f})),n.d(e,\\\\\\\"xb\\\\\\\",(function(){return g})),n.d(e,\\\\\\\"e\\\\\\\",(function(){return v})),n.d(e,\\\\\\\"Sc\\\\\\\",(function(){return y})),n.d(e,\\\\\\\"mb\\\\\\\",(function(){return x})),n.d(e,\\\\\\\"v\\\\\\\",(function(){return b})),n.d(e,\\\\\\\"b\\\\\\\",(function(){return w})),n.d(e,\\\\\\\"Rc\\\\\\\",(function(){return T})),n.d(e,\\\\\\\"xc\\\\\\\",(function(){return A})),n.d(e,\\\\\\\"jb\\\\\\\",(function(){return E})),n.d(e,\\\\\\\"ib\\\\\\\",(function(){return M})),n.d(e,\\\\\\\"jd\\\\\\\",(function(){return S})),n.d(e,\\\\\\\"Ab\\\\\\\",(function(){return C})),n.d(e,\\\\\\\"Pc\\\\\\\",(function(){return N})),n.d(e,\\\\\\\"Eb\\\\\\\",(function(){return L})),n.d(e,\\\\\\\"Nc\\\\\\\",(function(){return O})),n.d(e,\\\\\\\"Db\\\\\\\",(function(){return R})),n.d(e,\\\\\\\"A\\\\\\\",(function(){return P})),n.d(e,\\\\\\\"Bb\\\\\\\",(function(){return I})),n.d(e,\\\\\\\"B\\\\\\\",(function(){return F})),n.d(e,\\\\\\\"Cb\\\\\\\",(function(){return D})),n.d(e,\\\\\\\"Oc\\\\\\\",(function(){return k})),n.d(e,\\\\\\\"tb\\\\\\\",(function(){return B})),n.d(e,\\\\\\\"g\\\\\\\",(function(){return z})),n.d(e,\\\\\\\"S\\\\\\\",(function(){return U})),n.d(e,\\\\\\\"T\\\\\\\",(function(){return G})),n.d(e,\\\\\\\"C\\\\\\\",(function(){return V})),n.d(e,\\\\\\\"L\\\\\\\",(function(){return H})),n.d(e,\\\\\\\"K\\\\\\\",(function(){return j})),n.d(e,\\\\\\\"yb\\\\\\\",(function(){return W})),n.d(e,\\\\\\\"nb\\\\\\\",(function(){return q})),n.d(e,\\\\\\\"lb\\\\\\\",(function(){return X})),n.d(e,\\\\\\\"c\\\\\\\",(function(){return Y})),n.d(e,\\\\\\\"vb\\\\\\\",(function(){return $})),n.d(e,\\\\\\\"ab\\\\\\\",(function(){return J})),n.d(e,\\\\\\\"vc\\\\\\\",(function(){return Z})),n.d(e,\\\\\\\"m\\\\\\\",(function(){return Q})),n.d(e,\\\\\\\"a\\\\\\\",(function(){return K})),n.d(e,\\\\\\\"w\\\\\\\",(function(){return tt})),n.d(e,\\\\\\\"Yc\\\\\\\",(function(){return et})),n.d(e,\\\\\\\"o\\\\\\\",(function(){return nt})),n.d(e,\\\\\\\"p\\\\\\\",(function(){return it})),n.d(e,\\\\\\\"D\\\\\\\",(function(){return rt})),n.d(e,\\\\\\\"E\\\\\\\",(function(){return st})),n.d(e,\\\\\\\"q\\\\\\\",(function(){return ot})),n.d(e,\\\\\\\"r\\\\\\\",(function(){return at})),n.d(e,\\\\\\\"wc\\\\\\\",(function(){return lt})),n.d(e,\\\\\\\"n\\\\\\\",(function(){return ct})),n.d(e,\\\\\\\"kb\\\\\\\",(function(){return ut})),n.d(e,\\\\\\\"ob\\\\\\\",(function(){return ht})),n.d(e,\\\\\\\"sb\\\\\\\",(function(){return dt})),n.d(e,\\\\\\\"qb\\\\\\\",(function(){return pt})),n.d(e,\\\\\\\"rb\\\\\\\",(function(){return _t})),n.d(e,\\\\\\\"pb\\\\\\\",(function(){return mt})),n.d(e,\\\\\\\"V\\\\\\\",(function(){return ft})),n.d(e,\\\\\\\"Z\\\\\\\",(function(){return gt})),n.d(e,\\\\\\\"X\\\\\\\",(function(){return vt})),n.d(e,\\\\\\\"Y\\\\\\\",(function(){return yt})),n.d(e,\\\\\\\"W\\\\\\\",(function(){return xt})),n.d(e,\\\\\\\"Zc\\\\\\\",(function(){return bt})),n.d(e,\\\\\\\"l\\\\\\\",(function(){return wt})),n.d(e,\\\\\\\"Mc\\\\\\\",(function(){return Tt})),n.d(e,\\\\\\\"fd\\\\\\\",(function(){return At})),n.d(e,\\\\\\\"N\\\\\\\",(function(){return Et})),n.d(e,\\\\\\\"bd\\\\\\\",(function(){return Mt})),n.d(e,\\\\\\\"G\\\\\\\",(function(){return St})),n.d(e,\\\\\\\"M\\\\\\\",(function(){return Ct})),n.d(e,\\\\\\\"cd\\\\\\\",(function(){return Nt})),n.d(e,\\\\\\\"dd\\\\\\\",(function(){return Lt})),n.d(e,\\\\\\\"ed\\\\\\\",(function(){return Ot})),n.d(e,\\\\\\\"ad\\\\\\\",(function(){return Rt})),n.d(e,\\\\\\\"f\\\\\\\",(function(){return Pt})),n.d(e,\\\\\\\"ic\\\\\\\",(function(){return It})),n.d(e,\\\\\\\"Ib\\\\\\\",(function(){return Ft})),n.d(e,\\\\\\\"gb\\\\\\\",(function(){return Dt})),n.d(e,\\\\\\\"fb\\\\\\\",(function(){return kt})),n.d(e,\\\\\\\"hc\\\\\\\",(function(){return Bt})),n.d(e,\\\\\\\"x\\\\\\\",(function(){return zt})),n.d(e,\\\\\\\"y\\\\\\\",(function(){return Ut})),n.d(e,\\\\\\\"tc\\\\\\\",(function(){return Gt})),n.d(e,\\\\\\\"uc\\\\\\\",(function(){return Vt})),n.d(e,\\\\\\\"rc\\\\\\\",(function(){return Ht})),n.d(e,\\\\\\\"sc\\\\\\\",(function(){return jt})),n.d(e,\\\\\\\"jc\\\\\\\",(function(){return Wt})),n.d(e,\\\\\\\"Jb\\\\\\\",(function(){return qt})),n.d(e,\\\\\\\"qc\\\\\\\",(function(){return Xt})),n.d(e,\\\\\\\"cc\\\\\\\",(function(){return Yt})),n.d(e,\\\\\\\"dc\\\\\\\",(function(){return $t})),n.d(e,\\\\\\\"ec\\\\\\\",(function(){return Jt})),n.d(e,\\\\\\\"pc\\\\\\\",(function(){return Zt})),n.d(e,\\\\\\\"oc\\\\\\\",(function(){return Qt})),n.d(e,\\\\\\\"bc\\\\\\\",(function(){return Kt})),n.d(e,\\\\\\\"ac\\\\\\\",(function(){return te})),n.d(e,\\\\\\\"mc\\\\\\\",(function(){return ee})),n.d(e,\\\\\\\"nc\\\\\\\",(function(){return ne})),n.d(e,\\\\\\\"Zb\\\\\\\",(function(){return ie})),n.d(e,\\\\\\\"Qb\\\\\\\",(function(){return re})),n.d(e,\\\\\\\"Rb\\\\\\\",(function(){return se})),n.d(e,\\\\\\\"Sb\\\\\\\",(function(){return oe})),n.d(e,\\\\\\\"Tb\\\\\\\",(function(){return ae})),n.d(e,\\\\\\\"Ub\\\\\\\",(function(){return le})),n.d(e,\\\\\\\"Vb\\\\\\\",(function(){return ce})),n.d(e,\\\\\\\"Wb\\\\\\\",(function(){return ue})),n.d(e,\\\\\\\"Xb\\\\\\\",(function(){return he})),n.d(e,\\\\\\\"Lb\\\\\\\",(function(){return de})),n.d(e,\\\\\\\"Mb\\\\\\\",(function(){return pe})),n.d(e,\\\\\\\"Nb\\\\\\\",(function(){return _e})),n.d(e,\\\\\\\"Kb\\\\\\\",(function(){return me})),n.d(e,\\\\\\\"Ob\\\\\\\",(function(){return fe})),n.d(e,\\\\\\\"Pb\\\\\\\",(function(){return ge})),n.d(e,\\\\\\\"Yb\\\\\\\",(function(){return ve})),n.d(e,\\\\\\\"Ec\\\\\\\",(function(){return ye})),n.d(e,\\\\\\\"Fc\\\\\\\",(function(){return xe})),n.d(e,\\\\\\\"Gc\\\\\\\",(function(){return be})),n.d(e,\\\\\\\"Hc\\\\\\\",(function(){return we})),n.d(e,\\\\\\\"Ic\\\\\\\",(function(){return Te})),n.d(e,\\\\\\\"Jc\\\\\\\",(function(){return Ae})),n.d(e,\\\\\\\"Kc\\\\\\\",(function(){return Ee})),n.d(e,\\\\\\\"Lc\\\\\\\",(function(){return Me})),n.d(e,\\\\\\\"zc\\\\\\\",(function(){return Se})),n.d(e,\\\\\\\"Ac\\\\\\\",(function(){return Ce})),n.d(e,\\\\\\\"Bc\\\\\\\",(function(){return Ne})),n.d(e,\\\\\\\"yc\\\\\\\",(function(){return Le})),n.d(e,\\\\\\\"Cc\\\\\\\",(function(){return Oe})),n.d(e,\\\\\\\"Dc\\\\\\\",(function(){return Re})),n.d(e,\\\\\\\"cb\\\\\\\",(function(){return Pe})),n.d(e,\\\\\\\"eb\\\\\\\",(function(){return Ie})),n.d(e,\\\\\\\"db\\\\\\\",(function(){return Fe})),n.d(e,\\\\\\\"O\\\\\\\",(function(){return De})),n.d(e,\\\\\\\"P\\\\\\\",(function(){return ke})),n.d(e,\\\\\\\"Q\\\\\\\",(function(){return Be})),n.d(e,\\\\\\\"id\\\\\\\",(function(){return ze})),n.d(e,\\\\\\\"kd\\\\\\\",(function(){return Ue})),n.d(e,\\\\\\\"hd\\\\\\\",(function(){return Ge})),n.d(e,\\\\\\\"wb\\\\\\\",(function(){return Ve})),n.d(e,\\\\\\\"d\\\\\\\",(function(){return He})),n.d(e,\\\\\\\"Xc\\\\\\\",(function(){return je})),n.d(e,\\\\\\\"Wc\\\\\\\",(function(){return We})),n.d(e,\\\\\\\"Vc\\\\\\\",(function(){return qe})),n.d(e,\\\\\\\"U\\\\\\\",(function(){return Xe})),n.d(e,\\\\\\\"ld\\\\\\\",(function(){return Ye})),n.d(e,\\\\\\\"J\\\\\\\",(function(){return $e})),n.d(e,\\\\\\\"gc\\\\\\\",(function(){return Je})),n.d(e,\\\\\\\"bb\\\\\\\",(function(){return Ze})),n.d(e,\\\\\\\"lc\\\\\\\",(function(){return Qe})),n.d(e,\\\\\\\"kc\\\\\\\",(function(){return Ke})),n.d(e,\\\\\\\"fc\\\\\\\",(function(){return tn})),n.d(e,\\\\\\\"j\\\\\\\",(function(){return en})),n.d(e,\\\\\\\"Hb\\\\\\\",(function(){return nn})),n.d(e,\\\\\\\"Uc\\\\\\\",(function(){return rn})),n.d(e,\\\\\\\"zb\\\\\\\",(function(){return sn})),n.d(e,\\\\\\\"R\\\\\\\",(function(){return on})),n.d(e,\\\\\\\"h\\\\\\\",(function(){return an})),n.d(e,\\\\\\\"Qc\\\\\\\",(function(){return ln})),n.d(e,\\\\\\\"I\\\\\\\",(function(){return cn}));const i={LEFT:0,MIDDLE:1,RIGHT:2,ROTATE:0,DOLLY:1,PAN:2},r={ROTATE:0,PAN:1,DOLLY_PAN:2,DOLLY_ROTATE:3},s=0,o=1,a=2,l=0,c=1,u=2,h=3,d=0,p=1,_=2,m=1,f=0,g=1,v=2,y=3,x=4,b=5,w=100,T=101,A=102,E=103,M=104,S=200,C=201,N=202,L=203,O=204,R=205,P=206,I=207,F=208,D=209,k=210,B=0,z=1,U=2,G=3,V=4,H=5,j=6,W=7,q=0,X=1,Y=2,$=0,J=1,Z=2,Q=3,K=4,tt=5,et=300,nt=301,it=302,rt=303,st=304,ot=306,at=307,lt=1e3,ct=1001,ut=1002,ht=1003,dt=1004,pt=1004,_t=1005,mt=1005,ft=1006,gt=1007,vt=1007,yt=1008,xt=1008,bt=1009,wt=1010,Tt=1011,At=1012,Et=1013,Mt=1014,St=1015,Ct=1016,Nt=1017,Lt=1018,Ot=1019,Rt=1020,Pt=1021,It=1022,Ft=1023,Dt=1024,kt=1025,Bt=Ft,zt=1026,Ut=1027,Gt=1028,Vt=1029,Ht=1030,jt=1031,Wt=1032,qt=1033,Xt=33776,Yt=33777,$t=33778,Jt=33779,Zt=35840,Qt=35841,Kt=35842,te=35843,ee=36196,ne=37492,ie=37496,re=37808,se=37809,oe=37810,ae=37811,le=37812,ce=37813,ue=37814,he=37815,de=37816,pe=37817,_e=37818,me=37819,fe=37820,ge=37821,ve=36492,ye=37840,xe=37841,be=37842,we=37843,Te=37844,Ae=37845,Ee=37846,Me=37847,Se=37848,Ce=37849,Ne=37850,Le=37851,Oe=37852,Re=37853,Pe=2200,Ie=2201,Fe=2202,De=2300,ke=2301,Be=2302,ze=2400,Ue=2401,Ge=2402,Ve=2500,He=2501,je=0,We=1,qe=2,Xe=3e3,Ye=3001,$e=3007,Je=3002,Ze=3003,Qe=3004,Ke=3005,tn=3006,en=3200,nn=3201,rn=0,sn=1,on=7680,an=519,ln=35044,cn=\\\\\\\"300 es\\\\\\\"},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return i}));class i{constructor(t=0,e=0){this.x=t,this.y=e}get width(){return this.x}set width(t){this.x=t}get height(){return this.y}set height(t){this.y=t}set(t,e){return this.x=t,this.y=e,this}setScalar(t){return this.x=t,this.y=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y)}copy(t){return this.x=t.x,this.y=t.y,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector2: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this)}addScalar(t){return this.x+=t,this.y+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this}sub(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector2: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this)}subScalar(t){return this.x-=t,this.y-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this}multiply(t){return this.x*=t.x,this.y*=t.y,this}multiplyScalar(t){return this.x*=t,this.y*=t,this}divide(t){return this.x/=t.x,this.y/=t.y,this}divideScalar(t){return this.multiplyScalar(1/t)}applyMatrix3(t){const e=this.x,n=this.y,i=t.elements;return this.x=i[0]*e+i[3]*n+i[6],this.y=i[1]*e+i[4]*n+i[7],this}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this}negate(){return this.x=-this.x,this.y=-this.y,this}dot(t){return this.x*t.x+this.y*t.y}cross(t){return this.x*t.y-this.y*t.x}lengthSq(){return this.x*this.x+this.y*this.y}length(){return Math.sqrt(this.x*this.x+this.y*this.y)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)}normalize(){return this.divideScalar(this.length()||1)}angle(){return Math.atan2(-this.y,-this.x)+Math.PI}distanceTo(t){return Math.sqrt(this.distanceToSquared(t))}distanceToSquared(t){const e=this.x-t.x,n=this.y-t.y;return e*e+n*n}manhattanDistanceTo(t){return Math.abs(this.x-t.x)+Math.abs(this.y-t.y)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this}equals(t){return t.x===this.x&&t.y===this.y}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn(\\\\\\\"THREE.Vector2: offset has been removed from .fromBufferAttribute().\\\\\\\"),this.x=t.getX(e),this.y=t.getY(e),this}rotateAround(t,e){const n=Math.cos(e),i=Math.sin(e),r=this.x-t.x,s=this.y-t.y;return this.x=r*n-s*i+t.x,this.y=r*i+s*n+t.y,this}random(){return this.x=Math.random(),this.y=Math.random(),this}*[Symbol.iterator](){yield this.x,yield this.y}}i.prototype.isVector2=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return i})),n.d(e,\\\\\\\"b\\\\\\\",(function(){return r})),n.d(e,\\\\\\\"h\\\\\\\",(function(){return a})),n.d(e,\\\\\\\"d\\\\\\\",(function(){return l})),n.d(e,\\\\\\\"f\\\\\\\",(function(){return c})),n.d(e,\\\\\\\"j\\\\\\\",(function(){return u})),n.d(e,\\\\\\\"e\\\\\\\",(function(){return h})),n.d(e,\\\\\\\"k\\\\\\\",(function(){return d})),n.d(e,\\\\\\\"i\\\\\\\",(function(){return p})),n.d(e,\\\\\\\"c\\\\\\\",(function(){return _})),n.d(e,\\\\\\\"g\\\\\\\",(function(){return m}));const i=Math.PI/180,r=180/Math.PI,s=[];for(let t=0;t<256;t++)s[t]=(t<16?\\\\\\\"0\\\\\\\":\\\\\\\"\\\\\\\")+t.toString(16);const o=\\\\\\\"undefined\\\\\\\"!=typeof crypto&&\\\\\\\"randomUUID\\\\\\\"in crypto;function a(){if(o)return crypto.randomUUID().toUpperCase();const t=4294967295*Math.random()|0,e=4294967295*Math.random()|0,n=4294967295*Math.random()|0,i=4294967295*Math.random()|0;return(s[255&t]+s[t>>8&255]+s[t>>16&255]+s[t>>24&255]+\\\\\\\"-\\\\\\\"+s[255&e]+s[e>>8&255]+\\\\\\\"-\\\\\\\"+s[e>>16&15|64]+s[e>>24&255]+\\\\\\\"-\\\\\\\"+s[63&n|128]+s[n>>8&255]+\\\\\\\"-\\\\\\\"+s[n>>16&255]+s[n>>24&255]+s[255&i]+s[i>>8&255]+s[i>>16&255]+s[i>>24&255]).toUpperCase()}function l(t,e,n){return Math.max(e,Math.min(n,t))}function c(t,e){return(t%e+e)%e}function u(t,e,n){return(1-n)*t+n*e}function h(t){return t*i}function d(t){return t*r}function p(t){return 0==(t&t-1)&&0!==t}function _(t){return Math.pow(2,Math.ceil(Math.log(t)/Math.LN2))}function m(t){return Math.pow(2,Math.floor(Math.log(t)/Math.LN2))}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"d\\\\\\\",(function(){return x})),n.d(e,\\\\\\\"c\\\\\\\",(function(){return y})),n.d(e,\\\\\\\"b\\\\\\\",(function(){return v})),n.d(e,\\\\\\\"i\\\\\\\",(function(){return g})),n.d(e,\\\\\\\"f\\\\\\\",(function(){return f})),n.d(e,\\\\\\\"h\\\\\\\",(function(){return m})),n.d(e,\\\\\\\"e\\\\\\\",(function(){return _})),n.d(e,\\\\\\\"k\\\\\\\",(function(){return p})),n.d(e,\\\\\\\"j\\\\\\\",(function(){return d})),n.d(e,\\\\\\\"g\\\\\\\",(function(){return h})),n.d(e,\\\\\\\"a\\\\\\\",(function(){return u}));var i=n(9),r=n(0),s=n(2),o=n(6),a=n(1);const l=new r.a,c=new s.a;class u{constructor(t,e,n){if(Array.isArray(t))throw new TypeError(\\\\\\\"THREE.BufferAttribute: array should be a Typed Array.\\\\\\\");this.name=\\\\\\\"\\\\\\\",this.array=t,this.itemSize=e,this.count=void 0!==t?t.length/e:0,this.normalized=!0===n,this.usage=a.Qc,this.updateRange={offset:0,count:-1},this.version=0}onUploadCallback(){}set needsUpdate(t){!0===t&&this.version++}setUsage(t){return this.usage=t,this}copy(t){return this.name=t.name,this.array=new t.array.constructor(t.array),this.itemSize=t.itemSize,this.count=t.count,this.normalized=t.normalized,this.usage=t.usage,this}copyAt(t,e,n){t*=this.itemSize,n*=e.itemSize;for(let i=0,r=this.itemSize;i0&&console.error(\\\\\\\"THREE.Matrix4: the constructor no longer reads arguments. use .set() instead.\\\\\\\")}set(t,e,n,i,r,s,o,a,l,c,u,h,d,p,_,m){const f=this.elements;return f[0]=t,f[4]=e,f[8]=n,f[12]=i,f[1]=r,f[5]=s,f[9]=o,f[13]=a,f[2]=l,f[6]=c,f[10]=u,f[14]=h,f[3]=d,f[7]=p,f[11]=_,f[15]=m,this}identity(){return this.set(1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1),this}clone(){return(new r).fromArray(this.elements)}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],e[9]=n[9],e[10]=n[10],e[11]=n[11],e[12]=n[12],e[13]=n[13],e[14]=n[14],e[15]=n[15],this}copyPosition(t){const e=this.elements,n=t.elements;return e[12]=n[12],e[13]=n[13],e[14]=n[14],this}setFromMatrix3(t){const e=t.elements;return this.set(e[0],e[3],e[6],0,e[1],e[4],e[7],0,e[2],e[5],e[8],0,0,0,0,1),this}extractBasis(t,e,n){return t.setFromMatrixColumn(this,0),e.setFromMatrixColumn(this,1),n.setFromMatrixColumn(this,2),this}makeBasis(t,e,n){return this.set(t.x,e.x,n.x,0,t.y,e.y,n.y,0,t.z,e.z,n.z,0,0,0,0,1),this}extractRotation(t){const e=this.elements,n=t.elements,i=1/s.setFromMatrixColumn(t,0).length(),r=1/s.setFromMatrixColumn(t,1).length(),o=1/s.setFromMatrixColumn(t,2).length();return e[0]=n[0]*i,e[1]=n[1]*i,e[2]=n[2]*i,e[3]=0,e[4]=n[4]*r,e[5]=n[5]*r,e[6]=n[6]*r,e[7]=0,e[8]=n[8]*o,e[9]=n[9]*o,e[10]=n[10]*o,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromEuler(t){t&&t.isEuler||console.error(\\\\\\\"THREE.Matrix4: .makeRotationFromEuler() now expects a Euler rotation rather than a Vector3 and order.\\\\\\\");const e=this.elements,n=t.x,i=t.y,r=t.z,s=Math.cos(n),o=Math.sin(n),a=Math.cos(i),l=Math.sin(i),c=Math.cos(r),u=Math.sin(r);if(\\\\\\\"XYZ\\\\\\\"===t.order){const t=s*c,n=s*u,i=o*c,r=o*u;e[0]=a*c,e[4]=-a*u,e[8]=l,e[1]=n+i*l,e[5]=t-r*l,e[9]=-o*a,e[2]=r-t*l,e[6]=i+n*l,e[10]=s*a}else if(\\\\\\\"YXZ\\\\\\\"===t.order){const t=a*c,n=a*u,i=l*c,r=l*u;e[0]=t+r*o,e[4]=i*o-n,e[8]=s*l,e[1]=s*u,e[5]=s*c,e[9]=-o,e[2]=n*o-i,e[6]=r+t*o,e[10]=s*a}else if(\\\\\\\"ZXY\\\\\\\"===t.order){const t=a*c,n=a*u,i=l*c,r=l*u;e[0]=t-r*o,e[4]=-s*u,e[8]=i+n*o,e[1]=n+i*o,e[5]=s*c,e[9]=r-t*o,e[2]=-s*l,e[6]=o,e[10]=s*a}else if(\\\\\\\"ZYX\\\\\\\"===t.order){const t=s*c,n=s*u,i=o*c,r=o*u;e[0]=a*c,e[4]=i*l-n,e[8]=t*l+r,e[1]=a*u,e[5]=r*l+t,e[9]=n*l-i,e[2]=-l,e[6]=o*a,e[10]=s*a}else if(\\\\\\\"YZX\\\\\\\"===t.order){const t=s*a,n=s*l,i=o*a,r=o*l;e[0]=a*c,e[4]=r-t*u,e[8]=i*u+n,e[1]=u,e[5]=s*c,e[9]=-o*c,e[2]=-l*c,e[6]=n*u+i,e[10]=t-r*u}else if(\\\\\\\"XZY\\\\\\\"===t.order){const t=s*a,n=s*l,i=o*a,r=o*l;e[0]=a*c,e[4]=-u,e[8]=l*c,e[1]=t*u+r,e[5]=s*c,e[9]=n*u-i,e[2]=i*u-n,e[6]=o*c,e[10]=r*u+t}return e[3]=0,e[7]=0,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromQuaternion(t){return this.compose(a,t,l)}lookAt(t,e,n){const i=this.elements;return h.subVectors(t,e),0===h.lengthSq()&&(h.z=1),h.normalize(),c.crossVectors(n,h),0===c.lengthSq()&&(1===Math.abs(n.z)?h.x+=1e-4:h.z+=1e-4,h.normalize(),c.crossVectors(n,h)),c.normalize(),u.crossVectors(h,c),i[0]=c.x,i[4]=u.x,i[8]=h.x,i[1]=c.y,i[5]=u.y,i[9]=h.y,i[2]=c.z,i[6]=u.z,i[10]=h.z,this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Matrix4: .multiply() now only accepts one argument. Use .multiplyMatrices( a, b ) instead.\\\\\\\"),this.multiplyMatrices(t,e)):this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,r=this.elements,s=n[0],o=n[4],a=n[8],l=n[12],c=n[1],u=n[5],h=n[9],d=n[13],p=n[2],_=n[6],m=n[10],f=n[14],g=n[3],v=n[7],y=n[11],x=n[15],b=i[0],w=i[4],T=i[8],A=i[12],E=i[1],M=i[5],S=i[9],C=i[13],N=i[2],L=i[6],O=i[10],R=i[14],P=i[3],I=i[7],F=i[11],D=i[15];return r[0]=s*b+o*E+a*N+l*P,r[4]=s*w+o*M+a*L+l*I,r[8]=s*T+o*S+a*O+l*F,r[12]=s*A+o*C+a*R+l*D,r[1]=c*b+u*E+h*N+d*P,r[5]=c*w+u*M+h*L+d*I,r[9]=c*T+u*S+h*O+d*F,r[13]=c*A+u*C+h*R+d*D,r[2]=p*b+_*E+m*N+f*P,r[6]=p*w+_*M+m*L+f*I,r[10]=p*T+_*S+m*O+f*F,r[14]=p*A+_*C+m*R+f*D,r[3]=g*b+v*E+y*N+x*P,r[7]=g*w+v*M+y*L+x*I,r[11]=g*T+v*S+y*O+x*F,r[15]=g*A+v*C+y*R+x*D,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[4]*=t,e[8]*=t,e[12]*=t,e[1]*=t,e[5]*=t,e[9]*=t,e[13]*=t,e[2]*=t,e[6]*=t,e[10]*=t,e[14]*=t,e[3]*=t,e[7]*=t,e[11]*=t,e[15]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[4],i=t[8],r=t[12],s=t[1],o=t[5],a=t[9],l=t[13],c=t[2],u=t[6],h=t[10],d=t[14];return t[3]*(+r*a*u-i*l*u-r*o*h+n*l*h+i*o*d-n*a*d)+t[7]*(+e*a*d-e*l*h+r*s*h-i*s*d+i*l*c-r*a*c)+t[11]*(+e*l*u-e*o*d-r*s*u+n*s*d+r*o*c-n*l*c)+t[15]*(-i*o*c-e*a*u+e*o*h+i*s*u-n*s*h+n*a*c)}transpose(){const t=this.elements;let e;return e=t[1],t[1]=t[4],t[4]=e,e=t[2],t[2]=t[8],t[8]=e,e=t[6],t[6]=t[9],t[9]=e,e=t[3],t[3]=t[12],t[12]=e,e=t[7],t[7]=t[13],t[13]=e,e=t[11],t[11]=t[14],t[14]=e,this}setPosition(t,e,n){const i=this.elements;return t.isVector3?(i[12]=t.x,i[13]=t.y,i[14]=t.z):(i[12]=t,i[13]=e,i[14]=n),this}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],s=t[4],o=t[5],a=t[6],l=t[7],c=t[8],u=t[9],h=t[10],d=t[11],p=t[12],_=t[13],m=t[14],f=t[15],g=u*m*l-_*h*l+_*a*d-o*m*d-u*a*f+o*h*f,v=p*h*l-c*m*l-p*a*d+s*m*d+c*a*f-s*h*f,y=c*_*l-p*u*l+p*o*d-s*_*d-c*o*f+s*u*f,x=p*u*a-c*_*a-p*o*h+s*_*h+c*o*m-s*u*m,b=e*g+n*v+i*y+r*x;if(0===b)return this.set(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0);const w=1/b;return t[0]=g*w,t[1]=(_*h*r-u*m*r-_*i*d+n*m*d+u*i*f-n*h*f)*w,t[2]=(o*m*r-_*a*r+_*i*l-n*m*l-o*i*f+n*a*f)*w,t[3]=(u*a*r-o*h*r-u*i*l+n*h*l+o*i*d-n*a*d)*w,t[4]=v*w,t[5]=(c*m*r-p*h*r+p*i*d-e*m*d-c*i*f+e*h*f)*w,t[6]=(p*a*r-s*m*r-p*i*l+e*m*l+s*i*f-e*a*f)*w,t[7]=(s*h*r-c*a*r+c*i*l-e*h*l-s*i*d+e*a*d)*w,t[8]=y*w,t[9]=(p*u*r-c*_*r-p*n*d+e*_*d+c*n*f-e*u*f)*w,t[10]=(s*_*r-p*o*r+p*n*l-e*_*l-s*n*f+e*o*f)*w,t[11]=(c*o*r-s*u*r-c*n*l+e*u*l+s*n*d-e*o*d)*w,t[12]=x*w,t[13]=(c*_*i-p*u*i+p*n*h-e*_*h-c*n*m+e*u*m)*w,t[14]=(p*o*i-s*_*i-p*n*a+e*_*a+s*n*m-e*o*m)*w,t[15]=(s*u*i-c*o*i+c*n*a-e*u*a-s*n*h+e*o*h)*w,this}scale(t){const e=this.elements,n=t.x,i=t.y,r=t.z;return e[0]*=n,e[4]*=i,e[8]*=r,e[1]*=n,e[5]*=i,e[9]*=r,e[2]*=n,e[6]*=i,e[10]*=r,e[3]*=n,e[7]*=i,e[11]*=r,this}getMaxScaleOnAxis(){const t=this.elements,e=t[0]*t[0]+t[1]*t[1]+t[2]*t[2],n=t[4]*t[4]+t[5]*t[5]+t[6]*t[6],i=t[8]*t[8]+t[9]*t[9]+t[10]*t[10];return Math.sqrt(Math.max(e,n,i))}makeTranslation(t,e,n){return this.set(1,0,0,t,0,1,0,e,0,0,1,n,0,0,0,1),this}makeRotationX(t){const e=Math.cos(t),n=Math.sin(t);return this.set(1,0,0,0,0,e,-n,0,0,n,e,0,0,0,0,1),this}makeRotationY(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,0,n,0,0,1,0,0,-n,0,e,0,0,0,0,1),this}makeRotationZ(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,-n,0,0,n,e,0,0,0,0,1,0,0,0,0,1),this}makeRotationAxis(t,e){const n=Math.cos(e),i=Math.sin(e),r=1-n,s=t.x,o=t.y,a=t.z,l=r*s,c=r*o;return this.set(l*s+n,l*o-i*a,l*a+i*o,0,l*o+i*a,c*o+n,c*a-i*s,0,l*a-i*o,c*a+i*s,r*a*a+n,0,0,0,0,1),this}makeScale(t,e,n){return this.set(t,0,0,0,0,e,0,0,0,0,n,0,0,0,0,1),this}makeShear(t,e,n,i,r,s){return this.set(1,n,r,0,t,1,s,0,e,i,1,0,0,0,0,1),this}compose(t,e,n){const i=this.elements,r=e._x,s=e._y,o=e._z,a=e._w,l=r+r,c=s+s,u=o+o,h=r*l,d=r*c,p=r*u,_=s*c,m=s*u,f=o*u,g=a*l,v=a*c,y=a*u,x=n.x,b=n.y,w=n.z;return i[0]=(1-(_+f))*x,i[1]=(d+y)*x,i[2]=(p-v)*x,i[3]=0,i[4]=(d-y)*b,i[5]=(1-(h+f))*b,i[6]=(m+g)*b,i[7]=0,i[8]=(p+v)*w,i[9]=(m-g)*w,i[10]=(1-(h+_))*w,i[11]=0,i[12]=t.x,i[13]=t.y,i[14]=t.z,i[15]=1,this}decompose(t,e,n){const i=this.elements;let r=s.set(i[0],i[1],i[2]).length();const a=s.set(i[4],i[5],i[6]).length(),l=s.set(i[8],i[9],i[10]).length();this.determinant()<0&&(r=-r),t.x=i[12],t.y=i[13],t.z=i[14],o.copy(this);const c=1/r,u=1/a,h=1/l;return o.elements[0]*=c,o.elements[1]*=c,o.elements[2]*=c,o.elements[4]*=u,o.elements[5]*=u,o.elements[6]*=u,o.elements[8]*=h,o.elements[9]*=h,o.elements[10]*=h,e.setFromRotationMatrix(o),n.x=r,n.y=a,n.z=l,this}makePerspective(t,e,n,i,r,s){void 0===s&&console.warn(\\\\\\\"THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.\\\\\\\");const o=this.elements,a=2*r/(e-t),l=2*r/(n-i),c=(e+t)/(e-t),u=(n+i)/(n-i),h=-(s+r)/(s-r),d=-2*s*r/(s-r);return o[0]=a,o[4]=0,o[8]=c,o[12]=0,o[1]=0,o[5]=l,o[9]=u,o[13]=0,o[2]=0,o[6]=0,o[10]=h,o[14]=d,o[3]=0,o[7]=0,o[11]=-1,o[15]=0,this}makeOrthographic(t,e,n,i,r,s){const o=this.elements,a=1/(e-t),l=1/(n-i),c=1/(s-r),u=(e+t)*a,h=(n+i)*l,d=(s+r)*c;return o[0]=2*a,o[4]=0,o[8]=0,o[12]=-u,o[1]=0,o[5]=2*l,o[9]=0,o[13]=-h,o[2]=0,o[6]=0,o[10]=-2*c,o[14]=-d,o[3]=0,o[7]=0,o[11]=0,o[15]=1,this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<16;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<16;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t[e+9]=n[9],t[e+10]=n[10],t[e+11]=n[11],t[e+12]=n[12],t[e+13]=n[13],t[e+14]=n[14],t[e+15]=n[15],t}}r.prototype.isMatrix4=!0;const s=new i.a,o=new r,a=new i.a(0,0,0),l=new i.a(1,1,1),c=new i.a,u=new i.a,h=new i.a},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return u}));var i=n(3);const r={aliceblue:15792383,antiquewhite:16444375,aqua:65535,aquamarine:8388564,azure:15794175,beige:16119260,bisque:16770244,black:0,blanchedalmond:16772045,blue:255,blueviolet:9055202,brown:10824234,burlywood:14596231,cadetblue:6266528,chartreuse:8388352,chocolate:13789470,coral:16744272,cornflowerblue:6591981,cornsilk:16775388,crimson:14423100,cyan:65535,darkblue:139,darkcyan:35723,darkgoldenrod:12092939,darkgray:11119017,darkgreen:25600,darkgrey:11119017,darkkhaki:12433259,darkmagenta:9109643,darkolivegreen:5597999,darkorange:16747520,darkorchid:10040012,darkred:9109504,darksalmon:15308410,darkseagreen:9419919,darkslateblue:4734347,darkslategray:3100495,darkslategrey:3100495,darkturquoise:52945,darkviolet:9699539,deeppink:16716947,deepskyblue:49151,dimgray:6908265,dimgrey:6908265,dodgerblue:2003199,firebrick:11674146,floralwhite:16775920,forestgreen:2263842,fuchsia:16711935,gainsboro:14474460,ghostwhite:16316671,gold:16766720,goldenrod:14329120,gray:8421504,green:32768,greenyellow:11403055,grey:8421504,honeydew:15794160,hotpink:16738740,indianred:13458524,indigo:4915330,ivory:16777200,khaki:15787660,lavender:15132410,lavenderblush:16773365,lawngreen:8190976,lemonchiffon:16775885,lightblue:11393254,lightcoral:15761536,lightcyan:14745599,lightgoldenrodyellow:16448210,lightgray:13882323,lightgreen:9498256,lightgrey:13882323,lightpink:16758465,lightsalmon:16752762,lightseagreen:2142890,lightskyblue:8900346,lightslategray:7833753,lightslategrey:7833753,lightsteelblue:11584734,lightyellow:16777184,lime:65280,limegreen:3329330,linen:16445670,magenta:16711935,maroon:8388608,mediumaquamarine:6737322,mediumblue:205,mediumorchid:12211667,mediumpurple:9662683,mediumseagreen:3978097,mediumslateblue:8087790,mediumspringgreen:64154,mediumturquoise:4772300,mediumvioletred:13047173,midnightblue:1644912,mintcream:16121850,mistyrose:16770273,moccasin:16770229,navajowhite:16768685,navy:128,oldlace:16643558,olive:8421376,olivedrab:7048739,orange:16753920,orangered:16729344,orchid:14315734,palegoldenrod:15657130,palegreen:10025880,paleturquoise:11529966,palevioletred:14381203,papayawhip:16773077,peachpuff:16767673,peru:13468991,pink:16761035,plum:14524637,powderblue:11591910,purple:8388736,rebeccapurple:6697881,red:16711680,rosybrown:12357519,royalblue:4286945,saddlebrown:9127187,salmon:16416882,sandybrown:16032864,seagreen:3050327,seashell:16774638,sienna:10506797,silver:12632256,skyblue:8900331,slateblue:6970061,slategray:7372944,slategrey:7372944,snow:16775930,springgreen:65407,steelblue:4620980,tan:13808780,teal:32896,thistle:14204888,tomato:16737095,turquoise:4251856,violet:15631086,wheat:16113331,white:16777215,whitesmoke:16119285,yellow:16776960,yellowgreen:10145074},s={h:0,s:0,l:0},o={h:0,s:0,l:0};function a(t,e,n){return n<0&&(n+=1),n>1&&(n-=1),n<1/6?t+6*(e-t)*n:n<.5?e:n<2/3?t+6*(e-t)*(2/3-n):t}function l(t){return t<.04045?.0773993808*t:Math.pow(.9478672986*t+.0521327014,2.4)}function c(t){return t<.0031308?12.92*t:1.055*Math.pow(t,.41666)-.055}class u{constructor(t,e,n){return void 0===e&&void 0===n?this.set(t):this.setRGB(t,e,n)}set(t){return t&&t.isColor?this.copy(t):\\\\\\\"number\\\\\\\"==typeof t?this.setHex(t):\\\\\\\"string\\\\\\\"==typeof t&&this.setStyle(t),this}setScalar(t){return this.r=t,this.g=t,this.b=t,this}setHex(t){return t=Math.floor(t),this.r=(t>>16&255)/255,this.g=(t>>8&255)/255,this.b=(255&t)/255,this}setRGB(t,e,n){return this.r=t,this.g=e,this.b=n,this}setHSL(t,e,n){if(t=i.f(t,1),e=i.d(e,0,1),n=i.d(n,0,1),0===e)this.r=this.g=this.b=n;else{const i=n<=.5?n*(1+e):n+e-n*e,r=2*n-i;this.r=a(r,i,t+1/3),this.g=a(r,i,t),this.b=a(r,i,t-1/3)}return this}setStyle(t){function e(e){void 0!==e&&parseFloat(e)<1&&console.warn(\\\\\\\"THREE.Color: Alpha component of \\\\\\\"+t+\\\\\\\" will be ignored.\\\\\\\")}let n;if(n=/^((?:rgb|hsl)a?)\\\\(([^\\\\)]*)\\\\)/.exec(t)){let t;const i=n[1],r=n[2];switch(i){case\\\\\\\"rgb\\\\\\\":case\\\\\\\"rgba\\\\\\\":if(t=/^\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r))return this.r=Math.min(255,parseInt(t[1],10))/255,this.g=Math.min(255,parseInt(t[2],10))/255,this.b=Math.min(255,parseInt(t[3],10))/255,e(t[4]),this;if(t=/^\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r))return this.r=Math.min(100,parseInt(t[1],10))/100,this.g=Math.min(100,parseInt(t[2],10))/100,this.b=Math.min(100,parseInt(t[3],10))/100,e(t[4]),this;break;case\\\\\\\"hsl\\\\\\\":case\\\\\\\"hsla\\\\\\\":if(t=/^\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r)){const n=parseFloat(t[1])/360,i=parseInt(t[2],10)/100,r=parseInt(t[3],10)/100;return e(t[4]),this.setHSL(n,i,r)}}}else if(n=/^\\\\#([A-Fa-f\\\\d]+)$/.exec(t)){const t=n[1],e=t.length;if(3===e)return this.r=parseInt(t.charAt(0)+t.charAt(0),16)/255,this.g=parseInt(t.charAt(1)+t.charAt(1),16)/255,this.b=parseInt(t.charAt(2)+t.charAt(2),16)/255,this;if(6===e)return this.r=parseInt(t.charAt(0)+t.charAt(1),16)/255,this.g=parseInt(t.charAt(2)+t.charAt(3),16)/255,this.b=parseInt(t.charAt(4)+t.charAt(5),16)/255,this}return t&&t.length>0?this.setColorName(t):this}setColorName(t){const e=r[t.toLowerCase()];return void 0!==e?this.setHex(e):console.warn(\\\\\\\"THREE.Color: Unknown color \\\\\\\"+t),this}clone(){return new this.constructor(this.r,this.g,this.b)}copy(t){return this.r=t.r,this.g=t.g,this.b=t.b,this}copyGammaToLinear(t,e=2){return this.r=Math.pow(t.r,e),this.g=Math.pow(t.g,e),this.b=Math.pow(t.b,e),this}copyLinearToGamma(t,e=2){const n=e>0?1/e:1;return this.r=Math.pow(t.r,n),this.g=Math.pow(t.g,n),this.b=Math.pow(t.b,n),this}convertGammaToLinear(t){return this.copyGammaToLinear(this,t),this}convertLinearToGamma(t){return this.copyLinearToGamma(this,t),this}copySRGBToLinear(t){return this.r=l(t.r),this.g=l(t.g),this.b=l(t.b),this}copyLinearToSRGB(t){return this.r=c(t.r),this.g=c(t.g),this.b=c(t.b),this}convertSRGBToLinear(){return this.copySRGBToLinear(this),this}convertLinearToSRGB(){return this.copyLinearToSRGB(this),this}getHex(){return 255*this.r<<16^255*this.g<<8^255*this.b<<0}getHexString(){return(\\\\\\\"000000\\\\\\\"+this.getHex().toString(16)).slice(-6)}getHSL(t){const e=this.r,n=this.g,i=this.b,r=Math.max(e,n,i),s=Math.min(e,n,i);let o,a;const l=(s+r)/2;if(s===r)o=0,a=0;else{const t=r-s;switch(a=l<=.5?t/(r+s):t/(2-r-s),r){case e:o=(n-i)/t+(n65535?a.i:a.h)(t,1):this.index=t,this}getAttribute(t){return this.attributes[t]}setAttribute(t,e){return this.attributes[t]=e,this}deleteAttribute(t){return delete this.attributes[t],this}hasAttribute(t){return void 0!==this.attributes[t]}addGroup(t,e,n=0){this.groups.push({start:t,count:e,materialIndex:n})}clearGroups(){this.groups=[]}setDrawRange(t,e){this.drawRange.start=t,this.drawRange.count=e}applyMatrix4(t){const e=this.attributes.position;void 0!==e&&(e.applyMatrix4(t),e.needsUpdate=!0);const n=this.attributes.normal;if(void 0!==n){const e=(new h.a).getNormalMatrix(t);n.applyNormalMatrix(e),n.needsUpdate=!0}const i=this.attributes.tangent;return void 0!==i&&(i.transformDirection(t),i.needsUpdate=!0),null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this}applyQuaternion(t){return m.makeRotationFromQuaternion(t),this.applyMatrix4(m),this}rotateX(t){return m.makeRotationX(t),this.applyMatrix4(m),this}rotateY(t){return m.makeRotationY(t),this.applyMatrix4(m),this}rotateZ(t){return m.makeRotationZ(t),this.applyMatrix4(m),this}translate(t,e,n){return m.makeTranslation(t,e,n),this.applyMatrix4(m),this}scale(t,e,n){return m.makeScale(t,e,n),this.applyMatrix4(m),this}lookAt(t){return f.lookAt(t),f.updateMatrix(),this.applyMatrix4(f.matrix),this}center(){return this.computeBoundingBox(),this.boundingBox.getCenter(g).negate(),this.translate(g.x,g.y,g.z),this}setFromPoints(t){const e=[];for(let n=0,i=t.length;n0&&(t.userData=this.userData),void 0!==this.parameters){const e=this.parameters;for(const n in e)void 0!==e[n]&&(t[n]=e[n]);return t}t.data={attributes:{}};const e=this.index;null!==e&&(t.data.index={type:e.array.constructor.name,array:Array.prototype.slice.call(e.array)});const n=this.attributes;for(const e in n){const i=n[e];t.data.attributes[e]=i.toJSON(t.data)}const i={};let r=!1;for(const e in this.morphAttributes){const n=this.morphAttributes[e],s=[];for(let e=0,i=n.length;e0&&(i[e]=s,r=!0)}r&&(t.data.morphAttributes=i,t.data.morphTargetsRelative=this.morphTargetsRelative);const s=this.groups;s.length>0&&(t.data.groups=JSON.parse(JSON.stringify(s)));const o=this.boundingSphere;return null!==o&&(t.data.boundingSphere={center:o.center.toArray(),radius:o.radius}),t}clone(){return(new this.constructor).copy(this)}copy(t){this.index=null,this.attributes={},this.morphAttributes={},this.groups=[],this.boundingBox=null,this.boundingSphere=null;const e={};this.name=t.name;const n=t.index;null!==n&&this.setIndex(n.clone(e));const i=t.attributes;for(const t in i){const n=i[t];this.setAttribute(t,n.clone(e))}const r=t.morphAttributes;for(const t in r){const n=[],i=r[t];for(let t=0,r=i.length;t=0?1:-1,i=1-e*e;if(i>Number.EPSILON){const r=Math.sqrt(i),s=Math.atan2(r,e*n);t=Math.sin(t*s)/r,o=Math.sin(o*s)/r}const r=o*n;if(a=a*t+h*r,l=l*t+d*r,c=c*t+p*r,u=u*t+_*r,t===1-o){const t=1/Math.sqrt(a*a+l*l+c*c+u*u);a*=t,l*=t,c*=t,u*=t}}t[e]=a,t[e+1]=l,t[e+2]=c,t[e+3]=u}static multiplyQuaternionsFlat(t,e,n,i,r,s){const o=n[i],a=n[i+1],l=n[i+2],c=n[i+3],u=r[s],h=r[s+1],d=r[s+2],p=r[s+3];return t[e]=o*p+c*u+a*d-l*h,t[e+1]=a*p+c*h+l*u-o*d,t[e+2]=l*p+c*d+o*h-a*u,t[e+3]=c*p-o*u-a*h-l*d,t}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get w(){return this._w}set w(t){this._w=t,this._onChangeCallback()}set(t,e,n,i){return this._x=t,this._y=e,this._z=n,this._w=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._w)}copy(t){return this._x=t.x,this._y=t.y,this._z=t.z,this._w=t.w,this._onChangeCallback(),this}setFromEuler(t,e){if(!t||!t.isEuler)throw new Error(\\\\\\\"THREE.Quaternion: .setFromEuler() now expects an Euler rotation rather than a Vector3 and order.\\\\\\\");const n=t._x,i=t._y,r=t._z,s=t._order,o=Math.cos,a=Math.sin,l=o(n/2),c=o(i/2),u=o(r/2),h=a(n/2),d=a(i/2),p=a(r/2);switch(s){case\\\\\\\"XYZ\\\\\\\":this._x=h*c*u+l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u-h*d*p;break;case\\\\\\\"YXZ\\\\\\\":this._x=h*c*u+l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u+h*d*p;break;case\\\\\\\"ZXY\\\\\\\":this._x=h*c*u-l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u-h*d*p;break;case\\\\\\\"ZYX\\\\\\\":this._x=h*c*u-l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u+h*d*p;break;case\\\\\\\"YZX\\\\\\\":this._x=h*c*u+l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u-h*d*p;break;case\\\\\\\"XZY\\\\\\\":this._x=h*c*u-l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u+h*d*p;break;default:console.warn(\\\\\\\"THREE.Quaternion: .setFromEuler() encountered an unknown order: \\\\\\\"+s)}return!1!==e&&this._onChangeCallback(),this}setFromAxisAngle(t,e){const n=e/2,i=Math.sin(n);return this._x=t.x*i,this._y=t.y*i,this._z=t.z*i,this._w=Math.cos(n),this._onChangeCallback(),this}setFromRotationMatrix(t){const e=t.elements,n=e[0],i=e[4],r=e[8],s=e[1],o=e[5],a=e[9],l=e[2],c=e[6],u=e[10],h=n+o+u;if(h>0){const t=.5/Math.sqrt(h+1);this._w=.25/t,this._x=(c-a)*t,this._y=(r-l)*t,this._z=(s-i)*t}else if(n>o&&n>u){const t=2*Math.sqrt(1+n-o-u);this._w=(c-a)/t,this._x=.25*t,this._y=(i+s)/t,this._z=(r+l)/t}else if(o>u){const t=2*Math.sqrt(1+o-n-u);this._w=(r-l)/t,this._x=(i+s)/t,this._y=.25*t,this._z=(a+c)/t}else{const t=2*Math.sqrt(1+u-n-o);this._w=(s-i)/t,this._x=(r+l)/t,this._y=(a+c)/t,this._z=.25*t}return this._onChangeCallback(),this}setFromUnitVectors(t,e){let n=t.dot(e)+1;return nMath.abs(t.z)?(this._x=-t.y,this._y=t.x,this._z=0,this._w=n):(this._x=0,this._y=-t.z,this._z=t.y,this._w=n)):(this._x=t.y*e.z-t.z*e.y,this._y=t.z*e.x-t.x*e.z,this._z=t.x*e.y-t.y*e.x,this._w=n),this.normalize()}angleTo(t){return 2*Math.acos(Math.abs(i.d(this.dot(t),-1,1)))}rotateTowards(t,e){const n=this.angleTo(t);if(0===n)return this;const i=Math.min(1,e/n);return this.slerp(t,i),this}identity(){return this.set(0,0,0,1)}invert(){return this.conjugate()}conjugate(){return this._x*=-1,this._y*=-1,this._z*=-1,this._onChangeCallback(),this}dot(t){return this._x*t._x+this._y*t._y+this._z*t._z+this._w*t._w}lengthSq(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w}length(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w)}normalize(){let t=this.length();return 0===t?(this._x=0,this._y=0,this._z=0,this._w=1):(t=1/t,this._x=this._x*t,this._y=this._y*t,this._z=this._z*t,this._w=this._w*t),this._onChangeCallback(),this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Quaternion: .multiply() now only accepts one argument. Use .multiplyQuaternions( a, b ) instead.\\\\\\\"),this.multiplyQuaternions(t,e)):this.multiplyQuaternions(this,t)}premultiply(t){return this.multiplyQuaternions(t,this)}multiplyQuaternions(t,e){const n=t._x,i=t._y,r=t._z,s=t._w,o=e._x,a=e._y,l=e._z,c=e._w;return this._x=n*c+s*o+i*l-r*a,this._y=i*c+s*a+r*o-n*l,this._z=r*c+s*l+n*a-i*o,this._w=s*c-n*o-i*a-r*l,this._onChangeCallback(),this}slerp(t,e){if(0===e)return this;if(1===e)return this.copy(t);const n=this._x,i=this._y,r=this._z,s=this._w;let o=s*t._w+n*t._x+i*t._y+r*t._z;if(o<0?(this._w=-t._w,this._x=-t._x,this._y=-t._y,this._z=-t._z,o=-o):this.copy(t),o>=1)return this._w=s,this._x=n,this._y=i,this._z=r,this;const a=1-o*o;if(a<=Number.EPSILON){const t=1-e;return this._w=t*s+e*this._w,this._x=t*n+e*this._x,this._y=t*i+e*this._y,this._z=t*r+e*this._z,this.normalize(),this._onChangeCallback(),this}const l=Math.sqrt(a),c=Math.atan2(l,o),u=Math.sin((1-e)*c)/l,h=Math.sin(e*c)/l;return this._w=s*u+this._w*h,this._x=n*u+this._x*h,this._y=i*u+this._y*h,this._z=r*u+this._z*h,this._onChangeCallback(),this}slerpQuaternions(t,e,n){this.copy(t).slerp(e,n)}random(){const t=Math.random(),e=Math.sqrt(1-t),n=Math.sqrt(t),i=2*Math.PI*Math.random(),r=2*Math.PI*Math.random();return this.set(e*Math.cos(i),n*Math.sin(r),n*Math.cos(r),e*Math.sin(i))}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._w===this._w}fromArray(t,e=0){return this._x=t[e],this._y=t[e+1],this._z=t[e+2],this._w=t[e+3],this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._w,t}fromBufferAttribute(t,e){return this._x=t.getX(e),this._y=t.getY(e),this._z=t.getZ(e),this._w=t.getW(e),this}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}r.prototype.isQuaternion=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return i}));class i{constructor(t=0,e=0,n=0,i=1){this.x=t,this.y=e,this.z=n,this.w=i}get width(){return this.z}set width(t){this.z=t}get height(){return this.w}set height(t){this.w=t}set(t,e,n,i){return this.x=t,this.y=e,this.z=n,this.w=i,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this.w=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setW(t){return this.w=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;case 3:this.w=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;case 3:return this.w;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y,this.z,this.w)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this.w=void 0!==t.w?t.w:1,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this.w+=t.w,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this.w+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this.w=t.w+e.w,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this.w+=t.w*e,this}sub(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this.w-=t.w,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this.w-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this.w=t.w-e.w,this}multiply(t){return this.x*=t.x,this.y*=t.y,this.z*=t.z,this.w*=t.w,this}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this.w*=t,this}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,r=this.w,s=t.elements;return this.x=s[0]*e+s[4]*n+s[8]*i+s[12]*r,this.y=s[1]*e+s[5]*n+s[9]*i+s[13]*r,this.z=s[2]*e+s[6]*n+s[10]*i+s[14]*r,this.w=s[3]*e+s[7]*n+s[11]*i+s[15]*r,this}divideScalar(t){return this.multiplyScalar(1/t)}setAxisAngleFromQuaternion(t){this.w=2*Math.acos(t.w);const e=Math.sqrt(1-t.w*t.w);return e<1e-4?(this.x=1,this.y=0,this.z=0):(this.x=t.x/e,this.y=t.y/e,this.z=t.z/e),this}setAxisAngleFromRotationMatrix(t){let e,n,i,r;const s=.01,o=.1,a=t.elements,l=a[0],c=a[4],u=a[8],h=a[1],d=a[5],p=a[9],_=a[2],m=a[6],f=a[10];if(Math.abs(c-h)a&&t>g?tg?a1){for(let t=0;t1){for(let t=0;t0){i.children=[];for(let e=0;e0){i.animations=[];for(let e=0;e0&&(n.geometries=e),i.length>0&&(n.materials=i),r.length>0&&(n.textures=r),o.length>0&&(n.images=o),a.length>0&&(n.shapes=a),l.length>0&&(n.skeletons=l),c.length>0&&(n.animations=c)}return n.object=i,n;function s(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}}clone(t){return(new this.constructor).copy(this,t)}copy(t,e=!0){if(this.name=t.name,this.up.copy(t.up),this.position.copy(t.position),this.rotation.order=t.rotation.order,this.quaternion.copy(t.quaternion),this.scale.copy(t.scale),this.matrix.copy(t.matrix),this.matrixWorld.copy(t.matrixWorld),this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrixWorldNeedsUpdate=t.matrixWorldNeedsUpdate,this.layers.mask=t.layers.mask,this.visible=t.visible,this.castShadow=t.castShadow,this.receiveShadow=t.receiveShadow,this.frustumCulled=t.frustumCulled,this.renderOrder=t.renderOrder,this.userData=JSON.parse(JSON.stringify(t.userData)),!0===e)for(let e=0;e0&&console.error(\\\\\\\"THREE.Matrix3: the constructor no longer reads arguments. use .set() instead.\\\\\\\")}set(t,e,n,i,r,s,o,a,l){const c=this.elements;return c[0]=t,c[1]=i,c[2]=o,c[3]=e,c[4]=r,c[5]=a,c[6]=n,c[7]=s,c[8]=l,this}identity(){return this.set(1,0,0,0,1,0,0,0,1),this}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],this}extractBasis(t,e,n){return t.setFromMatrix3Column(this,0),e.setFromMatrix3Column(this,1),n.setFromMatrix3Column(this,2),this}setFromMatrix4(t){const e=t.elements;return this.set(e[0],e[4],e[8],e[1],e[5],e[9],e[2],e[6],e[10]),this}multiply(t){return this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,r=this.elements,s=n[0],o=n[3],a=n[6],l=n[1],c=n[4],u=n[7],h=n[2],d=n[5],p=n[8],_=i[0],m=i[3],f=i[6],g=i[1],v=i[4],y=i[7],x=i[2],b=i[5],w=i[8];return r[0]=s*_+o*g+a*x,r[3]=s*m+o*v+a*b,r[6]=s*f+o*y+a*w,r[1]=l*_+c*g+u*x,r[4]=l*m+c*v+u*b,r[7]=l*f+c*y+u*w,r[2]=h*_+d*g+p*x,r[5]=h*m+d*v+p*b,r[8]=h*f+d*y+p*w,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[3]*=t,e[6]*=t,e[1]*=t,e[4]*=t,e[7]*=t,e[2]*=t,e[5]*=t,e[8]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],s=t[4],o=t[5],a=t[6],l=t[7],c=t[8];return e*s*c-e*o*l-n*r*c+n*o*a+i*r*l-i*s*a}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],s=t[4],o=t[5],a=t[6],l=t[7],c=t[8],u=c*s-o*l,h=o*a-c*r,d=l*r-s*a,p=e*u+n*h+i*d;if(0===p)return this.set(0,0,0,0,0,0,0,0,0);const _=1/p;return t[0]=u*_,t[1]=(i*l-c*n)*_,t[2]=(o*n-i*s)*_,t[3]=h*_,t[4]=(c*e-i*a)*_,t[5]=(i*r-o*e)*_,t[6]=d*_,t[7]=(n*a-l*e)*_,t[8]=(s*e-n*r)*_,this}transpose(){let t;const e=this.elements;return t=e[1],e[1]=e[3],e[3]=t,t=e[2],e[2]=e[6],e[6]=t,t=e[5],e[5]=e[7],e[7]=t,this}getNormalMatrix(t){return this.setFromMatrix4(t).invert().transpose()}transposeIntoArray(t){const e=this.elements;return t[0]=e[0],t[1]=e[3],t[2]=e[6],t[3]=e[1],t[4]=e[4],t[5]=e[7],t[6]=e[2],t[7]=e[5],t[8]=e[8],this}setUvTransform(t,e,n,i,r,s,o){const a=Math.cos(r),l=Math.sin(r);return this.set(n*a,n*l,-n*(a*s+l*o)+s+t,-i*l,i*a,-i*(-l*s+a*o)+o+e,0,0,1),this}scale(t,e){const n=this.elements;return n[0]*=t,n[3]*=t,n[6]*=t,n[1]*=e,n[4]*=e,n[7]*=e,this}rotate(t){const e=Math.cos(t),n=Math.sin(t),i=this.elements,r=i[0],s=i[3],o=i[6],a=i[1],l=i[4],c=i[7];return i[0]=e*r+n*a,i[3]=e*s+n*l,i[6]=e*o+n*c,i[1]=-n*r+e*a,i[4]=-n*s+e*l,i[7]=-n*o+e*c,this}translate(t,e){const n=this.elements;return n[0]+=t*n[2],n[3]+=t*n[5],n[6]+=t*n[8],n[1]+=e*n[2],n[4]+=e*n[5],n[7]+=e*n[8],this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<9;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<9;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t}clone(){return(new this.constructor).fromArray(this.elements)}}i.prototype.isMatrix3=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return a}));var i=n(15),r=n(1),s=n(3);let o=0;class a extends i.a{constructor(){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:o++}),this.uuid=s.h(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Material\\\\\\\",this.fog=!0,this.blending=r.xb,this.side=r.H,this.vertexColors=!1,this.opacity=1,this.format=r.Ib,this.transparent=!1,this.blendSrc=r.Nc,this.blendDst=r.Db,this.blendEquation=r.b,this.blendSrcAlpha=null,this.blendDstAlpha=null,this.blendEquationAlpha=null,this.depthFunc=r.T,this.depthTest=!0,this.depthWrite=!0,this.stencilWriteMask=255,this.stencilFunc=r.h,this.stencilRef=0,this.stencilFuncMask=255,this.stencilFail=r.R,this.stencilZFail=r.R,this.stencilZPass=r.R,this.stencilWrite=!1,this.clippingPlanes=null,this.clipIntersection=!1,this.clipShadows=!1,this.shadowSide=null,this.colorWrite=!0,this.precision=null,this.polygonOffset=!1,this.polygonOffsetFactor=0,this.polygonOffsetUnits=0,this.dithering=!1,this.alphaToCoverage=!1,this.premultipliedAlpha=!1,this.visible=!0,this.toneMapped=!0,this.userData={},this.version=0,this._alphaTest=0}get alphaTest(){return this._alphaTest}set alphaTest(t){this._alphaTest>0!=t>0&&this.version++,this._alphaTest=t}onBuild(){}onBeforeRender(){}onBeforeCompile(){}customProgramCacheKey(){return this.onBeforeCompile.toString()}setValues(t){if(void 0!==t)for(const e in t){const n=t[e];if(void 0===n){console.warn(\\\\\\\"THREE.Material: '\\\\\\\"+e+\\\\\\\"' parameter is undefined.\\\\\\\");continue}if(\\\\\\\"shading\\\\\\\"===e){console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": .shading has been removed. Use the boolean .flatShading instead.\\\\\\\"),this.flatShading=n===r.F;continue}const i=this[e];void 0!==i?i&&i.isColor?i.set(n):i&&i.isVector3&&n&&n.isVector3?i.copy(n):this[e]=n:console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": '\\\\\\\"+e+\\\\\\\"' is not a property of this material.\\\\\\\")}}toJSON(t){const e=void 0===t||\\\\\\\"string\\\\\\\"==typeof t;e&&(t={textures:{},images:{}});const n={metadata:{version:4.5,type:\\\\\\\"Material\\\\\\\",generator:\\\\\\\"Material.toJSON\\\\\\\"}};function i(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}if(n.uuid=this.uuid,n.type=this.type,\\\\\\\"\\\\\\\"!==this.name&&(n.name=this.name),this.color&&this.color.isColor&&(n.color=this.color.getHex()),void 0!==this.roughness&&(n.roughness=this.roughness),void 0!==this.metalness&&(n.metalness=this.metalness),void 0!==this.sheen&&(n.sheen=this.sheen),this.sheenTint&&this.sheenTint.isColor&&(n.sheenTint=this.sheenTint.getHex()),void 0!==this.sheenRoughness&&(n.sheenRoughness=this.sheenRoughness),this.emissive&&this.emissive.isColor&&(n.emissive=this.emissive.getHex()),this.emissiveIntensity&&1!==this.emissiveIntensity&&(n.emissiveIntensity=this.emissiveIntensity),this.specular&&this.specular.isColor&&(n.specular=this.specular.getHex()),void 0!==this.specularIntensity&&(n.specularIntensity=this.specularIntensity),this.specularTint&&this.specularTint.isColor&&(n.specularTint=this.specularTint.getHex()),void 0!==this.shininess&&(n.shininess=this.shininess),void 0!==this.clearcoat&&(n.clearcoat=this.clearcoat),void 0!==this.clearcoatRoughness&&(n.clearcoatRoughness=this.clearcoatRoughness),this.clearcoatMap&&this.clearcoatMap.isTexture&&(n.clearcoatMap=this.clearcoatMap.toJSON(t).uuid),this.clearcoatRoughnessMap&&this.clearcoatRoughnessMap.isTexture&&(n.clearcoatRoughnessMap=this.clearcoatRoughnessMap.toJSON(t).uuid),this.clearcoatNormalMap&&this.clearcoatNormalMap.isTexture&&(n.clearcoatNormalMap=this.clearcoatNormalMap.toJSON(t).uuid,n.clearcoatNormalScale=this.clearcoatNormalScale.toArray()),this.map&&this.map.isTexture&&(n.map=this.map.toJSON(t).uuid),this.matcap&&this.matcap.isTexture&&(n.matcap=this.matcap.toJSON(t).uuid),this.alphaMap&&this.alphaMap.isTexture&&(n.alphaMap=this.alphaMap.toJSON(t).uuid),this.lightMap&&this.lightMap.isTexture&&(n.lightMap=this.lightMap.toJSON(t).uuid,n.lightMapIntensity=this.lightMapIntensity),this.aoMap&&this.aoMap.isTexture&&(n.aoMap=this.aoMap.toJSON(t).uuid,n.aoMapIntensity=this.aoMapIntensity),this.bumpMap&&this.bumpMap.isTexture&&(n.bumpMap=this.bumpMap.toJSON(t).uuid,n.bumpScale=this.bumpScale),this.normalMap&&this.normalMap.isTexture&&(n.normalMap=this.normalMap.toJSON(t).uuid,n.normalMapType=this.normalMapType,n.normalScale=this.normalScale.toArray()),this.displacementMap&&this.displacementMap.isTexture&&(n.displacementMap=this.displacementMap.toJSON(t).uuid,n.displacementScale=this.displacementScale,n.displacementBias=this.displacementBias),this.roughnessMap&&this.roughnessMap.isTexture&&(n.roughnessMap=this.roughnessMap.toJSON(t).uuid),this.metalnessMap&&this.metalnessMap.isTexture&&(n.metalnessMap=this.metalnessMap.toJSON(t).uuid),this.emissiveMap&&this.emissiveMap.isTexture&&(n.emissiveMap=this.emissiveMap.toJSON(t).uuid),this.specularMap&&this.specularMap.isTexture&&(n.specularMap=this.specularMap.toJSON(t).uuid),this.specularIntensityMap&&this.specularIntensityMap.isTexture&&(n.specularIntensityMap=this.specularIntensityMap.toJSON(t).uuid),this.specularTintMap&&this.specularTintMap.isTexture&&(n.specularTintMap=this.specularTintMap.toJSON(t).uuid),this.envMap&&this.envMap.isTexture&&(n.envMap=this.envMap.toJSON(t).uuid,void 0!==this.combine&&(n.combine=this.combine)),void 0!==this.envMapIntensity&&(n.envMapIntensity=this.envMapIntensity),void 0!==this.reflectivity&&(n.reflectivity=this.reflectivity),void 0!==this.refractionRatio&&(n.refractionRatio=this.refractionRatio),this.gradientMap&&this.gradientMap.isTexture&&(n.gradientMap=this.gradientMap.toJSON(t).uuid),void 0!==this.transmission&&(n.transmission=this.transmission),this.transmissionMap&&this.transmissionMap.isTexture&&(n.transmissionMap=this.transmissionMap.toJSON(t).uuid),void 0!==this.thickness&&(n.thickness=this.thickness),this.thicknessMap&&this.thicknessMap.isTexture&&(n.thicknessMap=this.thicknessMap.toJSON(t).uuid),void 0!==this.attenuationDistance&&(n.attenuationDistance=this.attenuationDistance),void 0!==this.attenuationTint&&(n.attenuationTint=this.attenuationTint.getHex()),void 0!==this.size&&(n.size=this.size),null!==this.shadowSide&&(n.shadowSide=this.shadowSide),void 0!==this.sizeAttenuation&&(n.sizeAttenuation=this.sizeAttenuation),this.blending!==r.xb&&(n.blending=this.blending),this.side!==r.H&&(n.side=this.side),this.vertexColors&&(n.vertexColors=!0),this.opacity<1&&(n.opacity=this.opacity),this.format!==r.Ib&&(n.format=this.format),!0===this.transparent&&(n.transparent=this.transparent),n.depthFunc=this.depthFunc,n.depthTest=this.depthTest,n.depthWrite=this.depthWrite,n.colorWrite=this.colorWrite,n.stencilWrite=this.stencilWrite,n.stencilWriteMask=this.stencilWriteMask,n.stencilFunc=this.stencilFunc,n.stencilRef=this.stencilRef,n.stencilFuncMask=this.stencilFuncMask,n.stencilFail=this.stencilFail,n.stencilZFail=this.stencilZFail,n.stencilZPass=this.stencilZPass,this.rotation&&0!==this.rotation&&(n.rotation=this.rotation),!0===this.polygonOffset&&(n.polygonOffset=!0),0!==this.polygonOffsetFactor&&(n.polygonOffsetFactor=this.polygonOffsetFactor),0!==this.polygonOffsetUnits&&(n.polygonOffsetUnits=this.polygonOffsetUnits),this.linewidth&&1!==this.linewidth&&(n.linewidth=this.linewidth),void 0!==this.dashSize&&(n.dashSize=this.dashSize),void 0!==this.gapSize&&(n.gapSize=this.gapSize),void 0!==this.scale&&(n.scale=this.scale),!0===this.dithering&&(n.dithering=!0),this.alphaTest>0&&(n.alphaTest=this.alphaTest),!0===this.alphaToCoverage&&(n.alphaToCoverage=this.alphaToCoverage),!0===this.premultipliedAlpha&&(n.premultipliedAlpha=this.premultipliedAlpha),!0===this.wireframe&&(n.wireframe=this.wireframe),this.wireframeLinewidth>1&&(n.wireframeLinewidth=this.wireframeLinewidth),\\\\\\\"round\\\\\\\"!==this.wireframeLinecap&&(n.wireframeLinecap=this.wireframeLinecap),\\\\\\\"round\\\\\\\"!==this.wireframeLinejoin&&(n.wireframeLinejoin=this.wireframeLinejoin),!0===this.flatShading&&(n.flatShading=this.flatShading),!1===this.visible&&(n.visible=!1),!1===this.toneMapped&&(n.toneMapped=!1),\\\\\\\"{}\\\\\\\"!==JSON.stringify(this.userData)&&(n.userData=this.userData),e){const e=i(t.textures),r=i(t.images);e.length>0&&(n.textures=e),r.length>0&&(n.images=r)}return n}clone(){return(new this.constructor).copy(this)}copy(t){this.name=t.name,this.fog=t.fog,this.blending=t.blending,this.side=t.side,this.vertexColors=t.vertexColors,this.opacity=t.opacity,this.format=t.format,this.transparent=t.transparent,this.blendSrc=t.blendSrc,this.blendDst=t.blendDst,this.blendEquation=t.blendEquation,this.blendSrcAlpha=t.blendSrcAlpha,this.blendDstAlpha=t.blendDstAlpha,this.blendEquationAlpha=t.blendEquationAlpha,this.depthFunc=t.depthFunc,this.depthTest=t.depthTest,this.depthWrite=t.depthWrite,this.stencilWriteMask=t.stencilWriteMask,this.stencilFunc=t.stencilFunc,this.stencilRef=t.stencilRef,this.stencilFuncMask=t.stencilFuncMask,this.stencilFail=t.stencilFail,this.stencilZFail=t.stencilZFail,this.stencilZPass=t.stencilZPass,this.stencilWrite=t.stencilWrite;const e=t.clippingPlanes;let n=null;if(null!==e){const t=e.length;n=new Array(t);for(let i=0;i!==t;++i)n[i]=e[i].clone()}return this.clippingPlanes=n,this.clipIntersection=t.clipIntersection,this.clipShadows=t.clipShadows,this.shadowSide=t.shadowSide,this.colorWrite=t.colorWrite,this.precision=t.precision,this.polygonOffset=t.polygonOffset,this.polygonOffsetFactor=t.polygonOffsetFactor,this.polygonOffsetUnits=t.polygonOffsetUnits,this.dithering=t.dithering,this.alphaTest=t.alphaTest,this.alphaToCoverage=t.alphaToCoverage,this.premultipliedAlpha=t.premultipliedAlpha,this.visible=t.visible,this.toneMapped=t.toneMapped,this.userData=JSON.parse(JSON.stringify(t.userData)),this}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}set needsUpdate(t){!0===t&&this.version++}}a.prototype.isMaterial=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return r}));var i=n(29);class r{constructor(t){this.manager=void 0!==t?t:i.a,this.crossOrigin=\\\\\\\"anonymous\\\\\\\",this.withCredentials=!1,this.path=\\\\\\\"\\\\\\\",this.resourcePath=\\\\\\\"\\\\\\\",this.requestHeader={}}load(){}loadAsync(t,e){const n=this;return new Promise((function(i,r){n.load(t,i,e,r)}))}parse(){}setCrossOrigin(t){return this.crossOrigin=t,this}setWithCredentials(t){return this.withCredentials=t,this}setPath(t){return this.path=t,this}setResourcePath(t){return this.resourcePath=t,this}setRequestHeader(t){return this.requestHeader=t,this}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return L}));var i=n(0),r=n(2),s=n(18),o=n(39),a=n(5),l=n(10),c=n(40),u=n(1),h=n(27),d=n(7);const p=new a.a,_=new o.a,m=new s.a,f=new i.a,g=new i.a,v=new i.a,y=new i.a,x=new i.a,b=new i.a,w=new i.a,T=new i.a,A=new i.a,E=new r.a,M=new r.a,S=new r.a,C=new i.a,N=new i.a;class L extends l.a{constructor(t=new d.a,e=new h.a){super(),this.type=\\\\\\\"Mesh\\\\\\\",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),void 0!==t.morphTargetInfluences&&(this.morphTargetInfluences=t.morphTargetInfluences.slice()),void 0!==t.morphTargetDictionary&&(this.morphTargetDictionary=Object.assign({},t.morphTargetDictionary)),this.material=t.material,this.geometry=t.geometry,this}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e0&&console.error(\\\\\\\"THREE.Mesh.updateMorphTargets() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}raycast(t,e){const n=this.geometry,i=this.material,r=this.matrixWorld;if(void 0===i)return;if(null===n.boundingSphere&&n.computeBoundingSphere(),m.copy(n.boundingSphere),m.applyMatrix4(r),!1===t.ray.intersectsSphere(m))return;if(p.copy(r).invert(),_.copy(t.ray).applyMatrix4(p),null!==n.boundingBox&&!1===_.intersectsBox(n.boundingBox))return;let s;if(n.isBufferGeometry){const r=n.index,o=n.attributes.position,a=n.morphAttributes.position,l=n.morphTargetsRelative,c=n.attributes.uv,u=n.attributes.uv2,h=n.groups,d=n.drawRange;if(null!==r)if(Array.isArray(i))for(let n=0,p=h.length;nn.far?null:{distance:c,point:N.clone(),object:t}}(t,e,n,s,f,g,v,C);if(O){h&&(E.fromBufferAttribute(h,p),M.fromBufferAttribute(h,_),S.fromBufferAttribute(h,m),O.uv=c.a.getUV(C,f,g,v,E,M,S,new r.a)),d&&(E.fromBufferAttribute(d,p),M.fromBufferAttribute(d,_),S.fromBufferAttribute(d,m),O.uv2=c.a.getUV(C,f,g,v,E,M,S,new r.a));const t={a:p,b:_,c:m,normal:new i.a,materialIndex:0};c.a.getNormal(f,g,v,t.normal),O.face=t}return O}L.prototype.isMesh=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return i}));class i{addEventListener(t,e){void 0===this._listeners&&(this._listeners={});const n=this._listeners;void 0===n[t]&&(n[t]=[]),-1===n[t].indexOf(e)&&n[t].push(e)}hasEventListener(t,e){if(void 0===this._listeners)return!1;const n=this._listeners;return void 0!==n[t]&&-1!==n[t].indexOf(e)}removeEventListener(t,e){if(void 0===this._listeners)return;const n=this._listeners[t];if(void 0!==n){const t=n.indexOf(e);-1!==t&&n.splice(t,1)}}dispatchEvent(t){if(void 0===this._listeners)return;const e=this._listeners[t.type];if(void 0!==e){t.target=this;const n=e.slice(0);for(let e=0,i=n.length;er&&(r=l),c>s&&(s=c),u>o&&(o=u)}return this.min.set(e,n,i),this.max.set(r,s,o),this}setFromBufferAttribute(t){let e=1/0,n=1/0,i=1/0,r=-1/0,s=-1/0,o=-1/0;for(let a=0,l=t.count;ar&&(r=l),c>s&&(s=c),u>o&&(o=u)}return this.min.set(e,n,i),this.max.set(r,s,o),this}setFromPoints(t){this.makeEmpty();for(let e=0,n=t.length;ethis.max.x||t.ythis.max.y||t.zthis.max.z)}containsBox(t){return this.min.x<=t.min.x&&t.max.x<=this.max.x&&this.min.y<=t.min.y&&t.max.y<=this.max.y&&this.min.z<=t.min.z&&t.max.z<=this.max.z}getParameter(t,e){return e.set((t.x-this.min.x)/(this.max.x-this.min.x),(t.y-this.min.y)/(this.max.y-this.min.y),(t.z-this.min.z)/(this.max.z-this.min.z))}intersectsBox(t){return!(t.max.xthis.max.x||t.max.ythis.max.y||t.max.zthis.max.z)}intersectsSphere(t){return this.clampPoint(t.center,o),o.distanceToSquared(t.center)<=t.radius*t.radius}intersectsPlane(t){let e,n;return t.normal.x>0?(e=t.normal.x*this.min.x,n=t.normal.x*this.max.x):(e=t.normal.x*this.max.x,n=t.normal.x*this.min.x),t.normal.y>0?(e+=t.normal.y*this.min.y,n+=t.normal.y*this.max.y):(e+=t.normal.y*this.max.y,n+=t.normal.y*this.min.y),t.normal.z>0?(e+=t.normal.z*this.min.z,n+=t.normal.z*this.max.z):(e+=t.normal.z*this.max.z,n+=t.normal.z*this.min.z),e<=-t.constant&&n>=-t.constant}intersectsTriangle(t){if(this.isEmpty())return!1;this.getCenter(_),m.subVectors(this.max,_),l.subVectors(t.a,_),c.subVectors(t.b,_),u.subVectors(t.c,_),h.subVectors(c,l),d.subVectors(u,c),p.subVectors(l,u);let e=[0,-h.z,h.y,0,-d.z,d.y,0,-p.z,p.y,h.z,0,-h.x,d.z,0,-d.x,p.z,0,-p.x,-h.y,h.x,0,-d.y,d.x,0,-p.y,p.x,0];return!!v(e,l,c,u,m)&&(e=[1,0,0,0,1,0,0,0,1],!!v(e,l,c,u,m)&&(f.crossVectors(h,d),e=[f.x,f.y,f.z],v(e,l,c,u,m)))}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return o.copy(t).clamp(this.min,this.max).sub(t).length()}getBoundingSphere(t){return this.getCenter(t.center),t.radius=.5*this.getSize(o).length(),t}intersect(t){return this.min.max(t.min),this.max.min(t.max),this.isEmpty()&&this.makeEmpty(),this}union(t){return this.min.min(t.min),this.max.max(t.max),this}applyMatrix4(t){return this.isEmpty()||(s[0].set(this.min.x,this.min.y,this.min.z).applyMatrix4(t),s[1].set(this.min.x,this.min.y,this.max.z).applyMatrix4(t),s[2].set(this.min.x,this.max.y,this.min.z).applyMatrix4(t),s[3].set(this.min.x,this.max.y,this.max.z).applyMatrix4(t),s[4].set(this.max.x,this.min.y,this.min.z).applyMatrix4(t),s[5].set(this.max.x,this.min.y,this.max.z).applyMatrix4(t),s[6].set(this.max.x,this.max.y,this.min.z).applyMatrix4(t),s[7].set(this.max.x,this.max.y,this.max.z).applyMatrix4(t),this.setFromPoints(s)),this}translate(t){return this.min.add(t),this.max.add(t),this}equals(t){return t.min.equals(this.min)&&t.max.equals(this.max)}}r.prototype.isBox3=!0;const s=[new i.a,new i.a,new i.a,new i.a,new i.a,new i.a,new i.a,new i.a],o=new i.a,a=new r,l=new i.a,c=new i.a,u=new i.a,h=new i.a,d=new i.a,p=new i.a,_=new i.a,m=new i.a,f=new i.a,g=new i.a;function v(t,e,n,i,r){for(let s=0,o=t.length-3;s<=o;s+=3){g.fromArray(t,s);const o=r.x*Math.abs(g.x)+r.y*Math.abs(g.y)+r.z*Math.abs(g.z),a=e.dot(g),l=n.dot(g),c=i.dot(g);if(Math.max(-Math.max(a,l,c),Math.min(a,l,c))>o)return!1}return!0}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return i}));const i={enabled:!1,files:{},add:function(t,e){!1!==this.enabled&&(this.files[t]=e)},get:function(t){if(!1!==this.enabled)return this.files[t]},remove:function(t){delete this.files[t]},clear:function(){this.files={}}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return c}));var i=n(16),r=n(0);const s=new i.a,o=new r.a,a=new r.a,l=new r.a;class c{constructor(t=new r.a,e=-1){this.center=t,this.radius=e}set(t,e){return this.center.copy(t),this.radius=e,this}setFromPoints(t,e){const n=this.center;void 0!==e?n.copy(e):s.setFromPoints(t).getCenter(n);let i=0;for(let e=0,r=t.length;ethis.radius*this.radius&&(e.sub(this.center).normalize(),e.multiplyScalar(this.radius).add(this.center)),e}getBoundingBox(t){return this.isEmpty()?(t.makeEmpty(),t):(t.set(this.center,this.center),t.expandByScalar(this.radius),t)}applyMatrix4(t){return this.center.applyMatrix4(t),this.radius=this.radius*t.getMaxScaleOnAxis(),this}translate(t){return this.center.add(t),this}expandByPoint(t){l.subVectors(t,this.center);const e=l.lengthSq();if(e>this.radius*this.radius){const t=Math.sqrt(e),n=.5*(t-this.radius);this.center.add(l.multiplyScalar(n/t)),this.radius+=n}return this}union(t){return a.subVectors(t.center,this.center).normalize().multiplyScalar(t.radius),this.expandByPoint(o.copy(t.center).add(a)),this.expandByPoint(o.copy(t.center).sub(a)),this}equals(t){return t.center.equals(this.center)&&t.radius===this.radius}clone(){return(new this.constructor).copy(this)}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return s}));var i=n(8),r=n(1);const s={arraySlice:function(t,e,n){return s.isTypedArray(t)?new t.constructor(t.subarray(e,void 0!==n?n:t.length)):t.slice(e,n)},convertArray:function(t,e,n){return!t||!n&&t.constructor===e?t:\\\\\\\"number\\\\\\\"==typeof e.BYTES_PER_ELEMENT?new e(t):Array.prototype.slice.call(t)},isTypedArray:function(t){return ArrayBuffer.isView(t)&&!(t instanceof DataView)},getKeyframeOrder:function(t){const e=t.length,n=new Array(e);for(let t=0;t!==e;++t)n[t]=t;return n.sort((function(e,n){return t[e]-t[n]})),n},sortedArray:function(t,e,n){const i=t.length,r=new t.constructor(i);for(let s=0,o=0;o!==i;++s){const i=n[s]*e;for(let n=0;n!==e;++n)r[o++]=t[i+n]}return r},flattenJSON:function(t,e,n,i){let r=1,s=t[0];for(;void 0!==s&&void 0===s[i];)s=t[r++];if(void 0===s)return;let o=s[i];if(void 0!==o)if(Array.isArray(o))do{o=s[i],void 0!==o&&(e.push(s.time),n.push.apply(n,o)),s=t[r++]}while(void 0!==s);else if(void 0!==o.toArray)do{o=s[i],void 0!==o&&(e.push(s.time),o.toArray(n,n.length)),s=t[r++]}while(void 0!==s);else do{o=s[i],void 0!==o&&(e.push(s.time),n.push(o)),s=t[r++]}while(void 0!==s)},subclip:function(t,e,n,i,r=30){const o=t.clone();o.name=e;const a=[];for(let t=0;t=i)){c.push(e.times[t]);for(let n=0;no.tracks[t].times[0]&&(l=o.tracks[t].times[0]);for(let t=0;t=r.times[p]){const t=p*u+c,e=t+u-c;_=s.arraySlice(r.values,t,e)}else{const t=r.createInterpolant(),e=c,n=u-c;t.evaluate(l),_=s.arraySlice(t.resultBuffer,e,n)}if(\\\\\\\"quaternion\\\\\\\"===o){(new i.a).fromArray(_).normalize().conjugate().toArray(_)}const m=a.times.length;for(let t=0;te&&(e=t[n]);return e}n.d(e,\\\\\\\"a\\\\\\\",(function(){return i})),n.d(e,\\\\\\\"c\\\\\\\",(function(){return s})),n.d(e,\\\\\\\"b\\\\\\\",(function(){return o}));const r={Int8Array:Int8Array,Uint8Array:Uint8Array,Uint8ClampedArray:Uint8ClampedArray,Int16Array:Int16Array,Uint16Array:Uint16Array,Int32Array:Int32Array,Uint32Array:Uint32Array,Float32Array:Float32Array,Float64Array:Float64Array};function s(t,e){return new r[t](e)}function o(t){return document.createElementNS(\\\\\\\"http://www.w3.org/1999/xhtml\\\\\\\",t)}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return o}));var i=n(17),r=n(13);const s={};class o extends r.a{constructor(t){super(t)}load(t,e,n,r){void 0===t&&(t=\\\\\\\"\\\\\\\"),void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const o=this,a=i.a.get(t);if(void 0!==a)return o.manager.itemStart(t),setTimeout((function(){e&&e(a),o.manager.itemEnd(t)}),0),a;if(void 0!==s[t])return void s[t].push({onLoad:e,onProgress:n,onError:r});const l=t.match(/^data:(.*?)(;base64)?,(.*)$/);let c;if(l){const n=l[1],i=!!l[2];let s=l[3];s=decodeURIComponent(s),i&&(s=atob(s));try{let i;const r=(this.responseType||\\\\\\\"\\\\\\\").toLowerCase();switch(r){case\\\\\\\"arraybuffer\\\\\\\":case\\\\\\\"blob\\\\\\\":const t=new Uint8Array(s.length);for(let e=0;e1)switch(this.wrapS){case r.wc:t.x=t.x-Math.floor(t.x);break;case r.n:t.x=t.x<0?0:1;break;case r.kb:1===Math.abs(Math.floor(t.x)%2)?t.x=Math.ceil(t.x)-t.x:t.x=t.x-Math.floor(t.x)}if(t.y<0||t.y>1)switch(this.wrapT){case r.wc:t.y=t.y-Math.floor(t.y);break;case r.n:t.y=t.y<0?0:1;break;case r.kb:1===Math.abs(Math.floor(t.y)%2)?t.y=Math.ceil(t.y)-t.y:t.y=t.y-Math.floor(t.y)}return this.flipY&&(t.y=1-t.y),t}set needsUpdate(t){!0===t&&this.version++}}function d(t){return\\\\\\\"undefined\\\\\\\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\\\\\\\"undefined\\\\\\\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\\\\\\\"undefined\\\\\\\"!=typeof ImageBitmap&&t instanceof ImageBitmap?class{static getDataURL(t){if(/^data:/i.test(t.src))return t.src;if(\\\\\\\"undefined\\\\\\\"==typeof HTMLCanvasElement)return t.src;let e;if(t instanceof HTMLCanvasElement)e=t;else{void 0===c&&(c=Object(l.b)(\\\\\\\"canvas\\\\\\\")),c.width=t.width,c.height=t.height;const n=c.getContext(\\\\\\\"2d\\\\\\\");t instanceof ImageData?n.putImageData(t,0,0):n.drawImage(t,0,0,t.width,t.height),e=c}return e.width>2048||e.height>2048?(console.warn(\\\\\\\"THREE.ImageUtils.getDataURL: Image converted to jpg for performance reasons\\\\\\\",t),e.toDataURL(\\\\\\\"image/jpeg\\\\\\\",.6)):e.toDataURL(\\\\\\\"image/png\\\\\\\")}}.getDataURL(t):t.data?{data:Array.prototype.slice.call(t.data),width:t.width,height:t.height,type:t.data.constructor.name}:(console.warn(\\\\\\\"THREE.Texture: Unable to serialize Texture.\\\\\\\"),{})}h.DEFAULT_IMAGE=void 0,h.DEFAULT_MAPPING=r.Yc,h.prototype.isTexture=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return s}));var i=n(12),r=n(6);class s extends i.a{constructor(t){super(),this.type=\\\\\\\"LineBasicMaterial\\\\\\\",this.color=new r.a(16777215),this.linewidth=1,this.linecap=\\\\\\\"round\\\\\\\",this.linejoin=\\\\\\\"round\\\\\\\",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.linewidth=t.linewidth,this.linecap=t.linecap,this.linejoin=t.linejoin,this}}s.prototype.isLineBasicMaterial=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return a}));var i=n(3),r=n(2),s=n(0),o=n(5);class a{constructor(){this.type=\\\\\\\"Curve\\\\\\\",this.arcLengthDivisions=200}getPoint(){return console.warn(\\\\\\\"THREE.Curve: .getPoint() not implemented.\\\\\\\"),null}getPointAt(t,e){const n=this.getUtoTmapping(t);return this.getPoint(n,e)}getPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPoint(n/t));return e}getSpacedPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPointAt(n/t));return e}getLength(){const t=this.getLengths();return t[t.length-1]}getLengths(t=this.arcLengthDivisions){if(this.cacheArcLengths&&this.cacheArcLengths.length===t+1&&!this.needsUpdate)return this.cacheArcLengths;this.needsUpdate=!1;const e=[];let n,i=this.getPoint(0),r=0;e.push(0);for(let s=1;s<=t;s++)n=this.getPoint(s/t),r+=n.distanceTo(i),e.push(r),i=n;return this.cacheArcLengths=e,e}updateArcLengths(){this.needsUpdate=!0,this.getLengths()}getUtoTmapping(t,e){const n=this.getLengths();let i=0;const r=n.length;let s;s=e||t*n[r-1];let o,a=0,l=r-1;for(;a<=l;)if(i=Math.floor(a+(l-a)/2),o=n[i]-s,o<0)a=i+1;else{if(!(o>0)){l=i;break}l=i-1}if(i=l,n[i]===s)return i/(r-1);const c=n[i];return(i+(s-c)/(n[i+1]-c))/(r-1)}getTangent(t,e){const n=1e-4;let i=t-n,o=t+n;i<0&&(i=0),o>1&&(o=1);const a=this.getPoint(i),l=this.getPoint(o),c=e||(a.isVector2?new r.a:new s.a);return c.copy(l).sub(a).normalize(),c}getTangentAt(t,e){const n=this.getUtoTmapping(t);return this.getTangent(n,e)}computeFrenetFrames(t,e){const n=new s.a,r=[],a=[],l=[],c=new s.a,u=new o.a;for(let e=0;e<=t;e++){const n=e/t;r[e]=this.getTangentAt(n,new s.a)}a[0]=new s.a,l[0]=new s.a;let h=Number.MAX_VALUE;const d=Math.abs(r[0].x),p=Math.abs(r[0].y),_=Math.abs(r[0].z);d<=h&&(h=d,n.set(1,0,0)),p<=h&&(h=p,n.set(0,1,0)),_<=h&&n.set(0,0,1),c.crossVectors(r[0],n).normalize(),a[0].crossVectors(r[0],c),l[0].crossVectors(r[0],a[0]);for(let e=1;e<=t;e++){if(a[e]=a[e-1].clone(),l[e]=l[e-1].clone(),c.crossVectors(r[e-1],r[e]),c.length()>Number.EPSILON){c.normalize();const t=Math.acos(i.d(r[e-1].dot(r[e]),-1,1));a[e].applyMatrix4(u.makeRotationAxis(c,t))}l[e].crossVectors(r[e],a[e])}if(!0===e){let e=Math.acos(i.d(a[0].dot(a[t]),-1,1));e/=t,r[0].dot(c.crossVectors(a[0],a[t]))>0&&(e=-e);for(let n=1;n<=t;n++)a[n].applyMatrix4(u.makeRotationAxis(r[n],e*n)),l[n].crossVectors(r[n],a[n])}return{tangents:r,normals:a,binormals:l}}clone(){return(new this.constructor).copy(this)}copy(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}toJSON(){const t={metadata:{version:4.5,type:\\\\\\\"Curve\\\\\\\",generator:\\\\\\\"Curve.toJSON\\\\\\\"}};return t.arcLengthDivisions=this.arcLengthDivisions,t.type=this.type,t}fromJSON(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return c}));var i=n(1),r=n(70),s=n(71),o=n(38);class a extends o.a{constructor(t,e,n,i){super(t,e,n,i)}interpolate_(t){return this.copySampleValue_(t-1)}}var l=n(19);class c{constructor(t,e,n,i){if(void 0===t)throw new Error(\\\\\\\"THREE.KeyframeTrack: track name is undefined\\\\\\\");if(void 0===e||0===e.length)throw new Error(\\\\\\\"THREE.KeyframeTrack: no keyframes in track named \\\\\\\"+t);this.name=t,this.times=l.a.convertArray(e,this.TimeBufferType),this.values=l.a.convertArray(n,this.ValueBufferType),this.setInterpolation(i||this.DefaultInterpolation)}static toJSON(t){const e=t.constructor;let n;if(e.toJSON!==this.toJSON)n=e.toJSON(t);else{n={name:t.name,times:l.a.convertArray(t.times,Array),values:l.a.convertArray(t.values,Array)};const e=t.getInterpolation();e!==t.DefaultInterpolation&&(n.interpolation=e)}return n.type=t.ValueTypeName,n}InterpolantFactoryMethodDiscrete(t){return new a(this.times,this.values,this.getValueSize(),t)}InterpolantFactoryMethodLinear(t){return new s.a(this.times,this.values,this.getValueSize(),t)}InterpolantFactoryMethodSmooth(t){return new r.a(this.times,this.values,this.getValueSize(),t)}setInterpolation(t){let e;switch(t){case i.O:e=this.InterpolantFactoryMethodDiscrete;break;case i.P:e=this.InterpolantFactoryMethodLinear;break;case i.Q:e=this.InterpolantFactoryMethodSmooth}if(void 0===e){const e=\\\\\\\"unsupported interpolation for \\\\\\\"+this.ValueTypeName+\\\\\\\" keyframe track named \\\\\\\"+this.name;if(void 0===this.createInterpolant){if(t===this.DefaultInterpolation)throw new Error(e);this.setInterpolation(this.DefaultInterpolation)}return console.warn(\\\\\\\"THREE.KeyframeTrack:\\\\\\\",e),this}return this.createInterpolant=e,this}getInterpolation(){switch(this.createInterpolant){case this.InterpolantFactoryMethodDiscrete:return i.O;case this.InterpolantFactoryMethodLinear:return i.P;case this.InterpolantFactoryMethodSmooth:return i.Q}}getValueSize(){return this.values.length/this.times.length}shift(t){if(0!==t){const e=this.times;for(let n=0,i=e.length;n!==i;++n)e[n]+=t}return this}scale(t){if(1!==t){const e=this.times;for(let n=0,i=e.length;n!==i;++n)e[n]*=t}return this}trim(t,e){const n=this.times,i=n.length;let r=0,s=i-1;for(;r!==i&&n[r]e;)--s;if(++s,0!==r||s!==i){r>=s&&(s=Math.max(s,1),r=s-1);const t=this.getValueSize();this.times=l.a.arraySlice(n,r,s),this.values=l.a.arraySlice(this.values,r*t,s*t)}return this}validate(){let t=!0;const e=this.getValueSize();e-Math.floor(e)!=0&&(console.error(\\\\\\\"THREE.KeyframeTrack: Invalid value size in track.\\\\\\\",this),t=!1);const n=this.times,i=this.values,r=n.length;0===r&&(console.error(\\\\\\\"THREE.KeyframeTrack: Track is empty.\\\\\\\",this),t=!1);let s=null;for(let e=0;e!==r;e++){const i=n[e];if(\\\\\\\"number\\\\\\\"==typeof i&&isNaN(i)){console.error(\\\\\\\"THREE.KeyframeTrack: Time is not a valid number.\\\\\\\",this,e,i),t=!1;break}if(null!==s&&s>i){console.error(\\\\\\\"THREE.KeyframeTrack: Out of order keys.\\\\\\\",this,e,i,s),t=!1;break}s=i}if(void 0!==i&&l.a.isTypedArray(i))for(let e=0,n=i.length;e!==n;++e){const n=i[e];if(isNaN(n)){console.error(\\\\\\\"THREE.KeyframeTrack: Value is not a valid number.\\\\\\\",this,e,n),t=!1;break}}return t}optimize(){const t=l.a.arraySlice(this.times),e=l.a.arraySlice(this.values),n=this.getValueSize(),r=this.getInterpolation()===i.Q,s=t.length-1;let o=1;for(let i=1;i0){t[o]=t[s];for(let t=s*n,i=o*n,r=0;r!==n;++r)e[i+r]=e[t+r];++o}return o!==t.length?(this.times=l.a.arraySlice(t,0,o),this.values=l.a.arraySlice(e,0,o*n)):(this.times=t,this.values=e),this}clone(){const t=l.a.arraySlice(this.times,0),e=l.a.arraySlice(this.values,0),n=new(0,this.constructor)(this.name,t,e);return n.createInterpolant=this.createInterpolant,n}}c.prototype.TimeBufferType=Float32Array,c.prototype.ValueBufferType=Float32Array,c.prototype.DefaultInterpolation=i.P},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return o}));var i=n(12),r=n(1),s=n(6);class o extends i.a{constructor(t){super(),this.type=\\\\\\\"MeshBasicMaterial\\\\\\\",this.color=new s.a(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=r.nb,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}o.prototype.isMeshBasicMaterial=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return c}));var i=n(8),r=n(0),s=n(5),o=n(3);const a=new s.a,l=new i.a;class c{constructor(t=0,e=0,n=0,i=c.DefaultOrder){this._x=t,this._y=e,this._z=n,this._order=i}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get order(){return this._order}set order(t){this._order=t,this._onChangeCallback()}set(t,e,n,i=this._order){return this._x=t,this._y=e,this._z=n,this._order=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._order)}copy(t){return this._x=t._x,this._y=t._y,this._z=t._z,this._order=t._order,this._onChangeCallback(),this}setFromRotationMatrix(t,e=this._order,n=!0){const i=t.elements,r=i[0],s=i[4],a=i[8],l=i[1],c=i[5],u=i[9],h=i[2],d=i[6],p=i[10];switch(e){case\\\\\\\"XYZ\\\\\\\":this._y=Math.asin(Object(o.d)(a,-1,1)),Math.abs(a)<.9999999?(this._x=Math.atan2(-u,p),this._z=Math.atan2(-s,r)):(this._x=Math.atan2(d,c),this._z=0);break;case\\\\\\\"YXZ\\\\\\\":this._x=Math.asin(-Object(o.d)(u,-1,1)),Math.abs(u)<.9999999?(this._y=Math.atan2(a,p),this._z=Math.atan2(l,c)):(this._y=Math.atan2(-h,r),this._z=0);break;case\\\\\\\"ZXY\\\\\\\":this._x=Math.asin(Object(o.d)(d,-1,1)),Math.abs(d)<.9999999?(this._y=Math.atan2(-h,p),this._z=Math.atan2(-s,c)):(this._y=0,this._z=Math.atan2(l,r));break;case\\\\\\\"ZYX\\\\\\\":this._y=Math.asin(-Object(o.d)(h,-1,1)),Math.abs(h)<.9999999?(this._x=Math.atan2(d,p),this._z=Math.atan2(l,r)):(this._x=0,this._z=Math.atan2(-s,c));break;case\\\\\\\"YZX\\\\\\\":this._z=Math.asin(Object(o.d)(l,-1,1)),Math.abs(l)<.9999999?(this._x=Math.atan2(-u,c),this._y=Math.atan2(-h,r)):(this._x=0,this._y=Math.atan2(a,p));break;case\\\\\\\"XZY\\\\\\\":this._z=Math.asin(-Object(o.d)(s,-1,1)),Math.abs(s)<.9999999?(this._x=Math.atan2(d,c),this._y=Math.atan2(a,r)):(this._x=Math.atan2(-u,p),this._y=0);break;default:console.warn(\\\\\\\"THREE.Euler: .setFromRotationMatrix() encountered an unknown order: \\\\\\\"+e)}return this._order=e,!0===n&&this._onChangeCallback(),this}setFromQuaternion(t,e,n){return a.makeRotationFromQuaternion(t),this.setFromRotationMatrix(a,e,n)}setFromVector3(t,e=this._order){return this.set(t.x,t.y,t.z,e)}reorder(t){return l.setFromEuler(this),this.setFromQuaternion(l,t)}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._order===this._order}fromArray(t){return this._x=t[0],this._y=t[1],this._z=t[2],void 0!==t[3]&&(this._order=t[3]),this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._order,t}toVector3(t){return t?t.set(this._x,this._y,this._z):new r.a(this._x,this._y,this._z)}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}c.prototype.isEuler=!0,c.DefaultOrder=\\\\\\\"XYZ\\\\\\\",c.RotationOrders=[\\\\\\\"XYZ\\\\\\\",\\\\\\\"YZX\\\\\\\",\\\\\\\"ZXY\\\\\\\",\\\\\\\"XZY\\\\\\\",\\\\\\\"YXZ\\\\\\\",\\\\\\\"ZYX\\\\\\\"]},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return r})),n.d(e,\\\\\\\"b\\\\\\\",(function(){return i}));class i{constructor(t,e,n){const i=this;let r,s=!1,o=0,a=0;const l=[];this.onStart=void 0,this.onLoad=t,this.onProgress=e,this.onError=n,this.itemStart=function(t){a++,!1===s&&void 0!==i.onStart&&i.onStart(t,o,a),s=!0},this.itemEnd=function(t){o++,void 0!==i.onProgress&&i.onProgress(t,o,a),o===a&&(s=!1,void 0!==i.onLoad&&i.onLoad())},this.itemError=function(t){void 0!==i.onError&&i.onError(t)},this.resolveURL=function(t){return r?r(t):t},this.setURLModifier=function(t){return r=t,this},this.addHandler=function(t,e){return l.push(t,e),this},this.removeHandler=function(t){const e=l.indexOf(t);return-1!==e&&l.splice(e,2),this},this.getHandler=function(t){for(let e=0,n=l.length;e1?null:e.copy(n).multiplyScalar(r).add(t.start)}intersectsLine(t){const e=this.distanceToPoint(t.start),n=this.distanceToPoint(t.end);return e<0&&n>0||n<0&&e>0}intersectsBox(t){return t.intersectsPlane(this)}intersectsSphere(t){return t.intersectsPlane(this)}coplanarPoint(t){return t.copy(this.normal).multiplyScalar(-this.constant)}applyMatrix4(t,e){const n=e||a.getNormalMatrix(t),i=this.coplanarPoint(s).applyMatrix4(t),r=this.normal.applyMatrix3(n).normalize();return this.constant=-i.dot(r),this}translate(t){return this.constant-=t.dot(this.normal),this}equals(t){return t.normal.equals(this.normal)&&t.constant===this.constant}clone(){return(new this.constructor).copy(this)}}l.prototype.isPlane=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return l}));var i=n(41),r=n(0),s=n(4);const o=new r.a,a=new r.a;class l extends i.a{constructor(t,e){super(t,e),this.type=\\\\\\\"LineSegments\\\\\\\"}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[];for(let t=0,i=e.count;t=r)break t;{const o=e[1];t=r)break e}s=n,n=0}}for(;n>>1;t0)if(p=l*u-c,_=l*c-u,f=r*d,p>=0)if(_>=-f)if(_<=f){const t=1/d;p*=t,_*=t,m=p*(p+l*_+2*c)+_*(l*p+_+2*u)+h}else _=r,p=Math.max(0,-(l*_+c)),m=-p*p+_*(_+2*u)+h;else _=-r,p=Math.max(0,-(l*_+c)),m=-p*p+_*(_+2*u)+h;else _<=-f?(p=Math.max(0,-(-l*r+c)),_=p>0?-r:Math.min(Math.max(-r,-u),r),m=-p*p+_*(_+2*u)+h):_<=f?(p=0,_=Math.min(Math.max(-r,-u),r),m=_*(_+2*u)+h):(p=Math.max(0,-(l*r+c)),_=p>0?r:Math.min(Math.max(-r,-u),r),m=-p*p+_*(_+2*u)+h);else _=l>0?-r:r,p=Math.max(0,-(l*_+c)),m=-p*p+_*(_+2*u)+h;return n&&n.copy(this.direction).multiplyScalar(p).add(this.origin),i&&i.copy(o).multiplyScalar(_).add(s),m}intersectSphere(t,e){r.subVectors(t.center,this.origin);const n=r.dot(this.direction),i=r.dot(r)-n*n,s=t.radius*t.radius;if(i>s)return null;const o=Math.sqrt(s-i),a=n-o,l=n+o;return a<0&&l<0?null:a<0?this.at(l,e):this.at(a,e)}intersectsSphere(t){return this.distanceSqToPoint(t.center)<=t.radius*t.radius}distanceToPlane(t){const e=t.normal.dot(this.direction);if(0===e)return 0===t.distanceToPoint(this.origin)?0:null;const n=-(this.origin.dot(t.normal)+t.constant)/e;return n>=0?n:null}intersectPlane(t,e){const n=this.distanceToPlane(t);return null===n?null:this.at(n,e)}intersectsPlane(t){const e=t.distanceToPoint(this.origin);if(0===e)return!0;return t.normal.dot(this.direction)*e<0}intersectBox(t,e){let n,i,r,s,o,a;const l=1/this.direction.x,c=1/this.direction.y,u=1/this.direction.z,h=this.origin;return l>=0?(n=(t.min.x-h.x)*l,i=(t.max.x-h.x)*l):(n=(t.max.x-h.x)*l,i=(t.min.x-h.x)*l),c>=0?(r=(t.min.y-h.y)*c,s=(t.max.y-h.y)*c):(r=(t.max.y-h.y)*c,s=(t.min.y-h.y)*c),n>s||r>i?null:((r>n||n!=n)&&(n=r),(s=0?(o=(t.min.z-h.z)*u,a=(t.max.z-h.z)*u):(o=(t.max.z-h.z)*u,a=(t.min.z-h.z)*u),n>a||o>i?null:((o>n||n!=n)&&(n=o),(a=0?n:i,e)))}intersectsBox(t){return null!==this.intersectBox(t,r)}intersectTriangle(t,e,n,i,r){l.subVectors(e,t),c.subVectors(n,t),u.crossVectors(l,c);let s,o=this.direction.dot(u);if(o>0){if(i)return null;s=1}else{if(!(o<0))return null;s=-1,o=-o}a.subVectors(this.origin,t);const h=s*this.direction.dot(c.crossVectors(a,c));if(h<0)return null;const d=s*this.direction.dot(l.cross(a));if(d<0)return null;if(h+d>o)return null;const p=-s*a.dot(u);return p<0?null:this.at(p/o,r)}applyMatrix4(t){return this.origin.applyMatrix4(t),this.direction.transformDirection(t),this}equals(t){return t.origin.equals(this.origin)&&t.direction.equals(this.direction)}clone(){return(new this.constructor).copy(this)}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return _}));var i=n(0);const r=new i.a,s=new i.a,o=new i.a,a=new i.a,l=new i.a,c=new i.a,u=new i.a,h=new i.a,d=new i.a,p=new i.a;class _{constructor(t=new i.a,e=new i.a,n=new i.a){this.a=t,this.b=e,this.c=n}static getNormal(t,e,n,i){i.subVectors(n,e),r.subVectors(t,e),i.cross(r);const s=i.lengthSq();return s>0?i.multiplyScalar(1/Math.sqrt(s)):i.set(0,0,0)}static getBarycoord(t,e,n,i,a){r.subVectors(i,e),s.subVectors(n,e),o.subVectors(t,e);const l=r.dot(r),c=r.dot(s),u=r.dot(o),h=s.dot(s),d=s.dot(o),p=l*h-c*c;if(0===p)return a.set(-2,-1,-1);const _=1/p,m=(h*u-c*d)*_,f=(l*d-c*u)*_;return a.set(1-m-f,f,m)}static containsPoint(t,e,n,i){return this.getBarycoord(t,e,n,i,a),a.x>=0&&a.y>=0&&a.x+a.y<=1}static getUV(t,e,n,i,r,s,o,l){return this.getBarycoord(t,e,n,i,a),l.set(0,0),l.addScaledVector(r,a.x),l.addScaledVector(s,a.y),l.addScaledVector(o,a.z),l}static isFrontFacing(t,e,n,i){return r.subVectors(n,e),s.subVectors(t,e),r.cross(s).dot(i)<0}set(t,e,n){return this.a.copy(t),this.b.copy(e),this.c.copy(n),this}setFromPointsAndIndices(t,e,n,i){return this.a.copy(t[e]),this.b.copy(t[n]),this.c.copy(t[i]),this}setFromAttributeAndIndices(t,e,n,i){return this.a.fromBufferAttribute(t,e),this.b.fromBufferAttribute(t,n),this.c.fromBufferAttribute(t,i),this}clone(){return(new this.constructor).copy(this)}copy(t){return this.a.copy(t.a),this.b.copy(t.b),this.c.copy(t.c),this}getArea(){return r.subVectors(this.c,this.b),s.subVectors(this.a,this.b),.5*r.cross(s).length()}getMidpoint(t){return t.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}getNormal(t){return _.getNormal(this.a,this.b,this.c,t)}getPlane(t){return t.setFromCoplanarPoints(this.a,this.b,this.c)}getBarycoord(t,e){return _.getBarycoord(t,this.a,this.b,this.c,e)}getUV(t,e,n,i,r){return _.getUV(t,this.a,this.b,this.c,e,n,i,r)}containsPoint(t){return _.containsPoint(t,this.a,this.b,this.c)}isFrontFacing(t){return _.isFrontFacing(this.a,this.b,this.c,t)}intersectsBox(t){return t.intersectsTriangle(this)}closestPointToPoint(t,e){const n=this.a,i=this.b,r=this.c;let s,o;l.subVectors(i,n),c.subVectors(r,n),h.subVectors(t,n);const a=l.dot(h),_=c.dot(h);if(a<=0&&_<=0)return e.copy(n);d.subVectors(t,i);const m=l.dot(d),f=c.dot(d);if(m>=0&&f<=m)return e.copy(i);const g=a*f-m*_;if(g<=0&&a>=0&&m<=0)return s=a/(a-m),e.copy(n).addScaledVector(l,s);p.subVectors(t,r);const v=l.dot(p),y=c.dot(p);if(y>=0&&v<=y)return e.copy(r);const x=v*_-a*y;if(x<=0&&_>=0&&y<=0)return o=_/(_-y),e.copy(n).addScaledVector(c,o);const b=m*y-v*f;if(b<=0&&f-m>=0&&v-y>=0)return u.subVectors(r,i),o=(f-m)/(f-m+(v-y)),e.copy(i).addScaledVector(u,o);const w=1/(b+x+g);return s=x*w,o=g*w,e.copy(n).addScaledVector(l,s).addScaledVector(c,o)}equals(t){return t.a.equals(this.a)&&t.b.equals(this.b)&&t.c.equals(this.c)}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return f}));var i=n(18),r=n(39),s=n(5),o=n(10),a=n(0),l=n(24),c=n(7),u=n(4);const h=new a.a,d=new a.a,p=new s.a,_=new r.a,m=new i.a;class f extends o.a{constructor(t=new c.a,e=new l.a){super(),this.type=\\\\\\\"Line\\\\\\\",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),this.material=t.material,this.geometry=t.geometry,this}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[0];for(let t=1,i=e.count;tl)continue;d.applyMatrix4(this.matrixWorld);const a=t.ray.origin.distanceTo(d);at.far||e.push({distance:a,point:h.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}else{for(let n=Math.max(0,s.start),i=Math.min(r.count,s.start+s.count)-1;nl)continue;d.applyMatrix4(this.matrixWorld);const i=t.ray.origin.distanceTo(d);it.far||e.push({distance:i,point:h.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}}else n.isGeometry&&console.error(\\\\\\\"THREE.Line.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e0&&console.error(\\\\\\\"THREE.Line.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}}f.prototype.isLine=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return s}));var i=n(12),r=n(6);class s extends i.a{constructor(t){super(),this.type=\\\\\\\"PointsMaterial\\\\\\\",this.color=new r.a(16777215),this.map=null,this.alphaMap=null,this.size=1,this.sizeAttenuation=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.alphaMap=t.alphaMap,this.size=t.size,this.sizeAttenuation=t.sizeAttenuation,this}}s.prototype.isPointsMaterial=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return s}));var i=n(5),r=n(10);class s extends r.a{constructor(){super(),this.type=\\\\\\\"Camera\\\\\\\",this.matrixWorldInverse=new i.a,this.projectionMatrix=new i.a,this.projectionMatrixInverse=new i.a}copy(t,e){return super.copy(t,e),this.matrixWorldInverse.copy(t.matrixWorldInverse),this.projectionMatrix.copy(t.projectionMatrix),this.projectionMatrixInverse.copy(t.projectionMatrixInverse),this}getWorldDirection(t){this.updateWorldMatrix(!0,!1);const e=this.matrixWorld.elements;return t.set(-e[8],-e[9],-e[10]).normalize()}updateMatrixWorld(t){super.updateMatrixWorld(t),this.matrixWorldInverse.copy(this.matrixWorld).invert()}updateWorldMatrix(t,e){super.updateWorldMatrix(t,e),this.matrixWorldInverse.copy(this.matrixWorld).invert()}clone(){return(new this.constructor).copy(this)}}s.prototype.isCamera=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return i}));class i{static decodeText(t){if(\\\\\\\"undefined\\\\\\\"!=typeof TextDecoder)return(new TextDecoder).decode(t);let e=\\\\\\\"\\\\\\\";for(let n=0,i=t.length;n=n){const t=i[r]-n,s=this.curves[r],o=s.getLength(),a=0===o?0:1-t/o;return s.getPointAt(a,e)}r++}return null}getLength(){const t=this.getCurveLengths();return t[t.length-1]}updateArcLengths(){this.needsUpdate=!0,this.cacheLengths=null,this.getCurveLengths()}getCurveLengths(){if(this.cacheLengths&&this.cacheLengths.length===this.curves.length)return this.cacheLengths;const t=[];let e=0;for(let n=0,i=this.curves.length;n1&&!e[e.length-1].equals(e[0])&&e.push(e[0]),e}copy(t){super.copy(t),this.curves=[];for(let e=0,n=t.curves.length;e0){const t=c.getPoint(0);t.equals(this.currentPoint)||this.lineTo(t.x,t.y)}this.curves.push(c);const u=c.getPoint(1);return this.currentPoint.copy(u),this}copy(t){return super.copy(t),this.currentPoint.copy(t.currentPoint),this}toJSON(){const t=super.toJSON();return t.currentPoint=this.currentPoint.toArray(),t}fromJSON(t){return super.fromJSON(t),this.currentPoint.fromArray(t.currentPoint),this}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return a}));var i=n(6),r=n(47),s=n(46),o=n(53);class a{constructor(){this.type=\\\\\\\"ShapePath\\\\\\\",this.color=new i.a,this.subPaths=[],this.currentPath=null}moveTo(t,e){return this.currentPath=new r.a,this.subPaths.push(this.currentPath),this.currentPath.moveTo(t,e),this}lineTo(t,e){return this.currentPath.lineTo(t,e),this}quadraticCurveTo(t,e,n,i){return this.currentPath.quadraticCurveTo(t,e,n,i),this}bezierCurveTo(t,e,n,i,r,s){return this.currentPath.bezierCurveTo(t,e,n,i,r,s),this}splineThru(t){return this.currentPath.splineThru(t),this}toShapes(t,e){function n(t){const e=[];for(let n=0,i=t.length;nNumber.EPSILON){if(l<0&&(n=e[s],a=-a,o=e[r],l=-l),t.yo.y)continue;if(t.y===n.y){if(t.x===n.x)return!0}else{const e=l*(t.x-n.x)-a*(t.y-n.y);if(0===e)return!0;if(e<0)continue;i=!i}}else{if(t.y!==n.y)continue;if(o.x<=t.x&&t.x<=n.x||n.x<=t.x&&t.x<=o.x)return!0}}return i}const r=o.a.isClockWise,a=this.subPaths;if(0===a.length)return[];if(!0===e)return n(a);let l,c,u;const h=[];if(1===a.length)return c=a[0],u=new s.a,u.curves=c.curves,h.push(u),h;let d=!r(a[0].getPoints());d=t?!d:d;const p=[],_=[];let m,f,g=[],v=0;_[v]=void 0,g[v]=[];for(let e=0,n=a.length;e1){let t=!1;const e=[];for(let t=0,e=_.length;t0&&(t||(g=p))}for(let t=0,e=_.length;t0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e0&&console.error(\\\\\\\"THREE.Points.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}}function m(t,e,n,i,r,s,o){const l=h.distanceSqToPoint(t);if(lr.far)return;s.push({distance:c,distanceToRay:Math.sqrt(l),point:n,index:e,face:null,object:o})}}_.prototype.isPoints=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return r}));var i=n(26);class r extends i.a{}r.prototype.ValueTypeName=\\\\\\\"number\\\\\\\"},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return r}));var i=n(26);class r extends i.a{}r.prototype.ValueTypeName=\\\\\\\"vector\\\\\\\"},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return r}));var i=n(10);class r extends i.a{constructor(){super(),this.type=\\\\\\\"Bone\\\\\\\"}}r.prototype.isBone=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return C}));const i=function(t,e,n=2){const i=e&&e.length,a=i?e[0]*n:t.length;let l=r(t,0,a,n,!0);const c=[];if(!l||l.next===l.prev)return c;let u,p,_,f,g,v,y;if(i&&(l=function(t,e,n,i){const o=[];let a,l,c,u,p;for(a=0,l=e.length;a80*n){u=_=t[0],p=f=t[1];for(let e=n;e_&&(_=g),v>f&&(f=v);y=Math.max(_-u,f-p),y=0!==y?1/y:0}return o(l,c,n,u,p,y),c};function r(t,e,n,i,r){let s,o;if(r===function(t,e,n,i){let r=0;for(let s=e,o=n-i;s0)for(s=e;s=e;s-=i)o=E(s,t[s],t[s+1],o);return o&&y(o,o.next)&&(M(o),o=o.next),o}function s(t,e){if(!t)return t;e||(e=t);let n,i=t;do{if(n=!1,i.steiner||!y(i,i.next)&&0!==v(i.prev,i,i.next))i=i.next;else{if(M(i),i=e=i.prev,i===i.next)break;n=!0}}while(n||i!==e);return e}function o(t,e,n,i,r,h,d){if(!t)return;!d&&h&&function(t,e,n,i){let r=t;do{null===r.z&&(r.z=_(r.x,r.y,e,n,i)),r.prevZ=r.prev,r.nextZ=r.next,r=r.next}while(r!==t);r.prevZ.nextZ=null,r.prevZ=null,function(t){let e,n,i,r,s,o,a,l,c=1;do{for(n=t,t=null,s=null,o=0;n;){for(o++,i=n,a=0,e=0;e0||l>0&&i;)0!==a&&(0===l||!i||n.z<=i.z)?(r=n,n=n.nextZ,a--):(r=i,i=i.nextZ,l--),s?s.nextZ=r:t=r,r.prevZ=s,s=r;n=i}s.nextZ=null,c*=2}while(o>1)}(r)}(t,i,r,h);let p,m,f=t;for(;t.prev!==t.next;)if(p=t.prev,m=t.next,h?l(t,i,r,h):a(t))e.push(p.i/n),e.push(t.i/n),e.push(m.i/n),M(t),t=m.next,f=m.next;else if((t=m)===f){d?1===d?o(t=c(s(t),e,n),e,n,i,r,h,2):2===d&&u(t,e,n,i,r,h):o(s(t),e,n,i,r,h,1);break}}function a(t){const e=t.prev,n=t,i=t.next;if(v(e,n,i)>=0)return!1;let r=t.next.next;for(;r!==t.prev;){if(f(e.x,e.y,n.x,n.y,i.x,i.y,r.x,r.y)&&v(r.prev,r,r.next)>=0)return!1;r=r.next}return!0}function l(t,e,n,i){const r=t.prev,s=t,o=t.next;if(v(r,s,o)>=0)return!1;const a=r.xs.x?r.x>o.x?r.x:o.x:s.x>o.x?s.x:o.x,u=r.y>s.y?r.y>o.y?r.y:o.y:s.y>o.y?s.y:o.y,h=_(a,l,e,n,i),d=_(c,u,e,n,i);let p=t.prevZ,m=t.nextZ;for(;p&&p.z>=h&&m&&m.z<=d;){if(p!==t.prev&&p!==t.next&&f(r.x,r.y,s.x,s.y,o.x,o.y,p.x,p.y)&&v(p.prev,p,p.next)>=0)return!1;if(p=p.prevZ,m!==t.prev&&m!==t.next&&f(r.x,r.y,s.x,s.y,o.x,o.y,m.x,m.y)&&v(m.prev,m,m.next)>=0)return!1;m=m.nextZ}for(;p&&p.z>=h;){if(p!==t.prev&&p!==t.next&&f(r.x,r.y,s.x,s.y,o.x,o.y,p.x,p.y)&&v(p.prev,p,p.next)>=0)return!1;p=p.prevZ}for(;m&&m.z<=d;){if(m!==t.prev&&m!==t.next&&f(r.x,r.y,s.x,s.y,o.x,o.y,m.x,m.y)&&v(m.prev,m,m.next)>=0)return!1;m=m.nextZ}return!0}function c(t,e,n){let i=t;do{const r=i.prev,s=i.next.next;!y(r,s)&&x(r,i,i.next,s)&&T(r,s)&&T(s,r)&&(e.push(r.i/n),e.push(i.i/n),e.push(s.i/n),M(i),M(i.next),i=t=s),i=i.next}while(i!==t);return s(i)}function u(t,e,n,i,r,a){let l=t;do{let t=l.next.next;for(;t!==l.prev;){if(l.i!==t.i&&g(l,t)){let c=A(l,t);return l=s(l,l.next),c=s(c,c.next),o(l,e,n,i,r,a),void o(c,e,n,i,r,a)}t=t.next}l=l.next}while(l!==t)}function h(t,e){return t.x-e.x}function d(t,e){if(e=function(t,e){let n=e;const i=t.x,r=t.y;let s,o=-1/0;do{if(r<=n.y&&r>=n.next.y&&n.next.y!==n.y){const t=n.x+(r-n.y)*(n.next.x-n.x)/(n.next.y-n.y);if(t<=i&&t>o){if(o=t,t===i){if(r===n.y)return n;if(r===n.next.y)return n.next}s=n.x=n.x&&n.x>=l&&i!==n.x&&f(rs.x||n.x===s.x&&p(s,n)))&&(s=n,h=u)),n=n.next}while(n!==a);return s}(t,e)){const n=A(e,t);s(e,e.next),s(n,n.next)}}function p(t,e){return v(t.prev,t,e.prev)<0&&v(e.next,t,t.next)<0}function _(t,e,n,i,r){return(t=1431655765&((t=858993459&((t=252645135&((t=16711935&((t=32767*(t-n)*r)|t<<8))|t<<4))|t<<2))|t<<1))|(e=1431655765&((e=858993459&((e=252645135&((e=16711935&((e=32767*(e-i)*r)|e<<8))|e<<4))|e<<2))|e<<1))<<1}function m(t){let e=t,n=t;do{(e.x=0&&(t-o)*(i-a)-(n-o)*(e-a)>=0&&(n-o)*(s-a)-(r-o)*(i-a)>=0}function g(t,e){return t.next.i!==e.i&&t.prev.i!==e.i&&!function(t,e){let n=t;do{if(n.i!==t.i&&n.next.i!==t.i&&n.i!==e.i&&n.next.i!==e.i&&x(n,n.next,t,e))return!0;n=n.next}while(n!==t);return!1}(t,e)&&(T(t,e)&&T(e,t)&&function(t,e){let n=t,i=!1;const r=(t.x+e.x)/2,s=(t.y+e.y)/2;do{n.y>s!=n.next.y>s&&n.next.y!==n.y&&r<(n.next.x-n.x)*(s-n.y)/(n.next.y-n.y)+n.x&&(i=!i),n=n.next}while(n!==t);return i}(t,e)&&(v(t.prev,t,e.prev)||v(t,e.prev,e))||y(t,e)&&v(t.prev,t,t.next)>0&&v(e.prev,e,e.next)>0)}function v(t,e,n){return(e.y-t.y)*(n.x-e.x)-(e.x-t.x)*(n.y-e.y)}function y(t,e){return t.x===e.x&&t.y===e.y}function x(t,e,n,i){const r=w(v(t,e,n)),s=w(v(t,e,i)),o=w(v(n,i,t)),a=w(v(n,i,e));return r!==s&&o!==a||(!(0!==r||!b(t,n,e))||(!(0!==s||!b(t,i,e))||(!(0!==o||!b(n,t,i))||!(0!==a||!b(n,e,i)))))}function b(t,e,n){return e.x<=Math.max(t.x,n.x)&&e.x>=Math.min(t.x,n.x)&&e.y<=Math.max(t.y,n.y)&&e.y>=Math.min(t.y,n.y)}function w(t){return t>0?1:t<0?-1:0}function T(t,e){return v(t.prev,t,t.next)<0?v(t,e,t.next)>=0&&v(t,t.prev,e)>=0:v(t,e,t.prev)<0||v(t,t.next,e)<0}function A(t,e){const n=new S(t.i,t.x,t.y),i=new S(e.i,e.x,e.y),r=t.next,s=e.prev;return t.next=e,e.prev=t,n.next=r,r.prev=n,i.next=n,n.prev=i,s.next=i,i.prev=s,i}function E(t,e,n,i){const r=new S(t,e,n);return i?(r.next=i.next,r.prev=i,i.next.prev=r,i.next=r):(r.prev=r,r.next=r),r}function M(t){t.next.prev=t.prev,t.prev.next=t.next,t.prevZ&&(t.prevZ.nextZ=t.nextZ),t.nextZ&&(t.nextZ.prevZ=t.prevZ)}function S(t,e,n){this.i=t,this.x=e,this.y=n,this.prev=null,this.next=null,this.z=null,this.prevZ=null,this.nextZ=null,this.steiner=!1}class C{static area(t){const e=t.length;let n=0;for(let i=e-1,r=0;r2&&t[e-1].equals(t[0])&&t.pop()}function L(t,e){for(let n=0;ni;)s-=i;s0?t.max.x:t.min.x,a.y=i.normal.y>0?t.max.y:t.min.y,a.z=i.normal.z>0?t.max.z:t.min.z,i.distanceToPoint(a)<0)return!1}return!0}containsPoint(t){const e=this.planes;for(let n=0;n<6;n++)if(e[n].distanceToPoint(t)<0)return!1;return!0}clone(){return(new this.constructor).copy(this)}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return s}));const i=new Float32Array(1),r=new Int32Array(i.buffer);class s{static toHalfFloat(t){t>65504&&(console.warn(\\\\\\\"THREE.DataUtils.toHalfFloat(): value exceeds 65504.\\\\\\\"),t=65504),i[0]=t;const e=r[0];let n=e>>16&32768,s=e>>12&2047;const o=e>>23&255;return o<103?n:o>142?(n|=31744,n|=(255==o?0:1)&&8388607&e,n):o<113?(s|=2048,n|=(s>>114-o)+(s>>113-o&1),n):(n|=o-112<<10|s>>1,n+=1&s,n)}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return o}));var i=n(12),r=n(1),s=n(6);class o extends i.a{constructor(t){super(),this.type=\\\\\\\"MeshLambertMaterial\\\\\\\",this.color=new s.a(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new s.a(0),this.emissiveIntensity=1,this.emissiveMap=null,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=r.nb,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}o.prototype.isMeshLambertMaterial=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return o}));var i=n(17),r=n(13),s=n(20);class o extends r.a{constructor(t){super(t)}load(t,e,n,r){void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const o=this,a=i.a.get(t);if(void 0!==a)return o.manager.itemStart(t),setTimeout((function(){e&&e(a),o.manager.itemEnd(t)}),0),a;const l=Object(s.b)(\\\\\\\"img\\\\\\\");function c(){l.removeEventListener(\\\\\\\"load\\\\\\\",c,!1),l.removeEventListener(\\\\\\\"error\\\\\\\",u,!1),i.a.add(t,this),e&&e(this),o.manager.itemEnd(t)}function u(e){l.removeEventListener(\\\\\\\"load\\\\\\\",c,!1),l.removeEventListener(\\\\\\\"error\\\\\\\",u,!1),r&&r(e),o.manager.itemError(t),o.manager.itemEnd(t)}return l.addEventListener(\\\\\\\"load\\\\\\\",c,!1),l.addEventListener(\\\\\\\"error\\\\\\\",u,!1),\\\\\\\"data:\\\\\\\"!==t.substr(0,5)&&void 0!==this.crossOrigin&&(l.crossOrigin=this.crossOrigin),o.manager.itemStart(t),l.src=t,l}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return p}));var i=n(19),r=n(26),s=n(1);class o extends r.a{}o.prototype.ValueTypeName=\\\\\\\"bool\\\\\\\",o.prototype.ValueBufferType=Array,o.prototype.DefaultInterpolation=s.O,o.prototype.InterpolantFactoryMethodLinear=void 0,o.prototype.InterpolantFactoryMethodSmooth=void 0;class a extends r.a{}a.prototype.ValueTypeName=\\\\\\\"color\\\\\\\";var l=n(50),c=n(54);class u extends r.a{}u.prototype.ValueTypeName=\\\\\\\"string\\\\\\\",u.prototype.ValueBufferType=Array,u.prototype.DefaultInterpolation=s.O,u.prototype.InterpolantFactoryMethodLinear=void 0,u.prototype.InterpolantFactoryMethodSmooth=void 0;var h=n(51),d=n(3);class p{constructor(t,e=-1,n,i=s.wb){this.name=t,this.tracks=n,this.duration=e,this.blendMode=i,this.uuid=d.h(),this.duration<0&&this.resetDuration()}static parse(t){const e=[],n=t.tracks,i=1/(t.fps||1);for(let t=0,r=n.length;t!==r;++t)e.push(_(n[t]).scale(i));const r=new this(t.name,t.duration,e,t.blendMode);return r.uuid=t.uuid,r}static toJSON(t){const e=[],n=t.tracks,i={name:t.name,duration:t.duration,tracks:e,uuid:t.uuid,blendMode:t.blendMode};for(let t=0,i=n.length;t!==i;++t)e.push(r.a.toJSON(n[t]));return i}static CreateFromMorphTargetSequence(t,e,n,r){const s=e.length,o=[];for(let t=0;t1){const t=s[1];let e=i[t];e||(i[t]=e=[]),e.push(n)}}const s=[];for(const t in i)s.push(this.CreateFromMorphTargetSequence(t,i[t],e,n));return s}static parseAnimation(t,e){if(!t)return console.error(\\\\\\\"THREE.AnimationClip: No animation in JSONLoader data.\\\\\\\"),null;const n=function(t,e,n,r,s){if(0!==n.length){const o=[],a=[];i.a.flattenJSON(n,o,a,r),0!==o.length&&s.push(new t(e,o,a))}},r=[],s=t.name||\\\\\\\"default\\\\\\\",o=t.fps||30,a=t.blendMode;let u=t.length||-1;const d=t.hierarchy||[];for(let t=0;t0!=t>0&&this.version++,this._sheen=t}get clearcoat(){return this._clearcoat}set clearcoat(t){this._clearcoat>0!=t>0&&this.version++,this._clearcoat=t}get transmission(){return this._transmission}set transmission(t){this._transmission>0!=t>0&&this.version++,this._transmission=t}copy(t){return super.copy(t),this.defines={STANDARD:\\\\\\\"\\\\\\\",PHYSICAL:\\\\\\\"\\\\\\\"},this.clearcoat=t.clearcoat,this.clearcoatMap=t.clearcoatMap,this.clearcoatRoughness=t.clearcoatRoughness,this.clearcoatRoughnessMap=t.clearcoatRoughnessMap,this.clearcoatNormalMap=t.clearcoatNormalMap,this.clearcoatNormalScale.copy(t.clearcoatNormalScale),this.ior=t.ior,this.sheen=t.sheen,this.sheenTint.copy(t.sheenTint),this.sheenRoughness=t.sheenRoughness,this.transmission=t.transmission,this.transmissionMap=t.transmissionMap,this.thickness=t.thickness,this.thicknessMap=t.thicknessMap,this.attenuationDistance=t.attenuationDistance,this.attenuationTint.copy(t.attenuationTint),this.specularIntensity=t.specularIntensity,this.specularIntensityMap=t.specularIntensityMap,this.specularTint.copy(t.specularTint),this.specularTintMap=t.specularTintMap,this}}a.prototype.isMeshPhysicalMaterial=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return p}));const i=\\\\\\\"\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/\\\\\\\",r=new RegExp(\\\\\\\"[\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",\\\\\\\"g\\\\\\\"),s=\\\\\\\"[^\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",o=\\\\\\\"[^\\\\\\\"+i.replace(\\\\\\\"\\\\\\\\.\\\\\\\",\\\\\\\"\\\\\\\")+\\\\\\\"]\\\\\\\",a=/((?:WC+[\\\\/:])*)/.source.replace(\\\\\\\"WC\\\\\\\",s),l=/(WCOD+)?/.source.replace(\\\\\\\"WCOD\\\\\\\",o),c=/(?:\\\\.(WC+)(?:\\\\[(.+)\\\\])?)?/.source.replace(\\\\\\\"WC\\\\\\\",s),u=/\\\\.(WC+)(?:\\\\[(.+)\\\\])?/.source.replace(\\\\\\\"WC\\\\\\\",s),h=new RegExp(\\\\\\\"^\\\\\\\"+a+l+c+u+\\\\\\\"$\\\\\\\"),d=[\\\\\\\"material\\\\\\\",\\\\\\\"materials\\\\\\\",\\\\\\\"bones\\\\\\\"];class p{constructor(t,e,n){this.path=e,this.parsedPath=n||p.parseTrackName(e),this.node=p.findNode(t,this.parsedPath.nodeName)||t,this.rootNode=t,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}static create(t,e,n){return t&&t.isAnimationObjectGroup?new p.Composite(t,e,n):new p(t,e,n)}static sanitizeNodeName(t){return t.replace(/\\\\s/g,\\\\\\\"_\\\\\\\").replace(r,\\\\\\\"\\\\\\\")}static parseTrackName(t){const e=h.exec(t);if(!e)throw new Error(\\\\\\\"PropertyBinding: Cannot parse trackName: \\\\\\\"+t);const n={nodeName:e[2],objectName:e[3],objectIndex:e[4],propertyName:e[5],propertyIndex:e[6]},i=n.nodeName&&n.nodeName.lastIndexOf(\\\\\\\".\\\\\\\");if(void 0!==i&&-1!==i){const t=n.nodeName.substring(i+1);-1!==d.indexOf(t)&&(n.nodeName=n.nodeName.substring(0,i),n.objectName=t)}if(null===n.propertyName||0===n.propertyName.length)throw new Error(\\\\\\\"PropertyBinding: can not parse propertyName from trackName: \\\\\\\"+t);return n}static findNode(t,e){if(!e||\\\\\\\"\\\\\\\"===e||\\\\\\\".\\\\\\\"===e||-1===e||e===t.name||e===t.uuid)return t;if(t.skeleton){const n=t.skeleton.getBoneByName(e);if(void 0!==n)return n}if(t.children){const n=function(t){for(let i=0;ii.length-2?i.length-1:a+1],d=i[a>i.length-3?i.length-1:a+2];return n.set(Object(r.a)(l,c.x,u.x,h.x,d.x),Object(r.a)(l,c.y,u.y,h.y,d.y)),n}copy(t){super.copy(t),this.points=[];for(let e=0,n=t.points.length;ethis.max.x||t.ythis.max.y)}containsBox(t){return this.min.x<=t.min.x&&t.max.x<=this.max.x&&this.min.y<=t.min.y&&t.max.y<=this.max.y}getParameter(t,e){return e.set((t.x-this.min.x)/(this.max.x-this.min.x),(t.y-this.min.y)/(this.max.y-this.min.y))}intersectsBox(t){return!(t.max.xthis.max.x||t.max.ythis.max.y)}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return r.copy(t).clamp(this.min,this.max).sub(t).length()}intersect(t){return this.min.max(t.min),this.max.min(t.max),this}union(t){return this.min.min(t.min),this.max.max(t.max),this}translate(t){return this.min.add(t),this.max.add(t),this}equals(t){return t.min.equals(this.min)&&t.max.equals(this.max)}}s.prototype.isBox2=!0;var o=n(7),a=n(21),l=n(4),c=n(13),u=n(11),h=n(47),d=n(46),p=n(48),_=n(53),m=n(0);class f extends c.a{constructor(t){super(t),this.defaultDPI=90,this.defaultUnit=\\\\\\\"px\\\\\\\"}load(t,e,n,i){const r=this,s=new a.a(r.manager);s.setPath(r.path),s.setRequestHeader(r.requestHeader),s.setWithCredentials(r.withCredentials),s.load(t,(function(n){try{e(r.parse(n))}catch(e){i?i(e):console.error(e),r.manager.itemError(t)}}),n,i)}parse(t){const e=this;function n(t,e,n,i,s,o,a,l){if(0==e||0==n)return void t.lineTo(l.x,l.y);i=i*Math.PI/180,e=Math.abs(e),n=Math.abs(n);const c=(a.x-l.x)/2,u=(a.y-l.y)/2,h=Math.cos(i)*c+Math.sin(i)*u,d=-Math.sin(i)*c+Math.cos(i)*u;let p=e*e,_=n*n;const m=h*h,f=d*d,g=m/p+f/_;if(g>1){const t=Math.sqrt(g);p=(e*=t)*e,_=(n*=t)*n}const v=p*f+_*m,y=(p*_-v)/v;let x=Math.sqrt(Math.max(0,y));s===o&&(x=-x);const b=x*e*d/n,w=-x*n*h/e,T=Math.cos(i)*b-Math.sin(i)*w+(a.x+l.x)/2,A=Math.sin(i)*b+Math.cos(i)*w+(a.y+l.y)/2,E=r(1,0,(h-b)/e,(d-w)/n),M=r((h-b)/e,(d-w)/n,(-h-b)/e,(-d-w)/n)%(2*Math.PI);t.currentPath.absellipse(T,A,e,n,E,E+M,0===o,i)}function r(t,e,n,i){const r=t*n+e*i,s=Math.sqrt(t*t+e*e)*Math.sqrt(n*n+i*i);let o=Math.acos(Math.max(-1,Math.min(1,r/s)));return t*i-e*n<0&&(o=-o),o}function s(t,e){e=Object.assign({},e);let n={};if(t.hasAttribute(\\\\\\\"class\\\\\\\")){const e=t.getAttribute(\\\\\\\"class\\\\\\\").split(/\\\\s/).filter(Boolean).map((t=>t.trim()));for(let t=0;t=0;t--){const r=i[t].trim();if(\\\\\\\"\\\\\\\"===r)continue;const s=r.indexOf(\\\\\\\"(\\\\\\\"),o=r.length;if(s>0&&s=1){const t=e[0];let i=t;e.length>=2&&(i=e[1]),n.translate(t,i)}break;case\\\\\\\"rotate\\\\\\\":if(e.length>=1){let t=0,i=0,r=0;t=-e[0]*Math.PI/180,e.length>=3&&(i=e[1],r=e[2]),b.identity().translate(-i,-r),w.identity().rotate(t),T.multiplyMatrices(w,b),b.identity().translate(i,r),n.multiplyMatrices(b,T)}break;case\\\\\\\"scale\\\\\\\":if(e.length>=1){const t=e[0];let i=t;e.length>=2&&(i=e[1]),n.scale(t,i)}break;case\\\\\\\"skewX\\\\\\\":1===e.length&&n.set(1,Math.tan(e[0]*Math.PI/180),0,0,1,0,0,0,1);break;case\\\\\\\"skewY\\\\\\\":1===e.length&&n.set(1,0,0,Math.tan(e[0]*Math.PI/180),1,0,0,0,1);break;case\\\\\\\"matrix\\\\\\\":6===e.length&&n.set(e[0],e[2],e[4],e[1],e[3],e[5],0,0,1)}}e.premultiply(n)}}return e}(t);y.length>0&&e.premultiply(y[y.length-1]);return M.copy(e),y.push(e),e}(e);let c=!0,m=null;switch(e.nodeName){case\\\\\\\"svg\\\\\\\":break;case\\\\\\\"style\\\\\\\":!function(t){if(!t.sheet||!t.sheet.cssRules||!t.sheet.cssRules.length)return;for(let e=0;et.trim()));for(let t=0;t0&&(r.copy(l),e.currentPath.currentPoint.copy(r),c=!0);break;default:console.warn(i)}u=!1}return e}(e));break;case\\\\\\\"rect\\\\\\\":r=s(e,r),m=function(t){const e=d(t.getAttribute(\\\\\\\"x\\\\\\\")||0),n=d(t.getAttribute(\\\\\\\"y\\\\\\\")||0),i=d(t.getAttribute(\\\\\\\"rx\\\\\\\")||t.getAttribute(\\\\\\\"ry\\\\\\\")||0),r=d(t.getAttribute(\\\\\\\"ry\\\\\\\")||t.getAttribute(\\\\\\\"rx\\\\\\\")||0),s=d(t.getAttribute(\\\\\\\"width\\\\\\\")),o=d(t.getAttribute(\\\\\\\"height\\\\\\\")),a=.448084975506,l=new p.a;l.moveTo(e+i,n),l.lineTo(e+s-i,n),(0!==i||0!==r)&&l.bezierCurveTo(e+s-i*a,n,e+s,n+r*a,e+s,n+r);l.lineTo(e+s,n+o-r),(0!==i||0!==r)&&l.bezierCurveTo(e+s,n+o-r*a,e+s-i*a,n+o,e+s-i,n+o);l.lineTo(e+i,n+o),(0!==i||0!==r)&&l.bezierCurveTo(e+i*a,n+o,e,n+o-r*a,e,n+o-r);l.lineTo(e,n+r),(0!==i||0!==r)&&l.bezierCurveTo(e,n+r*a,e+i*a,n,e+i,n);return l}(e);break;case\\\\\\\"polygon\\\\\\\":r=s(e,r),m=function(t){function e(t,e,n){const s=d(e),o=d(n);0===r?i.moveTo(s,o):i.lineTo(s,o),r++}const n=/(-?[\\\\d\\\\.?]+)[,|\\\\s](-?[\\\\d\\\\.?]+)/g,i=new p.a;let r=0;return t.getAttribute(\\\\\\\"points\\\\\\\").replace(n,e),i.currentPath.autoClose=!0,i}(e);break;case\\\\\\\"polyline\\\\\\\":r=s(e,r),m=function(t){function e(t,e,n){const s=d(e),o=d(n);0===r?i.moveTo(s,o):i.lineTo(s,o),r++}const n=/(-?[\\\\d\\\\.?]+)[,|\\\\s](-?[\\\\d\\\\.?]+)/g,i=new p.a;let r=0;return t.getAttribute(\\\\\\\"points\\\\\\\").replace(n,e),i.currentPath.autoClose=!1,i}(e);break;case\\\\\\\"circle\\\\\\\":r=s(e,r),m=function(t){const e=d(t.getAttribute(\\\\\\\"cx\\\\\\\")||0),n=d(t.getAttribute(\\\\\\\"cy\\\\\\\")||0),i=d(t.getAttribute(\\\\\\\"r\\\\\\\")||0),r=new h.a;r.absarc(e,n,i,0,2*Math.PI);const s=new p.a;return s.subPaths.push(r),s}(e);break;case\\\\\\\"ellipse\\\\\\\":r=s(e,r),m=function(t){const e=d(t.getAttribute(\\\\\\\"cx\\\\\\\")||0),n=d(t.getAttribute(\\\\\\\"cy\\\\\\\")||0),i=d(t.getAttribute(\\\\\\\"rx\\\\\\\")||0),r=d(t.getAttribute(\\\\\\\"ry\\\\\\\")||0),s=new h.a;s.absellipse(e,n,i,r,0,2*Math.PI);const o=new p.a;return o.subPaths.push(s),o}(e);break;case\\\\\\\"line\\\\\\\":r=s(e,r),m=function(t){const e=d(t.getAttribute(\\\\\\\"x1\\\\\\\")||0),n=d(t.getAttribute(\\\\\\\"y1\\\\\\\")||0),i=d(t.getAttribute(\\\\\\\"x2\\\\\\\")||0),r=d(t.getAttribute(\\\\\\\"y2\\\\\\\")||0),s=new p.a;return s.moveTo(e,n),s.lineTo(i,r),s.currentPath.autoClose=!1,s}(e);break;case\\\\\\\"defs\\\\\\\":c=!1;break;case\\\\\\\"use\\\\\\\":r=s(e,r);const l=e.href.baseVal.substring(1),u=e.viewportElement.getElementById(l);u?t(u,r):console.warn(\\\\\\\"SVGLoader: 'use node' references non-existent node id: \\\\\\\"+l)}if(m&&(void 0!==r.fill&&\\\\\\\"none\\\\\\\"!==r.fill&&m.color.setStyle(r.fill),function(t,e){function n(t){E.set(t.x,t.y,1).applyMatrix3(e),t.set(E.x,E.y)}const i=function(t){return 0!==t.elements[1]||0!==t.elements[3]}(e),r=t.subPaths;for(let t=0,s=r.length;t0?M.copy(y[y.length-1]):M.identity())}(S.documentElement,{fill:\\\\\\\"#000\\\\\\\",fillOpacity:1,strokeOpacity:1,strokeWidth:1,strokeLineJoin:\\\\\\\"miter\\\\\\\",strokeLineCap:\\\\\\\"butt\\\\\\\",strokeMiterLimit:4});return{paths:g,xml:S.documentElement}}static createShapes(t){const e=999999999,n=0,r=1,o=2,a=3,l=4,c=5,u=6,p={loc:n,t:0};function m(t,e,i,r){const s=t.x,a=e.x,l=i.x,c=r.x,u=t.y,h=e.y,d=i.y,_=r.y,m=(c-l)*(u-d)-(_-d)*(s-l),g=(_-d)*(a-s)-(c-l)*(h-u),v=m/g,y=((a-s)*(u-d)-(h-u)*(s-l))/g;if(0===g&&0!==m||v<=0||v>=1||y<0||y>1)return null;if(0===m&&0===g){for(let l=0;l<2;l++){if(f(0===l?i:r,t,e),p.loc==n){const t=0===l?i:r;return{x:t.x,y:t.y,t:p.t}}if(p.loc==o){return{x:+(s+p.t*(a-s)).toPrecision(10),y:+(u+p.t*(h-u)).toPrecision(10),t:p.t}}}return null}for(let s=0;s<2;s++)if(f(0===s?i:r,t,e),p.loc==n){const t=0===s?i:r;return{x:t.x,y:t.y,t:p.t}}return{x:+(s+v*(a-s)).toPrecision(10),y:+(u+v*(h-u)).toPrecision(10),t:v}}function f(t,e,i){const s=i.x-e.x,h=i.y-e.y,d=t.x-e.x,_=t.y-e.y,m=s*_-d*h;if(t.x===e.x&&t.y===e.y)return p.loc=n,void(p.t=0);if(t.x===i.x&&t.y===i.y)return p.loc=r,void(p.t=1);if(m<-Number.EPSILON)return void(p.loc=a);if(m>Number.EPSILON)return void(p.loc=l);if(s*d<0||h*_<0)return void(p.loc=c);if(Math.sqrt(s*s+h*h){if(e.boundingBox.containsPoint(r)){(function(t,e){const n=[],r=[];for(let s=1;st.t<=s.t+Number.EPSILON&&t.t>=s.t-Number.EPSILON))&&(n.push(s),r.push(new i.a(s.x,s.y)))}}return r})(t,e.points).forEach((t=>{s.push({identifier:e.identifier,isCW:e.isCW,point:t})}))}})),s.sort(((t,e)=>t.point.x-e.point.x)),s}let v=0,y=e,x=-999999999,b=t.subPaths.map((t=>{const n=t.getPoints();let r=-999999999,o=e,a=-999999999,l=e;for(let t=0;tr&&(r=e.y),e.ya&&(a=e.x),e.x=l&&(y=l-1),{points:n,isCW:_.a.isClockWise(n),identifier:v++,boundingBox:new s(new i.a(l,o),new i.a(a,r))}}));b=b.filter((t=>t.points.length>1));const w=b.map((e=>function(t,e,n,r,s){null!=s&&\\\\\\\"\\\\\\\"!==s||(s=\\\\\\\"nonzero\\\\\\\");const o=new i.a;t.boundingBox.getCenter(o);const a=g([new i.a(n,o.y),new i.a(r,o.y)],t.boundingBox,e);a.sort(((t,e)=>t.point.x-e.point.x));const l=[],c=[];a.forEach((e=>{e.identifier===t.identifier?l.push(e):c.push(e)}));const u=l[0].point.x,h=[];let d=0;for(;d0&&h[h.length-1]===c[d].identifier?h.pop():h.push(c[d].identifier),d++;if(h.push(t.identifier),\\\\\\\"evenodd\\\\\\\"===s){const e=h.length%2==0,n=h[h.length-2];return{identifier:t.identifier,isHole:e,for:n}}if(\\\\\\\"nonzero\\\\\\\"===s){let n=!0,i=null,r=null;for(let t=0;t{if(!w[t.identifier].isHole){const e=new d.a(t.points);w.filter((e=>e.isHole&&e.for===t.identifier)).forEach((t=>{const n=b[t.identifier];e.holes.push(new h.a(n.points))})),T.push(e)}})),T}static getStrokeStyle(t,e,n,i,r){return{strokeColor:e=void 0!==e?e:\\\\\\\"#000\\\\\\\",strokeWidth:t=void 0!==t?t:1,strokeLineJoin:n=void 0!==n?n:\\\\\\\"miter\\\\\\\",strokeLineCap:i=void 0!==i?i:\\\\\\\"butt\\\\\\\",strokeMiterLimit:r=void 0!==r?r:4}}static pointsToStroke(t,e,n,i){const r=[],s=[],a=[];if(0===f.pointsToStrokeWithBuffers(t,e,n,i,r,s,a))return null;const c=new o.a;return c.setAttribute(\\\\\\\"position\\\\\\\",new l.c(r,3)),c.setAttribute(\\\\\\\"normal\\\\\\\",new l.c(s,3)),c.setAttribute(\\\\\\\"uv\\\\\\\",new l.c(a,2)),c}static pointsToStrokeWithBuffers(t,e,n,r,s,o,a,l){const c=new i.a,u=new i.a,h=new i.a,d=new i.a,p=new i.a,_=new i.a,m=new i.a,f=new i.a,g=new i.a,v=new i.a,y=new i.a,x=new i.a,b=new i.a,w=new i.a,T=new i.a,A=new i.a,E=new i.a;n=void 0!==n?n:12,r=void 0!==r?r:.001,l=void 0!==l?l:0;const M=(t=function(t){let e=!1;for(let n=1,i=t.length-1;n=r&&n.push(t[e]);return n.push(t[t.length-1]),n}(t)).length;if(M<2)return 0;const S=t[0].equals(t[M-1]);let C,N,L=t[0];const O=e.strokeWidth/2,R=1/(M-1);let P,I,F,D,k=0,B=!1,z=0,U=3*l,G=2*l;V(t[0],t[1],c).multiplyScalar(O),f.copy(t[0]).sub(c),g.copy(t[0]).add(c),v.copy(f),y.copy(g);for(let n=1;n0?0:(Math.floor(Math.abs(p)/s)+1)*s:0===_&&p===s-1&&(p=s-2,_=1),this.closed||p>0?h=r[(p-1)%s]:(o.subVectors(r[0],r[1]).add(r[0]),h=o);const m=r[p%s],f=r[(p+1)%s];if(this.closed||p+2>>0)+\\\\\\\"_\\\\\\\",r=0;return function t(i){if(this instanceof t)throw new TypeError(\\\\\\\"Symbol is not a constructor\\\\\\\");return new e(n+(i||\\\\\\\"\\\\\\\")+\\\\\\\"_\\\\\\\"+r++,i)}})),s(\\\\\\\"Symbol.iterator\\\\\\\",(function(t){if(t)return t;t=Symbol(\\\\\\\"Symbol.iterator\\\\\\\");for(var e=\\\\\\\"Array Int8Array Uint8Array Uint8ClampedArray Int16Array Uint16Array Int32Array Uint32Array Float32Array Float64Array\\\\\\\".split(\\\\\\\" \\\\\\\"),s=0;s(e=e||0)&&(e=Math.max(e+i,0));es)i[n++]=s;else{if(2048>s)i[n++]=s>>6|192;else{if(55296<=s&&57343>=s){if(56319>=s&&r=o){s=1024*(s-55296)+o-56320+65536,i[n++]=s>>18|240,i[n++]=s>>12&63|128,i[n++]=s>>6&63|128,i[n++]=63&s|128;continue}r--}if(e)throw Error(\\\\\\\"Found an unpaired surrogate\\\\\\\");s=65533}i[n++]=s>>12|224,i[n++]=s>>6&63|128}i[n++]=63&s|128}}t=i.subarray(0,n)}return t}var P=\\\\\\\"function\\\\\\\"==typeof Uint8Array.prototype.slice,I=0;function F(t){this.h=null,this.g=this.j=this.l=0,this.m=!1,t&&D(this,t)}function D(t,e){e=e.constructor===Uint8Array?e:e.constructor===ArrayBuffer||e.constructor===Array?new Uint8Array(e):e.constructor===String?function(t){var e=t.length,n=3*e/4;n%3?n=Math.floor(n):-1!=\\\\\\\"=.\\\\\\\".indexOf(t[e-1])&&(n=-1!=\\\\\\\"=.\\\\\\\".indexOf(t[e-2])?n-2:n-1);var i=new Uint8Array(n),r=0;return function(t,e){function n(e){for(;i>4),64!=o&&(e(s<<4&240|o>>2),64!=a&&e(o<<6&192|a))}}(t,(function(t){i[r++]=t})),i.subarray(0,r)}(e):e instanceof Uint8Array?new Uint8Array(e.buffer,e.byteOffset,e.byteLength):new Uint8Array(0),t.h=e,t.l=0,t.j=t.h.length,t.g=t.l}function k(t){for(var e=128,n=0,i=0,r=0;4>r&&128<=e;r++)n|=(127&(e=t.h[t.g++]))<<7*r;if(128<=e&&(n|=(127&(e=t.h[t.g++]))<<28,i|=(127&e)>>4),128<=e)for(r=0;5>r&&128<=e;r++)i|=(127&(e=t.h[t.g++]))<<7*r+3;if(128>e)return t=n>>>0,(i=2147483648&(e=i>>>0))&&(e=~e>>>0,0==(t=1+~t>>>0)&&(e=e+1>>>0)),t=4294967296*e+(t>>>0),i?-t:t;t.m=!0}F.prototype.reset=function(){this.g=this.l},F.prototype.i=function(){var t=this.h,e=t[this.g],n=127&e;return 128>e?(this.g+=1,n):(n|=(127&(e=t[this.g+1]))<<7,128>e?(this.g+=2,n):(n|=(127&(e=t[this.g+2]))<<14,128>e?(this.g+=3,n):(n|=(127&(e=t[this.g+3]))<<21,128>e?(this.g+=4,n):(n|=(15&(e=t[this.g+4]))<<28,128>e?(this.g+=5,n>>>0):(this.g+=5,128<=t[this.g++]&&128<=t[this.g++]&&128<=t[this.g++]&&128<=t[this.g++]&&this.g++,n)))))},F.prototype.o=function(){var t=this.h[this.g],e=this.h[this.g+1],n=this.h[this.g+2],i=this.h[this.g+3];return this.g+=4,t=2*((n=(t<<0|e<<8|n<<16|i<<24)>>>0)>>31)+1,e=n>>>23&255,n&=8388607,255==e?n?NaN:1/0*t:0==e?t*Math.pow(2,-149)*n:t*Math.pow(2,e-150)*(n+Math.pow(2,23))};var B=[];function z(){this.g=new Uint8Array(64),this.h=0}function U(t,e){for(;127>>=7;t.push(e)}function G(t){if(B.length){var e=B.pop();t&&D(e,t),t=e}else t=new F(t);this.g=t,this.h=this.i=this.l=-1,this.j=!1}function V(t){var e=t.g;if((e=e.g==e.j)||(e=t.j)||(e=(e=t.g).m||0>e.g||e.g>e.j),e)return!1;var n=7&(e=t.g.i());return 0!=n&&5!=n&&1!=n&&2!=n&&3!=n&&4!=n?(t.j=!0,!1):(t.i=e,t.l=e>>>3,t.h=n,!0)}function H(t){switch(t.h){case 0:if(0!=t.h)H(t);else{for(t=t.g;128&t.h[t.g];)t.g++;t.g++}break;case 1:1!=t.h?H(t):(t=t.g).g+=8;break;case 2:if(2!=t.h)H(t);else{var e=t.g.i();(t=t.g).g+=e}break;case 5:5!=t.h?H(t):(t=t.g).g+=4;break;case 3:for(e=t.l;;){if(!V(t)){t.j=!0;break}if(4==t.h){t.l!=e&&(t.j=!0);break}H(t)}break;default:t.j=!0}}function j(t,e,n){var i=t.g.j,r=t.g.i();r=t.g.g+r,t.g.j=r,n(e,t),t.g.g=r,t.g.j=i}function W(t){return t.g.o()}function q(t){var e,n=t.g.i(),i=(t=t.g).g;if(t.g+=n,t=t.h,L)(e=C)||(e=C=new TextDecoder(\\\\\\\"utf-8\\\\\\\",{fatal:!1})),e=e.decode(t.subarray(i,i+n));else{n=i+n;for(var r,s,o,a=[],l=null;i(r=t[i++])?a.push(r):224>r?i>=n?a.push(65533):(s=t[i++],194>r||128!=(192&s)?(i--,a.push(65533)):a.push((31&r)<<6|63&s)):240>r?i>=n-1?a.push(65533):128!=(192&(s=t[i++]))||224===r&&160>s||237===r&&160<=s||128!=(192&(e=t[i++]))?(i--,a.push(65533)):a.push((15&r)<<12|(63&s)<<6|63&e):244>=r?i>=n-2?a.push(65533):128!=(192&(s=t[i++]))||0!=s-144+(r<<28)>>30||128!=(192&(e=t[i++]))||128!=(192&(o=t[i++]))?(i--,a.push(65533)):(r=(7&r)<<18|(63&s)<<12|(63&e)<<6|63&o,r-=65536,a.push(55296+(r>>10&1023),56320+(1023&r))):a.push(65533),8192<=a.length&&(l=S(l,a),a.length=0);e=S(l,a)}return e}function X(t,e){var n=t.g.i();n=t.g.g+n;for(var i=[];t.g.gn;n++){var i=t.concat(e[n].split(\\\\\\\"\\\\\\\"));$[n]=i;for(var r=0;ri?1:0)?-i:i)?0<1/i?I=0:(0,I=2147483648):isNaN(i)?(0,I=2147483647):34028234663852886e22>>0):11754943508222875e-54>i?(i=Math.round(i/Math.pow(2,-149)),0,I=(n<<31|i)>>>0):(e=Math.floor(Math.log(i)/Math.LN2),i*=Math.pow(2,-e),i=8388607&Math.round(8388608*i),0,I=(n<<31|e+127<<23|i)>>>0),n=I,t.push(n>>>0&255),t.push(n>>>8&255),t.push(n>>>16&255),t.push(n>>>24&255)}}function et(t,e,n){U(t.g,8*e+2),U(t.g,n.length),e=t.g.end(),t.h.push(e),t.h.push(n),t.i+=e.length+n.length}var nt=\\\\\\\"function\\\\\\\"==typeof Uint8Array;function it(t,e,n){return\\\\\\\"object\\\\\\\"==typeof t?nt&&!Array.isArray(t)&&t instanceof Uint8Array?n(t):rt(t,e,n):e(t)}function rt(t,e,n){if(Array.isArray(t)){for(var i=Array(t.length),r=0;r>2];o=e[(3&o)<<4|a>>4],a=e[(15&a)<<2|l>>6],l=e[63&l],n[s++]=c+o+a+l}switch(c=0,l=i,t.length-r){case 2:l=e[(15&(c=t[r+1]))<<2]||i;case 1:t=t[r],n[s]=e[t>>2]+e[(3&t)<<4|c>>4]+l+i}return n.join(\\\\\\\"\\\\\\\")}))}(xt(this,!1))},lt.prototype.toString=function(){return xt(this,!1).toString()},_(wt,lt),wt.prototype.getRows=function(){return ht(this,1)},wt.prototype.getCols=function(){return ht(this,2)},wt.prototype.getPackedDataList=function(){return dt(this)},wt.prototype.getLayout=function(){return pt(this,4,0)};var At=[3];function Et(t,e){var n=void 0;return new(n||(n=Promise))((function(i,r){function s(t){try{a(e.next(t))}catch(t){r(t)}}function o(t){try{a(e.throw(t))}catch(t){r(t)}}function a(t){t.done?i(t.value):new n((function(e){e(t.value)})).then(s,o)}a((e=e.apply(t,void 0)).next())}))}function Mt(t){lt.call(this,t)}function St(t,e){for(;V(e);)switch(e.i){case 8:var n=e.g.i();mt(t,1,n);break;case 21:mt(t,2,n=W(e));break;case 26:mt(t,3,n=q(e));break;case 34:mt(t,4,n=q(e));break;default:if(!bt(e))return t}return t}function Ct(t){lt.call(this,t,-1,Nt)}_(Mt,lt),_(Ct,lt),Ct.prototype.addClassification=function(t,e){return yt(this,t,Mt,e)};var Nt=[1];function Lt(t){lt.call(this,t)}function Ot(t,e){for(;V(e);)switch(e.i){case 13:var n=W(e);mt(t,1,n);break;case 21:mt(t,2,n=W(e));break;case 29:mt(t,3,n=W(e));break;case 37:mt(t,4,n=W(e));break;case 45:mt(t,5,n=W(e));break;default:if(!bt(e))return t}return t}function Rt(t){lt.call(this,t,-1,It)}function Pt(t){t:{var e=new Rt;for(t=new G(t);V(t);)switch(t.i){case 10:var n=new Lt;j(t,n,Ot),yt(e,n,Lt,void 0);break;default:if(!bt(t))break t}}return e}_(Lt,lt),_(Rt,lt);var It=[1];function Ft(t){lt.call(this,t)}function Dt(t){lt.call(this,t,-1,Bt)}function kt(t,e){for(;V(e);)switch(e.i){case 8:var n=k(e.g);mt(t,1,n);break;case 16:mt(t,2,n=k(e.g));break;case 29:case 26:n=Y(e);for(var i=0;iM,A=(M=Math.abs(M))>>>0,M=Math.floor((M-A)/4294967296),M>>>=0,E&&(M=~M>>>0,4294967295<(A=1+(~A>>>0))&&(A=0,4294967295<++M&&(M=0))),E=I=A,A=M;0>>7|A<<25)>>>0,A>>>=7;T.push(E)}if(w=K(w),x.call(y,b,w),g.M)for(y=0;yC;C++)M.push(127&S|128),S>>=7;M.push(1)}}null!=(M=ht(E,2))&&tt(A,2,M),null!=(M=ht(E,3))&&null!=M&&et(A,3,R(M)),null!=(M=ht(E,4))&&null!=M&&et(A,4,R(M)),T=K(T),b.call(x,w,T)}}_=_.data;break t;default:_={}}}switch(u=_,h=c.stream,c.type){case\\\\\\\"video\\\\\\\":r.pushTexture2d(Object.assign(Object.assign({},u),{stream:h,timestamp:i}));break;case\\\\\\\"detections\\\\\\\":(d=u).stream=h,d.timestamp=i,r.pushDetectionList(d);break;default:throw Error(\\\\\\\"Unknown input config type: '\\\\\\\"+c.type+\\\\\\\"'\\\\\\\")}}return p.i.send(r),v(n,p.B,4);case 4:r.delete(),n.g=0}}))}))},e.onResults=function(t,e){this.listeners[e||\\\\\\\"$\\\\\\\"]=t},M(\\\\\\\"Solution\\\\\\\",Jt),M(\\\\\\\"OptionType\\\\\\\",{BOOL:0,NUMBER:1,Z:2,0:\\\\\\\"BOOL\\\\\\\",1:\\\\\\\"NUMBER\\\\\\\",2:\\\\\\\"STRING\\\\\\\"});var ne={files:[{url:\\\\\\\"face_mesh_solution_packed_assets_loader.js\\\\\\\"},{simd:!0,url:\\\\\\\"face_mesh_solution_simd_wasm_bin.js\\\\\\\"},{simd:!1,url:\\\\\\\"face_mesh_solution_wasm_bin.js\\\\\\\"}],graph:{url:\\\\\\\"face_mesh.binarypb\\\\\\\"},listeners:[{wants:[\\\\\\\"multi_face_geometry\\\\\\\",\\\\\\\"image_transformed\\\\\\\",\\\\\\\"multi_face_landmarks\\\\\\\"],outs:{image:\\\\\\\"image_transformed\\\\\\\",multiFaceGeometry:{type:\\\\\\\"proto_list\\\\\\\",stream:\\\\\\\"multi_face_geometry\\\\\\\",transform:function(t){return t.map(ee)}},multiFaceLandmarks:{type:\\\\\\\"proto_list\\\\\\\",stream:\\\\\\\"multi_face_landmarks\\\\\\\",transform:function(t){return t.map((function(t){return gt(Pt(t),Lt).map(Vt)}))}}}}],inputs:{image:{type:\\\\\\\"video\\\\\\\",stream:\\\\\\\"input_frames_gpu\\\\\\\"}},options:{useCpuInference:{type:0,graphOptionXref:{calculatorType:\\\\\\\"InferenceCalculator\\\\\\\",fieldName:\\\\\\\"use_cpu_inference\\\\\\\"},default:\\\\\\\"iPad Simulator;iPhone Simulator;iPod Simulator;iPad;iPhone;iPod\\\\\\\".split(\\\\\\\";\\\\\\\").includes(navigator.platform)||navigator.userAgent.includes(\\\\\\\"Mac\\\\\\\")&&\\\\\\\"ontouchend\\\\\\\"in document},enableFaceGeometry:{type:0,graphOptionXref:{calculatorName:\\\\\\\"EnableFaceGeometryConstant\\\\\\\",calculatorType:\\\\\\\"ConstantSidePacketCalculator\\\\\\\",fieldName:\\\\\\\"bool_value\\\\\\\"}},selfieMode:{type:0,graphOptionXref:{calculatorType:\\\\\\\"GlScalerCalculator\\\\\\\",calculatorIndex:1,fieldName:\\\\\\\"flip_horizontal\\\\\\\"}},maxNumFaces:{type:1,graphOptionXref:{calculatorType:\\\\\\\"ConstantSidePacketCalculator\\\\\\\",calculatorName:\\\\\\\"ConstantSidePacketCalculator\\\\\\\",fieldName:\\\\\\\"int_value\\\\\\\"}},minDetectionConfidence:{type:1,graphOptionXref:{calculatorType:\\\\\\\"TensorsToDetectionsCalculator\\\\\\\",calculatorName:\\\\\\\"facelandmarkfrontgpu__facedetectionshortrangegpu__facedetectionshortrangecommon__TensorsToDetectionsCalculator\\\\\\\",fieldName:\\\\\\\"min_score_thresh\\\\\\\"}},minTrackingConfidence:{type:1,graphOptionXref:{calculatorType:\\\\\\\"ThresholdingCalculator\\\\\\\",calculatorName:\\\\\\\"facelandmarkfrontgpu__facelandmarkgpu__ThresholdingCalculator\\\\\\\",fieldName:\\\\\\\"threshold\\\\\\\"}},cameraNear:{type:1,graphOptionXref:{calculatorType:\\\\\\\"FaceGeometryEnvGeneratorCalculator\\\\\\\",fieldName:\\\\\\\"near\\\\\\\"}},cameraFar:{type:1,graphOptionXref:{calculatorType:\\\\\\\"FaceGeometryEnvGeneratorCalculator\\\\\\\",fieldName:\\\\\\\"far\\\\\\\"}},cameraVerticalFovDegrees:{type:1,graphOptionXref:{calculatorType:\\\\\\\"FaceGeometryEnvGeneratorCalculator\\\\\\\",fieldName:\\\\\\\"vertical_fov_degrees\\\\\\\"}}}},ie=[[61,146],[146,91],[91,181],[181,84],[84,17],[17,314],[314,405],[405,321],[321,375],[375,291],[61,185],[185,40],[40,39],[39,37],[37,0],[0,267],[267,269],[269,270],[270,409],[409,291],[78,95],[95,88],[88,178],[178,87],[87,14],[14,317],[317,402],[402,318],[318,324],[324,308],[78,191],[191,80],[80,81],[81,82],[82,13],[13,312],[312,311],[311,310],[310,415],[415,308]],re=[[263,249],[249,390],[390,373],[373,374],[374,380],[380,381],[381,382],[382,362],[263,466],[466,388],[388,387],[387,386],[386,385],[385,384],[384,398],[398,362]],se=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],oe=[[33,7],[7,163],[163,144],[144,145],[145,153],[153,154],[154,155],[155,133],[33,246],[246,161],[161,160],[160,159],[159,158],[158,157],[157,173],[173,133]],ae=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],le=[[10,338],[338,297],[297,332],[332,284],[284,251],[251,389],[389,356],[356,454],[454,323],[323,361],[361,288],[288,397],[397,365],[365,379],[379,378],[378,400],[400,377],[377,152],[152,148],[148,176],[176,149],[149,150],[150,136],[136,172],[172,58],[58,132],[132,93],[93,234],[234,127],[127,162],[162,21],[21,54],[54,103],[103,67],[67,109],[109,10]],ce=[].concat(l(ie),l(re),l(se),l(oe),l(ae),l(le));function ue(t){t=t||{},t=Object.assign(Object.assign({},ne),t),this.g=new Jt(t)}(e=ue.prototype).close=function(){return this.g.close(),Promise.resolve()},e.onResults=function(t){this.g.onResults(t)},e.initialize=function(){return Et(this,(function t(){var e=this;return T(t,(function(t){return v(t,e.g.initialize(),0)}))}))},e.reset=function(){this.g.reset()},e.send=function(t){return Et(this,(function e(){var n=this;return T(e,(function(e){return v(e,n.g.send(t),0)}))}))},e.setOptions=function(t){this.g.setOptions(t)},M(\\\\\\\"FACE_GEOMETRY\\\\\\\",{Layout:{COLUMN_MAJOR:0,ROW_MAJOR:1,0:\\\\\\\"COLUMN_MAJOR\\\\\\\",1:\\\\\\\"ROW_MAJOR\\\\\\\"},PrimitiveType:{TRIANGLE:0,0:\\\\\\\"TRIANGLE\\\\\\\"},VertexType:{VERTEX_PT:0,0:\\\\\\\"VERTEX_PT\\\\\\\"},DEFAULT_CAMERA_PARAMS:{verticalFovDegrees:63,near:1,far:1e4}}),M(\\\\\\\"FaceMesh\\\\\\\",ue),M(\\\\\\\"FACEMESH_LIPS\\\\\\\",ie),M(\\\\\\\"FACEMESH_LEFT_EYE\\\\\\\",re),M(\\\\\\\"FACEMESH_LEFT_EYEBROW\\\\\\\",se),M(\\\\\\\"FACEMESH_RIGHT_EYE\\\\\\\",oe),M(\\\\\\\"FACEMESH_RIGHT_EYEBROW\\\\\\\",ae),M(\\\\\\\"FACEMESH_FACE_OVAL\\\\\\\",le),M(\\\\\\\"FACEMESH_CONTOURS\\\\\\\",ce),M(\\\\\\\"FACEMESH_TESSELATION\\\\\\\",[[127,34],[34,139],[139,127],[11,0],[0,37],[37,11],[232,231],[231,120],[120,232],[72,37],[37,39],[39,72],[128,121],[121,47],[47,128],[232,121],[121,128],[128,232],[104,69],[69,67],[67,104],[175,171],[171,148],[148,175],[118,50],[50,101],[101,118],[73,39],[39,40],[40,73],[9,151],[151,108],[108,9],[48,115],[115,131],[131,48],[194,204],[204,211],[211,194],[74,40],[40,185],[185,74],[80,42],[42,183],[183,80],[40,92],[92,186],[186,40],[230,229],[229,118],[118,230],[202,212],[212,214],[214,202],[83,18],[18,17],[17,83],[76,61],[61,146],[146,76],[160,29],[29,30],[30,160],[56,157],[157,173],[173,56],[106,204],[204,194],[194,106],[135,214],[214,192],[192,135],[203,165],[165,98],[98,203],[21,71],[71,68],[68,21],[51,45],[45,4],[4,51],[144,24],[24,23],[23,144],[77,146],[146,91],[91,77],[205,50],[50,187],[187,205],[201,200],[200,18],[18,201],[91,106],[106,182],[182,91],[90,91],[91,181],[181,90],[85,84],[84,17],[17,85],[206,203],[203,36],[36,206],[148,171],[171,140],[140,148],[92,40],[40,39],[39,92],[193,189],[189,244],[244,193],[159,158],[158,28],[28,159],[247,246],[246,161],[161,247],[236,3],[3,196],[196,236],[54,68],[68,104],[104,54],[193,168],[168,8],[8,193],[117,228],[228,31],[31,117],[189,193],[193,55],[55,189],[98,97],[97,99],[99,98],[126,47],[47,100],[100,126],[166,79],[79,218],[218,166],[155,154],[154,26],[26,155],[209,49],[49,131],[131,209],[135,136],[136,150],[150,135],[47,126],[126,217],[217,47],[223,52],[52,53],[53,223],[45,51],[51,134],[134,45],[211,170],[170,140],[140,211],[67,69],[69,108],[108,67],[43,106],[106,91],[91,43],[230,119],[119,120],[120,230],[226,130],[130,247],[247,226],[63,53],[53,52],[52,63],[238,20],[20,242],[242,238],[46,70],[70,156],[156,46],[78,62],[62,96],[96,78],[46,53],[53,63],[63,46],[143,34],[34,227],[227,143],[123,117],[117,111],[111,123],[44,125],[125,19],[19,44],[236,134],[134,51],[51,236],[216,206],[206,205],[205,216],[154,153],[153,22],[22,154],[39,37],[37,167],[167,39],[200,201],[201,208],[208,200],[36,142],[142,100],[100,36],[57,212],[212,202],[202,57],[20,60],[60,99],[99,20],[28,158],[158,157],[157,28],[35,226],[226,113],[113,35],[160,159],[159,27],[27,160],[204,202],[202,210],[210,204],[113,225],[225,46],[46,113],[43,202],[202,204],[204,43],[62,76],[76,77],[77,62],[137,123],[123,116],[116,137],[41,38],[38,72],[72,41],[203,129],[129,142],[142,203],[64,98],[98,240],[240,64],[49,102],[102,64],[64,49],[41,73],[73,74],[74,41],[212,216],[216,207],[207,212],[42,74],[74,184],[184,42],[169,170],[170,211],[211,169],[170,149],[149,176],[176,170],[105,66],[66,69],[69,105],[122,6],[6,168],[168,122],[123,147],[147,187],[187,123],[96,77],[77,90],[90,96],[65,55],[55,107],[107,65],[89,90],[90,180],[180,89],[101,100],[100,120],[120,101],[63,105],[105,104],[104,63],[93,137],[137,227],[227,93],[15,86],[86,85],[85,15],[129,102],[102,49],[49,129],[14,87],[87,86],[86,14],[55,8],[8,9],[9,55],[100,47],[47,121],[121,100],[145,23],[23,22],[22,145],[88,89],[89,179],[179,88],[6,122],[122,196],[196,6],[88,95],[95,96],[96,88],[138,172],[172,136],[136,138],[215,58],[58,172],[172,215],[115,48],[48,219],[219,115],[42,80],[80,81],[81,42],[195,3],[3,51],[51,195],[43,146],[146,61],[61,43],[171,175],[175,199],[199,171],[81,82],[82,38],[38,81],[53,46],[46,225],[225,53],[144,163],[163,110],[110,144],[52,65],[65,66],[66,52],[229,228],[228,117],[117,229],[34,127],[127,234],[234,34],[107,108],[108,69],[69,107],[109,108],[108,151],[151,109],[48,64],[64,235],[235,48],[62,78],[78,191],[191,62],[129,209],[209,126],[126,129],[111,35],[35,143],[143,111],[117,123],[123,50],[50,117],[222,65],[65,52],[52,222],[19,125],[125,141],[141,19],[221,55],[55,65],[65,221],[3,195],[195,197],[197,3],[25,7],[7,33],[33,25],[220,237],[237,44],[44,220],[70,71],[71,139],[139,70],[122,193],[193,245],[245,122],[247,130],[130,33],[33,247],[71,21],[21,162],[162,71],[170,169],[169,150],[150,170],[188,174],[174,196],[196,188],[216,186],[186,92],[92,216],[2,97],[97,167],[167,2],[141,125],[125,241],[241,141],[164,167],[167,37],[37,164],[72,38],[38,12],[12,72],[38,82],[82,13],[13,38],[63,68],[68,71],[71,63],[226,35],[35,111],[111,226],[101,50],[50,205],[205,101],[206,92],[92,165],[165,206],[209,198],[198,217],[217,209],[165,167],[167,97],[97,165],[220,115],[115,218],[218,220],[133,112],[112,243],[243,133],[239,238],[238,241],[241,239],[214,135],[135,169],[169,214],[190,173],[173,133],[133,190],[171,208],[208,32],[32,171],[125,44],[44,237],[237,125],[86,87],[87,178],[178,86],[85,86],[86,179],[179,85],[84,85],[85,180],[180,84],[83,84],[84,181],[181,83],[201,83],[83,182],[182,201],[137,93],[93,132],[132,137],[76,62],[62,183],[183,76],[61,76],[76,184],[184,61],[57,61],[61,185],[185,57],[212,57],[57,186],[186,212],[214,207],[207,187],[187,214],[34,143],[143,156],[156,34],[79,239],[239,237],[237,79],[123,137],[137,177],[177,123],[44,1],[1,4],[4,44],[201,194],[194,32],[32,201],[64,102],[102,129],[129,64],[213,215],[215,138],[138,213],[59,166],[166,219],[219,59],[242,99],[99,97],[97,242],[2,94],[94,141],[141,2],[75,59],[59,235],[235,75],[24,110],[110,228],[228,24],[25,130],[130,226],[226,25],[23,24],[24,229],[229,23],[22,23],[23,230],[230,22],[26,22],[22,231],[231,26],[112,26],[26,232],[232,112],[189,190],[190,243],[243,189],[221,56],[56,190],[190,221],[28,56],[56,221],[221,28],[27,28],[28,222],[222,27],[29,27],[27,223],[223,29],[30,29],[29,224],[224,30],[247,30],[30,225],[225,247],[238,79],[79,20],[20,238],[166,59],[59,75],[75,166],[60,75],[75,240],[240,60],[147,177],[177,215],[215,147],[20,79],[79,166],[166,20],[187,147],[147,213],[213,187],[112,233],[233,244],[244,112],[233,128],[128,245],[245,233],[128,114],[114,188],[188,128],[114,217],[217,174],[174,114],[131,115],[115,220],[220,131],[217,198],[198,236],[236,217],[198,131],[131,134],[134,198],[177,132],[132,58],[58,177],[143,35],[35,124],[124,143],[110,163],[163,7],[7,110],[228,110],[110,25],[25,228],[356,389],[389,368],[368,356],[11,302],[302,267],[267,11],[452,350],[350,349],[349,452],[302,303],[303,269],[269,302],[357,343],[343,277],[277,357],[452,453],[453,357],[357,452],[333,332],[332,297],[297,333],[175,152],[152,377],[377,175],[347,348],[348,330],[330,347],[303,304],[304,270],[270,303],[9,336],[336,337],[337,9],[278,279],[279,360],[360,278],[418,262],[262,431],[431,418],[304,408],[408,409],[409,304],[310,415],[415,407],[407,310],[270,409],[409,410],[410,270],[450,348],[348,347],[347,450],[422,430],[430,434],[434,422],[313,314],[314,17],[17,313],[306,307],[307,375],[375,306],[387,388],[388,260],[260,387],[286,414],[414,398],[398,286],[335,406],[406,418],[418,335],[364,367],[367,416],[416,364],[423,358],[358,327],[327,423],[251,284],[284,298],[298,251],[281,5],[5,4],[4,281],[373,374],[374,253],[253,373],[307,320],[320,321],[321,307],[425,427],[427,411],[411,425],[421,313],[313,18],[18,421],[321,405],[405,406],[406,321],[320,404],[404,405],[405,320],[315,16],[16,17],[17,315],[426,425],[425,266],[266,426],[377,400],[400,369],[369,377],[322,391],[391,269],[269,322],[417,465],[465,464],[464,417],[386,257],[257,258],[258,386],[466,260],[260,388],[388,466],[456,399],[399,419],[419,456],[284,332],[332,333],[333,284],[417,285],[285,8],[8,417],[346,340],[340,261],[261,346],[413,441],[441,285],[285,413],[327,460],[460,328],[328,327],[355,371],[371,329],[329,355],[392,439],[439,438],[438,392],[382,341],[341,256],[256,382],[429,420],[420,360],[360,429],[364,394],[394,379],[379,364],[277,343],[343,437],[437,277],[443,444],[444,283],[283,443],[275,440],[440,363],[363,275],[431,262],[262,369],[369,431],[297,338],[338,337],[337,297],[273,375],[375,321],[321,273],[450,451],[451,349],[349,450],[446,342],[342,467],[467,446],[293,334],[334,282],[282,293],[458,461],[461,462],[462,458],[276,353],[353,383],[383,276],[308,324],[324,325],[325,308],[276,300],[300,293],[293,276],[372,345],[345,447],[447,372],[352,345],[345,340],[340,352],[274,1],[1,19],[19,274],[456,248],[248,281],[281,456],[436,427],[427,425],[425,436],[381,256],[256,252],[252,381],[269,391],[391,393],[393,269],[200,199],[199,428],[428,200],[266,330],[330,329],[329,266],[287,273],[273,422],[422,287],[250,462],[462,328],[328,250],[258,286],[286,384],[384,258],[265,353],[353,342],[342,265],[387,259],[259,257],[257,387],[424,431],[431,430],[430,424],[342,353],[353,276],[276,342],[273,335],[335,424],[424,273],[292,325],[325,307],[307,292],[366,447],[447,345],[345,366],[271,303],[303,302],[302,271],[423,266],[266,371],[371,423],[294,455],[455,460],[460,294],[279,278],[278,294],[294,279],[271,272],[272,304],[304,271],[432,434],[434,427],[427,432],[272,407],[407,408],[408,272],[394,430],[430,431],[431,394],[395,369],[369,400],[400,395],[334,333],[333,299],[299,334],[351,417],[417,168],[168,351],[352,280],[280,411],[411,352],[325,319],[319,320],[320,325],[295,296],[296,336],[336,295],[319,403],[403,404],[404,319],[330,348],[348,349],[349,330],[293,298],[298,333],[333,293],[323,454],[454,447],[447,323],[15,16],[16,315],[315,15],[358,429],[429,279],[279,358],[14,15],[15,316],[316,14],[285,336],[336,9],[9,285],[329,349],[349,350],[350,329],[374,380],[380,252],[252,374],[318,402],[402,403],[403,318],[6,197],[197,419],[419,6],[318,319],[319,325],[325,318],[367,364],[364,365],[365,367],[435,367],[367,397],[397,435],[344,438],[438,439],[439,344],[272,271],[271,311],[311,272],[195,5],[5,281],[281,195],[273,287],[287,291],[291,273],[396,428],[428,199],[199,396],[311,271],[271,268],[268,311],[283,444],[444,445],[445,283],[373,254],[254,339],[339,373],[282,334],[334,296],[296,282],[449,347],[347,346],[346,449],[264,447],[447,454],[454,264],[336,296],[296,299],[299,336],[338,10],[10,151],[151,338],[278,439],[439,455],[455,278],[292,407],[407,415],[415,292],[358,371],[371,355],[355,358],[340,345],[345,372],[372,340],[346,347],[347,280],[280,346],[442,443],[443,282],[282,442],[19,94],[94,370],[370,19],[441,442],[442,295],[295,441],[248,419],[419,197],[197,248],[263,255],[255,359],[359,263],[440,275],[275,274],[274,440],[300,383],[383,368],[368,300],[351,412],[412,465],[465,351],[263,467],[467,466],[466,263],[301,368],[368,389],[389,301],[395,378],[378,379],[379,395],[412,351],[351,419],[419,412],[436,426],[426,322],[322,436],[2,164],[164,393],[393,2],[370,462],[462,461],[461,370],[164,0],[0,267],[267,164],[302,11],[11,12],[12,302],[268,12],[12,13],[13,268],[293,300],[300,301],[301,293],[446,261],[261,340],[340,446],[330,266],[266,425],[425,330],[426,423],[423,391],[391,426],[429,355],[355,437],[437,429],[391,327],[327,326],[326,391],[440,457],[457,438],[438,440],[341,382],[382,362],[362,341],[459,457],[457,461],[461,459],[434,430],[430,394],[394,434],[414,463],[463,362],[362,414],[396,369],[369,262],[262,396],[354,461],[461,457],[457,354],[316,403],[403,402],[402,316],[315,404],[404,403],[403,315],[314,405],[405,404],[404,314],[313,406],[406,405],[405,313],[421,418],[418,406],[406,421],[366,401],[401,361],[361,366],[306,408],[408,407],[407,306],[291,409],[409,408],[408,291],[287,410],[410,409],[409,287],[432,436],[436,410],[410,432],[434,416],[416,411],[411,434],[264,368],[368,383],[383,264],[309,438],[438,457],[457,309],[352,376],[376,401],[401,352],[274,275],[275,4],[4,274],[421,428],[428,262],[262,421],[294,327],[327,358],[358,294],[433,416],[416,367],[367,433],[289,455],[455,439],[439,289],[462,370],[370,326],[326,462],[2,326],[326,370],[370,2],[305,460],[460,455],[455,305],[254,449],[449,448],[448,254],[255,261],[261,446],[446,255],[253,450],[450,449],[449,253],[252,451],[451,450],[450,252],[256,452],[452,451],[451,256],[341,453],[453,452],[452,341],[413,464],[464,463],[463,413],[441,413],[413,414],[414,441],[258,442],[442,441],[441,258],[257,443],[443,442],[442,257],[259,444],[444,443],[443,259],[260,445],[445,444],[444,260],[467,342],[342,445],[445,467],[459,458],[458,250],[250,459],[289,392],[392,290],[290,289],[290,328],[328,460],[460,290],[376,433],[433,435],[435,376],[250,290],[290,392],[392,250],[411,416],[416,433],[433,411],[341,463],[463,464],[464,341],[453,464],[464,465],[465,453],[357,465],[465,412],[412,357],[343,412],[412,399],[399,343],[360,363],[363,440],[440,360],[437,399],[399,456],[456,437],[420,456],[456,363],[363,420],[401,435],[435,288],[288,401],[372,383],[383,353],[353,372],[339,255],[255,249],[249,339],[448,261],[261,255],[255,448],[133,243],[243,190],[190,133],[133,155],[155,112],[112,133],[33,246],[246,247],[247,33],[33,130],[130,25],[25,33],[398,384],[384,286],[286,398],[362,398],[398,414],[414,362],[362,463],[463,341],[341,362],[263,359],[359,467],[467,263],[263,249],[249,255],[255,263],[466,467],[467,260],[260,466],[75,60],[60,166],[166,75],[238,239],[239,79],[79,238],[162,127],[127,139],[139,162],[72,11],[11,37],[37,72],[121,232],[232,120],[120,121],[73,72],[72,39],[39,73],[114,128],[128,47],[47,114],[233,232],[232,128],[128,233],[103,104],[104,67],[67,103],[152,175],[175,148],[148,152],[119,118],[118,101],[101,119],[74,73],[73,40],[40,74],[107,9],[9,108],[108,107],[49,48],[48,131],[131,49],[32,194],[194,211],[211,32],[184,74],[74,185],[185,184],[191,80],[80,183],[183,191],[185,40],[40,186],[186,185],[119,230],[230,118],[118,119],[210,202],[202,214],[214,210],[84,83],[83,17],[17,84],[77,76],[76,146],[146,77],[161,160],[160,30],[30,161],[190,56],[56,173],[173,190],[182,106],[106,194],[194,182],[138,135],[135,192],[192,138],[129,203],[203,98],[98,129],[54,21],[21,68],[68,54],[5,51],[51,4],[4,5],[145,144],[144,23],[23,145],[90,77],[77,91],[91,90],[207,205],[205,187],[187,207],[83,201],[201,18],[18,83],[181,91],[91,182],[182,181],[180,90],[90,181],[181,180],[16,85],[85,17],[17,16],[205,206],[206,36],[36,205],[176,148],[148,140],[140,176],[165,92],[92,39],[39,165],[245,193],[193,244],[244,245],[27,159],[159,28],[28,27],[30,247],[247,161],[161,30],[174,236],[236,196],[196,174],[103,54],[54,104],[104,103],[55,193],[193,8],[8,55],[111,117],[117,31],[31,111],[221,189],[189,55],[55,221],[240,98],[98,99],[99,240],[142,126],[126,100],[100,142],[219,166],[166,218],[218,219],[112,155],[155,26],[26,112],[198,209],[209,131],[131,198],[169,135],[135,150],[150,169],[114,47],[47,217],[217,114],[224,223],[223,53],[53,224],[220,45],[45,134],[134,220],[32,211],[211,140],[140,32],[109,67],[67,108],[108,109],[146,43],[43,91],[91,146],[231,230],[230,120],[120,231],[113,226],[226,247],[247,113],[105,63],[63,52],[52,105],[241,238],[238,242],[242,241],[124,46],[46,156],[156,124],[95,78],[78,96],[96,95],[70,46],[46,63],[63,70],[116,143],[143,227],[227,116],[116,123],[123,111],[111,116],[1,44],[44,19],[19,1],[3,236],[236,51],[51,3],[207,216],[216,205],[205,207],[26,154],[154,22],[22,26],[165,39],[39,167],[167,165],[199,200],[200,208],[208,199],[101,36],[36,100],[100,101],[43,57],[57,202],[202,43],[242,20],[20,99],[99,242],[56,28],[28,157],[157,56],[124,35],[35,113],[113,124],[29,160],[160,27],[27,29],[211,204],[204,210],[210,211],[124,113],[113,46],[46,124],[106,43],[43,204],[204,106],[96,62],[62,77],[77,96],[227,137],[137,116],[116,227],[73,41],[41,72],[72,73],[36,203],[203,142],[142,36],[235,64],[64,240],[240,235],[48,49],[49,64],[64,48],[42,41],[41,74],[74,42],[214,212],[212,207],[207,214],[183,42],[42,184],[184,183],[210,169],[169,211],[211,210],[140,170],[170,176],[176,140],[104,105],[105,69],[69,104],[193,122],[122,168],[168,193],[50,123],[123,187],[187,50],[89,96],[96,90],[90,89],[66,65],[65,107],[107,66],[179,89],[89,180],[180,179],[119,101],[101,120],[120,119],[68,63],[63,104],[104,68],[234,93],[93,227],[227,234],[16,15],[15,85],[85,16],[209,129],[129,49],[49,209],[15,14],[14,86],[86,15],[107,55],[55,9],[9,107],[120,100],[100,121],[121,120],[153,145],[145,22],[22,153],[178,88],[88,179],[179,178],[197,6],[6,196],[196,197],[89,88],[88,96],[96,89],[135,138],[138,136],[136,135],[138,215],[215,172],[172,138],[218,115],[115,219],[219,218],[41,42],[42,81],[81,41],[5,195],[195,51],[51,5],[57,43],[43,61],[61,57],[208,171],[171,199],[199,208],[41,81],[81,38],[38,41],[224,53],[53,225],[225,224],[24,144],[144,110],[110,24],[105,52],[52,66],[66,105],[118,229],[229,117],[117,118],[227,34],[34,234],[234,227],[66,107],[107,69],[69,66],[10,109],[109,151],[151,10],[219,48],[48,235],[235,219],[183,62],[62,191],[191,183],[142,129],[129,126],[126,142],[116,111],[111,143],[143,116],[118,117],[117,50],[50,118],[223,222],[222,52],[52,223],[94,19],[19,141],[141,94],[222,221],[221,65],[65,222],[196,3],[3,197],[197,196],[45,220],[220,44],[44,45],[156,70],[70,139],[139,156],[188,122],[122,245],[245,188],[139,71],[71,162],[162,139],[149,170],[170,150],[150,149],[122,188],[188,196],[196,122],[206,216],[216,92],[92,206],[164,2],[2,167],[167,164],[242,141],[141,241],[241,242],[0,164],[164,37],[37,0],[11,72],[72,12],[12,11],[12,38],[38,13],[13,12],[70,63],[63,71],[71,70],[31,226],[226,111],[111,31],[36,101],[101,205],[205,36],[203,206],[206,165],[165,203],[126,209],[209,217],[217,126],[98,165],[165,97],[97,98],[237,220],[220,218],[218,237],[237,239],[239,241],[241,237],[210,214],[214,169],[169,210],[140,171],[171,32],[32,140],[241,125],[125,237],[237,241],[179,86],[86,178],[178,179],[180,85],[85,179],[179,180],[181,84],[84,180],[180,181],[182,83],[83,181],[181,182],[194,201],[201,182],[182,194],[177,137],[137,132],[132,177],[184,76],[76,183],[183,184],[185,61],[61,184],[184,185],[186,57],[57,185],[185,186],[216,212],[212,186],[186,216],[192,214],[214,187],[187,192],[139,34],[34,156],[156,139],[218,79],[79,237],[237,218],[147,123],[123,177],[177,147],[45,44],[44,4],[4,45],[208,201],[201,32],[32,208],[98,64],[64,129],[129,98],[192,213],[213,138],[138,192],[235,59],[59,219],[219,235],[141,242],[242,97],[97,141],[97,2],[2,141],[141,97],[240,75],[75,235],[235,240],[229,24],[24,228],[228,229],[31,25],[25,226],[226,31],[230,23],[23,229],[229,230],[231,22],[22,230],[230,231],[232,26],[26,231],[231,232],[233,112],[112,232],[232,233],[244,189],[189,243],[243,244],[189,221],[221,190],[190,189],[222,28],[28,221],[221,222],[223,27],[27,222],[222,223],[224,29],[29,223],[223,224],[225,30],[30,224],[224,225],[113,247],[247,225],[225,113],[99,60],[60,240],[240,99],[213,147],[147,215],[215,213],[60,20],[20,166],[166,60],[192,187],[187,213],[213,192],[243,112],[112,244],[244,243],[244,233],[233,245],[245,244],[245,128],[128,188],[188,245],[188,114],[114,174],[174,188],[134,131],[131,220],[220,134],[174,217],[217,236],[236,174],[236,198],[198,134],[134,236],[215,177],[177,58],[58,215],[156,143],[143,124],[124,156],[25,110],[110,7],[7,25],[31,228],[228,25],[25,31],[264,356],[356,368],[368,264],[0,11],[11,267],[267,0],[451,452],[452,349],[349,451],[267,302],[302,269],[269,267],[350,357],[357,277],[277,350],[350,452],[452,357],[357,350],[299,333],[333,297],[297,299],[396,175],[175,377],[377,396],[280,347],[347,330],[330,280],[269,303],[303,270],[270,269],[151,9],[9,337],[337,151],[344,278],[278,360],[360,344],[424,418],[418,431],[431,424],[270,304],[304,409],[409,270],[272,310],[310,407],[407,272],[322,270],[270,410],[410,322],[449,450],[450,347],[347,449],[432,422],[422,434],[434,432],[18,313],[313,17],[17,18],[291,306],[306,375],[375,291],[259,387],[387,260],[260,259],[424,335],[335,418],[418,424],[434,364],[364,416],[416,434],[391,423],[423,327],[327,391],[301,251],[251,298],[298,301],[275,281],[281,4],[4,275],[254,373],[373,253],[253,254],[375,307],[307,321],[321,375],[280,425],[425,411],[411,280],[200,421],[421,18],[18,200],[335,321],[321,406],[406,335],[321,320],[320,405],[405,321],[314,315],[315,17],[17,314],[423,426],[426,266],[266,423],[396,377],[377,369],[369,396],[270,322],[322,269],[269,270],[413,417],[417,464],[464,413],[385,386],[386,258],[258,385],[248,456],[456,419],[419,248],[298,284],[284,333],[333,298],[168,417],[417,8],[8,168],[448,346],[346,261],[261,448],[417,413],[413,285],[285,417],[326,327],[327,328],[328,326],[277,355],[355,329],[329,277],[309,392],[392,438],[438,309],[381,382],[382,256],[256,381],[279,429],[429,360],[360,279],[365,364],[364,379],[379,365],[355,277],[277,437],[437,355],[282,443],[443,283],[283,282],[281,275],[275,363],[363,281],[395,431],[431,369],[369,395],[299,297],[297,337],[337,299],[335,273],[273,321],[321,335],[348,450],[450,349],[349,348],[359,446],[446,467],[467,359],[283,293],[293,282],[282,283],[250,458],[458,462],[462,250],[300,276],[276,383],[383,300],[292,308],[308,325],[325,292],[283,276],[276,293],[293,283],[264,372],[372,447],[447,264],[346,352],[352,340],[340,346],[354,274],[274,19],[19,354],[363,456],[456,281],[281,363],[426,436],[436,425],[425,426],[380,381],[381,252],[252,380],[267,269],[269,393],[393,267],[421,200],[200,428],[428,421],[371,266],[266,329],[329,371],[432,287],[287,422],[422,432],[290,250],[250,328],[328,290],[385,258],[258,384],[384,385],[446,265],[265,342],[342,446],[386,387],[387,257],[257,386],[422,424],[424,430],[430,422],[445,342],[342,276],[276,445],[422,273],[273,424],[424,422],[306,292],[292,307],[307,306],[352,366],[366,345],[345,352],[268,271],[271,302],[302,268],[358,423],[423,371],[371,358],[327,294],[294,460],[460,327],[331,279],[279,294],[294,331],[303,271],[271,304],[304,303],[436,432],[432,427],[427,436],[304,272],[272,408],[408,304],[395,394],[394,431],[431,395],[378,395],[395,400],[400,378],[296,334],[334,299],[299,296],[6,351],[351,168],[168,6],[376,352],[352,411],[411,376],[307,325],[325,320],[320,307],[285,295],[295,336],[336,285],[320,319],[319,404],[404,320],[329,330],[330,349],[349,329],[334,293],[293,333],[333,334],[366,323],[323,447],[447,366],[316,15],[15,315],[315,316],[331,358],[358,279],[279,331],[317,14],[14,316],[316,317],[8,285],[285,9],[9,8],[277,329],[329,350],[350,277],[253,374],[374,252],[252,253],[319,318],[318,403],[403,319],[351,6],[6,419],[419,351],[324,318],[318,325],[325,324],[397,367],[367,365],[365,397],[288,435],[435,397],[397,288],[278,344],[344,439],[439,278],[310,272],[272,311],[311,310],[248,195],[195,281],[281,248],[375,273],[273,291],[291,375],[175,396],[396,199],[199,175],[312,311],[311,268],[268,312],[276,283],[283,445],[445,276],[390,373],[373,339],[339,390],[295,282],[282,296],[296,295],[448,449],[449,346],[346,448],[356,264],[264,454],[454,356],[337,336],[336,299],[299,337],[337,338],[338,151],[151,337],[294,278],[278,455],[455,294],[308,292],[292,415],[415,308],[429,358],[358,355],[355,429],[265,340],[340,372],[372,265],[352,346],[346,280],[280,352],[295,442],[442,282],[282,295],[354,19],[19,370],[370,354],[285,441],[441,295],[295,285],[195,248],[248,197],[197,195],[457,440],[440,274],[274,457],[301,300],[300,368],[368,301],[417,351],[351,465],[465,417],[251,301],[301,389],[389,251],[394,395],[395,379],[379,394],[399,412],[412,419],[419,399],[410,436],[436,322],[322,410],[326,2],[2,393],[393,326],[354,370],[370,461],[461,354],[393,164],[164,267],[267,393],[268,302],[302,12],[12,268],[312,268],[268,13],[13,312],[298,293],[293,301],[301,298],[265,446],[446,340],[340,265],[280,330],[330,425],[425,280],[322,426],[426,391],[391,322],[420,429],[429,437],[437,420],[393,391],[391,326],[326,393],[344,440],[440,438],[438,344],[458,459],[459,461],[461,458],[364,434],[434,394],[394,364],[428,396],[396,262],[262,428],[274,354],[354,457],[457,274],[317,316],[316,402],[402,317],[316,315],[315,403],[403,316],[315,314],[314,404],[404,315],[314,313],[313,405],[405,314],[313,421],[421,406],[406,313],[323,366],[366,361],[361,323],[292,306],[306,407],[407,292],[306,291],[291,408],[408,306],[291,287],[287,409],[409,291],[287,432],[432,410],[410,287],[427,434],[434,411],[411,427],[372,264],[264,383],[383,372],[459,309],[309,457],[457,459],[366,352],[352,401],[401,366],[1,274],[274,4],[4,1],[418,421],[421,262],[262,418],[331,294],[294,358],[358,331],[435,433],[433,367],[367,435],[392,289],[289,439],[439,392],[328,462],[462,326],[326,328],[94,2],[2,370],[370,94],[289,305],[305,455],[455,289],[339,254],[254,448],[448,339],[359,255],[255,446],[446,359],[254,253],[253,449],[449,254],[253,252],[252,450],[450,253],[252,256],[256,451],[451,252],[256,341],[341,452],[452,256],[414,413],[413,463],[463,414],[286,441],[441,414],[414,286],[286,258],[258,441],[441,286],[258,257],[257,442],[442,258],[257,259],[259,443],[443,257],[259,260],[260,444],[444,259],[260,467],[467,445],[445,260],[309,459],[459,250],[250,309],[305,289],[289,290],[290,305],[305,290],[290,460],[460,305],[401,376],[376,435],[435,401],[309,250],[250,392],[392,309],[376,411],[411,433],[433,376],[453,341],[341,464],[464,453],[357,453],[453,465],[465,357],[343,357],[357,412],[412,343],[437,343],[343,399],[399,437],[344,360],[360,440],[440,344],[420,437],[437,456],[456,420],[360,420],[420,363],[363,360],[361,401],[401,288],[288,361],[265,372],[372,353],[353,265],[390,339],[339,249],[249,390],[339,448],[448,255],[255,339]]),M(\\\\\\\"matrixDataToMatrix\\\\\\\",(function(t){for(var e=t.getCols(),n=t.getRows(),i=t.getPackedDataList(),r=[],s=0;s{const n=setInterval((()=>{t.style.opacity||(t.style.opacity=\\\\\\\"1\\\\\\\");const i=parseFloat(t.style.opacity);i>0?t.style.opacity=\\\\\\\"\\\\\\\"+(i-.05):(e(),clearInterval(n))}),20)}))}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return r}));var i=n(41);class r extends i.a{constructor(t,e){super(t,e),this.type=\\\\\\\"LineLoop\\\\\\\"}}r.prototype.isLineLoop=!0},,function(t,e,n){!function(n){\\\\\\\"use strict\\\\\\\";var i=\\\\\\\"Compound\\\\\\\",r=\\\\\\\"MemberExpression\\\\\\\",s=\\\\\\\"Literal\\\\\\\",o=function(t,e){var n=new Error(t+\\\\\\\" at character \\\\\\\"+e);throw n.index=e,n.description=t,n},a=!0,l={\\\\\\\"-\\\\\\\":a,\\\\\\\"!\\\\\\\":a,\\\\\\\"~\\\\\\\":a,\\\\\\\"+\\\\\\\":a},c={\\\\\\\"||\\\\\\\":1,\\\\\\\"&&\\\\\\\":2,\\\\\\\"|\\\\\\\":3,\\\\\\\"^\\\\\\\":4,\\\\\\\"&\\\\\\\":5,\\\\\\\"==\\\\\\\":6,\\\\\\\"!=\\\\\\\":6,\\\\\\\"===\\\\\\\":6,\\\\\\\"!==\\\\\\\":6,\\\\\\\"<\\\\\\\":7,\\\\\\\">\\\\\\\":7,\\\\\\\"<=\\\\\\\":7,\\\\\\\">=\\\\\\\":7,\\\\\\\"<<\\\\\\\":8,\\\\\\\">>\\\\\\\":8,\\\\\\\">>>\\\\\\\":8,\\\\\\\"+\\\\\\\":9,\\\\\\\"-\\\\\\\":9,\\\\\\\"*\\\\\\\":10,\\\\\\\"/\\\\\\\":10,\\\\\\\"%\\\\\\\":10},u=function(t){var e,n=0;for(var i in t)(e=i.length)>n&&t.hasOwnProperty(i)&&(n=e);return n},h=u(l),d=u(c),p={true:!0,false:!1,null:null},_=function(t){return c[t]||0},m=function(t,e,n){return{type:\\\\\\\"||\\\\\\\"===t||\\\\\\\"&&\\\\\\\"===t?\\\\\\\"LogicalExpression\\\\\\\":\\\\\\\"BinaryExpression\\\\\\\",operator:t,left:e,right:n}},f=function(t){return t>=48&&t<=57},g=function(t){return 36===t||95===t||t>=65&&t<=90||t>=97&&t<=122||t>=128&&!c[String.fromCharCode(t)]},v=function(t){return 36===t||95===t||t>=65&&t<=90||t>=97&&t<=122||t>=48&&t<=57||t>=128&&!c[String.fromCharCode(t)]},y=function(t){for(var e,n,a=0,u=t.charAt,y=t.charCodeAt,x=function(e){return u.call(t,e)},b=function(e){return y.call(t,e)},w=t.length,T=function(){for(var t=b(a);32===t||9===t||10===t||13===t;)t=b(++a)},A=function(){var t,e,n=M();return T(),63!==b(a)?n:(a++,(t=A())||o(\\\\\\\"Expected expression\\\\\\\",a),T(),58===b(a)?(a++,(e=A())||o(\\\\\\\"Expected expression\\\\\\\",a),{type:\\\\\\\"ConditionalExpression\\\\\\\",test:n,consequent:t,alternate:e}):void o(\\\\\\\"Expected :\\\\\\\",a))},E=function(){T();for(var e=t.substr(a,d),n=e.length;n>0;){if(c.hasOwnProperty(e)&&(!g(b(a))||a+e.length2&&n<=i[i.length-2].prec;)l=i.pop(),e=i.pop().value,s=i.pop(),t=m(e,s,l),i.push(t);(t=S())||o(\\\\\\\"Expected expression after \\\\\\\"+u,a),i.push(r,t)}for(t=i[c=i.length-1];c>1;)t=m(i[c-1].value,i[c-2],t),c-=2;return t},S=function(){var e,n,i;if(T(),e=b(a),f(e)||46===e)return C();if(39===e||34===e)return N();if(91===e)return I();for(i=(n=t.substr(a,h)).length;i>0;){if(l.hasOwnProperty(n)&&(!g(b(a))||a+n.length=r.length&&o(\\\\\\\"Unexpected token \\\\\\\"+String.fromCharCode(t),a);break}if(44===e){if(a++,++l!==r.length)if(41===t)o(\\\\\\\"Unexpected token ,\\\\\\\",a);else if(93===t)for(var c=r.length;c-1&&t%1==0&&t<=9007199254740991}},function(t,e){t.exports=function(t,e){return function(n){return t(e(n))}}},function(t,e,n){var i=n(119),r=n(126);t.exports=function(t){return null!=t&&r(t.length)&&!i(t)}},function(t,e){t.exports=function(){return[]}},function(t,e,n){var i=n(131),r=n(132),s=n(112),o=n(129),a=Object.getOwnPropertySymbols?function(t){for(var e=[];t;)i(e,s(t)),t=r(t);return e}:o;t.exports=a},function(t,e){t.exports=function(t,e){for(var n=-1,i=e.length,r=t.length;++n2&&void 0!==arguments[2]?arguments[2]:{},r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:i;if(Array.isArray(n))n.forEach((function(n){return t(e,n,i,r)}));else if(\\\\\\\"function\\\\\\\"==typeof n)n(e,i,r,t);else{var s=Object.keys(n)[0];Array.isArray(n[s])?(r[s]={},t(e,n[s],i,r[s])):r[s]=n[s](e,i,r,t)}return i};e.conditional=function(t,e){return function(n,i,r,s){e(n,i,r)&&s(n,t,i,r)}};e.loop=function(t,e){return function(n,i,r,s){for(var o=[];e(n,i,r);){var a={};s(n,t,i,a),o.push(a)}return o}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";Object.defineProperty(e,\\\\\\\"__esModule\\\\\\\",{value:!0}),e.readBits=e.readArray=e.readUnsigned=e.readString=e.peekBytes=e.readBytes=e.peekByte=e.readByte=e.buildStream=void 0;e.buildStream=function(t){return{data:t,pos:0}};var i=function(){return function(t){return t.data[t.pos++]}};e.readByte=i;e.peekByte=function(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return function(e){return e.data[e.pos+t]}};var r=function(t){return function(e){return e.data.subarray(e.pos,e.pos+=t)}};e.readBytes=r;e.peekBytes=function(t){return function(e){return e.data.subarray(e.pos,e.pos+t)}};e.readString=function(t){return function(e){return Array.from(r(t)(e)).map((function(t){return String.fromCharCode(t)})).join(\\\\\\\"\\\\\\\")}};e.readUnsigned=function(t){return function(e){var n=r(2)(e);return t?(n[1]<<8)+n[0]:(n[0]<<8)+n[1]}};e.readArray=function(t,e){return function(n,i,s){for(var o=\\\\\\\"function\\\\\\\"==typeof e?e(n,i,s):e,a=r(t),l=new Array(o),c=0;c-1}},function(t,e,n){var i=n(98);t.exports=function(t,e){var n=this.__data__,r=i(n,t);return r<0?(++this.size,n.push([t,e])):n[r][1]=e,this}},function(t,e,n){var i=n(97);t.exports=function(){this.__data__=new i,this.size=0}},function(t,e){t.exports=function(t){var e=this.__data__,n=e.delete(t);return this.size=e.size,n}},function(t,e){t.exports=function(t){return this.__data__.get(t)}},function(t,e){t.exports=function(t){return this.__data__.has(t)}},function(t,e,n){var i=n(97),r=n(103),s=n(155);t.exports=function(t,e){var n=this.__data__;if(n instanceof i){var o=n.__data__;if(!r||o.length<199)return o.push([t,e]),this.size=++n.size,this;n=this.__data__=new s(o)}return n.set(t,e),this.size=n.size,this}},function(t,e,n){var i=n(119),r=n(152),s=n(90),o=n(121),a=/^\\\\[object .+?Constructor\\\\]$/,l=Function.prototype,c=Object.prototype,u=l.toString,h=c.hasOwnProperty,d=RegExp(\\\\\\\"^\\\\\\\"+u.call(h).replace(/[\\\\\\\\^$.*+?()[\\\\]{}|]/g,\\\\\\\"\\\\\\\\$&\\\\\\\").replace(/hasOwnProperty|(function).*?(?=\\\\\\\\\\\\()| for .+?(?=\\\\\\\\\\\\])/g,\\\\\\\"$1.*?\\\\\\\")+\\\\\\\"$\\\\\\\");t.exports=function(t){return!(!s(t)||r(t))&&(i(t)?d:a).test(o(t))}},function(t,e,n){var i=n(104),r=Object.prototype,s=r.hasOwnProperty,o=r.toString,a=i?i.toStringTag:void 0;t.exports=function(t){var e=s.call(t,a),n=t[a];try{t[a]=void 0;var i=!0}catch(t){}var r=o.call(t);return i&&(e?t[a]=n:delete t[a]),r}},function(t,e){var n=Object.prototype.toString;t.exports=function(t){return n.call(t)}},function(t,e,n){var i,r=n(153),s=(i=/[^.]+$/.exec(r&&r.keys&&r.keys.IE_PROTO||\\\\\\\"\\\\\\\"))?\\\\\\\"Symbol(src)_1.\\\\\\\"+i:\\\\\\\"\\\\\\\";t.exports=function(t){return!!s&&s in t}},function(t,e,n){var i=n(68)[\\\\\\\"__core-js_shared__\\\\\\\"];t.exports=i},function(t,e){t.exports=function(t,e){return null==t?void 0:t[e]}},function(t,e,n){var i=n(156),r=n(163),s=n(165),o=n(166),a=n(167);function l(t){var e=-1,n=null==t?0:t.length;for(this.clear();++e-1&&t%1==0&&t=n){var o=n-t.pos;e.push((0,r.readBytes)(o)(t)),i+=o;break}e.push((0,r.readBytes)(s)(t)),i+=s}for(var a=new Uint8Array(i),l=0,c=0;c>=o,_-=o,u>i||u==a)break;if(u==r){s=(1<<(o=d+1))-1,i=r+2,c=-1;continue}if(-1==c){A[f++]=T[u],c=u,m=u;continue}for(l=u,u==i&&(A[f++]=m,u=c);u>r;)A[f++]=T[u],u=w[u];m=255&T[u],A[f++]=m,i0}enqueue(t,e){this._queue.set(t.graphNodeId(),e)}process_queue(){this.blocked||this._queue.forEach(this._process_item_bound)}_process_item(t,e){const n=this._scene.graph.nodeFromId(e);n&&(this._queue.delete(e),n.dirtyController.runPostDirtyHooks(t))}}class a{constructor(){this._cooking_nodes_by_id=new Map,this._resolves=[]}addNode(t){this._cooking_nodes_by_id.set(t.graphNodeId(),t)}removeNode(t){this._cooking_nodes_by_id.delete(t.graphNodeId()),0==this._cooking_nodes_by_id.size&&this.flush()}cookingNodes(){const t=[];return this._cooking_nodes_by_id.forEach(((e,n)=>{t.push(e)})),t}flush(){let t;for(;t=this._resolves.pop();)t()}async waitForCooksCompleted(){return 0==this._cooking_nodes_by_id.size?void 0:new Promise(((t,e)=>{this._resolves.push(t)}))}}class l{constructor(){this._next_id=0,this._successors=new Map,this._predecessors=new Map,this._nodes_by_id=new Map,this._nodesCount=0,this._debugging=!1,this._addedNodesDuringDebugging=new Map}startDebugging(){this._debugging=!0,console.log(\\\\\\\"CoreGraph.startDebugging\\\\\\\",this._next_id)}stopDebugging(){this._debugging=!1,console.log(\\\\\\\"CoreGraph.stopDebugging\\\\\\\",this._next_id)}printDebug(){this._addedNodesDuringDebugging.forEach(((t,e)=>{console.log(e,t,t.graphPredecessors(),t.graphSuccessors())}))}setScene(t){this._scene=t}scene(){return this._scene}nextId(){return this._next_id+=1,this._next_id}nodesFromIds(t){const e=[];for(let n of t){const t=this.nodeFromId(n);t&&e.push(t)}return e}nodeFromId(t){return this._nodes_by_id.get(t)}hasNode(t){return null!=this._nodes_by_id.get(t.graphNodeId())}addNode(t){this._nodes_by_id.set(t.graphNodeId(),t),this._nodesCount+=1,this._debugging&&this._addedNodesDuringDebugging.set(t.graphNodeId(),t)}removeNode(t){this._nodes_by_id.delete(t.graphNodeId()),this._successors.delete(t.graphNodeId()),this._predecessors.delete(t.graphNodeId()),this._nodesCount-=1,this._debugging&&this._addedNodesDuringDebugging.delete(t.graphNodeId())}nodesCount(){return this._nodesCount}connect(t,e,n=!0){const i=t.graphNodeId(),r=e.graphNodeId();if(this.hasNode(t)&&this.hasNode(e)){if(n){n=!(!this._scene||this._scene.loadingController.isLoading())}let e=!1;return n&&(e=this._hasPredecessor(i,r)),!e&&(this._createConnection(i,r),t.dirtyController.clearSuccessorsCacheWithPredecessors(),!0)}return console.warn(`attempt to connect non existing node ${i} or ${r}`),!1}disconnect(t,e){this._remove_connection(t.graphNodeId(),e.graphNodeId()),t.dirtyController.clearSuccessorsCacheWithPredecessors()}disconnectPredecessors(t){const e=this.predecessors(t);for(let n of e)this.disconnect(n,t)}disconnectSuccessors(t){const e=this.successors(t);for(let n of e)this.disconnect(t,n)}predecessorIds(t){const e=this._predecessors.get(t);if(e){const t=[];return e.forEach(((e,n)=>{t.push(n)})),t}return[]}predecessors(t){const e=this.predecessorIds(t.graphNodeId());return this.nodesFromIds(e)}successorIds(t){const e=this._successors.get(t);if(e){const t=[];return e.forEach(((e,n)=>{t.push(n)})),t}return[]}successors(t){const e=this.successorIds(t.graphNodeId())||[];return this.nodesFromIds(e)}allPredecessorIds(t){return this.allNextIds(t,\\\\\\\"predecessorIds\\\\\\\")}allSuccessorIds(t){return this.allNextIds(t,\\\\\\\"successorIds\\\\\\\")}allPredecessors(t){const e=this.allPredecessorIds(t);return this.nodesFromIds(e)}allSuccessors(t){const e=this.allSuccessorIds(t);return this.nodesFromIds(e)}_createConnection(t,e){let n=this._successors.get(t);if(n||(n=new Set,this._successors.set(t,n)),n.has(e))return;n.add(e);let i=this._predecessors.get(e);i||(i=new Set,this._predecessors.set(e,i)),i.add(t)}_remove_connection(t,e){let n=this._successors.get(t);n&&(n.delete(e),0==n.size&&this._successors.delete(t));let i=this._predecessors.get(e);i&&(i.delete(t),0==i.size&&this._predecessors.delete(e))}allNextIds(t,e){const n=new Map,i=[];let r=this[e](t.graphNodeId());for(;r.length>0;){const t=[];for(let n of r)for(let i of this[e](n))t.push(i);for(let t of r)n.set(t,!0);for(let e of t)r.push(e);r=t}return n.forEach(((t,e)=>{i.push(e)})),i}_hasPredecessor(t,e){const n=this.predecessorIds(t);if(n){if(n.includes(e))return!0;for(let t of n)return this._hasPredecessor(t,e)}return!1}}class c{constructor(t){this._node=t,this._cooks_count=0,this._total_cook_time=0,this._total_inputs_time=0,this._total_params_time=0}update_cook_data(t){this._cooks_count+=1,this._total_cook_time+=t.cookTime,this._total_inputs_time+=t.inputsTime,this._total_params_time+=t.paramsTime}total_time(){return this._total_cook_time+this._total_inputs_time+this._total_params_time}total_cook_time(){return this._total_cook_time}cook_time_per_iteration(){return this._cooks_count>0?this._total_cook_time/this._cooks_count:0}total_inputs_time(){return this._total_inputs_time}inputs_time_per_iteration(){return this._cooks_count>0?this._total_inputs_time/this._cooks_count:0}total_params_time2(){return this._total_params_time}params_time_per_iteration2(){return this._cooks_count>0?this._total_params_time/this._cooks_count:0}cooks_count(){return this._cooks_count}print_object(){return{fullPath:this._node.path(),cooks_count:this.cooks_count(),total_time:this.total_time(),total_cook_time:this.total_cook_time(),cook_time_per_iteration:this.cook_time_per_iteration(),inputs_time_per_iteration:this.inputs_time_per_iteration(),params_time_per_iteration:this.params_time_per_iteration2()}}}class u{static pushOnArrayAtEntry(t,e,n){t.has(e)?t.get(e).push(n):t.set(e,[n])}static popFromArrayAtEntry(t,e,n){if(t.has(e)){const i=t.get(e),r=i.indexOf(n);r>=0&&i.splice(r,1)}}static unshiftOnArrayAtEntry(t,e,n){t.has(e)?t.get(e).unshift(n):t.set(e,[n])}static concatOnArrayAtEntry(t,e,n){if(t.has(e)){let i=t.get(e);for(let t of n)i.push(t)}else t.set(e,n)}}class h{static union(t,e){const n=new Set;return t.forEach((t=>n.add(t))),e.forEach((t=>n.add(t))),n}static intersection(t,e){const n=new Set;return t.forEach((t=>{e.has(t)&&n.add(t)})),e.forEach((e=>{t.has(e)&&n.add(e)})),n}static difference(t,e){const n=new Set;return t.forEach((t=>{e.has(t)||n.add(t)})),e.forEach((e=>{t.has(e)||n.add(e)})),n}}var d=n(2),p=n(0),_=n(9);class m{static isNumber(t){return\\\\\\\"number\\\\\\\"==typeof t}static isVector(t){return t instanceof d.a||t instanceof p.a||t instanceof _.a}static isString(t){return\\\\\\\"string\\\\\\\"==typeof t}static isBoolean(t){return!0===t||!1===t}static isNaN(t){return isNaN(t)}static isArray(t){return Array.isArray(t)}static isObject(t){var e=typeof t;return null!=t&&(\\\\\\\"object\\\\\\\"==e||\\\\\\\"function\\\\\\\"==e)}}class f{static min(t){let e=t[0];for(let n of t)ne&&(e=n);return e}static sum(t){let e=0;for(let n of t)e+=n;return e}static compact(t){const e=[];for(let n of t)null!=n&&e.push(n);return e}static uniq(t){const e=new Set;for(let n of t)e.add(n);const n=new Array(e.size);let i=0;return e.forEach((t=>{n[i]=t,i++})),n}static chunk(t,e){const n=[];let i=[];n.push(i);for(let r=0;rn.push(t))),n}static intersection(t,e){const n=[];return h.intersection(this.toSet(t),this.toSet(e)).forEach((t=>n.push(t))),n}static difference(t,e){const n=[];return h.difference(this.toSet(t),this.toSet(e)).forEach((t=>n.push(t))),n}static toSet(t){const e=new Set;for(let n of t)e.add(n);return e}static isEqual(t,e){if(t.length!=e.length)return!1;const n=t.length;for(let i=0;i{r[s]=t,s++})),m.isString(r[0])?r.sort():r.sort(((t,e)=>t-e));const o=new Array(t.length);s=0;for(let t of r){const e=n.get(t);if(e)for(let t of e)o[s]=t,s++}return o}static range(t,e,n=1){null==e&&(e=t,t=0);const i=Math.floor((e-t)/n),r=new Array(i);for(let e=0;e0?1:-1,c.push(C.x,C.y,C.z),u.push(a/f),u.push(1-s/g),M+=1}}for(let t=0;t0&&(e.defines=this.defines),e.vertexShader=this.vertexShader,e.fragmentShader=this.fragmentShader;const n={};for(const t in this.extensions)!0===this.extensions[t]&&(n[t]=!0);return Object.keys(n).length>0&&(e.extensions=n),e}}F.prototype.isShaderMaterial=!0;var D=n(6),k=n(14),B=\\\\\\\"\\\\n#ifdef USE_SHADOWMAP\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform sampler2D directionalShadowMap[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct DirectionalLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform sampler2D spotShadowMap[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct SpotLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform sampler2D pointShadowMap[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct PointLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraNear;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraFar;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t/*\\\\n\\\\t#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\t\\\\t// TODO (abelnation): create uniforms for area light shadows\\\\n\\\\n\\\\t#endif\\\\n\\\\t*/\\\\n\\\\n\\\\tfloat texture2DCompare( sampler2D depths, vec2 uv, float compare ) {\\\\n\\\\n\\\\t\\\\treturn step( compare, unpackRGBAToDepth( texture2D( depths, uv ) ) );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec2 texture2DDistribution( sampler2D shadow, vec2 uv ) {\\\\n\\\\n\\\\t\\\\treturn unpackRGBATo2Half( texture2D( shadow, uv ) );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tfloat VSMShadow (sampler2D shadow, vec2 uv, float compare ){\\\\n\\\\n\\\\t\\\\tfloat occlusion = 1.0;\\\\n\\\\n\\\\t\\\\tvec2 distribution = texture2DDistribution( shadow, uv );\\\\n\\\\n\\\\t\\\\tfloat hard_shadow = step( compare , distribution.x ); // Hard Shadow\\\\n\\\\n\\\\t\\\\tif (hard_shadow != 1.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat distance = compare - distribution.x ;\\\\n\\\\t\\\\t\\\\tfloat variance = max( 0.00000, distribution.y * distribution.y );\\\\n\\\\t\\\\t\\\\tfloat softness_probability = variance / (variance + distance * distance ); // Chebeyshevs inequality\\\\n\\\\t\\\\t\\\\tsoftness_probability = clamp( ( softness_probability - 0.3 ) / ( 0.95 - 0.3 ), 0.0, 1.0 ); // 0.3 reduces light bleed\\\\n\\\\t\\\\t\\\\tocclusion = clamp( max( hard_shadow, softness_probability ), 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn occlusion;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tfloat getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord ) {\\\\n\\\\n\\\\t\\\\tfloat shadow = 1.0;\\\\n\\\\n\\\\t\\\\tshadowCoord.xyz /= shadowCoord.w;\\\\n\\\\t\\\\tshadowCoord.z += shadowBias;\\\\n\\\\n\\\\t\\\\t// if ( something && something ) breaks ATI OpenGL shader compiler\\\\n\\\\t\\\\t// if ( all( something, something ) ) using this instead\\\\n\\\\n\\\\t\\\\tbvec4 inFrustumVec = bvec4 ( shadowCoord.x >= 0.0, shadowCoord.x <= 1.0, shadowCoord.y >= 0.0, shadowCoord.y <= 1.0 );\\\\n\\\\t\\\\tbool inFrustum = all( inFrustumVec );\\\\n\\\\n\\\\t\\\\tbvec2 frustumTestVec = bvec2( inFrustum, shadowCoord.z <= 1.0 );\\\\n\\\\n\\\\t\\\\tbool frustumTest = all( frustumTestVec );\\\\n\\\\n\\\\t\\\\tif ( frustumTest ) {\\\\n\\\\n\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF )\\\\n\\\\n\\\\t\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\\\n\\\\n\\\\t\\\\t\\\\tfloat dx0 = - texelSize.x * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dy0 = - texelSize.y * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dx1 = + texelSize.x * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dy1 = + texelSize.y * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dx2 = dx0 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dy2 = dy0 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dx3 = dx1 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dy3 = dy1 / 2.0;\\\\n\\\\n\\\\t\\\\t\\\\tshadow = (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 17.0 );\\\\n\\\\n\\\\t\\\\t#elif defined( SHADOWMAP_TYPE_PCF_SOFT )\\\\n\\\\n\\\\t\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat dx = texelSize.x;\\\\n\\\\t\\\\t\\\\tfloat dy = texelSize.y;\\\\n\\\\n\\\\t\\\\t\\\\tvec2 uv = shadowCoord.xy;\\\\n\\\\t\\\\t\\\\tvec2 f = fract( uv * shadowMapSize + 0.5 );\\\\n\\\\t\\\\t\\\\tuv -= f * texelSize;\\\\n\\\\n\\\\t\\\\t\\\\tshadow = (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + vec2( dx, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + vec2( 0.0, dy ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + texelSize, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, 0.0 ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 0.0 ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.x ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.x ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( 0.0, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 0.0, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( dx, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( dx, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( mix( texture2DCompare( shadowMap, uv + vec2( -dx, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, -dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t f.x ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t mix( texture2DCompare( shadowMap, uv + vec2( -dx, 2.0 * dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t f.x ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 9.0 );\\\\n\\\\n\\\\t\\\\t#elif defined( SHADOWMAP_TYPE_VSM )\\\\n\\\\n\\\\t\\\\t\\\\tshadow = VSMShadow( shadowMap, shadowCoord.xy, shadowCoord.z );\\\\n\\\\n\\\\t\\\\t#else // no percentage-closer filtering:\\\\n\\\\n\\\\t\\\\t\\\\tshadow = texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn shadow;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\t// cubeToUV() maps a 3D direction vector suitable for cube texture mapping to a 2D\\\\n\\\\t// vector suitable for 2D texture mapping. This code uses the following layout for the\\\\n\\\\t// 2D texture:\\\\n\\\\t//\\\\n\\\\t// xzXZ\\\\n\\\\t// y Y\\\\n\\\\t//\\\\n\\\\t// Y - Positive y direction\\\\n\\\\t// y - Negative y direction\\\\n\\\\t// X - Positive x direction\\\\n\\\\t// x - Negative x direction\\\\n\\\\t// Z - Positive z direction\\\\n\\\\t// z - Negative z direction\\\\n\\\\t//\\\\n\\\\t// Source and test bed:\\\\n\\\\t// https://gist.github.com/tschw/da10c43c467ce8afd0c4\\\\n\\\\n\\\\tvec2 cubeToUV( vec3 v, float texelSizeY ) {\\\\n\\\\n\\\\t\\\\t// Number of texels to avoid at the edge of each square\\\\n\\\\n\\\\t\\\\tvec3 absV = abs( v );\\\\n\\\\n\\\\t\\\\t// Intersect unit cube\\\\n\\\\n\\\\t\\\\tfloat scaleToCube = 1.0 / max( absV.x, max( absV.y, absV.z ) );\\\\n\\\\t\\\\tabsV *= scaleToCube;\\\\n\\\\n\\\\t\\\\t// Apply scale to avoid seams\\\\n\\\\n\\\\t\\\\t// two texels less per square (one texel will do for NEAREST)\\\\n\\\\t\\\\tv *= scaleToCube * ( 1.0 - 2.0 * texelSizeY );\\\\n\\\\n\\\\t\\\\t// Unwrap\\\\n\\\\n\\\\t\\\\t// space: -1 ... 1 range for each square\\\\n\\\\t\\\\t//\\\\n\\\\t\\\\t// #X##\\\\t\\\\tdim := ( 4 , 2 )\\\\n\\\\t\\\\t// # #\\\\t\\\\tcenter := ( 1 , 1 )\\\\n\\\\n\\\\t\\\\tvec2 planar = v.xy;\\\\n\\\\n\\\\t\\\\tfloat almostATexel = 1.5 * texelSizeY;\\\\n\\\\t\\\\tfloat almostOne = 1.0 - almostATexel;\\\\n\\\\n\\\\t\\\\tif ( absV.z >= almostOne ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( v.z > 0.0 )\\\\n\\\\t\\\\t\\\\t\\\\tplanar.x = 4.0 - v.x;\\\\n\\\\n\\\\t\\\\t} else if ( absV.x >= almostOne ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat signX = sign( v.x );\\\\n\\\\t\\\\t\\\\tplanar.x = v.z * signX + 2.0 * signX;\\\\n\\\\n\\\\t\\\\t} else if ( absV.y >= almostOne ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat signY = sign( v.y );\\\\n\\\\t\\\\t\\\\tplanar.x = v.x + 2.0 * signY + 2.0;\\\\n\\\\t\\\\t\\\\tplanar.y = v.z * signY - 2.0;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t// Transform to UV space\\\\n\\\\n\\\\t\\\\t// scale := 0.5 / dim\\\\n\\\\t\\\\t// translate := ( center + 0.5 ) / dim\\\\n\\\\t\\\\treturn vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tfloat getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {\\\\n\\\\n\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );\\\\n\\\\n\\\\t\\\\t// for point lights, the uniform @vShadowCoord is re-purposed to hold\\\\n\\\\t\\\\t// the vector from the light to the world-space position of the fragment.\\\\n\\\\t\\\\tvec3 lightToPosition = shadowCoord.xyz;\\\\n\\\\n\\\\t\\\\t// dp = normalized distance from light to fragment position\\\\n\\\\t\\\\tfloat dp = ( length( lightToPosition ) - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear ); // need to clamp?\\\\n\\\\t\\\\tdp += shadowBias;\\\\n\\\\n\\\\t\\\\t// bd3D = base direction 3D\\\\n\\\\t\\\\tvec3 bd3D = normalize( lightToPosition );\\\\n\\\\n\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT ) || defined( SHADOWMAP_TYPE_VSM )\\\\n\\\\n\\\\t\\\\t\\\\tvec2 offset = vec2( - 1, 1 ) * shadowRadius * texelSize.y;\\\\n\\\\n\\\\t\\\\t\\\\treturn (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 9.0 );\\\\n\\\\n\\\\t\\\\t#else // no percentage-closer filtering\\\\n\\\\n\\\\t\\\\t\\\\treturn texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\";const z={alphamap_fragment:\\\\\\\"\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\n\\\\tdiffuseColor.a *= texture2D( alphaMap, vUv ).g;\\\\n\\\\n#endif\\\\n\\\\\\\",alphamap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\n\\\\tuniform sampler2D alphaMap;\\\\n\\\\n#endif\\\\n\\\\\\\",alphatest_fragment:\\\\\\\"\\\\n#ifdef USE_ALPHATEST\\\\n\\\\n\\\\tif ( diffuseColor.a < alphaTest ) discard;\\\\n\\\\n#endif\\\\n\\\\\\\",alphatest_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ALPHATEST\\\\n\\\\tuniform float alphaTest;\\\\n#endif\\\\n\\\\\\\",aomap_fragment:\\\\\\\"\\\\n#ifdef USE_AOMAP\\\\n\\\\n\\\\t// reads channel R, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\tfloat ambientOcclusion = ( texture2D( aoMap, vUv2 ).r - 1.0 ) * aoMapIntensity + 1.0;\\\\n\\\\n\\\\treflectedLight.indirectDiffuse *= ambientOcclusion;\\\\n\\\\n\\\\t#if defined( USE_ENVMAP ) && defined( STANDARD )\\\\n\\\\n\\\\t\\\\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\\\\n\\\\n\\\\t\\\\treflectedLight.indirectSpecular *= computeSpecularOcclusion( dotNV, ambientOcclusion, material.roughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",aomap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_AOMAP\\\\n\\\\n\\\\tuniform sampler2D aoMap;\\\\n\\\\tuniform float aoMapIntensity;\\\\n\\\\n#endif\\\\n\\\\\\\",begin_vertex:\\\\\\\"\\\\nvec3 transformed = vec3( position );\\\\n\\\\\\\",beginnormal_vertex:\\\\\\\"\\\\nvec3 objectNormal = vec3( normal );\\\\n\\\\n#ifdef USE_TANGENT\\\\n\\\\n\\\\tvec3 objectTangent = vec3( tangent.xyz );\\\\n\\\\n#endif\\\\n\\\\\\\",bsdfs:'\\\\n\\\\nvec3 BRDF_Lambert( const in vec3 diffuseColor ) {\\\\n\\\\n\\\\treturn RECIPROCAL_PI * diffuseColor;\\\\n\\\\n} // validated\\\\n\\\\nvec3 F_Schlick( const in vec3 f0, const in float f90, const in float dotVH ) {\\\\n\\\\n\\\\t// Original approximation by Christophe Schlick \\\\'94\\\\n\\\\t// float fresnel = pow( 1.0 - dotVH, 5.0 );\\\\n\\\\n\\\\t// Optimized variant (presented by Epic at SIGGRAPH \\\\'13)\\\\n\\\\t// https://cdn2.unrealengine.com/Resources/files/2013SiggraphPresentationsNotes-26915738.pdf\\\\n\\\\tfloat fresnel = exp2( ( - 5.55473 * dotVH - 6.98316 ) * dotVH );\\\\n\\\\n\\\\treturn f0 * ( 1.0 - fresnel ) + ( f90 * fresnel );\\\\n\\\\n} // validated\\\\n\\\\n// Moving Frostbite to Physically Based Rendering 3.0 - page 12, listing 2\\\\n// https://seblagarde.files.wordpress.com/2015/07/course_notes_moving_frostbite_to_pbr_v32.pdf\\\\nfloat V_GGX_SmithCorrelated( const in float alpha, const in float dotNL, const in float dotNV ) {\\\\n\\\\n\\\\tfloat a2 = pow2( alpha );\\\\n\\\\n\\\\tfloat gv = dotNL * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\\\\n\\\\tfloat gl = dotNV * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\\\\n\\\\n\\\\treturn 0.5 / max( gv + gl, EPSILON );\\\\n\\\\n}\\\\n\\\\n// Microfacet Models for Refraction through Rough Surfaces - equation (33)\\\\n// http://graphicrants.blogspot.com/2013/08/specular-brdf-reference.html\\\\n// alpha is \\\\\\\"roughness squared\\\\\\\" in Disney’s reparameterization\\\\nfloat D_GGX( const in float alpha, const in float dotNH ) {\\\\n\\\\n\\\\tfloat a2 = pow2( alpha );\\\\n\\\\n\\\\tfloat denom = pow2( dotNH ) * ( a2 - 1.0 ) + 1.0; // avoid alpha = 0 with dotNH = 1\\\\n\\\\n\\\\treturn RECIPROCAL_PI * a2 / pow2( denom );\\\\n\\\\n}\\\\n\\\\n// GGX Distribution, Schlick Fresnel, GGX_SmithCorrelated Visibility\\\\nvec3 BRDF_GGX( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 f0, const in float f90, const in float roughness ) {\\\\n\\\\n\\\\tfloat alpha = pow2( roughness ); // UE4\\\\'s roughness\\\\n\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\n\\\\tfloat dotNL = saturate( dot( normal, lightDir ) );\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\tfloat dotVH = saturate( dot( viewDir, halfDir ) );\\\\n\\\\n\\\\tvec3 F = F_Schlick( f0, f90, dotVH );\\\\n\\\\n\\\\tfloat V = V_GGX_SmithCorrelated( alpha, dotNL, dotNV );\\\\n\\\\n\\\\tfloat D = D_GGX( alpha, dotNH );\\\\n\\\\n\\\\treturn F * ( V * D );\\\\n\\\\n}\\\\n\\\\n// Rect Area Light\\\\n\\\\n// Real-Time Polygonal-Light Shading with Linearly Transformed Cosines\\\\n// by Eric Heitz, Jonathan Dupuy, Stephen Hill and David Neubelt\\\\n// code: https://github.com/selfshadow/ltc_code/\\\\n\\\\nvec2 LTC_Uv( const in vec3 N, const in vec3 V, const in float roughness ) {\\\\n\\\\n\\\\tconst float LUT_SIZE = 64.0;\\\\n\\\\tconst float LUT_SCALE = ( LUT_SIZE - 1.0 ) / LUT_SIZE;\\\\n\\\\tconst float LUT_BIAS = 0.5 / LUT_SIZE;\\\\n\\\\n\\\\tfloat dotNV = saturate( dot( N, V ) );\\\\n\\\\n\\\\t// texture parameterized by sqrt( GGX alpha ) and sqrt( 1 - cos( theta ) )\\\\n\\\\tvec2 uv = vec2( roughness, sqrt( 1.0 - dotNV ) );\\\\n\\\\n\\\\tuv = uv * LUT_SCALE + LUT_BIAS;\\\\n\\\\n\\\\treturn uv;\\\\n\\\\n}\\\\n\\\\nfloat LTC_ClippedSphereFormFactor( const in vec3 f ) {\\\\n\\\\n\\\\t// Real-Time Area Lighting: a Journey from Research to Production (p.102)\\\\n\\\\t// An approximation of the form factor of a horizon-clipped rectangle.\\\\n\\\\n\\\\tfloat l = length( f );\\\\n\\\\n\\\\treturn max( ( l * l + f.z ) / ( l + 1.0 ), 0.0 );\\\\n\\\\n}\\\\n\\\\nvec3 LTC_EdgeVectorFormFactor( const in vec3 v1, const in vec3 v2 ) {\\\\n\\\\n\\\\tfloat x = dot( v1, v2 );\\\\n\\\\n\\\\tfloat y = abs( x );\\\\n\\\\n\\\\t// rational polynomial approximation to theta / sin( theta ) / 2PI\\\\n\\\\tfloat a = 0.8543985 + ( 0.4965155 + 0.0145206 * y ) * y;\\\\n\\\\tfloat b = 3.4175940 + ( 4.1616724 + y ) * y;\\\\n\\\\tfloat v = a / b;\\\\n\\\\n\\\\tfloat theta_sintheta = ( x > 0.0 ) ? v : 0.5 * inversesqrt( max( 1.0 - x * x, 1e-7 ) ) - v;\\\\n\\\\n\\\\treturn cross( v1, v2 ) * theta_sintheta;\\\\n\\\\n}\\\\n\\\\nvec3 LTC_Evaluate( const in vec3 N, const in vec3 V, const in vec3 P, const in mat3 mInv, const in vec3 rectCoords[ 4 ] ) {\\\\n\\\\n\\\\t// bail if point is on back side of plane of light\\\\n\\\\t// assumes ccw winding order of light vertices\\\\n\\\\tvec3 v1 = rectCoords[ 1 ] - rectCoords[ 0 ];\\\\n\\\\tvec3 v2 = rectCoords[ 3 ] - rectCoords[ 0 ];\\\\n\\\\tvec3 lightNormal = cross( v1, v2 );\\\\n\\\\n\\\\tif( dot( lightNormal, P - rectCoords[ 0 ] ) < 0.0 ) return vec3( 0.0 );\\\\n\\\\n\\\\t// construct orthonormal basis around N\\\\n\\\\tvec3 T1, T2;\\\\n\\\\tT1 = normalize( V - N * dot( V, N ) );\\\\n\\\\tT2 = - cross( N, T1 ); // negated from paper; possibly due to a different handedness of world coordinate system\\\\n\\\\n\\\\t// compute transform\\\\n\\\\tmat3 mat = mInv * transposeMat3( mat3( T1, T2, N ) );\\\\n\\\\n\\\\t// transform rect\\\\n\\\\tvec3 coords[ 4 ];\\\\n\\\\tcoords[ 0 ] = mat * ( rectCoords[ 0 ] - P );\\\\n\\\\tcoords[ 1 ] = mat * ( rectCoords[ 1 ] - P );\\\\n\\\\tcoords[ 2 ] = mat * ( rectCoords[ 2 ] - P );\\\\n\\\\tcoords[ 3 ] = mat * ( rectCoords[ 3 ] - P );\\\\n\\\\n\\\\t// project rect onto sphere\\\\n\\\\tcoords[ 0 ] = normalize( coords[ 0 ] );\\\\n\\\\tcoords[ 1 ] = normalize( coords[ 1 ] );\\\\n\\\\tcoords[ 2 ] = normalize( coords[ 2 ] );\\\\n\\\\tcoords[ 3 ] = normalize( coords[ 3 ] );\\\\n\\\\n\\\\t// calculate vector form factor\\\\n\\\\tvec3 vectorFormFactor = vec3( 0.0 );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 0 ], coords[ 1 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 1 ], coords[ 2 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 2 ], coords[ 3 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 3 ], coords[ 0 ] );\\\\n\\\\n\\\\t// adjust for horizon clipping\\\\n\\\\tfloat result = LTC_ClippedSphereFormFactor( vectorFormFactor );\\\\n\\\\n/*\\\\n\\\\t// alternate method of adjusting for horizon clipping (see referece)\\\\n\\\\t// refactoring required\\\\n\\\\tfloat len = length( vectorFormFactor );\\\\n\\\\tfloat z = vectorFormFactor.z / len;\\\\n\\\\n\\\\tconst float LUT_SIZE = 64.0;\\\\n\\\\tconst float LUT_SCALE = ( LUT_SIZE - 1.0 ) / LUT_SIZE;\\\\n\\\\tconst float LUT_BIAS = 0.5 / LUT_SIZE;\\\\n\\\\n\\\\t// tabulated horizon-clipped sphere, apparently...\\\\n\\\\tvec2 uv = vec2( z * 0.5 + 0.5, len );\\\\n\\\\tuv = uv * LUT_SCALE + LUT_BIAS;\\\\n\\\\n\\\\tfloat scale = texture2D( ltc_2, uv ).w;\\\\n\\\\n\\\\tfloat result = len * scale;\\\\n*/\\\\n\\\\n\\\\treturn vec3( result );\\\\n\\\\n}\\\\n\\\\n// End Rect Area Light\\\\n\\\\n\\\\nfloat G_BlinnPhong_Implicit( /* const in float dotNL, const in float dotNV */ ) {\\\\n\\\\n\\\\t// geometry term is (n dot l)(n dot v) / 4(n dot l)(n dot v)\\\\n\\\\treturn 0.25;\\\\n\\\\n}\\\\n\\\\nfloat D_BlinnPhong( const in float shininess, const in float dotNH ) {\\\\n\\\\n\\\\treturn RECIPROCAL_PI * ( shininess * 0.5 + 1.0 ) * pow( dotNH, shininess );\\\\n\\\\n}\\\\n\\\\nvec3 BRDF_BlinnPhong( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float shininess ) {\\\\n\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\tfloat dotVH = saturate( dot( viewDir, halfDir ) );\\\\n\\\\n\\\\tvec3 F = F_Schlick( specularColor, 1.0, dotVH );\\\\n\\\\n\\\\tfloat G = G_BlinnPhong_Implicit( /* dotNL, dotNV */ );\\\\n\\\\n\\\\tfloat D = D_BlinnPhong( shininess, dotNH );\\\\n\\\\n\\\\treturn F * ( G * D );\\\\n\\\\n} // validated\\\\n\\\\n#if defined( USE_SHEEN )\\\\n\\\\n// https://github.com/google/filament/blob/master/shaders/src/brdf.fs\\\\nfloat D_Charlie( float roughness, float dotNH ) {\\\\n\\\\n\\\\tfloat alpha = pow2( roughness );\\\\n\\\\n\\\\t// Estevez and Kulla 2017, \\\\\\\"Production Friendly Microfacet Sheen BRDF\\\\\\\"\\\\n\\\\tfloat invAlpha = 1.0 / alpha;\\\\n\\\\tfloat cos2h = dotNH * dotNH;\\\\n\\\\tfloat sin2h = max( 1.0 - cos2h, 0.0078125 ); // 2^(-14/2), so sin2h^2 > 0 in fp16\\\\n\\\\n\\\\treturn ( 2.0 + invAlpha ) * pow( sin2h, invAlpha * 0.5 ) / ( 2.0 * PI );\\\\n\\\\n}\\\\n\\\\n// https://github.com/google/filament/blob/master/shaders/src/brdf.fs\\\\nfloat V_Neubelt( float dotNV, float dotNL ) {\\\\n\\\\n\\\\t// Neubelt and Pettineo 2013, \\\\\\\"Crafting a Next-gen Material Pipeline for The Order: 1886\\\\\\\"\\\\n\\\\treturn saturate( 1.0 / ( 4.0 * ( dotNL + dotNV - dotNL * dotNV ) ) );\\\\n\\\\n}\\\\n\\\\nvec3 BRDF_Sheen( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, vec3 sheenTint, const in float sheenRoughness ) {\\\\n\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\n\\\\tfloat dotNL = saturate( dot( normal, lightDir ) );\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\n\\\\tfloat D = D_Charlie( sheenRoughness, dotNH );\\\\n\\\\tfloat V = V_Neubelt( dotNV, dotNL );\\\\n\\\\n\\\\treturn sheenTint * ( D * V );\\\\n\\\\n}\\\\n\\\\n#endif\\\\n',bumpmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_BUMPMAP\\\\n\\\\n\\\\tuniform sampler2D bumpMap;\\\\n\\\\tuniform float bumpScale;\\\\n\\\\n\\\\t// Bump Mapping Unparametrized Surfaces on the GPU by Morten S. Mikkelsen\\\\n\\\\t// http://api.unrealengine.com/attachments/Engine/Rendering/LightingAndShadows/BumpMappingWithoutTangentSpace/mm_sfgrad_bump.pdf\\\\n\\\\n\\\\t// Evaluate the derivative of the height w.r.t. screen-space using forward differencing (listing 2)\\\\n\\\\n\\\\tvec2 dHdxy_fwd() {\\\\n\\\\n\\\\t\\\\tvec2 dSTdx = dFdx( vUv );\\\\n\\\\t\\\\tvec2 dSTdy = dFdy( vUv );\\\\n\\\\n\\\\t\\\\tfloat Hll = bumpScale * texture2D( bumpMap, vUv ).x;\\\\n\\\\t\\\\tfloat dBx = bumpScale * texture2D( bumpMap, vUv + dSTdx ).x - Hll;\\\\n\\\\t\\\\tfloat dBy = bumpScale * texture2D( bumpMap, vUv + dSTdy ).x - Hll;\\\\n\\\\n\\\\t\\\\treturn vec2( dBx, dBy );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy, float faceDirection ) {\\\\n\\\\n\\\\t\\\\t// Workaround for Adreno 3XX dFd*( vec3 ) bug. See #9988\\\\n\\\\n\\\\t\\\\tvec3 vSigmaX = vec3( dFdx( surf_pos.x ), dFdx( surf_pos.y ), dFdx( surf_pos.z ) );\\\\n\\\\t\\\\tvec3 vSigmaY = vec3( dFdy( surf_pos.x ), dFdy( surf_pos.y ), dFdy( surf_pos.z ) );\\\\n\\\\t\\\\tvec3 vN = surf_norm;\\\\t\\\\t// normalized\\\\n\\\\n\\\\t\\\\tvec3 R1 = cross( vSigmaY, vN );\\\\n\\\\t\\\\tvec3 R2 = cross( vN, vSigmaX );\\\\n\\\\n\\\\t\\\\tfloat fDet = dot( vSigmaX, R1 ) * faceDirection;\\\\n\\\\n\\\\t\\\\tvec3 vGrad = sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );\\\\n\\\\t\\\\treturn normalize( abs( fDet ) * surf_norm - vGrad );\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",clipping_planes_fragment:\\\\\\\"\\\\n#if NUM_CLIPPING_PLANES > 0\\\\n\\\\n\\\\tvec4 plane;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < UNION_CLIPPING_PLANES; i ++ ) {\\\\n\\\\n\\\\t\\\\tplane = clippingPlanes[ i ];\\\\n\\\\t\\\\tif ( dot( vClipPosition, plane.xyz ) > plane.w ) discard;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#if UNION_CLIPPING_PLANES < NUM_CLIPPING_PLANES\\\\n\\\\n\\\\t\\\\tbool clipped = true;\\\\n\\\\n\\\\t\\\\t#pragma unroll_loop_start\\\\n\\\\t\\\\tfor ( int i = UNION_CLIPPING_PLANES; i < NUM_CLIPPING_PLANES; i ++ ) {\\\\n\\\\n\\\\t\\\\t\\\\tplane = clippingPlanes[ i ];\\\\n\\\\t\\\\t\\\\tclipped = ( dot( vClipPosition, plane.xyz ) > plane.w ) && clipped;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t\\\\tif ( clipped ) discard;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",clipping_planes_pars_fragment:\\\\\\\"\\\\n#if NUM_CLIPPING_PLANES > 0\\\\n\\\\n\\\\tvarying vec3 vClipPosition;\\\\n\\\\n\\\\tuniform vec4 clippingPlanes[ NUM_CLIPPING_PLANES ];\\\\n\\\\n#endif\\\\n\\\\\\\",clipping_planes_pars_vertex:\\\\\\\"\\\\n#if NUM_CLIPPING_PLANES > 0\\\\n\\\\n\\\\tvarying vec3 vClipPosition;\\\\n\\\\n#endif\\\\n\\\\\\\",clipping_planes_vertex:\\\\\\\"\\\\n#if NUM_CLIPPING_PLANES > 0\\\\n\\\\n\\\\tvClipPosition = - mvPosition.xyz;\\\\n\\\\n#endif\\\\n\\\\\\\",color_fragment:\\\\\\\"\\\\n#if defined( USE_COLOR_ALPHA )\\\\n\\\\n\\\\tdiffuseColor *= vColor;\\\\n\\\\n#elif defined( USE_COLOR )\\\\n\\\\n\\\\tdiffuseColor.rgb *= vColor;\\\\n\\\\n#endif\\\\n\\\\\\\",color_pars_fragment:\\\\\\\"\\\\n#if defined( USE_COLOR_ALPHA )\\\\n\\\\n\\\\tvarying vec4 vColor;\\\\n\\\\n#elif defined( USE_COLOR )\\\\n\\\\n\\\\tvarying vec3 vColor;\\\\n\\\\n#endif\\\\n\\\\\\\",color_pars_vertex:\\\\\\\"\\\\n#if defined( USE_COLOR_ALPHA )\\\\n\\\\n\\\\tvarying vec4 vColor;\\\\n\\\\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\\\\n\\\\n\\\\tvarying vec3 vColor;\\\\n\\\\n#endif\\\\n\\\\\\\",color_vertex:\\\\\\\"\\\\n#if defined( USE_COLOR_ALPHA )\\\\n\\\\n\\\\tvColor = vec4( 1.0 );\\\\n\\\\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\\\\n\\\\n\\\\tvColor = vec3( 1.0 );\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_COLOR\\\\n\\\\n\\\\tvColor *= color;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_INSTANCING_COLOR\\\\n\\\\n\\\\tvColor.xyz *= instanceColor.xyz;\\\\n\\\\n#endif\\\\n\\\\\\\",common:\\\\\\\"\\\\n#define PI 3.141592653589793\\\\n#define PI2 6.283185307179586\\\\n#define PI_HALF 1.5707963267948966\\\\n#define RECIPROCAL_PI 0.3183098861837907\\\\n#define RECIPROCAL_PI2 0.15915494309189535\\\\n#define EPSILON 1e-6\\\\n\\\\n#ifndef saturate\\\\n// may have defined saturate() already\\\\n#define saturate( a ) clamp( a, 0.0, 1.0 )\\\\n#endif\\\\n#define whiteComplement( a ) ( 1.0 - saturate( a ) )\\\\n\\\\nfloat pow2( const in float x ) { return x*x; }\\\\nfloat pow3( const in float x ) { return x*x*x; }\\\\nfloat pow4( const in float x ) { float x2 = x*x; return x2*x2; }\\\\nfloat max3( const in vec3 v ) { return max( max( v.x, v.y ), v.z ); }\\\\nfloat average( const in vec3 color ) { return dot( color, vec3( 0.3333 ) ); }\\\\n\\\\n// expects values in the range of [0,1]x[0,1], returns values in the [0,1] range.\\\\n// do not collapse into a single function per: http://byteblacksmith.com/improvements-to-the-canonical-one-liner-glsl-rand-for-opengl-es-2-0/\\\\nhighp float rand( const in vec2 uv ) {\\\\n\\\\n\\\\tconst highp float a = 12.9898, b = 78.233, c = 43758.5453;\\\\n\\\\thighp float dt = dot( uv.xy, vec2( a,b ) ), sn = mod( dt, PI );\\\\n\\\\n\\\\treturn fract( sin( sn ) * c );\\\\n\\\\n}\\\\n\\\\n#ifdef HIGH_PRECISION\\\\n\\\\tfloat precisionSafeLength( vec3 v ) { return length( v ); }\\\\n#else\\\\n\\\\tfloat precisionSafeLength( vec3 v ) {\\\\n\\\\t\\\\tfloat maxComponent = max3( abs( v ) );\\\\n\\\\t\\\\treturn length( v / maxComponent ) * maxComponent;\\\\n\\\\t}\\\\n#endif\\\\n\\\\nstruct IncidentLight {\\\\n\\\\tvec3 color;\\\\n\\\\tvec3 direction;\\\\n\\\\tbool visible;\\\\n};\\\\n\\\\nstruct ReflectedLight {\\\\n\\\\tvec3 directDiffuse;\\\\n\\\\tvec3 directSpecular;\\\\n\\\\tvec3 indirectDiffuse;\\\\n\\\\tvec3 indirectSpecular;\\\\n};\\\\n\\\\nstruct GeometricContext {\\\\n\\\\tvec3 position;\\\\n\\\\tvec3 normal;\\\\n\\\\tvec3 viewDir;\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tvec3 clearcoatNormal;\\\\n#endif\\\\n};\\\\n\\\\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\n\\\\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\\\\n\\\\n}\\\\n\\\\nvec3 inverseTransformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\n\\\\t// dir can be either a direction vector or a normal vector\\\\n\\\\t// upper-left 3x3 of matrix is assumed to be orthogonal\\\\n\\\\n\\\\treturn normalize( ( vec4( dir, 0.0 ) * matrix ).xyz );\\\\n\\\\n}\\\\n\\\\nmat3 transposeMat3( const in mat3 m ) {\\\\n\\\\n\\\\tmat3 tmp;\\\\n\\\\n\\\\ttmp[ 0 ] = vec3( m[ 0 ].x, m[ 1 ].x, m[ 2 ].x );\\\\n\\\\ttmp[ 1 ] = vec3( m[ 0 ].y, m[ 1 ].y, m[ 2 ].y );\\\\n\\\\ttmp[ 2 ] = vec3( m[ 0 ].z, m[ 1 ].z, m[ 2 ].z );\\\\n\\\\n\\\\treturn tmp;\\\\n\\\\n}\\\\n\\\\n// https://en.wikipedia.org/wiki/Relative_luminance\\\\nfloat linearToRelativeLuminance( const in vec3 color ) {\\\\n\\\\n\\\\tvec3 weights = vec3( 0.2126, 0.7152, 0.0722 );\\\\n\\\\n\\\\treturn dot( weights, color.rgb );\\\\n\\\\n}\\\\n\\\\nbool isPerspectiveMatrix( mat4 m ) {\\\\n\\\\n\\\\treturn m[ 2 ][ 3 ] == - 1.0;\\\\n\\\\n}\\\\n\\\\nvec2 equirectUv( in vec3 dir ) {\\\\n\\\\n\\\\t// dir is assumed to be unit length\\\\n\\\\n\\\\tfloat u = atan( dir.z, dir.x ) * RECIPROCAL_PI2 + 0.5;\\\\n\\\\n\\\\tfloat v = asin( clamp( dir.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\\\\n\\\\n\\\\treturn vec2( u, v );\\\\n\\\\n}\\\\n\\\\\\\",cube_uv_reflection_fragment:\\\\\\\"\\\\n#ifdef ENVMAP_TYPE_CUBE_UV\\\\n\\\\n\\\\t#define cubeUV_maxMipLevel 8.0\\\\n\\\\t#define cubeUV_minMipLevel 4.0\\\\n\\\\t#define cubeUV_maxTileSize 256.0\\\\n\\\\t#define cubeUV_minTileSize 16.0\\\\n\\\\n\\\\t// These shader functions convert between the UV coordinates of a single face of\\\\n\\\\t// a cubemap, the 0-5 integer index of a cube face, and the direction vector for\\\\n\\\\t// sampling a textureCube (not generally normalized ).\\\\n\\\\n\\\\tfloat getFace( vec3 direction ) {\\\\n\\\\n\\\\t\\\\tvec3 absDirection = abs( direction );\\\\n\\\\n\\\\t\\\\tfloat face = - 1.0;\\\\n\\\\n\\\\t\\\\tif ( absDirection.x > absDirection.z ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( absDirection.x > absDirection.y )\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.x > 0.0 ? 0.0 : 3.0;\\\\n\\\\n\\\\t\\\\t\\\\telse\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tif ( absDirection.z > absDirection.y )\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.z > 0.0 ? 2.0 : 5.0;\\\\n\\\\n\\\\t\\\\t\\\\telse\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn face;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\t// RH coordinate system; PMREM face-indexing convention\\\\n\\\\tvec2 getUV( vec3 direction, float face ) {\\\\n\\\\n\\\\t\\\\tvec2 uv;\\\\n\\\\n\\\\t\\\\tif ( face == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( direction.z, direction.y ) / abs( direction.x ); // pos x\\\\n\\\\n\\\\t\\\\t} else if ( face == 1.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, - direction.z ) / abs( direction.y ); // pos y\\\\n\\\\n\\\\t\\\\t} else if ( face == 2.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, direction.y ) / abs( direction.z ); // pos z\\\\n\\\\n\\\\t\\\\t} else if ( face == 3.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.z, direction.y ) / abs( direction.x ); // neg x\\\\n\\\\n\\\\t\\\\t} else if ( face == 4.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, direction.z ) / abs( direction.y ); // neg y\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( direction.x, direction.y ) / abs( direction.z ); // neg z\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn 0.5 * ( uv + 1.0 );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 bilinearCubeUV( sampler2D envMap, vec3 direction, float mipInt ) {\\\\n\\\\n\\\\t\\\\tfloat face = getFace( direction );\\\\n\\\\n\\\\t\\\\tfloat filterInt = max( cubeUV_minMipLevel - mipInt, 0.0 );\\\\n\\\\n\\\\t\\\\tmipInt = max( mipInt, cubeUV_minMipLevel );\\\\n\\\\n\\\\t\\\\tfloat faceSize = exp2( mipInt );\\\\n\\\\n\\\\t\\\\tfloat texelSize = 1.0 / ( 3.0 * cubeUV_maxTileSize );\\\\n\\\\n\\\\t\\\\tvec2 uv = getUV( direction, face ) * ( faceSize - 1.0 );\\\\n\\\\n\\\\t\\\\tvec2 f = fract( uv );\\\\n\\\\n\\\\t\\\\tuv += 0.5 - f;\\\\n\\\\n\\\\t\\\\tif ( face > 2.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv.y += faceSize;\\\\n\\\\n\\\\t\\\\t\\\\tface -= 3.0;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tuv.x += face * faceSize;\\\\n\\\\n\\\\t\\\\tif ( mipInt < cubeUV_maxMipLevel ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv.y += 2.0 * cubeUV_maxTileSize;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tuv.y += filterInt * 2.0 * cubeUV_minTileSize;\\\\n\\\\n\\\\t\\\\tuv.x += 3.0 * max( 0.0, cubeUV_maxTileSize - 2.0 * faceSize );\\\\n\\\\n\\\\t\\\\tuv *= texelSize;\\\\n\\\\n\\\\t\\\\tvec3 tl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\tuv.x += texelSize;\\\\n\\\\n\\\\t\\\\tvec3 tr = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\tuv.y += texelSize;\\\\n\\\\n\\\\t\\\\tvec3 br = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\tuv.x -= texelSize;\\\\n\\\\n\\\\t\\\\tvec3 bl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\tvec3 tm = mix( tl, tr, f.x );\\\\n\\\\n\\\\t\\\\tvec3 bm = mix( bl, br, f.x );\\\\n\\\\n\\\\t\\\\treturn mix( tm, bm, f.y );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\t// These defines must match with PMREMGenerator\\\\n\\\\n\\\\t#define r0 1.0\\\\n\\\\t#define v0 0.339\\\\n\\\\t#define m0 - 2.0\\\\n\\\\t#define r1 0.8\\\\n\\\\t#define v1 0.276\\\\n\\\\t#define m1 - 1.0\\\\n\\\\t#define r4 0.4\\\\n\\\\t#define v4 0.046\\\\n\\\\t#define m4 2.0\\\\n\\\\t#define r5 0.305\\\\n\\\\t#define v5 0.016\\\\n\\\\t#define m5 3.0\\\\n\\\\t#define r6 0.21\\\\n\\\\t#define v6 0.0038\\\\n\\\\t#define m6 4.0\\\\n\\\\n\\\\tfloat roughnessToMip( float roughness ) {\\\\n\\\\n\\\\t\\\\tfloat mip = 0.0;\\\\n\\\\n\\\\t\\\\tif ( roughness >= r1 ) {\\\\n\\\\n\\\\t\\\\t\\\\tmip = ( r0 - roughness ) * ( m1 - m0 ) / ( r0 - r1 ) + m0;\\\\n\\\\n\\\\t\\\\t} else if ( roughness >= r4 ) {\\\\n\\\\n\\\\t\\\\t\\\\tmip = ( r1 - roughness ) * ( m4 - m1 ) / ( r1 - r4 ) + m1;\\\\n\\\\n\\\\t\\\\t} else if ( roughness >= r5 ) {\\\\n\\\\n\\\\t\\\\t\\\\tmip = ( r4 - roughness ) * ( m5 - m4 ) / ( r4 - r5 ) + m4;\\\\n\\\\n\\\\t\\\\t} else if ( roughness >= r6 ) {\\\\n\\\\n\\\\t\\\\t\\\\tmip = ( r5 - roughness ) * ( m6 - m5 ) / ( r5 - r6 ) + m5;\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tmip = - 2.0 * log2( 1.16 * roughness ); // 1.16 = 1.79^0.25\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn mip;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec4 textureCubeUV( sampler2D envMap, vec3 sampleDir, float roughness ) {\\\\n\\\\n\\\\t\\\\tfloat mip = clamp( roughnessToMip( roughness ), m0, cubeUV_maxMipLevel );\\\\n\\\\n\\\\t\\\\tfloat mipF = fract( mip );\\\\n\\\\n\\\\t\\\\tfloat mipInt = floor( mip );\\\\n\\\\n\\\\t\\\\tvec3 color0 = bilinearCubeUV( envMap, sampleDir, mipInt );\\\\n\\\\n\\\\t\\\\tif ( mipF == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn vec4( color0, 1.0 );\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tvec3 color1 = bilinearCubeUV( envMap, sampleDir, mipInt + 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\treturn vec4( mix( color0, color1, mipF ), 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",defaultnormal_vertex:\\\\\\\"\\\\nvec3 transformedNormal = objectNormal;\\\\n\\\\n#ifdef USE_INSTANCING\\\\n\\\\n\\\\t// this is in lieu of a per-instance normal-matrix\\\\n\\\\t// shear transforms in the instance matrix are not supported\\\\n\\\\n\\\\tmat3 m = mat3( instanceMatrix );\\\\n\\\\n\\\\ttransformedNormal /= vec3( dot( m[ 0 ], m[ 0 ] ), dot( m[ 1 ], m[ 1 ] ), dot( m[ 2 ], m[ 2 ] ) );\\\\n\\\\n\\\\ttransformedNormal = m * transformedNormal;\\\\n\\\\n#endif\\\\n\\\\ntransformedNormal = normalMatrix * transformedNormal;\\\\n\\\\n#ifdef FLIP_SIDED\\\\n\\\\n\\\\ttransformedNormal = - transformedNormal;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_TANGENT\\\\n\\\\n\\\\tvec3 transformedTangent = ( modelViewMatrix * vec4( objectTangent, 0.0 ) ).xyz;\\\\n\\\\n\\\\t#ifdef FLIP_SIDED\\\\n\\\\n\\\\t\\\\ttransformedTangent = - transformedTangent;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",displacementmap_pars_vertex:\\\\\\\"\\\\n#ifdef USE_DISPLACEMENTMAP\\\\n\\\\n\\\\tuniform sampler2D displacementMap;\\\\n\\\\tuniform float displacementScale;\\\\n\\\\tuniform float displacementBias;\\\\n\\\\n#endif\\\\n\\\\\\\",displacementmap_vertex:\\\\\\\"\\\\n#ifdef USE_DISPLACEMENTMAP\\\\n\\\\n\\\\ttransformed += normalize( objectNormal ) * ( texture2D( displacementMap, vUv ).x * displacementScale + displacementBias );\\\\n\\\\n#endif\\\\n\\\\\\\",emissivemap_fragment:\\\\\\\"\\\\n#ifdef USE_EMISSIVEMAP\\\\n\\\\n\\\\tvec4 emissiveColor = texture2D( emissiveMap, vUv );\\\\n\\\\n\\\\temissiveColor.rgb = emissiveMapTexelToLinear( emissiveColor ).rgb;\\\\n\\\\n\\\\ttotalEmissiveRadiance *= emissiveColor.rgb;\\\\n\\\\n#endif\\\\n\\\\\\\",emissivemap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_EMISSIVEMAP\\\\n\\\\n\\\\tuniform sampler2D emissiveMap;\\\\n\\\\n#endif\\\\n\\\\\\\",encodings_fragment:\\\\\\\"\\\\ngl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\\\\",encodings_pars_fragment:\\\\\\\"\\\\n// For a discussion of what this is, please read this: http://lousodrome.net/blog/light/2013/05/26/gamma-correct-and-hdr-rendering-in-a-32-bits-buffer/\\\\n\\\\nvec4 LinearToLinear( in vec4 value ) {\\\\n\\\\treturn value;\\\\n}\\\\n\\\\nvec4 GammaToLinear( in vec4 value, in float gammaFactor ) {\\\\n\\\\treturn vec4( pow( value.rgb, vec3( gammaFactor ) ), value.a );\\\\n}\\\\n\\\\nvec4 LinearToGamma( in vec4 value, in float gammaFactor ) {\\\\n\\\\treturn vec4( pow( value.rgb, vec3( 1.0 / gammaFactor ) ), value.a );\\\\n}\\\\n\\\\nvec4 sRGBToLinear( in vec4 value ) {\\\\n\\\\treturn vec4( mix( pow( value.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), value.rgb * 0.0773993808, vec3( lessThanEqual( value.rgb, vec3( 0.04045 ) ) ) ), value.a );\\\\n}\\\\n\\\\nvec4 LinearTosRGB( in vec4 value ) {\\\\n\\\\treturn vec4( mix( pow( value.rgb, vec3( 0.41666 ) ) * 1.055 - vec3( 0.055 ), value.rgb * 12.92, vec3( lessThanEqual( value.rgb, vec3( 0.0031308 ) ) ) ), value.a );\\\\n}\\\\n\\\\nvec4 RGBEToLinear( in vec4 value ) {\\\\n\\\\treturn vec4( value.rgb * exp2( value.a * 255.0 - 128.0 ), 1.0 );\\\\n}\\\\n\\\\nvec4 LinearToRGBE( in vec4 value ) {\\\\n\\\\tfloat maxComponent = max( max( value.r, value.g ), value.b );\\\\n\\\\tfloat fExp = clamp( ceil( log2( maxComponent ) ), -128.0, 127.0 );\\\\n\\\\treturn vec4( value.rgb / exp2( fExp ), ( fExp + 128.0 ) / 255.0 );\\\\n\\\\t// return vec4( value.brg, ( 3.0 + 128.0 ) / 256.0 );\\\\n}\\\\n\\\\n// reference: http://iwasbeingirony.blogspot.ca/2010/06/difference-between-rgbm-and-rgbd.html\\\\nvec4 RGBMToLinear( in vec4 value, in float maxRange ) {\\\\n\\\\treturn vec4( value.rgb * value.a * maxRange, 1.0 );\\\\n}\\\\n\\\\nvec4 LinearToRGBM( in vec4 value, in float maxRange ) {\\\\n\\\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\\\n\\\\tfloat M = clamp( maxRGB / maxRange, 0.0, 1.0 );\\\\n\\\\tM = ceil( M * 255.0 ) / 255.0;\\\\n\\\\treturn vec4( value.rgb / ( M * maxRange ), M );\\\\n}\\\\n\\\\n// reference: http://iwasbeingirony.blogspot.ca/2010/06/difference-between-rgbm-and-rgbd.html\\\\nvec4 RGBDToLinear( in vec4 value, in float maxRange ) {\\\\n\\\\treturn vec4( value.rgb * ( ( maxRange / 255.0 ) / value.a ), 1.0 );\\\\n}\\\\n\\\\nvec4 LinearToRGBD( in vec4 value, in float maxRange ) {\\\\n\\\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\\\n\\\\tfloat D = max( maxRange / maxRGB, 1.0 );\\\\n\\\\t// NOTE: The implementation with min causes the shader to not compile on\\\\n\\\\t// a common Alcatel A502DL in Chrome 78/Android 8.1. Some research suggests \\\\n\\\\t// that the chipset is Mediatek MT6739 w/ IMG PowerVR GE8100 GPU.\\\\n\\\\t// D = min( floor( D ) / 255.0, 1.0 );\\\\n\\\\tD = clamp( floor( D ) / 255.0, 0.0, 1.0 );\\\\n\\\\treturn vec4( value.rgb * ( D * ( 255.0 / maxRange ) ), D );\\\\n}\\\\n\\\\n// LogLuv reference: http://graphicrants.blogspot.ca/2009/04/rgbm-color-encoding.html\\\\n\\\\n// M matrix, for encoding\\\\nconst mat3 cLogLuvM = mat3( 0.2209, 0.3390, 0.4184, 0.1138, 0.6780, 0.7319, 0.0102, 0.1130, 0.2969 );\\\\nvec4 LinearToLogLuv( in vec4 value ) {\\\\n\\\\tvec3 Xp_Y_XYZp = cLogLuvM * value.rgb;\\\\n\\\\tXp_Y_XYZp = max( Xp_Y_XYZp, vec3( 1e-6, 1e-6, 1e-6 ) );\\\\n\\\\tvec4 vResult;\\\\n\\\\tvResult.xy = Xp_Y_XYZp.xy / Xp_Y_XYZp.z;\\\\n\\\\tfloat Le = 2.0 * log2(Xp_Y_XYZp.y) + 127.0;\\\\n\\\\tvResult.w = fract( Le );\\\\n\\\\tvResult.z = ( Le - ( floor( vResult.w * 255.0 ) ) / 255.0 ) / 255.0;\\\\n\\\\treturn vResult;\\\\n}\\\\n\\\\n// Inverse M matrix, for decoding\\\\nconst mat3 cLogLuvInverseM = mat3( 6.0014, -2.7008, -1.7996, -1.3320, 3.1029, -5.7721, 0.3008, -1.0882, 5.6268 );\\\\nvec4 LogLuvToLinear( in vec4 value ) {\\\\n\\\\tfloat Le = value.z * 255.0 + value.w;\\\\n\\\\tvec3 Xp_Y_XYZp;\\\\n\\\\tXp_Y_XYZp.y = exp2( ( Le - 127.0 ) / 2.0 );\\\\n\\\\tXp_Y_XYZp.z = Xp_Y_XYZp.y / value.y;\\\\n\\\\tXp_Y_XYZp.x = value.x * Xp_Y_XYZp.z;\\\\n\\\\tvec3 vRGB = cLogLuvInverseM * Xp_Y_XYZp.rgb;\\\\n\\\\treturn vec4( max( vRGB, 0.0 ), 1.0 );\\\\n}\\\\n\\\\\\\",envmap_fragment:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\n\\\\t\\\\tvec3 cameraToFrag;\\\\n\\\\n\\\\t\\\\tif ( isOrthographic ) {\\\\n\\\\n\\\\t\\\\t\\\\tcameraToFrag = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tcameraToFrag = normalize( vWorldPosition - cameraPosition );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t// Transforming Normal Vectors with the Inverse Transformation\\\\n\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\n\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = reflect( cameraToFrag, worldNormal );\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = refract( cameraToFrag, worldNormal, refractionRatio );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvec3 reflectVec = vReflect;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\n\\\\t\\\\tvec4 envColor = textureCube( envMap, vec3( flipEnvMap * reflectVec.x, reflectVec.yz ) );\\\\n\\\\n\\\\t\\\\tenvColor = envMapTexelToLinear( envColor );\\\\n\\\\n\\\\t#elif defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\n\\\\t\\\\tvec4 envColor = textureCubeUV( envMap, reflectVec, 0.0 );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvec4 envColor = vec4( 0.0 );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef ENVMAP_BLENDING_MULTIPLY\\\\n\\\\n\\\\t\\\\toutgoingLight = mix( outgoingLight, outgoingLight * envColor.xyz, specularStrength * reflectivity );\\\\n\\\\n\\\\t#elif defined( ENVMAP_BLENDING_MIX )\\\\n\\\\n\\\\t\\\\toutgoingLight = mix( outgoingLight, envColor.xyz, specularStrength * reflectivity );\\\\n\\\\n\\\\t#elif defined( ENVMAP_BLENDING_ADD )\\\\n\\\\n\\\\t\\\\toutgoingLight += envColor.xyz * specularStrength * reflectivity;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",envmap_common_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\tuniform float envMapIntensity;\\\\n\\\\tuniform float flipEnvMap;\\\\n\\\\tuniform int maxMipLevel;\\\\n\\\\n\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\t\\\\tuniform samplerCube envMap;\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t#endif\\\\n\\\\t\\\\n#endif\\\\n\\\\\\\",envmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\tuniform float reflectivity;\\\\n\\\\n\\\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG )\\\\n\\\\n\\\\t\\\\t#define ENV_WORLDPOS\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\t#else\\\\n\\\\t\\\\tvarying vec3 vReflect;\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",envmap_pars_vertex:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) ||defined( PHONG )\\\\n\\\\n\\\\t\\\\t#define ENV_WORLDPOS\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\t\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvarying vec3 vReflect;\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",envmap_physical_pars_fragment:\\\\\\\"\\\\n#if defined( USE_ENVMAP )\\\\n\\\\n\\\\t#ifdef ENVMAP_MODE_REFRACTION\\\\n\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tvec3 getIBLIrradiance( const in vec3 normal ) {\\\\n\\\\n\\\\t\\\\t#if defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\n\\\\t\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeUV( envMap, worldNormal, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\treturn PI * envMapColor.rgb * envMapIntensity;\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\treturn vec3( 0.0 );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 getIBLRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness ) {\\\\n\\\\n\\\\t\\\\t#if defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\n\\\\t\\\\t\\\\tvec3 reflectVec;\\\\n\\\\n\\\\t\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = reflect( - viewDir, normal );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t// Mixing the reflection with the normal is more accurate and keeps rough objects from gathering light from behind their tangent plane.\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = normalize( mix( reflectVec, normal, roughness * roughness) );\\\\n\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = refract( - viewDir, normal, refractionRatio );\\\\n\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t\\\\treflectVec = inverseTransformDirection( reflectVec, viewMatrix );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeUV( envMap, reflectVec, roughness );\\\\n\\\\n\\\\t\\\\t\\\\treturn envMapColor.rgb * envMapIntensity;\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\treturn vec3( 0.0 );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",envmap_vertex:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\n\\\\t\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvec3 cameraToVertex;\\\\n\\\\n\\\\t\\\\tif ( isOrthographic ) {\\\\n\\\\n\\\\t\\\\t\\\\tcameraToVertex = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tcameraToVertex = normalize( worldPosition.xyz - cameraPosition );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\\\\n\\\\n\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\n\\\\t\\\\t\\\\tvReflect = reflect( cameraToVertex, worldNormal );\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\tvReflect = refract( cameraToVertex, worldNormal, refractionRatio );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",fog_vertex:\\\\\\\"\\\\n#ifdef USE_FOG\\\\n\\\\n\\\\tvFogDepth = - mvPosition.z;\\\\n\\\\n#endif\\\\n\\\\\\\",fog_pars_vertex:\\\\\\\"\\\\n#ifdef USE_FOG\\\\n\\\\n\\\\tvarying float vFogDepth;\\\\n\\\\n#endif\\\\n\\\\\\\",fog_fragment:\\\\\\\"\\\\n#ifdef USE_FOG\\\\n\\\\n\\\\t#ifdef FOG_EXP2\\\\n\\\\n\\\\t\\\\tfloat fogFactor = 1.0 - exp( - fogDensity * fogDensity * vFogDepth * vFogDepth );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tfloat fogFactor = smoothstep( fogNear, fogFar, vFogDepth );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tgl_FragColor.rgb = mix( gl_FragColor.rgb, fogColor, fogFactor );\\\\n\\\\n#endif\\\\n\\\\\\\",fog_pars_fragment:\\\\\\\"\\\\n#ifdef USE_FOG\\\\n\\\\n\\\\tuniform vec3 fogColor;\\\\n\\\\tvarying float vFogDepth;\\\\n\\\\n\\\\t#ifdef FOG_EXP2\\\\n\\\\n\\\\t\\\\tuniform float fogDensity;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tuniform float fogNear;\\\\n\\\\t\\\\tuniform float fogFar;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",gradientmap_pars_fragment:\\\\\\\"\\\\n\\\\n#ifdef USE_GRADIENTMAP\\\\n\\\\n\\\\tuniform sampler2D gradientMap;\\\\n\\\\n#endif\\\\n\\\\nvec3 getGradientIrradiance( vec3 normal, vec3 lightDirection ) {\\\\n\\\\n\\\\t// dotNL will be from -1.0 to 1.0\\\\n\\\\tfloat dotNL = dot( normal, lightDirection );\\\\n\\\\tvec2 coord = vec2( dotNL * 0.5 + 0.5, 0.0 );\\\\n\\\\n\\\\t#ifdef USE_GRADIENTMAP\\\\n\\\\n\\\\t\\\\treturn texture2D( gradientMap, coord ).rgb;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\treturn ( coord.x < 0.7 ) ? vec3( 0.7 ) : vec3( 1.0 );\\\\n\\\\n\\\\t#endif\\\\n\\\\n}\\\\n\\\\\\\",lightmap_fragment:\\\\\\\"\\\\n#ifdef USE_LIGHTMAP\\\\n\\\\n\\\\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\\\\n\\\\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\n\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\n\\\\t\\\\tlightMapIrradiance *= PI;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += lightMapIrradiance;\\\\n\\\\n#endif\\\\n\\\\\\\",lightmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_LIGHTMAP\\\\n\\\\n\\\\tuniform sampler2D lightMap;\\\\n\\\\tuniform float lightMapIntensity;\\\\n\\\\n#endif\\\\n\\\\\\\",lights_lambert_vertex:\\\\\\\"\\\\nvec3 diffuse = vec3( 1.0 );\\\\n\\\\nGeometricContext geometry;\\\\ngeometry.position = mvPosition.xyz;\\\\ngeometry.normal = normalize( transformedNormal );\\\\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( -mvPosition.xyz );\\\\n\\\\nGeometricContext backGeometry;\\\\nbackGeometry.position = geometry.position;\\\\nbackGeometry.normal = -geometry.normal;\\\\nbackGeometry.viewDir = geometry.viewDir;\\\\n\\\\nvLightFront = vec3( 0.0 );\\\\nvIndirectFront = vec3( 0.0 );\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvLightBack = vec3( 0.0 );\\\\n\\\\tvIndirectBack = vec3( 0.0 );\\\\n#endif\\\\n\\\\nIncidentLight directLight;\\\\nfloat dotNL;\\\\nvec3 directLightColor_Diffuse;\\\\n\\\\nvIndirectFront += getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\nvIndirectFront += getLightProbeIrradiance( lightProbe, geometry.normal );\\\\n\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\tvIndirectBack += getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\n\\\\tvIndirectBack += getLightProbeIrradiance( lightProbe, backGeometry.normal );\\\\n\\\\n#endif\\\\n\\\\n#if NUM_POINT_LIGHTS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tgetPointLightInfo( pointLights[ i ], geometry, directLight );\\\\n\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if NUM_SPOT_LIGHTS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tgetSpotLightInfo( spotLights[ i ], geometry, directLight );\\\\n\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if NUM_DIR_LIGHTS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tgetDirectionalLightInfo( directionalLights[ i ], geometry, directLight );\\\\n\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if NUM_HEMI_LIGHTS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tvIndirectFront += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry.normal );\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\tvIndirectBack += getHemisphereLightIrradiance( hemisphereLights[ i ], backGeometry.normal );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\\\\",lights_pars_begin:\\\\\\\"\\\\nuniform bool receiveShadow;\\\\nuniform vec3 ambientLightColor;\\\\nuniform vec3 lightProbe[ 9 ];\\\\n\\\\n// get the irradiance (radiance convolved with cosine lobe) at the point 'normal' on the unit sphere\\\\n// source: https://graphics.stanford.edu/papers/envmap/envmap.pdf\\\\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\\\\n\\\\n\\\\t// normal is assumed to have unit length\\\\n\\\\n\\\\tfloat x = normal.x, y = normal.y, z = normal.z;\\\\n\\\\n\\\\t// band 0\\\\n\\\\tvec3 result = shCoefficients[ 0 ] * 0.886227;\\\\n\\\\n\\\\t// band 1\\\\n\\\\tresult += shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\\\\n\\\\tresult += shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\\\\n\\\\tresult += shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\\\\n\\\\n\\\\t// band 2\\\\n\\\\tresult += shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\\\\n\\\\tresult += shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\\\\n\\\\tresult += shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\\\\n\\\\tresult += shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\\\\n\\\\tresult += shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\\\\n\\\\n\\\\treturn result;\\\\n\\\\n}\\\\n\\\\nvec3 getLightProbeIrradiance( const in vec3 lightProbe[ 9 ], const in vec3 normal ) {\\\\n\\\\n\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\n\\\\tvec3 irradiance = shGetIrradianceAt( worldNormal, lightProbe );\\\\n\\\\n\\\\treturn irradiance;\\\\n\\\\n}\\\\n\\\\nvec3 getAmbientLightIrradiance( const in vec3 ambientLightColor ) {\\\\n\\\\n\\\\tvec3 irradiance = ambientLightColor;\\\\n\\\\n\\\\treturn irradiance;\\\\n\\\\n}\\\\n\\\\nfloat getDistanceAttenuation( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {\\\\n\\\\n\\\\t#if defined ( PHYSICALLY_CORRECT_LIGHTS )\\\\n\\\\n\\\\t\\\\t// based upon Frostbite 3 Moving to Physically-based Rendering\\\\n\\\\t\\\\t// page 32, equation 26: E[window1]\\\\n\\\\t\\\\t// https://seblagarde.files.wordpress.com/2015/07/course_notes_moving_frostbite_to_pbr_v32.pdf\\\\n\\\\t\\\\tfloat distanceFalloff = 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );\\\\n\\\\n\\\\t\\\\tif ( cutoffDistance > 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tdistanceFalloff *= pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn distanceFalloff;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tif ( cutoffDistance > 0.0 && decayExponent > 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn pow( saturate( - lightDistance / cutoffDistance + 1.0 ), decayExponent );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn 1.0;\\\\n\\\\n\\\\t#endif\\\\n\\\\n}\\\\n\\\\nfloat getSpotAttenuation( const in float coneCosine, const in float penumbraCosine, const in float angleCosine ) {\\\\n\\\\n\\\\treturn smoothstep( coneCosine, penumbraCosine, angleCosine );\\\\n\\\\n}\\\\n\\\\n#if NUM_DIR_LIGHTS > 0\\\\n\\\\n\\\\tstruct DirectionalLight {\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t};\\\\n\\\\n\\\\tuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\\\\n\\\\n\\\\tvoid getDirectionalLightInfo( const in DirectionalLight directionalLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\n\\\\t\\\\tlight.color = directionalLight.color;\\\\n\\\\t\\\\tlight.direction = directionalLight.direction;\\\\n\\\\t\\\\tlight.visible = true;\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\n\\\\n#if NUM_POINT_LIGHTS > 0\\\\n\\\\n\\\\tstruct PointLight {\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tfloat distance;\\\\n\\\\t\\\\tfloat decay;\\\\n\\\\t};\\\\n\\\\n\\\\tuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\\\\n\\\\n\\\\t// light is an out parameter as having it as a return value caused compiler errors on some devices\\\\n\\\\tvoid getPointLightInfo( const in PointLight pointLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\n\\\\t\\\\tvec3 lVector = pointLight.position - geometry.position;\\\\n\\\\n\\\\t\\\\tlight.direction = normalize( lVector );\\\\n\\\\n\\\\t\\\\tfloat lightDistance = length( lVector );\\\\n\\\\n\\\\t\\\\tlight.color = pointLight.color;\\\\n\\\\t\\\\tlight.color *= getDistanceAttenuation( lightDistance, pointLight.distance, pointLight.decay );\\\\n\\\\t\\\\tlight.visible = ( light.color != vec3( 0.0 ) );\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\n\\\\n#if NUM_SPOT_LIGHTS > 0\\\\n\\\\n\\\\tstruct SpotLight {\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tfloat distance;\\\\n\\\\t\\\\tfloat decay;\\\\n\\\\t\\\\tfloat coneCos;\\\\n\\\\t\\\\tfloat penumbraCos;\\\\n\\\\t};\\\\n\\\\n\\\\tuniform SpotLight spotLights[ NUM_SPOT_LIGHTS ];\\\\n\\\\n\\\\t// light is an out parameter as having it as a return value caused compiler errors on some devices\\\\n\\\\tvoid getSpotLightInfo( const in SpotLight spotLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\n\\\\t\\\\tvec3 lVector = spotLight.position - geometry.position;\\\\n\\\\n\\\\t\\\\tlight.direction = normalize( lVector );\\\\n\\\\n\\\\t\\\\tfloat angleCos = dot( light.direction, spotLight.direction );\\\\n\\\\n\\\\t\\\\tfloat spotAttenuation = getSpotAttenuation( spotLight.coneCos, spotLight.penumbraCos, angleCos );\\\\n\\\\n\\\\t\\\\tif ( spotAttenuation > 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat lightDistance = length( lVector );\\\\n\\\\n\\\\t\\\\t\\\\tlight.color = spotLight.color * spotAttenuation;\\\\n\\\\t\\\\t\\\\tlight.color *= getDistanceAttenuation( lightDistance, spotLight.distance, spotLight.decay );\\\\n\\\\t\\\\t\\\\tlight.visible = ( light.color != vec3( 0.0 ) );\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tlight.color = vec3( 0.0 );\\\\n\\\\t\\\\t\\\\tlight.visible = false;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\n\\\\n#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\tstruct RectAreaLight {\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 halfWidth;\\\\n\\\\t\\\\tvec3 halfHeight;\\\\n\\\\t};\\\\n\\\\n\\\\t// Pre-computed values of LinearTransformedCosine approximation of BRDF\\\\n\\\\t// BRDF approximation Texture is 64x64\\\\n\\\\tuniform sampler2D ltc_1; // RGBA Float\\\\n\\\\tuniform sampler2D ltc_2; // RGBA Float\\\\n\\\\n\\\\tuniform RectAreaLight rectAreaLights[ NUM_RECT_AREA_LIGHTS ];\\\\n\\\\n#endif\\\\n\\\\n\\\\n#if NUM_HEMI_LIGHTS > 0\\\\n\\\\n\\\\tstruct HemisphereLight {\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 skyColor;\\\\n\\\\t\\\\tvec3 groundColor;\\\\n\\\\t};\\\\n\\\\n\\\\tuniform HemisphereLight hemisphereLights[ NUM_HEMI_LIGHTS ];\\\\n\\\\n\\\\tvec3 getHemisphereLightIrradiance( const in HemisphereLight hemiLight, const in vec3 normal ) {\\\\n\\\\n\\\\t\\\\tfloat dotNL = dot( normal, hemiLight.direction );\\\\n\\\\t\\\\tfloat hemiDiffuseWeight = 0.5 * dotNL + 0.5;\\\\n\\\\n\\\\t\\\\tvec3 irradiance = mix( hemiLight.groundColor, hemiLight.skyColor, hemiDiffuseWeight );\\\\n\\\\n\\\\t\\\\treturn irradiance;\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",lights_toon_fragment:\\\\\\\"\\\\nToonMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb;\\\\n\\\\\\\",lights_toon_pars_fragment:\\\\\\\"\\\\nvarying vec3 vViewPosition;\\\\n\\\\nstruct ToonMaterial {\\\\n\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\n};\\\\n\\\\nvoid RE_Direct_Toon( const in IncidentLight directLight, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\tvec3 irradiance = getGradientIrradiance( geometry.normal, directLight.direction ) * directLight.color;\\\\n\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n}\\\\n\\\\nvoid RE_IndirectDiffuse_Toon( const in vec3 irradiance, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n}\\\\n\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_Toon\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_Toon\\\\n\\\\n#define Material_LightProbeLOD( material )\\\\t(0)\\\\n\\\\\\\",lights_phong_fragment:\\\\\\\"\\\\nBlinnPhongMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb;\\\\nmaterial.specularColor = specular;\\\\nmaterial.specularShininess = shininess;\\\\nmaterial.specularStrength = specularStrength;\\\\n\\\\\\\",lights_phong_pars_fragment:\\\\\\\"\\\\nvarying vec3 vViewPosition;\\\\n\\\\nstruct BlinnPhongMaterial {\\\\n\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\tvec3 specularColor;\\\\n\\\\tfloat specularShininess;\\\\n\\\\tfloat specularStrength;\\\\n\\\\n};\\\\n\\\\nvoid RE_Direct_BlinnPhong( const in IncidentLight directLight, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\\\n\\\\tvec3 irradiance = dotNL * directLight.color;\\\\n\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n\\\\treflectedLight.directSpecular += irradiance * BRDF_BlinnPhong( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularShininess ) * material.specularStrength;\\\\n\\\\n}\\\\n\\\\nvoid RE_IndirectDiffuse_BlinnPhong( const in vec3 irradiance, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n}\\\\n\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_BlinnPhong\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_BlinnPhong\\\\n\\\\n#define Material_LightProbeLOD( material )\\\\t(0)\\\\n\\\\\\\",lights_physical_fragment:\\\\\\\"\\\\nPhysicalMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb * ( 1.0 - metalnessFactor );\\\\n\\\\nvec3 dxy = max( abs( dFdx( geometryNormal ) ), abs( dFdy( geometryNormal ) ) );\\\\nfloat geometryRoughness = max( max( dxy.x, dxy.y ), dxy.z );\\\\n\\\\nmaterial.roughness = max( roughnessFactor, 0.0525 );// 0.0525 corresponds to the base mip of a 256 cubemap.\\\\nmaterial.roughness += geometryRoughness;\\\\nmaterial.roughness = min( material.roughness, 1.0 );\\\\n\\\\n#ifdef IOR\\\\n\\\\n\\\\t#ifdef SPECULAR\\\\n\\\\n\\\\t\\\\tfloat specularIntensityFactor = specularIntensity;\\\\n\\\\t\\\\tvec3 specularTintFactor = specularTint;\\\\n\\\\n\\\\t\\\\t#ifdef USE_SPECULARINTENSITYMAP\\\\n\\\\n\\\\t\\\\t\\\\tspecularIntensityFactor *= texture2D( specularIntensityMap, vUv ).a;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t#ifdef USE_SPECULARTINTMAP\\\\n\\\\n\\\\t\\\\t\\\\tspecularTintFactor *= specularTintMapTexelToLinear( texture2D( specularTintMap, vUv ) ).rgb;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tmaterial.specularF90 = mix( specularIntensityFactor, 1.0, metalnessFactor );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tfloat specularIntensityFactor = 1.0;\\\\n\\\\t\\\\tvec3 specularTintFactor = vec3( 1.0 );\\\\n\\\\t\\\\tmaterial.specularF90 = 1.0;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tmaterial.specularColor = mix( min( pow2( ( ior - 1.0 ) / ( ior + 1.0 ) ) * specularTintFactor, vec3( 1.0 ) ) * specularIntensityFactor, diffuseColor.rgb, metalnessFactor );\\\\n\\\\n#else\\\\n\\\\n\\\\tmaterial.specularColor = mix( vec3( 0.04 ), diffuseColor.rgb, metalnessFactor );\\\\n\\\\tmaterial.specularF90 = 1.0;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\tmaterial.clearcoat = clearcoat;\\\\n\\\\tmaterial.clearcoatRoughness = clearcoatRoughness;\\\\n\\\\tmaterial.clearcoatF0 = vec3( 0.04 );\\\\n\\\\tmaterial.clearcoatF90 = 1.0;\\\\n\\\\n\\\\t#ifdef USE_CLEARCOATMAP\\\\n\\\\n\\\\t\\\\tmaterial.clearcoat *= texture2D( clearcoatMap, vUv ).x;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT_ROUGHNESSMAP\\\\n\\\\n\\\\t\\\\tmaterial.clearcoatRoughness *= texture2D( clearcoatRoughnessMap, vUv ).y;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tmaterial.clearcoat = saturate( material.clearcoat ); // Burley clearcoat model\\\\n\\\\tmaterial.clearcoatRoughness = max( material.clearcoatRoughness, 0.0525 );\\\\n\\\\tmaterial.clearcoatRoughness += geometryRoughness;\\\\n\\\\tmaterial.clearcoatRoughness = min( material.clearcoatRoughness, 1.0 );\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_SHEEN\\\\n\\\\n\\\\tmaterial.sheenTint = sheenTint;\\\\n\\\\tmaterial.sheenRoughness = clamp( sheenRoughness, 0.07, 1.0 );\\\\n\\\\n#endif\\\\n\\\\\\\",lights_physical_pars_fragment:'\\\\nstruct PhysicalMaterial {\\\\n\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\tfloat roughness;\\\\n\\\\tvec3 specularColor;\\\\n\\\\tfloat specularF90;\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tfloat clearcoat;\\\\n\\\\t\\\\tfloat clearcoatRoughness;\\\\n\\\\t\\\\tvec3 clearcoatF0;\\\\n\\\\t\\\\tfloat clearcoatF90;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_SHEEN\\\\n\\\\t\\\\tvec3 sheenTint;\\\\n\\\\t\\\\tfloat sheenRoughness;\\\\n\\\\t#endif\\\\n\\\\n};\\\\n\\\\n// temporary\\\\nvec3 clearcoatSpecular = vec3( 0.0 );\\\\n\\\\n// Analytical approximation of the DFG LUT, one half of the\\\\n// split-sum approximation used in indirect specular lighting.\\\\n// via \\\\'environmentBRDF\\\\' from \\\\\\\"Physically Based Shading on Mobile\\\\\\\"\\\\n// https://www.unrealengine.com/blog/physically-based-shading-on-mobile\\\\nvec2 DFGApprox( const in vec3 normal, const in vec3 viewDir, const in float roughness ) {\\\\n\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\n\\\\tconst vec4 c0 = vec4( - 1, - 0.0275, - 0.572, 0.022 );\\\\n\\\\n\\\\tconst vec4 c1 = vec4( 1, 0.0425, 1.04, - 0.04 );\\\\n\\\\n\\\\tvec4 r = roughness * c0 + c1;\\\\n\\\\n\\\\tfloat a004 = min( r.x * r.x, exp2( - 9.28 * dotNV ) ) * r.x + r.y;\\\\n\\\\n\\\\tvec2 fab = vec2( - 1.04, 1.04 ) * a004 + r.zw;\\\\n\\\\n\\\\treturn fab;\\\\n\\\\n}\\\\n\\\\nvec3 EnvironmentBRDF( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness ) {\\\\n\\\\n\\\\tvec2 fab = DFGApprox( normal, viewDir, roughness );\\\\n\\\\n\\\\treturn specularColor * fab.x + specularF90 * fab.y;\\\\n\\\\n}\\\\n\\\\n// Fdez-Agüera\\\\'s \\\\\\\"Multiple-Scattering Microfacet Model for Real-Time Image Based Lighting\\\\\\\"\\\\n// Approximates multiscattering in order to preserve energy.\\\\n// http://www.jcgt.org/published/0008/01/03/\\\\nvoid computeMultiscattering( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\\\\n\\\\n\\\\tvec2 fab = DFGApprox( normal, viewDir, roughness );\\\\n\\\\n\\\\tvec3 FssEss = specularColor * fab.x + specularF90 * fab.y;\\\\n\\\\n\\\\tfloat Ess = fab.x + fab.y;\\\\n\\\\tfloat Ems = 1.0 - Ess;\\\\n\\\\n\\\\tvec3 Favg = specularColor + ( 1.0 - specularColor ) * 0.047619; // 1/21\\\\n\\\\tvec3 Fms = FssEss * Favg / ( 1.0 - Ems * Favg );\\\\n\\\\n\\\\tsingleScatter += FssEss;\\\\n\\\\tmultiScatter += Fms * Ems;\\\\n\\\\n}\\\\n\\\\n#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\tvoid RE_Direct_RectArea_Physical( const in RectAreaLight rectAreaLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\t\\\\tvec3 normal = geometry.normal;\\\\n\\\\t\\\\tvec3 viewDir = geometry.viewDir;\\\\n\\\\t\\\\tvec3 position = geometry.position;\\\\n\\\\t\\\\tvec3 lightPos = rectAreaLight.position;\\\\n\\\\t\\\\tvec3 halfWidth = rectAreaLight.halfWidth;\\\\n\\\\t\\\\tvec3 halfHeight = rectAreaLight.halfHeight;\\\\n\\\\t\\\\tvec3 lightColor = rectAreaLight.color;\\\\n\\\\t\\\\tfloat roughness = material.roughness;\\\\n\\\\n\\\\t\\\\tvec3 rectCoords[ 4 ];\\\\n\\\\t\\\\trectCoords[ 0 ] = lightPos + halfWidth - halfHeight; // counterclockwise; light shines in local neg z direction\\\\n\\\\t\\\\trectCoords[ 1 ] = lightPos - halfWidth - halfHeight;\\\\n\\\\t\\\\trectCoords[ 2 ] = lightPos - halfWidth + halfHeight;\\\\n\\\\t\\\\trectCoords[ 3 ] = lightPos + halfWidth + halfHeight;\\\\n\\\\n\\\\t\\\\tvec2 uv = LTC_Uv( normal, viewDir, roughness );\\\\n\\\\n\\\\t\\\\tvec4 t1 = texture2D( ltc_1, uv );\\\\n\\\\t\\\\tvec4 t2 = texture2D( ltc_2, uv );\\\\n\\\\n\\\\t\\\\tmat3 mInv = mat3(\\\\n\\\\t\\\\t\\\\tvec3( t1.x, 0, t1.y ),\\\\n\\\\t\\\\t\\\\tvec3( 0, 1, 0 ),\\\\n\\\\t\\\\t\\\\tvec3( t1.z, 0, t1.w )\\\\n\\\\t\\\\t);\\\\n\\\\n\\\\t\\\\t// LTC Fresnel Approximation by Stephen Hill\\\\n\\\\t\\\\t// http://blog.selfshadow.com/publications/s2016-advances/s2016_ltc_fresnel.pdf\\\\n\\\\t\\\\tvec3 fresnel = ( material.specularColor * t2.x + ( vec3( 1.0 ) - material.specularColor ) * t2.y );\\\\n\\\\n\\\\t\\\\treflectedLight.directSpecular += lightColor * fresnel * LTC_Evaluate( normal, viewDir, position, mInv, rectCoords );\\\\n\\\\n\\\\t\\\\treflectedLight.directDiffuse += lightColor * material.diffuseColor * LTC_Evaluate( normal, viewDir, position, mat3( 1.0 ), rectCoords );\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\nvoid RE_Direct_Physical( const in IncidentLight directLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\\\n\\\\n\\\\tvec3 irradiance = dotNL * directLight.color;\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\t\\\\tfloat dotNLcc = saturate( dot( geometry.clearcoatNormal, directLight.direction ) );\\\\n\\\\n\\\\t\\\\tvec3 ccIrradiance = dotNLcc * directLight.color;\\\\n\\\\n\\\\t\\\\tclearcoatSpecular += ccIrradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.clearcoatNormal, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_SHEEN\\\\n\\\\n\\\\t\\\\treflectedLight.directSpecular += irradiance * BRDF_Sheen( directLight.direction, geometry.viewDir, geometry.normal, material.sheenTint, material.sheenRoughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\treflectedLight.directSpecular += irradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularF90, material.roughness );\\\\n\\\\n\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\n\\\\nvoid RE_IndirectDiffuse_Physical( const in vec3 irradiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n}\\\\n\\\\nvoid RE_IndirectSpecular_Physical( const in vec3 radiance, const in vec3 irradiance, const in vec3 clearcoatRadiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight) {\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\t\\\\tclearcoatSpecular += clearcoatRadiance * EnvironmentBRDF( geometry.clearcoatNormal, geometry.viewDir, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t// Both indirect specular and indirect diffuse light accumulate here\\\\n\\\\n\\\\tvec3 singleScattering = vec3( 0.0 );\\\\n\\\\tvec3 multiScattering = vec3( 0.0 );\\\\n\\\\tvec3 cosineWeightedIrradiance = irradiance * RECIPROCAL_PI;\\\\n\\\\n\\\\tcomputeMultiscattering( geometry.normal, geometry.viewDir, material.specularColor, material.specularF90, material.roughness, singleScattering, multiScattering );\\\\n\\\\n\\\\tvec3 diffuse = material.diffuseColor * ( 1.0 - ( singleScattering + multiScattering ) );\\\\n\\\\n\\\\treflectedLight.indirectSpecular += radiance * singleScattering;\\\\n\\\\treflectedLight.indirectSpecular += multiScattering * cosineWeightedIrradiance;\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += diffuse * cosineWeightedIrradiance;\\\\n\\\\n}\\\\n\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_Physical\\\\n#define RE_Direct_RectArea\\\\t\\\\tRE_Direct_RectArea_Physical\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_Physical\\\\n#define RE_IndirectSpecular\\\\t\\\\tRE_IndirectSpecular_Physical\\\\n\\\\n// ref: https://seblagarde.files.wordpress.com/2015/07/course_notes_moving_frostbite_to_pbr_v32.pdf\\\\nfloat computeSpecularOcclusion( const in float dotNV, const in float ambientOcclusion, const in float roughness ) {\\\\n\\\\n\\\\treturn saturate( pow( dotNV + ambientOcclusion, exp2( - 16.0 * roughness - 1.0 ) ) - 1.0 + ambientOcclusion );\\\\n\\\\n}\\\\n',lights_fragment_begin:\\\\\\\"\\\\n/**\\\\n * This is a template that can be used to light a material, it uses pluggable\\\\n * RenderEquations (RE)for specific lighting scenarios.\\\\n *\\\\n * Instructions for use:\\\\n * - Ensure that both RE_Direct, RE_IndirectDiffuse and RE_IndirectSpecular are defined\\\\n * - If you have defined an RE_IndirectSpecular, you need to also provide a Material_LightProbeLOD. <---- ???\\\\n * - Create a material parameter that is to be passed as the third parameter to your lighting functions.\\\\n *\\\\n * TODO:\\\\n * - Add area light support.\\\\n * - Add sphere light support.\\\\n * - Add diffuse light probe (irradiance cubemap) support.\\\\n */\\\\n\\\\nGeometricContext geometry;\\\\n\\\\ngeometry.position = - vViewPosition;\\\\ngeometry.normal = normal;\\\\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( vViewPosition );\\\\n\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\tgeometry.clearcoatNormal = clearcoatNormal;\\\\n\\\\n#endif\\\\n\\\\nIncidentLight directLight;\\\\n\\\\n#if ( NUM_POINT_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\n\\\\tPointLight pointLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\tPointLightShadow pointLightShadow;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tpointLight = pointLights[ i ];\\\\n\\\\n\\\\t\\\\tgetPointLightInfo( pointLight, geometry, directLight );\\\\n\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_POINT_LIGHT_SHADOWS )\\\\n\\\\t\\\\tpointLightShadow = pointLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getPointShadow( pointShadowMap[ i ], pointLightShadow.shadowMapSize, pointLightShadow.shadowBias, pointLightShadow.shadowRadius, vPointShadowCoord[ i ], pointLightShadow.shadowCameraNear, pointLightShadow.shadowCameraFar ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if ( NUM_SPOT_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\n\\\\tSpotLight spotLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\tSpotLightShadow spotLightShadow;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tspotLight = spotLights[ i ];\\\\n\\\\n\\\\t\\\\tgetSpotLightInfo( spotLight, geometry, directLight );\\\\n\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\\\\n\\\\t\\\\tspotLightShadow = spotLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( spotShadowMap[ i ], spotLightShadow.shadowMapSize, spotLightShadow.shadowBias, spotLightShadow.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if ( NUM_DIR_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\n\\\\tDirectionalLight directionalLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\tDirectionalLightShadow directionalLightShadow;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tdirectionalLight = directionalLights[ i ];\\\\n\\\\n\\\\t\\\\tgetDirectionalLightInfo( directionalLight, geometry, directLight );\\\\n\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_DIR_LIGHT_SHADOWS )\\\\n\\\\t\\\\tdirectionalLightShadow = directionalLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( directionalShadowMap[ i ], directionalLightShadow.shadowMapSize, directionalLightShadow.shadowBias, directionalLightShadow.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if ( NUM_RECT_AREA_LIGHTS > 0 ) && defined( RE_Direct_RectArea )\\\\n\\\\n\\\\tRectAreaLight rectAreaLight;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_RECT_AREA_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\trectAreaLight = rectAreaLights[ i ];\\\\n\\\\t\\\\tRE_Direct_RectArea( rectAreaLight, geometry, material, reflectedLight );\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if defined( RE_IndirectDiffuse )\\\\n\\\\n\\\\tvec3 iblIrradiance = vec3( 0.0 );\\\\n\\\\n\\\\tvec3 irradiance = getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\n\\\\tirradiance += getLightProbeIrradiance( lightProbe, geometry.normal );\\\\n\\\\n\\\\t#if ( NUM_HEMI_LIGHTS > 0 )\\\\n\\\\n\\\\t\\\\t#pragma unroll_loop_start\\\\n\\\\t\\\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\t\\\\tirradiance += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry.normal );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\n#if defined( RE_IndirectSpecular )\\\\n\\\\n\\\\tvec3 radiance = vec3( 0.0 );\\\\n\\\\tvec3 clearcoatRadiance = vec3( 0.0 );\\\\n\\\\n#endif\\\\n\\\\\\\",lights_fragment_maps:\\\\\\\"\\\\n#if defined( RE_IndirectDiffuse )\\\\n\\\\n\\\\t#ifdef USE_LIGHTMAP\\\\n\\\\n\\\\t\\\\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\\\\n\\\\t\\\\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\n\\\\t\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\n\\\\t\\\\t\\\\tlightMapIrradiance *= PI;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tirradiance += lightMapIrradiance;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if defined( USE_ENVMAP ) && defined( STANDARD ) && defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\n\\\\t\\\\tiblIrradiance += getIBLIrradiance( geometry.normal );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\n#if defined( USE_ENVMAP ) && defined( RE_IndirectSpecular )\\\\n\\\\n\\\\tradiance += getIBLRadiance( geometry.viewDir, geometry.normal, material.roughness );\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\t\\\\tclearcoatRadiance += getIBLRadiance( geometry.viewDir, geometry.clearcoatNormal, material.clearcoatRoughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",lights_fragment_end:\\\\\\\"\\\\n#if defined( RE_IndirectDiffuse )\\\\n\\\\n\\\\tRE_IndirectDiffuse( irradiance, geometry, material, reflectedLight );\\\\n\\\\n#endif\\\\n\\\\n#if defined( RE_IndirectSpecular )\\\\n\\\\n\\\\tRE_IndirectSpecular( radiance, iblIrradiance, clearcoatRadiance, geometry, material, reflectedLight );\\\\n\\\\n#endif\\\\n\\\\\\\",logdepthbuf_fragment:\\\\\\\"\\\\n#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\\\n\\\\n\\\\t// Doing a strict comparison with == 1.0 can cause noise artifacts\\\\n\\\\t// on some platforms. See issue #17623.\\\\n\\\\tgl_FragDepthEXT = vIsPerspective == 0.0 ? gl_FragCoord.z : log2( vFragDepth ) * logDepthBufFC * 0.5;\\\\n\\\\n#endif\\\\n\\\\\\\",logdepthbuf_pars_fragment:\\\\\\\"\\\\n#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\\\n\\\\n\\\\tuniform float logDepthBufFC;\\\\n\\\\tvarying float vFragDepth;\\\\n\\\\tvarying float vIsPerspective;\\\\n\\\\n#endif\\\\n\\\\\\\",logdepthbuf_pars_vertex:\\\\\\\"\\\\n#ifdef USE_LOGDEPTHBUF\\\\n\\\\n\\\\t#ifdef USE_LOGDEPTHBUF_EXT\\\\n\\\\n\\\\t\\\\tvarying float vFragDepth;\\\\n\\\\t\\\\tvarying float vIsPerspective;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tuniform float logDepthBufFC;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",logdepthbuf_vertex:\\\\\\\"\\\\n#ifdef USE_LOGDEPTHBUF\\\\n\\\\n\\\\t#ifdef USE_LOGDEPTHBUF_EXT\\\\n\\\\n\\\\t\\\\tvFragDepth = 1.0 + gl_Position.w;\\\\n\\\\t\\\\tvIsPerspective = float( isPerspectiveMatrix( projectionMatrix ) );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tif ( isPerspectiveMatrix( projectionMatrix ) ) {\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position.z = log2( max( EPSILON, gl_Position.w + 1.0 ) ) * logDepthBufFC - 1.0;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position.z *= gl_Position.w;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",map_fragment:\\\\\\\"\\\\n#ifdef USE_MAP\\\\n\\\\n\\\\tvec4 texelColor = texture2D( map, vUv );\\\\n\\\\n\\\\ttexelColor = mapTexelToLinear( texelColor );\\\\n\\\\tdiffuseColor *= texelColor;\\\\n\\\\n#endif\\\\n\\\\\\\",map_pars_fragment:\\\\\\\"\\\\n#ifdef USE_MAP\\\\n\\\\n\\\\tuniform sampler2D map;\\\\n\\\\n#endif\\\\n\\\\\\\",map_particle_fragment:\\\\\\\"\\\\n#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\\\\n\\\\n\\\\tvec2 uv = ( uvTransform * vec3( gl_PointCoord.x, 1.0 - gl_PointCoord.y, 1 ) ).xy;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_MAP\\\\n\\\\n\\\\tvec4 mapTexel = texture2D( map, uv );\\\\n\\\\tdiffuseColor *= mapTexelToLinear( mapTexel );\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\n\\\\tdiffuseColor.a *= texture2D( alphaMap, uv ).g;\\\\n\\\\n#endif\\\\n\\\\\\\",map_particle_pars_fragment:\\\\\\\"\\\\n#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\\\\n\\\\n\\\\tuniform mat3 uvTransform;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_MAP\\\\n\\\\n\\\\tuniform sampler2D map;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\n\\\\tuniform sampler2D alphaMap;\\\\n\\\\n#endif\\\\n\\\\\\\",metalnessmap_fragment:\\\\\\\"\\\\nfloat metalnessFactor = metalness;\\\\n\\\\n#ifdef USE_METALNESSMAP\\\\n\\\\n\\\\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\\\\n\\\\n\\\\t// reads channel B, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\tmetalnessFactor *= texelMetalness.b;\\\\n\\\\n#endif\\\\n\\\\\\\",metalnessmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_METALNESSMAP\\\\n\\\\n\\\\tuniform sampler2D metalnessMap;\\\\n\\\\n#endif\\\\n\\\\\\\",morphnormal_vertex:\\\\\\\"\\\\n#ifdef USE_MORPHNORMALS\\\\n\\\\n\\\\t// morphTargetBaseInfluence is set based on BufferGeometry.morphTargetsRelative value:\\\\n\\\\t// When morphTargetsRelative is false, this is set to 1 - sum(influences); this results in normal = sum((target - base) * influence)\\\\n\\\\t// When morphTargetsRelative is true, this is set to 1; as a result, all morph targets are simply added to the base after weighting\\\\n\\\\tobjectNormal *= morphTargetBaseInfluence;\\\\n\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\n\\\\t\\\\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) objectNormal += getMorph( gl_VertexID, i, 1, 2 ) * morphTargetInfluences[ i ];\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tobjectNormal += morphNormal0 * morphTargetInfluences[ 0 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal1 * morphTargetInfluences[ 1 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal2 * morphTargetInfluences[ 2 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal3 * morphTargetInfluences[ 3 ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",morphtarget_pars_vertex:\\\\\\\"\\\\n#ifdef USE_MORPHTARGETS\\\\n\\\\n\\\\tuniform float morphTargetBaseInfluence;\\\\n\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\n\\\\t\\\\tuniform float morphTargetInfluences[ MORPHTARGETS_COUNT ];\\\\n\\\\t\\\\tuniform sampler2DArray morphTargetsTexture;\\\\n\\\\t\\\\tuniform vec2 morphTargetsTextureSize;\\\\n\\\\n\\\\t\\\\tvec3 getMorph( const in int vertexIndex, const in int morphTargetIndex, const in int offset, const in int stride ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat texelIndex = float( vertexIndex * stride + offset );\\\\n\\\\t\\\\t\\\\tfloat y = floor( texelIndex / morphTargetsTextureSize.x );\\\\n\\\\t\\\\t\\\\tfloat x = texelIndex - y * morphTargetsTextureSize.x;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 morphUV = vec3( ( x + 0.5 ) / morphTargetsTextureSize.x, y / morphTargetsTextureSize.y, morphTargetIndex );\\\\n\\\\t\\\\t\\\\treturn texture( morphTargetsTexture, morphUV ).xyz;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\n\\\\t\\\\t\\\\tuniform float morphTargetInfluences[ 8 ];\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\tuniform float morphTargetInfluences[ 4 ];\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",morphtarget_vertex:\\\\\\\"\\\\n#ifdef USE_MORPHTARGETS\\\\n\\\\n\\\\t// morphTargetBaseInfluence is set based on BufferGeometry.morphTargetsRelative value:\\\\n\\\\t// When morphTargetsRelative is false, this is set to 1 - sum(influences); this results in position = sum((target - base) * influence)\\\\n\\\\t// When morphTargetsRelative is true, this is set to 1; as a result, all morph targets are simply added to the base after weighting\\\\n\\\\ttransformed *= morphTargetBaseInfluence;\\\\n\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\n\\\\t\\\\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\\\\n\\\\n\\\\t\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) transformed += getMorph( gl_VertexID, i, 0, 1 ) * morphTargetInfluences[ i ];\\\\n\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) transformed += getMorph( gl_VertexID, i, 0, 2 ) * morphTargetInfluences[ i ];\\\\n\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\ttransformed += morphTarget0 * morphTargetInfluences[ 0 ];\\\\n\\\\t\\\\ttransformed += morphTarget1 * morphTargetInfluences[ 1 ];\\\\n\\\\t\\\\ttransformed += morphTarget2 * morphTargetInfluences[ 2 ];\\\\n\\\\t\\\\ttransformed += morphTarget3 * morphTargetInfluences[ 3 ];\\\\n\\\\n\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget4 * morphTargetInfluences[ 4 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget5 * morphTargetInfluences[ 5 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget6 * morphTargetInfluences[ 6 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget7 * morphTargetInfluences[ 7 ];\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",normal_fragment_begin:\\\\\\\"\\\\nfloat faceDirection = gl_FrontFacing ? 1.0 : - 1.0;\\\\n\\\\n#ifdef FLAT_SHADED\\\\n\\\\n\\\\t// Workaround for Adreno GPUs not able to do dFdx( vViewPosition )\\\\n\\\\n\\\\tvec3 fdx = vec3( dFdx( vViewPosition.x ), dFdx( vViewPosition.y ), dFdx( vViewPosition.z ) );\\\\n\\\\tvec3 fdy = vec3( dFdy( vViewPosition.x ), dFdy( vViewPosition.y ), dFdy( vViewPosition.z ) );\\\\n\\\\tvec3 normal = normalize( cross( fdx, fdy ) );\\\\n\\\\n#else\\\\n\\\\n\\\\tvec3 normal = normalize( vNormal );\\\\n\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\tnormal = normal * faceDirection;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tvec3 tangent = normalize( vTangent );\\\\n\\\\t\\\\tvec3 bitangent = normalize( vBitangent );\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\ttangent = tangent * faceDirection;\\\\n\\\\t\\\\t\\\\tbitangent = bitangent * faceDirection;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t#if defined( TANGENTSPACE_NORMALMAP ) || defined( USE_CLEARCOAT_NORMALMAP )\\\\n\\\\n\\\\t\\\\t\\\\tmat3 vTBN = mat3( tangent, bitangent, normal );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\n// non perturbed normal for clearcoat among others\\\\n\\\\nvec3 geometryNormal = normal;\\\\n\\\\n\\\\\\\",normal_fragment_maps:\\\\\\\"\\\\n\\\\n#ifdef OBJECTSPACE_NORMALMAP\\\\n\\\\n\\\\tnormal = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0; // overrides both flatShading and attribute normals\\\\n\\\\n\\\\t#ifdef FLIP_SIDED\\\\n\\\\n\\\\t\\\\tnormal = - normal;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\tnormal = normal * faceDirection;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tnormal = normalize( normalMatrix * normal );\\\\n\\\\n#elif defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\n\\\\tvec3 mapN = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\tmapN.xy *= normalScale;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tnormal = normalize( vTBN * mapN );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tnormal = perturbNormal2Arb( - vViewPosition, normal, mapN, faceDirection );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#elif defined( USE_BUMPMAP )\\\\n\\\\n\\\\tnormal = perturbNormalArb( - vViewPosition, normal, dHdxy_fwd(), faceDirection );\\\\n\\\\n#endif\\\\n\\\\\\\",normal_pars_fragment:\\\\\\\"\\\\n#ifndef FLAT_SHADED\\\\n\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",normal_pars_vertex:\\\\\\\"\\\\n#ifndef FLAT_SHADED\\\\n\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",normal_vertex:\\\\\\\"\\\\n#ifndef FLAT_SHADED // normal is computed with derivatives when FLAT_SHADED\\\\n\\\\n\\\\tvNormal = normalize( transformedNormal );\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tvTangent = normalize( transformedTangent );\\\\n\\\\t\\\\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",normalmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_NORMALMAP\\\\n\\\\n\\\\tuniform sampler2D normalMap;\\\\n\\\\tuniform vec2 normalScale;\\\\n\\\\n#endif\\\\n\\\\n#ifdef OBJECTSPACE_NORMALMAP\\\\n\\\\n\\\\tuniform mat3 normalMatrix;\\\\n\\\\n#endif\\\\n\\\\n#if ! defined ( USE_TANGENT ) && ( defined ( TANGENTSPACE_NORMALMAP ) || defined ( USE_CLEARCOAT_NORMALMAP ) )\\\\n\\\\n\\\\t// Normal Mapping Without Precomputed Tangents\\\\n\\\\t// http://www.thetenthplanet.de/archives/1180\\\\n\\\\n\\\\tvec3 perturbNormal2Arb( vec3 eye_pos, vec3 surf_norm, vec3 mapN, float faceDirection ) {\\\\n\\\\n\\\\t\\\\t// Workaround for Adreno 3XX dFd*( vec3 ) bug. See #9988\\\\n\\\\n\\\\t\\\\tvec3 q0 = vec3( dFdx( eye_pos.x ), dFdx( eye_pos.y ), dFdx( eye_pos.z ) );\\\\n\\\\t\\\\tvec3 q1 = vec3( dFdy( eye_pos.x ), dFdy( eye_pos.y ), dFdy( eye_pos.z ) );\\\\n\\\\t\\\\tvec2 st0 = dFdx( vUv.st );\\\\n\\\\t\\\\tvec2 st1 = dFdy( vUv.st );\\\\n\\\\n\\\\t\\\\tvec3 N = surf_norm; // normalized\\\\n\\\\n\\\\t\\\\tvec3 q1perp = cross( q1, N );\\\\n\\\\t\\\\tvec3 q0perp = cross( N, q0 );\\\\n\\\\n\\\\t\\\\tvec3 T = q1perp * st0.x + q0perp * st1.x;\\\\n\\\\t\\\\tvec3 B = q1perp * st0.y + q0perp * st1.y;\\\\n\\\\n\\\\t\\\\tfloat det = max( dot( T, T ), dot( B, B ) );\\\\n\\\\t\\\\tfloat scale = ( det == 0.0 ) ? 0.0 : faceDirection * inversesqrt( det );\\\\n\\\\n\\\\t\\\\treturn normalize( T * ( mapN.x * scale ) + B * ( mapN.y * scale ) + N * mapN.z );\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",clearcoat_normal_fragment_begin:\\\\\\\"\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\tvec3 clearcoatNormal = geometryNormal;\\\\n\\\\n#endif\\\\n\\\\\\\",clearcoat_normal_fragment_maps:\\\\\\\"\\\\n#ifdef USE_CLEARCOAT_NORMALMAP\\\\n\\\\n\\\\tvec3 clearcoatMapN = texture2D( clearcoatNormalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\tclearcoatMapN.xy *= clearcoatNormalScale;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tclearcoatNormal = normalize( vTBN * clearcoatMapN );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tclearcoatNormal = perturbNormal2Arb( - vViewPosition, clearcoatNormal, clearcoatMapN, faceDirection );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",clearcoat_pars_fragment:\\\\\\\"\\\\n\\\\n#ifdef USE_CLEARCOATMAP\\\\n\\\\n\\\\tuniform sampler2D clearcoatMap;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_CLEARCOAT_ROUGHNESSMAP\\\\n\\\\n\\\\tuniform sampler2D clearcoatRoughnessMap;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_CLEARCOAT_NORMALMAP\\\\n\\\\n\\\\tuniform sampler2D clearcoatNormalMap;\\\\n\\\\tuniform vec2 clearcoatNormalScale;\\\\n\\\\n#endif\\\\n\\\\\\\",output_fragment:\\\\\\\"\\\\n#ifdef OPAQUE\\\\ndiffuseColor.a = 1.0;\\\\n#endif\\\\n\\\\n// https://github.com/mrdoob/three.js/pull/22425\\\\n#ifdef USE_TRANSMISSION\\\\ndiffuseColor.a *= transmissionAlpha + 0.1;\\\\n#endif\\\\n\\\\ngl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\\\\",packing:\\\\\\\"\\\\nvec3 packNormalToRGB( const in vec3 normal ) {\\\\n\\\\treturn normalize( normal ) * 0.5 + 0.5;\\\\n}\\\\n\\\\nvec3 unpackRGBToNormal( const in vec3 rgb ) {\\\\n\\\\treturn 2.0 * rgb.xyz - 1.0;\\\\n}\\\\n\\\\nconst float PackUpscale = 256. / 255.; // fraction -> 0..1 (including 1)\\\\nconst float UnpackDownscale = 255. / 256.; // 0..1 -> fraction (excluding 1)\\\\n\\\\nconst vec3 PackFactors = vec3( 256. * 256. * 256., 256. * 256., 256. );\\\\nconst vec4 UnpackFactors = UnpackDownscale / vec4( PackFactors, 1. );\\\\n\\\\nconst float ShiftRight8 = 1. / 256.;\\\\n\\\\nvec4 packDepthToRGBA( const in float v ) {\\\\n\\\\tvec4 r = vec4( fract( v * PackFactors ), v );\\\\n\\\\tr.yzw -= r.xyz * ShiftRight8; // tidy overflow\\\\n\\\\treturn r * PackUpscale;\\\\n}\\\\n\\\\nfloat unpackRGBAToDepth( const in vec4 v ) {\\\\n\\\\treturn dot( v, UnpackFactors );\\\\n}\\\\n\\\\nvec4 pack2HalfToRGBA( vec2 v ) {\\\\n\\\\tvec4 r = vec4( v.x, fract( v.x * 255.0 ), v.y, fract( v.y * 255.0 ) );\\\\n\\\\treturn vec4( r.x - r.y / 255.0, r.y, r.z - r.w / 255.0, r.w );\\\\n}\\\\n\\\\nvec2 unpackRGBATo2Half( vec4 v ) {\\\\n\\\\treturn vec2( v.x + ( v.y / 255.0 ), v.z + ( v.w / 255.0 ) );\\\\n}\\\\n\\\\n// NOTE: viewZ/eyeZ is < 0 when in front of the camera per OpenGL conventions\\\\n\\\\nfloat viewZToOrthographicDepth( const in float viewZ, const in float near, const in float far ) {\\\\n\\\\treturn ( viewZ + near ) / ( near - far );\\\\n}\\\\nfloat orthographicDepthToViewZ( const in float linearClipZ, const in float near, const in float far ) {\\\\n\\\\treturn linearClipZ * ( near - far ) - near;\\\\n}\\\\n\\\\n// NOTE: https://twitter.com/gonnavis/status/1377183786949959682\\\\n\\\\nfloat viewZToPerspectiveDepth( const in float viewZ, const in float near, const in float far ) {\\\\n\\\\treturn ( ( near + viewZ ) * far ) / ( ( far - near ) * viewZ );\\\\n}\\\\nfloat perspectiveDepthToViewZ( const in float invClipZ, const in float near, const in float far ) {\\\\n\\\\treturn ( near * far ) / ( ( far - near ) * invClipZ - far );\\\\n}\\\\n\\\\\\\",premultiplied_alpha_fragment:\\\\\\\"\\\\n#ifdef PREMULTIPLIED_ALPHA\\\\n\\\\n\\\\t// Get get normal blending with premultipled, use with CustomBlending, OneFactor, OneMinusSrcAlphaFactor, AddEquation.\\\\n\\\\tgl_FragColor.rgb *= gl_FragColor.a;\\\\n\\\\n#endif\\\\n\\\\\\\",project_vertex:\\\\\\\"\\\\nvec4 mvPosition = vec4( transformed, 1.0 );\\\\n\\\\n#ifdef USE_INSTANCING\\\\n\\\\n\\\\tmvPosition = instanceMatrix * mvPosition;\\\\n\\\\n#endif\\\\n\\\\nmvPosition = modelViewMatrix * mvPosition;\\\\n\\\\ngl_Position = projectionMatrix * mvPosition;\\\\n\\\\\\\",dithering_fragment:\\\\\\\"\\\\n#ifdef DITHERING\\\\n\\\\n\\\\tgl_FragColor.rgb = dithering( gl_FragColor.rgb );\\\\n\\\\n#endif\\\\n\\\\\\\",dithering_pars_fragment:\\\\\\\"\\\\n#ifdef DITHERING\\\\n\\\\n\\\\t// based on https://www.shadertoy.com/view/MslGR8\\\\n\\\\tvec3 dithering( vec3 color ) {\\\\n\\\\t\\\\t//Calculate grid position\\\\n\\\\t\\\\tfloat grid_position = rand( gl_FragCoord.xy );\\\\n\\\\n\\\\t\\\\t//Shift the individual colors differently, thus making it even harder to see the dithering pattern\\\\n\\\\t\\\\tvec3 dither_shift_RGB = vec3( 0.25 / 255.0, -0.25 / 255.0, 0.25 / 255.0 );\\\\n\\\\n\\\\t\\\\t//modify shift acording to grid position.\\\\n\\\\t\\\\tdither_shift_RGB = mix( 2.0 * dither_shift_RGB, -2.0 * dither_shift_RGB, grid_position );\\\\n\\\\n\\\\t\\\\t//shift the color by dither_shift\\\\n\\\\t\\\\treturn color + dither_shift_RGB;\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",roughnessmap_fragment:\\\\\\\"\\\\nfloat roughnessFactor = roughness;\\\\n\\\\n#ifdef USE_ROUGHNESSMAP\\\\n\\\\n\\\\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\\\\n\\\\n\\\\t// reads channel G, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\troughnessFactor *= texelRoughness.g;\\\\n\\\\n#endif\\\\n\\\\\\\",roughnessmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ROUGHNESSMAP\\\\n\\\\n\\\\tuniform sampler2D roughnessMap;\\\\n\\\\n#endif\\\\n\\\\\\\",shadowmap_pars_fragment:B,shadowmap_pars_vertex:\\\\\\\"\\\\n#ifdef USE_SHADOWMAP\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform mat4 directionalShadowMatrix[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct DirectionalLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform mat4 spotShadowMatrix[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct SpotLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform mat4 pointShadowMatrix[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct PointLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraNear;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraFar;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t/*\\\\n\\\\t#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\t\\\\t// TODO (abelnation): uniforms for area light shadows\\\\n\\\\n\\\\t#endif\\\\n\\\\t*/\\\\n\\\\n#endif\\\\n\\\\\\\",shadowmap_vertex:\\\\\\\"\\\\n#ifdef USE_SHADOWMAP\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0 || NUM_SPOT_LIGHT_SHADOWS > 0 || NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\t// Offsetting the position used for querying occlusion along the world normal can be used to reduce shadow acne.\\\\n\\\\t\\\\tvec3 shadowWorldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\\\\n\\\\t\\\\tvec4 shadowWorldPosition;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * directionalLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvDirectionalShadowCoord[ i ] = directionalShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * spotLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvSpotShadowCoord[ i ] = spotShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * pointLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvPointShadowCoord[ i ] = pointShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t/*\\\\n\\\\t#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\t\\\\t// TODO (abelnation): update vAreaShadowCoord with area light info\\\\n\\\\n\\\\t#endif\\\\n\\\\t*/\\\\n\\\\n#endif\\\\n\\\\\\\",shadowmask_pars_fragment:\\\\\\\"\\\\nfloat getShadowMask() {\\\\n\\\\n\\\\tfloat shadow = 1.0;\\\\n\\\\n\\\\t#ifdef USE_SHADOWMAP\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\tDirectionalLightShadow directionalLight;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tdirectionalLight = directionalLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\tSpotLightShadow spotLight;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tspotLight = spotLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\tPointLightShadow pointLight;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tpointLight = pointLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t/*\\\\n\\\\t#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\t\\\\t// TODO (abelnation): update shadow for Area light\\\\n\\\\n\\\\t#endif\\\\n\\\\t*/\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\treturn shadow;\\\\n\\\\n}\\\\n\\\\\\\",skinbase_vertex:\\\\\\\"\\\\n#ifdef USE_SKINNING\\\\n\\\\n\\\\tmat4 boneMatX = getBoneMatrix( skinIndex.x );\\\\n\\\\tmat4 boneMatY = getBoneMatrix( skinIndex.y );\\\\n\\\\tmat4 boneMatZ = getBoneMatrix( skinIndex.z );\\\\n\\\\tmat4 boneMatW = getBoneMatrix( skinIndex.w );\\\\n\\\\n#endif\\\\n\\\\\\\",skinning_pars_vertex:\\\\\\\"\\\\n#ifdef USE_SKINNING\\\\n\\\\n\\\\tuniform mat4 bindMatrix;\\\\n\\\\tuniform mat4 bindMatrixInverse;\\\\n\\\\n\\\\t#ifdef BONE_TEXTURE\\\\n\\\\n\\\\t\\\\tuniform highp sampler2D boneTexture;\\\\n\\\\t\\\\tuniform int boneTextureSize;\\\\n\\\\n\\\\t\\\\tmat4 getBoneMatrix( const in float i ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat j = i * 4.0;\\\\n\\\\t\\\\t\\\\tfloat x = mod( j, float( boneTextureSize ) );\\\\n\\\\t\\\\t\\\\tfloat y = floor( j / float( boneTextureSize ) );\\\\n\\\\n\\\\t\\\\t\\\\tfloat dx = 1.0 / float( boneTextureSize );\\\\n\\\\t\\\\t\\\\tfloat dy = 1.0 / float( boneTextureSize );\\\\n\\\\n\\\\t\\\\t\\\\ty = dy * ( y + 0.5 );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 v1 = texture2D( boneTexture, vec2( dx * ( x + 0.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v2 = texture2D( boneTexture, vec2( dx * ( x + 1.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v3 = texture2D( boneTexture, vec2( dx * ( x + 2.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v4 = texture2D( boneTexture, vec2( dx * ( x + 3.5 ), y ) );\\\\n\\\\n\\\\t\\\\t\\\\tmat4 bone = mat4( v1, v2, v3, v4 );\\\\n\\\\n\\\\t\\\\t\\\\treturn bone;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tuniform mat4 boneMatrices[ MAX_BONES ];\\\\n\\\\n\\\\t\\\\tmat4 getBoneMatrix( const in float i ) {\\\\n\\\\n\\\\t\\\\t\\\\tmat4 bone = boneMatrices[ int(i) ];\\\\n\\\\t\\\\t\\\\treturn bone;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",skinning_vertex:\\\\\\\"\\\\n#ifdef USE_SKINNING\\\\n\\\\n\\\\tvec4 skinVertex = bindMatrix * vec4( transformed, 1.0 );\\\\n\\\\n\\\\tvec4 skinned = vec4( 0.0 );\\\\n\\\\tskinned += boneMatX * skinVertex * skinWeight.x;\\\\n\\\\tskinned += boneMatY * skinVertex * skinWeight.y;\\\\n\\\\tskinned += boneMatZ * skinVertex * skinWeight.z;\\\\n\\\\tskinned += boneMatW * skinVertex * skinWeight.w;\\\\n\\\\n\\\\ttransformed = ( bindMatrixInverse * skinned ).xyz;\\\\n\\\\n#endif\\\\n\\\\\\\",skinnormal_vertex:\\\\\\\"\\\\n#ifdef USE_SKINNING\\\\n\\\\n\\\\tmat4 skinMatrix = mat4( 0.0 );\\\\n\\\\tskinMatrix += skinWeight.x * boneMatX;\\\\n\\\\tskinMatrix += skinWeight.y * boneMatY;\\\\n\\\\tskinMatrix += skinWeight.z * boneMatZ;\\\\n\\\\tskinMatrix += skinWeight.w * boneMatW;\\\\n\\\\tskinMatrix = bindMatrixInverse * skinMatrix * bindMatrix;\\\\n\\\\n\\\\tobjectNormal = vec4( skinMatrix * vec4( objectNormal, 0.0 ) ).xyz;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tobjectTangent = vec4( skinMatrix * vec4( objectTangent, 0.0 ) ).xyz;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",specularmap_fragment:\\\\\\\"\\\\nfloat specularStrength;\\\\n\\\\n#ifdef USE_SPECULARMAP\\\\n\\\\n\\\\tvec4 texelSpecular = texture2D( specularMap, vUv );\\\\n\\\\tspecularStrength = texelSpecular.r;\\\\n\\\\n#else\\\\n\\\\n\\\\tspecularStrength = 1.0;\\\\n\\\\n#endif\\\\n\\\\\\\",specularmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_SPECULARMAP\\\\n\\\\n\\\\tuniform sampler2D specularMap;\\\\n\\\\n#endif\\\\n\\\\\\\",tonemapping_fragment:\\\\\\\"\\\\n#if defined( TONE_MAPPING )\\\\n\\\\n\\\\tgl_FragColor.rgb = toneMapping( gl_FragColor.rgb );\\\\n\\\\n#endif\\\\n\\\\\\\",tonemapping_pars_fragment:\\\\\\\"\\\\n#ifndef saturate\\\\n// may have defined saturate() already\\\\n#define saturate( a ) clamp( a, 0.0, 1.0 )\\\\n#endif\\\\n\\\\nuniform float toneMappingExposure;\\\\n\\\\n// exposure only\\\\nvec3 LinearToneMapping( vec3 color ) {\\\\n\\\\n\\\\treturn toneMappingExposure * color;\\\\n\\\\n}\\\\n\\\\n// source: https://www.cs.utah.edu/~reinhard/cdrom/\\\\nvec3 ReinhardToneMapping( vec3 color ) {\\\\n\\\\n\\\\tcolor *= toneMappingExposure;\\\\n\\\\treturn saturate( color / ( vec3( 1.0 ) + color ) );\\\\n\\\\n}\\\\n\\\\n// source: http://filmicworlds.com/blog/filmic-tonemapping-operators/\\\\nvec3 OptimizedCineonToneMapping( vec3 color ) {\\\\n\\\\n\\\\t// optimized filmic operator by Jim Hejl and Richard Burgess-Dawson\\\\n\\\\tcolor *= toneMappingExposure;\\\\n\\\\tcolor = max( vec3( 0.0 ), color - 0.004 );\\\\n\\\\treturn pow( ( color * ( 6.2 * color + 0.5 ) ) / ( color * ( 6.2 * color + 1.7 ) + 0.06 ), vec3( 2.2 ) );\\\\n\\\\n}\\\\n\\\\n// source: https://github.com/selfshadow/ltc_code/blob/master/webgl/shaders/ltc/ltc_blit.fs\\\\nvec3 RRTAndODTFit( vec3 v ) {\\\\n\\\\n\\\\tvec3 a = v * ( v + 0.0245786 ) - 0.000090537;\\\\n\\\\tvec3 b = v * ( 0.983729 * v + 0.4329510 ) + 0.238081;\\\\n\\\\treturn a / b;\\\\n\\\\n}\\\\n\\\\n// this implementation of ACES is modified to accommodate a brighter viewing environment.\\\\n// the scale factor of 1/0.6 is subjective. see discussion in #19621.\\\\n\\\\nvec3 ACESFilmicToneMapping( vec3 color ) {\\\\n\\\\n\\\\t// sRGB => XYZ => D65_2_D60 => AP1 => RRT_SAT\\\\n\\\\tconst mat3 ACESInputMat = mat3(\\\\n\\\\t\\\\tvec3( 0.59719, 0.07600, 0.02840 ), // transposed from source\\\\n\\\\t\\\\tvec3( 0.35458, 0.90834, 0.13383 ),\\\\n\\\\t\\\\tvec3( 0.04823, 0.01566, 0.83777 )\\\\n\\\\t);\\\\n\\\\n\\\\t// ODT_SAT => XYZ => D60_2_D65 => sRGB\\\\n\\\\tconst mat3 ACESOutputMat = mat3(\\\\n\\\\t\\\\tvec3( 1.60475, -0.10208, -0.00327 ), // transposed from source\\\\n\\\\t\\\\tvec3( -0.53108, 1.10813, -0.07276 ),\\\\n\\\\t\\\\tvec3( -0.07367, -0.00605, 1.07602 )\\\\n\\\\t);\\\\n\\\\n\\\\tcolor *= toneMappingExposure / 0.6;\\\\n\\\\n\\\\tcolor = ACESInputMat * color;\\\\n\\\\n\\\\t// Apply RRT and ODT\\\\n\\\\tcolor = RRTAndODTFit( color );\\\\n\\\\n\\\\tcolor = ACESOutputMat * color;\\\\n\\\\n\\\\t// Clamp to [0, 1]\\\\n\\\\treturn saturate( color );\\\\n\\\\n}\\\\n\\\\nvec3 CustomToneMapping( vec3 color ) { return color; }\\\\n\\\\\\\",transmission_fragment:\\\\\\\"\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\n\\\\tfloat transmissionAlpha = 1.0;\\\\n\\\\tfloat transmissionFactor = transmission;\\\\n\\\\tfloat thicknessFactor = thickness;\\\\n\\\\n\\\\t#ifdef USE_TRANSMISSIONMAP\\\\n\\\\n\\\\t\\\\ttransmissionFactor *= texture2D( transmissionMap, vUv ).r;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_THICKNESSMAP\\\\n\\\\n\\\\t\\\\tthicknessFactor *= texture2D( thicknessMap, vUv ).g;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tvec3 pos = vWorldPosition;\\\\n\\\\tvec3 v = normalize( cameraPosition - pos );\\\\n\\\\tvec3 n = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\n\\\\tvec4 transmission = getIBLVolumeRefraction(\\\\n\\\\t\\\\tn, v, roughnessFactor, material.diffuseColor, material.specularColor, material.specularF90,\\\\n\\\\t\\\\tpos, modelMatrix, viewMatrix, projectionMatrix, ior, thicknessFactor,\\\\n\\\\t\\\\tattenuationTint, attenuationDistance );\\\\n\\\\n\\\\ttotalDiffuse = mix( totalDiffuse, transmission.rgb, transmissionFactor );\\\\n\\\\ttransmissionAlpha = mix( transmissionAlpha, transmission.a, transmissionFactor );\\\\n#endif\\\\n\\\\\\\",transmission_pars_fragment:\\\\\\\"\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\n\\\\t// Transmission code is based on glTF-Sampler-Viewer\\\\n\\\\t// https://github.com/KhronosGroup/glTF-Sample-Viewer\\\\n\\\\n\\\\tuniform float transmission;\\\\n\\\\tuniform float thickness;\\\\n\\\\tuniform float attenuationDistance;\\\\n\\\\tuniform vec3 attenuationTint;\\\\n\\\\n\\\\t#ifdef USE_TRANSMISSIONMAP\\\\n\\\\n\\\\t\\\\tuniform sampler2D transmissionMap;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_THICKNESSMAP\\\\n\\\\n\\\\t\\\\tuniform sampler2D thicknessMap;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tuniform vec2 transmissionSamplerSize;\\\\n\\\\tuniform sampler2D transmissionSamplerMap;\\\\n\\\\n\\\\tuniform mat4 modelMatrix;\\\\n\\\\tuniform mat4 projectionMatrix;\\\\n\\\\n\\\\tvarying vec3 vWorldPosition;\\\\n\\\\n\\\\tvec3 getVolumeTransmissionRay( vec3 n, vec3 v, float thickness, float ior, mat4 modelMatrix ) {\\\\n\\\\n\\\\t\\\\t// Direction of refracted light.\\\\n\\\\t\\\\tvec3 refractionVector = refract( - v, normalize( n ), 1.0 / ior );\\\\n\\\\n\\\\t\\\\t// Compute rotation-independant scaling of the model matrix.\\\\n\\\\t\\\\tvec3 modelScale;\\\\n\\\\t\\\\tmodelScale.x = length( vec3( modelMatrix[ 0 ].xyz ) );\\\\n\\\\t\\\\tmodelScale.y = length( vec3( modelMatrix[ 1 ].xyz ) );\\\\n\\\\t\\\\tmodelScale.z = length( vec3( modelMatrix[ 2 ].xyz ) );\\\\n\\\\n\\\\t\\\\t// The thickness is specified in local space.\\\\n\\\\t\\\\treturn normalize( refractionVector ) * thickness * modelScale;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tfloat applyIorToRoughness( float roughness, float ior ) {\\\\n\\\\n\\\\t\\\\t// Scale roughness with IOR so that an IOR of 1.0 results in no microfacet refraction and\\\\n\\\\t\\\\t// an IOR of 1.5 results in the default amount of microfacet refraction.\\\\n\\\\t\\\\treturn roughness * clamp( ior * 2.0 - 2.0, 0.0, 1.0 );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec4 getTransmissionSample( vec2 fragCoord, float roughness, float ior ) {\\\\n\\\\n\\\\t\\\\tfloat framebufferLod = log2( transmissionSamplerSize.x ) * applyIorToRoughness( roughness, ior );\\\\n\\\\n\\\\t\\\\t#ifdef TEXTURE_LOD_EXT\\\\n\\\\n\\\\t\\\\t\\\\treturn texture2DLodEXT( transmissionSamplerMap, fragCoord.xy, framebufferLod );\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\treturn texture2D( transmissionSamplerMap, fragCoord.xy, framebufferLod );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 applyVolumeAttenuation( vec3 radiance, float transmissionDistance, vec3 attenuationColor, float attenuationDistance ) {\\\\n\\\\n\\\\t\\\\tif ( attenuationDistance == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t// Attenuation distance is +∞ (which we indicate by zero), i.e. the transmitted color is not attenuated at all.\\\\n\\\\t\\\\t\\\\treturn radiance;\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t// Compute light attenuation using Beer's law.\\\\n\\\\t\\\\t\\\\tvec3 attenuationCoefficient = -log( attenuationColor ) / attenuationDistance;\\\\n\\\\t\\\\t\\\\tvec3 transmittance = exp( - attenuationCoefficient * transmissionDistance ); // Beer's law\\\\n\\\\t\\\\t\\\\treturn transmittance * radiance;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec4 getIBLVolumeRefraction( vec3 n, vec3 v, float roughness, vec3 diffuseColor, vec3 specularColor, float specularF90,\\\\n\\\\t\\\\tvec3 position, mat4 modelMatrix, mat4 viewMatrix, mat4 projMatrix, float ior, float thickness,\\\\n\\\\t\\\\tvec3 attenuationColor, float attenuationDistance ) {\\\\n\\\\n\\\\t\\\\tvec3 transmissionRay = getVolumeTransmissionRay( n, v, thickness, ior, modelMatrix );\\\\n\\\\t\\\\tvec3 refractedRayExit = position + transmissionRay;\\\\n\\\\n\\\\t\\\\t// Project refracted vector on the framebuffer, while mapping to normalized device coordinates.\\\\n\\\\t\\\\tvec4 ndcPos = projMatrix * viewMatrix * vec4( refractedRayExit, 1.0 );\\\\n\\\\t\\\\tvec2 refractionCoords = ndcPos.xy / ndcPos.w;\\\\n\\\\t\\\\trefractionCoords += 1.0;\\\\n\\\\t\\\\trefractionCoords /= 2.0;\\\\n\\\\n\\\\t\\\\t// Sample framebuffer to get pixel the refracted ray hits.\\\\n\\\\t\\\\tvec4 transmittedLight = getTransmissionSample( refractionCoords, roughness, ior );\\\\n\\\\n\\\\t\\\\tvec3 attenuatedColor = applyVolumeAttenuation( transmittedLight.rgb, length( transmissionRay ), attenuationColor, attenuationDistance );\\\\n\\\\n\\\\t\\\\t// Get the specular component.\\\\n\\\\t\\\\tvec3 F = EnvironmentBRDF( n, v, specularColor, specularF90, roughness );\\\\n\\\\n\\\\t\\\\treturn vec4( ( 1.0 - F ) * attenuatedColor * diffuseColor, transmittedLight.a );\\\\n\\\\n\\\\t}\\\\n#endif\\\\n\\\\\\\",uv_pars_fragment:\\\\\\\"\\\\n#if ( defined( USE_UV ) && ! defined( UVS_VERTEX_ONLY ) )\\\\n\\\\n\\\\tvarying vec2 vUv;\\\\n\\\\n#endif\\\\n\\\\\\\",uv_pars_vertex:\\\\\\\"\\\\n#ifdef USE_UV\\\\n\\\\n\\\\t#ifdef UVS_VERTEX_ONLY\\\\n\\\\n\\\\t\\\\tvec2 vUv;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tuniform mat3 uvTransform;\\\\n\\\\n#endif\\\\n\\\\\\\",uv_vertex:\\\\\\\"\\\\n#ifdef USE_UV\\\\n\\\\n\\\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\\\n\\\\n#endif\\\\n\\\\\\\",uv2_pars_fragment:\\\\\\\"\\\\n#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\n\\\\tvarying vec2 vUv2;\\\\n\\\\n#endif\\\\n\\\\\\\",uv2_pars_vertex:\\\\\\\"\\\\n#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\n\\\\tattribute vec2 uv2;\\\\n\\\\tvarying vec2 vUv2;\\\\n\\\\n\\\\tuniform mat3 uv2Transform;\\\\n\\\\n#endif\\\\n\\\\\\\",uv2_vertex:\\\\\\\"\\\\n#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\n\\\\tvUv2 = ( uv2Transform * vec3( uv2, 1 ) ).xy;\\\\n\\\\n#endif\\\\n\\\\\\\",worldpos_vertex:\\\\\\\"\\\\n#if defined( USE_ENVMAP ) || defined( DISTANCE ) || defined ( USE_SHADOWMAP ) || defined ( USE_TRANSMISSION )\\\\n\\\\n\\\\tvec4 worldPosition = vec4( transformed, 1.0 );\\\\n\\\\n\\\\t#ifdef USE_INSTANCING\\\\n\\\\n\\\\t\\\\tworldPosition = instanceMatrix * worldPosition;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tworldPosition = modelMatrix * worldPosition;\\\\n\\\\n#endif\\\\n\\\\\\\",background_vert:\\\\\\\"\\\\nvarying vec2 vUv;\\\\nuniform mat3 uvTransform;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\\\n\\\\n\\\\tgl_Position = vec4( position.xy, 1.0, 1.0 );\\\\n\\\\n}\\\\n\\\\\\\",background_frag:\\\\\\\"\\\\nuniform sampler2D t2D;\\\\n\\\\nvarying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 texColor = texture2D( t2D, vUv );\\\\n\\\\n\\\\tgl_FragColor = mapTexelToLinear( texColor );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",cube_vert:\\\\\\\"\\\\nvarying vec3 vWorldDirection;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tgl_Position.z = gl_Position.w; // set z to camera.far\\\\n\\\\n}\\\\n\\\\\\\",cube_frag:\\\\\\\"\\\\n#include \\\\nuniform float opacity;\\\\n\\\\nvarying vec3 vWorldDirection;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec3 vReflect = vWorldDirection;\\\\n\\\\t#include \\\\n\\\\n\\\\tgl_FragColor = envColor;\\\\n\\\\tgl_FragColor.a *= opacity;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",depth_vert:\\\\\\\"\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\n// This is used for computing an equivalent of gl_FragCoord.z that is as high precision as possible.\\\\n// Some platforms compute gl_FragCoord at a lower precision which makes the manually computed value better for\\\\n// depth-based postprocessing effects. Reproduced on iPad with A10 processor / iPadOS 13.3.1.\\\\nvarying vec2 vHighPrecisionZW;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvHighPrecisionZW = gl_Position.zw;\\\\n\\\\n}\\\\n\\\\\\\",depth_frag:\\\\\\\"\\\\n#if DEPTH_PACKING == 3200\\\\n\\\\n\\\\tuniform float opacity;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvarying vec2 vHighPrecisionZW;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( 1.0 );\\\\n\\\\n\\\\t#if DEPTH_PACKING == 3200\\\\n\\\\n\\\\t\\\\tdiffuseColor.a = opacity;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t// Higher precision equivalent of gl_FragCoord.z. This assumes depthRange has been left to its default values.\\\\n\\\\tfloat fragCoordZ = 0.5 * vHighPrecisionZW[0] / vHighPrecisionZW[1] + 0.5;\\\\n\\\\n\\\\t#if DEPTH_PACKING == 3200\\\\n\\\\n\\\\t\\\\tgl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\\\\n\\\\n\\\\t#elif DEPTH_PACKING == 3201\\\\n\\\\n\\\\t\\\\tgl_FragColor = packDepthToRGBA( fragCoordZ );\\\\n\\\\n\\\\t#endif\\\\n\\\\n}\\\\n\\\\\\\",distanceRGBA_vert:\\\\\\\"\\\\n#define DISTANCE\\\\n\\\\nvarying vec3 vWorldPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\n}\\\\n\\\\\\\",distanceRGBA_frag:\\\\\\\"\\\\n#define DISTANCE\\\\n\\\\nuniform vec3 referencePosition;\\\\nuniform float nearDistance;\\\\nuniform float farDistance;\\\\nvarying vec3 vWorldPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main () {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( 1.0 );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tfloat dist = length( vWorldPosition - referencePosition );\\\\n\\\\tdist = ( dist - nearDistance ) / ( farDistance - nearDistance );\\\\n\\\\tdist = saturate( dist ); // clamp to [ 0, 1 ]\\\\n\\\\n\\\\tgl_FragColor = packDepthToRGBA( dist );\\\\n\\\\n}\\\\n\\\\\\\",equirect_vert:\\\\\\\"\\\\nvarying vec3 vWorldDirection;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",equirect_frag:\\\\\\\"\\\\nuniform sampler2D tEquirect;\\\\n\\\\nvarying vec3 vWorldDirection;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec3 direction = normalize( vWorldDirection );\\\\n\\\\n\\\\tvec2 sampleUV = equirectUv( direction );\\\\n\\\\n\\\\tvec4 texColor = texture2D( tEquirect, sampleUV );\\\\n\\\\n\\\\tgl_FragColor = mapTexelToLinear( texColor );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",linedashed_vert:\\\\\\\"\\\\nuniform float scale;\\\\nattribute float lineDistance;\\\\n\\\\nvarying float vLineDistance;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvLineDistance = scale * lineDistance;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",linedashed_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\n\\\\nuniform float dashSize;\\\\nuniform float totalSize;\\\\n\\\\nvarying float vLineDistance;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tif ( mod( vLineDistance, totalSize ) > dashSize ) {\\\\n\\\\n\\\\t\\\\tdiscard;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\toutgoingLight = diffuseColor.rgb; // simple shader\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshbasic_vert:\\\\\\\"\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#if defined ( USE_ENVMAP ) || defined ( USE_SKINNING )\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshbasic_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\n\\\\n#ifndef FLAT_SHADED\\\\n\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\n\\\\t// accumulation (baked indirect lighting only)\\\\n\\\\t#ifdef USE_LIGHTMAP\\\\n\\\\n\\\\t\\\\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += vec3( 1.0 );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t// modulation\\\\n\\\\t#include \\\\n\\\\n\\\\treflectedLight.indirectDiffuse *= diffuseColor.rgb;\\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.indirectDiffuse;\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshlambert_vert:\\\\\\\"\\\\n#define LAMBERT\\\\n\\\\nvarying vec3 vLightFront;\\\\nvarying vec3 vIndirectFront;\\\\n\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvarying vec3 vLightBack;\\\\n\\\\tvarying vec3 vIndirectBack;\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\n\\\\\\\",meshlambert_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float opacity;\\\\n\\\\nvarying vec3 vLightFront;\\\\nvarying vec3 vIndirectFront;\\\\n\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvarying vec3 vLightBack;\\\\n\\\\tvarying vec3 vIndirectBack;\\\\n#endif\\\\n\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// accumulation\\\\n\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += ( gl_FrontFacing ) ? vIndirectFront : vIndirectBack;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += vIndirectFront;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\treflectedLight.indirectDiffuse *= BRDF_Lambert( diffuseColor.rgb );\\\\n\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\treflectedLight.directDiffuse = ( gl_FrontFacing ) ? vLightFront : vLightBack;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\treflectedLight.directDiffuse = vLightFront;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\treflectedLight.directDiffuse *= BRDF_Lambert( diffuseColor.rgb ) * getShadowMask();\\\\n\\\\n\\\\t// modulation\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\n\\\\\\\",meshmatcap_vert:\\\\\\\"\\\\n#define MATCAP\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n}\\\\n\\\\\\\",meshmatcap_frag:\\\\\\\"\\\\n#define MATCAP\\\\n\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\nuniform sampler2D matcap;\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 viewDir = normalize( vViewPosition );\\\\n\\\\tvec3 x = normalize( vec3( viewDir.z, 0.0, - viewDir.x ) );\\\\n\\\\tvec3 y = cross( viewDir, x );\\\\n\\\\tvec2 uv = vec2( dot( x, normal ), dot( y, normal ) ) * 0.495 + 0.5; // 0.495 to remove artifacts caused by undersized matcap disks\\\\n\\\\n\\\\t#ifdef USE_MATCAP\\\\n\\\\n\\\\t\\\\tvec4 matcapColor = texture2D( matcap, uv );\\\\n\\\\t\\\\tmatcapColor = matcapTexelToLinear( matcapColor );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvec4 matcapColor = vec4( 1.0 );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tvec3 outgoingLight = diffuseColor.rgb * matcapColor.rgb;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshnormal_vert:\\\\\\\"\\\\n#define NORMAL\\\\n\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\n\\\\tvarying vec3 vViewPosition;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n#endif\\\\n\\\\n}\\\\n\\\\\\\",meshnormal_frag:\\\\\\\"\\\\n#define NORMAL\\\\n\\\\nuniform float opacity;\\\\n\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\n\\\\tvarying vec3 vViewPosition;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tgl_FragColor = vec4( packNormalToRGB( normal ), opacity );\\\\n\\\\n}\\\\n\\\\\\\",meshphong_vert:\\\\\\\"\\\\n#define PHONG\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshphong_frag:\\\\\\\"\\\\n#define PHONG\\\\n\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform vec3 specular;\\\\nuniform float shininess;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// accumulation\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// modulation\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshphysical_vert:\\\\\\\"\\\\n#define STANDARD\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\n\\\\tvarying vec3 vWorldPosition;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\n#endif\\\\n}\\\\n\\\\\\\",meshphysical_frag:\\\\\\\"\\\\n#define STANDARD\\\\n\\\\n#ifdef PHYSICAL\\\\n\\\\t#define IOR\\\\n\\\\t#define SPECULAR\\\\n#endif\\\\n\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float roughness;\\\\nuniform float metalness;\\\\nuniform float opacity;\\\\n\\\\n#ifdef IOR\\\\n\\\\tuniform float ior;\\\\n#endif\\\\n\\\\n#ifdef SPECULAR\\\\n\\\\tuniform float specularIntensity;\\\\n\\\\tuniform vec3 specularTint;\\\\n\\\\n\\\\t#ifdef USE_SPECULARINTENSITYMAP\\\\n\\\\t\\\\tuniform sampler2D specularIntensityMap;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_SPECULARTINTMAP\\\\n\\\\t\\\\tuniform sampler2D specularTintMap;\\\\n\\\\t#endif\\\\n#endif\\\\n\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tuniform float clearcoat;\\\\n\\\\tuniform float clearcoatRoughness;\\\\n#endif\\\\n\\\\n#ifdef USE_SHEEN\\\\n\\\\tuniform vec3 sheenTint;\\\\n\\\\tuniform float sheenRoughness;\\\\n#endif\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// accumulation\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// modulation\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 totalDiffuse = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse;\\\\n\\\\tvec3 totalSpecular = reflectedLight.directSpecular + reflectedLight.indirectSpecular;\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = totalDiffuse + totalSpecular + totalEmissiveRadiance;\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\t\\\\tfloat dotNVcc = saturate( dot( geometry.clearcoatNormal, geometry.viewDir ) );\\\\n\\\\n\\\\t\\\\tvec3 Fcc = F_Schlick( material.clearcoatF0, material.clearcoatF90, dotNVcc );\\\\n\\\\n\\\\t\\\\toutgoingLight = outgoingLight * ( 1.0 - clearcoat * Fcc ) + clearcoatSpecular * clearcoat;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshtoon_vert:\\\\\\\"\\\\n#define TOON\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshtoon_frag:\\\\\\\"\\\\n#define TOON\\\\n\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// accumulation\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// modulation\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",points_vert:\\\\\\\"\\\\nuniform float size;\\\\nuniform float scale;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tgl_PointSize = size;\\\\n\\\\n\\\\t#ifdef USE_SIZEATTENUATION\\\\n\\\\n\\\\t\\\\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\\\\n\\\\n\\\\t\\\\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",points_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",shadow_vert:\\\\\\\"\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",shadow_frag:\\\\\\\"\\\\nuniform vec3 color;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tgl_FragColor = vec4( color, opacity * ( 1.0 - getShadowMask() ) );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",sprite_vert:\\\\\\\"\\\\nuniform float rotation;\\\\nuniform vec2 center;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 mvPosition = modelViewMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\n\\\\tvec2 scale;\\\\n\\\\tscale.x = length( vec3( modelMatrix[ 0 ].x, modelMatrix[ 0 ].y, modelMatrix[ 0 ].z ) );\\\\n\\\\tscale.y = length( vec3( modelMatrix[ 1 ].x, modelMatrix[ 1 ].y, modelMatrix[ 1 ].z ) );\\\\n\\\\n\\\\t#ifndef USE_SIZEATTENUATION\\\\n\\\\n\\\\t\\\\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\\\\n\\\\n\\\\t\\\\tif ( isPerspective ) scale *= - mvPosition.z;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tvec2 alignedPosition = ( position.xy - ( center - vec2( 0.5 ) ) ) * scale;\\\\n\\\\n\\\\tvec2 rotatedPosition;\\\\n\\\\trotatedPosition.x = cos( rotation ) * alignedPosition.x - sin( rotation ) * alignedPosition.y;\\\\n\\\\trotatedPosition.y = sin( rotation ) * alignedPosition.x + cos( rotation ) * alignedPosition.y;\\\\n\\\\n\\\\tmvPosition.xy += rotatedPosition;\\\\n\\\\n\\\\tgl_Position = projectionMatrix * mvPosition;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",sprite_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\"};var U=n(11);const G={common:{diffuse:{value:new D.a(16777215)},opacity:{value:1},map:{value:null},uvTransform:{value:new U.a},uv2Transform:{value:new U.a},alphaMap:{value:null},alphaTest:{value:0}},specularmap:{specularMap:{value:null}},envmap:{envMap:{value:null},flipEnvMap:{value:-1},reflectivity:{value:1},ior:{value:1.5},refractionRatio:{value:.98},maxMipLevel:{value:0}},aomap:{aoMap:{value:null},aoMapIntensity:{value:1}},lightmap:{lightMap:{value:null},lightMapIntensity:{value:1}},emissivemap:{emissiveMap:{value:null}},bumpmap:{bumpMap:{value:null},bumpScale:{value:1}},normalmap:{normalMap:{value:null},normalScale:{value:new d.a(1,1)}},displacementmap:{displacementMap:{value:null},displacementScale:{value:1},displacementBias:{value:0}},roughnessmap:{roughnessMap:{value:null}},metalnessmap:{metalnessMap:{value:null}},gradientmap:{gradientMap:{value:null}},fog:{fogDensity:{value:25e-5},fogNear:{value:1},fogFar:{value:2e3},fogColor:{value:new D.a(16777215)}},lights:{ambientLightColor:{value:[]},lightProbe:{value:[]},directionalLights:{value:[],properties:{direction:{},color:{}}},directionalLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},directionalShadowMap:{value:[]},directionalShadowMatrix:{value:[]},spotLights:{value:[],properties:{color:{},position:{},direction:{},distance:{},coneCos:{},penumbraCos:{},decay:{}}},spotLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},spotShadowMap:{value:[]},spotShadowMatrix:{value:[]},pointLights:{value:[],properties:{color:{},position:{},decay:{},distance:{}}},pointLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{},shadowCameraNear:{},shadowCameraFar:{}}},pointShadowMap:{value:[]},pointShadowMatrix:{value:[]},hemisphereLights:{value:[],properties:{direction:{},skyColor:{},groundColor:{}}},rectAreaLights:{value:[],properties:{color:{},position:{},width:{},height:{}}},ltc_1:{value:null},ltc_2:{value:null}},points:{diffuse:{value:new D.a(16777215)},opacity:{value:1},size:{value:1},scale:{value:1},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new U.a}},sprite:{diffuse:{value:new D.a(16777215)},opacity:{value:1},center:{value:new d.a(.5,.5)},rotation:{value:0},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new U.a}}},V={basic:{uniforms:P([G.common,G.specularmap,G.envmap,G.aomap,G.lightmap,G.fog]),vertexShader:z.meshbasic_vert,fragmentShader:z.meshbasic_frag},lambert:{uniforms:P([G.common,G.specularmap,G.envmap,G.aomap,G.lightmap,G.emissivemap,G.fog,G.lights,{emissive:{value:new D.a(0)}}]),vertexShader:z.meshlambert_vert,fragmentShader:z.meshlambert_frag},phong:{uniforms:P([G.common,G.specularmap,G.envmap,G.aomap,G.lightmap,G.emissivemap,G.bumpmap,G.normalmap,G.displacementmap,G.fog,G.lights,{emissive:{value:new D.a(0)},specular:{value:new D.a(1118481)},shininess:{value:30}}]),vertexShader:z.meshphong_vert,fragmentShader:z.meshphong_frag},standard:{uniforms:P([G.common,G.envmap,G.aomap,G.lightmap,G.emissivemap,G.bumpmap,G.normalmap,G.displacementmap,G.roughnessmap,G.metalnessmap,G.fog,G.lights,{emissive:{value:new D.a(0)},roughness:{value:1},metalness:{value:0},envMapIntensity:{value:1}}]),vertexShader:z.meshphysical_vert,fragmentShader:z.meshphysical_frag},toon:{uniforms:P([G.common,G.aomap,G.lightmap,G.emissivemap,G.bumpmap,G.normalmap,G.displacementmap,G.gradientmap,G.fog,G.lights,{emissive:{value:new D.a(0)}}]),vertexShader:z.meshtoon_vert,fragmentShader:z.meshtoon_frag},matcap:{uniforms:P([G.common,G.bumpmap,G.normalmap,G.displacementmap,G.fog,{matcap:{value:null}}]),vertexShader:z.meshmatcap_vert,fragmentShader:z.meshmatcap_frag},points:{uniforms:P([G.points,G.fog]),vertexShader:z.points_vert,fragmentShader:z.points_frag},dashed:{uniforms:P([G.common,G.fog,{scale:{value:1},dashSize:{value:1},totalSize:{value:2}}]),vertexShader:z.linedashed_vert,fragmentShader:z.linedashed_frag},depth:{uniforms:P([G.common,G.displacementmap]),vertexShader:z.depth_vert,fragmentShader:z.depth_frag},normal:{uniforms:P([G.common,G.bumpmap,G.normalmap,G.displacementmap,{opacity:{value:1}}]),vertexShader:z.meshnormal_vert,fragmentShader:z.meshnormal_frag},sprite:{uniforms:P([G.sprite,G.fog]),vertexShader:z.sprite_vert,fragmentShader:z.sprite_frag},background:{uniforms:{uvTransform:{value:new U.a},t2D:{value:null}},vertexShader:z.background_vert,fragmentShader:z.background_frag},cube:{uniforms:P([G.envmap,{opacity:{value:1}}]),vertexShader:z.cube_vert,fragmentShader:z.cube_frag},equirect:{uniforms:{tEquirect:{value:null}},vertexShader:z.equirect_vert,fragmentShader:z.equirect_frag},distanceRGBA:{uniforms:P([G.common,G.displacementmap,{referencePosition:{value:new p.a},nearDistance:{value:1},farDistance:{value:1e3}}]),vertexShader:z.distanceRGBA_vert,fragmentShader:z.distanceRGBA_frag},shadow:{uniforms:P([G.lights,G.fog,{color:{value:new D.a(0)},opacity:{value:1}}]),vertexShader:z.shadow_vert,fragmentShader:z.shadow_frag}};function H(t,e,n,i,r){const s=new D.a(0);let o,a,l=0,c=null,u=0,h=null;function d(t,e){n.buffers.color.setClear(t.r,t.g,t.b,e,r)}return{getClearColor:function(){return s},setClearColor:function(t,e=1){s.set(t),l=e,d(s,l)},getClearAlpha:function(){return l},setClearAlpha:function(t){l=t,d(s,l)},render:function(n,r){let p=!1,_=!0===r.isScene?r.background:null;_&&_.isTexture&&(_=e.get(_));const m=t.xr,f=m.getSession&&m.getSession();f&&\\\\\\\"additive\\\\\\\"===f.environmentBlendMode&&(_=null),null===_?d(s,l):_&&_.isColor&&(d(_,1),p=!0),(t.autoClear||p)&&t.clear(t.autoClearColor,t.autoClearDepth,t.autoClearStencil),_&&(_.isCubeTexture||_.mapping===w.q)?(void 0===a&&(a=new k.a(new N(1,1,1),new F({name:\\\\\\\"BackgroundCubeMaterial\\\\\\\",uniforms:R(V.cube.uniforms),vertexShader:V.cube.vertexShader,fragmentShader:V.cube.fragmentShader,side:w.i,depthTest:!1,depthWrite:!1,fog:!1})),a.geometry.deleteAttribute(\\\\\\\"normal\\\\\\\"),a.geometry.deleteAttribute(\\\\\\\"uv\\\\\\\"),a.onBeforeRender=function(t,e,n){this.matrixWorld.copyPosition(n.matrixWorld)},Object.defineProperty(a.material,\\\\\\\"envMap\\\\\\\",{get:function(){return this.uniforms.envMap.value}}),i.update(a)),a.material.uniforms.envMap.value=_,a.material.uniforms.flipEnvMap.value=_.isCubeTexture&&!1===_.isRenderTargetTexture?-1:1,c===_&&u===_.version&&h===t.toneMapping||(a.material.needsUpdate=!0,c=_,u=_.version,h=t.toneMapping),n.unshift(a,a.geometry,a.material,0,0,null)):_&&_.isTexture&&(void 0===o&&(o=new k.a(new L(2,2),new F({name:\\\\\\\"BackgroundMaterial\\\\\\\",uniforms:R(V.background.uniforms),vertexShader:V.background.vertexShader,fragmentShader:V.background.fragmentShader,side:w.H,depthTest:!1,depthWrite:!1,fog:!1})),o.geometry.deleteAttribute(\\\\\\\"normal\\\\\\\"),Object.defineProperty(o.material,\\\\\\\"map\\\\\\\",{get:function(){return this.uniforms.t2D.value}}),i.update(o)),o.material.uniforms.t2D.value=_,!0===_.matrixAutoUpdate&&_.updateMatrix(),o.material.uniforms.uvTransform.value.copy(_.matrix),c===_&&u===_.version&&h===t.toneMapping||(o.material.needsUpdate=!0,c=_,u=_.version,h=t.toneMapping),n.unshift(o,o.geometry,o.material,0,0,null))}}}function j(t,e,n,i){const r=t.getParameter(t.MAX_VERTEX_ATTRIBS),s=i.isWebGL2?null:e.get(\\\\\\\"OES_vertex_array_object\\\\\\\"),o=i.isWebGL2||null!==s,a={},l=d(null);let c=l;function u(e){return i.isWebGL2?t.bindVertexArray(e):s.bindVertexArrayOES(e)}function h(e){return i.isWebGL2?t.deleteVertexArray(e):s.deleteVertexArrayOES(e)}function d(t){const e=[],n=[],i=[];for(let t=0;t=0){let s=l[e];if(void 0===s&&(\\\\\\\"instanceMatrix\\\\\\\"===e&&r.instanceMatrix&&(s=r.instanceMatrix),\\\\\\\"instanceColor\\\\\\\"===e&&r.instanceColor&&(s=r.instanceColor)),void 0!==s){const e=s.normalized,o=s.itemSize,l=n.get(s);if(void 0===l)continue;const c=l.buffer,u=l.type,h=l.bytesPerElement;if(s.isInterleavedBufferAttribute){const n=s.data,l=n.stride,d=s.offset;if(n&&n.isInstancedInterleavedBuffer){for(let t=0;t0&&t.getShaderPrecisionFormat(t.FRAGMENT_SHADER,t.HIGH_FLOAT).precision>0)return\\\\\\\"highp\\\\\\\";e=\\\\\\\"mediump\\\\\\\"}return\\\\\\\"mediump\\\\\\\"===e&&t.getShaderPrecisionFormat(t.VERTEX_SHADER,t.MEDIUM_FLOAT).precision>0&&t.getShaderPrecisionFormat(t.FRAGMENT_SHADER,t.MEDIUM_FLOAT).precision>0?\\\\\\\"mediump\\\\\\\":\\\\\\\"lowp\\\\\\\"}const s=\\\\\\\"undefined\\\\\\\"!=typeof WebGL2RenderingContext&&t instanceof WebGL2RenderingContext||\\\\\\\"undefined\\\\\\\"!=typeof WebGL2ComputeRenderingContext&&t instanceof WebGL2ComputeRenderingContext;let o=void 0!==n.precision?n.precision:\\\\\\\"highp\\\\\\\";const a=r(o);a!==o&&(console.warn(\\\\\\\"THREE.WebGLRenderer:\\\\\\\",o,\\\\\\\"not supported, using\\\\\\\",a,\\\\\\\"instead.\\\\\\\"),o=a);const l=s||e.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\"),c=!0===n.logarithmicDepthBuffer,u=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS),h=t.getParameter(t.MAX_VERTEX_TEXTURE_IMAGE_UNITS),d=t.getParameter(t.MAX_TEXTURE_SIZE),p=t.getParameter(t.MAX_CUBE_MAP_TEXTURE_SIZE),_=t.getParameter(t.MAX_VERTEX_ATTRIBS),m=t.getParameter(t.MAX_VERTEX_UNIFORM_VECTORS),f=t.getParameter(t.MAX_VARYING_VECTORS),g=t.getParameter(t.MAX_FRAGMENT_UNIFORM_VECTORS),v=h>0,y=s||e.has(\\\\\\\"OES_texture_float\\\\\\\");return{isWebGL2:s,drawBuffers:l,getMaxAnisotropy:function(){if(void 0!==i)return i;if(!0===e.has(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\")){const n=e.get(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\");i=t.getParameter(n.MAX_TEXTURE_MAX_ANISOTROPY_EXT)}else i=0;return i},getMaxPrecision:r,precision:o,logarithmicDepthBuffer:c,maxTextures:u,maxVertexTextures:h,maxTextureSize:d,maxCubemapSize:p,maxAttributes:_,maxVertexUniforms:m,maxVaryings:f,maxFragmentUniforms:g,vertexTextures:v,floatFragmentTextures:y,floatVertexTextures:v&&y,maxSamples:s?t.getParameter(t.MAX_SAMPLES):0}}V.physical={uniforms:P([V.standard.uniforms,{clearcoat:{value:0},clearcoatMap:{value:null},clearcoatRoughness:{value:0},clearcoatRoughnessMap:{value:null},clearcoatNormalScale:{value:new d.a(1,1)},clearcoatNormalMap:{value:null},sheen:{value:0},sheenTint:{value:new D.a(0)},sheenRoughness:{value:0},transmission:{value:0},transmissionMap:{value:null},transmissionSamplerSize:{value:new d.a},transmissionSamplerMap:{value:null},thickness:{value:0},thicknessMap:{value:null},attenuationDistance:{value:0},attenuationTint:{value:new D.a(0)},specularIntensity:{value:0},specularIntensityMap:{value:null},specularTint:{value:new D.a(1,1,1)},specularTintMap:{value:null}}]),vertexShader:z.meshphysical_vert,fragmentShader:z.meshphysical_frag};var X=n(34);function Y(t){const e=this;let n=null,i=0,r=!1,s=!1;const o=new X.a,a=new U.a,l={value:null,needsUpdate:!1};function c(){l.value!==n&&(l.value=n,l.needsUpdate=i>0),e.numPlanes=i,e.numIntersection=0}function u(t,n,i,r){const s=null!==t?t.length:0;let c=null;if(0!==s){if(c=l.value,!0!==r||null===c){const e=i+4*s,r=n.matrixWorldInverse;a.getNormalMatrix(r),(null===c||c.length\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D tEquirect;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec3 vWorldDirection;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec3 direction = normalize( vWorldDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 sampleUV = equirectUv( direction );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = texture2D( tEquirect, sampleUV );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\\\\"},i=new N(5,5,5),r=new F({name:\\\\\\\"CubemapFromEquirect\\\\\\\",uniforms:R(n.uniforms),vertexShader:n.vertexShader,fragmentShader:n.fragmentShader,side:w.i,blending:w.ub});r.uniforms.tEquirect.value=e;const s=new k.a(i,r),o=e.minFilter;e.minFilter===w.Y&&(e.minFilter=w.V);return new et(1,10,this).update(t,s),e.minFilter=o,s.geometry.dispose(),s.material.dispose(),this}clear(t,e,n,i){const r=t.getRenderTarget();for(let r=0;r<6;r++)t.setRenderTarget(this,r),t.clear(e,n,i);t.setRenderTarget(r)}}function rt(t){let e=new WeakMap;function n(t,e){return e===w.D?t.mapping=w.o:e===w.E&&(t.mapping=w.p),t}function i(t){const n=t.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",i);const r=e.get(n);void 0!==r&&(e.delete(n),r.dispose())}return{get:function(r){if(r&&r.isTexture&&!1===r.isRenderTargetTexture){const s=r.mapping;if(s===w.D||s===w.E){if(e.has(r)){return n(e.get(r).texture,r.mapping)}{const s=r.image;if(s&&s.height>0){const o=t.getRenderTarget(),a=new it(s.height/2);return a.fromEquirectangularTexture(t,r),e.set(r,a),t.setRenderTarget(o),r.addEventListener(\\\\\\\"dispose\\\\\\\",i),n(a.texture,r.mapping)}return null}}}return r},dispose:function(){e=new WeakMap}}}it.prototype.isWebGLCubeRenderTarget=!0;var st=n(37);class ot extends F{constructor(t){super(t),this.type=\\\\\\\"RawShaderMaterial\\\\\\\"}}ot.prototype.isRawShaderMaterial=!0;var at=n(27);const lt=Math.pow(2,8),ct=[.125,.215,.35,.446,.526,.582],ut=5+ct.length,ht=20,dt={[w.U]:0,[w.ld]:1,[w.gc]:2,[w.lc]:3,[w.kc]:4,[w.fc]:5,[w.J]:6},pt=new st.a,{_lodPlanes:_t,_sizeLods:mt,_sigmas:ft}=At(),gt=new D.a;let vt=null;const yt=(1+Math.sqrt(5))/2,xt=1/yt,bt=[new p.a(1,1,1),new p.a(-1,1,1),new p.a(1,1,-1),new p.a(-1,1,-1),new p.a(0,yt,xt),new p.a(0,yt,-xt),new p.a(xt,0,yt),new p.a(-xt,0,yt),new p.a(yt,xt,0),new p.a(-yt,xt,0)];class wt{constructor(t){this._renderer=t,this._pingPongRenderTarget=null,this._blurMaterial=function(t){const e=new Float32Array(t),n=new p.a(0,1,0);return new ot({name:\\\\\\\"SphericalGaussianBlur\\\\\\\",defines:{n:t},uniforms:{envMap:{value:null},samples:{value:1},weights:{value:e},latitudinal:{value:!1},dTheta:{value:0},mipInt:{value:0},poleAxis:{value:n},inputEncoding:{value:dt[w.U]},outputEncoding:{value:dt[w.U]}},vertexShader:Nt(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform int samples;\\\\n\\\\t\\\\t\\\\tuniform float weights[ n ];\\\\n\\\\t\\\\t\\\\tuniform bool latitudinal;\\\\n\\\\t\\\\t\\\\tuniform float dTheta;\\\\n\\\\t\\\\t\\\\tuniform float mipInt;\\\\n\\\\t\\\\t\\\\tuniform vec3 poleAxis;\\\\n\\\\n\\\\t\\\\t\\\\t${Lt()}\\\\n\\\\n\\\\t\\\\t\\\\t#define ENVMAP_TYPE_CUBE_UV\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\tvec3 getSample( float theta, vec3 axis ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat cosTheta = cos( theta );\\\\n\\\\t\\\\t\\\\t\\\\t// Rodrigues' axis-angle rotation\\\\n\\\\t\\\\t\\\\t\\\\tvec3 sampleDirection = vOutputDirection * cosTheta\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t+ cross( axis, vOutputDirection ) * sin( theta )\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t+ axis * dot( axis, vOutputDirection ) * ( 1.0 - cosTheta );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn bilinearCubeUV( envMap, sampleDirection, mipInt );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 axis = latitudinal ? poleAxis : cross( poleAxis, vOutputDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tif ( all( equal( axis, vec3( 0.0 ) ) ) ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\taxis = vec3( vOutputDirection.z, 0.0, - vOutputDirection.x );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\taxis = normalize( axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ 0 ] * getSample( 0.0, axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfor ( int i = 1; i < n; i++ ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tif ( i >= samples ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tbreak;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat theta = dTheta * float( i );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ i ] * getSample( -1.0 * theta, axis );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ i ] * getSample( theta, axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:w.ub,depthTest:!1,depthWrite:!1})}(ht),this._equirectShader=null,this._cubemapShader=null,this._compileMaterial(this._blurMaterial)}fromScene(t,e=0,n=.1,i=100){vt=this._renderer.getRenderTarget();const r=this._allocateTargets();return this._sceneToCubeUV(t,n,i,r),e>0&&this._blur(r,0,0,e),this._applyPMREM(r),this._cleanup(r),r}fromEquirectangular(t){return this._fromTexture(t)}fromCubemap(t){return this._fromTexture(t)}compileCubemapShader(){null===this._cubemapShader&&(this._cubemapShader=Ct(),this._compileMaterial(this._cubemapShader))}compileEquirectangularShader(){null===this._equirectShader&&(this._equirectShader=St(),this._compileMaterial(this._equirectShader))}dispose(){this._blurMaterial.dispose(),null!==this._cubemapShader&&this._cubemapShader.dispose(),null!==this._equirectShader&&this._equirectShader.dispose();for(let t=0;t<_t.length;t++)_t[t].dispose()}_cleanup(t){this._pingPongRenderTarget.dispose(),this._renderer.setRenderTarget(vt),t.scissorTest=!1,Mt(t,0,0,t.width,t.height)}_fromTexture(t){vt=this._renderer.getRenderTarget();const e=this._allocateTargets(t);return this._textureToCubeUV(t,e),this._applyPMREM(e),this._cleanup(e),e}_allocateTargets(t){const e={magFilter:w.ob,minFilter:w.ob,generateMipmaps:!1,type:w.Zc,format:w.hc,encoding:Tt(t)?t.encoding:w.gc,depthBuffer:!1},n=Et(e);return n.depthBuffer=!t,this._pingPongRenderTarget=Et(e),n}_compileMaterial(t){const e=new k.a(_t[0],t);this._renderer.compile(e,pt)}_sceneToCubeUV(t,e,n,i){const r=new K.a(90,1,e,n),s=[1,-1,1,1,1,1],o=[1,1,1,-1,-1,-1],a=this._renderer,l=a.autoClear,c=a.outputEncoding,u=a.toneMapping;a.getClearColor(gt),a.toneMapping=w.vb,a.outputEncoding=w.U,a.autoClear=!1;const h=new at.a({name:\\\\\\\"PMREM.Background\\\\\\\",side:w.i,depthWrite:!1,depthTest:!1}),d=new k.a(new N,h);let p=!1;const _=t.background;_?_.isColor&&(h.color.copy(_),t.background=null,p=!0):(h.color.copy(gt),p=!0);for(let e=0;e<6;e++){const n=e%3;0==n?(r.up.set(0,s[e],0),r.lookAt(o[e],0,0)):1==n?(r.up.set(0,0,s[e]),r.lookAt(0,o[e],0)):(r.up.set(0,s[e],0),r.lookAt(0,0,o[e])),Mt(i,n*lt,e>2?lt:0,lt,lt),a.setRenderTarget(i),p&&a.render(d,r),a.render(t,r)}d.geometry.dispose(),d.material.dispose(),a.toneMapping=u,a.outputEncoding=c,a.autoClear=l,t.background=_}_setEncoding(t,e){!0===this._renderer.capabilities.isWebGL2&&e.format===w.Ib&&e.type===w.Zc&&e.encoding===w.ld?t.value=dt[w.U]:t.value=dt[e.encoding]}_textureToCubeUV(t,e){const n=this._renderer;t.isCubeTexture?null==this._cubemapShader&&(this._cubemapShader=Ct()):null==this._equirectShader&&(this._equirectShader=St());const i=t.isCubeTexture?this._cubemapShader:this._equirectShader,r=new k.a(_t[0],i),s=i.uniforms;s.envMap.value=t,t.isCubeTexture||s.texelSize.value.set(1/t.image.width,1/t.image.height),this._setEncoding(s.inputEncoding,t),this._setEncoding(s.outputEncoding,e.texture),Mt(e,0,0,3*lt,2*lt),n.setRenderTarget(e),n.render(r,pt)}_applyPMREM(t){const e=this._renderer,n=e.autoClear;e.autoClear=!1;for(let e=1;eht&&console.warn(`sigmaRadians, ${r}, is too large and will clip, as it requested ${_} samples when the maximum is set to 20`);const m=[];let f=0;for(let t=0;t4?i-8+4:0),3*g,2*g),a.setRenderTarget(e),a.render(c,pt)}}function Tt(t){return void 0!==t&&t.type===w.Zc&&(t.encoding===w.U||t.encoding===w.ld||t.encoding===w.J)}function At(){const t=[],e=[],n=[];let i=8;for(let r=0;r4?o=ct[r-8+4-1]:0==r&&(o=0),n.push(o);const a=1/(s-1),l=-a/2,c=1+a/2,u=[l,l,c,l,c,c,l,l,c,c,l,c],h=6,d=6,p=3,_=2,m=1,f=new Float32Array(p*d*h),g=new Float32Array(_*d*h),v=new Float32Array(m*d*h);for(let t=0;t2?0:-1,i=[e,n,0,e+2/3,n,0,e+2/3,n+1,0,e,n,0,e+2/3,n+1,0,e,n+1,0];f.set(i,p*d*t),g.set(u,_*d*t);const r=[t,t,t,t,t,t];v.set(r,m*d*t)}const y=new S.a;y.setAttribute(\\\\\\\"position\\\\\\\",new C.a(f,p)),y.setAttribute(\\\\\\\"uv\\\\\\\",new C.a(g,_)),y.setAttribute(\\\\\\\"faceIndex\\\\\\\",new C.a(v,m)),t.push(y),i>4&&i--}return{_lodPlanes:t,_sizeLods:e,_sigmas:n}}function Et(t){const e=new Z(3*lt,3*lt,t);return e.texture.mapping=w.q,e.texture.name=\\\\\\\"PMREM.cubeUv\\\\\\\",e.scissorTest=!0,e}function Mt(t,e,n,i,r){t.viewport.set(e,n,i,r),t.scissor.set(e,n,i,r)}function St(){const t=new d.a(1,1);return new ot({name:\\\\\\\"EquirectangularToCubeUV\\\\\\\",uniforms:{envMap:{value:null},texelSize:{value:t},inputEncoding:{value:dt[w.U]},outputEncoding:{value:dt[w.U]}},vertexShader:Nt(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform vec2 texelSize;\\\\n\\\\n\\\\t\\\\t\\\\t${Lt()}\\\\n\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 outputDirection = normalize( vOutputDirection );\\\\n\\\\t\\\\t\\\\t\\\\tvec2 uv = equirectUv( outputDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec2 f = fract( uv / texelSize - 0.5 );\\\\n\\\\t\\\\t\\\\t\\\\tuv -= f * texelSize;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x += texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tr = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.y += texelSize.y;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 br = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x -= texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tm = mix( tl, tr, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bm = mix( bl, br, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = mix( tm, bm, f.y );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:w.ub,depthTest:!1,depthWrite:!1})}function Ct(){return new ot({name:\\\\\\\"CubemapToCubeUV\\\\\\\",uniforms:{envMap:{value:null},inputEncoding:{value:dt[w.U]},outputEncoding:{value:dt[w.U]}},vertexShader:Nt(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform samplerCube envMap;\\\\n\\\\n\\\\t\\\\t\\\\t${Lt()}\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = envMapTexelToLinear( textureCube( envMap, vec3( - vOutputDirection.x, vOutputDirection.yz ) ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:w.ub,depthTest:!1,depthWrite:!1})}function Nt(){return\\\\\\\"\\\\n\\\\n\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\tattribute vec3 position;\\\\n\\\\t\\\\tattribute vec2 uv;\\\\n\\\\t\\\\tattribute float faceIndex;\\\\n\\\\n\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t// RH coordinate system; PMREM face-indexing convention\\\\n\\\\t\\\\tvec3 getDirection( vec2 uv, float face ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = 2.0 * uv - 1.0;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 direction = vec3( uv, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\tif ( face == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.zyx; // ( 1, v, u ) pos x\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 1.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.xzy;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 2.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 3.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.zyx;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 4.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.xzy;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 5.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\treturn direction;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvOutputDirection = getDirection( uv, faceIndex );\\\\n\\\\t\\\\t\\\\tgl_Position = vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\\\\"}function Lt(){return\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform int inputEncoding;\\\\n\\\\t\\\\tuniform int outputEncoding;\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\tvec4 inputTexelToLinear( vec4 value ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( inputEncoding == 0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn value;\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 1 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn sRGBToLinear( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 2 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBEToLinear( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 3 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBMToLinear( value, 7.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 4 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBMToLinear( value, 16.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 5 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBDToLinear( value, 256.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn GammaToLinear( value, 2.2 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec4 linearToOutputTexel( vec4 value ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( outputEncoding == 0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn value;\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 1 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearTosRGB( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 2 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBE( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 3 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBM( value, 7.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 4 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBM( value, 16.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 5 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBD( value, 256.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToGamma( value, 2.2 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec4 envMapTexelToLinear( vec4 color ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn inputTexelToLinear( color );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\\\\"}function Ot(t){let e=new WeakMap,n=null;function i(t){const n=t.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",i);const r=e.get(n);void 0!==r&&(e.delete(n),r.dispose())}return{get:function(r){if(r&&r.isTexture&&!1===r.isRenderTargetTexture){const s=r.mapping,o=s===w.D||s===w.E,a=s===w.o||s===w.p;if(o||a){if(e.has(r))return e.get(r).texture;{const s=r.image;if(o&&s&&s.height>0||a&&s&&function(t){let e=0;const n=6;for(let i=0;i65535?C.i:C.h)(n,1);a.version=o;const l=s.get(t);l&&e.remove(l),s.set(t,a)}return{get:function(t,e){return!0===r[e.id]||(e.addEventListener(\\\\\\\"dispose\\\\\\\",o),r[e.id]=!0,n.memory.geometries++),e},update:function(n){const i=n.attributes;for(const n in i)e.update(i[n],t.ARRAY_BUFFER);const r=n.morphAttributes;for(const n in r){const i=r[n];for(let n=0,r=i.length;ne.maxTextureSize&&(h=Math.ceil(u/e.maxTextureSize),u=e.maxTextureSize);const p=new Float32Array(u*h*4*i),_=new kt(p,u,h,i);_.format=w.Ib,_.type=w.G;const m=4*l;for(let e=0;e0)return t;const r=e*n;let s=$t[r];if(void 0===s&&(s=new Float32Array(r),$t[r]=s),0!==e){i.toArray(s,0);for(let i=1,r=0;i!==e;++i)r+=n,t[i].toArray(s,r)}return s}function ee(t,e){if(t.length!==e.length)return!1;for(let n=0,i=t.length;n/gm;function sn(t){return t.replace(rn,on)}function on(t,e){const n=z[e];if(void 0===n)throw new Error(\\\\\\\"Can not resolve #include <\\\\\\\"+e+\\\\\\\">\\\\\\\");return sn(n)}const an=/#pragma unroll_loop[\\\\s]+?for \\\\( int i \\\\= (\\\\d+)\\\\; i < (\\\\d+)\\\\; i \\\\+\\\\+ \\\\) \\\\{([\\\\s\\\\S]+?)(?=\\\\})\\\\}/g,ln=/#pragma unroll_loop_start\\\\s+for\\\\s*\\\\(\\\\s*int\\\\s+i\\\\s*=\\\\s*(\\\\d+)\\\\s*;\\\\s*i\\\\s*<\\\\s*(\\\\d+)\\\\s*;\\\\s*i\\\\s*\\\\+\\\\+\\\\s*\\\\)\\\\s*{([\\\\s\\\\S]+?)}\\\\s+#pragma unroll_loop_end/g;function cn(t){return t.replace(ln,hn).replace(an,un)}function un(t,e,n,i){return console.warn(\\\\\\\"WebGLProgram: #pragma unroll_loop shader syntax is deprecated. Please use #pragma unroll_loop_start syntax instead.\\\\\\\"),hn(t,e,n,i)}function hn(t,e,n,i){let r=\\\\\\\"\\\\\\\";for(let t=parseInt(e);t0?t.gammaFactor:1,p=n.isWebGL2?\\\\\\\"\\\\\\\":function(t){return[t.extensionDerivatives||t.envMapCubeUV||t.bumpMap||t.tangentSpaceNormalMap||t.clearcoatNormalMap||t.flatShading||\\\\\\\"physical\\\\\\\"===t.shaderID?\\\\\\\"#extension GL_OES_standard_derivatives : enable\\\\\\\":\\\\\\\"\\\\\\\",(t.extensionFragDepth||t.logarithmicDepthBuffer)&&t.rendererExtensionFragDepth?\\\\\\\"#extension GL_EXT_frag_depth : enable\\\\\\\":\\\\\\\"\\\\\\\",t.extensionDrawBuffers&&t.rendererExtensionDrawBuffers?\\\\\\\"#extension GL_EXT_draw_buffers : require\\\\\\\":\\\\\\\"\\\\\\\",(t.extensionShaderTextureLOD||t.envMap||t.transmission)&&t.rendererExtensionShaderTextureLod?\\\\\\\"#extension GL_EXT_shader_texture_lod : enable\\\\\\\":\\\\\\\"\\\\\\\"].filter(tn).join(\\\\\\\"\\\\n\\\\\\\")}(n),_=function(t){const e=[];for(const n in t){const i=t[n];!1!==i&&e.push(\\\\\\\"#define \\\\\\\"+n+\\\\\\\" \\\\\\\"+i)}return e.join(\\\\\\\"\\\\n\\\\\\\")}(s),m=r.createProgram();let f,g,v=n.glslVersion?\\\\\\\"#version \\\\\\\"+n.glslVersion+\\\\\\\"\\\\n\\\\\\\":\\\\\\\"\\\\\\\";n.isRawShaderMaterial?(f=[_].filter(tn).join(\\\\\\\"\\\\n\\\\\\\"),f.length>0&&(f+=\\\\\\\"\\\\n\\\\\\\"),g=[p,_].filter(tn).join(\\\\\\\"\\\\n\\\\\\\"),g.length>0&&(g+=\\\\\\\"\\\\n\\\\\\\")):(f=[dn(n),\\\\\\\"#define SHADER_NAME \\\\\\\"+n.shaderName,_,n.instancing?\\\\\\\"#define USE_INSTANCING\\\\\\\":\\\\\\\"\\\\\\\",n.instancingColor?\\\\\\\"#define USE_INSTANCING_COLOR\\\\\\\":\\\\\\\"\\\\\\\",n.supportsVertexTextures?\\\\\\\"#define VERTEX_TEXTURES\\\\\\\":\\\\\\\"\\\\\\\",\\\\\\\"#define GAMMA_FACTOR \\\\\\\"+d,\\\\\\\"#define MAX_BONES \\\\\\\"+n.maxBones,n.useFog&&n.fog?\\\\\\\"#define USE_FOG\\\\\\\":\\\\\\\"\\\\\\\",n.useFog&&n.fogExp2?\\\\\\\"#define FOG_EXP2\\\\\\\":\\\\\\\"\\\\\\\",n.map?\\\\\\\"#define USE_MAP\\\\\\\":\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define USE_ENVMAP\\\\\\\":\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define \\\\\\\"+u:\\\\\\\"\\\\\\\",n.lightMap?\\\\\\\"#define USE_LIGHTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.aoMap?\\\\\\\"#define USE_AOMAP\\\\\\\":\\\\\\\"\\\\\\\",n.emissiveMap?\\\\\\\"#define USE_EMISSIVEMAP\\\\\\\":\\\\\\\"\\\\\\\",n.bumpMap?\\\\\\\"#define USE_BUMPMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap?\\\\\\\"#define USE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap&&n.objectSpaceNormalMap?\\\\\\\"#define OBJECTSPACE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap&&n.tangentSpaceNormalMap?\\\\\\\"#define TANGENTSPACE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatMap?\\\\\\\"#define USE_CLEARCOATMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatRoughnessMap?\\\\\\\"#define USE_CLEARCOAT_ROUGHNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatNormalMap?\\\\\\\"#define USE_CLEARCOAT_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.displacementMap&&n.supportsVertexTextures?\\\\\\\"#define USE_DISPLACEMENTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularMap?\\\\\\\"#define USE_SPECULARMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularIntensityMap?\\\\\\\"#define USE_SPECULARINTENSITYMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularTintMap?\\\\\\\"#define USE_SPECULARTINTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.roughnessMap?\\\\\\\"#define USE_ROUGHNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.metalnessMap?\\\\\\\"#define USE_METALNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.alphaMap?\\\\\\\"#define USE_ALPHAMAP\\\\\\\":\\\\\\\"\\\\\\\",n.transmission?\\\\\\\"#define USE_TRANSMISSION\\\\\\\":\\\\\\\"\\\\\\\",n.transmissionMap?\\\\\\\"#define USE_TRANSMISSIONMAP\\\\\\\":\\\\\\\"\\\\\\\",n.thicknessMap?\\\\\\\"#define USE_THICKNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.vertexTangents?\\\\\\\"#define USE_TANGENT\\\\\\\":\\\\\\\"\\\\\\\",n.vertexColors?\\\\\\\"#define USE_COLOR\\\\\\\":\\\\\\\"\\\\\\\",n.vertexAlphas?\\\\\\\"#define USE_COLOR_ALPHA\\\\\\\":\\\\\\\"\\\\\\\",n.vertexUvs?\\\\\\\"#define USE_UV\\\\\\\":\\\\\\\"\\\\\\\",n.uvsVertexOnly?\\\\\\\"#define UVS_VERTEX_ONLY\\\\\\\":\\\\\\\"\\\\\\\",n.flatShading?\\\\\\\"#define FLAT_SHADED\\\\\\\":\\\\\\\"\\\\\\\",n.skinning?\\\\\\\"#define USE_SKINNING\\\\\\\":\\\\\\\"\\\\\\\",n.useVertexTexture?\\\\\\\"#define BONE_TEXTURE\\\\\\\":\\\\\\\"\\\\\\\",n.morphTargets?\\\\\\\"#define USE_MORPHTARGETS\\\\\\\":\\\\\\\"\\\\\\\",n.morphNormals&&!1===n.flatShading?\\\\\\\"#define USE_MORPHNORMALS\\\\\\\":\\\\\\\"\\\\\\\",n.morphTargets&&n.isWebGL2?\\\\\\\"#define MORPHTARGETS_TEXTURE\\\\\\\":\\\\\\\"\\\\\\\",n.morphTargets&&n.isWebGL2?\\\\\\\"#define MORPHTARGETS_COUNT \\\\\\\"+n.morphTargetsCount:\\\\\\\"\\\\\\\",n.doubleSided?\\\\\\\"#define DOUBLE_SIDED\\\\\\\":\\\\\\\"\\\\\\\",n.flipSided?\\\\\\\"#define FLIP_SIDED\\\\\\\":\\\\\\\"\\\\\\\",n.shadowMapEnabled?\\\\\\\"#define USE_SHADOWMAP\\\\\\\":\\\\\\\"\\\\\\\",n.shadowMapEnabled?\\\\\\\"#define \\\\\\\"+l:\\\\\\\"\\\\\\\",n.sizeAttenuation?\\\\\\\"#define USE_SIZEATTENUATION\\\\\\\":\\\\\\\"\\\\\\\",n.logarithmicDepthBuffer?\\\\\\\"#define USE_LOGDEPTHBUF\\\\\\\":\\\\\\\"\\\\\\\",n.logarithmicDepthBuffer&&n.rendererExtensionFragDepth?\\\\\\\"#define USE_LOGDEPTHBUF_EXT\\\\\\\":\\\\\\\"\\\\\\\",\\\\\\\"uniform mat4 modelMatrix;\\\\\\\",\\\\\\\"uniform mat4 modelViewMatrix;\\\\\\\",\\\\\\\"uniform mat4 projectionMatrix;\\\\\\\",\\\\\\\"uniform mat4 viewMatrix;\\\\\\\",\\\\\\\"uniform mat3 normalMatrix;\\\\\\\",\\\\\\\"uniform vec3 cameraPosition;\\\\\\\",\\\\\\\"uniform bool isOrthographic;\\\\\\\",\\\\\\\"#ifdef USE_INSTANCING\\\\\\\",\\\\\\\"\\\\tattribute mat4 instanceMatrix;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#ifdef USE_INSTANCING_COLOR\\\\\\\",\\\\\\\"\\\\tattribute vec3 instanceColor;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"attribute vec3 position;\\\\\\\",\\\\\\\"attribute vec3 normal;\\\\\\\",\\\\\\\"attribute vec2 uv;\\\\\\\",\\\\\\\"#ifdef USE_TANGENT\\\\\\\",\\\\\\\"\\\\tattribute vec4 tangent;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\\\\",\\\\\\\"\\\\tattribute vec4 color;\\\\\\\",\\\\\\\"#elif defined( USE_COLOR )\\\\\\\",\\\\\\\"\\\\tattribute vec3 color;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#if ( defined( USE_MORPHTARGETS ) && ! defined( MORPHTARGETS_TEXTURE ) )\\\\\\\",\\\\\\\"\\\\tattribute vec3 morphTarget0;\\\\\\\",\\\\\\\"\\\\tattribute vec3 morphTarget1;\\\\\\\",\\\\\\\"\\\\tattribute vec3 morphTarget2;\\\\\\\",\\\\\\\"\\\\tattribute vec3 morphTarget3;\\\\\\\",\\\\\\\"\\\\t#ifdef USE_MORPHNORMALS\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphNormal0;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphNormal1;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphNormal2;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphNormal3;\\\\\\\",\\\\\\\"\\\\t#else\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphTarget4;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphTarget5;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphTarget6;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphTarget7;\\\\\\\",\\\\\\\"\\\\t#endif\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#ifdef USE_SKINNING\\\\\\\",\\\\\\\"\\\\tattribute vec4 skinIndex;\\\\\\\",\\\\\\\"\\\\tattribute vec4 skinWeight;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"\\\\n\\\\\\\"].filter(tn).join(\\\\\\\"\\\\n\\\\\\\"),g=[p,dn(n),\\\\\\\"#define SHADER_NAME \\\\\\\"+n.shaderName,_,\\\\\\\"#define GAMMA_FACTOR \\\\\\\"+d,n.useFog&&n.fog?\\\\\\\"#define USE_FOG\\\\\\\":\\\\\\\"\\\\\\\",n.useFog&&n.fogExp2?\\\\\\\"#define FOG_EXP2\\\\\\\":\\\\\\\"\\\\\\\",n.map?\\\\\\\"#define USE_MAP\\\\\\\":\\\\\\\"\\\\\\\",n.matcap?\\\\\\\"#define USE_MATCAP\\\\\\\":\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define USE_ENVMAP\\\\\\\":\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define \\\\\\\"+c:\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define \\\\\\\"+u:\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define \\\\\\\"+h:\\\\\\\"\\\\\\\",n.lightMap?\\\\\\\"#define USE_LIGHTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.aoMap?\\\\\\\"#define USE_AOMAP\\\\\\\":\\\\\\\"\\\\\\\",n.emissiveMap?\\\\\\\"#define USE_EMISSIVEMAP\\\\\\\":\\\\\\\"\\\\\\\",n.bumpMap?\\\\\\\"#define USE_BUMPMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap?\\\\\\\"#define USE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap&&n.objectSpaceNormalMap?\\\\\\\"#define OBJECTSPACE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap&&n.tangentSpaceNormalMap?\\\\\\\"#define TANGENTSPACE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoat?\\\\\\\"#define USE_CLEARCOAT\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatMap?\\\\\\\"#define USE_CLEARCOATMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatRoughnessMap?\\\\\\\"#define USE_CLEARCOAT_ROUGHNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatNormalMap?\\\\\\\"#define USE_CLEARCOAT_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularMap?\\\\\\\"#define USE_SPECULARMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularIntensityMap?\\\\\\\"#define USE_SPECULARINTENSITYMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularTintMap?\\\\\\\"#define USE_SPECULARTINTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.roughnessMap?\\\\\\\"#define USE_ROUGHNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.metalnessMap?\\\\\\\"#define USE_METALNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.alphaMap?\\\\\\\"#define USE_ALPHAMAP\\\\\\\":\\\\\\\"\\\\\\\",n.alphaTest?\\\\\\\"#define USE_ALPHATEST\\\\\\\":\\\\\\\"\\\\\\\",n.sheen?\\\\\\\"#define USE_SHEEN\\\\\\\":\\\\\\\"\\\\\\\",n.transmission?\\\\\\\"#define USE_TRANSMISSION\\\\\\\":\\\\\\\"\\\\\\\",n.transmissionMap?\\\\\\\"#define USE_TRANSMISSIONMAP\\\\\\\":\\\\\\\"\\\\\\\",n.thicknessMap?\\\\\\\"#define USE_THICKNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.vertexTangents?\\\\\\\"#define USE_TANGENT\\\\\\\":\\\\\\\"\\\\\\\",n.vertexColors||n.instancingColor?\\\\\\\"#define USE_COLOR\\\\\\\":\\\\\\\"\\\\\\\",n.vertexAlphas?\\\\\\\"#define USE_COLOR_ALPHA\\\\\\\":\\\\\\\"\\\\\\\",n.vertexUvs?\\\\\\\"#define USE_UV\\\\\\\":\\\\\\\"\\\\\\\",n.uvsVertexOnly?\\\\\\\"#define UVS_VERTEX_ONLY\\\\\\\":\\\\\\\"\\\\\\\",n.gradientMap?\\\\\\\"#define USE_GRADIENTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.flatShading?\\\\\\\"#define FLAT_SHADED\\\\\\\":\\\\\\\"\\\\\\\",n.doubleSided?\\\\\\\"#define DOUBLE_SIDED\\\\\\\":\\\\\\\"\\\\\\\",n.flipSided?\\\\\\\"#define FLIP_SIDED\\\\\\\":\\\\\\\"\\\\\\\",n.shadowMapEnabled?\\\\\\\"#define USE_SHADOWMAP\\\\\\\":\\\\\\\"\\\\\\\",n.shadowMapEnabled?\\\\\\\"#define \\\\\\\"+l:\\\\\\\"\\\\\\\",n.premultipliedAlpha?\\\\\\\"#define PREMULTIPLIED_ALPHA\\\\\\\":\\\\\\\"\\\\\\\",n.physicallyCorrectLights?\\\\\\\"#define PHYSICALLY_CORRECT_LIGHTS\\\\\\\":\\\\\\\"\\\\\\\",n.logarithmicDepthBuffer?\\\\\\\"#define USE_LOGDEPTHBUF\\\\\\\":\\\\\\\"\\\\\\\",n.logarithmicDepthBuffer&&n.rendererExtensionFragDepth?\\\\\\\"#define USE_LOGDEPTHBUF_EXT\\\\\\\":\\\\\\\"\\\\\\\",(n.extensionShaderTextureLOD||n.envMap)&&n.rendererExtensionShaderTextureLod?\\\\\\\"#define TEXTURE_LOD_EXT\\\\\\\":\\\\\\\"\\\\\\\",\\\\\\\"uniform mat4 viewMatrix;\\\\\\\",\\\\\\\"uniform vec3 cameraPosition;\\\\\\\",\\\\\\\"uniform bool isOrthographic;\\\\\\\",n.toneMapping!==w.vb?\\\\\\\"#define TONE_MAPPING\\\\\\\":\\\\\\\"\\\\\\\",n.toneMapping!==w.vb?z.tonemapping_pars_fragment:\\\\\\\"\\\\\\\",n.toneMapping!==w.vb?Ke(\\\\\\\"toneMapping\\\\\\\",n.toneMapping):\\\\\\\"\\\\\\\",n.dithering?\\\\\\\"#define DITHERING\\\\\\\":\\\\\\\"\\\\\\\",n.format===w.ic?\\\\\\\"#define OPAQUE\\\\\\\":\\\\\\\"\\\\\\\",z.encodings_pars_fragment,n.map?Ze(\\\\\\\"mapTexelToLinear\\\\\\\",n.mapEncoding):\\\\\\\"\\\\\\\",n.matcap?Ze(\\\\\\\"matcapTexelToLinear\\\\\\\",n.matcapEncoding):\\\\\\\"\\\\\\\",n.envMap?Ze(\\\\\\\"envMapTexelToLinear\\\\\\\",n.envMapEncoding):\\\\\\\"\\\\\\\",n.emissiveMap?Ze(\\\\\\\"emissiveMapTexelToLinear\\\\\\\",n.emissiveMapEncoding):\\\\\\\"\\\\\\\",n.specularTintMap?Ze(\\\\\\\"specularTintMapTexelToLinear\\\\\\\",n.specularTintMapEncoding):\\\\\\\"\\\\\\\",n.lightMap?Ze(\\\\\\\"lightMapTexelToLinear\\\\\\\",n.lightMapEncoding):\\\\\\\"\\\\\\\",Qe(\\\\\\\"linearToOutputTexel\\\\\\\",n.outputEncoding),n.depthPacking?\\\\\\\"#define DEPTH_PACKING \\\\\\\"+n.depthPacking:\\\\\\\"\\\\\\\",\\\\\\\"\\\\n\\\\\\\"].filter(tn).join(\\\\\\\"\\\\n\\\\\\\")),o=sn(o),o=en(o,n),o=nn(o,n),a=sn(a),a=en(a,n),a=nn(a,n),o=cn(o),a=cn(a),n.isWebGL2&&!0!==n.isRawShaderMaterial&&(v=\\\\\\\"#version 300 es\\\\n\\\\\\\",f=[\\\\\\\"precision mediump sampler2DArray;\\\\\\\",\\\\\\\"#define attribute in\\\\\\\",\\\\\\\"#define varying out\\\\\\\",\\\\\\\"#define texture2D texture\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")+\\\\\\\"\\\\n\\\\\\\"+f,g=[\\\\\\\"#define varying in\\\\\\\",n.glslVersion===w.I?\\\\\\\"\\\\\\\":\\\\\\\"out highp vec4 pc_fragColor;\\\\\\\",n.glslVersion===w.I?\\\\\\\"\\\\\\\":\\\\\\\"#define gl_FragColor pc_fragColor\\\\\\\",\\\\\\\"#define gl_FragDepthEXT gl_FragDepth\\\\\\\",\\\\\\\"#define texture2D texture\\\\\\\",\\\\\\\"#define textureCube texture\\\\\\\",\\\\\\\"#define texture2DProj textureProj\\\\\\\",\\\\\\\"#define texture2DLodEXT textureLod\\\\\\\",\\\\\\\"#define texture2DProjLodEXT textureProjLod\\\\\\\",\\\\\\\"#define textureCubeLodEXT textureLod\\\\\\\",\\\\\\\"#define texture2DGradEXT textureGrad\\\\\\\",\\\\\\\"#define texture2DProjGradEXT textureProjGrad\\\\\\\",\\\\\\\"#define textureCubeGradEXT textureGrad\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")+\\\\\\\"\\\\n\\\\\\\"+g);const y=v+f+o,x=v+g+a,b=Xe(r,r.VERTEX_SHADER,y),T=Xe(r,r.FRAGMENT_SHADER,x);if(r.attachShader(m,b),r.attachShader(m,T),void 0!==n.index0AttributeName?r.bindAttribLocation(m,0,n.index0AttributeName):!0===n.morphTargets&&r.bindAttribLocation(m,0,\\\\\\\"position\\\\\\\"),r.linkProgram(m),t.debug.checkShaderErrors){const t=r.getProgramInfoLog(m).trim(),e=r.getShaderInfoLog(b).trim(),n=r.getShaderInfoLog(T).trim();let i=!0,s=!0;if(!1===r.getProgramParameter(m,r.LINK_STATUS)){i=!1;const e=Je(r,b,\\\\\\\"vertex\\\\\\\"),n=Je(r,T,\\\\\\\"fragment\\\\\\\");console.error(\\\\\\\"THREE.WebGLProgram: Shader Error \\\\\\\"+r.getError()+\\\\\\\" - VALIDATE_STATUS \\\\\\\"+r.getProgramParameter(m,r.VALIDATE_STATUS)+\\\\\\\"\\\\n\\\\nProgram Info Log: \\\\\\\"+t+\\\\\\\"\\\\n\\\\\\\"+e+\\\\\\\"\\\\n\\\\\\\"+n)}else\\\\\\\"\\\\\\\"!==t?console.warn(\\\\\\\"THREE.WebGLProgram: Program Info Log:\\\\\\\",t):\\\\\\\"\\\\\\\"!==e&&\\\\\\\"\\\\\\\"!==n||(s=!1);s&&(this.diagnostics={runnable:i,programLog:t,vertexShader:{log:e,prefix:f},fragmentShader:{log:n,prefix:g}})}let A,E;return r.deleteShader(b),r.deleteShader(T),this.getUniforms=function(){return void 0===A&&(A=new qe(r,m)),A},this.getAttributes=function(){return void 0===E&&(E=function(t,e){const n={},i=t.getProgramParameter(e,t.ACTIVE_ATTRIBUTES);for(let r=0;r0,N=s.clearcoat>0;return{isWebGL2:l,shaderID:T,shaderName:s.type,vertexShader:E,fragmentShader:M,defines:s.defines,isRawShaderMaterial:!0===s.isRawShaderMaterial,glslVersion:s.glslVersion,precision:p,instancing:!0===v.isInstancedMesh,instancingColor:!0===v.isInstancedMesh&&null!==v.instanceColor,supportsVertexTextures:d,outputEncoding:null!==S?f(S.texture):t.outputEncoding,map:!!s.map,mapEncoding:f(s.map),matcap:!!s.matcap,matcapEncoding:f(s.matcap),envMap:!!b,envMapMode:b&&b.mapping,envMapEncoding:f(b),envMapCubeUV:!!b&&(b.mapping===w.q||b.mapping===w.r),lightMap:!!s.lightMap,lightMapEncoding:f(s.lightMap),aoMap:!!s.aoMap,emissiveMap:!!s.emissiveMap,emissiveMapEncoding:f(s.emissiveMap),bumpMap:!!s.bumpMap,normalMap:!!s.normalMap,objectSpaceNormalMap:s.normalMapType===w.zb,tangentSpaceNormalMap:s.normalMapType===w.Uc,clearcoat:N,clearcoatMap:N&&!!s.clearcoatMap,clearcoatRoughnessMap:N&&!!s.clearcoatRoughnessMap,clearcoatNormalMap:N&&!!s.clearcoatNormalMap,displacementMap:!!s.displacementMap,roughnessMap:!!s.roughnessMap,metalnessMap:!!s.metalnessMap,specularMap:!!s.specularMap,specularIntensityMap:!!s.specularIntensityMap,specularTintMap:!!s.specularTintMap,specularTintMapEncoding:f(s.specularTintMap),alphaMap:!!s.alphaMap,alphaTest:C,gradientMap:!!s.gradientMap,sheen:s.sheen>0,transmission:s.transmission>0,transmissionMap:!!s.transmissionMap,thicknessMap:!!s.thicknessMap,combine:s.combine,vertexTangents:!!s.normalMap&&!!v.geometry&&!!v.geometry.attributes.tangent,vertexColors:s.vertexColors,vertexAlphas:!0===s.vertexColors&&!!v.geometry&&!!v.geometry.attributes.color&&4===v.geometry.attributes.color.itemSize,vertexUvs:!!(s.map||s.bumpMap||s.normalMap||s.specularMap||s.alphaMap||s.emissiveMap||s.roughnessMap||s.metalnessMap||s.clearcoatMap||s.clearcoatRoughnessMap||s.clearcoatNormalMap||s.displacementMap||s.transmissionMap||s.thicknessMap||s.specularIntensityMap||s.specularTintMap),uvsVertexOnly:!(s.map||s.bumpMap||s.normalMap||s.specularMap||s.alphaMap||s.emissiveMap||s.roughnessMap||s.metalnessMap||s.clearcoatNormalMap||s.transmission>0||s.transmissionMap||s.thicknessMap||s.specularIntensityMap||s.specularTintMap||!s.displacementMap),fog:!!y,useFog:s.fog,fogExp2:y&&y.isFogExp2,flatShading:!!s.flatShading,sizeAttenuation:s.sizeAttenuation,logarithmicDepthBuffer:c,skinning:!0===v.isSkinnedMesh&&A>0,maxBones:A,useVertexTexture:u,morphTargets:!!v.geometry&&!!v.geometry.morphAttributes.position,morphNormals:!!v.geometry&&!!v.geometry.morphAttributes.normal,morphTargetsCount:v.geometry&&v.geometry.morphAttributes.position?v.geometry.morphAttributes.position.length:0,numDirLights:a.directional.length,numPointLights:a.point.length,numSpotLights:a.spot.length,numRectAreaLights:a.rectArea.length,numHemiLights:a.hemi.length,numDirLightShadows:a.directionalShadowMap.length,numPointLightShadows:a.pointShadowMap.length,numSpotLightShadows:a.spotShadowMap.length,numClippingPlanes:o.numPlanes,numClipIntersection:o.numIntersection,format:s.format,dithering:s.dithering,shadowMapEnabled:t.shadowMap.enabled&&m.length>0,shadowMapType:t.shadowMap.type,toneMapping:s.toneMapped?t.toneMapping:w.vb,physicallyCorrectLights:t.physicallyCorrectLights,premultipliedAlpha:s.premultipliedAlpha,doubleSided:s.side===w.z,flipSided:s.side===w.i,depthPacking:void 0!==s.depthPacking&&s.depthPacking,index0AttributeName:s.index0AttributeName,extensionDerivatives:s.extensions&&s.extensions.derivatives,extensionFragDepth:s.extensions&&s.extensions.fragDepth,extensionDrawBuffers:s.extensions&&s.extensions.drawBuffers,extensionShaderTextureLOD:s.extensions&&s.extensions.shaderTextureLOD,rendererExtensionFragDepth:l||i.has(\\\\\\\"EXT_frag_depth\\\\\\\"),rendererExtensionDrawBuffers:l||i.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\"),rendererExtensionShaderTextureLod:l||i.has(\\\\\\\"EXT_shader_texture_lod\\\\\\\"),customProgramCacheKey:s.customProgramCacheKey()}},getProgramCacheKey:function(e){const n=[];if(e.shaderID?n.push(e.shaderID):(n.push(e.fragmentShader),n.push(e.vertexShader)),void 0!==e.defines)for(const t in e.defines)n.push(t),n.push(e.defines[t]);if(!1===e.isRawShaderMaterial){for(let t=0;t0?r.push(u):!0===n.transparent?s.push(u):i.push(u)},unshift:function(t,e,n,o,l,c){const u=a(t,e,n,o,l,c);n.transmission>0?r.unshift(u):!0===n.transparent?s.unshift(u):i.unshift(u)},finish:function(){for(let t=n,i=e.length;t1&&i.sort(t||fn),r.length>1&&r.sort(e||gn),s.length>1&&s.sort(e||gn)}}}function yn(t){let e=new WeakMap;return{get:function(n,i){let r;return!1===e.has(n)?(r=new vn(t),e.set(n,[r])):i>=e.get(n).length?(r=new vn(t),e.get(n).push(r)):r=e.get(n)[i],r},dispose:function(){e=new WeakMap}}}function xn(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case\\\\\\\"DirectionalLight\\\\\\\":n={direction:new p.a,color:new D.a};break;case\\\\\\\"SpotLight\\\\\\\":n={position:new p.a,direction:new p.a,color:new D.a,distance:0,coneCos:0,penumbraCos:0,decay:0};break;case\\\\\\\"PointLight\\\\\\\":n={position:new p.a,color:new D.a,distance:0,decay:0};break;case\\\\\\\"HemisphereLight\\\\\\\":n={direction:new p.a,skyColor:new D.a,groundColor:new D.a};break;case\\\\\\\"RectAreaLight\\\\\\\":n={color:new D.a,position:new p.a,halfWidth:new p.a,halfHeight:new p.a}}return t[e.id]=n,n}}}let bn=0;function wn(t,e){return(e.castShadow?1:0)-(t.castShadow?1:0)}function Tn(t,e){const n=new xn,i=function(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case\\\\\\\"DirectionalLight\\\\\\\":case\\\\\\\"SpotLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new d.a};break;case\\\\\\\"PointLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new d.a,shadowCameraNear:1,shadowCameraFar:1e3}}return t[e.id]=n,n}}}(),r={version:0,hash:{directionalLength:-1,pointLength:-1,spotLength:-1,rectAreaLength:-1,hemiLength:-1,numDirectionalShadows:-1,numPointShadows:-1,numSpotShadows:-1},ambient:[0,0,0],probe:[],directional:[],directionalShadow:[],directionalShadowMap:[],directionalShadowMatrix:[],spot:[],spotShadow:[],spotShadowMap:[],spotShadowMatrix:[],rectArea:[],rectAreaLTC1:null,rectAreaLTC2:null,point:[],pointShadow:[],pointShadowMap:[],pointShadowMatrix:[],hemi:[]};for(let t=0;t<9;t++)r.probe.push(new p.a);const s=new p.a,o=new A.a,a=new A.a;return{setup:function(s,o){let a=0,l=0,c=0;for(let t=0;t<9;t++)r.probe[t].set(0,0,0);let u=0,h=0,d=0,p=0,_=0,m=0,f=0,g=0;s.sort(wn);const v=!0!==o?Math.PI:1;for(let t=0,e=s.length;t0&&(e.isWebGL2||!0===t.has(\\\\\\\"OES_texture_float_linear\\\\\\\")?(r.rectAreaLTC1=G.LTC_FLOAT_1,r.rectAreaLTC2=G.LTC_FLOAT_2):!0===t.has(\\\\\\\"OES_texture_half_float_linear\\\\\\\")?(r.rectAreaLTC1=G.LTC_HALF_1,r.rectAreaLTC2=G.LTC_HALF_2):console.error(\\\\\\\"THREE.WebGLRenderer: Unable to use RectAreaLight. Missing WebGL extensions.\\\\\\\")),r.ambient[0]=a,r.ambient[1]=l,r.ambient[2]=c;const y=r.hash;y.directionalLength===u&&y.pointLength===h&&y.spotLength===d&&y.rectAreaLength===p&&y.hemiLength===_&&y.numDirectionalShadows===m&&y.numPointShadows===f&&y.numSpotShadows===g||(r.directional.length=u,r.spot.length=d,r.rectArea.length=p,r.point.length=h,r.hemi.length=_,r.directionalShadow.length=m,r.directionalShadowMap.length=m,r.pointShadow.length=f,r.pointShadowMap.length=f,r.spotShadow.length=g,r.spotShadowMap.length=g,r.directionalShadowMatrix.length=m,r.pointShadowMatrix.length=f,r.spotShadowMatrix.length=g,y.directionalLength=u,y.pointLength=h,y.spotLength=d,y.rectAreaLength=p,y.hemiLength=_,y.numDirectionalShadows=m,y.numPointShadows=f,y.numSpotShadows=g,r.version=bn++)},setupView:function(t,e){let n=0,i=0,l=0,c=0,u=0;const h=e.matrixWorldInverse;for(let e=0,d=t.length;e=n.get(i).length?(s=new An(t,e),n.get(i).push(s)):s=n.get(i)[r],s},dispose:function(){n=new WeakMap}}}class Mn extends O.a{constructor(t){super(),this.type=\\\\\\\"MeshDepthMaterial\\\\\\\",this.depthPacking=w.j,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.depthPacking=t.depthPacking,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this}}Mn.prototype.isMeshDepthMaterial=!0;class Sn extends O.a{constructor(t){super(),this.type=\\\\\\\"MeshDistanceMaterial\\\\\\\",this.referencePosition=new p.a,this.nearDistance=1,this.farDistance=1e3,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.referencePosition.copy(t.referencePosition),this.nearDistance=t.nearDistance,this.farDistance=t.farDistance,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this}}Sn.prototype.isMeshDistanceMaterial=!0;function Cn(t,e,n){let i=new T.a;const r=new d.a,s=new d.a,o=new _.a,a=new Mn({depthPacking:w.Hb}),l=new Sn,c={},u=n.maxTextureSize,h={0:w.i,1:w.H,2:w.z},p=new F({uniforms:{shadow_pass:{value:null},resolution:{value:new d.a},radius:{value:4},samples:{value:8}},vertexShader:\\\\\\\"\\\\nvoid main() {\\\\n\\\\n\\\\tgl_Position = vec4( position, 1.0 );\\\\n\\\\n}\\\\n\\\\\\\",fragmentShader:\\\\\\\"\\\\nuniform sampler2D shadow_pass;\\\\nuniform vec2 resolution;\\\\nuniform float radius;\\\\nuniform float samples;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tfloat mean = 0.0;\\\\n\\\\tfloat squared_mean = 0.0;\\\\n\\\\n\\\\t// This seems totally useless but it's a crazy work around for a Adreno compiler bug\\\\n\\\\t// float depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy ) / resolution ) );\\\\n\\\\n\\\\tfloat uvStride = samples <= 1.0 ? 0.0 : 2.0 / ( samples - 1.0 );\\\\n\\\\tfloat uvStart = samples <= 1.0 ? 0.0 : - 1.0;\\\\n\\\\tfor ( float i = 0.0; i < samples; i ++ ) {\\\\n\\\\n\\\\t\\\\tfloat uvOffset = uvStart + i * uvStride;\\\\n\\\\n\\\\t\\\\t#ifdef HORIZONTAL_PASS\\\\n\\\\n\\\\t\\\\t\\\\tvec2 distribution = unpackRGBATo2Half( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( uvOffset, 0.0 ) * radius ) / resolution ) );\\\\n\\\\t\\\\t\\\\tmean += distribution.x;\\\\n\\\\t\\\\t\\\\tsquared_mean += distribution.y * distribution.y + distribution.x * distribution.x;\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( 0.0, uvOffset ) * radius ) / resolution ) );\\\\n\\\\t\\\\t\\\\tmean += depth;\\\\n\\\\t\\\\t\\\\tsquared_mean += depth * depth;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tmean = mean / samples;\\\\n\\\\tsquared_mean = squared_mean / samples;\\\\n\\\\n\\\\tfloat std_dev = sqrt( squared_mean - mean * mean );\\\\n\\\\n\\\\tgl_FragColor = pack2HalfToRGBA( vec2( mean, std_dev ) );\\\\n\\\\n}\\\\n\\\\\\\"}),m=p.clone();m.defines.HORIZONTAL_PASS=1;const f=new S.a;f.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array([-1,-1,.5,3,-1,.5,-1,3,.5]),3));const g=new k.a(f,p),v=this;function y(n,i){const r=e.update(g);p.uniforms.shadow_pass.value=n.map.texture,p.uniforms.resolution.value=n.mapSize,p.uniforms.radius.value=n.radius,p.uniforms.samples.value=n.blurSamples,t.setRenderTarget(n.mapPass),t.clear(),t.renderBufferDirect(i,null,r,p,g,null),m.uniforms.shadow_pass.value=n.mapPass.texture,m.uniforms.resolution.value=n.mapSize,m.uniforms.radius.value=n.radius,m.uniforms.samples.value=n.blurSamples,t.setRenderTarget(n.map),t.clear(),t.renderBufferDirect(i,null,r,m,g,null)}function x(e,n,i,r,s,o,u){let d=null;const p=!0===r.isPointLight?e.customDistanceMaterial:e.customDepthMaterial;if(d=void 0!==p?p:!0===r.isPointLight?l:a,t.localClippingEnabled&&!0===i.clipShadows&&0!==i.clippingPlanes.length||i.displacementMap&&0!==i.displacementScale||i.alphaMap&&i.alphaTest>0){const t=d.uuid,e=i.uuid;let n=c[t];void 0===n&&(n={},c[t]=n);let r=n[e];void 0===r&&(r=d.clone(),n[e]=r),d=r}return d.visible=i.visible,d.wireframe=i.wireframe,u===w.gd?d.side=null!==i.shadowSide?i.shadowSide:i.side:d.side=null!==i.shadowSide?i.shadowSide:h[i.side],d.alphaMap=i.alphaMap,d.alphaTest=i.alphaTest,d.clipShadows=i.clipShadows,d.clippingPlanes=i.clippingPlanes,d.clipIntersection=i.clipIntersection,d.displacementMap=i.displacementMap,d.displacementScale=i.displacementScale,d.displacementBias=i.displacementBias,d.wireframeLinewidth=i.wireframeLinewidth,d.linewidth=i.linewidth,!0===r.isPointLight&&!0===d.isMeshDistanceMaterial&&(d.referencePosition.setFromMatrixPosition(r.matrixWorld),d.nearDistance=s,d.farDistance=o),d}function b(n,r,s,o,a){if(!1===n.visible)return;if(n.layers.test(r.layers)&&(n.isMesh||n.isLine||n.isPoints)&&(n.castShadow||n.receiveShadow&&a===w.gd)&&(!n.frustumCulled||i.intersectsObject(n))){n.modelViewMatrix.multiplyMatrices(s.matrixWorldInverse,n.matrixWorld);const i=e.update(n),r=n.material;if(Array.isArray(r)){const e=i.groups;for(let l=0,c=e.length;lu||r.y>u)&&(r.x>u&&(s.x=Math.floor(u/p.x),r.x=s.x*p.x,h.mapSize.x=s.x),r.y>u&&(s.y=Math.floor(u/p.y),r.y=s.y*p.y,h.mapSize.y=s.y)),null===h.map&&!h.isPointLightShadow&&this.type===w.gd){const t={minFilter:w.V,magFilter:w.V,format:w.Ib};h.map=new Z(r.x,r.y,t),h.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",h.mapPass=new Z(r.x,r.y,t),h.camera.updateProjectionMatrix()}if(null===h.map){const t={minFilter:w.ob,magFilter:w.ob,format:w.Ib};h.map=new Z(r.x,r.y,t),h.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",h.camera.updateProjectionMatrix()}t.setRenderTarget(h.map),t.clear();const _=h.getViewportCount();for(let t=0;t<_;t++){const e=h.getViewport(t);o.set(s.x*e.x,s.y*e.y,s.x*e.z,s.y*e.w),d.viewport(o),h.updateMatrices(c,t),i=h.getFrustum(),b(n,a,h.camera,c,this.type)}h.isPointLightShadow||this.type!==w.gd||y(h,a),h.needsUpdate=!1}v.needsUpdate=!1,t.setRenderTarget(l,c,h)}}function Nn(t,e,n){const i=n.isWebGL2;const r=new function(){let e=!1;const n=new _.a;let i=null;const r=new _.a(0,0,0,0);return{setMask:function(n){i===n||e||(t.colorMask(n,n,n,n),i=n)},setLocked:function(t){e=t},setClear:function(e,i,s,o,a){!0===a&&(e*=o,i*=o,s*=o),n.set(e,i,s,o),!1===r.equals(n)&&(t.clearColor(e,i,s,o),r.copy(n))},reset:function(){e=!1,i=null,r.set(-1,0,0,0)}}},s=new function(){let e=!1,n=null,i=null,r=null;return{setTest:function(e){e?z(t.DEPTH_TEST):U(t.DEPTH_TEST)},setMask:function(i){n===i||e||(t.depthMask(i),n=i)},setFunc:function(e){if(i!==e){if(e)switch(e){case w.tb:t.depthFunc(t.NEVER);break;case w.g:t.depthFunc(t.ALWAYS);break;case w.S:t.depthFunc(t.LESS);break;case w.T:t.depthFunc(t.LEQUAL);break;case w.C:t.depthFunc(t.EQUAL);break;case w.L:t.depthFunc(t.GEQUAL);break;case w.K:t.depthFunc(t.GREATER);break;case w.yb:t.depthFunc(t.NOTEQUAL);break;default:t.depthFunc(t.LEQUAL)}else t.depthFunc(t.LEQUAL);i=e}},setLocked:function(t){e=t},setClear:function(e){r!==e&&(t.clearDepth(e),r=e)},reset:function(){e=!1,n=null,i=null,r=null}}},o=new function(){let e=!1,n=null,i=null,r=null,s=null,o=null,a=null,l=null,c=null;return{setTest:function(n){e||(n?z(t.STENCIL_TEST):U(t.STENCIL_TEST))},setMask:function(i){n===i||e||(t.stencilMask(i),n=i)},setFunc:function(e,n,o){i===e&&r===n&&s===o||(t.stencilFunc(e,n,o),i=e,r=n,s=o)},setOp:function(e,n,i){o===e&&a===n&&l===i||(t.stencilOp(e,n,i),o=e,a=n,l=i)},setLocked:function(t){e=t},setClear:function(e){c!==e&&(t.clearStencil(e),c=e)},reset:function(){e=!1,n=null,i=null,r=null,s=null,o=null,a=null,l=null,c=null}}};let a={},l=null,c={},u=null,h=!1,d=null,p=null,m=null,f=null,g=null,v=null,y=null,x=!1,b=null,T=null,A=null,E=null,M=null;const S=t.getParameter(t.MAX_COMBINED_TEXTURE_IMAGE_UNITS);let C=!1,N=0;const L=t.getParameter(t.VERSION);-1!==L.indexOf(\\\\\\\"WebGL\\\\\\\")?(N=parseFloat(/^WebGL (\\\\d)/.exec(L)[1]),C=N>=1):-1!==L.indexOf(\\\\\\\"OpenGL ES\\\\\\\")&&(N=parseFloat(/^OpenGL ES (\\\\d)/.exec(L)[1]),C=N>=2);let O=null,R={};const P=t.getParameter(t.SCISSOR_BOX),I=t.getParameter(t.VIEWPORT),F=(new _.a).fromArray(P),D=(new _.a).fromArray(I);function k(e,n,i){const r=new Uint8Array(4),s=t.createTexture();t.bindTexture(e,s),t.texParameteri(e,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(e,t.TEXTURE_MAG_FILTER,t.NEAREST);for(let e=0;ei||t.height>i)&&(r=i/Math.max(t.width,t.height)),r<1||!0===e){if(\\\\\\\"undefined\\\\\\\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\\\\\\\"undefined\\\\\\\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\\\\\\\"undefined\\\\\\\"!=typeof ImageBitmap&&t instanceof ImageBitmap){const i=e?Ln.g:Math.floor,s=i(r*t.width),o=i(r*t.height);void 0===p&&(p=m(s,o));const a=n?m(s,o):p;a.width=s,a.height=o;return a.getContext(\\\\\\\"2d\\\\\\\").drawImage(t,0,0,s,o),console.warn(\\\\\\\"THREE.WebGLRenderer: Texture has been resized from (\\\\\\\"+t.width+\\\\\\\"x\\\\\\\"+t.height+\\\\\\\") to (\\\\\\\"+s+\\\\\\\"x\\\\\\\"+o+\\\\\\\").\\\\\\\"),a}return\\\\\\\"data\\\\\\\"in t&&console.warn(\\\\\\\"THREE.WebGLRenderer: Image in DataTexture is too big (\\\\\\\"+t.width+\\\\\\\"x\\\\\\\"+t.height+\\\\\\\").\\\\\\\"),t}return t}function g(t){return Ln.i(t.width)&&Ln.i(t.height)}function v(t,e){return t.generateMipmaps&&e&&t.minFilter!==w.ob&&t.minFilter!==w.V}function y(e,n,r,s,o=1){t.generateMipmap(e);i.get(n).__maxMipLevel=Math.log2(Math.max(r,s,o))}function x(n,i,r,s){if(!1===a)return i;if(null!==n){if(void 0!==t[n])return t[n];console.warn(\\\\\\\"THREE.WebGLRenderer: Attempt to use non-existing WebGL internal format '\\\\\\\"+n+\\\\\\\"'\\\\\\\")}let o=i;return i===t.RED&&(r===t.FLOAT&&(o=t.R32F),r===t.HALF_FLOAT&&(o=t.R16F),r===t.UNSIGNED_BYTE&&(o=t.R8)),i===t.RGB&&(r===t.FLOAT&&(o=t.RGB32F),r===t.HALF_FLOAT&&(o=t.RGB16F),r===t.UNSIGNED_BYTE&&(o=t.RGB8)),i===t.RGBA&&(r===t.FLOAT&&(o=t.RGBA32F),r===t.HALF_FLOAT&&(o=t.RGBA16F),r===t.UNSIGNED_BYTE&&(o=s===w.ld?t.SRGB8_ALPHA8:t.RGBA8)),o!==t.R16F&&o!==t.R32F&&o!==t.RGBA16F&&o!==t.RGBA32F||e.get(\\\\\\\"EXT_color_buffer_float\\\\\\\"),o}function b(e){return e===w.ob||e===w.sb||e===w.rb?t.NEAREST:t.LINEAR}function T(e){const n=e.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",T),function(e){const n=i.get(e);if(void 0===n.__webglInit)return;t.deleteTexture(n.__webglTexture),i.remove(e)}(n),n.isVideoTexture&&d.delete(n),o.memory.textures--}function A(e){const n=e.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",A),function(e){const n=e.texture,r=i.get(e),s=i.get(n);if(!e)return;void 0!==s.__webglTexture&&(t.deleteTexture(s.__webglTexture),o.memory.textures--);e.depthTexture&&e.depthTexture.dispose();if(e.isWebGLCubeRenderTarget)for(let e=0;e<6;e++)t.deleteFramebuffer(r.__webglFramebuffer[e]),r.__webglDepthbuffer&&t.deleteRenderbuffer(r.__webglDepthbuffer[e]);else t.deleteFramebuffer(r.__webglFramebuffer),r.__webglDepthbuffer&&t.deleteRenderbuffer(r.__webglDepthbuffer),r.__webglMultisampledFramebuffer&&t.deleteFramebuffer(r.__webglMultisampledFramebuffer),r.__webglColorRenderbuffer&&t.deleteRenderbuffer(r.__webglColorRenderbuffer),r.__webglDepthRenderbuffer&&t.deleteRenderbuffer(r.__webglDepthRenderbuffer);if(e.isWebGLMultipleRenderTargets)for(let e=0,r=n.length;e0&&s.__version!==e.version){const t=e.image;if(void 0===t)console.warn(\\\\\\\"THREE.WebGLRenderer: Texture marked for update but image is undefined\\\\\\\");else{if(!1!==t.complete)return void R(s,e,r);console.warn(\\\\\\\"THREE.WebGLRenderer: Texture marked for update but image is incomplete\\\\\\\")}}n.activeTexture(t.TEXTURE0+r),n.bindTexture(t.TEXTURE_2D,s.__webglTexture)}function S(e,r){const o=i.get(e);e.version>0&&o.__version!==e.version?function(e,i,r){if(6!==i.image.length)return;O(e,i),n.activeTexture(t.TEXTURE0+r),n.bindTexture(t.TEXTURE_CUBE_MAP,e.__webglTexture),t.pixelStorei(t.UNPACK_FLIP_Y_WEBGL,i.flipY),t.pixelStorei(t.UNPACK_PREMULTIPLY_ALPHA_WEBGL,i.premultiplyAlpha),t.pixelStorei(t.UNPACK_ALIGNMENT,i.unpackAlignment),t.pixelStorei(t.UNPACK_COLORSPACE_CONVERSION_WEBGL,t.NONE);const o=i&&(i.isCompressedTexture||i.image[0].isCompressedTexture),l=i.image[0]&&i.image[0].isDataTexture,u=[];for(let t=0;t<6;t++)u[t]=o||l?l?i.image[t].image:i.image[t]:f(i.image[t],!1,!0,c);const h=u[0],d=g(h)||a,p=s.convert(i.format),_=s.convert(i.type),m=x(i.internalFormat,p,_,i.encoding);let b;if(L(t.TEXTURE_CUBE_MAP,i,d),o){for(let e=0;e<6;e++){b=u[e].mipmaps;for(let r=0;r1||i.get(s).__currentAnisotropy)&&(t.texParameterf(n,o.TEXTURE_MAX_ANISOTROPY_EXT,Math.min(s.anisotropy,r.getMaxAnisotropy())),i.get(s).__currentAnisotropy=s.anisotropy)}}function O(e,n){void 0===e.__webglInit&&(e.__webglInit=!0,n.addEventListener(\\\\\\\"dispose\\\\\\\",T),e.__webglTexture=t.createTexture(),o.memory.textures++)}function R(e,i,r){let o=t.TEXTURE_2D;i.isDataTexture2DArray&&(o=t.TEXTURE_2D_ARRAY),i.isDataTexture3D&&(o=t.TEXTURE_3D),O(e,i),n.activeTexture(t.TEXTURE0+r),n.bindTexture(o,e.__webglTexture),t.pixelStorei(t.UNPACK_FLIP_Y_WEBGL,i.flipY),t.pixelStorei(t.UNPACK_PREMULTIPLY_ALPHA_WEBGL,i.premultiplyAlpha),t.pixelStorei(t.UNPACK_ALIGNMENT,i.unpackAlignment),t.pixelStorei(t.UNPACK_COLORSPACE_CONVERSION_WEBGL,t.NONE);const l=function(t){return!a&&(t.wrapS!==w.n||t.wrapT!==w.n||t.minFilter!==w.ob&&t.minFilter!==w.V)}(i)&&!1===g(i.image),c=f(i.image,l,!1,u),h=g(c)||a,d=s.convert(i.format);let p,_=s.convert(i.type),m=x(i.internalFormat,d,_,i.encoding);L(o,i,h);const b=i.mipmaps;if(i.isDepthTexture)m=t.DEPTH_COMPONENT,a?m=i.type===w.G?t.DEPTH_COMPONENT32F:i.type===w.bd?t.DEPTH_COMPONENT24:i.type===w.ad?t.DEPTH24_STENCIL8:t.DEPTH_COMPONENT16:i.type===w.G&&console.error(\\\\\\\"WebGLRenderer: Floating point depth texture requires WebGL2.\\\\\\\"),i.format===w.x&&m===t.DEPTH_COMPONENT&&i.type!==w.fd&&i.type!==w.bd&&(console.warn(\\\\\\\"THREE.WebGLRenderer: Use UnsignedShortType or UnsignedIntType for DepthFormat DepthTexture.\\\\\\\"),i.type=w.fd,_=s.convert(i.type)),i.format===w.y&&m===t.DEPTH_COMPONENT&&(m=t.DEPTH_STENCIL,i.type!==w.ad&&(console.warn(\\\\\\\"THREE.WebGLRenderer: Use UnsignedInt248Type for DepthStencilFormat DepthTexture.\\\\\\\"),i.type=w.ad,_=s.convert(i.type))),n.texImage2D(t.TEXTURE_2D,0,m,c.width,c.height,0,d,_,null);else if(i.isDataTexture)if(b.length>0&&h){for(let e=0,i=b.length;e0&&h){for(let e=0,i=b.length;e=l&&console.warn(\\\\\\\"THREE.WebGLTextures: Trying to use \\\\\\\"+t+\\\\\\\" texture units while this GPU supports only \\\\\\\"+l),E+=1,t},this.resetTextureUnits=function(){E=0},this.setTexture2D=M,this.setTexture2DArray=function(e,r){const s=i.get(e);e.version>0&&s.__version!==e.version?R(s,e,r):(n.activeTexture(t.TEXTURE0+r),n.bindTexture(t.TEXTURE_2D_ARRAY,s.__webglTexture))},this.setTexture3D=function(e,r){const s=i.get(e);e.version>0&&s.__version!==e.version?R(s,e,r):(n.activeTexture(t.TEXTURE0+r),n.bindTexture(t.TEXTURE_3D,s.__webglTexture))},this.setTextureCube=S,this.setupRenderTarget=function(e){const l=e.texture,c=i.get(e),u=i.get(l);e.addEventListener(\\\\\\\"dispose\\\\\\\",A),!0!==e.isWebGLMultipleRenderTargets&&(u.__webglTexture=t.createTexture(),u.__version=l.version,o.memory.textures++);const h=!0===e.isWebGLCubeRenderTarget,d=!0===e.isWebGLMultipleRenderTargets,p=!0===e.isWebGLMultisampleRenderTarget,_=l.isDataTexture3D||l.isDataTexture2DArray,m=g(e)||a;if(!a||l.format!==w.ic||l.type!==w.G&&l.type!==w.M||(l.format=w.Ib,console.warn(\\\\\\\"THREE.WebGLRenderer: Rendering to textures with RGB format is not supported. Using RGBA format instead.\\\\\\\")),h){c.__webglFramebuffer=[];for(let e=0;e<6;e++)c.__webglFramebuffer[e]=t.createFramebuffer()}else if(c.__webglFramebuffer=t.createFramebuffer(),d)if(r.drawBuffers){const n=e.texture;for(let e=0,r=n.length;ea+c?(l.inputState.pinching=!1,this.dispatchEvent({type:\\\\\\\"pinchend\\\\\\\",handedness:t.handedness,target:this})):!l.inputState.pinching&&o<=a-c&&(l.inputState.pinching=!0,this.dispatchEvent({type:\\\\\\\"pinchstart\\\\\\\",handedness:t.handedness,target:this}))}else null!==a&&t.gripSpace&&(r=e.getPose(t.gripSpace,n),null!==r&&(a.matrix.fromArray(r.transform.matrix),a.matrix.decompose(a.position,a.rotation,a.scale),r.linearVelocity?(a.hasLinearVelocity=!0,a.linearVelocity.copy(r.linearVelocity)):a.hasLinearVelocity=!1,r.angularVelocity?(a.hasAngularVelocity=!0,a.angularVelocity.copy(r.angularVelocity)):a.hasAngularVelocity=!1));return null!==o&&(o.visible=null!==i),null!==a&&(a.visible=null!==r),null!==l&&(l.visible=null!==s),this}}class kn extends $.a{constructor(t,e){super();const n=this,i=t.state;let r=null,s=1,o=null,a=\\\\\\\"local-floor\\\\\\\",l=null,c=null,u=null,h=null,d=null,m=!1,f=null,g=null,v=null,y=null,x=null,b=null;const w=[],T=new Map,A=new K.a;A.layers.enable(1),A.viewport=new _.a;const M=new K.a;M.layers.enable(2),M.viewport=new _.a;const S=[A,M],C=new Pn;C.layers.enable(1),C.layers.enable(2);let N=null,L=null;function O(t){const e=T.get(t.inputSource);e&&e.dispatchEvent({type:t.type,data:t.inputSource})}function R(){T.forEach((function(t,e){t.disconnect(e)})),T.clear(),N=null,L=null,i.bindXRFramebuffer(null),t.setRenderTarget(t.getRenderTarget()),u&&e.deleteFramebuffer(u),f&&e.deleteFramebuffer(f),g&&e.deleteRenderbuffer(g),v&&e.deleteRenderbuffer(v),u=null,f=null,g=null,v=null,d=null,h=null,c=null,r=null,B.stop(),n.isPresenting=!1,n.dispatchEvent({type:\\\\\\\"sessionend\\\\\\\"})}function P(t){const e=r.inputSources;for(let t=0;t0&&(e.alphaTest.value=n.alphaTest);const i=t.get(n).envMap;if(i){e.envMap.value=i,e.flipEnvMap.value=i.isCubeTexture&&!1===i.isRenderTargetTexture?-1:1,e.reflectivity.value=n.reflectivity,e.ior.value=n.ior,e.refractionRatio.value=n.refractionRatio;const r=t.get(i).__maxMipLevel;void 0!==r&&(e.maxMipLevel.value=r)}let r,s;n.lightMap&&(e.lightMap.value=n.lightMap,e.lightMapIntensity.value=n.lightMapIntensity),n.aoMap&&(e.aoMap.value=n.aoMap,e.aoMapIntensity.value=n.aoMapIntensity),n.map?r=n.map:n.specularMap?r=n.specularMap:n.displacementMap?r=n.displacementMap:n.normalMap?r=n.normalMap:n.bumpMap?r=n.bumpMap:n.roughnessMap?r=n.roughnessMap:n.metalnessMap?r=n.metalnessMap:n.alphaMap?r=n.alphaMap:n.emissiveMap?r=n.emissiveMap:n.clearcoatMap?r=n.clearcoatMap:n.clearcoatNormalMap?r=n.clearcoatNormalMap:n.clearcoatRoughnessMap?r=n.clearcoatRoughnessMap:n.specularIntensityMap?r=n.specularIntensityMap:n.specularTintMap?r=n.specularTintMap:n.transmissionMap?r=n.transmissionMap:n.thicknessMap&&(r=n.thicknessMap),void 0!==r&&(r.isWebGLRenderTarget&&(r=r.texture),!0===r.matrixAutoUpdate&&r.updateMatrix(),e.uvTransform.value.copy(r.matrix)),n.aoMap?s=n.aoMap:n.lightMap&&(s=n.lightMap),void 0!==s&&(s.isWebGLRenderTarget&&(s=s.texture),!0===s.matrixAutoUpdate&&s.updateMatrix(),e.uv2Transform.value.copy(s.matrix))}function n(e,n){e.roughness.value=n.roughness,e.metalness.value=n.metalness,n.roughnessMap&&(e.roughnessMap.value=n.roughnessMap),n.metalnessMap&&(e.metalnessMap.value=n.metalnessMap),n.emissiveMap&&(e.emissiveMap.value=n.emissiveMap),n.bumpMap&&(e.bumpMap.value=n.bumpMap,e.bumpScale.value=n.bumpScale,n.side===w.i&&(e.bumpScale.value*=-1)),n.normalMap&&(e.normalMap.value=n.normalMap,e.normalScale.value.copy(n.normalScale),n.side===w.i&&e.normalScale.value.negate()),n.displacementMap&&(e.displacementMap.value=n.displacementMap,e.displacementScale.value=n.displacementScale,e.displacementBias.value=n.displacementBias);t.get(n).envMap&&(e.envMapIntensity.value=n.envMapIntensity)}return{refreshFogUniforms:function(t,e){t.fogColor.value.copy(e.color),e.isFog?(t.fogNear.value=e.near,t.fogFar.value=e.far):e.isFogExp2&&(t.fogDensity.value=e.density)},refreshMaterialUniforms:function(t,i,r,s,o){i.isMeshBasicMaterial?e(t,i):i.isMeshLambertMaterial?(e(t,i),function(t,e){e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap)}(t,i)):i.isMeshToonMaterial?(e(t,i),function(t,e){e.gradientMap&&(t.gradientMap.value=e.gradientMap);e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,e.side===w.i&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),e.side===w.i&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshPhongMaterial?(e(t,i),function(t,e){t.specular.value.copy(e.specular),t.shininess.value=Math.max(e.shininess,1e-4),e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,e.side===w.i&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),e.side===w.i&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshStandardMaterial?(e(t,i),i.isMeshPhysicalMaterial?function(t,e,i){n(t,e),t.ior.value=e.ior,e.sheen>0&&(t.sheenTint.value.copy(e.sheenTint).multiplyScalar(e.sheen),t.sheenRoughness.value=e.sheenRoughness);e.clearcoat>0&&(t.clearcoat.value=e.clearcoat,t.clearcoatRoughness.value=e.clearcoatRoughness,e.clearcoatMap&&(t.clearcoatMap.value=e.clearcoatMap),e.clearcoatRoughnessMap&&(t.clearcoatRoughnessMap.value=e.clearcoatRoughnessMap),e.clearcoatNormalMap&&(t.clearcoatNormalScale.value.copy(e.clearcoatNormalScale),t.clearcoatNormalMap.value=e.clearcoatNormalMap,e.side===w.i&&t.clearcoatNormalScale.value.negate()));e.transmission>0&&(t.transmission.value=e.transmission,t.transmissionSamplerMap.value=i.texture,t.transmissionSamplerSize.value.set(i.width,i.height),e.transmissionMap&&(t.transmissionMap.value=e.transmissionMap),t.thickness.value=e.thickness,e.thicknessMap&&(t.thicknessMap.value=e.thicknessMap),t.attenuationDistance.value=e.attenuationDistance,t.attenuationTint.value.copy(e.attenuationTint));t.specularIntensity.value=e.specularIntensity,t.specularTint.value.copy(e.specularTint),e.specularIntensityMap&&(t.specularIntensityMap.value=e.specularIntensityMap);e.specularTintMap&&(t.specularTintMap.value=e.specularTintMap)}(t,i,o):n(t,i)):i.isMeshMatcapMaterial?(e(t,i),function(t,e){e.matcap&&(t.matcap.value=e.matcap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,e.side===w.i&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),e.side===w.i&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDepthMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDistanceMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias);t.referencePosition.value.copy(e.referencePosition),t.nearDistance.value=e.nearDistance,t.farDistance.value=e.farDistance}(t,i)):i.isMeshNormalMaterial?(e(t,i),function(t,e){e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,e.side===w.i&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),e.side===w.i&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isLineBasicMaterial?(function(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity}(t,i),i.isLineDashedMaterial&&function(t,e){t.dashSize.value=e.dashSize,t.totalSize.value=e.dashSize+e.gapSize,t.scale.value=e.scale}(t,i)):i.isPointsMaterial?function(t,e,n,i){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.size.value=e.size*n,t.scale.value=.5*i,e.map&&(t.map.value=e.map);e.alphaMap&&(t.alphaMap.value=e.alphaMap);e.alphaTest>0&&(t.alphaTest.value=e.alphaTest);let r;e.map?r=e.map:e.alphaMap&&(r=e.alphaMap);void 0!==r&&(!0===r.matrixAutoUpdate&&r.updateMatrix(),t.uvTransform.value.copy(r.matrix))}(t,i,r,s):i.isSpriteMaterial?function(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.rotation.value=e.rotation,e.map&&(t.map.value=e.map);e.alphaMap&&(t.alphaMap.value=e.alphaMap);e.alphaTest>0&&(t.alphaTest.value=e.alphaTest);let n;e.map?n=e.map:e.alphaMap&&(n=e.alphaMap);void 0!==n&&(!0===n.matrixAutoUpdate&&n.updateMatrix(),t.uvTransform.value.copy(n.matrix))}(t,i):i.isShadowMaterial?(t.color.value.copy(i.color),t.opacity.value=i.opacity):i.isShaderMaterial&&(i.uniformsNeedUpdate=!1)}}}function zn(t={}){const e=void 0!==t.canvas?t.canvas:function(){const t=Object(Pt.b)(\\\\\\\"canvas\\\\\\\");return t.style.display=\\\\\\\"block\\\\\\\",t}(),n=void 0!==t.context?t.context:null,i=void 0!==t.alpha&&t.alpha,r=void 0===t.depth||t.depth,s=void 0===t.stencil||t.stencil,o=void 0!==t.antialias&&t.antialias,a=void 0===t.premultipliedAlpha||t.premultipliedAlpha,l=void 0!==t.preserveDrawingBuffer&&t.preserveDrawingBuffer,c=void 0!==t.powerPreference?t.powerPreference:\\\\\\\"default\\\\\\\",u=void 0!==t.failIfMajorPerformanceCaveat&&t.failIfMajorPerformanceCaveat;let h=null,d=null;const m=[],f=[];this.domElement=e,this.debug={checkShaderErrors:!0},this.autoClear=!0,this.autoClearColor=!0,this.autoClearDepth=!0,this.autoClearStencil=!0,this.sortObjects=!0,this.clippingPlanes=[],this.localClippingEnabled=!1,this.gammaFactor=2,this.outputEncoding=w.U,this.physicallyCorrectLights=!1,this.toneMapping=w.vb,this.toneMappingExposure=1;const g=this;let v=!1,y=0,x=0,b=null,S=-1,C=null;const N=new _.a,L=new _.a;let O=null,R=e.width,P=e.height,I=1,F=null,D=null;const k=new _.a(0,0,R,P),B=new _.a(0,0,R,P);let z=!1;const U=[],G=new T.a;let V=!1,X=!1,$=null;const J=new A.a,Q=new p.a,K={background:null,fog:null,environment:null,overrideMaterial:null,isScene:!0};function tt(){return null===b?I:1}let et,nt,it,st,ot,at,lt,ct,ut,ht,dt,pt,_t,mt,ft,gt,vt,yt,xt,bt,wt,Tt,At,Et=n;function Mt(t,n){for(let i=0;i0&&function(t,e,n){if(null===$){const t=!0===o&&!0===nt.isWebGL2;$=new(t?Vt:Z)(1024,1024,{generateMipmaps:!0,type:null!==Tt.convert(w.M)?w.M:w.Zc,minFilter:w.Y,magFilter:w.ob,wrapS:w.n,wrapT:w.n})}const i=g.getRenderTarget();g.setRenderTarget($),g.clear();const r=g.toneMapping;g.toneMapping=w.vb,Xt(t,e,n),g.toneMapping=r,at.updateMultisampleRenderTarget($),at.updateRenderTargetMipmap($),g.setRenderTarget(i)}(r,e,n),i&&it.viewport(N.copy(i)),r.length>0&&Xt(r,e,n),s.length>0&&Xt(s,e,n),a.length>0&&Xt(a,e,n)}function Xt(t,e,n){const i=!0===e.isScene?e.overrideMaterial:null;for(let r=0,s=t.length;r0?f[f.length-1]:null,m.pop(),h=m.length>0?m[m.length-1]:null},this.getActiveCubeFace=function(){return y},this.getActiveMipmapLevel=function(){return x},this.getRenderTarget=function(){return b},this.setRenderTarget=function(t,e=0,n=0){b=t,y=e,x=n,t&&void 0===ot.get(t).__webglFramebuffer&&at.setupRenderTarget(t);let i=null,r=!1,s=!1;if(t){const n=t.texture;(n.isDataTexture3D||n.isDataTexture2DArray)&&(s=!0);const o=ot.get(t).__webglFramebuffer;t.isWebGLCubeRenderTarget?(i=o[e],r=!0):i=t.isWebGLMultisampleRenderTarget?ot.get(t).__webglMultisampledFramebuffer:o,N.copy(t.viewport),L.copy(t.scissor),O=t.scissorTest}else N.copy(k).multiplyScalar(I).floor(),L.copy(B).multiplyScalar(I).floor(),O=z;if(it.bindFramebuffer(Et.FRAMEBUFFER,i)&&nt.drawBuffers){let e=!1;if(t)if(t.isWebGLMultipleRenderTargets){const n=t.texture;if(U.length!==n.length||U[0]!==Et.COLOR_ATTACHMENT0){for(let t=0,e=n.length;t=0&&e<=t.width-i&&n>=0&&n<=t.height-r&&Et.readPixels(e,n,i,r,Tt.convert(a),Tt.convert(l),s):console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: readPixels from renderTarget failed. Framebuffer not complete.\\\\\\\")}finally{const t=null!==b?ot.get(b).__webglFramebuffer:null;it.bindFramebuffer(Et.FRAMEBUFFER,t)}}},this.copyFramebufferToTexture=function(t,e,n=0){const i=Math.pow(2,-n),r=Math.floor(e.image.width*i),s=Math.floor(e.image.height*i);let o=Tt.convert(e.format);nt.isWebGL2&&(o===Et.RGB&&(o=Et.RGB8),o===Et.RGBA&&(o=Et.RGBA8)),at.setTexture2D(e,0),Et.copyTexImage2D(Et.TEXTURE_2D,n,o,t.x,t.y,r,s,0),it.unbindTexture()},this.copyTextureToTexture=function(t,e,n,i=0){const r=e.image.width,s=e.image.height,o=Tt.convert(n.format),a=Tt.convert(n.type);at.setTexture2D(n,0),Et.pixelStorei(Et.UNPACK_FLIP_Y_WEBGL,n.flipY),Et.pixelStorei(Et.UNPACK_PREMULTIPLY_ALPHA_WEBGL,n.premultiplyAlpha),Et.pixelStorei(Et.UNPACK_ALIGNMENT,n.unpackAlignment),e.isDataTexture?Et.texSubImage2D(Et.TEXTURE_2D,i,t.x,t.y,r,s,o,a,e.image.data):e.isCompressedTexture?Et.compressedTexSubImage2D(Et.TEXTURE_2D,i,t.x,t.y,e.mipmaps[0].width,e.mipmaps[0].height,o,e.mipmaps[0].data):Et.texSubImage2D(Et.TEXTURE_2D,i,t.x,t.y,o,a,e.image),0===i&&n.generateMipmaps&&Et.generateMipmap(Et.TEXTURE_2D),it.unbindTexture()},this.copyTextureToTexture3D=function(t,e,n,i,r=0){if(g.isWebGL1Renderer)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: can only be used with WebGL2.\\\\\\\");const s=t.max.x-t.min.x+1,o=t.max.y-t.min.y+1,a=t.max.z-t.min.z+1,l=Tt.convert(i.format),c=Tt.convert(i.type);let u;if(i.isDataTexture3D)at.setTexture3D(i,0),u=Et.TEXTURE_3D;else{if(!i.isDataTexture2DArray)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: only supports THREE.DataTexture3D and THREE.DataTexture2DArray.\\\\\\\");at.setTexture2DArray(i,0),u=Et.TEXTURE_2D_ARRAY}Et.pixelStorei(Et.UNPACK_FLIP_Y_WEBGL,i.flipY),Et.pixelStorei(Et.UNPACK_PREMULTIPLY_ALPHA_WEBGL,i.premultiplyAlpha),Et.pixelStorei(Et.UNPACK_ALIGNMENT,i.unpackAlignment);const h=Et.getParameter(Et.UNPACK_ROW_LENGTH),d=Et.getParameter(Et.UNPACK_IMAGE_HEIGHT),p=Et.getParameter(Et.UNPACK_SKIP_PIXELS),_=Et.getParameter(Et.UNPACK_SKIP_ROWS),m=Et.getParameter(Et.UNPACK_SKIP_IMAGES),f=n.isCompressedTexture?n.mipmaps[0]:n.image;Et.pixelStorei(Et.UNPACK_ROW_LENGTH,f.width),Et.pixelStorei(Et.UNPACK_IMAGE_HEIGHT,f.height),Et.pixelStorei(Et.UNPACK_SKIP_PIXELS,t.min.x),Et.pixelStorei(Et.UNPACK_SKIP_ROWS,t.min.y),Et.pixelStorei(Et.UNPACK_SKIP_IMAGES,t.min.z),n.isDataTexture||n.isDataTexture3D?Et.texSubImage3D(u,r,e.x,e.y,e.z,s,o,a,l,c,f.data):n.isCompressedTexture?(console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: untested support for compressed srcTexture.\\\\\\\"),Et.compressedTexSubImage3D(u,r,e.x,e.y,e.z,s,o,a,l,f.data)):Et.texSubImage3D(u,r,e.x,e.y,e.z,s,o,a,l,c,f),Et.pixelStorei(Et.UNPACK_ROW_LENGTH,h),Et.pixelStorei(Et.UNPACK_IMAGE_HEIGHT,d),Et.pixelStorei(Et.UNPACK_SKIP_PIXELS,p),Et.pixelStorei(Et.UNPACK_SKIP_ROWS,_),Et.pixelStorei(Et.UNPACK_SKIP_IMAGES,m),0===r&&i.generateMipmaps&&Et.generateMipmap(u),it.unbindTexture()},this.initTexture=function(t){at.setTexture2D(t,0),it.unbindTexture()},this.resetState=function(){y=0,x=0,b=null,it.reset(),At.reset()},\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}const Un={};var Gn,Vn,Hn;!function(t){t.WEBGL=\\\\\\\"webgl\\\\\\\",t.WEBGL2=\\\\\\\"webgl2\\\\\\\",t.EXPERIMENTAL_WEBGL=\\\\\\\"experimental-webgl\\\\\\\",t.EXPERIMENTAL_WEBGL2=\\\\\\\"experimental-webgl2\\\\\\\"}(Gn||(Gn={}));class jn{constructor(){this._next_renderer_id=0,this._renderers={},this._printDebug=!1,this._require_webgl2=!1,this._resolves=[]}setPrintDebug(t=!0){this._printDebug=t}printDebug(){return this._printDebug}printDebugMessage(t){this._printDebug&&console.warn(\\\\\\\"[Poly debug]\\\\\\\",t)}setRequireWebGL2(){this._require_webgl2||(this._require_webgl2=!0)}webgl2Available(){return void 0===this._webgl2_available&&(this._webgl2_available=this._set_webgl2_available()),this._webgl2_available}_set_webgl2_available(){const t=document.createElement(\\\\\\\"canvas\\\\\\\");return null!=(window.WebGL2RenderingContext&&t.getContext(Gn.WEBGL2))}createWebGLRenderer(t){const e=new zn(t);return this.printDebugMessage([\\\\\\\"create renderer:\\\\\\\",t]),e}createRenderingContext(t){let e=null;return this._require_webgl2&&(e=this._getRenderingContextWebgl(t,!0),e||console.warn(\\\\\\\"failed to create webgl2 context\\\\\\\")),e||(e=this._getRenderingContextWebgl(t,!1)),e}_getRenderingContextWebgl(t,e){let n;n=this.webgl2Available()||e?Gn.WEBGL2:Gn.WEBGL;let i=t.getContext(n,Un);return i?this.printDebugMessage(`create gl context: ${n}.`):(n=e?Gn.EXPERIMENTAL_WEBGL2:Gn.EXPERIMENTAL_WEBGL,this.printDebugMessage(`create gl context: ${n}.`),i=t.getContext(n,Un)),i}registerRenderer(t){if(t._polygon_id)throw new Error(\\\\\\\"render already registered\\\\\\\");t._polygon_id=this._next_renderer_id+=1,this._renderers[t._polygon_id]=t,1==Object.keys(this._renderers).length&&this.flush_callbacks_with_renderer(t)}deregisterRenderer(t){delete this._renderers[t._polygon_id],t.dispose()}firstRenderer(){const t=Object.keys(this._renderers)[0];return t?this._renderers[t]:null}renderers(){return Object.values(this._renderers)}flush_callbacks_with_renderer(t){let e;for(;e=this._resolves.pop();)e(t)}async waitForRenderer(){const t=this.firstRenderer();return t||new Promise(((t,e)=>{this._resolves.push(t)}))}renderTarget(t,e,n){return this.webgl2Available()?new Vt(t,e,n):new Z(t,e,n)}}class Wn{constructor(){this._root=\\\\\\\"/three/js/libs\\\\\\\",this._BASISPath=\\\\\\\"/basis\\\\\\\",this._DRACOPath=\\\\\\\"/draco\\\\\\\",this._DRACOGLTFPath=\\\\\\\"/draco/gltf\\\\\\\"}root(){return this._root}setRoot(t){this._root=t}BASISPath(){return this._BASISPath}DRACOPath(){return this._DRACOPath}DRACOGLTFPath(){return this._DRACOGLTFPath}}class qn{constructor(t){this.poly=t,this._node_register=new Map,this._node_register_categories=new Map,this._node_register_options=new Map}register(t,e,n){const i=t.context(),r=t.type().toLowerCase();let s=this._node_register.get(i);s||(s=new Map,this._node_register.set(i,s));if(s.get(r))console.error(`node ${i}/${r} already registered`);else{if(s.set(r,t),e){let t=this._node_register_categories.get(i);t||(t=new Map,this._node_register_categories.set(i,t)),t.set(r,e)}if(n){let t=this._node_register_options.get(i);t||(t=new Map,this._node_register_options.set(i,t)),t.set(r,n)}this.poly.pluginsRegister.registerNode(t)}}deregister(t,e){var n,i,r;null===(n=this._node_register.get(t))||void 0===n||n.delete(e),null===(i=this._node_register_categories.get(t))||void 0===i||i.delete(e),null===(r=this._node_register_options.get(t))||void 0===r||r.delete(e)}isRegistered(t,e){const n=this._node_register.get(t);return!!n&&null!=n.get(e)}registeredNodesForContextAndParentType(t,e){var n;if(this._node_register.get(t)){const i=[];return null===(n=this._node_register.get(t))||void 0===n||n.forEach(((t,e)=>{i.push(t)})),i.filter((n=>{var i;const r=n.type().toLowerCase(),s=null===(i=this._node_register_options.get(t))||void 0===i?void 0:i.get(r);if(s){const n=s.only,i=s.except,r=`${t}/${e}`;return n?n.includes(r):!i||!i.includes(r)}return!0}))}return[]}registeredNodes(t,e){const n={},i=this.registeredNodesForContextAndParentType(t,e);for(let t of i){n[t.type().toLowerCase()]=t}return n}registeredCategory(t,e){var n;return null===(n=this._node_register_categories.get(t))||void 0===n?void 0:n.get(e.toLowerCase())}map(){return this._node_register}}class Xn{constructor(t){this.poly=t,this._operation_register=new Map}register(t){const e=t.context();let n=this._operation_register.get(e);n||(n=new Map,this._operation_register.set(e,n));const i=t.type().toLowerCase();if(n.get(i)){const t=`operation ${e}/${i} already registered`;console.error(t)}else n.set(i,t),this.poly.pluginsRegister.registerOperation(t)}registeredOperationsForContextAndParentType(t,e){var n;if(this._operation_register.get(t)){const e=[];return null===(n=this._operation_register.get(t))||void 0===n||n.forEach(((t,n)=>{e.push(t)})),e}return[]}registeredOperation(t,e){const n=this._operation_register.get(t);if(n)return n.get(e.toLowerCase())}}class Yn extends class{constructor(){this._methods_names=[],this._methods_by_name=new Map}register(t,e){this._methods_names.push(e),this._methods_by_name.set(e,t)}getMethod(t){return this._methods_by_name.get(t)}availableMethods(){return this._methods_names}}{getMethod(t){return super.getMethod(t)}}!function(t){t.BasisTextureLoader=\\\\\\\"BasisTextureLoader\\\\\\\",t.DRACOLoader=\\\\\\\"DRACOLoader\\\\\\\",t.EXRLoader=\\\\\\\"EXRLoader\\\\\\\",t.FBXLoader=\\\\\\\"FBXLoader\\\\\\\",t.GLTFLoader=\\\\\\\"GLTFLoader\\\\\\\",t.OBJLoader=\\\\\\\"OBJLoader\\\\\\\",t.PDBLoader=\\\\\\\"PDBLoader\\\\\\\",t.PLYLoader=\\\\\\\"PLYLoader\\\\\\\",t.RGBELoader=\\\\\\\"RGBELoader\\\\\\\",t.SVGLoader=\\\\\\\"SVGLoader\\\\\\\",t.STLLoader=\\\\\\\"STLLoader\\\\\\\",t.TTFLoader=\\\\\\\"TTFLoader\\\\\\\"}(Vn||(Vn={}));class $n extends class{constructor(){this._module_by_name=new Map}register(t,e){this._module_by_name.set(t,e)}moduleNames(){const t=[];return this._module_by_name.forEach(((e,n)=>{t.push(n)})),t}module(t){return this._module_by_name.get(t)}}{}!function(t){t.GL_MESH_BASIC=\\\\\\\"GL_MESH_BASIC\\\\\\\",t.GL_MESH_LAMBERT=\\\\\\\"GL_MESH_LAMBERT\\\\\\\",t.GL_MESH_STANDARD=\\\\\\\"GL_MESH_STANDARD\\\\\\\",t.GL_MESH_PHONG=\\\\\\\"GL_MESH_PHONG\\\\\\\",t.GL_MESH_PHYSICAL=\\\\\\\"GL_MESH_PHYSICAL\\\\\\\",t.GL_PARTICLES=\\\\\\\"GL_PARTICLES\\\\\\\",t.GL_POINTS=\\\\\\\"GL_POINTS\\\\\\\",t.GL_LINE=\\\\\\\"GL_LINE\\\\\\\",t.GL_TEXTURE=\\\\\\\"GL_TEXTURE\\\\\\\",t.GL_VOLUME=\\\\\\\"GL_VOLUME\\\\\\\"}(Hn||(Hn={}));class Jn extends class{constructor(){this._controller_assembler_by_name=new Map}register(t,e,n){this._controller_assembler_by_name.set(t,{controller:e,assembler:n})}unregister(t){this._controller_assembler_by_name.delete(t)}}{assembler(t,e){const n=this._controller_assembler_by_name.get(e);if(n){return new(0,n.controller)(t,n.assembler)}return n}unregister(t){const e=this._controller_assembler_by_name.get(t);return super.unregister(t),e}}class Zn{constructor(t){this.poly=t,this._plugins_by_name=new Map,this._plugin_name_by_node_context_by_type=new Map,this._plugin_name_by_operation_context_by_type=new Map}register(t){this._current_plugin=t,this._plugins_by_name.set(t.name(),t),t.init(this.poly),this._current_plugin=void 0}pluginByName(t){return this._plugins_by_name.get(t)}registerNode(t){if(!this._current_plugin)return;const e=t.context(),n=t.type();let i=this._plugin_name_by_node_context_by_type.get(e);i||(i=new Map,this._plugin_name_by_node_context_by_type.set(e,i)),i.set(n,this._current_plugin.name())}registerOperation(t){if(!this._current_plugin)return;const e=t.context(),n=t.type();let i=this._plugin_name_by_operation_context_by_type.get(e);i||(i=new Map,this._plugin_name_by_operation_context_by_type.set(e,i)),i.set(n,this._current_plugin.name())}toJson(){const t={plugins:{},nodes:{},operations:{}};return this._plugins_by_name.forEach(((e,n)=>{t.plugins[n]=e.toJSON()})),this._plugin_name_by_node_context_by_type.forEach(((e,n)=>{t.nodes[n]={},e.forEach(((e,i)=>{t.nodes[n][i]=e}))})),this._plugin_name_by_operation_context_by_type.forEach(((e,n)=>{t.operations[n]={},e.forEach(((e,i)=>{t.operations[n][i]=e}))})),t}}class Qn{constructor(t){this._camera_types=[]}register(t){const e=t.type();this._camera_types.includes(e)||this._camera_types.push(e)}registeredTypes(){return this._camera_types}}var Kn=n(86);class ti{constructor(){this._blobUrlsByStoredUrl=new Map,this._blobsByStoredUrl=new Map,this._blobDataByNodeId=new Map,this._globalBlobsByStoredUrl=new Map}registerBlobUrl(t){console.log(\\\\\\\"registerBlobUrl\\\\\\\",t,ai.playerMode()),ai.playerMode()&&this._blobUrlsByStoredUrl.set(t.storedUrl,t.blobUrl)}blobUrl(t){return this._blobUrlsByStoredUrl.get(t)}clear(){this._blobUrlsByStoredUrl.clear(),this._blobsByStoredUrl.clear(),this._blobDataByNodeId.clear()}_clearBlobForNode(t){const e=this._blobDataByNodeId.get(t.graphNodeId());e&&(this._blobsByStoredUrl.delete(e.storedUrl),this._blobUrlsByStoredUrl.delete(e.storedUrl)),this._blobDataByNodeId.delete(t.graphNodeId())}_assignBlobToNode(t,e){this._clearBlobForNode(t),this._blobDataByNodeId.set(t.graphNodeId(),{storedUrl:e.storedUrl,fullUrl:e.fullUrl})}async fetchBlobGlobal(t){if(ai.playerMode())return{};try{if(this._blobUrlsByStoredUrl.get(t.storedUrl))return{};const e=ai.assetUrls.remapedUrl(t.fullUrl),n=await fetch(e||t.fullUrl);if(n.ok){const e=await n.blob();return this._blobsByStoredUrl.set(t.storedUrl,e),this._blobUrlsByStoredUrl.set(t.storedUrl,this.createBlobUrl(e)),this._globalBlobsByStoredUrl.set(t.storedUrl,e),{blobData:{storedUrl:t.storedUrl,fullUrl:t.fullUrl}}}return{error:`failed to fetch ${t.fullUrl}`}}catch(e){return{error:`failed to fetch ${t.fullUrl}`}}}async fetchBlobForNode(t){if(ai.playerMode())return{};try{if(this._blobUrlsByStoredUrl.get(t.storedUrl))return{};const e=ai.assetUrls.remapedUrl(t.fullUrl),n=await fetch(e||t.fullUrl);if(n.ok){const e=await n.blob();return this._blobsByStoredUrl.set(t.storedUrl,e),this._blobUrlsByStoredUrl.set(t.storedUrl,this.createBlobUrl(e)),this._scene=t.node.scene(),this._assignBlobToNode(t.node,{storedUrl:t.storedUrl,fullUrl:t.fullUrl}),{blobData:{storedUrl:t.storedUrl,fullUrl:t.fullUrl}}}return{error:`failed to fetch ${t.fullUrl}`}}catch(e){return{error:`failed to fetch ${t.fullUrl}`}}}forEachBlob(t){this._blobDataByNodeId.forEach(((e,n)=>{if(this._scene){if(this._scene.graph.nodeFromId(n)){const{storedUrl:n}=e,i=this._blobsByStoredUrl.get(n);i&&t(i,n)}}}));let e=[];const n=new Map;this._globalBlobsByStoredUrl.forEach(((t,i)=>{e.push(i),n.set(i,t)})),e=e.sort(),e.forEach((e=>{const n=this._globalBlobsByStoredUrl.get(e);n&&t(n,e)}))}createBlobUrl(t){return Object(Kn.a)(t)}}class ei{setMap(t){this._map=t}remapedUrl(t){if(!this._map)return;const e=t.split(\\\\\\\"?\\\\\\\"),n=e[0],i=e[1],r=this._map[n];return r?i?`${r}?${i}`:r:void 0}}var ni=n(93),ii=n(83);class ri{markAsLoaded(t,e){this._sceneJsonImporterContructor=e,t()}load(t){if(!this._sceneJsonImporterContructor)return;const e=[];t.forEach(((t,n)=>{e.push(n)}));for(let n of e){const e=t.get(n);e&&(this._loadElement(n,e,this._sceneJsonImporterContructor),t.delete(n))}}async _loadElement(t,e,n){const{sceneData:i,assetsManifest:r,unzippedData:s}=e,o=Object.keys(r);for(let t of o){const e=s[`assets/${r[t]}`];if(!e)return void console.error(t,e);const n=new Blob([e]),i={storedUrl:t,blobUrl:ai.blobs.createBlobUrl(n)};ai.blobs.registerBlobUrl(i)}ai.setPlayerMode(!0),ai.libs.setRoot(null);const a=`${Math.random()}`.replace(\\\\\\\".\\\\\\\",\\\\\\\"_\\\\\\\"),l={Poly:`___POLY_polyConfig_configurePolygonjs_${a}`,scriptElementId:`___POLY_polyConfig_scriptElement_${a}`,loadSceneArgs:`___POLY_polyConfig_loadSceneArgs_${a}`};window[l.Poly]=ai;const c={method:this._loadScene.bind(this),element:t,sceneData:i,sceneJsonImporterContructor:n};window[l.loadSceneArgs]=c;this._loadPolyConfig(l,s)||this._loadScene(t,i,n)}_loadPolyConfig(t,e){const n=e[ii.a.POLY_CONFIG];if(!n)return!1;const i=this._createJsBlob(n,\\\\\\\"polyConfig\\\\\\\");let r=document.getElementById(t.scriptElementId);const s=[];return s.push(`import {configurePolygonjs, configureScene} from '${i}';`),s.push(`configurePolygonjs(window.${t.Poly});`),s.push(`window.${t.loadSceneArgs}.method(window.${t.loadSceneArgs}.element, window.${t.loadSceneArgs}.sceneData, window.${t.loadSceneArgs}.sceneJsonImporterContructor, configureScene);`),s.push(`delete window.${t.loadSceneArgs};`),r||(r=document.createElement(\\\\\\\"script\\\\\\\"),r.setAttribute(\\\\\\\"type\\\\\\\",\\\\\\\"module\\\\\\\"),r.text=s.join(\\\\\\\"\\\\n\\\\\\\"),document.body.append(r)),!0}async _loadScene(t,e,n,i){this._fadeOutPoster(t);const r=new n(e),s=await r.scene();i&&i(s);const o=s.mainCameraNode();if(!o)return void console.warn(\\\\\\\"no master camera found\\\\\\\");const a=o.createViewer(t);s.play(),t.scene=s,t.viewer=a}_fadeOutPoster(t){const e=t.firstElementChild;e&&(e.style.pointerEvents=\\\\\\\"none\\\\\\\",ni.a.fadeOut(e).then((()=>{var t;null===(t=e.parentElement)||void 0===t||t.removeChild(e)})))}_createJsBlob(t,e){const n=new Blob([t]),i=new File([n],`${e}.js`,{type:\\\\\\\"application/javascript\\\\\\\"});return Object(Kn.a)(i)}}class si{setPerformanceManager(t){this._performanceManager=t}performanceManager(){return this._performanceManager||window.performance}}class oi{constructor(){this.renderersController=new jn,this.nodesRegister=new qn(this),this.operationsRegister=new Xn(this),this.expressionsRegister=new Yn,this.modulesRegister=new $n,this.assemblersRegister=new Jn,this.pluginsRegister=new Zn(this),this.camerasRegister=new Qn(this),this.blobs=new ti,this.assetUrls=new ei,this.selfContainedScenesLoader=new ri,this.performance=new si,this.scenesByUuid={},this._player_mode=!0,this._logger=null}static _instance_(){if(window.__POLYGONJS_POLY_INSTANCE__)return window.__POLYGONJS_POLY_INSTANCE__;{const t=new oi;return window.__POLYGONJS_POLY_INSTANCE__=t,window.__POLYGONJS_POLY_INSTANCE__}}setPlayerMode(t){this._player_mode=t}playerMode(){return this._player_mode}registerNode(t,e,n){this.nodesRegister.register(t,e,n)}registerOperation(t){this.operationsRegister.register(t)}registerCamera(t){this.camerasRegister.register(t)}registerPlugin(t){this.pluginsRegister.register(t)}registeredNodes(t,e){return this.nodesRegister.registeredNodes(t,e)}registeredOperation(t,e){return this.operationsRegister.registeredOperation(t,e)}registeredCameraTypes(){return this.camerasRegister.registeredTypes()}inWorkerThread(){return!1}desktopController(){}get libs(){return this._libs_controller=this._libs_controller||new Wn}setEnv(t){this._env=t}env(){return this._env}setLogger(t){this._logger=t}get logger(){return this._logger}log(t,...e){var n;null===(n=this.logger)||void 0===n||n.log(t,...e)}warn(t,...e){var n;null===(n=this.logger)||void 0===n||n.warn(t,...e)}error(t,...e){var n;null===(n=this.logger)||void 0===n||n.error(t,...e)}}const ai=oi._instance_();class li{constructor(){this._started=!1,this._start_time=0,this._previous_timestamp=0,this._nodes_cook_data={},this._durations_by_name={},this._durations_count_by_name={}}profile(t,e){const n=ai.performance.performanceManager(),i=n.now();e();const r=n.now()-i;console.log(`${t}: ${r}`)}start(){if(!this._started){this.reset(),this._started=!0;const t=ai.performance.performanceManager();this._start_time=t.now(),this._nodes_cook_data={},this._previous_timestamp=this._start_time}}stop(){this.reset()}reset(){this._started=!1,this._start_time=null,this._durations_by_name={},this._durations_count_by_name={},this._nodes_cook_data={}}started(){return this._started}record_node_cook_data(t,e){const n=t.graphNodeId();null==this._nodes_cook_data[n]&&(this._nodes_cook_data[n]=new c(t)),this._nodes_cook_data[n].update_cook_data(e)}record(t){this.started()||this.start();const e=performance.now();return null==this._durations_by_name[t]&&(this._durations_by_name[t]=0),this._durations_by_name[t]+=e-this._previous_timestamp,null==this._durations_count_by_name[t]&&(this._durations_count_by_name[t]=0),this._durations_count_by_name[t]+=1,this._previous_timestamp=e}print(){this.print_node_cook_data(),this.print_recordings()}print_node_cook_data(){let t=Object.values(this._nodes_cook_data);t=f.sortBy(t,(t=>t.total_cook_time()));const e=t.map((t=>t.print_object()));console.log(\\\\\\\"--------------- NODES COOK TIME -----------\\\\\\\");const n=[],i=f.sortBy(e,(t=>-t.total_cook_time));for(let t of i)n.push(t);return console.table(n),e}print_recordings(){const t=b.clone(this._durations_by_name),e=b.clone(this._durations_count_by_name),n=[],i={};for(let e of Object.keys(t)){const r=t[e];n.push(r),null==i[r]&&(i[r]=[]),i[r].push(e)}n.sort(((t,e)=>t-e));const r=f.uniq(n);console.log(\\\\\\\"--------------- PERF RECORDINGS -----------\\\\\\\");const s=[];for(let t of r){const n=i[t];for(let i of n){const n=e[i],r={duration:t,name:i,count:n,duration_per_iteration:t/n};s.push(r)}}return console.table(s),s}}class ci{constructor(t){this.scene=t}setListener(t){this._events_listener?console.warn(\\\\\\\"scene already has a listener\\\\\\\"):(this._events_listener=t,this.run_on_add_listener_callbacks())}onAddListener(t){this._events_listener?t():(this._on_add_listener_callbacks=this._on_add_listener_callbacks||[],this._on_add_listener_callbacks.push(t))}run_on_add_listener_callbacks(){if(this._on_add_listener_callbacks){let t;for(;t=this._on_add_listener_callbacks.pop();)t();this._on_add_listener_callbacks=void 0}}get eventsListener(){return this._events_listener}dispatch(t,e,n){var i;null===(i=this._events_listener)||void 0===i||i.process_events(t,e,n)}emitAllowed(){return null!=this._events_listener&&this.scene.loadingController.loaded()&&this.scene.loadingController.autoUpdating()}}class ui{constructor(){this._params_by_id=new Map}register_param(t){this._params_by_id.set(t.graphNodeId(),t)}deregister_param(t){this._params_by_id.delete(t.graphNodeId())}regenerate_referring_expressions(t){t.nameController.graph_node.setSuccessorsDirty(t)}}class hi{constructor(t){this.scene=t,this._lifecycle_on_create_allowed=!0}onCreateHookAllowed(){return this.scene.loadingController.loaded()&&this._lifecycle_on_create_allowed}onCreatePrevent(t){this._lifecycle_on_create_allowed=!1,t(),this._lifecycle_on_create_allowed=!0}}class di{constructor(t){this.dispatcher=t,this._nodes_by_graph_node_id=new Map,this._require_canvas_event_listeners=!1,this._activeEventDatas=[]}registerNode(t){this._nodes_by_graph_node_id.set(t.graphNodeId(),t),this.updateViewerEventListeners()}unregisterNode(t){this._nodes_by_graph_node_id.delete(t.graphNodeId()),this.updateViewerEventListeners()}processEvent(t){0!=this._activeEventDatas.length&&this._nodes_by_graph_node_id.forEach((e=>e.processEvent(t)))}updateViewerEventListeners(){this._update_active_event_types(),this._require_canvas_event_listeners&&this.dispatcher.scene.viewersRegister.traverseViewers((t=>{t.eventsController.updateEvents(this)}))}activeEventDatas(){return this._activeEventDatas}_update_active_event_types(){const t=new Map;this._nodes_by_graph_node_id.forEach((e=>{if(e.parent()){const n=e.activeEventDatas();for(let e of n)t.set(e,!0)}})),this._activeEventDatas=[],t.forEach(((t,e)=>{this._activeEventDatas.push(e)}))}}var pi;!function(t){t.LOADED=\\\\\\\"sceneLoaded\\\\\\\",t.PLAY=\\\\\\\"play\\\\\\\",t.PAUSE=\\\\\\\"pause\\\\\\\",t.TICK=\\\\\\\"tick\\\\\\\"}(pi||(pi={}));const _i=[pi.LOADED,pi.PLAY,pi.PAUSE,pi.TICK];class mi extends di{type(){return\\\\\\\"scene\\\\\\\"}acceptedEventTypes(){return _i.map((t=>`${t}`))}}class fi{constructor(t){this.scene=t,this._loading_state=!1,this._auto_updating=!0,this._first_object_loaded=!1}get LOADED_EVENT_CONTEXT(){return this._LOADED_EVENT_CONTEXT=this._LOADED_EVENT_CONTEXT||{event:new Event(pi.LOADED)}}markAsLoading(){this._set_loading_state(!0)}async markAsLoaded(){this.scene.missingExpressionReferencesController.resolve_missing_references(),await this._set_loading_state(!1),this.trigger_loaded_event()}trigger_loaded_event(){globalThis.Event&&this.scene.eventsDispatcher.sceneEventsController.processEvent(this.LOADED_EVENT_CONTEXT)}async _set_loading_state(t){this._loading_state=t,await this.set_auto_update(!this._loading_state)}isLoading(){return this._loading_state}loaded(){return!this._loading_state}autoUpdating(){return this._auto_updating}async set_auto_update(t){if(this._auto_updating!==t&&(this._auto_updating=t,this._auto_updating)){const t=this.scene.root();t&&await t.processQueue()}}on_first_object_loaded(){var t;if(!this._first_object_loaded){this._first_object_loaded=!0;const e=document.getElementById(\\\\\\\"scene_loading_container\\\\\\\");e&&(null===(t=e.parentElement)||void 0===t||t.removeChild(e))}}}const gi={EMPTY:\\\\\\\"\\\\\\\",UV:\\\\\\\"/COP/imageUv\\\\\\\",ENV_MAP:\\\\\\\"/COP/envMap\\\\\\\",CUBE_MAP:\\\\\\\"/COP/cubeCamera\\\\\\\"};class vi{constructor(t=\\\\\\\"\\\\\\\"){this._path=t,this._node=null}set_path(t){this._path=t}set_node(t){this._node=t}path(){return this._path}node(){return this._node}resolve(t){this._node=xi.findNode(t,this._path)}clone(){const t=new vi(this._path);return t.set_node(this._node),t}nodeWithContext(t,e){const n=this.node();if(!n)return void(null==e||e.set(`no node found at ${this.path()}`));const i=n.context();return i==t?n:void(null==e||e.set(`expected ${t} node, but got a ${i}`))}}class yi{constructor(t=\\\\\\\"\\\\\\\"){this._path=t,this._param=null}set_path(t){this._path=t}set_param(t){this._param=t}path(){return this._path}param(){return this._param}resolve(t){this._param=xi.findParam(t,this._path)}clone(){const t=new yi(this._path);return t.set_param(this._param),t}paramWithType(t,e){const n=this.param();if(n)return n.type()==t?n:void(null==e||e.set(`expected ${t} node, but got a ${n.type()}`));null==e||e.set(`no param found at ${this.path()}`)}}class xi{static split_parent_child(t){const e=t.split(xi.SEPARATOR).filter((t=>t.length>0)),n=e.pop();return{parent:e.join(xi.SEPARATOR),child:n}}static findNode(t,e,n){if(!t)return null;const i=e.split(xi.SEPARATOR).filter((t=>t.length>0)),r=i[0];let s=null;if(e[0]!==xi.SEPARATOR){switch(r){case xi.PARENT:null==n||n.add_path_element(r),s=t.parent();break;case xi.CURRENT:null==n||n.add_path_element(r),s=t;break;default:s=t.node(r),s&&(null==n||n.add_node(r,s))}if(null!=s&&i.length>1){const t=i.slice(1).join(xi.SEPARATOR);s=this.findNode(s,t,n)}return s}{const i=e.substr(1);s=this.findNode(t.root(),i,n)}return s}static findParam(t,e,n){if(!t)return null;const i=e.split(xi.SEPARATOR);if(1===i.length)return t.params.get(i[0]);{const e=i.slice(0,+(i.length-2)+1||void 0).join(xi.SEPARATOR),r=this.findNode(t,e,n);if(null!=r){const t=i[i.length-1],e=r.params.get(t);return n&&e&&n.add_node(t,e),e}return null}}static relativePath(t,e){const n=this.closestCommonParent(t,e);if(n){const i=this.distanceToParent(t,n);let r=\\\\\\\"\\\\\\\";if(i>0){let t=0;const e=[];for(;t++t.length>0)),o=e.path().split(xi.SEPARATOR).filter((t=>t.length>0)),a=[];let l=0;for(let t of o)s[l]||a.push(t),l++;return`${r}${a.join(xi.SEPARATOR)}`}return e.path()}static closestCommonParent(t,e){const n=this.parents(t).reverse().concat([t]),i=this.parents(e).reverse().concat([e]),r=Math.min(n.length,i.length);let s=null;for(let t=0;t{for(let n of e)this._is_reference_resolvable(n)&&t.push(n)}));for(let e of t)e.resolve_missing_dependencies()}_is_reference_resolvable(t){const e=t.absolute_path();if(e){if(this.scene.node(e))return!0;{const t=xi.split_parent_child(e);if(t.child){const e=this.scene.node(t.parent);if(e){if(e.params.get(t.child))return!0}}}}}check_for_missing_references(t){this._check_for_missing_references_for_node(t);for(let e of t.params.all)this._check_for_missing_references_for_param(e)}_check_for_missing_references_for_node(t){const e=t.graphNodeId();this.references.forEach(((n,i)=>{let r=!1;for(let e of n)e.matches_path(t.path())&&(r=!0,e.resolve_missing_dependencies());r&&this.references.delete(e)}))}_check_for_missing_references_for_param(t){const e=t.graphNodeId();this.references.forEach(((n,i)=>{let r=!1;for(let e of n)e.matches_path(t.path())&&(r=!0,e.resolve_missing_dependencies());r&&this.references.delete(e)}))}}class Ti{constructor(t){this.node=t,this._dirty_count=0,this._dirty=!0}dispose(){this._cached_successors=void 0,this._post_dirty_hooks=void 0,this._post_dirty_hook_names=void 0}isDirty(){return!0===this._dirty}dirtyTimestamp(){return this._dirty_timestamp}dirtyCount(){return this._dirty_count}addPostDirtyHook(t,e){this._post_dirty_hook_names=this._post_dirty_hook_names||[],this._post_dirty_hooks=this._post_dirty_hooks||[],this._post_dirty_hook_names.includes(t)?console.warn(`hook with name ${t} already exists`,this.node):(this._post_dirty_hook_names.push(t),this._post_dirty_hooks.push(e))}removePostDirtyHook(t){if(this._post_dirty_hook_names&&this._post_dirty_hooks){const e=this._post_dirty_hook_names.indexOf(t);e>=0&&(this._post_dirty_hook_names.splice(e,1),this._post_dirty_hooks.splice(e,1))}}hasHook(t){return!!this._post_dirty_hook_names&&this._post_dirty_hook_names.includes(t)}removeDirtyState(){this._dirty=!1}setForbiddenTriggerNodes(t){this._forbidden_trigger_nodes=t.map((t=>t.graphNodeId()))}setDirty(t,e){if(null==e&&(e=!0),t&&this._forbidden_trigger_nodes&&this._forbidden_trigger_nodes.includes(t.graphNodeId()))return;null==t&&(t=this.node),this._dirty=!0;const n=ai.performance.performanceManager();this._dirty_timestamp=n.now(),this._dirty_count+=1,this.runPostDirtyHooks(t),!0===e&&this.setSuccessorsDirty(t)}runPostDirtyHooks(t){if(this._post_dirty_hooks){const e=this.node.scene().cooker;if(e.blocked)e.enqueue(this.node,t);else for(let e of this._post_dirty_hooks)e(t)}}setSuccessorsDirty(t){this._cached_successors=this._cached_successors||this.node.graphAllSuccessors();for(let e of this._cached_successors)e.dirtyController.setDirty(t,false)}clearSuccessorsCache(){this._cached_successors=void 0}clearSuccessorsCacheWithPredecessors(){this.clearSuccessorsCache();for(let t of this.node.graphAllPredecessors())t.dirtyController.clearSuccessorsCache()}}class Ai{constructor(t,e){this._scene=t,this._name=e,this._dirty_controller=new Ti(this),this._graph_node_id=t.graph.nextId(),t.graph.addNode(this),this._graph=t.graph}dispose(){this._dirty_controller.dispose(),this.graphRemove()}name(){return this._name}setName(t){this._name=t}scene(){return this._scene}graphNodeId(){return this._graph_node_id}get dirtyController(){return this._dirty_controller}setDirty(t){t=t||this,this._dirty_controller.setDirty(t)}setSuccessorsDirty(t){this._dirty_controller.setSuccessorsDirty(t)}removeDirtyState(){this._dirty_controller.removeDirtyState()}isDirty(){return this._dirty_controller.isDirty()}addPostDirtyHook(t,e){this._dirty_controller.addPostDirtyHook(t,e)}graphRemove(){this._graph.removeNode(this)}addGraphInput(t,e=!0){return this._graph.connect(t,this,e)}removeGraphInput(t){this._graph.disconnect(t,this)}graphDisconnectPredecessors(){this._graph.disconnectPredecessors(this)}graphDisconnectSuccessors(){this._graph.disconnectSuccessors(this)}graphPredecessorIds(){return this._graph.predecessorIds(this._graph_node_id)||[]}graphPredecessors(){return this._graph.predecessors(this)}graphSuccessors(){return this._graph.successors(this)}graphAllPredecessors(){return this._graph.allPredecessors(this)}graphAllSuccessors(){return this._graph.allSuccessors(this)}}var Ei;!function(t){t.CREATED=\\\\\\\"node_created\\\\\\\",t.DELETED=\\\\\\\"node_deleted\\\\\\\",t.NAME_UPDATED=\\\\\\\"node_name_update\\\\\\\",t.OVERRIDE_CLONABLE_STATE_UPDATE=\\\\\\\"node_override_clonable_state_update\\\\\\\",t.NAMED_OUTPUTS_UPDATED=\\\\\\\"node_named_outputs_updated\\\\\\\",t.NAMED_INPUTS_UPDATED=\\\\\\\"node_named_inputs_updated\\\\\\\",t.INPUTS_UPDATED=\\\\\\\"node_inputs_updated\\\\\\\",t.PARAMS_UPDATED=\\\\\\\"node_params_updated\\\\\\\",t.UI_DATA_POSITION_UPDATED=\\\\\\\"node_ui_data_position_updated\\\\\\\",t.UI_DATA_COMMENT_UPDATED=\\\\\\\"node_ui_data_comment_updated\\\\\\\",t.ERROR_UPDATED=\\\\\\\"node_error_updated\\\\\\\",t.FLAG_BYPASS_UPDATED=\\\\\\\"bypass_flag_updated\\\\\\\",t.FLAG_DISPLAY_UPDATED=\\\\\\\"display_flag_updated\\\\\\\",t.FLAG_OPTIMIZE_UPDATED=\\\\\\\"optimize_flag_updated\\\\\\\",t.SELECTION_UPDATED=\\\\\\\"selection_updated\\\\\\\"}(Ei||(Ei={}));class Mi{constructor(t,e=0,n=0){this.node=t,this._position=new d.a,this._width=50,this._color=new D.a(.75,.75,.75),this._layout_vertical=!0,this._json={x:0,y:0},this._position.x=e,this._position.y=n}setComment(t){this._comment=t,this.node.emit(Ei.UI_DATA_COMMENT_UPDATED)}comment(){return this._comment}setColor(t){this._color=t}color(){return this._color}setLayoutHorizontal(){this._layout_vertical=!1}isLayoutVertical(){return this._layout_vertical}copy(t){this._position.copy(t.position()),this._color.copy(t.color())}position(){return this._position}setPosition(t,e=0){if(m.isNumber(t)){const n=t;this._position.set(n,e)}else this._position.copy(t);this.node.emit(Ei.UI_DATA_POSITION_UPDATED)}translate(t,e=!1){this._position.add(t),e&&(this._position.x=Math.round(this._position.x),this._position.y=Math.round(this._position.y)),this.node.emit(Ei.UI_DATA_POSITION_UPDATED)}toJSON(){return this._json.x=this._position.x,this._json.y=this._position.y,this._json.comment=this._comment,this._json}}class Si{constructor(t){this.node=t,this._state=!0,this._hooks=null}onUpdate(t){this._hooks=this._hooks||[],this._hooks.push(t)}_on_update(){}set(t){this._state!=t&&(this._state=t,this._on_update(),this.runHooks())}active(){return this._state}toggle(){this.set(!this._state)}runHooks(){if(this._hooks)for(let t of this._hooks)t()}}class Ci extends Si{constructor(){super(...arguments),this._state=!1}_on_update(){this.node.emit(Ei.FLAG_BYPASS_UPDATED),this.node.setDirty()}}class Ni extends Si{_on_update(){this.node.emit(Ei.FLAG_DISPLAY_UPDATED)}}class Li extends Si{constructor(){super(...arguments),this._state=!1}_on_update(){this.node.emit(Ei.FLAG_OPTIMIZE_UPDATED)}}class Oi{constructor(t){this.node=t}hasDisplay(){return!1}hasBypass(){return!1}hasOptimize(){return!1}}function Ri(t){return class extends t{constructor(){super(...arguments),this.display=new Ni(this.node)}hasDisplay(){return!0}}}function Pi(t){return class extends t{constructor(){super(...arguments),this.bypass=new Ci(this.node)}hasBypass(){return!0}}}function Ii(t){return class extends t{constructor(){super(...arguments),this.optimize=new Li(this.node)}hasOptimize(){return!0}}}class Fi extends(Ri(Oi)){}class Di extends(Pi(Oi)){}class ki extends(Pi(Ri(Oi))){}class Bi extends(Ii(Pi(Oi))){}class zi extends(Ii(Pi(Ri(Oi)))){}class Ui{constructor(t){this.node=t}}class Gi extends Ui{active(){return this.paramsTimeDependent()||this.inputsTimeDependent()}paramsTimeDependent(){const t=this.node.params.names;for(let e of t){const t=this.node.params.get(e);if(t&&t.states.timeDependent.active())return!0}return!1}inputsTimeDependent(){const t=this.node.io.inputs.inputs();for(let e of t)if(e&&e.states.timeDependent.active())return!0;return!1}forceTimeDependent(){const t=this.node.graphPredecessors().map((t=>t.graphNodeId())),e=this.node.scene().timeController.graphNode;t.includes(e.graphNodeId())||this.node.addGraphInput(e,!1)}unforceTimeDependent(){const t=this.node.scene().timeController.graphNode;this.node.removeGraphInput(t)}}class Vi extends Ui{set(t){this._message!=t&&(t&&ai.warn(`[${this.node.path()}] error: '${t}'`),this._message=t,this.onUpdate())}message(){return this._message}clear(){this.set(void 0)}active(){return null!=this._message}onUpdate(){null!=this._message&&this.node._setContainer(null,`from error '${this._message}'`),this.node.emit(Ei.ERROR_UPDATED)}}class Hi{constructor(t){this.node=t,this.timeDependent=new Gi(this.node),this.error=new Vi(this.node)}}class ji{constructor(t){this.node=t,this._graph_node=new Ai(t.scene(),\\\\\\\"node_name_controller\\\\\\\")}dispose(){this._graph_node.dispose(),this._on_set_name_hooks=void 0,this._on_set_fullPath_hooks=void 0}get graph_node(){return this._graph_node}static base_name(t){let e=t.type();const n=e[e.length-1];return m.isNaN(parseInt(n))||(e+=\\\\\\\"_\\\\\\\"),`${e}1`}request_name_to_parent(t){const e=this.node.parent();e&&e.childrenAllowed()&&e.childrenController?e.childrenController.set_child_name(this.node,t):console.warn(\\\\\\\"request_name_to_parent failed, no parent found\\\\\\\")}setName(t){t!=this.node.name()&&this.request_name_to_parent(t)}update_name_from_parent(t){var e;if(this.node._set_core_name(t),this.post_setName(),this.run_post_set_fullPath_hooks(),this.node.childrenAllowed()){const t=null===(e=this.node.childrenController)||void 0===e?void 0:e.children();if(t)for(let e of t)e.nameController.run_post_set_fullPath_hooks()}this.node.lifecycle.creation_completed&&(this.node.scene().missingExpressionReferencesController.check_for_missing_references(this.node),this.node.scene().expressionsController.regenerate_referring_expressions(this.node)),this.node.scene().referencesController.notify_name_updated(this.node),this.node.emit(Ei.NAME_UPDATED)}add_post_set_name_hook(t){this._on_set_name_hooks=this._on_set_name_hooks||[],this._on_set_name_hooks.push(t)}add_post_set_fullPath_hook(t){this._on_set_fullPath_hooks=this._on_set_fullPath_hooks||[],this._on_set_fullPath_hooks.push(t)}post_setName(){if(this._on_set_name_hooks)for(let t of this._on_set_name_hooks)t()}run_post_set_fullPath_hooks(){if(this._on_set_fullPath_hooks)for(let t of this._on_set_fullPath_hooks)t()}}class Wi{constructor(t){this.node=t,this._parent=null}parent(){return this._parent}setParent(t){t!=this.node.parentController.parent()&&(this._parent=t,this._parent&&this.node.nameController.request_name_to_parent(ji.base_name(this.node)))}is_selected(){var t,e,n;return(null===(n=null===(e=null===(t=this.parent())||void 0===t?void 0:t.childrenController)||void 0===e?void 0:e.selection)||void 0===n?void 0:n.contains(this.node))||!1}path(t){const e=xi.SEPARATOR;if(null!=this._parent){if(this._parent==t)return this.node.name();{const n=this._parent.path(t);return n===e?n+this.node.name():n+e+this.node.name()}}return e}onSetParent(){if(this._on_set_parent_hooks)for(let t of this._on_set_parent_hooks)t()}findNode(t){if(null==t)return null;if(t==xi.CURRENT||t==xi.CURRENT_WITH_SLASH)return this.node;if(t==xi.PARENT||t==xi.PARENT_WITH_SLASH)return this.node.parent();const e=xi.SEPARATOR;if(t===e)return this.node.scene().root();if(t[0]===e)return t=t.substring(1,t.length),this.node.scene().root().node(t);if(t.split){const n=t.split(e);if(1===n.length){const t=n[0];return this.node.childrenController?this.node.childrenController.child_by_name(t):null}return xi.findNode(this.node,t)}return console.error(\\\\\\\"unexpected path given:\\\\\\\",t),null}}const qi=/[, ]/,Xi=/\\\\d+$/,Yi=/^0+/,$i=/,| /,Ji=/^-?\\\\d+\\\\.?\\\\d*$/;var Zi,Qi,Ki,tr,er,nr,ir,rr;!function(t){t.TRUE=\\\\\\\"true\\\\\\\",t.FALSE=\\\\\\\"false\\\\\\\"}(Zi||(Zi={}));class sr{static isBoolean(t){return t==Zi.TRUE||t==Zi.FALSE}static toBoolean(t){return t==Zi.TRUE}static isNumber(t){return Ji.test(t)}static tailDigits(t){const e=t.match(Xi);return e?parseInt(e[0]):0}static increment(t){const e=t.match(Xi);if(e){let n=e[0],i=\\\\\\\"\\\\\\\";const r=n.match(Yi);r&&(i=r[0]);const s=parseInt(n);0==s&&i.length>0&&\\\\\\\"0\\\\\\\"==i[i.length-1]&&(i=i.slice(0,-1));return`${t.substring(0,t.length-e[0].length)}${i}${s+1}`}return`${t}1`}static pluralize(t){return\\\\\\\"s\\\\\\\"!==t[t.length-1]?`${t}s`:t}static camelCase(t){const e=t.replace(/_/g,\\\\\\\" \\\\\\\").split(\\\\\\\" \\\\\\\");let n=\\\\\\\"\\\\\\\";for(let t=0;t0&&(i=this.upperFirst(i)),n+=i}return n}static upperFirst(t){return t[0].toUpperCase()+t.substr(1)}static titleize(t){return t.split(/\\\\s|_/g).map((t=>this.upperFirst(t))).join(\\\\\\\" \\\\\\\")}static precision(t,e=2){e=Math.max(e,0);const n=`${t}`.split(\\\\\\\".\\\\\\\");if(e<=0)return n[0];let i=n[1];if(void 0!==i)return i.length>e&&(i=i.substring(0,e)),i=i.padEnd(e,\\\\\\\"0\\\\\\\"),`${n[0]}.${i}`;{const n=`${t}.`,i=n.length+e;return n.padEnd(i,\\\\\\\"0\\\\\\\")}}static ensureFloat(t){const e=`${t}`;return e.indexOf(\\\\\\\".\\\\\\\")>=0?e:`${e}.0`}static ensureInteger(t){const e=`${t}`;return e.indexOf(\\\\\\\".\\\\\\\")>=0?e.split(\\\\\\\".\\\\\\\")[0]:e}static matchMask(t,e){if(\\\\\\\"*\\\\\\\"===e)return!0;if(t==e)return!0;const n=e.split(\\\\\\\" \\\\\\\");if(n.length>1){for(let e of n){if(this.matchMask(t,e))return!0}return!1}e=`^${e=e.split(\\\\\\\"*\\\\\\\").join(\\\\\\\".*\\\\\\\")}$`;return new RegExp(e).test(t)}static matchesOneMask(t,e){let n=!1;for(let i of e)sr.matchMask(t,i)&&(n=!0);return n}static attribNames(t){const e=t.split(qi),n=new Set;for(let t of e)t=t.trim(),t.length>0&&n.add(t);const i=new Array(n.size);let r=0;return n.forEach((t=>{i[r]=t,r++})),i}static indices(t){const e=t.split($i);if(e.length>1){const t=e.flatMap((t=>this.indices(t)));return f.uniq(t).sort(((t,e)=>t-e))}{const t=e[0];if(t){const e=\\\\\\\"-\\\\\\\";if(t.indexOf(e)>0){const n=t.split(e);return f.range(parseInt(n[0]),parseInt(n[1])+1)}{const e=parseInt(t);return m.isNumber(e)?[e]:[]}}return[]}}static escapeLineBreaks(t){return t.replace(/(\\\\r\\\\n|\\\\n|\\\\r)/gm,\\\\\\\"\\\\\\\\n\\\\\\\")}static sanitizeName(t){return t=(t=t.replace(/[^A-Za-z0-9]/g,\\\\\\\"_\\\\\\\")).replace(/^[0-9]/,\\\\\\\"_\\\\\\\")}}class or{constructor(t){this._node=t,this._node_ids=[],this._json=[]}node(){return this._node}nodes(){return this._node.scene().graph.nodesFromIds(this._node_ids)}contains(t){return this._node_ids.includes(t.graphNodeId())}equals(t){const e=t.map((t=>t.graphNodeId())).sort();return f.isEqual(e,this._node_ids)}clear(){this._node_ids=[],this.send_update_event()}set(t){this._node_ids=[],this.add(t)}add(t){const e=t.map((t=>t.graphNodeId()));this._node_ids=f.union(this._node_ids,e),this.send_update_event()}remove(t){const e=t.map((t=>t.graphNodeId()));this._node_ids=f.difference(this._node_ids,e),this.send_update_event()}send_update_event(){this._node.emit(Ei.SELECTION_UPDATED)}toJSON(){return this._json=this._json||[],this._json=this._node_ids.map((t=>t)),this._json}}!function(t){t.ALWAYS=\\\\\\\"always\\\\\\\",t.NEVER=\\\\\\\"never\\\\\\\",t.FROM_NODE=\\\\\\\"from_node\\\\\\\"}(Qi||(Qi={}));class ar{static unreachable(t){throw new Error(\\\\\\\"Didn't expect to get here\\\\\\\")}}class lr{constructor(t){this.inputs_controller=t,this._clone_required_states=[],this._overridden=!1}init_inputs_cloned_state(t){m.isArray(t)?this._cloned_states=t:this._cloned_state=t,this._update_clone_required_state()}override_cloned_state_allowed(){if(this._cloned_states)for(let t of this._cloned_states)if(t==Qi.FROM_NODE)return!0;return!!this._cloned_state&&this._cloned_state==Qi.FROM_NODE}clone_required_state(t){return this._clone_required_states[t]}clone_required_states(){return this._clone_required_states}_get_clone_required_state(t){const e=this._cloned_states;if(e){const n=e[t];if(null!=n)return this.clone_required_from_state(n)}return!this._cloned_state||this.clone_required_from_state(this._cloned_state)}clone_required_from_state(t){switch(t){case Qi.ALWAYS:return!0;case Qi.NEVER:return!1;case Qi.FROM_NODE:return!this._overridden}return ar.unreachable(t)}override_cloned_state(t){this._overridden=t,this._update_clone_required_state()}overriden(){return this._overridden}_update_clone_required_state(){if(this._cloned_states){const t=[];for(let e=0;e=1:e;if(m.isString(e)){if(n){if(n instanceof vi)return n.set_path(e);if(n instanceof yi)return n.set_path(e)}return e}m.isArray(e)&&this.params[t].fromArray(e)}setInput(t,e){this._inputs=this._inputs||[],this._inputs[t]=e}inputs_count(){return this._inputs?this._inputs.length:0}inputsController(){return this._inputs_controller=this._inputs_controller||new cr(this)}_init_cloned_states(){const t=this.operation.constructor.INPUT_CLONED_STATE;this.inputsController().init_inputs_cloned_state(t)}input_clone_required(t){return!this._inputs_controller||this._inputs_controller.clone_required(t)}override_input_clone_state(t){this.inputsController().override_cloned_state(t)}cook(t){return this.operation.cook(t,this.params)}}{constructor(t,e,n){super(t,e,n),this.operation=t,this.name=e,this.init_params=n,this._inputs=[],this._current_input_index=0,this._dirty=!0}add_input(t){super.setInput(this._current_input_index,t),this.increment_input_index()}increment_input_index(){this._current_input_index++}current_input_index(){return this._current_input_index}setDirty(){if(!this._dirty){this._compute_result=void 0;for(let t=0;t{n[i]=t[e]}));for(let i=0;i=0&&(this._children_by_type[n].splice(t,1),0==this._children_by_type[n].length&&delete this._children_by_type[n])}this.remove_from_children_and_grandchildren_by_context(t)}add_to_children_and_grandchildren_by_context(t){var e;const n=t.graphNodeId(),i=t.context();this._children_and_grandchildren_by_context[i]=this._children_and_grandchildren_by_context[i]||[],this._children_and_grandchildren_by_context[i].includes(n)||this._children_and_grandchildren_by_context[i].push(n);const r=this.node.parent();r&&r.childrenAllowed()&&(null===(e=r.childrenController)||void 0===e||e.add_to_children_and_grandchildren_by_context(t))}remove_from_children_and_grandchildren_by_context(t){var e;const n=t.graphNodeId(),i=t.context();if(this._children_and_grandchildren_by_context[i]){const t=this._children_and_grandchildren_by_context[i].indexOf(n);t>=0&&(this._children_and_grandchildren_by_context[i].splice(t,1),0==this._children_and_grandchildren_by_context[i].length&&delete this._children_and_grandchildren_by_context[i])}const r=this.node.parent();r&&r.childrenAllowed()&&(null===(e=r.childrenController)||void 0===e||e.remove_from_children_and_grandchildren_by_context(t))}nodesByType(t){const e=this._children_by_type[t]||[],n=this.node.scene().graph,i=[];for(let t of e){const e=n.nodeFromId(t);e&&i.push(e)}return i}child_by_name(t){return this._children[t]}has_children_and_grandchildren_with_context(t){return null!=this._children_and_grandchildren_by_context[t]}children(){return Object.values(this._children)}children_names(){return Object.keys(this._children).sort()}traverse_children(t){var e;for(let n of this.children())t(n),null===(e=n.childrenController)||void 0===e||e.traverse_children(t)}}class dr{constructor(t){this.node=t,this._creation_completed=!1}dispose(){this._on_child_add_hooks=void 0,this._on_child_remove_hooks=void 0,this._on_create_hooks=void 0,this._on_add_hooks=void 0,this._on_delete_hooks=void 0}set_creation_completed(){this._creation_completed||(this._creation_completed=!0)}get creation_completed(){return this.node.scene().loadingController.loaded()&&this._creation_completed}add_on_child_add_hook(t){this._on_child_add_hooks=this._on_child_add_hooks||[],this._on_child_add_hooks.push(t)}run_on_child_add_hooks(t){this.execute_hooks_with_child_node(this._on_child_add_hooks,t)}add_on_child_remove_hook(t){this._on_child_remove_hooks=this._on_child_remove_hooks||[],this._on_child_remove_hooks.push(t)}run_on_child_remove_hooks(t){this.execute_hooks_with_child_node(this._on_child_remove_hooks,t)}add_on_create_hook(t){this._on_create_hooks=this._on_create_hooks||[],this._on_create_hooks.push(t)}run_on_create_hooks(){this.execute_hooks(this._on_create_hooks)}add_on_add_hook(t){this._on_add_hooks=this._on_add_hooks||[],this._on_add_hooks.push(t)}run_on_add_hooks(){this.execute_hooks(this._on_add_hooks)}add_delete_hook(t){this._on_delete_hooks=this._on_delete_hooks||[],this._on_delete_hooks.push(t)}run_on_delete_hooks(){this.execute_hooks(this._on_delete_hooks)}execute_hooks(t){if(t){let e;for(e of t)e()}}execute_hooks_with_child_node(t,e){if(t){let n;for(n of t)n(e)}}}!function(t){t.ANIM=\\\\\\\"anim\\\\\\\",t.COP=\\\\\\\"cop\\\\\\\",t.EVENT=\\\\\\\"event\\\\\\\",t.GL=\\\\\\\"gl\\\\\\\",t.JS=\\\\\\\"js\\\\\\\",t.MANAGER=\\\\\\\"manager\\\\\\\",t.MAT=\\\\\\\"mat\\\\\\\",t.OBJ=\\\\\\\"obj\\\\\\\",t.POST=\\\\\\\"post\\\\\\\",t.ROP=\\\\\\\"rop\\\\\\\",t.SOP=\\\\\\\"sop\\\\\\\"}(Ki||(Ki={})),function(t){t.ANIM=\\\\\\\"animationsNetwork\\\\\\\",t.COP=\\\\\\\"copNetwork\\\\\\\",t.EVENT=\\\\\\\"eventsNetwork\\\\\\\",t.MAT=\\\\\\\"materialsNetwork\\\\\\\",t.POST=\\\\\\\"postProcessNetwork\\\\\\\",t.ROP=\\\\\\\"renderersNetwork\\\\\\\"}(tr||(tr={})),function(t){t.INPUT=\\\\\\\"subnetInput\\\\\\\",t.OUTPUT=\\\\\\\"subnetOutput\\\\\\\"}(er||(er={})),function(t){t.PERSPECTIVE=\\\\\\\"perspectiveCamera\\\\\\\",t.ORTHOGRAPHIC=\\\\\\\"orthographicCamera\\\\\\\"}(nr||(nr={})),function(t){t.ATTRIBUTE=\\\\\\\"attribute\\\\\\\"}(ir||(ir={})),function(t){t.DEVICE_ORIENTATION=\\\\\\\"cameraDeviceOrientationControls\\\\\\\",t.MAP=\\\\\\\"cameraMapControls\\\\\\\",t.ORBIT=\\\\\\\"cameraOrbitControls\\\\\\\",t.FIRST_PERSON=\\\\\\\"firstPersonControls\\\\\\\",t.MOBILE_JOYSTICK=\\\\\\\"mobileJoystickControls\\\\\\\"}(rr||(rr={}));const pr=[rr.DEVICE_ORIENTATION,rr.MAP,rr.ORBIT,rr.FIRST_PERSON,rr.MOBILE_JOYSTICK];class _r{constructor(t){this._node=t}set_node(t){this._node=t}node(){return this._node}set_content(t){this._content=t,this._post_set_content()}has_content(){return null!=this._content}content(){return this._content}_post_set_content(){}coreContent(){return this._content}coreContentCloned(){return this._content}infos(){return[]}}var mr=n(69);class fr extends Q.a{constructor(){super(),this.type=\\\\\\\"Scene\\\\\\\",this.background=null,this.environment=null,this.fog=null,this.overrideMaterial=null,this.autoUpdate=!0,\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}copy(t,e){return super.copy(t,e),null!==t.background&&(this.background=t.background.clone()),null!==t.environment&&(this.environment=t.environment.clone()),null!==t.fog&&(this.fog=t.fog.clone()),null!==t.overrideMaterial&&(this.overrideMaterial=t.overrideMaterial.clone()),this.autoUpdate=t.autoUpdate,this.matrixAutoUpdate=t.matrixAutoUpdate,this}toJSON(t){const e=super.toJSON(t);return null!==this.fog&&(e.object.fog=this.fog.toJSON()),e}}fr.prototype.isScene=!0;var gr=n(49),vr=n(52),yr=n(42),xr=n(55),br=n(61),wr=n(24),Tr=n(35);const Ar=new p.a,Er=new p.a;class Mr extends Q.a{constructor(){super(),this._currentLevel=0,this.type=\\\\\\\"LOD\\\\\\\",Object.defineProperties(this,{levels:{enumerable:!0,value:[]},isLOD:{value:!0}}),this.autoUpdate=!0}copy(t){super.copy(t,!1);const e=t.levels;for(let t=0,n=e.length;t0){let n,i;for(n=1,i=e.length;n0){Ar.setFromMatrixPosition(this.matrixWorld);const n=t.ray.origin.distanceTo(Ar);this.getObjectForDistance(n).raycast(t,e)}}update(t){const e=this.levels;if(e.length>1){Ar.setFromMatrixPosition(t.matrixWorld),Er.setFromMatrixPosition(this.matrixWorld);const n=Ar.distanceTo(Er)/t.zoom;let i,r;for(e[0].object.visible=!0,i=1,r=e.length;i=e[i].distance;i++)e[i-1].object.visible=!1,e[i].object.visible=!0;for(this._currentLevel=i-1;in?n:t}static fit01(t,e,n){return this.fit(t,0,1,e,n)}static fit(t,e,n,i,r){return(t-e)/(n-e)*(r-i)+i}static blend(t,e,n){return(1-n)*t+n*e}static degrees_to_radians(t){return t*is}static radians_to_degrees(t){return t/is}static deg2rad(t){return this.degrees_to_radians(t)}static rad2deg(t){return this.radians_to_degrees(t)}static rand(t){return m.isNumber(t)?this.randFloat(t):this.randVec2(t)}static round(t,e){const n=t/e;return(t<0?Math.ceil(n):Math.floor(n))*e}static highest_even(t){return 2*Math.ceil(.5*t)}static randFloat(t,e=136574){return this._vec.x=t,this._vec.y=e,this.randVec2(this._vec)}static randVec2(t){const e=(12.9898*t.x+78.233*t.y)%Math.PI;return this.fract(43758.5453*Math.sin(e))}static geodesic_distance(t,e){var n=this.deg2rad(t.lat),i=this.deg2rad(e.lat),r=this.deg2rad(e.lat-t.lat),s=this.deg2rad(e.lng-t.lng),o=Math.sin(r/2)*Math.sin(r/2)+Math.cos(n)*Math.cos(i)*Math.sin(s/2)*Math.sin(s/2);return 6371e3*(2*Math.atan2(Math.sqrt(o),Math.sqrt(1-o)))}static expand_triangle(t,e){t.getMidpoint(this._triangle_mid),this._triangle_mid_to_corner.copy(t.a).sub(this._triangle_mid),this._triangle_mid_to_corner.normalize().multiplyScalar(e),t.a.add(this._triangle_mid_to_corner),this._triangle_mid_to_corner.copy(t.b).sub(this._triangle_mid),this._triangle_mid_to_corner.normalize().multiplyScalar(e),t.b.add(this._triangle_mid_to_corner),this._triangle_mid_to_corner.copy(t.c).sub(this._triangle_mid),this._triangle_mid_to_corner.normalize().multiplyScalar(e),t.c.add(this._triangle_mid_to_corner)}static nearestPower2(t){return Math.pow(2,Math.ceil(Math.log(t)/Math.log(2)))}}rs.Easing=ns,rs.fract=t=>t-Math.floor(t),rs._vec={x:0,y:136574},rs._triangle_mid=new p.a,rs._triangle_mid_to_corner=new p.a;class ss{constructor(t,e){this._core_geometry=t,this._index=e,this._geometry=this._core_geometry.geometry()}index(){return this._index}points(){return this._points=this._points||this._get_points()}applyMatrix4(t){for(let e of this.points())e.applyMatrix4(t)}_get_points(){var t;const e=(null===(t=this._geometry.index)||void 0===t?void 0:t.array)||[],n=3*this._index;return[new Zr(this._core_geometry,e[n+0]),new Zr(this._core_geometry,e[n+1]),new Zr(this._core_geometry,e[n+2])]}positions(){return this._positions=this._positions||this._get_positions()}_get_positions(){const t=this.points();return[t[0].position(),t[1].position(),t[2].position()]}triangle(){return this._triangle=this._triangle||this._get_triangle()}_get_triangle(){const t=this.positions();return new Qr.a(t[0],t[1],t[2])}deltas(){return this._deltas=this._deltas||this._get_deltas()}_get_deltas(){const t=this.positions();return[t[1].clone().sub(t[0]),t[2].clone().sub(t[0])]}area(){return this.triangle().getArea()}center(t){const e=this.positions();return t.x=(e[0].x+e[1].x+e[2].x)/3,t.y=(e[0].y+e[1].y+e[2].y)/3,t.z=(e[0].z+e[1].z+e[2].z)/3,t}random_position(t){let e=[rs.randFloat(t),rs.randFloat(6541*t)];return e[0]+e[1]>1&&(e[0]=1-e[0],e[1]=1-e[1]),this.positions()[0].clone().add(this.deltas()[0].clone().multiplyScalar(e[0])).add(this.deltas()[1].clone().multiplyScalar(e[1]))}attrib_value_at_position(t,e){const n=new p.a;this.triangle().getBarycoord(e,n);const i=n.toArray(),r=this._geometry.attributes[t].itemSize,s=this.points().map((e=>e.attribValue(t)));let o,a,l=0;switch(r){case 1:a=0;for(let t of s)a+=t*i[l],l++;o=a;break;default:for(let t of s){const e=t.multiplyScalar(i[l]);a?a.add(e):a=e,l++}o=a}return o}static interpolated_value(t,e,n,i){const r=[e.a,e.b,e.c],s=t.getAttribute(\\\\\\\"position\\\\\\\").array,o=r.map((t=>new p.a(s[3*t+0],s[3*t+1],s[3*t+2]))),a=i.itemSize,l=i.array;let c=[];switch(a){case 1:c=r.map((t=>l[t]));break;case 2:c=r.map((t=>new d.a(l[2*t+0],l[2*t+1])));break;case 3:c=r.map((t=>new p.a(l[3*t+0],l[3*t+1],l[3*t+2])))}const u=r.map(((t,e)=>n.distanceTo(o[e]))),h=f.sum([u[0]*u[1],u[0]*u[2],u[1]*u[2]]),_=[u[1]*u[2]/h,u[0]*u[2]/h,u[0]*u[1]/h];let m;switch(a){case 1:m=f.sum(r.map(((t,e)=>_[e]*c[e])));break;default:var g=r.map(((t,e)=>c[e].multiplyScalar(_[e])));m=null;for(let t of g)m?m.add(t):m=t}return m}}class os{from_points(t){t=this._filter_points(t);const e=new S.a,n=new ps(e),i=t[0];if(null!=i){const r=i.geometry(),s=i.core_geometry(),o={};for(let e=0;et.indexedAttribValue(i)))),s={};r.forEach(((t,e)=>s[t]=e)),n.userDataAttribs()[i]=r;const o=[];for(let e of t){const t=s[e.indexedAttribValue(i)];o.push(t)}e.setAttribute(i,new C.c(o,1))}else{const n=l[i].itemSize,r=new Array(t.length*n);switch(n){case 1:for(let e=0;enew ps(t))),n=e[0].indexedAttributeNames(),i={};for(let t of n){const n={},r=[];for(let i of e){const e=i.points();for(let i of e){r.push(i);const e=i.indexedAttribValue(t);null!=n[e]?n[e]:n[e]=Object.keys(n).length}}const s=Object.keys(n);for(let e of r){const i=n[e.indexedAttribValue(t)];e.setAttribIndex(t,i)}i[t]=s}const r=cs(t),s=new ps(r);return Object.keys(i).forEach((t=>{const e=i[t];s.setIndexedAttributeValues(t,e)})),r&&delete r.userData.mergedUserData,r}}class ps{constructor(t){this._geometry=t}geometry(){return this._geometry}uuid(){return this._geometry.uuid}boundingBox(){return this._bounding_box=this._bounding_box||this._create_bounding_box()}_create_bounding_box(){if(this._geometry.computeBoundingBox(),this._geometry.boundingBox)return this._geometry.boundingBox}markAsInstance(){this._geometry.userData.isInstance=!0}static markedAsInstance(t){return!0===t.userData.isInstance}markedAsInstance(){return ps.markedAsInstance(this._geometry)}positionAttribName(){let t=\\\\\\\"position\\\\\\\";return this.markedAsInstance()&&(t=\\\\\\\"instancePosition\\\\\\\"),t}computeVertexNormals(){this._geometry.computeVertexNormals()}userDataAttribs(){const t=\\\\\\\"indexed_attrib_values\\\\\\\";return this._geometry.userData[t]=this._geometry.userData[t]||{}}indexedAttributeNames(){return Object.keys(this.userDataAttribs()||{})}userDataAttrib(t){return t=Wr.remapName(t),this.userDataAttribs()[t]}isAttribIndexed(t){return t=Wr.remapName(t),null!=this.userDataAttrib(t)}hasAttrib(t){return\\\\\\\"ptnum\\\\\\\"===t||(t=Wr.remapName(t),null!=this._geometry.attributes[t])}attribType(t){return this.isAttribIndexed(t)?Dr.STRING:Dr.NUMERIC}static attribNames(t){return Object.keys(t.attributes)}attribNames(){return ps.attribNames(this._geometry)}static attribNamesMatchingMask(t,e){const n=sr.attribNames(e),i=[];for(let e of this.attribNames(t))for(let t of n)sr.matchMask(e,t)&&i.push(e);return f.uniq(i)}attribSizes(){const t={};for(let e of this.attribNames())t[e]=this._geometry.attributes[e].itemSize;return t}attribSize(t){let e;return t=Wr.remapName(t),null!=(e=this._geometry.attributes[t])?e.itemSize:\\\\\\\"ptnum\\\\\\\"===t?1:0}setIndexedAttributeValues(t,e){this.userDataAttribs()[t]=e}setIndexedAttribute(t,e,n){this.setIndexedAttributeValues(t,e),this._geometry.setAttribute(t,new C.f(n,1))}addNumericAttrib(t,e=1,n=0){const i=[];let r=!1;if(m.isNumber(n)){for(let t=0;t1)if(m.isArray(n)){for(let t=0;tnew ss(this,t)))}}var _s;ps._mesh_builder=new class extends os{_filter_points(t){var e;const n=t[0];if(n){const i=null===(e=n.geometry().getIndex())||void 0===e?void 0:e.array;if(i){const e={};for(let n of t)e[n.index()]=n;const n=[],r=i.length;let s,o,a;for(let t=0;t{};class fs{static node(t,e){return t.node(e.name)}static clone(t){const e=t.clone(),n=t.uniforms;return n&&(e.uniforms=I.clone(n)),e}static add_user_data_render_hook(t,e){t.userData.POLY_render_hook=e}static apply_render_hook(t,e){if(e.userData){const n=e.userData.POLY_render_hook;if(n)return void(t.onBeforeRender=(e,i,r,s,o,a)=>{n(e,i,r,s,o,a,t)})}t.onBeforeRender=ms}static applyCustomMaterials(t,e){const n=e;if(n.customMaterials)for(let e of Object.keys(n.customMaterials)){const i=e,r=n.customMaterials[i];r&&(t[i]=r,r.needsUpdate=!0)}}static assign_custom_uniforms(t,e,n){const i=t;if(i.customMaterials)for(let t of Object.keys(i.customMaterials)){const r=t,s=i.customMaterials[r];s&&(s.uniforms[e].value=n)}}static init_custom_material_uniforms(t,e,n){const i=t;if(i.customMaterials)for(let t of Object.keys(i.customMaterials)){const r=t,s=i.customMaterials[r];s&&(s.uniforms[e]=s.uniforms[e]||n)}}}const gs=\\\\\\\"name\\\\\\\";class vs extends qr{constructor(t,e){super(e),this._object=t,null==this._object.userData.attributes&&(this._object.userData.attributes={})}object(){return this._object}geometry(){return this._object.geometry}coreGeometry(){const t=this.geometry();return t?new ps(t):null}points(){var t;return(null===(t=this.coreGeometry())||void 0===t?void 0:t.points())||[]}pointsFromGroup(t){if(t){const e=sr.indices(t);if(e){const t=this.points();return e.map((e=>t[e]))}return[]}return this.points()}static isInGroup(t,e){const n=t.trim();if(0==n.length)return!0;const i=n.split(\\\\\\\"=\\\\\\\"),r=i[0];if(\\\\\\\"@\\\\\\\"==r[0]){const t=r.substr(1);return i[1]==this.attribValue(e,t)}return!1}computeVertexNormals(){var t;null===(t=this.coreGeometry())||void 0===t||t.computeVertexNormals()}static _convert_array_to_vector(t){switch(t.length){case 1:return t[0];case 2:return new d.a(t[0],t[1]);case 3:return new p.a(t[0],t[1],t[2]);case 4:return new _.a(t[0],t[1],t[2],t[3])}}static addAttribute(t,e,n){if(m.isArray(n)){if(!this._convert_array_to_vector(n)){const t=\\\\\\\"attribute_value invalid\\\\\\\";throw console.error(t,n),new Error(t)}}const i=n,r=t.userData;r.attributes=r.attributes||{},r.attributes[e]=i}addAttribute(t,e){vs.addAttribute(this._object,t,e)}addNumericAttrib(t,e){this.addAttribute(t,e)}setAttribValue(t,e){this.addAttribute(t,e)}addNumericVertexAttrib(t,e,n){var i;null==n&&(n=Wr.default_value(e)),null===(i=this.coreGeometry())||void 0===i||i.addNumericAttrib(t,e,n)}attributeNames(){return Object.keys(this._object.userData.attributes)}attribNames(){return this.attributeNames()}hasAttrib(t){return this.attributeNames().includes(t)}renameAttrib(t,e){const n=this.attribValue(t);null!=n?(this.addAttribute(e,n),this.deleteAttribute(t)):console.warn(`attribute ${t} not found`)}deleteAttribute(t){delete this._object.userData.attributes[t]}static attribValue(t,e,n=0,i){if(\\\\\\\"ptnum\\\\\\\"===e)return n;if(t.userData&&t.userData.attributes){const n=t.userData.attributes[e];if(null==n){if(e==gs)return t.name}else if(m.isArray(n)&&i)return i.fromArray(n),i;return n}return e==gs?t.name:void 0}static stringAttribValue(t,e,n=0){const i=this.attribValue(t,e,n);if(null!=i)return m.isString(i)?i:`${i}`}attribValue(t,e){return vs.attribValue(this._object,t,this._index,e)}stringAttribValue(t){return vs.stringAttribValue(this._object,t,this._index)}name(){return this.attribValue(gs)}humanType(){return Vr.CONSTRUCTOR_NAMES_BY_CONSTRUCTOR_NAME[this._object.constructor.name]}attribTypes(){const t={};for(let e of this.attribNames()){const n=this.attribType(e);null!=n&&(t[e]=n)}return t}attribType(t){const e=this.attribValue(t);return m.isString(e)?Dr.STRING:Dr.NUMERIC}attribSizes(){const t={};for(let e of this.attribNames()){const n=this.attribSize(e);null!=n&&(t[e]=n)}return t}attribSize(t){const e=this.attribValue(t);return null==e?null:Wr.attribSizeFromValue(e)}clone(){return vs.clone(this._object)}static clone(t){const e=t.clone();var n=new Map,i=new Map;return vs.parallelTraverse(t,e,(function(t,e){n.set(e,t),i.set(t,e)})),e.traverse((function(e){const r=n.get(e),s=e;if(s.geometry){const t=r.geometry;s.geometry=ps.clone(t);const e=s.geometry;e.userData&&(e.userData=b.cloneDeep(t.userData))}if(s.material){s.material=r.material,fs.applyCustomMaterials(e,s.material);const t=s.material;null==t.color&&(t.color=new D.a(1,1,1))}t.userData&&(e.userData=b.cloneDeep(r.userData));const o=r;o.animations&&(e.animations=o.animations.map((t=>t.clone())));const a=e;if(a.isSkinnedMesh){var l=a,c=r,u=c.skeleton.bones;l.skeleton=c.skeleton.clone(),l.bindMatrix.copy(c.bindMatrix);const t=u.map((function(t){return i.get(t)}));l.skeleton.bones=t,l.bind(l.skeleton,l.bindMatrix)}})),e}static parallelTraverse(t,e,n){n(t,e);for(var i=0;i{const i=e.attributes[n];t[n]=i.itemSize})),t}objectAttributeSizesByName(){let t={};const e=this.firstCoreObject();if(e){const n=e.attribNames();for(let i of n){const n=e.attribSize(i);null!=n&&(t[i]=n)}}return t}pointAttributeTypesByName(){let t={};const e=this.firstGeometry();if(e){const n=new ps(e);Object.keys(e.attributes).forEach((e=>{t[e]=n.attribType(e)}))}return t}objectAttributeTypesByName(){let t={};const e=this.firstCoreObject();if(e)for(let n of e.attribNames())t[n]=e.attribType(n);return t}objectAttributeNames(){let t=[];const e=this.firstObject();return e&&(t=Object.keys(e.userData.attributes||{})),t}pointsCount(){return this._content?this._content.pointsCount():0}totalPointsCount(){return this._content?this._content.totalPointsCount():0}objectsData(){return this._content?this._content.objectsData():[]}boundingBox(){return this._content.boundingBox()}center(){return this._content.center()}size(){return this._content.size()}}};class xs{constructor(t){this.node=t,this._callbacks=[],this._callbacks_tmp=[];const e=ys[t.context()];this._container=new e(this.node)}container(){return this._container}async compute(){var t,e;if(null===(e=null===(t=this.node.flags)||void 0===t?void 0:t.bypass)||void 0===e?void 0:e.active()){const t=await this.requestInputContainer(0)||this._container;return this.node.cookController.endCook(),t}return this.node.isDirty()?new Promise(((t,e)=>{this._callbacks.push(t),this.node.cookController.cookMain()})):this._container}async requestInputContainer(t){const e=this.node.io.inputs.input(t);return e?await e.compute():(this.node.states.error.set(`input ${t} required`),this.notifyRequesters(),null)}notifyRequesters(t){let e;for(this._callbacks_tmp=this._callbacks.slice(),this._callbacks.splice(0,this._callbacks.length),t||(t=this.node.containerController.container());e=this._callbacks_tmp.pop();)e(t);this.node.scene().cookController.removeNode(this.node)}}const bs=ai.performance.performanceManager();class ws{constructor(t){this.cookController=t,this._inputs_start=0,this._params_start=0,this._cook_start=0,this._cooksCount=0,this._data={inputsTime:0,paramsTime:0,cookTime:0}}cooksCount(){return this._cooksCount}data2(){return this._data}active(){return this.cookController.performanceRecordStarted()}recordInputsStart(){this.active()&&(this._inputs_start=bs.now())}recordInputsEnd(){this.active()&&(this._data.inputsTime=bs.now()-this._inputs_start)}recordParamsStart(){this.active()&&(this._params_start=bs.now())}recordParamsEnd(){this.active()&&(this._data.paramsTime=bs.now()-this._params_start)}recordCookStart(){this.active()&&(this._cook_start=bs.now())}recordCookEnd(){this.active()&&(this._data.cookTime=bs.now()-this._cook_start,this._cooksCount+=1)}}class Ts{constructor(t){this.node=t,this._cooking=!1,this._performanceController=new ws(this),this._inputs_evaluation_required=!0,this._core_performance=this.node.scene().performance}performanceRecordStarted(){return this._core_performance.started()}disallowInputsEvaluation(){this._inputs_evaluation_required=!1}isCooking(){return!0===this._cooking}_start_cook_if_no_errors(t){if(this.node.states.error.active())this.endCook();else try{this._performanceController.recordCookStart(),this.node.cook(t)}catch(t){this.node.states.error.set(`node internal error: '${t}'.`),ai.warn(t),this.endCook()}}async cookMain(){if(this.isCooking())return;let t;this._initCookingState(),this.node.states.error.clear(),this.node.scene().cookController.addNode(this.node),t=this._inputs_evaluation_required?await this._evaluateInputs():[],this.node.params.paramsEvalRequired()&&await this._evaluateParams(),this._start_cook_if_no_errors(t)}async cookMainWithoutInputs(){this.node.scene().cookController.addNode(this.node),this.isCooking()?ai.warn(\\\\\\\"cook_main_without_inputs already cooking\\\\\\\",this.node.path()):(this._initCookingState(),this.node.states.error.clear(),this.node.params.paramsEvalRequired()&&await this._evaluateParams(),this._start_cook_if_no_errors([]))}endCook(t){this._finalizeCookPerformance();const e=this.node.dirtyController.dirtyTimestamp();null==e||e===this._cooking_dirty_timestamp?(this.node.removeDirtyState(),this._terminateCookProcess()):(ai.log(\\\\\\\"COOK AGAIN\\\\\\\",e,this._cooking_dirty_timestamp,this.node.path()),this._cooking=!1,this.cookMain())}_initCookingState(){this._cooking=!0,this._cooking_dirty_timestamp=this.node.dirtyController.dirtyTimestamp()}_terminateCookProcess(){this.isCooking()&&(this._cooking=!1,this.node.containerController.notifyRequesters(),this._run_on_cook_complete_hooks())}async _evaluateInputs(){this._performanceController.recordInputsStart();let t=[];const e=this.node.io.inputs;this._inputs_evaluation_required&&(t=e.is_any_input_dirty()?await e.eval_required_inputs():await e.containers_without_evaluation());const n=e.inputs(),i=[];let r;for(let s=0;st.graphNodeId()))}maxInputsCount(){return this.node.io.inputs.maxInputsCount()}inputIds(){return this.node.io.inputs.inputs().map((t=>null!=t?t.graphNodeId():void 0))}inputConnectionOutputIndices(){var t;return null===(t=this.node.io.connections.inputConnections())||void 0===t?void 0:t.map((t=>null!=t?t.output_index:void 0))}namedInputConnectionPoints(){return this.node.io.inputs.namedInputConnectionPoints().map((t=>t.toJSON()))}namedOutputConnectionPoints(){return this.node.io.outputs.namedOutputConnectionPoints().map((t=>t.toJSON()))}to_json_params_from_names(t,e=!1){return t.map((t=>this.node.params.get(t).graphNodeId()))}to_json_params(t=!1){return this.to_json_params_from_names(this.node.params.names,t)}}var Es,Ms;!function(t){t.BOOLEAN=\\\\\\\"boolean\\\\\\\",t.BUTTON=\\\\\\\"button\\\\\\\",t.COLOR=\\\\\\\"color\\\\\\\",t.FLOAT=\\\\\\\"float\\\\\\\",t.FOLDER=\\\\\\\"folder\\\\\\\",t.INTEGER=\\\\\\\"integer\\\\\\\",t.OPERATOR_PATH=\\\\\\\"operator_path\\\\\\\",t.PARAM_PATH=\\\\\\\"param_path\\\\\\\",t.NODE_PATH=\\\\\\\"node_path\\\\\\\",t.RAMP=\\\\\\\"ramp\\\\\\\",t.STRING=\\\\\\\"string\\\\\\\",t.VECTOR2=\\\\\\\"vector2\\\\\\\",t.VECTOR3=\\\\\\\"vector3\\\\\\\",t.VECTOR4=\\\\\\\"vector4\\\\\\\"}(Es||(Es={})),function(t){t.VISIBLE_UPDATED=\\\\\\\"param_visible_updated\\\\\\\",t.RAW_INPUT_UPDATED=\\\\\\\"raw_input_updated\\\\\\\",t.VALUE_UPDATED=\\\\\\\"param_value_updated\\\\\\\",t.EXPRESSION_UPDATED=\\\\\\\"param_expression_update\\\\\\\",t.ERROR_UPDATED=\\\\\\\"param_error_updated\\\\\\\",t.DELETED=\\\\\\\"param_deleted\\\\\\\"}(Ms||(Ms={}));const Ss=\\\\\\\"dependentOnFoundNode\\\\\\\",Cs=\\\\\\\"visibleIf\\\\\\\";var Ns,Ls;!function(t){t.TYPESCRIPT=\\\\\\\"typescript\\\\\\\"}(Ns||(Ns={})),function(t){t.AUDIO=\\\\\\\"audio\\\\\\\",t.TEXTURE_IMAGE=\\\\\\\"texture_image\\\\\\\",t.TEXTURE_VIDEO=\\\\\\\"texture_video\\\\\\\",t.GEOMETRY=\\\\\\\"geometry\\\\\\\",t.FONT=\\\\\\\"font\\\\\\\",t.SVG=\\\\\\\"svg\\\\\\\",t.JSON=\\\\\\\"json\\\\\\\"}(Ls||(Ls={}));class Os{constructor(t){this._param=t,this._programatic_visible_state=!0,this._callbackAllowed=!1,this._updateVisibilityAndRemoveDirtyBound=this.updateVisibilityAndRemoveDirty.bind(this),this._ui_data_dependency_set=!1}dispose(){var t;try{this._options.callback=void 0,this._options.callbackString=void 0}catch(t){}null===(t=this._visibility_graph_node)||void 0===t||t.dispose()}set(t){this._default_options=t,this._options=b.cloneDeep(this._default_options),this.post_set_options()}copy(t){this._default_options=b.cloneDeep(t.default()),this._options=b.cloneDeep(t.current()),this.post_set_options()}setOption(t,e){if(this._options[t]=e,this._param.components)for(let n of this._param.components)n.options.setOption(t,e)}post_set_options(){this._handleComputeOnDirty()}param(){return this._param}node(){return this._param.node}default(){return this._default_options}current(){return this._options}hasOptionsOverridden(){return!b.isEqual(this._options,this._default_options)}overriddenOptions(){const t={},e=Object.keys(this._options);for(let n of e)if(!b.isEqual(this._options[n],this._default_options[n])){const e=b.cloneDeep(this._options[n]);Object.assign(t,{[n]:e})}return t}overriddenOptionNames(){return Object.keys(this.overriddenOptions())}computeOnDirty(){return this._options.computeOnDirty||!1}_handleComputeOnDirty(){this.computeOnDirty()&&(this._computeOnDirty_callback_added||(this.param().addPostDirtyHook(\\\\\\\"computeOnDirty\\\\\\\",this._computeParam.bind(this)),this._computeOnDirty_callback_added=!0))}async _computeParam(){await this.param().compute()}hasCallback(){return null!=this._options.callback||null!=this._options.callbackString}allowCallback(){this._callbackAllowed=!0}executeCallback(){if(!this._callbackAllowed)return;if(!this.node())return;const t=this.getCallback();if(!t)return;if(!this.node().scene().loadingController.loaded())return;const e=this.param().parent_param;e?e.options.executeCallback():t(this.node(),this.param())}getCallback(){if(this.hasCallback())return this._options.callback=this._options.callback||this.createCallbackFromString()}createCallbackFromString(){const t=this._options.callbackString;if(t){const e=new Function(\\\\\\\"node\\\\\\\",\\\\\\\"scene\\\\\\\",\\\\\\\"window\\\\\\\",\\\\\\\"location\\\\\\\",t);return()=>{e(this.node(),this.node().scene(),null,null)}}}colorConversion(){return this._options.conversion}makesNodeDirtyWhenDirty(){let t;if(null!=this.param().parent_param)return!1;let e=!0;return null!=(t=this._options.cook)&&(e=t),e}fileBrowseOption(){return this._options.fileBrowse}fileBrowseAllowed(){return null!=this.fileBrowseOption()}fileBrowseType(){const t=this.fileBrowseOption();return t?t.type:null}separatorBefore(){return this._options.separatorBefore}separatorAfter(){return this._options.separatorAfter}isExpressionForEntities(){const t=this._options.expression;return t&&t.forEntities||!1}level(){return this._options.level||0}hasMenu(){return null!=this.menuOptions()||null!=this.menuStringOptions()}menuOptions(){return this._options.menu}menuStringOptions(){return this._options.menuString}menuEntries(){const t=this.menuOptions()||this.menuStringOptions();return t?t.entries:[]}isMultiline(){return!0===this._options.multiline}language(){return this._options.language}isCode(){return null!=this.language()}nodeSelectionOptions(){return this._options.nodeSelection}nodeSelectionContext(){const t=this.nodeSelectionOptions();if(t)return t.context}nodeSelectionTypes(){const t=this.nodeSelectionOptions();if(t)return t.types}dependentOnFoundNode(){return!(Ss in this._options)||this._options.dependentOnFoundNode}isSelectingParam(){return null!=this.paramSelectionOptions()}paramSelectionOptions(){return this._options.paramSelection}paramSelectionType(){const t=this.paramSelectionOptions();if(t){const e=t;if(!m.isBoolean(e))return e}}range(){return this._options.range||[0,1]}step(){return this._options.step}rangeLocked(){return this._options.rangeLocked||[!1,!1]}ensureInRange(t){const e=this.range();return t>=e[0]&&t<=e[1]?t:tObject.keys(t))).flat()):Object.keys(t);const n=this.param().node;return f.compact(e.map((t=>{const e=n.params.get(t);if(e)return e;console.error(`param ${t} not found as visibility condition for ${this.param().name()} in node ${this.param().node.type()}`)})))}setUiDataDependency(){if(this._ui_data_dependency_set)return;this._ui_data_dependency_set=!0;const t=this.visibilityPredecessors();if(t.length>0){this._visibility_graph_node=new Ai(this.param().scene(),\\\\\\\"param_visibility\\\\\\\");for(let e of t)this._visibility_graph_node.addGraphInput(e);this._visibility_graph_node.addPostDirtyHook(\\\\\\\"_update_visibility_and_remove_dirty\\\\\\\",this._updateVisibilityAndRemoveDirtyBound)}}updateVisibilityAndRemoveDirty(){this.updateVisibility(),this.param().removeDirtyState()}async updateVisibility(){const t=this._options.visibleIf;if(t){const e=this.visibilityPredecessors(),n=e.map((t=>{if(t.isDirty())return t.compute()}));if(this._programatic_visible_state=!1,await Promise.all(n),m.isArray(t))for(let n of t){e.filter((t=>t.value==n[t.name()])).length==e.length&&(this._programatic_visible_state=!0)}else{const n=e.filter((e=>e.value==t[e.name()]));this._programatic_visible_state=n.length==e.length}this.param().emit(Ms.VISIBLE_UPDATED)}}}class Rs{constructor(t){this.param=t,this._blocked_emit=!1,this._blocked_parent_emit=!1,this._count_by_event_name={}}emitAllowed(){return!0!==this._blocked_emit&&(!this.param.scene().loadingController.isLoading()&&this.param.scene().dispatchController.emitAllowed())}blockEmit(){if(this._blocked_emit=!0,this.param.isMultiple()&&this.param.components)for(let t of this.param.components)t.emitController.blockEmit();return!0}unblockEmit(){if(this._blocked_emit=!1,this.param.isMultiple()&&this.param.components)for(let t of this.param.components)t.emitController.unblockEmit();return!0}blockParentEmit(){return this._blocked_parent_emit=!0,!0}unblockParentEmit(){return this._blocked_parent_emit=!1,!0}incrementCount(t){this._count_by_event_name[t]=this._count_by_event_name[t]||0,this._count_by_event_name[t]+=1}eventsCount(t){return this._count_by_event_name[t]||0}emit(t){this.emitAllowed()&&(this.param.emit(t),null!=this.param.parent_param&&!0!==this._blocked_parent_emit&&this.param.parent_param.emit(t))}}class Ps{constructor(t){this.param=t}toJSON(){const t={name:this.param.name(),type:this.param.type(),raw_input:this.rawInput(),value:this.value(),value_pre_conversion:this.value_pre_conversion(),expression:this.expression(),graph_node_id:this.param.graphNodeId(),error_message:this.error_message(),is_visible:this.is_visible(),components:void 0};return this.param.isMultiple()&&this.param.components&&(t.components=this.param.components.map((t=>t.graphNodeId()))),t}rawInput(){return this.param.rawInputSerialized()}value(){return this.param.valueSerialized()}value_pre_conversion(){return this.param.valuePreConversionSerialized()}expression(){var t;return this.param.hasExpression()?null===(t=this.param.expressionController)||void 0===t?void 0:t.expression():void 0}error_message(){return this.param.states.error.message()}is_visible(){return this.param.options.isVisible()}}class Is{constructor(t){this.param=t}active(){const t=this.param.scene().timeController.graphNode.graphNodeId();return this.param.graphPredecessorIds().includes(t)}}class Fs{constructor(t){this.param=t}set(t){this._message!=t&&(this._message=t,this._message&&ai.warn(this.param.path(),this._message),this.param.emitController.emit(Ms.ERROR_UPDATED))}message(){return this._message}clear(){this.set(void 0)}active(){return null!=this._message}}class Ds{constructor(t){this.param=t,this.timeDependent=new Is(this.param),this.error=new Fs(this.param)}}class ks extends Ai{constructor(t,e,n){var i;super(t.scene(),\\\\\\\"MethodDependency\\\\\\\"),this.param=t,this.path_argument=e,this.decomposed_path=n,this._update_from_name_change_bound=this._update_from_name_change.bind(this),null===(i=t.expressionController)||void 0===i||i.registerMethodDependency(this),this.addPostDirtyHook(\\\\\\\"_update_from_name_change\\\\\\\",this._update_from_name_change_bound)}_update_from_name_change(t){if(t&&this.decomposed_path){const e=t;this.decomposed_path.update_from_name_change(e);const n=this.decomposed_path.to_path(),i=this.jsep_node;i&&(i.value=`${i.value}`.replace(`${this.path_argument}`,n),i.raw=i.raw.replace(`${this.path_argument}`,n)),this.param.expressionController&&this.param.expressionController.update_from_method_dependency_name_change()}}reset(){this.graphDisconnectPredecessors()}listen_for_name_changes(){if(this.jsep_node&&this.decomposed_path)for(let t of this.decomposed_path.named_nodes())if(t){const e=t;e.nameController&&this.addGraphInput(e.nameController.graph_node)}}set_jsep_node(t){this.jsep_node=t}set_resolved_graph_node(t){this.resolved_graph_node=t}set_unresolved_path(t){this.unresolved_path=t}static create(t,e,n,i){const r=m.isNumber(e),s=new ks(t,e,i);if(n)s.set_resolved_graph_node(n);else if(!r){const t=e;s.set_unresolved_path(t)}return s}}const Bs=[];class zs extends Ai{constructor(t,e){super(t,\\\\\\\"BaseParam\\\\\\\"),this._options=new Os(this),this._emit_controller=new Rs(this),this._is_computing=!1,this._node=e,this.initialize_param()}get options(){return this._options=this._options||new Os(this)}get emitController(){return this._emit_controller=this._emit_controller||new Rs(this)}get expressionController(){return this._expression_controller}get serializer(){return this._serializer=this._serializer||new Ps(this)}get states(){return this._states=this._states||new Ds(this)}dispose(){var t,e;const n=this.graphPredecessors();for(let t of n)t instanceof ks&&t.dispose();null===(t=this._expression_controller)||void 0===t||t.dispose(),super.dispose(),null===(e=this._options)||void 0===e||e.dispose()}initialize_param(){}static type(){return Es.FLOAT}type(){return this.constructor.type()}isNumeric(){return!1}setName(t){super.setName(t)}get value(){return this._value}copy_value(t){t.type()==this.type()?this._copy_value(t):console.warn(`cannot copy value from ${t.type()} to ${this.type()}`)}_copy_value(t){throw\\\\\\\"abstract method param._copy_value\\\\\\\"}valuePreConversionSerialized(){}convert(t){return null}static are_raw_input_equal(t,e){return!1}is_raw_input_equal(t){return this.constructor.are_raw_input_equal(this._raw_input,t)}static are_values_equal(t,e){return!1}is_value_equal(t){return this.constructor.are_values_equal(this.value,t)}_clone_raw_input(t){return t}set(t){this._raw_input=this._clone_raw_input(this._prefilter_invalid_raw_input(t)),this.emitController.emit(Ms.RAW_INPUT_UPDATED),this.processRawInput()}_prefilter_invalid_raw_input(t){return t}defaultValue(){return this._default_value}isDefault(){return this._raw_input==this._default_value}rawInput(){return this._raw_input}processRawInput(){}async compute(){if(this.scene().loadingController.isLoading()&&console.warn(`param attempt to compute ${this.path()}`),this.isDirty()){if(this._is_computing)return new Promise(((t,e)=>{this._compute_resolves=this._compute_resolves||[],this._compute_resolves.push(t)}));if(this._is_computing=!0,await this.processComputation(),this._is_computing=!1,this._compute_resolves){let t;for(;t=this._compute_resolves.pop();)t()}}}async processComputation(){}setInitValue(t){this._default_value=this._clone_raw_input(this._prefilter_invalid_raw_input(t))}_setupNodeDependencies(t){var e,n;if(t?(this.options.allowCallback(),this.parent_param||(this.options.makesNodeDirtyWhenDirty()?null===(n=t.params.params_node)||void 0===n||n.addGraphInput(this,!1):this.dirtyController.addPostDirtyHook(\\\\\\\"run callback\\\\\\\",(async()=>{await this.compute(),this.options.executeCallback()})))):this._node&&(null===(e=this._node.params.params_node)||void 0===e||e.removeGraphInput(this)),this.components)for(let e of this.components)e._setupNodeDependencies(t)}get node(){return this._node}parent(){return this.node}set_parent_param(t){t.addGraphInput(this,!1),this._parent_param=t}get parent_param(){return this._parent_param}has_parent_param(){return null!=this._parent_param}path(){var t;return(null===(t=this.node)||void 0===t?void 0:t.path())+\\\\\\\"/\\\\\\\"+this.name()}pathRelativeTo(t){const e=xi.relativePath(t,this.node);return e.length>0?`${e}${xi.SEPARATOR}${this.name()}`:this.name()}emit(t){this.emitController.emitAllowed()&&(this.emitController.incrementCount(t),this.scene().dispatchController.dispatch(this,t))}get components(){return this._components}componentNames(){return Bs}isMultiple(){return this.componentNames().length>0}initComponents(){}hasExpression(){return null!=this.expressionController&&this.expressionController.active()}toJSON(){return this.serializer.toJSON()}}var Us=n(96),Gs=n.n(Us);Gs.a.addUnaryOp(\\\\\\\"@\\\\\\\");Gs.a.addBinaryOp(\\\\\\\"**\\\\\\\",10);class Vs{constructor(){}parse_expression(t){try{this.reset(),this.node=Gs()(t)}catch(e){const n=`could not parse the expression '${t}' (error: ${e})`;this.error_message=n}}parse_expression_for_string_param(t){try{this.reset();const e=Vs.string_value_elements(t),n=[];for(let t=0;t{e.push(js.assign_attribute_line(t))})),e.join(\\\\\\\";\\\\n\\\\\\\")}return\\\\\\\"\\\\\\\"}assign_arrays_lines(){var t;if(this._attribute_names){const e=[];return null===(t=this._attribute_names)||void 0===t||t.forEach((t=>{e.push(js.assign_item_size_line(t)),e.push(js.assign_array_line(t))})),e.join(\\\\\\\";\\\\n\\\\\\\")}return\\\\\\\"\\\\\\\"}attribute_presence_check_line(){var t;if(this._attribute_names){const e=[];if(null===(t=this._attribute_names)||void 0===t||t.forEach((t=>{const n=js.var_attribute(t);e.push(n)})),e.length>0)return e.join(\\\\\\\" && \\\\\\\")}return\\\\\\\"true\\\\\\\"}add(t){this._attribute_names=this._attribute_names||new Set,this._attribute_names.add(t)}static assign_attribute_line(t){return`const ${this.var_attribute(t)} = entities[0].geometry().attributes['${t}']`}static assign_item_size_line(t){const e=this.var_attribute(t);return`const ${this.var_attribute_size(t)} = ${e}.itemSize`}static assign_array_line(t){const e=this.var_attribute(t);return`const ${this.var_array(t)} = ${e}.array`}static var_attribute(t){return`attrib_${t}`}static var_attribute_size(t){return`attrib_size_${t}`}static var_array(t){return`array_${t}`}var_attribute_size(t){return js.var_attribute_size(t)}var_array(t){return js.var_array(t)}}const Ws={math_random:\\\\\\\"random\\\\\\\"},qs=Object.keys(ns),Xs={};[\\\\\\\"abs\\\\\\\",\\\\\\\"acos\\\\\\\",\\\\\\\"acosh\\\\\\\",\\\\\\\"asin\\\\\\\",\\\\\\\"asinh\\\\\\\",\\\\\\\"atan\\\\\\\",\\\\\\\"atan2\\\\\\\",\\\\\\\"atanh\\\\\\\",\\\\\\\"ceil\\\\\\\",\\\\\\\"cos\\\\\\\",\\\\\\\"cosh\\\\\\\",\\\\\\\"exp\\\\\\\",\\\\\\\"expm1\\\\\\\",\\\\\\\"floor\\\\\\\",\\\\\\\"log\\\\\\\",\\\\\\\"log1p\\\\\\\",\\\\\\\"log2\\\\\\\",\\\\\\\"log10\\\\\\\",\\\\\\\"max\\\\\\\",\\\\\\\"min\\\\\\\",\\\\\\\"pow\\\\\\\",\\\\\\\"round\\\\\\\",\\\\\\\"sign\\\\\\\",\\\\\\\"sin\\\\\\\",\\\\\\\"sinh\\\\\\\",\\\\\\\"sqrt\\\\\\\",\\\\\\\"tan\\\\\\\",\\\\\\\"tanh\\\\\\\"].forEach((t=>{Xs[t]=`Math.${t}`})),[\\\\\\\"cbrt\\\\\\\",\\\\\\\"hypot\\\\\\\",\\\\\\\"log10\\\\\\\",\\\\\\\"trunc\\\\\\\"].forEach((t=>{Xs[t]=`Math.${t}`})),Object.keys(Ws).forEach((t=>{const e=Ws[t];Xs[t]=`Math.${e}`})),[\\\\\\\"fit\\\\\\\",\\\\\\\"fit01\\\\\\\",\\\\\\\"fract\\\\\\\",\\\\\\\"deg2rad\\\\\\\",\\\\\\\"rad2deg\\\\\\\",\\\\\\\"rand\\\\\\\",\\\\\\\"clamp\\\\\\\"].forEach((t=>{Xs[t]=`Core.Math.${t}`})),qs.forEach((t=>{Xs[t]=`Core.Math.Easing.${t}`})),[\\\\\\\"precision\\\\\\\"].forEach((t=>{Xs[t]=`Core.String.${t}`}));const Ys={if:class{static if(t){return`(${t[0]}) ? (${t[1]}) : (${t[2]})`}}.if},$s={};[\\\\\\\"E\\\\\\\",\\\\\\\"LN2\\\\\\\",\\\\\\\"LN10\\\\\\\",\\\\\\\"LOG10E\\\\\\\",\\\\\\\"LOG2E\\\\\\\",\\\\\\\"PI\\\\\\\",\\\\\\\"SQRT1_2\\\\\\\",\\\\\\\"SQRT2\\\\\\\"].forEach((t=>{$s[t]=`Math.${t}`}));const Js={x:0,y:1,z:2,w:3,r:0,g:1,b:2};class Zs extends Hs{constructor(t){super(t),this.param=t,this._attribute_requirements_controller=new js,this.methods=[],this.method_index=-1,this.method_dependencies=[],this.immutable_dependencies=[]}parse_tree(t){if(this.reset(),null==t.error_message){try{if(this._attribute_requirements_controller.reset(),t.node){const e=this.traverse_node(t.node);e&&!this.is_errored()&&(this.function_main_string=e)}else console.warn(\\\\\\\"no parsed_tree.node\\\\\\\")}catch(t){console.warn(`error in expression for param ${this.param.path()}`),console.warn(t)}if(this.function_main_string)try{this.function=new Function(\\\\\\\"Core\\\\\\\",\\\\\\\"param\\\\\\\",\\\\\\\"methods\\\\\\\",\\\\\\\"_set_error_from_error\\\\\\\",`\\\\n\\\\t\\\\t\\\\t\\\\t\\\\ttry {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t${this.function_body()}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t} catch(e) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t_set_error_from_error(e)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treturn null;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}`)}catch(t){console.warn(t),this.set_error(\\\\\\\"cannot generate function\\\\\\\")}else this.set_error(\\\\\\\"cannot generate function body\\\\\\\")}else this.set_error(\\\\\\\"cannot parse expression\\\\\\\")}reset(){super.reset(),this.function_main_string=void 0,this.methods=[],this.method_index=-1,this.function=void 0,this.method_dependencies=[],this.immutable_dependencies=[]}function_body(){return this.param.options.isExpressionForEntities()?`\\\\n\\\\t\\\\t\\\\tconst entities = param.expressionController.entities();\\\\n\\\\t\\\\t\\\\tif(entities){\\\\n\\\\t\\\\t\\\\t\\\\treturn new Promise( async (resolve, reject)=>{\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tlet entity;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tconst entity_callback = param.expressionController.entity_callback();\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t${this._attribute_requirements_controller.assign_attributes_lines()}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tif( ${this._attribute_requirements_controller.attribute_presence_check_line()} ){\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t${this._attribute_requirements_controller.assign_arrays_lines()}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfor(let index=0; index < entities.length; index++){\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tentity = entities[index];\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tresult = ${this.function_main_string};\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tentity_callback(entity, result);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tresolve()\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tconst error = new Error('attribute not found')\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t_set_error_from_error(error)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treject(error)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t})\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\treturn []`:`\\\\n\\\\t\\\\t\\\\treturn new Promise( async (resolve, reject)=>{\\\\n\\\\t\\\\t\\\\t\\\\ttry {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tconst value = ${this.function_main_string}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tresolve(value)\\\\n\\\\t\\\\t\\\\t\\\\t} catch(e) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t_set_error_from_error(e)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treject()\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t})\\\\n\\\\t\\\\t\\\\t`}eval_allowed(){return null!=this.function}eval_function(){if(this.function){this.clear_error();const t={Math:rs,String:sr};return this.function(t,this.param,this.methods,this._set_error_from_error_bound)}}traverse_CallExpression(t){const e=t.arguments.map((t=>this.traverse_node(t))),n=t.callee.name;if(n){const i=Ys[n];if(i)return i(e);const r=`${e.join(\\\\\\\", \\\\\\\")}`,s=Xs[n];if(s)return`${s}(${r})`;const o=ai.expressionsRegister;if(o.getMethod(n)){const i=t.arguments[0],s=`return ${e[0]}`;let o,a=[];try{o=new Function(s),a=o()}catch{}return this._create_method_and_dependencies(n,a,i),`(await methods[${this.method_index}].processArguments([${r}]))`}{const t=`method not found (${n}), available methods are: ${o.availableMethods().join(\\\\\\\", \\\\\\\")}`;ai.warn(t)}}this.set_error(`unknown method: ${n}`)}traverse_BinaryExpression(t){return`(${this.traverse_node(t.left)} ${t.operator} ${this.traverse_node(t.right)})`}traverse_LogicalExpression(t){return`(${this.traverse_node(t.left)} ${t.operator} ${this.traverse_node(t.right)})`}traverse_MemberExpression(t){return`${this.traverse_node(t.object)}.${this.traverse_node(t.property)}`}traverse_UnaryExpression(t){if(\\\\\\\"@\\\\\\\"===t.operator){let e,n,i=t.argument;switch(i.type){case\\\\\\\"Identifier\\\\\\\":e=i.name;break;case\\\\\\\"MemberExpression\\\\\\\":{const t=i,r=t.object,s=t.property;e=r.name,n=s.name;break}}if(e){if(e=Wr.remapName(e),\\\\\\\"ptnum\\\\\\\"==e)return\\\\\\\"((entity != null) ? entity.index() : 0)\\\\\\\";{const t=this._attribute_requirements_controller.var_attribute_size(e),i=this._attribute_requirements_controller.var_array(e);if(this._attribute_requirements_controller.add(e),n){return`${i}[entity.index()*${t}+${Js[n]}]`}return`${i}[entity.index()*${t}]`}}return console.warn(\\\\\\\"attribute not found\\\\\\\"),\\\\\\\"\\\\\\\"}return`${t.operator}${this.traverse_node(t.argument)}`}traverse_Literal(t){return`${t.raw}`}traverse_Identifier(t){if(\\\\\\\"$\\\\\\\"!=t.name[0])return t.name;{const e=t.name.substr(1),n=$s[e];if(n)return n;const i=`traverse_Identifier_${e}`;if(this[i])return this[i]();this.set_error(`identifier unknown: ${t.name}`)}}traverse_Identifier_F(){return this.immutable_dependencies.push(this.param.scene().timeController.graphNode),\\\\\\\"param.scene().timeController.frame()\\\\\\\"}traverse_Identifier_T(){return this.immutable_dependencies.push(this.param.scene().timeController.graphNode),\\\\\\\"param.scene().timeController.time()\\\\\\\"}traverse_Identifier_OS(){return`'${this.param.node.name()}'`}traverse_Identifier_CH(){return`'${this.param.name()}'`}traverse_Identifier_CEX(){return this._method_centroid(\\\\\\\"x\\\\\\\")}traverse_Identifier_CEY(){return this._method_centroid(\\\\\\\"y\\\\\\\")}traverse_Identifier_CEZ(){return this._method_centroid(\\\\\\\"z\\\\\\\")}_method_centroid(t){const e=[0,`'${t}'`].join(\\\\\\\", \\\\\\\");return this._create_method_and_dependencies(\\\\\\\"centroid\\\\\\\",0),`(await methods[${this.method_index}].processArguments([${e}]))`}_create_method_and_dependencies(t,e,n){const i=ai.expressionsRegister,r=i.getMethod(t);if(!r){const e=`method not found (${t}), available methods are: ${i.availableMethods().join(\\\\\\\", \\\\\\\")}`;return this.set_error(e),void ai.warn(e)}const s=new r(this.param);if(this.method_index+=1,this.methods[this.method_index]=s,s.require_dependency()){const t=s.findDependency(e);t?(n&&t.set_jsep_node(n),this.method_dependencies.push(t)):n&&m.isString(e)&&this.param.scene().missingExpressionReferencesController.register(this.param,n,e)}}}class Qs extends Hs{constructor(t){super(t),this.param=t}parse_tree(t){if(null==t.error_message&&t.node)try{return this.traverse_node(t.node)}catch(t){this.set_error(\\\\\\\"could not traverse tree\\\\\\\")}else this.set_error(\\\\\\\"cannot parse tree\\\\\\\")}traverse_CallExpression(t){const e=`${t.arguments.map((t=>this.traverse_node(t))).join(\\\\\\\", \\\\\\\")}`;return`${t.callee.name}(${e})`}traverse_UnaryExpression(t){return`${t.operator}${this.traverse_node(t.argument)}`}traverse_Identifier(t){return`${t.name}`}}class Ks{constructor(t){this.param=t,this.cyclic_graph_detected=!1,this.method_dependencies=[]}set_error(t){this.error_message=this.error_message||t}reset(){this.param.graphDisconnectPredecessors(),this.method_dependencies.forEach((t=>{t.reset()})),this.method_dependencies=[]}update(t){this.cyclic_graph_detected=!1,this.connect_immutable_dependencies(t),this.method_dependencies=t.method_dependencies,this.handle_method_dependencies(),this.listen_for_name_changes()}connect_immutable_dependencies(t){t.immutable_dependencies.forEach((t=>{if(0==this.cyclic_graph_detected&&0==this.param.addGraphInput(t))return this.cyclic_graph_detected=!0,this.set_error(\\\\\\\"cannot create expression, infinite graph detected\\\\\\\"),void this.reset()}))}handle_method_dependencies(){this.method_dependencies.forEach((t=>{0==this.cyclic_graph_detected&&this.handle_method_dependency(t)}))}handle_method_dependency(t){const e=t.resolved_graph_node;if(e&&!this.param.addGraphInput(e))return this.cyclic_graph_detected=!0,this.set_error(\\\\\\\"cannot create expression, infinite graph detected\\\\\\\"),void this.reset()}listen_for_name_changes(){this.method_dependencies.forEach((t=>{t.listen_for_name_changes()}))}}class to{constructor(t){this.param=t,this.parse_completed=!1,this.parse_started=!1,this.parsed_tree=new Vs,this.function_generator=new Zs(this.param),this.dependencies_controller=new Ks(this.param)}parse_expression(t){if(this.parse_started)throw new Error(`parse in progress for param ${this.param.path()}`);this.parse_started=!0,this.parse_completed=!1,this.parsed_tree=this.parsed_tree||new Vs,this.reset(),this.param.type()==Es.STRING?this.parsed_tree.parse_expression_for_string_param(t):this.parsed_tree.parse_expression(t),this.function_generator.parse_tree(this.parsed_tree),null==this.function_generator.error_message()&&(this.dependencies_controller.update(this.function_generator),this.dependencies_controller.error_message?this.param.states.error.set(this.dependencies_controller.error_message):(this.parse_completed=!0,this.parse_started=!1))}async compute_function(){if(!this.compute_allowed())return new Promise(((t,e)=>{t(null)}));try{return await this.function_generator.eval_function()}catch(t){return}}reset(){this.parse_completed=!1,this.parse_started=!1,this.dependencies_controller.reset(),this.function_generator.reset()}is_errored(){return this.function_generator.is_errored()}error_message(){return this.function_generator.error_message()}compute_allowed(){return this.function_generator.eval_allowed()}update_from_method_dependency_name_change(){this.expression_string_generator=this.expression_string_generator||new Qs(this.param);const t=this.expression_string_generator.parse_tree(this.parsed_tree);t?this.param.set(t):console.warn(\\\\\\\"failed to regenerate expression\\\\\\\")}}class eo{constructor(t){this.param=t}dispose(){this._resetMethodDependencies()}_resetMethodDependencies(){var t,e;null===(t=this._method_dependencies_by_graph_node_id)||void 0===t||t.forEach((t=>{t.dispose()})),null===(e=this._method_dependencies_by_graph_node_id)||void 0===e||e.clear()}registerMethodDependency(t){this._method_dependencies_by_graph_node_id=this._method_dependencies_by_graph_node_id||new Map,this._method_dependencies_by_graph_node_id.set(t.graphNodeId(),t)}active(){return null!=this._expression}expression(){return this._expression}is_errored(){return!!this._manager&&this._manager.is_errored()}error_message(){return this._manager?this._manager.error_message():null}requires_entities(){return this.param.options.isExpressionForEntities()}set_expression(t,e=!0){var n;this.param.scene().missingExpressionReferencesController.deregister_param(this.param),this.param.scene().expressionsController.deregister_param(this.param),this._expression!=t&&(this._resetMethodDependencies(),this._expression=t,this._expression?(this._manager=this._manager||new to(this.param),this._manager.parse_expression(this._expression)):null===(n=this._manager)||void 0===n||n.reset(),e&&this.param.setDirty())}update_from_method_dependency_name_change(){this._manager&&this.active()&&this._manager.update_from_method_dependency_name_change()}async compute_expression(){if(this._manager&&this.active()){return await this._manager.compute_function()}}async compute_expression_for_entities(t,e){var n,i;this.set_entities(t,e),await this.compute_expression(),(null===(n=this._manager)||void 0===n?void 0:n.error_message())&&this.param.node.states.error.set(`expression evalution error: ${null===(i=this._manager)||void 0===i?void 0:i.error_message()}`),this.reset_entities()}compute_expression_for_points(t,e){return this.compute_expression_for_entities(t,e)}compute_expression_for_objects(t,e){return this.compute_expression_for_entities(t,e)}entities(){return this._entities}entity_callback(){return this._entity_callback}set_entities(t,e){this._entities=t,this._entity_callback=e}reset_entities(){this._entities=void 0,this._entity_callback=void 0}}class no extends zs{isNumeric(){return!0}isDefault(){return this._raw_input==this._default_value}_prefilter_invalid_raw_input(t){return m.isArray(t)?t[0]:t}processRawInput(){this.states.error.clear();const t=this.convert(this._raw_input);null!=t?(this._expression_controller&&(this._expression_controller.set_expression(void 0,!1),this.emitController.emit(Ms.EXPRESSION_UPDATED)),t!=this._value&&(this._update_value(t),this.setSuccessorsDirty(this))):m.isString(this._raw_input)?(this._expression_controller=this._expression_controller||new eo(this),this._raw_input!=this._expression_controller.expression()&&(this._expression_controller.set_expression(this._raw_input),this.emitController.emit(Ms.EXPRESSION_UPDATED))):this.states.error.set(`param input is invalid (${this.path()})`)}async processComputation(){var t;if((null===(t=this.expressionController)||void 0===t?void 0:t.active())&&!this.expressionController.requires_entities()){const t=await this.expressionController.compute_expression();if(this.expressionController.is_errored())this.states.error.set(`expression error: \\\\\\\"${this.expressionController.expression()}\\\\\\\" (${this.expressionController.error_message()})`);else{const e=this.convert(t);null!=e?(this.states.error.active()&&this.states.error.clear(),this._update_value(e)):this.states.error.set(`expression returns an invalid type (${t}) (${this.expressionController.expression()})`)}}}_update_value(t){this._value=t,this.parent_param&&this.parent_param.set_value_from_components(),this.options.executeCallback(),this.emitController.emit(Ms.VALUE_UPDATED),this.removeDirtyState()}}class io extends no{static type(){return Es.FLOAT}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){this.set(t.valueSerialized())}_prefilter_invalid_raw_input(t){return m.isArray(t)?t[0]:m.isString(t)&&sr.isNumber(t)?parseFloat(t):t}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}static convert(t){if(m.isNumber(t))return t;if(m.isBoolean(t))return t?1:0;if(sr.isNumber(t)){const e=parseFloat(t);if(m.isNumber(e))return e}return null}convert(t){const e=io.convert(t);return e?this.options.ensureInRange(e):e}}class ro extends zs{constructor(){super(...arguments),this._components_contructor=io}get components(){return this._components}isNumeric(){return!0}isDefault(){for(let t of this.components)if(!t.isDefault())return!1;return!0}rawInput(){return this._components.map((t=>t.rawInput()))}rawInputSerialized(){return this._components.map((t=>t.rawInputSerialized()))}_copy_value(t){for(let e=0;ee))}}processRawInput(){const t=this.scene().cooker;t.block();const e=this.components;for(let t of e)t.emitController.blockParentEmit();const n=this._raw_input;let i=0;if(m.isArray(n))for(let t=0;t linear\\\\\\\",t.LINEAR_TO_GAMMA=\\\\\\\"linear -> gamma\\\\\\\",t.SRGB_TO_LINEAR=\\\\\\\"sRGB -> linear\\\\\\\",t.LINEAR_TO_SRGB=\\\\\\\"linear -> sRGB\\\\\\\"}(so||(so={}));so.NONE,so.GAMMA_TO_LINEAR,so.LINEAR_TO_GAMMA,so.SRGB_TO_LINEAR,so.LINEAR_TO_SRGB;class oo{static set_hsv(t,e,n,i){t=Object(Ln.f)(t,1),e=Object(Ln.d)(e,0,1),n=Object(Ln.d)(n,0,1),i.setHSL(t,e*n/((t=(2-e)*n)<1?t:2-t),.5*t)}}const ao=[\\\\\\\"r\\\\\\\",\\\\\\\"g\\\\\\\",\\\\\\\"b\\\\\\\"];class lo extends no{static type(){return Es.INTEGER}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){this.set(t.valueSerialized())}_prefilter_invalid_raw_input(t){return m.isArray(t)?t[0]:m.isString(t)&&sr.isNumber(t)?parseInt(t):t}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}static convert(t){if(m.isNumber(t))return Math.round(t);if(m.isBoolean(t))return t?1:0;if(sr.isNumber(t)){const e=parseInt(t);if(m.isNumber(e))return e}return null}convert(t){const e=lo.convert(t);return e?this.options.ensureInRange(e):e}}class co{constructor(){this._index=-1,this._path_elements=[],this._named_nodes=[],this._graph_node_ids=[],this._node_element_by_graph_node_id=new Map}reset(){this._index=-1,this._path_elements=[],this._named_nodes=[],this._graph_node_ids=[],this._node_element_by_graph_node_id.clear()}add_node(t,e){this._index+=1,t==e.name()&&(this._named_nodes[this._index]=e),this._graph_node_ids[this._index]=e.graphNodeId(),this._node_element_by_graph_node_id.set(e.graphNodeId(),t)}add_path_element(t){this._index+=1,this._path_elements[this._index]=t}named_graph_nodes(){return this._named_nodes}named_nodes(){const t=[];for(let e of this._named_nodes)if(e){const n=e;n.nameController&&t.push(n)}return t}update_from_name_change(t){this._named_nodes.map((t=>null==t?void 0:t.graphNodeId())).includes(t.graphNodeId())&&this._node_element_by_graph_node_id.set(t.graphNodeId(),t.name())}to_path(){const t=new Array(this._index);for(let e=0;e<=this._index;e++){const n=this._named_nodes[e];if(n){const i=this._node_element_by_graph_node_id.get(n.graphNodeId());i&&(t[e]=i)}else{const n=this._path_elements[e];n&&(t[e]=n)}}let e=t.join(xi.SEPARATOR);const n=e[0];return n&&(xi.NON_LETTER_PREFIXES.includes(n)||(e=`${xi.SEPARATOR}${e}`)),e}}class uo extends zs{constructor(){super(...arguments),this.decomposed_path=new co}}var ho;!function(t){t.NODE=\\\\\\\"NODE\\\\\\\",t.PARAM=\\\\\\\"PARAM\\\\\\\"}(ho||(ho={}));class po extends uo{constructor(){super(...arguments),this._found_node=null,this._found_node_with_expected_type=null,this._found_param=null,this._found_param_with_expected_type=null}static type(){return Es.OPERATOR_PATH}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(t){this.set(t.valueSerialized())}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}isDefault(){return this._value==this._default_value}setNode(t){this.set(t.path())}processRawInput(){this._value!=this._raw_input&&(this._value=this._raw_input,this.setDirty(),this.emitController.emit(Ms.VALUE_UPDATED))}async processComputation(){this.find_target()}find_target(){if(!this.node)return;const t=this._value;let e=null,n=null;const i=null!=t&&\\\\\\\"\\\\\\\"!==t,r=this.options.paramSelectionOptions()?ho.PARAM:ho.NODE;this.scene().referencesController.reset_reference_from_param(this),this.decomposed_path.reset(),i&&(r==ho.PARAM?n=xi.findParam(this.node,t,this.decomposed_path):e=xi.findNode(this.node,t,this.decomposed_path));const s=r==ho.PARAM?this._found_param:this._found_node,o=r==ho.PARAM?n:e;if(this.scene().referencesController.set_named_nodes_from_param(this),e&&this.scene().referencesController.set_reference_from_param(this,e),(null==s?void 0:s.graphNodeId())!==(null==o?void 0:o.graphNodeId())){const t=this.options.dependentOnFoundNode();this._found_node&&t&&this.removeGraphInput(this._found_node),r==ho.PARAM?(this._found_param=n,this._found_node=null):(this._found_node=e,this._found_param=null),e&&this._assign_found_node(e),n&&this._assign_found_param(n),this.options.executeCallback()}this.removeDirtyState()}_assign_found_node(t){const e=this.options.dependentOnFoundNode();this._is_node_expected_context(t)?this._is_node_expected_type(t)?(this._found_node_with_expected_type=t,e&&this.addGraphInput(t)):this.states.error.set(`node type is ${t.type()} but the params expects one of ${(this._expected_node_types()||[]).join(\\\\\\\", \\\\\\\")}`):this.states.error.set(`node context is ${t.context()} but the params expects a ${this._expected_context()}`)}_assign_found_param(t){this._is_param_expected_type(t)?this._found_param_with_expected_type=t:this.states.error.set(`param type is ${t.type()} but the params expects a ${this._expected_param_type()}`)}found_node(){return this._found_node}found_param(){return this._found_param}found_node_with_context(t){return this._found_node_with_expected_type}found_node_with_context_and_type(t,e){const n=this.found_node_with_context(t);if(n)if(m.isArray(e)){for(let t of e)if(n.type()==t)return n;this.states.error.set(`expected node type to be ${e.join(\\\\\\\", \\\\\\\")}, but was instead ${n.type()}`)}else{const t=e;if(n.type()==t)return n;this.states.error.set(`expected node type to be ${t}, but was instead ${n.type()}`)}}found_param_with_type(t){if(this._found_param_with_expected_type)return this._found_param_with_expected_type}found_node_with_expected_type(){return this._found_node_with_expected_type}_expected_context(){return this.options.nodeSelectionContext()}_is_node_expected_context(t){var e,n;const i=this._expected_context();if(null==i)return!0;return i==(null===(n=null===(e=t.parent())||void 0===e?void 0:e.childrenController)||void 0===n?void 0:n.context)}_expected_node_types(){return this.options.nodeSelectionTypes()}_expected_param_type(){return this.options.paramSelectionType()}_is_node_expected_type(t){const e=this._expected_node_types();return null==e||(null==e?void 0:e.includes(t.type()))}_is_param_expected_type(t){const e=this._expected_node_types();return null==e||e.includes(t.type())}notify_path_rebuild_required(t){this.decomposed_path.update_from_name_change(t);const e=this.decomposed_path.to_path();this.set(e)}notify_target_param_owner_params_updated(t){this.setDirty()}}var _o,mo=n(33),fo=n(70);class go{constructor(t=0,e=0){this._position=t,this._value=e}toJSON(){return{position:this._position,value:this._value}}get position(){return this._position}get value(){return this._value}copy(t){this._position=t.position,this._value=t.value}clone(){const t=new go;return t.copy(this),t}is_equal(t){return this._position==t.position&&this._value==t.value}is_equal_json(t){return this._position==t.position&&this._value==t.value}from_json(t){this._position=t.position,this._value=t.value}static are_equal_json(t,e){return t.position==e.position&&t.value==e.value}static from_json(t){return new go(t.position,t.value)}}!function(t){t.LINEAR=\\\\\\\"linear\\\\\\\"}(_o||(_o={}));class vo{constructor(t=_o.LINEAR,e=[]){this._interpolation=t,this._points=e,this._uuid=Object(Ln.h)()}get uuid(){return this._uuid}get interpolation(){return this._interpolation}get points(){return this._points}static from_json(t){const e=[];for(let n of t.points)e.push(go.from_json(n));return new vo(t.interpolation,e)}toJSON(){return{interpolation:this._interpolation,points:this._points.map((t=>t.toJSON()))}}clone(){const t=new vo;return t.copy(this),t}copy(t){this._interpolation=t.interpolation;let e=0;for(let n of t.points){const t=this._points[e];t?t.copy(n):this._points.push(n.clone()),e+=1}}is_equal(t){if(this._interpolation!=t.interpolation)return!1;const e=t.points;if(this._points.length!=e.length)return!1;let n=0;for(let t of this._points){const i=e[n];if(!t.is_equal(i))return!1;n+=1}return!0}is_equal_json(t){if(this._interpolation!=t.interpolation)return!1;if(this._points.length!=t.points.length)return!1;let e=0;for(let n of this._points){const i=t.points[e];if(!n.is_equal_json(i))return!1;e+=1}return!0}static are_json_equal(t,e){if(t.interpolation!=e.interpolation)return!1;if(t.points.length!=e.points.length)return!1;let n=0;for(let i of t.points){const t=e.points[n];if(!go.are_equal_json(i,t))return!1;n+=1}return!0}from_json(t){this._interpolation=t.interpolation;let e=0;for(let n of t.points){const t=this._points[e];t?t.from_json(n):this._points.push(go.from_json(n)),e+=1}}}const yo=1024;class xo extends zs{constructor(){super(...arguments),this._texture_data=new Uint8Array(3072),this._ramp_texture=new mo.a(this._texture_data,yo,1,w.ic)}static type(){return Es.RAMP}defaultValueSerialized(){return this._default_value instanceof vo?this._default_value.toJSON():this._default_value}_clone_raw_input(t){return t instanceof vo?t.clone():vo.from_json(t).toJSON()}rawInputSerialized(){return this._raw_input instanceof vo?this._raw_input.toJSON():vo.from_json(this._raw_input).toJSON()}valueSerialized(){return this.value.toJSON()}_copy_value(t){this.set(t.valueSerialized())}static are_raw_input_equal(t,e){return t instanceof vo?e instanceof vo?t.is_equal(e):t.is_equal_json(e):e instanceof vo?e.is_equal_json(t):vo.are_json_equal(t,e)}static are_values_equal(t,e){return t.is_equal(e)}isDefault(){return this._default_value instanceof vo?this.value.is_equal(this._default_value):this.value.is_equal_json(this._default_value)}processRawInput(){this._raw_input instanceof vo?this._value?this._value.copy(this._raw_input):this._value=this._raw_input:this._value?this._value.from_json(this._raw_input):this._value=vo.from_json(this._raw_input),this._reset_ramp_interpolant(),this._update_rampTexture(),this.options.executeCallback(),this.emitController.emit(Ms.VALUE_UPDATED),this.setSuccessorsDirty(this)}hasExpression(){return!1}_reset_ramp_interpolant(){this._ramp_interpolant=void 0}rampTexture(){return this._ramp_texture}_update_rampTexture(){this._update_ramp_texture_data(),this.rampTexture().needsUpdate=!0}_update_ramp_texture_data(){let t=0,e=0,n=0;for(var i=0;i<1024;i++)t=3*i,e=i/yo,n=this.value_at_position(e),this._texture_data[t]=255*n}static create_interpolant(t,e){const n=new Float32Array(1);return new fo.a(t,e,1,n)}interpolant(){return this._ramp_interpolant=this._ramp_interpolant||this._create_interpolant()}_create_interpolant(){const t=this.value.points,e=f.sortBy(t,(t=>t.position)),n=new Float32Array(e.length),i=new Float32Array(e.length);let r=0;for(let t of e)n[r]=t.position,i[r]=t.value,r++;return xo.create_interpolant(n,i)}value_at_position(t){return this.interpolant().evaluate(t)[0]}}xo.DEFAULT_VALUE=new vo(_o.LINEAR,[new go(0,0),new go(1,1)]),xo.DEFAULT_VALUE_JSON=xo.DEFAULT_VALUE.toJSON();class bo extends zs{static type(){return Es.STRING}defaultValueSerialized(){return this._default_value}_clone_raw_input(t){return`${t}`}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(t){this.set(t.value)}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}isDefault(){return this._raw_input==this._default_value}convert(t){return m.isString(t)?t:`${t}`}rawInput(){return this._raw_input}processRawInput(){this.states.error.clear(),this._value_elements(this._raw_input).length>=3?(this._expression_controller=this._expression_controller||new eo(this),this._raw_input!=this._expression_controller.expression()&&(this._expression_controller.set_expression(this._raw_input),this.setDirty(),this.emitController.emit(Ms.EXPRESSION_UPDATED))):this._raw_input!=this._value&&(this._value=this._raw_input,this.removeDirtyState(),this.setSuccessorsDirty(this),this.emitController.emit(Ms.VALUE_UPDATED),this.options.executeCallback(),this._expression_controller&&(this._expression_controller.set_expression(void 0,!1),this.emitController.emit(Ms.EXPRESSION_UPDATED)))}async processComputation(){var t;if((null===(t=this.expressionController)||void 0===t?void 0:t.active())&&!this.expressionController.requires_entities()){const t=await this.expressionController.compute_expression();if(this.expressionController.is_errored())this.states.error.set(`expression error: ${this.expressionController.error_message()}`);else{const e=this.convert(t);null!=e?(this._value=e,this.emitController.emit(Ms.VALUE_UPDATED),this.options.executeCallback()):this.states.error.set(`expression returns an invalid type (${t})`),this.removeDirtyState()}}}_value_elements(t){return Vs.string_value_elements(t)}}const wo=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"];const To=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"];const Ao=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];const Eo={[Es.BOOLEAN]:class extends no{static type(){return Es.BOOLEAN}defaultValueSerialized(){return m.isString(this._default_value)?this._default_value:this.convert(this._default_value)||!1}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){this.set(t.value)}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}convert(t){if(m.isBoolean(t))return t;if(m.isNumber(t))return t>=1;if(m.isString(t)){if(sr.isBoolean(t))return sr.toBoolean(t);if(sr.isNumber(t)){return parseFloat(t)>=1}}return null}},[Es.BUTTON]:class extends zs{static type(){return Es.BUTTON}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){}static are_raw_input_equal(t,e){return!0}static are_values_equal(t,e){return!0}async pressButton(){(this.node.isDirty()||this.node.cookController.isCooking())&&await this.node.compute(),this.options.executeCallback()}},[Es.COLOR]:class extends ro{constructor(){super(...arguments),this._value=new D.a,this._value_pre_conversion=new D.a,this._value_serialized_dirty=!1,this._value_serialized=[0,0,0],this._value_pre_conversion_serialized=[0,0,0],this._copied_value=[0,0,0]}static type(){return Es.COLOR}componentNames(){return ao}defaultValueSerialized(){return m.isArray(this._default_value)?this._default_value:this._default_value.toArray()}valueSerialized(){return this._update_value_serialized_if_required(),this._value_serialized}valuePreConversionSerialized(){return this._update_value_serialized_if_required(),this._value_pre_conversion_serialized}_copy_value(t){t.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(t){if(t instanceof D.a)return t.clone();{const e=[t[0],t[1],t[2]];return null==e[0]&&(e[0]=e[0]||0),null==e[1]&&(e[1]=e[1]||e[0]),null==e[2]&&(e[2]=e[2]||e[1]),e}}static are_raw_input_equal(t,e){return t instanceof D.a?e instanceof D.a?t.equals(e):t.r==e[0]&&t.g==e[1]&&t.b==e[2]:e instanceof D.a?t[0]==e.r&&t[1]==e.g&&t[2]==e.b:t[0]==e[0]&&t[1]==e[1]&&t[2]==e[2]}static are_values_equal(t,e){return t.equals(e)}initComponents(){super.initComponents(),this.r=this.components[0],this.g=this.components[1],this.b=this.components[2],this._value_serialized_dirty=!0}_update_value_serialized_if_required(){this._value_serialized_dirty&&(this._value_serialized[0]=this._value.r,this._value_serialized[1]=this._value.g,this._value_serialized[2]=this._value.b,this._value_pre_conversion_serialized[0]=this._value_pre_conversion.r,this._value_pre_conversion_serialized[1]=this._value_pre_conversion.g,this._value_pre_conversion_serialized[2]=this._value_pre_conversion.b)}valuePreConversion(){return this._value_pre_conversion}set_value_from_components(){this._value_pre_conversion.r=this.r.value,this._value_pre_conversion.g=this.g.value,this._value_pre_conversion.b=this.b.value,this._value.copy(this._value_pre_conversion);const t=this.options.colorConversion();if(null!=t&&t!=so.NONE){switch(t){case so.GAMMA_TO_LINEAR:return void this._value.convertGammaToLinear();case so.LINEAR_TO_GAMMA:return void this._value.convertLinearToGamma();case so.SRGB_TO_LINEAR:return void this._value.convertSRGBToLinear();case so.LINEAR_TO_SRGB:return void this._value.convertLinearToSRGB()}ar.unreachable(t)}this._value_serialized_dirty=!0}},[Es.FLOAT]:io,[Es.FOLDER]:class extends zs{static type(){return Es.FOLDER}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){}static are_raw_input_equal(t,e){return!0}static are_values_equal(t,e){return!0}},[Es.INTEGER]:lo,[Es.OPERATOR_PATH]:po,[Es.PARAM_PATH]:class extends uo{static type(){return Es.PARAM_PATH}initialize_param(){this._value=new yi}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(t){this.set(t.valueSerialized())}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}isDefault(){return this._raw_input==this._default_value}setParam(t){this.set(t.path())}processRawInput(){this._value.path()!=this._raw_input&&(this._value.set_path(this._raw_input),this.find_target(),this.setDirty(),this.emitController.emit(Ms.VALUE_UPDATED))}async processComputation(){this.find_target()}find_target(){if(!this.node)return;const t=this._raw_input;let e=null;const n=null!=t&&\\\\\\\"\\\\\\\"!==t;this.scene().referencesController.reset_reference_from_param(this),this.decomposed_path.reset(),n&&(e=xi.findParam(this.node,t,this.decomposed_path));const i=this._value.param(),r=e;if(this.scene().referencesController.set_named_nodes_from_param(this),e&&this.scene().referencesController.set_reference_from_param(this,e),(null==i?void 0:i.graphNodeId())!==(null==r?void 0:r.graphNodeId())){const t=this.options.dependentOnFoundNode(),n=this._value.param();n&&t&&this.removeGraphInput(n),e?this._assign_found_node(e):this._value.set_param(null),this.options.executeCallback()}this.removeDirtyState()}_assign_found_node(t){const e=this.options.dependentOnFoundNode();this._value.set_param(t),e&&this.addGraphInput(t)}notify_path_rebuild_required(t){this.decomposed_path.update_from_name_change(t);const e=this.decomposed_path.to_path();this.set(e)}notify_target_param_owner_params_updated(t){this.setDirty()}},[Es.NODE_PATH]:class extends uo{static type(){return Es.NODE_PATH}initialize_param(){this._value=new vi}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(t){this.set(t.valueSerialized())}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}isDefault(){return this._raw_input==this._default_value}setNode(t){this.set(t.path())}processRawInput(){this._value.path()!=this._raw_input&&(this._value.set_path(this._raw_input),this._findTarget(),this.setDirty(),this.emitController.emit(Ms.VALUE_UPDATED))}async processComputation(){this._findTarget()}_findTarget(){if(!this.node)return;const t=this._raw_input;let e=null;const n=null!=t&&\\\\\\\"\\\\\\\"!==t;this.scene().referencesController.reset_reference_from_param(this),this.decomposed_path.reset(),n&&(e=xi.findNode(this.node,t,this.decomposed_path));const i=this._value.node(),r=e;if(this.scene().referencesController.set_named_nodes_from_param(this),e&&this.scene().referencesController.set_reference_from_param(this,e),(null==i?void 0:i.graphNodeId())!==(null==r?void 0:r.graphNodeId())){const t=this.options.dependentOnFoundNode(),n=this._value.node();n&&t&&this.removeGraphInput(n),e?this._assign_found_node(e):this._value.set_node(null),this.options.executeCallback()}n&&!e&&this.scene().loadingController.loaded()&&n&&this.states.error.set(`no node found at path '${t}'`),this.removeDirtyState()}_assign_found_node(t){const e=this.options.dependentOnFoundNode();this._isNodeExpectedContext(t)?this._is_node_expected_type(t)?(this.states.error.clear(),this._value.set_node(t),e&&this.addGraphInput(t)):this.states.error.set(`node type is ${t.type()} but the params expects one of ${(this._expected_node_types()||[]).join(\\\\\\\", \\\\\\\")}`):this.states.error.set(`node context is ${t.context()} but the params expects a ${this._expectedContext()}`)}_expectedContext(){return this.options.nodeSelectionContext()}_isNodeExpectedContext(t){var e,n;const i=this._expectedContext();if(null==i)return!0;return i==(null===(n=null===(e=t.parent())||void 0===e?void 0:e.childrenController)||void 0===n?void 0:n.context)}_expected_node_types(){return this.options.nodeSelectionTypes()}_is_node_expected_type(t){const e=this._expected_node_types();return null==e||(null==e?void 0:e.includes(t.type()))}notify_path_rebuild_required(t){this.decomposed_path.update_from_name_change(t);const e=this.decomposed_path.to_path();this.set(e)}notify_target_param_owner_params_updated(t){this.setDirty()}},[Es.RAMP]:xo,[Es.STRING]:bo,[Es.VECTOR2]:class extends ro{constructor(){super(...arguments),this._value=new d.a,this._copied_value=[0,0]}static type(){return Es.VECTOR2}componentNames(){return wo}defaultValueSerialized(){return m.isArray(this._default_value)?this._default_value:this._default_value.toArray()}valueSerialized(){return this.value.toArray()}_copy_value(t){t.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(t){if(t instanceof d.a)return t.clone();{const e=[t[0],t[1]];return null==e[0]&&(e[0]=e[0]||0),null==e[1]&&(e[1]=e[1]||e[0]),e}}static are_raw_input_equal(t,e){return t instanceof d.a?e instanceof d.a?t.equals(e):t.x==e[0]&&t.y==e[1]:e instanceof d.a?t[0]==e.x&&t[1]==e.y:t[0]==e[0]&&t[1]==e[1]}static are_values_equal(t,e){return t.equals(e)}initComponents(){super.initComponents(),this.x=this.components[0],this.y=this.components[1]}set_value_from_components(){this._value.x=this.x.value,this._value.y=this.y.value}},[Es.VECTOR3]:class extends ro{constructor(){super(...arguments),this._value=new p.a,this._copied_value=[0,0,0]}static type(){return Es.VECTOR3}componentNames(){return To}defaultValueSerialized(){return m.isArray(this._default_value)?this._default_value:this._default_value.toArray()}valueSerialized(){return this.value.toArray()}_copy_value(t){t.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(t){if(t instanceof p.a)return t.clone();{const e=[t[0],t[1],t[2]];return null==e[0]&&(e[0]=e[0]||0),null==e[1]&&(e[1]=e[1]||e[0]),null==e[2]&&(e[2]=e[2]||e[1]),e}}static are_raw_input_equal(t,e){return t instanceof p.a?e instanceof p.a?t.equals(e):t.x==e[0]&&t.y==e[1]&&t.z==e[2]:e instanceof p.a?t[0]==e.x&&t[1]==e.y&&t[2]==e.z:t[0]==e[0]&&t[1]==e[1]&&t[2]==e[2]}static are_values_equal(t,e){return t.equals(e)}initComponents(){super.initComponents(),this.x=this.components[0],this.y=this.components[1],this.z=this.components[2]}set_value_from_components(){this._value.x=this.x.value,this._value.y=this.y.value,this._value.z=this.z.value}},[Es.VECTOR4]:class extends ro{constructor(){super(...arguments),this._value=new _.a,this._copied_value=[0,0,0,0]}static type(){return Es.VECTOR4}componentNames(){return Ao}defaultValueSerialized(){return m.isArray(this._default_value)?this._default_value:this._default_value.toArray()}valueSerialized(){return this.value.toArray()}_copy_value(t){t.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(t){if(t instanceof _.a)return t.clone();{const e=[t[0],t[1],t[2],t[3]];return null==e[0]&&(e[0]=e[0]||0),null==e[1]&&(e[1]=e[1]||e[0]),null==e[2]&&(e[2]=e[2]||e[1]),null==e[3]&&(e[3]=e[3]||e[2]),e}}static are_raw_input_equal(t,e){return t instanceof _.a?e instanceof _.a?t.equals(e):t.x==e[0]&&t.y==e[1]&&t.z==e[2]&&t.w==e[3]:e instanceof _.a?t[0]==e.x&&t[1]==e.y&&t[2]==e.z&&t[3]==e.w:t[0]==e[0]&&t[1]==e[1]&&t[2]==e[2]&&t[3]==e[3]}static are_values_equal(t,e){return t.equals(e)}initComponents(){super.initComponents(),this.x=this.components[0],this.y=this.components[1],this.z=this.components[2],this.w=this.components[3]}set_value_from_components(){this._value.x=this.x.value,this._value.y=this.y.value,this._value.z=this.z.value,this._value.w=this.w.value}}};class Mo{dispose(){this._callback=void 0}params(){return this._params}callback(){return this._callback}init(t,e){if(this._params=t,e)this._callback=e;else{const t=this._params[0];switch(t.type()){case Es.STRING:return this._handle_string_param(t);case Es.OPERATOR_PATH:return this._handle_operator_path_param(t);case Es.NODE_PATH:return this._handle_node_path_param(t);case Es.PARAM_PATH:return this._handle_param_path_param(t);case Es.FLOAT:case Es.INTEGER:return this._handle_number_param(t)}}}_handle_string_param(t){this._callback=()=>t.value}_handle_operator_path_param(t){this._callback=()=>t.value}_handle_node_path_param(t){this._callback=()=>t.value.path()}_handle_param_path_param(t){this._callback=()=>t.value.path()}_handle_number_param(t){this._callback=()=>`${t.value}`}}class So{constructor(t){this.node=t,this._param_create_mode=!1,this._params_created=!1,this._params_by_name={},this._params_list=[],this._param_names=[],this._non_spare_params=[],this._spare_params=[],this._non_spare_param_names=[],this._spare_param_names=[],this._params_added_since_last_params_eval=!1}get label(){return this._label_controller=this._label_controller||new Mo}hasLabelController(){return null!=this._label_controller}dispose(){var t;this._params_node&&this._params_node.dispose();for(let t of this.all)t.dispose();this._post_create_params_hook_names=void 0,this._post_create_params_hooks=void 0,this._on_scene_load_hooks=void 0,this._on_scene_load_hook_names=void 0,null===(t=this._label_controller)||void 0===t||t.dispose()}initDependencyNode(){this._params_node||(this._params_node=new Ai(this.node.scene(),\\\\\\\"params\\\\\\\"),this.node.addGraphInput(this._params_node,!1))}init(){this.initDependencyNode(),this._param_create_mode=!0,this._initFromParamsConfig(),this.node.createParams(),this._postCreateParams()}_postCreateParams(){this._updateCaches(),this._initParamAccessors(),this._param_create_mode=!1,this._params_created=!0,this._runPostCreateParamsHooks()}postCreateSpareParams(){this._updateCaches(),this._initParamAccessors(),this.node.scene().referencesController.notify_params_updated(this.node),this.node.emit(Ei.PARAMS_UPDATED)}updateParams(t){let e=!1,n=!1;if(t.namesToDelete)for(let e of t.namesToDelete)this.has(e)&&(this._deleteParam(e),n=!0);if(t.toAdd)for(let n of t.toAdd){const t=this.addParam(n.type,n.name,n.init_value,n.options);t&&(null!=n.raw_input&&t.set(n.raw_input),e=!0)}(n||e)&&this.postCreateSpareParams()}_initFromParamsConfig(){const t=this.node.paramsConfig;let e=!1;if(t)for(let n of Object.keys(t)){const i=t[n];let r;this.node.params_init_value_overrides&&(r=this.node.params_init_value_overrides[n],e=!0),this.addParam(i.type,n,i.init_value,i.options,r)}e&&this.node.setDirty(),this.node.params_init_value_overrides=void 0}_initParamAccessors(){let t=Object.getOwnPropertyNames(this.node.pv);this._removeUnneededAccessors(t),t=Object.getOwnPropertyNames(this.node.pv);for(let e of this.all){const n=e.options.isSpare();(!t.includes(e.name())||n)&&(Object.defineProperty(this.node.pv,e.name(),{get:()=>e.value,configurable:n}),Object.defineProperty(this.node.p,e.name(),{get:()=>e,configurable:n}))}}_removeUnneededAccessors(t){const e=this._param_names,n=[];for(let i of t)e.includes(i)||n.push(i);for(let t of n)Object.defineProperty(this.node.pv,t,{get:()=>{},configurable:!0}),Object.defineProperty(this.node.p,t,{get:()=>{},configurable:!0})}get params_node(){return this._params_node}get all(){return this._params_list}get non_spare(){return this._non_spare_params}get spare(){return this._spare_params}get names(){return this._param_names}get non_spare_names(){return this._non_spare_param_names}get spare_names(){return this._spare_param_names}set_with_type(t,e,n){const i=this.param_with_type(t,n);i?i.set(e):ai.warn(`param ${t} not found with type ${n}`)}set_float(t,e){this.set_with_type(t,e,Es.FLOAT)}set_vector3(t,e){this.set_with_type(t,e,Es.VECTOR3)}has_param(t){return null!=this._params_by_name[t]}has(t){return this.has_param(t)}get(t){return this.param(t)}param_with_type(t,e){const n=this.param(t);if(n&&n.type()==e)return n}get_float(t){return this.param_with_type(t,Es.FLOAT)}get_operator_path(t){return this.param_with_type(t,Es.OPERATOR_PATH)}value(t){var e;return null===(e=this.param(t))||void 0===e?void 0:e.value}value_with_type(t,e){var n;return null===(n=this.param_with_type(t,e))||void 0===n?void 0:n.value}boolean(t){return this.value_with_type(t,Es.BOOLEAN)}float(t){return this.value_with_type(t,Es.FLOAT)}integer(t){return this.value_with_type(t,Es.INTEGER)}string(t){return this.value_with_type(t,Es.STRING)}vector2(t){return this.value_with_type(t,Es.VECTOR2)}vector3(t){return this.value_with_type(t,Es.VECTOR3)}color(t){return this.value_with_type(t,Es.COLOR)}param(t){const e=this._params_by_name[t];return null!=e?e:(ai.warn(`tried to access param '${t}' in node ${this.node.path()}, but existing params are: ${this.names} on node ${this.node.path()}`),null)}_deleteParam(t){const e=this._params_by_name[t];if(!e)throw new Error(`param '${t}' does not exist on node ${this.node.path()}`);if(this._params_node&&this._params_node.removeGraphInput(this._params_by_name[t]),e._setupNodeDependencies(null),delete this._params_by_name[t],e.isMultiple()&&e.components)for(let t of e.components){const e=t.name();delete this._params_by_name[e]}}addParam(t,e,n,i={},r){const s=i.spare||!1;!1!==this._param_create_mode||s||ai.warn(`node ${this.node.path()} (${this.node.type()}) param '${e}' cannot be created outside of create_params`),null==this.node.scene()&&ai.warn(`node ${this.node.path()} (${this.node.type()}) has no scene assigned`);const o=Eo[t];if(null!=o){const a=this._params_by_name[e];a&&(s?a.type()!=t&&this._deleteParam(a.name()):ai.warn(`a param named ${e} already exists`,this.node));const l=new o(this.node.scene(),this.node);if(l.options.set(i),l.setName(e),l.setInitValue(n),l.initComponents(),null==r)l.set(n);else if(l.options.isExpressionForEntities()&&l.set(n),null!=r.raw_input)l.set(r.raw_input);else if(null!=r.simple_data)l.set(r.simple_data);else if(null!=r.complex_data){const t=r.complex_data.raw_input;t?l.set(t):l.set(n);const e=r.complex_data.overriden_options;if(null!=e){const t=Object.keys(e);for(let n of t)l.options.setOption(n,e[n])}}if(l._setupNodeDependencies(this.node),this._params_by_name[l.name()]=l,l.isMultiple()&&l.components)for(let t of l.components)this._params_by_name[t.name()]=t;return this._params_added_since_last_params_eval=!0,l}}_updateCaches(){this._params_list=Object.values(this._params_by_name),this._param_names=Object.keys(this._params_by_name),this._non_spare_params=Object.values(this._params_by_name).filter((t=>!t.options.isSpare())),this._spare_params=Object.values(this._params_by_name).filter((t=>t.options.isSpare())),this._non_spare_param_names=Object.values(this._params_by_name).filter((t=>!t.options.isSpare())).map((t=>t.name())),this._spare_param_names=Object.values(this._params_by_name).filter((t=>t.options.isSpare())).map((t=>t.name()))}async _evalParam(t){t.isDirty()&&(await t.compute(),t.states.error.active()&&this.node.states.error.set(`param '${t.name()}' error: ${t.states.error.message()}`))}async evalParams(t){const e=[];for(let n of t)n.isDirty()&&e.push(this._evalParam(n));await Promise.all(e),this.node.states.error.active()&&this.node._setContainer(null)}paramsEvalRequired(){return null!=this._params_node&&(this._params_node.isDirty()||this._params_added_since_last_params_eval)}async evalAll(){var t;this.paramsEvalRequired()&&(await this.evalParams(this._params_list),null===(t=this._params_node)||void 0===t||t.removeDirtyState(),this._params_added_since_last_params_eval=!1)}onParamsCreated(t,e){if(this._params_created)e();else{if(this._post_create_params_hook_names&&this._post_create_params_hook_names.includes(t))return void ai.error(`hook name ${t} already exists`);this._post_create_params_hook_names=this._post_create_params_hook_names||[],this._post_create_params_hook_names.push(t),this._post_create_params_hooks=this._post_create_params_hooks||[],this._post_create_params_hooks.push(e)}}addOnSceneLoadHook(t,e){this._on_scene_load_hook_names=this._on_scene_load_hook_names||[],this._on_scene_load_hooks=this._on_scene_load_hooks||[],this._on_scene_load_hook_names.includes(t)?ai.warn(`hook with name ${t} already exists`,this.node):(this._on_scene_load_hook_names.push(t),this._on_scene_load_hooks.push(e))}_runPostCreateParamsHooks(){if(this._post_create_params_hooks)for(let t of this._post_create_params_hooks)t()}runOnSceneLoadHooks(){if(this._on_scene_load_hooks)for(let t of this._on_scene_load_hooks)t()}}class Co{constructor(){}}class No{constructor(t,e,n=0,i=0){if(this._node_src=t,this._node_dest=e,this._output_index=n,this._input_index=i,null==this._output_index)throw\\\\\\\"bad output index\\\\\\\";if(null==this._input_index)throw\\\\\\\"bad input index\\\\\\\";this._id=No._next_id++,this._node_src.io.connections&&this._node_dest.io.connections&&(this._node_src.io.connections.addOutputConnection(this),this._node_dest.io.connections.addInputConnection(this))}get id(){return this._id}get node_src(){return this._node_src}get node_dest(){return this._node_dest}get output_index(){return this._output_index}get input_index(){return this._input_index}src_connection_point(){const t=this._node_src,e=this._output_index;return t.io.outputs.namedOutputConnectionPoints()[e]}dest_connection_point(){const t=this._node_dest,e=this._input_index;return t.io.inputs.namedInputConnectionPoints()[e]}disconnect(t={}){this._node_src.io.connections&&this._node_dest.io.connections&&(this._node_src.io.connections.removeOutputConnection(this),this._node_dest.io.connections.removeInputConnection(this)),!0===t.setInput&&this._node_dest.io.inputs.setInput(this._input_index,null)}}No._next_id=0;class Lo{constructor(t){this.inputs_controller=t,this._clone_required_states=[],this._overridden=!1,this.node=t.node}initInputsClonedState(t){m.isArray(t)?this._cloned_states=t:this._cloned_state=t,this._update_clone_required_state()}overrideClonedStateAllowed(){if(this._cloned_states)for(let t of this._cloned_states)if(t==Qi.FROM_NODE)return!0;return!!this._cloned_state&&this._cloned_state==Qi.FROM_NODE}cloneRequiredState(t){return this._clone_required_states[t]}cloneRequiredStates(){return this._clone_required_states}_get_clone_required_state(t){const e=this._cloned_states;if(e){const n=e[t];if(null!=n)return this.clone_required_from_state(n)}return!this._cloned_state||this.clone_required_from_state(this._cloned_state)}clone_required_from_state(t){switch(t){case Qi.ALWAYS:return!0;case Qi.NEVER:return!1;case Qi.FROM_NODE:return!this._overridden}return ar.unreachable(t)}overrideClonedState(t){this._overridden=t,this._update_clone_required_state(),this.node.emit(Ei.OVERRIDE_CLONABLE_STATE_UPDATE),this.node.setDirty()}overriden(){return this._overridden}_update_clone_required_state(){if(this._cloned_states){const t=[];for(let e=0;e=t.length&&n.disconnect({setInput:!0});this._named_input_connection_points=t,this.set_min_inputs_count(0),this.set_max_inputs_count(t.length),this.init_graph_node_inputs(),this.node.emit(Ei.NAMED_INPUTS_UPDATED)}hasNamedInputs(){return this._has_named_inputs}namedInputConnectionPoints(){return this._named_input_connection_points||[]}init_graph_node_inputs(){for(let t=0;t0)for(let e=0;e0){const n=this.existing_input_indices();if(n.length0){const n=[];let i;for(let t=0;tt.name()));return void console.warn(`node ${e.path()} does not have an output named ${n}. inputs are: ${t.join(\\\\\\\", \\\\\\\")}`)}const s=this._graph_node_inputs[i];if(null==s){const t=`graph_input_node not found at index ${i}`;throw console.warn(t),new Error(t)}if(e&&this.node.parent()!=e.parent())return;const o=this._inputs[i];let a,l=null;this.node.io.connections&&(a=this.node.io.connections.inputConnection(i)),a&&(l=a.output_index),e===o&&r==l||(null!=o&&this._depends_on_inputs&&s.removeGraphInput(o),null!=e?s.addGraphInput(e)?(this._depends_on_inputs||s.removeGraphInput(e),a&&a.disconnect({setInput:!1}),this._inputs[i]=e,new No(e,this.node,r,i)):console.warn(`cannot connect ${e.path()} to ${this.node.path()}`):(this._inputs[i]=null,a&&a.disconnect({setInput:!1})),this._run_on_set_input_hooks(),s.setSuccessorsDirty(),this.node.emit(Ei.INPUTS_UPDATED))}remove_input(t){const e=this.inputs();let n;for(let i=0;i=0}has_input(t){return null!=this._inputs[t]}inputs(){return this._inputs}initInputsClonedState(t){this._cloned_states_controller||(this._cloned_states_controller=new Lo(this),this._cloned_states_controller.initInputsClonedState(t))}overrideClonedStateAllowed(){var t;return(null===(t=this._cloned_states_controller)||void 0===t?void 0:t.overrideClonedStateAllowed())||!1}overrideClonedState(t){var e;null===(e=this._cloned_states_controller)||void 0===e||e.overrideClonedState(t)}clonedStateOverriden(){var t;return(null===(t=this._cloned_states_controller)||void 0===t?void 0:t.overriden())||!1}cloneRequired(t){var e;const n=null===(e=this._cloned_states_controller)||void 0===e?void 0:e.cloneRequiredState(t);return null==n||n}cloneRequiredStates(){var t;const e=null===(t=this._cloned_states_controller)||void 0===t?void 0:t.cloneRequiredStates();return null==e||e}add_on_set_input_hook(t,e){this._on_update_hooks=this._on_update_hooks||[],this._on_update_hook_names=this._on_update_hook_names||[],this._on_update_hook_names.includes(t)?console.warn(`hook with name ${t} already exists`,this.node):(this._on_update_hooks.push(e),this._on_update_hook_names.push(t))}_run_on_set_input_hooks(){if(this._on_update_hooks)for(let t of this._on_update_hooks)t()}}class Ro{constructor(t){this.node=t,this._has_outputs=!1,this._has_named_outputs=!1}setHasOneOutput(){this._has_outputs=!0}setHasNoOutput(){this._has_outputs=!1}hasOutputs(){return this._has_outputs}hasNamedOutputs(){return this._has_named_outputs}hasNamedOutput(t){return this.getNamedOutputIndex(t)>=0}namedOutputConnectionPoints(){return this._named_output_connection_points||[]}namedOutputConnection(t){if(this._named_output_connection_points)return this._named_output_connection_points[t]}getNamedOutputIndex(t){var e;if(this._named_output_connection_points)for(let n=0;n=t.length&&e.disconnect({setInput:!0});this._named_output_connection_points=t,e&&this.node.scene()&&this.node.setDirty(this.node),this.node.emit(Ei.NAMED_OUTPUTS_UPDATED)}used_output_names(){var t;const e=this.node.io.connections;if(e){let n=e.outputConnections().map((t=>t?t.output_index:null));n=f.uniq(n);const i=[];n.forEach((t=>{m.isNumber(t)&&i.push(t)}));const r=[];for(let e of i){const n=null===(t=this.namedOutputConnectionPoints()[e])||void 0===t?void 0:t.name();n&&r.push(n)}return r}return[]}}class Po{constructor(t){this._node=t,this._output_connections=new Map}initInputs(){const t=this._node.io.inputs.maxInputsCount();for(this._input_connections=this._input_connections||new Array(t);this._input_connections.length1&&void 0===t[t.length-1];)t.pop();return t}addOutputConnection(t){const e=t.output_index,n=t.id;let i=this._output_connections.get(e);i||(i=new Map,this._output_connections.set(e,i)),i.set(n,t)}removeOutputConnection(t){const e=t.output_index,n=t.id;let i=this._output_connections.get(e);i&&i.delete(n)}outputConnections(){let t=[];return this._output_connections.forEach(((e,n)=>{e.forEach(((e,n)=>{e&&t.push(e)}))})),t}}class Io{constructor(t){this._node=t}set_in(t){this._in=t}set_out(t){this._out=t}clear(){this._in=void 0,this._out=void 0}in(){return this._in}out(){return this._out}}class Fo{constructor(t,e,n){this._name=t,this._type=e,this._init_value=n}get init_value(){return this._init_value}name(){return this._name}type(){return this._type}are_types_matched(t,e){return!0}toJSON(){return this._json=this._json||this._create_json()}_create_json(){return{name:this._name,type:this._type}}}var Do;!function(t){t.BOOL=\\\\\\\"bool\\\\\\\",t.INT=\\\\\\\"int\\\\\\\",t.FLOAT=\\\\\\\"float\\\\\\\",t.VEC2=\\\\\\\"vec2\\\\\\\",t.VEC3=\\\\\\\"vec3\\\\\\\",t.VEC4=\\\\\\\"vec4\\\\\\\",t.SAMPLER_2D=\\\\\\\"sampler2D\\\\\\\",t.SSS_MODEL=\\\\\\\"SSSModel\\\\\\\"}(Do||(Do={}));const ko=[Do.BOOL,Do.INT,Do.FLOAT,Do.VEC2,Do.VEC3,Do.VEC4],Bo={[Do.BOOL]:Es.BOOLEAN,[Do.INT]:Es.INTEGER,[Do.FLOAT]:Es.FLOAT,[Do.VEC2]:Es.VECTOR2,[Do.VEC3]:Es.VECTOR3,[Do.VEC4]:Es.VECTOR4,[Do.SAMPLER_2D]:Es.RAMP,[Do.SSS_MODEL]:Es.STRING},zo={[Es.BOOLEAN]:Do.BOOL,[Es.COLOR]:Do.VEC3,[Es.INTEGER]:Do.INT,[Es.FLOAT]:Do.FLOAT,[Es.FOLDER]:void 0,[Es.VECTOR2]:Do.VEC2,[Es.VECTOR3]:Do.VEC3,[Es.VECTOR4]:Do.VEC4,[Es.BUTTON]:void 0,[Es.OPERATOR_PATH]:void 0,[Es.PARAM_PATH]:void 0,[Es.NODE_PATH]:void 0,[Es.RAMP]:void 0,[Es.STRING]:void 0},Uo={[Do.BOOL]:!1,[Do.INT]:0,[Do.FLOAT]:0,[Do.VEC2]:[0,0],[Do.VEC3]:[0,0,0],[Do.VEC4]:[0,0,0,0],[Do.SAMPLER_2D]:xo.DEFAULT_VALUE_JSON,[Do.SSS_MODEL]:\\\\\\\"SSSModel()\\\\\\\"},Go={[Do.BOOL]:1,[Do.INT]:1,[Do.FLOAT]:1,[Do.VEC2]:2,[Do.VEC3]:3,[Do.VEC4]:4,[Do.SAMPLER_2D]:1,[Do.SSS_MODEL]:1};class Vo extends Fo{constructor(t,e,n){super(t,e),this._name=t,this._type=e,this._init_value=n,this._init_value=this._init_value||Uo[this._type]}type(){return this._type}are_types_matched(t,e){return t==e}get param_type(){return Bo[this._type]}get init_value(){return this._init_value}toJSON(){return this._json=this._json||this._create_json()}_create_json(){return{name:this._name,type:this._type}}}var Ho;!function(t){t.BOOL=\\\\\\\"bool\\\\\\\",t.INT=\\\\\\\"int\\\\\\\",t.FLOAT=\\\\\\\"float\\\\\\\",t.VEC2=\\\\\\\"vec2\\\\\\\",t.VEC3=\\\\\\\"vec3\\\\\\\",t.VEC4=\\\\\\\"vec4\\\\\\\"}(Ho||(Ho={}));const jo=[Ho.BOOL,Ho.INT,Ho.FLOAT,Ho.VEC2,Ho.VEC3,Ho.VEC4],Wo={[Ho.BOOL]:Es.BOOLEAN,[Ho.INT]:Es.INTEGER,[Ho.FLOAT]:Es.FLOAT,[Ho.VEC2]:Es.VECTOR2,[Ho.VEC3]:Es.VECTOR3,[Ho.VEC4]:Es.VECTOR4},qo={[Es.BOOLEAN]:Ho.BOOL,[Es.COLOR]:Ho.VEC3,[Es.INTEGER]:Ho.INT,[Es.FLOAT]:Ho.FLOAT,[Es.FOLDER]:void 0,[Es.VECTOR2]:Ho.VEC2,[Es.VECTOR3]:Ho.VEC3,[Es.VECTOR4]:Ho.VEC4,[Es.BUTTON]:void 0,[Es.OPERATOR_PATH]:void 0,[Es.PARAM_PATH]:void 0,[Es.NODE_PATH]:void 0,[Es.RAMP]:void 0,[Es.STRING]:void 0},Xo={[Ho.BOOL]:!1,[Ho.INT]:0,[Ho.FLOAT]:0,[Ho.VEC2]:[0,0],[Ho.VEC3]:[0,0,0],[Ho.VEC4]:[0,0,0,0]};Ho.BOOL,Ho.INT,Ho.FLOAT,Ho.VEC2,Ho.VEC3,Ho.VEC4;class Yo extends Fo{constructor(t,e){super(t,e),this._name=t,this._type=e,this._init_value=Xo[this._type]}type(){return this._type}are_types_matched(t,e){return t==e}get param_type(){return Wo[this._type]}get init_value(){return this._init_value}toJSON(){return this._json=this._json||this._create_json()}_create_json(){return{name:this._name,type:this._type}}}var $o;!function(t){t.BASE=\\\\\\\"base\\\\\\\",t.DRAG=\\\\\\\"drag\\\\\\\",t.KEYBOARD=\\\\\\\"keyboard\\\\\\\",t.MOUSE=\\\\\\\"mouse\\\\\\\",t.POINTER=\\\\\\\"pointer\\\\\\\"}($o||($o={}));class Jo extends Fo{constructor(t,e,n){super(t,e),this._name=t,this._type=e,this._event_listener=n}type(){return this._type}get param_type(){return Es.FLOAT}are_types_matched(t,e){return e==$o.BASE||t==e}get event_listener(){return this._event_listener}toJSON(){return this._json=this._json||this._create_json()}_create_json(){return{name:this._name,type:this._type}}}const Zo={[Ki.ANIM]:void 0,[Ki.COP]:void 0,[Ki.EVENT]:$o.BASE,[Ki.GL]:Do.FLOAT,[Ki.JS]:Ho.FLOAT,[Ki.MANAGER]:void 0,[Ki.MAT]:void 0,[Ki.OBJ]:void 0,[Ki.POST]:void 0,[Ki.ROP]:void 0,[Ki.SOP]:void 0};function Qo(t,e,n){switch(t){case Ki.EVENT:return new Jo(e,n);case Ki.GL:return new Vo(e,n);case Ki.JS:return new Yo(e,n);default:return}}class Ko{constructor(t,e){this.node=t,this._context=e,this._raw_input_serialized_by_param_name=new Map,this._default_value_serialized_by_param_name=new Map,this._initialized=!1}initializeNode(){this._initialized?console.warn(\\\\\\\"already initialized\\\\\\\",this.node):(this._initialized=!0,this.node.params.onParamsCreated(\\\\\\\"create_inputs_from_params\\\\\\\",this.create_inputs_from_params.bind(this)))}initialized(){return this._initialized}create_inputs_from_params(){const t=function(t){switch(t){case Ki.EVENT:return;case Ki.GL:return zo;case Ki.JS:return qo;default:return}}(this._context);if(!t)return;const e=[];for(let n of this.node.params.names){let i=!0;if(this._inputless_param_names&&this._inputless_param_names.length>0&&this._inputless_param_names.includes(n)&&(i=!1),i&&this.node.params.has(n)){const i=this.node.params.get(n);if(i&&!i.parent_param){const n=t[i.type()];if(n){const t=Qo(this._context,i.name(),n);t&&e.push(t)}}}}this.node.io.inputs.setNamedInputConnectionPoints(e)}set_inputless_param_names(t){return this._inputless_param_names=t}createSpareParameters(){if(this.node.scene().loadingController.isLoading())return;const t=this.node.params.spare_names,e={};for(let n of t)if(this.node.params.has(n)){const t=this.node.params.get(n);t&&(this._raw_input_serialized_by_param_name.set(n,t.rawInputSerialized()),this._default_value_serialized_by_param_name.set(n,t.defaultValueSerialized()),e.namesToDelete=e.namesToDelete||[],e.namesToDelete.push(n))}for(let t of this.node.io.inputs.namedInputConnectionPoints())if(t){const n=t.name(),i=t.param_type;let r=t.init_value;const s=this._default_value_serialized_by_param_name.get(n);let o=this.node.paramDefaultValue(n);if(r=null!=o?o:null!=s?s:t.init_value,m.isArray(t.init_value))if(m.isNumber(r)){const e=new Array(t.init_value.length);e.fill(r),r=e}else m.isArray(r)&&r.length==t.init_value.length&&null!=s&&(r=t.init_value);null!=r&&(e.toAdd=e.toAdd||[],e.toAdd.push({name:n,type:i,init_value:b.clone(r),raw_input:b.clone(r),options:{spare:!0}}))}this.node.params.updateParams(e);for(let t of this.node.params.spare)if(!t.parent_param){const e=this._raw_input_serialized_by_param_name.get(t.name());e&&t.set(e)}}}class ta{constructor(t,e){this.node=t,this._context=e,this._create_spare_params_from_inputs=!0,this._functions_overridden=!1,this._input_name_function=t=>`in${t}`,this._output_name_function=t=>0==t?\\\\\\\"val\\\\\\\":`val${t}`,this._expected_input_types_function=()=>{const t=this.first_input_connection_type()||this.default_connection_type();return[t,t]},this._expected_output_types_function=()=>[this._expected_input_types_function()[0]],this._update_signature_if_required_bound=this.update_signature_if_required.bind(this),this._initialized=!1,this._spare_params_controller=new Ko(this.node,this._context)}default_connection_type(){return Zo[this._context]}create_connection_point(t,e){return Qo(this._context,t,e)}functions_overridden(){return this._functions_overridden}initialized(){return this._initialized}set_create_spare_params_from_inputs(t){this._create_spare_params_from_inputs=t}set_input_name_function(t){this._initialize_if_required(),this._input_name_function=t}set_output_name_function(t){this._initialize_if_required(),this._output_name_function=t}set_expected_input_types_function(t){this._initialize_if_required(),this._functions_overridden=!0,this._expected_input_types_function=t}set_expected_output_types_function(t){this._initialize_if_required(),this._functions_overridden=!0,this._expected_output_types_function=t}input_name(t){return this._wrapped_input_name_function(t)}output_name(t){return this._wrapped_output_name_function(t)}initializeNode(){this._initialized?console.warn(\\\\\\\"already initialized\\\\\\\",this.node):(this._initialized=!0,this.node.io.inputs.add_on_set_input_hook(\\\\\\\"_update_signature_if_required\\\\\\\",this._update_signature_if_required_bound),this.node.params.addOnSceneLoadHook(\\\\\\\"_update_signature_if_required\\\\\\\",this._update_signature_if_required_bound),this.node.params.onParamsCreated(\\\\\\\"_update_signature_if_required_bound\\\\\\\",this._update_signature_if_required_bound),this.node.addPostDirtyHook(\\\\\\\"_update_signature_if_required\\\\\\\",this._update_signature_if_required_bound),this._spare_params_controller.initialized()||this._spare_params_controller.initializeNode())}_initialize_if_required(){this._initialized||this.initializeNode()}get spare_params(){return this._spare_params_controller}update_signature_if_required(t){this.node.lifecycle.creation_completed&&this._connections_match_inputs()||(this.update_connection_types(),this.node.removeDirtyState(),this.node.scene().loadingController.isLoading()||this.make_successors_update_signatures())}make_successors_update_signatures(){const t=this.node.graphAllSuccessors();if(this.node.childrenAllowed()){const e=this.node.nodesByType(er.INPUT),n=this.node.nodesByType(er.OUTPUT);for(let n of e)t.push(n);for(let e of n)t.push(e)}for(let e of t){const t=e;t.io&&t.io.has_connection_points_controller&&t.io.connection_points.initialized()&&t.io.connection_points.update_signature_if_required(this.node)}}update_connection_types(){const t=this._wrapped_expected_input_types_function(),e=this._wrapped_expected_output_types_function(),n=[];for(let e=0;enull==t?void 0:t.type())),e=this.node.io.outputs.namedOutputConnectionPoints().map((t=>null==t?void 0:t.type())),n=this._wrapped_expected_input_types_function(),i=this._wrapped_expected_output_types_function();if(n.length!=t.length)return!1;if(i.length!=e.length)return!1;for(let e=0;et.type))}return this._expected_input_types_function()}_wrapped_expected_output_types_function(){if(this.node.scene().loadingController.isLoading()){const t=this.node.io.saved_connection_points_data.out();if(t)return t.map((t=>t.type))}return this._expected_output_types_function()}_wrapped_input_name_function(t){if(this.node.scene().loadingController.isLoading()){const e=this.node.io.saved_connection_points_data.in();if(e)return e[t].name}return this._input_name_function(t)}_wrapped_output_name_function(t){if(this.node.scene().loadingController.isLoading()){const e=this.node.io.saved_connection_points_data.out();if(e)return e[t].name}return this._output_name_function(t)}first_input_connection_type(){return this.input_connection_type(0)}input_connection_type(t){const e=this.node.io.connections.inputConnections();if(e){const n=e[t];if(n)return n.src_connection_point().type()}}}class ea{constructor(t){this.node=t,this._connections=new Po(this.node)}get connections(){return this._connections}get inputs(){return this._inputs=this._inputs||new Oo(this.node)}has_inputs(){return null!=this._inputs}get outputs(){return this._outputs=this._outputs||new Ro(this.node)}has_outputs(){return null!=this._outputs}get connection_points(){return this._connection_points=this._connection_points||new ta(this.node,this.node.context())}get has_connection_points_controller(){return null!=this._connection_points}get saved_connection_points_data(){return this._saved_connection_points_data=this._saved_connection_points_data||new Io(this.node)}clear_saved_connection_points_data(){this._saved_connection_points_data&&(this._saved_connection_points_data.clear(),this._saved_connection_points_data=void 0)}}class na{constructor(){}}class ia extends Ai{constructor(t,e=\\\\\\\"BaseNode\\\\\\\",n){super(t,e),this.params_init_value_overrides=n,this.containerController=new xs(this),this.pv=new Co,this.p=new na,this._initialized=!1}copy_param_values(t){const e=this.params.non_spare;for(let n of e){const e=t.params.get(n.name());e&&n.copy_value(e)}}get parentController(){return this._parent_controller=this._parent_controller||new Wi(this)}static displayedInputNames(){return[]}get childrenControllerContext(){return this._children_controller_context}_create_children_controller(){if(this._children_controller_context)return new hr(this,this._children_controller_context)}get childrenController(){return this._children_controller=this._children_controller||this._create_children_controller()}childrenAllowed(){return null!=this._children_controller_context}get uiData(){return this._ui_data=this._ui_data||new Mi(this)}get states(){return this._states=this._states||new Hi(this)}get lifecycle(){return this._lifecycle=this._lifecycle||new dr(this)}get serializer(){return this._serializer=this._serializer||new As(this)}get cookController(){return this._cook_controller=this._cook_controller||new Ts(this)}get io(){return this._io=this._io||new ea(this)}get nameController(){return this._name_controller=this._name_controller||new ji(this)}setName(t){this.nameController.setName(t)}_set_core_name(t){this._name=t}get params(){return this._params_controller=this._params_controller||new So(this)}initialize_base_and_node(){var t;this._initialized?console.warn(\\\\\\\"node already initialized\\\\\\\"):(this._initialized=!0,null===(t=this.displayNodeController)||void 0===t||t.initializeNode(),this.initializeBaseNode(),this.initializeNode(),this.polyNodeController&&this.polyNodeController.initializeNode())}initializeBaseNode(){}initializeNode(){}static type(){throw\\\\\\\"type to be overriden\\\\\\\"}type(){return this.constructor.type()}static context(){throw console.error(\\\\\\\"node has no node_context\\\\\\\",this),\\\\\\\"context requires override\\\\\\\"}context(){return this.constructor.context()}static require_webgl2(){return!1}require_webgl2(){return this.constructor.require_webgl2()}setParent(t){this.parentController.setParent(t)}parent(){return this.parentController.parent()}root(){return this._scene.root()}path(t){return this.parentController.path(t)}createParams(){}addParam(t,e,n,i){var r;return null===(r=this._params_controller)||void 0===r?void 0:r.addParam(t,e,n,i)}paramDefaultValue(t){return null}cook(t){return null}onCookEnd(t,e){this.cookController.registerOnCookEnd(t,e)}async compute(){var t,e;return this.isDirty()||(null===(e=null===(t=this.flags)||void 0===t?void 0:t.bypass)||void 0===e?void 0:e.active())?await this.containerController.compute():this.containerController.container()}_setContainer(t,e=null){this.containerController.container().set_content(t),null!=t&&(t.name||(t.name=this.path()),t.node||(t.node=this)),this.cookController.endCook(e)}createNode(t,e){var n;return null===(n=this.childrenController)||void 0===n?void 0:n.createNode(t,e)}create_operation_container(t,e,n){var i;return null===(i=this.childrenController)||void 0===i?void 0:i.create_operation_container(t,e,n)}removeNode(t){var e;null===(e=this.childrenController)||void 0===e||e.removeNode(t)}dispose(){var t,e;super.dispose(),this.setParent(null),this.io.inputs.dispose(),this.lifecycle.dispose(),null===(t=this.displayNodeController)||void 0===t||t.dispose(),this.nameController.dispose(),null===(e=this.childrenController)||void 0===e||e.dispose(),this.params.dispose()}children(){var t;return(null===(t=this.childrenController)||void 0===t?void 0:t.children())||[]}node(t){var e;return(null===(e=this.parentController)||void 0===e?void 0:e.findNode(t))||null}nodeSibbling(t){var e;const n=this.parent();if(n){const i=null===(e=n.childrenController)||void 0===e?void 0:e.child_by_name(t);if(i)return i}return null}nodesByType(t){var e;return(null===(e=this.childrenController)||void 0===e?void 0:e.nodesByType(t))||[]}setInput(t,e,n=0){this.io.inputs.setInput(t,e,n)}emit(t,e=null){this.scene().dispatchController.dispatch(this,t,e)}toJSON(t=!1){return this.serializer.toJSON(t)}async requiredModules(){}usedAssembler(){}integrationData(){}}class ra extends ia{static context(){return Ki.MANAGER}}class sa{constructor(t,e,n){this.type=t,this.init_value=e,this.options=n}}class oa{static BUTTON(t,e){return new sa(Es.BUTTON,t,e)}static BOOLEAN(t,e){return new sa(Es.BOOLEAN,t,e)}static COLOR(t,e){return t instanceof D.a&&(t=t.toArray()),new sa(Es.COLOR,t,e)}static FLOAT(t,e){return new sa(Es.FLOAT,t,e)}static FOLDER(t=null,e){return new sa(Es.FOLDER,t,e)}static INTEGER(t,e){return new sa(Es.INTEGER,t,e)}static RAMP(t=xo.DEFAULT_VALUE,e){return new sa(Es.RAMP,t,e)}static STRING(t=\\\\\\\"\\\\\\\",e){return new sa(Es.STRING,t,e)}static VECTOR2(t,e){return t instanceof d.a&&(t=t.toArray()),new sa(Es.VECTOR2,t,e)}static VECTOR3(t,e){return t instanceof p.a&&(t=t.toArray()),new sa(Es.VECTOR3,t,e)}static VECTOR4(t,e){return t instanceof _.a&&(t=t.toArray()),new sa(Es.VECTOR4,t,e)}static OPERATOR_PATH(t,e){return new sa(Es.OPERATOR_PATH,t,e)}static NODE_PATH(t,e){return new sa(Es.NODE_PATH,t,e)}static PARAM_PATH(t,e){return new sa(Es.PARAM_PATH,t,e)}}class aa{}class la{constructor(t){this.scene=t}findObjectByMask(t){return this.findObjectByMaskInObject(t,this.scene.threejsScene())}findObjectByMaskInObject(t,e,n=\\\\\\\"\\\\\\\"){for(let i of e.children){const e=this._removeTrailingOrHeadingSlash(i.name),r=`${n=this._removeTrailingOrHeadingSlash(n)}/${e}`;if(sr.matchMask(r,t))return i;const s=this.findObjectByMaskInObject(t,i,r);if(s)return s}}objectsByMask(t){return this.objectsByMaskInObject(t,this.scene.threejsScene(),[],\\\\\\\"\\\\\\\")}objectsByMaskInObject(t,e,n=[],i=\\\\\\\"\\\\\\\"){for(let r of e.children){const e=this._removeTrailingOrHeadingSlash(r.name),s=`${i=this._removeTrailingOrHeadingSlash(i)}/${e}`;sr.matchMask(s,t)&&n.push(r),this.objectsByMaskInObject(t,r,n,s)}return n}_removeTrailingOrHeadingSlash(t){return\\\\\\\"/\\\\\\\"==t[0]&&(t=t.substr(1)),\\\\\\\"/\\\\\\\"==t[t.length-1]&&(t=t.substr(0,t.length-1)),t}}const ca={computeOnDirty:!1,callback:t=>{ha.update(t)}};function ua(t){return class extends t{constructor(){super(...arguments),this.autoUpdate=oa.BOOLEAN(1,ca)}}}ua(aa);class ha{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;e.autoUpdate!=t.autoUpdate&&(t.autoUpdate=e.autoUpdate)}static async update(t){t.sceneAutoUpdateController.update()}}var da;!function(t){t.NONE=\\\\\\\"none\\\\\\\",t.COLOR=\\\\\\\"color\\\\\\\",t.TEXTURE=\\\\\\\"texture\\\\\\\"}(da||(da={}));const pa=[da.NONE,da.COLOR,da.TEXTURE],_a={computeOnDirty:!1,callback:t=>{fa.update(t)}};function ma(t){return class extends t{constructor(){super(...arguments),this.backgroundMode=oa.INTEGER(pa.indexOf(da.NONE),{menu:{entries:pa.map(((t,e)=>({name:t,value:e})))},..._a}),this.bgColor=oa.COLOR([0,0,0],{visibleIf:{backgroundMode:pa.indexOf(da.COLOR)},..._a}),this.bgTexture=oa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{backgroundMode:pa.indexOf(da.TEXTURE)},nodeSelection:{context:Ki.COP},dependentOnFoundNode:!1,..._a})}}}ma(aa);class fa{constructor(t){this.node=t}update(){const t=this.node.object,e=this.node.pv;if(e.backgroundMode==pa.indexOf(da.NONE))t.background=null;else if(e.backgroundMode==pa.indexOf(da.COLOR))t.background=e.bgColor;else{const n=e.bgTexture.nodeWithContext(Ki.COP);n?n.compute().then((e=>{t.background=e.texture()})):this.node.states.error.set(\\\\\\\"bgTexture node not found\\\\\\\")}}static update(t){t.sceneBackgroundController.update()}}const ga={computeOnDirty:!1,callback:t=>{ya.update(t)}};function va(t){return class extends t{constructor(){super(...arguments),this.useEnvironment=oa.BOOLEAN(0,ga),this.environment=oa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{useEnvironment:1},nodeSelection:{context:Ki.COP},dependentOnFoundNode:!1,...ga})}}}va(aa);class ya{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;if(e.useEnvironment){const n=e.environment.nodeWithContext(Ki.COP);n?n.compute().then((e=>{t.environment=e.texture()})):this.node.states.error.set(\\\\\\\"bgTexture node not found\\\\\\\")}else t.environment=null}static async update(t){t.sceneEnvController.update()}}class xa{constructor(t,e=1,n=1e3){this.name=\\\\\\\"\\\\\\\",this.color=new D.a(t),this.near=e,this.far=n}clone(){return new xa(this.color,this.near,this.far)}toJSON(){return{type:\\\\\\\"Fog\\\\\\\",color:this.color.getHex(),near:this.near,far:this.far}}}xa.prototype.isFog=!0;class ba{constructor(t,e=25e-5){this.name=\\\\\\\"\\\\\\\",this.color=new D.a(t),this.density=e}clone(){return new ba(this.color,this.density)}toJSON(){return{type:\\\\\\\"FogExp2\\\\\\\",color:this.color.getHex(),density:this.density}}}ba.prototype.isFogExp2=!0;const wa={computeOnDirty:!1,callback:t=>{Ma.update(t)}};var Ta;!function(t){t.LINEAR=\\\\\\\"linear\\\\\\\",t.EXPONENTIAL=\\\\\\\"exponential\\\\\\\"}(Ta||(Ta={}));const Aa=[Ta.LINEAR,Ta.EXPONENTIAL];function Ea(t){return class extends t{constructor(){super(...arguments),this.useFog=oa.BOOLEAN(0,wa),this.fogType=oa.INTEGER(Aa.indexOf(Ta.EXPONENTIAL),{visibleIf:{useFog:1},menu:{entries:Aa.map(((t,e)=>({name:t,value:e})))},...wa}),this.fogColor=oa.COLOR([1,1,1],{visibleIf:{useFog:1},...wa}),this.fogNear=oa.FLOAT(1,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useFog:1,fogType:Aa.indexOf(Ta.LINEAR)},...wa}),this.fogFar=oa.FLOAT(100,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useFog:1,fogType:Aa.indexOf(Ta.LINEAR)},...wa}),this.fogDensity=oa.FLOAT(25e-5,{visibleIf:{useFog:1,fogType:Aa.indexOf(Ta.EXPONENTIAL)},...wa})}}}Ea(aa);class Ma{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;if(e.useFog)if(e.fogType==Aa.indexOf(Ta.LINEAR)){const n=this.fog2(e);t.fog=n,n.color=e.fogColor,n.near=e.fogNear,n.far=e.fogFar}else{const n=this.fogExp2(e);t.fog=this.fogExp2(e),n.color=e.fogColor,n.density=e.fogDensity}else{t.fog&&(t.fog=null)}}fog2(t){return this._fog=this._fog||new xa(16777215,t.fogNear,t.fogFar)}fogExp2(t){return this._fogExp2=this._fogExp2||new ba(16777215,t.fogDensity)}static async update(t){t.sceneFogController.update()}}const Sa={computeOnDirty:!1,callback:t=>{Na.update(t)}};function Ca(t){return class extends t{constructor(){super(...arguments),this.useOverrideMaterial=oa.BOOLEAN(0,Sa),this.overrideMaterial=oa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{useOverrideMaterial:1},nodeSelection:{context:Ki.MAT},dependentOnFoundNode:!1,...Sa})}}}Ca(aa);class Na{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;if(e.useOverrideMaterial){const n=e.overrideMaterial.nodeWithContext(Ki.MAT);n?n.compute().then((e=>{t.overrideMaterial=e.material()})):this.node.states.error.set(\\\\\\\"bgTexture node not found\\\\\\\")}else t.overrideMaterial=null}static async update(t){t.SceneMaterialOverrideController.update()}}class La extends(Ca(va(Ea(ma(ua(aa)))))){}const Oa=new La;class Ra extends ra{constructor(){super(...arguments),this.paramsConfig=Oa,this._object=this._createScene(),this._queued_nodes_by_id=new Map,this.sceneAutoUpdateController=new ha(this),this.sceneBackgroundController=new fa(this),this.sceneEnvController=new ya(this),this.sceneFogController=new Ma(this),this.sceneMaterialOverrideController=new Na(this),this._children_controller_context=Ki.OBJ}static type(){return\\\\\\\"obj\\\\\\\"}initializeNode(){this._object.matrixAutoUpdate=!1,this.lifecycle.add_on_child_add_hook(this._on_child_add.bind(this)),this.lifecycle.add_on_child_remove_hook(this._on_child_remove.bind(this))}_createScene(){const t=new fr;return t.name=\\\\\\\"/\\\\\\\",t.matrixAutoUpdate=!1,t}get object(){return this._object}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}_updateScene(){this.sceneAutoUpdateController.update(),this.sceneBackgroundController.update(),this.sceneEnvController.update(),this.sceneFogController.update(),this.sceneMaterialOverrideController.update()}_addToQueue(t){const e=t.graphNodeId();return this._queued_nodes_by_id.has(e)||this._queued_nodes_by_id.set(e,t),t}async processQueue(){this._updateScene();const t=new Map,e=[];this._queued_nodes_by_id.forEach(((n,i)=>{const r=`_____${n.renderOrder}__${n.path()}`;e.push(r),t.set(r,n)})),this._queued_nodes_by_id.clear();for(let n of e){const e=t.get(n);e&&(t.delete(n),this._addToScene(e))}}_update_object(t){return this.scene().loadingController.autoUpdating()?this._addToScene(t):this._addToQueue(t)}getParentForNode(t){if(t.attachableToHierarchy()){const e=t.io.inputs.input(0);return e?e.children_group:this._object}return null}_addToScene(t){var e;if(t.attachableToHierarchy()){const n=this.getParentForNode(t);n&&(t.usedInScene()?(null===(e=t.childrenDisplayController)||void 0===e||e.request_display_node_container(),t.addObjectToParent(n)):t.removeObjectFromParent())}}_removeFromScene(t){t.removeObjectFromParent()}areChildrenCooking(){const t=this.children();for(let e of t)if(e.cookController.isCooking()||e.isDisplayNodeCooking())return!0;return!1}addToParentTransform(t){this._update_object(t)}removeFromParentTransform(t){this._update_object(t)}_on_child_add(t){t&&this._update_object(t)}_on_child_remove(t){t&&this._removeFromScene(t)}}class Pa{constructor(t){this.scene=t,this._node_context_signatures={},this._instanciated_nodes_by_context_and_type={}}init(){this._root=new Ra(this.scene),this._root.initialize_base_and_node(),this._root.params.init(),this._root._set_core_name(\\\\\\\"RootNode\\\\\\\")}root(){return this._root}_traverseNode(t,e){const n=t.children();if(n&&0!=n.length)for(let t of n)e(t),t.childrenController&&this._traverseNode(t,e)}clear(){var t;const e=this.root().children();for(let n of e)null===(t=this.root().childrenController)||void 0===t||t.removeNode(n)}node(t){return\\\\\\\"/\\\\\\\"===t?this.root():this.root().node(t)}allNodes(){let t=[this.root()],e=[this.root()],n=0;for(;e.length>0&&n<10;){const i=e.map((t=>t.childrenAllowed()?t.children():[])).flat();t=t.concat(i),e=i,n+=1}return t.flat()}nodesFromMask(t){const e=this.allNodes(),n=[];for(let i of e){const e=i.path();sr.matchMask(e,t)&&n.push(i)}return n}reset_node_context_signatures(){this._node_context_signatures={}}register_node_context_signature(t){t.childrenAllowed()&&t.childrenController&&(this._node_context_signatures[t.childrenController.node_context_signature()]=!0)}node_context_signatures(){return Object.keys(this._node_context_signatures).sort().map((t=>t.toLowerCase()))}addToInstanciatedNode(t){const e=t.context(),n=t.type();this._instanciated_nodes_by_context_and_type[e]=this._instanciated_nodes_by_context_and_type[e]||{},this._instanciated_nodes_by_context_and_type[e][n]=this._instanciated_nodes_by_context_and_type[e][n]||{},this._instanciated_nodes_by_context_and_type[e][n][t.graphNodeId()]=t}removeFromInstanciatedNode(t){const e=t.context(),n=t.type();delete this._instanciated_nodes_by_context_and_type[e][n][t.graphNodeId()]}nodesByType(t){const e=[];return this._traverseNode(this.scene.root(),(n=>{n.type()==t&&e.push(n)})),e}nodesByContextAndType(t,e){const n=[],i=this._instanciated_nodes_by_context_and_type[t];if(i){const t=i[e];if(t)for(let e of Object.keys(t))n.push(t[e])}return n}}class Ia{constructor(t){this.scene=t}toJSON(t=!1){const e={},n={};for(let i of this.scene.nodesController.allNodes()){const r=new As(i);e[i.graphNodeId()]=r.toJSON(t);const s=i.params.all;for(let t of s)n[t.graphNodeId()]=t.toJSON()}return{nodes_by_graph_node_id:e,params_by_graph_node_id:n}}}var Fa;!function(t){t.auxclick=\\\\\\\"auxclick\\\\\\\",t.click=\\\\\\\"click\\\\\\\",t.contextmenu=\\\\\\\"contextmenu\\\\\\\",t.dblclick=\\\\\\\"dblclick\\\\\\\",t.mousedown=\\\\\\\"mousedown\\\\\\\",t.mouseenter=\\\\\\\"mouseenter\\\\\\\",t.mouseleave=\\\\\\\"mouseleave\\\\\\\",t.mousemove=\\\\\\\"mousemove\\\\\\\",t.mouseover=\\\\\\\"mouseover\\\\\\\",t.mouseout=\\\\\\\"mouseout\\\\\\\",t.mouseup=\\\\\\\"mouseup\\\\\\\",t.pointerlockchange=\\\\\\\"pointerlockchange\\\\\\\",t.pointerlockerror=\\\\\\\"pointerlockerror\\\\\\\",t.select=\\\\\\\"select\\\\\\\",t.wheel=\\\\\\\"wheel\\\\\\\"}(Fa||(Fa={}));const Da=[Fa.auxclick,Fa.click,Fa.contextmenu,Fa.dblclick,Fa.mousedown,Fa.mouseenter,Fa.mouseleave,Fa.mousemove,Fa.mouseover,Fa.mouseout,Fa.mouseup,Fa.pointerlockchange,Fa.pointerlockerror,Fa.select,Fa.wheel];class ka extends di{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"mouse\\\\\\\"}acceptedEventTypes(){return Da.map((t=>`${t}`))}}class Ba extends ia{constructor(){super(...arguments),this._cook_without_inputs_bound=this._cook_without_inputs.bind(this)}static context(){return Ki.EVENT}initializeBaseNode(){this.uiData.setLayoutHorizontal(),this.addPostDirtyHook(\\\\\\\"cook_without_inputs_on_dirty\\\\\\\",this._cook_without_inputs_bound),this.io.inputs.set_depends_on_inputs(!1),this.io.connections.initInputs(),this.io.connection_points.spare_params.initializeNode()}_cook_without_inputs(){this.cookController.cookMainWithoutInputs()}cook(){this.cookController.endCook()}processEventViaConnectionPoint(t,e){e.event_listener?e.event_listener(t):this.processEvent(t)}processEvent(t){}async dispatchEventToOutput(t,e){this.run_on_dispatch_hook(t,e);const n=this.io.outputs.getOutputIndex(t);if(n>=0){const t=this.io.connections.outputConnections().filter((t=>t.output_index==n));let i;for(let n of t){i=n.node_dest;const t=i.io.inputs.namedInputConnectionPoints()[n.input_index];i.processEventViaConnectionPoint(e,t)}}else console.warn(`requested output '${t}' does not exist on node '${this.path()}'`)}onDispatch(t,e){this._on_dispatch_hooks_by_output_name=this._on_dispatch_hooks_by_output_name||new Map,u.pushOnArrayAtEntry(this._on_dispatch_hooks_by_output_name,t,e)}run_on_dispatch_hook(t,e){if(this._on_dispatch_hooks_by_output_name){const n=this._on_dispatch_hooks_by_output_name.get(t);if(n)for(let t of n)t(e)}}}var za;!function(t){t.CANVAS=\\\\\\\"canvas\\\\\\\",t.DOCUMENT=\\\\\\\"document\\\\\\\"}(za||(za={}));const Ua=[za.CANVAS,za.DOCUMENT];class Ga{constructor(t){this.viewer=t,this._bound_listener_map_by_event_controller_type=new Map}updateEvents(t){const e=this.canvas();if(!e)return;const n=t.type();let i=this._bound_listener_map_by_event_controller_type.get(n);i||(i=new Map,this._bound_listener_map_by_event_controller_type.set(n,i)),i.forEach(((t,n)=>{this._eventOwner(t.data,e).removeEventListener(n,t.listener)})),i.clear();const r=e=>{this.processEvent(e,t)};for(let n of t.activeEventDatas()){this._eventOwner(n,e).addEventListener(n.type,r),i.set(n.type,{listener:r,data:n})}}_eventOwner(t,e){return\\\\\\\"resize\\\\\\\"==t.type?window:t.emitter==za.CANVAS?e:document}cameraNode(){return this.viewer.camerasController.cameraNode()}canvas(){return this.viewer.canvas()}init(){this.canvas&&this.viewer.scene().eventsDispatcher.traverseControllers((t=>{this.updateEvents(t)}))}registeredEventTypes(){const t=[];return this._bound_listener_map_by_event_controller_type.forEach((e=>{e.forEach(((e,n)=>{t.push(n)}))})),t}dispose(){const t=this.canvas();this._bound_listener_map_by_event_controller_type.forEach((e=>{t&&e.forEach(((e,n)=>{this._eventOwner(e.data,t).removeEventListener(n,e.listener)}))}))}processEvent(t,e){if(!this.canvas())return;const n={viewer:this.viewer,event:t,cameraNode:this.cameraNode()};e.processEvent(n)}}const Va={visibleIf:{active:1},callback:t=>{ja.PARAM_CALLBACK_updateRegister(t)}};class Ha extends Ba{constructor(){super(...arguments),this._activeEventDatas=[]}initializeBaseNode(){super.initializeBaseNode();this.lifecycle.add_on_add_hook((()=>{this.scene().eventsDispatcher.registerEventNode(this)})),this.lifecycle.add_delete_hook((()=>{this.scene().eventsDispatcher.unregisterEventNode(this)})),this.params.onParamsCreated(\\\\\\\"update_register\\\\\\\",(()=>{this._updateRegister()}))}processEvent(t){this.pv.active&&t.event&&this.dispatchEventToOutput(t.event.type,t)}static PARAM_CALLBACK_updateRegister(t){t._updateRegister()}_updateRegister(){this._updateActiveEventDatas(),this.scene().eventsDispatcher.updateViewerEventListeners(this)}_updateActiveEventDatas(){if(this._activeEventDatas=[],this.pv.active){const t=this.acceptedEventTypes();for(let e of t){const t=this.params.get(e);t&&t.value&&this._activeEventDatas.push({type:e,emitter:Ua[this.pv.element]})}}}activeEventDatas(){return this._activeEventDatas}}class ja extends Ha{acceptedEventTypes(){return[]}}const Wa=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:t=>{qa.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(Ua.indexOf(za.CANVAS),{menu:{entries:Ua.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.auxclick=oa.BOOLEAN(0,Va),this.click=oa.BOOLEAN(0,Va),this.contextmenu=oa.BOOLEAN(0,Va),this.dblclick=oa.BOOLEAN(0,Va),this.mousedown=oa.BOOLEAN(1,Va),this.mouseenter=oa.BOOLEAN(0,Va),this.mouseleave=oa.BOOLEAN(0,Va),this.mousemove=oa.BOOLEAN(1,Va),this.mouseover=oa.BOOLEAN(0,Va),this.mouseout=oa.BOOLEAN(0,Va),this.mouseup=oa.BOOLEAN(1,Va),this.pointerlockchange=oa.BOOLEAN(0,Va),this.pointerlockerror=oa.BOOLEAN(0,Va),this.select=oa.BOOLEAN(0,Va),this.wheel=oa.BOOLEAN(0,Va),this.ctrlKey=oa.BOOLEAN(0,{...Va,separatorBefore:!0}),this.altKey=oa.BOOLEAN(0,Va),this.shiftKey=oa.BOOLEAN(0,Va),this.metaKey=oa.BOOLEAN(0,Va)}};class qa extends Ha{constructor(){super(...arguments),this.paramsConfig=Wa}static type(){return\\\\\\\"mouse\\\\\\\"}acceptedEventTypes(){return Da.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(Da.map((t=>new Jo(t,$o.MOUSE)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.auxclick,this.p.click,this.p.dblclick,this.p.mousedown,this.p.mouseenter,this.p.mouseleave,this.p.mousemove,this.p.mouseout,this.p.mouseout,this.p.mouseup,this.p.pointerlockchange,this.p.pointerlockerror,this.p.select,this.p.wheel];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;e.ctrlKey==this.pv.ctrlKey&&e.shiftKey==this.pv.shiftKey&&e.altKey==this.pv.altKey&&e.metaKey==this.pv.metaKey&&this.dispatchEventToOutput(t.event.type,t)}}var Xa;!function(t){t.pointerdown=\\\\\\\"pointerdown\\\\\\\",t.pointermove=\\\\\\\"pointermove\\\\\\\",t.pointerup=\\\\\\\"pointerup\\\\\\\"}(Xa||(Xa={}));const Ya=[Xa.pointerdown,Xa.pointermove,Xa.pointerup];class $a extends di{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"pointer\\\\\\\"}acceptedEventTypes(){return Ya.map((t=>`${t}`))}}const Ja=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:t=>{Za.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(Ua.indexOf(za.CANVAS),{menu:{entries:Ua.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.pointerdown=oa.BOOLEAN(1,Va),this.pointermove=oa.BOOLEAN(0,Va),this.pointerup=oa.BOOLEAN(0,Va),this.ctrlKey=oa.BOOLEAN(0,{...Va,separatorBefore:!0}),this.altKey=oa.BOOLEAN(0,Va),this.shiftKey=oa.BOOLEAN(0,Va),this.metaKey=oa.BOOLEAN(0,Va)}};class Za extends Ha{constructor(){super(...arguments),this.paramsConfig=Ja}static type(){return\\\\\\\"pointer\\\\\\\"}acceptedEventTypes(){return Ya.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(Ya.map((t=>new Jo(t,$o.POINTER)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.pointerdown,this.p.pointermove,this.p.pointerup];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;e.ctrlKey==this.pv.ctrlKey&&e.shiftKey==this.pv.shiftKey&&e.altKey==this.pv.altKey&&e.metaKey==this.pv.metaKey&&this.dispatchEventToOutput(t.event.type,t)}}var Qa,Ka;!function(t){t.SET_FRAME=\\\\\\\"setFrame\\\\\\\"}(Qa||(Qa={})),function(t){t.TIME_REACHED=\\\\\\\"timeReached\\\\\\\"}(Ka||(Ka={}));const tl=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:(t,e)=>{el.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(0,{hidden:!0}),this.sceneLoaded=oa.BOOLEAN(1,Va),this.play=oa.BOOLEAN(1,Va),this.pause=oa.BOOLEAN(1,Va),this.tick=oa.BOOLEAN(1,{separatorAfter:!0,...Va}),this.treachedTime=oa.BOOLEAN(0,{callback:t=>{el.PARAM_CALLBACK_update_time_dependency(t)}}),this.reachedTime=oa.INTEGER(10,{visibleIf:{treachedTime:1},range:[0,100],separatorAfter:!0}),this.setFrameValue=oa.INTEGER(1,{range:[0,100]}),this.setFrame=oa.BUTTON(null,{callback:t=>{el.PARAM_CALLBACK_setFrame(t)}})}};class el extends Ha{constructor(){super(...arguments),this.paramsConfig=tl}static type(){return\\\\\\\"scene\\\\\\\"}acceptedEventTypes(){return _i.map((t=>`${t}`))}dispose(){var t;null===(t=this.graph_node)||void 0===t||t.dispose(),super.dispose()}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(Qa.SET_FRAME,$o.BASE,this.onSetFrame.bind(this))]);const t=_i.map((t=>new Jo(t,$o.BASE)));t.push(new Jo(Ka.TIME_REACHED,$o.BASE)),this.io.outputs.setNamedOutputConnectionPoints(t),this.params.onParamsCreated(\\\\\\\"update_time_dependency\\\\\\\",(()=>{this.update_time_dependency()}))}onSetFrame(t){this.scene().setFrame(this.pv.setFrameValue)}on_frame_update(){this.scene().time()>=this.pv.reachedTime&&this.dispatchEventToOutput(Ka.TIME_REACHED,{})}update_time_dependency(){this.pv.treachedTime?(this.graph_node=this.graph_node||new Ai(this.scene(),\\\\\\\"scene_node_time_graph_node\\\\\\\"),this.graph_node.addGraphInput(this.scene().timeController.graphNode),this.graph_node.addPostDirtyHook(\\\\\\\"time_update\\\\\\\",this.on_frame_update.bind(this))):this.graph_node&&this.graph_node.graphDisconnectPredecessors()}static PARAM_CALLBACK_setFrame(t){t.onSetFrame({})}static PARAM_CALLBACK_update_time_dependency(t){t.update_time_dependency()}}var nl;!function(t){t.keydown=\\\\\\\"keydown\\\\\\\",t.keypress=\\\\\\\"keypress\\\\\\\",t.keyup=\\\\\\\"keyup\\\\\\\"}(nl||(nl={}));const il=[nl.keydown,nl.keypress,nl.keyup];class rl extends di{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"keyboard\\\\\\\"}acceptedEventTypes(){return il.map((t=>`${t}`))}}const sl=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:(t,e)=>{ol.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(Ua.indexOf(za.CANVAS),{menu:{entries:Ua.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.keydown=oa.BOOLEAN(1,Va),this.keypress=oa.BOOLEAN(0,Va),this.keyup=oa.BOOLEAN(0,Va),this.keyCodes=oa.STRING(\\\\\\\"Digit1 KeyE ArrowDown\\\\\\\",Va),this.ctrlKey=oa.BOOLEAN(0,Va),this.altKey=oa.BOOLEAN(0,Va),this.shiftKey=oa.BOOLEAN(0,Va),this.metaKey=oa.BOOLEAN(0,Va)}};class ol extends Ha{constructor(){super(...arguments),this.paramsConfig=sl}static type(){return\\\\\\\"keyboard\\\\\\\"}acceptedEventTypes(){return il.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(il.map((t=>new Jo(t,$o.KEYBOARD)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.keydown,this.p.keypress,this.p.keyup];this.params.label.init(t.concat([this.p.keyCodes]),(()=>`${t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")} (${this.pv.keyCodes})`))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;if(e.ctrlKey!=this.pv.ctrlKey)return;if(e.shiftKey!=this.pv.shiftKey)return;if(e.altKey!=this.pv.altKey)return;if(e.metaKey!=this.pv.metaKey)return;if(this.pv.keyCodes.trim().length>0){if(!this.pv.keyCodes.split(\\\\\\\" \\\\\\\").includes(e.code))return}this.dispatchEventToOutput(t.event.type,t)}}var al;!function(t){t.resize=\\\\\\\"resize\\\\\\\"}(al||(al={}));const ll=[al.resize];class cl extends di{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"window\\\\\\\"}acceptedEventTypes(){return ll.map((t=>`${t}`))}}const ul=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:t=>{hl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(0,{hidden:!0}),this.resize=oa.BOOLEAN(1,Va)}};class hl extends Ha{constructor(){super(...arguments),this.paramsConfig=ul}static type(){return\\\\\\\"window\\\\\\\"}acceptedEventTypes(){return ll.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(ll.map((t=>new Jo(t,$o.POINTER)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.resize];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){this.pv.active&&t.event&&this.dispatchEventToOutput(t.event.type,t)}}var dl;!function(t){t.dragover=\\\\\\\"dragover\\\\\\\"}(dl||(dl={}));const pl=[dl.dragover];class _l extends di{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"drag\\\\\\\"}acceptedEventTypes(){return pl.map((t=>`${t}`))}}var ml;!function(t){t.touchstart=\\\\\\\"touchstart\\\\\\\",t.touchmove=\\\\\\\"touchmove\\\\\\\",t.touchend=\\\\\\\"touchend\\\\\\\"}(ml||(ml={}));const fl=[ml.touchstart,ml.touchmove,ml.touchend];class gl extends di{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"touch\\\\\\\"}acceptedEventTypes(){return fl.map((t=>`${t}`))}}const vl=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:t=>{yl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(Ua.indexOf(za.CANVAS),{menu:{entries:Ua.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.dragover=oa.BOOLEAN(1,Va),this.ctrlKey=oa.BOOLEAN(0,{...Va,separatorBefore:!0}),this.altKey=oa.BOOLEAN(0,Va),this.shiftKey=oa.BOOLEAN(0,Va),this.metaKey=oa.BOOLEAN(0,Va)}};class yl extends Ha{constructor(){super(...arguments),this.paramsConfig=vl}static type(){return\\\\\\\"drag\\\\\\\"}acceptedEventTypes(){return pl.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(pl.map((t=>new Jo(t,$o.DRAG)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.dragover];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;e.ctrlKey==this.pv.ctrlKey&&e.shiftKey==this.pv.shiftKey&&e.altKey==this.pv.altKey&&e.metaKey==this.pv.metaKey&&this.dispatchEventToOutput(t.event.type,t)}}const xl=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:t=>{bl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(Ua.indexOf(za.CANVAS),{menu:{entries:Ua.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.touchstart=oa.BOOLEAN(1,Va),this.touchmove=oa.BOOLEAN(0,Va),this.touchend=oa.BOOLEAN(0,Va)}};class bl extends Ha{constructor(){super(...arguments),this.paramsConfig=xl}static type(){return\\\\\\\"touch\\\\\\\"}acceptedEventTypes(){return fl.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(fl.map((t=>new Jo(t,$o.DRAG)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.touchstart,this.p.touchmove,this.p.touchend];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){this.pv.active&&t.event&&this.dispatchEventToOutput(t.event.type,t)}}class wl{constructor(t){this.scene=t,this._controllers=[]}registerEventNode(t){const e=this._find_or_create_controller_for_node(t);e&&e.registerNode(t)}unregisterEventNode(t){const e=this._find_or_create_controller_for_node(t);e&&e.unregisterNode(t)}updateViewerEventListeners(t){const e=this._find_or_create_controller_for_node(t);e&&e.updateViewerEventListeners()}traverseControllers(t){for(let e of this._controllers)t(e)}_find_or_create_controller_for_node(t){switch(t.type()){case ol.type():return this.keyboardEventsController;case qa.type():return this.mouseEventsController;case yl.type():return this.dragEventsController;case Za.type():return this.pointerEventsController;case el.type():return this.sceneEventsController;case bl.type():return this.touchEventsController;case hl.type():return this.windowEventsController}}get keyboardEventsController(){return this._keyboard_events_controller=this._keyboard_events_controller||this._create_controller(rl)}get mouseEventsController(){return this._mouse_events_controller=this._mouse_events_controller||this._create_controller(ka)}get dragEventsController(){return this._drag_events_controller=this._drag_events_controller||this._create_controller(_l)}get pointerEventsController(){return this._pointer_events_controller=this._pointer_events_controller||this._create_controller($a)}get sceneEventsController(){return this._scene_events_controller=this._scene_events_controller||this._create_controller(mi)}get windowEventsController(){return this._window_events_controller=this._window_events_controller||this._create_controller(cl)}get touchEventsController(){return this._touch_events_controller=this._touch_events_controller||this._create_controller(gl)}_create_controller(t){const e=new t(this);return this._controllers.includes(e)||this._controllers.push(e),e}}class Tl{constructor(t){this.scene=t,this._referenced_nodes_by_src_param_id=new Map,this._referencing_params_by_referenced_node_id=new Map,this._referencing_params_by_all_named_node_ids=new Map}set_reference_from_param(t,e){this._referenced_nodes_by_src_param_id.set(t.graphNodeId(),e),u.pushOnArrayAtEntry(this._referencing_params_by_referenced_node_id,e.graphNodeId(),t)}set_named_nodes_from_param(t){const e=t.decomposed_path.named_nodes();for(let n of e)u.pushOnArrayAtEntry(this._referencing_params_by_all_named_node_ids,n.graphNodeId(),t)}reset_reference_from_param(t){const e=this._referenced_nodes_by_src_param_id.get(t.graphNodeId());if(e){u.popFromArrayAtEntry(this._referencing_params_by_referenced_node_id,e.graphNodeId(),t);const n=t.decomposed_path.named_nodes();for(let e of n)u.popFromArrayAtEntry(this._referencing_params_by_all_named_node_ids,e.graphNodeId(),t);this._referenced_nodes_by_src_param_id.delete(t.graphNodeId())}}referencing_params(t){return this._referencing_params_by_referenced_node_id.get(t.graphNodeId())}referencing_nodes(t){const e=this._referencing_params_by_referenced_node_id.get(t.graphNodeId());if(e){const t=new Map;for(let n of e){const e=n.node;t.set(e.graphNodeId(),e)}const n=[];return t.forEach((t=>{n.push(t)})),n}}nodes_referenced_by(t){const e=new Set([Es.OPERATOR_PATH,Es.NODE_PATH]),n=[];for(let i of t.params.all)e.has(i.type())&&n.push(i);const i=new Map,r=[];for(let t of n)this._check_param(t,i,r);for(let t of r)i.set(t.node.graphNodeId(),t.node);const s=[];return i.forEach((t=>{s.push(t)})),s}_check_param(t,e,n){if(t instanceof po){const i=t.found_node(),r=t.found_param();return i&&e.set(i.graphNodeId(),i),void(r&&n.push(r))}}notify_name_updated(t){const e=this._referencing_params_by_all_named_node_ids.get(t.graphNodeId());if(e)for(let n of e)n.notify_path_rebuild_required(t)}notify_params_updated(t){const e=this._referencing_params_by_all_named_node_ids.get(t.graphNodeId());if(e)for(let n of e)n.options.isSelectingParam()&&n.notify_target_param_owner_params_updated(t)}}var Al;!function(t){t.MAX_FRAME_UPDATED=\\\\\\\"scene_maxFrameUpdated\\\\\\\",t.REALTIME_STATUS_UPDATED=\\\\\\\"scene_realtime_status_updated\\\\\\\",t.FRAME_UPDATED=\\\\\\\"scene_frame_updated\\\\\\\",t.PLAY_STATE_UPDATED=\\\\\\\"scene_play_state_updated\\\\\\\"}(Al||(Al={}));const El=ai.performance.performanceManager();class Ml{constructor(t){this.scene=t,this._frame=0,this._time=0,this._prev_performance_now=0,this._realtimeState=!0,this._maxFrame=600,this._maxFrameLocked=!1,this._playing=!1,this._graph_node=new Ai(t,\\\\\\\"time controller\\\\\\\")}get PLAY_EVENT_CONTEXT(){return this._PLAY_EVENT_CONTEXT=this._PLAY_EVENT_CONTEXT||{event:new Event(pi.PLAY)}}get PAUSE_EVENT_CONTEXT(){return this._PAUSE_EVENT_CONTEXT=this._PAUSE_EVENT_CONTEXT||{event:new Event(pi.PAUSE)}}get TICK_EVENT_CONTEXT(){return this._TICK_EVENT_CONTEXT=this._TICK_EVENT_CONTEXT||{event:new Event(pi.TICK)}}get graphNode(){return this._graph_node}frame(){return this._frame}time(){return this._time}maxFrame(){return this._maxFrame}maxFrameLocked(){return this._maxFrameLocked}realtimeState(){return this._realtimeState}setMaxFrame(t){this._maxFrame=Math.floor(t),this.scene.dispatchController.dispatch(this._graph_node,Al.MAX_FRAME_UPDATED)}setMaxFrameLocked(t){this._maxFrameLocked=t,this.scene.dispatchController.dispatch(this._graph_node,Al.MAX_FRAME_UPDATED)}setRealtimeState(t){this._realtimeState=t,this.scene.dispatchController.dispatch(this._graph_node,Al.REALTIME_STATUS_UPDATED)}setTime(t,e=!0){if(t!=this._time){if(this._time=t,this._onBeforeTickCallbacks)for(let t of this._onBeforeTickCallbacks)t();if(e){const t=Math.floor(60*this._time),e=this._ensureFrameWithinBounds(t);t!=e?this.setFrame(e,!0):this._frame=t}if(this.scene.dispatchController.dispatch(this._graph_node,Al.FRAME_UPDATED),this.scene.uniformsController.updateTimeDependentUniformOwners(),this.scene.cooker.block(),this.graphNode.setSuccessorsDirty(),this.scene.cooker.unblock(),this.scene.eventsDispatcher.sceneEventsController.processEvent(this.TICK_EVENT_CONTEXT),this._onAfterTickCallbacks)for(let t of this._onAfterTickCallbacks)t()}}setFrame(t,e=!0){t!=this._frame&&(t=this._ensureFrameWithinBounds(t))!=this._frame&&(this._frame=t,e&&this.setTime(this._frame/60,!1))}setFrameToStart(){this.setFrame(Ml.START_FRAME,!0)}incrementTimeIfPlaying(){this._playing&&(this.scene.root().areChildrenCooking()||this.incrementTime())}incrementTime(){if(this._realtimeState){const t=El.now(),e=(t-this._prev_performance_now)/1e3,n=this._time+e;this._prev_performance_now=t,this.setTime(n)}else this.setFrame(this.frame()+1)}_ensureFrameWithinBounds(t){if(this._playing){if(this._maxFrameLocked&&t>this._maxFrame)return Ml.START_FRAME}else{if(this._maxFrameLocked&&t>this._maxFrame)return this._maxFrame;if(t=0&&this._time_dependent_uniform_owners_ids.splice(e,1)}}updateTimeDependentUniformOwners(){const t=this.scene.time();if(this._time_dependent_uniform_owners_ids)for(let e of this._time_dependent_uniform_owners_ids){this._time_dependent_uniform_owners[e].time.value=t}}addResolutionDependentUniformOwner(t,e){this._resolution_dependent_uniform_owners[t]=e,this._resolution_dependent_uniform_owners_ids||(this._resolution_dependent_uniform_owners_ids=[]),this._resolution_dependent_uniform_owners_ids.includes(t)||this._resolution_dependent_uniform_owners_ids.push(t),this._resolution&&this.updateResolutionDependentUniforms(e)}removeResolutionDependentUniformOwner(t){if(delete this._resolution_dependent_uniform_owners[t],this._resolution_dependent_uniform_owners_ids){const e=this._resolution_dependent_uniform_owners_ids.indexOf(t);e>=0&&this._resolution_dependent_uniform_owners_ids.splice(e,1)}}updateResolutionDependentUniformOwners(t){this._resolution.copy(t);for(let t of this._resolution_dependent_uniform_owners_ids){const e=this._resolution_dependent_uniform_owners[t];this.updateResolutionDependentUniforms(e)}}updateResolutionDependentUniforms(t){t.resolution.value.x=this._resolution.x,t.resolution.value.y=this._resolution.y}}class Cl{constructor(t){this.scene=t,this._viewers_by_id=new Map}registerViewer(t){this._viewers_by_id.set(t.id(),t)}unregisterViewer(t){this._viewers_by_id.delete(t.id())}traverseViewers(t){this._viewers_by_id.forEach(t)}}class Nl{constructor(){this._require_webgl2=!1}require_webgl2(){return this._require_webgl2}set_require_webgl2(){this._require_webgl2||(this._require_webgl2=!0,ai.renderersController.setRequireWebGL2())}}class Ll{constructor(t){this._scene=t,this._onWindowResizeBound=this._onWindowResize.bind(this)}graphNode(){return this._coreGraphNode=this._coreGraphNode||this._createGraphNode()}_createGraphNode(){const t=new Ai(this._scene,\\\\\\\"SceneWindowController\\\\\\\");return window.addEventListener(\\\\\\\"resize\\\\\\\",this._onWindowResizeBound),t}_onWindowResize(){this.graphNode().setSuccessorsDirty()}dispose(){window.removeEventListener(\\\\\\\"resize\\\\\\\",this._onWindowResizeBound)}}class Ol{constructor(){this._params_by_id=new Map,this._assets_root=null}register_param(t){this._params_by_id.set(t.graphNodeId(),t)}deregister_param(t){this._params_by_id.delete(t.graphNodeId())}traverse_params(t){this._params_by_id.forEach(((e,n)=>{t(e)}))}root(){return this._assets_root}setRoot(t){\\\\\\\"\\\\\\\"==t&&(t=null),this._assets_root=t}}class Rl{constructor(){this._cameras_controller=new s(this),this._cooker=new o(this),this.cookController=new a,this._graph=new l,this._missing_expression_references_controller=new wi(this),this._expressions_controller=new ui,this._nodes_controller=new Pa(this),this._objects_controller=new la(this),this._references_controller=new Tl(this),this._time_controller=new Ml(this),this._read_only=!1,this._graph.setScene(this),this.nodesController.init()}threejsScene(){return this.root().object}setUuid(t){return this._uuid=t}get uuid(){return this._uuid}setName(t){return t=sr.sanitizeName(t),this._name=t}name(){return this._name}get camerasController(){return this._cameras_controller}mainCameraNode(){return this.camerasController.mainCameraNode()}get cooker(){return this._cooker}get assets(){return this._assets_controller=this._assets_controller||new Ol}async waitForCooksCompleted(){return this.cookController.waitForCooksCompleted()}get dispatchController(){return this._dispatch_controller=this._dispatch_controller||new ci(this)}get eventsDispatcher(){return this._events_dispatcher=this._events_dispatcher||new wl(this)}get graph(){return this._graph}get lifecycleController(){return this._lifecycle_controller=this._lifecycle_controller||new hi(this)}get loadingController(){return this._loading_controller=this._loading_controller||new fi(this)}get missingExpressionReferencesController(){return this._missing_expression_references_controller}get expressionsController(){return this._expressions_controller}get nodesController(){return this._nodes_controller}createNode(t,e){return this.root().createNode(t,e)}nodesByType(t){return this.nodesController.nodesByType(t)}get objectsController(){return this._objects_controller}findObjectByMask(t){return this._objects_controller.findObjectByMask(t)}objectsByMask(t){return this._objects_controller.objectsByMask(t)}get referencesController(){return this._references_controller}get performance(){return this._performance=this._performance||new li}get viewersRegister(){return this._viewers_register=this._viewers_register||new Cl(this)}get timeController(){return this._time_controller}setFrame(t){this.timeController.setFrame(t)}setFrameToStart(){this.timeController.setFrameToStart()}frame(){return this.timeController.frame()}time(){return this.timeController.time()}maxFrame(){return this.timeController.maxFrame()}play(){this.timeController.play()}pause(){this.timeController.pause()}get serializer(){return this._serializer=this._serializer||new Ia(this)}toJSON(){return this.serializer.toJSON()}markAsReadOnly(t){this._read_only||(this._read_only_requester=t,this._read_only=!0)}readOnly(){return this._read_only}readOnlyRequester(){return this._read_only_requester}get uniformsController(){return this._uniformsController=this._uniformsController||new Sl(this)}get webgl_controller(){return this._webgl_controller=this._webgl_controller||new Nl}get windowController(){return this._windowController=this._windowController||new Ll(this)}dispose(){var t;null===(t=this._windowController)||void 0===t||t.dispose()}batchUpdates(t){this._cooker.block(),t(),this._cooker.unblock()}node(t){return this.nodesController.node(t)}root(){return this.nodesController.root()}registerOnBeforeTick(t,e){this.timeController.registerOnBeforeTick(t,e)}unRegisterOnBeforeTick(t){this.timeController.unRegisterOnBeforeTick(t)}registeredBeforeTickCallbackNames(){return this.timeController.registeredBeforeTickCallbackNames()}registerOnAfterTick(t,e){this.timeController.registerOnAfterTick(t,e)}unRegisterOnAfterTick(t){this.timeController.unRegisterOnAfterTick(t)}registeredAfterTickCallbackNames(){return this.timeController.registeredAfterTickCallbackNames()}}class Pl{constructor(t){this._param=t}process_data(t){const e=t.raw_input;void 0!==e&&this._param.set(e),this.add_main(t)}add_main(t){}static spare_params_data(t){return this.params_data(!0,t)}static non_spare_params_data_value(t){return this.params_data_value(!1,t)}static params_data(t,e){let n;if(e){n={};const t=Object.keys(e);let i;for(let r of t)i=e[r],i&&(n[r]=e)}return n}static params_data_value(t,e){let n;if(e){n={};const i=Object.keys(e);let r;for(let s of i)if(r=e[s],null!=r){const e=r.options,i=r.overriden_options;if(e||i){const o=r;e&&e.spare==t?null!=o.raw_input&&(n[s]={complex_data:o}):i&&(n[s]={complex_data:o})}else{const t=r;(i||null!=t)&&(n[s]={simple_data:t})}}}return n}}const Il=\\\\\\\"operationsComposer\\\\\\\";class Fl{constructor(t,e,n){this._scene=t,this.states=e,this._node=n}static type(){throw\\\\\\\"type to be overriden\\\\\\\"}type(){return this.constructor.type()}static context(){throw console.error(\\\\\\\"operation has no node_context\\\\\\\",this),\\\\\\\"context requires override\\\\\\\"}context(){return this.constructor.context()}scene(){return this._scene}cook(t,e){}}Fl.DEFAULT_PARAMS={},Fl.INPUT_CLONED_STATE=[];class Dl{constructor(t){this._node=t,this._nodes=[],this._optimized_root_node_names=new Set,this._operation_containers_by_name=new Map,this._node_inputs=[]}nodes(){return this._nodes}process_data(t,e){var n,i,r;if(!e)return;if(!this._node.childrenAllowed()||!this._node.childrenController)return;const{optimized_names:s}=Dl.child_names_by_optimized_state(e);this._nodes=[],this._optimized_root_node_names=new Set;for(let t of s)Dl.is_optimized_root_node(e,t)&&this._optimized_root_node_names.add(t);for(let s of this._optimized_root_node_names){const o=e[s],a=this._node.createNode(Il);if(a){a.setName(s),this._nodes.push(a),(null===(n=o.flags)||void 0===n?void 0:n.display)&&(null===(r=null===(i=a.flags)||void 0===i?void 0:i.display)||void 0===r||r.set(!0));const e=this._create_operation_container(t,a,o,a.name());a.set_output_operation_container(e)}}for(let n of this._nodes){const i=n.output_operation_container();if(i){this._node_inputs=[],this._add_optimized_node_inputs(t,n,e,n.name(),i),n.io.inputs.setCount(this._node_inputs.length);for(let t=0;t0}static is_optimized_root_node(t,e){const n=this.node_outputs(t,e);let i=0;return n.forEach((e=>{const n=t[e];this.is_node_optimized(n)||i++})),this.is_optimized_root_node_generic({outputs_count:n.size,non_optimized_count:i})}static is_optimized_root_node_from_node(t){var e,n,i,r;if(!(null===(n=null===(e=t.flags)||void 0===e?void 0:e.optimize)||void 0===n?void 0:n.active()))return!1;const s=t.io.connections.outputConnections().map((t=>t.node_dest));let o=0;for(let t of s)(null===(r=null===(i=t.flags)||void 0===i?void 0:i.optimize)||void 0===r?void 0:r.active())||o++;return this.is_optimized_root_node_generic({outputs_count:s.length,non_optimized_count:o})}static node_outputs(t,e){const n=Object.keys(t),i=new Set;for(let r of n)if(r!=e){const n=t[r].inputs;if(n)for(let t of n)if(m.isString(t)){t==e&&i.add(r)}}return i}_create_operation_container(t,e,n,i){const r=Pl.non_spare_params_data_value(n.params),s=Dl.operation_type(n),o=this._node.create_operation_container(s,i,r);return o&&(this._operation_containers_by_name.set(i,o),o.path_param_resolve_required()&&(e.add_operation_container_with_path_param_resolve_required(o),t.add_operations_composer_node_with_path_param_resolve_required(e))),o}static operation_type(t){return Dl.is_node_bypassed(t)?\\\\\\\"null\\\\\\\":t.type}static is_node_optimized(t){const e=t.flags;return!(!e||!e.optimize)}static is_node_bypassed(t){const e=t.flags;return!(!e||!e.bypass)}}class kl{constructor(t){this._node=t}process_data(t,e){var n;if(!e)return;if(!this._node.childrenAllowed()||!this._node.childrenController)return;const{optimized_names:i,non_optimized_names:r}=Dl.child_names_by_optimized_state(e),s=[];for(let n of r){const i=e[n],r=i.type.toLowerCase(),o=Pl.non_spare_params_data_value(i.params);try{const t=this._node.createNode(r,o);t&&(t.setName(n),s.push(t))}catch(e){console.error(`error importing node: cannot create with type ${r}`,e);const i=sr.camelCase(r);try{const t=this._node.createNode(i,o);t&&(t.setName(n),s.push(t))}catch(e){const a=`${r}Network`;try{const t=this._node.createNode(a,o);t&&(t.setName(n),s.push(t))}catch(e){const n=`failed to create node with type '${r}', '${i}' or '${a}'`;t.report.addWarning(n),ai.warn(n,e)}}}}if(i.length>0){const i=new Dl(this._node);if(i.process_data(t,e),this._node.childrenController.context==Ki.SOP){const t=Object.keys(e);let r;for(let i of t){(null===(n=e[i].flags)||void 0===n?void 0:n.display)&&(r=i)}if(r){const t=s.map((t=>t.name())),e=i.nodes();for(let n of e)t.push(n.name());if(!t.includes(r)){const t=`node '${`${this._node.path()}/${r}`}' with display flag has been optimized and does not exist in player mode`;console.error(t)}}}}const o=new Map;for(let n of s){if(e[n.name()]){const i=Wl.dispatch_node(n);o.set(n.name(),i),i.process_data(t,e[n.name()])}else ai.warn(`possible import error for node ${n.name()}`)}for(let t of s){const n=o.get(t.name());n&&n.process_inputs_data(e[t.name()])}}}const Bl=[\\\\\\\"overriden_options\\\\\\\",\\\\\\\"type\\\\\\\"];class zl{constructor(t){this._node=t}process_data(t,e){if(this.set_connection_points(e.connection_points),this._node.childrenAllowed()&&this.create_nodes(t,e.nodes),this.set_selection(e.selection),this._node.io.inputs.overrideClonedStateAllowed()){const t=e.cloned_state_overriden;t&&this._node.io.inputs.overrideClonedState(t)}this.set_flags(e),this.set_params(e.params),e.persisted_config&&this.set_persisted_config(e.persisted_config),this.from_data_custom(e)}process_inputs_data(t){const e=t.maxInputsCount;null!=e&&this._node.io.inputs.setCount(1,e),this.setInputs(t.inputs)}process_ui_data(t,e){if(!e)return;if(ai.playerMode())return;const n=this._node.uiData,i=e.pos;if(i){const t=(new d.a).fromArray(i);n.setPosition(t)}const r=e.comment;r&&n.setComment(r),this._node.childrenAllowed()&&this.process_nodes_ui_data(t,e.nodes)}create_nodes(t,e){if(!e)return;new kl(this._node).process_data(t,e)}set_selection(t){if(this._node.childrenAllowed()&&this._node.childrenController&&t&&t.length>0){const e=[];t.forEach((t=>{const n=this._node.node(t);n&&e.push(n)})),this._node.childrenController.selection.set(e)}}set_flags(t){var e,n,i,r,s,o;const a=t.flags;if(a){const t=a.bypass;null!=t&&(null===(n=null===(e=this._node.flags)||void 0===e?void 0:e.bypass)||void 0===n||n.set(t));const l=a.display;null!=l&&(null===(r=null===(i=this._node.flags)||void 0===i?void 0:i.display)||void 0===r||r.set(l));const c=a.optimize;null!=c&&(null===(o=null===(s=this._node.flags)||void 0===s?void 0:s.optimize)||void 0===o||o.set(c))}}set_connection_points(t){t&&(t.in&&this._node.io.saved_connection_points_data.set_in(t.in),t.out&&this._node.io.saved_connection_points_data.set_out(t.out),this._node.io.has_connection_points_controller&&this._node.io.connection_points.update_signature_if_required())}setInputs(t){if(!t)return;let e;for(let n=0;n0,r=n.toAdd&&n.toAdd.length>0;if(i||r){this._node.params.updateParams(n);for(let e of this._node.params.spare){const n=t[e.name()];!e.parent_param&&n&&(this._is_param_data_complex(n)?this._process_param_data_complex(e.name(),n):this._process_param_data_simple(e.name(),n))}}this._node.params.runOnSceneLoadHooks()}_process_param_data_simple(t,e){var n;null===(n=this._node.params.get(t))||void 0===n||n.set(e)}_process_param_data_complex(t,e){const n=this._node.params.get(t);n&&Wl.dispatch_param(n).process_data(e)}_is_param_data_complex(t){if(m.isString(t)||m.isNumber(t)||m.isArray(t)||m.isBoolean(t))return!1;if(m.isObject(t)){const e=Object.keys(t);for(let t of Bl)if(e.includes(t))return!0}return!1}set_persisted_config(t){this._node.persisted_config&&this._node.persisted_config.load(t)}from_data_custom(t){}}class Ul extends Pl{add_main(t){}}const Gl=/\\\\\\\\n+/g;class Vl extends Pl{add_main(t){let e=t.raw_input;void 0!==e&&(e=e.replace(Gl,\\\\\\\"\\\\n\\\\\\\"),this._param.set(e))}}class Hl extends Pl{add_main(t){const e=t.raw_input;e&&this._param.set(e)}}class jl extends zl{create_nodes(t,e){const n=this._node.polyNodeController;n&&n.createChildNodesFromDefinition()}}class Wl{static dispatch_node(t){return t.polyNodeController?new jl(t):new zl(t)}static dispatch_param(t){return t instanceof ro?new Ul(t):t instanceof bo?new Vl(t):t instanceof xo?new Hl(t):new Pl(t)}}class ql{constructor(t){this._warnings=[]}warnings(){return this._warnings}reset(){this._warnings=[]}addWarning(t){this._warnings.push(t)}}class Xl{constructor(t){this._data=t,this.report=new ql(this)}static async loadData(t){const e=new Xl(t);return await e.scene()}async scene(){const t=new Rl;t.loadingController.markAsLoading();const e=this._data.properties;if(e){const n=e.maxFrame||600;t.timeController.setMaxFrame(n);const i=e.maxFrameLocked;i&&t.timeController.setMaxFrameLocked(i);const r=e.realtimeState;null!=r&&t.timeController.setRealtimeState(r),t.setFrame(e.frame||Ml.START_FRAME),e.mainCameraNodePath&&t.camerasController.setMainCameraNodePath(e.mainCameraNodePath)}t.cooker.block(),this._base_operations_composer_nodes_with_resolve_required=void 0;const n=Wl.dispatch_node(t.root());return this._data.root&&n.process_data(this,this._data.root),this._data.ui&&n.process_ui_data(this,this._data.ui),this._resolve_operation_containers_with_path_param_resolve(),await t.loadingController.markAsLoaded(),t.cooker.unblock(),t}add_operations_composer_node_with_path_param_resolve_required(t){this._base_operations_composer_nodes_with_resolve_required||(this._base_operations_composer_nodes_with_resolve_required=[]),this._base_operations_composer_nodes_with_resolve_required.push(t)}_resolve_operation_containers_with_path_param_resolve(){if(this._base_operations_composer_nodes_with_resolve_required)for(let t of this._base_operations_composer_nodes_with_resolve_required)t.resolve_operation_containers_path_params()}}class Yl{static async importSceneData(t){null==t.editorMode&&(t.editorMode=!1);const{manifest:e,urlPrefix:n}=t,i=Object.keys(e.nodes),r=[];for(let t of i){const i=`${n}/root/${t}.json?t=${e.nodes[t]}`;r.push(i)}const s=[`${n}/root.json?t=${e.root}`,`${n}/properties.json?t=${e.properties}`];if(t.editorMode){const t=Date.now();s.push(`${n}/ui.json?t=${t}`)}for(let t of r)s.push(t);let o=0;const a=s.length,l=s.map((async e=>{const n=await fetch(e);return t.onProgress&&(o++,t.onProgress({count:o,total:a})),n})),c=await Promise.all(l),u=[];for(let t of c)u.push(await t.json());const h={root:u[0],properties:u[1]};let d=2;t.editorMode&&(h.ui=u[2],d+=1);const p={},_=Object.keys(e.nodes);for(let t=0;t<_.length;t++){const e=_[t],n=u[t+d];p[e]=n}return this.assemble(h,_,p)}static async assemble(t,e,n){const i={root:t.root,properties:t.properties,ui:t.ui};for(let t=0;t{s.matrixAutoUpdate=!0},c.onComplete=()=>{s.matrixAutoUpdate=t,s.matrixAutoUpdate||s.updateMatrix()}}if(o instanceof au.a&&this._target_value instanceof au.a){const t={value:0},e=o,n=(new au.a).copy(o),i=this._target_value;c.onUpdate=()=>{e.slerpQuaternions(n,i,t.value)},a=t,c.value=1}if(m.isNumber(this._target_value)){if(m.isNumber(o))for(let t of l)c[t]=this.withOp(o,this._target_value,i)}else if(!m.isNumber(o))for(let t of l)c[t]=this.withOp(o[t],this._target_value[t],i);a&&this._startTimeline(e,n,c,a)}}}_sceneGraphProps(t,e){const n=e.split(\\\\\\\".\\\\\\\");if(!(n.length>1)){const n=t[e];let i=null;const r=[];return m.isNumber(n)?(i=t,r.push(e)):(i=n,this._target_value instanceof d.a&&r.push(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"),this._target_value instanceof p.a&&r.push(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"),this._target_value instanceof _.a&&r.push(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"),this._target_value,au.a),{target_property:n,to_target:i,property_names:r}}{const e=t[n.shift()];if(e){const t=n.join(\\\\\\\".\\\\\\\");return this._sceneGraphProps(e,t)}}}_populateWithNode(t,e,n){this._printDebug([\\\\\\\"_populateWithNode\\\\\\\",t]);const i=t.p[this._property_name];this._printDebug([\\\\\\\"target_param\\\\\\\",i]),i?i&&this._populateVarsForParam(i,e,n):ai.warn(`${this._property_name} not found on node ${t.path()}`)}_populateVarsForParam(t,e,n){switch(this._printDebug([\\\\\\\"_populateVarsForParam\\\\\\\",t]),t.type()){case Es.INTEGER:return this._populateVarsForParamInteger(t,e,n);case Es.FLOAT:return this._populateVarsForParamFloat(t,e,n);case Es.VECTOR2:return this._populateVarsForParamVector2(t,e,n);case Es.VECTOR3:return this._populateVarsForParamVector3(t,e,n);case Es.VECTOR4:return this._populateVarsForParamVector4(t,e,n)}this._printDebug(`param type cannot be animated (yet): '${t.type()}' '${t.path()}'`)}_populateVarsForParamInteger(t,e,n){if(!m.isNumber(this._target_value))return void ai.warn(\\\\\\\"value is not a numbber\\\\\\\",this._target_value);const i=this._commonVars(e),r={num:t.value};i.onUpdate=()=>{t.set(r.num)};const s=e.operation();i.num=this.withOp(t.value,this._target_value,s),this._startTimeline(e,n,i,r)}_populateVarsForParamFloat(t,e,n){if(!m.isNumber(this._target_value))return void ai.warn(\\\\\\\"value is not a numbber\\\\\\\",this._target_value);const i=this._commonVars(e),r={num:t.value};i.onUpdate=()=>{t.set(r.num)};const s=e.operation();i.num=this.withOp(t.value,this._target_value,s),this._startTimeline(e,n,i,r)}_populateVarsForParamVector2(t,e,n){if(!(this._target_value instanceof d.a))return;const i=this._commonVars(e),r=t.value.clone(),s=[0,0];i.onUpdate=()=>{r.toArray(s),t.set(s)};const o=e.operation();i.x=this.withOp(t.value.x,this._target_value.x,o),i.y=this.withOp(t.value.y,this._target_value.y,o),this._startTimeline(e,n,i,r)}_populateVarsForParamVector3(t,e,n){if(!(this._target_value instanceof p.a))return;const i=this._commonVars(e),r=t.value.clone(),s=[0,0,0];i.onUpdate=()=>{r.toArray(s),t.set(s)};const o=e.operation();i.x=this.withOp(t.value.x,this._target_value.x,o),i.y=this.withOp(t.value.y,this._target_value.y,o),i.z=this.withOp(t.value.z,this._target_value.z,o),this._startTimeline(e,n,i,r)}_populateVarsForParamVector4(t,e,n){if(!(this._target_value instanceof _.a))return;const i=this._commonVars(e),r=t.value.clone(),s=[0,0,0,0];i.onUpdate=()=>{r.toArray(s),t.set(s)};const o=e.operation();i.x=this.withOp(t.value.x,this._target_value.x,o),i.y=this.withOp(t.value.y,this._target_value.y,o),i.z=this.withOp(t.value.z,this._target_value.z,o),i.w=this.withOp(t.value.w,this._target_value.w,o),this._startTimeline(e,n,i,r)}withOp(t,e,n){switch(n){case N_.SET:return e;case N_.ADD:return t+e;case N_.SUBSTRACT:return t-e}ar.unreachable(n)}_commonVars(t){const e={duration:t.duration()},n=t.easing()||ou.NONE;n&&(e.ease=n);const i=t.delay();null!=i&&(e.delay=i);const r=t.repeatParams();return r&&(e.repeat=r.count,e.repeatDelay=r.delay,e.yoyo=r.yoyo),e}_startTimeline(t,e,n,i){const r=t.position(),s=r?r.toParameter():void 0;e.to(i,n,s)}}function du(t){if(void 0===t)throw new ReferenceError(\\\\\\\"this hasn't been initialised - super() hasn't been called\\\\\\\");return t}function pu(t,e){t.prototype=Object.create(e.prototype),t.prototype.constructor=t,t.__proto__=e}var _u,mu,fu,gu,vu,yu,xu,bu,wu,Tu,Au,Eu,Mu,Su={autoSleep:120,force3D:\\\\\\\"auto\\\\\\\",nullTargetWarn:1,units:{lineHeight:\\\\\\\"\\\\\\\"}},Cu={duration:.5,overwrite:!1,delay:0},Nu=1e8,Lu=1e-8,Ou=2*Math.PI,Ru=Ou/4,Pu=0,Iu=Math.sqrt,Fu=Math.cos,Du=Math.sin,ku=function(t){return\\\\\\\"string\\\\\\\"==typeof t},Bu=function(t){return\\\\\\\"function\\\\\\\"==typeof t},zu=function(t){return\\\\\\\"number\\\\\\\"==typeof t},Uu=function(t){return void 0===t},Gu=function(t){return\\\\\\\"object\\\\\\\"==typeof t},Vu=function(t){return!1!==t},Hu=function(){return\\\\\\\"undefined\\\\\\\"!=typeof window},ju=function(t){return Bu(t)||ku(t)},Wu=\\\\\\\"function\\\\\\\"==typeof ArrayBuffer&&ArrayBuffer.isView||function(){},qu=Array.isArray,Xu=/(?:-?\\\\.?\\\\d|\\\\.)+/gi,Yu=/[-+=.]*\\\\d+[.e\\\\-+]*\\\\d*[e\\\\-+]*\\\\d*/g,$u=/[-+=.]*\\\\d+[.e-]*\\\\d*[a-z%]*/g,Ju=/[-+=.]*\\\\d+\\\\.?\\\\d*(?:e-|e\\\\+)?\\\\d*/gi,Zu=/[+-]=-?[.\\\\d]+/,Qu=/[#\\\\-+.]*\\\\b[a-z\\\\d-=+%.]+/gi,Ku=/[\\\\d.+\\\\-=]+(?:e[-+]\\\\d*)*/i,th={},eh={},nh=function(t){return(eh=Ch(t,th))&&pp},ih=function(t,e){return console.warn(\\\\\\\"Invalid property\\\\\\\",t,\\\\\\\"set to\\\\\\\",e,\\\\\\\"Missing plugin? gsap.registerPlugin()\\\\\\\")},rh=function(t,e){return!e&&console.warn(t)},sh=function(t,e){return t&&(th[t]=e)&&eh&&(eh[t]=e)||th},oh=function(){return 0},ah={},lh=[],ch={},uh={},hh={},dh=30,ph=[],_h=\\\\\\\"\\\\\\\",mh=function(t){var e,n,i=t[0];if(Gu(i)||Bu(i)||(t=[t]),!(e=(i._gsap||{}).harness)){for(n=ph.length;n--&&!ph[n].targetTest(i););e=ph[n]}for(n=t.length;n--;)t[n]&&(t[n]._gsap||(t[n]._gsap=new Bd(t[n],e)))||t.splice(n,1);return t},fh=function(t){return t._gsap||mh(id(t))[0]._gsap},gh=function(t,e,n){return(n=t[e])&&Bu(n)?t[e]():Uu(n)&&t.getAttribute&&t.getAttribute(e)||n},vh=function(t,e){return(t=t.split(\\\\\\\",\\\\\\\")).forEach(e)||t},yh=function(t){return Math.round(1e5*t)/1e5||0},xh=function(t,e){for(var n=e.length,i=0;t.indexOf(e[i])<0&&++it._dur||e._start<0))for(var n=t;n;)n._dirty=1,n=n.parent;return t},Fh=function(t){for(var e=t.parent;e&&e.parent;)e._dirty=1,e.totalDuration(),e=e.parent;return t},Dh=function t(e){return!e||e._ts&&t(e.parent)},kh=function(t){return t._repeat?Bh(t._tTime,t=t.duration()+t._rDelay)*t:0},Bh=function(t,e){var n=Math.floor(t/=e);return t&&n===t?n-1:n},zh=function(t,e){return(t-e._start)*e._ts+(e._ts>=0?0:e._dirty?e.totalDuration():e._tDur)},Uh=function(t){return t._end=yh(t._start+(t._tDur/Math.abs(t._ts||t._rts||Lu)||0))},Gh=function(t,e){var n=t._dp;return n&&n.smoothChildTiming&&t._ts&&(t._start=yh(n._time-(t._ts>0?e/t._ts:((t._dirty?t.totalDuration():t._tDur)-e)/-t._ts)),Uh(t),n._dirty||Ih(n,t)),t},Vh=function(t,e){var n;if((e._time||e._initted&&!e._dur)&&(n=zh(t.rawTime(),e),(!e._dur||Qh(0,e.totalDuration(),n)-e._tTime>Lu)&&e.render(n,!0)),Ih(t,e)._dp&&t._initted&&t._time>=t._dur&&t._ts){if(t._dur=0&&n.totalTime(n._tTime),n=n._dp;t._zTime=-1e-8}},Hh=function(t,e,n,i){return e.parent&&Ph(e),e._start=yh(n+e._delay),e._end=yh(e._start+(e.totalDuration()/Math.abs(e.timeScale())||0)),function(t,e,n,i,r){void 0===n&&(n=\\\\\\\"_first\\\\\\\"),void 0===i&&(i=\\\\\\\"_last\\\\\\\");var s,o=t[i];if(r)for(s=e[r];o&&o[r]>s;)o=o._prev;o?(e._next=o._next,o._next=e):(e._next=t[n],t[n]=e),e._next?e._next._prev=e:t[i]=e,e._prev=o,e.parent=e._dp=t}(t,e,\\\\\\\"_first\\\\\\\",\\\\\\\"_last\\\\\\\",t._sort?\\\\\\\"_start\\\\\\\":0),t._recent=e,i||Vh(t,e),t},jh=function(t,e){return(th.ScrollTrigger||ih(\\\\\\\"scrollTrigger\\\\\\\",e))&&th.ScrollTrigger.create(e,t)},Wh=function(t,e,n,i){return Wd(t,e),t._initted?!n&&t._pt&&(t._dur&&!1!==t.vars.lazy||!t._dur&&t.vars.lazy)&&xu!==Ed.frame?(lh.push(t),t._lazy=[e,i],1):void 0:1},qh=function t(e){var n=e.parent;return n&&n._ts&&n._initted&&!n._lock&&(n.rawTime()<0||t(n))},Xh=function(t,e,n,i){var r=t._repeat,s=yh(e)||0,o=t._tTime/t._tDur;return o&&!i&&(t._time*=s/t._dur),t._dur=s,t._tDur=r?r<0?1e10:yh(s*(r+1)+t._rDelay*r):s,o&&!i?Gh(t,t._tTime=t._tDur*o):t.parent&&Uh(t),n||Ih(t.parent,t),t},Yh=function(t){return t instanceof Ud?Ih(t):Xh(t,t._dur)},$h={_start:0,endTime:oh},Jh=function t(e,n){var i,r,s=e.labels,o=e._recent||$h,a=e.duration()>=Nu?o.endTime(!1):e._dur;return ku(n)&&(isNaN(n)||n in s)?\\\\\\\"<\\\\\\\"===(i=n.charAt(0))||\\\\\\\">\\\\\\\"===i?(\\\\\\\"<\\\\\\\"===i?o._start:o.endTime(o._repeat>=0))+(parseFloat(n.substr(1))||0):(i=n.indexOf(\\\\\\\"=\\\\\\\"))<0?(n in s||(s[n]=a),s[n]):(r=+(n.charAt(i-1)+n.substr(i+1)),i>1?t(e,n.substr(0,i-1))+r:a+r):null==n?a:+n},Zh=function(t,e){return t||0===t?e(t):e},Qh=function(t,e,n){return ne?e:n},Kh=function(t){if(\\\\\\\"string\\\\\\\"!=typeof t)return\\\\\\\"\\\\\\\";var e=Ku.exec(t);return e?t.substr(e.index+e[0].length):\\\\\\\"\\\\\\\"},td=[].slice,ed=function(t,e){return t&&Gu(t)&&\\\\\\\"length\\\\\\\"in t&&(!e&&!t.length||t.length-1 in t&&Gu(t[0]))&&!t.nodeType&&t!==fu},nd=function(t,e,n){return void 0===n&&(n=[]),t.forEach((function(t){var i;return ku(t)&&!e||ed(t,1)?(i=n).push.apply(i,id(t)):n.push(t)}))||n},id=function(t,e){return!ku(t)||e||!gu&&Md()?qu(t)?nd(t,e):ed(t)?td.call(t,0):t?[t]:[]:td.call(vu.querySelectorAll(t),0)},rd=function(t){return t.sort((function(){return.5-Math.random()}))},sd=function(t){if(Bu(t))return t;var e=Gu(t)?t:{each:t},n=Pd(e.ease),i=e.from||0,r=parseFloat(e.base)||0,s={},o=i>0&&i<1,a=isNaN(i)||o,l=e.axis,c=i,u=i;return ku(i)?c=u={center:.5,edges:.5,end:1}[i]||0:!o&&a&&(c=i[0],u=i[1]),function(t,o,h){var d,p,_,m,f,g,v,y,x,b=(h||e).length,w=s[b];if(!w){if(!(x=\\\\\\\"auto\\\\\\\"===e.grid?0:(e.grid||[1,Nu])[1])){for(v=-Nu;v<(v=h[x++].getBoundingClientRect().left)&&xv&&(v=f),fb?b-1:l?\\\\\\\"y\\\\\\\"===l?b/x:x:Math.max(x,b/x))||0)*(\\\\\\\"edges\\\\\\\"===i?-1:1),w.b=b<0?r-b:r,w.u=Kh(e.amount||e.each)||0,n=n&&b<0?Od(n):n}return b=(w[t]-w.min)/w.max||0,yh(w.b+(n?n(b):b)*w.v)+w.u}},od=function(t){var e=t<1?Math.pow(10,(t+\\\\\\\"\\\\\\\").length-2):1;return function(n){var i=Math.round(parseFloat(n)/t)*t*e;return(i-i%1)/e+(zu(n)?0:Kh(n))}},ad=function(t,e){var n,i,r=qu(t);return!r&&Gu(t)&&(n=r=t.radius||Nu,t.values?(t=id(t.values),(i=!zu(t[0]))&&(n*=n)):t=od(t.increment)),Zh(e,r?Bu(t)?function(e){return i=t(e),Math.abs(i-e)<=n?i:e}:function(e){for(var r,s,o=parseFloat(i?e.x:e),a=parseFloat(i?e.y:0),l=Nu,c=0,u=t.length;u--;)(r=i?(r=t[u].x-o)*r+(s=t[u].y-a)*s:Math.abs(t[u]-o))(r=Math.abs(r))&&(s=i,a=r);return s},pd=function(t,e,n){var i,r,s=t.vars,o=s[e];if(o)return i=s[e+\\\\\\\"Params\\\\\\\"],r=s.callbackScope||t,n&&lh.length&&wh(),i?o.apply(r,i):o.call(r)},_d=function(t){return Ph(t),t.scrollTrigger&&t.scrollTrigger.kill(!1),t.progress()<1&&pd(t,\\\\\\\"onInterrupt\\\\\\\"),t},md=function(t){var e=(t=!t.name&&t.default||t).name,n=Bu(t),i=e&&!n&&t.init?function(){this._props=[]}:t,r={init:oh,render:rp,add:Hd,kill:op,modifier:sp,rawVars:0},s={targetTest:0,get:0,getSetter:tp,aliases:{},register:0};if(Md(),t!==i){if(uh[e])return;Mh(i,Mh(Lh(t,r),s)),Ch(i.prototype,Ch(r,Lh(t,s))),uh[i.prop=e]=i,t.targetTest&&(ph.push(i),ah[e]=1),e=(\\\\\\\"css\\\\\\\"===e?\\\\\\\"CSS\\\\\\\":e.charAt(0).toUpperCase()+e.substr(1))+\\\\\\\"Plugin\\\\\\\"}sh(e,i),t.register&&t.register(pp,i,cp)},fd=255,gd={aqua:[0,fd,fd],lime:[0,fd,0],silver:[192,192,192],black:[0,0,0],maroon:[128,0,0],teal:[0,128,128],blue:[0,0,fd],navy:[0,0,128],white:[fd,fd,fd],olive:[128,128,0],yellow:[fd,fd,0],orange:[fd,165,0],gray:[128,128,128],purple:[128,0,128],green:[0,128,0],red:[fd,0,0],pink:[fd,192,203],cyan:[0,fd,fd],transparent:[fd,fd,fd,0]},vd=function(t,e,n){return(6*(t=t<0?t+1:t>1?t-1:t)<1?e+(n-e)*t*6:t<.5?n:3*t<2?e+(n-e)*(2/3-t)*6:e)*fd+.5|0},yd=function(t,e,n){var i,r,s,o,a,l,c,u,h,d,p=t?zu(t)?[t>>16,t>>8&fd,t&fd]:0:gd.black;if(!p){if(\\\\\\\",\\\\\\\"===t.substr(-1)&&(t=t.substr(0,t.length-1)),gd[t])p=gd[t];else if(\\\\\\\"#\\\\\\\"===t.charAt(0)){if(t.length<6&&(i=t.charAt(1),r=t.charAt(2),s=t.charAt(3),t=\\\\\\\"#\\\\\\\"+i+i+r+r+s+s+(5===t.length?t.charAt(4)+t.charAt(4):\\\\\\\"\\\\\\\")),9===t.length)return[(p=parseInt(t.substr(1,6),16))>>16,p>>8&fd,p&fd,parseInt(t.substr(7),16)/255];p=[(t=parseInt(t.substr(1),16))>>16,t>>8&fd,t&fd]}else if(\\\\\\\"hsl\\\\\\\"===t.substr(0,3))if(p=d=t.match(Xu),e){if(~t.indexOf(\\\\\\\"=\\\\\\\"))return p=t.match(Yu),n&&p.length<4&&(p[3]=1),p}else o=+p[0]%360/360,a=+p[1]/100,i=2*(l=+p[2]/100)-(r=l<=.5?l*(a+1):l+a-l*a),p.length>3&&(p[3]*=1),p[0]=vd(o+1/3,i,r),p[1]=vd(o,i,r),p[2]=vd(o-1/3,i,r);else p=t.match(Xu)||gd.transparent;p=p.map(Number)}return e&&!d&&(i=p[0]/fd,r=p[1]/fd,s=p[2]/fd,l=((c=Math.max(i,r,s))+(u=Math.min(i,r,s)))/2,c===u?o=a=0:(h=c-u,a=l>.5?h/(2-c-u):h/(c+u),o=c===i?(r-s)/h+(ra&&(c+=y-l),((m=(g=(u+=y)-c)-d)>0||x)&&(v=++i.frame,r=g-1e3*i.time,i.time=g/=1e3,d+=m+(m>=h?4:h-m),f=1),x||(t=e(n)),f)for(s=0;s=e&&s--},_listeners:p}}(),Md=function(){return!wu&&Ed.wake()},Sd={},Cd=/^[\\\\d.\\\\-M][\\\\d.\\\\-,\\\\s]/,Nd=/[\\\\\\\"']/g,Ld=function(t){for(var e,n,i,r={},s=t.substr(1,t.length-3).split(\\\\\\\":\\\\\\\"),o=s[0],a=1,l=s.length;a1&&o.config?o.config.apply(null,~t.indexOf(\\\\\\\"{\\\\\\\")?[Ld(s[1])]:(e=t,n=e.indexOf(\\\\\\\"(\\\\\\\")+1,i=e.indexOf(\\\\\\\")\\\\\\\"),r=e.indexOf(\\\\\\\"(\\\\\\\",n),e.substring(n,~r&&r=1?n:1,s=(i||(e?.3:.45))/(n<1?n:1),o=s/Ou*(Math.asin(1/r)||0),a=function(t){return 1===t?1:r*Math.pow(2,-10*t)*Du((t-o)*s)+1},l=\\\\\\\"out\\\\\\\"===e?a:\\\\\\\"in\\\\\\\"===e?function(t){return 1-a(1-t)}:Fd(a);return s=Ou/s,l.config=function(n,i){return t(e,n,i)},l},kd=function t(e,n){void 0===n&&(n=1.70158);var i=function(t){return t?--t*t*((n+1)*t+n)+1:0},r=\\\\\\\"out\\\\\\\"===e?i:\\\\\\\"in\\\\\\\"===e?function(t){return 1-i(1-t)}:Fd(i);return r.config=function(n){return t(e,n)},r};vh(\\\\\\\"Linear,Quad,Cubic,Quart,Quint,Strong\\\\\\\",(function(t,e){var n=e<5?e+1:e;Id(t+\\\\\\\",Power\\\\\\\"+(n-1),e?function(t){return Math.pow(t,n)}:function(t){return t},(function(t){return 1-Math.pow(1-t,n)}),(function(t){return t<.5?Math.pow(2*t,n)/2:1-Math.pow(2*(1-t),n)/2}))})),Sd.Linear.easeNone=Sd.none=Sd.Linear.easeIn,Id(\\\\\\\"Elastic\\\\\\\",Dd(\\\\\\\"in\\\\\\\"),Dd(\\\\\\\"out\\\\\\\"),Dd()),Tu=7.5625,Eu=1/(Au=2.75),Id(\\\\\\\"Bounce\\\\\\\",(function(t){return 1-Mu(1-t)}),Mu=function(t){return t0?t+(t+this._rDelay)*this._repeat:t):this.totalDuration()&&this._dur},e.totalDuration=function(t){return arguments.length?(this._dirty=0,Xh(this,this._repeat<0?t:(t-this._repeat*this._rDelay)/(this._repeat+1))):this._tDur},e.totalTime=function(t,e){if(Md(),!arguments.length)return this._tTime;var n=this._dp;if(n&&n.smoothChildTiming&&this._ts){for(Gh(this,t),!n._dp||n.parent||Vh(n,this);n.parent;)n.parent._time!==n._start+(n._ts>=0?n._tTime/n._ts:(n.totalDuration()-n._tTime)/-n._ts)&&n.totalTime(n._tTime,!0),n=n.parent;!this.parent&&this._dp.autoRemoveChildren&&(this._ts>0&&t0||!this._tDur&&!t)&&Hh(this._dp,this,this._start-this._delay)}return(this._tTime!==t||!this._dur&&!e||this._initted&&Math.abs(this._zTime)===Lu||!t&&!this._initted&&(this.add||this._ptLookup))&&(this._ts||(this._pTime=t),Th(this,t,e)),this},e.time=function(t,e){return arguments.length?this.totalTime(Math.min(this.totalDuration(),t+kh(this))%this._dur||(t?this._dur:0),e):this._time},e.totalProgress=function(t,e){return arguments.length?this.totalTime(this.totalDuration()*t,e):this.totalDuration()?Math.min(1,this._tTime/this._tDur):this.ratio},e.progress=function(t,e){return arguments.length?this.totalTime(this.duration()*(!this._yoyo||1&this.iteration()?t:1-t)+kh(this),e):this.duration()?Math.min(1,this._time/this._dur):this.ratio},e.iteration=function(t,e){var n=this.duration()+this._rDelay;return arguments.length?this.totalTime(this._time+(t-1)*n,e):this._repeat?Bh(this._tTime,n)+1:1},e.timeScale=function(t){if(!arguments.length)return-1e-8===this._rts?0:this._rts;if(this._rts===t)return this;var e=this.parent&&this._ts?zh(this.parent._time,this):this._tTime;return this._rts=+t||0,this._ts=this._ps||-1e-8===t?0:this._rts,Fh(this.totalTime(Qh(-this._delay,this._tDur,e),!0))},e.paused=function(t){return arguments.length?(this._ps!==t&&(this._ps=t,t?(this._pTime=this._tTime||Math.max(-this._delay,this.rawTime()),this._ts=this._act=0):(Md(),this._ts=this._rts,this.totalTime(this.parent&&!this.parent.smoothChildTiming?this.rawTime():this._tTime||this._pTime,1===this.progress()&&(this._tTime-=Lu)&&Math.abs(this._zTime)!==Lu))),this):this._ps},e.startTime=function(t){if(arguments.length){this._start=t;var e=this.parent||this._dp;return e&&(e._sort||!this.parent)&&Hh(e,this,t-this._delay),this}return this._start},e.endTime=function(t){return this._start+(Vu(t)?this.totalDuration():this.duration())/Math.abs(this._ts)},e.rawTime=function(t){var e=this.parent||this._dp;return e?t&&(!this._ts||this._repeat&&this._time&&this.totalProgress()<1)?this._tTime%(this._dur+this._rDelay):this._ts?zh(e.rawTime(t),this):this._tTime:this._tTime},e.globalTime=function(t){for(var e=this,n=arguments.length?t:e.rawTime();e;)n=e._start+n/(e._ts||1),e=e._dp;return n},e.repeat=function(t){return arguments.length?(this._repeat=t===1/0?-2:t,Yh(this)):-2===this._repeat?1/0:this._repeat},e.repeatDelay=function(t){return arguments.length?(this._rDelay=t,Yh(this)):this._rDelay},e.yoyo=function(t){return arguments.length?(this._yoyo=t,this):this._yoyo},e.seek=function(t,e){return this.totalTime(Jh(this,t),Vu(e))},e.restart=function(t,e){return this.play().totalTime(t?-this._delay:0,Vu(e))},e.play=function(t,e){return null!=t&&this.seek(t,e),this.reversed(!1).paused(!1)},e.reverse=function(t,e){return null!=t&&this.seek(t||this.totalDuration(),e),this.reversed(!0).paused(!1)},e.pause=function(t,e){return null!=t&&this.seek(t,e),this.paused(!0)},e.resume=function(){return this.paused(!1)},e.reversed=function(t){return arguments.length?(!!t!==this.reversed()&&this.timeScale(-this._rts||(t?-1e-8:0)),this):this._rts<0},e.invalidate=function(){return this._initted=this._act=0,this._zTime=-1e-8,this},e.isActive=function(){var t,e=this.parent||this._dp,n=this._start;return!(e&&!(this._ts&&this._initted&&e.isActive()&&(t=e.rawTime(!0))>=n&&t1?(e?(i[t]=e,n&&(i[t+\\\\\\\"Params\\\\\\\"]=n),\\\\\\\"onUpdate\\\\\\\"===t&&(this._onUpdate=e)):delete i[t],this):i[t]},e.then=function(t){var e=this;return new Promise((function(n){var i=Bu(t)?t:Eh,r=function(){var t=e.then;e.then=null,Bu(i)&&(i=i(e))&&(i.then||i===e)&&(e.then=t),n(i),e.then=t};e._initted&&1===e.totalProgress()&&e._ts>=0||!e._tTime&&e._ts<0?r():e._prom=r}))},e.kill=function(){_d(this)},t}();Mh(zd.prototype,{_time:0,_start:0,_end:0,_tTime:0,_tDur:0,_dirty:0,_repeat:0,_yoyo:!1,parent:null,_initted:!1,_rDelay:0,_ts:1,_dp:0,ratio:0,_zTime:-1e-8,_prom:0,_ps:!1,_rts:1});var Ud=function(t){function e(e,n){var i;return void 0===e&&(e={}),(i=t.call(this,e,n)||this).labels={},i.smoothChildTiming=!!e.smoothChildTiming,i.autoRemoveChildren=!!e.autoRemoveChildren,i._sort=Vu(e.sortChildren),i.parent&&Vh(i.parent,du(i)),e.scrollTrigger&&jh(du(i),e.scrollTrigger),i}pu(e,t);var n=e.prototype;return n.to=function(t,e,n){return new $d(t,bh(arguments,0,this),Jh(this,zu(e)?arguments[3]:n)),this},n.from=function(t,e,n){return new $d(t,bh(arguments,1,this),Jh(this,zu(e)?arguments[3]:n)),this},n.fromTo=function(t,e,n,i){return new $d(t,bh(arguments,2,this),Jh(this,zu(e)?arguments[4]:i)),this},n.set=function(t,e,n){return e.duration=0,e.parent=this,Oh(e).repeatDelay||(e.repeat=0),e.immediateRender=!!e.immediateRender,new $d(t,e,Jh(this,n),1),this},n.call=function(t,e,n){return Hh(this,$d.delayedCall(0,t,e),Jh(this,n))},n.staggerTo=function(t,e,n,i,r,s,o){return n.duration=e,n.stagger=n.stagger||i,n.onComplete=s,n.onCompleteParams=o,n.parent=this,new $d(t,n,Jh(this,r)),this},n.staggerFrom=function(t,e,n,i,r,s,o){return n.runBackwards=1,Oh(n).immediateRender=Vu(n.immediateRender),this.staggerTo(t,e,n,i,r,s,o)},n.staggerFromTo=function(t,e,n,i,r,s,o,a){return i.startAt=n,Oh(i).immediateRender=Vu(i.immediateRender),this.staggerTo(t,e,i,r,s,o,a)},n.render=function(t,e,n){var i,r,s,o,a,l,c,u,h,d,p,_,m=this._time,f=this._dirty?this.totalDuration():this._tDur,g=this._dur,v=this!==mu&&t>f-Lu&&t>=0?f:tg&&(i=g)),d=Bh(this._tTime,a),!m&&this._tTime&&d!==o&&(d=o),p&&1&o&&(i=g-i,_=1),o!==d&&!this._lock){var x=p&&1&d,b=x===(p&&1&o);if(oe)for(i=t._first;i&&i._start<=n;){if(!i._dur&&\\\\\\\"isPause\\\\\\\"===i.data&&i._start>e)return i;i=i._next}else for(i=t._last;i&&i._start>=n;){if(!i._dur&&\\\\\\\"isPause\\\\\\\"===i.data&&i._start=m&&t>=0)for(r=this._first;r;){if(s=r._next,(r._act||i>=r._start)&&r._ts&&c!==r){if(r.parent!==this)return this.render(t,e,n);if(r.render(r._ts>0?(i-r._start)*r._ts:(r._dirty?r.totalDuration():r._tDur)+(i-r._start)*r._ts,e,n),i!==this._time||!this._ts&&!l){c=0,s&&(v+=this._zTime=-1e-8);break}}r=s}else{r=this._last;for(var w=t<0?t:i;r;){if(s=r._prev,(r._act||w<=r._end)&&r._ts&&c!==r){if(r.parent!==this)return this.render(t,e,n);if(r.render(r._ts>0?(w-r._start)*r._ts:(r._dirty?r.totalDuration():r._tDur)+(w-r._start)*r._ts,e,n),i!==this._time||!this._ts&&!l){c=0,s&&(v+=this._zTime=w?-1e-8:Lu);break}}r=s}}if(c&&!e&&(this.pause(),c.render(i>=m?0:-1e-8)._zTime=i>=m?1:-1,this._ts))return this._start=h,Uh(this),this.render(t,e,n);this._onUpdate&&!e&&pd(this,\\\\\\\"onUpdate\\\\\\\",!0),(v===f&&f>=this.totalDuration()||!v&&m)&&(h!==this._start&&Math.abs(u)===Math.abs(this._ts)||this._lock||((t||!g)&&(v===f&&this._ts>0||!v&&this._ts<0)&&Ph(this,1),e||t<0&&!m||!v&&!m||(pd(this,v===f?\\\\\\\"onComplete\\\\\\\":\\\\\\\"onReverseComplete\\\\\\\",!0),this._prom&&!(v0)&&this._prom())))}return this},n.add=function(t,e){var n=this;if(zu(e)||(e=Jh(this,e)),!(t instanceof zd)){if(qu(t))return t.forEach((function(t){return n.add(t,e)})),this;if(ku(t))return this.addLabel(t,e);if(!Bu(t))return this;t=$d.delayedCall(0,t)}return this!==t?Hh(this,t,e):this},n.getChildren=function(t,e,n,i){void 0===t&&(t=!0),void 0===e&&(e=!0),void 0===n&&(n=!0),void 0===i&&(i=-Nu);for(var r=[],s=this._first;s;)s._start>=i&&(s instanceof $d?e&&r.push(s):(n&&r.push(s),t&&r.push.apply(r,s.getChildren(!0,e,n)))),s=s._next;return r},n.getById=function(t){for(var e=this.getChildren(1,1,1),n=e.length;n--;)if(e[n].vars.id===t)return e[n]},n.remove=function(t){return ku(t)?this.removeLabel(t):Bu(t)?this.killTweensOf(t):(Rh(this,t),t===this._recent&&(this._recent=this._last),Ih(this))},n.totalTime=function(e,n){return arguments.length?(this._forcing=1,!this._dp&&this._ts&&(this._start=yh(Ed.time-(this._ts>0?e/this._ts:(this.totalDuration()-e)/-this._ts))),t.prototype.totalTime.call(this,e,n),this._forcing=0,this):this._tTime},n.addLabel=function(t,e){return this.labels[t]=Jh(this,e),this},n.removeLabel=function(t){return delete this.labels[t],this},n.addPause=function(t,e,n){var i=$d.delayedCall(0,e||oh,n);return i.data=\\\\\\\"isPause\\\\\\\",this._hasPause=1,Hh(this,i,Jh(this,t))},n.removePause=function(t){var e=this._first;for(t=Jh(this,t);e;)e._start===t&&\\\\\\\"isPause\\\\\\\"===e.data&&Ph(e),e=e._next},n.killTweensOf=function(t,e,n){for(var i=this.getTweensOf(t,n),r=i.length;r--;)Gd!==i[r]&&i[r].kill(t,e);return this},n.getTweensOf=function(t,e){for(var n,i=[],r=id(t),s=this._first,o=zu(e);s;)s instanceof $d?xh(s._targets,r)&&(o?(!Gd||s._initted&&s._ts)&&s.globalTime(0)<=e&&s.globalTime(s.totalDuration())>e:!e||s.isActive())&&i.push(s):(n=s.getTweensOf(r,e)).length&&i.push.apply(i,n),s=s._next;return i},n.tweenTo=function(t,e){e=e||{};var n=this,i=Jh(n,t),r=e,s=r.startAt,o=r.onStart,a=r.onStartParams,l=r.immediateRender,c=$d.to(n,Mh({ease:e.ease||\\\\\\\"none\\\\\\\",lazy:!1,immediateRender:!1,time:i,overwrite:\\\\\\\"auto\\\\\\\",duration:e.duration||Math.abs((i-(s&&\\\\\\\"time\\\\\\\"in s?s.time:n._time))/n.timeScale())||Lu,onStart:function(){n.pause();var t=e.duration||Math.abs((i-n._time)/n.timeScale());c._dur!==t&&Xh(c,t,0,1).render(c._time,!0,!0),o&&o.apply(c,a||[])}},e));return l?c.render(0):c},n.tweenFromTo=function(t,e,n){return this.tweenTo(e,Mh({startAt:{time:Jh(this,t)}},n))},n.recent=function(){return this._recent},n.nextLabel=function(t){return void 0===t&&(t=this._time),dd(this,Jh(this,t))},n.previousLabel=function(t){return void 0===t&&(t=this._time),dd(this,Jh(this,t),1)},n.currentLabel=function(t){return arguments.length?this.seek(t,!0):this.previousLabel(this._time+Lu)},n.shiftChildren=function(t,e,n){void 0===n&&(n=0);for(var i,r=this._first,s=this.labels;r;)r._start>=n&&(r._start+=t,r._end+=t),r=r._next;if(e)for(i in s)s[i]>=n&&(s[i]+=t);return Ih(this)},n.invalidate=function(){var e=this._first;for(this._lock=0;e;)e.invalidate(),e=e._next;return t.prototype.invalidate.call(this)},n.clear=function(t){void 0===t&&(t=!0);for(var e,n=this._first;n;)e=n._next,this.remove(n),n=e;return this._dp&&(this._time=this._tTime=this._pTime=0),t&&(this.labels={}),Ih(this)},n.totalDuration=function(t){var e,n,i,r=0,s=this,o=s._last,a=Nu;if(arguments.length)return s.timeScale((s._repeat<0?s.duration():s.totalDuration())/(s.reversed()?-t:t));if(s._dirty){for(i=s.parent;o;)e=o._prev,o._dirty&&o.totalDuration(),(n=o._start)>a&&s._sort&&o._ts&&!s._lock?(s._lock=1,Hh(s,o,n-o._delay,1)._lock=0):a=n,n<0&&o._ts&&(r-=n,(!i&&!s._dp||i&&i.smoothChildTiming)&&(s._start+=n/s._ts,s._time-=n,s._tTime-=n),s.shiftChildren(-n,!1,-Infinity),a=0),o._end>r&&o._ts&&(r=o._end),o=e;Xh(s,s===mu&&s._time>r?s._time:r,1,1),s._dirty=0}return s._tDur},e.updateRoot=function(t){if(mu._ts&&(Th(mu,zh(t,mu)),xu=Ed.frame),Ed.frame>=dh){dh+=Su.autoSleep||120;var e=mu._first;if((!e||!e._ts)&&Su.autoSleep&&Ed._listeners.length<2){for(;e&&!e._ts;)e=e._next;e||Ed.sleep()}}},e}(zd);Mh(Ud.prototype,{_lock:0,_hasPause:0,_forcing:0});var Gd,Vd=function(t,e,n,i,r,s,o){var a,l,c,u,h,d,p,_,m=new cp(this._pt,t,e,0,1,ip,null,r),f=0,g=0;for(m.b=n,m.e=i,n+=\\\\\\\"\\\\\\\",(p=~(i+=\\\\\\\"\\\\\\\").indexOf(\\\\\\\"random(\\\\\\\"))&&(i=ud(i)),s&&(s(_=[n,i],t,e),n=_[0],i=_[1]),l=n.match(Ju)||[];a=Ju.exec(i);)u=a[0],h=i.substring(f,a.index),c?c=(c+1)%5:\\\\\\\"rgba(\\\\\\\"===h.substr(-5)&&(c=1),u!==l[g++]&&(d=parseFloat(l[g-1])||0,m._pt={_next:m._pt,p:h||1===g?h:\\\\\\\",\\\\\\\",s:d,c:\\\\\\\"=\\\\\\\"===u.charAt(1)?parseFloat(u.substr(2))*(\\\\\\\"-\\\\\\\"===u.charAt(0)?-1:1):parseFloat(u)-d,m:c&&c<4?Math.round:0},f=Ju.lastIndex);return m.c=f0)S||(e._startAt=0);else if(C&&!(n<0&&N))return void(n&&(e._zTime=n))}else!1===S&&(e._startAt=0);else if(A&&C)if(N)!S&&(e._startAt=0);else if(n&&(y=!1),s=Mh({overwrite:!1,data:\\\\\\\"isFromStart\\\\\\\",lazy:y&&Vu(x),immediateRender:y,stagger:0,parent:O},i),_&&(s[u.prop]=_),Ph(e._startAt=$d.set(L,s)),y){if(!n)return}else t(e._startAt,Lu);for(e._pt=0,x=C&&Vu(x)||x&&!C,r=0;r\\\\\\\")}));else{if(c=E.length,d=v?sd(v):oh,Gu(v))for(u in v)~Xd.indexOf(u)&&(p||(p={}),p[u]=v[u]);for(a=0;ap-Lu&&t>=0?p:t_&&(i=_)),(l=this._yoyo&&1&s)&&(h=this._yEase,i=_-i),a=Bh(this._tTime,o),i===d&&!n&&this._initted)return this;s!==a&&(u&&this._yEase&&Rd(u,l),!this.vars.repeatRefresh||l||this._lock||(this._lock=n=1,this.render(yh(o*s),!0).invalidate()._lock=0))}if(!this._initted){if(Wh(this,t<0?t:i,n,e))return this._tTime=0,this;if(_!==this._dur)return this.render(t,e,n)}for(this._tTime=m,this._time=i,!this._act&&this._ts&&(this._act=1,this._lazy=0),this.ratio=c=(h||this._ease)(i/_),this._from&&(this.ratio=c=1-c),i&&!d&&!e&&pd(this,\\\\\\\"onStart\\\\\\\"),r=this._pt;r;)r.r(c,r.d),r=r._next;u&&u.render(t<0?t:!i&&l?-1e-8:u._dur*c,e,n)||this._startAt&&(this._zTime=t),this._onUpdate&&!e&&(t<0&&this._startAt&&this._startAt.render(t,!0,n),pd(this,\\\\\\\"onUpdate\\\\\\\")),this._repeat&&s!==a&&this.vars.onRepeat&&!e&&this.parent&&pd(this,\\\\\\\"onRepeat\\\\\\\"),m!==this._tDur&&m||this._tTime!==m||(t<0&&this._startAt&&!this._onUpdate&&this._startAt.render(t,!0,!0),(t||!_)&&(m===this._tDur&&this._ts>0||!m&&this._ts<0)&&Ph(this,1),e||t<0&&!d||!m&&!d||(pd(this,m===p?\\\\\\\"onComplete\\\\\\\":\\\\\\\"onReverseComplete\\\\\\\",!0),this._prom&&!(m0)&&this._prom()))}}else!function(t,e,n,i){var r,s,o,a=t.ratio,l=e<0||!e&&(!t._start&&qh(t)||(t._ts<0||t._dp._ts<0)&&\\\\\\\"isFromStart\\\\\\\"!==t.data&&\\\\\\\"isStart\\\\\\\"!==t.data)?0:1,c=t._rDelay,u=0;if(c&&t._repeat&&(u=Qh(0,t._tDur,e),s=Bh(u,c),o=Bh(t._tTime,c),t._yoyo&&1&s&&(l=1-l),s!==o&&(a=1-l,t.vars.repeatRefresh&&t._initted&&t.invalidate())),l!==a||i||t._zTime===Lu||!e&&t._zTime){if(!t._initted&&Wh(t,e,i,n))return;for(o=t._zTime,t._zTime=e||(n?Lu:0),n||(n=e&&!o),t.ratio=l,t._from&&(l=1-l),t._time=0,t._tTime=u,r=t._pt;r;)r.r(l,r.d),r=r._next;t._startAt&&e<0&&t._startAt.render(e,!0,!0),t._onUpdate&&!n&&pd(t,\\\\\\\"onUpdate\\\\\\\"),u&&t._repeat&&!n&&t.parent&&pd(t,\\\\\\\"onRepeat\\\\\\\"),(e>=t._tDur||e<0)&&t.ratio===l&&(l&&Ph(t,1),n||(pd(t,l?\\\\\\\"onComplete\\\\\\\":\\\\\\\"onReverseComplete\\\\\\\",!0),t._prom&&t._prom()))}else t._zTime||(t._zTime=e)}(this,t,e,n);return this},n.targets=function(){return this._targets},n.invalidate=function(){return this._pt=this._op=this._startAt=this._onUpdate=this._lazy=this.ratio=0,this._ptLookup=[],this.timeline&&this.timeline.invalidate(),t.prototype.invalidate.call(this)},n.kill=function(t,e){if(void 0===e&&(e=\\\\\\\"all\\\\\\\"),!(t||e&&\\\\\\\"all\\\\\\\"!==e))return this._lazy=this._pt=0,this.parent?_d(this):this;if(this.timeline){var n=this.timeline.totalDuration();return this.timeline.killTweensOf(t,e,Gd&&!0!==Gd.vars.overwrite)._first||_d(this),this.parent&&n!==this.timeline.totalDuration()&&Xh(this,this._dur*this.timeline._tDur/n,0,1),this}var i,r,s,o,a,l,c,u=this._targets,h=t?id(t):u,d=this._ptLookup,p=this._pt;if((!e||\\\\\\\"all\\\\\\\"===e)&&function(t,e){for(var n=t.length,i=n===e.length;i&&n--&&t[n]===e[n];);return n<0}(u,h))return\\\\\\\"all\\\\\\\"===e&&(this._pt=0),_d(this);for(i=this._op=this._op||[],\\\\\\\"all\\\\\\\"!==e&&(ku(e)&&(a={},vh(e,(function(t){return a[t]=1})),e=a),e=function(t,e){var n,i,r,s,o=t[0]?fh(t[0]).harness:0,a=o&&o.aliases;if(!a)return e;for(i in n=Ch({},e),a)if(i in n)for(r=(s=a[i].split(\\\\\\\",\\\\\\\")).length;r--;)n[s[r]]=n[i];return n}(u,e)),c=u.length;c--;)if(~h.indexOf(u[c]))for(a in r=d[c],\\\\\\\"all\\\\\\\"===e?(i[c]=e,o=r,s={}):(s=i[c]=i[c]||{},o=e),o)(l=r&&r[a])&&(\\\\\\\"kill\\\\\\\"in l.d&&!0!==l.d.kill(a)||Rh(this,l,\\\\\\\"_pt\\\\\\\"),delete r[a]),\\\\\\\"all\\\\\\\"!==s&&(s[a]=1);return this._initted&&!this._pt&&p&&_d(this),this},e.to=function(t,n){return new e(t,n,arguments[2])},e.from=function(t,n){return new e(t,bh(arguments,1))},e.delayedCall=function(t,n,i,r){return new e(n,0,{immediateRender:!1,lazy:!1,overwrite:!1,delay:t,onComplete:n,onReverseComplete:n,onCompleteParams:i,onReverseCompleteParams:i,callbackScope:r})},e.fromTo=function(t,n,i){return new e(t,bh(arguments,2))},e.set=function(t,n){return n.duration=0,n.repeatDelay||(n.repeat=0),new e(t,n)},e.killTweensOf=function(t,e,n){return mu.killTweensOf(t,e,n)},e}(zd);Mh($d.prototype,{_targets:[],_lazy:0,_startAt:0,_op:0,_onInit:0}),vh(\\\\\\\"staggerTo,staggerFrom,staggerFromTo\\\\\\\",(function(t){$d[t]=function(){var e=new Ud,n=td.call(arguments,0);return n.splice(\\\\\\\"staggerFromTo\\\\\\\"===t?5:4,0,0),e[t].apply(e,n)}}));var Jd=function(t,e,n){return t[e]=n},Zd=function(t,e,n){return t[e](n)},Qd=function(t,e,n,i){return t[e](i.fp,n)},Kd=function(t,e,n){return t.setAttribute(e,n)},tp=function(t,e){return Bu(t[e])?Zd:Uu(t[e])&&t.setAttribute?Kd:Jd},ep=function(t,e){return e.set(e.t,e.p,Math.round(1e4*(e.s+e.c*t))/1e4,e)},np=function(t,e){return e.set(e.t,e.p,!!(e.s+e.c*t),e)},ip=function(t,e){var n=e._pt,i=\\\\\\\"\\\\\\\";if(!t&&e.b)i=e.b;else if(1===t&&e.e)i=e.e;else{for(;n;)i=n.p+(n.m?n.m(n.s+n.c*t):Math.round(1e4*(n.s+n.c*t))/1e4)+i,n=n._next;i+=e.c}e.set(e.t,e.p,i,e)},rp=function(t,e){for(var n=e._pt;n;)n.r(t,n.d),n=n._next},sp=function(t,e,n,i){for(var r,s=this._pt;s;)r=s._next,s.p===i&&s.modifier(t,e,n),s=r},op=function(t){for(var e,n,i=this._pt;i;)n=i._next,i.p===t&&!i.op||i.op===t?Rh(this,i,\\\\\\\"_pt\\\\\\\"):i.dep||(e=1),i=n;return!e},ap=function(t,e,n,i){i.mSet(t,e,i.m.call(i.tween,n,i.mt),i)},lp=function(t){for(var e,n,i,r,s=t._pt;s;){for(e=s._next,n=i;n&&n.pr>s.pr;)n=n._next;(s._prev=n?n._prev:r)?s._prev._next=s:i=s,(s._next=n)?n._prev=s:r=s,s=e}t._pt=i},cp=function(){function t(t,e,n,i,r,s,o,a,l){this.t=e,this.s=i,this.c=r,this.p=n,this.r=s||ep,this.d=o||this,this.set=a||Jd,this.pr=l||0,this._next=t,t&&(t._prev=this)}return t.prototype.modifier=function(t,e,n){this.mSet=this.mSet||this.set,this.set=ap,this.m=t,this.mt=n,this.tween=e},t}();vh(_h+\\\\\\\"parent,duration,ease,delay,overwrite,runBackwards,startAt,yoyo,immediateRender,repeat,repeatDelay,data,paused,reversed,lazy,callbackScope,stringFilter,id,yoyoEase,stagger,inherit,repeatRefresh,keyframes,autoRevert,scrollTrigger\\\\\\\",(function(t){return ah[t]=1})),th.TweenMax=th.TweenLite=$d,th.TimelineLite=th.TimelineMax=Ud,mu=new Ud({sortChildren:!1,defaults:Cu,autoRemoveChildren:!0,id:\\\\\\\"root\\\\\\\",smoothChildTiming:!0}),Su.stringFilter=Ad;var up={registerPlugin:function(){for(var t=arguments.length,e=new Array(t),n=0;n1){var i=t.map((function(t){return pp.quickSetter(t,e,n)})),r=i.length;return function(t){for(var e=r;e--;)i[e](t)}}t=t[0]||{};var s=uh[e],o=fh(t),a=o.harness&&(o.harness.aliases||{})[e]||e,l=s?function(e){var i=new s;bu._pt=0,i.init(t,n?e+n:e,bu,0,[t]),i.render(1,i),bu._pt&&rp(1,bu)}:o.set(t,a);return s?l:function(e){return l(t,a,n?e+n:e,o,1)}},isTweening:function(t){return mu.getTweensOf(t,!0).length>0},defaults:function(t){return t&&t.ease&&(t.ease=Pd(t.ease,Cu.ease)),Nh(Cu,t||{})},config:function(t){return Nh(Su,t||{})},registerEffect:function(t){var e=t.name,n=t.effect,i=t.plugins,r=t.defaults,s=t.extendTimeline;(i||\\\\\\\"\\\\\\\").split(\\\\\\\",\\\\\\\").forEach((function(t){return t&&!uh[t]&&!th[t]&&rh(e+\\\\\\\" effect requires \\\\\\\"+t+\\\\\\\" plugin.\\\\\\\")})),hh[e]=function(t,e,i){return n(id(t),Mh(e||{},r),i)},s&&(Ud.prototype[e]=function(t,n,i){return this.add(hh[e](t,Gu(n)?n:(i=n)&&{},this),i)})},registerEase:function(t,e){Sd[t]=Pd(e)},parseEase:function(t,e){return arguments.length?Pd(t,e):Sd},getById:function(t){return mu.getById(t)},exportRoot:function(t,e){void 0===t&&(t={});var n,i,r=new Ud(t);for(r.smoothChildTiming=Vu(t.smoothChildTiming),mu.remove(r),r._dp=0,r._time=r._tTime=mu._time,n=mu._first;n;)i=n._next,!e&&!n._dur&&n instanceof $d&&n.vars.onComplete===n._targets[0]||Hh(r,n,n._start-n._delay),n=i;return Hh(mu,r,0),r},utils:{wrap:function t(e,n,i){var r=n-e;return qu(e)?cd(e,t(0,e.length),n):Zh(i,(function(t){return(r+(t-e)%r)%r+e}))},wrapYoyo:function t(e,n,i){var r=n-e,s=2*r;return qu(e)?cd(e,t(0,e.length-1),n):Zh(i,(function(t){return e+((t=(s+(t-e)%s)%s||0)>r?s-t:t)}))},distribute:sd,random:ld,snap:ad,normalize:function(t,e,n){return hd(t,e,0,1,n)},getUnit:Kh,clamp:function(t,e,n){return Zh(n,(function(n){return Qh(t,e,n)}))},splitColor:yd,toArray:id,mapRange:hd,pipe:function(){for(var t=arguments.length,e=new Array(t),n=0;n=0?Wp[r]:\\\\\\\"\\\\\\\")+t},Xp=function(){\\\\\\\"undefined\\\\\\\"!=typeof window&&window.document&&(_p=window,mp=_p.document,fp=mp.documentElement,vp=Hp(\\\\\\\"div\\\\\\\")||{style:{}},Hp(\\\\\\\"div\\\\\\\"),Gp=qp(Gp),Vp=Gp+\\\\\\\"Origin\\\\\\\",vp.style.cssText=\\\\\\\"border-width:0;line-height:0;position:absolute;padding:0\\\\\\\",xp=!!qp(\\\\\\\"perspective\\\\\\\"),gp=1)},Yp=function t(e){var n,i=Hp(\\\\\\\"svg\\\\\\\",this.ownerSVGElement&&this.ownerSVGElement.getAttribute(\\\\\\\"xmlns\\\\\\\")||\\\\\\\"http://www.w3.org/2000/svg\\\\\\\"),r=this.parentNode,s=this.nextSibling,o=this.style.cssText;if(fp.appendChild(i),i.appendChild(this),this.style.display=\\\\\\\"block\\\\\\\",e)try{n=this.getBBox(),this._gsapBBox=this.getBBox,this.getBBox=t}catch(t){}else this._gsapBBox&&(n=this._gsapBBox());return r&&(s?r.insertBefore(this,s):r.appendChild(this)),fp.removeChild(i),this.style.cssText=o,n},$p=function(t,e){for(var n=e.length;n--;)if(t.hasAttribute(e[n]))return t.getAttribute(e[n])},Jp=function(t){var e;try{e=t.getBBox()}catch(n){e=Yp.call(t,!0)}return e&&(e.width||e.height)||t.getBBox===Yp||(e=Yp.call(t,!0)),!e||e.width||e.x||e.y?e:{x:+$p(t,[\\\\\\\"x\\\\\\\",\\\\\\\"cx\\\\\\\",\\\\\\\"x1\\\\\\\"])||0,y:+$p(t,[\\\\\\\"y\\\\\\\",\\\\\\\"cy\\\\\\\",\\\\\\\"y1\\\\\\\"])||0,width:0,height:0}},Zp=function(t){return!(!t.getCTM||t.parentNode&&!t.ownerSVGElement||!Jp(t))},Qp=function(t,e){if(e){var n=t.style;e in bp&&e!==Vp&&(e=Gp),n.removeProperty?(\\\\\\\"ms\\\\\\\"!==e.substr(0,2)&&\\\\\\\"webkit\\\\\\\"!==e.substr(0,6)||(e=\\\\\\\"-\\\\\\\"+e),n.removeProperty(e.replace(Ep,\\\\\\\"-$1\\\\\\\").toLowerCase())):n.removeAttribute(e)}},Kp=function(t,e,n,i,r,s){var o=new cp(t._pt,e,n,0,1,s?Ip:Pp);return t._pt=o,o.b=i,o.e=r,t._props.push(n),o},t_={deg:1,rad:1,turn:1},e_=function t(e,n,i,r){var s,o,a,l,c=parseFloat(i)||0,u=(i+\\\\\\\"\\\\\\\").trim().substr((c+\\\\\\\"\\\\\\\").length)||\\\\\\\"px\\\\\\\",h=vp.style,d=Mp.test(n),p=\\\\\\\"svg\\\\\\\"===e.tagName.toLowerCase(),_=(p?\\\\\\\"client\\\\\\\":\\\\\\\"offset\\\\\\\")+(d?\\\\\\\"Width\\\\\\\":\\\\\\\"Height\\\\\\\"),m=100,f=\\\\\\\"px\\\\\\\"===r,g=\\\\\\\"%\\\\\\\"===r;return r===u||!c||t_[r]||t_[u]?c:(\\\\\\\"px\\\\\\\"!==u&&!f&&(c=t(e,n,i,\\\\\\\"px\\\\\\\")),l=e.getCTM&&Zp(e),!g&&\\\\\\\"%\\\\\\\"!==u||!bp[n]&&!~n.indexOf(\\\\\\\"adius\\\\\\\")?(h[d?\\\\\\\"width\\\\\\\":\\\\\\\"height\\\\\\\"]=m+(f?u:r),o=~n.indexOf(\\\\\\\"adius\\\\\\\")||\\\\\\\"em\\\\\\\"===r&&e.appendChild&&!p?e:e.parentNode,l&&(o=(e.ownerSVGElement||{}).parentNode),o&&o!==mp&&o.appendChild||(o=mp.body),(a=o._gsap)&&g&&a.width&&d&&a.time===Ed.time?yh(c/a.width*m):((g||\\\\\\\"%\\\\\\\"===u)&&(h.position=jp(e,\\\\\\\"position\\\\\\\")),o===e&&(h.position=\\\\\\\"static\\\\\\\"),o.appendChild(vp),s=vp[_],o.removeChild(vp),h.position=\\\\\\\"absolute\\\\\\\",d&&g&&((a=fh(o)).time=Ed.time,a.width=o[_]),yh(f?s*c/m:s&&c?m/s*c:0))):(s=l?e.getBBox()[d?\\\\\\\"width\\\\\\\":\\\\\\\"height\\\\\\\"]:e[_],yh(g?c/s*m:c/100*s)))},n_=function(t,e,n,i){var r;return gp||Xp(),e in Cp&&\\\\\\\"transform\\\\\\\"!==e&&~(e=Cp[e]).indexOf(\\\\\\\",\\\\\\\")&&(e=e.split(\\\\\\\",\\\\\\\")[0]),bp[e]&&\\\\\\\"transform\\\\\\\"!==e?(r=p_(t,i),r=\\\\\\\"transformOrigin\\\\\\\"!==e?r[e]:__(jp(t,Vp))+\\\\\\\" \\\\\\\"+r.zOrigin+\\\\\\\"px\\\\\\\"):(!(r=t.style[e])||\\\\\\\"auto\\\\\\\"===r||i||~(r+\\\\\\\"\\\\\\\").indexOf(\\\\\\\"calc(\\\\\\\"))&&(r=o_[e]&&o_[e](t,e,n)||jp(t,e)||gh(t,e)||(\\\\\\\"opacity\\\\\\\"===e?1:0)),n&&!~(r+\\\\\\\"\\\\\\\").trim().indexOf(\\\\\\\" \\\\\\\")?e_(t,e,r,n)+n:r},i_=function(t,e,n,i){if(!n||\\\\\\\"none\\\\\\\"===n){var r=qp(e,t,1),s=r&&jp(t,r,1);s&&s!==n?(e=r,n=s):\\\\\\\"borderColor\\\\\\\"===e&&(n=jp(t,\\\\\\\"borderTopColor\\\\\\\"))}var o,a,l,c,u,h,d,p,_,m,f,g,v=new cp(this._pt,t.style,e,0,1,ip),y=0,x=0;if(v.b=n,v.e=i,n+=\\\\\\\"\\\\\\\",\\\\\\\"auto\\\\\\\"===(i+=\\\\\\\"\\\\\\\")&&(t.style[e]=i,i=jp(t,e)||i,t.style[e]=n),Ad(o=[n,i]),i=o[1],l=(n=o[0]).match($u)||[],(i.match($u)||[]).length){for(;a=$u.exec(i);)d=a[0],_=i.substring(y,a.index),u?u=(u+1)%5:\\\\\\\"rgba(\\\\\\\"!==_.substr(-5)&&\\\\\\\"hsla(\\\\\\\"!==_.substr(-5)||(u=1),d!==(h=l[x++]||\\\\\\\"\\\\\\\")&&(c=parseFloat(h)||0,f=h.substr((c+\\\\\\\"\\\\\\\").length),(g=\\\\\\\"=\\\\\\\"===d.charAt(1)?+(d.charAt(0)+\\\\\\\"1\\\\\\\"):0)&&(d=d.substr(2)),p=parseFloat(d),m=d.substr((p+\\\\\\\"\\\\\\\").length),y=$u.lastIndex-m.length,m||(m=m||Su.units[e]||f,y===i.length&&(i+=m,v.e+=m)),f!==m&&(c=e_(t,e,h,m)||0),v._pt={_next:v._pt,p:_||1===x?_:\\\\\\\",\\\\\\\",s:c,c:g?g*p:p-c,m:u&&u<4||\\\\\\\"zIndex\\\\\\\"===e?Math.round:0});v.c=y-1;)n=a[r],bp[n]&&(i=1,n=\\\\\\\"transformOrigin\\\\\\\"===n?Vp:Gp),Qp(s,n);i&&(Qp(s,Gp),l&&(l.svg&&s.removeAttribute(\\\\\\\"transform\\\\\\\"),p_(s,1),l.uncache=1))}},o_={clearProps:function(t,e,n,i,r){if(\\\\\\\"isFromStart\\\\\\\"!==r.data){var s=t._pt=new cp(t._pt,e,n,0,0,s_);return s.u=i,s.pr=-10,s.tween=r,t._props.push(n),1}}},a_=[1,0,0,1,0,0],l_={},c_=function(t){return\\\\\\\"matrix(1, 0, 0, 1, 0, 0)\\\\\\\"===t||\\\\\\\"none\\\\\\\"===t||!t},u_=function(t){var e=jp(t,Gp);return c_(e)?a_:e.substr(7).match(Yu).map(yh)},h_=function(t,e){var n,i,r,s,o=t._gsap||fh(t),a=t.style,l=u_(t);return o.svg&&t.getAttribute(\\\\\\\"transform\\\\\\\")?\\\\\\\"1,0,0,1,0,0\\\\\\\"===(l=[(r=t.transform.baseVal.consolidate().matrix).a,r.b,r.c,r.d,r.e,r.f]).join(\\\\\\\",\\\\\\\")?a_:l:(l!==a_||t.offsetParent||t===fp||o.svg||(r=a.display,a.display=\\\\\\\"block\\\\\\\",(n=t.parentNode)&&t.offsetParent||(s=1,i=t.nextSibling,fp.appendChild(t)),l=u_(t),r?a.display=r:Qp(t,\\\\\\\"display\\\\\\\"),s&&(i?n.insertBefore(t,i):n?n.appendChild(t):fp.removeChild(t))),e&&l.length>6?[l[0],l[1],l[4],l[5],l[12],l[13]]:l)},d_=function(t,e,n,i,r,s){var o,a,l,c=t._gsap,u=r||h_(t,!0),h=c.xOrigin||0,d=c.yOrigin||0,p=c.xOffset||0,_=c.yOffset||0,m=u[0],f=u[1],g=u[2],v=u[3],y=u[4],x=u[5],b=e.split(\\\\\\\" \\\\\\\"),w=parseFloat(b[0])||0,T=parseFloat(b[1])||0;n?u!==a_&&(a=m*v-f*g)&&(l=w*(-f/a)+T*(m/a)-(m*x-f*y)/a,w=w*(v/a)+T*(-g/a)+(g*x-v*y)/a,T=l):(w=(o=Jp(t)).x+(~b[0].indexOf(\\\\\\\"%\\\\\\\")?w/100*o.width:w),T=o.y+(~(b[1]||b[0]).indexOf(\\\\\\\"%\\\\\\\")?T/100*o.height:T)),i||!1!==i&&c.smooth?(y=w-h,x=T-d,c.xOffset=p+(y*m+x*g)-y,c.yOffset=_+(y*f+x*v)-x):c.xOffset=c.yOffset=0,c.xOrigin=w,c.yOrigin=T,c.smooth=!!i,c.origin=e,c.originIsAbsolute=!!n,t.style[Vp]=\\\\\\\"0px 0px\\\\\\\",s&&(Kp(s,c,\\\\\\\"xOrigin\\\\\\\",h,w),Kp(s,c,\\\\\\\"yOrigin\\\\\\\",d,T),Kp(s,c,\\\\\\\"xOffset\\\\\\\",p,c.xOffset),Kp(s,c,\\\\\\\"yOffset\\\\\\\",_,c.yOffset)),t.setAttribute(\\\\\\\"data-svg-origin\\\\\\\",w+\\\\\\\" \\\\\\\"+T)},p_=function(t,e){var n=t._gsap||new Bd(t);if(\\\\\\\"x\\\\\\\"in n&&!e&&!n.uncache)return n;var i,r,s,o,a,l,c,u,h,d,p,_,m,f,g,v,y,x,b,w,T,A,E,M,S,C,N,L,O,R,P,I,F=t.style,D=n.scaleX<0,k=\\\\\\\"px\\\\\\\",B=\\\\\\\"deg\\\\\\\",z=jp(t,Vp)||\\\\\\\"0\\\\\\\";return i=r=s=l=c=u=h=d=p=0,o=a=1,n.svg=!(!t.getCTM||!Zp(t)),f=h_(t,n.svg),n.svg&&(M=!n.uncache&&!e&&t.getAttribute(\\\\\\\"data-svg-origin\\\\\\\"),d_(t,M||z,!!M||n.originIsAbsolute,!1!==n.smooth,f)),_=n.xOrigin||0,m=n.yOrigin||0,f!==a_&&(x=f[0],b=f[1],w=f[2],T=f[3],i=A=f[4],r=E=f[5],6===f.length?(o=Math.sqrt(x*x+b*b),a=Math.sqrt(T*T+w*w),l=x||b?Ap(b,x)*wp:0,(h=w||T?Ap(w,T)*wp+l:0)&&(a*=Math.abs(Math.cos(h*Tp))),n.svg&&(i-=_-(_*x+m*w),r-=m-(_*b+m*T))):(I=f[6],R=f[7],N=f[8],L=f[9],O=f[10],P=f[11],i=f[12],r=f[13],s=f[14],c=(g=Ap(I,O))*wp,g&&(M=A*(v=Math.cos(-g))+N*(y=Math.sin(-g)),S=E*v+L*y,C=I*v+O*y,N=A*-y+N*v,L=E*-y+L*v,O=I*-y+O*v,P=R*-y+P*v,A=M,E=S,I=C),u=(g=Ap(-w,O))*wp,g&&(v=Math.cos(-g),P=T*(y=Math.sin(-g))+P*v,x=M=x*v-N*y,b=S=b*v-L*y,w=C=w*v-O*y),l=(g=Ap(b,x))*wp,g&&(M=x*(v=Math.cos(g))+b*(y=Math.sin(g)),S=A*v+E*y,b=b*v-x*y,E=E*v-A*y,x=M,A=S),c&&Math.abs(c)+Math.abs(l)>359.9&&(c=l=0,u=180-u),o=yh(Math.sqrt(x*x+b*b+w*w)),a=yh(Math.sqrt(E*E+I*I)),g=Ap(A,E),h=Math.abs(g)>2e-4?g*wp:0,p=P?1/(P<0?-P:P):0),n.svg&&(M=t.getAttribute(\\\\\\\"transform\\\\\\\"),n.forceCSS=t.setAttribute(\\\\\\\"transform\\\\\\\",\\\\\\\"\\\\\\\")||!c_(jp(t,Gp)),M&&t.setAttribute(\\\\\\\"transform\\\\\\\",M))),Math.abs(h)>90&&Math.abs(h)<270&&(D?(o*=-1,h+=l<=0?180:-180,l+=l<=0?180:-180):(a*=-1,h+=h<=0?180:-180)),n.x=i-((n.xPercent=i&&(n.xPercent||(Math.round(t.offsetWidth/2)===Math.round(-i)?-50:0)))?t.offsetWidth*n.xPercent/100:0)+k,n.y=r-((n.yPercent=r&&(n.yPercent||(Math.round(t.offsetHeight/2)===Math.round(-r)?-50:0)))?t.offsetHeight*n.yPercent/100:0)+k,n.z=s+k,n.scaleX=yh(o),n.scaleY=yh(a),n.rotation=yh(l)+B,n.rotationX=yh(c)+B,n.rotationY=yh(u)+B,n.skewX=h+B,n.skewY=d+B,n.transformPerspective=p+k,(n.zOrigin=parseFloat(z.split(\\\\\\\" \\\\\\\")[2])||0)&&(F[Vp]=__(z)),n.xOffset=n.yOffset=0,n.force3D=Su.force3D,n.renderTransform=n.svg?b_:xp?x_:f_,n.uncache=0,n},__=function(t){return(t=t.split(\\\\\\\" \\\\\\\"))[0]+\\\\\\\" \\\\\\\"+t[1]},m_=function(t,e,n){var i=Kh(e);return yh(parseFloat(e)+parseFloat(e_(t,\\\\\\\"x\\\\\\\",n+\\\\\\\"px\\\\\\\",i)))+i},f_=function(t,e){e.z=\\\\\\\"0px\\\\\\\",e.rotationY=e.rotationX=\\\\\\\"0deg\\\\\\\",e.force3D=0,x_(t,e)},g_=\\\\\\\"0deg\\\\\\\",v_=\\\\\\\"0px\\\\\\\",y_=\\\\\\\") \\\\\\\",x_=function(t,e){var n=e||this,i=n.xPercent,r=n.yPercent,s=n.x,o=n.y,a=n.z,l=n.rotation,c=n.rotationY,u=n.rotationX,h=n.skewX,d=n.skewY,p=n.scaleX,_=n.scaleY,m=n.transformPerspective,f=n.force3D,g=n.target,v=n.zOrigin,y=\\\\\\\"\\\\\\\",x=\\\\\\\"auto\\\\\\\"===f&&t&&1!==t||!0===f;if(v&&(u!==g_||c!==g_)){var b,w=parseFloat(c)*Tp,T=Math.sin(w),A=Math.cos(w);w=parseFloat(u)*Tp,b=Math.cos(w),s=m_(g,s,T*b*-v),o=m_(g,o,-Math.sin(w)*-v),a=m_(g,a,A*b*-v+v)}m!==v_&&(y+=\\\\\\\"perspective(\\\\\\\"+m+y_),(i||r)&&(y+=\\\\\\\"translate(\\\\\\\"+i+\\\\\\\"%, \\\\\\\"+r+\\\\\\\"%) \\\\\\\"),(x||s!==v_||o!==v_||a!==v_)&&(y+=a!==v_||x?\\\\\\\"translate3d(\\\\\\\"+s+\\\\\\\", \\\\\\\"+o+\\\\\\\", \\\\\\\"+a+\\\\\\\") \\\\\\\":\\\\\\\"translate(\\\\\\\"+s+\\\\\\\", \\\\\\\"+o+y_),l!==g_&&(y+=\\\\\\\"rotate(\\\\\\\"+l+y_),c!==g_&&(y+=\\\\\\\"rotateY(\\\\\\\"+c+y_),u!==g_&&(y+=\\\\\\\"rotateX(\\\\\\\"+u+y_),h===g_&&d===g_||(y+=\\\\\\\"skew(\\\\\\\"+h+\\\\\\\", \\\\\\\"+d+y_),1===p&&1===_||(y+=\\\\\\\"scale(\\\\\\\"+p+\\\\\\\", \\\\\\\"+_+y_),g.style[Gp]=y||\\\\\\\"translate(0, 0)\\\\\\\"},b_=function(t,e){var n,i,r,s,o,a=e||this,l=a.xPercent,c=a.yPercent,u=a.x,h=a.y,d=a.rotation,p=a.skewX,_=a.skewY,m=a.scaleX,f=a.scaleY,g=a.target,v=a.xOrigin,y=a.yOrigin,x=a.xOffset,b=a.yOffset,w=a.forceCSS,T=parseFloat(u),A=parseFloat(h);d=parseFloat(d),p=parseFloat(p),(_=parseFloat(_))&&(p+=_=parseFloat(_),d+=_),d||p?(d*=Tp,p*=Tp,n=Math.cos(d)*m,i=Math.sin(d)*m,r=Math.sin(d-p)*-f,s=Math.cos(d-p)*f,p&&(_*=Tp,o=Math.tan(p-_),r*=o=Math.sqrt(1+o*o),s*=o,_&&(o=Math.tan(_),n*=o=Math.sqrt(1+o*o),i*=o)),n=yh(n),i=yh(i),r=yh(r),s=yh(s)):(n=m,s=f,i=r=0),(T&&!~(u+\\\\\\\"\\\\\\\").indexOf(\\\\\\\"px\\\\\\\")||A&&!~(h+\\\\\\\"\\\\\\\").indexOf(\\\\\\\"px\\\\\\\"))&&(T=e_(g,\\\\\\\"x\\\\\\\",u,\\\\\\\"px\\\\\\\"),A=e_(g,\\\\\\\"y\\\\\\\",h,\\\\\\\"px\\\\\\\")),(v||y||x||b)&&(T=yh(T+v-(v*n+y*r)+x),A=yh(A+y-(v*i+y*s)+b)),(l||c)&&(o=g.getBBox(),T=yh(T+l/100*o.width),A=yh(A+c/100*o.height)),o=\\\\\\\"matrix(\\\\\\\"+n+\\\\\\\",\\\\\\\"+i+\\\\\\\",\\\\\\\"+r+\\\\\\\",\\\\\\\"+s+\\\\\\\",\\\\\\\"+T+\\\\\\\",\\\\\\\"+A+\\\\\\\")\\\\\\\",g.setAttribute(\\\\\\\"transform\\\\\\\",o),w&&(g.style[Gp]=o)},w_=function(t,e,n,i,r,s){var o,a,l=360,c=ku(r),u=parseFloat(r)*(c&&~r.indexOf(\\\\\\\"rad\\\\\\\")?wp:1),h=s?u*s:u-i,d=i+h+\\\\\\\"deg\\\\\\\";return c&&(\\\\\\\"short\\\\\\\"===(o=r.split(\\\\\\\"_\\\\\\\")[1])&&(h%=l)!==h%180&&(h+=h<0?l:-360),\\\\\\\"cw\\\\\\\"===o&&h<0?h=(h+36e9)%l-~~(h/l)*l:\\\\\\\"ccw\\\\\\\"===o&&h>0&&(h=(h-36e9)%l-~~(h/l)*l)),t._pt=a=new cp(t._pt,e,n,i,h,Lp),a.e=d,a.u=\\\\\\\"deg\\\\\\\",t._props.push(n),a},T_=function(t,e){for(var n in e)t[n]=e[n];return t},A_=function(t,e,n){var i,r,s,o,a,l,c,u=T_({},n._gsap),h=n.style;for(r in u.svg?(s=n.getAttribute(\\\\\\\"transform\\\\\\\"),n.setAttribute(\\\\\\\"transform\\\\\\\",\\\\\\\"\\\\\\\"),h[Gp]=e,i=p_(n,1),Qp(n,Gp),n.setAttribute(\\\\\\\"transform\\\\\\\",s)):(s=getComputedStyle(n)[Gp],h[Gp]=e,i=p_(n,1),h[Gp]=s),bp)(s=u[r])!==(o=i[r])&&\\\\\\\"perspective,force3D,transformOrigin,svgOrigin\\\\\\\".indexOf(r)<0&&(a=Kh(s)!==(c=Kh(o))?e_(n,r,s,c):parseFloat(s),l=parseFloat(o),t._pt=new cp(t._pt,i,r,a,l-a,Np),t._pt.u=c||0,t._props.push(r));T_(i,u)};vh(\\\\\\\"padding,margin,Width,Radius\\\\\\\",(function(t,e){var n=\\\\\\\"Top\\\\\\\",i=\\\\\\\"Right\\\\\\\",r=\\\\\\\"Bottom\\\\\\\",s=\\\\\\\"Left\\\\\\\",o=(e<3?[n,i,r,s]:[n+s,n+i,r+i,r+s]).map((function(n){return e<2?t+n:\\\\\\\"border\\\\\\\"+n+t}));o_[e>1?\\\\\\\"border\\\\\\\"+t:t]=function(t,e,n,i,r){var s,a;if(arguments.length<4)return s=o.map((function(e){return n_(t,e,n)})),5===(a=s.join(\\\\\\\" \\\\\\\")).split(s[0]).length?s[0]:a;s=(i+\\\\\\\"\\\\\\\").split(\\\\\\\" \\\\\\\"),a={},o.forEach((function(t,e){return a[t]=s[e]=s[e]||s[(e-1)/2|0]})),t.init(e,a,r)}}));var E_,M_,S_,C_={name:\\\\\\\"css\\\\\\\",register:Xp,targetTest:function(t){return t.style&&t.nodeType},init:function(t,e,n,i,r){var s,o,a,l,c,u,h,d,p,_,m,f,g,v,y,x,b,w,T,A=this._props,E=t.style,M=n.vars.startAt;for(h in gp||Xp(),e)if(\\\\\\\"autoRound\\\\\\\"!==h&&(o=e[h],!uh[h]||!jd(h,e,n,i,t,r)))if(c=typeof o,u=o_[h],\\\\\\\"function\\\\\\\"===c&&(c=typeof(o=o.call(n,i,t,r))),\\\\\\\"string\\\\\\\"===c&&~o.indexOf(\\\\\\\"random(\\\\\\\")&&(o=ud(o)),u)u(this,t,h,o,n)&&(y=1);else if(\\\\\\\"--\\\\\\\"===h.substr(0,2))s=(getComputedStyle(t).getPropertyValue(h)+\\\\\\\"\\\\\\\").trim(),o+=\\\\\\\"\\\\\\\",wd.lastIndex=0,wd.test(s)||(d=Kh(s),p=Kh(o)),p?d!==p&&(s=e_(t,h,s,p)+p):d&&(o+=d),this.add(E,\\\\\\\"setProperty\\\\\\\",s,o,i,r,0,0,h);else if(\\\\\\\"undefined\\\\\\\"!==c){if(M&&h in M?(s=\\\\\\\"function\\\\\\\"==typeof M[h]?M[h].call(n,i,t,r):M[h],h in Su.units&&!Kh(s)&&(s+=Su.units[h]),\\\\\\\"=\\\\\\\"===(s+\\\\\\\"\\\\\\\").charAt(1)&&(s=n_(t,h))):s=n_(t,h),l=parseFloat(s),(_=\\\\\\\"string\\\\\\\"===c&&\\\\\\\"=\\\\\\\"===o.charAt(1)?+(o.charAt(0)+\\\\\\\"1\\\\\\\"):0)&&(o=o.substr(2)),a=parseFloat(o),h in Cp&&(\\\\\\\"autoAlpha\\\\\\\"===h&&(1===l&&\\\\\\\"hidden\\\\\\\"===n_(t,\\\\\\\"visibility\\\\\\\")&&a&&(l=0),Kp(this,E,\\\\\\\"visibility\\\\\\\",l?\\\\\\\"inherit\\\\\\\":\\\\\\\"hidden\\\\\\\",a?\\\\\\\"inherit\\\\\\\":\\\\\\\"hidden\\\\\\\",!a)),\\\\\\\"scale\\\\\\\"!==h&&\\\\\\\"transform\\\\\\\"!==h&&~(h=Cp[h]).indexOf(\\\\\\\",\\\\\\\")&&(h=h.split(\\\\\\\",\\\\\\\")[0])),m=h in bp)if(f||((g=t._gsap).renderTransform&&!e.parseTransform||p_(t,e.parseTransform),v=!1!==e.smoothOrigin&&g.smooth,(f=this._pt=new cp(this._pt,E,Gp,0,1,g.renderTransform,g,0,-1)).dep=1),\\\\\\\"scale\\\\\\\"===h)this._pt=new cp(this._pt,g,\\\\\\\"scaleY\\\\\\\",g.scaleY,_?_*a:a-g.scaleY),A.push(\\\\\\\"scaleY\\\\\\\",h),h+=\\\\\\\"X\\\\\\\";else{if(\\\\\\\"transformOrigin\\\\\\\"===h){b=void 0,w=void 0,T=void 0,b=(x=o).split(\\\\\\\" \\\\\\\"),w=b[0],T=b[1]||\\\\\\\"50%\\\\\\\",\\\\\\\"top\\\\\\\"!==w&&\\\\\\\"bottom\\\\\\\"!==w&&\\\\\\\"left\\\\\\\"!==T&&\\\\\\\"right\\\\\\\"!==T||(x=w,w=T,T=x),b[0]=r_[w]||w,b[1]=r_[T]||T,o=b.join(\\\\\\\" \\\\\\\"),g.svg?d_(t,o,0,v,0,this):((p=parseFloat(o.split(\\\\\\\" \\\\\\\")[2])||0)!==g.zOrigin&&Kp(this,g,\\\\\\\"zOrigin\\\\\\\",g.zOrigin,p),Kp(this,E,h,__(s),__(o)));continue}if(\\\\\\\"svgOrigin\\\\\\\"===h){d_(t,o,1,v,0,this);continue}if(h in l_){w_(this,g,h,l,o,_);continue}if(\\\\\\\"smoothOrigin\\\\\\\"===h){Kp(this,g,\\\\\\\"smooth\\\\\\\",g.smooth,o);continue}if(\\\\\\\"force3D\\\\\\\"===h){g[h]=o;continue}if(\\\\\\\"transform\\\\\\\"===h){A_(this,o,t);continue}}else h in E||(h=qp(h)||h);if(m||(a||0===a)&&(l||0===l)&&!Sp.test(o)&&h in E)a||(a=0),(d=(s+\\\\\\\"\\\\\\\").substr((l+\\\\\\\"\\\\\\\").length))!==(p=Kh(o)||(h in Su.units?Su.units[h]:d))&&(l=e_(t,h,s,p)),this._pt=new cp(this._pt,m?g:E,h,l,_?_*a:a-l,m||\\\\\\\"px\\\\\\\"!==p&&\\\\\\\"zIndex\\\\\\\"!==h||!1===e.autoRound?Np:Rp),this._pt.u=p||0,d!==p&&(this._pt.b=s,this._pt.r=Op);else if(h in E)i_.call(this,t,h,s,o);else{if(!(h in t)){ih(h,o);continue}this.add(t,h,t[h],o,i,r)}A.push(h)}y&&lp(this)},get:n_,aliases:Cp,getSetter:function(t,e,n){var i=Cp[e];return i&&i.indexOf(\\\\\\\",\\\\\\\")<0&&(e=i),e in bp&&e!==Vp&&(t._gsap.x||n_(t,\\\\\\\"x\\\\\\\"))?n&&yp===n?\\\\\\\"scale\\\\\\\"===e?Bp:kp:(yp=n||{})&&(\\\\\\\"scale\\\\\\\"===e?zp:Up):t.style&&!Uu(t.style[e])?Fp:~e.indexOf(\\\\\\\"-\\\\\\\")?Dp:tp(t,e)},core:{_removeProperty:Qp,_getMatrix:h_}};pp.utils.checkPrefix=qp,S_=vh((E_=\\\\\\\"x,y,z,scale,scaleX,scaleY,xPercent,yPercent\\\\\\\")+\\\\\\\",\\\\\\\"+(M_=\\\\\\\"rotation,rotationX,rotationY,skewX,skewY\\\\\\\")+\\\\\\\",transform,transformOrigin,svgOrigin,force3D,smoothOrigin,transformPerspective\\\\\\\",(function(t){bp[t]=1})),vh(M_,(function(t){Su.units[t]=\\\\\\\"deg\\\\\\\",l_[t]=1})),Cp[S_[13]]=E_+\\\\\\\",\\\\\\\"+M_,vh(\\\\\\\"0:translateX,1:translateY,2:translateZ,8:rotate,8:rotationZ,8:rotateZ,9:rotateX,10:rotateY\\\\\\\",(function(t){var e=t.split(\\\\\\\":\\\\\\\");Cp[e[1]]=S_[e[0]]})),vh(\\\\\\\"x,y,z,top,right,bottom,left,width,height,fontSize,padding,margin,perspective\\\\\\\",(function(t){Su.units[t]=\\\\\\\"px\\\\\\\"})),pp.registerPlugin(C_);var N_,L_=pp.registerPlugin(C_)||pp;L_.core.Tween;!function(t){t.SET=\\\\\\\"set\\\\\\\",t.ADD=\\\\\\\"add\\\\\\\",t.SUBSTRACT=\\\\\\\"substract\\\\\\\"}(N_||(N_={}));const O_=[N_.SET,N_.ADD,N_.SUBSTRACT];class R_{constructor(){this._timeline_builders=[],this._duration=1,this._operation=N_.SET,this._delay=0,this._debug=!1}setDebug(t){this._debug=t}_printDebug(t){this._debug&&console.log(t)}addTimelineBuilder(t){this._timeline_builders.push(t),t.setParent(this)}timelineBuilders(){return this._timeline_builders}setParent(t){this._parent=t}parent(){return this._parent}setTarget(t){this._target=t;for(let e of this._timeline_builders)e.setTarget(t)}target(){return this._target}setDuration(t){if(t>=0){this._duration=t;for(let e of this._timeline_builders)e.setDuration(t)}}duration(){return this._duration}setEasing(t){this._easing=t;for(let e of this._timeline_builders)e.setEasing(t)}easing(){return this._easing}setOperation(t){this._operation=t;for(let e of this._timeline_builders)e.setOperation(t)}operation(){return this._operation}setRepeatParams(t){this._repeat_params=t;for(let e of this._timeline_builders)e.setRepeatParams(t)}repeatParams(){return this._repeat_params}setDelay(t){this._delay=t;for(let e of this._timeline_builders)e.setDelay(t)}delay(){return this._delay}setPosition(t){this._position=t}position(){return this._position}setUpdateCallback(t){this._update_callback=t}updateCallback(){return this._update_callback}clone(){const t=new R_;if(t.setDuration(this._duration),t.setOperation(this._operation),t.setDelay(this._delay),this._target&&t.setTarget(this._target.clone()),this._easing&&t.setEasing(this._easing),this._delay&&t.setDelay(this._delay),this._update_callback&&t.setUpdateCallback(this._update_callback.clone()),this._repeat_params&&t.setRepeatParams({count:this._repeat_params.count,delay:this._repeat_params.delay,yoyo:this._repeat_params.yoyo}),this._property){const e=this._property.name();e&&t.setPropertyName(e);const n=this._property.targetValue();null!=n&&t.setPropertyValue(n)}this._position&&t.setPosition(this._position.clone());for(let e of this._timeline_builders){const n=e.clone();t.addTimelineBuilder(n)}return t}setPropertyName(t){this.property().setName(t)}property(){return this._property=this._property||new hu}propertyName(){return this.property().name()}setPropertyValue(t){this.property().setTargetValue(t)}populate(t){var e;this._printDebug([\\\\\\\"populate\\\\\\\",this,t]);for(let n of this._timeline_builders){const i=L_.timeline();n.setDebug(this._debug),n.populate(i);const r=(null===(e=n.position())||void 0===e?void 0:e.toParameter())||void 0;t.add(i,r)}this._property&&this._target&&(this._property.setDebug(this._debug),this._property.addToTimeline(this,t,this._target))}}const P_=new class extends aa{constructor(){super(...arguments),this.count=oa.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1]})}};class I_ extends iu{constructor(){super(...arguments),this.paramsConfig=P_}static type(){return\\\\\\\"copy\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}async cook(t){const e=new R_;for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.delay])}))}))}cook(t){const e=t[0]||new R_;e.setDelay(this.pv.delay),this.setTimelineBuilder(e)}}const k_=new class extends aa{constructor(){super(...arguments),this.duration=oa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]})}};class B_ extends iu{constructor(){super(...arguments),this.paramsConfig=k_}static type(){return\\\\\\\"duration\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.duration])}))}))}cook(t){const e=t[0]||new R_;e.setDuration(this.pv.duration),this.setTimelineBuilder(e)}}const z_=new class extends aa{constructor(){super(...arguments),this.name=oa.INTEGER(lu.indexOf(ou.POWER4),{menu:{entries:lu.map(((t,e)=>({name:t,value:e})))}}),this.inOut=oa.INTEGER(uu.indexOf(cu.OUT),{menu:{entries:uu.map(((t,e)=>({name:t,value:e})))}})}};class U_ extends iu{constructor(){super(...arguments),this.paramsConfig=z_}static type(){return\\\\\\\"easing\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name,this.p.inOut],(()=>this.easing_full_name()))}))}))}easing_full_name(){const t=lu[this.pv.name];if(t==ou.NONE)return t;return`${t}.${uu[this.pv.inOut]}`}cook(t){const e=t[0]||new R_,n=this.easing_full_name();e.setEasing(n),this.setTimelineBuilder(e)}}var G_;!function(t){t.RELATIVE=\\\\\\\"relative\\\\\\\",t.ABSOLUTE=\\\\\\\"absolute\\\\\\\"}(G_||(G_={}));const V_=[G_.RELATIVE,G_.ABSOLUTE];var H_;!function(t){t.START=\\\\\\\"start\\\\\\\",t.END=\\\\\\\"end\\\\\\\"}(H_||(H_={}));const j_=[H_.START,H_.END];class W_{constructor(){this._mode=G_.RELATIVE,this._relativeTo=H_.END,this._offset=0}clone(){const t=new W_;return t.setMode(this._mode),t.setRelativeTo(this._relativeTo),t.setOffset(this._offset),t}setMode(t){this._mode=t}mode(){return this._mode}setRelativeTo(t){this._relativeTo=t}relativeTo(){return this._relativeTo}setOffset(t){this._offset=t}offset(){return this._offset}toParameter(){switch(this._mode){case G_.RELATIVE:return this._relative_position_param();case G_.ABSOLUTE:return this._absolutePositionParam()}ar.unreachable(this._mode)}_relative_position_param(){switch(this._relativeTo){case H_.END:return this._offsetString();case H_.START:return`<${this._offset}`}ar.unreachable(this._relativeTo)}_absolutePositionParam(){return this._offset}_offsetString(){return this._offset>0?`+=${this._offset}`:`-=${Math.abs(this._offset)}`}}var q_;!function(t){t.ALL_TOGETHER=\\\\\\\"play all together\\\\\\\",t.ONE_AT_A_TIME=\\\\\\\"play one at a time\\\\\\\"}(q_||(q_={}));const X_=[q_.ALL_TOGETHER,q_.ONE_AT_A_TIME];const Y_=new class extends aa{constructor(){super(...arguments),this.mode=oa.INTEGER(0,{menu:{entries:X_.map(((t,e)=>({name:t,value:e})))}}),this.offset=oa.FLOAT(0,{range:[-1,1]}),this.overridePositions=oa.BOOLEAN(0),this.inputsCount=oa.INTEGER(4,{range:[1,32],rangeLocked:[!0,!1],callback:t=>{$_.PARAM_CALLBACK_setInputsCount(t)}})}};class $_ extends iu{constructor(){super(...arguments),this.paramsConfig=Y_}static type(){return\\\\\\\"merge\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.mode],(()=>X_[this.pv.mode]))})),this.params.addOnSceneLoadHook(\\\\\\\"update inputs\\\\\\\",(()=>{this._callbackUpdateInputsCount()}))}))}cook(t){const e=new R_;let n=0;for(let i of t)i&&(n>0&&this._update_timeline_builder(i),e.addTimelineBuilder(i),n++);this.setTimelineBuilder(e)}_update_timeline_builder(t){const e=X_[this.pv.mode];switch(e){case q_.ALL_TOGETHER:return this._set_play_all_together(t);case q_.ONE_AT_A_TIME:return this._set_play_one_at_a_time(t)}ar.unreachable(e)}_set_play_all_together(t){let e=t.position();e&&!this.pv.overridePositions||(e=new W_,e.setMode(G_.RELATIVE),e.setRelativeTo(H_.START),e.setOffset(this.pv.offset),t.setPosition(e))}_set_play_one_at_a_time(t){let e=t.position();e&&!this.pv.overridePositions||(e=new W_,e.setMode(G_.RELATIVE),e.setRelativeTo(H_.END),e.setOffset(this.pv.offset),t.setPosition(e))}_callbackUpdateInputsCount(){this.io.inputs.setCount(1,this.pv.inputsCount),this.emit(Ei.INPUTS_UPDATED)}static PARAM_CALLBACK_setInputsCount(t){t._callbackUpdateInputsCount()}}const J_=new class extends aa{constructor(){super(...arguments),this.play=oa.BUTTON(null,{callback:t=>{Z_.PARAM_CALLBACK_play(t)}}),this.pause=oa.BUTTON(null,{callback:t=>{Z_.PARAM_CALLBACK_pause(t)}}),this.debug=oa.BOOLEAN(0)}};class Z_ extends iu{constructor(){super(...arguments),this.paramsConfig=J_}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}cook(t){const e=t[0]||new R_;this.setTimelineBuilder(e)}async play(){return new Promise((async t=>{const e=await this.compute();e&&(this._timeline_builder=e.coreContent(),this._timeline_builder&&(this._timeline&&this._timeline.kill(),this._timeline=L_.timeline({onComplete:t}),this.pv.debug&&console.log(`play from '${this.path()}'`),this._timeline_builder.setDebug(this.pv.debug),this._timeline_builder.populate(this._timeline)))}))}async pause(){this._timeline&&this._timeline.pause()}static PARAM_CALLBACK_play(t){t.play()}static PARAM_CALLBACK_pause(t){t.pause()}}const Q_=new class extends aa{constructor(){super(...arguments),this.operation=oa.INTEGER(0,{menu:{entries:O_.map(((t,e)=>({value:e,name:t})))}})}};class K_ extends iu{constructor(){super(...arguments),this.paramsConfig=Q_}static type(){return\\\\\\\"operation\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.operation],(()=>O_[this.pv.operation]))}))}))}cook(t){const e=t[0]||new R_;e.setOperation(O_[this.pv.operation]),this.setTimelineBuilder(e)}}const tm=new class extends aa{constructor(){super(...arguments),this.mode=oa.INTEGER(0,{menu:{entries:V_.map(((t,e)=>({name:t,value:e})))}}),this.relativeTo=oa.INTEGER(0,{menu:{entries:j_.map(((t,e)=>({name:t,value:e})))}}),this.offset=oa.FLOAT(0)}};class em extends iu{constructor(){super(...arguments),this.paramsConfig=tm}static type(){return\\\\\\\"position\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.mode,this.p.relativeTo,this.p.offset],(()=>{switch(V_[this.pv.mode]){case G_.RELATIVE:return this._relative_label();case G_.ABSOLUTE:return this._absolute_label()}}))}))}))}_relative_label(){const t=this.pv.offset>0?\\\\\\\"after\\\\\\\":\\\\\\\"before\\\\\\\",e=j_[this.pv.relativeTo];return`${Math.abs(this.pv.offset)} ${t} ${e}`}_absolute_label(){return\\\\\\\"absolute\\\\\\\"}cook(t){const e=t[0]||new R_,n=new W_;n.setMode(V_[this.pv.mode]),n.setRelativeTo(j_[this.pv.relativeTo]),n.setOffset(this.pv.offset),e.setPosition(n),this.setTimelineBuilder(e)}}const nm=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"position\\\\\\\")}};class im extends iu{constructor(){super(...arguments),this.paramsConfig=nm}static type(){return\\\\\\\"propertyName\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}cook(t){const e=t[0]||new R_;e.setPropertyName(this.pv.name),this.setTimelineBuilder(e)}}var rm;!function(t){t.CUSTOM=\\\\\\\"custom\\\\\\\",t.FROM_SCENE_GRAPH=\\\\\\\"from scene graph\\\\\\\",t.FROM_NODE=\\\\\\\"from node\\\\\\\"}(rm||(rm={}));const sm=[rm.CUSTOM,rm.FROM_SCENE_GRAPH,rm.FROM_NODE],om=sm.indexOf(rm.CUSTOM),am=sm.indexOf(rm.FROM_SCENE_GRAPH),lm=sm.indexOf(rm.FROM_NODE);const cm=new class extends aa{constructor(){super(...arguments),this.mode=oa.INTEGER(om,{menu:{entries:sm.map(((t,e)=>({name:t,value:e})))}}),this.nodePath=oa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{mode:lm}}),this.objectMask=oa.STRING(\\\\\\\"*geo1\\\\\\\",{visibleIf:{mode:am}}),this.printResolve=oa.BUTTON(null,{visibleIf:{mode:am},callback:t=>{um.PARAM_CALLBACK_print_resolve(t)}}),this.overridePropertyName=oa.BOOLEAN(0,{visibleIf:[{mode:am},{mode:lm}]}),this.propertyName=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:[{overridePropertyName:!0,mode:am},{overridePropertyName:!0,mode:lm}]}),this.size=oa.INTEGER(3,{range:[1,4],rangeLocked:[!0,!0],visibleIf:{mode:om}}),this.value1=oa.FLOAT(0,{visibleIf:{mode:om,size:1}}),this.value2=oa.VECTOR2([0,0],{visibleIf:{mode:om,size:2}}),this.value3=oa.VECTOR3([0,0,0],{visibleIf:{mode:om,size:3}}),this.value4=oa.VECTOR4([0,0,0,0],{visibleIf:{mode:om,size:4}})}};class um extends iu{constructor(){super(...arguments),this.paramsConfig=cm}static type(){return\\\\\\\"propertyValue\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1)}async cook(t){const e=t[0]||new R_;await this._prepare_timeline_builder(e),this.setTimelineBuilder(e)}setMode(t){this.p.mode.set(sm.indexOf(t))}async _prepare_timeline_builder(t){const e=sm[this.pv.mode];switch(e){case rm.CUSTOM:return this._prepare_timebuilder_custom(t);case rm.FROM_SCENE_GRAPH:return this._prepare_timebuilder_from_scene_graph(t);case rm.FROM_NODE:return await this._prepare_timebuilder_from_node(t)}ar.unreachable(e)}_prepare_timebuilder_custom(t){const e=[this.pv.value1,this.pv.value2.clone(),this.pv.value3.clone(),this.pv.value4.clone()][this.pv.size-1];t.setPropertyValue(e)}_prepare_timebuilder_from_scene_graph(t){const e=this.pv.overridePropertyName?this.pv.propertyName:t.propertyName();if(!e)return;const n=this._foundObjectFromSceneGraph();if(n){const i=n[e];i&&(m.isNumber(i)||m.isVector(i)||i instanceof au.a)&&t.setPropertyValue(i)}}async _prepare_timebuilder_from_node(t){const e=this.pv.overridePropertyName?this.pv.propertyName:t.propertyName();if(!e)return;const n=this.pv.nodePath.node();if(!n)return;const i=n.params.get(e);if(!i)return;i.isDirty()&&await i.compute();const r=i.value;r&&(m.isNumber(r)||m.isVector(r))&&t.setPropertyValue(r)}static PARAM_CALLBACK_print_resolve(t){t.printResolve()}_foundObjectFromSceneGraph(){return this.scene().findObjectByMask(this.pv.objectMask)}printResolve(){const t=this._foundObjectFromSceneGraph();console.log(t)}}const hm=new class extends aa{constructor(){super(...arguments),this.unlimited=oa.BOOLEAN(0),this.count=oa.INTEGER(1,{range:[0,10],visibleIf:{unlimited:0}}),this.delay=oa.FLOAT(0),this.yoyo=oa.BOOLEAN(0)}};class dm extends iu{constructor(){super(...arguments),this.paramsConfig=hm}static type(){return\\\\\\\"repeat\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.unlimited,this.p.count,this.p.yoyo],(()=>`${`${this.p.unlimited?\\\\\\\"unlimited\\\\\\\":this.pv.count}`} (yoyo: ${this.pv.yoyo})`))}))}))}_repeat_params(){return{count:this.pv.unlimited?-1:this.pv.count,delay:this.pv.delay,yoyo:this.pv.yoyo}}cook(t){const e=t[0]||new R_;e.setRepeatParams(this._repeat_params()),this.setTimelineBuilder(e)}}const pm=new class extends aa{constructor(){super(...arguments),this.input=oa.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class _m extends iu{constructor(){super(...arguments),this.paramsConfig=pm}static type(){return\\\\\\\"switch\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4)}cook(t){const e=t[this.pv.input];e?this.setTimelineBuilder(e):this.states.error.set(`input ${this.pv.input} is not valid`)}}class mm{constructor(t,e){this._scene=t,this._options=e}clone(){return new mm(this._scene,this._options)}objects(){const t=this._options.objectMask;if(t)return this._scene.objectsByMask(t)}node(){if(!this._options.node)return;const t=this._options.node;return t.relativeTo.node(t.path)}}class fm{constructor(){this._update_matrix=!1}clone(){const t=new fm;return t.setUpdateMatrix(this._update_matrix),t}setUpdateMatrix(t){this._update_matrix=t}updateMatrix(){return this._update_matrix}}var gm;!function(t){t.SCENE_GRAPH=\\\\\\\"scene graph\\\\\\\",t.NODE=\\\\\\\"node\\\\\\\"}(gm||(gm={}));const vm=[gm.SCENE_GRAPH,gm.NODE],ym=vm.indexOf(gm.SCENE_GRAPH),xm=vm.indexOf(gm.NODE);const bm=new class extends aa{constructor(){super(...arguments),this.type=oa.INTEGER(ym,{menu:{entries:vm.map(((t,e)=>({name:t,value:e})))}}),this.nodePath=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{type:xm}}),this.objectMask=oa.STRING(\\\\\\\"/geo*\\\\\\\",{visibleIf:{type:ym}}),this.updateMatrix=oa.BOOLEAN(0,{visibleIf:{type:ym}}),this.printResolve=oa.BUTTON(null,{callback:(t,e)=>{wm.PARAM_CALLBACK_print_resolve(t)}})}};class wm extends iu{constructor(){super(...arguments),this.paramsConfig=bm}static type(){return\\\\\\\"target\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.type,this.p.nodePath,this.p.objectMask],(()=>{const t=vm[this.pv.type];switch(t){case gm.NODE:return this.pv.nodePath;case gm.SCENE_GRAPH:return this.pv.objectMask}ar.unreachable(t)}))}))}))}cook(t){const e=t[0]||new R_,n=this._create_target(e);e.setTarget(n),this._set_update_callback(e),this.setTimelineBuilder(e)}setTargetType(t){this.p.type.set(vm.indexOf(t))}_create_target(t){const e=vm[this.pv.type];switch(e){case gm.NODE:return new mm(this.scene(),{node:{path:this.pv.nodePath,relativeTo:this}});case gm.SCENE_GRAPH:return new mm(this.scene(),{objectMask:this.pv.objectMask})}ar.unreachable(e)}_set_update_callback(t){const e=vm[this.pv.type];let n=t.updateCallback();switch(e){case gm.NODE:return;case gm.SCENE_GRAPH:return void(this.pv.updateMatrix&&(n=n||new fm,n.setUpdateMatrix(this.pv.updateMatrix),t.setUpdateCallback(n)))}ar.unreachable(e)}static PARAM_CALLBACK_print_resolve(t){t.print_resolve()}print_resolve(){const t=vm[this.pv.type],e=new R_,n=this._create_target(e);switch(t){case gm.NODE:return console.log(n.node());case gm.SCENE_GRAPH:return console.log(n.objects())}}}class Tm extends ia{static context(){return Ki.ANIM}cook(){this.cookController.endCook()}}class Am extends Tm{}class Em extends Am{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Mm extends Am{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Sm extends Am{constructor(){super(...arguments),this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Cm extends Am{constructor(){super(...arguments),this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}const Nm={dependsOnDisplayNode:!0};class Lm{constructor(t,e,n=Nm){this.node=t,this.options=n,this._initialized=!1,this._display_node=void 0,this._graph_node=new Ai(t.scene(),\\\\\\\"DisplayNodeController\\\\\\\"),this._graph_node.node=t,this._on_display_node_remove_callback=e.onDisplayNodeRemove,this._on_display_node_set_callback=e.onDisplayNodeSet,this._on_display_node_update_callback=e.onDisplayNodeUpdate}dispose(){this._graph_node.dispose()}displayNode(){return this._display_node}initializeNode(){this._initialized?console.error(\\\\\\\"display node controller already initialed\\\\\\\",this.node):(this._initialized=!0,this.node.lifecycle.add_on_child_add_hook((t=>{var e,n;this._display_node||null===(n=null===(e=t.flags)||void 0===e?void 0:e.display)||void 0===n||n.set(!0)})),this.node.lifecycle.add_on_child_remove_hook((t=>{var e,n,i;if(t.graphNodeId()==(null===(e=this._display_node)||void 0===e?void 0:e.graphNodeId())){const t=this.node.children(),e=t[t.length-1];e?null===(i=null===(n=e.flags)||void 0===n?void 0:n.display)||void 0===i||i.set(!0):this.setDisplayNode(void 0)}})),this._graph_node.dirtyController.addPostDirtyHook(\\\\\\\"_request_display_node_container\\\\\\\",(()=>{this._on_display_node_update_callback&&this._on_display_node_update_callback()})))}async setDisplayNode(t){if(this._initialized||console.error(\\\\\\\"display node controller not initialized\\\\\\\",this.node),this._display_node!=t){const e=this._display_node;e&&(e.flags.display.set(!1),this.options.dependsOnDisplayNode&&this._graph_node.removeGraphInput(e),this._on_display_node_remove_callback&&this._on_display_node_remove_callback()),this._display_node=t,this._display_node&&(this.options.dependsOnDisplayNode&&this._graph_node.addGraphInput(this._display_node),this._on_display_node_set_callback&&this._on_display_node_set_callback())}}}class Om{constructor(t=!0){this.autoStart=t,this.startTime=0,this.oldTime=0,this.elapsedTime=0,this.running=!1}start(){this.startTime=Rm(),this.oldTime=this.startTime,this.elapsedTime=0,this.running=!0}stop(){this.getElapsedTime(),this.running=!1,this.autoStart=!1}getElapsedTime(){return this.getDelta(),this.elapsedTime}getDelta(){let t=0;if(this.autoStart&&!this.running)return this.start(),0;if(this.running){const e=Rm();t=(e-this.oldTime)/1e3,this.oldTime=e,this.elapsedTime+=t}return t}}function Rm(){return(\\\\\\\"undefined\\\\\\\"==typeof performance?Date:performance).now()}var Pm={uniforms:{tDiffuse:{value:null},opacity:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float opacity;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\t\\\\t\\\\tgl_FragColor = opacity * texel;\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class Im{constructor(){this.enabled=!0,this.needsSwap=!0,this.clear=!1,this.renderToScreen=!1}setSize(){}render(){console.error(\\\\\\\"THREE.Pass: .render() must be implemented in derived pass.\\\\\\\")}}const Fm=new st.a(-1,1,1,-1,0,1),Dm=new S.a;Dm.setAttribute(\\\\\\\"position\\\\\\\",new C.c([-1,3,0,-1,-1,0,3,-1,0],3)),Dm.setAttribute(\\\\\\\"uv\\\\\\\",new C.c([0,2,0,0,2,0],2));class km{constructor(t){this._mesh=new k.a(Dm,t)}dispose(){this._mesh.geometry.dispose()}render(t){t.render(this._mesh,Fm)}get material(){return this._mesh.material}set material(t){this._mesh.material=t}}class Bm extends Im{constructor(t,e){super(),this.textureID=void 0!==e?e:\\\\\\\"tDiffuse\\\\\\\",t instanceof F?(this.uniforms=t.uniforms,this.material=t):t&&(this.uniforms=I.clone(t.uniforms),this.material=new F({defines:Object.assign({},t.defines),uniforms:this.uniforms,vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})),this.fsQuad=new km(this.material)}render(t,e,n){this.uniforms[this.textureID]&&(this.uniforms[this.textureID].value=n.texture),this.fsQuad.material=this.material,this.renderToScreen?(t.setRenderTarget(null),this.fsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(t.autoClearColor,t.autoClearDepth,t.autoClearStencil),this.fsQuad.render(t))}}class zm extends Im{constructor(t,e){super(),this.scene=t,this.camera=e,this.clear=!0,this.needsSwap=!1,this.inverse=!1}render(t,e,n){const i=t.getContext(),r=t.state;let s,o;r.buffers.color.setMask(!1),r.buffers.depth.setMask(!1),r.buffers.color.setLocked(!0),r.buffers.depth.setLocked(!0),this.inverse?(s=0,o=1):(s=1,o=0),r.buffers.stencil.setTest(!0),r.buffers.stencil.setOp(i.REPLACE,i.REPLACE,i.REPLACE),r.buffers.stencil.setFunc(i.ALWAYS,s,4294967295),r.buffers.stencil.setClear(o),r.buffers.stencil.setLocked(!0),t.setRenderTarget(n),this.clear&&t.clear(),t.render(this.scene,this.camera),t.setRenderTarget(e),this.clear&&t.clear(),t.render(this.scene,this.camera),r.buffers.color.setLocked(!1),r.buffers.depth.setLocked(!1),r.buffers.stencil.setLocked(!1),r.buffers.stencil.setFunc(i.EQUAL,1,4294967295),r.buffers.stencil.setOp(i.KEEP,i.KEEP,i.KEEP),r.buffers.stencil.setLocked(!0)}}class Um extends Im{constructor(){super(),this.needsSwap=!1}render(t){t.state.buffers.stencil.setLocked(!1),t.state.buffers.stencil.setTest(!1)}}class Gm{constructor(t,e){if(this.renderer=t,void 0===e){const n={minFilter:w.V,magFilter:w.V,format:w.Ib},i=t.getSize(new d.a);this._pixelRatio=t.getPixelRatio(),this._width=i.width,this._height=i.height,(e=new Z(this._width*this._pixelRatio,this._height*this._pixelRatio,n)).texture.name=\\\\\\\"EffectComposer.rt1\\\\\\\"}else this._pixelRatio=1,this._width=e.width,this._height=e.height;this.renderTarget1=e,this.renderTarget2=e.clone(),this.renderTarget2.texture.name=\\\\\\\"EffectComposer.rt2\\\\\\\",this.writeBuffer=this.renderTarget1,this.readBuffer=this.renderTarget2,this.renderToScreen=!0,this.passes=[],void 0===Pm&&console.error(\\\\\\\"THREE.EffectComposer relies on CopyShader\\\\\\\"),void 0===Bm&&console.error(\\\\\\\"THREE.EffectComposer relies on ShaderPass\\\\\\\"),this.copyPass=new Bm(Pm),this.clock=new Om}swapBuffers(){const t=this.readBuffer;this.readBuffer=this.writeBuffer,this.writeBuffer=t}addPass(t){this.passes.push(t),t.setSize(this._width*this._pixelRatio,this._height*this._pixelRatio)}insertPass(t,e){this.passes.splice(e,0,t),t.setSize(this._width*this._pixelRatio,this._height*this._pixelRatio)}removePass(t){const e=this.passes.indexOf(t);-1!==e&&this.passes.splice(e,1)}isLastEnabledPass(t){for(let e=t+1;e({name:Object.keys(t)[0],value:Object.values(t)[0]}))),$m=Wm.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})));class Jm extends aa{constructor(){super(...arguments),this.prependRenderPass=oa.BOOLEAN(1),this.useRenderTarget=oa.BOOLEAN(1),this.tmagFilter=oa.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.magFilter=oa.INTEGER(qm,{visibleIf:{useRenderTarget:1,tmagFilter:1},menu:{entries:Ym}}),this.tminFilter=oa.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.minFilter=oa.INTEGER(Xm,{visibleIf:{useRenderTarget:1,tminFilter:1},menu:{entries:$m}}),this.stencilBuffer=oa.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.sampling=oa.INTEGER(1,{range:[1,4],rangeLocked:[!0,!1]})}}class Zm{constructor(t){this.node=t,this._renderer_size=new d.a}displayNodeControllerCallbacks(){return{onDisplayNodeRemove:()=>{},onDisplayNodeSet:()=>{this.node.setDirty()},onDisplayNodeUpdate:()=>{this.node.setDirty()}}}createEffectsComposer(t){const e=t.renderer;let n;if(this.node.pv.useRenderTarget){const t=this._create_render_target(e);n=new Gm(e,t)}else n=new Gm(e);return n.setPixelRatio(window.devicePixelRatio*this.node.pv.sampling),this._build_passes(n,t),n}_create_render_target(t){let e;t.autoClear=!1;const n={format:w.ic,stencilBuffer:this.node.pv.stencilBuffer};return this.node.pv.tminFilter&&(n.minFilter=this.node.pv.minFilter),this.node.pv.tmagFilter&&(n.magFilter=this.node.pv.magFilter),t.getDrawingBufferSize(this._renderer_size),e=ai.renderersController.renderTarget(this._renderer_size.x,this._renderer_size.y,n),e}_build_passes(t,e){if(this.node.pv.prependRenderPass){const n=new Hm(e.scene,e.camera);t.addPass(n)}const n=this.node.displayNodeController.displayNode();n&&n.setupComposer({composer:t,camera:e.camera,resolution:e.resolution,camera_node:e.camera_node,scene:e.scene,requester:e.requester})}}class Qm extends Tm{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Km extends Am{constructor(){super(...arguments),this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}var tf=n(43);const ef=\\\\\\\"input texture\\\\\\\",nf=[ef,ef,ef,ef];for(var rf=new Uint16Array(32),sf=0;sf<32;sf++)rf[sf]=28898;const of=new mo.a(rf,32,1,w.gb,w.M);class af extends ia{constructor(t){super(t,\\\\\\\"BaseCopNode\\\\\\\"),this.flags=new Bi(this)}static context(){return Ki.COP}static displayedInputNames(){return nf}initializeBaseNode(){this.io.outputs.setHasOneOutput()}setTexture(t){t.name=this.path();const e=this.containerController.container().texture();if(e){if(e.uuid!=t.uuid){const n=Object.keys(t);for(let i of n)e[i]=t[i];e.needsUpdate=!0}this._setContainer(e)}else this._setContainer(t)}_clearTexture(){this._setContainer(of)}}class lf extends af{}class cf{constructor(){this._id=cf.__next_id++}id(){return this._id}handle_globals_node(t,e,n){}}cf.__next_id=0;class uf{static any(t){return m.isString(t)?t:m.isBoolean(t)?`${t}`:m.isNumber(t)?`${sr.ensureFloat(t)}`:m.isArray(t)?this.numeric_array(t):t instanceof d.a||t instanceof p.a||t instanceof _.a||t instanceof D.a?this.numeric_array(t.toArray()):`ThreeToGl error: unknown value type '${t}'`}static numeric_array(t){const e=new Array(t.length);for(let n=0;n`${sr.ensureFloat(t)}`)).join(\\\\\\\", \\\\\\\")})`}static vector3(t){if(m.isString(t))return t;return`vec3(${t.toArray().map((t=>`${sr.ensureFloat(t)}`)).join(\\\\\\\", \\\\\\\")})`}static vector2(t){if(m.isString(t))return t;return`vec2(${t.toArray().map((t=>`${sr.ensureFloat(t)}`)).join(\\\\\\\", \\\\\\\")})`}static vector3_float(t,e){return m.isNumber(e)&&(e=sr.ensureFloat(e)),`vec4(${this.vector3(t)}, ${e})`}static float4(t,e,n,i){return m.isNumber(t)&&(t=sr.ensureFloat(t)),m.isNumber(e)&&(e=sr.ensureFloat(e)),m.isNumber(n)&&(n=sr.ensureFloat(n)),m.isNumber(i)&&(i=sr.ensureFloat(i)),`vec4(${t}, ${e}, ${n}, ${i})`}static float3(t,e,n){return m.isNumber(t)&&(t=sr.ensureFloat(t)),m.isNumber(e)&&(e=sr.ensureFloat(e)),m.isNumber(n)&&(n=sr.ensureFloat(n)),`vec3(${t}, ${e}, ${n})`}static float2(t,e){return m.isNumber(t)&&(t=sr.ensureFloat(t)),m.isNumber(e)&&(e=sr.ensureFloat(e)),`vec2(${t}, ${e})`}static float(t){if(m.isNumber(t))return sr.ensureFloat(t);{const e=parseFloat(t);return m.isNaN(e)?t:sr.ensureFloat(e)}}static integer(t){if(m.isNumber(t))return sr.ensureInteger(t);{const e=parseInt(t);return m.isNaN(e)?t:sr.ensureInteger(e)}}static bool(t){return m.isBoolean(t)?`${t}`:t}}const hf=/\\\\/+/g;class df extends ia{static context(){return Ki.GL}initializeBaseNode(){this.uiData.setLayoutHorizontal(),this.io.connections.initInputs(),this.io.connection_points.spare_params.initializeNode()}cook(){console.warn(\\\\\\\"gl nodes should never cook\\\\\\\")}_set_mat_to_recompile(){var t,e;null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e||e.set_compilation_required_and_dirty(this)}get material_node(){var t;const e=this.parent();if(e)return e.context()==Ki.GL?null===(t=e)||void 0===t?void 0:t.material_node:e}glVarName(t){return`v_POLY_${this.path(this.material_node).replace(hf,\\\\\\\"_\\\\\\\")}_${t}`}variableForInputParam(t){return this.variableForInput(t.name())}variableForInput(t){var e;const n=this.io.inputs.get_input_index(t),i=this.io.connections.inputConnection(n);if(i){const e=i.node_src,n=e.io.outputs.namedOutputConnectionPoints()[i.output_index];if(n){const t=n.name();return e.glVarName(t)}throw console.warn(`no output called '${t}' for gl node ${e.path()}`),\\\\\\\"variable_for_input ERROR\\\\\\\"}if(this.params.has(t))return uf.any(null===(e=this.params.get(t))||void 0===e?void 0:e.value);{const t=this.io.inputs.namedInputConnectionPoints()[n];return uf.any(t.init_value)}}setLines(t){}reset_code(){var t;null===(t=this._param_configs_controller)||void 0===t||t.reset()}setParamConfigs(){}param_configs(){var t;return null===(t=this._param_configs_controller)||void 0===t?void 0:t.list()}paramsGenerating(){return!1}paramDefaultValue(t){return null}}const pf=new class extends aa{};class _f extends df{constructor(){super(...arguments),this.paramsConfig=pf}}const mf=[Do.FLOAT,Do.VEC2,Do.VEC3,Do.VEC4];const ff=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"\\\\\\\"),this.type=oa.INTEGER(0,{menu:{entries:mf.map(((t,e)=>({name:t,value:e})))}}),this.texportWhenConnected=oa.BOOLEAN(0,{hidden:!0}),this.exportWhenConnected=oa.BOOLEAN(0,{visibleIf:{texportWhenConnected:1}})}};class gf extends df{constructor(){super(...arguments),this.paramsConfig=ff,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this),this._bound_setExportWhenConnectedStatus=this._setExportWhenConnectedStatus.bind(this)}static type(){return ir.ATTRIBUTE}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile_if_is_exporting.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function((()=>{var t,e;return(null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e?void 0:e.allow_attribute_exports())?[mf[this.pv.type]]:[]})),this.io.connection_points.set_input_name_function((t=>gf.INPUT_NAME)),this.io.connection_points.set_expected_output_types_function((()=>[mf[this.pv.type]])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name,this.p.exportWhenConnected],(()=>this.pv.exportWhenConnected?`${this.pv.name} (EXPORTED)`:this.pv.name))}))})),this.lifecycle.add_on_add_hook(this._bound_setExportWhenConnectedStatus),this.params.addOnSceneLoadHook(\\\\\\\"prepare params\\\\\\\",this._bound_setExportWhenConnectedStatus)}_setExportWhenConnectedStatus(){var t,e;(null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e?void 0:e.allow_attribute_exports())&&this.p.texportWhenConnected.set(1)}setAttribSize(t){this.p.type.set(t-1)}get input_name(){return gf.INPUT_NAME}get output_name(){return gf.OUTPUT_NAME}setLines(t){t.assembler().set_node_lines_attribute(this,t)}get attribute_name(){return this.pv.name.trim()}gl_type(){return this.io.outputs.namedOutputConnectionPoints()[0].type()}set_gl_type(t){this.p.type.set(mf.indexOf(t))}connected_input_node(){return this.io.inputs.named_input(gf.INPUT_NAME)}connected_input_connection_point(){return this.io.inputs.named_input_connection_point(gf.INPUT_NAME)}output_connection_point(){return this.io.outputs.namedOutputConnectionPointsByName(this.output_name)}isImporting(){return this.io.outputs.used_output_names().length>0}isExporting(){if(this.pv.exportWhenConnected){return null!=this.io.inputs.named_input(gf.INPUT_NAME)}return!1}_set_mat_to_recompile_if_is_exporting(){this.isExporting()&&this._set_mat_to_recompile()}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}gf.INPUT_NAME=\\\\\\\"in\\\\\\\",gf.OUTPUT_NAME=\\\\\\\"val\\\\\\\";class vf{constructor(t=[]){this._definitions=t,this._errored=!1}get errored(){return this._errored}get error_message(){return this._error_message}uniq(){const t=new Map,e=[];for(let n of this._definitions)if(!this._errored){const i=n.name(),r=t.get(i);r?r.data_type!=n.data_type&&(this._errored=!0,this._error_message=`attempt to create '${n.name()}' with types '${n.data_type}' by node '${n.node.path()}', when there is already an existing with type ${r.data_type} from node '${r.node.path()}'`,console.warn(\\\\\\\"emitting error message:\\\\\\\",this._error_message)):(t.set(i,n),e.push(i))}const n=[];for(let i of e){const e=t.get(i);e&&n.push(e)}return n}}var yf,xf;!function(t){t.ATTRIBUTE=\\\\\\\"attribute\\\\\\\",t.FUNCTION=\\\\\\\"function\\\\\\\",t.UNIFORM=\\\\\\\"uniform\\\\\\\",t.VARYING=\\\\\\\"varying\\\\\\\"}(yf||(yf={}));class bf{constructor(t,e,n,i){this._definition_type=t,this._data_type=e,this._node=n,this._name=i}get definition_type(){return this._definition_type}get data_type(){return this._data_type}get node(){return this._node}name(){return this._name}collection_instance(){return new vf}}class wf extends bf{constructor(t,e,n){super(yf.ATTRIBUTE,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`attribute ${this.data_type} ${this.name()}`}}class Tf extends bf{constructor(t,e){super(yf.FUNCTION,Do.FLOAT,t,e),this._node=t,this._name=e}get line(){return this.name()}}class Af extends bf{constructor(t,e,n){super(yf.UNIFORM,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`uniform ${this.data_type} ${this.name()}`}}class Ef extends bf{constructor(t,e,n){super(yf.VARYING,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`varying ${this.data_type} ${this.name()}`}}!function(t){t.VERTEX=\\\\\\\"vertex\\\\\\\",t.FRAGMENT=\\\\\\\"fragment\\\\\\\",t.LEAVES_FROM_NODES_SHADER=\\\\\\\"leaves_from_nodes_shader\\\\\\\"}(xf||(xf={}));const Mf={position:\\\\\\\"vec3( position )\\\\\\\"};class Sf extends cf{handle_globals_node(t,e,n){var i,r;const s=t.io.outputs.namedOutputConnectionPointsByName(e);if(!s)return;const o=t.glVarName(e),a=s.type(),l=new Ef(t,a,o);n.addDefinitions(t,[l]);const c=null===(r=null===(i=t.material_node)||void 0===i?void 0:i.assemblerController)||void 0===r?void 0:r.assembler;if(!c)return;const u=c.shader_config(n.current_shader_name);if(!u)return;const h=u.dependencies(),d=[],p=`${o} = modelMatrix * vec4( position, 1.0 )`,_=`${o} = normalize( mat3( modelMatrix[0].xyz, modelMatrix[1].xyz, modelMatrix[2].xyz ) * normal )`;switch(e){case\\\\\\\"worldPosition\\\\\\\":d.push(p);break;case\\\\\\\"worldNormal\\\\\\\":d.push(_);break;default:d.push(`${o} = ${a}(${e})`)}for(let e of h)n.addDefinitions(t,[l],e),n.addBodyLines(t,d,e);0==h.length&&n.addBodyLines(t,d)}static variable_config_default(t){return Mf[t]}variable_config_default(t){return Sf.variable_config_default(t)}read_attribute(t,e,n,i){return Sf.read_attribute(t,e,n,i)}static read_attribute(t,e,n,i){var r,s;Sf.PRE_DEFINED_ATTRIBUTES.indexOf(n)<0&&i.addDefinitions(t,[new wf(t,e,n)],xf.VERTEX);const o=i.current_shader_name;switch(o){case xf.VERTEX:return n;case xf.FRAGMENT:{if(!(t instanceof gf))return;const a=\\\\\\\"varying_\\\\\\\"+t.glVarName(t.output_name),l=new Ef(t,e,a),c=new Map;c.set(xf.FRAGMENT,[]);const h=new Map;h.set(xf.FRAGMENT,[]),u.pushOnArrayAtEntry(c,o,l);const d=`${a} = ${e}(${n})`,p=null===(s=null===(r=t.material_node)||void 0===r?void 0:r.assemblerController)||void 0===s?void 0:s.assembler.shader_config(o);if(p){const e=p.dependencies();for(let t of e)u.pushOnArrayAtEntry(c,t,l),u.pushOnArrayAtEntry(h,t,d);c.forEach(((e,n)=>{i.addDefinitions(t,e,n)})),h.forEach(((e,n)=>{i.addBodyLines(t,e,n)}))}return a}}}handle_attribute_node(t,e,n,i){return Sf.read_attribute(t,e,n,i)}}Sf.PRE_DEFINED_ATTRIBUTES=[\\\\\\\"position\\\\\\\",\\\\\\\"color\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",\\\\\\\"uv2\\\\\\\",\\\\\\\"morphTarget0\\\\\\\",\\\\\\\"morphTarget1\\\\\\\",\\\\\\\"morphTarget2\\\\\\\",\\\\\\\"morphTarget3\\\\\\\",\\\\\\\"skinIndex\\\\\\\",\\\\\\\"skinWeight\\\\\\\"],Sf.IF_RULE={uv:\\\\\\\"defined( USE_MAP ) || defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( USE_SPECULARMAP ) || defined( USE_ALPHAMAP ) || defined( USE_EMISSIVEMAP ) || defined( USE_ROUGHNESSMAP ) || defined( USE_METALNESSMAP )\\\\\\\"};const Cf=[Do.FLOAT,Do.VEC2,Do.VEC3,Do.VEC4];const Nf=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"\\\\\\\"),this.type=oa.INTEGER(0,{menu:{entries:Cf.map(((t,e)=>({name:t,value:e})))}})}};class Lf extends df{constructor(){super(...arguments),this.paramsConfig=Nf,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"varyingWrite\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_input_name_function((()=>this.input_name)),this.io.connection_points.set_expected_input_types_function((()=>[Cf[this.pv.type]])),this.io.connection_points.set_expected_output_types_function((()=>[])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}get input_name(){return Lf.INPUT_NAME}setLines(t){if(t.current_shader_name==xf.VERTEX){const e=this.gl_type();if(!e)return;const n=this.pv.name,i=new Ef(this,e,n),r=`${n} = ${uf.any(this.variableForInput(Lf.INPUT_NAME))}`;t.addDefinitions(this,[i],xf.VERTEX),t.addBodyLines(this,[r],xf.VERTEX)}}get attribute_name(){return this.pv.name.trim()}gl_type(){const t=this.io.inputs.namedInputConnectionPoints()[0];if(t)return t.type()}set_gl_type(t){this.p.type.set(Cf.indexOf(t))}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}Lf.INPUT_NAME=\\\\\\\"vertex\\\\\\\";class Of{static findOutputNodes(t){return t.nodesByType(\\\\\\\"output\\\\\\\")}static findParamGeneratingNodes(t){var e;const n=[];return null===(e=t.childrenController)||void 0===e||e.traverse_children((t=>{const e=t;e.paramsGenerating()&&n.push(e)})),n}static findVaryingNodes(t){return t.nodesByType(Lf.type())}static findAttributeExportNodes(t){return t.nodesByType(gf.type()).filter((t=>t.isExporting()))}}class Rf{static overlay(t,e){return new Promise(((n,i)=>{let r=document.createElement(\\\\\\\"canvas\\\\\\\");r.width=Math.max(t.width,e.width),r.height=Math.max(t.height,e.height);let s=r.getContext(\\\\\\\"2d\\\\\\\");s.drawImage(t,0,0,t.width,t.height),s.drawImage(e,0,0,e.width,e.height);const o=r.toDataURL(\\\\\\\"image/png\\\\\\\"),a=new Image;a.onload=()=>{n(a)},a.src=o}))}static create_white_image(t,e){return new Promise(((n,i)=>{let r=document.createElement(\\\\\\\"canvas\\\\\\\");r.width=t,r.height=e;let s=r.getContext(\\\\\\\"2d\\\\\\\");s.beginPath(),s.rect(0,0,t,e),s.fillStyle=\\\\\\\"white\\\\\\\",s.fill();const o=r.toDataURL(\\\\\\\"image/png\\\\\\\"),a=new Image;a.onload=()=>{n(a)},a.src=o}))}static make_square(t){return new Promise(((e,n)=>{let i=document.createElement(\\\\\\\"canvas\\\\\\\");const r=Math.min(t.width,t.height),s=t.width/t.height;i.width=r,i.height=r;let o=i.getContext(\\\\\\\"2d\\\\\\\");const a=s>1,l=a?(t.width-r)/2:(t.height-r)/2;a?o.drawImage(t,l,0,r,r,0,0,r,r):o.drawImage(t,0,l,r,r,0,0,r,r);const c=i.toDataURL(\\\\\\\"image/png\\\\\\\"),u=new Image;u.onload=()=>{e(u)},u.src=c}))}static async image_to_blob(t){return new Promise((function(e,n){try{let i=new XMLHttpRequest;i.open(\\\\\\\"GET\\\\\\\",t.src),i.responseType=\\\\\\\"blob\\\\\\\",i.onerror=function(){n(\\\\\\\"Network error.\\\\\\\")},i.onload=function(){200===i.status?e(i.response):n(\\\\\\\"Loading error:\\\\\\\"+i.statusText)},i.send()}catch(t){n(t.message)}}))}static data_from_url(t){return new Promise(((e,n)=>{const i=new Image;i.crossOrigin=\\\\\\\"Anonymous\\\\\\\",i.onload=()=>{const t=this.data_from_image(i);e(t)},i.src=t}))}static data_from_image(t){const e=document.createElement(\\\\\\\"canvas\\\\\\\");e.width=t.width,e.height=t.height;const n=e.getContext(\\\\\\\"2d\\\\\\\");return n.drawImage(t,0,0,t.width,t.height),n.getImageData(0,0,t.width,t.height)}}var Pf;!function(t){t.Uint8Array=\\\\\\\"Uint8Array\\\\\\\",t.Uint8ClampedArray=\\\\\\\"Uint8ClampedArray\\\\\\\",t.Float32Array=\\\\\\\"Float32Array\\\\\\\"}(Pf||(Pf={}));class If{constructor(t){this.buffer_type=t}from_render_target(t,e){return this._data_texture&&this._same_dimensions(e.texture)||(this._data_texture=this._create_data_texture(e.texture)),this._copy_to_data_texture(t,e),this._data_texture}from_texture(t){const e=Rf.data_from_image(t.image);this._data_texture&&this._same_dimensions(t)||(this._data_texture=this._create_data_texture(t));const n=e.width*e.height,i=e.data,r=this._data_texture.image.data,s=4*n;for(let t=0;t0:r.vertexColors=t.vertexColors),void 0!==t.uniforms)for(const e in t.uniforms){const i=t.uniforms[e];switch(r.uniforms[e]={},i.type){case\\\\\\\"t\\\\\\\":r.uniforms[e].value=n(i.value);break;case\\\\\\\"c\\\\\\\":r.uniforms[e].value=(new D.a).setHex(i.value);break;case\\\\\\\"v2\\\\\\\":r.uniforms[e].value=(new d.a).fromArray(i.value);break;case\\\\\\\"v3\\\\\\\":r.uniforms[e].value=(new p.a).fromArray(i.value);break;case\\\\\\\"v4\\\\\\\":r.uniforms[e].value=(new _.a).fromArray(i.value);break;case\\\\\\\"m3\\\\\\\":r.uniforms[e].value=(new U.a).fromArray(i.value);break;case\\\\\\\"m4\\\\\\\":r.uniforms[e].value=(new A.a).fromArray(i.value);break;default:r.uniforms[e].value=i.value}}if(void 0!==t.defines&&(r.defines=t.defines),void 0!==t.vertexShader&&(r.vertexShader=t.vertexShader),void 0!==t.fragmentShader&&(r.fragmentShader=t.fragmentShader),void 0!==t.extensions)for(const e in t.extensions)r.extensions[e]=t.extensions[e];if(void 0!==t.shading&&(r.flatShading=1===t.shading),void 0!==t.size&&(r.size=t.size),void 0!==t.sizeAttenuation&&(r.sizeAttenuation=t.sizeAttenuation),void 0!==t.map&&(r.map=n(t.map)),void 0!==t.matcap&&(r.matcap=n(t.matcap)),void 0!==t.alphaMap&&(r.alphaMap=n(t.alphaMap)),void 0!==t.bumpMap&&(r.bumpMap=n(t.bumpMap)),void 0!==t.bumpScale&&(r.bumpScale=t.bumpScale),void 0!==t.normalMap&&(r.normalMap=n(t.normalMap)),void 0!==t.normalMapType&&(r.normalMapType=t.normalMapType),void 0!==t.normalScale){let e=t.normalScale;!1===Array.isArray(e)&&(e=[e,e]),r.normalScale=(new d.a).fromArray(e)}return void 0!==t.displacementMap&&(r.displacementMap=n(t.displacementMap)),void 0!==t.displacementScale&&(r.displacementScale=t.displacementScale),void 0!==t.displacementBias&&(r.displacementBias=t.displacementBias),void 0!==t.roughnessMap&&(r.roughnessMap=n(t.roughnessMap)),void 0!==t.metalnessMap&&(r.metalnessMap=n(t.metalnessMap)),void 0!==t.emissiveMap&&(r.emissiveMap=n(t.emissiveMap)),void 0!==t.emissiveIntensity&&(r.emissiveIntensity=t.emissiveIntensity),void 0!==t.specularMap&&(r.specularMap=n(t.specularMap)),void 0!==t.specularIntensityMap&&(r.specularIntensityMap=n(t.specularIntensityMap)),void 0!==t.specularTintMap&&(r.specularTintMap=n(t.specularTintMap)),void 0!==t.envMap&&(r.envMap=n(t.envMap)),void 0!==t.envMapIntensity&&(r.envMapIntensity=t.envMapIntensity),void 0!==t.reflectivity&&(r.reflectivity=t.reflectivity),void 0!==t.refractionRatio&&(r.refractionRatio=t.refractionRatio),void 0!==t.lightMap&&(r.lightMap=n(t.lightMap)),void 0!==t.lightMapIntensity&&(r.lightMapIntensity=t.lightMapIntensity),void 0!==t.aoMap&&(r.aoMap=n(t.aoMap)),void 0!==t.aoMapIntensity&&(r.aoMapIntensity=t.aoMapIntensity),void 0!==t.gradientMap&&(r.gradientMap=n(t.gradientMap)),void 0!==t.clearcoatMap&&(r.clearcoatMap=n(t.clearcoatMap)),void 0!==t.clearcoatRoughnessMap&&(r.clearcoatRoughnessMap=n(t.clearcoatRoughnessMap)),void 0!==t.clearcoatNormalMap&&(r.clearcoatNormalMap=n(t.clearcoatNormalMap)),void 0!==t.clearcoatNormalScale&&(r.clearcoatNormalScale=(new d.a).fromArray(t.clearcoatNormalScale)),void 0!==t.transmissionMap&&(r.transmissionMap=n(t.transmissionMap)),void 0!==t.thicknessMap&&(r.thicknessMap=n(t.thicknessMap)),r}setTextures(t){return this.textures=t,this}}class Xf{constructor(t){this.node=t,this._found_uniform_texture_by_id=new Map,this._found_uniform_textures_id_by_uniform_name=new Map,this._found_param_texture_by_id=new Map,this._found_param_textures_id_by_uniform_name=new Map}toJSON(){}load(t){}_materialToJson(t,e){let n;this._unassignTextures(t);try{n=t.toJSON({}),n&&(n.shadowSide=t.shadowSide,n.colorWrite=t.colorWrite)}catch(e){console.error(\\\\\\\"failed to save material data\\\\\\\"),console.log(t)}return n&&null!=t.lights&&(n.lights=t.lights),n&&(n.uuid=`${e.node.path()}-${e.suffix}`),this._reassignTextures(t),n}_unassignTextures(t){this._found_uniform_texture_by_id.clear(),this._found_uniform_textures_id_by_uniform_name.clear(),this._found_param_texture_by_id.clear(),this._found_param_textures_id_by_uniform_name.clear();const e=t.uniforms,n=Object.keys(e);for(let t of n){const n=e[t].value;if(n&&n.uuid){const i=n;this._found_uniform_texture_by_id.set(i.uuid,n),this._found_uniform_textures_id_by_uniform_name.set(t,i.uuid),e[t].value=null}}const i=Object.keys(t);for(let e of i){const n=t[e];if(n&&n.uuid){const i=n;this._found_param_texture_by_id.set(i.uuid,i),this._found_param_textures_id_by_uniform_name.set(e,i.uuid),t[e]=null}}}_reassignTextures(t){const e=[],n=[];this._found_uniform_textures_id_by_uniform_name.forEach(((t,n)=>{e.push(n)})),this._found_param_textures_id_by_uniform_name.forEach(((t,e)=>{n.push(e)}));const i=t.uniforms;for(let t of e){const e=this._found_uniform_textures_id_by_uniform_name.get(t);if(e){const n=this._found_uniform_texture_by_id.get(e);n&&(i[t].value=n)}}for(let e of n){const n=this._found_param_textures_id_by_uniform_name.get(e);if(n){const i=this._found_param_texture_by_id.get(n);i&&(t[e]=i)}}}_loadMaterial(t){t.color=void 0;const e=(new qf).parse(t);t.shadowSide&&(e.shadowSide=t.shadowSide),null!=t.lights&&(e.lights=t.lights);const n=e.uniforms.uv2Transform;n&&this.mat4ToMat3(n);const i=e.uniforms.uvTransform;return i&&this.mat4ToMat3(i),e}mat4ToMat3(t){const e=t.value;if(null==e.elements[e.elements.length-1]){const n=new U.a;for(let t=0;t{const n=t.texture();e.value=n}));else{const t=n.containerController.container().texture();e.value=t}else e.value=null}static async set_uniform_value_from_texture_from_node_path_param(t,e){t.isDirty()&&await t.compute();const n=t.value.nodeWithContext(Ki.COP);if(n)if(n.isDirty())n.compute().then((t=>{const n=t.texture();e.value=n}));else{const t=n.containerController.container().texture();e.value=t}else e.value=null}set_uniform_value_from_ramp(t,e){e.value=t.rampTexture()}}class Jf extends Xf{constructor(t){super(t),this.node=t}toJSON(){const t=this.node.assemblerController;if(!t)return;const e=[],n=t.assembler.param_configs();for(let t of n)e.push([t.name(),t.uniform_name]);return{fragment_shader:this.node.texture_material.fragmentShader,uniforms:this.node.texture_material.uniforms,param_uniform_pairs:e,uniforms_time_dependent:t.assembler.uniformsTimeDependent(),uniforms_resolution_dependent:t.assembler.uniforms_resolution_dependent()}}load(t){this.node.texture_material.fragmentShader=t.fragment_shader,this.node.texture_material.uniforms=t.uniforms,tg.handle_dependencies(this.node,t.uniforms_time_dependent||!1,t.uniforms);for(let e of t.param_uniform_pairs){const n=this.node.params.get(e[0]),i=t.uniforms[e[1]];n&&i&&n.options.set({callback:()=>{$f.callback(n,i)}})}}}class Zf{static isChrome(){return navigator&&null!=navigator.userAgent&&-1!=navigator.userAgent.indexOf(\\\\\\\"Chrome\\\\\\\")}static isMobile(){return/Android|webOS|iPhone|iPad|iPod|BlackBerry|IEMobile|Opera Mini/i.test(navigator.userAgent)}static isiOS(){return/(iPad|iPhone|iPod)/g.test(navigator.userAgent)}static isAndroid(){return/(Android)/g.test(navigator.userAgent)}static isTouchDevice(){var t=document.createElement(\\\\\\\"div\\\\\\\");return t.setAttribute(\\\\\\\"ongesturestart\\\\\\\",\\\\\\\"return;\\\\\\\"),\\\\\\\"function\\\\\\\"==typeof t.ongesturestart}}const Qf=[256,256];const Kf=new class extends aa{constructor(){super(...arguments),this.resolution=oa.VECTOR2(Qf),this.useCameraRenderer=oa.BOOLEAN(0)}};class tg extends af{constructor(){super(...arguments),this.paramsConfig=Kf,this.persisted_config=new Jf(this),this._assembler_controller=this._create_assembler_controller(),this._texture_mesh=new k.a(new L(2,2)),this.texture_material=new F({uniforms:{},vertexShader:\\\\\\\"\\\\nvoid main()\\\\t{\\\\n\\\\tgl_Position = vec4( position, 1.0 );\\\\n}\\\\n\\\\\\\",fragmentShader:\\\\\\\"\\\\\\\"}),this._texture_scene=new fr,this._texture_camera=new tf.a,this._children_controller_context=Ki.GL,this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this)}static type(){return\\\\\\\"builder\\\\\\\"}usedAssembler(){return Hn.GL_TEXTURE}_create_assembler_controller(){const t=ai.assemblersRegister.assembler(this,this.usedAssembler());if(t){const e=new Sf;return t.set_assembler_globals_handler(e),t}}get assemblerController(){return this._assembler_controller}initializeNode(){this._texture_mesh.material=this.texture_material,this._texture_mesh.scale.multiplyScalar(.25),this._texture_scene.add(this._texture_mesh),this._texture_camera.position.z=1,this.addPostDirtyHook(\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",(()=>{setTimeout(this._cook_main_without_inputs_when_dirty_bound,0)}))}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}childrenAllowed(){return this.assemblerController?super.childrenAllowed():(this.scene().markAsReadOnly(this),!1)}async _cook_main_without_inputs_when_dirty(){await this.cookController.cookMainWithoutInputs()}async cook(){this.compileIfRequired(),this.renderOnTarget()}shaders_by_name(){return{fragment:this._fragment_shader}}compileIfRequired(){var t;(null===(t=this.assemblerController)||void 0===t?void 0:t.compileRequired())&&this.compile()}compile(){const t=this.assemblerController;if(!t)return;const e=Of.findOutputNodes(this);if(e.length>1)return void this.states.error.set(\\\\\\\"only one output node allowed\\\\\\\");if(e[0]){const n=e;t.assembler.set_root_nodes(n),t.assembler.update_fragment_shader();const i=t.assembler.fragment_shader(),r=t.assembler.uniforms();i&&r&&(this._fragment_shader=i,this._uniforms=r),tg.handle_dependencies(this,t.assembler.uniformsTimeDependent())}this._fragment_shader&&this._uniforms&&(this.texture_material.fragmentShader=this._fragment_shader,this.texture_material.uniforms=this._uniforms,this.texture_material.needsUpdate=!0,this.texture_material.uniforms.resolution={value:this.pv.resolution}),t.post_compile()}static handle_dependencies(t,e,n){const i=t.scene(),r=`${t.graphNodeId()}`;e?(t.states.timeDependent.forceTimeDependent(),n&&i.uniformsController.addTimeDependentUniformOwner(r,n)):(t.states.timeDependent.unforceTimeDependent(),i.uniformsController.removeTimeDependentUniformOwner(r))}async renderOnTarget(){if(this.createRenderTargetIfRequired(),!this._render_target)return;this._renderer_controller=this._renderer_controller||new Ff(this);const t=await this._renderer_controller.renderer(),e=t.getRenderTarget();if(t.setRenderTarget(this._render_target),t.clear(),t.render(this._texture_scene,this._texture_camera),t.setRenderTarget(e),this._render_target.texture)if(this.pv.useCameraRenderer)this.setTexture(this._render_target.texture);else{this._data_texture_controller=this._data_texture_controller||new If(Pf.Float32Array);const e=this._data_texture_controller.from_render_target(t,this._render_target);this.setTexture(e)}else this.cookController.endCook()}renderTarget(){return this._render_target=this._render_target||this._createRenderTarget(this.pv.resolution.x,this.pv.resolution.y)}createRenderTargetIfRequired(){var t;this._render_target&&this._renderTargetResolutionValid()||(this._render_target=this._createRenderTarget(this.pv.resolution.x,this.pv.resolution.y),null===(t=this._data_texture_controller)||void 0===t||t.reset())}_renderTargetResolutionValid(){if(this._render_target){const t=this._render_target.texture.image;return t.width==this.pv.resolution.x&&t.height==this.pv.resolution.y}return!1}_createRenderTarget(t,e){if(this._render_target){const n=this._render_target.texture.image;if(n.width==t&&n.height==e)return this._render_target}const n=w.n,i=w.n,r=w.V,s=w.ob;var o=new Z(t,e,{wrapS:n,wrapT:i,minFilter:r,magFilter:s,format:w.Ib,type:Zf.isiOS()?w.M:w.G,stencilBuffer:!1,depthBuffer:!1});return ai.warn(\\\\\\\"created render target\\\\\\\",this.path(),t,e),o}}const eg=[{LinearEncoding:w.U},{sRGBEncoding:w.ld},{GammaEncoding:w.J},{RGBEEncoding:w.gc},{LogLuvEncoding:w.bb},{RGBM7Encoding:w.lc},{RGBM16Encoding:w.kc},{RGBDEncoding:w.fc},{BasicDepthPacking:w.j},{RGBADepthPacking:w.Hb}],ng=[{ClampToEdgeWrapping:w.n},{RepeatWrapping:w.wc},{MirroredRepeatWrapping:w.kb}],ig=[{UVMapping:w.Yc},{CubeReflectionMapping:w.o},{CubeRefractionMapping:w.p},{EquirectangularReflectionMapping:w.D},{EquirectangularRefractionMapping:w.E},{CubeUVReflectionMapping:w.q},{CubeUVRefractionMapping:w.r}],rg=[{UnsignedByteType:w.Zc},{ByteType:w.l},{ShortType:w.Mc},{UnsignedShortType:w.fd},{IntType:w.N},{UnsignedIntType:w.bd},{FloatType:w.G},{HalfFloatType:w.M},{UnsignedShort4444Type:w.cd},{UnsignedShort5551Type:w.dd},{UnsignedShort565Type:w.ed},{UnsignedInt248Type:w.ad}],sg=[{AlphaFormat:w.f},{RedFormat:w.tc},{RedIntegerFormat:w.uc},{RGFormat:w.rc},{RGIntegerFormat:w.sc},{RGBFormat:w.ic},{RGBIntegerFormat:w.jc},{RGBAFormat:w.Ib},{RGBAIntegerFormat:w.Jb},{LuminanceFormat:w.gb},{LuminanceAlphaFormat:w.fb},{DepthFormat:w.x},{DepthStencilFormat:w.y}];function og(t){return{cook:!1,callback:e=>{wg[t](e)}}}const ag={ENCODING:w.U,FORMAT:w.Ib,MAPPING:w.Yc,MIN_FILTER:w.V,MAG_FILTER:w.V,TYPE:w.Zc,WRAPPING:w.wc},lg=og(\\\\\\\"PARAM_CALLBACK_update_encoding\\\\\\\"),cg=og(\\\\\\\"PARAM_CALLBACK_update_mapping\\\\\\\"),ug=og(\\\\\\\"PARAM_CALLBACK_update_wrap\\\\\\\"),hg=og(\\\\\\\"PARAM_CALLBACK_update_filter\\\\\\\"),dg=og(\\\\\\\"PARAM_CALLBACK_update_anisotropy\\\\\\\"),pg=og(\\\\\\\"PARAM_CALLBACK_update_flipY\\\\\\\"),_g=og(\\\\\\\"PARAM_CALLBACK_update_transform\\\\\\\"),mg=og(\\\\\\\"PARAM_CALLBACK_update_repeat\\\\\\\"),fg=og(\\\\\\\"PARAM_CALLBACK_update_offset\\\\\\\"),gg=og(\\\\\\\"PARAM_CALLBACK_update_rotation\\\\\\\"),vg=og(\\\\\\\"PARAM_CALLBACK_update_center\\\\\\\"),yg=og(\\\\\\\"PARAM_CALLBACK_update_advanced\\\\\\\");function xg(t){return class extends t{constructor(){super(...arguments),this.tencoding=oa.BOOLEAN(0,{...lg}),this.encoding=oa.INTEGER(ag.ENCODING,{visibleIf:{tencoding:1},menu:{entries:eg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...lg}),this.tmapping=oa.BOOLEAN(0,{...cg}),this.mapping=oa.INTEGER(ag.MAPPING,{visibleIf:{tmapping:1},menu:{entries:ig.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...cg}),this.twrap=oa.BOOLEAN(0,{...ug}),this.wrapS=oa.INTEGER(ag.WRAPPING,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...ug}),this.wrapT=oa.INTEGER(ag.WRAPPING,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},separatorAfter:!0,...ug}),this.tminFilter=oa.BOOLEAN(0,{...hg}),this.minFilter=oa.INTEGER(Xm,{visibleIf:{tminFilter:1},menu:{entries:$m},...hg}),this.tmagFilter=oa.BOOLEAN(0,{...hg}),this.magFilter=oa.INTEGER(qm,{visibleIf:{tmagFilter:1},menu:{entries:Ym},...hg}),this.tanisotropy=oa.BOOLEAN(0,{...dg}),this.useRendererMaxAnisotropy=oa.BOOLEAN(0,{visibleIf:{tanisotropy:1},...dg}),this.anisotropy=oa.INTEGER(2,{visibleIf:{tanisotropy:1,useRendererMaxAnisotropy:0},range:[0,32],rangeLocked:[!0,!1],...dg}),this.tflipY=oa.BOOLEAN(0,{...pg}),this.flipY=oa.BOOLEAN(0,{visibleIf:{tflipY:1},...pg}),this.ttransform=oa.BOOLEAN(0,{..._g}),this.offset=oa.VECTOR2([0,0],{visibleIf:{ttransform:1},...fg}),this.repeat=oa.VECTOR2([1,1],{visibleIf:{ttransform:1},...mg}),this.rotation=oa.FLOAT(0,{range:[-1,1],visibleIf:{ttransform:1},...gg}),this.center=oa.VECTOR2([0,0],{visibleIf:{ttransform:1},...vg}),this.tadvanced=oa.BOOLEAN(0,{...yg}),this.tformat=oa.BOOLEAN(0,{visibleIf:{tadvanced:1},...yg}),this.format=oa.INTEGER(ag.FORMAT,{visibleIf:{tadvanced:1,tformat:1},menu:{entries:sg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...yg}),this.ttype=oa.BOOLEAN(0,{visibleIf:{tadvanced:1},...yg}),this.type=oa.INTEGER(ag.TYPE,{visibleIf:{tadvanced:1,ttype:1},menu:{entries:rg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...yg})}}}class bg extends(xg(aa)){}new bg;class wg{constructor(t){this.node=t}async update(t){const e=this.node.pv;this._updateEncoding(t,e),this._updateAdvanced(t,e),this._updateMapping(t,e),this._updateWrap(t,e),this._updateFilter(t,e),this._updateFlip(t,e),await this._updateAnisotropy(t,e),this._updateTransform(t)}_updateEncoding(t,e){e.tencoding?t.encoding=e.encoding:t.encoding=ag.ENCODING,t.needsUpdate=!0}_updateAdvanced(t,e){e.tadvanced&&(e.tformat?t.format=e.format:t.format=ag.FORMAT,e.ttype?t.type=e.type:t.type=ag.TYPE),t.needsUpdate=!0}_updateMapping(t,e){e.tmapping?t.mapping=e.mapping:t.mapping=ag.MAPPING,t.needsUpdate=!0}_updateWrap(t,e){e.twrap?(t.wrapS=e.wrapS,t.wrapT=e.wrapT):(t.wrapS=ag.WRAPPING,t.wrapT=ag.WRAPPING),t.needsUpdate=!0}_updateFilter(t,e){e.tminFilter?t.minFilter=e.minFilter:t.minFilter=w.V,e.tmagFilter?t.magFilter=e.magFilter:t.magFilter=w.V,t.needsUpdate=!0}_updateFlip(t,e){t.flipY=e.tflipY&&e.flipY,t.needsUpdate=!0}async _updateAnisotropy(t,e){if(e.tanisotropy){if(e.useRendererMaxAnisotropy)t.anisotropy=await this._maxRendererAnisotropy();else{const n=e.anisotropy;t.anisotropy=n<=2?n:Math.min(n,await this._maxRendererAnisotropy())}t.needsUpdate=!0}else t.anisotropy=1}async _maxRendererAnisotropy(){this._renderer_controller=this._renderer_controller||new Ff(this.node);return(await this._renderer_controller.renderer()).capabilities.getMaxAnisotropy()}_updateTransform(t){if(!this.node.pv.ttransform)return t.offset.set(0,0),t.rotation=0,t.repeat.set(1,1),void t.center.set(0,0);this._updateTransformOffset(t,!1),this._updateTransformRepeat(t,!1),this._updateTransformRotation(t,!1),this._updateTransformCenter(t,!1),t.updateMatrix()}async _updateTransformOffset(t,e){t.offset.copy(this.node.pv.offset),e&&t.updateMatrix()}async _updateTransformRepeat(t,e){t.repeat.copy(this.node.pv.repeat),e&&t.updateMatrix()}async _updateTransformRotation(t,e){t.rotation=this.node.pv.rotation,e&&t.updateMatrix()}async _updateTransformCenter(t,e){t.center.copy(this.node.pv.center),e&&t.updateMatrix()}static PARAM_CALLBACK_update_encoding(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateEncoding(e,t.pv)}static PARAM_CALLBACK_update_mapping(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateMapping(e,t.pv)}static PARAM_CALLBACK_update_wrap(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateWrap(e,t.pv)}static PARAM_CALLBACK_update_filter(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateFilter(e,t.pv)}static PARAM_CALLBACK_update_anisotropy(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateAnisotropy(e,t.pv)}static PARAM_CALLBACK_update_flipY(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateFlip(e,t.pv)}static PARAM_CALLBACK_update_transform(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransform(e)}static PARAM_CALLBACK_update_offset(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransformOffset(e,!0)}static PARAM_CALLBACK_update_repeat(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransformRepeat(e,!0)}static PARAM_CALLBACK_update_rotation(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransformRotation(e,!0)}static PARAM_CALLBACK_update_center(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransformCenter(e,!0)}static PARAM_CALLBACK_update_advanced(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateAdvanced(e,t.pv)}static copyTextureAttributes(t,e){t.encoding=e.encoding,t.mapping=e.mapping,t.wrapS=e.wrapS,t.wrapT=e.wrapT,t.minFilter=e.minFilter,t.magFilter=e.magFilter,t.magFilter=e.magFilter,t.anisotropy=e.anisotropy,t.flipY=e.flipY,t.repeat.copy(e.repeat),t.offset.copy(e.offset),t.center.copy(e.center),t.rotation=e.rotation,t.type=e.type,t.format=e.format,t.needsUpdate=!0}paramLabelsParams(){const t=this.node.p;return[t.tencoding,t.encoding,t.tmapping,t.mapping,t.twrap,t.wrapS,t.wrapT,t.tminFilter,t.minFilter,t.tmagFilter,t.magFilter,t.tflipY,t.flipY]}paramLabels(){const t=[],e=this.node.pv;if(e.tencoding)for(let n of eg){const i=Object.keys(n)[0];n[i]==e.encoding&&t.push(`encoding: ${i}`)}if(e.tmapping)for(let n of ig){const i=Object.keys(n)[0];n[i]==e.mapping&&t.push(`mapping: ${i}`)}if(e.twrap){function n(n){for(let i of ng){const r=Object.keys(i)[0];i[r]==e[n]&&t.push(`${n}: ${r}`)}}n(\\\\\\\"wrapS\\\\\\\"),n(\\\\\\\"wrapT\\\\\\\")}if(e.tminFilter)for(let n of Wm){const i=Object.keys(n)[0];n[i]==e.minFilter&&t.push(`minFilter: ${i}`)}if(e.tmagFilter)for(let n of jm){const i=Object.keys(n)[0];n[i]==e.magFilter&&t.push(`magFilter: ${i}`)}return e.tflipY&&t.push(`flipY: ${e.flipY}`),t}}class Tg extends J.a{constructor(t,e,n,i,r,s,o,a,l){super(t,e,n,i,r,s,o,a,l),this.needsUpdate=!0}}Tg.prototype.isCanvasTexture=!0;class Ag extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.canvasId=oa.STRING(\\\\\\\"canvas-id\\\\\\\"),this.update=oa.BUTTON(null,{cook:!1,callback:t=>{Mg.PARAM_CALLBACK_update(t)}})}}}(aa))){}const Eg=new Ag;class Mg extends af{constructor(){super(...arguments),this.paramsConfig=Eg,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"canvas\\\\\\\"}async cook(){const t=this.pv.canvasId,e=document.getElementById(t);if(!e)return this.states.error.set(`element with id '${t}' not found`),void this.cookController.endCook();if(!(e instanceof HTMLCanvasElement))return this.states.error.set(\\\\\\\"element found is not a canvas\\\\\\\"),void this.cookController.endCook();const n=new Tg(e);await this.textureParamsController.update(n),this.setTexture(n)}static PARAM_CALLBACK_update(t){t.markTextureNeedsUpdate()}markTextureNeedsUpdate(){const t=this.containerController.container().texture();t&&(t.needsUpdate=!0)}}const Sg=new class extends aa{constructor(){super(...arguments),this.resolution=oa.VECTOR2([256,256],{callback:t=>{Cg.PARAM_CALLBACK_reset(t)}}),this.color=oa.COLOR([1,1,1])}};class Cg extends af{constructor(){super(...arguments),this.paramsConfig=Sg}static type(){return\\\\\\\"color\\\\\\\"}cook(){const t=this.pv.resolution.x,e=this.pv.resolution.y;this._data_texture=this._data_texture||this._create_data_texture(t,e);const n=e*t,i=this.pv.color.toArray(),r=255*i[0],s=255*i[1],o=255*i[2],a=this._data_texture.image.data;for(let t=0;t({name:t,value:e})))}})}};class Ig extends af{constructor(){super(...arguments),this.paramsConfig=Pg}static type(){return Lg.CUBE_CAMERA}async cook(){const t=this.pv.cubeCamera.nodeWithContext(Ki.OBJ,this.states.error);if(!t)return this.states.error.set(`cubeCamera not found at '${this.pv.cubeCamera.path()}'`),this.cookController.endCook();const e=t.renderTarget();if(!e)return this.states.error.set(\\\\\\\"cubeCamera has no render target'\\\\\\\"),this.cookController.endCook();const n=e.texture;Rg[this.pv.mode]==Og.REFLECTION?n.mapping=w.o:n.mapping=w.p,this.setTexture(n)}}var Fg;!function(t){t.REFLECTION=\\\\\\\"reflection\\\\\\\",t.REFRACTION=\\\\\\\"refraction\\\\\\\"}(Fg||(Fg={}));const Dg=[Fg.REFLECTION,Fg.REFRACTION];const kg=new class extends aa{constructor(){super(...arguments),this.useCameraRenderer=oa.BOOLEAN(1),this.mode=oa.INTEGER(0,{menu:{entries:Dg.map(((t,e)=>({name:t,value:e})))}})}};class Bg extends af{constructor(){super(...arguments),this.paramsConfig=kg}static type(){return\\\\\\\"envMap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.NEVER)}async cook(t){const e=t[0];this.convert_texture_to_env_map(e)}async convert_texture_to_env_map(t){this._renderer_controller=this._renderer_controller||new Ff(this);const e=await this._renderer_controller.renderer();if(e){const n=new wt(e).fromEquirectangular(t);if(this.pv.useCameraRenderer)this._set_mapping(n.texture),this.setTexture(n.texture);else{this._data_texture_controller=this._data_texture_controller||new If(Pf.Uint8Array);const t=this._data_texture_controller.from_render_target(e,n);this._set_mapping(t),this.setTexture(t)}}else this.states.error.set(\\\\\\\"no renderer found to convert the texture to an env map\\\\\\\"),this.cookController.endCook()}_set_mapping(t){Dg[this.pv.mode]==Fg.REFLECTION?t.mapping=w.q:t.mapping=w.r}}class zg extends J.a{constructor(t,e,n,i,r,s,o,a,l){super(t,e,n,i,r,s,o,a,l),this.format=void 0!==o?o:w.ic,this.minFilter=void 0!==s?s:w.V,this.magFilter=void 0!==r?r:w.V,this.generateMipmaps=!1;const c=this;\\\\\\\"requestVideoFrameCallback\\\\\\\"in t&&t.requestVideoFrameCallback((function e(){c.needsUpdate=!0,t.requestVideoFrameCallback(e)}))}clone(){return new this.constructor(this.image).copy(this)}update(){const t=this.image;!1===\\\\\\\"requestVideoFrameCallback\\\\\\\"in t&&t.readyState>=t.HAVE_CURRENT_DATA&&(this.needsUpdate=!0)}}zg.prototype.isVideoTexture=!0;var Ug=n(80);const Gg=\\\\\\\"https://raw.githubusercontent.com/polygonjs/polygonjs-assets/master/\\\\\\\";var Vg=n(29);const Hg=new Vg.b;Hg.setURLModifier((t=>{const e=ai.assetUrls.remapedUrl(t);if(e)return e;const n=ai.blobs.blobUrl(t);return n||t}));class jg{constructor(t,e,n){this._url=t,this._scene=e,this._node=n,this.loadingManager=Hg}static extension(t){let e=null;try{e=new URL(t).searchParams.get(\\\\\\\"ext\\\\\\\")}catch(t){}if(!e){const n=t.split(\\\\\\\"?\\\\\\\")[0].split(\\\\\\\".\\\\\\\");e=n[n.length-1].toLowerCase()}return e}extension(){return jg.extension(this._url)}async _urlToLoad(){let t=this._url;const e=this._url.split(\\\\\\\"?\\\\\\\")[0];if(\\\\\\\"h\\\\\\\"!=t[0]){const e=this._scene.assets.root();e&&(t=`${e}${t}`)}this._node&&await ai.blobs.fetchBlobForNode({storedUrl:e,fullUrl:t,node:this._node});return ai.blobs.blobUrl(e)||t}static async _loadMultipleBlobGlobal(t){const e=[];for(let n of t.files){const i=n.storedUrl,r=n.fullUrl,s=t.node;e.push(ai.blobs.fetchBlobGlobal({storedUrl:i,fullUrl:r,node:s}))}const n=await Promise.all(e);for(let e of n)e.error&&t.node.states.error.set(t.error)}}var Wg;jg.loadingManager=Hg,function(t){t.JPG=\\\\\\\"jpg\\\\\\\",t.JPEG=\\\\\\\"jpeg\\\\\\\",t.PNG=\\\\\\\"png\\\\\\\",t.EXR=\\\\\\\"exr\\\\\\\",t.BASIS=\\\\\\\"basis\\\\\\\",t.HDR=\\\\\\\"hdr\\\\\\\"}(Wg||(Wg={}));Wg.JPEG,Wg.JPG,Wg.PNG,Wg.EXR,Wg.BASIS,Wg.HDR;class qg extends jg{constructor(t,e,n,i){super(t,i,n),this._param=e,this._node=n,this._scene=i}static onTextureLoaded(t){this._onTextureLoadedCallback=t}async load_texture_from_url_or_op(t){let e=null,n=null;if(\\\\\\\"op:\\\\\\\"==this._url.substring(0,3)){const t=this._url.substring(3);if(n=xi.findNode(this._node,t),n)if(n instanceof lf){e=(await n.compute()).texture()}else this._node.states.error.set(\\\\\\\"found node is not a texture node\\\\\\\");else this._node.states.error.set(`no node found in path '${t}'`)}else e=await this._loadUrl(t),e||this._node.states.error.set(`could not load texture ${this._url}`);return n&&this._param.graphPredecessors()[0]!=n&&(this._param.graphDisconnectPredecessors(),this._param.addGraphInput(n)),e}async _loadUrl(t){return new Promise((async(e,n)=>{const i=this.extension(),r=await this._urlToLoad();if(qg.VIDEO_EXTENSIONS.includes(i)){const t=await this._load_as_video(r);e(t)}else this.loader_for_ext(i,t).then((async t=>{t?(qg.increment_in_progress_loads_count(),await qg.wait_for_max_concurrent_loads_queue_freed(),t.load(r,(t=>{qg.decrement_in_progress_loads_count();const n=qg._onTextureLoadedCallback;n&&n(r,t),e(t)}),void 0,(t=>{qg.decrement_in_progress_loads_count(),ai.warn(\\\\\\\"error\\\\\\\",t),n()}))):n()}))}))}static module_names(t){switch(t){case Wg.EXR:return[Vn.EXRLoader];case Wg.HDR:return[Vn.RGBELoader];case Wg.BASIS:return[Vn.BasisTextureLoader]}}async loader_for_ext(t,e){switch(t.toLowerCase()){case Wg.EXR:return await this._exr_loader(e);case Wg.HDR:return await this._hdr_loader(e);case Wg.BASIS:return await qg._basis_loader(this._node)}return new Ug.a(this.loadingManager)}async _exr_loader(t){const e=await ai.modulesRegister.module(Vn.EXRLoader);if(e){const n=new e(this.loadingManager);return t.tdataType&&n.setDataType(t.dataType),n}}async _hdr_loader(t){const e=await ai.modulesRegister.module(Vn.RGBELoader);if(e){const n=new e(this.loadingManager);return t.tdataType&&n.setDataType(t.dataType),n}}static async _basis_loader(t){const e=await ai.modulesRegister.module(Vn.BasisTextureLoader);if(e){const n=new e(this.loadingManager),i=ai.libs.root(),r=ai.libs.BASISPath();if(i||r){const e=`${i||\\\\\\\"\\\\\\\"}${r||\\\\\\\"\\\\\\\"}/`;if(t){const n=[\\\\\\\"basis_transcoder.js\\\\\\\",\\\\\\\"basis_transcoder.wasm\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((t=>({storedUrl:`${r}/${t}`,fullUrl:`${e}${t}`}))),node:t,error:\\\\\\\"failed to load basis libraries. Make sure to install them to load .basis files\\\\\\\"})}n.setTranscoderPath(e)}else n.setTranscoderPath(void 0);const s=await ai.renderersController.waitForRenderer();return s?n.detectSupport(s):ai.warn(\\\\\\\"texture loader found no renderer for basis texture loader\\\\\\\"),n}}_load_as_video(t){return new Promise(((e,n)=>{const i=document.createElement(\\\\\\\"video\\\\\\\");i.setAttribute(\\\\\\\"crossOrigin\\\\\\\",\\\\\\\"anonymous\\\\\\\"),i.setAttribute(\\\\\\\"autoplay\\\\\\\",\\\\\\\"true\\\\\\\"),i.setAttribute(\\\\\\\"loop\\\\\\\",\\\\\\\"true\\\\\\\"),i.onloadedmetadata=function(){i.pause();const t=new zg(i);e(t)};const r=document.createElement(\\\\\\\"source\\\\\\\"),s=jg.extension(t);let o=qg.VIDEO_SOURCE_TYPE_BY_EXT[s];o=o||qg._default_video_source_type(t),r.setAttribute(\\\\\\\"type\\\\\\\",o),r.setAttribute(\\\\\\\"src\\\\\\\",t),i.appendChild(r);let a=t;a=\\\\\\\"mp4\\\\\\\"==s?qg.replaceExtension(t,\\\\\\\"ogv\\\\\\\"):qg.replaceExtension(t,\\\\\\\"mp4\\\\\\\");const l=document.createElement(\\\\\\\"source\\\\\\\"),c=jg.extension(a);o=qg.VIDEO_SOURCE_TYPE_BY_EXT[c],o=o||qg._default_video_source_type(t),l.setAttribute(\\\\\\\"type\\\\\\\",o),l.setAttribute(\\\\\\\"src\\\\\\\",t),i.appendChild(l)}))}static _default_video_source_type(t){return`video/${jg.extension(t)}`}static pixel_data(t){const e=t.image,n=document.createElement(\\\\\\\"canvas\\\\\\\");n.width=e.width,n.height=e.height;const i=n.getContext(\\\\\\\"2d\\\\\\\");if(i)return i.drawImage(e,0,0,e.width,e.height),i.getImageData(0,0,e.width,e.height)}static replaceExtension(t,e){const n=t.split(\\\\\\\"?\\\\\\\"),i=n[0].split(\\\\\\\".\\\\\\\");return i.pop(),i.push(e),[i.join(\\\\\\\".\\\\\\\"),n[1]].join(\\\\\\\"?\\\\\\\")}static setMaxConcurrentLoadsCount(t){this._maxConcurrentLoadsCountMethod=t}static _init_max_concurrent_loads_count(){return this._maxConcurrentLoadsCountMethod?this._maxConcurrentLoadsCountMethod():Zf.isChrome()?10:4}static _init_concurrent_loads_delay(){return Zf.isChrome()?0:10}static increment_in_progress_loads_count(){this.in_progress_loads_count++}static decrement_in_progress_loads_count(){this.in_progress_loads_count--;const t=this._queue.pop();if(t){const e=this.CONCURRENT_LOADS_DELAY;setTimeout((()=>{t()}),e)}}static async wait_for_max_concurrent_loads_queue_freed(){return this.in_progress_loads_count<=this.MAX_CONCURRENT_LOADS_COUNT?void 0:new Promise((t=>{this._queue.push(t)}))}}qg.PARAM_DEFAULT=`${Gg}/textures/uv.jpg`,qg.PARAM_ENV_DEFAULT=`${Gg}/textures/piz_compressed.exr`,qg.VIDEO_EXTENSIONS=[\\\\\\\"mp4\\\\\\\",\\\\\\\"webm\\\\\\\",\\\\\\\"ogv\\\\\\\"],qg.VIDEO_SOURCE_TYPE_BY_EXT={ogg:'video/ogg; codecs=\\\\\\\"theora, vorbis\\\\\\\"',ogv:'video/ogg; codecs=\\\\\\\"theora, vorbis\\\\\\\"',mp4:'video/mp4; codecs=\\\\\\\"avc1.42E01E, mp4a.40.2\\\\\\\"'},qg.MAX_CONCURRENT_LOADS_COUNT=qg._init_max_concurrent_loads_count(),qg.CONCURRENT_LOADS_DELAY=qg._init_concurrent_loads_delay(),qg.in_progress_loads_count=0,qg._queue=[];var Xg=n(115);class Yg extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.url=oa.STRING(qg.PARAM_DEFAULT,{fileBrowse:{type:[Ls.TEXTURE_IMAGE]}}),this.reload=oa.BUTTON(null,{callback:(t,e)=>{Jg.PARAM_CALLBACK_reload(t)}}),this.play=oa.BOOLEAN(1,{cook:!1,callback:t=>{Jg.PARAM_CALLBACK_gifUpdatePlay(t)}}),this.gifFrame=oa.INTEGER(0,{cook:!1,range:[0,100],rangeLocked:[!0,!1],callback:t=>{Jg.PARAM_CALLBACK_gifUpdateFrameIndex(t)}})}}}(aa))){}const $g=new Yg;class Jg extends af{constructor(){super(...arguments),this.paramsConfig=$g,this.textureParamsController=new wg(this),this._gifCanvasContext=null,this._tmpCanvasContext=null,this._parsedFrames=[],this._frameDelay=100,this._frameIndex=0}static type(){return\\\\\\\"gif\\\\\\\"}async requiredModules(){this.p.url.isDirty()&&await this.p.url.compute();const t=jg.extension(this.pv.url||\\\\\\\"\\\\\\\");return qg.module_names(t)}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Qi.NEVER),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{let t=[this.p.url];t=t.concat(this.textureParamsController.paramLabelsParams()),this.params.label.init(t,(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\"),n=e[e.length-1],i=this.textureParamsController.paramLabels();return[n].concat(i)}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){const e=await fetch(this.pv.url),n=await e.arrayBuffer(),i=await Object(Xg.parseGIF)(n);this._parsedFrames=await Object(Xg.decompressFrames)(i,!0);const r=this._parsedFrames[0];if(this._frameDelay=r.delay,this._frameIndex=this.pv.gifFrame-1,this._createCanvas(),this._gifCanvasElement){const t=new Tg(this._gifCanvasElement);await this.textureParamsController.update(t),this.setTexture(t)}else this.states.error.set(\\\\\\\"failed to create canvas\\\\\\\")}_createCanvas(){const t=this._parsedFrames[0];this._gifCanvasElement=document.createElement(\\\\\\\"canvas\\\\\\\"),this._tmpCanvasElement=document.createElement(\\\\\\\"canvas\\\\\\\"),this._gifCanvasElement.width=t.dims.width,this._gifCanvasElement.height=t.dims.height,this._tmpCanvasElement.width=t.dims.width,this._tmpCanvasElement.height=t.dims.height,this._gifCanvasContext=this._gifCanvasElement.getContext(\\\\\\\"2d\\\\\\\"),this._tmpCanvasContext=this._tmpCanvasElement.getContext(\\\\\\\"2d\\\\\\\"),this._drawNextFrame()}_drawOnCanvas(){if(!(this._gifCanvasContext&&this._tmpCanvasElement&&this._tmpCanvasContext))return;let t=this._parsedFrames[this._frameIndex];if(t||(console.warn(`no frame at index ${this._frameIndex}, using last frame`),t=this._parsedFrames[this._parsedFrames.length-1]),t){const e=t.dims;this._frameImageData&&e.width==this._frameImageData.width&&e.height==this._frameImageData.height||(this._tmpCanvasElement.width=e.width,this._tmpCanvasElement.height=e.height,this._frameImageData=this._tmpCanvasContext.createImageData(e.width,e.height)),this._frameImageData.data.set(t.patch),this._tmpCanvasContext.putImageData(this._frameImageData,0,0),this._gifCanvasContext.drawImage(this._tmpCanvasElement,e.left,e.top);const n=this.containerController.container().texture();if(!n)return;n.needsUpdate=!0}}_drawNextFrame(){this._frameIndex++,this._frameIndex>=this._parsedFrames.length&&(this._frameIndex=0),this._drawOnCanvas(),this.pv.play&&setTimeout((()=>{this._drawNextFrame()}),this._frameDelay)}gifUpdateFrameIndex(){this._frameIndex=this.pv.gifFrame,this._drawOnCanvas()}static PARAM_CALLBACK_reload(t){t.paramCallbackReload()}paramCallbackReload(){this.p.url.setDirty()}static PARAM_CALLBACK_gifUpdatePlay(t){t.gifUpdatePlay()}gifUpdatePlay(){this.pv.play&&this._drawNextFrame()}static PARAM_CALLBACK_gifUpdateFrameIndex(t){t.gifUpdateFrameIndex()}}const Zg=[\\\\\\\"mp4\\\\\\\",\\\\\\\"ogv\\\\\\\"];class Qg{static isStaticImageUrl(t){const e=t.split(\\\\\\\"?\\\\\\\")[0].split(\\\\\\\".\\\\\\\"),n=e[e.length-1];return!Zg.includes(n)}}class Kg extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.url=oa.STRING(qg.PARAM_DEFAULT,{fileBrowse:{type:[Ls.TEXTURE_IMAGE]}}),this.reload=oa.BUTTON(null,{callback:(t,e)=>{ev.PARAM_CALLBACK_reload(t,e)}})}}}(aa))){}const tv=new Kg;class ev extends af{constructor(){super(...arguments),this.paramsConfig=tv,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"image\\\\\\\"}async requiredModules(){this.p.url.isDirty()&&await this.p.url.compute();const t=jg.extension(this.pv.url||\\\\\\\"\\\\\\\");return qg.module_names(t)}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Qi.NEVER),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{let t=[this.p.url];t=t.concat(this.textureParamsController.paramLabelsParams()),this.params.label.init(t,(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\"),n=e[e.length-1],i=this.textureParamsController.paramLabels();return[n].concat(i)}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){if(Qg.isStaticImageUrl(this.pv.url)){const e=await this._loadTexture(this.pv.url);if(e){const n=t[0];n&&wg.copyTextureAttributes(e,n),await this.textureParamsController.update(e),this.setTexture(e)}else this._clearTexture()}else this.states.error.set(\\\\\\\"input is not an image\\\\\\\")}static PARAM_CALLBACK_reload(t,e){t.paramCallbackReload()}paramCallbackReload(){this.p.url.setDirty()}async _loadTexture(t){let e=null;const n=this.p.url,i=new qg(t,n,this,this.scene());try{e=await i.load_texture_from_url_or_op({tdataType:this.pv.ttype&&this.pv.tadvanced,dataType:this.pv.type}),e&&(e.matrixAutoUpdate=!1)}catch(t){}return e||this.states.error.set(`could not load texture '${t}'`),e}}var nv=n(32);const iv=.005;class rv{constructor(t,e=1024){this.renderer=t,this.res=e,this.objectTargets=[],this.lights=[],this.scene=new fr,this.buffer1Active=!1,this._params={lightRadius:1,iterations:1,iterationBlend:iv,blur:!1,blurAmount:0},this._objectStateByObject=new WeakMap,this._previousRenderTarget=null,this._lightHierarchyStateByLight=new WeakMap,this._lightMatrixStateByLight=new WeakMap,this._t=new p.a,this._q=new au.a,this._s=new p.a;const n=Zf.isAndroid()||Zf.isiOS()?w.M:w.G;this.progressiveLightMap1=new Z(this.res,this.res,{type:n}),this.progressiveLightMap2=new Z(this.res,this.res,{type:n}),this.uvMat=this._createUVMat()}textureRenderTarget(){return this.progressiveLightMap2}texture(){return this.textureRenderTarget().texture}setParams(t){this._params.lightRadius=t.lightRadius,this._params.iterations=t.iterations,this._params.iterationBlend=t.iterationBlend,this._params.blur=t.blur,this._params.blurAmount=t.blurAmount}init(t,e){this._setObjects(t),this._setLights(e)}_setObjects(t){this.objectTargets=[];for(let e of t)null==this.blurringPlane&&this._initializeBlurPlane(this.res,this.progressiveLightMap1),this.objectTargets.push(e);this._saveObjectsState()}_setLights(t){this.lights=t;for(let e of t)this._saveLightHierarchyState(e),this.scene.attach(e),this._saveLightMatrixState(e)}_saveLightHierarchyState(t){this._lightHierarchyStateByLight.set(t,{parent:t.parent,matrixAutoUpdate:t.matrixAutoUpdate}),t.matrixAutoUpdate=!0}_saveLightMatrixState(t){t.updateMatrix(),t.matrix.decompose(this._t,this._q,this._s),this._lightMatrixStateByLight.set(t,{matrix:t.matrix.clone(),position:this._t.clone()})}_saveObjectsState(){let t=0;for(let e of this.objectTargets)this._objectStateByObject.set(e,{frustumCulled:e.frustumCulled,material:e.material,parent:e.parent,castShadow:e.castShadow,receiveShadow:e.receiveShadow}),e.material=this.uvMat,e.frustumCulled=!1,e.castShadow=!0,e.receiveShadow=!0,e.renderOrder=1e3+t,this.scene.attach(e),t++;this._previousRenderTarget=this.renderer.getRenderTarget()}_moveLights(){const t=this._params.lightRadius;for(let e of this.lights){const n=this._lightMatrixStateByLight.get(e);if(n){const i=n.position;e.position.x=i.x+t*(Math.random()-.5),e.position.y=i.y+t*(Math.random()-.5),e.position.z=i.z+t*(Math.random()-.5)}}}restoreState(){this._restoreObjectsState(),this._restoreLightsState(),this.renderer.setRenderTarget(this._previousRenderTarget)}_restoreObjectsState(){for(let t of this.objectTargets){const e=this._objectStateByObject.get(t);if(e){t.frustumCulled=e.frustumCulled,t.castShadow=e.castShadow,t.receiveShadow=e.receiveShadow,t.material=e.material;const n=e.parent;n&&n.add(t)}}}_restoreLightsState(){var t;for(let e of this.lights){const n=this._lightHierarchyStateByLight.get(e),i=this._lightMatrixStateByLight.get(e);n&&i&&(e.matrixAutoUpdate=n.matrixAutoUpdate,e.matrix.copy(i.matrix),e.matrix.decompose(e.position,e.quaternion,e.scale),e.updateMatrix(),null===(t=n.parent)||void 0===t||t.attach(e))}}runUpdates(t){if(!this.blurMaterial)return;if(null==this.blurringPlane)return;const e=this._params.iterations;this.blurMaterial.uniforms.pixelOffset.value=this._params.blurAmount/this.res,this.blurringPlane.visible=this._params.blur,this.uvMat.uniforms.iterationBlend.value=this._params.iterationBlend,this._clear(t);for(let n=0;n{i.vertexShader=\\\\\\\"#define USE_UV\\\\n\\\\\\\"+i.vertexShader.slice(0,-2)+\\\\\\\"\\\\tgl_Position = vec4((uv - 0.5) * 2.0, 1.0, 1.0); }\\\\\\\";const r=i.fragmentShader.indexOf(\\\\\\\"void main() {\\\\\\\");i.fragmentShader=\\\\\\\"#define USE_UV\\\\n\\\\\\\"+i.fragmentShader.slice(0,r)+\\\\\\\"\\\\tuniform sampler2D previousShadowMap;\\\\n\\\\tuniform float pixelOffset;\\\\n\\\\\\\"+i.fragmentShader.slice(r-1,-2)+\\\\\\\"\\\\tgl_FragColor.rgb = (\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( pixelOffset, 0.0 )).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( 0.0 , pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( 0.0 , -pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2(-pixelOffset, 0.0 )).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( pixelOffset, pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2(-pixelOffset, pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( pixelOffset, -pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2(-pixelOffset, -pixelOffset)).rgb)/8.0;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\";const s={previousShadowMap:{value:e.texture},pixelOffset:{value:.5/t}};i.uniforms.previousShadowMap=s.previousShadowMap,i.uniforms.pixelOffset=s.pixelOffset,n.uniforms.previousShadowMap=s.previousShadowMap,n.uniforms.pixelOffset=s.pixelOffset,n.userData.shader=i},n}_createUVMat(){const t=new Gf.a;return t.uniforms={previousShadowMap:{value:null},iterationBlend:{value:iv}},t.name=\\\\\\\"uvMat\\\\\\\",t.onBeforeCompile=e=>{e.vertexShader=\\\\\\\"#define USE_LIGHTMAP\\\\n\\\\\\\"+e.vertexShader.slice(0,-2)+\\\\\\\"\\\\tgl_Position = vec4((uv2 - 0.5) * 2.0, 1.0, 1.0); }\\\\\\\";const n=e.fragmentShader.indexOf(\\\\\\\"void main() {\\\\\\\");e.fragmentShader=\\\\\\\"varying vec2 vUv2;\\\\n\\\\\\\"+e.fragmentShader.slice(0,n)+\\\\\\\"\\\\tuniform sampler2D previousShadowMap;\\\\n\\\\tuniform float iterationBlend;\\\\n\\\\\\\"+e.fragmentShader.slice(n-1,-2)+\\\\\\\"\\\\nvec3 texelOld = texture2D(previousShadowMap, vUv2).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = mix(texelOld, gl_FragColor.rgb, iterationBlend);\\\\n\\\\t\\\\t\\\\t\\\\t// gl_FragColor.rgb = vec3(vUv2,1.0);\\\\n\\\\t\\\\t\\\\t}\\\\\\\";const i={previousShadowMap:{value:this.progressiveLightMap1.texture},iterationBlend:{value:iv}};e.uniforms.previousShadowMap=i.previousShadowMap,e.uniforms.iterationBlend=i.iterationBlend,t.uniforms.previousShadowMap=i.previousShadowMap,t.uniforms.iterationBlend=i.iterationBlend,t.userData.shader=e},t}}const sv=new class extends aa{constructor(){super(...arguments),this.update=oa.BUTTON(null,{callback:t=>{ov.PARAM_CALLBACK_updateManual(t)}}),this.useCameraRenderer=oa.BOOLEAN(1),this.lightMapRes=oa.INTEGER(1024,{range:[1,2048],rangeLocked:[!0,!1]}),this.iterations=oa.INTEGER(512,{range:[1,2048],rangeLocked:[!0,!1]}),this.iterationBlend=oa.FLOAT(iv,{range:[0,1],rangeLocked:[!0,!0]}),this.blur=oa.BOOLEAN(1),this.blurAmount=oa.FLOAT(1,{visibleIf:{blur:1},range:[0,1],rangeLocked:[!0,!1]}),this.lightRadius=oa.FLOAT(1,{range:[0,10]}),this.objectsMask=oa.STRING(\\\\\\\"\\\\\\\"),this.lightsMask=oa.STRING(\\\\\\\"*\\\\\\\"),this.printResolveObjectsList=oa.BUTTON(null,{callback:t=>{ov.PARAM_CALLBACK_printResolveObjectsList(t)}})}};class ov extends af{constructor(){super(...arguments),this.paramsConfig=sv,this._includedObjects=[],this._includedLights=[]}static type(){return\\\\\\\"lightMap\\\\\\\"}async cook(){this._updateManual()}async _createLightMapController(){const t=await ai.renderersController.firstRenderer();if(!t)return void console.warn(\\\\\\\"no renderer found\\\\\\\");return new rv(t,this.pv.lightMapRes)}static PARAM_CALLBACK_update_updateMode(t){}async _updateManual(){if(this.lightMapController=this.lightMapController||await this._createLightMapController(),!this.lightMapController)return;const t=this.scene().mainCameraNode();if(!t)return;this._updateObjectsAndLightsList(),this.lightMapController.init(this._includedObjects,this._includedLights);const e=t.camera();this.lightMapController.setParams({lightRadius:this.pv.lightRadius,iterations:this.pv.iterations,iterationBlend:this.pv.iterationBlend,blur:this.pv.blur,blurAmount:this.pv.blurAmount}),this.lightMapController.runUpdates(e),this.lightMapController.restoreState();const n=this.lightMapController.textureRenderTarget();if(this.pv.useCameraRenderer)this.setTexture(n.texture);else{this._data_texture_controller=this._data_texture_controller||new If(Pf.Float32Array),this._renderer_controller=this._renderer_controller||new Ff(this);const t=await this._renderer_controller.renderer(),e=this._data_texture_controller.from_render_target(t,n);this.setTexture(e)}}static PARAM_CALLBACK_updateManual(t){t._updateManual()}_updateObjectsAndLightsList(){let t=[],e=[];this._includedLights=[],this._includedObjects=[];const n=new WeakSet;if(\\\\\\\"\\\\\\\"!=this.pv.lightsMask){e=this.scene().objectsByMask(this.pv.lightsMask);for(let t of e)t instanceof nv.a&&(this._includedLights.push(t),n.add(t))}if(\\\\\\\"\\\\\\\"!=this.pv.objectsMask){t=this.scene().objectsByMask(this.pv.objectsMask);for(let e of t)e instanceof nv.a||!n.has(e)&&e instanceof k.a&&this._includedObjects.push(e)}}static PARAM_CALLBACK_printResolveObjectsList(t){t._printResolveObjectsList()}_printResolveObjectsList(){this._updateObjectsAndLightsList(),console.log(\\\\\\\"included objects:\\\\\\\"),console.log(this._includedObjects),console.log(\\\\\\\"included lights:\\\\\\\"),console.log(this._includedLights)}}const av=new aa;class lv extends af{constructor(){super(...arguments),this.paramsConfig=av}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.NEVER)}async cook(t){const e=t[0];this.setTexture(e)}}const cv=[nr.ORTHOGRAPHIC,nr.PERSPECTIVE];class uv extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.camera=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.OBJ,types:cv}}),this.resolution=oa.VECTOR2([1024,1024]),this.useCameraRenderer=oa.BOOLEAN(1),this.render=oa.BUTTON(null,{callback:t=>{dv.PARAM_CALLBACK_render(t)}})}}}(aa))){}const hv=new uv;class dv extends af{constructor(){super(...arguments),this.paramsConfig=hv,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"render\\\\\\\"}async cook(){this._texture_scene=this.scene().threejsScene(),this._camera_node=this.pv.camera.nodeWithContext(Ki.OBJ),this._camera_node&&cv.includes(this._camera_node.type())?(this._texture_camera=this._camera_node.object,await this._camera_node.compute(),this.renderOnTarget()):this._texture_camera=void 0}async renderOnTarget(){if(await this.createRenderTargetIfRequired(),!(this._render_target&&this._texture_scene&&this._texture_camera))return;this._renderer_controller=this._renderer_controller||new Ff(this);const t=await this._renderer_controller.renderer(),e=t.getRenderTarget();if(t.setRenderTarget(this._render_target),t.clear(),t.render(this._texture_scene,this._texture_camera),t.setRenderTarget(e),this._render_target.texture)if(this.pv.useCameraRenderer)this.setTexture(this._render_target.texture);else{this._data_texture_controller=this._data_texture_controller||new If(Pf.Float32Array);const e=this._data_texture_controller.from_render_target(t,this._render_target);await this.textureParamsController.update(e),this.setTexture(e)}else this.cookController.endCook()}async renderTarget(){return this._render_target=this._render_target||await this._createRenderTarget(this.pv.resolution.x,this.pv.resolution.y)}async createRenderTargetIfRequired(){var t;this._render_target&&this._renderTargetResolutionValid()||(this._render_target=await this._createRenderTarget(this.pv.resolution.x,this.pv.resolution.y),null===(t=this._data_texture_controller)||void 0===t||t.reset())}_renderTargetResolutionValid(){if(this._render_target){const t=this._render_target.texture.image;return t.width==this.pv.resolution.x&&t.height==this.pv.resolution.y}return!1}async _createRenderTarget(t,e){if(this._render_target){const n=this._render_target.texture.image;if(n.width==t&&n.height==e)return this._render_target}const n=w.n,i=w.n,r=w.V,s=w.ob;var o=new Z(t,e,{wrapS:n,wrapT:i,minFilter:r,magFilter:s,format:w.Ib,generateMipmaps:!0,type:Zf.isiOS()?w.M:w.G,stencilBuffer:!1,depthBuffer:!1});return await this.textureParamsController.update(o.texture),ai.warn(\\\\\\\"created render target\\\\\\\",this.path(),t,e),o}static PARAM_CALLBACK_render(t){t.renderOnTarget()}}const pv=new class extends aa{constructor(){super(...arguments),this.input=oa.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class _v extends af{constructor(){super(...arguments),this.paramsConfig=pv}static type(){return\\\\\\\"switch\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4),this.io.inputs.initInputsClonedState(Qi.NEVER),this.cookController.disallowInputsEvaluation()}async cook(){const t=this.pv.input;if(this.io.inputs.has_input(t)){const e=await this.containerController.requestInputContainer(t);if(e)return void this.setTexture(e.texture())}else this.states.error.set(`no input ${t}`);this.cookController.endCook()}}class mv extends(xg(aa)){}const fv=new mv;class gv extends af{constructor(){super(...arguments),this.paramsConfig=fv,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"textureProperties\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Qi.FROM_NODE])}async cook(t){const e=t[0];this.textureParamsController.update(e),this.setTexture(e)}}class vv extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.url=oa.STRING(qg.PARAM_DEFAULT,{fileBrowse:{type:[Ls.TEXTURE_VIDEO]}}),this.reload=oa.BUTTON(null,{callback:(t,e)=>{xv.PARAM_CALLBACK_reload(t,e)}}),this.play=oa.BOOLEAN(1,{cook:!1,callback:t=>{xv.PARAM_CALLBACK_video_update_play(t)}}),this.muted=oa.BOOLEAN(1,{cook:!1,callback:t=>{xv.PARAM_CALLBACK_video_update_muted(t)}}),this.loop=oa.BOOLEAN(1,{cook:!1,callback:t=>{xv.PARAM_CALLBACK_video_update_loop(t)}}),this.videoTime=oa.FLOAT(0,{cook:!1}),this.setVideoTime=oa.BUTTON(null,{cook:!1,callback:t=>{xv.PARAM_CALLBACK_video_update_time(t)}})}}}(aa))){}const yv=new vv;class xv extends af{constructor(){super(...arguments),this.paramsConfig=yv,this.textureParamsController=new wg(this)}static type(){return Lg.VIDEO}async requiredModules(){this.p.url.isDirty()&&await this.p.url.compute();const t=jg.extension(this.pv.url||\\\\\\\"\\\\\\\");return qg.module_names(t)}HTMLVideoElement(){return this._video}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Qi.NEVER),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\");return e[e.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){if(Qg.isStaticImageUrl(this.pv.url))this.states.error.set(\\\\\\\"input is not a video\\\\\\\");else{const e=await this._load_texture(this.pv.url);if(e){this._video=e.image,this._video&&document.body.appendChild(this._video);const n=t[0];n&&wg.copyTextureAttributes(e,n),this.video_update_loop(),this.video_update_muted(),this.video_update_play(),this.video_update_time(),await this.textureParamsController.update(e),this.setTexture(e)}else this.cookController.endCook()}}static PARAM_CALLBACK_video_update_time(t){t.video_update_time()}static PARAM_CALLBACK_video_update_play(t){t.video_update_play()}static PARAM_CALLBACK_video_update_muted(t){t.video_update_muted()}static PARAM_CALLBACK_video_update_loop(t){t.video_update_loop()}async video_update_time(){if(this._video){const t=this.p.videoTime;t.isDirty()&&await t.compute(),this._video.currentTime=t.value}}video_update_muted(){this._video&&(this._video.muted=this.pv.muted)}video_update_loop(){this._video&&(this._video.loop=this.pv.loop)}video_update_play(){this._video&&(this.pv.play?this._video.play():this._video.pause())}static PARAM_CALLBACK_reload(t,e){t.paramCallbackReload()}paramCallbackReload(){this.p.url.setDirty()}async _load_texture(t){let e=null;const n=this.p.url;this._texture_loader=this._texture_loader||new qg(t,n,this,this.scene());try{e=await this._texture_loader.load_texture_from_url_or_op({tdataType:this.pv.ttype&&this.pv.tadvanced,dataType:this.pv.type}),e&&(e.matrixAutoUpdate=!1)}catch(t){}return e||this.states.error.set(`could not load texture '${t}'`),e}}class bv extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.res=oa.VECTOR2([1024,1024])}}}(aa))){}const wv=new bv;class Tv extends af{constructor(){super(...arguments),this.paramsConfig=wv,this.textureParamsController=new wg(this)}static type(){return Lg.WEB_CAM}HTMLVideoElement(){return this._video}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Qi.NEVER)}_createHTMLVideoElement(){this._video&&document.body.removeChild(this._video);const t=document.createElement(\\\\\\\"video\\\\\\\");return t.style.display=\\\\\\\"none\\\\\\\",t.width=this.pv.res.x,t.height=this.pv.res.y,t.autoplay=!0,t.setAttribute(\\\\\\\"autoplay\\\\\\\",\\\\\\\"true\\\\\\\"),t.setAttribute(\\\\\\\"muted\\\\\\\",\\\\\\\"true\\\\\\\"),t.setAttribute(\\\\\\\"playsinline\\\\\\\",\\\\\\\"true\\\\\\\"),document.body.appendChild(t),t}async cook(t){this._video=this._createHTMLVideoElement();const e=new zg(this._video),n=t[0];if(n&&wg.copyTextureAttributes(e,n),await this.textureParamsController.update(e),navigator&&navigator.mediaDevices&&navigator.mediaDevices.getUserMedia){const t={video:{width:this.pv.res.x,height:this.pv.res.y,facingMode:\\\\\\\"user\\\\\\\"}};navigator.mediaDevices.getUserMedia(t).then((t=>{this._video&&(this._video.srcObject=t,this._video.play(),this.setTexture(e))})).catch((t=>{this.states.error.set(\\\\\\\"Unable to access the camera/webcam\\\\\\\")}))}else this.states.error.set(\\\\\\\"MediaDevices interface not available.\\\\\\\")}}class Av extends ia{static context(){return Ki.COP}cook(){this.cookController.endCook()}}class Ev extends Av{}class Mv extends Ev{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Sv extends Ev{constructor(){super(...arguments),this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Cv extends Ev{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Nv extends Ev{constructor(){super(...arguments),this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Lv extends Av{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Ov extends Ev{constructor(){super(...arguments),this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}var Rv,Pv;!function(t){t.START=\\\\\\\"start\\\\\\\",t.STOP=\\\\\\\"stop\\\\\\\",t.UPDATE=\\\\\\\"update\\\\\\\"}(Rv||(Rv={})),function(t){t.START=\\\\\\\"start\\\\\\\",t.COMPLETE=\\\\\\\"completed\\\\\\\"}(Pv||(Pv={}));const Iv=new class extends aa{constructor(){super(...arguments),this.animation=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.ANIM},dependentOnFoundNode:!1}),this.play=oa.BUTTON(null,{callback:t=>{Fv.PARAM_CALLBACK_play(t)}}),this.pause=oa.BUTTON(null,{callback:t=>{Fv.PARAM_CALLBACK_pause(t)}})}};class Fv extends Ba{constructor(){super(...arguments),this.paramsConfig=Iv}static type(){return\\\\\\\"animation\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(Rv.START,$o.BASE,this._play.bind(this)),new Jo(Rv.STOP,$o.BASE,this._pause.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(Pv.START,$o.BASE),new Jo(Pv.COMPLETE,$o.BASE)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.animation],(()=>this.pv.animation.path()))}))}))}processEvent(t){}static PARAM_CALLBACK_play(t){t._play({})}static PARAM_CALLBACK_pause(t){t._pause()}async _play(t){const e=this.p.animation;e.isDirty()&&await e.compute();const n=e.value.nodeWithContext(Ki.ANIM);if(!n)return;const i=await n.compute();i&&(this._timeline_builder=i.coreContent(),this._timeline_builder&&(this._timeline&&this._timeline.kill(),this._timeline=L_.timeline(),this._timeline_builder.populate(this._timeline),this._timeline.vars.onStart=()=>{this.trigger_animation_started(t)},this._timeline.vars.onComplete=()=>{this._timeline&&this._timeline.kill(),this.trigger_animation_completed(t)}))}_pause(){this._timeline&&this._timeline.pause()}trigger_animation_started(t){this.dispatchEventToOutput(Pv.START,t)}trigger_animation_completed(t){this.dispatchEventToOutput(Pv.COMPLETE,t)}}const Dv=\\\\\\\"event\\\\\\\";const kv=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(1),this.inputsCount=oa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1]})}};class Bv extends Ba{constructor(){super(...arguments),this.paramsConfig=kv}static type(){return\\\\\\\"any\\\\\\\"}initializeNode(){this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_input_name_function(this._input_name.bind(this)),this.io.connection_points.set_output_name_function((()=>Dv)),this.io.connection_points.set_expected_output_types_function((()=>[$o.BASE]))}_expected_input_types(){const t=new Array(this.pv.inputsCount);return t.fill($o.BASE),t}_input_name(t){return`trigger${t}`}async processEvent(t){this.p.active.isDirty()&&await this.p.active.compute(),this.pv.active&&this.dispatchEventToOutput(Dv,t)}}const zv=new class extends aa{constructor(){super(...arguments),this.blocking=oa.BOOLEAN(1)}};class Uv extends Ba{constructor(){super(...arguments),this.paramsConfig=zv}static type(){return\\\\\\\"block\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(\\\\\\\"in\\\\\\\",$o.BASE,this._process_incoming_event.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(Uv.OUTPUT,$o.BASE)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.blocking],(()=>this.pv.blocking?\\\\\\\"blocking (X)\\\\\\\":\\\\\\\"pass-through (--\\\\x3e)\\\\\\\"))}))}))}trigger_output(t){this.dispatchEventToOutput(Uv.OUTPUT,t)}_process_incoming_event(t){this.pv.blocking||this.trigger_output(t)}}var Gv;Uv.OUTPUT=\\\\\\\"output\\\\\\\",function(t){t.OUT=\\\\\\\"out\\\\\\\"}(Gv||(Gv={}));const Vv=new class extends aa{constructor(){super(...arguments),this.dispatch=oa.BUTTON(null,{callback:t=>{Hv.PARAM_CALLBACK_execute(t)}})}};class Hv extends Ba{constructor(){super(...arguments),this.paramsConfig=Vv}static type(){return\\\\\\\"button\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Jo(Gv.OUT,$o.BASE)])}processEvent(t){}process_event_execute(t){this.dispatchEventToOutput(Gv.OUT,t)}static PARAM_CALLBACK_execute(t){t.process_event_execute({})}}class jv extends Ba{constructor(){super(...arguments),this._controls_by_viewer=new Map}async apply_controls(t,e){var n;null===(n=e.controlsController)||void 0===n||n.dispose_controls();const i=e.canvas();if(!i)return;const r=await this.create_controls_instance(t,i),s=this._controls_by_viewer.get(e);s&&s.dispose(),this._controls_by_viewer.set(e,r);const o=ai.performance.performanceManager().now();return r.name=`${this.path()}:${t.name}:${o}:${this.controls_id()}`,await this.params.evalAll(),this.setup_controls(r),r}controls_id(){return JSON.stringify(this.params.all.map((t=>t.valueSerialized())))}}var Wv=n(28);const qv=new p.a(0,0,1),Xv=new Wv.a,Yv=new au.a,$v=new au.a(-Math.sqrt(.5),0,0,Math.sqrt(.5)),Jv={type:\\\\\\\"change\\\\\\\"};class Zv extends $.a{constructor(t){super(),!1===window.isSecureContext&&console.error(\\\\\\\"THREE.DeviceOrientationControls: DeviceOrientationEvent is only available in secure contexts (https)\\\\\\\");const e=this,n=new au.a;this.object=t,this.object.rotation.reorder(\\\\\\\"YXZ\\\\\\\"),this.enabled=!0,this.deviceOrientation={},this.screenOrientation=0,this.alphaOffset=0;const i=function(t){e.deviceOrientation=t},r=function(){e.screenOrientation=window.orientation||0};this.connect=function(){r(),void 0!==window.DeviceOrientationEvent&&\\\\\\\"function\\\\\\\"==typeof window.DeviceOrientationEvent.requestPermission?window.DeviceOrientationEvent.requestPermission().then((function(t){\\\\\\\"granted\\\\\\\"==t&&(window.addEventListener(\\\\\\\"orientationchange\\\\\\\",r),window.addEventListener(\\\\\\\"deviceorientation\\\\\\\",i))})).catch((function(t){console.error(\\\\\\\"THREE.DeviceOrientationControls: Unable to use DeviceOrientation API:\\\\\\\",t)})):(window.addEventListener(\\\\\\\"orientationchange\\\\\\\",r),window.addEventListener(\\\\\\\"deviceorientation\\\\\\\",i)),e.enabled=!0},this.disconnect=function(){window.removeEventListener(\\\\\\\"orientationchange\\\\\\\",r),window.removeEventListener(\\\\\\\"deviceorientation\\\\\\\",i),e.enabled=!1},this.update=function(){if(!1===e.enabled)return;const t=e.deviceOrientation;if(t){const i=t.alpha?Ln.e(t.alpha)+e.alphaOffset:0,r=t.beta?Ln.e(t.beta):0,s=t.gamma?Ln.e(t.gamma):0,o=e.screenOrientation?Ln.e(e.screenOrientation):0;!function(t,e,n,i,r){Xv.set(n,e,-i,\\\\\\\"YXZ\\\\\\\"),t.setFromEuler(Xv),t.multiply($v),t.multiply(Yv.setFromAxisAngle(qv,-r))}(e.object.quaternion,i,r,s,o),8*(1-n.dot(e.object.quaternion))>1e-6&&(n.copy(e.object.quaternion),e.dispatchEvent(Jv))}},this.dispose=function(){e.disconnect()},this.connect()}}const Qv=new class extends aa{constructor(){super(...arguments),this.enabled=oa.BOOLEAN(1)}};class Kv extends jv{constructor(){super(...arguments),this.paramsConfig=Qv,this._controls_by_element_id=new Map}static type(){return rr.DEVICE_ORIENTATION}endEventName(){return\\\\\\\"end\\\\\\\"}async create_controls_instance(t,e){const n=new Zv(t);return this._controls_by_element_id.set(e.id,n),n}setup_controls(t){t.enabled=this.pv.enabled}update_required(){return!0}dispose_controls_for_html_element_id(t){const e=this._controls_by_element_id.get(t);e&&(e.dispose(),this._controls_by_element_id.delete(t))}}class ty{constructor(t=1,e=0,n=0){return this.radius=t,this.phi=e,this.theta=n,this}set(t,e,n){return this.radius=t,this.phi=e,this.theta=n,this}copy(t){return this.radius=t.radius,this.phi=t.phi,this.theta=t.theta,this}makeSafe(){const t=1e-6;return this.phi=Math.max(t,Math.min(Math.PI-t,this.phi)),this}setFromVector3(t){return this.setFromCartesianCoords(t.x,t.y,t.z)}setFromCartesianCoords(t,e,n){return this.radius=Math.sqrt(t*t+e*e+n*n),0===this.radius?(this.theta=0,this.phi=0):(this.theta=Math.atan2(t,n),this.phi=Math.acos(Ln.d(e/this.radius,-1,1))),this}clone(){return(new this.constructor).copy(this)}}const ey={type:\\\\\\\"change\\\\\\\"},ny={type:\\\\\\\"start\\\\\\\"},iy={type:\\\\\\\"end\\\\\\\"};class ry extends $.a{constructor(t,e){super(),void 0===e&&console.warn('THREE.OrbitControls: The second parameter \\\\\\\"domElement\\\\\\\" is now mandatory.'),e===document&&console.error('THREE.OrbitControls: \\\\\\\"document\\\\\\\" should not be used as the target \\\\\\\"domElement\\\\\\\". Please use \\\\\\\"renderer.domElement\\\\\\\" instead.'),this.object=t,this.domElement=e,this.domElement.style.touchAction=\\\\\\\"none\\\\\\\",this.enabled=!0,this.target=new p.a,this.minDistance=0,this.maxDistance=1/0,this.minZoom=0,this.maxZoom=1/0,this.minPolarAngle=0,this.maxPolarAngle=Math.PI,this.minAzimuthAngle=-1/0,this.maxAzimuthAngle=1/0,this.enableDamping=!1,this.dampingFactor=.05,this.enableZoom=!0,this.zoomSpeed=1,this.enableRotate=!0,this.rotateSpeed=1,this.enablePan=!0,this.panSpeed=1,this.screenSpacePanning=!0,this.keyPanSpeed=7,this.autoRotate=!1,this.autoRotateSpeed=2,this.enableKeys=!0,this.keyMode=\\\\\\\"pan\\\\\\\",this.keyRotateSpeedVertical=1,this.keyRotateSpeedHorizontal=1,this.keys={LEFT:\\\\\\\"ArrowLeft\\\\\\\",UP:\\\\\\\"ArrowUp\\\\\\\",RIGHT:\\\\\\\"ArrowRight\\\\\\\",BOTTOM:\\\\\\\"ArrowDown\\\\\\\"},this.mouseButtons={LEFT:w.hb.ROTATE,MIDDLE:w.hb.DOLLY,RIGHT:w.hb.PAN},this.touches={ONE:w.Tc.ROTATE,TWO:w.Tc.DOLLY_PAN},this.target0=this.target.clone(),this.position0=this.object.position.clone(),this.zoom0=this.object.zoom,this._domElementKeyEvents=null,this.getPolarAngle=function(){return o.phi},this.getAzimuthalAngle=function(){return o.theta},this.getDistance=function(){return this.object.position.distanceTo(this.target)},this.listenToKeyEvents=function(t){t.addEventListener(\\\\\\\"keydown\\\\\\\",q),this._domElementKeyEvents=t},this.saveState=function(){n.target0.copy(n.target),n.position0.copy(n.object.position),n.zoom0=n.object.zoom},this.reset=function(){n.target.copy(n.target0),n.object.position.copy(n.position0),n.object.zoom=n.zoom0,n.object.updateProjectionMatrix(),n.dispatchEvent(ey),n.update(),r=i.NONE},this.update=function(){const e=new p.a,h=(new au.a).setFromUnitVectors(t.up,new p.a(0,1,0)),d=h.clone().invert(),_=new p.a,m=new au.a,f=2*Math.PI;let g=!1;return function(){const t=n.object.position;if(e.copy(t).sub(n.target),e.applyQuaternion(h),o.setFromVector3(e),n.autoRotate&&r===i.NONE&&M(2*Math.PI/60/60*n.autoRotateSpeed),n.enableDamping){const t=a.theta*n.dampingFactor,e=a.phi*n.dampingFactor;tMath.PI&&(p-=f),v<-Math.PI?v+=f:v>Math.PI&&(v-=f),o.theta=p(p+v)/2?Math.max(p,o.theta):Math.min(v,o.theta)),o.phi=Math.max(n.minPolarAngle,Math.min(n.maxPolarAngle,o.phi)),o.makeSafe(),o.radius*=l,o.radius=Math.max(n.minDistance,Math.min(n.maxDistance,o.radius)),!0===n.enableDamping?n.target.addScaledVector(c,n.dampingFactor):n.target.add(c),e.setFromSpherical(o),e.applyQuaternion(d),t.copy(n.target).add(e),n.object.lookAt(n.target),!0===n.enableDamping?(a.theta*=1-n.dampingFactor,a.phi*=1-n.dampingFactor,c.multiplyScalar(1-n.dampingFactor)):(a.set(0,0,0),c.set(0,0,0)),l=1,!!(u||_.distanceToSquared(n.object.position)>s||8*(1-m.dot(n.object.quaternion))>s)&&(n.dispatchEvent(ey),_.copy(n.object.position),m.copy(n.object.quaternion),u=!1,!0)}}(),this.dispose=function(){n.domElement.removeEventListener(\\\\\\\"contextmenu\\\\\\\",X,!1),n.domElement.removeEventListener(\\\\\\\"pointerdown\\\\\\\",G,!1),n.domElement.removeEventListener(\\\\\\\"pointercancel\\\\\\\",j),n.domElement.removeEventListener(\\\\\\\"wheel\\\\\\\",W,!1),n.domElement.ownerDocument.removeEventListener(\\\\\\\"pointermove\\\\\\\",V,!1),n.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerup\\\\\\\",H,!1),null!==n._domElementKeyEvents&&n._domElementKeyEvents.removeEventListener(\\\\\\\"keydown\\\\\\\",q)};const n=this,i={NONE:-1,ROTATE:0,DOLLY:1,PAN:2,TOUCH_ROTATE:3,TOUCH_PAN:4,TOUCH_DOLLY_PAN:5,TOUCH_DOLLY_ROTATE:6};let r=i.NONE;const s=1e-6,o=new ty,a=new ty;let l=1;const c=new p.a;let u=!1;const h=new d.a,_=new d.a,m=new d.a,f=new d.a,g=new d.a,v=new d.a,y=new d.a,x=new d.a,b=new d.a,T=[],A={};function E(){return Math.pow(.95,n.zoomSpeed)}function M(t){a.theta-=t}function S(t){a.phi-=t}const C=function(){const t=new p.a;return function(e,n){t.setFromMatrixColumn(n,0),t.multiplyScalar(-e),c.add(t)}}(),N=function(){const t=new p.a;return function(e,i){!0===n.screenSpacePanning?t.setFromMatrixColumn(i,1):(t.setFromMatrixColumn(i,0),t.crossVectors(n.object.up,t)),t.multiplyScalar(e),c.add(t)}}(),L=function(){const t=new p.a;return function(e,i){const r=n.domElement;if(n.object.isPerspectiveCamera){const s=n.object.position;t.copy(s).sub(n.target);let o=t.length();o*=Math.tan(n.object.fov/2*Math.PI/180),C(2*e*o/r.clientHeight,n.object.matrix),N(2*i*o/r.clientHeight,n.object.matrix)}else n.object.isOrthographicCamera?(C(e*(n.object.right-n.object.left)/n.object.zoom/r.clientWidth,n.object.matrix),N(i*(n.object.top-n.object.bottom)/n.object.zoom/r.clientHeight,n.object.matrix)):(console.warn(\\\\\\\"WARNING: OrbitControls.js encountered an unknown camera type - pan disabled.\\\\\\\"),n.enablePan=!1)}}();function O(t){n.object.isPerspectiveCamera?l/=t:n.object.isOrthographicCamera?(n.object.zoom=Math.max(n.minZoom,Math.min(n.maxZoom,n.object.zoom*t)),n.object.updateProjectionMatrix(),u=!0):(console.warn(\\\\\\\"WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled.\\\\\\\"),n.enableZoom=!1)}function R(t){n.object.isPerspectiveCamera?l*=t:n.object.isOrthographicCamera?(n.object.zoom=Math.max(n.minZoom,Math.min(n.maxZoom,n.object.zoom/t)),n.object.updateProjectionMatrix(),u=!0):(console.warn(\\\\\\\"WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled.\\\\\\\"),n.enableZoom=!1)}function P(t){h.set(t.clientX,t.clientY)}function I(t){f.set(t.clientX,t.clientY)}function F(){if(1===T.length)h.set(T[0].pageX,T[0].pageY);else{const t=.5*(T[0].pageX+T[1].pageX),e=.5*(T[0].pageY+T[1].pageY);h.set(t,e)}}function D(){if(1===T.length)f.set(T[0].pageX,T[0].pageY);else{const t=.5*(T[0].pageX+T[1].pageX),e=.5*(T[0].pageY+T[1].pageY);f.set(t,e)}}function k(){const t=T[0].pageX-T[1].pageX,e=T[0].pageY-T[1].pageY,n=Math.sqrt(t*t+e*e);y.set(0,n)}function B(t){if(1==T.length)_.set(t.pageX,t.pageY);else{const e=J(t),n=.5*(t.pageX+e.x),i=.5*(t.pageY+e.y);_.set(n,i)}m.subVectors(_,h).multiplyScalar(n.rotateSpeed);const e=n.domElement;M(2*Math.PI*m.x/e.clientHeight),S(2*Math.PI*m.y/e.clientHeight),h.copy(_)}function z(t){if(1===T.length)g.set(t.pageX,t.pageY);else{const e=J(t),n=.5*(t.pageX+e.x),i=.5*(t.pageY+e.y);g.set(n,i)}v.subVectors(g,f).multiplyScalar(n.panSpeed),L(v.x,v.y),f.copy(g)}function U(t){const e=J(t),i=t.pageX-e.x,r=t.pageY-e.y,s=Math.sqrt(i*i+r*r);x.set(0,s),b.set(0,Math.pow(x.y/y.y,n.zoomSpeed)),O(b.y),y.copy(x)}function G(t){!1!==n.enabled&&(0===T.length&&(n.domElement.setPointerCapture(t.pointerId),n.domElement.ownerDocument.addEventListener(\\\\\\\"pointermove\\\\\\\",V),n.domElement.ownerDocument.addEventListener(\\\\\\\"pointerup\\\\\\\",H)),function(t){T.push(t)}(t),\\\\\\\"touch\\\\\\\"===t.pointerType?function(t){switch($(t),T.length){case 1:switch(n.touches.ONE){case w.Tc.ROTATE:if(!1===n.enableRotate)return;F(),r=i.TOUCH_ROTATE;break;case w.Tc.PAN:if(!1===n.enablePan)return;D(),r=i.TOUCH_PAN;break;default:r=i.NONE}break;case 2:switch(n.touches.TWO){case w.Tc.DOLLY_PAN:if(!1===n.enableZoom&&!1===n.enablePan)return;n.enableZoom&&k(),n.enablePan&&D(),r=i.TOUCH_DOLLY_PAN;break;case w.Tc.DOLLY_ROTATE:if(!1===n.enableZoom&&!1===n.enableRotate)return;n.enableZoom&&k(),n.enableRotate&&F(),r=i.TOUCH_DOLLY_ROTATE;break;default:r=i.NONE}break;default:r=i.NONE}r!==i.NONE&&n.dispatchEvent(ny)}(t):function(t){let e;switch(t.button){case 0:e=n.mouseButtons.LEFT;break;case 1:e=n.mouseButtons.MIDDLE;break;case 2:e=n.mouseButtons.RIGHT;break;default:e=-1}switch(e){case w.hb.DOLLY:if(!1===n.enableZoom)return;!function(t){y.set(t.clientX,t.clientY)}(t),r=i.DOLLY;break;case w.hb.ROTATE:if(t.ctrlKey||t.metaKey||t.shiftKey){if(!1===n.enablePan)return;I(t),r=i.PAN}else{if(!1===n.enableRotate)return;P(t),r=i.ROTATE}break;case w.hb.PAN:if(t.ctrlKey||t.metaKey||t.shiftKey){if(!1===n.enableRotate)return;P(t),r=i.ROTATE}else{if(!1===n.enablePan)return;I(t),r=i.PAN}break;default:r=i.NONE}r!==i.NONE&&n.dispatchEvent(ny)}(t))}function V(t){!1!==n.enabled&&(\\\\\\\"touch\\\\\\\"===t.pointerType?function(t){switch($(t),r){case i.TOUCH_ROTATE:if(!1===n.enableRotate)return;B(t),n.update();break;case i.TOUCH_PAN:if(!1===n.enablePan)return;z(t),n.update();break;case i.TOUCH_DOLLY_PAN:if(!1===n.enableZoom&&!1===n.enablePan)return;!function(t){n.enableZoom&&U(t),n.enablePan&&z(t)}(t),n.update();break;case i.TOUCH_DOLLY_ROTATE:if(!1===n.enableZoom&&!1===n.enableRotate)return;!function(t){n.enableZoom&&U(t),n.enableRotate&&B(t)}(t),n.update();break;default:r=i.NONE}}(t):function(t){if(!1===n.enabled)return;switch(r){case i.ROTATE:if(!1===n.enableRotate)return;!function(t){_.set(t.clientX,t.clientY),m.subVectors(_,h).multiplyScalar(n.rotateSpeed);var e=n.domElement;M(2*Math.PI*m.x/e.clientHeight),S(2*Math.PI*m.y/e.clientHeight),h.copy(_),n.update()}(t);break;case i.DOLLY:if(!1===n.enableZoom)return;!function(t){x.set(t.clientX,t.clientY),b.subVectors(x,y),b.y>0?O(E()):b.y<0&&R(E()),y.copy(x),n.update()}(t);break;case i.PAN:if(!1===n.enablePan)return;!function(t){g.set(t.clientX,t.clientY),v.subVectors(g,f).multiplyScalar(n.panSpeed),L(v.x,v.y),f.copy(g),n.update()}(t)}}(t))}function H(t){!1!==n.enabled&&(t.pointerType,n.dispatchEvent(iy),r=i.NONE,Y(t),0===T.length&&(n.domElement.releasePointerCapture(t.pointerId),n.domElement.ownerDocument.removeEventListener(\\\\\\\"pointermove\\\\\\\",V),n.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerup\\\\\\\",H)))}function j(t){Y(t)}function W(t){!1===n.enabled||!1===n.enableZoom||r!==i.NONE&&r!==i.ROTATE||(t.preventDefault(),n.dispatchEvent(ny),function(t){t.deltaY<0?R(E()):t.deltaY>0&&O(E()),n.update()}(t),n.dispatchEvent(iy))}function q(t){!1!==n.enabled&&!1!==n.enablePan&&function(t){let e=!1;if(\\\\\\\"pan\\\\\\\"==n.keyMode)switch(t.code){case n.keys.UP:L(0,n.keyPanSpeed),e=!0;break;case n.keys.BOTTOM:L(0,-n.keyPanSpeed),e=!0;break;case n.keys.LEFT:L(n.keyPanSpeed,0),e=!0;break;case n.keys.RIGHT:L(-n.keyPanSpeed,0),e=!0}else switch(t.code){case n.keys.UP:S(n.keyRotateSpeedVertical),e=!0;break;case n.keys.BOTTOM:S(-n.keyRotateSpeedVertical),e=!0;break;case n.keys.LEFT:M(n.keyRotateSpeedHorizontal),e=!0;break;case n.keys.RIGHT:M(-n.keyRotateSpeedHorizontal),e=!0}e&&(t.preventDefault(),n.update())}(t)}function X(t){!1!==n.enabled&&t.preventDefault()}function Y(t){delete A[t.pointerId];for(let e=0;e{hy.PARAM_CALLBACK_update_target(t)}}),this.enableKeys=oa.BOOLEAN(0),this.keysMode=oa.INTEGER(cy.indexOf(ly.PAN),{visibleIf:{enableKeys:1},menu:{entries:cy.map(((t,e)=>({name:t,value:e})))}}),this.keysPanSpeed=oa.FLOAT(7,{range:[0,10],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:cy.indexOf(ly.PAN)}}),this.keysRotateSpeedVertical=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:cy.indexOf(ly.ROTATE)}}),this.keysRotateSpeedHorizontal=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:cy.indexOf(ly.ROTATE)}})}};class hy extends jv{constructor(){super(...arguments),this.paramsConfig=uy,this._controls_by_element_id=new Map,this._target_array=[0,0,0]}static type(){return rr.ORBIT}endEventName(){return\\\\\\\"end\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Jo(oy,$o.BASE),new Jo(ay,$o.BASE),new Jo(\\\\\\\"end\\\\\\\",$o.BASE)])}async create_controls_instance(t,e){const n=new ry(t,e);return n.addEventListener(\\\\\\\"end\\\\\\\",(()=>{this._on_controls_end(n)})),this._controls_by_element_id.set(e.id,n),this._bind_listeners_to_controls_instance(n),n}_bind_listeners_to_controls_instance(t){t.addEventListener(\\\\\\\"start\\\\\\\",(()=>{this.dispatchEventToOutput(oy,{})})),t.addEventListener(\\\\\\\"change\\\\\\\",(()=>{this.dispatchEventToOutput(ay,{})})),t.addEventListener(\\\\\\\"end\\\\\\\",(()=>{this.dispatchEventToOutput(\\\\\\\"end\\\\\\\",{})}))}setup_controls(t){t.enabled=this.pv.enabled,t.enablePan=this.pv.allowPan,t.enableRotate=this.pv.allowRotate,t.enableZoom=this.pv.allowZoom,t.enableDamping=this.pv.tdamping,t.dampingFactor=this.pv.damping,t.rotateSpeed=this.pv.rotateSpeed,t.screenSpacePanning=this.pv.screenSpacePanning,t.minDistance=this.pv.minDistance,t.maxDistance=this.pv.maxDistance,this._set_azimuth_angle(t),t.minPolarAngle=this.pv.polarAngleRange.x,t.maxPolarAngle=this.pv.polarAngleRange.y,t.target.copy(this.pv.target),t.enabled&&t.update(),t.enableKeys=this.pv.enableKeys,t.enableKeys&&(t.keyMode=cy[this.pv.keysMode],t.keyRotateSpeedVertical=this.pv.keysRotateSpeedVertical,t.keyRotateSpeedHorizontal=this.pv.keysRotateSpeedHorizontal,t.keyPanSpeed=this.pv.keysPanSpeed)}_set_azimuth_angle(t){this.pv.limitAzimuthAngle?(t.minAzimuthAngle=this.pv.azimuthAngleRange.x,t.maxAzimuthAngle=this.pv.azimuthAngleRange.y):(t.minAzimuthAngle=1/0,t.maxAzimuthAngle=1/0)}update_required(){return this.pv.tdamping}_on_controls_end(t){this.pv.allowPan&&(t.target.toArray(this._target_array),this.p.target.set(this._target_array))}static PARAM_CALLBACK_update_target(t){t._update_target()}_update_target(){const t=this.pv.target;this._controls_by_element_id.forEach(((e,n)=>{const i=e.target;i.equals(t)||(i.copy(t),e.update())}))}dispose_controls_for_html_element_id(t){this._controls_by_element_id.get(t)&&this._controls_by_element_id.delete(t)}}class dy extends hy{static type(){return rr.MAP}async create_controls_instance(t,e){const n=new sy(t,e);return this._bind_listeners_to_controls_instance(n),n}}const py=new class extends aa{constructor(){super(...arguments),this.delay=oa.INTEGER(1e3,{range:[0,1e3],rangeLocked:[!0,!1]})}};class _y extends Ba{constructor(){super(...arguments),this.paramsConfig=py}static type(){return\\\\\\\"delay\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(\\\\\\\"in\\\\\\\",$o.BASE,this._process_input.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(\\\\\\\"out\\\\\\\",$o.BASE)])}_process_input(t){setTimeout((()=>{this.dispatchEventToOutput(\\\\\\\"out\\\\\\\",t)}),this.pv.delay)}}const my=100,fy=301,gy=302,vy=303,yy=304,xy=306,by=307,wy=1e3,Ty=1001,Ay=1002,Ey=1003,My=1004,Sy=1005,Cy=1006,Ny=1007,Ly=1008,Oy=1009,Ry=1012,Py=1014,Iy=1015,Fy=1016,Dy=1020,ky=1022,By=1023,zy=1026,Uy=1027,Gy=2300,Vy=2301,Hy=2302,jy=2400,Wy=2401,qy=2402,Xy=2500,Yy=3e3,$y=3001,Jy=3007,Zy=3002,Qy=7680,Ky=35044,tx=35048,ex=\\\\\\\"300 es\\\\\\\";class nx{addEventListener(t,e){void 0===this._listeners&&(this._listeners={});const n=this._listeners;void 0===n[t]&&(n[t]=[]),-1===n[t].indexOf(e)&&n[t].push(e)}hasEventListener(t,e){if(void 0===this._listeners)return!1;const n=this._listeners;return void 0!==n[t]&&-1!==n[t].indexOf(e)}removeEventListener(t,e){if(void 0===this._listeners)return;const n=this._listeners[t];if(void 0!==n){const t=n.indexOf(e);-1!==t&&n.splice(t,1)}}dispatchEvent(t){if(void 0===this._listeners)return;const e=this._listeners[t.type];if(void 0!==e){t.target=this;const n=e.slice(0);for(let e=0,i=n.length;e>8&255]+ox[t>>16&255]+ox[t>>24&255]+\\\\\\\"-\\\\\\\"+ox[255&e]+ox[e>>8&255]+\\\\\\\"-\\\\\\\"+ox[e>>16&15|64]+ox[e>>24&255]+\\\\\\\"-\\\\\\\"+ox[63&n|128]+ox[n>>8&255]+\\\\\\\"-\\\\\\\"+ox[n>>16&255]+ox[n>>24&255]+ox[255&i]+ox[i>>8&255]+ox[i>>16&255]+ox[i>>24&255]).toUpperCase()}function cx(t,e,n){return Math.max(e,Math.min(n,t))}function ux(t,e){return(t%e+e)%e}function hx(t,e,n){return(1-n)*t+n*e}function dx(t){return 0==(t&t-1)&&0!==t}function px(t){return Math.pow(2,Math.ceil(Math.log(t)/Math.LN2))}function _x(t){return Math.pow(2,Math.floor(Math.log(t)/Math.LN2))}var mx=Object.freeze({__proto__:null,DEG2RAD:rx,RAD2DEG:sx,generateUUID:lx,clamp:cx,euclideanModulo:ux,mapLinear:function(t,e,n,i,r){return i+(t-e)*(r-i)/(n-e)},inverseLerp:function(t,e,n){return t!==e?(n-t)/(e-t):0},lerp:hx,damp:function(t,e,n,i){return hx(t,e,1-Math.exp(-n*i))},pingpong:function(t,e=1){return e-Math.abs(ux(t,2*e)-e)},smoothstep:function(t,e,n){return t<=e?0:t>=n?1:(t=(t-e)/(n-e))*t*(3-2*t)},smootherstep:function(t,e,n){return t<=e?0:t>=n?1:(t=(t-e)/(n-e))*t*t*(t*(6*t-15)+10)},randInt:function(t,e){return t+Math.floor(Math.random()*(e-t+1))},randFloat:function(t,e){return t+Math.random()*(e-t)},randFloatSpread:function(t){return t*(.5-Math.random())},seededRandom:function(t){return void 0!==t&&(ix=t%2147483647),ix=16807*ix%2147483647,(ix-1)/2147483646},degToRad:function(t){return t*rx},radToDeg:function(t){return t*sx},isPowerOfTwo:dx,ceilPowerOfTwo:px,floorPowerOfTwo:_x,setQuaternionFromProperEuler:function(t,e,n,i,r){const s=Math.cos,o=Math.sin,a=s(n/2),l=o(n/2),c=s((e+i)/2),u=o((e+i)/2),h=s((e-i)/2),d=o((e-i)/2),p=s((i-e)/2),_=o((i-e)/2);switch(r){case\\\\\\\"XYX\\\\\\\":t.set(a*u,l*h,l*d,a*c);break;case\\\\\\\"YZY\\\\\\\":t.set(l*d,a*u,l*h,a*c);break;case\\\\\\\"ZXZ\\\\\\\":t.set(l*h,l*d,a*u,a*c);break;case\\\\\\\"XZX\\\\\\\":t.set(a*u,l*_,l*p,a*c);break;case\\\\\\\"YXY\\\\\\\":t.set(l*p,a*u,l*_,a*c);break;case\\\\\\\"ZYZ\\\\\\\":t.set(l*_,l*p,a*u,a*c);break;default:console.warn(\\\\\\\"THREE.MathUtils: .setQuaternionFromProperEuler() encountered an unknown order: \\\\\\\"+r)}}});class fx{constructor(t=0,e=0){this.x=t,this.y=e}get width(){return this.x}set width(t){this.x=t}get height(){return this.y}set height(t){this.y=t}set(t,e){return this.x=t,this.y=e,this}setScalar(t){return this.x=t,this.y=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y)}copy(t){return this.x=t.x,this.y=t.y,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector2: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this)}addScalar(t){return this.x+=t,this.y+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this}sub(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector2: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this)}subScalar(t){return this.x-=t,this.y-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this}multiply(t){return this.x*=t.x,this.y*=t.y,this}multiplyScalar(t){return this.x*=t,this.y*=t,this}divide(t){return this.x/=t.x,this.y/=t.y,this}divideScalar(t){return this.multiplyScalar(1/t)}applyMatrix3(t){const e=this.x,n=this.y,i=t.elements;return this.x=i[0]*e+i[3]*n+i[6],this.y=i[1]*e+i[4]*n+i[7],this}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this}negate(){return this.x=-this.x,this.y=-this.y,this}dot(t){return this.x*t.x+this.y*t.y}cross(t){return this.x*t.y-this.y*t.x}lengthSq(){return this.x*this.x+this.y*this.y}length(){return Math.sqrt(this.x*this.x+this.y*this.y)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)}normalize(){return this.divideScalar(this.length()||1)}angle(){return Math.atan2(-this.y,-this.x)+Math.PI}distanceTo(t){return Math.sqrt(this.distanceToSquared(t))}distanceToSquared(t){const e=this.x-t.x,n=this.y-t.y;return e*e+n*n}manhattanDistanceTo(t){return Math.abs(this.x-t.x)+Math.abs(this.y-t.y)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this}equals(t){return t.x===this.x&&t.y===this.y}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn(\\\\\\\"THREE.Vector2: offset has been removed from .fromBufferAttribute().\\\\\\\"),this.x=t.getX(e),this.y=t.getY(e),this}rotateAround(t,e){const n=Math.cos(e),i=Math.sin(e),r=this.x-t.x,s=this.y-t.y;return this.x=r*n-s*i+t.x,this.y=r*i+s*n+t.y,this}random(){return this.x=Math.random(),this.y=Math.random(),this}*[Symbol.iterator](){yield this.x,yield this.y}}fx.prototype.isVector2=!0;class gx{constructor(){this.elements=[1,0,0,0,1,0,0,0,1],arguments.length>0&&console.error(\\\\\\\"THREE.Matrix3: the constructor no longer reads arguments. use .set() instead.\\\\\\\")}set(t,e,n,i,r,s,o,a,l){const c=this.elements;return c[0]=t,c[1]=i,c[2]=o,c[3]=e,c[4]=r,c[5]=a,c[6]=n,c[7]=s,c[8]=l,this}identity(){return this.set(1,0,0,0,1,0,0,0,1),this}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],this}extractBasis(t,e,n){return t.setFromMatrix3Column(this,0),e.setFromMatrix3Column(this,1),n.setFromMatrix3Column(this,2),this}setFromMatrix4(t){const e=t.elements;return this.set(e[0],e[4],e[8],e[1],e[5],e[9],e[2],e[6],e[10]),this}multiply(t){return this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,r=this.elements,s=n[0],o=n[3],a=n[6],l=n[1],c=n[4],u=n[7],h=n[2],d=n[5],p=n[8],_=i[0],m=i[3],f=i[6],g=i[1],v=i[4],y=i[7],x=i[2],b=i[5],w=i[8];return r[0]=s*_+o*g+a*x,r[3]=s*m+o*v+a*b,r[6]=s*f+o*y+a*w,r[1]=l*_+c*g+u*x,r[4]=l*m+c*v+u*b,r[7]=l*f+c*y+u*w,r[2]=h*_+d*g+p*x,r[5]=h*m+d*v+p*b,r[8]=h*f+d*y+p*w,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[3]*=t,e[6]*=t,e[1]*=t,e[4]*=t,e[7]*=t,e[2]*=t,e[5]*=t,e[8]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],s=t[4],o=t[5],a=t[6],l=t[7],c=t[8];return e*s*c-e*o*l-n*r*c+n*o*a+i*r*l-i*s*a}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],s=t[4],o=t[5],a=t[6],l=t[7],c=t[8],u=c*s-o*l,h=o*a-c*r,d=l*r-s*a,p=e*u+n*h+i*d;if(0===p)return this.set(0,0,0,0,0,0,0,0,0);const _=1/p;return t[0]=u*_,t[1]=(i*l-c*n)*_,t[2]=(o*n-i*s)*_,t[3]=h*_,t[4]=(c*e-i*a)*_,t[5]=(i*r-o*e)*_,t[6]=d*_,t[7]=(n*a-l*e)*_,t[8]=(s*e-n*r)*_,this}transpose(){let t;const e=this.elements;return t=e[1],e[1]=e[3],e[3]=t,t=e[2],e[2]=e[6],e[6]=t,t=e[5],e[5]=e[7],e[7]=t,this}getNormalMatrix(t){return this.setFromMatrix4(t).invert().transpose()}transposeIntoArray(t){const e=this.elements;return t[0]=e[0],t[1]=e[3],t[2]=e[6],t[3]=e[1],t[4]=e[4],t[5]=e[7],t[6]=e[2],t[7]=e[5],t[8]=e[8],this}setUvTransform(t,e,n,i,r,s,o){const a=Math.cos(r),l=Math.sin(r);return this.set(n*a,n*l,-n*(a*s+l*o)+s+t,-i*l,i*a,-i*(-l*s+a*o)+o+e,0,0,1),this}scale(t,e){const n=this.elements;return n[0]*=t,n[3]*=t,n[6]*=t,n[1]*=e,n[4]*=e,n[7]*=e,this}rotate(t){const e=Math.cos(t),n=Math.sin(t),i=this.elements,r=i[0],s=i[3],o=i[6],a=i[1],l=i[4],c=i[7];return i[0]=e*r+n*a,i[3]=e*s+n*l,i[6]=e*o+n*c,i[1]=-n*r+e*a,i[4]=-n*s+e*l,i[7]=-n*o+e*c,this}translate(t,e){const n=this.elements;return n[0]+=t*n[2],n[3]+=t*n[5],n[6]+=t*n[8],n[1]+=e*n[2],n[4]+=e*n[5],n[7]+=e*n[8],this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<9;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<9;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t}clone(){return(new this.constructor).fromArray(this.elements)}}function vx(t){if(0===t.length)return-1/0;let e=t[0];for(let n=1,i=t.length;ne&&(e=t[n]);return e}gx.prototype.isMatrix3=!0;Int8Array,Uint8Array,Uint8ClampedArray,Int16Array,Uint16Array,Int32Array,Uint32Array,Float32Array,Float64Array;function yx(t){return document.createElementNS(\\\\\\\"http://www.w3.org/1999/xhtml\\\\\\\",t)}let xx;class bx{static getDataURL(t){if(/^data:/i.test(t.src))return t.src;if(\\\\\\\"undefined\\\\\\\"==typeof HTMLCanvasElement)return t.src;let e;if(t instanceof HTMLCanvasElement)e=t;else{void 0===xx&&(xx=yx(\\\\\\\"canvas\\\\\\\")),xx.width=t.width,xx.height=t.height;const n=xx.getContext(\\\\\\\"2d\\\\\\\");t instanceof ImageData?n.putImageData(t,0,0):n.drawImage(t,0,0,t.width,t.height),e=xx}return e.width>2048||e.height>2048?(console.warn(\\\\\\\"THREE.ImageUtils.getDataURL: Image converted to jpg for performance reasons\\\\\\\",t),e.toDataURL(\\\\\\\"image/jpeg\\\\\\\",.6)):e.toDataURL(\\\\\\\"image/png\\\\\\\")}}let wx=0;class Tx extends nx{constructor(t=Tx.DEFAULT_IMAGE,e=Tx.DEFAULT_MAPPING,n=1001,i=1001,r=1006,s=1008,o=1023,a=1009,l=1,c=3e3){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:wx++}),this.uuid=lx(),this.name=\\\\\\\"\\\\\\\",this.image=t,this.mipmaps=[],this.mapping=e,this.wrapS=n,this.wrapT=i,this.magFilter=r,this.minFilter=s,this.anisotropy=l,this.format=o,this.internalFormat=null,this.type=a,this.offset=new fx(0,0),this.repeat=new fx(1,1),this.center=new fx(0,0),this.rotation=0,this.matrixAutoUpdate=!0,this.matrix=new gx,this.generateMipmaps=!0,this.premultiplyAlpha=!1,this.flipY=!0,this.unpackAlignment=4,this.encoding=c,this.version=0,this.onUpdate=null,this.isRenderTargetTexture=!1}updateMatrix(){this.matrix.setUvTransform(this.offset.x,this.offset.y,this.repeat.x,this.repeat.y,this.rotation,this.center.x,this.center.y)}clone(){return(new this.constructor).copy(this)}copy(t){return this.name=t.name,this.image=t.image,this.mipmaps=t.mipmaps.slice(0),this.mapping=t.mapping,this.wrapS=t.wrapS,this.wrapT=t.wrapT,this.magFilter=t.magFilter,this.minFilter=t.minFilter,this.anisotropy=t.anisotropy,this.format=t.format,this.internalFormat=t.internalFormat,this.type=t.type,this.offset.copy(t.offset),this.repeat.copy(t.repeat),this.center.copy(t.center),this.rotation=t.rotation,this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrix.copy(t.matrix),this.generateMipmaps=t.generateMipmaps,this.premultiplyAlpha=t.premultiplyAlpha,this.flipY=t.flipY,this.unpackAlignment=t.unpackAlignment,this.encoding=t.encoding,this}toJSON(t){const e=void 0===t||\\\\\\\"string\\\\\\\"==typeof t;if(!e&&void 0!==t.textures[this.uuid])return t.textures[this.uuid];const n={metadata:{version:4.5,type:\\\\\\\"Texture\\\\\\\",generator:\\\\\\\"Texture.toJSON\\\\\\\"},uuid:this.uuid,name:this.name,mapping:this.mapping,repeat:[this.repeat.x,this.repeat.y],offset:[this.offset.x,this.offset.y],center:[this.center.x,this.center.y],rotation:this.rotation,wrap:[this.wrapS,this.wrapT],format:this.format,type:this.type,encoding:this.encoding,minFilter:this.minFilter,magFilter:this.magFilter,anisotropy:this.anisotropy,flipY:this.flipY,premultiplyAlpha:this.premultiplyAlpha,unpackAlignment:this.unpackAlignment};if(void 0!==this.image){const i=this.image;if(void 0===i.uuid&&(i.uuid=lx()),!e&&void 0===t.images[i.uuid]){let e;if(Array.isArray(i)){e=[];for(let t=0,n=i.length;t1)switch(this.wrapS){case wy:t.x=t.x-Math.floor(t.x);break;case Ty:t.x=t.x<0?0:1;break;case Ay:1===Math.abs(Math.floor(t.x)%2)?t.x=Math.ceil(t.x)-t.x:t.x=t.x-Math.floor(t.x)}if(t.y<0||t.y>1)switch(this.wrapT){case wy:t.y=t.y-Math.floor(t.y);break;case Ty:t.y=t.y<0?0:1;break;case Ay:1===Math.abs(Math.floor(t.y)%2)?t.y=Math.ceil(t.y)-t.y:t.y=t.y-Math.floor(t.y)}return this.flipY&&(t.y=1-t.y),t}set needsUpdate(t){!0===t&&this.version++}}function Ax(t){return\\\\\\\"undefined\\\\\\\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\\\\\\\"undefined\\\\\\\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\\\\\\\"undefined\\\\\\\"!=typeof ImageBitmap&&t instanceof ImageBitmap?bx.getDataURL(t):t.data?{data:Array.prototype.slice.call(t.data),width:t.width,height:t.height,type:t.data.constructor.name}:(console.warn(\\\\\\\"THREE.Texture: Unable to serialize Texture.\\\\\\\"),{})}Tx.DEFAULT_IMAGE=void 0,Tx.DEFAULT_MAPPING=300,Tx.prototype.isTexture=!0;class Ex{constructor(t=0,e=0,n=0,i=1){this.x=t,this.y=e,this.z=n,this.w=i}get width(){return this.z}set width(t){this.z=t}get height(){return this.w}set height(t){this.w=t}set(t,e,n,i){return this.x=t,this.y=e,this.z=n,this.w=i,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this.w=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setW(t){return this.w=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;case 3:this.w=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;case 3:return this.w;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y,this.z,this.w)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this.w=void 0!==t.w?t.w:1,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this.w+=t.w,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this.w+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this.w=t.w+e.w,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this.w+=t.w*e,this}sub(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this.w-=t.w,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this.w-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this.w=t.w-e.w,this}multiply(t){return this.x*=t.x,this.y*=t.y,this.z*=t.z,this.w*=t.w,this}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this.w*=t,this}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,r=this.w,s=t.elements;return this.x=s[0]*e+s[4]*n+s[8]*i+s[12]*r,this.y=s[1]*e+s[5]*n+s[9]*i+s[13]*r,this.z=s[2]*e+s[6]*n+s[10]*i+s[14]*r,this.w=s[3]*e+s[7]*n+s[11]*i+s[15]*r,this}divideScalar(t){return this.multiplyScalar(1/t)}setAxisAngleFromQuaternion(t){this.w=2*Math.acos(t.w);const e=Math.sqrt(1-t.w*t.w);return e<1e-4?(this.x=1,this.y=0,this.z=0):(this.x=t.x/e,this.y=t.y/e,this.z=t.z/e),this}setAxisAngleFromRotationMatrix(t){let e,n,i,r;const s=.01,o=.1,a=t.elements,l=a[0],c=a[4],u=a[8],h=a[1],d=a[5],p=a[9],_=a[2],m=a[6],f=a[10];if(Math.abs(c-h)a&&t>g?tg?a=0?1:-1,i=1-e*e;if(i>Number.EPSILON){const r=Math.sqrt(i),s=Math.atan2(r,e*n);t=Math.sin(t*s)/r,o=Math.sin(o*s)/r}const r=o*n;if(a=a*t+h*r,l=l*t+d*r,c=c*t+p*r,u=u*t+_*r,t===1-o){const t=1/Math.sqrt(a*a+l*l+c*c+u*u);a*=t,l*=t,c*=t,u*=t}}t[e]=a,t[e+1]=l,t[e+2]=c,t[e+3]=u}static multiplyQuaternionsFlat(t,e,n,i,r,s){const o=n[i],a=n[i+1],l=n[i+2],c=n[i+3],u=r[s],h=r[s+1],d=r[s+2],p=r[s+3];return t[e]=o*p+c*u+a*d-l*h,t[e+1]=a*p+c*h+l*u-o*d,t[e+2]=l*p+c*d+o*h-a*u,t[e+3]=c*p-o*u-a*h-l*d,t}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get w(){return this._w}set w(t){this._w=t,this._onChangeCallback()}set(t,e,n,i){return this._x=t,this._y=e,this._z=n,this._w=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._w)}copy(t){return this._x=t.x,this._y=t.y,this._z=t.z,this._w=t.w,this._onChangeCallback(),this}setFromEuler(t,e){if(!t||!t.isEuler)throw new Error(\\\\\\\"THREE.Quaternion: .setFromEuler() now expects an Euler rotation rather than a Vector3 and order.\\\\\\\");const n=t._x,i=t._y,r=t._z,s=t._order,o=Math.cos,a=Math.sin,l=o(n/2),c=o(i/2),u=o(r/2),h=a(n/2),d=a(i/2),p=a(r/2);switch(s){case\\\\\\\"XYZ\\\\\\\":this._x=h*c*u+l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u-h*d*p;break;case\\\\\\\"YXZ\\\\\\\":this._x=h*c*u+l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u+h*d*p;break;case\\\\\\\"ZXY\\\\\\\":this._x=h*c*u-l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u-h*d*p;break;case\\\\\\\"ZYX\\\\\\\":this._x=h*c*u-l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u+h*d*p;break;case\\\\\\\"YZX\\\\\\\":this._x=h*c*u+l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u-h*d*p;break;case\\\\\\\"XZY\\\\\\\":this._x=h*c*u-l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u+h*d*p;break;default:console.warn(\\\\\\\"THREE.Quaternion: .setFromEuler() encountered an unknown order: \\\\\\\"+s)}return!1!==e&&this._onChangeCallback(),this}setFromAxisAngle(t,e){const n=e/2,i=Math.sin(n);return this._x=t.x*i,this._y=t.y*i,this._z=t.z*i,this._w=Math.cos(n),this._onChangeCallback(),this}setFromRotationMatrix(t){const e=t.elements,n=e[0],i=e[4],r=e[8],s=e[1],o=e[5],a=e[9],l=e[2],c=e[6],u=e[10],h=n+o+u;if(h>0){const t=.5/Math.sqrt(h+1);this._w=.25/t,this._x=(c-a)*t,this._y=(r-l)*t,this._z=(s-i)*t}else if(n>o&&n>u){const t=2*Math.sqrt(1+n-o-u);this._w=(c-a)/t,this._x=.25*t,this._y=(i+s)/t,this._z=(r+l)/t}else if(o>u){const t=2*Math.sqrt(1+o-n-u);this._w=(r-l)/t,this._x=(i+s)/t,this._y=.25*t,this._z=(a+c)/t}else{const t=2*Math.sqrt(1+u-n-o);this._w=(s-i)/t,this._x=(r+l)/t,this._y=(a+c)/t,this._z=.25*t}return this._onChangeCallback(),this}setFromUnitVectors(t,e){let n=t.dot(e)+1;return nMath.abs(t.z)?(this._x=-t.y,this._y=t.x,this._z=0,this._w=n):(this._x=0,this._y=-t.z,this._z=t.y,this._w=n)):(this._x=t.y*e.z-t.z*e.y,this._y=t.z*e.x-t.x*e.z,this._z=t.x*e.y-t.y*e.x,this._w=n),this.normalize()}angleTo(t){return 2*Math.acos(Math.abs(cx(this.dot(t),-1,1)))}rotateTowards(t,e){const n=this.angleTo(t);if(0===n)return this;const i=Math.min(1,e/n);return this.slerp(t,i),this}identity(){return this.set(0,0,0,1)}invert(){return this.conjugate()}conjugate(){return this._x*=-1,this._y*=-1,this._z*=-1,this._onChangeCallback(),this}dot(t){return this._x*t._x+this._y*t._y+this._z*t._z+this._w*t._w}lengthSq(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w}length(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w)}normalize(){let t=this.length();return 0===t?(this._x=0,this._y=0,this._z=0,this._w=1):(t=1/t,this._x=this._x*t,this._y=this._y*t,this._z=this._z*t,this._w=this._w*t),this._onChangeCallback(),this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Quaternion: .multiply() now only accepts one argument. Use .multiplyQuaternions( a, b ) instead.\\\\\\\"),this.multiplyQuaternions(t,e)):this.multiplyQuaternions(this,t)}premultiply(t){return this.multiplyQuaternions(t,this)}multiplyQuaternions(t,e){const n=t._x,i=t._y,r=t._z,s=t._w,o=e._x,a=e._y,l=e._z,c=e._w;return this._x=n*c+s*o+i*l-r*a,this._y=i*c+s*a+r*o-n*l,this._z=r*c+s*l+n*a-i*o,this._w=s*c-n*o-i*a-r*l,this._onChangeCallback(),this}slerp(t,e){if(0===e)return this;if(1===e)return this.copy(t);const n=this._x,i=this._y,r=this._z,s=this._w;let o=s*t._w+n*t._x+i*t._y+r*t._z;if(o<0?(this._w=-t._w,this._x=-t._x,this._y=-t._y,this._z=-t._z,o=-o):this.copy(t),o>=1)return this._w=s,this._x=n,this._y=i,this._z=r,this;const a=1-o*o;if(a<=Number.EPSILON){const t=1-e;return this._w=t*s+e*this._w,this._x=t*n+e*this._x,this._y=t*i+e*this._y,this._z=t*r+e*this._z,this.normalize(),this._onChangeCallback(),this}const l=Math.sqrt(a),c=Math.atan2(l,o),u=Math.sin((1-e)*c)/l,h=Math.sin(e*c)/l;return this._w=s*u+this._w*h,this._x=n*u+this._x*h,this._y=i*u+this._y*h,this._z=r*u+this._z*h,this._onChangeCallback(),this}slerpQuaternions(t,e,n){this.copy(t).slerp(e,n)}random(){const t=Math.random(),e=Math.sqrt(1-t),n=Math.sqrt(t),i=2*Math.PI*Math.random(),r=2*Math.PI*Math.random();return this.set(e*Math.cos(i),n*Math.sin(r),n*Math.cos(r),e*Math.sin(i))}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._w===this._w}fromArray(t,e=0){return this._x=t[e],this._y=t[e+1],this._z=t[e+2],this._w=t[e+3],this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._w,t}fromBufferAttribute(t,e){return this._x=t.getX(e),this._y=t.getY(e),this._z=t.getZ(e),this._w=t.getW(e),this}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}Cx.prototype.isQuaternion=!0;class Nx{constructor(t=0,e=0,n=0){this.x=t,this.y=e,this.z=n}set(t,e,n){return void 0===n&&(n=this.z),this.x=t,this.y=e,this.z=n,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y,this.z)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this}sub(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .multiply() now only accepts one argument. Use .multiplyVectors( a, b ) instead.\\\\\\\"),this.multiplyVectors(t,e)):(this.x*=t.x,this.y*=t.y,this.z*=t.z,this)}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this}multiplyVectors(t,e){return this.x=t.x*e.x,this.y=t.y*e.y,this.z=t.z*e.z,this}applyEuler(t){return t&&t.isEuler||console.error(\\\\\\\"THREE.Vector3: .applyEuler() now expects an Euler rotation rather than a Vector3 and order.\\\\\\\"),this.applyQuaternion(Ox.setFromEuler(t))}applyAxisAngle(t,e){return this.applyQuaternion(Ox.setFromAxisAngle(t,e))}applyMatrix3(t){const e=this.x,n=this.y,i=this.z,r=t.elements;return this.x=r[0]*e+r[3]*n+r[6]*i,this.y=r[1]*e+r[4]*n+r[7]*i,this.z=r[2]*e+r[5]*n+r[8]*i,this}applyNormalMatrix(t){return this.applyMatrix3(t).normalize()}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,r=t.elements,s=1/(r[3]*e+r[7]*n+r[11]*i+r[15]);return this.x=(r[0]*e+r[4]*n+r[8]*i+r[12])*s,this.y=(r[1]*e+r[5]*n+r[9]*i+r[13])*s,this.z=(r[2]*e+r[6]*n+r[10]*i+r[14])*s,this}applyQuaternion(t){const e=this.x,n=this.y,i=this.z,r=t.x,s=t.y,o=t.z,a=t.w,l=a*e+s*i-o*n,c=a*n+o*e-r*i,u=a*i+r*n-s*e,h=-r*e-s*n-o*i;return this.x=l*a+h*-r+c*-o-u*-s,this.y=c*a+h*-s+u*-r-l*-o,this.z=u*a+h*-o+l*-s-c*-r,this}project(t){return this.applyMatrix4(t.matrixWorldInverse).applyMatrix4(t.projectionMatrix)}unproject(t){return this.applyMatrix4(t.projectionMatrixInverse).applyMatrix4(t.matrixWorld)}transformDirection(t){const e=this.x,n=this.y,i=this.z,r=t.elements;return this.x=r[0]*e+r[4]*n+r[8]*i,this.y=r[1]*e+r[5]*n+r[9]*i,this.z=r[2]*e+r[6]*n+r[10]*i,this.normalize()}divide(t){return this.x/=t.x,this.y/=t.y,this.z/=t.z,this}divideScalar(t){return this.multiplyScalar(1/t)}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this.z=Math.min(this.z,t.z),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this.z=Math.max(this.z,t.z),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this.z=Math.max(t.z,Math.min(e.z,this.z)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this.z=Math.max(t,Math.min(e,this.z)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this.z=Math.floor(this.z),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this.z=this.z<0?Math.ceil(this.z):Math.floor(this.z),this}negate(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z}lengthSq(){return this.x*this.x+this.y*this.y+this.z*this.z}length(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)}normalize(){return this.divideScalar(this.length()||1)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this.z+=(t.z-this.z)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this.z=t.z+(e.z-t.z)*n,this}cross(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .cross() now only accepts one argument. Use .crossVectors( a, b ) instead.\\\\\\\"),this.crossVectors(t,e)):this.crossVectors(this,t)}crossVectors(t,e){const n=t.x,i=t.y,r=t.z,s=e.x,o=e.y,a=e.z;return this.x=i*a-r*o,this.y=r*s-n*a,this.z=n*o-i*s,this}projectOnVector(t){const e=t.lengthSq();if(0===e)return this.set(0,0,0);const n=t.dot(this)/e;return this.copy(t).multiplyScalar(n)}projectOnPlane(t){return Lx.copy(this).projectOnVector(t),this.sub(Lx)}reflect(t){return this.sub(Lx.copy(t).multiplyScalar(2*this.dot(t)))}angleTo(t){const e=Math.sqrt(this.lengthSq()*t.lengthSq());if(0===e)return Math.PI/2;const n=this.dot(t)/e;return Math.acos(cx(n,-1,1))}distanceTo(t){return Math.sqrt(this.distanceToSquared(t))}distanceToSquared(t){const e=this.x-t.x,n=this.y-t.y,i=this.z-t.z;return e*e+n*n+i*i}manhattanDistanceTo(t){return Math.abs(this.x-t.x)+Math.abs(this.y-t.y)+Math.abs(this.z-t.z)}setFromSpherical(t){return this.setFromSphericalCoords(t.radius,t.phi,t.theta)}setFromSphericalCoords(t,e,n){const i=Math.sin(e)*t;return this.x=i*Math.sin(n),this.y=Math.cos(e)*t,this.z=i*Math.cos(n),this}setFromCylindrical(t){return this.setFromCylindricalCoords(t.radius,t.theta,t.y)}setFromCylindricalCoords(t,e,n){return this.x=t*Math.sin(e),this.y=n,this.z=t*Math.cos(e),this}setFromMatrixPosition(t){const e=t.elements;return this.x=e[12],this.y=e[13],this.z=e[14],this}setFromMatrixScale(t){const e=this.setFromMatrixColumn(t,0).length(),n=this.setFromMatrixColumn(t,1).length(),i=this.setFromMatrixColumn(t,2).length();return this.x=e,this.y=n,this.z=i,this}setFromMatrixColumn(t,e){return this.fromArray(t.elements,4*e)}setFromMatrix3Column(t,e){return this.fromArray(t.elements,3*e)}equals(t){return t.x===this.x&&t.y===this.y&&t.z===this.z}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this.z=t[e+2],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t[e+2]=this.z,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn(\\\\\\\"THREE.Vector3: offset has been removed from .fromBufferAttribute().\\\\\\\"),this.x=t.getX(e),this.y=t.getY(e),this.z=t.getZ(e),this}random(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this}randomDirection(){const t=2*(Math.random()-.5),e=Math.random()*Math.PI*2,n=Math.sqrt(1-t**2);return this.x=n*Math.cos(e),this.y=n*Math.sin(e),this.z=t,this}*[Symbol.iterator](){yield this.x,yield this.y,yield this.z}}Nx.prototype.isVector3=!0;const Lx=new Nx,Ox=new Cx;class Rx{constructor(t=new Nx(1/0,1/0,1/0),e=new Nx(-1/0,-1/0,-1/0)){this.min=t,this.max=e}set(t,e){return this.min.copy(t),this.max.copy(e),this}setFromArray(t){let e=1/0,n=1/0,i=1/0,r=-1/0,s=-1/0,o=-1/0;for(let a=0,l=t.length;ar&&(r=l),c>s&&(s=c),u>o&&(o=u)}return this.min.set(e,n,i),this.max.set(r,s,o),this}setFromBufferAttribute(t){let e=1/0,n=1/0,i=1/0,r=-1/0,s=-1/0,o=-1/0;for(let a=0,l=t.count;ar&&(r=l),c>s&&(s=c),u>o&&(o=u)}return this.min.set(e,n,i),this.max.set(r,s,o),this}setFromPoints(t){this.makeEmpty();for(let e=0,n=t.length;ethis.max.x||t.ythis.max.y||t.zthis.max.z)}containsBox(t){return this.min.x<=t.min.x&&t.max.x<=this.max.x&&this.min.y<=t.min.y&&t.max.y<=this.max.y&&this.min.z<=t.min.z&&t.max.z<=this.max.z}getParameter(t,e){return e.set((t.x-this.min.x)/(this.max.x-this.min.x),(t.y-this.min.y)/(this.max.y-this.min.y),(t.z-this.min.z)/(this.max.z-this.min.z))}intersectsBox(t){return!(t.max.xthis.max.x||t.max.ythis.max.y||t.max.zthis.max.z)}intersectsSphere(t){return this.clampPoint(t.center,Ix),Ix.distanceToSquared(t.center)<=t.radius*t.radius}intersectsPlane(t){let e,n;return t.normal.x>0?(e=t.normal.x*this.min.x,n=t.normal.x*this.max.x):(e=t.normal.x*this.max.x,n=t.normal.x*this.min.x),t.normal.y>0?(e+=t.normal.y*this.min.y,n+=t.normal.y*this.max.y):(e+=t.normal.y*this.max.y,n+=t.normal.y*this.min.y),t.normal.z>0?(e+=t.normal.z*this.min.z,n+=t.normal.z*this.max.z):(e+=t.normal.z*this.max.z,n+=t.normal.z*this.min.z),e<=-t.constant&&n>=-t.constant}intersectsTriangle(t){if(this.isEmpty())return!1;this.getCenter(Vx),Hx.subVectors(this.max,Vx),Dx.subVectors(t.a,Vx),kx.subVectors(t.b,Vx),Bx.subVectors(t.c,Vx),zx.subVectors(kx,Dx),Ux.subVectors(Bx,kx),Gx.subVectors(Dx,Bx);let e=[0,-zx.z,zx.y,0,-Ux.z,Ux.y,0,-Gx.z,Gx.y,zx.z,0,-zx.x,Ux.z,0,-Ux.x,Gx.z,0,-Gx.x,-zx.y,zx.x,0,-Ux.y,Ux.x,0,-Gx.y,Gx.x,0];return!!qx(e,Dx,kx,Bx,Hx)&&(e=[1,0,0,0,1,0,0,0,1],!!qx(e,Dx,kx,Bx,Hx)&&(jx.crossVectors(zx,Ux),e=[jx.x,jx.y,jx.z],qx(e,Dx,kx,Bx,Hx)))}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return Ix.copy(t).clamp(this.min,this.max).sub(t).length()}getBoundingSphere(t){return this.getCenter(t.center),t.radius=.5*this.getSize(Ix).length(),t}intersect(t){return this.min.max(t.min),this.max.min(t.max),this.isEmpty()&&this.makeEmpty(),this}union(t){return this.min.min(t.min),this.max.max(t.max),this}applyMatrix4(t){return this.isEmpty()||(Px[0].set(this.min.x,this.min.y,this.min.z).applyMatrix4(t),Px[1].set(this.min.x,this.min.y,this.max.z).applyMatrix4(t),Px[2].set(this.min.x,this.max.y,this.min.z).applyMatrix4(t),Px[3].set(this.min.x,this.max.y,this.max.z).applyMatrix4(t),Px[4].set(this.max.x,this.min.y,this.min.z).applyMatrix4(t),Px[5].set(this.max.x,this.min.y,this.max.z).applyMatrix4(t),Px[6].set(this.max.x,this.max.y,this.min.z).applyMatrix4(t),Px[7].set(this.max.x,this.max.y,this.max.z).applyMatrix4(t),this.setFromPoints(Px)),this}translate(t){return this.min.add(t),this.max.add(t),this}equals(t){return t.min.equals(this.min)&&t.max.equals(this.max)}}Rx.prototype.isBox3=!0;const Px=[new Nx,new Nx,new Nx,new Nx,new Nx,new Nx,new Nx,new Nx],Ix=new Nx,Fx=new Rx,Dx=new Nx,kx=new Nx,Bx=new Nx,zx=new Nx,Ux=new Nx,Gx=new Nx,Vx=new Nx,Hx=new Nx,jx=new Nx,Wx=new Nx;function qx(t,e,n,i,r){for(let s=0,o=t.length-3;s<=o;s+=3){Wx.fromArray(t,s);const o=r.x*Math.abs(Wx.x)+r.y*Math.abs(Wx.y)+r.z*Math.abs(Wx.z),a=e.dot(Wx),l=n.dot(Wx),c=i.dot(Wx);if(Math.max(-Math.max(a,l,c),Math.min(a,l,c))>o)return!1}return!0}const Xx=new Rx,Yx=new Nx,$x=new Nx,Jx=new Nx;class Zx{constructor(t=new Nx,e=-1){this.center=t,this.radius=e}set(t,e){return this.center.copy(t),this.radius=e,this}setFromPoints(t,e){const n=this.center;void 0!==e?n.copy(e):Xx.setFromPoints(t).getCenter(n);let i=0;for(let e=0,r=t.length;ethis.radius*this.radius&&(e.sub(this.center).normalize(),e.multiplyScalar(this.radius).add(this.center)),e}getBoundingBox(t){return this.isEmpty()?(t.makeEmpty(),t):(t.set(this.center,this.center),t.expandByScalar(this.radius),t)}applyMatrix4(t){return this.center.applyMatrix4(t),this.radius=this.radius*t.getMaxScaleOnAxis(),this}translate(t){return this.center.add(t),this}expandByPoint(t){Jx.subVectors(t,this.center);const e=Jx.lengthSq();if(e>this.radius*this.radius){const t=Math.sqrt(e),n=.5*(t-this.radius);this.center.add(Jx.multiplyScalar(n/t)),this.radius+=n}return this}union(t){return $x.subVectors(t.center,this.center).normalize().multiplyScalar(t.radius),this.expandByPoint(Yx.copy(t.center).add($x)),this.expandByPoint(Yx.copy(t.center).sub($x)),this}equals(t){return t.center.equals(this.center)&&t.radius===this.radius}clone(){return(new this.constructor).copy(this)}}const Qx=new Nx,Kx=new Nx,tb=new Nx,eb=new Nx,nb=new Nx,ib=new Nx,rb=new Nx;class sb{constructor(t=new Nx,e=new Nx(0,0,-1)){this.origin=t,this.direction=e}set(t,e){return this.origin.copy(t),this.direction.copy(e),this}copy(t){return this.origin.copy(t.origin),this.direction.copy(t.direction),this}at(t,e){return e.copy(this.direction).multiplyScalar(t).add(this.origin)}lookAt(t){return this.direction.copy(t).sub(this.origin).normalize(),this}recast(t){return this.origin.copy(this.at(t,Qx)),this}closestPointToPoint(t,e){e.subVectors(t,this.origin);const n=e.dot(this.direction);return n<0?e.copy(this.origin):e.copy(this.direction).multiplyScalar(n).add(this.origin)}distanceToPoint(t){return Math.sqrt(this.distanceSqToPoint(t))}distanceSqToPoint(t){const e=Qx.subVectors(t,this.origin).dot(this.direction);return e<0?this.origin.distanceToSquared(t):(Qx.copy(this.direction).multiplyScalar(e).add(this.origin),Qx.distanceToSquared(t))}distanceSqToSegment(t,e,n,i){Kx.copy(t).add(e).multiplyScalar(.5),tb.copy(e).sub(t).normalize(),eb.copy(this.origin).sub(Kx);const r=.5*t.distanceTo(e),s=-this.direction.dot(tb),o=eb.dot(this.direction),a=-eb.dot(tb),l=eb.lengthSq(),c=Math.abs(1-s*s);let u,h,d,p;if(c>0)if(u=s*a-o,h=s*o-a,p=r*c,u>=0)if(h>=-p)if(h<=p){const t=1/c;u*=t,h*=t,d=u*(u+s*h+2*o)+h*(s*u+h+2*a)+l}else h=r,u=Math.max(0,-(s*h+o)),d=-u*u+h*(h+2*a)+l;else h=-r,u=Math.max(0,-(s*h+o)),d=-u*u+h*(h+2*a)+l;else h<=-p?(u=Math.max(0,-(-s*r+o)),h=u>0?-r:Math.min(Math.max(-r,-a),r),d=-u*u+h*(h+2*a)+l):h<=p?(u=0,h=Math.min(Math.max(-r,-a),r),d=h*(h+2*a)+l):(u=Math.max(0,-(s*r+o)),h=u>0?r:Math.min(Math.max(-r,-a),r),d=-u*u+h*(h+2*a)+l);else h=s>0?-r:r,u=Math.max(0,-(s*h+o)),d=-u*u+h*(h+2*a)+l;return n&&n.copy(this.direction).multiplyScalar(u).add(this.origin),i&&i.copy(tb).multiplyScalar(h).add(Kx),d}intersectSphere(t,e){Qx.subVectors(t.center,this.origin);const n=Qx.dot(this.direction),i=Qx.dot(Qx)-n*n,r=t.radius*t.radius;if(i>r)return null;const s=Math.sqrt(r-i),o=n-s,a=n+s;return o<0&&a<0?null:o<0?this.at(a,e):this.at(o,e)}intersectsSphere(t){return this.distanceSqToPoint(t.center)<=t.radius*t.radius}distanceToPlane(t){const e=t.normal.dot(this.direction);if(0===e)return 0===t.distanceToPoint(this.origin)?0:null;const n=-(this.origin.dot(t.normal)+t.constant)/e;return n>=0?n:null}intersectPlane(t,e){const n=this.distanceToPlane(t);return null===n?null:this.at(n,e)}intersectsPlane(t){const e=t.distanceToPoint(this.origin);if(0===e)return!0;return t.normal.dot(this.direction)*e<0}intersectBox(t,e){let n,i,r,s,o,a;const l=1/this.direction.x,c=1/this.direction.y,u=1/this.direction.z,h=this.origin;return l>=0?(n=(t.min.x-h.x)*l,i=(t.max.x-h.x)*l):(n=(t.max.x-h.x)*l,i=(t.min.x-h.x)*l),c>=0?(r=(t.min.y-h.y)*c,s=(t.max.y-h.y)*c):(r=(t.max.y-h.y)*c,s=(t.min.y-h.y)*c),n>s||r>i?null:((r>n||n!=n)&&(n=r),(s=0?(o=(t.min.z-h.z)*u,a=(t.max.z-h.z)*u):(o=(t.max.z-h.z)*u,a=(t.min.z-h.z)*u),n>a||o>i?null:((o>n||n!=n)&&(n=o),(a=0?n:i,e)))}intersectsBox(t){return null!==this.intersectBox(t,Qx)}intersectTriangle(t,e,n,i,r){nb.subVectors(e,t),ib.subVectors(n,t),rb.crossVectors(nb,ib);let s,o=this.direction.dot(rb);if(o>0){if(i)return null;s=1}else{if(!(o<0))return null;s=-1,o=-o}eb.subVectors(this.origin,t);const a=s*this.direction.dot(ib.crossVectors(eb,ib));if(a<0)return null;const l=s*this.direction.dot(nb.cross(eb));if(l<0)return null;if(a+l>o)return null;const c=-s*eb.dot(rb);return c<0?null:this.at(c/o,r)}applyMatrix4(t){return this.origin.applyMatrix4(t),this.direction.transformDirection(t),this}equals(t){return t.origin.equals(this.origin)&&t.direction.equals(this.direction)}clone(){return(new this.constructor).copy(this)}}class ob{constructor(){this.elements=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],arguments.length>0&&console.error(\\\\\\\"THREE.Matrix4: the constructor no longer reads arguments. use .set() instead.\\\\\\\")}set(t,e,n,i,r,s,o,a,l,c,u,h,d,p,_,m){const f=this.elements;return f[0]=t,f[4]=e,f[8]=n,f[12]=i,f[1]=r,f[5]=s,f[9]=o,f[13]=a,f[2]=l,f[6]=c,f[10]=u,f[14]=h,f[3]=d,f[7]=p,f[11]=_,f[15]=m,this}identity(){return this.set(1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1),this}clone(){return(new ob).fromArray(this.elements)}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],e[9]=n[9],e[10]=n[10],e[11]=n[11],e[12]=n[12],e[13]=n[13],e[14]=n[14],e[15]=n[15],this}copyPosition(t){const e=this.elements,n=t.elements;return e[12]=n[12],e[13]=n[13],e[14]=n[14],this}setFromMatrix3(t){const e=t.elements;return this.set(e[0],e[3],e[6],0,e[1],e[4],e[7],0,e[2],e[5],e[8],0,0,0,0,1),this}extractBasis(t,e,n){return t.setFromMatrixColumn(this,0),e.setFromMatrixColumn(this,1),n.setFromMatrixColumn(this,2),this}makeBasis(t,e,n){return this.set(t.x,e.x,n.x,0,t.y,e.y,n.y,0,t.z,e.z,n.z,0,0,0,0,1),this}extractRotation(t){const e=this.elements,n=t.elements,i=1/ab.setFromMatrixColumn(t,0).length(),r=1/ab.setFromMatrixColumn(t,1).length(),s=1/ab.setFromMatrixColumn(t,2).length();return e[0]=n[0]*i,e[1]=n[1]*i,e[2]=n[2]*i,e[3]=0,e[4]=n[4]*r,e[5]=n[5]*r,e[6]=n[6]*r,e[7]=0,e[8]=n[8]*s,e[9]=n[9]*s,e[10]=n[10]*s,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromEuler(t){t&&t.isEuler||console.error(\\\\\\\"THREE.Matrix4: .makeRotationFromEuler() now expects a Euler rotation rather than a Vector3 and order.\\\\\\\");const e=this.elements,n=t.x,i=t.y,r=t.z,s=Math.cos(n),o=Math.sin(n),a=Math.cos(i),l=Math.sin(i),c=Math.cos(r),u=Math.sin(r);if(\\\\\\\"XYZ\\\\\\\"===t.order){const t=s*c,n=s*u,i=o*c,r=o*u;e[0]=a*c,e[4]=-a*u,e[8]=l,e[1]=n+i*l,e[5]=t-r*l,e[9]=-o*a,e[2]=r-t*l,e[6]=i+n*l,e[10]=s*a}else if(\\\\\\\"YXZ\\\\\\\"===t.order){const t=a*c,n=a*u,i=l*c,r=l*u;e[0]=t+r*o,e[4]=i*o-n,e[8]=s*l,e[1]=s*u,e[5]=s*c,e[9]=-o,e[2]=n*o-i,e[6]=r+t*o,e[10]=s*a}else if(\\\\\\\"ZXY\\\\\\\"===t.order){const t=a*c,n=a*u,i=l*c,r=l*u;e[0]=t-r*o,e[4]=-s*u,e[8]=i+n*o,e[1]=n+i*o,e[5]=s*c,e[9]=r-t*o,e[2]=-s*l,e[6]=o,e[10]=s*a}else if(\\\\\\\"ZYX\\\\\\\"===t.order){const t=s*c,n=s*u,i=o*c,r=o*u;e[0]=a*c,e[4]=i*l-n,e[8]=t*l+r,e[1]=a*u,e[5]=r*l+t,e[9]=n*l-i,e[2]=-l,e[6]=o*a,e[10]=s*a}else if(\\\\\\\"YZX\\\\\\\"===t.order){const t=s*a,n=s*l,i=o*a,r=o*l;e[0]=a*c,e[4]=r-t*u,e[8]=i*u+n,e[1]=u,e[5]=s*c,e[9]=-o*c,e[2]=-l*c,e[6]=n*u+i,e[10]=t-r*u}else if(\\\\\\\"XZY\\\\\\\"===t.order){const t=s*a,n=s*l,i=o*a,r=o*l;e[0]=a*c,e[4]=-u,e[8]=l*c,e[1]=t*u+r,e[5]=s*c,e[9]=n*u-i,e[2]=i*u-n,e[6]=o*c,e[10]=r*u+t}return e[3]=0,e[7]=0,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromQuaternion(t){return this.compose(cb,t,ub)}lookAt(t,e,n){const i=this.elements;return pb.subVectors(t,e),0===pb.lengthSq()&&(pb.z=1),pb.normalize(),hb.crossVectors(n,pb),0===hb.lengthSq()&&(1===Math.abs(n.z)?pb.x+=1e-4:pb.z+=1e-4,pb.normalize(),hb.crossVectors(n,pb)),hb.normalize(),db.crossVectors(pb,hb),i[0]=hb.x,i[4]=db.x,i[8]=pb.x,i[1]=hb.y,i[5]=db.y,i[9]=pb.y,i[2]=hb.z,i[6]=db.z,i[10]=pb.z,this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Matrix4: .multiply() now only accepts one argument. Use .multiplyMatrices( a, b ) instead.\\\\\\\"),this.multiplyMatrices(t,e)):this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,r=this.elements,s=n[0],o=n[4],a=n[8],l=n[12],c=n[1],u=n[5],h=n[9],d=n[13],p=n[2],_=n[6],m=n[10],f=n[14],g=n[3],v=n[7],y=n[11],x=n[15],b=i[0],w=i[4],T=i[8],A=i[12],E=i[1],M=i[5],S=i[9],C=i[13],N=i[2],L=i[6],O=i[10],R=i[14],P=i[3],I=i[7],F=i[11],D=i[15];return r[0]=s*b+o*E+a*N+l*P,r[4]=s*w+o*M+a*L+l*I,r[8]=s*T+o*S+a*O+l*F,r[12]=s*A+o*C+a*R+l*D,r[1]=c*b+u*E+h*N+d*P,r[5]=c*w+u*M+h*L+d*I,r[9]=c*T+u*S+h*O+d*F,r[13]=c*A+u*C+h*R+d*D,r[2]=p*b+_*E+m*N+f*P,r[6]=p*w+_*M+m*L+f*I,r[10]=p*T+_*S+m*O+f*F,r[14]=p*A+_*C+m*R+f*D,r[3]=g*b+v*E+y*N+x*P,r[7]=g*w+v*M+y*L+x*I,r[11]=g*T+v*S+y*O+x*F,r[15]=g*A+v*C+y*R+x*D,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[4]*=t,e[8]*=t,e[12]*=t,e[1]*=t,e[5]*=t,e[9]*=t,e[13]*=t,e[2]*=t,e[6]*=t,e[10]*=t,e[14]*=t,e[3]*=t,e[7]*=t,e[11]*=t,e[15]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[4],i=t[8],r=t[12],s=t[1],o=t[5],a=t[9],l=t[13],c=t[2],u=t[6],h=t[10],d=t[14];return t[3]*(+r*a*u-i*l*u-r*o*h+n*l*h+i*o*d-n*a*d)+t[7]*(+e*a*d-e*l*h+r*s*h-i*s*d+i*l*c-r*a*c)+t[11]*(+e*l*u-e*o*d-r*s*u+n*s*d+r*o*c-n*l*c)+t[15]*(-i*o*c-e*a*u+e*o*h+i*s*u-n*s*h+n*a*c)}transpose(){const t=this.elements;let e;return e=t[1],t[1]=t[4],t[4]=e,e=t[2],t[2]=t[8],t[8]=e,e=t[6],t[6]=t[9],t[9]=e,e=t[3],t[3]=t[12],t[12]=e,e=t[7],t[7]=t[13],t[13]=e,e=t[11],t[11]=t[14],t[14]=e,this}setPosition(t,e,n){const i=this.elements;return t.isVector3?(i[12]=t.x,i[13]=t.y,i[14]=t.z):(i[12]=t,i[13]=e,i[14]=n),this}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],s=t[4],o=t[5],a=t[6],l=t[7],c=t[8],u=t[9],h=t[10],d=t[11],p=t[12],_=t[13],m=t[14],f=t[15],g=u*m*l-_*h*l+_*a*d-o*m*d-u*a*f+o*h*f,v=p*h*l-c*m*l-p*a*d+s*m*d+c*a*f-s*h*f,y=c*_*l-p*u*l+p*o*d-s*_*d-c*o*f+s*u*f,x=p*u*a-c*_*a-p*o*h+s*_*h+c*o*m-s*u*m,b=e*g+n*v+i*y+r*x;if(0===b)return this.set(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0);const w=1/b;return t[0]=g*w,t[1]=(_*h*r-u*m*r-_*i*d+n*m*d+u*i*f-n*h*f)*w,t[2]=(o*m*r-_*a*r+_*i*l-n*m*l-o*i*f+n*a*f)*w,t[3]=(u*a*r-o*h*r-u*i*l+n*h*l+o*i*d-n*a*d)*w,t[4]=v*w,t[5]=(c*m*r-p*h*r+p*i*d-e*m*d-c*i*f+e*h*f)*w,t[6]=(p*a*r-s*m*r-p*i*l+e*m*l+s*i*f-e*a*f)*w,t[7]=(s*h*r-c*a*r+c*i*l-e*h*l-s*i*d+e*a*d)*w,t[8]=y*w,t[9]=(p*u*r-c*_*r-p*n*d+e*_*d+c*n*f-e*u*f)*w,t[10]=(s*_*r-p*o*r+p*n*l-e*_*l-s*n*f+e*o*f)*w,t[11]=(c*o*r-s*u*r-c*n*l+e*u*l+s*n*d-e*o*d)*w,t[12]=x*w,t[13]=(c*_*i-p*u*i+p*n*h-e*_*h-c*n*m+e*u*m)*w,t[14]=(p*o*i-s*_*i-p*n*a+e*_*a+s*n*m-e*o*m)*w,t[15]=(s*u*i-c*o*i+c*n*a-e*u*a-s*n*h+e*o*h)*w,this}scale(t){const e=this.elements,n=t.x,i=t.y,r=t.z;return e[0]*=n,e[4]*=i,e[8]*=r,e[1]*=n,e[5]*=i,e[9]*=r,e[2]*=n,e[6]*=i,e[10]*=r,e[3]*=n,e[7]*=i,e[11]*=r,this}getMaxScaleOnAxis(){const t=this.elements,e=t[0]*t[0]+t[1]*t[1]+t[2]*t[2],n=t[4]*t[4]+t[5]*t[5]+t[6]*t[6],i=t[8]*t[8]+t[9]*t[9]+t[10]*t[10];return Math.sqrt(Math.max(e,n,i))}makeTranslation(t,e,n){return this.set(1,0,0,t,0,1,0,e,0,0,1,n,0,0,0,1),this}makeRotationX(t){const e=Math.cos(t),n=Math.sin(t);return this.set(1,0,0,0,0,e,-n,0,0,n,e,0,0,0,0,1),this}makeRotationY(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,0,n,0,0,1,0,0,-n,0,e,0,0,0,0,1),this}makeRotationZ(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,-n,0,0,n,e,0,0,0,0,1,0,0,0,0,1),this}makeRotationAxis(t,e){const n=Math.cos(e),i=Math.sin(e),r=1-n,s=t.x,o=t.y,a=t.z,l=r*s,c=r*o;return this.set(l*s+n,l*o-i*a,l*a+i*o,0,l*o+i*a,c*o+n,c*a-i*s,0,l*a-i*o,c*a+i*s,r*a*a+n,0,0,0,0,1),this}makeScale(t,e,n){return this.set(t,0,0,0,0,e,0,0,0,0,n,0,0,0,0,1),this}makeShear(t,e,n,i,r,s){return this.set(1,n,r,0,t,1,s,0,e,i,1,0,0,0,0,1),this}compose(t,e,n){const i=this.elements,r=e._x,s=e._y,o=e._z,a=e._w,l=r+r,c=s+s,u=o+o,h=r*l,d=r*c,p=r*u,_=s*c,m=s*u,f=o*u,g=a*l,v=a*c,y=a*u,x=n.x,b=n.y,w=n.z;return i[0]=(1-(_+f))*x,i[1]=(d+y)*x,i[2]=(p-v)*x,i[3]=0,i[4]=(d-y)*b,i[5]=(1-(h+f))*b,i[6]=(m+g)*b,i[7]=0,i[8]=(p+v)*w,i[9]=(m-g)*w,i[10]=(1-(h+_))*w,i[11]=0,i[12]=t.x,i[13]=t.y,i[14]=t.z,i[15]=1,this}decompose(t,e,n){const i=this.elements;let r=ab.set(i[0],i[1],i[2]).length();const s=ab.set(i[4],i[5],i[6]).length(),o=ab.set(i[8],i[9],i[10]).length();this.determinant()<0&&(r=-r),t.x=i[12],t.y=i[13],t.z=i[14],lb.copy(this);const a=1/r,l=1/s,c=1/o;return lb.elements[0]*=a,lb.elements[1]*=a,lb.elements[2]*=a,lb.elements[4]*=l,lb.elements[5]*=l,lb.elements[6]*=l,lb.elements[8]*=c,lb.elements[9]*=c,lb.elements[10]*=c,e.setFromRotationMatrix(lb),n.x=r,n.y=s,n.z=o,this}makePerspective(t,e,n,i,r,s){void 0===s&&console.warn(\\\\\\\"THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.\\\\\\\");const o=this.elements,a=2*r/(e-t),l=2*r/(n-i),c=(e+t)/(e-t),u=(n+i)/(n-i),h=-(s+r)/(s-r),d=-2*s*r/(s-r);return o[0]=a,o[4]=0,o[8]=c,o[12]=0,o[1]=0,o[5]=l,o[9]=u,o[13]=0,o[2]=0,o[6]=0,o[10]=h,o[14]=d,o[3]=0,o[7]=0,o[11]=-1,o[15]=0,this}makeOrthographic(t,e,n,i,r,s){const o=this.elements,a=1/(e-t),l=1/(n-i),c=1/(s-r),u=(e+t)*a,h=(n+i)*l,d=(s+r)*c;return o[0]=2*a,o[4]=0,o[8]=0,o[12]=-u,o[1]=0,o[5]=2*l,o[9]=0,o[13]=-h,o[2]=0,o[6]=0,o[10]=-2*c,o[14]=-d,o[3]=0,o[7]=0,o[11]=0,o[15]=1,this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<16;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<16;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t[e+9]=n[9],t[e+10]=n[10],t[e+11]=n[11],t[e+12]=n[12],t[e+13]=n[13],t[e+14]=n[14],t[e+15]=n[15],t}}ob.prototype.isMatrix4=!0;const ab=new Nx,lb=new ob,cb=new Nx(0,0,0),ub=new Nx(1,1,1),hb=new Nx,db=new Nx,pb=new Nx,_b=new ob,mb=new Cx;class fb{constructor(t=0,e=0,n=0,i=fb.DefaultOrder){this._x=t,this._y=e,this._z=n,this._order=i}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get order(){return this._order}set order(t){this._order=t,this._onChangeCallback()}set(t,e,n,i=this._order){return this._x=t,this._y=e,this._z=n,this._order=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._order)}copy(t){return this._x=t._x,this._y=t._y,this._z=t._z,this._order=t._order,this._onChangeCallback(),this}setFromRotationMatrix(t,e=this._order,n=!0){const i=t.elements,r=i[0],s=i[4],o=i[8],a=i[1],l=i[5],c=i[9],u=i[2],h=i[6],d=i[10];switch(e){case\\\\\\\"XYZ\\\\\\\":this._y=Math.asin(cx(o,-1,1)),Math.abs(o)<.9999999?(this._x=Math.atan2(-c,d),this._z=Math.atan2(-s,r)):(this._x=Math.atan2(h,l),this._z=0);break;case\\\\\\\"YXZ\\\\\\\":this._x=Math.asin(-cx(c,-1,1)),Math.abs(c)<.9999999?(this._y=Math.atan2(o,d),this._z=Math.atan2(a,l)):(this._y=Math.atan2(-u,r),this._z=0);break;case\\\\\\\"ZXY\\\\\\\":this._x=Math.asin(cx(h,-1,1)),Math.abs(h)<.9999999?(this._y=Math.atan2(-u,d),this._z=Math.atan2(-s,l)):(this._y=0,this._z=Math.atan2(a,r));break;case\\\\\\\"ZYX\\\\\\\":this._y=Math.asin(-cx(u,-1,1)),Math.abs(u)<.9999999?(this._x=Math.atan2(h,d),this._z=Math.atan2(a,r)):(this._x=0,this._z=Math.atan2(-s,l));break;case\\\\\\\"YZX\\\\\\\":this._z=Math.asin(cx(a,-1,1)),Math.abs(a)<.9999999?(this._x=Math.atan2(-c,l),this._y=Math.atan2(-u,r)):(this._x=0,this._y=Math.atan2(o,d));break;case\\\\\\\"XZY\\\\\\\":this._z=Math.asin(-cx(s,-1,1)),Math.abs(s)<.9999999?(this._x=Math.atan2(h,l),this._y=Math.atan2(o,r)):(this._x=Math.atan2(-c,d),this._y=0);break;default:console.warn(\\\\\\\"THREE.Euler: .setFromRotationMatrix() encountered an unknown order: \\\\\\\"+e)}return this._order=e,!0===n&&this._onChangeCallback(),this}setFromQuaternion(t,e,n){return _b.makeRotationFromQuaternion(t),this.setFromRotationMatrix(_b,e,n)}setFromVector3(t,e=this._order){return this.set(t.x,t.y,t.z,e)}reorder(t){return mb.setFromEuler(this),this.setFromQuaternion(mb,t)}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._order===this._order}fromArray(t){return this._x=t[0],this._y=t[1],this._z=t[2],void 0!==t[3]&&(this._order=t[3]),this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._order,t}toVector3(t){return t?t.set(this._x,this._y,this._z):new Nx(this._x,this._y,this._z)}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}fb.prototype.isEuler=!0,fb.DefaultOrder=\\\\\\\"XYZ\\\\\\\",fb.RotationOrders=[\\\\\\\"XYZ\\\\\\\",\\\\\\\"YZX\\\\\\\",\\\\\\\"ZXY\\\\\\\",\\\\\\\"XZY\\\\\\\",\\\\\\\"YXZ\\\\\\\",\\\\\\\"ZYX\\\\\\\"];class gb{constructor(){this.mask=1}set(t){this.mask=1<1){for(let t=0;t1){for(let t=0;t0){i.children=[];for(let e=0;e0){i.animations=[];for(let e=0;e0&&(n.geometries=e),i.length>0&&(n.materials=i),r.length>0&&(n.textures=r),o.length>0&&(n.images=o),a.length>0&&(n.shapes=a),l.length>0&&(n.skeletons=l),c.length>0&&(n.animations=c)}return n.object=i,n;function s(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}}clone(t){return(new this.constructor).copy(this,t)}copy(t,e=!0){if(this.name=t.name,this.up.copy(t.up),this.position.copy(t.position),this.rotation.order=t.rotation.order,this.quaternion.copy(t.quaternion),this.scale.copy(t.scale),this.matrix.copy(t.matrix),this.matrixWorld.copy(t.matrixWorld),this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrixWorldNeedsUpdate=t.matrixWorldNeedsUpdate,this.layers.mask=t.layers.mask,this.visible=t.visible,this.castShadow=t.castShadow,this.receiveShadow=t.receiveShadow,this.frustumCulled=t.frustumCulled,this.renderOrder=t.renderOrder,this.userData=JSON.parse(JSON.stringify(t.userData)),!0===e)for(let e=0;e0?i.multiplyScalar(1/Math.sqrt(r)):i.set(0,0,0)}static getBarycoord(t,e,n,i,r){Rb.subVectors(i,e),Pb.subVectors(n,e),Ib.subVectors(t,e);const s=Rb.dot(Rb),o=Rb.dot(Pb),a=Rb.dot(Ib),l=Pb.dot(Pb),c=Pb.dot(Ib),u=s*l-o*o;if(0===u)return r.set(-2,-1,-1);const h=1/u,d=(l*a-o*c)*h,p=(s*c-o*a)*h;return r.set(1-d-p,p,d)}static containsPoint(t,e,n,i){return this.getBarycoord(t,e,n,i,Fb),Fb.x>=0&&Fb.y>=0&&Fb.x+Fb.y<=1}static getUV(t,e,n,i,r,s,o,a){return this.getBarycoord(t,e,n,i,Fb),a.set(0,0),a.addScaledVector(r,Fb.x),a.addScaledVector(s,Fb.y),a.addScaledVector(o,Fb.z),a}static isFrontFacing(t,e,n,i){return Rb.subVectors(n,e),Pb.subVectors(t,e),Rb.cross(Pb).dot(i)<0}set(t,e,n){return this.a.copy(t),this.b.copy(e),this.c.copy(n),this}setFromPointsAndIndices(t,e,n,i){return this.a.copy(t[e]),this.b.copy(t[n]),this.c.copy(t[i]),this}setFromAttributeAndIndices(t,e,n,i){return this.a.fromBufferAttribute(t,e),this.b.fromBufferAttribute(t,n),this.c.fromBufferAttribute(t,i),this}clone(){return(new this.constructor).copy(this)}copy(t){return this.a.copy(t.a),this.b.copy(t.b),this.c.copy(t.c),this}getArea(){return Rb.subVectors(this.c,this.b),Pb.subVectors(this.a,this.b),.5*Rb.cross(Pb).length()}getMidpoint(t){return t.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}getNormal(t){return Vb.getNormal(this.a,this.b,this.c,t)}getPlane(t){return t.setFromCoplanarPoints(this.a,this.b,this.c)}getBarycoord(t,e){return Vb.getBarycoord(t,this.a,this.b,this.c,e)}getUV(t,e,n,i,r){return Vb.getUV(t,this.a,this.b,this.c,e,n,i,r)}containsPoint(t){return Vb.containsPoint(t,this.a,this.b,this.c)}isFrontFacing(t){return Vb.isFrontFacing(this.a,this.b,this.c,t)}intersectsBox(t){return t.intersectsTriangle(this)}closestPointToPoint(t,e){const n=this.a,i=this.b,r=this.c;let s,o;Db.subVectors(i,n),kb.subVectors(r,n),zb.subVectors(t,n);const a=Db.dot(zb),l=kb.dot(zb);if(a<=0&&l<=0)return e.copy(n);Ub.subVectors(t,i);const c=Db.dot(Ub),u=kb.dot(Ub);if(c>=0&&u<=c)return e.copy(i);const h=a*u-c*l;if(h<=0&&a>=0&&c<=0)return s=a/(a-c),e.copy(n).addScaledVector(Db,s);Gb.subVectors(t,r);const d=Db.dot(Gb),p=kb.dot(Gb);if(p>=0&&d<=p)return e.copy(r);const _=d*l-a*p;if(_<=0&&l>=0&&p<=0)return o=l/(l-p),e.copy(n).addScaledVector(kb,o);const m=c*p-d*u;if(m<=0&&u-c>=0&&d-p>=0)return Bb.subVectors(r,i),o=(u-c)/(u-c+(d-p)),e.copy(i).addScaledVector(Bb,o);const f=1/(m+_+h);return s=_*f,o=h*f,e.copy(n).addScaledVector(Db,s).addScaledVector(kb,o)}equals(t){return t.a.equals(this.a)&&t.b.equals(this.b)&&t.c.equals(this.c)}}let Hb=0;class jb extends nx{constructor(){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:Hb++}),this.uuid=lx(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Material\\\\\\\",this.fog=!0,this.blending=1,this.side=0,this.vertexColors=!1,this.opacity=1,this.format=By,this.transparent=!1,this.blendSrc=204,this.blendDst=205,this.blendEquation=my,this.blendSrcAlpha=null,this.blendDstAlpha=null,this.blendEquationAlpha=null,this.depthFunc=3,this.depthTest=!0,this.depthWrite=!0,this.stencilWriteMask=255,this.stencilFunc=519,this.stencilRef=0,this.stencilFuncMask=255,this.stencilFail=Qy,this.stencilZFail=Qy,this.stencilZPass=Qy,this.stencilWrite=!1,this.clippingPlanes=null,this.clipIntersection=!1,this.clipShadows=!1,this.shadowSide=null,this.colorWrite=!0,this.precision=null,this.polygonOffset=!1,this.polygonOffsetFactor=0,this.polygonOffsetUnits=0,this.dithering=!1,this.alphaToCoverage=!1,this.premultipliedAlpha=!1,this.visible=!0,this.toneMapped=!0,this.userData={},this.version=0,this._alphaTest=0}get alphaTest(){return this._alphaTest}set alphaTest(t){this._alphaTest>0!=t>0&&this.version++,this._alphaTest=t}onBuild(){}onBeforeRender(){}onBeforeCompile(){}customProgramCacheKey(){return this.onBeforeCompile.toString()}setValues(t){if(void 0!==t)for(const e in t){const n=t[e];if(void 0===n){console.warn(\\\\\\\"THREE.Material: '\\\\\\\"+e+\\\\\\\"' parameter is undefined.\\\\\\\");continue}if(\\\\\\\"shading\\\\\\\"===e){console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": .shading has been removed. Use the boolean .flatShading instead.\\\\\\\"),this.flatShading=1===n;continue}const i=this[e];void 0!==i?i&&i.isColor?i.set(n):i&&i.isVector3&&n&&n.isVector3?i.copy(n):this[e]=n:console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": '\\\\\\\"+e+\\\\\\\"' is not a property of this material.\\\\\\\")}}toJSON(t){const e=void 0===t||\\\\\\\"string\\\\\\\"==typeof t;e&&(t={textures:{},images:{}});const n={metadata:{version:4.5,type:\\\\\\\"Material\\\\\\\",generator:\\\\\\\"Material.toJSON\\\\\\\"}};function i(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}if(n.uuid=this.uuid,n.type=this.type,\\\\\\\"\\\\\\\"!==this.name&&(n.name=this.name),this.color&&this.color.isColor&&(n.color=this.color.getHex()),void 0!==this.roughness&&(n.roughness=this.roughness),void 0!==this.metalness&&(n.metalness=this.metalness),void 0!==this.sheen&&(n.sheen=this.sheen),this.sheenTint&&this.sheenTint.isColor&&(n.sheenTint=this.sheenTint.getHex()),void 0!==this.sheenRoughness&&(n.sheenRoughness=this.sheenRoughness),this.emissive&&this.emissive.isColor&&(n.emissive=this.emissive.getHex()),this.emissiveIntensity&&1!==this.emissiveIntensity&&(n.emissiveIntensity=this.emissiveIntensity),this.specular&&this.specular.isColor&&(n.specular=this.specular.getHex()),void 0!==this.specularIntensity&&(n.specularIntensity=this.specularIntensity),this.specularTint&&this.specularTint.isColor&&(n.specularTint=this.specularTint.getHex()),void 0!==this.shininess&&(n.shininess=this.shininess),void 0!==this.clearcoat&&(n.clearcoat=this.clearcoat),void 0!==this.clearcoatRoughness&&(n.clearcoatRoughness=this.clearcoatRoughness),this.clearcoatMap&&this.clearcoatMap.isTexture&&(n.clearcoatMap=this.clearcoatMap.toJSON(t).uuid),this.clearcoatRoughnessMap&&this.clearcoatRoughnessMap.isTexture&&(n.clearcoatRoughnessMap=this.clearcoatRoughnessMap.toJSON(t).uuid),this.clearcoatNormalMap&&this.clearcoatNormalMap.isTexture&&(n.clearcoatNormalMap=this.clearcoatNormalMap.toJSON(t).uuid,n.clearcoatNormalScale=this.clearcoatNormalScale.toArray()),this.map&&this.map.isTexture&&(n.map=this.map.toJSON(t).uuid),this.matcap&&this.matcap.isTexture&&(n.matcap=this.matcap.toJSON(t).uuid),this.alphaMap&&this.alphaMap.isTexture&&(n.alphaMap=this.alphaMap.toJSON(t).uuid),this.lightMap&&this.lightMap.isTexture&&(n.lightMap=this.lightMap.toJSON(t).uuid,n.lightMapIntensity=this.lightMapIntensity),this.aoMap&&this.aoMap.isTexture&&(n.aoMap=this.aoMap.toJSON(t).uuid,n.aoMapIntensity=this.aoMapIntensity),this.bumpMap&&this.bumpMap.isTexture&&(n.bumpMap=this.bumpMap.toJSON(t).uuid,n.bumpScale=this.bumpScale),this.normalMap&&this.normalMap.isTexture&&(n.normalMap=this.normalMap.toJSON(t).uuid,n.normalMapType=this.normalMapType,n.normalScale=this.normalScale.toArray()),this.displacementMap&&this.displacementMap.isTexture&&(n.displacementMap=this.displacementMap.toJSON(t).uuid,n.displacementScale=this.displacementScale,n.displacementBias=this.displacementBias),this.roughnessMap&&this.roughnessMap.isTexture&&(n.roughnessMap=this.roughnessMap.toJSON(t).uuid),this.metalnessMap&&this.metalnessMap.isTexture&&(n.metalnessMap=this.metalnessMap.toJSON(t).uuid),this.emissiveMap&&this.emissiveMap.isTexture&&(n.emissiveMap=this.emissiveMap.toJSON(t).uuid),this.specularMap&&this.specularMap.isTexture&&(n.specularMap=this.specularMap.toJSON(t).uuid),this.specularIntensityMap&&this.specularIntensityMap.isTexture&&(n.specularIntensityMap=this.specularIntensityMap.toJSON(t).uuid),this.specularTintMap&&this.specularTintMap.isTexture&&(n.specularTintMap=this.specularTintMap.toJSON(t).uuid),this.envMap&&this.envMap.isTexture&&(n.envMap=this.envMap.toJSON(t).uuid,void 0!==this.combine&&(n.combine=this.combine)),void 0!==this.envMapIntensity&&(n.envMapIntensity=this.envMapIntensity),void 0!==this.reflectivity&&(n.reflectivity=this.reflectivity),void 0!==this.refractionRatio&&(n.refractionRatio=this.refractionRatio),this.gradientMap&&this.gradientMap.isTexture&&(n.gradientMap=this.gradientMap.toJSON(t).uuid),void 0!==this.transmission&&(n.transmission=this.transmission),this.transmissionMap&&this.transmissionMap.isTexture&&(n.transmissionMap=this.transmissionMap.toJSON(t).uuid),void 0!==this.thickness&&(n.thickness=this.thickness),this.thicknessMap&&this.thicknessMap.isTexture&&(n.thicknessMap=this.thicknessMap.toJSON(t).uuid),void 0!==this.attenuationDistance&&(n.attenuationDistance=this.attenuationDistance),void 0!==this.attenuationTint&&(n.attenuationTint=this.attenuationTint.getHex()),void 0!==this.size&&(n.size=this.size),null!==this.shadowSide&&(n.shadowSide=this.shadowSide),void 0!==this.sizeAttenuation&&(n.sizeAttenuation=this.sizeAttenuation),1!==this.blending&&(n.blending=this.blending),0!==this.side&&(n.side=this.side),this.vertexColors&&(n.vertexColors=!0),this.opacity<1&&(n.opacity=this.opacity),this.format!==By&&(n.format=this.format),!0===this.transparent&&(n.transparent=this.transparent),n.depthFunc=this.depthFunc,n.depthTest=this.depthTest,n.depthWrite=this.depthWrite,n.colorWrite=this.colorWrite,n.stencilWrite=this.stencilWrite,n.stencilWriteMask=this.stencilWriteMask,n.stencilFunc=this.stencilFunc,n.stencilRef=this.stencilRef,n.stencilFuncMask=this.stencilFuncMask,n.stencilFail=this.stencilFail,n.stencilZFail=this.stencilZFail,n.stencilZPass=this.stencilZPass,this.rotation&&0!==this.rotation&&(n.rotation=this.rotation),!0===this.polygonOffset&&(n.polygonOffset=!0),0!==this.polygonOffsetFactor&&(n.polygonOffsetFactor=this.polygonOffsetFactor),0!==this.polygonOffsetUnits&&(n.polygonOffsetUnits=this.polygonOffsetUnits),this.linewidth&&1!==this.linewidth&&(n.linewidth=this.linewidth),void 0!==this.dashSize&&(n.dashSize=this.dashSize),void 0!==this.gapSize&&(n.gapSize=this.gapSize),void 0!==this.scale&&(n.scale=this.scale),!0===this.dithering&&(n.dithering=!0),this.alphaTest>0&&(n.alphaTest=this.alphaTest),!0===this.alphaToCoverage&&(n.alphaToCoverage=this.alphaToCoverage),!0===this.premultipliedAlpha&&(n.premultipliedAlpha=this.premultipliedAlpha),!0===this.wireframe&&(n.wireframe=this.wireframe),this.wireframeLinewidth>1&&(n.wireframeLinewidth=this.wireframeLinewidth),\\\\\\\"round\\\\\\\"!==this.wireframeLinecap&&(n.wireframeLinecap=this.wireframeLinecap),\\\\\\\"round\\\\\\\"!==this.wireframeLinejoin&&(n.wireframeLinejoin=this.wireframeLinejoin),!0===this.flatShading&&(n.flatShading=this.flatShading),!1===this.visible&&(n.visible=!1),!1===this.toneMapped&&(n.toneMapped=!1),\\\\\\\"{}\\\\\\\"!==JSON.stringify(this.userData)&&(n.userData=this.userData),e){const e=i(t.textures),r=i(t.images);e.length>0&&(n.textures=e),r.length>0&&(n.images=r)}return n}clone(){return(new this.constructor).copy(this)}copy(t){this.name=t.name,this.fog=t.fog,this.blending=t.blending,this.side=t.side,this.vertexColors=t.vertexColors,this.opacity=t.opacity,this.format=t.format,this.transparent=t.transparent,this.blendSrc=t.blendSrc,this.blendDst=t.blendDst,this.blendEquation=t.blendEquation,this.blendSrcAlpha=t.blendSrcAlpha,this.blendDstAlpha=t.blendDstAlpha,this.blendEquationAlpha=t.blendEquationAlpha,this.depthFunc=t.depthFunc,this.depthTest=t.depthTest,this.depthWrite=t.depthWrite,this.stencilWriteMask=t.stencilWriteMask,this.stencilFunc=t.stencilFunc,this.stencilRef=t.stencilRef,this.stencilFuncMask=t.stencilFuncMask,this.stencilFail=t.stencilFail,this.stencilZFail=t.stencilZFail,this.stencilZPass=t.stencilZPass,this.stencilWrite=t.stencilWrite;const e=t.clippingPlanes;let n=null;if(null!==e){const t=e.length;n=new Array(t);for(let i=0;i!==t;++i)n[i]=e[i].clone()}return this.clippingPlanes=n,this.clipIntersection=t.clipIntersection,this.clipShadows=t.clipShadows,this.shadowSide=t.shadowSide,this.colorWrite=t.colorWrite,this.precision=t.precision,this.polygonOffset=t.polygonOffset,this.polygonOffsetFactor=t.polygonOffsetFactor,this.polygonOffsetUnits=t.polygonOffsetUnits,this.dithering=t.dithering,this.alphaTest=t.alphaTest,this.alphaToCoverage=t.alphaToCoverage,this.premultipliedAlpha=t.premultipliedAlpha,this.visible=t.visible,this.toneMapped=t.toneMapped,this.userData=JSON.parse(JSON.stringify(t.userData)),this}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}set needsUpdate(t){!0===t&&this.version++}}jb.prototype.isMaterial=!0;const Wb={aliceblue:15792383,antiquewhite:16444375,aqua:65535,aquamarine:8388564,azure:15794175,beige:16119260,bisque:16770244,black:0,blanchedalmond:16772045,blue:255,blueviolet:9055202,brown:10824234,burlywood:14596231,cadetblue:6266528,chartreuse:8388352,chocolate:13789470,coral:16744272,cornflowerblue:6591981,cornsilk:16775388,crimson:14423100,cyan:65535,darkblue:139,darkcyan:35723,darkgoldenrod:12092939,darkgray:11119017,darkgreen:25600,darkgrey:11119017,darkkhaki:12433259,darkmagenta:9109643,darkolivegreen:5597999,darkorange:16747520,darkorchid:10040012,darkred:9109504,darksalmon:15308410,darkseagreen:9419919,darkslateblue:4734347,darkslategray:3100495,darkslategrey:3100495,darkturquoise:52945,darkviolet:9699539,deeppink:16716947,deepskyblue:49151,dimgray:6908265,dimgrey:6908265,dodgerblue:2003199,firebrick:11674146,floralwhite:16775920,forestgreen:2263842,fuchsia:16711935,gainsboro:14474460,ghostwhite:16316671,gold:16766720,goldenrod:14329120,gray:8421504,green:32768,greenyellow:11403055,grey:8421504,honeydew:15794160,hotpink:16738740,indianred:13458524,indigo:4915330,ivory:16777200,khaki:15787660,lavender:15132410,lavenderblush:16773365,lawngreen:8190976,lemonchiffon:16775885,lightblue:11393254,lightcoral:15761536,lightcyan:14745599,lightgoldenrodyellow:16448210,lightgray:13882323,lightgreen:9498256,lightgrey:13882323,lightpink:16758465,lightsalmon:16752762,lightseagreen:2142890,lightskyblue:8900346,lightslategray:7833753,lightslategrey:7833753,lightsteelblue:11584734,lightyellow:16777184,lime:65280,limegreen:3329330,linen:16445670,magenta:16711935,maroon:8388608,mediumaquamarine:6737322,mediumblue:205,mediumorchid:12211667,mediumpurple:9662683,mediumseagreen:3978097,mediumslateblue:8087790,mediumspringgreen:64154,mediumturquoise:4772300,mediumvioletred:13047173,midnightblue:1644912,mintcream:16121850,mistyrose:16770273,moccasin:16770229,navajowhite:16768685,navy:128,oldlace:16643558,olive:8421376,olivedrab:7048739,orange:16753920,orangered:16729344,orchid:14315734,palegoldenrod:15657130,palegreen:10025880,paleturquoise:11529966,palevioletred:14381203,papayawhip:16773077,peachpuff:16767673,peru:13468991,pink:16761035,plum:14524637,powderblue:11591910,purple:8388736,rebeccapurple:6697881,red:16711680,rosybrown:12357519,royalblue:4286945,saddlebrown:9127187,salmon:16416882,sandybrown:16032864,seagreen:3050327,seashell:16774638,sienna:10506797,silver:12632256,skyblue:8900331,slateblue:6970061,slategray:7372944,slategrey:7372944,snow:16775930,springgreen:65407,steelblue:4620980,tan:13808780,teal:32896,thistle:14204888,tomato:16737095,turquoise:4251856,violet:15631086,wheat:16113331,white:16777215,whitesmoke:16119285,yellow:16776960,yellowgreen:10145074},qb={h:0,s:0,l:0},Xb={h:0,s:0,l:0};function Yb(t,e,n){return n<0&&(n+=1),n>1&&(n-=1),n<1/6?t+6*(e-t)*n:n<.5?e:n<2/3?t+6*(e-t)*(2/3-n):t}function $b(t){return t<.04045?.0773993808*t:Math.pow(.9478672986*t+.0521327014,2.4)}function Jb(t){return t<.0031308?12.92*t:1.055*Math.pow(t,.41666)-.055}class Zb{constructor(t,e,n){return void 0===e&&void 0===n?this.set(t):this.setRGB(t,e,n)}set(t){return t&&t.isColor?this.copy(t):\\\\\\\"number\\\\\\\"==typeof t?this.setHex(t):\\\\\\\"string\\\\\\\"==typeof t&&this.setStyle(t),this}setScalar(t){return this.r=t,this.g=t,this.b=t,this}setHex(t){return t=Math.floor(t),this.r=(t>>16&255)/255,this.g=(t>>8&255)/255,this.b=(255&t)/255,this}setRGB(t,e,n){return this.r=t,this.g=e,this.b=n,this}setHSL(t,e,n){if(t=ux(t,1),e=cx(e,0,1),n=cx(n,0,1),0===e)this.r=this.g=this.b=n;else{const i=n<=.5?n*(1+e):n+e-n*e,r=2*n-i;this.r=Yb(r,i,t+1/3),this.g=Yb(r,i,t),this.b=Yb(r,i,t-1/3)}return this}setStyle(t){function e(e){void 0!==e&&parseFloat(e)<1&&console.warn(\\\\\\\"THREE.Color: Alpha component of \\\\\\\"+t+\\\\\\\" will be ignored.\\\\\\\")}let n;if(n=/^((?:rgb|hsl)a?)\\\\(([^\\\\)]*)\\\\)/.exec(t)){let t;const i=n[1],r=n[2];switch(i){case\\\\\\\"rgb\\\\\\\":case\\\\\\\"rgba\\\\\\\":if(t=/^\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r))return this.r=Math.min(255,parseInt(t[1],10))/255,this.g=Math.min(255,parseInt(t[2],10))/255,this.b=Math.min(255,parseInt(t[3],10))/255,e(t[4]),this;if(t=/^\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r))return this.r=Math.min(100,parseInt(t[1],10))/100,this.g=Math.min(100,parseInt(t[2],10))/100,this.b=Math.min(100,parseInt(t[3],10))/100,e(t[4]),this;break;case\\\\\\\"hsl\\\\\\\":case\\\\\\\"hsla\\\\\\\":if(t=/^\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r)){const n=parseFloat(t[1])/360,i=parseInt(t[2],10)/100,r=parseInt(t[3],10)/100;return e(t[4]),this.setHSL(n,i,r)}}}else if(n=/^\\\\#([A-Fa-f\\\\d]+)$/.exec(t)){const t=n[1],e=t.length;if(3===e)return this.r=parseInt(t.charAt(0)+t.charAt(0),16)/255,this.g=parseInt(t.charAt(1)+t.charAt(1),16)/255,this.b=parseInt(t.charAt(2)+t.charAt(2),16)/255,this;if(6===e)return this.r=parseInt(t.charAt(0)+t.charAt(1),16)/255,this.g=parseInt(t.charAt(2)+t.charAt(3),16)/255,this.b=parseInt(t.charAt(4)+t.charAt(5),16)/255,this}return t&&t.length>0?this.setColorName(t):this}setColorName(t){const e=Wb[t.toLowerCase()];return void 0!==e?this.setHex(e):console.warn(\\\\\\\"THREE.Color: Unknown color \\\\\\\"+t),this}clone(){return new this.constructor(this.r,this.g,this.b)}copy(t){return this.r=t.r,this.g=t.g,this.b=t.b,this}copyGammaToLinear(t,e=2){return this.r=Math.pow(t.r,e),this.g=Math.pow(t.g,e),this.b=Math.pow(t.b,e),this}copyLinearToGamma(t,e=2){const n=e>0?1/e:1;return this.r=Math.pow(t.r,n),this.g=Math.pow(t.g,n),this.b=Math.pow(t.b,n),this}convertGammaToLinear(t){return this.copyGammaToLinear(this,t),this}convertLinearToGamma(t){return this.copyLinearToGamma(this,t),this}copySRGBToLinear(t){return this.r=$b(t.r),this.g=$b(t.g),this.b=$b(t.b),this}copyLinearToSRGB(t){return this.r=Jb(t.r),this.g=Jb(t.g),this.b=Jb(t.b),this}convertSRGBToLinear(){return this.copySRGBToLinear(this),this}convertLinearToSRGB(){return this.copyLinearToSRGB(this),this}getHex(){return 255*this.r<<16^255*this.g<<8^255*this.b<<0}getHexString(){return(\\\\\\\"000000\\\\\\\"+this.getHex().toString(16)).slice(-6)}getHSL(t){const e=this.r,n=this.g,i=this.b,r=Math.max(e,n,i),s=Math.min(e,n,i);let o,a;const l=(s+r)/2;if(s===r)o=0,a=0;else{const t=r-s;switch(a=l<=.5?t/(r+s):t/(2-r-s),r){case e:o=(n-i)/t+(n65535?iw:nw)(t,1):this.index=t,this}getAttribute(t){return this.attributes[t]}setAttribute(t,e){return this.attributes[t]=e,this}deleteAttribute(t){return delete this.attributes[t],this}hasAttribute(t){return void 0!==this.attributes[t]}addGroup(t,e,n=0){this.groups.push({start:t,count:e,materialIndex:n})}clearGroups(){this.groups=[]}setDrawRange(t,e){this.drawRange.start=t,this.drawRange.count=e}applyMatrix4(t){const e=this.attributes.position;void 0!==e&&(e.applyMatrix4(t),e.needsUpdate=!0);const n=this.attributes.normal;if(void 0!==n){const e=(new gx).getNormalMatrix(t);n.applyNormalMatrix(e),n.needsUpdate=!0}const i=this.attributes.tangent;return void 0!==i&&(i.transformDirection(t),i.needsUpdate=!0),null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this}applyQuaternion(t){return ow.makeRotationFromQuaternion(t),this.applyMatrix4(ow),this}rotateX(t){return ow.makeRotationX(t),this.applyMatrix4(ow),this}rotateY(t){return ow.makeRotationY(t),this.applyMatrix4(ow),this}rotateZ(t){return ow.makeRotationZ(t),this.applyMatrix4(ow),this}translate(t,e,n){return ow.makeTranslation(t,e,n),this.applyMatrix4(ow),this}scale(t,e,n){return ow.makeScale(t,e,n),this.applyMatrix4(ow),this}lookAt(t){return aw.lookAt(t),aw.updateMatrix(),this.applyMatrix4(aw.matrix),this}center(){return this.computeBoundingBox(),this.boundingBox.getCenter(lw).negate(),this.translate(lw.x,lw.y,lw.z),this}setFromPoints(t){const e=[];for(let n=0,i=t.length;n0&&(t.userData=this.userData),void 0!==this.parameters){const e=this.parameters;for(const n in e)void 0!==e[n]&&(t[n]=e[n]);return t}t.data={attributes:{}};const e=this.index;null!==e&&(t.data.index={type:e.array.constructor.name,array:Array.prototype.slice.call(e.array)});const n=this.attributes;for(const e in n){const i=n[e];t.data.attributes[e]=i.toJSON(t.data)}const i={};let r=!1;for(const e in this.morphAttributes){const n=this.morphAttributes[e],s=[];for(let e=0,i=n.length;e0&&(i[e]=s,r=!0)}r&&(t.data.morphAttributes=i,t.data.morphTargetsRelative=this.morphTargetsRelative);const s=this.groups;s.length>0&&(t.data.groups=JSON.parse(JSON.stringify(s)));const o=this.boundingSphere;return null!==o&&(t.data.boundingSphere={center:o.center.toArray(),radius:o.radius}),t}clone(){return(new this.constructor).copy(this)}copy(t){this.index=null,this.attributes={},this.morphAttributes={},this.groups=[],this.boundingBox=null,this.boundingSphere=null;const e={};this.name=t.name;const n=t.index;null!==n&&this.setIndex(n.clone(e));const i=t.attributes;for(const t in i){const n=i[t];this.setAttribute(t,n.clone(e))}const r=t.morphAttributes;for(const t in r){const n=[],i=r[t];for(let t=0,r=i.length;t0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e0&&console.error(\\\\\\\"THREE.Mesh.updateMorphTargets() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}raycast(t,e){const n=this.geometry,i=this.material,r=this.matrixWorld;if(void 0===i)return;if(null===n.boundingSphere&&n.computeBoundingSphere(),mw.copy(n.boundingSphere),mw.applyMatrix4(r),!1===t.ray.intersectsSphere(mw))return;if(pw.copy(r).invert(),_w.copy(t.ray).applyMatrix4(pw),null!==n.boundingBox&&!1===_w.intersectsBox(n.boundingBox))return;let s;if(n.isBufferGeometry){const r=n.index,o=n.attributes.position,a=n.morphAttributes.position,l=n.morphTargetsRelative,c=n.attributes.uv,u=n.attributes.uv2,h=n.groups,d=n.drawRange;if(null!==r)if(Array.isArray(i))for(let n=0,p=h.length;nn.far?null:{distance:c,point:Nw.clone(),object:t}}(t,e,n,i,fw,gw,vw,Cw);if(p){a&&(Ew.fromBufferAttribute(a,c),Mw.fromBufferAttribute(a,u),Sw.fromBufferAttribute(a,h),p.uv=Vb.getUV(Cw,fw,gw,vw,Ew,Mw,Sw,new fx)),l&&(Ew.fromBufferAttribute(l,c),Mw.fromBufferAttribute(l,u),Sw.fromBufferAttribute(l,h),p.uv2=Vb.getUV(Cw,fw,gw,vw,Ew,Mw,Sw,new fx));const t={a:c,b:u,c:h,normal:new Nx,materialIndex:0};Vb.getNormal(fw,gw,vw,t.normal),p.face=t}return p}Lw.prototype.isMesh=!0;class Rw extends dw{constructor(t=1,e=1,n=1,i=1,r=1,s=1){super(),this.type=\\\\\\\"BoxGeometry\\\\\\\",this.parameters={width:t,height:e,depth:n,widthSegments:i,heightSegments:r,depthSegments:s};const o=this;i=Math.floor(i),r=Math.floor(r),s=Math.floor(s);const a=[],l=[],c=[],u=[];let h=0,d=0;function p(t,e,n,i,r,s,p,_,m,f,g){const v=s/m,y=p/f,x=s/2,b=p/2,w=_/2,T=m+1,A=f+1;let E=0,M=0;const S=new Nx;for(let s=0;s0?1:-1,c.push(S.x,S.y,S.z),u.push(a/m),u.push(1-s/f),E+=1}}for(let t=0;t0&&(e.defines=this.defines),e.vertexShader=this.vertexShader,e.fragmentShader=this.fragmentShader;const n={};for(const t in this.extensions)!0===this.extensions[t]&&(n[t]=!0);return Object.keys(n).length>0&&(e.extensions=n),e}}Dw.prototype.isShaderMaterial=!0;class kw extends Ob{constructor(){super(),this.type=\\\\\\\"Camera\\\\\\\",this.matrixWorldInverse=new ob,this.projectionMatrix=new ob,this.projectionMatrixInverse=new ob}copy(t,e){return super.copy(t,e),this.matrixWorldInverse.copy(t.matrixWorldInverse),this.projectionMatrix.copy(t.projectionMatrix),this.projectionMatrixInverse.copy(t.projectionMatrixInverse),this}getWorldDirection(t){this.updateWorldMatrix(!0,!1);const e=this.matrixWorld.elements;return t.set(-e[8],-e[9],-e[10]).normalize()}updateMatrixWorld(t){super.updateMatrixWorld(t),this.matrixWorldInverse.copy(this.matrixWorld).invert()}updateWorldMatrix(t,e){super.updateWorldMatrix(t,e),this.matrixWorldInverse.copy(this.matrixWorld).invert()}clone(){return(new this.constructor).copy(this)}}kw.prototype.isCamera=!0;class Bw extends kw{constructor(t=50,e=1,n=.1,i=2e3){super(),this.type=\\\\\\\"PerspectiveCamera\\\\\\\",this.fov=t,this.zoom=1,this.near=n,this.far=i,this.focus=10,this.aspect=e,this.view=null,this.filmGauge=35,this.filmOffset=0,this.updateProjectionMatrix()}copy(t,e){return super.copy(t,e),this.fov=t.fov,this.zoom=t.zoom,this.near=t.near,this.far=t.far,this.focus=t.focus,this.aspect=t.aspect,this.view=null===t.view?null:Object.assign({},t.view),this.filmGauge=t.filmGauge,this.filmOffset=t.filmOffset,this}setFocalLength(t){const e=.5*this.getFilmHeight()/t;this.fov=2*sx*Math.atan(e),this.updateProjectionMatrix()}getFocalLength(){const t=Math.tan(.5*rx*this.fov);return.5*this.getFilmHeight()/t}getEffectiveFOV(){return 2*sx*Math.atan(Math.tan(.5*rx*this.fov)/this.zoom)}getFilmWidth(){return this.filmGauge*Math.min(this.aspect,1)}getFilmHeight(){return this.filmGauge/Math.max(this.aspect,1)}setViewOffset(t,e,n,i,r,s){this.aspect=t/e,null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=i,this.view.width=r,this.view.height=s,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=this.near;let e=t*Math.tan(.5*rx*this.fov)/this.zoom,n=2*e,i=this.aspect*n,r=-.5*i;const s=this.view;if(null!==this.view&&this.view.enabled){const t=s.fullWidth,o=s.fullHeight;r+=s.offsetX*i/t,e-=s.offsetY*n/o,i*=s.width/t,n*=s.height/o}const o=this.filmOffset;0!==o&&(r+=t*o/this.getFilmWidth()),this.projectionMatrix.makePerspective(r,r+i,e,e-n,t,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(t){const e=super.toJSON(t);return e.object.fov=this.fov,e.object.zoom=this.zoom,e.object.near=this.near,e.object.far=this.far,e.object.focus=this.focus,e.object.aspect=this.aspect,null!==this.view&&(e.object.view=Object.assign({},this.view)),e.object.filmGauge=this.filmGauge,e.object.filmOffset=this.filmOffset,e}}Bw.prototype.isPerspectiveCamera=!0;const zw=90;class Uw extends Ob{constructor(t,e,n){if(super(),this.type=\\\\\\\"CubeCamera\\\\\\\",!0!==n.isWebGLCubeRenderTarget)return void console.error(\\\\\\\"THREE.CubeCamera: The constructor now expects an instance of WebGLCubeRenderTarget as third parameter.\\\\\\\");this.renderTarget=n;const i=new Bw(zw,1,t,e);i.layers=this.layers,i.up.set(0,-1,0),i.lookAt(new Nx(1,0,0)),this.add(i);const r=new Bw(zw,1,t,e);r.layers=this.layers,r.up.set(0,-1,0),r.lookAt(new Nx(-1,0,0)),this.add(r);const s=new Bw(zw,1,t,e);s.layers=this.layers,s.up.set(0,0,1),s.lookAt(new Nx(0,1,0)),this.add(s);const o=new Bw(zw,1,t,e);o.layers=this.layers,o.up.set(0,0,-1),o.lookAt(new Nx(0,-1,0)),this.add(o);const a=new Bw(zw,1,t,e);a.layers=this.layers,a.up.set(0,-1,0),a.lookAt(new Nx(0,0,1)),this.add(a);const l=new Bw(zw,1,t,e);l.layers=this.layers,l.up.set(0,-1,0),l.lookAt(new Nx(0,0,-1)),this.add(l)}update(t,e){null===this.parent&&this.updateMatrixWorld();const n=this.renderTarget,[i,r,s,o,a,l]=this.children,c=t.xr.enabled,u=t.getRenderTarget();t.xr.enabled=!1;const h=n.texture.generateMipmaps;n.texture.generateMipmaps=!1,t.setRenderTarget(n,0),t.render(e,i),t.setRenderTarget(n,1),t.render(e,r),t.setRenderTarget(n,2),t.render(e,s),t.setRenderTarget(n,3),t.render(e,o),t.setRenderTarget(n,4),t.render(e,a),n.texture.generateMipmaps=h,t.setRenderTarget(n,5),t.render(e,l),t.setRenderTarget(u),t.xr.enabled=c}}class Gw extends Tx{constructor(t,e,n,i,r,s,o,a,l,c){super(t=void 0!==t?t:[],e=void 0!==e?e:fy,n,i,r,s,o,a,l,c),this.flipY=!1}get images(){return this.image}set images(t){this.image=t}}Gw.prototype.isCubeTexture=!0;class Vw extends Mx{constructor(t,e,n){Number.isInteger(e)&&(console.warn(\\\\\\\"THREE.WebGLCubeRenderTarget: constructor signature is now WebGLCubeRenderTarget( size, options )\\\\\\\"),e=n),super(t,t,e),e=e||{},this.texture=new Gw(void 0,e.mapping,e.wrapS,e.wrapT,e.magFilter,e.minFilter,e.format,e.type,e.anisotropy,e.encoding),this.texture.isRenderTargetTexture=!0,this.texture.generateMipmaps=void 0!==e.generateMipmaps&&e.generateMipmaps,this.texture.minFilter=void 0!==e.minFilter?e.minFilter:Cy,this.texture._needsFlipEnvMap=!1}fromEquirectangularTexture(t,e){this.texture.type=e.type,this.texture.format=By,this.texture.encoding=e.encoding,this.texture.generateMipmaps=e.generateMipmaps,this.texture.minFilter=e.minFilter,this.texture.magFilter=e.magFilter;const n={uniforms:{tEquirect:{value:null}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec3 vWorldDirection;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D tEquirect;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec3 vWorldDirection;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec3 direction = normalize( vWorldDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 sampleUV = equirectUv( direction );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = texture2D( tEquirect, sampleUV );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\\\\"},i=new Rw(5,5,5),r=new Dw({name:\\\\\\\"CubemapFromEquirect\\\\\\\",uniforms:Pw(n.uniforms),vertexShader:n.vertexShader,fragmentShader:n.fragmentShader,side:1,blending:0});r.uniforms.tEquirect.value=e;const s=new Lw(i,r),o=e.minFilter;e.minFilter===Ly&&(e.minFilter=Cy);return new Uw(1,10,this).update(t,s),e.minFilter=o,s.geometry.dispose(),s.material.dispose(),this}clear(t,e,n,i){const r=t.getRenderTarget();for(let r=0;r<6;r++)t.setRenderTarget(this,r),t.clear(e,n,i);t.setRenderTarget(r)}}Vw.prototype.isWebGLCubeRenderTarget=!0;const Hw=new Nx,jw=new Nx,Ww=new gx;class qw{constructor(t=new Nx(1,0,0),e=0){this.normal=t,this.constant=e}set(t,e){return this.normal.copy(t),this.constant=e,this}setComponents(t,e,n,i){return this.normal.set(t,e,n),this.constant=i,this}setFromNormalAndCoplanarPoint(t,e){return this.normal.copy(t),this.constant=-e.dot(this.normal),this}setFromCoplanarPoints(t,e,n){const i=Hw.subVectors(n,e).cross(jw.subVectors(t,e)).normalize();return this.setFromNormalAndCoplanarPoint(i,t),this}copy(t){return this.normal.copy(t.normal),this.constant=t.constant,this}normalize(){const t=1/this.normal.length();return this.normal.multiplyScalar(t),this.constant*=t,this}negate(){return this.constant*=-1,this.normal.negate(),this}distanceToPoint(t){return this.normal.dot(t)+this.constant}distanceToSphere(t){return this.distanceToPoint(t.center)-t.radius}projectPoint(t,e){return e.copy(this.normal).multiplyScalar(-this.distanceToPoint(t)).add(t)}intersectLine(t,e){const n=t.delta(Hw),i=this.normal.dot(n);if(0===i)return 0===this.distanceToPoint(t.start)?e.copy(t.start):null;const r=-(t.start.dot(this.normal)+this.constant)/i;return r<0||r>1?null:e.copy(n).multiplyScalar(r).add(t.start)}intersectsLine(t){const e=this.distanceToPoint(t.start),n=this.distanceToPoint(t.end);return e<0&&n>0||n<0&&e>0}intersectsBox(t){return t.intersectsPlane(this)}intersectsSphere(t){return t.intersectsPlane(this)}coplanarPoint(t){return t.copy(this.normal).multiplyScalar(-this.constant)}applyMatrix4(t,e){const n=e||Ww.getNormalMatrix(t),i=this.coplanarPoint(Hw).applyMatrix4(t),r=this.normal.applyMatrix3(n).normalize();return this.constant=-i.dot(r),this}translate(t){return this.constant-=t.dot(this.normal),this}equals(t){return t.normal.equals(this.normal)&&t.constant===this.constant}clone(){return(new this.constructor).copy(this)}}qw.prototype.isPlane=!0;const Xw=new Zx,Yw=new Nx;class $w{constructor(t=new qw,e=new qw,n=new qw,i=new qw,r=new qw,s=new qw){this.planes=[t,e,n,i,r,s]}set(t,e,n,i,r,s){const o=this.planes;return o[0].copy(t),o[1].copy(e),o[2].copy(n),o[3].copy(i),o[4].copy(r),o[5].copy(s),this}copy(t){const e=this.planes;for(let n=0;n<6;n++)e[n].copy(t.planes[n]);return this}setFromProjectionMatrix(t){const e=this.planes,n=t.elements,i=n[0],r=n[1],s=n[2],o=n[3],a=n[4],l=n[5],c=n[6],u=n[7],h=n[8],d=n[9],p=n[10],_=n[11],m=n[12],f=n[13],g=n[14],v=n[15];return e[0].setComponents(o-i,u-a,_-h,v-m).normalize(),e[1].setComponents(o+i,u+a,_+h,v+m).normalize(),e[2].setComponents(o+r,u+l,_+d,v+f).normalize(),e[3].setComponents(o-r,u-l,_-d,v-f).normalize(),e[4].setComponents(o-s,u-c,_-p,v-g).normalize(),e[5].setComponents(o+s,u+c,_+p,v+g).normalize(),this}intersectsObject(t){const e=t.geometry;return null===e.boundingSphere&&e.computeBoundingSphere(),Xw.copy(e.boundingSphere).applyMatrix4(t.matrixWorld),this.intersectsSphere(Xw)}intersectsSprite(t){return Xw.center.set(0,0,0),Xw.radius=.7071067811865476,Xw.applyMatrix4(t.matrixWorld),this.intersectsSphere(Xw)}intersectsSphere(t){const e=this.planes,n=t.center,i=-t.radius;for(let t=0;t<6;t++){if(e[t].distanceToPoint(n)0?t.max.x:t.min.x,Yw.y=i.normal.y>0?t.max.y:t.min.y,Yw.z=i.normal.z>0?t.max.z:t.min.z,i.distanceToPoint(Yw)<0)return!1}return!0}containsPoint(t){const e=this.planes;for(let n=0;n<6;n++)if(e[n].distanceToPoint(t)<0)return!1;return!0}clone(){return(new this.constructor).copy(this)}}function Jw(){let t=null,e=!1,n=null,i=null;function r(e,s){n(e,s),i=t.requestAnimationFrame(r)}return{start:function(){!0!==e&&null!==n&&(i=t.requestAnimationFrame(r),e=!0)},stop:function(){t.cancelAnimationFrame(i),e=!1},setAnimationLoop:function(t){n=t},setContext:function(e){t=e}}}function Zw(t,e){const n=e.isWebGL2,i=new WeakMap;return{get:function(t){return t.isInterleavedBufferAttribute&&(t=t.data),i.get(t)},remove:function(e){e.isInterleavedBufferAttribute&&(e=e.data);const n=i.get(e);n&&(t.deleteBuffer(n.buffer),i.delete(e))},update:function(e,r){if(e.isGLBufferAttribute){const t=i.get(e);return void((!t||t.version 0.0 ) ? v : 0.5 * inversesqrt( max( 1.0 - x * x, 1e-7 ) ) - v;\\\\n\\\\treturn cross( v1, v2 ) * theta_sintheta;\\\\n}\\\\nvec3 LTC_Evaluate( const in vec3 N, const in vec3 V, const in vec3 P, const in mat3 mInv, const in vec3 rectCoords[ 4 ] ) {\\\\n\\\\tvec3 v1 = rectCoords[ 1 ] - rectCoords[ 0 ];\\\\n\\\\tvec3 v2 = rectCoords[ 3 ] - rectCoords[ 0 ];\\\\n\\\\tvec3 lightNormal = cross( v1, v2 );\\\\n\\\\tif( dot( lightNormal, P - rectCoords[ 0 ] ) < 0.0 ) return vec3( 0.0 );\\\\n\\\\tvec3 T1, T2;\\\\n\\\\tT1 = normalize( V - N * dot( V, N ) );\\\\n\\\\tT2 = - cross( N, T1 );\\\\n\\\\tmat3 mat = mInv * transposeMat3( mat3( T1, T2, N ) );\\\\n\\\\tvec3 coords[ 4 ];\\\\n\\\\tcoords[ 0 ] = mat * ( rectCoords[ 0 ] - P );\\\\n\\\\tcoords[ 1 ] = mat * ( rectCoords[ 1 ] - P );\\\\n\\\\tcoords[ 2 ] = mat * ( rectCoords[ 2 ] - P );\\\\n\\\\tcoords[ 3 ] = mat * ( rectCoords[ 3 ] - P );\\\\n\\\\tcoords[ 0 ] = normalize( coords[ 0 ] );\\\\n\\\\tcoords[ 1 ] = normalize( coords[ 1 ] );\\\\n\\\\tcoords[ 2 ] = normalize( coords[ 2 ] );\\\\n\\\\tcoords[ 3 ] = normalize( coords[ 3 ] );\\\\n\\\\tvec3 vectorFormFactor = vec3( 0.0 );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 0 ], coords[ 1 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 1 ], coords[ 2 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 2 ], coords[ 3 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 3 ], coords[ 0 ] );\\\\n\\\\tfloat result = LTC_ClippedSphereFormFactor( vectorFormFactor );\\\\n\\\\treturn vec3( result );\\\\n}\\\\nfloat G_BlinnPhong_Implicit( ) {\\\\n\\\\treturn 0.25;\\\\n}\\\\nfloat D_BlinnPhong( const in float shininess, const in float dotNH ) {\\\\n\\\\treturn RECIPROCAL_PI * ( shininess * 0.5 + 1.0 ) * pow( dotNH, shininess );\\\\n}\\\\nvec3 BRDF_BlinnPhong( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float shininess ) {\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\tfloat dotVH = saturate( dot( viewDir, halfDir ) );\\\\n\\\\tvec3 F = F_Schlick( specularColor, 1.0, dotVH );\\\\n\\\\tfloat G = G_BlinnPhong_Implicit( );\\\\n\\\\tfloat D = D_BlinnPhong( shininess, dotNH );\\\\n\\\\treturn F * ( G * D );\\\\n}\\\\n#if defined( USE_SHEEN )\\\\nfloat D_Charlie( float roughness, float dotNH ) {\\\\n\\\\tfloat alpha = pow2( roughness );\\\\n\\\\tfloat invAlpha = 1.0 / alpha;\\\\n\\\\tfloat cos2h = dotNH * dotNH;\\\\n\\\\tfloat sin2h = max( 1.0 - cos2h, 0.0078125 );\\\\n\\\\treturn ( 2.0 + invAlpha ) * pow( sin2h, invAlpha * 0.5 ) / ( 2.0 * PI );\\\\n}\\\\nfloat V_Neubelt( float dotNV, float dotNL ) {\\\\n\\\\treturn saturate( 1.0 / ( 4.0 * ( dotNL + dotNV - dotNL * dotNV ) ) );\\\\n}\\\\nvec3 BRDF_Sheen( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, vec3 sheenTint, const in float sheenRoughness ) {\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\tfloat dotNL = saturate( dot( normal, lightDir ) );\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\tfloat D = D_Charlie( sheenRoughness, dotNH );\\\\n\\\\tfloat V = V_Neubelt( dotNV, dotNL );\\\\n\\\\treturn sheenTint * ( D * V );\\\\n}\\\\n#endif\\\\\\\",bumpmap_pars_fragment:\\\\\\\"#ifdef USE_BUMPMAP\\\\n\\\\tuniform sampler2D bumpMap;\\\\n\\\\tuniform float bumpScale;\\\\n\\\\tvec2 dHdxy_fwd() {\\\\n\\\\t\\\\tvec2 dSTdx = dFdx( vUv );\\\\n\\\\t\\\\tvec2 dSTdy = dFdy( vUv );\\\\n\\\\t\\\\tfloat Hll = bumpScale * texture2D( bumpMap, vUv ).x;\\\\n\\\\t\\\\tfloat dBx = bumpScale * texture2D( bumpMap, vUv + dSTdx ).x - Hll;\\\\n\\\\t\\\\tfloat dBy = bumpScale * texture2D( bumpMap, vUv + dSTdy ).x - Hll;\\\\n\\\\t\\\\treturn vec2( dBx, dBy );\\\\n\\\\t}\\\\n\\\\tvec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy, float faceDirection ) {\\\\n\\\\t\\\\tvec3 vSigmaX = vec3( dFdx( surf_pos.x ), dFdx( surf_pos.y ), dFdx( surf_pos.z ) );\\\\n\\\\t\\\\tvec3 vSigmaY = vec3( dFdy( surf_pos.x ), dFdy( surf_pos.y ), dFdy( surf_pos.z ) );\\\\n\\\\t\\\\tvec3 vN = surf_norm;\\\\n\\\\t\\\\tvec3 R1 = cross( vSigmaY, vN );\\\\n\\\\t\\\\tvec3 R2 = cross( vN, vSigmaX );\\\\n\\\\t\\\\tfloat fDet = dot( vSigmaX, R1 ) * faceDirection;\\\\n\\\\t\\\\tvec3 vGrad = sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );\\\\n\\\\t\\\\treturn normalize( abs( fDet ) * surf_norm - vGrad );\\\\n\\\\t}\\\\n#endif\\\\\\\",clipping_planes_fragment:\\\\\\\"#if NUM_CLIPPING_PLANES > 0\\\\n\\\\tvec4 plane;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < UNION_CLIPPING_PLANES; i ++ ) {\\\\n\\\\t\\\\tplane = clippingPlanes[ i ];\\\\n\\\\t\\\\tif ( dot( vClipPosition, plane.xyz ) > plane.w ) discard;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#if UNION_CLIPPING_PLANES < NUM_CLIPPING_PLANES\\\\n\\\\t\\\\tbool clipped = true;\\\\n\\\\t\\\\t#pragma unroll_loop_start\\\\n\\\\t\\\\tfor ( int i = UNION_CLIPPING_PLANES; i < NUM_CLIPPING_PLANES; i ++ ) {\\\\n\\\\t\\\\t\\\\tplane = clippingPlanes[ i ];\\\\n\\\\t\\\\t\\\\tclipped = ( dot( vClipPosition, plane.xyz ) > plane.w ) && clipped;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t#pragma unroll_loop_end\\\\n\\\\t\\\\tif ( clipped ) discard;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",clipping_planes_pars_fragment:\\\\\\\"#if NUM_CLIPPING_PLANES > 0\\\\n\\\\tvarying vec3 vClipPosition;\\\\n\\\\tuniform vec4 clippingPlanes[ NUM_CLIPPING_PLANES ];\\\\n#endif\\\\\\\",clipping_planes_pars_vertex:\\\\\\\"#if NUM_CLIPPING_PLANES > 0\\\\n\\\\tvarying vec3 vClipPosition;\\\\n#endif\\\\\\\",clipping_planes_vertex:\\\\\\\"#if NUM_CLIPPING_PLANES > 0\\\\n\\\\tvClipPosition = - mvPosition.xyz;\\\\n#endif\\\\\\\",color_fragment:\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\n\\\\tdiffuseColor *= vColor;\\\\n#elif defined( USE_COLOR )\\\\n\\\\tdiffuseColor.rgb *= vColor;\\\\n#endif\\\\\\\",color_pars_fragment:\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\n\\\\tvarying vec4 vColor;\\\\n#elif defined( USE_COLOR )\\\\n\\\\tvarying vec3 vColor;\\\\n#endif\\\\\\\",color_pars_vertex:\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\n\\\\tvarying vec4 vColor;\\\\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\\\\n\\\\tvarying vec3 vColor;\\\\n#endif\\\\\\\",color_vertex:\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\n\\\\tvColor = vec4( 1.0 );\\\\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\\\\n\\\\tvColor = vec3( 1.0 );\\\\n#endif\\\\n#ifdef USE_COLOR\\\\n\\\\tvColor *= color;\\\\n#endif\\\\n#ifdef USE_INSTANCING_COLOR\\\\n\\\\tvColor.xyz *= instanceColor.xyz;\\\\n#endif\\\\\\\",common:\\\\\\\"#define PI 3.141592653589793\\\\n#define PI2 6.283185307179586\\\\n#define PI_HALF 1.5707963267948966\\\\n#define RECIPROCAL_PI 0.3183098861837907\\\\n#define RECIPROCAL_PI2 0.15915494309189535\\\\n#define EPSILON 1e-6\\\\n#ifndef saturate\\\\n#define saturate( a ) clamp( a, 0.0, 1.0 )\\\\n#endif\\\\n#define whiteComplement( a ) ( 1.0 - saturate( a ) )\\\\nfloat pow2( const in float x ) { return x*x; }\\\\nfloat pow3( const in float x ) { return x*x*x; }\\\\nfloat pow4( const in float x ) { float x2 = x*x; return x2*x2; }\\\\nfloat max3( const in vec3 v ) { return max( max( v.x, v.y ), v.z ); }\\\\nfloat average( const in vec3 color ) { return dot( color, vec3( 0.3333 ) ); }\\\\nhighp float rand( const in vec2 uv ) {\\\\n\\\\tconst highp float a = 12.9898, b = 78.233, c = 43758.5453;\\\\n\\\\thighp float dt = dot( uv.xy, vec2( a,b ) ), sn = mod( dt, PI );\\\\n\\\\treturn fract( sin( sn ) * c );\\\\n}\\\\n#ifdef HIGH_PRECISION\\\\n\\\\tfloat precisionSafeLength( vec3 v ) { return length( v ); }\\\\n#else\\\\n\\\\tfloat precisionSafeLength( vec3 v ) {\\\\n\\\\t\\\\tfloat maxComponent = max3( abs( v ) );\\\\n\\\\t\\\\treturn length( v / maxComponent ) * maxComponent;\\\\n\\\\t}\\\\n#endif\\\\nstruct IncidentLight {\\\\n\\\\tvec3 color;\\\\n\\\\tvec3 direction;\\\\n\\\\tbool visible;\\\\n};\\\\nstruct ReflectedLight {\\\\n\\\\tvec3 directDiffuse;\\\\n\\\\tvec3 directSpecular;\\\\n\\\\tvec3 indirectDiffuse;\\\\n\\\\tvec3 indirectSpecular;\\\\n};\\\\nstruct GeometricContext {\\\\n\\\\tvec3 position;\\\\n\\\\tvec3 normal;\\\\n\\\\tvec3 viewDir;\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tvec3 clearcoatNormal;\\\\n#endif\\\\n};\\\\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\\\\n}\\\\nvec3 inverseTransformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\treturn normalize( ( vec4( dir, 0.0 ) * matrix ).xyz );\\\\n}\\\\nmat3 transposeMat3( const in mat3 m ) {\\\\n\\\\tmat3 tmp;\\\\n\\\\ttmp[ 0 ] = vec3( m[ 0 ].x, m[ 1 ].x, m[ 2 ].x );\\\\n\\\\ttmp[ 1 ] = vec3( m[ 0 ].y, m[ 1 ].y, m[ 2 ].y );\\\\n\\\\ttmp[ 2 ] = vec3( m[ 0 ].z, m[ 1 ].z, m[ 2 ].z );\\\\n\\\\treturn tmp;\\\\n}\\\\nfloat linearToRelativeLuminance( const in vec3 color ) {\\\\n\\\\tvec3 weights = vec3( 0.2126, 0.7152, 0.0722 );\\\\n\\\\treturn dot( weights, color.rgb );\\\\n}\\\\nbool isPerspectiveMatrix( mat4 m ) {\\\\n\\\\treturn m[ 2 ][ 3 ] == - 1.0;\\\\n}\\\\nvec2 equirectUv( in vec3 dir ) {\\\\n\\\\tfloat u = atan( dir.z, dir.x ) * RECIPROCAL_PI2 + 0.5;\\\\n\\\\tfloat v = asin( clamp( dir.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\\\\n\\\\treturn vec2( u, v );\\\\n}\\\\\\\",cube_uv_reflection_fragment:\\\\\\\"#ifdef ENVMAP_TYPE_CUBE_UV\\\\n\\\\t#define cubeUV_maxMipLevel 8.0\\\\n\\\\t#define cubeUV_minMipLevel 4.0\\\\n\\\\t#define cubeUV_maxTileSize 256.0\\\\n\\\\t#define cubeUV_minTileSize 16.0\\\\n\\\\tfloat getFace( vec3 direction ) {\\\\n\\\\t\\\\tvec3 absDirection = abs( direction );\\\\n\\\\t\\\\tfloat face = - 1.0;\\\\n\\\\t\\\\tif ( absDirection.x > absDirection.z ) {\\\\n\\\\t\\\\t\\\\tif ( absDirection.x > absDirection.y )\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.x > 0.0 ? 0.0 : 3.0;\\\\n\\\\t\\\\t\\\\telse\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tif ( absDirection.z > absDirection.y )\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.z > 0.0 ? 2.0 : 5.0;\\\\n\\\\t\\\\t\\\\telse\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn face;\\\\n\\\\t}\\\\n\\\\tvec2 getUV( vec3 direction, float face ) {\\\\n\\\\t\\\\tvec2 uv;\\\\n\\\\t\\\\tif ( face == 0.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( direction.z, direction.y ) / abs( direction.x );\\\\n\\\\t\\\\t} else if ( face == 1.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, - direction.z ) / abs( direction.y );\\\\n\\\\t\\\\t} else if ( face == 2.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, direction.y ) / abs( direction.z );\\\\n\\\\t\\\\t} else if ( face == 3.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.z, direction.y ) / abs( direction.x );\\\\n\\\\t\\\\t} else if ( face == 4.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, direction.z ) / abs( direction.y );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tuv = vec2( direction.x, direction.y ) / abs( direction.z );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn 0.5 * ( uv + 1.0 );\\\\n\\\\t}\\\\n\\\\tvec3 bilinearCubeUV( sampler2D envMap, vec3 direction, float mipInt ) {\\\\n\\\\t\\\\tfloat face = getFace( direction );\\\\n\\\\t\\\\tfloat filterInt = max( cubeUV_minMipLevel - mipInt, 0.0 );\\\\n\\\\t\\\\tmipInt = max( mipInt, cubeUV_minMipLevel );\\\\n\\\\t\\\\tfloat faceSize = exp2( mipInt );\\\\n\\\\t\\\\tfloat texelSize = 1.0 / ( 3.0 * cubeUV_maxTileSize );\\\\n\\\\t\\\\tvec2 uv = getUV( direction, face ) * ( faceSize - 1.0 );\\\\n\\\\t\\\\tvec2 f = fract( uv );\\\\n\\\\t\\\\tuv += 0.5 - f;\\\\n\\\\t\\\\tif ( face > 2.0 ) {\\\\n\\\\t\\\\t\\\\tuv.y += faceSize;\\\\n\\\\t\\\\t\\\\tface -= 3.0;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tuv.x += face * faceSize;\\\\n\\\\t\\\\tif ( mipInt < cubeUV_maxMipLevel ) {\\\\n\\\\t\\\\t\\\\tuv.y += 2.0 * cubeUV_maxTileSize;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tuv.y += filterInt * 2.0 * cubeUV_minTileSize;\\\\n\\\\t\\\\tuv.x += 3.0 * max( 0.0, cubeUV_maxTileSize - 2.0 * faceSize );\\\\n\\\\t\\\\tuv *= texelSize;\\\\n\\\\t\\\\tvec3 tl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\t\\\\tuv.x += texelSize;\\\\n\\\\t\\\\tvec3 tr = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\t\\\\tuv.y += texelSize;\\\\n\\\\t\\\\tvec3 br = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\t\\\\tuv.x -= texelSize;\\\\n\\\\t\\\\tvec3 bl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\t\\\\tvec3 tm = mix( tl, tr, f.x );\\\\n\\\\t\\\\tvec3 bm = mix( bl, br, f.x );\\\\n\\\\t\\\\treturn mix( tm, bm, f.y );\\\\n\\\\t}\\\\n\\\\t#define r0 1.0\\\\n\\\\t#define v0 0.339\\\\n\\\\t#define m0 - 2.0\\\\n\\\\t#define r1 0.8\\\\n\\\\t#define v1 0.276\\\\n\\\\t#define m1 - 1.0\\\\n\\\\t#define r4 0.4\\\\n\\\\t#define v4 0.046\\\\n\\\\t#define m4 2.0\\\\n\\\\t#define r5 0.305\\\\n\\\\t#define v5 0.016\\\\n\\\\t#define m5 3.0\\\\n\\\\t#define r6 0.21\\\\n\\\\t#define v6 0.0038\\\\n\\\\t#define m6 4.0\\\\n\\\\tfloat roughnessToMip( float roughness ) {\\\\n\\\\t\\\\tfloat mip = 0.0;\\\\n\\\\t\\\\tif ( roughness >= r1 ) {\\\\n\\\\t\\\\t\\\\tmip = ( r0 - roughness ) * ( m1 - m0 ) / ( r0 - r1 ) + m0;\\\\n\\\\t\\\\t} else if ( roughness >= r4 ) {\\\\n\\\\t\\\\t\\\\tmip = ( r1 - roughness ) * ( m4 - m1 ) / ( r1 - r4 ) + m1;\\\\n\\\\t\\\\t} else if ( roughness >= r5 ) {\\\\n\\\\t\\\\t\\\\tmip = ( r4 - roughness ) * ( m5 - m4 ) / ( r4 - r5 ) + m4;\\\\n\\\\t\\\\t} else if ( roughness >= r6 ) {\\\\n\\\\t\\\\t\\\\tmip = ( r5 - roughness ) * ( m6 - m5 ) / ( r5 - r6 ) + m5;\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tmip = - 2.0 * log2( 1.16 * roughness );\\\\t\\\\t}\\\\n\\\\t\\\\treturn mip;\\\\n\\\\t}\\\\n\\\\tvec4 textureCubeUV( sampler2D envMap, vec3 sampleDir, float roughness ) {\\\\n\\\\t\\\\tfloat mip = clamp( roughnessToMip( roughness ), m0, cubeUV_maxMipLevel );\\\\n\\\\t\\\\tfloat mipF = fract( mip );\\\\n\\\\t\\\\tfloat mipInt = floor( mip );\\\\n\\\\t\\\\tvec3 color0 = bilinearCubeUV( envMap, sampleDir, mipInt );\\\\n\\\\t\\\\tif ( mipF == 0.0 ) {\\\\n\\\\t\\\\t\\\\treturn vec4( color0, 1.0 );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tvec3 color1 = bilinearCubeUV( envMap, sampleDir, mipInt + 1.0 );\\\\n\\\\t\\\\t\\\\treturn vec4( mix( color0, color1, mipF ), 1.0 );\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n#endif\\\\\\\",defaultnormal_vertex:\\\\\\\"vec3 transformedNormal = objectNormal;\\\\n#ifdef USE_INSTANCING\\\\n\\\\tmat3 m = mat3( instanceMatrix );\\\\n\\\\ttransformedNormal /= vec3( dot( m[ 0 ], m[ 0 ] ), dot( m[ 1 ], m[ 1 ] ), dot( m[ 2 ], m[ 2 ] ) );\\\\n\\\\ttransformedNormal = m * transformedNormal;\\\\n#endif\\\\ntransformedNormal = normalMatrix * transformedNormal;\\\\n#ifdef FLIP_SIDED\\\\n\\\\ttransformedNormal = - transformedNormal;\\\\n#endif\\\\n#ifdef USE_TANGENT\\\\n\\\\tvec3 transformedTangent = ( modelViewMatrix * vec4( objectTangent, 0.0 ) ).xyz;\\\\n\\\\t#ifdef FLIP_SIDED\\\\n\\\\t\\\\ttransformedTangent = - transformedTangent;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",displacementmap_pars_vertex:\\\\\\\"#ifdef USE_DISPLACEMENTMAP\\\\n\\\\tuniform sampler2D displacementMap;\\\\n\\\\tuniform float displacementScale;\\\\n\\\\tuniform float displacementBias;\\\\n#endif\\\\\\\",displacementmap_vertex:\\\\\\\"#ifdef USE_DISPLACEMENTMAP\\\\n\\\\ttransformed += normalize( objectNormal ) * ( texture2D( displacementMap, vUv ).x * displacementScale + displacementBias );\\\\n#endif\\\\\\\",emissivemap_fragment:\\\\\\\"#ifdef USE_EMISSIVEMAP\\\\n\\\\tvec4 emissiveColor = texture2D( emissiveMap, vUv );\\\\n\\\\temissiveColor.rgb = emissiveMapTexelToLinear( emissiveColor ).rgb;\\\\n\\\\ttotalEmissiveRadiance *= emissiveColor.rgb;\\\\n#endif\\\\\\\",emissivemap_pars_fragment:\\\\\\\"#ifdef USE_EMISSIVEMAP\\\\n\\\\tuniform sampler2D emissiveMap;\\\\n#endif\\\\\\\",encodings_fragment:\\\\\\\"gl_FragColor = linearToOutputTexel( gl_FragColor );\\\\\\\",encodings_pars_fragment:\\\\\\\"\\\\nvec4 LinearToLinear( in vec4 value ) {\\\\n\\\\treturn value;\\\\n}\\\\nvec4 GammaToLinear( in vec4 value, in float gammaFactor ) {\\\\n\\\\treturn vec4( pow( value.rgb, vec3( gammaFactor ) ), value.a );\\\\n}\\\\nvec4 LinearToGamma( in vec4 value, in float gammaFactor ) {\\\\n\\\\treturn vec4( pow( value.rgb, vec3( 1.0 / gammaFactor ) ), value.a );\\\\n}\\\\nvec4 sRGBToLinear( in vec4 value ) {\\\\n\\\\treturn vec4( mix( pow( value.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), value.rgb * 0.0773993808, vec3( lessThanEqual( value.rgb, vec3( 0.04045 ) ) ) ), value.a );\\\\n}\\\\nvec4 LinearTosRGB( in vec4 value ) {\\\\n\\\\treturn vec4( mix( pow( value.rgb, vec3( 0.41666 ) ) * 1.055 - vec3( 0.055 ), value.rgb * 12.92, vec3( lessThanEqual( value.rgb, vec3( 0.0031308 ) ) ) ), value.a );\\\\n}\\\\nvec4 RGBEToLinear( in vec4 value ) {\\\\n\\\\treturn vec4( value.rgb * exp2( value.a * 255.0 - 128.0 ), 1.0 );\\\\n}\\\\nvec4 LinearToRGBE( in vec4 value ) {\\\\n\\\\tfloat maxComponent = max( max( value.r, value.g ), value.b );\\\\n\\\\tfloat fExp = clamp( ceil( log2( maxComponent ) ), -128.0, 127.0 );\\\\n\\\\treturn vec4( value.rgb / exp2( fExp ), ( fExp + 128.0 ) / 255.0 );\\\\n}\\\\nvec4 RGBMToLinear( in vec4 value, in float maxRange ) {\\\\n\\\\treturn vec4( value.rgb * value.a * maxRange, 1.0 );\\\\n}\\\\nvec4 LinearToRGBM( in vec4 value, in float maxRange ) {\\\\n\\\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\\\n\\\\tfloat M = clamp( maxRGB / maxRange, 0.0, 1.0 );\\\\n\\\\tM = ceil( M * 255.0 ) / 255.0;\\\\n\\\\treturn vec4( value.rgb / ( M * maxRange ), M );\\\\n}\\\\nvec4 RGBDToLinear( in vec4 value, in float maxRange ) {\\\\n\\\\treturn vec4( value.rgb * ( ( maxRange / 255.0 ) / value.a ), 1.0 );\\\\n}\\\\nvec4 LinearToRGBD( in vec4 value, in float maxRange ) {\\\\n\\\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\\\n\\\\tfloat D = max( maxRange / maxRGB, 1.0 );\\\\n\\\\tD = clamp( floor( D ) / 255.0, 0.0, 1.0 );\\\\n\\\\treturn vec4( value.rgb * ( D * ( 255.0 / maxRange ) ), D );\\\\n}\\\\nconst mat3 cLogLuvM = mat3( 0.2209, 0.3390, 0.4184, 0.1138, 0.6780, 0.7319, 0.0102, 0.1130, 0.2969 );\\\\nvec4 LinearToLogLuv( in vec4 value ) {\\\\n\\\\tvec3 Xp_Y_XYZp = cLogLuvM * value.rgb;\\\\n\\\\tXp_Y_XYZp = max( Xp_Y_XYZp, vec3( 1e-6, 1e-6, 1e-6 ) );\\\\n\\\\tvec4 vResult;\\\\n\\\\tvResult.xy = Xp_Y_XYZp.xy / Xp_Y_XYZp.z;\\\\n\\\\tfloat Le = 2.0 * log2(Xp_Y_XYZp.y) + 127.0;\\\\n\\\\tvResult.w = fract( Le );\\\\n\\\\tvResult.z = ( Le - ( floor( vResult.w * 255.0 ) ) / 255.0 ) / 255.0;\\\\n\\\\treturn vResult;\\\\n}\\\\nconst mat3 cLogLuvInverseM = mat3( 6.0014, -2.7008, -1.7996, -1.3320, 3.1029, -5.7721, 0.3008, -1.0882, 5.6268 );\\\\nvec4 LogLuvToLinear( in vec4 value ) {\\\\n\\\\tfloat Le = value.z * 255.0 + value.w;\\\\n\\\\tvec3 Xp_Y_XYZp;\\\\n\\\\tXp_Y_XYZp.y = exp2( ( Le - 127.0 ) / 2.0 );\\\\n\\\\tXp_Y_XYZp.z = Xp_Y_XYZp.y / value.y;\\\\n\\\\tXp_Y_XYZp.x = value.x * Xp_Y_XYZp.z;\\\\n\\\\tvec3 vRGB = cLogLuvInverseM * Xp_Y_XYZp.rgb;\\\\n\\\\treturn vec4( max( vRGB, 0.0 ), 1.0 );\\\\n}\\\\\\\",envmap_fragment:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\tvec3 cameraToFrag;\\\\n\\\\t\\\\tif ( isOrthographic ) {\\\\n\\\\t\\\\t\\\\tcameraToFrag = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tcameraToFrag = normalize( vWorldPosition - cameraPosition );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = reflect( cameraToFrag, worldNormal );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = refract( cameraToFrag, worldNormal, refractionRatio );\\\\n\\\\t\\\\t#endif\\\\n\\\\t#else\\\\n\\\\t\\\\tvec3 reflectVec = vReflect;\\\\n\\\\t#endif\\\\n\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\t\\\\tvec4 envColor = textureCube( envMap, vec3( flipEnvMap * reflectVec.x, reflectVec.yz ) );\\\\n\\\\t\\\\tenvColor = envMapTexelToLinear( envColor );\\\\n\\\\t#elif defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\t\\\\tvec4 envColor = textureCubeUV( envMap, reflectVec, 0.0 );\\\\n\\\\t#else\\\\n\\\\t\\\\tvec4 envColor = vec4( 0.0 );\\\\n\\\\t#endif\\\\n\\\\t#ifdef ENVMAP_BLENDING_MULTIPLY\\\\n\\\\t\\\\toutgoingLight = mix( outgoingLight, outgoingLight * envColor.xyz, specularStrength * reflectivity );\\\\n\\\\t#elif defined( ENVMAP_BLENDING_MIX )\\\\n\\\\t\\\\toutgoingLight = mix( outgoingLight, envColor.xyz, specularStrength * reflectivity );\\\\n\\\\t#elif defined( ENVMAP_BLENDING_ADD )\\\\n\\\\t\\\\toutgoingLight += envColor.xyz * specularStrength * reflectivity;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",envmap_common_pars_fragment:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\tuniform float envMapIntensity;\\\\n\\\\tuniform float flipEnvMap;\\\\n\\\\tuniform int maxMipLevel;\\\\n\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\t\\\\tuniform samplerCube envMap;\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t#endif\\\\n\\\\t\\\\n#endif\\\\\\\",envmap_pars_fragment:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\tuniform float reflectivity;\\\\n\\\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG )\\\\n\\\\t\\\\t#define ENV_WORLDPOS\\\\n\\\\t#endif\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\t#else\\\\n\\\\t\\\\tvarying vec3 vReflect;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",envmap_pars_vertex:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) ||defined( PHONG )\\\\n\\\\t\\\\t#define ENV_WORLDPOS\\\\n\\\\t#endif\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\t\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t#else\\\\n\\\\t\\\\tvarying vec3 vReflect;\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",envmap_physical_pars_fragment:\\\\\\\"#if defined( USE_ENVMAP )\\\\n\\\\t#ifdef ENVMAP_MODE_REFRACTION\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\t#endif\\\\n\\\\tvec3 getIBLIrradiance( const in vec3 normal ) {\\\\n\\\\t\\\\t#if defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\t\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeUV( envMap, worldNormal, 1.0 );\\\\n\\\\t\\\\t\\\\treturn PI * envMapColor.rgb * envMapIntensity;\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\treturn vec3( 0.0 );\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\tvec3 getIBLRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness ) {\\\\n\\\\t\\\\t#if defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\t\\\\t\\\\tvec3 reflectVec;\\\\n\\\\t\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = reflect( - viewDir, normal );\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = normalize( mix( reflectVec, normal, roughness * roughness) );\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = refract( - viewDir, normal, refractionRatio );\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\treflectVec = inverseTransformDirection( reflectVec, viewMatrix );\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeUV( envMap, reflectVec, roughness );\\\\n\\\\t\\\\t\\\\treturn envMapColor.rgb * envMapIntensity;\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\treturn vec3( 0.0 );\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n#endif\\\\\\\",envmap_vertex:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\t#else\\\\n\\\\t\\\\tvec3 cameraToVertex;\\\\n\\\\t\\\\tif ( isOrthographic ) {\\\\n\\\\t\\\\t\\\\tcameraToVertex = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tcameraToVertex = normalize( worldPosition.xyz - cameraPosition );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\\\\n\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\t\\\\t\\\\tvReflect = reflect( cameraToVertex, worldNormal );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tvReflect = refract( cameraToVertex, worldNormal, refractionRatio );\\\\n\\\\t\\\\t#endif\\\\n\\\\t#endif\\\\n#endif\\\\\\\",fog_vertex:\\\\\\\"#ifdef USE_FOG\\\\n\\\\tvFogDepth = - mvPosition.z;\\\\n#endif\\\\\\\",fog_pars_vertex:\\\\\\\"#ifdef USE_FOG\\\\n\\\\tvarying float vFogDepth;\\\\n#endif\\\\\\\",fog_fragment:\\\\\\\"#ifdef USE_FOG\\\\n\\\\t#ifdef FOG_EXP2\\\\n\\\\t\\\\tfloat fogFactor = 1.0 - exp( - fogDensity * fogDensity * vFogDepth * vFogDepth );\\\\n\\\\t#else\\\\n\\\\t\\\\tfloat fogFactor = smoothstep( fogNear, fogFar, vFogDepth );\\\\n\\\\t#endif\\\\n\\\\tgl_FragColor.rgb = mix( gl_FragColor.rgb, fogColor, fogFactor );\\\\n#endif\\\\\\\",fog_pars_fragment:\\\\\\\"#ifdef USE_FOG\\\\n\\\\tuniform vec3 fogColor;\\\\n\\\\tvarying float vFogDepth;\\\\n\\\\t#ifdef FOG_EXP2\\\\n\\\\t\\\\tuniform float fogDensity;\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform float fogNear;\\\\n\\\\t\\\\tuniform float fogFar;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",gradientmap_pars_fragment:\\\\\\\"#ifdef USE_GRADIENTMAP\\\\n\\\\tuniform sampler2D gradientMap;\\\\n#endif\\\\nvec3 getGradientIrradiance( vec3 normal, vec3 lightDirection ) {\\\\n\\\\tfloat dotNL = dot( normal, lightDirection );\\\\n\\\\tvec2 coord = vec2( dotNL * 0.5 + 0.5, 0.0 );\\\\n\\\\t#ifdef USE_GRADIENTMAP\\\\n\\\\t\\\\treturn texture2D( gradientMap, coord ).rgb;\\\\n\\\\t#else\\\\n\\\\t\\\\treturn ( coord.x < 0.7 ) ? vec3( 0.7 ) : vec3( 1.0 );\\\\n\\\\t#endif\\\\n}\\\\\\\",lightmap_fragment:\\\\\\\"#ifdef USE_LIGHTMAP\\\\n\\\\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\\\\n\\\\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\t\\\\tlightMapIrradiance *= PI;\\\\n\\\\t#endif\\\\n\\\\treflectedLight.indirectDiffuse += lightMapIrradiance;\\\\n#endif\\\\\\\",lightmap_pars_fragment:\\\\\\\"#ifdef USE_LIGHTMAP\\\\n\\\\tuniform sampler2D lightMap;\\\\n\\\\tuniform float lightMapIntensity;\\\\n#endif\\\\\\\",lights_lambert_vertex:\\\\\\\"vec3 diffuse = vec3( 1.0 );\\\\nGeometricContext geometry;\\\\ngeometry.position = mvPosition.xyz;\\\\ngeometry.normal = normalize( transformedNormal );\\\\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( -mvPosition.xyz );\\\\nGeometricContext backGeometry;\\\\nbackGeometry.position = geometry.position;\\\\nbackGeometry.normal = -geometry.normal;\\\\nbackGeometry.viewDir = geometry.viewDir;\\\\nvLightFront = vec3( 0.0 );\\\\nvIndirectFront = vec3( 0.0 );\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvLightBack = vec3( 0.0 );\\\\n\\\\tvIndirectBack = vec3( 0.0 );\\\\n#endif\\\\nIncidentLight directLight;\\\\nfloat dotNL;\\\\nvec3 directLightColor_Diffuse;\\\\nvIndirectFront += getAmbientLightIrradiance( ambientLightColor );\\\\nvIndirectFront += getLightProbeIrradiance( lightProbe, geometry.normal );\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvIndirectBack += getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\tvIndirectBack += getLightProbeIrradiance( lightProbe, backGeometry.normal );\\\\n#endif\\\\n#if NUM_POINT_LIGHTS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tgetPointLightInfo( pointLights[ i ], geometry, directLight );\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if NUM_SPOT_LIGHTS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tgetSpotLightInfo( spotLights[ i ], geometry, directLight );\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if NUM_DIR_LIGHTS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tgetDirectionalLightInfo( directionalLights[ i ], geometry, directLight );\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if NUM_HEMI_LIGHTS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tvIndirectFront += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry.normal );\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\tvIndirectBack += getHemisphereLightIrradiance( hemisphereLights[ i ], backGeometry.normal );\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\\\\",lights_pars_begin:\\\\\\\"uniform bool receiveShadow;\\\\nuniform vec3 ambientLightColor;\\\\nuniform vec3 lightProbe[ 9 ];\\\\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\\\\n\\\\tfloat x = normal.x, y = normal.y, z = normal.z;\\\\n\\\\tvec3 result = shCoefficients[ 0 ] * 0.886227;\\\\n\\\\tresult += shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\\\\n\\\\tresult += shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\\\\n\\\\tresult += shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\\\\n\\\\tresult += shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\\\\n\\\\tresult += shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\\\\n\\\\tresult += shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\\\\n\\\\tresult += shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\\\\n\\\\tresult += shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\\\\n\\\\treturn result;\\\\n}\\\\nvec3 getLightProbeIrradiance( const in vec3 lightProbe[ 9 ], const in vec3 normal ) {\\\\n\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\tvec3 irradiance = shGetIrradianceAt( worldNormal, lightProbe );\\\\n\\\\treturn irradiance;\\\\n}\\\\nvec3 getAmbientLightIrradiance( const in vec3 ambientLightColor ) {\\\\n\\\\tvec3 irradiance = ambientLightColor;\\\\n\\\\treturn irradiance;\\\\n}\\\\nfloat getDistanceAttenuation( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {\\\\n\\\\t#if defined ( PHYSICALLY_CORRECT_LIGHTS )\\\\n\\\\t\\\\tfloat distanceFalloff = 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );\\\\n\\\\t\\\\tif ( cutoffDistance > 0.0 ) {\\\\n\\\\t\\\\t\\\\tdistanceFalloff *= pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn distanceFalloff;\\\\n\\\\t#else\\\\n\\\\t\\\\tif ( cutoffDistance > 0.0 && decayExponent > 0.0 ) {\\\\n\\\\t\\\\t\\\\treturn pow( saturate( - lightDistance / cutoffDistance + 1.0 ), decayExponent );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn 1.0;\\\\n\\\\t#endif\\\\n}\\\\nfloat getSpotAttenuation( const in float coneCosine, const in float penumbraCosine, const in float angleCosine ) {\\\\n\\\\treturn smoothstep( coneCosine, penumbraCosine, angleCosine );\\\\n}\\\\n#if NUM_DIR_LIGHTS > 0\\\\n\\\\tstruct DirectionalLight {\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t};\\\\n\\\\tuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\\\\n\\\\tvoid getDirectionalLightInfo( const in DirectionalLight directionalLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\t\\\\tlight.color = directionalLight.color;\\\\n\\\\t\\\\tlight.direction = directionalLight.direction;\\\\n\\\\t\\\\tlight.visible = true;\\\\n\\\\t}\\\\n#endif\\\\n#if NUM_POINT_LIGHTS > 0\\\\n\\\\tstruct PointLight {\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tfloat distance;\\\\n\\\\t\\\\tfloat decay;\\\\n\\\\t};\\\\n\\\\tuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\\\\n\\\\tvoid getPointLightInfo( const in PointLight pointLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\t\\\\tvec3 lVector = pointLight.position - geometry.position;\\\\n\\\\t\\\\tlight.direction = normalize( lVector );\\\\n\\\\t\\\\tfloat lightDistance = length( lVector );\\\\n\\\\t\\\\tlight.color = pointLight.color;\\\\n\\\\t\\\\tlight.color *= getDistanceAttenuation( lightDistance, pointLight.distance, pointLight.decay );\\\\n\\\\t\\\\tlight.visible = ( light.color != vec3( 0.0 ) );\\\\n\\\\t}\\\\n#endif\\\\n#if NUM_SPOT_LIGHTS > 0\\\\n\\\\tstruct SpotLight {\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tfloat distance;\\\\n\\\\t\\\\tfloat decay;\\\\n\\\\t\\\\tfloat coneCos;\\\\n\\\\t\\\\tfloat penumbraCos;\\\\n\\\\t};\\\\n\\\\tuniform SpotLight spotLights[ NUM_SPOT_LIGHTS ];\\\\n\\\\tvoid getSpotLightInfo( const in SpotLight spotLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\t\\\\tvec3 lVector = spotLight.position - geometry.position;\\\\n\\\\t\\\\tlight.direction = normalize( lVector );\\\\n\\\\t\\\\tfloat angleCos = dot( light.direction, spotLight.direction );\\\\n\\\\t\\\\tfloat spotAttenuation = getSpotAttenuation( spotLight.coneCos, spotLight.penumbraCos, angleCos );\\\\n\\\\t\\\\tif ( spotAttenuation > 0.0 ) {\\\\n\\\\t\\\\t\\\\tfloat lightDistance = length( lVector );\\\\n\\\\t\\\\t\\\\tlight.color = spotLight.color * spotAttenuation;\\\\n\\\\t\\\\t\\\\tlight.color *= getDistanceAttenuation( lightDistance, spotLight.distance, spotLight.decay );\\\\n\\\\t\\\\t\\\\tlight.visible = ( light.color != vec3( 0.0 ) );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tlight.color = vec3( 0.0 );\\\\n\\\\t\\\\t\\\\tlight.visible = false;\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n#endif\\\\n#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\tstruct RectAreaLight {\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 halfWidth;\\\\n\\\\t\\\\tvec3 halfHeight;\\\\n\\\\t};\\\\n\\\\tuniform sampler2D ltc_1;\\\\tuniform sampler2D ltc_2;\\\\n\\\\tuniform RectAreaLight rectAreaLights[ NUM_RECT_AREA_LIGHTS ];\\\\n#endif\\\\n#if NUM_HEMI_LIGHTS > 0\\\\n\\\\tstruct HemisphereLight {\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 skyColor;\\\\n\\\\t\\\\tvec3 groundColor;\\\\n\\\\t};\\\\n\\\\tuniform HemisphereLight hemisphereLights[ NUM_HEMI_LIGHTS ];\\\\n\\\\tvec3 getHemisphereLightIrradiance( const in HemisphereLight hemiLight, const in vec3 normal ) {\\\\n\\\\t\\\\tfloat dotNL = dot( normal, hemiLight.direction );\\\\n\\\\t\\\\tfloat hemiDiffuseWeight = 0.5 * dotNL + 0.5;\\\\n\\\\t\\\\tvec3 irradiance = mix( hemiLight.groundColor, hemiLight.skyColor, hemiDiffuseWeight );\\\\n\\\\t\\\\treturn irradiance;\\\\n\\\\t}\\\\n#endif\\\\\\\",lights_toon_fragment:\\\\\\\"ToonMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb;\\\\\\\",lights_toon_pars_fragment:\\\\\\\"varying vec3 vViewPosition;\\\\nstruct ToonMaterial {\\\\n\\\\tvec3 diffuseColor;\\\\n};\\\\nvoid RE_Direct_Toon( const in IncidentLight directLight, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\tvec3 irradiance = getGradientIrradiance( geometry.normal, directLight.direction ) * directLight.color;\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\nvoid RE_IndirectDiffuse_Toon( const in vec3 irradiance, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_Toon\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_Toon\\\\n#define Material_LightProbeLOD( material )\\\\t(0)\\\\\\\",lights_phong_fragment:\\\\\\\"BlinnPhongMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb;\\\\nmaterial.specularColor = specular;\\\\nmaterial.specularShininess = shininess;\\\\nmaterial.specularStrength = specularStrength;\\\\\\\",lights_phong_pars_fragment:\\\\\\\"varying vec3 vViewPosition;\\\\nstruct BlinnPhongMaterial {\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\tvec3 specularColor;\\\\n\\\\tfloat specularShininess;\\\\n\\\\tfloat specularStrength;\\\\n};\\\\nvoid RE_Direct_BlinnPhong( const in IncidentLight directLight, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\\\n\\\\tvec3 irradiance = dotNL * directLight.color;\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\treflectedLight.directSpecular += irradiance * BRDF_BlinnPhong( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularShininess ) * material.specularStrength;\\\\n}\\\\nvoid RE_IndirectDiffuse_BlinnPhong( const in vec3 irradiance, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_BlinnPhong\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_BlinnPhong\\\\n#define Material_LightProbeLOD( material )\\\\t(0)\\\\\\\",lights_physical_fragment:\\\\\\\"PhysicalMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb * ( 1.0 - metalnessFactor );\\\\nvec3 dxy = max( abs( dFdx( geometryNormal ) ), abs( dFdy( geometryNormal ) ) );\\\\nfloat geometryRoughness = max( max( dxy.x, dxy.y ), dxy.z );\\\\nmaterial.roughness = max( roughnessFactor, 0.0525 );material.roughness += geometryRoughness;\\\\nmaterial.roughness = min( material.roughness, 1.0 );\\\\n#ifdef IOR\\\\n\\\\t#ifdef SPECULAR\\\\n\\\\t\\\\tfloat specularIntensityFactor = specularIntensity;\\\\n\\\\t\\\\tvec3 specularTintFactor = specularTint;\\\\n\\\\t\\\\t#ifdef USE_SPECULARINTENSITYMAP\\\\n\\\\t\\\\t\\\\tspecularIntensityFactor *= texture2D( specularIntensityMap, vUv ).a;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t#ifdef USE_SPECULARTINTMAP\\\\n\\\\t\\\\t\\\\tspecularTintFactor *= specularTintMapTexelToLinear( texture2D( specularTintMap, vUv ) ).rgb;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tmaterial.specularF90 = mix( specularIntensityFactor, 1.0, metalnessFactor );\\\\n\\\\t#else\\\\n\\\\t\\\\tfloat specularIntensityFactor = 1.0;\\\\n\\\\t\\\\tvec3 specularTintFactor = vec3( 1.0 );\\\\n\\\\t\\\\tmaterial.specularF90 = 1.0;\\\\n\\\\t#endif\\\\n\\\\tmaterial.specularColor = mix( min( pow2( ( ior - 1.0 ) / ( ior + 1.0 ) ) * specularTintFactor, vec3( 1.0 ) ) * specularIntensityFactor, diffuseColor.rgb, metalnessFactor );\\\\n#else\\\\n\\\\tmaterial.specularColor = mix( vec3( 0.04 ), diffuseColor.rgb, metalnessFactor );\\\\n\\\\tmaterial.specularF90 = 1.0;\\\\n#endif\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tmaterial.clearcoat = clearcoat;\\\\n\\\\tmaterial.clearcoatRoughness = clearcoatRoughness;\\\\n\\\\tmaterial.clearcoatF0 = vec3( 0.04 );\\\\n\\\\tmaterial.clearcoatF90 = 1.0;\\\\n\\\\t#ifdef USE_CLEARCOATMAP\\\\n\\\\t\\\\tmaterial.clearcoat *= texture2D( clearcoatMap, vUv ).x;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_CLEARCOAT_ROUGHNESSMAP\\\\n\\\\t\\\\tmaterial.clearcoatRoughness *= texture2D( clearcoatRoughnessMap, vUv ).y;\\\\n\\\\t#endif\\\\n\\\\tmaterial.clearcoat = saturate( material.clearcoat );\\\\tmaterial.clearcoatRoughness = max( material.clearcoatRoughness, 0.0525 );\\\\n\\\\tmaterial.clearcoatRoughness += geometryRoughness;\\\\n\\\\tmaterial.clearcoatRoughness = min( material.clearcoatRoughness, 1.0 );\\\\n#endif\\\\n#ifdef USE_SHEEN\\\\n\\\\tmaterial.sheenTint = sheenTint;\\\\n\\\\tmaterial.sheenRoughness = clamp( sheenRoughness, 0.07, 1.0 );\\\\n#endif\\\\\\\",lights_physical_pars_fragment:\\\\\\\"struct PhysicalMaterial {\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\tfloat roughness;\\\\n\\\\tvec3 specularColor;\\\\n\\\\tfloat specularF90;\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tfloat clearcoat;\\\\n\\\\t\\\\tfloat clearcoatRoughness;\\\\n\\\\t\\\\tvec3 clearcoatF0;\\\\n\\\\t\\\\tfloat clearcoatF90;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_SHEEN\\\\n\\\\t\\\\tvec3 sheenTint;\\\\n\\\\t\\\\tfloat sheenRoughness;\\\\n\\\\t#endif\\\\n};\\\\nvec3 clearcoatSpecular = vec3( 0.0 );\\\\nvec2 DFGApprox( const in vec3 normal, const in vec3 viewDir, const in float roughness ) {\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\tconst vec4 c0 = vec4( - 1, - 0.0275, - 0.572, 0.022 );\\\\n\\\\tconst vec4 c1 = vec4( 1, 0.0425, 1.04, - 0.04 );\\\\n\\\\tvec4 r = roughness * c0 + c1;\\\\n\\\\tfloat a004 = min( r.x * r.x, exp2( - 9.28 * dotNV ) ) * r.x + r.y;\\\\n\\\\tvec2 fab = vec2( - 1.04, 1.04 ) * a004 + r.zw;\\\\n\\\\treturn fab;\\\\n}\\\\nvec3 EnvironmentBRDF( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness ) {\\\\n\\\\tvec2 fab = DFGApprox( normal, viewDir, roughness );\\\\n\\\\treturn specularColor * fab.x + specularF90 * fab.y;\\\\n}\\\\nvoid computeMultiscattering( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\\\\n\\\\tvec2 fab = DFGApprox( normal, viewDir, roughness );\\\\n\\\\tvec3 FssEss = specularColor * fab.x + specularF90 * fab.y;\\\\n\\\\tfloat Ess = fab.x + fab.y;\\\\n\\\\tfloat Ems = 1.0 - Ess;\\\\n\\\\tvec3 Favg = specularColor + ( 1.0 - specularColor ) * 0.047619;\\\\tvec3 Fms = FssEss * Favg / ( 1.0 - Ems * Favg );\\\\n\\\\tsingleScatter += FssEss;\\\\n\\\\tmultiScatter += Fms * Ems;\\\\n}\\\\n#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\tvoid RE_Direct_RectArea_Physical( const in RectAreaLight rectAreaLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\t\\\\tvec3 normal = geometry.normal;\\\\n\\\\t\\\\tvec3 viewDir = geometry.viewDir;\\\\n\\\\t\\\\tvec3 position = geometry.position;\\\\n\\\\t\\\\tvec3 lightPos = rectAreaLight.position;\\\\n\\\\t\\\\tvec3 halfWidth = rectAreaLight.halfWidth;\\\\n\\\\t\\\\tvec3 halfHeight = rectAreaLight.halfHeight;\\\\n\\\\t\\\\tvec3 lightColor = rectAreaLight.color;\\\\n\\\\t\\\\tfloat roughness = material.roughness;\\\\n\\\\t\\\\tvec3 rectCoords[ 4 ];\\\\n\\\\t\\\\trectCoords[ 0 ] = lightPos + halfWidth - halfHeight;\\\\t\\\\trectCoords[ 1 ] = lightPos - halfWidth - halfHeight;\\\\n\\\\t\\\\trectCoords[ 2 ] = lightPos - halfWidth + halfHeight;\\\\n\\\\t\\\\trectCoords[ 3 ] = lightPos + halfWidth + halfHeight;\\\\n\\\\t\\\\tvec2 uv = LTC_Uv( normal, viewDir, roughness );\\\\n\\\\t\\\\tvec4 t1 = texture2D( ltc_1, uv );\\\\n\\\\t\\\\tvec4 t2 = texture2D( ltc_2, uv );\\\\n\\\\t\\\\tmat3 mInv = mat3(\\\\n\\\\t\\\\t\\\\tvec3( t1.x, 0, t1.y ),\\\\n\\\\t\\\\t\\\\tvec3( 0, 1, 0 ),\\\\n\\\\t\\\\t\\\\tvec3( t1.z, 0, t1.w )\\\\n\\\\t\\\\t);\\\\n\\\\t\\\\tvec3 fresnel = ( material.specularColor * t2.x + ( vec3( 1.0 ) - material.specularColor ) * t2.y );\\\\n\\\\t\\\\treflectedLight.directSpecular += lightColor * fresnel * LTC_Evaluate( normal, viewDir, position, mInv, rectCoords );\\\\n\\\\t\\\\treflectedLight.directDiffuse += lightColor * material.diffuseColor * LTC_Evaluate( normal, viewDir, position, mat3( 1.0 ), rectCoords );\\\\n\\\\t}\\\\n#endif\\\\nvoid RE_Direct_Physical( const in IncidentLight directLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\\\n\\\\tvec3 irradiance = dotNL * directLight.color;\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tfloat dotNLcc = saturate( dot( geometry.clearcoatNormal, directLight.direction ) );\\\\n\\\\t\\\\tvec3 ccIrradiance = dotNLcc * directLight.color;\\\\n\\\\t\\\\tclearcoatSpecular += ccIrradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.clearcoatNormal, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_SHEEN\\\\n\\\\t\\\\treflectedLight.directSpecular += irradiance * BRDF_Sheen( directLight.direction, geometry.viewDir, geometry.normal, material.sheenTint, material.sheenRoughness );\\\\n\\\\t#endif\\\\n\\\\treflectedLight.directSpecular += irradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularF90, material.roughness );\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\nvoid RE_IndirectDiffuse_Physical( const in vec3 irradiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\nvoid RE_IndirectSpecular_Physical( const in vec3 radiance, const in vec3 irradiance, const in vec3 clearcoatRadiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight) {\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tclearcoatSpecular += clearcoatRadiance * EnvironmentBRDF( geometry.clearcoatNormal, geometry.viewDir, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\\\\n\\\\t#endif\\\\n\\\\tvec3 singleScattering = vec3( 0.0 );\\\\n\\\\tvec3 multiScattering = vec3( 0.0 );\\\\n\\\\tvec3 cosineWeightedIrradiance = irradiance * RECIPROCAL_PI;\\\\n\\\\tcomputeMultiscattering( geometry.normal, geometry.viewDir, material.specularColor, material.specularF90, material.roughness, singleScattering, multiScattering );\\\\n\\\\tvec3 diffuse = material.diffuseColor * ( 1.0 - ( singleScattering + multiScattering ) );\\\\n\\\\treflectedLight.indirectSpecular += radiance * singleScattering;\\\\n\\\\treflectedLight.indirectSpecular += multiScattering * cosineWeightedIrradiance;\\\\n\\\\treflectedLight.indirectDiffuse += diffuse * cosineWeightedIrradiance;\\\\n}\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_Physical\\\\n#define RE_Direct_RectArea\\\\t\\\\tRE_Direct_RectArea_Physical\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_Physical\\\\n#define RE_IndirectSpecular\\\\t\\\\tRE_IndirectSpecular_Physical\\\\nfloat computeSpecularOcclusion( const in float dotNV, const in float ambientOcclusion, const in float roughness ) {\\\\n\\\\treturn saturate( pow( dotNV + ambientOcclusion, exp2( - 16.0 * roughness - 1.0 ) ) - 1.0 + ambientOcclusion );\\\\n}\\\\\\\",lights_fragment_begin:\\\\\\\"\\\\nGeometricContext geometry;\\\\ngeometry.position = - vViewPosition;\\\\ngeometry.normal = normal;\\\\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( vViewPosition );\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tgeometry.clearcoatNormal = clearcoatNormal;\\\\n#endif\\\\nIncidentLight directLight;\\\\n#if ( NUM_POINT_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\tPointLight pointLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\tPointLightShadow pointLightShadow;\\\\n\\\\t#endif\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tpointLight = pointLights[ i ];\\\\n\\\\t\\\\tgetPointLightInfo( pointLight, geometry, directLight );\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_POINT_LIGHT_SHADOWS )\\\\n\\\\t\\\\tpointLightShadow = pointLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getPointShadow( pointShadowMap[ i ], pointLightShadow.shadowMapSize, pointLightShadow.shadowBias, pointLightShadow.shadowRadius, vPointShadowCoord[ i ], pointLightShadow.shadowCameraNear, pointLightShadow.shadowCameraFar ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if ( NUM_SPOT_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\tSpotLight spotLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\tSpotLightShadow spotLightShadow;\\\\n\\\\t#endif\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tspotLight = spotLights[ i ];\\\\n\\\\t\\\\tgetSpotLightInfo( spotLight, geometry, directLight );\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\\\\n\\\\t\\\\tspotLightShadow = spotLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( spotShadowMap[ i ], spotLightShadow.shadowMapSize, spotLightShadow.shadowBias, spotLightShadow.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if ( NUM_DIR_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\tDirectionalLight directionalLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\tDirectionalLightShadow directionalLightShadow;\\\\n\\\\t#endif\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tdirectionalLight = directionalLights[ i ];\\\\n\\\\t\\\\tgetDirectionalLightInfo( directionalLight, geometry, directLight );\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_DIR_LIGHT_SHADOWS )\\\\n\\\\t\\\\tdirectionalLightShadow = directionalLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( directionalShadowMap[ i ], directionalLightShadow.shadowMapSize, directionalLightShadow.shadowBias, directionalLightShadow.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if ( NUM_RECT_AREA_LIGHTS > 0 ) && defined( RE_Direct_RectArea )\\\\n\\\\tRectAreaLight rectAreaLight;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_RECT_AREA_LIGHTS; i ++ ) {\\\\n\\\\t\\\\trectAreaLight = rectAreaLights[ i ];\\\\n\\\\t\\\\tRE_Direct_RectArea( rectAreaLight, geometry, material, reflectedLight );\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if defined( RE_IndirectDiffuse )\\\\n\\\\tvec3 iblIrradiance = vec3( 0.0 );\\\\n\\\\tvec3 irradiance = getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\tirradiance += getLightProbeIrradiance( lightProbe, geometry.normal );\\\\n\\\\t#if ( NUM_HEMI_LIGHTS > 0 )\\\\n\\\\t\\\\t#pragma unroll_loop_start\\\\n\\\\t\\\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\\\n\\\\t\\\\t\\\\tirradiance += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry.normal );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n#endif\\\\n#if defined( RE_IndirectSpecular )\\\\n\\\\tvec3 radiance = vec3( 0.0 );\\\\n\\\\tvec3 clearcoatRadiance = vec3( 0.0 );\\\\n#endif\\\\\\\",lights_fragment_maps:\\\\\\\"#if defined( RE_IndirectDiffuse )\\\\n\\\\t#ifdef USE_LIGHTMAP\\\\n\\\\t\\\\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\\\\n\\\\t\\\\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\t\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\t\\\\t\\\\tlightMapIrradiance *= PI;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tirradiance += lightMapIrradiance;\\\\n\\\\t#endif\\\\n\\\\t#if defined( USE_ENVMAP ) && defined( STANDARD ) && defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\t\\\\tiblIrradiance += getIBLIrradiance( geometry.normal );\\\\n\\\\t#endif\\\\n#endif\\\\n#if defined( USE_ENVMAP ) && defined( RE_IndirectSpecular )\\\\n\\\\tradiance += getIBLRadiance( geometry.viewDir, geometry.normal, material.roughness );\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tclearcoatRadiance += getIBLRadiance( geometry.viewDir, geometry.clearcoatNormal, material.clearcoatRoughness );\\\\n\\\\t#endif\\\\n#endif\\\\\\\",lights_fragment_end:\\\\\\\"#if defined( RE_IndirectDiffuse )\\\\n\\\\tRE_IndirectDiffuse( irradiance, geometry, material, reflectedLight );\\\\n#endif\\\\n#if defined( RE_IndirectSpecular )\\\\n\\\\tRE_IndirectSpecular( radiance, iblIrradiance, clearcoatRadiance, geometry, material, reflectedLight );\\\\n#endif\\\\\\\",logdepthbuf_fragment:\\\\\\\"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\\\n\\\\tgl_FragDepthEXT = vIsPerspective == 0.0 ? gl_FragCoord.z : log2( vFragDepth ) * logDepthBufFC * 0.5;\\\\n#endif\\\\\\\",logdepthbuf_pars_fragment:\\\\\\\"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\\\n\\\\tuniform float logDepthBufFC;\\\\n\\\\tvarying float vFragDepth;\\\\n\\\\tvarying float vIsPerspective;\\\\n#endif\\\\\\\",logdepthbuf_pars_vertex:\\\\\\\"#ifdef USE_LOGDEPTHBUF\\\\n\\\\t#ifdef USE_LOGDEPTHBUF_EXT\\\\n\\\\t\\\\tvarying float vFragDepth;\\\\n\\\\t\\\\tvarying float vIsPerspective;\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform float logDepthBufFC;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",logdepthbuf_vertex:\\\\\\\"#ifdef USE_LOGDEPTHBUF\\\\n\\\\t#ifdef USE_LOGDEPTHBUF_EXT\\\\n\\\\t\\\\tvFragDepth = 1.0 + gl_Position.w;\\\\n\\\\t\\\\tvIsPerspective = float( isPerspectiveMatrix( projectionMatrix ) );\\\\n\\\\t#else\\\\n\\\\t\\\\tif ( isPerspectiveMatrix( projectionMatrix ) ) {\\\\n\\\\t\\\\t\\\\tgl_Position.z = log2( max( EPSILON, gl_Position.w + 1.0 ) ) * logDepthBufFC - 1.0;\\\\n\\\\t\\\\t\\\\tgl_Position.z *= gl_Position.w;\\\\n\\\\t\\\\t}\\\\n\\\\t#endif\\\\n#endif\\\\\\\",map_fragment:\\\\\\\"#ifdef USE_MAP\\\\n\\\\tvec4 texelColor = texture2D( map, vUv );\\\\n\\\\ttexelColor = mapTexelToLinear( texelColor );\\\\n\\\\tdiffuseColor *= texelColor;\\\\n#endif\\\\\\\",map_pars_fragment:\\\\\\\"#ifdef USE_MAP\\\\n\\\\tuniform sampler2D map;\\\\n#endif\\\\\\\",map_particle_fragment:\\\\\\\"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\\\\n\\\\tvec2 uv = ( uvTransform * vec3( gl_PointCoord.x, 1.0 - gl_PointCoord.y, 1 ) ).xy;\\\\n#endif\\\\n#ifdef USE_MAP\\\\n\\\\tvec4 mapTexel = texture2D( map, uv );\\\\n\\\\tdiffuseColor *= mapTexelToLinear( mapTexel );\\\\n#endif\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\tdiffuseColor.a *= texture2D( alphaMap, uv ).g;\\\\n#endif\\\\\\\",map_particle_pars_fragment:\\\\\\\"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\\\\n\\\\tuniform mat3 uvTransform;\\\\n#endif\\\\n#ifdef USE_MAP\\\\n\\\\tuniform sampler2D map;\\\\n#endif\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\tuniform sampler2D alphaMap;\\\\n#endif\\\\\\\",metalnessmap_fragment:\\\\\\\"float metalnessFactor = metalness;\\\\n#ifdef USE_METALNESSMAP\\\\n\\\\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\\\\n\\\\tmetalnessFactor *= texelMetalness.b;\\\\n#endif\\\\\\\",metalnessmap_pars_fragment:\\\\\\\"#ifdef USE_METALNESSMAP\\\\n\\\\tuniform sampler2D metalnessMap;\\\\n#endif\\\\\\\",morphnormal_vertex:\\\\\\\"#ifdef USE_MORPHNORMALS\\\\n\\\\tobjectNormal *= morphTargetBaseInfluence;\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\t\\\\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\\\\n\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) objectNormal += getMorph( gl_VertexID, i, 1, 2 ) * morphTargetInfluences[ i ];\\\\n\\\\t\\\\t}\\\\n\\\\t#else\\\\n\\\\t\\\\tobjectNormal += morphNormal0 * morphTargetInfluences[ 0 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal1 * morphTargetInfluences[ 1 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal2 * morphTargetInfluences[ 2 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal3 * morphTargetInfluences[ 3 ];\\\\n\\\\t#endif\\\\n#endif\\\\\\\",morphtarget_pars_vertex:\\\\\\\"#ifdef USE_MORPHTARGETS\\\\n\\\\tuniform float morphTargetBaseInfluence;\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\t\\\\tuniform float morphTargetInfluences[ MORPHTARGETS_COUNT ];\\\\n\\\\t\\\\tuniform sampler2DArray morphTargetsTexture;\\\\n\\\\t\\\\tuniform vec2 morphTargetsTextureSize;\\\\n\\\\t\\\\tvec3 getMorph( const in int vertexIndex, const in int morphTargetIndex, const in int offset, const in int stride ) {\\\\n\\\\t\\\\t\\\\tfloat texelIndex = float( vertexIndex * stride + offset );\\\\n\\\\t\\\\t\\\\tfloat y = floor( texelIndex / morphTargetsTextureSize.x );\\\\n\\\\t\\\\t\\\\tfloat x = texelIndex - y * morphTargetsTextureSize.x;\\\\n\\\\t\\\\t\\\\tvec3 morphUV = vec3( ( x + 0.5 ) / morphTargetsTextureSize.x, y / morphTargetsTextureSize.y, morphTargetIndex );\\\\n\\\\t\\\\t\\\\treturn texture( morphTargetsTexture, morphUV ).xyz;\\\\n\\\\t\\\\t}\\\\n\\\\t#else\\\\n\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\t\\\\t\\\\tuniform float morphTargetInfluences[ 8 ];\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tuniform float morphTargetInfluences[ 4 ];\\\\n\\\\t\\\\t#endif\\\\n\\\\t#endif\\\\n#endif\\\\\\\",morphtarget_vertex:\\\\\\\"#ifdef USE_MORPHTARGETS\\\\n\\\\ttransformed *= morphTargetBaseInfluence;\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\t\\\\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\\\\n\\\\t\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\t\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) transformed += getMorph( gl_VertexID, i, 0, 1 ) * morphTargetInfluences[ i ];\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) transformed += getMorph( gl_VertexID, i, 0, 2 ) * morphTargetInfluences[ i ];\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t}\\\\n\\\\t#else\\\\n\\\\t\\\\ttransformed += morphTarget0 * morphTargetInfluences[ 0 ];\\\\n\\\\t\\\\ttransformed += morphTarget1 * morphTargetInfluences[ 1 ];\\\\n\\\\t\\\\ttransformed += morphTarget2 * morphTargetInfluences[ 2 ];\\\\n\\\\t\\\\ttransformed += morphTarget3 * morphTargetInfluences[ 3 ];\\\\n\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget4 * morphTargetInfluences[ 4 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget5 * morphTargetInfluences[ 5 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget6 * morphTargetInfluences[ 6 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget7 * morphTargetInfluences[ 7 ];\\\\n\\\\t\\\\t#endif\\\\n\\\\t#endif\\\\n#endif\\\\\\\",normal_fragment_begin:\\\\\\\"float faceDirection = gl_FrontFacing ? 1.0 : - 1.0;\\\\n#ifdef FLAT_SHADED\\\\n\\\\tvec3 fdx = vec3( dFdx( vViewPosition.x ), dFdx( vViewPosition.y ), dFdx( vViewPosition.z ) );\\\\n\\\\tvec3 fdy = vec3( dFdy( vViewPosition.x ), dFdy( vViewPosition.y ), dFdy( vViewPosition.z ) );\\\\n\\\\tvec3 normal = normalize( cross( fdx, fdy ) );\\\\n#else\\\\n\\\\tvec3 normal = normalize( vNormal );\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\tnormal = normal * faceDirection;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvec3 tangent = normalize( vTangent );\\\\n\\\\t\\\\tvec3 bitangent = normalize( vBitangent );\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\ttangent = tangent * faceDirection;\\\\n\\\\t\\\\t\\\\tbitangent = bitangent * faceDirection;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t#if defined( TANGENTSPACE_NORMALMAP ) || defined( USE_CLEARCOAT_NORMALMAP )\\\\n\\\\t\\\\t\\\\tmat3 vTBN = mat3( tangent, bitangent, normal );\\\\n\\\\t\\\\t#endif\\\\n\\\\t#endif\\\\n#endif\\\\nvec3 geometryNormal = normal;\\\\\\\",normal_fragment_maps:\\\\\\\"#ifdef OBJECTSPACE_NORMALMAP\\\\n\\\\tnormal = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\t#ifdef FLIP_SIDED\\\\n\\\\t\\\\tnormal = - normal;\\\\n\\\\t#endif\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\tnormal = normal * faceDirection;\\\\n\\\\t#endif\\\\n\\\\tnormal = normalize( normalMatrix * normal );\\\\n#elif defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\tvec3 mapN = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\tmapN.xy *= normalScale;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tnormal = normalize( vTBN * mapN );\\\\n\\\\t#else\\\\n\\\\t\\\\tnormal = perturbNormal2Arb( - vViewPosition, normal, mapN, faceDirection );\\\\n\\\\t#endif\\\\n#elif defined( USE_BUMPMAP )\\\\n\\\\tnormal = perturbNormalArb( - vViewPosition, normal, dHdxy_fwd(), faceDirection );\\\\n#endif\\\\\\\",normal_pars_fragment:\\\\\\\"#ifndef FLAT_SHADED\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",normal_pars_vertex:\\\\\\\"#ifndef FLAT_SHADED\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",normal_vertex:\\\\\\\"#ifndef FLAT_SHADED\\\\n\\\\tvNormal = normalize( transformedNormal );\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvTangent = normalize( transformedTangent );\\\\n\\\\t\\\\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\\\\n\\\\t#endif\\\\n#endif\\\\\\\",normalmap_pars_fragment:\\\\\\\"#ifdef USE_NORMALMAP\\\\n\\\\tuniform sampler2D normalMap;\\\\n\\\\tuniform vec2 normalScale;\\\\n#endif\\\\n#ifdef OBJECTSPACE_NORMALMAP\\\\n\\\\tuniform mat3 normalMatrix;\\\\n#endif\\\\n#if ! defined ( USE_TANGENT ) && ( defined ( TANGENTSPACE_NORMALMAP ) || defined ( USE_CLEARCOAT_NORMALMAP ) )\\\\n\\\\tvec3 perturbNormal2Arb( vec3 eye_pos, vec3 surf_norm, vec3 mapN, float faceDirection ) {\\\\n\\\\t\\\\tvec3 q0 = vec3( dFdx( eye_pos.x ), dFdx( eye_pos.y ), dFdx( eye_pos.z ) );\\\\n\\\\t\\\\tvec3 q1 = vec3( dFdy( eye_pos.x ), dFdy( eye_pos.y ), dFdy( eye_pos.z ) );\\\\n\\\\t\\\\tvec2 st0 = dFdx( vUv.st );\\\\n\\\\t\\\\tvec2 st1 = dFdy( vUv.st );\\\\n\\\\t\\\\tvec3 N = surf_norm;\\\\n\\\\t\\\\tvec3 q1perp = cross( q1, N );\\\\n\\\\t\\\\tvec3 q0perp = cross( N, q0 );\\\\n\\\\t\\\\tvec3 T = q1perp * st0.x + q0perp * st1.x;\\\\n\\\\t\\\\tvec3 B = q1perp * st0.y + q0perp * st1.y;\\\\n\\\\t\\\\tfloat det = max( dot( T, T ), dot( B, B ) );\\\\n\\\\t\\\\tfloat scale = ( det == 0.0 ) ? 0.0 : faceDirection * inversesqrt( det );\\\\n\\\\t\\\\treturn normalize( T * ( mapN.x * scale ) + B * ( mapN.y * scale ) + N * mapN.z );\\\\n\\\\t}\\\\n#endif\\\\\\\",clearcoat_normal_fragment_begin:\\\\\\\"#ifdef USE_CLEARCOAT\\\\n\\\\tvec3 clearcoatNormal = geometryNormal;\\\\n#endif\\\\\\\",clearcoat_normal_fragment_maps:\\\\\\\"#ifdef USE_CLEARCOAT_NORMALMAP\\\\n\\\\tvec3 clearcoatMapN = texture2D( clearcoatNormalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\tclearcoatMapN.xy *= clearcoatNormalScale;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tclearcoatNormal = normalize( vTBN * clearcoatMapN );\\\\n\\\\t#else\\\\n\\\\t\\\\tclearcoatNormal = perturbNormal2Arb( - vViewPosition, clearcoatNormal, clearcoatMapN, faceDirection );\\\\n\\\\t#endif\\\\n#endif\\\\\\\",clearcoat_pars_fragment:\\\\\\\"#ifdef USE_CLEARCOATMAP\\\\n\\\\tuniform sampler2D clearcoatMap;\\\\n#endif\\\\n#ifdef USE_CLEARCOAT_ROUGHNESSMAP\\\\n\\\\tuniform sampler2D clearcoatRoughnessMap;\\\\n#endif\\\\n#ifdef USE_CLEARCOAT_NORMALMAP\\\\n\\\\tuniform sampler2D clearcoatNormalMap;\\\\n\\\\tuniform vec2 clearcoatNormalScale;\\\\n#endif\\\\\\\",output_fragment:\\\\\\\"#ifdef OPAQUE\\\\ndiffuseColor.a = 1.0;\\\\n#endif\\\\n#ifdef USE_TRANSMISSION\\\\ndiffuseColor.a *= transmissionAlpha + 0.1;\\\\n#endif\\\\ngl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\\\\",packing:\\\\\\\"vec3 packNormalToRGB( const in vec3 normal ) {\\\\n\\\\treturn normalize( normal ) * 0.5 + 0.5;\\\\n}\\\\nvec3 unpackRGBToNormal( const in vec3 rgb ) {\\\\n\\\\treturn 2.0 * rgb.xyz - 1.0;\\\\n}\\\\nconst float PackUpscale = 256. / 255.;const float UnpackDownscale = 255. / 256.;\\\\nconst vec3 PackFactors = vec3( 256. * 256. * 256., 256. * 256., 256. );\\\\nconst vec4 UnpackFactors = UnpackDownscale / vec4( PackFactors, 1. );\\\\nconst float ShiftRight8 = 1. / 256.;\\\\nvec4 packDepthToRGBA( const in float v ) {\\\\n\\\\tvec4 r = vec4( fract( v * PackFactors ), v );\\\\n\\\\tr.yzw -= r.xyz * ShiftRight8;\\\\treturn r * PackUpscale;\\\\n}\\\\nfloat unpackRGBAToDepth( const in vec4 v ) {\\\\n\\\\treturn dot( v, UnpackFactors );\\\\n}\\\\nvec4 pack2HalfToRGBA( vec2 v ) {\\\\n\\\\tvec4 r = vec4( v.x, fract( v.x * 255.0 ), v.y, fract( v.y * 255.0 ) );\\\\n\\\\treturn vec4( r.x - r.y / 255.0, r.y, r.z - r.w / 255.0, r.w );\\\\n}\\\\nvec2 unpackRGBATo2Half( vec4 v ) {\\\\n\\\\treturn vec2( v.x + ( v.y / 255.0 ), v.z + ( v.w / 255.0 ) );\\\\n}\\\\nfloat viewZToOrthographicDepth( const in float viewZ, const in float near, const in float far ) {\\\\n\\\\treturn ( viewZ + near ) / ( near - far );\\\\n}\\\\nfloat orthographicDepthToViewZ( const in float linearClipZ, const in float near, const in float far ) {\\\\n\\\\treturn linearClipZ * ( near - far ) - near;\\\\n}\\\\nfloat viewZToPerspectiveDepth( const in float viewZ, const in float near, const in float far ) {\\\\n\\\\treturn ( ( near + viewZ ) * far ) / ( ( far - near ) * viewZ );\\\\n}\\\\nfloat perspectiveDepthToViewZ( const in float invClipZ, const in float near, const in float far ) {\\\\n\\\\treturn ( near * far ) / ( ( far - near ) * invClipZ - far );\\\\n}\\\\\\\",premultiplied_alpha_fragment:\\\\\\\"#ifdef PREMULTIPLIED_ALPHA\\\\n\\\\tgl_FragColor.rgb *= gl_FragColor.a;\\\\n#endif\\\\\\\",project_vertex:\\\\\\\"vec4 mvPosition = vec4( transformed, 1.0 );\\\\n#ifdef USE_INSTANCING\\\\n\\\\tmvPosition = instanceMatrix * mvPosition;\\\\n#endif\\\\nmvPosition = modelViewMatrix * mvPosition;\\\\ngl_Position = projectionMatrix * mvPosition;\\\\\\\",dithering_fragment:\\\\\\\"#ifdef DITHERING\\\\n\\\\tgl_FragColor.rgb = dithering( gl_FragColor.rgb );\\\\n#endif\\\\\\\",dithering_pars_fragment:\\\\\\\"#ifdef DITHERING\\\\n\\\\tvec3 dithering( vec3 color ) {\\\\n\\\\t\\\\tfloat grid_position = rand( gl_FragCoord.xy );\\\\n\\\\t\\\\tvec3 dither_shift_RGB = vec3( 0.25 / 255.0, -0.25 / 255.0, 0.25 / 255.0 );\\\\n\\\\t\\\\tdither_shift_RGB = mix( 2.0 * dither_shift_RGB, -2.0 * dither_shift_RGB, grid_position );\\\\n\\\\t\\\\treturn color + dither_shift_RGB;\\\\n\\\\t}\\\\n#endif\\\\\\\",roughnessmap_fragment:\\\\\\\"float roughnessFactor = roughness;\\\\n#ifdef USE_ROUGHNESSMAP\\\\n\\\\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\\\\n\\\\troughnessFactor *= texelRoughness.g;\\\\n#endif\\\\\\\",roughnessmap_pars_fragment:\\\\\\\"#ifdef USE_ROUGHNESSMAP\\\\n\\\\tuniform sampler2D roughnessMap;\\\\n#endif\\\\\\\",shadowmap_pars_fragment:\\\\\\\"#ifdef USE_SHADOWMAP\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform sampler2D directionalShadowMap[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct DirectionalLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform sampler2D spotShadowMap[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct SpotLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform sampler2D pointShadowMap[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct PointLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraNear;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraFar;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\tfloat texture2DCompare( sampler2D depths, vec2 uv, float compare ) {\\\\n\\\\t\\\\treturn step( compare, unpackRGBAToDepth( texture2D( depths, uv ) ) );\\\\n\\\\t}\\\\n\\\\tvec2 texture2DDistribution( sampler2D shadow, vec2 uv ) {\\\\n\\\\t\\\\treturn unpackRGBATo2Half( texture2D( shadow, uv ) );\\\\n\\\\t}\\\\n\\\\tfloat VSMShadow (sampler2D shadow, vec2 uv, float compare ){\\\\n\\\\t\\\\tfloat occlusion = 1.0;\\\\n\\\\t\\\\tvec2 distribution = texture2DDistribution( shadow, uv );\\\\n\\\\t\\\\tfloat hard_shadow = step( compare , distribution.x );\\\\n\\\\t\\\\tif (hard_shadow != 1.0 ) {\\\\n\\\\t\\\\t\\\\tfloat distance = compare - distribution.x ;\\\\n\\\\t\\\\t\\\\tfloat variance = max( 0.00000, distribution.y * distribution.y );\\\\n\\\\t\\\\t\\\\tfloat softness_probability = variance / (variance + distance * distance );\\\\t\\\\t\\\\tsoftness_probability = clamp( ( softness_probability - 0.3 ) / ( 0.95 - 0.3 ), 0.0, 1.0 );\\\\t\\\\t\\\\tocclusion = clamp( max( hard_shadow, softness_probability ), 0.0, 1.0 );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn occlusion;\\\\n\\\\t}\\\\n\\\\tfloat getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord ) {\\\\n\\\\t\\\\tfloat shadow = 1.0;\\\\n\\\\t\\\\tshadowCoord.xyz /= shadowCoord.w;\\\\n\\\\t\\\\tshadowCoord.z += shadowBias;\\\\n\\\\t\\\\tbvec4 inFrustumVec = bvec4 ( shadowCoord.x >= 0.0, shadowCoord.x <= 1.0, shadowCoord.y >= 0.0, shadowCoord.y <= 1.0 );\\\\n\\\\t\\\\tbool inFrustum = all( inFrustumVec );\\\\n\\\\t\\\\tbvec2 frustumTestVec = bvec2( inFrustum, shadowCoord.z <= 1.0 );\\\\n\\\\t\\\\tbool frustumTest = all( frustumTestVec );\\\\n\\\\t\\\\tif ( frustumTest ) {\\\\n\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF )\\\\n\\\\t\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat dx0 = - texelSize.x * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dy0 = - texelSize.y * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dx1 = + texelSize.x * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dy1 = + texelSize.y * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dx2 = dx0 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dy2 = dy0 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dx3 = dx1 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dy3 = dy1 / 2.0;\\\\n\\\\t\\\\t\\\\tshadow = (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 17.0 );\\\\n\\\\t\\\\t#elif defined( SHADOWMAP_TYPE_PCF_SOFT )\\\\n\\\\t\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat dx = texelSize.x;\\\\n\\\\t\\\\t\\\\tfloat dy = texelSize.y;\\\\n\\\\t\\\\t\\\\tvec2 uv = shadowCoord.xy;\\\\n\\\\t\\\\t\\\\tvec2 f = fract( uv * shadowMapSize + 0.5 );\\\\n\\\\t\\\\t\\\\tuv -= f * texelSize;\\\\n\\\\t\\\\t\\\\tshadow = (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + vec2( dx, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + vec2( 0.0, dy ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + texelSize, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, 0.0 ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 0.0 ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.x ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.x ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( 0.0, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 0.0, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( dx, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( dx, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( mix( texture2DCompare( shadowMap, uv + vec2( -dx, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, -dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t f.x ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t mix( texture2DCompare( shadowMap, uv + vec2( -dx, 2.0 * dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t f.x ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 9.0 );\\\\n\\\\t\\\\t#elif defined( SHADOWMAP_TYPE_VSM )\\\\n\\\\t\\\\t\\\\tshadow = VSMShadow( shadowMap, shadowCoord.xy, shadowCoord.z );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tshadow = texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn shadow;\\\\n\\\\t}\\\\n\\\\tvec2 cubeToUV( vec3 v, float texelSizeY ) {\\\\n\\\\t\\\\tvec3 absV = abs( v );\\\\n\\\\t\\\\tfloat scaleToCube = 1.0 / max( absV.x, max( absV.y, absV.z ) );\\\\n\\\\t\\\\tabsV *= scaleToCube;\\\\n\\\\t\\\\tv *= scaleToCube * ( 1.0 - 2.0 * texelSizeY );\\\\n\\\\t\\\\tvec2 planar = v.xy;\\\\n\\\\t\\\\tfloat almostATexel = 1.5 * texelSizeY;\\\\n\\\\t\\\\tfloat almostOne = 1.0 - almostATexel;\\\\n\\\\t\\\\tif ( absV.z >= almostOne ) {\\\\n\\\\t\\\\t\\\\tif ( v.z > 0.0 )\\\\n\\\\t\\\\t\\\\t\\\\tplanar.x = 4.0 - v.x;\\\\n\\\\t\\\\t} else if ( absV.x >= almostOne ) {\\\\n\\\\t\\\\t\\\\tfloat signX = sign( v.x );\\\\n\\\\t\\\\t\\\\tplanar.x = v.z * signX + 2.0 * signX;\\\\n\\\\t\\\\t} else if ( absV.y >= almostOne ) {\\\\n\\\\t\\\\t\\\\tfloat signY = sign( v.y );\\\\n\\\\t\\\\t\\\\tplanar.x = v.x + 2.0 * signY + 2.0;\\\\n\\\\t\\\\t\\\\tplanar.y = v.z * signY - 2.0;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );\\\\n\\\\t}\\\\n\\\\tfloat getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {\\\\n\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );\\\\n\\\\t\\\\tvec3 lightToPosition = shadowCoord.xyz;\\\\n\\\\t\\\\tfloat dp = ( length( lightToPosition ) - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear );\\\\t\\\\tdp += shadowBias;\\\\n\\\\t\\\\tvec3 bd3D = normalize( lightToPosition );\\\\n\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT ) || defined( SHADOWMAP_TYPE_VSM )\\\\n\\\\t\\\\t\\\\tvec2 offset = vec2( - 1, 1 ) * shadowRadius * texelSize.y;\\\\n\\\\t\\\\t\\\\treturn (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 9.0 );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\treturn texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n#endif\\\\\\\",shadowmap_pars_vertex:\\\\\\\"#ifdef USE_SHADOWMAP\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform mat4 directionalShadowMatrix[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct DirectionalLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform mat4 spotShadowMatrix[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct SpotLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform mat4 pointShadowMatrix[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct PointLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraNear;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraFar;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n#endif\\\\\\\",shadowmap_vertex:\\\\\\\"#ifdef USE_SHADOWMAP\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0 || NUM_SPOT_LIGHT_SHADOWS > 0 || NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tvec3 shadowWorldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\\\\n\\\\t\\\\tvec4 shadowWorldPosition;\\\\n\\\\t#endif\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * directionalLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvDirectionalShadowCoord[ i ] = directionalShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * spotLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvSpotShadowCoord[ i ] = spotShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * pointLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvPointShadowCoord[ i ] = pointShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n#endif\\\\\\\",shadowmask_pars_fragment:\\\\\\\"float getShadowMask() {\\\\n\\\\tfloat shadow = 1.0;\\\\n\\\\t#ifdef USE_SHADOWMAP\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\tDirectionalLightShadow directionalLight;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tdirectionalLight = directionalLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\tSpotLightShadow spotLight;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tspotLight = spotLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\tPointLightShadow pointLight;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tpointLight = pointLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#endif\\\\n\\\\treturn shadow;\\\\n}\\\\\\\",skinbase_vertex:\\\\\\\"#ifdef USE_SKINNING\\\\n\\\\tmat4 boneMatX = getBoneMatrix( skinIndex.x );\\\\n\\\\tmat4 boneMatY = getBoneMatrix( skinIndex.y );\\\\n\\\\tmat4 boneMatZ = getBoneMatrix( skinIndex.z );\\\\n\\\\tmat4 boneMatW = getBoneMatrix( skinIndex.w );\\\\n#endif\\\\\\\",skinning_pars_vertex:\\\\\\\"#ifdef USE_SKINNING\\\\n\\\\tuniform mat4 bindMatrix;\\\\n\\\\tuniform mat4 bindMatrixInverse;\\\\n\\\\t#ifdef BONE_TEXTURE\\\\n\\\\t\\\\tuniform highp sampler2D boneTexture;\\\\n\\\\t\\\\tuniform int boneTextureSize;\\\\n\\\\t\\\\tmat4 getBoneMatrix( const in float i ) {\\\\n\\\\t\\\\t\\\\tfloat j = i * 4.0;\\\\n\\\\t\\\\t\\\\tfloat x = mod( j, float( boneTextureSize ) );\\\\n\\\\t\\\\t\\\\tfloat y = floor( j / float( boneTextureSize ) );\\\\n\\\\t\\\\t\\\\tfloat dx = 1.0 / float( boneTextureSize );\\\\n\\\\t\\\\t\\\\tfloat dy = 1.0 / float( boneTextureSize );\\\\n\\\\t\\\\t\\\\ty = dy * ( y + 0.5 );\\\\n\\\\t\\\\t\\\\tvec4 v1 = texture2D( boneTexture, vec2( dx * ( x + 0.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v2 = texture2D( boneTexture, vec2( dx * ( x + 1.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v3 = texture2D( boneTexture, vec2( dx * ( x + 2.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v4 = texture2D( boneTexture, vec2( dx * ( x + 3.5 ), y ) );\\\\n\\\\t\\\\t\\\\tmat4 bone = mat4( v1, v2, v3, v4 );\\\\n\\\\t\\\\t\\\\treturn bone;\\\\n\\\\t\\\\t}\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform mat4 boneMatrices[ MAX_BONES ];\\\\n\\\\t\\\\tmat4 getBoneMatrix( const in float i ) {\\\\n\\\\t\\\\t\\\\tmat4 bone = boneMatrices[ int(i) ];\\\\n\\\\t\\\\t\\\\treturn bone;\\\\n\\\\t\\\\t}\\\\n\\\\t#endif\\\\n#endif\\\\\\\",skinning_vertex:\\\\\\\"#ifdef USE_SKINNING\\\\n\\\\tvec4 skinVertex = bindMatrix * vec4( transformed, 1.0 );\\\\n\\\\tvec4 skinned = vec4( 0.0 );\\\\n\\\\tskinned += boneMatX * skinVertex * skinWeight.x;\\\\n\\\\tskinned += boneMatY * skinVertex * skinWeight.y;\\\\n\\\\tskinned += boneMatZ * skinVertex * skinWeight.z;\\\\n\\\\tskinned += boneMatW * skinVertex * skinWeight.w;\\\\n\\\\ttransformed = ( bindMatrixInverse * skinned ).xyz;\\\\n#endif\\\\\\\",skinnormal_vertex:\\\\\\\"#ifdef USE_SKINNING\\\\n\\\\tmat4 skinMatrix = mat4( 0.0 );\\\\n\\\\tskinMatrix += skinWeight.x * boneMatX;\\\\n\\\\tskinMatrix += skinWeight.y * boneMatY;\\\\n\\\\tskinMatrix += skinWeight.z * boneMatZ;\\\\n\\\\tskinMatrix += skinWeight.w * boneMatW;\\\\n\\\\tskinMatrix = bindMatrixInverse * skinMatrix * bindMatrix;\\\\n\\\\tobjectNormal = vec4( skinMatrix * vec4( objectNormal, 0.0 ) ).xyz;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tobjectTangent = vec4( skinMatrix * vec4( objectTangent, 0.0 ) ).xyz;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",specularmap_fragment:\\\\\\\"float specularStrength;\\\\n#ifdef USE_SPECULARMAP\\\\n\\\\tvec4 texelSpecular = texture2D( specularMap, vUv );\\\\n\\\\tspecularStrength = texelSpecular.r;\\\\n#else\\\\n\\\\tspecularStrength = 1.0;\\\\n#endif\\\\\\\",specularmap_pars_fragment:\\\\\\\"#ifdef USE_SPECULARMAP\\\\n\\\\tuniform sampler2D specularMap;\\\\n#endif\\\\\\\",tonemapping_fragment:\\\\\\\"#if defined( TONE_MAPPING )\\\\n\\\\tgl_FragColor.rgb = toneMapping( gl_FragColor.rgb );\\\\n#endif\\\\\\\",tonemapping_pars_fragment:\\\\\\\"#ifndef saturate\\\\n#define saturate( a ) clamp( a, 0.0, 1.0 )\\\\n#endif\\\\nuniform float toneMappingExposure;\\\\nvec3 LinearToneMapping( vec3 color ) {\\\\n\\\\treturn toneMappingExposure * color;\\\\n}\\\\nvec3 ReinhardToneMapping( vec3 color ) {\\\\n\\\\tcolor *= toneMappingExposure;\\\\n\\\\treturn saturate( color / ( vec3( 1.0 ) + color ) );\\\\n}\\\\nvec3 OptimizedCineonToneMapping( vec3 color ) {\\\\n\\\\tcolor *= toneMappingExposure;\\\\n\\\\tcolor = max( vec3( 0.0 ), color - 0.004 );\\\\n\\\\treturn pow( ( color * ( 6.2 * color + 0.5 ) ) / ( color * ( 6.2 * color + 1.7 ) + 0.06 ), vec3( 2.2 ) );\\\\n}\\\\nvec3 RRTAndODTFit( vec3 v ) {\\\\n\\\\tvec3 a = v * ( v + 0.0245786 ) - 0.000090537;\\\\n\\\\tvec3 b = v * ( 0.983729 * v + 0.4329510 ) + 0.238081;\\\\n\\\\treturn a / b;\\\\n}\\\\nvec3 ACESFilmicToneMapping( vec3 color ) {\\\\n\\\\tconst mat3 ACESInputMat = mat3(\\\\n\\\\t\\\\tvec3( 0.59719, 0.07600, 0.02840 ),\\\\t\\\\tvec3( 0.35458, 0.90834, 0.13383 ),\\\\n\\\\t\\\\tvec3( 0.04823, 0.01566, 0.83777 )\\\\n\\\\t);\\\\n\\\\tconst mat3 ACESOutputMat = mat3(\\\\n\\\\t\\\\tvec3( 1.60475, -0.10208, -0.00327 ),\\\\t\\\\tvec3( -0.53108, 1.10813, -0.07276 ),\\\\n\\\\t\\\\tvec3( -0.07367, -0.00605, 1.07602 )\\\\n\\\\t);\\\\n\\\\tcolor *= toneMappingExposure / 0.6;\\\\n\\\\tcolor = ACESInputMat * color;\\\\n\\\\tcolor = RRTAndODTFit( color );\\\\n\\\\tcolor = ACESOutputMat * color;\\\\n\\\\treturn saturate( color );\\\\n}\\\\nvec3 CustomToneMapping( vec3 color ) { return color; }\\\\\\\",transmission_fragment:\\\\\\\"#ifdef USE_TRANSMISSION\\\\n\\\\tfloat transmissionAlpha = 1.0;\\\\n\\\\tfloat transmissionFactor = transmission;\\\\n\\\\tfloat thicknessFactor = thickness;\\\\n\\\\t#ifdef USE_TRANSMISSIONMAP\\\\n\\\\t\\\\ttransmissionFactor *= texture2D( transmissionMap, vUv ).r;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_THICKNESSMAP\\\\n\\\\t\\\\tthicknessFactor *= texture2D( thicknessMap, vUv ).g;\\\\n\\\\t#endif\\\\n\\\\tvec3 pos = vWorldPosition;\\\\n\\\\tvec3 v = normalize( cameraPosition - pos );\\\\n\\\\tvec3 n = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\tvec4 transmission = getIBLVolumeRefraction(\\\\n\\\\t\\\\tn, v, roughnessFactor, material.diffuseColor, material.specularColor, material.specularF90,\\\\n\\\\t\\\\tpos, modelMatrix, viewMatrix, projectionMatrix, ior, thicknessFactor,\\\\n\\\\t\\\\tattenuationTint, attenuationDistance );\\\\n\\\\ttotalDiffuse = mix( totalDiffuse, transmission.rgb, transmissionFactor );\\\\n\\\\ttransmissionAlpha = mix( transmissionAlpha, transmission.a, transmissionFactor );\\\\n#endif\\\\\\\",transmission_pars_fragment:\\\\\\\"#ifdef USE_TRANSMISSION\\\\n\\\\tuniform float transmission;\\\\n\\\\tuniform float thickness;\\\\n\\\\tuniform float attenuationDistance;\\\\n\\\\tuniform vec3 attenuationTint;\\\\n\\\\t#ifdef USE_TRANSMISSIONMAP\\\\n\\\\t\\\\tuniform sampler2D transmissionMap;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_THICKNESSMAP\\\\n\\\\t\\\\tuniform sampler2D thicknessMap;\\\\n\\\\t#endif\\\\n\\\\tuniform vec2 transmissionSamplerSize;\\\\n\\\\tuniform sampler2D transmissionSamplerMap;\\\\n\\\\tuniform mat4 modelMatrix;\\\\n\\\\tuniform mat4 projectionMatrix;\\\\n\\\\tvarying vec3 vWorldPosition;\\\\n\\\\tvec3 getVolumeTransmissionRay( vec3 n, vec3 v, float thickness, float ior, mat4 modelMatrix ) {\\\\n\\\\t\\\\tvec3 refractionVector = refract( - v, normalize( n ), 1.0 / ior );\\\\n\\\\t\\\\tvec3 modelScale;\\\\n\\\\t\\\\tmodelScale.x = length( vec3( modelMatrix[ 0 ].xyz ) );\\\\n\\\\t\\\\tmodelScale.y = length( vec3( modelMatrix[ 1 ].xyz ) );\\\\n\\\\t\\\\tmodelScale.z = length( vec3( modelMatrix[ 2 ].xyz ) );\\\\n\\\\t\\\\treturn normalize( refractionVector ) * thickness * modelScale;\\\\n\\\\t}\\\\n\\\\tfloat applyIorToRoughness( float roughness, float ior ) {\\\\n\\\\t\\\\treturn roughness * clamp( ior * 2.0 - 2.0, 0.0, 1.0 );\\\\n\\\\t}\\\\n\\\\tvec4 getTransmissionSample( vec2 fragCoord, float roughness, float ior ) {\\\\n\\\\t\\\\tfloat framebufferLod = log2( transmissionSamplerSize.x ) * applyIorToRoughness( roughness, ior );\\\\n\\\\t\\\\t#ifdef TEXTURE_LOD_EXT\\\\n\\\\t\\\\t\\\\treturn texture2DLodEXT( transmissionSamplerMap, fragCoord.xy, framebufferLod );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\treturn texture2D( transmissionSamplerMap, fragCoord.xy, framebufferLod );\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\tvec3 applyVolumeAttenuation( vec3 radiance, float transmissionDistance, vec3 attenuationColor, float attenuationDistance ) {\\\\n\\\\t\\\\tif ( attenuationDistance == 0.0 ) {\\\\n\\\\t\\\\t\\\\treturn radiance;\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tvec3 attenuationCoefficient = -log( attenuationColor ) / attenuationDistance;\\\\n\\\\t\\\\t\\\\tvec3 transmittance = exp( - attenuationCoefficient * transmissionDistance );\\\\t\\\\t\\\\treturn transmittance * radiance;\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n\\\\tvec4 getIBLVolumeRefraction( vec3 n, vec3 v, float roughness, vec3 diffuseColor, vec3 specularColor, float specularF90,\\\\n\\\\t\\\\tvec3 position, mat4 modelMatrix, mat4 viewMatrix, mat4 projMatrix, float ior, float thickness,\\\\n\\\\t\\\\tvec3 attenuationColor, float attenuationDistance ) {\\\\n\\\\t\\\\tvec3 transmissionRay = getVolumeTransmissionRay( n, v, thickness, ior, modelMatrix );\\\\n\\\\t\\\\tvec3 refractedRayExit = position + transmissionRay;\\\\n\\\\t\\\\tvec4 ndcPos = projMatrix * viewMatrix * vec4( refractedRayExit, 1.0 );\\\\n\\\\t\\\\tvec2 refractionCoords = ndcPos.xy / ndcPos.w;\\\\n\\\\t\\\\trefractionCoords += 1.0;\\\\n\\\\t\\\\trefractionCoords /= 2.0;\\\\n\\\\t\\\\tvec4 transmittedLight = getTransmissionSample( refractionCoords, roughness, ior );\\\\n\\\\t\\\\tvec3 attenuatedColor = applyVolumeAttenuation( transmittedLight.rgb, length( transmissionRay ), attenuationColor, attenuationDistance );\\\\n\\\\t\\\\tvec3 F = EnvironmentBRDF( n, v, specularColor, specularF90, roughness );\\\\n\\\\t\\\\treturn vec4( ( 1.0 - F ) * attenuatedColor * diffuseColor, transmittedLight.a );\\\\n\\\\t}\\\\n#endif\\\\\\\",uv_pars_fragment:\\\\\\\"#if ( defined( USE_UV ) && ! defined( UVS_VERTEX_ONLY ) )\\\\n\\\\tvarying vec2 vUv;\\\\n#endif\\\\\\\",uv_pars_vertex:\\\\\\\"#ifdef USE_UV\\\\n\\\\t#ifdef UVS_VERTEX_ONLY\\\\n\\\\t\\\\tvec2 vUv;\\\\n\\\\t#else\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\t#endif\\\\n\\\\tuniform mat3 uvTransform;\\\\n#endif\\\\\\\",uv_vertex:\\\\\\\"#ifdef USE_UV\\\\n\\\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\\\n#endif\\\\\\\",uv2_pars_fragment:\\\\\\\"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\tvarying vec2 vUv2;\\\\n#endif\\\\\\\",uv2_pars_vertex:\\\\\\\"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\tattribute vec2 uv2;\\\\n\\\\tvarying vec2 vUv2;\\\\n\\\\tuniform mat3 uv2Transform;\\\\n#endif\\\\\\\",uv2_vertex:\\\\\\\"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\tvUv2 = ( uv2Transform * vec3( uv2, 1 ) ).xy;\\\\n#endif\\\\\\\",worldpos_vertex:\\\\\\\"#if defined( USE_ENVMAP ) || defined( DISTANCE ) || defined ( USE_SHADOWMAP ) || defined ( USE_TRANSMISSION )\\\\n\\\\tvec4 worldPosition = vec4( transformed, 1.0 );\\\\n\\\\t#ifdef USE_INSTANCING\\\\n\\\\t\\\\tworldPosition = instanceMatrix * worldPosition;\\\\n\\\\t#endif\\\\n\\\\tworldPosition = modelMatrix * worldPosition;\\\\n#endif\\\\\\\",background_vert:\\\\\\\"varying vec2 vUv;\\\\nuniform mat3 uvTransform;\\\\nvoid main() {\\\\n\\\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\\\n\\\\tgl_Position = vec4( position.xy, 1.0, 1.0 );\\\\n}\\\\\\\",background_frag:\\\\\\\"uniform sampler2D t2D;\\\\nvarying vec2 vUv;\\\\nvoid main() {\\\\n\\\\tvec4 texColor = texture2D( t2D, vUv );\\\\n\\\\tgl_FragColor = mapTexelToLinear( texColor );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",cube_vert:\\\\\\\"varying vec3 vWorldDirection;\\\\n#include \\\\nvoid main() {\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tgl_Position.z = gl_Position.w;\\\\n}\\\\\\\",cube_frag:\\\\\\\"#include \\\\nuniform float opacity;\\\\nvarying vec3 vWorldDirection;\\\\n#include \\\\nvoid main() {\\\\n\\\\tvec3 vReflect = vWorldDirection;\\\\n\\\\t#include \\\\n\\\\tgl_FragColor = envColor;\\\\n\\\\tgl_FragColor.a *= opacity;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",depth_vert:\\\\\\\"#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvarying vec2 vHighPrecisionZW;\\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvHighPrecisionZW = gl_Position.zw;\\\\n}\\\\\\\",depth_frag:\\\\\\\"#if DEPTH_PACKING == 3200\\\\n\\\\tuniform float opacity;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvarying vec2 vHighPrecisionZW;\\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( 1.0 );\\\\n\\\\t#if DEPTH_PACKING == 3200\\\\n\\\\t\\\\tdiffuseColor.a = opacity;\\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tfloat fragCoordZ = 0.5 * vHighPrecisionZW[0] / vHighPrecisionZW[1] + 0.5;\\\\n\\\\t#if DEPTH_PACKING == 3200\\\\n\\\\t\\\\tgl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\\\\n\\\\t#elif DEPTH_PACKING == 3201\\\\n\\\\t\\\\tgl_FragColor = packDepthToRGBA( fragCoordZ );\\\\n\\\\t#endif\\\\n}\\\\\\\",distanceRGBA_vert:\\\\\\\"#define DISTANCE\\\\nvarying vec3 vWorldPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n}\\\\\\\",distanceRGBA_frag:\\\\\\\"#define DISTANCE\\\\nuniform vec3 referencePosition;\\\\nuniform float nearDistance;\\\\nuniform float farDistance;\\\\nvarying vec3 vWorldPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main () {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( 1.0 );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tfloat dist = length( vWorldPosition - referencePosition );\\\\n\\\\tdist = ( dist - nearDistance ) / ( farDistance - nearDistance );\\\\n\\\\tdist = saturate( dist );\\\\n\\\\tgl_FragColor = packDepthToRGBA( dist );\\\\n}\\\\\\\",equirect_vert:\\\\\\\"varying vec3 vWorldDirection;\\\\n#include \\\\nvoid main() {\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",equirect_frag:\\\\\\\"uniform sampler2D tEquirect;\\\\nvarying vec3 vWorldDirection;\\\\n#include \\\\nvoid main() {\\\\n\\\\tvec3 direction = normalize( vWorldDirection );\\\\n\\\\tvec2 sampleUV = equirectUv( direction );\\\\n\\\\tvec4 texColor = texture2D( tEquirect, sampleUV );\\\\n\\\\tgl_FragColor = mapTexelToLinear( texColor );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",linedashed_vert:\\\\\\\"uniform float scale;\\\\nattribute float lineDistance;\\\\nvarying float vLineDistance;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\tvLineDistance = scale * lineDistance;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",linedashed_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\nuniform float dashSize;\\\\nuniform float totalSize;\\\\nvarying float vLineDistance;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tif ( mod( vLineDistance, totalSize ) > dashSize ) {\\\\n\\\\t\\\\tdiscard;\\\\n\\\\t}\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshbasic_vert:\\\\\\\"#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#if defined ( USE_ENVMAP ) || defined ( USE_SKINNING )\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshbasic_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\n#ifndef FLAT_SHADED\\\\n\\\\tvarying vec3 vNormal;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\t#ifdef USE_LIGHTMAP\\\\n\\\\t\\\\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\t#else\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += vec3( 1.0 );\\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\treflectedLight.indirectDiffuse *= diffuseColor.rgb;\\\\n\\\\tvec3 outgoingLight = reflectedLight.indirectDiffuse;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshlambert_vert:\\\\\\\"#define LAMBERT\\\\nvarying vec3 vLightFront;\\\\nvarying vec3 vIndirectFront;\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvarying vec3 vLightBack;\\\\n\\\\tvarying vec3 vIndirectBack;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshlambert_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float opacity;\\\\nvarying vec3 vLightFront;\\\\nvarying vec3 vIndirectFront;\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvarying vec3 vLightBack;\\\\n\\\\tvarying vec3 vIndirectBack;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += ( gl_FrontFacing ) ? vIndirectFront : vIndirectBack;\\\\n\\\\t#else\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += vIndirectFront;\\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\treflectedLight.indirectDiffuse *= BRDF_Lambert( diffuseColor.rgb );\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\treflectedLight.directDiffuse = ( gl_FrontFacing ) ? vLightFront : vLightBack;\\\\n\\\\t#else\\\\n\\\\t\\\\treflectedLight.directDiffuse = vLightFront;\\\\n\\\\t#endif\\\\n\\\\treflectedLight.directDiffuse *= BRDF_Lambert( diffuseColor.rgb ) * getShadowMask();\\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshmatcap_vert:\\\\\\\"#define MATCAP\\\\nvarying vec3 vViewPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n}\\\\\\\",meshmatcap_frag:\\\\\\\"#define MATCAP\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\nuniform sampler2D matcap;\\\\nvarying vec3 vViewPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvec3 viewDir = normalize( vViewPosition );\\\\n\\\\tvec3 x = normalize( vec3( viewDir.z, 0.0, - viewDir.x ) );\\\\n\\\\tvec3 y = cross( viewDir, x );\\\\n\\\\tvec2 uv = vec2( dot( x, normal ), dot( y, normal ) ) * 0.495 + 0.5;\\\\n\\\\t#ifdef USE_MATCAP\\\\n\\\\t\\\\tvec4 matcapColor = texture2D( matcap, uv );\\\\n\\\\t\\\\tmatcapColor = matcapTexelToLinear( matcapColor );\\\\n\\\\t#else\\\\n\\\\t\\\\tvec4 matcapColor = vec4( 1.0 );\\\\n\\\\t#endif\\\\n\\\\tvec3 outgoingLight = diffuseColor.rgb * matcapColor.rgb;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshnormal_vert:\\\\\\\"#define NORMAL\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\tvarying vec3 vViewPosition;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n#endif\\\\n}\\\\\\\",meshnormal_frag:\\\\\\\"#define NORMAL\\\\nuniform float opacity;\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\tvarying vec3 vViewPosition;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tgl_FragColor = vec4( packNormalToRGB( normal ), opacity );\\\\n}\\\\\\\",meshphong_vert:\\\\\\\"#define PHONG\\\\nvarying vec3 vViewPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshphong_frag:\\\\\\\"#define PHONG\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform vec3 specular;\\\\nuniform float shininess;\\\\nuniform float opacity;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshphysical_vert:\\\\\\\"#define STANDARD\\\\nvarying vec3 vViewPosition;\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\tvarying vec3 vWorldPosition;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n#ifdef USE_TRANSMISSION\\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n#endif\\\\n}\\\\\\\",meshphysical_frag:\\\\\\\"#define STANDARD\\\\n#ifdef PHYSICAL\\\\n\\\\t#define IOR\\\\n\\\\t#define SPECULAR\\\\n#endif\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float roughness;\\\\nuniform float metalness;\\\\nuniform float opacity;\\\\n#ifdef IOR\\\\n\\\\tuniform float ior;\\\\n#endif\\\\n#ifdef SPECULAR\\\\n\\\\tuniform float specularIntensity;\\\\n\\\\tuniform vec3 specularTint;\\\\n\\\\t#ifdef USE_SPECULARINTENSITYMAP\\\\n\\\\t\\\\tuniform sampler2D specularIntensityMap;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_SPECULARTINTMAP\\\\n\\\\t\\\\tuniform sampler2D specularTintMap;\\\\n\\\\t#endif\\\\n#endif\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tuniform float clearcoat;\\\\n\\\\tuniform float clearcoatRoughness;\\\\n#endif\\\\n#ifdef USE_SHEEN\\\\n\\\\tuniform vec3 sheenTint;\\\\n\\\\tuniform float sheenRoughness;\\\\n#endif\\\\nvarying vec3 vViewPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvec3 totalDiffuse = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse;\\\\n\\\\tvec3 totalSpecular = reflectedLight.directSpecular + reflectedLight.indirectSpecular;\\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = totalDiffuse + totalSpecular + totalEmissiveRadiance;\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tfloat dotNVcc = saturate( dot( geometry.clearcoatNormal, geometry.viewDir ) );\\\\n\\\\t\\\\tvec3 Fcc = F_Schlick( material.clearcoatF0, material.clearcoatF90, dotNVcc );\\\\n\\\\t\\\\toutgoingLight = outgoingLight * ( 1.0 - clearcoat * Fcc ) + clearcoatSpecular * clearcoat;\\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshtoon_vert:\\\\\\\"#define TOON\\\\nvarying vec3 vViewPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshtoon_frag:\\\\\\\"#define TOON\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float opacity;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",points_vert:\\\\\\\"uniform float size;\\\\nuniform float scale;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tgl_PointSize = size;\\\\n\\\\t#ifdef USE_SIZEATTENUATION\\\\n\\\\t\\\\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\\\\n\\\\t\\\\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",points_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",shadow_vert:\\\\\\\"#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",shadow_frag:\\\\\\\"uniform vec3 color;\\\\nuniform float opacity;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\tgl_FragColor = vec4( color, opacity * ( 1.0 - getShadowMask() ) );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",sprite_vert:\\\\\\\"uniform float rotation;\\\\nuniform vec2 center;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 mvPosition = modelViewMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\tvec2 scale;\\\\n\\\\tscale.x = length( vec3( modelMatrix[ 0 ].x, modelMatrix[ 0 ].y, modelMatrix[ 0 ].z ) );\\\\n\\\\tscale.y = length( vec3( modelMatrix[ 1 ].x, modelMatrix[ 1 ].y, modelMatrix[ 1 ].z ) );\\\\n\\\\t#ifndef USE_SIZEATTENUATION\\\\n\\\\t\\\\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\\\\n\\\\t\\\\tif ( isPerspective ) scale *= - mvPosition.z;\\\\n\\\\t#endif\\\\n\\\\tvec2 alignedPosition = ( position.xy - ( center - vec2( 0.5 ) ) ) * scale;\\\\n\\\\tvec2 rotatedPosition;\\\\n\\\\trotatedPosition.x = cos( rotation ) * alignedPosition.x - sin( rotation ) * alignedPosition.y;\\\\n\\\\trotatedPosition.y = sin( rotation ) * alignedPosition.x + cos( rotation ) * alignedPosition.y;\\\\n\\\\tmvPosition.xy += rotatedPosition;\\\\n\\\\tgl_Position = projectionMatrix * mvPosition;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",sprite_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\"},tT={common:{diffuse:{value:new Zb(16777215)},opacity:{value:1},map:{value:null},uvTransform:{value:new gx},uv2Transform:{value:new gx},alphaMap:{value:null},alphaTest:{value:0}},specularmap:{specularMap:{value:null}},envmap:{envMap:{value:null},flipEnvMap:{value:-1},reflectivity:{value:1},ior:{value:1.5},refractionRatio:{value:.98},maxMipLevel:{value:0}},aomap:{aoMap:{value:null},aoMapIntensity:{value:1}},lightmap:{lightMap:{value:null},lightMapIntensity:{value:1}},emissivemap:{emissiveMap:{value:null}},bumpmap:{bumpMap:{value:null},bumpScale:{value:1}},normalmap:{normalMap:{value:null},normalScale:{value:new fx(1,1)}},displacementmap:{displacementMap:{value:null},displacementScale:{value:1},displacementBias:{value:0}},roughnessmap:{roughnessMap:{value:null}},metalnessmap:{metalnessMap:{value:null}},gradientmap:{gradientMap:{value:null}},fog:{fogDensity:{value:25e-5},fogNear:{value:1},fogFar:{value:2e3},fogColor:{value:new Zb(16777215)}},lights:{ambientLightColor:{value:[]},lightProbe:{value:[]},directionalLights:{value:[],properties:{direction:{},color:{}}},directionalLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},directionalShadowMap:{value:[]},directionalShadowMatrix:{value:[]},spotLights:{value:[],properties:{color:{},position:{},direction:{},distance:{},coneCos:{},penumbraCos:{},decay:{}}},spotLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},spotShadowMap:{value:[]},spotShadowMatrix:{value:[]},pointLights:{value:[],properties:{color:{},position:{},decay:{},distance:{}}},pointLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{},shadowCameraNear:{},shadowCameraFar:{}}},pointShadowMap:{value:[]},pointShadowMatrix:{value:[]},hemisphereLights:{value:[],properties:{direction:{},skyColor:{},groundColor:{}}},rectAreaLights:{value:[],properties:{color:{},position:{},width:{},height:{}}},ltc_1:{value:null},ltc_2:{value:null}},points:{diffuse:{value:new Zb(16777215)},opacity:{value:1},size:{value:1},scale:{value:1},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new gx}},sprite:{diffuse:{value:new Zb(16777215)},opacity:{value:1},center:{value:new fx(.5,.5)},rotation:{value:0},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new gx}}},eT={basic:{uniforms:Iw([tT.common,tT.specularmap,tT.envmap,tT.aomap,tT.lightmap,tT.fog]),vertexShader:Kw.meshbasic_vert,fragmentShader:Kw.meshbasic_frag},lambert:{uniforms:Iw([tT.common,tT.specularmap,tT.envmap,tT.aomap,tT.lightmap,tT.emissivemap,tT.fog,tT.lights,{emissive:{value:new Zb(0)}}]),vertexShader:Kw.meshlambert_vert,fragmentShader:Kw.meshlambert_frag},phong:{uniforms:Iw([tT.common,tT.specularmap,tT.envmap,tT.aomap,tT.lightmap,tT.emissivemap,tT.bumpmap,tT.normalmap,tT.displacementmap,tT.fog,tT.lights,{emissive:{value:new Zb(0)},specular:{value:new Zb(1118481)},shininess:{value:30}}]),vertexShader:Kw.meshphong_vert,fragmentShader:Kw.meshphong_frag},standard:{uniforms:Iw([tT.common,tT.envmap,tT.aomap,tT.lightmap,tT.emissivemap,tT.bumpmap,tT.normalmap,tT.displacementmap,tT.roughnessmap,tT.metalnessmap,tT.fog,tT.lights,{emissive:{value:new Zb(0)},roughness:{value:1},metalness:{value:0},envMapIntensity:{value:1}}]),vertexShader:Kw.meshphysical_vert,fragmentShader:Kw.meshphysical_frag},toon:{uniforms:Iw([tT.common,tT.aomap,tT.lightmap,tT.emissivemap,tT.bumpmap,tT.normalmap,tT.displacementmap,tT.gradientmap,tT.fog,tT.lights,{emissive:{value:new Zb(0)}}]),vertexShader:Kw.meshtoon_vert,fragmentShader:Kw.meshtoon_frag},matcap:{uniforms:Iw([tT.common,tT.bumpmap,tT.normalmap,tT.displacementmap,tT.fog,{matcap:{value:null}}]),vertexShader:Kw.meshmatcap_vert,fragmentShader:Kw.meshmatcap_frag},points:{uniforms:Iw([tT.points,tT.fog]),vertexShader:Kw.points_vert,fragmentShader:Kw.points_frag},dashed:{uniforms:Iw([tT.common,tT.fog,{scale:{value:1},dashSize:{value:1},totalSize:{value:2}}]),vertexShader:Kw.linedashed_vert,fragmentShader:Kw.linedashed_frag},depth:{uniforms:Iw([tT.common,tT.displacementmap]),vertexShader:Kw.depth_vert,fragmentShader:Kw.depth_frag},normal:{uniforms:Iw([tT.common,tT.bumpmap,tT.normalmap,tT.displacementmap,{opacity:{value:1}}]),vertexShader:Kw.meshnormal_vert,fragmentShader:Kw.meshnormal_frag},sprite:{uniforms:Iw([tT.sprite,tT.fog]),vertexShader:Kw.sprite_vert,fragmentShader:Kw.sprite_frag},background:{uniforms:{uvTransform:{value:new gx},t2D:{value:null}},vertexShader:Kw.background_vert,fragmentShader:Kw.background_frag},cube:{uniforms:Iw([tT.envmap,{opacity:{value:1}}]),vertexShader:Kw.cube_vert,fragmentShader:Kw.cube_frag},equirect:{uniforms:{tEquirect:{value:null}},vertexShader:Kw.equirect_vert,fragmentShader:Kw.equirect_frag},distanceRGBA:{uniforms:Iw([tT.common,tT.displacementmap,{referencePosition:{value:new Nx},nearDistance:{value:1},farDistance:{value:1e3}}]),vertexShader:Kw.distanceRGBA_vert,fragmentShader:Kw.distanceRGBA_frag},shadow:{uniforms:Iw([tT.lights,tT.fog,{color:{value:new Zb(0)},opacity:{value:1}}]),vertexShader:Kw.shadow_vert,fragmentShader:Kw.shadow_frag}};function nT(t,e,n,i,r){const s=new Zb(0);let o,a,l=0,c=null,u=0,h=null;function d(t,e){n.buffers.color.setClear(t.r,t.g,t.b,e,r)}return{getClearColor:function(){return s},setClearColor:function(t,e=1){s.set(t),l=e,d(s,l)},getClearAlpha:function(){return l},setClearAlpha:function(t){l=t,d(s,l)},render:function(n,r){let p=!1,_=!0===r.isScene?r.background:null;_&&_.isTexture&&(_=e.get(_));const m=t.xr,f=m.getSession&&m.getSession();f&&\\\\\\\"additive\\\\\\\"===f.environmentBlendMode&&(_=null),null===_?d(s,l):_&&_.isColor&&(d(_,1),p=!0),(t.autoClear||p)&&t.clear(t.autoClearColor,t.autoClearDepth,t.autoClearStencil),_&&(_.isCubeTexture||_.mapping===xy)?(void 0===a&&(a=new Lw(new Rw(1,1,1),new Dw({name:\\\\\\\"BackgroundCubeMaterial\\\\\\\",uniforms:Pw(eT.cube.uniforms),vertexShader:eT.cube.vertexShader,fragmentShader:eT.cube.fragmentShader,side:1,depthTest:!1,depthWrite:!1,fog:!1})),a.geometry.deleteAttribute(\\\\\\\"normal\\\\\\\"),a.geometry.deleteAttribute(\\\\\\\"uv\\\\\\\"),a.onBeforeRender=function(t,e,n){this.matrixWorld.copyPosition(n.matrixWorld)},Object.defineProperty(a.material,\\\\\\\"envMap\\\\\\\",{get:function(){return this.uniforms.envMap.value}}),i.update(a)),a.material.uniforms.envMap.value=_,a.material.uniforms.flipEnvMap.value=_.isCubeTexture&&!1===_.isRenderTargetTexture?-1:1,c===_&&u===_.version&&h===t.toneMapping||(a.material.needsUpdate=!0,c=_,u=_.version,h=t.toneMapping),n.unshift(a,a.geometry,a.material,0,0,null)):_&&_.isTexture&&(void 0===o&&(o=new Lw(new Qw(2,2),new Dw({name:\\\\\\\"BackgroundMaterial\\\\\\\",uniforms:Pw(eT.background.uniforms),vertexShader:eT.background.vertexShader,fragmentShader:eT.background.fragmentShader,side:0,depthTest:!1,depthWrite:!1,fog:!1})),o.geometry.deleteAttribute(\\\\\\\"normal\\\\\\\"),Object.defineProperty(o.material,\\\\\\\"map\\\\\\\",{get:function(){return this.uniforms.t2D.value}}),i.update(o)),o.material.uniforms.t2D.value=_,!0===_.matrixAutoUpdate&&_.updateMatrix(),o.material.uniforms.uvTransform.value.copy(_.matrix),c===_&&u===_.version&&h===t.toneMapping||(o.material.needsUpdate=!0,c=_,u=_.version,h=t.toneMapping),n.unshift(o,o.geometry,o.material,0,0,null))}}}function iT(t,e,n,i){const r=t.getParameter(34921),s=i.isWebGL2?null:e.get(\\\\\\\"OES_vertex_array_object\\\\\\\"),o=i.isWebGL2||null!==s,a={},l=d(null);let c=l;function u(e){return i.isWebGL2?t.bindVertexArray(e):s.bindVertexArrayOES(e)}function h(e){return i.isWebGL2?t.deleteVertexArray(e):s.deleteVertexArrayOES(e)}function d(t){const e=[],n=[],i=[];for(let t=0;t=0){let s=l[e];if(void 0===s&&(\\\\\\\"instanceMatrix\\\\\\\"===e&&r.instanceMatrix&&(s=r.instanceMatrix),\\\\\\\"instanceColor\\\\\\\"===e&&r.instanceColor&&(s=r.instanceColor)),void 0!==s){const e=s.normalized,o=s.itemSize,l=n.get(s);if(void 0===l)continue;const c=l.buffer,u=l.type,h=l.bytesPerElement;if(s.isInterleavedBufferAttribute){const n=s.data,l=n.stride,d=s.offset;if(n&&n.isInstancedInterleavedBuffer){for(let t=0;t0&&t.getShaderPrecisionFormat(35632,36338).precision>0)return\\\\\\\"highp\\\\\\\";e=\\\\\\\"mediump\\\\\\\"}return\\\\\\\"mediump\\\\\\\"===e&&t.getShaderPrecisionFormat(35633,36337).precision>0&&t.getShaderPrecisionFormat(35632,36337).precision>0?\\\\\\\"mediump\\\\\\\":\\\\\\\"lowp\\\\\\\"}const s=\\\\\\\"undefined\\\\\\\"!=typeof WebGL2RenderingContext&&t instanceof WebGL2RenderingContext||\\\\\\\"undefined\\\\\\\"!=typeof WebGL2ComputeRenderingContext&&t instanceof WebGL2ComputeRenderingContext;let o=void 0!==n.precision?n.precision:\\\\\\\"highp\\\\\\\";const a=r(o);a!==o&&(console.warn(\\\\\\\"THREE.WebGLRenderer:\\\\\\\",o,\\\\\\\"not supported, using\\\\\\\",a,\\\\\\\"instead.\\\\\\\"),o=a);const l=s||e.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\"),c=!0===n.logarithmicDepthBuffer,u=t.getParameter(34930),h=t.getParameter(35660),d=t.getParameter(3379),p=t.getParameter(34076),_=t.getParameter(34921),m=t.getParameter(36347),f=t.getParameter(36348),g=t.getParameter(36349),v=h>0,y=s||e.has(\\\\\\\"OES_texture_float\\\\\\\");return{isWebGL2:s,drawBuffers:l,getMaxAnisotropy:function(){if(void 0!==i)return i;if(!0===e.has(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\")){const n=e.get(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\");i=t.getParameter(n.MAX_TEXTURE_MAX_ANISOTROPY_EXT)}else i=0;return i},getMaxPrecision:r,precision:o,logarithmicDepthBuffer:c,maxTextures:u,maxVertexTextures:h,maxTextureSize:d,maxCubemapSize:p,maxAttributes:_,maxVertexUniforms:m,maxVaryings:f,maxFragmentUniforms:g,vertexTextures:v,floatFragmentTextures:y,floatVertexTextures:v&&y,maxSamples:s?t.getParameter(36183):0}}function oT(t){const e=this;let n=null,i=0,r=!1,s=!1;const o=new qw,a=new gx,l={value:null,needsUpdate:!1};function c(){l.value!==n&&(l.value=n,l.needsUpdate=i>0),e.numPlanes=i,e.numIntersection=0}function u(t,n,i,r){const s=null!==t?t.length:0;let c=null;if(0!==s){if(c=l.value,!0!==r||null===c){const e=i+4*s,r=n.matrixWorldInverse;a.getNormalMatrix(r),(null===c||c.length0){const o=t.getRenderTarget(),a=new Vw(s.height/2);return a.fromEquirectangularTexture(t,r),e.set(r,a),t.setRenderTarget(o),r.addEventListener(\\\\\\\"dispose\\\\\\\",i),n(a.texture,r.mapping)}return null}}}return r},dispose:function(){e=new WeakMap}}}eT.physical={uniforms:Iw([eT.standard.uniforms,{clearcoat:{value:0},clearcoatMap:{value:null},clearcoatRoughness:{value:0},clearcoatRoughnessMap:{value:null},clearcoatNormalScale:{value:new fx(1,1)},clearcoatNormalMap:{value:null},sheen:{value:0},sheenTint:{value:new Zb(0)},sheenRoughness:{value:0},transmission:{value:0},transmissionMap:{value:null},transmissionSamplerSize:{value:new fx},transmissionSamplerMap:{value:null},thickness:{value:0},thicknessMap:{value:null},attenuationDistance:{value:0},attenuationTint:{value:new Zb(0)},specularIntensity:{value:0},specularIntensityMap:{value:null},specularTint:{value:new Zb(1,1,1)},specularTintMap:{value:null}}]),vertexShader:Kw.meshphysical_vert,fragmentShader:Kw.meshphysical_frag};class lT extends kw{constructor(t=-1,e=1,n=1,i=-1,r=.1,s=2e3){super(),this.type=\\\\\\\"OrthographicCamera\\\\\\\",this.zoom=1,this.view=null,this.left=t,this.right=e,this.top=n,this.bottom=i,this.near=r,this.far=s,this.updateProjectionMatrix()}copy(t,e){return super.copy(t,e),this.left=t.left,this.right=t.right,this.top=t.top,this.bottom=t.bottom,this.near=t.near,this.far=t.far,this.zoom=t.zoom,this.view=null===t.view?null:Object.assign({},t.view),this}setViewOffset(t,e,n,i,r,s){null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=i,this.view.width=r,this.view.height=s,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=(this.right-this.left)/(2*this.zoom),e=(this.top-this.bottom)/(2*this.zoom),n=(this.right+this.left)/2,i=(this.top+this.bottom)/2;let r=n-t,s=n+t,o=i+e,a=i-e;if(null!==this.view&&this.view.enabled){const t=(this.right-this.left)/this.view.fullWidth/this.zoom,e=(this.top-this.bottom)/this.view.fullHeight/this.zoom;r+=t*this.view.offsetX,s=r+t*this.view.width,o-=e*this.view.offsetY,a=o-e*this.view.height}this.projectionMatrix.makeOrthographic(r,s,o,a,this.near,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(t){const e=super.toJSON(t);return e.object.zoom=this.zoom,e.object.left=this.left,e.object.right=this.right,e.object.top=this.top,e.object.bottom=this.bottom,e.object.near=this.near,e.object.far=this.far,null!==this.view&&(e.object.view=Object.assign({},this.view)),e}}lT.prototype.isOrthographicCamera=!0;class cT extends Dw{constructor(t){super(t),this.type=\\\\\\\"RawShaderMaterial\\\\\\\"}}cT.prototype.isRawShaderMaterial=!0;const uT=Math.pow(2,8),hT=[.125,.215,.35,.446,.526,.582],dT=5+hT.length,pT=20,_T={[Yy]:0,[$y]:1,[Zy]:2,3004:3,3005:4,3006:5,[Jy]:6},mT=new lT,{_lodPlanes:fT,_sizeLods:gT,_sigmas:vT}=MT(),yT=new Zb;let xT=null;const bT=(1+Math.sqrt(5))/2,wT=1/bT,TT=[new Nx(1,1,1),new Nx(-1,1,1),new Nx(1,1,-1),new Nx(-1,1,-1),new Nx(0,bT,wT),new Nx(0,bT,-wT),new Nx(wT,0,bT),new Nx(-wT,0,bT),new Nx(bT,wT,0),new Nx(-bT,wT,0)];class AT{constructor(t){this._renderer=t,this._pingPongRenderTarget=null,this._blurMaterial=function(t){const e=new Float32Array(t),n=new Nx(0,1,0);return new cT({name:\\\\\\\"SphericalGaussianBlur\\\\\\\",defines:{n:t},uniforms:{envMap:{value:null},samples:{value:1},weights:{value:e},latitudinal:{value:!1},dTheta:{value:0},mipInt:{value:0},poleAxis:{value:n},inputEncoding:{value:_T[3e3]},outputEncoding:{value:_T[3e3]}},vertexShader:OT(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform int samples;\\\\n\\\\t\\\\t\\\\tuniform float weights[ n ];\\\\n\\\\t\\\\t\\\\tuniform bool latitudinal;\\\\n\\\\t\\\\t\\\\tuniform float dTheta;\\\\n\\\\t\\\\t\\\\tuniform float mipInt;\\\\n\\\\t\\\\t\\\\tuniform vec3 poleAxis;\\\\n\\\\n\\\\t\\\\t\\\\t${RT()}\\\\n\\\\n\\\\t\\\\t\\\\t#define ENVMAP_TYPE_CUBE_UV\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\tvec3 getSample( float theta, vec3 axis ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat cosTheta = cos( theta );\\\\n\\\\t\\\\t\\\\t\\\\t// Rodrigues' axis-angle rotation\\\\n\\\\t\\\\t\\\\t\\\\tvec3 sampleDirection = vOutputDirection * cosTheta\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t+ cross( axis, vOutputDirection ) * sin( theta )\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t+ axis * dot( axis, vOutputDirection ) * ( 1.0 - cosTheta );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn bilinearCubeUV( envMap, sampleDirection, mipInt );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 axis = latitudinal ? poleAxis : cross( poleAxis, vOutputDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tif ( all( equal( axis, vec3( 0.0 ) ) ) ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\taxis = vec3( vOutputDirection.z, 0.0, - vOutputDirection.x );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\taxis = normalize( axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ 0 ] * getSample( 0.0, axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfor ( int i = 1; i < n; i++ ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tif ( i >= samples ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tbreak;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat theta = dTheta * float( i );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ i ] * getSample( -1.0 * theta, axis );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ i ] * getSample( theta, axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:0,depthTest:!1,depthWrite:!1})}(pT),this._equirectShader=null,this._cubemapShader=null,this._compileMaterial(this._blurMaterial)}fromScene(t,e=0,n=.1,i=100){xT=this._renderer.getRenderTarget();const r=this._allocateTargets();return this._sceneToCubeUV(t,n,i,r),e>0&&this._blur(r,0,0,e),this._applyPMREM(r),this._cleanup(r),r}fromEquirectangular(t){return this._fromTexture(t)}fromCubemap(t){return this._fromTexture(t)}compileCubemapShader(){null===this._cubemapShader&&(this._cubemapShader=LT(),this._compileMaterial(this._cubemapShader))}compileEquirectangularShader(){null===this._equirectShader&&(this._equirectShader=NT(),this._compileMaterial(this._equirectShader))}dispose(){this._blurMaterial.dispose(),null!==this._cubemapShader&&this._cubemapShader.dispose(),null!==this._equirectShader&&this._equirectShader.dispose();for(let t=0;t2?uT:0,uT,uT),a.setRenderTarget(i),p&&a.render(d,r),a.render(t,r)}d.geometry.dispose(),d.material.dispose(),a.toneMapping=u,a.outputEncoding=c,a.autoClear=l,t.background=_}_setEncoding(t,e){!0===this._renderer.capabilities.isWebGL2&&e.format===By&&e.type===Oy&&e.encoding===$y?t.value=_T[3e3]:t.value=_T[e.encoding]}_textureToCubeUV(t,e){const n=this._renderer;t.isCubeTexture?null==this._cubemapShader&&(this._cubemapShader=LT()):null==this._equirectShader&&(this._equirectShader=NT());const i=t.isCubeTexture?this._cubemapShader:this._equirectShader,r=new Lw(fT[0],i),s=i.uniforms;s.envMap.value=t,t.isCubeTexture||s.texelSize.value.set(1/t.image.width,1/t.image.height),this._setEncoding(s.inputEncoding,t),this._setEncoding(s.outputEncoding,e.texture),CT(e,0,0,3*uT,2*uT),n.setRenderTarget(e),n.render(r,mT)}_applyPMREM(t){const e=this._renderer,n=e.autoClear;e.autoClear=!1;for(let e=1;epT&&console.warn(`sigmaRadians, ${r}, is too large and will clip, as it requested ${_} samples when the maximum is set to 20`);const m=[];let f=0;for(let t=0;t4?i-8+4:0),3*g,2*g),a.setRenderTarget(e),a.render(c,mT)}}function ET(t){return void 0!==t&&t.type===Oy&&(t.encoding===Yy||t.encoding===$y||t.encoding===Jy)}function MT(){const t=[],e=[],n=[];let i=8;for(let r=0;r4?o=hT[r-8+4-1]:0==r&&(o=0),n.push(o);const a=1/(s-1),l=-a/2,c=1+a/2,u=[l,l,c,l,c,c,l,l,c,c,l,c],h=6,d=6,p=3,_=2,m=1,f=new Float32Array(p*d*h),g=new Float32Array(_*d*h),v=new Float32Array(m*d*h);for(let t=0;t2?0:-1,i=[e,n,0,e+2/3,n,0,e+2/3,n+1,0,e,n,0,e+2/3,n+1,0,e,n+1,0];f.set(i,p*d*t),g.set(u,_*d*t);const r=[t,t,t,t,t,t];v.set(r,m*d*t)}const y=new dw;y.setAttribute(\\\\\\\"position\\\\\\\",new ew(f,p)),y.setAttribute(\\\\\\\"uv\\\\\\\",new ew(g,_)),y.setAttribute(\\\\\\\"faceIndex\\\\\\\",new ew(v,m)),t.push(y),i>4&&i--}return{_lodPlanes:t,_sizeLods:e,_sigmas:n}}function ST(t){const e=new Mx(3*uT,3*uT,t);return e.texture.mapping=xy,e.texture.name=\\\\\\\"PMREM.cubeUv\\\\\\\",e.scissorTest=!0,e}function CT(t,e,n,i,r){t.viewport.set(e,n,i,r),t.scissor.set(e,n,i,r)}function NT(){const t=new fx(1,1);return new cT({name:\\\\\\\"EquirectangularToCubeUV\\\\\\\",uniforms:{envMap:{value:null},texelSize:{value:t},inputEncoding:{value:_T[3e3]},outputEncoding:{value:_T[3e3]}},vertexShader:OT(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform vec2 texelSize;\\\\n\\\\n\\\\t\\\\t\\\\t${RT()}\\\\n\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 outputDirection = normalize( vOutputDirection );\\\\n\\\\t\\\\t\\\\t\\\\tvec2 uv = equirectUv( outputDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec2 f = fract( uv / texelSize - 0.5 );\\\\n\\\\t\\\\t\\\\t\\\\tuv -= f * texelSize;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x += texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tr = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.y += texelSize.y;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 br = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x -= texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tm = mix( tl, tr, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bm = mix( bl, br, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = mix( tm, bm, f.y );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:0,depthTest:!1,depthWrite:!1})}function LT(){return new cT({name:\\\\\\\"CubemapToCubeUV\\\\\\\",uniforms:{envMap:{value:null},inputEncoding:{value:_T[3e3]},outputEncoding:{value:_T[3e3]}},vertexShader:OT(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform samplerCube envMap;\\\\n\\\\n\\\\t\\\\t\\\\t${RT()}\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = envMapTexelToLinear( textureCube( envMap, vec3( - vOutputDirection.x, vOutputDirection.yz ) ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:0,depthTest:!1,depthWrite:!1})}function OT(){return\\\\\\\"\\\\n\\\\n\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\tattribute vec3 position;\\\\n\\\\t\\\\tattribute vec2 uv;\\\\n\\\\t\\\\tattribute float faceIndex;\\\\n\\\\n\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t// RH coordinate system; PMREM face-indexing convention\\\\n\\\\t\\\\tvec3 getDirection( vec2 uv, float face ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = 2.0 * uv - 1.0;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 direction = vec3( uv, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\tif ( face == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.zyx; // ( 1, v, u ) pos x\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 1.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.xzy;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 2.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 3.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.zyx;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 4.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.xzy;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 5.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\treturn direction;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvOutputDirection = getDirection( uv, faceIndex );\\\\n\\\\t\\\\t\\\\tgl_Position = vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\\\\"}function RT(){return\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform int inputEncoding;\\\\n\\\\t\\\\tuniform int outputEncoding;\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\tvec4 inputTexelToLinear( vec4 value ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( inputEncoding == 0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn value;\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 1 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn sRGBToLinear( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 2 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBEToLinear( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 3 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBMToLinear( value, 7.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 4 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBMToLinear( value, 16.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 5 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBDToLinear( value, 256.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn GammaToLinear( value, 2.2 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec4 linearToOutputTexel( vec4 value ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( outputEncoding == 0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn value;\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 1 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearTosRGB( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 2 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBE( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 3 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBM( value, 7.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 4 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBM( value, 16.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 5 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBD( value, 256.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToGamma( value, 2.2 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec4 envMapTexelToLinear( vec4 color ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn inputTexelToLinear( color );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\\\\"}function PT(t){let e=new WeakMap,n=null;function i(t){const n=t.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",i);const r=e.get(n);void 0!==r&&(e.delete(n),r.dispose())}return{get:function(r){if(r&&r.isTexture&&!1===r.isRenderTargetTexture){const s=r.mapping,o=s===vy||s===yy,a=s===fy||s===gy;if(o||a){if(e.has(r))return e.get(r).texture;{const s=r.image;if(o&&s&&s.height>0||a&&s&&function(t){let e=0;const n=6;for(let i=0;i65535?iw:nw)(n,1);a.version=o;const l=s.get(t);l&&e.remove(l),s.set(t,a)}return{get:function(t,e){return!0===r[e.id]||(e.addEventListener(\\\\\\\"dispose\\\\\\\",o),r[e.id]=!0,n.memory.geometries++),e},update:function(t){const n=t.attributes;for(const t in n)e.update(n[t],34962);const i=t.morphAttributes;for(const t in i){const n=i[t];for(let t=0,i=n.length;te.maxTextureSize&&(h=Math.ceil(u/e.maxTextureSize),u=e.maxTextureSize);const d=new Float32Array(u*h*4*i),p=new BT(d,u,h,i);p.format=By,p.type=Iy;const _=4*l;for(let e=0;e0)return t;const r=e*n;let s=$T[r];if(void 0===s&&(s=new Float32Array(r),$T[r]=s),0!==e){i.toArray(s,0);for(let i=1,r=0;i!==e;++i)r+=n,t[i].toArray(s,r)}return s}function eA(t,e){if(t.length!==e.length)return!1;for(let n=0,i=t.length;n/gm;function rE(t){return t.replace(iE,sE)}function sE(t,e){const n=Kw[e];if(void 0===n)throw new Error(\\\\\\\"Can not resolve #include <\\\\\\\"+e+\\\\\\\">\\\\\\\");return rE(n)}const oE=/#pragma unroll_loop[\\\\s]+?for \\\\( int i \\\\= (\\\\d+)\\\\; i < (\\\\d+)\\\\; i \\\\+\\\\+ \\\\) \\\\{([\\\\s\\\\S]+?)(?=\\\\})\\\\}/g,aE=/#pragma unroll_loop_start\\\\s+for\\\\s*\\\\(\\\\s*int\\\\s+i\\\\s*=\\\\s*(\\\\d+)\\\\s*;\\\\s*i\\\\s*<\\\\s*(\\\\d+)\\\\s*;\\\\s*i\\\\s*\\\\+\\\\+\\\\s*\\\\)\\\\s*{([\\\\s\\\\S]+?)}\\\\s+#pragma unroll_loop_end/g;function lE(t){return t.replace(aE,uE).replace(oE,cE)}function cE(t,e,n,i){return console.warn(\\\\\\\"WebGLProgram: #pragma unroll_loop shader syntax is deprecated. Please use #pragma unroll_loop_start syntax instead.\\\\\\\"),uE(t,e,n,i)}function uE(t,e,n,i){let r=\\\\\\\"\\\\\\\";for(let t=parseInt(e);t0?t.gammaFactor:1,p=n.isWebGL2?\\\\\\\"\\\\\\\":function(t){return[t.extensionDerivatives||t.envMapCubeUV||t.bumpMap||t.tangentSpaceNormalMap||t.clearcoatNormalMap||t.flatShading||\\\\\\\"physical\\\\\\\"===t.shaderID?\\\\\\\"#extension GL_OES_standard_derivatives : enable\\\\\\\":\\\\\\\"\\\\\\\",(t.extensionFragDepth||t.logarithmicDepthBuffer)&&t.rendererExtensionFragDepth?\\\\\\\"#extension GL_EXT_frag_depth : enable\\\\\\\":\\\\\\\"\\\\\\\",t.extensionDrawBuffers&&t.rendererExtensionDrawBuffers?\\\\\\\"#extension GL_EXT_draw_buffers : require\\\\\\\":\\\\\\\"\\\\\\\",(t.extensionShaderTextureLOD||t.envMap||t.transmission)&&t.rendererExtensionShaderTextureLod?\\\\\\\"#extension GL_EXT_shader_texture_lod : enable\\\\\\\":\\\\\\\"\\\\\\\"].filter(tE).join(\\\\\\\"\\\\n\\\\\\\")}(n),_=function(t){const e=[];for(const n in t){const i=t[n];!1!==i&&e.push(\\\\\\\"#define \\\\\\\"+n+\\\\\\\" \\\\\\\"+i)}return e.join(\\\\\\\"\\\\n\\\\\\\")}(s),m=r.createProgram();let f,g,v=n.glslVersion?\\\\\\\"#version \\\\\\\"+n.glslVersion+\\\\\\\"\\\\n\\\\\\\":\\\\\\\"\\\\\\\";n.isRawShaderMaterial?(f=[_].filter(tE).join(\\\\\\\"\\\\n\\\\\\\"),f.length>0&&(f+=\\\\\\\"\\\\n\\\\\\\"),g=[p,_].filter(tE).join(\\\\\\\"\\\\n\\\\\\\"),g.length>0&&(g+=\\\\\\\"\\\\n\\\\\\\")):(f=[hE(n),\\\\\\\"#define SHADER_NAME \\\\\\\"+n.shaderName,_,n.instancing?\\\\\\\"#define USE_INSTANCING\\\\\\\":\\\\\\\"\\\\\\\",n.instancingColor?\\\\\\\"#define USE_INSTANCING_COLOR\\\\\\\":\\\\\\\"\\\\\\\",n.supportsVertexTextures?\\\\\\\"#define VERTEX_TEXTURES\\\\\\\":\\\\\\\"\\\\\\\",\\\\\\\"#define GAMMA_FACTOR \\\\\\\"+d,\\\\\\\"#define MAX_BONES \\\\\\\"+n.maxBones,n.useFog&&n.fog?\\\\\\\"#define USE_FOG\\\\\\\":\\\\\\\"\\\\\\\",n.useFog&&n.fogExp2?\\\\\\\"#define FOG_EXP2\\\\\\\":\\\\\\\"\\\\\\\",n.map?\\\\\\\"#define USE_MAP\\\\\\\":\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define USE_ENVMAP\\\\\\\":\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define \\\\\\\"+u:\\\\\\\"\\\\\\\",n.lightMap?\\\\\\\"#define USE_LIGHTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.aoMap?\\\\\\\"#define USE_AOMAP\\\\\\\":\\\\\\\"\\\\\\\",n.emissiveMap?\\\\\\\"#define USE_EMISSIVEMAP\\\\\\\":\\\\\\\"\\\\\\\",n.bumpMap?\\\\\\\"#define USE_BUMPMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap?\\\\\\\"#define USE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap&&n.objectSpaceNormalMap?\\\\\\\"#define OBJECTSPACE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap&&n.tangentSpaceNormalMap?\\\\\\\"#define TANGENTSPACE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatMap?\\\\\\\"#define USE_CLEARCOATMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatRoughnessMap?\\\\\\\"#define USE_CLEARCOAT_ROUGHNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatNormalMap?\\\\\\\"#define USE_CLEARCOAT_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.displacementMap&&n.supportsVertexTextures?\\\\\\\"#define USE_DISPLACEMENTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularMap?\\\\\\\"#define USE_SPECULARMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularIntensityMap?\\\\\\\"#define USE_SPECULARINTENSITYMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularTintMap?\\\\\\\"#define USE_SPECULARTINTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.roughnessMap?\\\\\\\"#define USE_ROUGHNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.metalnessMap?\\\\\\\"#define USE_METALNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.alphaMap?\\\\\\\"#define USE_ALPHAMAP\\\\\\\":\\\\\\\"\\\\\\\",n.transmission?\\\\\\\"#define USE_TRANSMISSION\\\\\\\":\\\\\\\"\\\\\\\",n.transmissionMap?\\\\\\\"#define USE_TRANSMISSIONMAP\\\\\\\":\\\\\\\"\\\\\\\",n.thicknessMap?\\\\\\\"#define USE_THICKNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.vertexTangents?\\\\\\\"#define USE_TANGENT\\\\\\\":\\\\\\\"\\\\\\\",n.vertexColors?\\\\\\\"#define USE_COLOR\\\\\\\":\\\\\\\"\\\\\\\",n.vertexAlphas?\\\\\\\"#define USE_COLOR_ALPHA\\\\\\\":\\\\\\\"\\\\\\\",n.vertexUvs?\\\\\\\"#define USE_UV\\\\\\\":\\\\\\\"\\\\\\\",n.uvsVertexOnly?\\\\\\\"#define UVS_VERTEX_ONLY\\\\\\\":\\\\\\\"\\\\\\\",n.flatShading?\\\\\\\"#define FLAT_SHADED\\\\\\\":\\\\\\\"\\\\\\\",n.skinning?\\\\\\\"#define USE_SKINNING\\\\\\\":\\\\\\\"\\\\\\\",n.useVertexTexture?\\\\\\\"#define BONE_TEXTURE\\\\\\\":\\\\\\\"\\\\\\\",n.morphTargets?\\\\\\\"#define USE_MORPHTARGETS\\\\\\\":\\\\\\\"\\\\\\\",n.morphNormals&&!1===n.flatShading?\\\\\\\"#define USE_MORPHNORMALS\\\\\\\":\\\\\\\"\\\\\\\",n.morphTargets&&n.isWebGL2?\\\\\\\"#define MORPHTARGETS_TEXTURE\\\\\\\":\\\\\\\"\\\\\\\",n.morphTargets&&n.isWebGL2?\\\\\\\"#define MORPHTARGETS_COUNT \\\\\\\"+n.morphTargetsCount:\\\\\\\"\\\\\\\",n.doubleSided?\\\\\\\"#define DOUBLE_SIDED\\\\\\\":\\\\\\\"\\\\\\\",n.flipSided?\\\\\\\"#define FLIP_SIDED\\\\\\\":\\\\\\\"\\\\\\\",n.shadowMapEnabled?\\\\\\\"#define USE_SHADOWMAP\\\\\\\":\\\\\\\"\\\\\\\",n.shadowMapEnabled?\\\\\\\"#define \\\\\\\"+l:\\\\\\\"\\\\\\\",n.sizeAttenuation?\\\\\\\"#define USE_SIZEATTENUATION\\\\\\\":\\\\\\\"\\\\\\\",n.logarithmicDepthBuffer?\\\\\\\"#define USE_LOGDEPTHBUF\\\\\\\":\\\\\\\"\\\\\\\",n.logarithmicDepthBuffer&&n.rendererExtensionFragDepth?\\\\\\\"#define USE_LOGDEPTHBUF_EXT\\\\\\\":\\\\\\\"\\\\\\\",\\\\\\\"uniform mat4 modelMatrix;\\\\\\\",\\\\\\\"uniform mat4 modelViewMatrix;\\\\\\\",\\\\\\\"uniform mat4 projectionMatrix;\\\\\\\",\\\\\\\"uniform mat4 viewMatrix;\\\\\\\",\\\\\\\"uniform mat3 normalMatrix;\\\\\\\",\\\\\\\"uniform vec3 cameraPosition;\\\\\\\",\\\\\\\"uniform bool isOrthographic;\\\\\\\",\\\\\\\"#ifdef USE_INSTANCING\\\\\\\",\\\\\\\"\\\\tattribute mat4 instanceMatrix;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#ifdef USE_INSTANCING_COLOR\\\\\\\",\\\\\\\"\\\\tattribute vec3 instanceColor;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"attribute vec3 position;\\\\\\\",\\\\\\\"attribute vec3 normal;\\\\\\\",\\\\\\\"attribute vec2 uv;\\\\\\\",\\\\\\\"#ifdef USE_TANGENT\\\\\\\",\\\\\\\"\\\\tattribute vec4 tangent;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\\\\",\\\\\\\"\\\\tattribute vec4 color;\\\\\\\",\\\\\\\"#elif defined( USE_COLOR )\\\\\\\",\\\\\\\"\\\\tattribute vec3 color;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#if ( defined( USE_MORPHTARGETS ) && ! defined( MORPHTARGETS_TEXTURE ) )\\\\\\\",\\\\\\\"\\\\tattribute vec3 morphTarget0;\\\\\\\",\\\\\\\"\\\\tattribute vec3 morphTarget1;\\\\\\\",\\\\\\\"\\\\tattribute vec3 morphTarget2;\\\\\\\",\\\\\\\"\\\\tattribute vec3 morphTarget3;\\\\\\\",\\\\\\\"\\\\t#ifdef USE_MORPHNORMALS\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphNormal0;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphNormal1;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphNormal2;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphNormal3;\\\\\\\",\\\\\\\"\\\\t#else\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphTarget4;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphTarget5;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphTarget6;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphTarget7;\\\\\\\",\\\\\\\"\\\\t#endif\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#ifdef USE_SKINNING\\\\\\\",\\\\\\\"\\\\tattribute vec4 skinIndex;\\\\\\\",\\\\\\\"\\\\tattribute vec4 skinWeight;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"\\\\n\\\\\\\"].filter(tE).join(\\\\\\\"\\\\n\\\\\\\"),g=[p,hE(n),\\\\\\\"#define SHADER_NAME \\\\\\\"+n.shaderName,_,\\\\\\\"#define GAMMA_FACTOR \\\\\\\"+d,n.useFog&&n.fog?\\\\\\\"#define USE_FOG\\\\\\\":\\\\\\\"\\\\\\\",n.useFog&&n.fogExp2?\\\\\\\"#define FOG_EXP2\\\\\\\":\\\\\\\"\\\\\\\",n.map?\\\\\\\"#define USE_MAP\\\\\\\":\\\\\\\"\\\\\\\",n.matcap?\\\\\\\"#define USE_MATCAP\\\\\\\":\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define USE_ENVMAP\\\\\\\":\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define \\\\\\\"+c:\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define \\\\\\\"+u:\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define \\\\\\\"+h:\\\\\\\"\\\\\\\",n.lightMap?\\\\\\\"#define USE_LIGHTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.aoMap?\\\\\\\"#define USE_AOMAP\\\\\\\":\\\\\\\"\\\\\\\",n.emissiveMap?\\\\\\\"#define USE_EMISSIVEMAP\\\\\\\":\\\\\\\"\\\\\\\",n.bumpMap?\\\\\\\"#define USE_BUMPMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap?\\\\\\\"#define USE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap&&n.objectSpaceNormalMap?\\\\\\\"#define OBJECTSPACE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap&&n.tangentSpaceNormalMap?\\\\\\\"#define TANGENTSPACE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoat?\\\\\\\"#define USE_CLEARCOAT\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatMap?\\\\\\\"#define USE_CLEARCOATMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatRoughnessMap?\\\\\\\"#define USE_CLEARCOAT_ROUGHNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatNormalMap?\\\\\\\"#define USE_CLEARCOAT_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularMap?\\\\\\\"#define USE_SPECULARMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularIntensityMap?\\\\\\\"#define USE_SPECULARINTENSITYMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularTintMap?\\\\\\\"#define USE_SPECULARTINTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.roughnessMap?\\\\\\\"#define USE_ROUGHNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.metalnessMap?\\\\\\\"#define USE_METALNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.alphaMap?\\\\\\\"#define USE_ALPHAMAP\\\\\\\":\\\\\\\"\\\\\\\",n.alphaTest?\\\\\\\"#define USE_ALPHATEST\\\\\\\":\\\\\\\"\\\\\\\",n.sheen?\\\\\\\"#define USE_SHEEN\\\\\\\":\\\\\\\"\\\\\\\",n.transmission?\\\\\\\"#define USE_TRANSMISSION\\\\\\\":\\\\\\\"\\\\\\\",n.transmissionMap?\\\\\\\"#define USE_TRANSMISSIONMAP\\\\\\\":\\\\\\\"\\\\\\\",n.thicknessMap?\\\\\\\"#define USE_THICKNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.vertexTangents?\\\\\\\"#define USE_TANGENT\\\\\\\":\\\\\\\"\\\\\\\",n.vertexColors||n.instancingColor?\\\\\\\"#define USE_COLOR\\\\\\\":\\\\\\\"\\\\\\\",n.vertexAlphas?\\\\\\\"#define USE_COLOR_ALPHA\\\\\\\":\\\\\\\"\\\\\\\",n.vertexUvs?\\\\\\\"#define USE_UV\\\\\\\":\\\\\\\"\\\\\\\",n.uvsVertexOnly?\\\\\\\"#define UVS_VERTEX_ONLY\\\\\\\":\\\\\\\"\\\\\\\",n.gradientMap?\\\\\\\"#define USE_GRADIENTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.flatShading?\\\\\\\"#define FLAT_SHADED\\\\\\\":\\\\\\\"\\\\\\\",n.doubleSided?\\\\\\\"#define DOUBLE_SIDED\\\\\\\":\\\\\\\"\\\\\\\",n.flipSided?\\\\\\\"#define FLIP_SIDED\\\\\\\":\\\\\\\"\\\\\\\",n.shadowMapEnabled?\\\\\\\"#define USE_SHADOWMAP\\\\\\\":\\\\\\\"\\\\\\\",n.shadowMapEnabled?\\\\\\\"#define \\\\\\\"+l:\\\\\\\"\\\\\\\",n.premultipliedAlpha?\\\\\\\"#define PREMULTIPLIED_ALPHA\\\\\\\":\\\\\\\"\\\\\\\",n.physicallyCorrectLights?\\\\\\\"#define PHYSICALLY_CORRECT_LIGHTS\\\\\\\":\\\\\\\"\\\\\\\",n.logarithmicDepthBuffer?\\\\\\\"#define USE_LOGDEPTHBUF\\\\\\\":\\\\\\\"\\\\\\\",n.logarithmicDepthBuffer&&n.rendererExtensionFragDepth?\\\\\\\"#define USE_LOGDEPTHBUF_EXT\\\\\\\":\\\\\\\"\\\\\\\",(n.extensionShaderTextureLOD||n.envMap)&&n.rendererExtensionShaderTextureLod?\\\\\\\"#define TEXTURE_LOD_EXT\\\\\\\":\\\\\\\"\\\\\\\",\\\\\\\"uniform mat4 viewMatrix;\\\\\\\",\\\\\\\"uniform vec3 cameraPosition;\\\\\\\",\\\\\\\"uniform bool isOrthographic;\\\\\\\",0!==n.toneMapping?\\\\\\\"#define TONE_MAPPING\\\\\\\":\\\\\\\"\\\\\\\",0!==n.toneMapping?Kw.tonemapping_pars_fragment:\\\\\\\"\\\\\\\",0!==n.toneMapping?KA(\\\\\\\"toneMapping\\\\\\\",n.toneMapping):\\\\\\\"\\\\\\\",n.dithering?\\\\\\\"#define DITHERING\\\\\\\":\\\\\\\"\\\\\\\",n.format===ky?\\\\\\\"#define OPAQUE\\\\\\\":\\\\\\\"\\\\\\\",Kw.encodings_pars_fragment,n.map?ZA(\\\\\\\"mapTexelToLinear\\\\\\\",n.mapEncoding):\\\\\\\"\\\\\\\",n.matcap?ZA(\\\\\\\"matcapTexelToLinear\\\\\\\",n.matcapEncoding):\\\\\\\"\\\\\\\",n.envMap?ZA(\\\\\\\"envMapTexelToLinear\\\\\\\",n.envMapEncoding):\\\\\\\"\\\\\\\",n.emissiveMap?ZA(\\\\\\\"emissiveMapTexelToLinear\\\\\\\",n.emissiveMapEncoding):\\\\\\\"\\\\\\\",n.specularTintMap?ZA(\\\\\\\"specularTintMapTexelToLinear\\\\\\\",n.specularTintMapEncoding):\\\\\\\"\\\\\\\",n.lightMap?ZA(\\\\\\\"lightMapTexelToLinear\\\\\\\",n.lightMapEncoding):\\\\\\\"\\\\\\\",QA(\\\\\\\"linearToOutputTexel\\\\\\\",n.outputEncoding),n.depthPacking?\\\\\\\"#define DEPTH_PACKING \\\\\\\"+n.depthPacking:\\\\\\\"\\\\\\\",\\\\\\\"\\\\n\\\\\\\"].filter(tE).join(\\\\\\\"\\\\n\\\\\\\")),o=rE(o),o=eE(o,n),o=nE(o,n),a=rE(a),a=eE(a,n),a=nE(a,n),o=lE(o),a=lE(a),n.isWebGL2&&!0!==n.isRawShaderMaterial&&(v=\\\\\\\"#version 300 es\\\\n\\\\\\\",f=[\\\\\\\"precision mediump sampler2DArray;\\\\\\\",\\\\\\\"#define attribute in\\\\\\\",\\\\\\\"#define varying out\\\\\\\",\\\\\\\"#define texture2D texture\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")+\\\\\\\"\\\\n\\\\\\\"+f,g=[\\\\\\\"#define varying in\\\\\\\",n.glslVersion===ex?\\\\\\\"\\\\\\\":\\\\\\\"out highp vec4 pc_fragColor;\\\\\\\",n.glslVersion===ex?\\\\\\\"\\\\\\\":\\\\\\\"#define gl_FragColor pc_fragColor\\\\\\\",\\\\\\\"#define gl_FragDepthEXT gl_FragDepth\\\\\\\",\\\\\\\"#define texture2D texture\\\\\\\",\\\\\\\"#define textureCube texture\\\\\\\",\\\\\\\"#define texture2DProj textureProj\\\\\\\",\\\\\\\"#define texture2DLodEXT textureLod\\\\\\\",\\\\\\\"#define texture2DProjLodEXT textureProjLod\\\\\\\",\\\\\\\"#define textureCubeLodEXT textureLod\\\\\\\",\\\\\\\"#define texture2DGradEXT textureGrad\\\\\\\",\\\\\\\"#define texture2DProjGradEXT textureProjGrad\\\\\\\",\\\\\\\"#define textureCubeGradEXT textureGrad\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")+\\\\\\\"\\\\n\\\\\\\"+g);const y=v+g+a,x=XA(r,35633,v+f+o),b=XA(r,35632,y);if(r.attachShader(m,x),r.attachShader(m,b),void 0!==n.index0AttributeName?r.bindAttribLocation(m,0,n.index0AttributeName):!0===n.morphTargets&&r.bindAttribLocation(m,0,\\\\\\\"position\\\\\\\"),r.linkProgram(m),t.debug.checkShaderErrors){const t=r.getProgramInfoLog(m).trim(),e=r.getShaderInfoLog(x).trim(),n=r.getShaderInfoLog(b).trim();let i=!0,s=!0;if(!1===r.getProgramParameter(m,35714)){i=!1;const e=JA(r,x,\\\\\\\"vertex\\\\\\\"),n=JA(r,b,\\\\\\\"fragment\\\\\\\");console.error(\\\\\\\"THREE.WebGLProgram: Shader Error \\\\\\\"+r.getError()+\\\\\\\" - VALIDATE_STATUS \\\\\\\"+r.getProgramParameter(m,35715)+\\\\\\\"\\\\n\\\\nProgram Info Log: \\\\\\\"+t+\\\\\\\"\\\\n\\\\\\\"+e+\\\\\\\"\\\\n\\\\\\\"+n)}else\\\\\\\"\\\\\\\"!==t?console.warn(\\\\\\\"THREE.WebGLProgram: Program Info Log:\\\\\\\",t):\\\\\\\"\\\\\\\"!==e&&\\\\\\\"\\\\\\\"!==n||(s=!1);s&&(this.diagnostics={runnable:i,programLog:t,vertexShader:{log:e,prefix:f},fragmentShader:{log:n,prefix:g}})}let w,T;return r.deleteShader(x),r.deleteShader(b),this.getUniforms=function(){return void 0===w&&(w=new qA(r,m)),w},this.getAttributes=function(){return void 0===T&&(T=function(t,e){const n={},i=t.getProgramParameter(e,35721);for(let r=0;r0,C=s.clearcoat>0;return{isWebGL2:l,shaderID:w,shaderName:s.type,vertexShader:A,fragmentShader:E,defines:s.defines,isRawShaderMaterial:!0===s.isRawShaderMaterial,glslVersion:s.glslVersion,precision:p,instancing:!0===v.isInstancedMesh,instancingColor:!0===v.isInstancedMesh&&null!==v.instanceColor,supportsVertexTextures:d,outputEncoding:null!==M?f(M.texture):t.outputEncoding,map:!!s.map,mapEncoding:f(s.map),matcap:!!s.matcap,matcapEncoding:f(s.matcap),envMap:!!b,envMapMode:b&&b.mapping,envMapEncoding:f(b),envMapCubeUV:!!b&&(b.mapping===xy||b.mapping===by),lightMap:!!s.lightMap,lightMapEncoding:f(s.lightMap),aoMap:!!s.aoMap,emissiveMap:!!s.emissiveMap,emissiveMapEncoding:f(s.emissiveMap),bumpMap:!!s.bumpMap,normalMap:!!s.normalMap,objectSpaceNormalMap:1===s.normalMapType,tangentSpaceNormalMap:0===s.normalMapType,clearcoat:C,clearcoatMap:C&&!!s.clearcoatMap,clearcoatRoughnessMap:C&&!!s.clearcoatRoughnessMap,clearcoatNormalMap:C&&!!s.clearcoatNormalMap,displacementMap:!!s.displacementMap,roughnessMap:!!s.roughnessMap,metalnessMap:!!s.metalnessMap,specularMap:!!s.specularMap,specularIntensityMap:!!s.specularIntensityMap,specularTintMap:!!s.specularTintMap,specularTintMapEncoding:f(s.specularTintMap),alphaMap:!!s.alphaMap,alphaTest:S,gradientMap:!!s.gradientMap,sheen:s.sheen>0,transmission:s.transmission>0,transmissionMap:!!s.transmissionMap,thicknessMap:!!s.thicknessMap,combine:s.combine,vertexTangents:!!s.normalMap&&!!v.geometry&&!!v.geometry.attributes.tangent,vertexColors:s.vertexColors,vertexAlphas:!0===s.vertexColors&&!!v.geometry&&!!v.geometry.attributes.color&&4===v.geometry.attributes.color.itemSize,vertexUvs:!!(s.map||s.bumpMap||s.normalMap||s.specularMap||s.alphaMap||s.emissiveMap||s.roughnessMap||s.metalnessMap||s.clearcoatMap||s.clearcoatRoughnessMap||s.clearcoatNormalMap||s.displacementMap||s.transmissionMap||s.thicknessMap||s.specularIntensityMap||s.specularTintMap),uvsVertexOnly:!(s.map||s.bumpMap||s.normalMap||s.specularMap||s.alphaMap||s.emissiveMap||s.roughnessMap||s.metalnessMap||s.clearcoatNormalMap||s.transmission>0||s.transmissionMap||s.thicknessMap||s.specularIntensityMap||s.specularTintMap||!s.displacementMap),fog:!!y,useFog:s.fog,fogExp2:y&&y.isFogExp2,flatShading:!!s.flatShading,sizeAttenuation:s.sizeAttenuation,logarithmicDepthBuffer:c,skinning:!0===v.isSkinnedMesh&&T>0,maxBones:T,useVertexTexture:u,morphTargets:!!v.geometry&&!!v.geometry.morphAttributes.position,morphNormals:!!v.geometry&&!!v.geometry.morphAttributes.normal,morphTargetsCount:v.geometry&&v.geometry.morphAttributes.position?v.geometry.morphAttributes.position.length:0,numDirLights:a.directional.length,numPointLights:a.point.length,numSpotLights:a.spot.length,numRectAreaLights:a.rectArea.length,numHemiLights:a.hemi.length,numDirLightShadows:a.directionalShadowMap.length,numPointLightShadows:a.pointShadowMap.length,numSpotLightShadows:a.spotShadowMap.length,numClippingPlanes:o.numPlanes,numClipIntersection:o.numIntersection,format:s.format,dithering:s.dithering,shadowMapEnabled:t.shadowMap.enabled&&m.length>0,shadowMapType:t.shadowMap.type,toneMapping:s.toneMapped?t.toneMapping:0,physicallyCorrectLights:t.physicallyCorrectLights,premultipliedAlpha:s.premultipliedAlpha,doubleSided:2===s.side,flipSided:1===s.side,depthPacking:void 0!==s.depthPacking&&s.depthPacking,index0AttributeName:s.index0AttributeName,extensionDerivatives:s.extensions&&s.extensions.derivatives,extensionFragDepth:s.extensions&&s.extensions.fragDepth,extensionDrawBuffers:s.extensions&&s.extensions.drawBuffers,extensionShaderTextureLOD:s.extensions&&s.extensions.shaderTextureLOD,rendererExtensionFragDepth:l||i.has(\\\\\\\"EXT_frag_depth\\\\\\\"),rendererExtensionDrawBuffers:l||i.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\"),rendererExtensionShaderTextureLod:l||i.has(\\\\\\\"EXT_shader_texture_lod\\\\\\\"),customProgramCacheKey:s.customProgramCacheKey()}},getProgramCacheKey:function(e){const n=[];if(e.shaderID?n.push(e.shaderID):(n.push(e.fragmentShader),n.push(e.vertexShader)),void 0!==e.defines)for(const t in e.defines)n.push(t),n.push(e.defines[t]);if(!1===e.isRawShaderMaterial){for(let t=0;t0?r.push(u):!0===n.transparent?s.push(u):i.push(u)},unshift:function(t,e,n,o,l,c){const u=a(t,e,n,o,l,c);n.transmission>0?r.unshift(u):!0===n.transparent?s.unshift(u):i.unshift(u)},finish:function(){for(let t=n,i=e.length;t1&&i.sort(t||mE),r.length>1&&r.sort(e||fE),s.length>1&&s.sort(e||fE)}}}function vE(t){let e=new WeakMap;return{get:function(n,i){let r;return!1===e.has(n)?(r=new gE(t),e.set(n,[r])):i>=e.get(n).length?(r=new gE(t),e.get(n).push(r)):r=e.get(n)[i],r},dispose:function(){e=new WeakMap}}}function yE(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case\\\\\\\"DirectionalLight\\\\\\\":n={direction:new Nx,color:new Zb};break;case\\\\\\\"SpotLight\\\\\\\":n={position:new Nx,direction:new Nx,color:new Zb,distance:0,coneCos:0,penumbraCos:0,decay:0};break;case\\\\\\\"PointLight\\\\\\\":n={position:new Nx,color:new Zb,distance:0,decay:0};break;case\\\\\\\"HemisphereLight\\\\\\\":n={direction:new Nx,skyColor:new Zb,groundColor:new Zb};break;case\\\\\\\"RectAreaLight\\\\\\\":n={color:new Zb,position:new Nx,halfWidth:new Nx,halfHeight:new Nx}}return t[e.id]=n,n}}}let xE=0;function bE(t,e){return(e.castShadow?1:0)-(t.castShadow?1:0)}function wE(t,e){const n=new yE,i=function(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case\\\\\\\"DirectionalLight\\\\\\\":case\\\\\\\"SpotLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new fx};break;case\\\\\\\"PointLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new fx,shadowCameraNear:1,shadowCameraFar:1e3}}return t[e.id]=n,n}}}(),r={version:0,hash:{directionalLength:-1,pointLength:-1,spotLength:-1,rectAreaLength:-1,hemiLength:-1,numDirectionalShadows:-1,numPointShadows:-1,numSpotShadows:-1},ambient:[0,0,0],probe:[],directional:[],directionalShadow:[],directionalShadowMap:[],directionalShadowMatrix:[],spot:[],spotShadow:[],spotShadowMap:[],spotShadowMatrix:[],rectArea:[],rectAreaLTC1:null,rectAreaLTC2:null,point:[],pointShadow:[],pointShadowMap:[],pointShadowMatrix:[],hemi:[]};for(let t=0;t<9;t++)r.probe.push(new Nx);const s=new Nx,o=new ob,a=new ob;return{setup:function(s,o){let a=0,l=0,c=0;for(let t=0;t<9;t++)r.probe[t].set(0,0,0);let u=0,h=0,d=0,p=0,_=0,m=0,f=0,g=0;s.sort(bE);const v=!0!==o?Math.PI:1;for(let t=0,e=s.length;t0&&(e.isWebGL2||!0===t.has(\\\\\\\"OES_texture_float_linear\\\\\\\")?(r.rectAreaLTC1=tT.LTC_FLOAT_1,r.rectAreaLTC2=tT.LTC_FLOAT_2):!0===t.has(\\\\\\\"OES_texture_half_float_linear\\\\\\\")?(r.rectAreaLTC1=tT.LTC_HALF_1,r.rectAreaLTC2=tT.LTC_HALF_2):console.error(\\\\\\\"THREE.WebGLRenderer: Unable to use RectAreaLight. Missing WebGL extensions.\\\\\\\")),r.ambient[0]=a,r.ambient[1]=l,r.ambient[2]=c;const y=r.hash;y.directionalLength===u&&y.pointLength===h&&y.spotLength===d&&y.rectAreaLength===p&&y.hemiLength===_&&y.numDirectionalShadows===m&&y.numPointShadows===f&&y.numSpotShadows===g||(r.directional.length=u,r.spot.length=d,r.rectArea.length=p,r.point.length=h,r.hemi.length=_,r.directionalShadow.length=m,r.directionalShadowMap.length=m,r.pointShadow.length=f,r.pointShadowMap.length=f,r.spotShadow.length=g,r.spotShadowMap.length=g,r.directionalShadowMatrix.length=m,r.pointShadowMatrix.length=f,r.spotShadowMatrix.length=g,y.directionalLength=u,y.pointLength=h,y.spotLength=d,y.rectAreaLength=p,y.hemiLength=_,y.numDirectionalShadows=m,y.numPointShadows=f,y.numSpotShadows=g,r.version=xE++)},setupView:function(t,e){let n=0,i=0,l=0,c=0,u=0;const h=e.matrixWorldInverse;for(let e=0,d=t.length;e=n.get(i).length?(s=new TE(t,e),n.get(i).push(s)):s=n.get(i)[r],s},dispose:function(){n=new WeakMap}}}class EE extends jb{constructor(t){super(),this.type=\\\\\\\"MeshDepthMaterial\\\\\\\",this.depthPacking=3200,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.depthPacking=t.depthPacking,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this}}EE.prototype.isMeshDepthMaterial=!0;class ME extends jb{constructor(t){super(),this.type=\\\\\\\"MeshDistanceMaterial\\\\\\\",this.referencePosition=new Nx,this.nearDistance=1,this.farDistance=1e3,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.referencePosition.copy(t.referencePosition),this.nearDistance=t.nearDistance,this.farDistance=t.farDistance,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this}}ME.prototype.isMeshDistanceMaterial=!0;function SE(t,e,n){let i=new $w;const r=new fx,s=new fx,o=new Ex,a=new EE({depthPacking:3201}),l=new ME,c={},u=n.maxTextureSize,h={0:1,1:0,2:2},d=new Dw({uniforms:{shadow_pass:{value:null},resolution:{value:new fx},radius:{value:4},samples:{value:8}},vertexShader:\\\\\\\"void main() {\\\\n\\\\tgl_Position = vec4( position, 1.0 );\\\\n}\\\\\\\",fragmentShader:\\\\\\\"uniform sampler2D shadow_pass;\\\\nuniform vec2 resolution;\\\\nuniform float radius;\\\\nuniform float samples;\\\\n#include \\\\nvoid main() {\\\\n\\\\tfloat mean = 0.0;\\\\n\\\\tfloat squared_mean = 0.0;\\\\n\\\\tfloat uvStride = samples <= 1.0 ? 0.0 : 2.0 / ( samples - 1.0 );\\\\n\\\\tfloat uvStart = samples <= 1.0 ? 0.0 : - 1.0;\\\\n\\\\tfor ( float i = 0.0; i < samples; i ++ ) {\\\\n\\\\t\\\\tfloat uvOffset = uvStart + i * uvStride;\\\\n\\\\t\\\\t#ifdef HORIZONTAL_PASS\\\\n\\\\t\\\\t\\\\tvec2 distribution = unpackRGBATo2Half( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( uvOffset, 0.0 ) * radius ) / resolution ) );\\\\n\\\\t\\\\t\\\\tmean += distribution.x;\\\\n\\\\t\\\\t\\\\tsquared_mean += distribution.y * distribution.y + distribution.x * distribution.x;\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( 0.0, uvOffset ) * radius ) / resolution ) );\\\\n\\\\t\\\\t\\\\tmean += depth;\\\\n\\\\t\\\\t\\\\tsquared_mean += depth * depth;\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\tmean = mean / samples;\\\\n\\\\tsquared_mean = squared_mean / samples;\\\\n\\\\tfloat std_dev = sqrt( squared_mean - mean * mean );\\\\n\\\\tgl_FragColor = pack2HalfToRGBA( vec2( mean, std_dev ) );\\\\n}\\\\\\\"}),p=d.clone();p.defines.HORIZONTAL_PASS=1;const _=new dw;_.setAttribute(\\\\\\\"position\\\\\\\",new ew(new Float32Array([-1,-1,.5,3,-1,.5,-1,3,.5]),3));const m=new Lw(_,d),f=this;function g(n,i){const r=e.update(m);d.uniforms.shadow_pass.value=n.map.texture,d.uniforms.resolution.value=n.mapSize,d.uniforms.radius.value=n.radius,d.uniforms.samples.value=n.blurSamples,t.setRenderTarget(n.mapPass),t.clear(),t.renderBufferDirect(i,null,r,d,m,null),p.uniforms.shadow_pass.value=n.mapPass.texture,p.uniforms.resolution.value=n.mapSize,p.uniforms.radius.value=n.radius,p.uniforms.samples.value=n.blurSamples,t.setRenderTarget(n.map),t.clear(),t.renderBufferDirect(i,null,r,p,m,null)}function v(e,n,i,r,s,o,u){let d=null;const p=!0===r.isPointLight?e.customDistanceMaterial:e.customDepthMaterial;if(d=void 0!==p?p:!0===r.isPointLight?l:a,t.localClippingEnabled&&!0===i.clipShadows&&0!==i.clippingPlanes.length||i.displacementMap&&0!==i.displacementScale||i.alphaMap&&i.alphaTest>0){const t=d.uuid,e=i.uuid;let n=c[t];void 0===n&&(n={},c[t]=n);let r=n[e];void 0===r&&(r=d.clone(),n[e]=r),d=r}return d.visible=i.visible,d.wireframe=i.wireframe,d.side=3===u?null!==i.shadowSide?i.shadowSide:i.side:null!==i.shadowSide?i.shadowSide:h[i.side],d.alphaMap=i.alphaMap,d.alphaTest=i.alphaTest,d.clipShadows=i.clipShadows,d.clippingPlanes=i.clippingPlanes,d.clipIntersection=i.clipIntersection,d.displacementMap=i.displacementMap,d.displacementScale=i.displacementScale,d.displacementBias=i.displacementBias,d.wireframeLinewidth=i.wireframeLinewidth,d.linewidth=i.linewidth,!0===r.isPointLight&&!0===d.isMeshDistanceMaterial&&(d.referencePosition.setFromMatrixPosition(r.matrixWorld),d.nearDistance=s,d.farDistance=o),d}function y(n,r,s,o,a){if(!1===n.visible)return;if(n.layers.test(r.layers)&&(n.isMesh||n.isLine||n.isPoints)&&(n.castShadow||n.receiveShadow&&3===a)&&(!n.frustumCulled||i.intersectsObject(n))){n.modelViewMatrix.multiplyMatrices(s.matrixWorldInverse,n.matrixWorld);const i=e.update(n),r=n.material;if(Array.isArray(r)){const e=i.groups;for(let l=0,c=e.length;lu||r.y>u)&&(r.x>u&&(s.x=Math.floor(u/p.x),r.x=s.x*p.x,h.mapSize.x=s.x),r.y>u&&(s.y=Math.floor(u/p.y),r.y=s.y*p.y,h.mapSize.y=s.y)),null===h.map&&!h.isPointLightShadow&&3===this.type){const t={minFilter:Cy,magFilter:Cy,format:By};h.map=new Mx(r.x,r.y,t),h.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",h.mapPass=new Mx(r.x,r.y,t),h.camera.updateProjectionMatrix()}if(null===h.map){const t={minFilter:Ey,magFilter:Ey,format:By};h.map=new Mx(r.x,r.y,t),h.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",h.camera.updateProjectionMatrix()}t.setRenderTarget(h.map),t.clear();const _=h.getViewportCount();for(let t=0;t<_;t++){const e=h.getViewport(t);o.set(s.x*e.x,s.y*e.y,s.x*e.z,s.y*e.w),d.viewport(o),h.updateMatrices(c,t),i=h.getFrustum(),y(n,a,h.camera,c,this.type)}h.isPointLightShadow||3!==this.type||g(h,a),h.needsUpdate=!1}f.needsUpdate=!1,t.setRenderTarget(l,c,h)}}function CE(t,e,n){const i=n.isWebGL2;const r=new function(){let e=!1;const n=new Ex;let i=null;const r=new Ex(0,0,0,0);return{setMask:function(n){i===n||e||(t.colorMask(n,n,n,n),i=n)},setLocked:function(t){e=t},setClear:function(e,i,s,o,a){!0===a&&(e*=o,i*=o,s*=o),n.set(e,i,s,o),!1===r.equals(n)&&(t.clearColor(e,i,s,o),r.copy(n))},reset:function(){e=!1,i=null,r.set(-1,0,0,0)}}},s=new function(){let e=!1,n=null,i=null,r=null;return{setTest:function(t){t?k(2929):B(2929)},setMask:function(i){n===i||e||(t.depthMask(i),n=i)},setFunc:function(e){if(i!==e){if(e)switch(e){case 0:t.depthFunc(512);break;case 1:t.depthFunc(519);break;case 2:t.depthFunc(513);break;case 3:t.depthFunc(515);break;case 4:t.depthFunc(514);break;case 5:t.depthFunc(518);break;case 6:t.depthFunc(516);break;case 7:t.depthFunc(517);break;default:t.depthFunc(515)}else t.depthFunc(515);i=e}},setLocked:function(t){e=t},setClear:function(e){r!==e&&(t.clearDepth(e),r=e)},reset:function(){e=!1,n=null,i=null,r=null}}},o=new function(){let e=!1,n=null,i=null,r=null,s=null,o=null,a=null,l=null,c=null;return{setTest:function(t){e||(t?k(2960):B(2960))},setMask:function(i){n===i||e||(t.stencilMask(i),n=i)},setFunc:function(e,n,o){i===e&&r===n&&s===o||(t.stencilFunc(e,n,o),i=e,r=n,s=o)},setOp:function(e,n,i){o===e&&a===n&&l===i||(t.stencilOp(e,n,i),o=e,a=n,l=i)},setLocked:function(t){e=t},setClear:function(e){c!==e&&(t.clearStencil(e),c=e)},reset:function(){e=!1,n=null,i=null,r=null,s=null,o=null,a=null,l=null,c=null}}};let a={},l=null,c={},u=null,h=!1,d=null,p=null,_=null,m=null,f=null,g=null,v=null,y=!1,x=null,b=null,w=null,T=null,A=null;const E=t.getParameter(35661);let M=!1,S=0;const C=t.getParameter(7938);-1!==C.indexOf(\\\\\\\"WebGL\\\\\\\")?(S=parseFloat(/^WebGL (\\\\d)/.exec(C)[1]),M=S>=1):-1!==C.indexOf(\\\\\\\"OpenGL ES\\\\\\\")&&(S=parseFloat(/^OpenGL ES (\\\\d)/.exec(C)[1]),M=S>=2);let N=null,L={};const O=t.getParameter(3088),R=t.getParameter(2978),P=(new Ex).fromArray(O),I=(new Ex).fromArray(R);function F(e,n,i){const r=new Uint8Array(4),s=t.createTexture();t.bindTexture(e,s),t.texParameteri(e,10241,9728),t.texParameteri(e,10240,9728);for(let e=0;ei||t.height>i)&&(r=i/Math.max(t.width,t.height)),r<1||!0===e){if(\\\\\\\"undefined\\\\\\\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\\\\\\\"undefined\\\\\\\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\\\\\\\"undefined\\\\\\\"!=typeof ImageBitmap&&t instanceof ImageBitmap){const i=e?_x:Math.floor,s=i(r*t.width),o=i(r*t.height);void 0===p&&(p=m(s,o));const a=n?m(s,o):p;a.width=s,a.height=o;return a.getContext(\\\\\\\"2d\\\\\\\").drawImage(t,0,0,s,o),console.warn(\\\\\\\"THREE.WebGLRenderer: Texture has been resized from (\\\\\\\"+t.width+\\\\\\\"x\\\\\\\"+t.height+\\\\\\\") to (\\\\\\\"+s+\\\\\\\"x\\\\\\\"+o+\\\\\\\").\\\\\\\"),a}return\\\\\\\"data\\\\\\\"in t&&console.warn(\\\\\\\"THREE.WebGLRenderer: Image in DataTexture is too big (\\\\\\\"+t.width+\\\\\\\"x\\\\\\\"+t.height+\\\\\\\").\\\\\\\"),t}return t}function g(t){return dx(t.width)&&dx(t.height)}function v(t,e){return t.generateMipmaps&&e&&t.minFilter!==Ey&&t.minFilter!==Cy}function y(e,n,r,s,o=1){t.generateMipmap(e);i.get(n).__maxMipLevel=Math.log2(Math.max(r,s,o))}function x(n,i,r,s){if(!1===a)return i;if(null!==n){if(void 0!==t[n])return t[n];console.warn(\\\\\\\"THREE.WebGLRenderer: Attempt to use non-existing WebGL internal format '\\\\\\\"+n+\\\\\\\"'\\\\\\\")}let o=i;return 6403===i&&(5126===r&&(o=33326),5131===r&&(o=33325),5121===r&&(o=33321)),6407===i&&(5126===r&&(o=34837),5131===r&&(o=34843),5121===r&&(o=32849)),6408===i&&(5126===r&&(o=34836),5131===r&&(o=34842),5121===r&&(o=s===$y?35907:32856)),33325!==o&&33326!==o&&34842!==o&&34836!==o||e.get(\\\\\\\"EXT_color_buffer_float\\\\\\\"),o}function b(t){return t===Ey||t===My||t===Sy?9728:9729}function w(e){const n=e.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",w),function(e){const n=i.get(e);if(void 0===n.__webglInit)return;t.deleteTexture(n.__webglTexture),i.remove(e)}(n),n.isVideoTexture&&d.delete(n),o.memory.textures--}function T(e){const n=e.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",T),function(e){const n=e.texture,r=i.get(e),s=i.get(n);if(!e)return;void 0!==s.__webglTexture&&(t.deleteTexture(s.__webglTexture),o.memory.textures--);e.depthTexture&&e.depthTexture.dispose();if(e.isWebGLCubeRenderTarget)for(let e=0;e<6;e++)t.deleteFramebuffer(r.__webglFramebuffer[e]),r.__webglDepthbuffer&&t.deleteRenderbuffer(r.__webglDepthbuffer[e]);else t.deleteFramebuffer(r.__webglFramebuffer),r.__webglDepthbuffer&&t.deleteRenderbuffer(r.__webglDepthbuffer),r.__webglMultisampledFramebuffer&&t.deleteFramebuffer(r.__webglMultisampledFramebuffer),r.__webglColorRenderbuffer&&t.deleteRenderbuffer(r.__webglColorRenderbuffer),r.__webglDepthRenderbuffer&&t.deleteRenderbuffer(r.__webglDepthRenderbuffer);if(e.isWebGLMultipleRenderTargets)for(let e=0,r=n.length;e0&&r.__version!==t.version){const n=t.image;if(void 0===n)console.warn(\\\\\\\"THREE.WebGLRenderer: Texture marked for update but image is undefined\\\\\\\");else{if(!1!==n.complete)return void O(r,t,e);console.warn(\\\\\\\"THREE.WebGLRenderer: Texture marked for update but image is incomplete\\\\\\\")}}n.activeTexture(33984+e),n.bindTexture(3553,r.__webglTexture)}function M(e,r){const o=i.get(e);e.version>0&&o.__version!==e.version?function(e,i,r){if(6!==i.image.length)return;L(e,i),n.activeTexture(33984+r),n.bindTexture(34067,e.__webglTexture),t.pixelStorei(37440,i.flipY),t.pixelStorei(37441,i.premultiplyAlpha),t.pixelStorei(3317,i.unpackAlignment),t.pixelStorei(37443,0);const o=i&&(i.isCompressedTexture||i.image[0].isCompressedTexture),l=i.image[0]&&i.image[0].isDataTexture,u=[];for(let t=0;t<6;t++)u[t]=o||l?l?i.image[t].image:i.image[t]:f(i.image[t],!1,!0,c);const h=u[0],d=g(h)||a,p=s.convert(i.format),_=s.convert(i.type),m=x(i.internalFormat,p,_,i.encoding);let b;if(N(34067,i,d),o){for(let t=0;t<6;t++){b=u[t].mipmaps;for(let e=0;e1||i.get(s).__currentAnisotropy)&&(t.texParameterf(n,o.TEXTURE_MAX_ANISOTROPY_EXT,Math.min(s.anisotropy,r.getMaxAnisotropy())),i.get(s).__currentAnisotropy=s.anisotropy)}}function L(e,n){void 0===e.__webglInit&&(e.__webglInit=!0,n.addEventListener(\\\\\\\"dispose\\\\\\\",w),e.__webglTexture=t.createTexture(),o.memory.textures++)}function O(e,i,r){let o=3553;i.isDataTexture2DArray&&(o=35866),i.isDataTexture3D&&(o=32879),L(e,i),n.activeTexture(33984+r),n.bindTexture(o,e.__webglTexture),t.pixelStorei(37440,i.flipY),t.pixelStorei(37441,i.premultiplyAlpha),t.pixelStorei(3317,i.unpackAlignment),t.pixelStorei(37443,0);const l=function(t){return!a&&(t.wrapS!==Ty||t.wrapT!==Ty||t.minFilter!==Ey&&t.minFilter!==Cy)}(i)&&!1===g(i.image),c=f(i.image,l,!1,u),h=g(c)||a,d=s.convert(i.format);let p,_=s.convert(i.type),m=x(i.internalFormat,d,_,i.encoding);N(o,i,h);const b=i.mipmaps;if(i.isDepthTexture)m=6402,a?m=i.type===Iy?36012:i.type===Py?33190:i.type===Dy?35056:33189:i.type===Iy&&console.error(\\\\\\\"WebGLRenderer: Floating point depth texture requires WebGL2.\\\\\\\"),i.format===zy&&6402===m&&i.type!==Ry&&i.type!==Py&&(console.warn(\\\\\\\"THREE.WebGLRenderer: Use UnsignedShortType or UnsignedIntType for DepthFormat DepthTexture.\\\\\\\"),i.type=Ry,_=s.convert(i.type)),i.format===Uy&&6402===m&&(m=34041,i.type!==Dy&&(console.warn(\\\\\\\"THREE.WebGLRenderer: Use UnsignedInt248Type for DepthStencilFormat DepthTexture.\\\\\\\"),i.type=Dy,_=s.convert(i.type))),n.texImage2D(3553,0,m,c.width,c.height,0,d,_,null);else if(i.isDataTexture)if(b.length>0&&h){for(let t=0,e=b.length;t0&&h){for(let t=0,e=b.length;t=l&&console.warn(\\\\\\\"THREE.WebGLTextures: Trying to use \\\\\\\"+t+\\\\\\\" texture units while this GPU supports only \\\\\\\"+l),A+=1,t},this.resetTextureUnits=function(){A=0},this.setTexture2D=E,this.setTexture2DArray=function(t,e){const r=i.get(t);t.version>0&&r.__version!==t.version?O(r,t,e):(n.activeTexture(33984+e),n.bindTexture(35866,r.__webglTexture))},this.setTexture3D=function(t,e){const r=i.get(t);t.version>0&&r.__version!==t.version?O(r,t,e):(n.activeTexture(33984+e),n.bindTexture(32879,r.__webglTexture))},this.setTextureCube=M,this.setupRenderTarget=function(e){const l=e.texture,c=i.get(e),u=i.get(l);e.addEventListener(\\\\\\\"dispose\\\\\\\",T),!0!==e.isWebGLMultipleRenderTargets&&(u.__webglTexture=t.createTexture(),u.__version=l.version,o.memory.textures++);const h=!0===e.isWebGLCubeRenderTarget,d=!0===e.isWebGLMultipleRenderTargets,p=!0===e.isWebGLMultisampleRenderTarget,_=l.isDataTexture3D||l.isDataTexture2DArray,m=g(e)||a;if(!a||l.format!==ky||l.type!==Iy&&l.type!==Fy||(l.format=By,console.warn(\\\\\\\"THREE.WebGLRenderer: Rendering to textures with RGB format is not supported. Using RGBA format instead.\\\\\\\")),h){c.__webglFramebuffer=[];for(let e=0;e<6;e++)c.__webglFramebuffer[e]=t.createFramebuffer()}else if(c.__webglFramebuffer=t.createFramebuffer(),d)if(r.drawBuffers){const n=e.texture;for(let e=0,r=n.length;ea+c?(l.inputState.pinching=!1,this.dispatchEvent({type:\\\\\\\"pinchend\\\\\\\",handedness:t.handedness,target:this})):!l.inputState.pinching&&o<=a-c&&(l.inputState.pinching=!0,this.dispatchEvent({type:\\\\\\\"pinchstart\\\\\\\",handedness:t.handedness,target:this}))}else null!==a&&t.gripSpace&&(r=e.getPose(t.gripSpace,n),null!==r&&(a.matrix.fromArray(r.transform.matrix),a.matrix.decompose(a.position,a.rotation,a.scale),r.linearVelocity?(a.hasLinearVelocity=!0,a.linearVelocity.copy(r.linearVelocity)):a.hasLinearVelocity=!1,r.angularVelocity?(a.hasAngularVelocity=!0,a.angularVelocity.copy(r.angularVelocity)):a.hasAngularVelocity=!1));return null!==o&&(o.visible=null!==i),null!==a&&(a.visible=null!==r),null!==l&&(l.visible=null!==s),this}}class FE extends nx{constructor(t,e){super();const n=this,i=t.state;let r=null,s=1,o=null,a=\\\\\\\"local-floor\\\\\\\",l=null,c=null,u=null,h=null,d=null,p=!1,_=null,m=null,f=null,g=null,v=null,y=null;const x=[],b=new Map,w=new Bw;w.layers.enable(1),w.viewport=new Ex;const T=new Bw;T.layers.enable(2),T.viewport=new Ex;const A=[w,T],E=new OE;E.layers.enable(1),E.layers.enable(2);let M=null,S=null;function C(t){const e=b.get(t.inputSource);e&&e.dispatchEvent({type:t.type,data:t.inputSource})}function N(){b.forEach((function(t,e){t.disconnect(e)})),b.clear(),M=null,S=null,i.bindXRFramebuffer(null),t.setRenderTarget(t.getRenderTarget()),u&&e.deleteFramebuffer(u),_&&e.deleteFramebuffer(_),m&&e.deleteRenderbuffer(m),f&&e.deleteRenderbuffer(f),u=null,_=null,m=null,f=null,d=null,h=null,c=null,r=null,F.stop(),n.isPresenting=!1,n.dispatchEvent({type:\\\\\\\"sessionend\\\\\\\"})}function L(t){const e=r.inputSources;for(let t=0;t0&&(e.alphaTest.value=n.alphaTest);const i=t.get(n).envMap;if(i){e.envMap.value=i,e.flipEnvMap.value=i.isCubeTexture&&!1===i.isRenderTargetTexture?-1:1,e.reflectivity.value=n.reflectivity,e.ior.value=n.ior,e.refractionRatio.value=n.refractionRatio;const r=t.get(i).__maxMipLevel;void 0!==r&&(e.maxMipLevel.value=r)}let r,s;n.lightMap&&(e.lightMap.value=n.lightMap,e.lightMapIntensity.value=n.lightMapIntensity),n.aoMap&&(e.aoMap.value=n.aoMap,e.aoMapIntensity.value=n.aoMapIntensity),n.map?r=n.map:n.specularMap?r=n.specularMap:n.displacementMap?r=n.displacementMap:n.normalMap?r=n.normalMap:n.bumpMap?r=n.bumpMap:n.roughnessMap?r=n.roughnessMap:n.metalnessMap?r=n.metalnessMap:n.alphaMap?r=n.alphaMap:n.emissiveMap?r=n.emissiveMap:n.clearcoatMap?r=n.clearcoatMap:n.clearcoatNormalMap?r=n.clearcoatNormalMap:n.clearcoatRoughnessMap?r=n.clearcoatRoughnessMap:n.specularIntensityMap?r=n.specularIntensityMap:n.specularTintMap?r=n.specularTintMap:n.transmissionMap?r=n.transmissionMap:n.thicknessMap&&(r=n.thicknessMap),void 0!==r&&(r.isWebGLRenderTarget&&(r=r.texture),!0===r.matrixAutoUpdate&&r.updateMatrix(),e.uvTransform.value.copy(r.matrix)),n.aoMap?s=n.aoMap:n.lightMap&&(s=n.lightMap),void 0!==s&&(s.isWebGLRenderTarget&&(s=s.texture),!0===s.matrixAutoUpdate&&s.updateMatrix(),e.uv2Transform.value.copy(s.matrix))}function n(e,n){e.roughness.value=n.roughness,e.metalness.value=n.metalness,n.roughnessMap&&(e.roughnessMap.value=n.roughnessMap),n.metalnessMap&&(e.metalnessMap.value=n.metalnessMap),n.emissiveMap&&(e.emissiveMap.value=n.emissiveMap),n.bumpMap&&(e.bumpMap.value=n.bumpMap,e.bumpScale.value=n.bumpScale,1===n.side&&(e.bumpScale.value*=-1)),n.normalMap&&(e.normalMap.value=n.normalMap,e.normalScale.value.copy(n.normalScale),1===n.side&&e.normalScale.value.negate()),n.displacementMap&&(e.displacementMap.value=n.displacementMap,e.displacementScale.value=n.displacementScale,e.displacementBias.value=n.displacementBias);t.get(n).envMap&&(e.envMapIntensity.value=n.envMapIntensity)}return{refreshFogUniforms:function(t,e){t.fogColor.value.copy(e.color),e.isFog?(t.fogNear.value=e.near,t.fogFar.value=e.far):e.isFogExp2&&(t.fogDensity.value=e.density)},refreshMaterialUniforms:function(t,i,r,s,o){i.isMeshBasicMaterial?e(t,i):i.isMeshLambertMaterial?(e(t,i),function(t,e){e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap)}(t,i)):i.isMeshToonMaterial?(e(t,i),function(t,e){e.gradientMap&&(t.gradientMap.value=e.gradientMap);e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshPhongMaterial?(e(t,i),function(t,e){t.specular.value.copy(e.specular),t.shininess.value=Math.max(e.shininess,1e-4),e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshStandardMaterial?(e(t,i),i.isMeshPhysicalMaterial?function(t,e,i){n(t,e),t.ior.value=e.ior,e.sheen>0&&(t.sheenTint.value.copy(e.sheenTint).multiplyScalar(e.sheen),t.sheenRoughness.value=e.sheenRoughness);e.clearcoat>0&&(t.clearcoat.value=e.clearcoat,t.clearcoatRoughness.value=e.clearcoatRoughness,e.clearcoatMap&&(t.clearcoatMap.value=e.clearcoatMap),e.clearcoatRoughnessMap&&(t.clearcoatRoughnessMap.value=e.clearcoatRoughnessMap),e.clearcoatNormalMap&&(t.clearcoatNormalScale.value.copy(e.clearcoatNormalScale),t.clearcoatNormalMap.value=e.clearcoatNormalMap,1===e.side&&t.clearcoatNormalScale.value.negate()));e.transmission>0&&(t.transmission.value=e.transmission,t.transmissionSamplerMap.value=i.texture,t.transmissionSamplerSize.value.set(i.width,i.height),e.transmissionMap&&(t.transmissionMap.value=e.transmissionMap),t.thickness.value=e.thickness,e.thicknessMap&&(t.thicknessMap.value=e.thicknessMap),t.attenuationDistance.value=e.attenuationDistance,t.attenuationTint.value.copy(e.attenuationTint));t.specularIntensity.value=e.specularIntensity,t.specularTint.value.copy(e.specularTint),e.specularIntensityMap&&(t.specularIntensityMap.value=e.specularIntensityMap);e.specularTintMap&&(t.specularTintMap.value=e.specularTintMap)}(t,i,o):n(t,i)):i.isMeshMatcapMaterial?(e(t,i),function(t,e){e.matcap&&(t.matcap.value=e.matcap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDepthMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDistanceMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias);t.referencePosition.value.copy(e.referencePosition),t.nearDistance.value=e.nearDistance,t.farDistance.value=e.farDistance}(t,i)):i.isMeshNormalMaterial?(e(t,i),function(t,e){e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isLineBasicMaterial?(function(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity}(t,i),i.isLineDashedMaterial&&function(t,e){t.dashSize.value=e.dashSize,t.totalSize.value=e.dashSize+e.gapSize,t.scale.value=e.scale}(t,i)):i.isPointsMaterial?function(t,e,n,i){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.size.value=e.size*n,t.scale.value=.5*i,e.map&&(t.map.value=e.map);e.alphaMap&&(t.alphaMap.value=e.alphaMap);e.alphaTest>0&&(t.alphaTest.value=e.alphaTest);let r;e.map?r=e.map:e.alphaMap&&(r=e.alphaMap);void 0!==r&&(!0===r.matrixAutoUpdate&&r.updateMatrix(),t.uvTransform.value.copy(r.matrix))}(t,i,r,s):i.isSpriteMaterial?function(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.rotation.value=e.rotation,e.map&&(t.map.value=e.map);e.alphaMap&&(t.alphaMap.value=e.alphaMap);e.alphaTest>0&&(t.alphaTest.value=e.alphaTest);let n;e.map?n=e.map:e.alphaMap&&(n=e.alphaMap);void 0!==n&&(!0===n.matrixAutoUpdate&&n.updateMatrix(),t.uvTransform.value.copy(n.matrix))}(t,i):i.isShadowMaterial?(t.color.value.copy(i.color),t.opacity.value=i.opacity):i.isShaderMaterial&&(i.uniformsNeedUpdate=!1)}}}function kE(t={}){const e=void 0!==t.canvas?t.canvas:function(){const t=yx(\\\\\\\"canvas\\\\\\\");return t.style.display=\\\\\\\"block\\\\\\\",t}(),n=void 0!==t.context?t.context:null,i=void 0!==t.alpha&&t.alpha,r=void 0===t.depth||t.depth,s=void 0===t.stencil||t.stencil,o=void 0!==t.antialias&&t.antialias,a=void 0===t.premultipliedAlpha||t.premultipliedAlpha,l=void 0!==t.preserveDrawingBuffer&&t.preserveDrawingBuffer,c=void 0!==t.powerPreference?t.powerPreference:\\\\\\\"default\\\\\\\",u=void 0!==t.failIfMajorPerformanceCaveat&&t.failIfMajorPerformanceCaveat;let h=null,d=null;const p=[],_=[];this.domElement=e,this.debug={checkShaderErrors:!0},this.autoClear=!0,this.autoClearColor=!0,this.autoClearDepth=!0,this.autoClearStencil=!0,this.sortObjects=!0,this.clippingPlanes=[],this.localClippingEnabled=!1,this.gammaFactor=2,this.outputEncoding=Yy,this.physicallyCorrectLights=!1,this.toneMapping=0,this.toneMappingExposure=1;const m=this;let f=!1,g=0,v=0,y=null,x=-1,b=null;const w=new Ex,T=new Ex;let A=null,E=e.width,M=e.height,S=1,C=null,N=null;const L=new Ex(0,0,E,M),O=new Ex(0,0,E,M);let R=!1;const P=[],I=new $w;let F=!1,D=!1,k=null;const B=new ob,z=new Nx,U={background:null,fog:null,environment:null,overrideMaterial:null,isScene:!0};function G(){return null===y?S:1}let V,H,j,W,q,X,Y,$,J,Z,Q,K,tt,et,nt,it,rt,st,ot,at,lt,ct,ut,ht=n;function dt(t,n){for(let i=0;i0&&function(t,e,n){if(null===k){const t=!0===o&&!0===H.isWebGL2;k=new(t?Sx:Mx)(1024,1024,{generateMipmaps:!0,type:null!==ct.convert(Fy)?Fy:Oy,minFilter:Ly,magFilter:Ey,wrapS:Ty,wrapT:Ty})}const i=m.getRenderTarget();m.setRenderTarget(k),m.clear();const r=m.toneMapping;m.toneMapping=0,At(t,e,n),m.toneMapping=r,X.updateMultisampleRenderTarget(k),X.updateRenderTargetMipmap(k),m.setRenderTarget(i)}(r,e,n),i&&j.viewport(w.copy(i)),r.length>0&&At(r,e,n),s.length>0&&At(s,e,n),a.length>0&&At(a,e,n)}function At(t,e,n){const i=!0===e.isScene?e.overrideMaterial:null;for(let r=0,s=t.length;r0?_[_.length-1]:null,p.pop(),h=p.length>0?p[p.length-1]:null},this.getActiveCubeFace=function(){return g},this.getActiveMipmapLevel=function(){return v},this.getRenderTarget=function(){return y},this.setRenderTarget=function(t,e=0,n=0){y=t,g=e,v=n,t&&void 0===q.get(t).__webglFramebuffer&&X.setupRenderTarget(t);let i=null,r=!1,s=!1;if(t){const n=t.texture;(n.isDataTexture3D||n.isDataTexture2DArray)&&(s=!0);const o=q.get(t).__webglFramebuffer;t.isWebGLCubeRenderTarget?(i=o[e],r=!0):i=t.isWebGLMultisampleRenderTarget?q.get(t).__webglMultisampledFramebuffer:o,w.copy(t.viewport),T.copy(t.scissor),A=t.scissorTest}else w.copy(L).multiplyScalar(S).floor(),T.copy(O).multiplyScalar(S).floor(),A=R;if(j.bindFramebuffer(36160,i)&&H.drawBuffers){let e=!1;if(t)if(t.isWebGLMultipleRenderTargets){const n=t.texture;if(P.length!==n.length||36064!==P[0]){for(let t=0,e=n.length;t=0&&e<=t.width-i&&n>=0&&n<=t.height-r&&ht.readPixels(e,n,i,r,ct.convert(a),ct.convert(l),s):console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: readPixels from renderTarget failed. Framebuffer not complete.\\\\\\\")}finally{const t=null!==y?q.get(y).__webglFramebuffer:null;j.bindFramebuffer(36160,t)}}},this.copyFramebufferToTexture=function(t,e,n=0){const i=Math.pow(2,-n),r=Math.floor(e.image.width*i),s=Math.floor(e.image.height*i);let o=ct.convert(e.format);H.isWebGL2&&(6407===o&&(o=32849),6408===o&&(o=32856)),X.setTexture2D(e,0),ht.copyTexImage2D(3553,n,o,t.x,t.y,r,s,0),j.unbindTexture()},this.copyTextureToTexture=function(t,e,n,i=0){const r=e.image.width,s=e.image.height,o=ct.convert(n.format),a=ct.convert(n.type);X.setTexture2D(n,0),ht.pixelStorei(37440,n.flipY),ht.pixelStorei(37441,n.premultiplyAlpha),ht.pixelStorei(3317,n.unpackAlignment),e.isDataTexture?ht.texSubImage2D(3553,i,t.x,t.y,r,s,o,a,e.image.data):e.isCompressedTexture?ht.compressedTexSubImage2D(3553,i,t.x,t.y,e.mipmaps[0].width,e.mipmaps[0].height,o,e.mipmaps[0].data):ht.texSubImage2D(3553,i,t.x,t.y,o,a,e.image),0===i&&n.generateMipmaps&&ht.generateMipmap(3553),j.unbindTexture()},this.copyTextureToTexture3D=function(t,e,n,i,r=0){if(m.isWebGL1Renderer)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: can only be used with WebGL2.\\\\\\\");const s=t.max.x-t.min.x+1,o=t.max.y-t.min.y+1,a=t.max.z-t.min.z+1,l=ct.convert(i.format),c=ct.convert(i.type);let u;if(i.isDataTexture3D)X.setTexture3D(i,0),u=32879;else{if(!i.isDataTexture2DArray)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: only supports THREE.DataTexture3D and THREE.DataTexture2DArray.\\\\\\\");X.setTexture2DArray(i,0),u=35866}ht.pixelStorei(37440,i.flipY),ht.pixelStorei(37441,i.premultiplyAlpha),ht.pixelStorei(3317,i.unpackAlignment);const h=ht.getParameter(3314),d=ht.getParameter(32878),p=ht.getParameter(3316),_=ht.getParameter(3315),f=ht.getParameter(32877),g=n.isCompressedTexture?n.mipmaps[0]:n.image;ht.pixelStorei(3314,g.width),ht.pixelStorei(32878,g.height),ht.pixelStorei(3316,t.min.x),ht.pixelStorei(3315,t.min.y),ht.pixelStorei(32877,t.min.z),n.isDataTexture||n.isDataTexture3D?ht.texSubImage3D(u,r,e.x,e.y,e.z,s,o,a,l,c,g.data):n.isCompressedTexture?(console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: untested support for compressed srcTexture.\\\\\\\"),ht.compressedTexSubImage3D(u,r,e.x,e.y,e.z,s,o,a,l,g.data)):ht.texSubImage3D(u,r,e.x,e.y,e.z,s,o,a,l,c,g),ht.pixelStorei(3314,h),ht.pixelStorei(32878,d),ht.pixelStorei(3316,p),ht.pixelStorei(3315,_),ht.pixelStorei(32877,f),0===r&&i.generateMipmaps&&ht.generateMipmap(u),j.unbindTexture()},this.initTexture=function(t){X.setTexture2D(t,0),j.unbindTexture()},this.resetState=function(){g=0,v=0,y=null,j.reset(),ut.reset()},\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}(class extends kE{}).prototype.isWebGL1Renderer=!0;class BE{constructor(t,e=25e-5){this.name=\\\\\\\"\\\\\\\",this.color=new Zb(t),this.density=e}clone(){return new BE(this.color,this.density)}toJSON(){return{type:\\\\\\\"FogExp2\\\\\\\",color:this.color.getHex(),density:this.density}}}BE.prototype.isFogExp2=!0;class zE{constructor(t,e=1,n=1e3){this.name=\\\\\\\"\\\\\\\",this.color=new Zb(t),this.near=e,this.far=n}clone(){return new zE(this.color,this.near,this.far)}toJSON(){return{type:\\\\\\\"Fog\\\\\\\",color:this.color.getHex(),near:this.near,far:this.far}}}zE.prototype.isFog=!0;class UE extends Ob{constructor(){super(),this.type=\\\\\\\"Scene\\\\\\\",this.background=null,this.environment=null,this.fog=null,this.overrideMaterial=null,this.autoUpdate=!0,\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}copy(t,e){return super.copy(t,e),null!==t.background&&(this.background=t.background.clone()),null!==t.environment&&(this.environment=t.environment.clone()),null!==t.fog&&(this.fog=t.fog.clone()),null!==t.overrideMaterial&&(this.overrideMaterial=t.overrideMaterial.clone()),this.autoUpdate=t.autoUpdate,this.matrixAutoUpdate=t.matrixAutoUpdate,this}toJSON(t){const e=super.toJSON(t);return null!==this.fog&&(e.object.fog=this.fog.toJSON()),e}}UE.prototype.isScene=!0;class GE{constructor(t,e){this.array=t,this.stride=e,this.count=void 0!==t?t.length/e:0,this.usage=Ky,this.updateRange={offset:0,count:-1},this.version=0,this.uuid=lx()}onUploadCallback(){}set needsUpdate(t){!0===t&&this.version++}setUsage(t){return this.usage=t,this}copy(t){return this.array=new t.array.constructor(t.array),this.count=t.count,this.stride=t.stride,this.usage=t.usage,this}copyAt(t,e,n){t*=this.stride,n*=e.stride;for(let i=0,r=this.stride;it.far||e.push({distance:a,point:qE.clone(),uv:Vb.getUV(qE,QE,KE,tM,eM,nM,iM,new fx),face:null,object:this})}copy(t){return super.copy(t),void 0!==t.center&&this.center.copy(t.center),this.material=t.material,this}}function sM(t,e,n,i,r,s){$E.subVectors(t,n).addScalar(.5).multiply(i),void 0!==r?(JE.x=s*$E.x-r*$E.y,JE.y=r*$E.x+s*$E.y):JE.copy($E),t.copy(e),t.x+=JE.x,t.y+=JE.y,t.applyMatrix4(ZE)}rM.prototype.isSprite=!0;const oM=new Nx,aM=new Ex,lM=new Ex,cM=new Nx,uM=new ob;class hM extends Lw{constructor(t,e){super(t,e),this.type=\\\\\\\"SkinnedMesh\\\\\\\",this.bindMode=\\\\\\\"attached\\\\\\\",this.bindMatrix=new ob,this.bindMatrixInverse=new ob}copy(t){return super.copy(t),this.bindMode=t.bindMode,this.bindMatrix.copy(t.bindMatrix),this.bindMatrixInverse.copy(t.bindMatrixInverse),this.skeleton=t.skeleton,this}bind(t,e){this.skeleton=t,void 0===e&&(this.updateMatrixWorld(!0),this.skeleton.calculateInverses(),e=this.matrixWorld),this.bindMatrix.copy(e),this.bindMatrixInverse.copy(e).invert()}pose(){this.skeleton.pose()}normalizeSkinWeights(){const t=new Ex,e=this.geometry.attributes.skinWeight;for(let n=0,i=e.count;na)continue;h.applyMatrix4(this.matrixWorld);const d=t.ray.origin.distanceTo(h);dt.far||e.push({distance:d,point:u.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}else{for(let n=Math.max(0,s.start),i=Math.min(r.count,s.start+s.count)-1;na)continue;h.applyMatrix4(this.matrixWorld);const i=t.ray.origin.distanceTo(h);it.far||e.push({distance:i,point:u.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}}else n.isGeometry&&console.error(\\\\\\\"THREE.Line.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e0&&console.error(\\\\\\\"THREE.Line.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}}MM.prototype.isLine=!0;const SM=new Nx,CM=new Nx;class NM extends MM{constructor(t,e){super(t,e),this.type=\\\\\\\"LineSegments\\\\\\\"}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[];for(let t=0,i=e.count;t0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e0&&console.error(\\\\\\\"THREE.Points.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}}function kM(t,e,n,i,r,s,o){const a=PM.distanceSqToPoint(t);if(ar.far)return;s.push({distance:l,distanceToRay:Math.sqrt(a),point:n,index:e,face:null,object:o})}}DM.prototype.isPoints=!0;(class extends Tx{constructor(t,e,n,i,r,s,o,a,l){super(t,e,n,i,r,s,o,a,l),this.format=void 0!==o?o:ky,this.minFilter=void 0!==s?s:Cy,this.magFilter=void 0!==r?r:Cy,this.generateMipmaps=!1;const c=this;\\\\\\\"requestVideoFrameCallback\\\\\\\"in t&&t.requestVideoFrameCallback((function e(){c.needsUpdate=!0,t.requestVideoFrameCallback(e)}))}clone(){return new this.constructor(this.image).copy(this)}update(){const t=this.image;!1===\\\\\\\"requestVideoFrameCallback\\\\\\\"in t&&t.readyState>=t.HAVE_CURRENT_DATA&&(this.needsUpdate=!0)}}).prototype.isVideoTexture=!0;class BM extends Tx{constructor(t,e,n,i,r,s,o,a,l,c,u,h){super(null,s,o,a,l,c,i,r,u,h),this.image={width:e,height:n},this.mipmaps=t,this.flipY=!1,this.generateMipmaps=!1}}BM.prototype.isCompressedTexture=!0;(class extends Tx{constructor(t,e,n,i,r,s,o,a,l){super(t,e,n,i,r,s,o,a,l),this.needsUpdate=!0}}).prototype.isCanvasTexture=!0;(class extends Tx{constructor(t,e,n,i,r,s,o,a,l,c){if((c=void 0!==c?c:zy)!==zy&&c!==Uy)throw new Error(\\\\\\\"DepthTexture format must be either THREE.DepthFormat or THREE.DepthStencilFormat\\\\\\\");void 0===n&&c===zy&&(n=Ry),void 0===n&&c===Uy&&(n=Dy),super(null,i,r,s,o,a,c,n,l),this.image={width:t,height:e},this.magFilter=void 0!==o?o:Ey,this.minFilter=void 0!==a?a:Ey,this.flipY=!1,this.generateMipmaps=!1}}).prototype.isDepthTexture=!0;new Nx,new Nx,new Nx,new Vb;class zM{constructor(){this.type=\\\\\\\"Curve\\\\\\\",this.arcLengthDivisions=200}getPoint(){return console.warn(\\\\\\\"THREE.Curve: .getPoint() not implemented.\\\\\\\"),null}getPointAt(t,e){const n=this.getUtoTmapping(t);return this.getPoint(n,e)}getPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPoint(n/t));return e}getSpacedPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPointAt(n/t));return e}getLength(){const t=this.getLengths();return t[t.length-1]}getLengths(t=this.arcLengthDivisions){if(this.cacheArcLengths&&this.cacheArcLengths.length===t+1&&!this.needsUpdate)return this.cacheArcLengths;this.needsUpdate=!1;const e=[];let n,i=this.getPoint(0),r=0;e.push(0);for(let s=1;s<=t;s++)n=this.getPoint(s/t),r+=n.distanceTo(i),e.push(r),i=n;return this.cacheArcLengths=e,e}updateArcLengths(){this.needsUpdate=!0,this.getLengths()}getUtoTmapping(t,e){const n=this.getLengths();let i=0;const r=n.length;let s;s=e||t*n[r-1];let o,a=0,l=r-1;for(;a<=l;)if(i=Math.floor(a+(l-a)/2),o=n[i]-s,o<0)a=i+1;else{if(!(o>0)){l=i;break}l=i-1}if(i=l,n[i]===s)return i/(r-1);const c=n[i];return(i+(s-c)/(n[i+1]-c))/(r-1)}getTangent(t,e){const n=1e-4;let i=t-n,r=t+n;i<0&&(i=0),r>1&&(r=1);const s=this.getPoint(i),o=this.getPoint(r),a=e||(s.isVector2?new fx:new Nx);return a.copy(o).sub(s).normalize(),a}getTangentAt(t,e){const n=this.getUtoTmapping(t);return this.getTangent(n,e)}computeFrenetFrames(t,e){const n=new Nx,i=[],r=[],s=[],o=new Nx,a=new ob;for(let e=0;e<=t;e++){const n=e/t;i[e]=this.getTangentAt(n,new Nx)}r[0]=new Nx,s[0]=new Nx;let l=Number.MAX_VALUE;const c=Math.abs(i[0].x),u=Math.abs(i[0].y),h=Math.abs(i[0].z);c<=l&&(l=c,n.set(1,0,0)),u<=l&&(l=u,n.set(0,1,0)),h<=l&&n.set(0,0,1),o.crossVectors(i[0],n).normalize(),r[0].crossVectors(i[0],o),s[0].crossVectors(i[0],r[0]);for(let e=1;e<=t;e++){if(r[e]=r[e-1].clone(),s[e]=s[e-1].clone(),o.crossVectors(i[e-1],i[e]),o.length()>Number.EPSILON){o.normalize();const t=Math.acos(cx(i[e-1].dot(i[e]),-1,1));r[e].applyMatrix4(a.makeRotationAxis(o,t))}s[e].crossVectors(i[e],r[e])}if(!0===e){let e=Math.acos(cx(r[0].dot(r[t]),-1,1));e/=t,i[0].dot(o.crossVectors(r[0],r[t]))>0&&(e=-e);for(let n=1;n<=t;n++)r[n].applyMatrix4(a.makeRotationAxis(i[n],e*n)),s[n].crossVectors(i[n],r[n])}return{tangents:i,normals:r,binormals:s}}clone(){return(new this.constructor).copy(this)}copy(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}toJSON(){const t={metadata:{version:4.5,type:\\\\\\\"Curve\\\\\\\",generator:\\\\\\\"Curve.toJSON\\\\\\\"}};return t.arcLengthDivisions=this.arcLengthDivisions,t.type=this.type,t}fromJSON(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}}class UM extends zM{constructor(t=0,e=0,n=1,i=1,r=0,s=2*Math.PI,o=!1,a=0){super(),this.type=\\\\\\\"EllipseCurve\\\\\\\",this.aX=t,this.aY=e,this.xRadius=n,this.yRadius=i,this.aStartAngle=r,this.aEndAngle=s,this.aClockwise=o,this.aRotation=a}getPoint(t,e){const n=e||new fx,i=2*Math.PI;let r=this.aEndAngle-this.aStartAngle;const s=Math.abs(r)i;)r-=i;r0?0:(Math.floor(Math.abs(l)/r)+1)*r:0===c&&l===r-1&&(l=r-2,c=1),this.closed||l>0?o=i[(l-1)%r]:(HM.subVectors(i[0],i[1]).add(i[0]),o=HM);const u=i[l%r],h=i[(l+1)%r];if(this.closed||l+2i.length-2?i.length-1:s+1],u=i[s>i.length-3?i.length-1:s+2];return n.set(YM(o,a.x,l.x,c.x,u.x),YM(o,a.y,l.y,c.y,u.y)),n}copy(t){super.copy(t),this.points=[];for(let e=0,n=t.points.length;e=n){const t=i[r]-n,s=this.curves[r],o=s.getLength(),a=0===o?0:1-t/o;return s.getPointAt(a,e)}r++}return null}getLength(){const t=this.getCurveLengths();return t[t.length-1]}updateArcLengths(){this.needsUpdate=!0,this.cacheLengths=null,this.getCurveLengths()}getCurveLengths(){if(this.cacheLengths&&this.cacheLengths.length===this.curves.length)return this.cacheLengths;const t=[];let e=0;for(let n=0,i=this.curves.length;n1&&!e[e.length-1].equals(e[0])&&e.push(e[0]),e}copy(t){super.copy(t),this.curves=[];for(let e=0,n=t.curves.length;e0){const t=l.getPoint(0);t.equals(this.currentPoint)||this.lineTo(t.x,t.y)}this.curves.push(l);const c=l.getPoint(1);return this.currentPoint.copy(c),this}copy(t){return super.copy(t),this.currentPoint.copy(t.currentPoint),this}toJSON(){const t=super.toJSON();return t.currentPoint=this.currentPoint.toArray(),t}fromJSON(t){return super.fromJSON(t),this.currentPoint.fromArray(t.currentPoint),this}}class oS extends sS{constructor(t){super(t),this.uuid=lx(),this.type=\\\\\\\"Shape\\\\\\\",this.holes=[]}getPointsHoles(t){const e=[];for(let n=0,i=this.holes.length;n80*n){a=c=t[0],l=u=t[1];for(let e=n;ec&&(c=h),d>u&&(u=d);p=Math.max(c-a,u-l),p=0!==p?1/p:0}return uS(s,o,n,a,l,p),o};function lS(t,e,n,i,r){let s,o;if(r===function(t,e,n,i){let r=0;for(let s=e,o=n-i;s0)for(s=e;s=e;s-=i)o=NS(s,t[s],t[s+1],o);return o&&TS(o,o.next)&&(LS(o),o=o.next),o}function cS(t,e){if(!t)return t;e||(e=t);let n,i=t;do{if(n=!1,i.steiner||!TS(i,i.next)&&0!==wS(i.prev,i,i.next))i=i.next;else{if(LS(i),i=e=i.prev,i===i.next)break;n=!0}}while(n||i!==e);return e}function uS(t,e,n,i,r,s,o){if(!t)return;!o&&s&&function(t,e,n,i){let r=t;do{null===r.z&&(r.z=vS(r.x,r.y,e,n,i)),r.prevZ=r.prev,r.nextZ=r.next,r=r.next}while(r!==t);r.prevZ.nextZ=null,r.prevZ=null,function(t){let e,n,i,r,s,o,a,l,c=1;do{for(n=t,t=null,s=null,o=0;n;){for(o++,i=n,a=0,e=0;e0||l>0&&i;)0!==a&&(0===l||!i||n.z<=i.z)?(r=n,n=n.nextZ,a--):(r=i,i=i.nextZ,l--),s?s.nextZ=r:t=r,r.prevZ=s,s=r;n=i}s.nextZ=null,c*=2}while(o>1)}(r)}(t,i,r,s);let a,l,c=t;for(;t.prev!==t.next;)if(a=t.prev,l=t.next,s?dS(t,i,r,s):hS(t))e.push(a.i/n),e.push(t.i/n),e.push(l.i/n),LS(t),t=l.next,c=l.next;else if((t=l)===c){o?1===o?uS(t=pS(cS(t),e,n),e,n,i,r,s,2):2===o&&_S(t,e,n,i,r,s):uS(cS(t),e,n,i,r,s,1);break}}function hS(t){const e=t.prev,n=t,i=t.next;if(wS(e,n,i)>=0)return!1;let r=t.next.next;for(;r!==t.prev;){if(xS(e.x,e.y,n.x,n.y,i.x,i.y,r.x,r.y)&&wS(r.prev,r,r.next)>=0)return!1;r=r.next}return!0}function dS(t,e,n,i){const r=t.prev,s=t,o=t.next;if(wS(r,s,o)>=0)return!1;const a=r.xs.x?r.x>o.x?r.x:o.x:s.x>o.x?s.x:o.x,u=r.y>s.y?r.y>o.y?r.y:o.y:s.y>o.y?s.y:o.y,h=vS(a,l,e,n,i),d=vS(c,u,e,n,i);let p=t.prevZ,_=t.nextZ;for(;p&&p.z>=h&&_&&_.z<=d;){if(p!==t.prev&&p!==t.next&&xS(r.x,r.y,s.x,s.y,o.x,o.y,p.x,p.y)&&wS(p.prev,p,p.next)>=0)return!1;if(p=p.prevZ,_!==t.prev&&_!==t.next&&xS(r.x,r.y,s.x,s.y,o.x,o.y,_.x,_.y)&&wS(_.prev,_,_.next)>=0)return!1;_=_.nextZ}for(;p&&p.z>=h;){if(p!==t.prev&&p!==t.next&&xS(r.x,r.y,s.x,s.y,o.x,o.y,p.x,p.y)&&wS(p.prev,p,p.next)>=0)return!1;p=p.prevZ}for(;_&&_.z<=d;){if(_!==t.prev&&_!==t.next&&xS(r.x,r.y,s.x,s.y,o.x,o.y,_.x,_.y)&&wS(_.prev,_,_.next)>=0)return!1;_=_.nextZ}return!0}function pS(t,e,n){let i=t;do{const r=i.prev,s=i.next.next;!TS(r,s)&&AS(r,i,i.next,s)&&SS(r,s)&&SS(s,r)&&(e.push(r.i/n),e.push(i.i/n),e.push(s.i/n),LS(i),LS(i.next),i=t=s),i=i.next}while(i!==t);return cS(i)}function _S(t,e,n,i,r,s){let o=t;do{let t=o.next.next;for(;t!==o.prev;){if(o.i!==t.i&&bS(o,t)){let a=CS(o,t);return o=cS(o,o.next),a=cS(a,a.next),uS(o,e,n,i,r,s),void uS(a,e,n,i,r,s)}t=t.next}o=o.next}while(o!==t)}function mS(t,e){return t.x-e.x}function fS(t,e){if(e=function(t,e){let n=e;const i=t.x,r=t.y;let s,o=-1/0;do{if(r<=n.y&&r>=n.next.y&&n.next.y!==n.y){const t=n.x+(r-n.y)*(n.next.x-n.x)/(n.next.y-n.y);if(t<=i&&t>o){if(o=t,t===i){if(r===n.y)return n;if(r===n.next.y)return n.next}s=n.x=n.x&&n.x>=l&&i!==n.x&&xS(rs.x||n.x===s.x&&gS(s,n)))&&(s=n,h=u)),n=n.next}while(n!==a);return s}(t,e)){const n=CS(e,t);cS(e,e.next),cS(n,n.next)}}function gS(t,e){return wS(t.prev,t,e.prev)<0&&wS(e.next,t,t.next)<0}function vS(t,e,n,i,r){return(t=1431655765&((t=858993459&((t=252645135&((t=16711935&((t=32767*(t-n)*r)|t<<8))|t<<4))|t<<2))|t<<1))|(e=1431655765&((e=858993459&((e=252645135&((e=16711935&((e=32767*(e-i)*r)|e<<8))|e<<4))|e<<2))|e<<1))<<1}function yS(t){let e=t,n=t;do{(e.x=0&&(t-o)*(i-a)-(n-o)*(e-a)>=0&&(n-o)*(s-a)-(r-o)*(i-a)>=0}function bS(t,e){return t.next.i!==e.i&&t.prev.i!==e.i&&!function(t,e){let n=t;do{if(n.i!==t.i&&n.next.i!==t.i&&n.i!==e.i&&n.next.i!==e.i&&AS(n,n.next,t,e))return!0;n=n.next}while(n!==t);return!1}(t,e)&&(SS(t,e)&&SS(e,t)&&function(t,e){let n=t,i=!1;const r=(t.x+e.x)/2,s=(t.y+e.y)/2;do{n.y>s!=n.next.y>s&&n.next.y!==n.y&&r<(n.next.x-n.x)*(s-n.y)/(n.next.y-n.y)+n.x&&(i=!i),n=n.next}while(n!==t);return i}(t,e)&&(wS(t.prev,t,e.prev)||wS(t,e.prev,e))||TS(t,e)&&wS(t.prev,t,t.next)>0&&wS(e.prev,e,e.next)>0)}function wS(t,e,n){return(e.y-t.y)*(n.x-e.x)-(e.x-t.x)*(n.y-e.y)}function TS(t,e){return t.x===e.x&&t.y===e.y}function AS(t,e,n,i){const r=MS(wS(t,e,n)),s=MS(wS(t,e,i)),o=MS(wS(n,i,t)),a=MS(wS(n,i,e));return r!==s&&o!==a||(!(0!==r||!ES(t,n,e))||(!(0!==s||!ES(t,i,e))||(!(0!==o||!ES(n,t,i))||!(0!==a||!ES(n,e,i)))))}function ES(t,e,n){return e.x<=Math.max(t.x,n.x)&&e.x>=Math.min(t.x,n.x)&&e.y<=Math.max(t.y,n.y)&&e.y>=Math.min(t.y,n.y)}function MS(t){return t>0?1:t<0?-1:0}function SS(t,e){return wS(t.prev,t,t.next)<0?wS(t,e,t.next)>=0&&wS(t,t.prev,e)>=0:wS(t,e,t.prev)<0||wS(t,t.next,e)<0}function CS(t,e){const n=new OS(t.i,t.x,t.y),i=new OS(e.i,e.x,e.y),r=t.next,s=e.prev;return t.next=e,e.prev=t,n.next=r,r.prev=n,i.next=n,n.prev=i,s.next=i,i.prev=s,i}function NS(t,e,n,i){const r=new OS(t,e,n);return i?(r.next=i.next,r.prev=i,i.next.prev=r,i.next=r):(r.prev=r,r.next=r),r}function LS(t){t.next.prev=t.prev,t.prev.next=t.next,t.prevZ&&(t.prevZ.nextZ=t.nextZ),t.nextZ&&(t.nextZ.prevZ=t.prevZ)}function OS(t,e,n){this.i=t,this.x=e,this.y=n,this.prev=null,this.next=null,this.z=null,this.prevZ=null,this.nextZ=null,this.steiner=!1}class RS{static area(t){const e=t.length;let n=0;for(let i=e-1,r=0;r2&&t[e-1].equals(t[0])&&t.pop()}function IS(t,e){for(let n=0;nNumber.EPSILON){const h=Math.sqrt(u),d=Math.sqrt(l*l+c*c),p=e.x-a/h,_=e.y+o/h,m=((n.x-c/d-p)*c-(n.y+l/d-_)*l)/(o*c-a*l);i=p+o*m-t.x,r=_+a*m-t.y;const f=i*i+r*r;if(f<=2)return new fx(i,r);s=Math.sqrt(f/2)}else{let t=!1;o>Number.EPSILON?l>Number.EPSILON&&(t=!0):o<-Number.EPSILON?l<-Number.EPSILON&&(t=!0):Math.sign(a)===Math.sign(c)&&(t=!0),t?(i=-a,r=o,s=Math.sqrt(u)):(i=o,r=a,s=Math.sqrt(u/2))}return new fx(i/s,r/s)}const O=[];for(let t=0,e=M.length,n=e-1,i=t+1;t=0;t--){const e=t/p,n=u*Math.cos(e*Math.PI/2),i=h*Math.sin(e*Math.PI/2)+d;for(let t=0,e=M.length;t=0;){const i=n;let r=n-1;r<0&&(r=t.length-1);for(let t=0,n=a+2*p;t0!=t>0&&this.version++,this._sheen=t}get clearcoat(){return this._clearcoat}set clearcoat(t){this._clearcoat>0!=t>0&&this.version++,this._clearcoat=t}get transmission(){return this._transmission}set transmission(t){this._transmission>0!=t>0&&this.version++,this._transmission=t}copy(t){return super.copy(t),this.defines={STANDARD:\\\\\\\"\\\\\\\",PHYSICAL:\\\\\\\"\\\\\\\"},this.clearcoat=t.clearcoat,this.clearcoatMap=t.clearcoatMap,this.clearcoatRoughness=t.clearcoatRoughness,this.clearcoatRoughnessMap=t.clearcoatRoughnessMap,this.clearcoatNormalMap=t.clearcoatNormalMap,this.clearcoatNormalScale.copy(t.clearcoatNormalScale),this.ior=t.ior,this.sheen=t.sheen,this.sheenTint.copy(t.sheenTint),this.sheenRoughness=t.sheenRoughness,this.transmission=t.transmission,this.transmissionMap=t.transmissionMap,this.thickness=t.thickness,this.thicknessMap=t.thicknessMap,this.attenuationDistance=t.attenuationDistance,this.attenuationTint.copy(t.attenuationTint),this.specularIntensity=t.specularIntensity,this.specularIntensityMap=t.specularIntensityMap,this.specularTint.copy(t.specularTint),this.specularTintMap=t.specularTintMap,this}}US.prototype.isMeshPhysicalMaterial=!0;class GS extends jb{constructor(t){super(),this.type=\\\\\\\"MeshPhongMaterial\\\\\\\",this.color=new Zb(16777215),this.specular=new Zb(1118481),this.shininess=30,this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Zb(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new fx(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.specular.copy(t.specular),this.shininess=t.shininess,this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this.flatShading=t.flatShading,this}}GS.prototype.isMeshPhongMaterial=!0;class VS extends jb{constructor(t){super(),this.defines={TOON:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshToonMaterial\\\\\\\",this.color=new Zb(16777215),this.map=null,this.gradientMap=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Zb(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new fx(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.alphaMap=null,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.gradientMap=t.gradientMap,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.alphaMap=t.alphaMap,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}VS.prototype.isMeshToonMaterial=!0;class HS extends jb{constructor(t){super(),this.type=\\\\\\\"MeshNormalMaterial\\\\\\\",this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new fx(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.flatShading=t.flatShading,this}}HS.prototype.isMeshNormalMaterial=!0;class jS extends jb{constructor(t){super(),this.type=\\\\\\\"MeshLambertMaterial\\\\\\\",this.color=new Zb(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Zb(0),this.emissiveIntensity=1,this.emissiveMap=null,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}jS.prototype.isMeshLambertMaterial=!0;class WS extends jb{constructor(t){super(),this.defines={MATCAP:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshMatcapMaterial\\\\\\\",this.color=new Zb(16777215),this.matcap=null,this.map=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new fx(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.alphaMap=null,this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.defines={MATCAP:\\\\\\\"\\\\\\\"},this.color.copy(t.color),this.matcap=t.matcap,this.map=t.map,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.alphaMap=t.alphaMap,this.flatShading=t.flatShading,this}}WS.prototype.isMeshMatcapMaterial=!0;class qS extends xM{constructor(t){super(),this.type=\\\\\\\"LineDashedMaterial\\\\\\\",this.scale=1,this.dashSize=3,this.gapSize=1,this.setValues(t)}copy(t){return super.copy(t),this.scale=t.scale,this.dashSize=t.dashSize,this.gapSize=t.gapSize,this}}qS.prototype.isLineDashedMaterial=!0;const XS={arraySlice:function(t,e,n){return XS.isTypedArray(t)?new t.constructor(t.subarray(e,void 0!==n?n:t.length)):t.slice(e,n)},convertArray:function(t,e,n){return!t||!n&&t.constructor===e?t:\\\\\\\"number\\\\\\\"==typeof e.BYTES_PER_ELEMENT?new e(t):Array.prototype.slice.call(t)},isTypedArray:function(t){return ArrayBuffer.isView(t)&&!(t instanceof DataView)},getKeyframeOrder:function(t){const e=t.length,n=new Array(e);for(let t=0;t!==e;++t)n[t]=t;return n.sort((function(e,n){return t[e]-t[n]})),n},sortedArray:function(t,e,n){const i=t.length,r=new t.constructor(i);for(let s=0,o=0;o!==i;++s){const i=n[s]*e;for(let n=0;n!==e;++n)r[o++]=t[i+n]}return r},flattenJSON:function(t,e,n,i){let r=1,s=t[0];for(;void 0!==s&&void 0===s[i];)s=t[r++];if(void 0===s)return;let o=s[i];if(void 0!==o)if(Array.isArray(o))do{o=s[i],void 0!==o&&(e.push(s.time),n.push.apply(n,o)),s=t[r++]}while(void 0!==s);else if(void 0!==o.toArray)do{o=s[i],void 0!==o&&(e.push(s.time),o.toArray(n,n.length)),s=t[r++]}while(void 0!==s);else do{o=s[i],void 0!==o&&(e.push(s.time),n.push(o)),s=t[r++]}while(void 0!==s)},subclip:function(t,e,n,i,r=30){const s=t.clone();s.name=e;const o=[];for(let t=0;t=i)){l.push(e.times[t]);for(let n=0;ns.tracks[t].times[0]&&(a=s.tracks[t].times[0]);for(let t=0;t=i.times[h]){const t=h*l+a,e=t+l-a;d=XS.arraySlice(i.values,t,e)}else{const t=i.createInterpolant(),e=a,n=l-a;t.evaluate(s),d=XS.arraySlice(t.resultBuffer,e,n)}if(\\\\\\\"quaternion\\\\\\\"===r){(new Cx).fromArray(d).normalize().conjugate().toArray(d)}const p=o.times.length;for(let t=0;t=r)break t;{const o=e[1];t=r)break e}s=n,n=0}}for(;n>>1;te;)--s;if(++s,0!==r||s!==i){r>=s&&(s=Math.max(s,1),r=s-1);const t=this.getValueSize();this.times=XS.arraySlice(n,r,s),this.values=XS.arraySlice(this.values,r*t,s*t)}return this}validate(){let t=!0;const e=this.getValueSize();e-Math.floor(e)!=0&&(console.error(\\\\\\\"THREE.KeyframeTrack: Invalid value size in track.\\\\\\\",this),t=!1);const n=this.times,i=this.values,r=n.length;0===r&&(console.error(\\\\\\\"THREE.KeyframeTrack: Track is empty.\\\\\\\",this),t=!1);let s=null;for(let e=0;e!==r;e++){const i=n[e];if(\\\\\\\"number\\\\\\\"==typeof i&&isNaN(i)){console.error(\\\\\\\"THREE.KeyframeTrack: Time is not a valid number.\\\\\\\",this,e,i),t=!1;break}if(null!==s&&s>i){console.error(\\\\\\\"THREE.KeyframeTrack: Out of order keys.\\\\\\\",this,e,i,s),t=!1;break}s=i}if(void 0!==i&&XS.isTypedArray(i))for(let e=0,n=i.length;e!==n;++e){const n=i[e];if(isNaN(n)){console.error(\\\\\\\"THREE.KeyframeTrack: Value is not a valid number.\\\\\\\",this,e,n),t=!1;break}}return t}optimize(){const t=XS.arraySlice(this.times),e=XS.arraySlice(this.values),n=this.getValueSize(),i=this.getInterpolation()===Hy,r=t.length-1;let s=1;for(let o=1;o0){t[s]=t[r];for(let t=r*n,i=s*n,o=0;o!==n;++o)e[i+o]=e[t+o];++s}return s!==t.length?(this.times=XS.arraySlice(t,0,s),this.values=XS.arraySlice(e,0,s*n)):(this.times=t,this.values=e),this}clone(){const t=XS.arraySlice(this.times,0),e=XS.arraySlice(this.values,0),n=new(0,this.constructor)(this.name,t,e);return n.createInterpolant=this.createInterpolant,n}}QS.prototype.TimeBufferType=Float32Array,QS.prototype.ValueBufferType=Float32Array,QS.prototype.DefaultInterpolation=Vy;class KS extends QS{}KS.prototype.ValueTypeName=\\\\\\\"bool\\\\\\\",KS.prototype.ValueBufferType=Array,KS.prototype.DefaultInterpolation=Gy,KS.prototype.InterpolantFactoryMethodLinear=void 0,KS.prototype.InterpolantFactoryMethodSmooth=void 0;class tC extends QS{}tC.prototype.ValueTypeName=\\\\\\\"color\\\\\\\";class eC extends QS{}eC.prototype.ValueTypeName=\\\\\\\"number\\\\\\\";class nC extends YS{constructor(t,e,n,i){super(t,e,n,i)}interpolate_(t,e,n,i){const r=this.resultBuffer,s=this.sampleValues,o=this.valueSize,a=(n-e)/(i-e);let l=t*o;for(let t=l+o;l!==t;l+=4)Cx.slerpFlat(r,0,s,l-o,s,l,a);return r}}class iC extends QS{InterpolantFactoryMethodLinear(t){return new nC(this.times,this.values,this.getValueSize(),t)}}iC.prototype.ValueTypeName=\\\\\\\"quaternion\\\\\\\",iC.prototype.DefaultInterpolation=Vy,iC.prototype.InterpolantFactoryMethodSmooth=void 0;class rC extends QS{}rC.prototype.ValueTypeName=\\\\\\\"string\\\\\\\",rC.prototype.ValueBufferType=Array,rC.prototype.DefaultInterpolation=Gy,rC.prototype.InterpolantFactoryMethodLinear=void 0,rC.prototype.InterpolantFactoryMethodSmooth=void 0;class sC extends QS{}sC.prototype.ValueTypeName=\\\\\\\"vector\\\\\\\";class oC{constructor(t,e=-1,n,i=2500){this.name=t,this.tracks=n,this.duration=e,this.blendMode=i,this.uuid=lx(),this.duration<0&&this.resetDuration()}static parse(t){const e=[],n=t.tracks,i=1/(t.fps||1);for(let t=0,r=n.length;t!==r;++t)e.push(aC(n[t]).scale(i));const r=new this(t.name,t.duration,e,t.blendMode);return r.uuid=t.uuid,r}static toJSON(t){const e=[],n=t.tracks,i={name:t.name,duration:t.duration,tracks:e,uuid:t.uuid,blendMode:t.blendMode};for(let t=0,i=n.length;t!==i;++t)e.push(QS.toJSON(n[t]));return i}static CreateFromMorphTargetSequence(t,e,n,i){const r=e.length,s=[];for(let t=0;t1){const t=s[1];let e=i[t];e||(i[t]=e=[]),e.push(n)}}const s=[];for(const t in i)s.push(this.CreateFromMorphTargetSequence(t,i[t],e,n));return s}static parseAnimation(t,e){if(!t)return console.error(\\\\\\\"THREE.AnimationClip: No animation in JSONLoader data.\\\\\\\"),null;const n=function(t,e,n,i,r){if(0!==n.length){const s=[],o=[];XS.flattenJSON(n,s,o,i),0!==s.length&&r.push(new t(e,s,o))}},i=[],r=t.name||\\\\\\\"default\\\\\\\",s=t.fps||30,o=t.blendMode;let a=t.length||-1;const l=t.hierarchy||[];for(let t=0;t0){this.source.connect(this.filters[0]);for(let t=1,e=this.filters.length;t0){this.source.disconnect(this.filters[0]);for(let t=1,e=this.filters.length;t0&&this._mixBufferRegionAdditive(n,i,this._addIndex*e,1,e);for(let t=e,r=e+e;t!==r;++t)if(n[t]!==n[t+e]){o.setValue(n,i);break}}saveOriginalState(){const t=this.binding,e=this.buffer,n=this.valueSize,i=n*this._origIndex;t.getValue(e,i);for(let t=n,r=i;t!==r;++t)e[t]=e[i+t%n];this._setIdentity(),this.cumulativeWeight=0,this.cumulativeWeightAdditive=0}restoreOriginalState(){const t=3*this.valueSize;this.binding.setValue(this.buffer,t)}_setAdditiveIdentityNumeric(){const t=this._addIndex*this.valueSize,e=t+this.valueSize;for(let n=t;n=.5)for(let i=0;i!==r;++i)t[e+i]=t[n+i]}_slerp(t,e,n,i){Cx.slerpFlat(t,e,t,e,t,n,i)}_slerpAdditive(t,e,n,i,r){const s=this._workIndex*r;Cx.multiplyQuaternionsFlat(t,s,t,e,t,n),Cx.slerpFlat(t,e,t,e,t,s,i)}_lerp(t,e,n,i,r){const s=1-i;for(let o=0;o!==r;++o){const r=e+o;t[r]=t[r]*s+t[n+o]*i}}_lerpAdditive(t,e,n,i,r){for(let s=0;s!==r;++s){const r=e+s;t[r]=t[r]+t[n+s]*i}}}const HC=\\\\\\\"\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/\\\\\\\",jC=new RegExp(\\\\\\\"[\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",\\\\\\\"g\\\\\\\"),WC=\\\\\\\"[^\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",qC=\\\\\\\"[^\\\\\\\"+HC.replace(\\\\\\\"\\\\\\\\.\\\\\\\",\\\\\\\"\\\\\\\")+\\\\\\\"]\\\\\\\",XC=/((?:WC+[\\\\/:])*)/.source.replace(\\\\\\\"WC\\\\\\\",WC),YC=/(WCOD+)?/.source.replace(\\\\\\\"WCOD\\\\\\\",qC),$C=/(?:\\\\.(WC+)(?:\\\\[(.+)\\\\])?)?/.source.replace(\\\\\\\"WC\\\\\\\",WC),JC=/\\\\.(WC+)(?:\\\\[(.+)\\\\])?/.source.replace(\\\\\\\"WC\\\\\\\",WC),ZC=new RegExp(\\\\\\\"^\\\\\\\"+XC+YC+$C+JC+\\\\\\\"$\\\\\\\"),QC=[\\\\\\\"material\\\\\\\",\\\\\\\"materials\\\\\\\",\\\\\\\"bones\\\\\\\"];class KC{constructor(t,e,n){this.path=e,this.parsedPath=n||KC.parseTrackName(e),this.node=KC.findNode(t,this.parsedPath.nodeName)||t,this.rootNode=t,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}static create(t,e,n){return t&&t.isAnimationObjectGroup?new KC.Composite(t,e,n):new KC(t,e,n)}static sanitizeNodeName(t){return t.replace(/\\\\s/g,\\\\\\\"_\\\\\\\").replace(jC,\\\\\\\"\\\\\\\")}static parseTrackName(t){const e=ZC.exec(t);if(!e)throw new Error(\\\\\\\"PropertyBinding: Cannot parse trackName: \\\\\\\"+t);const n={nodeName:e[2],objectName:e[3],objectIndex:e[4],propertyName:e[5],propertyIndex:e[6]},i=n.nodeName&&n.nodeName.lastIndexOf(\\\\\\\".\\\\\\\");if(void 0!==i&&-1!==i){const t=n.nodeName.substring(i+1);-1!==QC.indexOf(t)&&(n.nodeName=n.nodeName.substring(0,i),n.objectName=t)}if(null===n.propertyName||0===n.propertyName.length)throw new Error(\\\\\\\"PropertyBinding: can not parse propertyName from trackName: \\\\\\\"+t);return n}static findNode(t,e){if(!e||\\\\\\\"\\\\\\\"===e||\\\\\\\".\\\\\\\"===e||-1===e||e===t.name||e===t.uuid)return t;if(t.skeleton){const n=t.skeleton.getBoneByName(e);if(void 0!==n)return n}if(t.children){const n=function(t){for(let i=0;i0){const t=this._interpolants,e=this._propertyBindings;switch(this.blendMode){case 2501:for(let n=0,i=t.length;n!==i;++n)t[n].evaluate(s),e[n].accumulateAdditive(o);break;case Xy:default:for(let n=0,r=t.length;n!==r;++n)t[n].evaluate(s),e[n].accumulate(i,o)}}}_updateWeight(t){let e=0;if(this.enabled){e=this.weight;const n=this._weightInterpolant;if(null!==n){const i=n.evaluate(t)[0];e*=i,t>n.parameterPositions[1]&&(this.stopFading(),0===i&&(this.enabled=!1))}}return this._effectiveWeight=e,e}_updateTimeScale(t){let e=0;if(!this.paused){e=this.timeScale;const n=this._timeScaleInterpolant;if(null!==n){e*=n.evaluate(t)[0],t>n.parameterPositions[1]&&(this.stopWarping(),0===e?this.paused=!0:this.timeScale=e)}}return this._effectiveTimeScale=e,e}_updateTime(t){const e=this._clip.duration,n=this.loop;let i=this.time+t,r=this._loopCount;const s=2202===n;if(0===t)return-1===r?i:s&&1==(1&r)?e-i:i;if(2200===n){-1===r&&(this._loopCount=0,this._setEndings(!0,!0,!1));t:{if(i>=e)i=e;else{if(!(i<0)){this.time=i;break t}i=0}this.clampWhenFinished?this.paused=!0:this.enabled=!1,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:t<0?-1:1})}}else{if(-1===r&&(t>=0?(r=0,this._setEndings(!0,0===this.repetitions,s)):this._setEndings(0===this.repetitions,!0,s)),i>=e||i<0){const n=Math.floor(i/e);i-=e*n,r+=Math.abs(n);const o=this.repetitions-r;if(o<=0)this.clampWhenFinished?this.paused=!0:this.enabled=!1,i=t>0?e:0,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:t>0?1:-1});else{if(1===o){const e=t<0;this._setEndings(e,!e,s)}else this._setEndings(!1,!1,s);this._loopCount=r,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"loop\\\\\\\",action:this,loopDelta:n})}}else this.time=i;if(s&&1==(1&r))return e-i}return i}_setEndings(t,e,n){const i=this._interpolantSettings;n?(i.endingStart=Wy,i.endingEnd=Wy):(i.endingStart=t?this.zeroSlopeAtStart?Wy:jy:qy,i.endingEnd=e?this.zeroSlopeAtEnd?Wy:jy:qy)}_scheduleFading(t,e,n){const i=this._mixer,r=i.time;let s=this._weightInterpolant;null===s&&(s=i._lendControlInterpolant(),this._weightInterpolant=s);const o=s.parameterPositions,a=s.sampleValues;return o[0]=r,a[0]=e,o[1]=r+t,a[1]=n,this}}(class extends nx{constructor(t){super(),this._root=t,this._initMemoryManager(),this._accuIndex=0,this.time=0,this.timeScale=1}_bindAction(t,e){const n=t._localRoot||this._root,i=t._clip.tracks,r=i.length,s=t._propertyBindings,o=t._interpolants,a=n.uuid,l=this._bindingsByRootAndName;let c=l[a];void 0===c&&(c={},l[a]=c);for(let t=0;t!==r;++t){const r=i[t],l=r.name;let u=c[l];if(void 0!==u)s[t]=u;else{if(u=s[t],void 0!==u){null===u._cacheIndex&&(++u.referenceCount,this._addInactiveBinding(u,a,l));continue}const i=e&&e._propertyBindings[t].binding.parsedPath;u=new VC(KC.create(n,l,i),r.ValueTypeName,r.getValueSize()),++u.referenceCount,this._addInactiveBinding(u,a,l),s[t]=u}o[t].resultBuffer=u.buffer}}_activateAction(t){if(!this._isActiveAction(t)){if(null===t._cacheIndex){const e=(t._localRoot||this._root).uuid,n=t._clip.uuid,i=this._actionsByClip[n];this._bindAction(t,i&&i.knownActions[0]),this._addInactiveAction(t,n,e)}const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==n.useCount++&&(this._lendBinding(n),n.saveOriginalState())}this._lendAction(t)}}_deactivateAction(t){if(this._isActiveAction(t)){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.useCount&&(n.restoreOriginalState(),this._takeBackBinding(n))}this._takeBackAction(t)}}_initMemoryManager(){this._actions=[],this._nActiveActions=0,this._actionsByClip={},this._bindings=[],this._nActiveBindings=0,this._bindingsByRootAndName={},this._controlInterpolants=[],this._nActiveControlInterpolants=0;const t=this;this.stats={actions:{get total(){return t._actions.length},get inUse(){return t._nActiveActions}},bindings:{get total(){return t._bindings.length},get inUse(){return t._nActiveBindings}},controlInterpolants:{get total(){return t._controlInterpolants.length},get inUse(){return t._nActiveControlInterpolants}}}}_isActiveAction(t){const e=t._cacheIndex;return null!==e&&e=0;--e)t[e].stop();return this}update(t){t*=this.timeScale;const e=this._actions,n=this._nActiveActions,i=this.time+=t,r=Math.sign(t),s=this._accuIndex^=1;for(let o=0;o!==n;++o){e[o]._update(i,t,r,s)}const o=this._bindings,a=this._nActiveBindings;for(let t=0;t!==a;++t)o[t].apply(s);return this}setTime(t){this.time=0;for(let t=0;tthis.max.x||t.ythis.max.y)}containsBox(t){return this.min.x<=t.min.x&&t.max.x<=this.max.x&&this.min.y<=t.min.y&&t.max.y<=this.max.y}getParameter(t,e){return e.set((t.x-this.min.x)/(this.max.x-this.min.x),(t.y-this.min.y)/(this.max.y-this.min.y))}intersectsBox(t){return!(t.max.xthis.max.x||t.max.ythis.max.y)}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return nN.copy(t).clamp(this.min,this.max).sub(t).length()}intersect(t){return this.min.max(t.min),this.max.min(t.max),this}union(t){return this.min.min(t.min),this.max.max(t.max),this}translate(t){return this.min.add(t),this.max.add(t),this}equals(t){return t.min.equals(this.min)&&t.max.equals(this.max)}}iN.prototype.isBox2=!0;const rN=new Nx,sN=new Nx;class oN{constructor(t=new Nx,e=new Nx){this.start=t,this.end=e}set(t,e){return this.start.copy(t),this.end.copy(e),this}copy(t){return this.start.copy(t.start),this.end.copy(t.end),this}getCenter(t){return t.addVectors(this.start,this.end).multiplyScalar(.5)}delta(t){return t.subVectors(this.end,this.start)}distanceSq(){return this.start.distanceToSquared(this.end)}distance(){return this.start.distanceTo(this.end)}at(t,e){return this.delta(e).multiplyScalar(t).add(this.start)}closestPointToPointParameter(t,e){rN.subVectors(t,this.start),sN.subVectors(this.end,this.start);const n=sN.dot(sN);let i=sN.dot(rN)/n;return e&&(i=cx(i,0,1)),i}closestPointToPoint(t,e,n){const i=this.closestPointToPointParameter(t,e);return this.delta(n).multiplyScalar(i).add(this.start)}applyMatrix4(t){return this.start.applyMatrix4(t),this.end.applyMatrix4(t),this}equals(t){return t.start.equals(this.start)&&t.end.equals(this.end)}clone(){return(new this.constructor).copy(this)}}(class extends Ob{constructor(t){super(),this.material=t,this.render=function(){},this.hasPositions=!1,this.hasNormals=!1,this.hasColors=!1,this.hasUvs=!1,this.positionArray=null,this.normalArray=null,this.colorArray=null,this.uvArray=null,this.count=0}}).prototype.isImmediateRenderObject=!0;const aN=new Nx,lN=new ob,cN=new ob;function uN(t){const e=[];t&&t.isBone&&e.push(t);for(let n=0;n8&&t<16&&e[n].split(t+1),0!==i&&this.subTrees.push(e[n])}return this}build(){return this.calcBox(),this.split(0),this}getRayTriangles(t,e){for(let n=0;n0)for(let t=0;t0&&i>0||n<-t.radius&&i<-t.radius)return!1;const r=Math.abs(n/(Math.abs(n)+Math.abs(i))),s=fN.copy(t.start).lerp(t.end,r);if(e.containsPoint(s))return{normal:vN.normal.clone(),point:s.clone(),depth:Math.abs(Math.min(n,i))};const o=t.radius*t.radius,a=yN.set(t.start,t.end),l=[[e.a,e.b],[e.b,e.c],[e.c,e.a]];for(let e=0;e0)for(let t=0;t0)for(let t=0;ta&&(i=o.clone().add(t.origin),r=a,n=e[s])}}return r<1e100&&{distance:r,triangle:n,position:i}}fromGraphNode(t){return t.updateWorldMatrix(!0,!0),t.traverse((t=>{if(!0===t.isMesh){let e,n=!1;null!==t.geometry.index?(n=!0,e=t.geometry.toNonIndexed()):e=t.geometry;const i=e.getAttribute(\\\\\\\"position\\\\\\\");for(let e=0;e{if(!e){const n=t;n.geometry&&(e=n)}})),e?this._playerCollisionController=new AN(e):console.error(\\\\\\\"no geo found in\\\\\\\",t)}else this._playerCollisionController=void 0}setCollisionCapsule(t){var e;null===(e=this._playerCollisionController)||void 0===e||e.setCapsule(t)}}const MN={type:\\\\\\\"change\\\\\\\"},SN={type:\\\\\\\"lock\\\\\\\"},CN={type:\\\\\\\"unlock\\\\\\\"},NN=Math.PI/2;class LN extends EN{constructor(t,e){super(),this.camera=t,this.domElement=e,this.isLocked=!1,this.minPolarAngle=0,this.maxPolarAngle=Math.PI,this.speed=1,this.euler=new Wv.a(0,0,0,\\\\\\\"YXZ\\\\\\\"),this.vec=new p.a,this.boundMethods={onMouseMove:this.onMouseMove.bind(this),onPointerlockChange:this.onPointerlockChange.bind(this),onPointerlockError:this.onPointerlockError.bind(this)},this._cameraTmp=new tf.a,this.velocity=new p.a,this.direction=new p.a,this._moveForward=!1,this._moveBackward=!1,this._moveLeft=!1,this._moveRight=!1,this.prevTime=0,this.connect()}onMouseMove(t){if(!1!==this.isLocked){var e=t.movementX||t.mozMovementX||t.webkitMovementX||0,n=t.movementY||t.mozMovementY||t.webkitMovementY||0;this.euler.setFromQuaternion(this.camera.quaternion),this.euler.y-=.002*e,this.euler.x-=.002*n,this.euler.x=Math.max(NN-this.maxPolarAngle,Math.min(NN-this.minPolarAngle,this.euler.x)),this.camera.quaternion.setFromEuler(this.euler),this.dispatchEvent(MN)}}onPointerlockChange(){this.velocity.set(0,0,0),this.domElement.ownerDocument.pointerLockElement===this.domElement?(this.dispatchEvent(SN),this.isLocked=!0):(this.dispatchEvent(CN),this.isLocked=!1)}onPointerlockError(){console.error(\\\\\\\"THREE.PointerLockControls: Unable to use Pointer Lock API (Note that you need to wait for 2 seconds to lock the pointer after having just unlocked it)\\\\\\\")}connect(){this.domElement.ownerDocument.addEventListener(\\\\\\\"mousemove\\\\\\\",this.boundMethods.onMouseMove),this.domElement.ownerDocument.addEventListener(\\\\\\\"pointerlockchange\\\\\\\",this.boundMethods.onPointerlockChange),this.domElement.ownerDocument.addEventListener(\\\\\\\"pointerlockerror\\\\\\\",this.boundMethods.onPointerlockError)}disconnect(){this.domElement.ownerDocument.removeEventListener(\\\\\\\"mousemove\\\\\\\",this.boundMethods.onMouseMove),this.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerlockchange\\\\\\\",this.boundMethods.onPointerlockChange),this.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerlockerror\\\\\\\",this.boundMethods.onPointerlockError)}dispose(){this.disconnect()}getObject(){return this.camera}moveForward(t,e){this.vec.setFromMatrixColumn(t.matrix,0),this.vec.crossVectors(t.up,this.vec),t.position.addScaledVector(this.vec,e)}moveRight(t,e){this.vec.setFromMatrixColumn(t.matrix,0),t.position.addScaledVector(this.vec,e)}_copyToCameraTmp(){this._cameraTmp.position.copy(this.camera.position),this._cameraTmp.matrix.copy(this.camera.matrix),this._cameraTmp.up.copy(this.camera.up)}lock(){this.domElement.requestPointerLock()}unlock(){this.domElement.ownerDocument.exitPointerLock()}setMoveForward(t){this._moveForward=t}setMoveBackward(t){this._moveBackward=t}setMoveLeft(t){this._moveLeft=t}setMoveRight(t){this._moveRight=t}update(){const t=performance.now();if(!0===this.isLocked){const e=(t-this.prevTime)/1e3;if(this.velocity.x-=10*this.velocity.x*e,this.velocity.z-=10*this.velocity.z*e,this.velocity.y-=9.8*100*e,this.direction.z=Number(this._moveForward)-Number(this._moveBackward),this.direction.x=Number(this._moveRight)-Number(this._moveLeft),this.direction.normalize(),(this._moveForward||this._moveBackward)&&(this.velocity.z-=400*this.direction.z*e*this.speed),(this._moveLeft||this._moveRight)&&(this.velocity.x-=400*this.direction.x*e*this.speed),this._playerCollisionController){this._copyToCameraTmp(),this.moveRight(this._cameraTmp,-this.velocity.x*e),this.moveForward(this._cameraTmp,-this.velocity.z*e);const t=this._playerCollisionController.testPosition(this._cameraTmp.position);t?(this._cameraTmp.position.add(t.normal.multiplyScalar(t.depth)),this.camera.position.copy(this._cameraTmp.position)):this._applyVelocity(e)}this._applyVelocity(e)}this.prevTime=t}_applyVelocity(t){this.moveRight(this.camera,-this.velocity.x*t),this.moveForward(this.camera,-this.velocity.z*t)}}async function ON(t,e){var n;if(e.pv.collideWithGeo){const i=e.pv.collidingGeo.nodeWithContext(Ki.OBJ);if(i){await i.compute();const r=await(null===(n=i.displayNodeController)||void 0===n?void 0:n.displayNode()),s=(await(null==r?void 0:r.compute())).coreContent();if(!s)return void console.error(\\\\\\\"obj node contains invalid sop\\\\\\\");const o=s.objectsWithGeo()[0];t.setCheckCollisions(o),t.setCollisionCapsule(new mN(new p.a(0,e.pv.capsuleHeightRange.x,0),new p.a(e.pv.capsuleHeightRange.y),e.pv.capsuleRadius))}}else t.setCheckCollisions()}const RN=\\\\\\\"lock\\\\\\\",PN=\\\\\\\"change\\\\\\\",IN=\\\\\\\"unlock\\\\\\\";const FN=new class extends aa{constructor(){super(...arguments),this.lock=oa.BUTTON(null,{callback:t=>{DN.PARAM_CALLBACK_lock_controls(t)}}),this.minPolarAngle=oa.FLOAT(0,{range:[0,Math.PI],rangeLocked:[!0,!0]}),this.maxPolarAngle=oa.FLOAT(\\\\\\\"$PI\\\\\\\",{range:[0,Math.PI],rangeLocked:[!0,!0]}),this.speed=oa.FLOAT(1),this.collideWithGeo=oa.BOOLEAN(0),this.collidingGeo=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.OBJ,types:[Ng.GEO]},visibleIf:{collideWithGeo:!0}}),this.recomputeCollidingGeo=oa.BUTTON(null,{callback:t=>{DN.PARAM_CALLBACK_recomputeCollidingGeo(t)},visibleIf:{collideWithGeo:!0}}),this.capsuleHeightRange=oa.VECTOR2([.3,1],{visibleIf:{collideWithGeo:!0}}),this.capsuleRadius=oa.FLOAT(.3,{visibleIf:{collideWithGeo:!0}})}};class DN extends jv{constructor(){super(...arguments),this.paramsConfig=FN,this._controls_by_element_id=new Map}static type(){return rr.FIRST_PERSON}endEventName(){return\\\\\\\"unlock\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(RN,$o.BASE,this.lockControls.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(RN,$o.BASE),new Jo(PN,$o.BASE),new Jo(IN,$o.BASE)])}async create_controls_instance(t,e){const n=new LN(t,e);return this._controls_by_element_id.set(e.id,n),this._bind_listeners_to_controls_instance(n),n}_bind_listeners_to_controls_instance(t){t.addEventListener(RN,(()=>{this._createKeysEvents(t),this.dispatchEventToOutput(RN,{})})),t.addEventListener(PN,(()=>{this.dispatchEventToOutput(PN,{})})),t.addEventListener(IN,(()=>{this._removeKeysEvents(),this.dispatchEventToOutput(IN,{})}))}update_required(){return!0}setup_controls(t){t.minPolarAngle=this.pv.minPolarAngle,t.maxPolarAngle=this.pv.maxPolarAngle,t.speed=this.pv.speed,this._setupCollisionGeo(t)}async _setupCollisionGeo(t){ON(t,this)}dispose_controls_for_html_element_id(t){this._controls_by_element_id.get(t)&&this._controls_by_element_id.delete(t)}static PARAM_CALLBACK_recomputeCollidingGeo(t){t._recomputeCollidingGeo()}_recomputeCollidingGeo(){this._controls_by_element_id.forEach(((t,e)=>{this._setupCollisionGeo(t)}))}lockControls(){let t;this._controls_by_element_id.forEach(((e,n)=>{t=t||e})),t&&t.lock()}static PARAM_CALLBACK_lock_controls(t){t.lockControls()}_onKeyDown(t,e){switch(t.code){case\\\\\\\"ArrowUp\\\\\\\":case\\\\\\\"KeyW\\\\\\\":e.setMoveForward(!0);break;case\\\\\\\"ArrowLeft\\\\\\\":case\\\\\\\"KeyA\\\\\\\":e.setMoveLeft(!0);break;case\\\\\\\"ArrowDown\\\\\\\":case\\\\\\\"KeyS\\\\\\\":e.setMoveBackward(!0);break;case\\\\\\\"ArrowRight\\\\\\\":case\\\\\\\"KeyD\\\\\\\":e.setMoveRight(!0)}}_onKeyUp(t,e){switch(t.code){case\\\\\\\"ArrowUp\\\\\\\":case\\\\\\\"KeyW\\\\\\\":e.setMoveForward(!1);break;case\\\\\\\"ArrowLeft\\\\\\\":case\\\\\\\"KeyA\\\\\\\":e.setMoveLeft(!1);break;case\\\\\\\"ArrowDown\\\\\\\":case\\\\\\\"KeyS\\\\\\\":e.setMoveBackward(!1);break;case\\\\\\\"ArrowRight\\\\\\\":case\\\\\\\"KeyD\\\\\\\":e.setMoveRight(!1)}}_createKeysEvents(t){this._onKeyDownBound=e=>{this._onKeyDown(e,t)},this._onKeyUpBound=e=>{this._onKeyUp(e,t)},document.addEventListener(\\\\\\\"keydown\\\\\\\",this._onKeyDownBound),document.addEventListener(\\\\\\\"keyup\\\\\\\",this._onKeyUpBound)}_removeKeysEvents(){this._onKeyDownBound&&this._onKeyUpBound&&(document.removeEventListener(\\\\\\\"keydown\\\\\\\",this._onKeyDownBound),document.removeEventListener(\\\\\\\"keyup\\\\\\\",this._onKeyUpBound))}}var kN,BN;!function(t){t.TRIGGER=\\\\\\\"trigger\\\\\\\",t.RESET=\\\\\\\"reset\\\\\\\"}(kN||(kN={})),function(t){t.OUT=\\\\\\\"out\\\\\\\",t.LAST=\\\\\\\"last\\\\\\\"}(BN||(BN={}));const zN=new class extends aa{constructor(){super(...arguments),this.maxCount=oa.INTEGER(5,{range:[0,10],rangeLocked:[!0,!1]}),this.reset=oa.BUTTON(null,{callback:t=>{UN.PARAM_CALLBACK_reset(t)}})}};class UN extends Ba{constructor(){super(...arguments),this.paramsConfig=zN,this._process_count=0,this._last_dispatched=!1}static type(){return\\\\\\\"limit\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(kN.TRIGGER,$o.BASE,this.process_event_trigger.bind(this)),new Jo(kN.RESET,$o.BASE,this.process_event_reset.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(BN.OUT,$o.BASE),new Jo(BN.LAST,$o.BASE)])}processEvent(t){}process_event_trigger(t){this._process_count(t.preventDefault(),!1);class jN{static disableContextMenu(){document.addEventListener(\\\\\\\"contextmenu\\\\\\\",HN)}static reEstablishContextMenu(){document.removeEventListener(\\\\\\\"contextmenu\\\\\\\",HN)}}const WN={rotationSpeed:1,rotationRange:{min:.25*-Math.PI,max:.25*Math.PI},translationSpeed:.1},qN={type:\\\\\\\"change\\\\\\\"};class XN extends EN{constructor(t,e){super(),this._camera=t,this.domElement=e,this.translationData={direction:{x:0,y:0}},this.rotationData={direction:{x:0,y:0}},this._boundMethods={onRotateStart:this._onRotateStart.bind(this),onRotateMove:this._onRotateMove.bind(this),onRotateEnd:this._onRotateEnd.bind(this),onTranslateStart:this._onTranslateStart.bind(this),onTranslateMove:this._onTranslateMove.bind(this),onTranslateEnd:this._onTranslateEnd.bind(this)},this._startCameraRotation=new Wv.a,this._velocity=new p.a,this._rotationSpeed=WN.rotationSpeed,this._rotationRange={min:WN.rotationRange.min,max:WN.rotationRange.max},this._translationSpeed=WN.translationSpeed,this._translateDomElement=this._createTranslateDomElement(),this._translateDomElementRect=this._translateDomElement.getBoundingClientRect(),this.vLeft=new p.a,this.vRight=new p.a,this.vTop=new p.a,this.vBottom=new p.a,this.angleY=0,this.angleX=0,this._rotationStartPosition=new d.a,this._rotationMovePosition=new d.a,this._rotationDelta=new d.a,this._startCameraPosition=new p.a,this._translationStartPosition=new d.a,this._translationMovePosition=new d.a,this._translationDelta=new d.a,this.prevTime=performance.now(),this._camTmpPost=new p.a,this._camWorldDir=new p.a,this._up=new p.a(0,1,0),this._camSideVector=new p.a,this._camera.rotation.order=\\\\\\\"ZYX\\\\\\\",this._addEvents()}dispose(){this._removeEvents()}_createTranslateDomElement(){const t=document.createElement(\\\\\\\"div\\\\\\\"),e=this.domElement.getBoundingClientRect(),n=Math.min(e.width,e.height),i=Math.round(.4*n),r=Math.round(.1*n);return t.style.width=`${i}px`,t.style.height=t.style.width,t.style.border=\\\\\\\"1px solid black\\\\\\\",t.style.borderRadius=`${i}px`,t.style.position=\\\\\\\"absolute\\\\\\\",t.style.bottom=`${r}px`,t.style.left=`${r}px`,t}_addEvents(){var t;jN.disableContextMenu(),this.domElement.addEventListener(\\\\\\\"touchstart\\\\\\\",this._boundMethods.onRotateStart),this.domElement.addEventListener(\\\\\\\"touchmove\\\\\\\",this._boundMethods.onRotateMove),this.domElement.addEventListener(\\\\\\\"touchend\\\\\\\",this._boundMethods.onRotateEnd),this._translateDomElement.addEventListener(\\\\\\\"touchstart\\\\\\\",this._boundMethods.onTranslateStart),this._translateDomElement.addEventListener(\\\\\\\"touchmove\\\\\\\",this._boundMethods.onTranslateMove),this._translateDomElement.addEventListener(\\\\\\\"touchend\\\\\\\",this._boundMethods.onTranslateEnd),null===(t=this.domElement.parentElement)||void 0===t||t.append(this._translateDomElement)}_removeEvents(){var t;jN.reEstablishContextMenu(),this.domElement.removeEventListener(\\\\\\\"touchstart\\\\\\\",this._boundMethods.onRotateStart),this.domElement.removeEventListener(\\\\\\\"touchmove\\\\\\\",this._boundMethods.onRotateMove),this.domElement.removeEventListener(\\\\\\\"touchend\\\\\\\",this._boundMethods.onRotateEnd),this._translateDomElement.removeEventListener(\\\\\\\"touchstart\\\\\\\",this._boundMethods.onTranslateStart),this._translateDomElement.removeEventListener(\\\\\\\"touchmove\\\\\\\",this._boundMethods.onTranslateMove),this._translateDomElement.removeEventListener(\\\\\\\"touchend\\\\\\\",this._boundMethods.onTranslateEnd),null===(t=this.domElement.parentElement)||void 0===t||t.removeChild(this._translateDomElement)}setRotationSpeed(t){this._rotationSpeed=t}setRotationRange(t){this._rotationRange.min=t.min,this._rotationRange.max=t.max}setTranslationSpeed(t){this._translationSpeed=t}_onRotateStart(t){this._startCameraRotation.copy(this._camera.rotation);const e=this._getTouch(t,this.domElement);e&&(this._rotationStartPosition.set(e.clientX,e.clientY),this.vLeft.set(-1,0,.5),this.vRight.set(1,0,.5),[this.vLeft,this.vRight].forEach((t=>{t.unproject(this._camera),this._camera.worldToLocal(t)})),this.angleY=this.vLeft.angleTo(this.vRight),this.vTop.set(0,1,.5),this.vBottom.set(0,-1,.5),[this.vTop,this.vBottom].forEach((t=>{t.unproject(this._camera),this._camera.worldToLocal(t)})),this.angleX=this.vTop.angleTo(this.vBottom))}_onRotateMove(t){const e=this._getTouch(t,this.domElement);e&&(this._rotationMovePosition.set(e.clientX,e.clientY),this._rotationDelta.copy(this._rotationMovePosition).sub(this._rotationStartPosition),this.rotationData.direction.x=this._rotationDelta.x/this.domElement.clientWidth,this.rotationData.direction.y=this._rotationDelta.y/this.domElement.clientHeight,this._rotateCamera(this.rotationData))}_onRotateEnd(){this.rotationData.direction.x=0,this.rotationData.direction.y=0}_rotateCamera(t){let e=this.angleY*t.direction.x*this._rotationSpeed;this._camera.rotation.y=this._startCameraRotation.y+-e;let n=this.angleX*t.direction.y*this._rotationSpeed;this._camera.rotation.x=rs.clamp(this._startCameraRotation.x+-n,this._rotationRange.min,this._rotationRange.max),this.dispatchEvent(qN)}_onTranslateStart(t){this._startCameraPosition.copy(this._camera.position);const e=this._getTouch(t,this._translateDomElement);e&&(this._translationStartPosition.set(e.clientX,e.clientY),this._translateDomElementRect=this._translateDomElement.getBoundingClientRect())}_onTranslateMove(t){const e=this._getTouch(t,this._translateDomElement);e&&(this._translationMovePosition.set(e.clientX,e.clientY),this._translationDelta.copy(this._translationMovePosition).sub(this._translationStartPosition),this.translationData.direction.x=this._translationSpeed*this._translationDelta.x/this._translateDomElementRect.width,this.translationData.direction.y=this._translationSpeed*-this._translationDelta.y/this._translateDomElementRect.height,this.dispatchEvent(qN))}_onTranslateEnd(){this.translationData.direction.x=0,this.translationData.direction.y=0}update(){const t=performance.now(),e=t-this.prevTime;this.prevTime=t,this._translateCamera(this.translationData,e)}_translateCamera(t,e){this._camera.getWorldDirection(this._camWorldDir),this._camWorldDir.y=0,this._camWorldDir.normalize(),this._camSideVector.crossVectors(this._up,this._camWorldDir),this._camSideVector.normalize(),this._camSideVector.multiplyScalar(-t.direction.x),this._camWorldDir.multiplyScalar(t.direction.y),this._velocity.copy(this._camWorldDir),this._velocity.add(this._camSideVector);const n=this._camera.position.y;if(this._camTmpPost.copy(this._camera.position),this._playerCollisionController){const t=1;this._velocity.addScaledVector(this._velocity,t);const n=this._velocity.clone().multiplyScalar(e);this._camTmpPost.add(n);const i=this._playerCollisionController.testPosition(this._camTmpPost);i&&this._camTmpPost.add(i.normal.multiplyScalar(i.depth)),this._camera.position.copy(this._camTmpPost)}else this._camTmpPost.add(this._camSideVector),this._camTmpPost.add(this._camWorldDir),this._camera.position.copy(this._camTmpPost);this._camera.position.y=n}_getTouch(t,e){for(let n=0;n{QN.PARAM_CALLBACK_recomputeCollidingGeo(t)},visibleIf:{collideWithGeo:!0}}),this.capsuleHeightRange=oa.VECTOR2([.3,1],{visibleIf:{collideWithGeo:!0}}),this.capsuleRadius=oa.FLOAT(.3,{visibleIf:{collideWithGeo:!0}})}};class QN extends jv{constructor(){super(...arguments),this.paramsConfig=ZN,this._controls_by_element_id=new Map}static type(){return rr.MOBILE_JOYSTICK}endEventName(){return\\\\\\\"end\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Jo(YN,$o.BASE),new Jo($N,$o.BASE),new Jo(JN,$o.BASE)])}async create_controls_instance(t,e){const n=new XN(t,e);return this._controls_by_element_id.set(e.id,n),this._bind_listeners_to_controls_instance(n),n}_bind_listeners_to_controls_instance(t){t.addEventListener(YN,(()=>{this.dispatchEventToOutput(YN,{})})),t.addEventListener($N,(()=>{this.dispatchEventToOutput($N,{})})),t.addEventListener(JN,(()=>{this.dispatchEventToOutput(JN,{})}))}update_required(){return!0}setup_controls(t){t.setRotationSpeed(this.pv.rotationSpeed),t.setRotationRange({min:this.pv.minPolarAngle,max:this.pv.maxPolarAngle}),t.setTranslationSpeed(this.pv.translationSpeed),this._setupCollisionGeo(t)}async _setupCollisionGeo(t){ON(t,this)}dispose_controls_for_html_element_id(t){this._controls_by_element_id.get(t)&&this._controls_by_element_id.delete(t)}static PARAM_CALLBACK_recomputeCollidingGeo(t){t._recomputeCollidingGeo()}_recomputeCollidingGeo(){this._controls_by_element_id.forEach(((t,e)=>{this._setupCollisionGeo(t)}))}}var KN;!function(t){t.ALL_TOGETHER=\\\\\\\"all together\\\\\\\",t.BATCH=\\\\\\\"batch\\\\\\\"}(KN||(KN={}));const tL=[KN.ALL_TOGETHER,KN.BATCH];const eL=new class extends aa{constructor(){super(...arguments),this.mask=oa.STRING(\\\\\\\"/geo*\\\\\\\",{callback:t=>{nL.PARAM_CALLBACK_update_resolved_nodes(t)}}),this.force=oa.BOOLEAN(0),this.cookMode=oa.INTEGER(tL.indexOf(KN.ALL_TOGETHER),{menu:{entries:tL.map(((t,e)=>({name:t,value:e})))}}),this.batchSize=oa.INTEGER(1,{visibleIf:{cookMode:tL.indexOf(KN.BATCH)},separatorAfter:!0}),this.updateResolve=oa.BUTTON(null,{callback:(t,e)=>{nL.PARAM_CALLBACK_update_resolve(t)}}),this.printResolve=oa.BUTTON(null,{callback:(t,e)=>{nL.PARAM_CALLBACK_print_resolve(t)}})}};class nL extends Ba{constructor(){super(...arguments),this.paramsConfig=eL,this._resolved_nodes=[],this._dispatched_first_node_cooked=!1,this._dispatched_all_nodes_cooked=!1,this._cook_state_by_node_id=new Map}static type(){return\\\\\\\"nodeCook\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(nL.INPUT_TRIGGER,$o.BASE,this.process_event_trigger.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(nL.OUTPUT_FIRST_NODE,$o.BASE),new Jo(nL.OUTPUT_EACH_NODE,$o.BASE),new Jo(nL.OUTPUT_ALL_NODES,$o.BASE)])}trigger(){this.process_event_trigger({})}cook(){this._update_resolved_nodes(),this.cookController.endCook()}dispose(){super.dispose(),this._reset()}process_event_trigger(t){this._cook_nodes_with_mode()}_cook_nodes_with_mode(){this._update_resolved_nodes();const t=tL[this.pv.cookMode];switch(t){case KN.ALL_TOGETHER:return this._cook_nodes_all_together();case KN.BATCH:return this._cook_nodes_batch()}ar.unreachable(t)}_cook_nodes_all_together(){this._cook_nodes(this._resolved_nodes)}async _cook_nodes_batch(){const t=this.pv.batchSize,e=Math.ceil(this._resolved_nodes.length/t);for(let n=0;n{this._on_node_cook_complete(t)})),this._cook_state_by_node_id.set(t.graphNodeId(),!1)}_callbackNameForNode(t){return`owner-${this.graphNodeId()}-target-${t.graphNodeId()}`}_reset(){this._dispatched_first_node_cooked=!1,this._cook_state_by_node_id.clear();for(let t of this._resolved_nodes)t.cookController.deregisterOnCookEnd(this._callbackNameForNode(t));this._resolved_nodes=[]}_all_nodes_have_cooked(){for(let t of this._resolved_nodes){if(!this._cook_state_by_node_id.get(t.graphNodeId()))return!1}return!0}_on_node_cook_complete(t){const e={value:{node:t}};this._dispatched_first_node_cooked||(this._dispatched_first_node_cooked=!0,this.dispatchEventToOutput(nL.OUTPUT_FIRST_NODE,e)),this._cook_state_by_node_id.get(t.graphNodeId())||this.dispatchEventToOutput(nL.OUTPUT_EACH_NODE,e),this._cook_state_by_node_id.set(t.graphNodeId(),!0),this._dispatched_all_nodes_cooked||this._all_nodes_have_cooked()&&(this._dispatched_all_nodes_cooked=!0,this.dispatchEventToOutput(nL.OUTPUT_ALL_NODES,{}))}static PARAM_CALLBACK_update_resolve(t){t._update_resolved_nodes()}static PARAM_CALLBACK_print_resolve(t){t.print_resolve()}print_resolve(){console.log(this._resolved_nodes)}}var iL,rL;nL.INPUT_TRIGGER=\\\\\\\"trigger\\\\\\\",nL.OUTPUT_FIRST_NODE=\\\\\\\"first\\\\\\\",nL.OUTPUT_EACH_NODE=\\\\\\\"each\\\\\\\",nL.OUTPUT_ALL_NODES=\\\\\\\"all\\\\\\\",function(t){t.TRIGGER=\\\\\\\"trigger\\\\\\\"}(iL||(iL={})),function(t){t.OUT=\\\\\\\"out\\\\\\\"}(rL||(rL={}));const sL=new class extends aa{};class oL extends Ba{constructor(){super(...arguments),this.paramsConfig=sL}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(iL.TRIGGER,$o.BASE,this.process_event_trigger.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(rL.OUT,$o.BASE)])}processEvent(t){}process_event_trigger(t){this.dispatchEventToOutput(rL.OUT,t)}}var aL,lL=n(39),cL=n(36);class uL{constructor(t,e,n=0,i=1/0){this.ray=new lL.a(t,e),this.near=n,this.far=i,this.camera=null,this.layers=new cL.a,this.params={Mesh:{},Line:{threshold:1},LOD:{},Points:{threshold:1},Sprite:{}}}set(t,e){this.ray.set(t,e)}setFromCamera(t,e){e&&e.isPerspectiveCamera?(this.ray.origin.setFromMatrixPosition(e.matrixWorld),this.ray.direction.set(t.x,t.y,.5).unproject(e).sub(this.ray.origin).normalize(),this.camera=e):e&&e.isOrthographicCamera?(this.ray.origin.set(t.x,t.y,(e.near+e.far)/(e.near-e.far)).unproject(e),this.ray.direction.set(0,0,-1).transformDirection(e.matrixWorld),this.camera=e):console.error(\\\\\\\"THREE.Raycaster: Unsupported camera type: \\\\\\\"+e.type)}intersectObject(t,e=!0,n=[]){return dL(t,this,n,e),n.sort(hL),n}intersectObjects(t,e=!0,n=[]){for(let i=0,r=t.length;i{this._mouse.x=t.offsetX/n.offsetWidth*2-1,this._mouse.y=-t.offsetY/n.offsetHeight*2+1,this._mouse.toArray(this._mouse_array),this._node.p.mouse.set(this._mouse_array)})(this._offset),this._raycaster.setFromCamera(this._mouse,i.object)}processEvent(t){this._prepareRaycaster(t);const e=mL[this._node.pv.intersectWith];switch(e){case _L.GEOMETRY:return this._intersect_with_geometry(t);case _L.PLANE:return this._intersect_with_plane(t)}ar.unreachable(e)}_intersect_with_plane(t){this._plane.normal.copy(this._node.pv.planeDirection),this._plane.constant=this._node.pv.planeOffset,this._raycaster.ray.intersectPlane(this._plane,this._plane_intersect_target),this._set_position_param(this._plane_intersect_target),this._node.trigger_hit(t)}_intersect_with_geometry(t){if(this._resolved_targets||this.update_target(),this._resolved_targets){this._intersections.length=0;const e=this._raycaster.intersectObjects(this._resolved_targets,this._node.pv.traverseChildren,this._intersections)[0];e?(this._set_position_param(e.point),this._node.pv.geoAttribute&&this._resolve_geometry_attribute(e),t.value={intersect:e},this._node.trigger_hit(t)):this._node.trigger_miss(t)}}_resolve_geometry_attribute(t){const e=kr[this._node.pv.geoAttributeType],n=gL.resolve_geometry_attribute(t,this._node.pv.geoAttributeName,e);if(null!=n){switch(e){case Dr.NUMERIC:return void this._node.p.geoAttributeValue1.set(n);case Dr.STRING:return void(m.isString(n)&&this._node.p.geoAttributeValues.set(n))}ar.unreachable(e)}}static resolve_geometry_attribute(t,e,n){switch(Nr(t.object.constructor)){case Sr.MESH:return this.resolve_geometry_attribute_for_mesh(t,e,n);case Sr.POINTS:return this.resolve_geometry_attribute_for_point(t,e,n)}}static resolve_geometry_attribute_for_mesh(t,e,n){const i=t.object.geometry;if(i){const r=i.getAttribute(e);if(r){switch(n){case Dr.NUMERIC:{const e=i.getAttribute(\\\\\\\"position\\\\\\\");return t.face?(this._vA.fromBufferAttribute(e,t.face.a),this._vB.fromBufferAttribute(e,t.face.b),this._vC.fromBufferAttribute(e,t.face.c),this._uvA.fromBufferAttribute(r,t.face.a),this._uvB.fromBufferAttribute(r,t.face.b),this._uvC.fromBufferAttribute(r,t.face.c),t.uv=Qr.a.getUV(t.point,this._vA,this._vB,this._vC,this._uvA,this._uvB,this._uvC,this._hitUV),this._hitUV.x):void 0}case Dr.STRING:{const t=new ps(i).points()[0];return t?t.stringAttribValue(e):void 0}}ar.unreachable(n)}}}static resolve_geometry_attribute_for_point(t,e,n){const i=t.object.geometry;if(i&&null!=t.index){switch(n){case Dr.NUMERIC:{const n=i.getAttribute(e);return n?n.array[t.index]:void 0}case Dr.STRING:{const n=new ps(i).points()[t.index];return n?n.stringAttribValue(e):void 0}}ar.unreachable(n)}}_set_position_param(t){if(t.toArray(this._hit_position_array),this._node.pv.tpositionTarget){if(ai.playerMode())this._found_position_target_param=this._found_position_target_param||this._node.pv.positionTarget.paramWithType(Es.VECTOR3);else{const t=this._node.pv.positionTarget;this._found_position_target_param=t.paramWithType(Es.VECTOR3)}this._found_position_target_param&&this._found_position_target_param.set(this._hit_position_array)}else this._node.p.position.set(this._hit_position_array);this.velocity_controller.process(t)}_prepareRaycaster(t){const e=this._raycaster.params.Points;e&&(e.threshold=this._node.pv.pointsThreshold);let n=t.cameraNode;if(this._node.pv.overrideCamera)if(this._node.pv.overrideRay)this._raycaster.ray.origin.copy(this._node.pv.rayOrigin),this._raycaster.ray.direction.copy(this._node.pv.rayDirection);else{const t=this._node.p.camera.found_node_with_context(Ki.OBJ);t&&(n=t)}n&&!this._node.pv.overrideRay&&n.prepareRaycaster(this._mouse,this._raycaster)}update_target(){const t=SL[this._node.pv.targetType];switch(t){case ML.NODE:return this._update_target_from_node();case ML.SCENE_GRAPH:return this._update_target_from_scene_graph()}ar.unreachable(t)}_update_target_from_node(){const t=this._node.p.targetNode.value.nodeWithContext(Ki.OBJ);if(t){const e=this._node.pv.traverseChildren?t.object:t.childrenDisplayController.sopGroup();this._resolved_targets=e?[e]:void 0}else this._node.states.error.set(\\\\\\\"node is not an object\\\\\\\")}_update_target_from_scene_graph(){const t=this._node.scene().objectsByMask(this._node.pv.objectMask);t.length>0?this._resolved_targets=t:this._resolved_targets=void 0}async update_position_target(){this._node.p.positionTarget.isDirty()&&await this._node.p.positionTarget.compute()}static PARAM_CALLBACK_update_target(t){t.cpuController.update_target()}static PARAM_CALLBACK_print_resolve(t){t.cpuController.print_resolve()}print_resolve(){this.update_target(),console.log(this._resolved_targets)}}gL._vA=new p.a,gL._vB=new p.a,gL._vC=new p.a,gL._uvA=new d.a,gL._uvB=new d.a,gL._uvC=new d.a,gL._hitUV=new d.a;class vL{constructor(t){this._node=t,this._resolved_material=null,this._restore_context={scene:{overrideMaterial:null},renderer:{toneMapping:-1,outputEncoding:-1}},this._mouse=new d.a,this._mouse_array=[0,0],this._read=new Float32Array(4),this._param_read=[0,0,0,0]}updateMouse(t){var e;const n=null===(e=t.viewer)||void 0===e?void 0:e.canvas();n&&t.event&&(t.event instanceof MouseEvent||t.event instanceof DragEvent||t.event instanceof PointerEvent?(this._mouse.x=t.event.offsetX/n.offsetWidth,this._mouse.y=1-t.event.offsetY/n.offsetHeight,this._mouse.toArray(this._mouse_array),this._node.p.mouse.set(this._mouse_array)):console.warn(\\\\\\\"event type not implemented\\\\\\\"))}processEvent(t){var e;const n=null===(e=t.viewer)||void 0===e?void 0:e.canvas();if(!n||!t.cameraNode)return;const i=t.cameraNode,r=i.renderController;if(r){if(this._render_target=this._render_target||new Z(n.offsetWidth,n.offsetHeight,{minFilter:w.V,magFilter:w.ob,format:w.Ib,type:w.G}),!this._resolved_material)return this.update_material(),void console.warn(\\\\\\\"no material found\\\\\\\");const e=i,s=r.resolved_scene||i.scene().threejsScene(),o=r.renderer(n);this._modify_scene_and_renderer(s,o),o.setRenderTarget(this._render_target),o.clear(),o.render(s,e.object),o.setRenderTarget(null),this._restore_scene_and_renderer(s,o),o.readRenderTargetPixels(this._render_target,Math.round(this._mouse.x*n.offsetWidth),Math.round(this._mouse.y*n.offsetHeight),1,1,this._read),this._param_read[0]=this._read[0],this._param_read[1]=this._read[1],this._param_read[2]=this._read[2],this._param_read[3]=this._read[3],this._node.p.pixelValue.set(this._param_read),this._node.pv.pixelValue.x>this._node.pv.hitThreshold?this._node.trigger_hit(t):this._node.trigger_miss(t)}}_modify_scene_and_renderer(t,e){this._restore_context.scene.overrideMaterial=t.overrideMaterial,this._restore_context.renderer.outputEncoding=e.outputEncoding,this._restore_context.renderer.toneMapping=e.toneMapping,t.overrideMaterial=this._resolved_material,e.toneMapping=w.vb,e.outputEncoding=w.U}_restore_scene_and_renderer(t,e){t.overrideMaterial=this._restore_context.scene.overrideMaterial,e.outputEncoding=this._restore_context.renderer.outputEncoding,e.toneMapping=this._restore_context.renderer.toneMapping}update_material(){const t=this._node.p.material.found_node();t?t.context()==Ki.MAT?this._resolved_material=t.material:this._node.states.error.set(\\\\\\\"target is not an obj\\\\\\\"):this._node.states.error.set(\\\\\\\"no target found\\\\\\\")}static PARAM_CALLBACK_update_material(t){t.gpuController.update_material()}}const yL=1e3/60;var xL;!function(t){t.CPU=\\\\\\\"cpu\\\\\\\",t.GPU=\\\\\\\"gpu\\\\\\\"}(xL||(xL={}));const bL=[xL.CPU,xL.GPU];function wL(t={}){return t.mode=bL.indexOf(xL.CPU),{visibleIf:t}}function TL(t={}){return t.mode=bL.indexOf(xL.CPU),t.intersectWith=mL.indexOf(_L.GEOMETRY),{visibleIf:t}}function AL(t={}){return t.mode=bL.indexOf(xL.CPU),t.intersectWith=mL.indexOf(_L.PLANE),{visibleIf:t}}function EL(t={}){return t.mode=bL.indexOf(xL.GPU),{visibleIf:t}}var ML;!function(t){t.SCENE_GRAPH=\\\\\\\"scene graph\\\\\\\",t.NODE=\\\\\\\"node\\\\\\\"}(ML||(ML={}));const SL=[ML.SCENE_GRAPH,ML.NODE];const CL=new class extends aa{constructor(){super(...arguments),this.mode=oa.INTEGER(bL.indexOf(xL.CPU),{menu:{entries:bL.map(((t,e)=>({name:t,value:e})))}}),this.mouse=oa.VECTOR2([0,0],{cook:!1}),this.overrideCamera=oa.BOOLEAN(0),this.overrideRay=oa.BOOLEAN(0,{visibleIf:{mode:bL.indexOf(xL.CPU),overrideCamera:1}}),this.camera=oa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{nodeSelection:{context:Ki.OBJ},dependentOnFoundNode:!1,visibleIf:{overrideCamera:1,overrideRay:0}}),this.rayOrigin=oa.VECTOR3([0,0,0],{visibleIf:{overrideCamera:1,overrideRay:1}}),this.rayDirection=oa.VECTOR3([0,0,1],{visibleIf:{overrideCamera:1,overrideRay:1}}),this.material=oa.OPERATOR_PATH(\\\\\\\"/MAT/mesh_basic_builder1\\\\\\\",{nodeSelection:{context:Ki.MAT},dependentOnFoundNode:!1,callback:(t,e)=>{vL.PARAM_CALLBACK_update_material(t)},...EL()}),this.pixelValue=oa.VECTOR4([0,0,0,0],{cook:!1,...EL()}),this.hitThreshold=oa.FLOAT(.5,{cook:!1,...EL()}),this.intersectWith=oa.INTEGER(mL.indexOf(_L.GEOMETRY),{menu:{entries:mL.map(((t,e)=>({name:t,value:e})))},...wL()}),this.pointsThreshold=oa.FLOAT(1,{range:[0,100],rangeLocked:[!0,!1],...wL()}),this.planeDirection=oa.VECTOR3([0,1,0],{...AL()}),this.planeOffset=oa.FLOAT(0,{...AL()}),this.targetType=oa.INTEGER(0,{menu:{entries:SL.map(((t,e)=>({name:t,value:e})))},...TL()}),this.targetNode=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.OBJ},dependentOnFoundNode:!1,callback:(t,e)=>{gL.PARAM_CALLBACK_update_target(t)},...TL({targetType:SL.indexOf(ML.NODE)})}),this.objectMask=oa.STRING(\\\\\\\"*geo1*\\\\\\\",{callback:(t,e)=>{gL.PARAM_CALLBACK_update_target(t)},...TL({targetType:SL.indexOf(ML.SCENE_GRAPH)})}),this.printFoundObjectsFromMask=oa.BUTTON(null,{callback:(t,e)=>{gL.PARAM_CALLBACK_print_resolve(t)},...TL({targetType:SL.indexOf(ML.SCENE_GRAPH)})}),this.traverseChildren=oa.BOOLEAN(!0,{callback:(t,e)=>{gL.PARAM_CALLBACK_update_target(t)},...TL(),separatorAfter:!0}),this.tpositionTarget=oa.BOOLEAN(0,{cook:!1,...wL()}),this.position=oa.VECTOR3([0,0,0],{cook:!1,...wL({tpositionTarget:0})}),this.positionTarget=oa.PARAM_PATH(\\\\\\\"\\\\\\\",{cook:!1,...wL({tpositionTarget:1}),paramSelection:Es.VECTOR3,computeOnDirty:!0}),this.tvelocity=oa.BOOLEAN(0,{cook:!1}),this.tvelocityTarget=oa.BOOLEAN(0,{cook:!1,...wL({tvelocity:1})}),this.velocity=oa.VECTOR3([0,0,0],{cook:!1,...wL({tvelocity:1,tvelocityTarget:0})}),this.velocityTarget=oa.PARAM_PATH(\\\\\\\"\\\\\\\",{cook:!1,...wL({tvelocity:1,tvelocityTarget:1}),paramSelection:Es.VECTOR3,computeOnDirty:!0}),this.geoAttribute=oa.BOOLEAN(0,TL()),this.geoAttributeName=oa.STRING(\\\\\\\"id\\\\\\\",{cook:!1,...TL({geoAttribute:1})}),this.geoAttributeType=oa.INTEGER(kr.indexOf(Dr.NUMERIC),{menu:{entries:Br},...TL({geoAttribute:1})}),this.geoAttributeValue1=oa.FLOAT(0,{cook:!1,...TL({geoAttribute:1,geoAttributeType:kr.indexOf(Dr.NUMERIC)})}),this.geoAttributeValues=oa.STRING(\\\\\\\"\\\\\\\",{...TL({geoAttribute:1,geoAttributeType:kr.indexOf(Dr.STRING)})})}};class NL extends Ba{constructor(){super(...arguments),this.paramsConfig=CL,this.cpuController=new gL(this),this.gpuController=new vL(this),this._last_event_processed_at=-1}static type(){return\\\\\\\"raycast\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(NL.INPUT_TRIGGER,$o.BASE,this._process_trigger_event_throttled.bind(this)),new Jo(NL.INPUT_MOUSE,$o.MOUSE,this._process_mouse_event.bind(this)),new Jo(NL.INPUT_UPDATE_OBJECTS,$o.BASE,this._process_trigger_update_objects.bind(this)),new Jo(NL.INPUT_TRIGGER_VEL_RESET,$o.BASE,this._process_trigger_vel_reset.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(NL.OUTPUT_HIT,$o.BASE),new Jo(NL.OUTPUT_MISS,$o.BASE)])}trigger_hit(t){this.dispatchEventToOutput(NL.OUTPUT_HIT,t)}trigger_miss(t){this.dispatchEventToOutput(NL.OUTPUT_MISS,t)}_process_mouse_event(t){this.pv.mode==bL.indexOf(xL.CPU)?this.cpuController.updateMouse(t):this.gpuController.updateMouse(t)}_process_trigger_event_throttled(t){const e=this._last_event_processed_at,n=ai.performance.performanceManager().now();this._last_event_processed_at=n;const i=n-e;i{this._process_trigger_event(t)}),yL-i):this._process_trigger_event(t)}_process_trigger_event(t){this.pv.mode==bL.indexOf(xL.CPU)?this.cpuController.processEvent(t):this.gpuController.processEvent(t)}_process_trigger_update_objects(t){this.pv.mode==bL.indexOf(xL.CPU)&&this.cpuController.update_target()}_process_trigger_vel_reset(t){this.pv.mode==bL.indexOf(xL.CPU)&&this.cpuController.velocity_controller.reset()}}var LL;NL.INPUT_TRIGGER=\\\\\\\"trigger\\\\\\\",NL.INPUT_MOUSE=\\\\\\\"mouse\\\\\\\",NL.INPUT_UPDATE_OBJECTS=\\\\\\\"updateObjects\\\\\\\",NL.INPUT_TRIGGER_VEL_RESET=\\\\\\\"triggerVelReset\\\\\\\",NL.OUTPUT_HIT=\\\\\\\"hit\\\\\\\",NL.OUTPUT_MISS=\\\\\\\"miss\\\\\\\",function(t){t.SET=\\\\\\\"set\\\\\\\",t.TOGGLE=\\\\\\\"toggle\\\\\\\"}(LL||(LL={}));const OL=[LL.SET,LL.TOGGLE];const RL=new class extends aa{constructor(){super(...arguments),this.mask=oa.STRING(\\\\\\\"/geo*\\\\\\\",{separatorAfter:!0}),this.tdisplay=oa.BOOLEAN(0),this.displayMode=oa.INTEGER(OL.indexOf(LL.SET),{visibleIf:{tdisplay:1},menu:{entries:OL.map(((t,e)=>({name:t,value:e})))}}),this.display=oa.BOOLEAN(0,{visibleIf:{tdisplay:1,displayMode:OL.indexOf(LL.SET)},separatorAfter:!0}),this.tbypass=oa.BOOLEAN(0),this.bypassMode=oa.INTEGER(OL.indexOf(LL.SET),{visibleIf:{tbypass:1},menu:{entries:OL.map(((t,e)=>({name:t,value:e})))}}),this.bypass=oa.BOOLEAN(0,{visibleIf:{tbypass:1,displayMode:OL.indexOf(LL.SET)}}),this.execute=oa.BUTTON(null,{callback:t=>{PL.PARAM_CALLBACK_execute(t)}})}};class PL extends Ba{constructor(){super(...arguments),this.paramsConfig=RL}static type(){return\\\\\\\"setFlag\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(\\\\\\\"trigger\\\\\\\",$o.BASE)])}async processEvent(t){let e=this.pv.mask;if(t.value){const n=t.value.node;if(n){const t=n.parent();t&&(e=`${t.path()}/${e}`)}}const n=this.scene().nodesController.nodesFromMask(e);for(let t of n)this._update_node_flags(t)}_update_node_flags(t){this._update_node_display_flag(t),this._update_node_bypass_flag(t)}_update_node_display_flag(t){var e;if(!this.pv.tdisplay)return;if(!(null===(e=t.flags)||void 0===e?void 0:e.hasDisplay()))return;const n=t.flags.display;if(!n)return;const i=OL[this.pv.displayMode];switch(i){case LL.SET:return void n.set(this.pv.display);case LL.TOGGLE:return void n.set(!n.active())}ar.unreachable(i)}_update_node_bypass_flag(t){var e;if(!this.pv.tbypass)return;if(!(null===(e=t.flags)||void 0===e?void 0:e.hasBypass()))return;const n=t.flags.bypass;if(!n)return;const i=OL[this.pv.bypassMode];switch(i){case LL.SET:return void n.set(this.pv.bypass);case LL.TOGGLE:return void n.set(!n.active())}ar.unreachable(i)}static PARAM_CALLBACK_execute(t){t.processEvent({})}}var IL;!function(t){t.BOOLEAN=\\\\\\\"boolean\\\\\\\",t.BUTTON=\\\\\\\"button\\\\\\\",t.NUMBER=\\\\\\\"number\\\\\\\",t.VECTOR2=\\\\\\\"vector2\\\\\\\",t.VECTOR3=\\\\\\\"vector3\\\\\\\",t.VECTOR4=\\\\\\\"vector4\\\\\\\",t.STRING=\\\\\\\"string\\\\\\\"}(IL||(IL={}));const FL=[IL.BOOLEAN,IL.BUTTON,IL.NUMBER,IL.VECTOR2,IL.VECTOR3,IL.VECTOR4,IL.STRING],DL=FL.indexOf(IL.BOOLEAN),kL=FL.indexOf(IL.NUMBER),BL=FL.indexOf(IL.VECTOR2),zL=FL.indexOf(IL.VECTOR3),UL=FL.indexOf(IL.VECTOR4),GL=FL.indexOf(IL.STRING),VL=\\\\\\\"output\\\\\\\";const HL=new class extends aa{constructor(){super(...arguments),this.param=oa.PARAM_PATH(\\\\\\\"\\\\\\\",{paramSelection:!0,computeOnDirty:!0}),this.type=oa.INTEGER(kL,{menu:{entries:FL.map(((t,e)=>({name:t,value:e})))}}),this.toggle=oa.BOOLEAN(0,{visibleIf:{type:DL}}),this.boolean=oa.BOOLEAN(0,{visibleIf:{type:DL,toggle:0}}),this.number=oa.FLOAT(0,{visibleIf:{type:kL}}),this.vector2=oa.VECTOR2([0,0],{visibleIf:{type:BL}}),this.vector3=oa.VECTOR3([0,0,0],{visibleIf:{type:zL}}),this.vector4=oa.VECTOR4([0,0,0,0],{visibleIf:{type:UL}}),this.increment=oa.BOOLEAN(0,{visibleIf:[{type:kL},{type:BL},{type:zL},{type:UL}]}),this.string=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{type:GL}}),this.execute=oa.BUTTON(null,{callback:t=>{jL.PARAM_CALLBACK_execute(t)}})}};class jL extends Ba{constructor(){super(...arguments),this.paramsConfig=HL,this._tmp_vector2=new d.a,this._tmp_vector3=new p.a,this._tmp_vector4=new _.a,this._tmp_array2=[0,0],this._tmp_array3=[0,0,0],this._tmp_array4=[0,0,0,0]}static type(){return\\\\\\\"setParam\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(\\\\\\\"trigger\\\\\\\",$o.BASE)]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(VL,$o.BASE)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.param])}))}))}async processEvent(t){this.p.param.isDirty()&&await this.p.param.compute();const e=this.p.param.value.param();if(e){const t=await this._new_param_value(e);null!=t&&e.set(t)}else this.states.error.set(\\\\\\\"target param not found\\\\\\\");this.dispatchEventToOutput(VL,t)}async _new_param_value(t){const e=FL[this.pv.type];switch(e){case IL.BOOLEAN:return await this._compute_params_if_dirty([this.p.toggle]),this.pv.toggle?t.value?0:1:this.pv.boolean?1:0;case IL.BUTTON:return t.options.executeCallback();case IL.NUMBER:return await this._compute_params_if_dirty([this.p.increment,this.p.number]),this.pv.increment?t.type()==Es.FLOAT?t.value+this.pv.number:t.value:this.pv.number;case IL.VECTOR2:return await this._compute_params_if_dirty([this.p.increment,this.p.vector2]),this.pv.increment?t.type()==Es.VECTOR2?(this._tmp_vector2.copy(t.value),this._tmp_vector2.add(this.pv.vector2),this._tmp_vector2.toArray(this._tmp_array2)):t.value.toArray(this._tmp_array2):this.pv.vector2.toArray(this._tmp_array2),this._tmp_array2;case IL.VECTOR3:return await this._compute_params_if_dirty([this.p.increment,this.p.vector3]),this.pv.increment?t.type()==Es.VECTOR3?(this._tmp_vector3.copy(t.value),this._tmp_vector3.add(this.pv.vector3),this._tmp_vector3.toArray(this._tmp_array3)):t.value.toArray(this._tmp_array3):this.pv.vector3.toArray(this._tmp_array3),this._tmp_array3;case IL.VECTOR4:return await this._compute_params_if_dirty([this.p.increment,this.p.vector4]),this.pv.increment?t.type()==Es.VECTOR4?(this._tmp_vector4.copy(t.value),this._tmp_vector4.add(this.pv.vector4),this._tmp_vector4.toArray(this._tmp_array4)):t.value.toArray(this._tmp_array4):this.pv.vector4.toArray(this._tmp_array4),this._tmp_array4;case IL.STRING:return await this._compute_params_if_dirty([this.p.string]),this.pv.string}ar.unreachable(e)}static PARAM_CALLBACK_execute(t){t.processEvent({})}async _compute_params_if_dirty(t){const e=[];for(let n of t)n.isDirty()&&e.push(n);const n=[];for(let t of e)n.push(t.compute());return await Promise.all(n)}}const WL=new class extends aa{constructor(){super(...arguments),this.outputsCount=oa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1]})}};class qL extends Ba{constructor(){super(...arguments),this.paramsConfig=WL}static type(){return\\\\\\\"sequence\\\\\\\"}initializeNode(){this.io.connection_points.set_input_name_function((()=>\\\\\\\"trigger\\\\\\\")),this.io.connection_points.set_expected_input_types_function((()=>[$o.BASE])),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_output_name_function(this._output_name.bind(this))}_expected_output_types(){const t=new Array(this.pv.outputsCount);return t.fill($o.BASE),t}_output_name(t){return`out${t}`}processEvent(t){const e=this.pv.outputsCount;for(let n=0;n{this._timer_active&&(this.pv.count<=0||this._current_count{var e;null===(e=t.controlsController)||void 0===e||e.create_controls()}))}_process_trigger_disposeControls(t){this.scene().viewersRegister.traverseViewers((t=>{var e;null===(e=t.controlsController)||void 0===e||e.dispose_controls()}))}}class QL extends ia{static context(){return Ki.EVENT}cook(){this.cookController.endCook()}}class KL extends QL{}class tO extends KL{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class eO extends KL{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class nO extends KL{constructor(){super(...arguments),this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class iO extends KL{constructor(){super(...arguments),this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class rO extends QL{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class sO extends KL{constructor(){super(...arguments),this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}const oO=\\\\\\\"int\\\\\\\";const aO=new class extends aa{constructor(){super(...arguments),this.float=oa.FLOAT(0)}};class lO extends df{constructor(){super(...arguments),this.paramsConfig=aO}static type(){return\\\\\\\"floatToInt\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(oO,Do.INT)])}setLines(t){const e=this.variableForInputParam(this.p.float),n=`int ${this.glVarName(oO)} = int(${uf.float(e)})`;t.addBodyLines(this,[n])}}const cO=\\\\\\\"float\\\\\\\";const uO=new class extends aa{constructor(){super(...arguments),this.int=oa.INTEGER(0)}};class hO extends df{constructor(){super(...arguments),this.paramsConfig=uO}static type(){return\\\\\\\"intToFloat\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(cO,Do.FLOAT)])}setLines(t){const e=this.variableForInputParam(this.p.int),n=`float ${this.glVarName(cO)} = float(${uf.integer(e)})`;t.addBodyLines(this,[n])}}const dO=\\\\\\\"bool\\\\\\\";const pO=new class extends aa{constructor(){super(...arguments),this.int=oa.INTEGER(0)}};class _O extends df{constructor(){super(...arguments),this.paramsConfig=pO}static type(){return\\\\\\\"intToBool\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(dO,Do.BOOL)])}setLines(t){const e=this.variableForInputParam(this.p.int),n=`bool ${this.glVarName(dO)} = bool(${uf.integer(e)})`;t.addBodyLines(this,[n])}}const mO=new class extends aa{constructor(){super(...arguments),this.bool=oa.BOOLEAN(0)}};class fO extends df{constructor(){super(...arguments),this.paramsConfig=mO}static type(){return\\\\\\\"boolToInt\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(oO,Do.INT)])}setLines(t){const e=this.variableForInputParam(this.p.bool),n=`int ${this.glVarName(oO)} = int(${uf.bool(e)})`;t.addBodyLines(this,[n])}}const gO=new class extends aa{constructor(){super(...arguments),this.x=oa.FLOAT(0),this.y=oa.FLOAT(0)}};class vO extends df{constructor(){super(...arguments),this.paramsConfig=gO}static type(){return\\\\\\\"floatToVec2\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(vO.OUTPUT_NAME,Do.VEC2)])}setLines(t){const e=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=`vec2 ${this.glVarName(vO.OUTPUT_NAME)} = ${uf.float2(e,n)}`;t.addBodyLines(this,[i])}}vO.OUTPUT_NAME=\\\\\\\"vec2\\\\\\\";const yO=new class extends aa{constructor(){super(...arguments),this.x=oa.FLOAT(0),this.y=oa.FLOAT(0),this.z=oa.FLOAT(0)}};class xO extends df{constructor(){super(...arguments),this.paramsConfig=yO}static type(){return\\\\\\\"floatToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(xO.OUTPUT_NAME,Do.VEC3)])}setLines(t){const e=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=this.variableForInputParam(this.p.z),r=`vec3 ${this.glVarName(xO.OUTPUT_NAME)} = ${uf.float3(e,n,i)}`;t.addBodyLines(this,[r])}}xO.OUTPUT_NAME=\\\\\\\"vec3\\\\\\\";const bO=new class extends aa{constructor(){super(...arguments),this.x=oa.FLOAT(0),this.y=oa.FLOAT(0),this.z=oa.FLOAT(0),this.w=oa.FLOAT(0)}};class wO extends df{constructor(){super(...arguments),this.paramsConfig=bO}static type(){return\\\\\\\"floatToVec4\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(wO.OUTPUT_NAME,Do.VEC4)])}setLines(t){const e=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=this.variableForInputParam(this.p.z),r=this.variableForInputParam(this.p.w),s=`vec4 ${this.glVarName(wO.OUTPUT_NAME)} = ${uf.float4(e,n,i,r)}`;t.addBodyLines(this,[s])}}wO.OUTPUT_NAME=\\\\\\\"vec4\\\\\\\";const TO=new class extends aa{};class AO extends df{constructor(){super(...arguments),this.paramsConfig=TO}}function EO(t,e){const n=e.components,i=e.param_type;return class extends AO{static type(){return t}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(n.map((t=>new Vo(t,Do.FLOAT))))}createParams(){this.addParam(i,\\\\\\\"vec\\\\\\\",n.map((t=>0)))}setLines(t){const e=[],n=this.variableForInput(\\\\\\\"vec\\\\\\\");this.io.outputs.used_output_names().forEach((t=>{const i=this.glVarName(t);e.push(`float ${i} = ${n}.${t}`)})),t.addBodyLines(this,e)}}}const MO=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"],SO=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"],CO=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];class NO extends(EO(\\\\\\\"vec2ToFloat\\\\\\\",{components:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"],param_type:Es.VECTOR2})){}class LO extends(EO(\\\\\\\"vec3ToFloat\\\\\\\",{components:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"],param_type:Es.VECTOR3})){}class OO extends(EO(\\\\\\\"vec4ToFloat\\\\\\\",{components:CO,param_type:Es.VECTOR4})){}class RO extends AO{static type(){return\\\\\\\"vec4ToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(RO.OUTPUT_NAME_VEC3,Do.VEC3),new Vo(RO.OUTPUT_NAME_W,Do.FLOAT)])}createParams(){this.addParam(Es.VECTOR4,RO.INPUT_NAME_VEC4,CO.map((t=>0)))}setLines(t){const e=[],n=RO.INPUT_NAME_VEC4,i=RO.OUTPUT_NAME_VEC3,r=RO.OUTPUT_NAME_W,s=this.variableForInput(n),o=this.io.outputs.used_output_names();if(o.indexOf(i)>=0){const t=this.glVarName(i);e.push(`vec3 ${t} = ${s}.xyz`)}if(o.indexOf(r)>=0){const t=this.glVarName(r);e.push(`float ${t} = ${s}.w`)}t.addBodyLines(this,e)}}RO.INPUT_NAME_VEC4=\\\\\\\"vec4\\\\\\\",RO.OUTPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",RO.OUTPUT_NAME_W=\\\\\\\"w\\\\\\\";class PO extends AO{static type(){return\\\\\\\"vec3ToVec2\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(PO.OUTPUT_NAME_VEC2,Do.VEC2),new Vo(PO.OUTPUT_NAME_Z,Do.FLOAT)])}createParams(){this.addParam(Es.VECTOR3,PO.INPUT_NAME_VEC3,SO.map((t=>0)))}setLines(t){const e=[],n=PO.INPUT_NAME_VEC3,i=PO.OUTPUT_NAME_VEC2,r=PO.OUTPUT_NAME_Z,s=this.variableForInput(n),o=this.io.outputs.used_output_names();if(o.indexOf(i)>=0){const t=this.glVarName(i);e.push(`vec2 ${t} = ${s}.xy`)}if(o.indexOf(r)>=0){const t=this.glVarName(r);e.push(`float ${t} = ${s}.z`)}t.addBodyLines(this,e)}}PO.INPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",PO.OUTPUT_NAME_VEC2=\\\\\\\"vec2\\\\\\\",PO.OUTPUT_NAME_Z=\\\\\\\"z\\\\\\\";class IO extends AO{static type(){return\\\\\\\"vec2ToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(IO.OUTPUT_NAME_VEC3,Do.VEC3)])}createParams(){this.addParam(Es.VECTOR2,IO.INPUT_NAME_VEC2,MO.map((t=>0))),this.addParam(Es.FLOAT,IO.INPUT_NAME_Z,0)}setLines(t){const e=[],n=IO.INPUT_NAME_VEC2,i=IO.INPUT_NAME_Z,r=IO.OUTPUT_NAME_VEC3,s=this.variableForInput(n),o=this.variableForInput(i),a=this.glVarName(r);e.push(`vec3 ${a} = vec3(${s}.xy, ${o})`),t.addBodyLines(this,e)}}IO.INPUT_NAME_VEC2=\\\\\\\"vec3\\\\\\\",IO.INPUT_NAME_Z=\\\\\\\"z\\\\\\\",IO.OUTPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\";class FO extends AO{static type(){return\\\\\\\"vec3ToVec4\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(FO.OUTPUT_NAME_VEC4,Do.VEC4)])}createParams(){this.addParam(Es.VECTOR3,FO.INPUT_NAME_VEC3,SO.map((t=>0))),this.addParam(Es.FLOAT,FO.INPUT_NAME_W,0)}setLines(t){const e=[],n=FO.INPUT_NAME_VEC3,i=FO.INPUT_NAME_W,r=FO.OUTPUT_NAME_VEC4,s=this.variableForInput(n),o=this.variableForInput(i),a=this.glVarName(r);e.push(`vec4 ${a} = vec4(${s}.xyz, ${o})`),t.addBodyLines(this,e)}}FO.INPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",FO.INPUT_NAME_W=\\\\\\\"w\\\\\\\",FO.OUTPUT_NAME_VEC4=\\\\\\\"vec4\\\\\\\";const DO=new class extends aa{};class kO extends df{constructor(){super(...arguments),this.paramsConfig=DO}gl_method_name(){return\\\\\\\"\\\\\\\"}gl_function_definitions(){return[]}initializeNode(){super.initializeNode(),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this))}_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.FLOAT;if(this.io.connections.firstInputConnection()){const e=this.io.connections.inputConnections();if(e){let n=Math.max(f.compact(e).length+1,2);return f.range(n).map((e=>t))}return[]}return f.range(2).map((e=>t))}_expected_output_types(){return[this._expected_input_types()[0]]}_gl_input_name(t){return\\\\\\\"in\\\\\\\"}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.io.inputs.namedInputConnectionPoints().map(((t,e)=>{const n=t.name();return uf.any(this.variableForInput(n))})).join(\\\\\\\", \\\\\\\"),i=`${e} ${this.glVarName(this.io.connection_points.output_name(0))} = ${this.gl_method_name()}(${n})`;t.addBodyLines(this,[i]),t.addDefinitions(this,this.gl_function_definitions())}}class BO extends kO{_gl_input_name(t){return\\\\\\\"in\\\\\\\"}_expected_input_types(){return[this.io.connection_points.first_input_connection_type()||Do.FLOAT]}}class zO extends kO{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.FLOAT;return[t,t]}}class UO extends kO{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.FLOAT;return[t,t,t]}}class GO extends kO{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.FLOAT;return[t,t,t,t]}}class VO extends kO{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.FLOAT;return[t,t,t,t,t]}}function HO(t,e={}){const n=e.method||t,i=e.out||\\\\\\\"val\\\\\\\",r=e.in||\\\\\\\"in\\\\\\\";return class extends BO{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}_gl_input_name(t){return r}_gl_output_name(t){return i}gl_method_name(){return n}}}class jO extends(HO(\\\\\\\"abs\\\\\\\")){}class WO extends(HO(\\\\\\\"acos\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class qO extends(HO(\\\\\\\"asin\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class XO extends(HO(\\\\\\\"atan\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class YO extends(HO(\\\\\\\"ceil\\\\\\\")){}class $O extends(HO(\\\\\\\"cos\\\\\\\",{in:\\\\\\\"radians\\\\\\\"})){}class JO extends(HO(\\\\\\\"degrees\\\\\\\",{in:\\\\\\\"radians\\\\\\\",out:\\\\\\\"degrees\\\\\\\"})){}class ZO extends(HO(\\\\\\\"exp\\\\\\\")){}class QO extends(HO(\\\\\\\"exp2\\\\\\\")){}class KO extends(HO(\\\\\\\"floor\\\\\\\")){}class tR extends(HO(\\\\\\\"fract\\\\\\\")){}class eR extends(HO(\\\\\\\"inverseSqrt\\\\\\\",{method:\\\\\\\"inversesqrt\\\\\\\"})){}class nR extends(HO(\\\\\\\"log\\\\\\\")){}class iR extends(HO(\\\\\\\"log2\\\\\\\")){}class rR extends(HO(\\\\\\\"normalize\\\\\\\",{out:\\\\\\\"normalized\\\\\\\"})){}class sR extends(HO(\\\\\\\"radians\\\\\\\",{in:\\\\\\\"degrees\\\\\\\",out:\\\\\\\"radians\\\\\\\"})){}class oR extends(HO(\\\\\\\"sign\\\\\\\")){}class aR extends(HO(\\\\\\\"sin\\\\\\\",{in:\\\\\\\"radians\\\\\\\"})){}class lR extends(HO(\\\\\\\"sqrt\\\\\\\")){}class cR extends(HO(\\\\\\\"tan\\\\\\\")){}function uR(t,e={}){const n=e.method||t,i=e.out||\\\\\\\"val\\\\\\\",r=e.in||[\\\\\\\"in0\\\\\\\",\\\\\\\"in1\\\\\\\"],s=e.default_in_type,o=e.allowed_in_types,a=e.out_type,l=e.functions||[];return class extends zO{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),a&&this.io.connection_points.set_expected_output_types_function((()=>[a]))}_gl_input_name(t){return r[t]}_gl_output_name(t){return i}gl_method_name(){return n}gl_function_definitions(){return l?l.map((t=>new Tf(this,t))):[]}_expected_input_types(){let t=this.io.connection_points.first_input_connection_type();if(t&&o&&!o.includes(t)){const e=this.io.inputs.namedInputConnectionPoints()[0];t=e?e.type():s}const e=t||s||Do.FLOAT;return[e,e]}}}class hR extends(uR(\\\\\\\"distance\\\\\\\",{in:[\\\\\\\"p0\\\\\\\",\\\\\\\"p1\\\\\\\"],default_in_type:Do.VEC3,allowed_in_types:[Do.VEC2,Do.VEC3,Do.VEC4],out_type:Do.FLOAT})){}class dR extends(uR(\\\\\\\"dot\\\\\\\",{in:[\\\\\\\"vec0\\\\\\\",\\\\\\\"vec1\\\\\\\"],default_in_type:Do.VEC3,allowed_in_types:[Do.VEC2,Do.VEC3,Do.VEC4],out_type:Do.FLOAT})){}class pR extends(uR(\\\\\\\"max\\\\\\\")){}class _R extends(uR(\\\\\\\"min\\\\\\\")){}class mR extends(uR(\\\\\\\"mod\\\\\\\")){paramDefaultValue(t){return{in1:1}[t]}_expected_input_types(){const t=Do.FLOAT;return[t,t]}}class fR extends(uR(\\\\\\\"pow\\\\\\\",{in:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"]})){}class gR extends(uR(\\\\\\\"reflect\\\\\\\",{in:[\\\\\\\"I\\\\\\\",\\\\\\\"N\\\\\\\"],default_in_type:Do.VEC3})){}class vR extends(uR(\\\\\\\"step\\\\\\\",{in:[\\\\\\\"edge\\\\\\\",\\\\\\\"x\\\\\\\"]})){}function yR(t,e={}){const n=e.method||t,i=e.out||\\\\\\\"val\\\\\\\",r=e.in||[\\\\\\\"in0\\\\\\\",\\\\\\\"in1\\\\\\\",\\\\\\\"in2\\\\\\\"],s=e.default||{},o=e.out_type||Do.FLOAT,a=e.functions||[];return class extends UO{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_gl_input_name(t){return r[t]}_gl_output_name(t){return i}gl_method_name(){return n}_expected_output_types(){return[o]}paramDefaultValue(t){return s[t]}gl_function_definitions(){return a.map((t=>new Tf(this,t)))}}}class xR extends(yR(\\\\\\\"clamp\\\\\\\",{in:[\\\\\\\"value\\\\\\\",\\\\\\\"min\\\\\\\",\\\\\\\"max\\\\\\\"],default:{max:1}})){_expected_output_types(){return[this._expected_input_types()[0]]}}class bR extends(yR(\\\\\\\"faceForward\\\\\\\",{in:[\\\\\\\"N\\\\\\\",\\\\\\\"I\\\\\\\",\\\\\\\"Nref\\\\\\\"]})){}class wR extends(yR(\\\\\\\"smoothstep\\\\\\\",{in:[\\\\\\\"edge0\\\\\\\",\\\\\\\"edge1\\\\\\\",\\\\\\\"x\\\\\\\"],default:{edge1:1}})){_expected_output_types(){return[this._expected_input_types()[0]]}}function TR(t,e){const n=e.in_prefix||t,i=e.out||\\\\\\\"val\\\\\\\",r=e.operation,s=e.allowed_in_types;return class extends zO{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.io.inputs.namedInputConnectionPoints().map(((t,e)=>{const n=t.name(),i=this.variableForInput(n);if(i)return uf.any(i)})).join(` ${this.gl_operation()} `),i=`${e} ${this.glVarName(this.io.connection_points.output_name(0))} = ${this.gl_method_name()}(${n})`;t.addBodyLines(this,[i])}_gl_input_name(t){return`${n}${t}`}_gl_output_name(t){return i}gl_operation(){return r}_expected_input_types(){let t=this.io.connection_points.first_input_connection_type();if(t&&s&&!s.includes(t)){const e=this.io.inputs.namedInputConnectionPoints()[0];e&&(t=e.type())}const e=t||Do.FLOAT,n=this.io.connections.existingInputConnections(),i=n?Math.max(n.length+1,2):2,r=[];for(let t=0;t{const n=t.name();return uf.any(this.variableForInput(n))})).join(` ${this.boolean_operation()} `),n=`bool ${this.glVarName(this.io.connection_points.output_name(0))} = ${e}`;t.addBodyLines(this,[n])}}function NR(t,e){return class extends CR{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}boolean_operation(){return e.op}_gl_output_name(e){return t}_gl_input_name(e=0){return`${t}${e}`}}}class LR extends(NR(\\\\\\\"and\\\\\\\",{op:\\\\\\\"&&\\\\\\\"})){}class OR extends(NR(\\\\\\\"or\\\\\\\",{op:\\\\\\\"||\\\\\\\"})){}var RR;!function(t){t.TIME=\\\\\\\"time\\\\\\\",t.DELTA_TIME=\\\\\\\"delta_time\\\\\\\"}(RR||(RR={}));var PR,IR;!function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.VELOCITY=\\\\\\\"velocity\\\\\\\",t.MASS=\\\\\\\"mass\\\\\\\",t.FORCE=\\\\\\\"force\\\\\\\"}(PR||(PR={})),function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.VELOCITY=\\\\\\\"velocity\\\\\\\"}(IR||(IR={}));const FR=[PR.POSITION,PR.VELOCITY,PR.MASS,PR.FORCE],DR=[IR.POSITION,IR.VELOCITY],kR={[PR.POSITION]:[0,0,0],[PR.VELOCITY]:[0,0,0],[PR.MASS]:1,[PR.FORCE]:[0,-9.8,0]};const BR=new class extends aa{};class zR extends df{constructor(){super(...arguments),this.paramsConfig=BR}static type(){return\\\\\\\"acceleration\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(IR.POSITION,Do.VEC3),new Vo(IR.VELOCITY,Do.VEC3)]),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.VEC3;return[t,t,Do.FLOAT,t]}_expected_output_types(){const t=this._expected_input_types()[0];return[t,t]}_gl_input_name(t){return FR[t]}_gl_output_name(t){return DR[t]}paramDefaultValue(t){return kR[t]}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=new Af(this,Do.FLOAT,RR.DELTA_TIME),i=new Tf(this,\\\\\\\"float compute_velocity_from_acceleration(float vel, float force, float mass, float time_delta){\\\\n\\\\tfloat impulse = (force * mass) * time_delta;\\\\n\\\\treturn vel + impulse;\\\\n}\\\\nvec2 compute_velocity_from_acceleration(vec2 vel, vec2 force, float mass, float time_delta){\\\\n\\\\tvec2 impulse = (force * mass) * time_delta;\\\\n\\\\treturn vel + impulse;\\\\n}\\\\nvec3 compute_velocity_from_acceleration(vec3 vel, vec3 force, float mass, float time_delta){\\\\n\\\\tvec3 impulse = (force * mass) * time_delta;\\\\n\\\\treturn vel + impulse;\\\\n}\\\\nvec4 compute_velocity_from_acceleration(vec4 vel, vec4 force, float mass, float time_delta){\\\\n\\\\tvec4 impulse = (force * mass) * time_delta;\\\\n\\\\treturn vel + impulse;\\\\n}\\\\nfloat compute_position_from_velocity(float position, float velocity, float time_delta){\\\\n\\\\treturn position + (velocity * time_delta);\\\\n}\\\\nvec2 compute_position_from_velocity(vec2 position, vec2 velocity, float time_delta){\\\\n\\\\treturn position + (velocity * time_delta);\\\\n}\\\\nvec3 compute_position_from_velocity(vec3 position, vec3 velocity, float time_delta){\\\\n\\\\treturn position + (velocity * time_delta);\\\\n}\\\\nvec4 compute_position_from_velocity(vec4 position, vec4 velocity, float time_delta){\\\\n\\\\treturn position + (velocity * time_delta);\\\\n}\\\\\\\");t.addDefinitions(this,[n,i]);const r=uf.any(this.variableForInput(PR.POSITION)),s=uf.any(this.variableForInput(PR.VELOCITY)),o=uf.float(this.variableForInput(PR.MASS)),a=uf.any(this.variableForInput(PR.FORCE)),l=this.glVarName(IR.POSITION),c=this.glVarName(IR.VELOCITY),u=`${e} ${c} = compute_velocity_from_acceleration(${[s,a,o,RR.DELTA_TIME].join(\\\\\\\", \\\\\\\")})`,h=`${e} ${l} = compute_position_from_velocity(${[r,c,RR.DELTA_TIME].join(\\\\\\\", \\\\\\\")})`;t.addBodyLines(this,[u,h])}}var UR,GR=\\\\\\\"\\\\n\\\\n// https://github.com/mattatz/ShibuyaCrowd/blob/master/source/shaders/common/quaternion.glsl\\\\nvec4 quatMult(vec4 q1, vec4 q2)\\\\n{\\\\n\\\\treturn vec4(\\\\n\\\\tq1.w * q2.x + q1.x * q2.w + q1.z * q2.y - q1.y * q2.z,\\\\n\\\\tq1.w * q2.y + q1.y * q2.w + q1.x * q2.z - q1.z * q2.x,\\\\n\\\\tq1.w * q2.z + q1.z * q2.w + q1.y * q2.x - q1.x * q2.y,\\\\n\\\\tq1.w * q2.w - q1.x * q2.x - q1.y * q2.y - q1.z * q2.z\\\\n\\\\t);\\\\n}\\\\n// http://glmatrix.net/docs/quat.js.html#line97\\\\n// let ax = a[0], ay = a[1], az = a[2], aw = a[3];\\\\n\\\\n// let bx = b[0], by = b[1], bz = b[2], bw = b[3];\\\\n\\\\n// out[0] = ax * bw + aw * bx + ay * bz - az * by;\\\\n\\\\n// out[1] = ay * bw + aw * by + az * bx - ax * bz;\\\\n\\\\n// out[2] = az * bw + aw * bz + ax * by - ay * bx;\\\\n\\\\n// out[3] = aw * bw - ax * bx - ay * by - az * bz;\\\\n\\\\n// return out\\\\n\\\\n\\\\n\\\\n// http://www.neilmendoza.com/glsl-rotation-about-an-arbitrary-axis/\\\\nmat4 rotationMatrix(vec3 axis, float angle)\\\\n{\\\\n\\\\taxis = normalize(axis);\\\\n\\\\tfloat s = sin(angle);\\\\n\\\\tfloat c = cos(angle);\\\\n\\\\tfloat oc = 1.0 - c;\\\\n\\\\n \\\\treturn mat4(oc * axis.x * axis.x + c, oc * axis.x * axis.y - axis.z * s, oc * axis.z * axis.x + axis.y * s, 0.0, oc * axis.x * axis.y + axis.z * s, oc * axis.y * axis.y + c, oc * axis.y * axis.z - axis.x * s, 0.0, oc * axis.z * axis.x - axis.y * s, oc * axis.y * axis.z + axis.x * s, oc * axis.z * axis.z + c, 0.0, 0.0, 0.0, 0.0, 1.0);\\\\n}\\\\n\\\\n// https://www.geeks3d.com/20141201/how-to-rotate-a-vertex-by-a-quaternion-in-glsl/\\\\nvec4 quatFromAxisAngle(vec3 axis, float angle)\\\\n{\\\\n\\\\tvec4 qr;\\\\n\\\\tfloat half_angle = (angle * 0.5); // * 3.14159 / 180.0;\\\\n\\\\tfloat sin_half_angle = sin(half_angle);\\\\n\\\\tqr.x = axis.x * sin_half_angle;\\\\n\\\\tqr.y = axis.y * sin_half_angle;\\\\n\\\\tqr.z = axis.z * sin_half_angle;\\\\n\\\\tqr.w = cos(half_angle);\\\\n\\\\treturn qr;\\\\n}\\\\nvec3 rotateWithAxisAngle(vec3 position, vec3 axis, float angle)\\\\n{\\\\n\\\\tvec4 q = quatFromAxisAngle(axis, angle);\\\\n\\\\tvec3 v = position.xyz;\\\\n\\\\treturn v + 2.0 * cross(q.xyz, cross(q.xyz, v) + q.w * v);\\\\n}\\\\n// vec3 applyQuaternionToVector( vec4 q, vec3 v ){\\\\n// \\\\treturn v + 2.0 * cross( q.xyz, cross( q.xyz, v ) + q.w * v );\\\\n// }\\\\nvec3 rotateWithQuat( vec3 v, vec4 q )\\\\n{\\\\n\\\\t// vec4 qv = multQuat( quat, vec4(vec, 0.0) );\\\\n\\\\t// return multQuat( qv, vec4(-quat.x, -quat.y, -quat.z, quat.w) ).xyz;\\\\n\\\\treturn v + 2.0 * cross( q.xyz, cross( q.xyz, v ) + q.w * v );\\\\n}\\\\n// https://github.com/glslify/glsl-look-at/blob/gh-pages/index.glsl\\\\n// mat3 rotation_matrix(vec3 origin, vec3 target, float roll) {\\\\n// \\\\tvec3 rr = vec3(sin(roll), cos(roll), 0.0);\\\\n// \\\\tvec3 ww = normalize(target - origin);\\\\n// \\\\tvec3 uu = normalize(cross(ww, rr));\\\\n// \\\\tvec3 vv = normalize(cross(uu, ww));\\\\n\\\\n// \\\\treturn mat3(uu, vv, ww);\\\\n// }\\\\n// mat3 rotation_matrix(vec3 target, float roll) {\\\\n// \\\\tvec3 rr = vec3(sin(roll), cos(roll), 0.0);\\\\n// \\\\tvec3 ww = normalize(target);\\\\n// \\\\tvec3 uu = normalize(cross(ww, rr));\\\\n// \\\\tvec3 vv = normalize(cross(uu, ww));\\\\n\\\\n// \\\\treturn mat3(uu, vv, ww);\\\\n// }\\\\n\\\\nfloat vectorAngle(vec3 start, vec3 dest){\\\\n\\\\tstart = normalize(start);\\\\n\\\\tdest = normalize(dest);\\\\n\\\\n\\\\tfloat cosTheta = dot(start, dest);\\\\n\\\\tvec3 c1 = cross(start, dest);\\\\n\\\\t// We use the dot product of the cross with the Y axis.\\\\n\\\\t// This is a little arbitrary, but can still give a good sense of direction\\\\n\\\\tvec3 y_axis = vec3(0.0, 1.0, 0.0);\\\\n\\\\tfloat d1 = dot(c1, y_axis);\\\\n\\\\tfloat angle = acos(cosTheta) * sign(d1);\\\\n\\\\treturn angle;\\\\n}\\\\n\\\\n// http://www.opengl-tutorial.org/intermediate-tutorials/tutorial-17-quaternions/#i-need-an-equivalent-of-glulookat-how-do-i-orient-an-object-towards-a-point-\\\\nvec4 vectorAlign(vec3 start, vec3 dest){\\\\n\\\\tstart = normalize(start);\\\\n\\\\tdest = normalize(dest);\\\\n\\\\n\\\\tfloat cosTheta = dot(start, dest);\\\\n\\\\tvec3 axis;\\\\n\\\\n\\\\t// if (cosTheta < -1 + 0.001f){\\\\n\\\\t// \\\\t// special case when vectors in opposite directions:\\\\n\\\\t// \\\\t// there is no ideal rotation axis\\\\n\\\\t// \\\\t// So guess one; any will do as long as it's perpendicular to start\\\\n\\\\t// \\\\taxis = cross(vec3(0.0f, 0.0f, 1.0f), start);\\\\n\\\\t// \\\\tif (length2(axis) < 0.01 ) // bad luck, they were parallel, try again!\\\\n\\\\t// \\\\t\\\\taxis = cross(vec3(1.0f, 0.0f, 0.0f), start);\\\\n\\\\n\\\\t// \\\\taxis = normalize(axis);\\\\n\\\\t// \\\\treturn gtx::quaternion::angleAxis(glm::radians(180.0f), axis);\\\\n\\\\t// }\\\\n\\\\tif(cosTheta > (1.0 - 0.0001) || cosTheta < (-1.0 + 0.0001) ){\\\\n\\\\t\\\\taxis = normalize(cross(start, vec3(0.0, 1.0, 0.0)));\\\\n\\\\t\\\\tif (length(axis) < 0.001 ){ // bad luck, they were parallel, try again!\\\\n\\\\t\\\\t\\\\taxis = normalize(cross(start, vec3(1.0, 0.0, 0.0)));\\\\n\\\\t\\\\t}\\\\n\\\\t} else {\\\\n\\\\t\\\\taxis = normalize(cross(start, dest));\\\\n\\\\t}\\\\n\\\\n\\\\tfloat angle = acos(cosTheta);\\\\n\\\\n\\\\treturn quatFromAxisAngle(axis, angle);\\\\n}\\\\nvec4 vectorAlignWithUp(vec3 start, vec3 dest, vec3 up){\\\\n\\\\tvec4 rot1 = vectorAlign(start, dest);\\\\n\\\\tup = normalize(up);\\\\n\\\\n\\\\t// Recompute desiredUp so that it's perpendicular to the direction\\\\n\\\\t// You can skip that part if you really want to force desiredUp\\\\n\\\\t// vec3 right = normalize(cross(dest, up));\\\\n\\\\t// up = normalize(cross(right, dest));\\\\n\\\\n\\\\t// Because of the 1rst rotation, the up is probably completely screwed up.\\\\n\\\\t// Find the rotation between the up of the rotated object, and the desired up\\\\n\\\\tvec3 newUp = rotateWithQuat(vec3(0.0, 1.0, 0.0), rot1);//rot1 * vec3(0.0, 1.0, 0.0);\\\\n\\\\tvec4 rot2 = vectorAlign(up, newUp);\\\\n\\\\n\\\\t// return rot1;\\\\n\\\\treturn rot2;\\\\n\\\\t// return multQuat(rot1, rot2);\\\\n\\\\t// return rot2 * rot1;\\\\n\\\\n}\\\\n\\\\n// https://www.euclideanspace.com/maths/geometry/rotations/conversions/quaternionToAngle/index.htm\\\\nfloat quatToAngle(vec4 q){\\\\n\\\\treturn 2.0 * acos(q.w);\\\\n}\\\\nvec3 quatToAxis(vec4 q){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tq.x / sqrt(1.0-q.w*q.w),\\\\n\\\\t\\\\tq.y / sqrt(1.0-q.w*q.w),\\\\n\\\\t\\\\tq.z / sqrt(1.0-q.w*q.w)\\\\n\\\\t);\\\\n}\\\\n\\\\nvec4 align(vec3 dir, vec3 up){\\\\n\\\\tvec3 start_dir = vec3(0.0, 0.0, 1.0);\\\\n\\\\tvec3 start_up = vec3(0.0, 1.0, 0.0);\\\\n\\\\tvec4 rot1 = vectorAlign(start_dir, dir);\\\\n\\\\tup = normalize(up);\\\\n\\\\n\\\\t// Recompute desiredUp so that it's perpendicular to the direction\\\\n\\\\t// You can skip that part if you really want to force desiredUp\\\\n\\\\tvec3 right = normalize(cross(dir, up));\\\\n\\\\tif(length(right)<0.001){\\\\n\\\\t\\\\tright = vec3(1.0, 0.0, 0.0);\\\\n\\\\t}\\\\n\\\\tup = normalize(cross(right, dir));\\\\n\\\\n\\\\t// Because of the 1rst rotation, the up is probably completely screwed up.\\\\n\\\\t// Find the rotation between the up of the rotated object, and the desired up\\\\n\\\\tvec3 newUp = rotateWithQuat(start_up, rot1);//rot1 * vec3(0.0, 1.0, 0.0);\\\\n\\\\tvec4 rot2 = vectorAlign(normalize(newUp), up);\\\\n\\\\n\\\\t// return rot1;\\\\n\\\\treturn quatMult(rot1, rot2);\\\\n\\\\t// return rot2 * rot1;\\\\n\\\\n}\\\\\\\";!function(t){t.DIR=\\\\\\\"dir\\\\\\\",t.UP=\\\\\\\"up\\\\\\\"}(UR||(UR={}));const VR=[UR.DIR,UR.UP],HR={[UR.DIR]:[0,0,1],[UR.UP]:[0,1,0]};class jR extends zO{static type(){return\\\\\\\"align\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>VR[t])),this.io.connection_points.set_expected_input_types_function((()=>[Do.VEC3,Do.VEC3])),this.io.connection_points.set_expected_output_types_function((()=>[Do.VEC4]))}paramDefaultValue(t){return HR[t]}gl_method_name(){return\\\\\\\"align\\\\\\\"}gl_function_definitions(){return[new Tf(this,GR)]}}var WR;!function(t){t.LINEAR=\\\\\\\"Linear\\\\\\\",t.GAMMA=\\\\\\\"Gamma\\\\\\\",t.SRGB=\\\\\\\"sRGB\\\\\\\",t.RGBE=\\\\\\\"RGBE\\\\\\\",t.RGBM=\\\\\\\"RGBM\\\\\\\",t.RGBD=\\\\\\\"RGBD\\\\\\\",t.LogLuv=\\\\\\\"LogLuv\\\\\\\"}(WR||(WR={}));const qR=[WR.LINEAR,WR.GAMMA,WR.SRGB,WR.RGBE,WR.RGBM,WR.RGBD,WR.LogLuv];const XR=new class extends aa{constructor(){super(...arguments),this.color=oa.VECTOR4([1,1,1,1]),this.from=oa.INTEGER(qR.indexOf(WR.LINEAR),{menu:{entries:qR.map(((t,e)=>({name:t,value:e})))}}),this.to=oa.INTEGER(qR.indexOf(WR.GAMMA),{menu:{entries:qR.map(((t,e)=>({name:t,value:e})))}}),this.gammaFactor=oa.FLOAT(2.2)}};class YR extends df{constructor(){super(...arguments),this.paramsConfig=XR}static type(){return\\\\\\\"colorCorrect\\\\\\\"}initializeNode(){this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"to\\\\\\\",\\\\\\\"from\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Vo(YR.OUTPUT_NAME,Do.VEC4)])}setLines(t){const e=qR[this.pv.from],n=qR[this.pv.to],i=this.glVarName(YR.OUTPUT_NAME),r=uf.any(this.variableForInput(YR.INPUT_NAME)),s=[];if(e!=n){const t=`${e}To${n}`,o=[];if(o.push(r),e==WR.GAMMA||n==WR.GAMMA){const t=uf.any(this.variableForInputParam(this.p.gammaFactor));o.push(t)}s.push(`vec4 ${i} = ${t}(${o.join(\\\\\\\", \\\\\\\")})`)}else s.push(`vec4 ${i} = ${r}`);t.addBodyLines(this,s)}}var $R,JR;YR.INPUT_NAME=\\\\\\\"color\\\\\\\",YR.INPUT_GAMMA_FACTOR=\\\\\\\"gammaFactor\\\\\\\",YR.OUTPUT_NAME=\\\\\\\"out\\\\\\\",function(t){t.EQUAL=\\\\\\\"Equal\\\\\\\",t.LESS_THAN=\\\\\\\"Less Than\\\\\\\",t.GREATER_THAN=\\\\\\\"Greater Than\\\\\\\",t.LESS_THAN_OR_EQUAL=\\\\\\\"Less Than Or Equal\\\\\\\",t.GREATER_THAN_OR_EQUAL=\\\\\\\"Greater Than Or Equal\\\\\\\",t.NOT_EQUAL=\\\\\\\"Not Equal\\\\\\\"}($R||($R={})),function(t){t.EQUAL=\\\\\\\"==\\\\\\\",t.LESS_THAN=\\\\\\\"<\\\\\\\",t.GREATER_THAN=\\\\\\\">\\\\\\\",t.LESS_THAN_OR_EQUAL=\\\\\\\"<=\\\\\\\",t.GREATER_THAN_OR_EQUAL=\\\\\\\">=\\\\\\\",t.NOT_EQUAL=\\\\\\\"!=\\\\\\\"}(JR||(JR={}));const ZR=[$R.EQUAL,$R.LESS_THAN,$R.GREATER_THAN,$R.LESS_THAN_OR_EQUAL,$R.GREATER_THAN_OR_EQUAL,$R.NOT_EQUAL],QR=[JR.EQUAL,JR.LESS_THAN,JR.GREATER_THAN,JR.LESS_THAN_OR_EQUAL,JR.GREATER_THAN_OR_EQUAL,JR.NOT_EQUAL],KR=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];const tP=new class extends aa{constructor(){super(...arguments),this.test=oa.INTEGER(0,{menu:{entries:ZR.map(((t,e)=>({name:`${QR[e].padEnd(2,\\\\\\\" \\\\\\\")} (${t})`,value:e})))}})}};class eP extends df{constructor(){super(...arguments),this.paramsConfig=tP}static type(){return\\\\\\\"compare\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"test\\\\\\\"]),this.io.connection_points.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function((t=>\\\\\\\"val\\\\\\\")),this.io.connection_points.set_expected_input_types_function(this._expected_input_type.bind(this)),this.io.connection_points.set_expected_output_types_function((()=>[Do.BOOL]))}set_test_name(t){this.p.test.set(ZR.indexOf(t))}_gl_input_name(t){return[\\\\\\\"value0\\\\\\\",\\\\\\\"value1\\\\\\\"][t]}_expected_input_type(){const t=this.io.connection_points.first_input_connection_type()||Do.FLOAT;return[t,t]}setLines(t){const e=[],n=this.glVarName(\\\\\\\"val\\\\\\\"),i=QR[this.pv.test],r=uf.any(this.variableForInput(this._gl_input_name(0))),s=uf.any(this.variableForInput(this._gl_input_name(1))),o=this.io.inputs.namedInputConnectionPoints()[0];let a=1;if(o&&(a=Go[o.type()]||1),a>1){let t=[];for(let n=0;n({name:t,value:e})))}}),this.bool=oa.BOOLEAN(0,iP(Do.BOOL)),this.int=oa.INTEGER(0,iP(Do.INT)),this.float=oa.FLOAT(0,iP(Do.FLOAT)),this.vec2=oa.VECTOR2([0,0],iP(Do.VEC2)),this.vec3=oa.VECTOR3([0,0,0],iP(Do.VEC3)),this.vec4=oa.VECTOR4([0,0,0,0],iP(Do.VEC4))}};class sP extends df{constructor(){super(...arguments),this.paramsConfig=rP,this._allow_inputs_created_from_params=!1}static type(){return\\\\\\\"constant\\\\\\\"}initializeNode(){this.io.connection_points.set_output_name_function((t=>sP.OUTPUT_NAME)),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[this._current_connection_type]))}setLines(t){const e=this._current_param;if(e){const n=this._current_connection_type;let i=uf.any(e.value);e.name()==this.p.int.name()&&m.isNumber(e.value)&&(i=uf.integer(e.value));const r=`${n} ${this._current_var_name} = ${i}`;t.addBodyLines(this,[r])}else console.warn(`no param found for constant node for type '${this.pv.type}'`)}get _current_connection_type(){null==this.pv.type&&console.warn(\\\\\\\"constant gl node type if not valid\\\\\\\");const t=ko[this.pv.type];return null==t&&console.warn(\\\\\\\"constant gl node type if not valid\\\\\\\"),t}get _current_param(){this._params_by_type=this._params_by_type||new Map([[Do.BOOL,this.p.bool],[Do.INT,this.p.int],[Do.FLOAT,this.p.float],[Do.VEC2,this.p.vec2],[Do.VEC3,this.p.vec3],[Do.VEC4,this.p.vec4]]);const t=ko[this.pv.type];return this._params_by_type.get(t)}get _current_var_name(){return this.glVarName(sP.OUTPUT_NAME)}set_gl_type(t){this.p.type.set(ko.indexOf(t))}}sP.OUTPUT_NAME=\\\\\\\"val\\\\\\\";const oP=\\\\\\\"cross\\\\\\\";const aP=new class extends aa{constructor(){super(...arguments),this.x=oa.VECTOR3([0,0,1]),this.y=oa.VECTOR3([0,1,0])}};class lP extends df{constructor(){super(...arguments),this.paramsConfig=aP}static type(){return\\\\\\\"cross\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(oP,Do.VEC3)])}setLines(t){const e=uf.float(this.variableForInputParam(this.p.x)),n=uf.float(this.variableForInputParam(this.p.y)),i=`vec3 ${this.glVarName(oP)} = cross(${e}, ${n})`;t.addBodyLines(this,[i])}}class cP extends(yR(\\\\\\\"cycle\\\\\\\",{in:[\\\\\\\"in\\\\\\\",\\\\\\\"min\\\\\\\",\\\\\\\"max\\\\\\\"],default:{max:1},functions:[\\\\\\\"float cycle(float val, float val_min, float val_max){\\\\n\\\\tif(val >= val_min && val < val_max){\\\\n\\\\t\\\\treturn val;\\\\n\\\\t} else {\\\\n\\\\t\\\\tfloat range = val_max - val_min;\\\\n\\\\t\\\\tif(val >= val_max){\\\\n\\\\t\\\\t\\\\tfloat delta = (val - val_max);\\\\n\\\\t\\\\t\\\\treturn val_min + mod(delta, range);\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tfloat delta = (val_min - val);\\\\n\\\\t\\\\t\\\\treturn val_max - mod(delta, range);\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n}\\\\\\\"]})){}var uP=\\\\\\\"float disk_feather(float dist, float radius, float feather){\\\\n\\\\tif(feather <= 0.0){\\\\n\\\\t\\\\tif(dist < radius){return 1.0;}else{return 0.0;}\\\\n\\\\t} else {\\\\n\\\\t\\\\tfloat half_feather = feather * 0.5;\\\\n\\\\t\\\\tif(dist < (radius - half_feather)){\\\\n\\\\t\\\\t\\\\treturn 1.0;\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tif(dist > (radius + half_feather)){\\\\n\\\\t\\\\t\\\\t\\\\treturn 0.0;\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\tfloat feather_start = (radius - half_feather);\\\\n\\\\t\\\\t\\\\t\\\\tfloat blend = 1.0 - (dist - feather_start) / feather;\\\\n\\\\t\\\\t\\\\t\\\\treturn blend;\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n}\\\\n\\\\nfloat disk2d(vec2 pos, vec2 center, float radius, float feather){\\\\n\\\\tfloat dist = distance(pos, center);\\\\n\\\\treturn disk_feather(dist, radius, feather);\\\\n}\\\\n\\\\n// function could be called sphere, but is an overload of disk, and is the same\\\\nfloat disk3d(vec3 pos, vec3 center, float radius, float feather){\\\\n\\\\tfloat dist = distance(pos, center);\\\\n\\\\treturn disk_feather(dist, radius, feather);\\\\n}\\\\\\\";const hP=new class extends aa{constructor(){super(...arguments),this.position=oa.VECTOR2([0,0]),this.center=oa.VECTOR2([0,0]),this.radius=oa.FLOAT(1),this.feather=oa.FLOAT(.1)}};class dP extends df{constructor(){super(...arguments),this.paramsConfig=hP}static type(){return\\\\\\\"disk\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"float\\\\\\\",Do.FLOAT)])}setLines(t){const e=uf.vector2(this.variableForInputParam(this.p.position)),n=uf.vector2(this.variableForInputParam(this.p.center)),i=uf.float(this.variableForInputParam(this.p.radius)),r=uf.float(this.variableForInputParam(this.p.feather)),s=`float ${this.glVarName(\\\\\\\"float\\\\\\\")} = disk2d(${e}, ${n}, ${i}, ${r})`;t.addBodyLines(this,[s]),t.addDefinitions(this,[new Tf(this,uP)])}}var pP=\\\\\\\"\\\\nfloat bounceOut(float t) {\\\\n const float a = 4.0 / 11.0;\\\\n const float b = 8.0 / 11.0;\\\\n const float c = 9.0 / 10.0;\\\\n\\\\n const float ca = 4356.0 / 361.0;\\\\n const float cb = 35442.0 / 1805.0;\\\\n const float cc = 16061.0 / 1805.0;\\\\n\\\\n float t2 = t * t;\\\\n\\\\n return t < a\\\\n ? 7.5625 * t2\\\\n : t < b\\\\n ? 9.075 * t2 - 9.9 * t + 3.4\\\\n : t < c\\\\n ? ca * t2 - cb * t + cc\\\\n : 10.8 * t * t - 20.52 * t + 10.72;\\\\n}\\\\n\\\\n\\\\\\\";const _P=[\\\\\\\"back-in-out\\\\\\\",\\\\\\\"back-in\\\\\\\",\\\\\\\"back-out\\\\\\\",\\\\\\\"bounce-in-out\\\\\\\",\\\\\\\"bounce-in\\\\\\\",\\\\\\\"bounce-out\\\\\\\",\\\\\\\"circular-in-out\\\\\\\",\\\\\\\"circular-in\\\\\\\",\\\\\\\"circular-out\\\\\\\",\\\\\\\"cubic-in-out\\\\\\\",\\\\\\\"cubic-in\\\\\\\",\\\\\\\"cubic-out\\\\\\\",\\\\\\\"elastic-in-out\\\\\\\",\\\\\\\"elastic-in\\\\\\\",\\\\\\\"elastic-out\\\\\\\",\\\\\\\"exponential-in-out\\\\\\\",\\\\\\\"exponential-in\\\\\\\",\\\\\\\"exponential-out\\\\\\\",\\\\\\\"linear\\\\\\\",\\\\\\\"quadratic-in-out\\\\\\\",\\\\\\\"quadratic-in\\\\\\\",\\\\\\\"quadratic-out\\\\\\\",\\\\\\\"sine-in-out\\\\\\\",\\\\\\\"sine-in\\\\\\\",\\\\\\\"sine-out\\\\\\\"],mP={\\\\\\\"circular-in-out\\\\\\\":\\\\\\\"float circularInOut(float t) {\\\\n return t < 0.5\\\\n ? 0.5 * (1.0 - sqrt(1.0 - 4.0 * t * t))\\\\n : 0.5 * (sqrt((3.0 - 2.0 * t) * (2.0 * t - 1.0)) + 1.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"exponential-in-out\\\\\\\":\\\\\\\"float exponentialInOut(float t) {\\\\n return t == 0.0 || t == 1.0\\\\n ? t\\\\n : t < 0.5\\\\n ? +0.5 * pow(2.0, (20.0 * t) - 10.0)\\\\n : -0.5 * pow(2.0, 10.0 - (t * 20.0)) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"circular-in\\\\\\\":\\\\\\\"float circularIn(float t) {\\\\n return 1.0 - sqrt(1.0 - t * t);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"elastic-out\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat elasticOut(float t) {\\\\n return sin(-13.0 * (t + 1.0) * HALF_PI) * pow(2.0, -10.0 * t) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"cubic-in\\\\\\\":\\\\\\\"float cubicIn(float t) {\\\\n return t * t * t;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"exponential-out\\\\\\\":\\\\\\\"float exponentialOut(float t) {\\\\n return t == 1.0 ? t : 1.0 - pow(2.0, -10.0 * t);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quintic-out\\\\\\\":\\\\\\\"float quinticOut(float t) {\\\\n return 1.0 - (pow(t - 1.0, 5.0));\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"elastic-in-out\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat elasticInOut(float t) {\\\\n return t < 0.5\\\\n ? 0.5 * sin(+13.0 * HALF_PI * 2.0 * t) * pow(2.0, 10.0 * (2.0 * t - 1.0))\\\\n : 0.5 * sin(-13.0 * HALF_PI * ((2.0 * t - 1.0) + 1.0)) * pow(2.0, -10.0 * (2.0 * t - 1.0)) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",linear:\\\\\\\"float linear(float t) {\\\\n return t;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"circular-out\\\\\\\":\\\\\\\"float circularOut(float t) {\\\\n return sqrt((2.0 - t) * t);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"back-in-out\\\\\\\":\\\\\\\"\\\\nfloat backInOut(float t) {\\\\n float f = t < 0.5\\\\n ? 2.0 * t\\\\n : 1.0 - (2.0 * t - 1.0);\\\\n\\\\n float g = pow(f, 3.0) - f * sin(f * PI);\\\\n\\\\n return t < 0.5\\\\n ? 0.5 * g\\\\n : 0.5 * (1.0 - g) + 0.5;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"back-in\\\\\\\":\\\\\\\"\\\\nfloat backIn(float t) {\\\\n return pow(t, 3.0) - t * sin(t * PI);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"sine-in\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat sineIn(float t) {\\\\n return sin((t - 1.0) * HALF_PI) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"back-out\\\\\\\":\\\\\\\"\\\\nfloat backOut(float t) {\\\\n float f = 1.0 - t;\\\\n return 1.0 - (pow(f, 3.0) - f * sin(f * PI));\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quartic-in-out\\\\\\\":\\\\\\\"float quarticInOut(float t) {\\\\n return t < 0.5\\\\n ? +8.0 * pow(t, 4.0)\\\\n : -8.0 * pow(t - 1.0, 4.0) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quadratic-in\\\\\\\":\\\\\\\"float quadraticIn(float t) {\\\\n return t * t;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"cubic-in-out\\\\\\\":\\\\\\\"float cubicInOut(float t) {\\\\n return t < 0.5\\\\n ? 4.0 * t * t * t\\\\n : 0.5 * pow(2.0 * t - 2.0, 3.0) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"elastic-in\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat elasticIn(float t) {\\\\n return sin(13.0 * t * HALF_PI) * pow(2.0, 10.0 * (t - 1.0));\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"bounce-out\\\\\\\":pP,\\\\\\\"quadratic-in-out\\\\\\\":\\\\\\\"float quadraticInOut(float t) {\\\\n float p = 2.0 * t * t;\\\\n return t < 0.5 ? p : -p + (4.0 * t) - 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"exponential-in\\\\\\\":\\\\\\\"float exponentialIn(float t) {\\\\n return t == 0.0 ? t : pow(2.0, 10.0 * (t - 1.0));\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quintic-in-out\\\\\\\":\\\\\\\"float quinticInOut(float t) {\\\\n return t < 0.5\\\\n ? +16.0 * pow(t, 5.0)\\\\n : -0.5 * pow(2.0 * t - 2.0, 5.0) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"sine-in-out\\\\\\\":\\\\\\\"\\\\nfloat sineInOut(float t) {\\\\n return -0.5 * (cos(PI * t) - 1.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"cubic-out\\\\\\\":\\\\\\\"float cubicOut(float t) {\\\\n float f = t - 1.0;\\\\n return f * f * f + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quadratic-out\\\\\\\":\\\\\\\"float quadraticOut(float t) {\\\\n return -t * (t - 2.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"bounce-in-out\\\\\\\":\\\\\\\"\\\\nfloat bounceInOut(float t) {\\\\n return t < 0.5\\\\n ? 0.5 * (1.0 - bounceOut(1.0 - t * 2.0))\\\\n : 0.5 * bounceOut(t * 2.0 - 1.0) + 0.5;\\\\n}\\\\n\\\\n\\\\n\\\\n\\\\\\\",\\\\\\\"quintic-in\\\\\\\":\\\\\\\"float quinticIn(float t) {\\\\n return pow(t, 5.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quartic-in\\\\\\\":\\\\\\\"float quarticIn(float t) {\\\\n return pow(t, 4.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quartic-out\\\\\\\":\\\\\\\"float quarticOut(float t) {\\\\n return pow(t - 1.0, 3.0) * (1.0 - t) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"bounce-in\\\\\\\":\\\\\\\"\\\\nfloat bounceIn(float t) {\\\\n return 1.0 - bounceOut(1.0 - t);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"sine-out\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat sineOut(float t) {\\\\n return sin(t * HALF_PI);\\\\n}\\\\n\\\\n\\\\\\\"},fP={\\\\\\\"bounce-in\\\\\\\":[pP],\\\\\\\"bounce-in-out\\\\\\\":[pP]},gP={\\\\\\\"circular-in-out\\\\\\\":\\\\\\\"circularInOut\\\\\\\",\\\\\\\"exponential-in-out\\\\\\\":\\\\\\\"exponentialInOut\\\\\\\",\\\\\\\"circular-in\\\\\\\":\\\\\\\"circularIn\\\\\\\",\\\\\\\"elastic-out\\\\\\\":\\\\\\\"elasticOut\\\\\\\",\\\\\\\"cubic-in\\\\\\\":\\\\\\\"cubicIn\\\\\\\",\\\\\\\"exponential-out\\\\\\\":\\\\\\\"exponentialOut\\\\\\\",\\\\\\\"quintic-out\\\\\\\":\\\\\\\"quinticOut\\\\\\\",\\\\\\\"elastic-in-out\\\\\\\":\\\\\\\"elasticInOut\\\\\\\",linear:\\\\\\\"linear\\\\\\\",\\\\\\\"circular-out\\\\\\\":\\\\\\\"circularOut\\\\\\\",\\\\\\\"back-in-out\\\\\\\":\\\\\\\"backInOut\\\\\\\",\\\\\\\"back-in\\\\\\\":\\\\\\\"backIn\\\\\\\",\\\\\\\"sine-in\\\\\\\":\\\\\\\"sineIn\\\\\\\",\\\\\\\"back-out\\\\\\\":\\\\\\\"backOut\\\\\\\",\\\\\\\"quartic-in-out\\\\\\\":\\\\\\\"quarticInOut\\\\\\\",\\\\\\\"quadratic-in\\\\\\\":\\\\\\\"quadraticIn\\\\\\\",\\\\\\\"cubic-in-out\\\\\\\":\\\\\\\"cubicInOut\\\\\\\",\\\\\\\"elastic-in\\\\\\\":\\\\\\\"elasticIn\\\\\\\",\\\\\\\"bounce-out\\\\\\\":\\\\\\\"bounceOut\\\\\\\",\\\\\\\"quadratic-in-out\\\\\\\":\\\\\\\"quadraticInOut\\\\\\\",\\\\\\\"exponential-in\\\\\\\":\\\\\\\"exponentialIn\\\\\\\",\\\\\\\"quintic-in-out\\\\\\\":\\\\\\\"quinticInOut\\\\\\\",\\\\\\\"sine-in-out\\\\\\\":\\\\\\\"sineInOut\\\\\\\",\\\\\\\"cubic-out\\\\\\\":\\\\\\\"cubicOut\\\\\\\",\\\\\\\"quadratic-out\\\\\\\":\\\\\\\"quadraticOut\\\\\\\",\\\\\\\"bounce-in-out\\\\\\\":\\\\\\\"bounceInOut\\\\\\\",\\\\\\\"quintic-in\\\\\\\":\\\\\\\"quinticIn\\\\\\\",\\\\\\\"quartic-in\\\\\\\":\\\\\\\"quarticIn\\\\\\\",\\\\\\\"quartic-out\\\\\\\":\\\\\\\"quarticOut\\\\\\\",\\\\\\\"bounce-in\\\\\\\":\\\\\\\"bounceIn\\\\\\\",\\\\\\\"sine-out\\\\\\\":\\\\\\\"sineOut\\\\\\\"},vP=_P.indexOf(\\\\\\\"sine-in-out\\\\\\\");const yP=new class extends aa{constructor(){super(...arguments),this.type=oa.INTEGER(vP,{menu:{entries:_P.map(((t,e)=>({name:t,value:e})))}}),this.input=oa.FLOAT(0)}};class xP extends df{constructor(){super(...arguments),this.paramsConfig=yP}static type(){return\\\\\\\"easing\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"type\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"out\\\\\\\",Do.FLOAT)])}setLines(t){const e=_P[this.pv.type],n=gP[e];let i=[new Tf(this,mP[e])];const r=(fP[e]||[]).map((t=>new Tf(this,t)));r&&(i=r.concat(i));const s=uf.float(this.variableForInputParam(this.p.input)),o=`float ${this.glVarName(\\\\\\\"out\\\\\\\")} = ${n}(${s})`;t.addDefinitions(this,i),t.addBodyLines(this,[o])}}var bP=\\\\\\\"//\\\\n//\\\\n// FIT\\\\n//\\\\n//\\\\nfloat fit(float val, float srcMin, float srcMax, float destMin, float destMax){\\\\n\\\\tfloat src_range = srcMax - srcMin;\\\\n\\\\tfloat dest_range = destMax - destMin;\\\\n\\\\n\\\\tfloat r = (val - srcMin) / src_range;\\\\n\\\\treturn (r * dest_range) + destMin;\\\\n}\\\\nvec2 fit(vec2 val, vec2 srcMin, vec2 srcMax, vec2 destMin, vec2 destMax){\\\\n\\\\treturn vec2(\\\\n\\\\t\\\\tfit(val.x, srcMin.x, srcMax.x, destMin.x, destMax.x),\\\\n\\\\t\\\\tfit(val.y, srcMin.y, srcMax.y, destMin.y, destMax.y)\\\\n\\\\t);\\\\n}\\\\nvec3 fit(vec3 val, vec3 srcMin, vec3 srcMax, vec3 destMin, vec3 destMax){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tfit(val.x, srcMin.x, srcMax.x, destMin.x, destMax.x),\\\\n\\\\t\\\\tfit(val.y, srcMin.y, srcMax.y, destMin.y, destMax.y),\\\\n\\\\t\\\\tfit(val.z, srcMin.z, srcMax.z, destMin.z, destMax.z)\\\\n\\\\t);\\\\n}\\\\nvec4 fit(vec4 val, vec4 srcMin, vec4 srcMax, vec4 destMin, vec4 destMax){\\\\n\\\\treturn vec4(\\\\n\\\\t\\\\tfit(val.x, srcMin.x, srcMax.x, destMin.x, destMax.x),\\\\n\\\\t\\\\tfit(val.y, srcMin.y, srcMax.y, destMin.y, destMax.y),\\\\n\\\\t\\\\tfit(val.z, srcMin.z, srcMax.z, destMin.z, destMax.z),\\\\n\\\\t\\\\tfit(val.w, srcMin.w, srcMax.w, destMin.w, destMax.w)\\\\n\\\\t);\\\\n}\\\\n\\\\n//\\\\n//\\\\n// FIT TO 01\\\\n// fits the range [srcMin, srcMax] to [0, 1]\\\\n//\\\\nfloat fitTo01(float val, float srcMin, float srcMax){\\\\n\\\\tfloat size = srcMax - srcMin;\\\\n\\\\treturn (val - srcMin) / size;\\\\n}\\\\nvec2 fitTo01(vec2 val, vec2 srcMin, vec2 srcMax){\\\\n\\\\treturn vec2(\\\\n\\\\t\\\\tfitTo01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitTo01(val.y, srcMin.y, srcMax.y)\\\\n\\\\t);\\\\n}\\\\nvec3 fitTo01(vec3 val, vec3 srcMin, vec3 srcMax){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tfitTo01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitTo01(val.y, srcMin.y, srcMax.y),\\\\n\\\\t\\\\tfitTo01(val.z, srcMin.z, srcMax.z)\\\\n\\\\t);\\\\n}\\\\nvec4 fitTo01(vec4 val, vec4 srcMin, vec4 srcMax){\\\\n\\\\treturn vec4(\\\\n\\\\t\\\\tfitTo01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitTo01(val.y, srcMin.y, srcMax.y),\\\\n\\\\t\\\\tfitTo01(val.z, srcMin.z, srcMax.z),\\\\n\\\\t\\\\tfitTo01(val.w, srcMin.w, srcMax.w)\\\\n\\\\t);\\\\n}\\\\n\\\\n//\\\\n//\\\\n// FIT FROM 01\\\\n// fits the range [0, 1] to [destMin, destMax]\\\\n//\\\\nfloat fitFrom01(float val, float destMin, float destMax){\\\\n\\\\treturn fit(val, 0.0, 1.0, destMin, destMax);\\\\n}\\\\nvec2 fitFrom01(vec2 val, vec2 srcMin, vec2 srcMax){\\\\n\\\\treturn vec2(\\\\n\\\\t\\\\tfitFrom01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitFrom01(val.y, srcMin.y, srcMax.y)\\\\n\\\\t);\\\\n}\\\\nvec3 fitFrom01(vec3 val, vec3 srcMin, vec3 srcMax){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tfitFrom01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitFrom01(val.y, srcMin.y, srcMax.y),\\\\n\\\\t\\\\tfitFrom01(val.z, srcMin.z, srcMax.z)\\\\n\\\\t);\\\\n}\\\\nvec4 fitFrom01(vec4 val, vec4 srcMin, vec4 srcMax){\\\\n\\\\treturn vec4(\\\\n\\\\t\\\\tfitFrom01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitFrom01(val.y, srcMin.y, srcMax.y),\\\\n\\\\t\\\\tfitFrom01(val.z, srcMin.z, srcMax.z),\\\\n\\\\t\\\\tfitFrom01(val.w, srcMin.w, srcMax.w)\\\\n\\\\t);\\\\n}\\\\n\\\\n//\\\\n//\\\\n// FIT FROM 01 TO VARIANCE\\\\n// fits the range [0, 1] to [center - variance, center + variance]\\\\n//\\\\nfloat fitFrom01ToVariance(float val, float center, float variance){\\\\n\\\\treturn fitFrom01(val, center - variance, center + variance);\\\\n}\\\\nvec2 fitFrom01ToVariance(vec2 val, vec2 center, vec2 variance){\\\\n\\\\treturn vec2(\\\\n\\\\t\\\\tfitFrom01ToVariance(val.x, center.x, variance.x),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.y, center.y, variance.y)\\\\n\\\\t);\\\\n}\\\\nvec3 fitFrom01ToVariance(vec3 val, vec3 center, vec3 variance){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tfitFrom01ToVariance(val.x, center.x, variance.x),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.y, center.y, variance.y),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.z, center.z, variance.z)\\\\n\\\\t);\\\\n}\\\\nvec4 fitFrom01ToVariance(vec4 val, vec4 center, vec4 variance){\\\\n\\\\treturn vec4(\\\\n\\\\t\\\\tfitFrom01ToVariance(val.x, center.x, variance.x),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.y, center.y, variance.y),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.z, center.z, variance.z),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.w, center.w, variance.w)\\\\n\\\\t);\\\\n}\\\\\\\";const wP={srcMin:0,srcMax:1,destMin:0,destMax:1};class TP extends VO{static type(){return\\\\\\\"fit\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"srcMin\\\\\\\",\\\\\\\"srcMax\\\\\\\",\\\\\\\"destMin\\\\\\\",\\\\\\\"destMax\\\\\\\"][t]}paramDefaultValue(t){return wP[t]}gl_method_name(){return\\\\\\\"fit\\\\\\\"}gl_function_definitions(){return[new Tf(this,bP)]}}const AP={srcMin:0,srcMax:1};class EP extends UO{static type(){return\\\\\\\"fitTo01\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"srcMin\\\\\\\",\\\\\\\"srcMax\\\\\\\"][t]}paramDefaultValue(t){return AP[t]}gl_method_name(){return\\\\\\\"fitTo01\\\\\\\"}gl_function_definitions(){return[new Tf(this,bP)]}}const MP={destMin:0,destMax:1};class SP extends UO{static type(){return\\\\\\\"fitFrom01\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"destMin\\\\\\\",\\\\\\\"destMax\\\\\\\"][t]}paramDefaultValue(t){return MP[t]}gl_method_name(){return\\\\\\\"fitFrom01\\\\\\\"}gl_function_definitions(){return[new Tf(this,bP)]}}const CP={center:.5,variance:.5};class NP extends UO{static type(){return\\\\\\\"fitFrom01ToVariance\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"center\\\\\\\",\\\\\\\"variance\\\\\\\"][t]}paramDefaultValue(t){return CP[t]}gl_method_name(){return\\\\\\\"fitFrom01ToVariance\\\\\\\"}gl_function_definitions(){return[new Tf(this,bP)]}}const LP=\\\\\\\"color\\\\\\\";const OP=new class extends aa{constructor(){super(...arguments),this.mvPosition=oa.VECTOR4([0,0,0,0]),this.baseColor=oa.COLOR([0,0,0]),this.fogColor=oa.COLOR([1,1,1]),this.near=oa.FLOAT(0),this.far=oa.FLOAT(0)}};class RP extends df{constructor(){super(...arguments),this.paramsConfig=OP}static type(){return\\\\\\\"fog\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(LP,Do.VEC3)])}setLines(t){if(t.current_shader_name==xf.FRAGMENT){const e=this.glVarName(this.name()),n=new Ef(this,Do.VEC4,e),i=`${e} = modelViewMatrix * vec4(position, 1.0)`;t.addDefinitions(this,[n],xf.VERTEX),t.addBodyLines(this,[i],xf.VERTEX);const r=new Tf(this,\\\\\\\"vec3 compute_fog(vec4 mvPosition, vec3 base_color, vec3 fog_color, float near, float far) {\\\\n\\\\tfloat blend = (-mvPosition.z - near) / (far - near);\\\\n\\\\tblend = clamp(blend, 0.0, 1.0);\\\\n\\\\treturn blend * fog_color + (1.0 - blend) * base_color;\\\\n}\\\\\\\"),s=uf.vector4(this.variableForInputParam(this.p.mvPosition)),o=uf.vector3(this.variableForInputParam(this.p.baseColor)),a=uf.vector3(this.variableForInputParam(this.p.fogColor)),l=uf.vector3(this.variableForInputParam(this.p.near)),c=uf.vector3(this.variableForInputParam(this.p.far)),u=`vec3 ${this.glVarName(LP)} = compute_fog(${[s,o,a,l,c].join(\\\\\\\", \\\\\\\")})`;t.addDefinitions(this,[n,r]),t.addBodyLines(this,[u])}}}const PP=new class extends aa{};class IP extends df{constructor(){super(...arguments),this.paramsConfig=PP}static type(){return er.OUTPUT}initializeNode(){this.io.connection_points.set_input_name_function(this._expected_input_name.bind(this)),this.io.connection_points.set_expected_output_types_function((()=>[])),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_create_spare_params_from_inputs(!1),this.addPostDirtyHook(\\\\\\\"setParentDirty\\\\\\\",(()=>{var t;null===(t=this.parent())||void 0===t||t.setDirty(this)}))}parent(){return super.parent()}_expected_input_name(t){const e=this.parent();return(null==e?void 0:e.child_expected_output_connection_point_name(t))||`in${t}`}_expected_input_types(){const t=this.parent();return(null==t?void 0:t.child_expected_output_connection_point_types())||[]}setLines(t){const e=this.parent();if(!e)return;const n=[],i=this.io.connections.inputConnections();if(i)for(let t of i)if(t){const i=t.dest_connection_point(),r=uf.any(this.variableForInput(i.name())),s=`\\\\t${e.glVarName(i.name())} = ${r}`;n.push(s)}t.addBodyLines(this,n),e.set_lines_block_end(t,this)}}class FP extends df{constructor(){super(...arguments),this._children_controller_context=Ki.GL}initializeNode(){var t;null===(t=this.childrenController)||void 0===t||t.set_output_node_find_method((()=>this.nodesByType(IP.type())[0])),this.io.connection_points.set_input_name_function(this._expected_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._expected_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_expected_inputs_count(){const t=this.io.connections.inputConnections();return t?t.length+1:1}_expected_input_types(){const t=[],e=Do.FLOAT,n=this.io.connections.inputConnections(),i=this._expected_inputs_count();for(let r=0;r=0){const e=t.dest_connection_point(),i=uf.any(this.variableForInput(e.name())),r=`${e.type()} ${this.glVarName(e.name())} = ${i}`;n.push(r)}const s=this.pv.start,o=this.pv.max,a=this.pv.step,l=uf.float(s),c=uf.float(o),u=uf.float(a),h=this.glVarName(\\\\\\\"i\\\\\\\"),d=`for(float ${h} = ${l}; ${h} < ${c}; ${h}+= ${u}){`;n.push(d);const p=`\\\\tfloat ${e.glVarName(BP.START_INDEX)} = ${h}`;if(n.push(p),r)for(let t of r)if(t&&t.input_index>=0){const i=t.dest_connection_point(),r=this.glVarName(i.name()),s=`\\\\t${i.type()} ${e.glVarName(i.name())} = ${r}`;n.push(s)}t.addBodyLines(e,n)}setLines(t){}}const VP=new class extends aa{};class HP extends df{constructor(){super(...arguments),this.paramsConfig=VP}static type(){return\\\\\\\"globals\\\\\\\"}initializeNode(){super.initializeNode(),this.lifecycle.add_on_add_hook((()=>{var t,e;null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e||e.add_globals_outputs(this)}))}setLines(t){t.assembler().set_node_lines_globals(this,t)}}const jP=new class extends aa{constructor(){super(...arguments),this.hsluv=oa.VECTOR3([1,1,1])}};class WP extends df{constructor(){super(...arguments),this.paramsConfig=jP}static type(){return\\\\\\\"hsluvToRgb\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"rgb\\\\\\\",Do.VEC3)])}setLines(t){const e=[],n=[];e.push(new Tf(this,\\\\\\\"// from https://github.com/williammalo/hsluv-glsl\\\\n/*\\\\nHSLUV-GLSL v4.2\\\\nHSLUV is a human-friendly alternative to HSL. ( http://www.hsluv.org )\\\\nGLSL port by William Malo ( https://github.com/williammalo )\\\\nPut this code in your fragment shader.\\\\n*/\\\\n\\\\nvec3 hsluv_intersectLineLine(vec3 line1x, vec3 line1y, vec3 line2x, vec3 line2y) {\\\\n\\\\treturn (line1y - line2y) / (line2x - line1x);\\\\n}\\\\n\\\\nvec3 hsluv_distanceFromPole(vec3 pointx,vec3 pointy) {\\\\n\\\\treturn sqrt(pointx*pointx + pointy*pointy);\\\\n}\\\\n\\\\nvec3 hsluv_lengthOfRayUntilIntersect(float theta, vec3 x, vec3 y) {\\\\n\\\\tvec3 len = y / (sin(theta) - x * cos(theta));\\\\n\\\\tif (len.r < 0.0) {len.r=1000.0;}\\\\n\\\\tif (len.g < 0.0) {len.g=1000.0;}\\\\n\\\\tif (len.b < 0.0) {len.b=1000.0;}\\\\n\\\\treturn len;\\\\n}\\\\n\\\\nfloat hsluv_maxSafeChromaForL(float L){\\\\n\\\\tmat3 m2 = mat3(\\\\n\\\\t\\\\t 3.2409699419045214 ,-0.96924363628087983 , 0.055630079696993609,\\\\n\\\\t\\\\t-1.5373831775700935 , 1.8759675015077207 ,-0.20397695888897657 ,\\\\n\\\\t\\\\t-0.49861076029300328 , 0.041555057407175613, 1.0569715142428786 \\\\n\\\\t);\\\\n\\\\tfloat sub0 = L + 16.0;\\\\n\\\\tfloat sub1 = sub0 * sub0 * sub0 * .000000641;\\\\n\\\\tfloat sub2 = sub1 > 0.0088564516790356308 ? sub1 : L / 903.2962962962963;\\\\n\\\\n\\\\tvec3 top1 = (284517.0 * m2[0] - 94839.0 * m2[2]) * sub2;\\\\n\\\\tvec3 bottom = (632260.0 * m2[2] - 126452.0 * m2[1]) * sub2;\\\\n\\\\tvec3 top2 = (838422.0 * m2[2] + 769860.0 * m2[1] + 731718.0 * m2[0]) * L * sub2;\\\\n\\\\n\\\\tvec3 bounds0x = top1 / bottom;\\\\n\\\\tvec3 bounds0y = top2 / bottom;\\\\n\\\\n\\\\tvec3 bounds1x = top1 / (bottom+126452.0);\\\\n\\\\tvec3 bounds1y = (top2-769860.0*L) / (bottom+126452.0);\\\\n\\\\n\\\\tvec3 xs0 = hsluv_intersectLineLine(bounds0x, bounds0y, -1.0/bounds0x, vec3(0.0) );\\\\n\\\\tvec3 xs1 = hsluv_intersectLineLine(bounds1x, bounds1y, -1.0/bounds1x, vec3(0.0) );\\\\n\\\\n\\\\tvec3 lengths0 = hsluv_distanceFromPole( xs0, bounds0y + xs0 * bounds0x );\\\\n\\\\tvec3 lengths1 = hsluv_distanceFromPole( xs1, bounds1y + xs1 * bounds1x );\\\\n\\\\n\\\\treturn min(lengths0.r,\\\\n\\\\t\\\\t\\\\tmin(lengths1.r,\\\\n\\\\t\\\\t\\\\tmin(lengths0.g,\\\\n\\\\t\\\\t\\\\tmin(lengths1.g,\\\\n\\\\t\\\\t\\\\tmin(lengths0.b,\\\\n\\\\t\\\\t\\\\t\\\\tlengths1.b)))));\\\\n}\\\\n\\\\nfloat hsluv_maxChromaForLH(float L, float H) {\\\\n\\\\n\\\\tfloat hrad = radians(H);\\\\n\\\\n\\\\tmat3 m2 = mat3(\\\\n\\\\t\\\\t 3.2409699419045214 ,-0.96924363628087983 , 0.055630079696993609,\\\\n\\\\t\\\\t-1.5373831775700935 , 1.8759675015077207 ,-0.20397695888897657 ,\\\\n\\\\t\\\\t-0.49861076029300328 , 0.041555057407175613, 1.0569715142428786 \\\\n\\\\t);\\\\n\\\\tfloat sub1 = pow(L + 16.0, 3.0) / 1560896.0;\\\\n\\\\tfloat sub2 = sub1 > 0.0088564516790356308 ? sub1 : L / 903.2962962962963;\\\\n\\\\n\\\\tvec3 top1 = (284517.0 * m2[0] - 94839.0 * m2[2]) * sub2;\\\\n\\\\tvec3 bottom = (632260.0 * m2[2] - 126452.0 * m2[1]) * sub2;\\\\n\\\\tvec3 top2 = (838422.0 * m2[2] + 769860.0 * m2[1] + 731718.0 * m2[0]) * L * sub2;\\\\n\\\\n\\\\tvec3 bound0x = top1 / bottom;\\\\n\\\\tvec3 bound0y = top2 / bottom;\\\\n\\\\n\\\\tvec3 bound1x = top1 / (bottom+126452.0);\\\\n\\\\tvec3 bound1y = (top2-769860.0*L) / (bottom+126452.0);\\\\n\\\\n\\\\tvec3 lengths0 = hsluv_lengthOfRayUntilIntersect(hrad, bound0x, bound0y );\\\\n\\\\tvec3 lengths1 = hsluv_lengthOfRayUntilIntersect(hrad, bound1x, bound1y );\\\\n\\\\n\\\\treturn min(lengths0.r,\\\\n\\\\t\\\\t\\\\tmin(lengths1.r,\\\\n\\\\t\\\\t\\\\tmin(lengths0.g,\\\\n\\\\t\\\\t\\\\tmin(lengths1.g,\\\\n\\\\t\\\\t\\\\tmin(lengths0.b,\\\\n\\\\t\\\\t\\\\t\\\\tlengths1.b)))));\\\\n}\\\\n\\\\nfloat hsluv_fromLinear(float c) {\\\\n\\\\treturn c <= 0.0031308 ? 12.92 * c : 1.055 * pow(c, 1.0 / 2.4) - 0.055;\\\\n}\\\\nvec3 hsluv_fromLinear(vec3 c) {\\\\n\\\\treturn vec3( hsluv_fromLinear(c.r), hsluv_fromLinear(c.g), hsluv_fromLinear(c.b) );\\\\n}\\\\n\\\\nfloat hsluv_toLinear(float c) {\\\\n\\\\treturn c > 0.04045 ? pow((c + 0.055) / (1.0 + 0.055), 2.4) : c / 12.92;\\\\n}\\\\n\\\\nvec3 hsluv_toLinear(vec3 c) {\\\\n\\\\treturn vec3( hsluv_toLinear(c.r), hsluv_toLinear(c.g), hsluv_toLinear(c.b) );\\\\n}\\\\n\\\\nfloat hsluv_yToL(float Y){\\\\n\\\\treturn Y <= 0.0088564516790356308 ? Y * 903.2962962962963 : 116.0 * pow(Y, 1.0 / 3.0) - 16.0;\\\\n}\\\\n\\\\nfloat hsluv_lToY(float L) {\\\\n\\\\treturn L <= 8.0 ? L / 903.2962962962963 : pow((L + 16.0) / 116.0, 3.0);\\\\n}\\\\n\\\\nvec3 xyzToRgb(vec3 tuple) {\\\\n\\\\tconst mat3 m = mat3( \\\\n\\\\t\\\\t3.2409699419045214 ,-1.5373831775700935 ,-0.49861076029300328 ,\\\\n\\\\t\\\\t-0.96924363628087983 , 1.8759675015077207 , 0.041555057407175613,\\\\n\\\\t\\\\t0.055630079696993609,-0.20397695888897657, 1.0569715142428786 );\\\\n\\\\t\\\\n\\\\treturn hsluv_fromLinear(tuple*m);\\\\n}\\\\n\\\\nvec3 rgbToXyz(vec3 tuple) {\\\\n\\\\tconst mat3 m = mat3(\\\\n\\\\t\\\\t0.41239079926595948 , 0.35758433938387796, 0.18048078840183429 ,\\\\n\\\\t\\\\t0.21263900587151036 , 0.71516867876775593, 0.072192315360733715,\\\\n\\\\t\\\\t0.019330818715591851, 0.11919477979462599, 0.95053215224966058 \\\\n\\\\t);\\\\n\\\\treturn hsluv_toLinear(tuple) * m;\\\\n}\\\\n\\\\nvec3 xyzToLuv(vec3 tuple){\\\\n\\\\tfloat X = tuple.x;\\\\n\\\\tfloat Y = tuple.y;\\\\n\\\\tfloat Z = tuple.z;\\\\n\\\\n\\\\tfloat L = hsluv_yToL(Y);\\\\n\\\\t\\\\n\\\\tfloat div = 1./dot(tuple,vec3(1,15,3)); \\\\n\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\t1.,\\\\n\\\\t\\\\t(52. * (X*div) - 2.57179),\\\\n\\\\t\\\\t(117.* (Y*div) - 6.08816)\\\\n\\\\t) * L;\\\\n}\\\\n\\\\n\\\\nvec3 luvToXyz(vec3 tuple) {\\\\n\\\\tfloat L = tuple.x;\\\\n\\\\n\\\\tfloat U = tuple.y / (13.0 * L) + 0.19783000664283681;\\\\n\\\\tfloat V = tuple.z / (13.0 * L) + 0.468319994938791;\\\\n\\\\n\\\\tfloat Y = hsluv_lToY(L);\\\\n\\\\tfloat X = 2.25 * U * Y / V;\\\\n\\\\tfloat Z = (3./V - 5.)*Y - (X/3.);\\\\n\\\\n\\\\treturn vec3(X, Y, Z);\\\\n}\\\\n\\\\nvec3 luvToLch(vec3 tuple) {\\\\n\\\\tfloat L = tuple.x;\\\\n\\\\tfloat U = tuple.y;\\\\n\\\\tfloat V = tuple.z;\\\\n\\\\n\\\\tfloat C = length(tuple.yz);\\\\n\\\\tfloat H = degrees(atan(V,U));\\\\n\\\\tif (H < 0.0) {\\\\n\\\\t\\\\tH = 360.0 + H;\\\\n\\\\t}\\\\n\\\\t\\\\n\\\\treturn vec3(L, C, H);\\\\n}\\\\n\\\\nvec3 lchToLuv(vec3 tuple) {\\\\n\\\\tfloat hrad = radians(tuple.b);\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\ttuple.r,\\\\n\\\\t\\\\tcos(hrad) * tuple.g,\\\\n\\\\t\\\\tsin(hrad) * tuple.g\\\\n\\\\t);\\\\n}\\\\n\\\\nvec3 hsluvToLch(vec3 tuple) {\\\\n\\\\ttuple.g *= hsluv_maxChromaForLH(tuple.b, tuple.r) * .01;\\\\n\\\\treturn tuple.bgr;\\\\n}\\\\n\\\\nvec3 lchToHsluv(vec3 tuple) {\\\\n\\\\ttuple.g /= hsluv_maxChromaForLH(tuple.r, tuple.b) * .01;\\\\n\\\\treturn tuple.bgr;\\\\n}\\\\n\\\\nvec3 hpluvToLch(vec3 tuple) {\\\\n\\\\ttuple.g *= hsluv_maxSafeChromaForL(tuple.b) * .01;\\\\n\\\\treturn tuple.bgr;\\\\n}\\\\n\\\\nvec3 lchToHpluv(vec3 tuple) {\\\\n\\\\ttuple.g /= hsluv_maxSafeChromaForL(tuple.r) * .01;\\\\n\\\\treturn tuple.bgr;\\\\n}\\\\n\\\\nvec3 lchToRgb(vec3 tuple) {\\\\n\\\\treturn xyzToRgb(luvToXyz(lchToLuv(tuple)));\\\\n}\\\\n\\\\nvec3 rgbToLch(vec3 tuple) {\\\\n\\\\treturn luvToLch(xyzToLuv(rgbToXyz(tuple)));\\\\n}\\\\n\\\\nvec3 hsluvToRgb(vec3 tuple) {\\\\n\\\\treturn lchToRgb(hsluvToLch(tuple));\\\\n}\\\\n\\\\nvec3 rgbToHsluv(vec3 tuple) {\\\\n\\\\treturn lchToHsluv(rgbToLch(tuple));\\\\n}\\\\n\\\\nvec3 hpluvToRgb(vec3 tuple) {\\\\n\\\\treturn lchToRgb(hpluvToLch(tuple));\\\\n}\\\\n\\\\nvec3 rgbToHpluv(vec3 tuple) {\\\\n\\\\treturn lchToHpluv(rgbToLch(tuple));\\\\n}\\\\n\\\\nvec3 luvToRgb(vec3 tuple){\\\\n\\\\treturn xyzToRgb(luvToXyz(tuple));\\\\n}\\\\n\\\\n// allow vec4's\\\\nvec4 xyzToRgb(vec4 c) {return vec4( xyzToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 rgbToXyz(vec4 c) {return vec4( rgbToXyz( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 xyzToLuv(vec4 c) {return vec4( xyzToLuv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 luvToXyz(vec4 c) {return vec4( luvToXyz( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 luvToLch(vec4 c) {return vec4( luvToLch( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 lchToLuv(vec4 c) {return vec4( lchToLuv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 hsluvToLch(vec4 c) {return vec4( hsluvToLch( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 lchToHsluv(vec4 c) {return vec4( lchToHsluv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 hpluvToLch(vec4 c) {return vec4( hpluvToLch( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 lchToHpluv(vec4 c) {return vec4( lchToHpluv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 lchToRgb(vec4 c) {return vec4( lchToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 rgbToLch(vec4 c) {return vec4( rgbToLch( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 hsluvToRgb(vec4 c) {return vec4( hsluvToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 rgbToHsluv(vec4 c) {return vec4( rgbToHsluv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 hpluvToRgb(vec4 c) {return vec4( hpluvToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 rgbToHpluv(vec4 c) {return vec4( rgbToHpluv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 luvToRgb(vec4 c) {return vec4( luvToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\n// allow 3 floats\\\\nvec3 xyzToRgb(float x, float y, float z) {return xyzToRgb( vec3(x,y,z) );}\\\\nvec3 rgbToXyz(float x, float y, float z) {return rgbToXyz( vec3(x,y,z) );}\\\\nvec3 xyzToLuv(float x, float y, float z) {return xyzToLuv( vec3(x,y,z) );}\\\\nvec3 luvToXyz(float x, float y, float z) {return luvToXyz( vec3(x,y,z) );}\\\\nvec3 luvToLch(float x, float y, float z) {return luvToLch( vec3(x,y,z) );}\\\\nvec3 lchToLuv(float x, float y, float z) {return lchToLuv( vec3(x,y,z) );}\\\\nvec3 hsluvToLch(float x, float y, float z) {return hsluvToLch( vec3(x,y,z) );}\\\\nvec3 lchToHsluv(float x, float y, float z) {return lchToHsluv( vec3(x,y,z) );}\\\\nvec3 hpluvToLch(float x, float y, float z) {return hpluvToLch( vec3(x,y,z) );}\\\\nvec3 lchToHpluv(float x, float y, float z) {return lchToHpluv( vec3(x,y,z) );}\\\\nvec3 lchToRgb(float x, float y, float z) {return lchToRgb( vec3(x,y,z) );}\\\\nvec3 rgbToLch(float x, float y, float z) {return rgbToLch( vec3(x,y,z) );}\\\\nvec3 hsluvToRgb(float x, float y, float z) {return hsluvToRgb( vec3(x,y,z) );}\\\\nvec3 rgbToHsluv(float x, float y, float z) {return rgbToHsluv( vec3(x,y,z) );}\\\\nvec3 hpluvToRgb(float x, float y, float z) {return hpluvToRgb( vec3(x,y,z) );}\\\\nvec3 rgbToHpluv(float x, float y, float z) {return rgbToHpluv( vec3(x,y,z) );}\\\\nvec3 luvToRgb(float x, float y, float z) {return luvToRgb( vec3(x,y,z) );}\\\\n// allow 4 floats\\\\nvec4 xyzToRgb(float x, float y, float z, float a) {return xyzToRgb( vec4(x,y,z,a) );}\\\\nvec4 rgbToXyz(float x, float y, float z, float a) {return rgbToXyz( vec4(x,y,z,a) );}\\\\nvec4 xyzToLuv(float x, float y, float z, float a) {return xyzToLuv( vec4(x,y,z,a) );}\\\\nvec4 luvToXyz(float x, float y, float z, float a) {return luvToXyz( vec4(x,y,z,a) );}\\\\nvec4 luvToLch(float x, float y, float z, float a) {return luvToLch( vec4(x,y,z,a) );}\\\\nvec4 lchToLuv(float x, float y, float z, float a) {return lchToLuv( vec4(x,y,z,a) );}\\\\nvec4 hsluvToLch(float x, float y, float z, float a) {return hsluvToLch( vec4(x,y,z,a) );}\\\\nvec4 lchToHsluv(float x, float y, float z, float a) {return lchToHsluv( vec4(x,y,z,a) );}\\\\nvec4 hpluvToLch(float x, float y, float z, float a) {return hpluvToLch( vec4(x,y,z,a) );}\\\\nvec4 lchToHpluv(float x, float y, float z, float a) {return lchToHpluv( vec4(x,y,z,a) );}\\\\nvec4 lchToRgb(float x, float y, float z, float a) {return lchToRgb( vec4(x,y,z,a) );}\\\\nvec4 rgbToLch(float x, float y, float z, float a) {return rgbToLch( vec4(x,y,z,a) );}\\\\nvec4 hsluvToRgb(float x, float y, float z, float a) {return hsluvToRgb( vec4(x,y,z,a) );}\\\\nvec4 rgbToHslul(float x, float y, float z, float a) {return rgbToHsluv( vec4(x,y,z,a) );}\\\\nvec4 hpluvToRgb(float x, float y, float z, float a) {return hpluvToRgb( vec4(x,y,z,a) );}\\\\nvec4 rgbToHpluv(float x, float y, float z, float a) {return rgbToHpluv( vec4(x,y,z,a) );}\\\\nvec4 luvToRgb(float x, float y, float z, float a) {return luvToRgb( vec4(x,y,z,a) );}\\\\n\\\\n/*\\\\nEND HSLUV-GLSL\\\\n*/\\\\n\\\\n\\\\n// from https://gist.github.com/mattatz/44f081cac87e2f7c8980\\\\n// converted to glsl by gui@polygonjs.com\\\\n// and made function names consistent with the ones above\\\\n/*\\\\n * Conversion between RGB and LAB colorspace.\\\\n * Import from flowabs glsl program : https://code.google.com/p/flowabs/source/browse/glsl/?r=f36cbdcf7790a28d90f09e2cf89ec9a64911f138\\\\n */\\\\n\\\\n\\\\n\\\\nvec3 xyzToLab( vec3 c ) {\\\\n\\\\tvec3 n = c / vec3(95.047, 100, 108.883);\\\\n\\\\tvec3 v;\\\\n\\\\tv.x = ( n.x > 0.008856 ) ? pow( n.x, 1.0 / 3.0 ) : ( 7.787 * n.x ) + ( 16.0 / 116.0 );\\\\n\\\\tv.y = ( n.y > 0.008856 ) ? pow( n.y, 1.0 / 3.0 ) : ( 7.787 * n.y ) + ( 16.0 / 116.0 );\\\\n\\\\tv.z = ( n.z > 0.008856 ) ? pow( n.z, 1.0 / 3.0 ) : ( 7.787 * n.z ) + ( 16.0 / 116.0 );\\\\n\\\\treturn vec3(( 116.0 * v.y ) - 16.0, 500.0 * ( v.x - v.y ), 200.0 * ( v.y - v.z ));\\\\n}\\\\n\\\\nvec3 rgbToLab( vec3 c ) {\\\\n\\\\tvec3 lab = xyzToLab( rgbToXyz( c ) );\\\\n\\\\treturn vec3( lab.x / 100.0, 0.5 + 0.5 * ( lab.y / 127.0 ), 0.5 + 0.5 * ( lab.z / 127.0 ));\\\\n}\\\\n\\\\nvec3 labToXyz( vec3 c ) {\\\\n\\\\tfloat fy = ( c.x + 16.0 ) / 116.0;\\\\n\\\\tfloat fx = c.y / 500.0 + fy;\\\\n\\\\tfloat fz = fy - c.z / 200.0;\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\t 95.047 * (( fx > 0.206897 ) ? fx * fx * fx : ( fx - 16.0 / 116.0 ) / 7.787),\\\\n\\\\t\\\\t100.000 * (( fy > 0.206897 ) ? fy * fy * fy : ( fy - 16.0 / 116.0 ) / 7.787),\\\\n\\\\t\\\\t108.883 * (( fz > 0.206897 ) ? fz * fz * fz : ( fz - 16.0 / 116.0 ) / 7.787)\\\\n\\\\t);\\\\n}\\\\n\\\\n\\\\n\\\\nvec3 labToRgb( vec3 c ) {\\\\n\\\\treturn xyzToRgb( labToXyz( vec3(100.0 * c.x, 2.0 * 127.0 * (c.y - 0.5), 2.0 * 127.0 * (c.z - 0.5)) ) );\\\\n}\\\\\\\"));const i=uf.vector3(this.variableForInputParam(this.p.hsluv)),r=this.glVarName(\\\\\\\"rgb\\\\\\\");n.push(`vec3 ${r} = hsluvToRgb(${i}.x * 360.0, ${i}.y * 100.0, ${i}.z * 100.0)`),t.addDefinitions(this,e),t.addBodyLines(this,n)}}const qP=new class extends aa{constructor(){super(...arguments),this.hsv=oa.VECTOR3([1,1,1])}};class XP extends df{constructor(){super(...arguments),this.paramsConfig=qP}static type(){return\\\\\\\"hsvToRgb\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"rgb\\\\\\\",Do.VEC3)])}setLines(t){const e=[],n=[];e.push(new Tf(this,\\\\\\\"// https://github.com/hughsk/glsl-hsv2rgb\\\\n// https://stackoverflow.com/questions/15095909/from-rgb-to-hsv-in-opengl-glsl\\\\nvec3 hsv2rgb(vec3 c) {\\\\n\\\\tvec4 K = vec4(1.0, 2.0 / 3.0, 1.0 / 3.0, 3.0);\\\\n\\\\tvec3 p = abs(fract(c.xxx + K.xyz) * 6.0 - K.www);\\\\n\\\\treturn c.z * mix(K.xxx, clamp(p - K.xxx, 0.0, 1.0), c.y);\\\\n}\\\\\\\"));const i=uf.vector3(this.variableForInputParam(this.p.hsv)),r=this.glVarName(\\\\\\\"rgb\\\\\\\");n.push(`vec3 ${r} = hsv2rgb(${i})`),t.addDefinitions(this,e),t.addBodyLines(this,n)}}const YP=\\\\\\\"condition\\\\\\\";const $P=new class extends aa{};class JP extends kP{constructor(){super(...arguments),this.paramsConfig=$P}static type(){return\\\\\\\"ifThen\\\\\\\"}_expected_inputs_count(){const t=this.io.connections.inputConnections();return t?Math.max(t.length+1,2):2}_expected_input_types(){const t=[Do.BOOL],e=Do.FLOAT,n=this.io.connections.inputConnections(),i=this._expected_inputs_count();for(let r=1;r[\\\\\\\"value0\\\\\\\",\\\\\\\"value1\\\\\\\",\\\\\\\"blend\\\\\\\"][t])),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_gl_output_name(){return\\\\\\\"mix\\\\\\\"}_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.FLOAT;return[t,t,Do.FLOAT]}_expected_output_types(){return[this._expected_input_types()[0]]}}const pI=\\\\\\\"mvMult\\\\\\\";const _I=new class extends aa{constructor(){super(...arguments),this.vector=oa.VECTOR3([0,0,0])}};class mI extends df{constructor(){super(...arguments),this.paramsConfig=_I}static type(){return\\\\\\\"modelViewMatrixMult\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(pI,Do.VEC4)])}setLines(t){if(t.current_shader_name==xf.VERTEX){const e=uf.vector3(this.variableForInputParam(this.p.vector)),n=`vec4 ${this.glVarName(pI)} = modelViewMatrix * vec4(${e}, 1.0)`;t.addBodyLines(this,[n],xf.VERTEX)}}}const fI={mult:1};var gI;!function(t){t.VALUE=\\\\\\\"value\\\\\\\",t.PRE_ADD=\\\\\\\"preAdd\\\\\\\",t.MULT=\\\\\\\"mult\\\\\\\",t.POST_ADD=\\\\\\\"postAdd\\\\\\\"}(gI||(gI={}));class vI extends GO{static type(){return\\\\\\\"multAdd\\\\\\\"}_gl_input_name(t){return[gI.VALUE,gI.PRE_ADD,gI.MULT,gI.POST_ADD][t]}paramDefaultValue(t){return fI[t]}setLines(t){const e=uf.any(this.variableForInput(gI.VALUE)),n=uf.any(this.variableForInput(gI.PRE_ADD)),i=uf.any(this.variableForInput(gI.MULT)),r=uf.any(this.variableForInput(gI.POST_ADD)),s=this._expected_output_types()[0],o=this.io.outputs.namedOutputConnectionPoints()[0].name(),a=`${s} ${this.glVarName(o)} = (${i}*(${e} + ${n})) + ${r}`;t.addBodyLines(this,[a])}}class yI extends BO{static type(){return\\\\\\\"negate\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"in\\\\\\\"][t]))}_gl_input_name(t){return[\\\\\\\"in\\\\\\\"][t]}setLines(t){const e=uf.any(this.variableForInput(this._gl_input_name(0))),n=`${this.io.inputs.namedInputConnectionPoints()[0].type()} ${this.glVarName(this.io.connection_points.output_name(0))} = -1.0 * ${e}`;t.addBodyLines(this,[n])}}var xI;!function(t){t.CLASSIC_PERLIN_2D=\\\\\\\"Classic Perlin 2D\\\\\\\",t.CLASSIC_PERLIN_3D=\\\\\\\"Classic Perlin 3D\\\\\\\",t.CLASSIC_PERLIN_4D=\\\\\\\"Classic Perlin 4D\\\\\\\",t.NOISE_2D=\\\\\\\"noise2D\\\\\\\",t.NOISE_3D=\\\\\\\"noise3D\\\\\\\",t.NOISE_4D=\\\\\\\"noise4D\\\\\\\"}(xI||(xI={}));const bI=[xI.CLASSIC_PERLIN_2D,xI.CLASSIC_PERLIN_3D,xI.CLASSIC_PERLIN_4D,xI.NOISE_2D,xI.NOISE_3D,xI.NOISE_4D],wI={[xI.CLASSIC_PERLIN_2D]:'//\\\\n// GLSL textureless classic 2D noise \\\\\\\"cnoise\\\\\\\",\\\\n// with an RSL-style periodic variant \\\\\\\"pnoise\\\\\\\".\\\\n// Author: Stefan Gustavson (stefan.gustavson@liu.se)\\\\n// Version: 2011-08-22\\\\n//\\\\n// Many thanks to Ian McEwan of Ashima Arts for the\\\\n// ideas for permutation and gradient selection.\\\\n//\\\\n// Copyright (c) 2011 Stefan Gustavson. All rights reserved.\\\\n// Distributed under the MIT license. See LICENSE file.\\\\n// https://github.com/stegu/webgl-noise\\\\n//\\\\n\\\\n\\\\n// Classic Perlin noise\\\\nfloat cnoise(vec2 P)\\\\n{\\\\n vec4 Pi = floor(P.xyxy) + vec4(0.0, 0.0, 1.0, 1.0);\\\\n vec4 Pf = fract(P.xyxy) - vec4(0.0, 0.0, 1.0, 1.0);\\\\n Pi = mod289(Pi); // To avoid truncation effects in permutation\\\\n vec4 ix = Pi.xzxz;\\\\n vec4 iy = Pi.yyww;\\\\n vec4 fx = Pf.xzxz;\\\\n vec4 fy = Pf.yyww;\\\\n\\\\n vec4 i = permute(permute(ix) + iy);\\\\n\\\\n vec4 gx = fract(i * (1.0 / 41.0)) * 2.0 - 1.0 ;\\\\n vec4 gy = abs(gx) - 0.5 ;\\\\n vec4 tx = floor(gx + 0.5);\\\\n gx = gx - tx;\\\\n\\\\n vec2 g00 = vec2(gx.x,gy.x);\\\\n vec2 g10 = vec2(gx.y,gy.y);\\\\n vec2 g01 = vec2(gx.z,gy.z);\\\\n vec2 g11 = vec2(gx.w,gy.w);\\\\n\\\\n vec4 norm = taylorInvSqrt(vec4(dot(g00, g00), dot(g01, g01), dot(g10, g10), dot(g11, g11)));\\\\n g00 *= norm.x; \\\\n g01 *= norm.y; \\\\n g10 *= norm.z; \\\\n g11 *= norm.w; \\\\n\\\\n float n00 = dot(g00, vec2(fx.x, fy.x));\\\\n float n10 = dot(g10, vec2(fx.y, fy.y));\\\\n float n01 = dot(g01, vec2(fx.z, fy.z));\\\\n float n11 = dot(g11, vec2(fx.w, fy.w));\\\\n\\\\n vec2 fade_xy = fade(Pf.xy);\\\\n vec2 n_x = mix(vec2(n00, n01), vec2(n10, n11), fade_xy.x);\\\\n float n_xy = mix(n_x.x, n_x.y, fade_xy.y);\\\\n return 2.3 * n_xy;\\\\n}\\\\n\\\\n// Classic Perlin noise, periodic variant\\\\nfloat pnoise(vec2 P, vec2 rep)\\\\n{\\\\n vec4 Pi = floor(P.xyxy) + vec4(0.0, 0.0, 1.0, 1.0);\\\\n vec4 Pf = fract(P.xyxy) - vec4(0.0, 0.0, 1.0, 1.0);\\\\n Pi = mod(Pi, rep.xyxy); // To create noise with explicit period\\\\n Pi = mod289(Pi); // To avoid truncation effects in permutation\\\\n vec4 ix = Pi.xzxz;\\\\n vec4 iy = Pi.yyww;\\\\n vec4 fx = Pf.xzxz;\\\\n vec4 fy = Pf.yyww;\\\\n\\\\n vec4 i = permute(permute(ix) + iy);\\\\n\\\\n vec4 gx = fract(i * (1.0 / 41.0)) * 2.0 - 1.0 ;\\\\n vec4 gy = abs(gx) - 0.5 ;\\\\n vec4 tx = floor(gx + 0.5);\\\\n gx = gx - tx;\\\\n\\\\n vec2 g00 = vec2(gx.x,gy.x);\\\\n vec2 g10 = vec2(gx.y,gy.y);\\\\n vec2 g01 = vec2(gx.z,gy.z);\\\\n vec2 g11 = vec2(gx.w,gy.w);\\\\n\\\\n vec4 norm = taylorInvSqrt(vec4(dot(g00, g00), dot(g01, g01), dot(g10, g10), dot(g11, g11)));\\\\n g00 *= norm.x; \\\\n g01 *= norm.y; \\\\n g10 *= norm.z; \\\\n g11 *= norm.w; \\\\n\\\\n float n00 = dot(g00, vec2(fx.x, fy.x));\\\\n float n10 = dot(g10, vec2(fx.y, fy.y));\\\\n float n01 = dot(g01, vec2(fx.z, fy.z));\\\\n float n11 = dot(g11, vec2(fx.w, fy.w));\\\\n\\\\n vec2 fade_xy = fade(Pf.xy);\\\\n vec2 n_x = mix(vec2(n00, n01), vec2(n10, n11), fade_xy.x);\\\\n float n_xy = mix(n_x.x, n_x.y, fade_xy.y);\\\\n return 2.3 * n_xy;\\\\n}\\\\n',[xI.CLASSIC_PERLIN_3D]:'//\\\\n// GLSL textureless classic 3D noise \\\\\\\"cnoise\\\\\\\",\\\\n// with an RSL-style periodic variant \\\\\\\"pnoise\\\\\\\".\\\\n// Author: Stefan Gustavson (stefan.gustavson@liu.se)\\\\n// Version: 2011-10-11\\\\n//\\\\n// Many thanks to Ian McEwan of Ashima Arts for the\\\\n// ideas for permutation and gradient selection.\\\\n//\\\\n// Copyright (c) 2011 Stefan Gustavson. All rights reserved.\\\\n// Distributed under the MIT license. See LICENSE file.\\\\n// https://github.com/stegu/webgl-noise\\\\n//\\\\n\\\\n// Classic Perlin noise\\\\nfloat cnoise(vec3 P)\\\\n{\\\\n vec3 Pi0 = floor(P); // Integer part for indexing\\\\n vec3 Pi1 = Pi0 + vec3(1.0); // Integer part + 1\\\\n Pi0 = mod289(Pi0);\\\\n Pi1 = mod289(Pi1);\\\\n vec3 Pf0 = fract(P); // Fractional part for interpolation\\\\n vec3 Pf1 = Pf0 - vec3(1.0); // Fractional part - 1.0\\\\n vec4 ix = vec4(Pi0.x, Pi1.x, Pi0.x, Pi1.x);\\\\n vec4 iy = vec4(Pi0.yy, Pi1.yy);\\\\n vec4 iz0 = Pi0.zzzz;\\\\n vec4 iz1 = Pi1.zzzz;\\\\n\\\\n vec4 ixy = permute(permute(ix) + iy);\\\\n vec4 ixy0 = permute(ixy + iz0);\\\\n vec4 ixy1 = permute(ixy + iz1);\\\\n\\\\n vec4 gx0 = ixy0 * (1.0 / 7.0);\\\\n vec4 gy0 = fract(floor(gx0) * (1.0 / 7.0)) - 0.5;\\\\n gx0 = fract(gx0);\\\\n vec4 gz0 = vec4(0.5) - abs(gx0) - abs(gy0);\\\\n vec4 sz0 = step(gz0, vec4(0.0));\\\\n gx0 -= sz0 * (step(0.0, gx0) - 0.5);\\\\n gy0 -= sz0 * (step(0.0, gy0) - 0.5);\\\\n\\\\n vec4 gx1 = ixy1 * (1.0 / 7.0);\\\\n vec4 gy1 = fract(floor(gx1) * (1.0 / 7.0)) - 0.5;\\\\n gx1 = fract(gx1);\\\\n vec4 gz1 = vec4(0.5) - abs(gx1) - abs(gy1);\\\\n vec4 sz1 = step(gz1, vec4(0.0));\\\\n gx1 -= sz1 * (step(0.0, gx1) - 0.5);\\\\n gy1 -= sz1 * (step(0.0, gy1) - 0.5);\\\\n\\\\n vec3 g000 = vec3(gx0.x,gy0.x,gz0.x);\\\\n vec3 g100 = vec3(gx0.y,gy0.y,gz0.y);\\\\n vec3 g010 = vec3(gx0.z,gy0.z,gz0.z);\\\\n vec3 g110 = vec3(gx0.w,gy0.w,gz0.w);\\\\n vec3 g001 = vec3(gx1.x,gy1.x,gz1.x);\\\\n vec3 g101 = vec3(gx1.y,gy1.y,gz1.y);\\\\n vec3 g011 = vec3(gx1.z,gy1.z,gz1.z);\\\\n vec3 g111 = vec3(gx1.w,gy1.w,gz1.w);\\\\n\\\\n vec4 norm0 = taylorInvSqrt(vec4(dot(g000, g000), dot(g010, g010), dot(g100, g100), dot(g110, g110)));\\\\n g000 *= norm0.x;\\\\n g010 *= norm0.y;\\\\n g100 *= norm0.z;\\\\n g110 *= norm0.w;\\\\n vec4 norm1 = taylorInvSqrt(vec4(dot(g001, g001), dot(g011, g011), dot(g101, g101), dot(g111, g111)));\\\\n g001 *= norm1.x;\\\\n g011 *= norm1.y;\\\\n g101 *= norm1.z;\\\\n g111 *= norm1.w;\\\\n\\\\n float n000 = dot(g000, Pf0);\\\\n float n100 = dot(g100, vec3(Pf1.x, Pf0.yz));\\\\n float n010 = dot(g010, vec3(Pf0.x, Pf1.y, Pf0.z));\\\\n float n110 = dot(g110, vec3(Pf1.xy, Pf0.z));\\\\n float n001 = dot(g001, vec3(Pf0.xy, Pf1.z));\\\\n float n101 = dot(g101, vec3(Pf1.x, Pf0.y, Pf1.z));\\\\n float n011 = dot(g011, vec3(Pf0.x, Pf1.yz));\\\\n float n111 = dot(g111, Pf1);\\\\n\\\\n vec3 fade_xyz = fade(Pf0);\\\\n vec4 n_z = mix(vec4(n000, n100, n010, n110), vec4(n001, n101, n011, n111), fade_xyz.z);\\\\n vec2 n_yz = mix(n_z.xy, n_z.zw, fade_xyz.y);\\\\n float n_xyz = mix(n_yz.x, n_yz.y, fade_xyz.x); \\\\n return 2.2 * n_xyz;\\\\n}\\\\n\\\\n// Classic Perlin noise, periodic variant\\\\nfloat pnoise(vec3 P, vec3 rep)\\\\n{\\\\n vec3 Pi0 = mod(floor(P), rep); // Integer part, modulo period\\\\n vec3 Pi1 = mod(Pi0 + vec3(1.0), rep); // Integer part + 1, mod period\\\\n Pi0 = mod289(Pi0);\\\\n Pi1 = mod289(Pi1);\\\\n vec3 Pf0 = fract(P); // Fractional part for interpolation\\\\n vec3 Pf1 = Pf0 - vec3(1.0); // Fractional part - 1.0\\\\n vec4 ix = vec4(Pi0.x, Pi1.x, Pi0.x, Pi1.x);\\\\n vec4 iy = vec4(Pi0.yy, Pi1.yy);\\\\n vec4 iz0 = Pi0.zzzz;\\\\n vec4 iz1 = Pi1.zzzz;\\\\n\\\\n vec4 ixy = permute(permute(ix) + iy);\\\\n vec4 ixy0 = permute(ixy + iz0);\\\\n vec4 ixy1 = permute(ixy + iz1);\\\\n\\\\n vec4 gx0 = ixy0 * (1.0 / 7.0);\\\\n vec4 gy0 = fract(floor(gx0) * (1.0 / 7.0)) - 0.5;\\\\n gx0 = fract(gx0);\\\\n vec4 gz0 = vec4(0.5) - abs(gx0) - abs(gy0);\\\\n vec4 sz0 = step(gz0, vec4(0.0));\\\\n gx0 -= sz0 * (step(0.0, gx0) - 0.5);\\\\n gy0 -= sz0 * (step(0.0, gy0) - 0.5);\\\\n\\\\n vec4 gx1 = ixy1 * (1.0 / 7.0);\\\\n vec4 gy1 = fract(floor(gx1) * (1.0 / 7.0)) - 0.5;\\\\n gx1 = fract(gx1);\\\\n vec4 gz1 = vec4(0.5) - abs(gx1) - abs(gy1);\\\\n vec4 sz1 = step(gz1, vec4(0.0));\\\\n gx1 -= sz1 * (step(0.0, gx1) - 0.5);\\\\n gy1 -= sz1 * (step(0.0, gy1) - 0.5);\\\\n\\\\n vec3 g000 = vec3(gx0.x,gy0.x,gz0.x);\\\\n vec3 g100 = vec3(gx0.y,gy0.y,gz0.y);\\\\n vec3 g010 = vec3(gx0.z,gy0.z,gz0.z);\\\\n vec3 g110 = vec3(gx0.w,gy0.w,gz0.w);\\\\n vec3 g001 = vec3(gx1.x,gy1.x,gz1.x);\\\\n vec3 g101 = vec3(gx1.y,gy1.y,gz1.y);\\\\n vec3 g011 = vec3(gx1.z,gy1.z,gz1.z);\\\\n vec3 g111 = vec3(gx1.w,gy1.w,gz1.w);\\\\n\\\\n vec4 norm0 = taylorInvSqrt(vec4(dot(g000, g000), dot(g010, g010), dot(g100, g100), dot(g110, g110)));\\\\n g000 *= norm0.x;\\\\n g010 *= norm0.y;\\\\n g100 *= norm0.z;\\\\n g110 *= norm0.w;\\\\n vec4 norm1 = taylorInvSqrt(vec4(dot(g001, g001), dot(g011, g011), dot(g101, g101), dot(g111, g111)));\\\\n g001 *= norm1.x;\\\\n g011 *= norm1.y;\\\\n g101 *= norm1.z;\\\\n g111 *= norm1.w;\\\\n\\\\n float n000 = dot(g000, Pf0);\\\\n float n100 = dot(g100, vec3(Pf1.x, Pf0.yz));\\\\n float n010 = dot(g010, vec3(Pf0.x, Pf1.y, Pf0.z));\\\\n float n110 = dot(g110, vec3(Pf1.xy, Pf0.z));\\\\n float n001 = dot(g001, vec3(Pf0.xy, Pf1.z));\\\\n float n101 = dot(g101, vec3(Pf1.x, Pf0.y, Pf1.z));\\\\n float n011 = dot(g011, vec3(Pf0.x, Pf1.yz));\\\\n float n111 = dot(g111, Pf1);\\\\n\\\\n vec3 fade_xyz = fade(Pf0);\\\\n vec4 n_z = mix(vec4(n000, n100, n010, n110), vec4(n001, n101, n011, n111), fade_xyz.z);\\\\n vec2 n_yz = mix(n_z.xy, n_z.zw, fade_xyz.y);\\\\n float n_xyz = mix(n_yz.x, n_yz.y, fade_xyz.x); \\\\n return 2.2 * n_xyz;\\\\n}\\\\n',[xI.CLASSIC_PERLIN_4D]:'//\\\\n// GLSL textureless classic 4D noise \\\\\\\"cnoise\\\\\\\",\\\\n// with an RSL-style periodic variant \\\\\\\"pnoise\\\\\\\".\\\\n// Author: Stefan Gustavson (stefan.gustavson@liu.se)\\\\n// Version: 2011-08-22\\\\n//\\\\n// Many thanks to Ian McEwan of Ashima Arts for the\\\\n// ideas for permutation and gradient selection.\\\\n//\\\\n// Copyright (c) 2011 Stefan Gustavson. All rights reserved.\\\\n// Distributed under the MIT license. See LICENSE file.\\\\n// https://github.com/stegu/webgl-noise\\\\n//\\\\n\\\\n\\\\n\\\\n// Classic Perlin noise\\\\nfloat cnoise(vec4 P)\\\\n{\\\\n vec4 Pi0 = floor(P); // Integer part for indexing\\\\n vec4 Pi1 = Pi0 + 1.0; // Integer part + 1\\\\n Pi0 = mod289(Pi0);\\\\n Pi1 = mod289(Pi1);\\\\n vec4 Pf0 = fract(P); // Fractional part for interpolation\\\\n vec4 Pf1 = Pf0 - 1.0; // Fractional part - 1.0\\\\n vec4 ix = vec4(Pi0.x, Pi1.x, Pi0.x, Pi1.x);\\\\n vec4 iy = vec4(Pi0.yy, Pi1.yy);\\\\n vec4 iz0 = vec4(Pi0.zzzz);\\\\n vec4 iz1 = vec4(Pi1.zzzz);\\\\n vec4 iw0 = vec4(Pi0.wwww);\\\\n vec4 iw1 = vec4(Pi1.wwww);\\\\n\\\\n vec4 ixy = permute(permute(ix) + iy);\\\\n vec4 ixy0 = permute(ixy + iz0);\\\\n vec4 ixy1 = permute(ixy + iz1);\\\\n vec4 ixy00 = permute(ixy0 + iw0);\\\\n vec4 ixy01 = permute(ixy0 + iw1);\\\\n vec4 ixy10 = permute(ixy1 + iw0);\\\\n vec4 ixy11 = permute(ixy1 + iw1);\\\\n\\\\n vec4 gx00 = ixy00 * (1.0 / 7.0);\\\\n vec4 gy00 = floor(gx00) * (1.0 / 7.0);\\\\n vec4 gz00 = floor(gy00) * (1.0 / 6.0);\\\\n gx00 = fract(gx00) - 0.5;\\\\n gy00 = fract(gy00) - 0.5;\\\\n gz00 = fract(gz00) - 0.5;\\\\n vec4 gw00 = vec4(0.75) - abs(gx00) - abs(gy00) - abs(gz00);\\\\n vec4 sw00 = step(gw00, vec4(0.0));\\\\n gx00 -= sw00 * (step(0.0, gx00) - 0.5);\\\\n gy00 -= sw00 * (step(0.0, gy00) - 0.5);\\\\n\\\\n vec4 gx01 = ixy01 * (1.0 / 7.0);\\\\n vec4 gy01 = floor(gx01) * (1.0 / 7.0);\\\\n vec4 gz01 = floor(gy01) * (1.0 / 6.0);\\\\n gx01 = fract(gx01) - 0.5;\\\\n gy01 = fract(gy01) - 0.5;\\\\n gz01 = fract(gz01) - 0.5;\\\\n vec4 gw01 = vec4(0.75) - abs(gx01) - abs(gy01) - abs(gz01);\\\\n vec4 sw01 = step(gw01, vec4(0.0));\\\\n gx01 -= sw01 * (step(0.0, gx01) - 0.5);\\\\n gy01 -= sw01 * (step(0.0, gy01) - 0.5);\\\\n\\\\n vec4 gx10 = ixy10 * (1.0 / 7.0);\\\\n vec4 gy10 = floor(gx10) * (1.0 / 7.0);\\\\n vec4 gz10 = floor(gy10) * (1.0 / 6.0);\\\\n gx10 = fract(gx10) - 0.5;\\\\n gy10 = fract(gy10) - 0.5;\\\\n gz10 = fract(gz10) - 0.5;\\\\n vec4 gw10 = vec4(0.75) - abs(gx10) - abs(gy10) - abs(gz10);\\\\n vec4 sw10 = step(gw10, vec4(0.0));\\\\n gx10 -= sw10 * (step(0.0, gx10) - 0.5);\\\\n gy10 -= sw10 * (step(0.0, gy10) - 0.5);\\\\n\\\\n vec4 gx11 = ixy11 * (1.0 / 7.0);\\\\n vec4 gy11 = floor(gx11) * (1.0 / 7.0);\\\\n vec4 gz11 = floor(gy11) * (1.0 / 6.0);\\\\n gx11 = fract(gx11) - 0.5;\\\\n gy11 = fract(gy11) - 0.5;\\\\n gz11 = fract(gz11) - 0.5;\\\\n vec4 gw11 = vec4(0.75) - abs(gx11) - abs(gy11) - abs(gz11);\\\\n vec4 sw11 = step(gw11, vec4(0.0));\\\\n gx11 -= sw11 * (step(0.0, gx11) - 0.5);\\\\n gy11 -= sw11 * (step(0.0, gy11) - 0.5);\\\\n\\\\n vec4 g0000 = vec4(gx00.x,gy00.x,gz00.x,gw00.x);\\\\n vec4 g1000 = vec4(gx00.y,gy00.y,gz00.y,gw00.y);\\\\n vec4 g0100 = vec4(gx00.z,gy00.z,gz00.z,gw00.z);\\\\n vec4 g1100 = vec4(gx00.w,gy00.w,gz00.w,gw00.w);\\\\n vec4 g0010 = vec4(gx10.x,gy10.x,gz10.x,gw10.x);\\\\n vec4 g1010 = vec4(gx10.y,gy10.y,gz10.y,gw10.y);\\\\n vec4 g0110 = vec4(gx10.z,gy10.z,gz10.z,gw10.z);\\\\n vec4 g1110 = vec4(gx10.w,gy10.w,gz10.w,gw10.w);\\\\n vec4 g0001 = vec4(gx01.x,gy01.x,gz01.x,gw01.x);\\\\n vec4 g1001 = vec4(gx01.y,gy01.y,gz01.y,gw01.y);\\\\n vec4 g0101 = vec4(gx01.z,gy01.z,gz01.z,gw01.z);\\\\n vec4 g1101 = vec4(gx01.w,gy01.w,gz01.w,gw01.w);\\\\n vec4 g0011 = vec4(gx11.x,gy11.x,gz11.x,gw11.x);\\\\n vec4 g1011 = vec4(gx11.y,gy11.y,gz11.y,gw11.y);\\\\n vec4 g0111 = vec4(gx11.z,gy11.z,gz11.z,gw11.z);\\\\n vec4 g1111 = vec4(gx11.w,gy11.w,gz11.w,gw11.w);\\\\n\\\\n vec4 norm00 = taylorInvSqrt(vec4(dot(g0000, g0000), dot(g0100, g0100), dot(g1000, g1000), dot(g1100, g1100)));\\\\n g0000 *= norm00.x;\\\\n g0100 *= norm00.y;\\\\n g1000 *= norm00.z;\\\\n g1100 *= norm00.w;\\\\n\\\\n vec4 norm01 = taylorInvSqrt(vec4(dot(g0001, g0001), dot(g0101, g0101), dot(g1001, g1001), dot(g1101, g1101)));\\\\n g0001 *= norm01.x;\\\\n g0101 *= norm01.y;\\\\n g1001 *= norm01.z;\\\\n g1101 *= norm01.w;\\\\n\\\\n vec4 norm10 = taylorInvSqrt(vec4(dot(g0010, g0010), dot(g0110, g0110), dot(g1010, g1010), dot(g1110, g1110)));\\\\n g0010 *= norm10.x;\\\\n g0110 *= norm10.y;\\\\n g1010 *= norm10.z;\\\\n g1110 *= norm10.w;\\\\n\\\\n vec4 norm11 = taylorInvSqrt(vec4(dot(g0011, g0011), dot(g0111, g0111), dot(g1011, g1011), dot(g1111, g1111)));\\\\n g0011 *= norm11.x;\\\\n g0111 *= norm11.y;\\\\n g1011 *= norm11.z;\\\\n g1111 *= norm11.w;\\\\n\\\\n float n0000 = dot(g0000, Pf0);\\\\n float n1000 = dot(g1000, vec4(Pf1.x, Pf0.yzw));\\\\n float n0100 = dot(g0100, vec4(Pf0.x, Pf1.y, Pf0.zw));\\\\n float n1100 = dot(g1100, vec4(Pf1.xy, Pf0.zw));\\\\n float n0010 = dot(g0010, vec4(Pf0.xy, Pf1.z, Pf0.w));\\\\n float n1010 = dot(g1010, vec4(Pf1.x, Pf0.y, Pf1.z, Pf0.w));\\\\n float n0110 = dot(g0110, vec4(Pf0.x, Pf1.yz, Pf0.w));\\\\n float n1110 = dot(g1110, vec4(Pf1.xyz, Pf0.w));\\\\n float n0001 = dot(g0001, vec4(Pf0.xyz, Pf1.w));\\\\n float n1001 = dot(g1001, vec4(Pf1.x, Pf0.yz, Pf1.w));\\\\n float n0101 = dot(g0101, vec4(Pf0.x, Pf1.y, Pf0.z, Pf1.w));\\\\n float n1101 = dot(g1101, vec4(Pf1.xy, Pf0.z, Pf1.w));\\\\n float n0011 = dot(g0011, vec4(Pf0.xy, Pf1.zw));\\\\n float n1011 = dot(g1011, vec4(Pf1.x, Pf0.y, Pf1.zw));\\\\n float n0111 = dot(g0111, vec4(Pf0.x, Pf1.yzw));\\\\n float n1111 = dot(g1111, Pf1);\\\\n\\\\n vec4 fade_xyzw = fade(Pf0);\\\\n vec4 n_0w = mix(vec4(n0000, n1000, n0100, n1100), vec4(n0001, n1001, n0101, n1101), fade_xyzw.w);\\\\n vec4 n_1w = mix(vec4(n0010, n1010, n0110, n1110), vec4(n0011, n1011, n0111, n1111), fade_xyzw.w);\\\\n vec4 n_zw = mix(n_0w, n_1w, fade_xyzw.z);\\\\n vec2 n_yzw = mix(n_zw.xy, n_zw.zw, fade_xyzw.y);\\\\n float n_xyzw = mix(n_yzw.x, n_yzw.y, fade_xyzw.x);\\\\n return 2.2 * n_xyzw;\\\\n}\\\\n\\\\n// Classic Perlin noise, periodic version\\\\nfloat pnoise(vec4 P, vec4 rep)\\\\n{\\\\n vec4 Pi0 = mod(floor(P), rep); // Integer part modulo rep\\\\n vec4 Pi1 = mod(Pi0 + 1.0, rep); // Integer part + 1 mod rep\\\\n Pi0 = mod289(Pi0);\\\\n Pi1 = mod289(Pi1);\\\\n vec4 Pf0 = fract(P); // Fractional part for interpolation\\\\n vec4 Pf1 = Pf0 - 1.0; // Fractional part - 1.0\\\\n vec4 ix = vec4(Pi0.x, Pi1.x, Pi0.x, Pi1.x);\\\\n vec4 iy = vec4(Pi0.yy, Pi1.yy);\\\\n vec4 iz0 = vec4(Pi0.zzzz);\\\\n vec4 iz1 = vec4(Pi1.zzzz);\\\\n vec4 iw0 = vec4(Pi0.wwww);\\\\n vec4 iw1 = vec4(Pi1.wwww);\\\\n\\\\n vec4 ixy = permute(permute(ix) + iy);\\\\n vec4 ixy0 = permute(ixy + iz0);\\\\n vec4 ixy1 = permute(ixy + iz1);\\\\n vec4 ixy00 = permute(ixy0 + iw0);\\\\n vec4 ixy01 = permute(ixy0 + iw1);\\\\n vec4 ixy10 = permute(ixy1 + iw0);\\\\n vec4 ixy11 = permute(ixy1 + iw1);\\\\n\\\\n vec4 gx00 = ixy00 * (1.0 / 7.0);\\\\n vec4 gy00 = floor(gx00) * (1.0 / 7.0);\\\\n vec4 gz00 = floor(gy00) * (1.0 / 6.0);\\\\n gx00 = fract(gx00) - 0.5;\\\\n gy00 = fract(gy00) - 0.5;\\\\n gz00 = fract(gz00) - 0.5;\\\\n vec4 gw00 = vec4(0.75) - abs(gx00) - abs(gy00) - abs(gz00);\\\\n vec4 sw00 = step(gw00, vec4(0.0));\\\\n gx00 -= sw00 * (step(0.0, gx00) - 0.5);\\\\n gy00 -= sw00 * (step(0.0, gy00) - 0.5);\\\\n\\\\n vec4 gx01 = ixy01 * (1.0 / 7.0);\\\\n vec4 gy01 = floor(gx01) * (1.0 / 7.0);\\\\n vec4 gz01 = floor(gy01) * (1.0 / 6.0);\\\\n gx01 = fract(gx01) - 0.5;\\\\n gy01 = fract(gy01) - 0.5;\\\\n gz01 = fract(gz01) - 0.5;\\\\n vec4 gw01 = vec4(0.75) - abs(gx01) - abs(gy01) - abs(gz01);\\\\n vec4 sw01 = step(gw01, vec4(0.0));\\\\n gx01 -= sw01 * (step(0.0, gx01) - 0.5);\\\\n gy01 -= sw01 * (step(0.0, gy01) - 0.5);\\\\n\\\\n vec4 gx10 = ixy10 * (1.0 / 7.0);\\\\n vec4 gy10 = floor(gx10) * (1.0 / 7.0);\\\\n vec4 gz10 = floor(gy10) * (1.0 / 6.0);\\\\n gx10 = fract(gx10) - 0.5;\\\\n gy10 = fract(gy10) - 0.5;\\\\n gz10 = fract(gz10) - 0.5;\\\\n vec4 gw10 = vec4(0.75) - abs(gx10) - abs(gy10) - abs(gz10);\\\\n vec4 sw10 = step(gw10, vec4(0.0));\\\\n gx10 -= sw10 * (step(0.0, gx10) - 0.5);\\\\n gy10 -= sw10 * (step(0.0, gy10) - 0.5);\\\\n\\\\n vec4 gx11 = ixy11 * (1.0 / 7.0);\\\\n vec4 gy11 = floor(gx11) * (1.0 / 7.0);\\\\n vec4 gz11 = floor(gy11) * (1.0 / 6.0);\\\\n gx11 = fract(gx11) - 0.5;\\\\n gy11 = fract(gy11) - 0.5;\\\\n gz11 = fract(gz11) - 0.5;\\\\n vec4 gw11 = vec4(0.75) - abs(gx11) - abs(gy11) - abs(gz11);\\\\n vec4 sw11 = step(gw11, vec4(0.0));\\\\n gx11 -= sw11 * (step(0.0, gx11) - 0.5);\\\\n gy11 -= sw11 * (step(0.0, gy11) - 0.5);\\\\n\\\\n vec4 g0000 = vec4(gx00.x,gy00.x,gz00.x,gw00.x);\\\\n vec4 g1000 = vec4(gx00.y,gy00.y,gz00.y,gw00.y);\\\\n vec4 g0100 = vec4(gx00.z,gy00.z,gz00.z,gw00.z);\\\\n vec4 g1100 = vec4(gx00.w,gy00.w,gz00.w,gw00.w);\\\\n vec4 g0010 = vec4(gx10.x,gy10.x,gz10.x,gw10.x);\\\\n vec4 g1010 = vec4(gx10.y,gy10.y,gz10.y,gw10.y);\\\\n vec4 g0110 = vec4(gx10.z,gy10.z,gz10.z,gw10.z);\\\\n vec4 g1110 = vec4(gx10.w,gy10.w,gz10.w,gw10.w);\\\\n vec4 g0001 = vec4(gx01.x,gy01.x,gz01.x,gw01.x);\\\\n vec4 g1001 = vec4(gx01.y,gy01.y,gz01.y,gw01.y);\\\\n vec4 g0101 = vec4(gx01.z,gy01.z,gz01.z,gw01.z);\\\\n vec4 g1101 = vec4(gx01.w,gy01.w,gz01.w,gw01.w);\\\\n vec4 g0011 = vec4(gx11.x,gy11.x,gz11.x,gw11.x);\\\\n vec4 g1011 = vec4(gx11.y,gy11.y,gz11.y,gw11.y);\\\\n vec4 g0111 = vec4(gx11.z,gy11.z,gz11.z,gw11.z);\\\\n vec4 g1111 = vec4(gx11.w,gy11.w,gz11.w,gw11.w);\\\\n\\\\n vec4 norm00 = taylorInvSqrt(vec4(dot(g0000, g0000), dot(g0100, g0100), dot(g1000, g1000), dot(g1100, g1100)));\\\\n g0000 *= norm00.x;\\\\n g0100 *= norm00.y;\\\\n g1000 *= norm00.z;\\\\n g1100 *= norm00.w;\\\\n\\\\n vec4 norm01 = taylorInvSqrt(vec4(dot(g0001, g0001), dot(g0101, g0101), dot(g1001, g1001), dot(g1101, g1101)));\\\\n g0001 *= norm01.x;\\\\n g0101 *= norm01.y;\\\\n g1001 *= norm01.z;\\\\n g1101 *= norm01.w;\\\\n\\\\n vec4 norm10 = taylorInvSqrt(vec4(dot(g0010, g0010), dot(g0110, g0110), dot(g1010, g1010), dot(g1110, g1110)));\\\\n g0010 *= norm10.x;\\\\n g0110 *= norm10.y;\\\\n g1010 *= norm10.z;\\\\n g1110 *= norm10.w;\\\\n\\\\n vec4 norm11 = taylorInvSqrt(vec4(dot(g0011, g0011), dot(g0111, g0111), dot(g1011, g1011), dot(g1111, g1111)));\\\\n g0011 *= norm11.x;\\\\n g0111 *= norm11.y;\\\\n g1011 *= norm11.z;\\\\n g1111 *= norm11.w;\\\\n\\\\n float n0000 = dot(g0000, Pf0);\\\\n float n1000 = dot(g1000, vec4(Pf1.x, Pf0.yzw));\\\\n float n0100 = dot(g0100, vec4(Pf0.x, Pf1.y, Pf0.zw));\\\\n float n1100 = dot(g1100, vec4(Pf1.xy, Pf0.zw));\\\\n float n0010 = dot(g0010, vec4(Pf0.xy, Pf1.z, Pf0.w));\\\\n float n1010 = dot(g1010, vec4(Pf1.x, Pf0.y, Pf1.z, Pf0.w));\\\\n float n0110 = dot(g0110, vec4(Pf0.x, Pf1.yz, Pf0.w));\\\\n float n1110 = dot(g1110, vec4(Pf1.xyz, Pf0.w));\\\\n float n0001 = dot(g0001, vec4(Pf0.xyz, Pf1.w));\\\\n float n1001 = dot(g1001, vec4(Pf1.x, Pf0.yz, Pf1.w));\\\\n float n0101 = dot(g0101, vec4(Pf0.x, Pf1.y, Pf0.z, Pf1.w));\\\\n float n1101 = dot(g1101, vec4(Pf1.xy, Pf0.z, Pf1.w));\\\\n float n0011 = dot(g0011, vec4(Pf0.xy, Pf1.zw));\\\\n float n1011 = dot(g1011, vec4(Pf1.x, Pf0.y, Pf1.zw));\\\\n float n0111 = dot(g0111, vec4(Pf0.x, Pf1.yzw));\\\\n float n1111 = dot(g1111, Pf1);\\\\n\\\\n vec4 fade_xyzw = fade(Pf0);\\\\n vec4 n_0w = mix(vec4(n0000, n1000, n0100, n1100), vec4(n0001, n1001, n0101, n1101), fade_xyzw.w);\\\\n vec4 n_1w = mix(vec4(n0010, n1010, n0110, n1110), vec4(n0011, n1011, n0111, n1111), fade_xyzw.w);\\\\n vec4 n_zw = mix(n_0w, n_1w, fade_xyzw.z);\\\\n vec2 n_yzw = mix(n_zw.xy, n_zw.zw, fade_xyzw.y);\\\\n float n_xyzw = mix(n_yzw.x, n_yzw.y, fade_xyzw.x);\\\\n return 2.2 * n_xyzw;\\\\n}\\\\n',[xI.NOISE_2D]:\\\\\\\"//\\\\n// Description : Array and textureless GLSL 2D simplex noise function.\\\\n// Author : Ian McEwan, Ashima Arts.\\\\n// Maintainer : stegu\\\\n// Lastmod : 20110822 (ijm)\\\\n// License : Copyright (C) 2011 Ashima Arts. All rights reserved.\\\\n// Distributed under the MIT License. See LICENSE file.\\\\n// https://github.com/ashima/webgl-noise\\\\n// https://github.com/stegu/webgl-noise\\\\n// \\\\n\\\\n\\\\nfloat snoise(vec2 v)\\\\n {\\\\n const vec4 C = vec4(0.211324865405187, // (3.0-sqrt(3.0))/6.0\\\\n 0.366025403784439, // 0.5*(sqrt(3.0)-1.0)\\\\n -0.577350269189626, // -1.0 + 2.0 * C.x\\\\n 0.024390243902439); // 1.0 / 41.0\\\\n// First corner\\\\n vec2 i = floor(v + dot(v, C.yy) );\\\\n vec2 x0 = v - i + dot(i, C.xx);\\\\n\\\\n// Other corners\\\\n vec2 i1;\\\\n //i1.x = step( x0.y, x0.x ); // x0.x > x0.y ? 1.0 : 0.0\\\\n //i1.y = 1.0 - i1.x;\\\\n i1 = (x0.x > x0.y) ? vec2(1.0, 0.0) : vec2(0.0, 1.0);\\\\n // x0 = x0 - 0.0 + 0.0 * C.xx ;\\\\n // x1 = x0 - i1 + 1.0 * C.xx ;\\\\n // x2 = x0 - 1.0 + 2.0 * C.xx ;\\\\n vec4 x12 = x0.xyxy + C.xxzz;\\\\n x12.xy -= i1;\\\\n\\\\n// Permutations\\\\n i = mod289(i); // Avoid truncation effects in permutation\\\\n vec3 p = permute( permute( i.y + vec3(0.0, i1.y, 1.0 ))\\\\n\\\\t\\\\t+ i.x + vec3(0.0, i1.x, 1.0 ));\\\\n\\\\n vec3 m = max(0.5 - vec3(dot(x0,x0), dot(x12.xy,x12.xy), dot(x12.zw,x12.zw)), 0.0);\\\\n m = m*m ;\\\\n m = m*m ;\\\\n\\\\n// Gradients: 41 points uniformly over a line, mapped onto a diamond.\\\\n// The ring size 17*17 = 289 is close to a multiple of 41 (41*7 = 287)\\\\n\\\\n vec3 x = 2.0 * fract(p * C.www) - 1.0;\\\\n vec3 h = abs(x) - 0.5;\\\\n vec3 ox = floor(x + 0.5);\\\\n vec3 a0 = x - ox;\\\\n\\\\n// Normalise gradients implicitly by scaling m\\\\n// Approximation of: m *= inversesqrt( a0*a0 + h*h );\\\\n m *= 1.79284291400159 - 0.85373472095314 * ( a0*a0 + h*h );\\\\n\\\\n// Compute final noise value at P\\\\n vec3 g;\\\\n g.x = a0.x * x0.x + h.x * x0.y;\\\\n g.yz = a0.yz * x12.xz + h.yz * x12.yw;\\\\n return 130.0 * dot(m, g);\\\\n}\\\\n\\\\\\\",[xI.NOISE_3D]:\\\\\\\"//\\\\n// Description : Array and textureless GLSL 2D/3D/4D simplex \\\\n// noise functions.\\\\n// Author : Ian McEwan, Ashima Arts.\\\\n// Maintainer : stegu\\\\n// Lastmod : 20110822 (ijm)\\\\n// License : Copyright (C) 2011 Ashima Arts. All rights reserved.\\\\n// Distributed under the MIT License. See LICENSE file.\\\\n// https://github.com/ashima/webgl-noise\\\\n// https://github.com/stegu/webgl-noise\\\\n// \\\\n\\\\n\\\\n\\\\nfloat snoise(vec3 v)\\\\n { \\\\n const vec2 C = vec2(1.0/6.0, 1.0/3.0) ;\\\\n const vec4 D = vec4(0.0, 0.5, 1.0, 2.0);\\\\n\\\\n// First corner\\\\n vec3 i = floor(v + dot(v, C.yyy) );\\\\n vec3 x0 = v - i + dot(i, C.xxx) ;\\\\n\\\\n// Other corners\\\\n vec3 g = step(x0.yzx, x0.xyz);\\\\n vec3 l = 1.0 - g;\\\\n vec3 i1 = min( g.xyz, l.zxy );\\\\n vec3 i2 = max( g.xyz, l.zxy );\\\\n\\\\n // x0 = x0 - 0.0 + 0.0 * C.xxx;\\\\n // x1 = x0 - i1 + 1.0 * C.xxx;\\\\n // x2 = x0 - i2 + 2.0 * C.xxx;\\\\n // x3 = x0 - 1.0 + 3.0 * C.xxx;\\\\n vec3 x1 = x0 - i1 + C.xxx;\\\\n vec3 x2 = x0 - i2 + C.yyy; // 2.0*C.x = 1/3 = C.y\\\\n vec3 x3 = x0 - D.yyy; // -1.0+3.0*C.x = -0.5 = -D.y\\\\n\\\\n// Permutations\\\\n i = mod289(i); \\\\n vec4 p = permute( permute( permute( \\\\n i.z + vec4(0.0, i1.z, i2.z, 1.0 ))\\\\n + i.y + vec4(0.0, i1.y, i2.y, 1.0 )) \\\\n + i.x + vec4(0.0, i1.x, i2.x, 1.0 ));\\\\n\\\\n// Gradients: 7x7 points over a square, mapped onto an octahedron.\\\\n// The ring size 17*17 = 289 is close to a multiple of 49 (49*6 = 294)\\\\n float n_ = 0.142857142857; // 1.0/7.0\\\\n vec3 ns = n_ * D.wyz - D.xzx;\\\\n\\\\n vec4 j = p - 49.0 * floor(p * ns.z * ns.z); // mod(p,7*7)\\\\n\\\\n vec4 x_ = floor(j * ns.z);\\\\n vec4 y_ = floor(j - 7.0 * x_ ); // mod(j,N)\\\\n\\\\n vec4 x = x_ *ns.x + ns.yyyy;\\\\n vec4 y = y_ *ns.x + ns.yyyy;\\\\n vec4 h = 1.0 - abs(x) - abs(y);\\\\n\\\\n vec4 b0 = vec4( x.xy, y.xy );\\\\n vec4 b1 = vec4( x.zw, y.zw );\\\\n\\\\n //vec4 s0 = vec4(lessThan(b0,0.0))*2.0 - 1.0;\\\\n //vec4 s1 = vec4(lessThan(b1,0.0))*2.0 - 1.0;\\\\n vec4 s0 = floor(b0)*2.0 + 1.0;\\\\n vec4 s1 = floor(b1)*2.0 + 1.0;\\\\n vec4 sh = -step(h, vec4(0.0));\\\\n\\\\n vec4 a0 = b0.xzyw + s0.xzyw*sh.xxyy ;\\\\n vec4 a1 = b1.xzyw + s1.xzyw*sh.zzww ;\\\\n\\\\n vec3 p0 = vec3(a0.xy,h.x);\\\\n vec3 p1 = vec3(a0.zw,h.y);\\\\n vec3 p2 = vec3(a1.xy,h.z);\\\\n vec3 p3 = vec3(a1.zw,h.w);\\\\n\\\\n//Normalise gradients\\\\n vec4 norm = taylorInvSqrt(vec4(dot(p0,p0), dot(p1,p1), dot(p2, p2), dot(p3,p3)));\\\\n p0 *= norm.x;\\\\n p1 *= norm.y;\\\\n p2 *= norm.z;\\\\n p3 *= norm.w;\\\\n\\\\n// Mix final noise value\\\\n vec4 m = max(0.6 - vec4(dot(x0,x0), dot(x1,x1), dot(x2,x2), dot(x3,x3)), 0.0);\\\\n m = m * m;\\\\n return 42.0 * dot( m*m, vec4( dot(p0,x0), dot(p1,x1), \\\\n dot(p2,x2), dot(p3,x3) ) );\\\\n }\\\\n\\\\\\\",[xI.NOISE_4D]:\\\\\\\"//\\\\n// Description : Array and textureless GLSL 2D/3D/4D simplex \\\\n// noise functions.\\\\n// Author : Ian McEwan, Ashima Arts.\\\\n// Maintainer : stegu\\\\n// Lastmod : 20110822 (ijm)\\\\n// License : Copyright (C) 2011 Ashima Arts. All rights reserved.\\\\n// Distributed under the MIT License. See LICENSE file.\\\\n// https://github.com/ashima/webgl-noise\\\\n// https://github.com/stegu/webgl-noise\\\\n// \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nvec4 grad4(float j, vec4 ip)\\\\n {\\\\n const vec4 ones = vec4(1.0, 1.0, 1.0, -1.0);\\\\n vec4 p,s;\\\\n\\\\n p.xyz = floor( fract (vec3(j) * ip.xyz) * 7.0) * ip.z - 1.0;\\\\n p.w = 1.5 - dot(abs(p.xyz), ones.xyz);\\\\n s = vec4(lessThan(p, vec4(0.0)));\\\\n p.xyz = p.xyz + (s.xyz*2.0 - 1.0) * s.www; \\\\n\\\\n return p;\\\\n }\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\n// (sqrt(5) - 1)/4 = F4, used once below\\\\n#define F4 0.309016994374947451\\\\n\\\\nfloat snoise(vec4 v)\\\\n {\\\\n const vec4 C = vec4( 0.138196601125011, // (5 - sqrt(5))/20 G4\\\\n 0.276393202250021, // 2 * G4\\\\n 0.414589803375032, // 3 * G4\\\\n -0.447213595499958); // -1 + 4 * G4\\\\n\\\\n// First corner\\\\n vec4 i = floor(v + dot(v, vec4(F4)) );\\\\n vec4 x0 = v - i + dot(i, C.xxxx);\\\\n\\\\n// Other corners\\\\n\\\\n// Rank sorting originally contributed by Bill Licea-Kane, AMD (formerly ATI)\\\\n vec4 i0;\\\\n vec3 isX = step( x0.yzw, x0.xxx );\\\\n vec3 isYZ = step( x0.zww, x0.yyz );\\\\n// i0.x = dot( isX, vec3( 1.0 ) );\\\\n i0.x = isX.x + isX.y + isX.z;\\\\n i0.yzw = 1.0 - isX;\\\\n// i0.y += dot( isYZ.xy, vec2( 1.0 ) );\\\\n i0.y += isYZ.x + isYZ.y;\\\\n i0.zw += 1.0 - isYZ.xy;\\\\n i0.z += isYZ.z;\\\\n i0.w += 1.0 - isYZ.z;\\\\n\\\\n // i0 now contains the unique values 0,1,2,3 in each channel\\\\n vec4 i3 = clamp( i0, 0.0, 1.0 );\\\\n vec4 i2 = clamp( i0-1.0, 0.0, 1.0 );\\\\n vec4 i1 = clamp( i0-2.0, 0.0, 1.0 );\\\\n\\\\n // x0 = x0 - 0.0 + 0.0 * C.xxxx\\\\n // x1 = x0 - i1 + 1.0 * C.xxxx\\\\n // x2 = x0 - i2 + 2.0 * C.xxxx\\\\n // x3 = x0 - i3 + 3.0 * C.xxxx\\\\n // x4 = x0 - 1.0 + 4.0 * C.xxxx\\\\n vec4 x1 = x0 - i1 + C.xxxx;\\\\n vec4 x2 = x0 - i2 + C.yyyy;\\\\n vec4 x3 = x0 - i3 + C.zzzz;\\\\n vec4 x4 = x0 + C.wwww;\\\\n\\\\n// Permutations\\\\n i = mod289(i); \\\\n float j0 = permute( permute( permute( permute(i.w) + i.z) + i.y) + i.x);\\\\n vec4 j1 = permute( permute( permute( permute (\\\\n i.w + vec4(i1.w, i2.w, i3.w, 1.0 ))\\\\n + i.z + vec4(i1.z, i2.z, i3.z, 1.0 ))\\\\n + i.y + vec4(i1.y, i2.y, i3.y, 1.0 ))\\\\n + i.x + vec4(i1.x, i2.x, i3.x, 1.0 ));\\\\n\\\\n// Gradients: 7x7x6 points over a cube, mapped onto a 4-cross polytope\\\\n// 7*7*6 = 294, which is close to the ring size 17*17 = 289.\\\\n vec4 ip = vec4(1.0/294.0, 1.0/49.0, 1.0/7.0, 0.0) ;\\\\n\\\\n vec4 p0 = grad4(j0, ip);\\\\n vec4 p1 = grad4(j1.x, ip);\\\\n vec4 p2 = grad4(j1.y, ip);\\\\n vec4 p3 = grad4(j1.z, ip);\\\\n vec4 p4 = grad4(j1.w, ip);\\\\n\\\\n// Normalise gradients\\\\n vec4 norm = taylorInvSqrt(vec4(dot(p0,p0), dot(p1,p1), dot(p2, p2), dot(p3,p3)));\\\\n p0 *= norm.x;\\\\n p1 *= norm.y;\\\\n p2 *= norm.z;\\\\n p3 *= norm.w;\\\\n p4 *= taylorInvSqrt(dot(p4,p4));\\\\n\\\\n// Mix contributions from the five corners\\\\n vec3 m0 = max(0.6 - vec3(dot(x0,x0), dot(x1,x1), dot(x2,x2)), 0.0);\\\\n vec2 m1 = max(0.6 - vec2(dot(x3,x3), dot(x4,x4) ), 0.0);\\\\n m0 = m0 * m0;\\\\n m1 = m1 * m1;\\\\n return 49.0 * ( dot(m0*m0, vec3( dot( p0, x0 ), dot( p1, x1 ), dot( p2, x2 )))\\\\n + dot(m1*m1, vec2( dot( p3, x3 ), dot( p4, x4 ) ) ) ) ;\\\\n\\\\n }\\\\n\\\\\\\"},TI={[xI.CLASSIC_PERLIN_2D]:Do.VEC2,[xI.CLASSIC_PERLIN_3D]:Do.VEC3,[xI.CLASSIC_PERLIN_4D]:Do.VEC4,[xI.NOISE_2D]:Do.VEC2,[xI.NOISE_3D]:Do.VEC3,[xI.NOISE_4D]:Do.VEC4},AI={[xI.CLASSIC_PERLIN_2D]:Do.FLOAT,[xI.CLASSIC_PERLIN_3D]:Do.FLOAT,[xI.CLASSIC_PERLIN_4D]:Do.FLOAT,[xI.NOISE_2D]:Do.FLOAT,[xI.NOISE_3D]:Do.FLOAT,[xI.NOISE_4D]:Do.FLOAT},EI={[xI.CLASSIC_PERLIN_2D]:\\\\\\\"cnoise\\\\\\\",[xI.CLASSIC_PERLIN_3D]:\\\\\\\"cnoise\\\\\\\",[xI.CLASSIC_PERLIN_4D]:\\\\\\\"cnoise\\\\\\\",[xI.NOISE_2D]:\\\\\\\"snoise\\\\\\\",[xI.NOISE_3D]:\\\\\\\"snoise\\\\\\\",[xI.NOISE_4D]:\\\\\\\"snoise\\\\\\\"};var MI;!function(t){t[t.NoChange=0]=\\\\\\\"NoChange\\\\\\\",t[t.Float=1]=\\\\\\\"Float\\\\\\\",t[t.Vec2=2]=\\\\\\\"Vec2\\\\\\\",t[t.Vec3=3]=\\\\\\\"Vec3\\\\\\\",t[t.Vec4=4]=\\\\\\\"Vec4\\\\\\\"}(MI||(MI={}));const SI=[MI.NoChange,MI.Float,MI.Vec2,MI.Vec3,MI.Vec4],CI={[MI.NoChange]:\\\\\\\"Same as noise\\\\\\\",[MI.Float]:\\\\\\\"Float\\\\\\\",[MI.Vec2]:\\\\\\\"Vec2\\\\\\\",[MI.Vec3]:\\\\\\\"Vec3\\\\\\\",[MI.Vec4]:\\\\\\\"Vec4\\\\\\\"},NI={[MI.NoChange]:Do.FLOAT,[MI.Float]:Do.FLOAT,[MI.Vec2]:Do.VEC2,[MI.Vec3]:Do.VEC3,[MI.Vec4]:Do.VEC4},LI=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"],OI=\\\\\\\"noise\\\\\\\",RI=bI.indexOf(xI.NOISE_3D),PI=MI.NoChange,II={amp:1,freq:1};var FI;!function(t){t.AMP=\\\\\\\"amp\\\\\\\",t.POSITION=\\\\\\\"position\\\\\\\",t.FREQ=\\\\\\\"freq\\\\\\\",t.OFFSET=\\\\\\\"offset\\\\\\\"}(FI||(FI={}));const DI=new class extends aa{constructor(){super(...arguments),this.type=oa.INTEGER(RI,{menu:{entries:bI.map(((t,e)=>({name:`${t} (output: ${AI[t]})`,value:e})))}}),this.outputType=oa.INTEGER(PI,{menu:{entries:SI.map((t=>{const e=SI[t];return{name:CI[e],value:e}}))}}),this.octaves=oa.INTEGER(3,{range:[1,10],rangeLocked:[!0,!1]}),this.ampAttenuation=oa.FLOAT(.5,{range:[0,1]}),this.freqIncrease=oa.FLOAT(2,{range:[0,10],separatorAfter:!0})}};class kI extends df{constructor(){super(...arguments),this.paramsConfig=DI}static type(){return\\\\\\\"noise\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.initializeNode(),this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"octaves\\\\\\\",\\\\\\\"ampAttenuation\\\\\\\",\\\\\\\"freqIncrease\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Vo(OI,Do.FLOAT)]),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function((()=>OI))}_gl_input_name(t){return[FI.AMP,FI.POSITION,FI.FREQ,FI.OFFSET][t]}paramDefaultValue(t){return II[t]}_expected_input_types(){const t=bI[this.pv.type],e=this._expected_output_types()[0],n=TI[t];return[e,n,n,n]}_expected_output_types(){const t=bI[this.pv.type],e=SI[this.pv.outputType];return e==MI.NoChange?[TI[t]]:[NI[e]]}setLines(t){const e=[],n=[],i=bI[this.pv.type],r=wI[i],s=AI[i];e.push(new Tf(this,\\\\\\\"// Modulo 289 without a division (only multiplications)\\\\nfloat mod289(float x) {\\\\n return x - floor(x * (1.0 / 289.0)) * 289.0;\\\\n}\\\\nvec2 mod289(vec2 x) {\\\\n return x - floor(x * (1.0 / 289.0)) * 289.0;\\\\n}\\\\nvec3 mod289(vec3 x) {\\\\n return x - floor(x * (1.0 / 289.0)) * 289.0;\\\\n}\\\\nvec4 mod289(vec4 x) {\\\\n return x - floor(x * (1.0 / 289.0)) * 289.0;\\\\n}\\\\n// Modulo 7 without a division\\\\nvec3 mod7(vec3 x) {\\\\n return x - floor(x * (1.0 / 7.0)) * 7.0;\\\\n}\\\\n\\\\n// Permutation polynomial: (34x^2 + x) mod 289\\\\nfloat permute(float x) {\\\\n return mod289(((x*34.0)+1.0)*x);\\\\n}\\\\nvec3 permute(vec3 x) {\\\\n return mod289((34.0 * x + 1.0) * x);\\\\n}\\\\nvec4 permute(vec4 x) {\\\\n return mod289(((x*34.0)+1.0)*x);\\\\n}\\\\n\\\\nfloat taylorInvSqrt(float r)\\\\n{\\\\n return 1.79284291400159 - 0.85373472095314 * r;\\\\n}\\\\nvec4 taylorInvSqrt(vec4 r)\\\\n{\\\\n return 1.79284291400159 - 0.85373472095314 * r;\\\\n}\\\\n\\\\nvec2 fade(vec2 t) {\\\\n return t*t*t*(t*(t*6.0-15.0)+10.0);\\\\n}\\\\nvec3 fade(vec3 t) {\\\\n return t*t*t*(t*(t*6.0-15.0)+10.0);\\\\n}\\\\nvec4 fade(vec4 t) {\\\\n return t*t*t*(t*(t*6.0-15.0)+10.0);\\\\n}\\\\\\\")),e.push(new Tf(this,r)),e.push(new Tf(this,this.fbm_function()));const o=this._expected_output_types()[0];if(o==s){const t=this.single_noise_line();n.push(t)}else{const t=Go[o],e=[],r=this.glVarName(\\\\\\\"noise\\\\\\\");for(let s=0;suf.float(1e3*s))).join(\\\\\\\", \\\\\\\")})`,c=this.single_noise_line(t,t,l);n.push(c)}const s=`vec${t} ${r} = vec${t}(${e.join(\\\\\\\", \\\\\\\")})`;n.push(s)}t.addDefinitions(this,e),t.addBodyLines(this,n)}fbm_method_name(){const t=bI[this.pv.type];return`fbm_${EI[t]}_${this.name()}`}fbm_function(){const t=bI[this.pv.type],e=EI[t],n=TI[t];return`\\\\nfloat ${this.fbm_method_name()} (in ${n} st) {\\\\n\\\\tfloat value = 0.0;\\\\n\\\\tfloat amplitude = 1.0;\\\\n\\\\tfor (int i = 0; i < ${uf.integer(this.pv.octaves)}; i++) {\\\\n\\\\t\\\\tvalue += amplitude * ${e}(st);\\\\n\\\\t\\\\tst *= ${uf.float(this.pv.freqIncrease)};\\\\n\\\\t\\\\tamplitude *= ${uf.float(this.pv.ampAttenuation)};\\\\n\\\\t}\\\\n\\\\treturn value;\\\\n}\\\\n`}single_noise_line(t,e,n){const i=this.fbm_method_name(),r=uf.any(this.variableForInput(FI.AMP)),s=uf.any(this.variableForInput(FI.POSITION)),o=uf.any(this.variableForInput(FI.FREQ));let a=uf.any(this.variableForInput(FI.OFFSET));n&&(a=`(${a}+${n})`);const l=[`(${s}*${o})+${a}`].join(\\\\\\\", \\\\\\\"),c=this.glVarName(OI),u=`${r}*${i}(${l})`;if(e)return`float ${c}${t} = (${u}).${e}`;return`${this.io.outputs.namedOutputConnectionPoints()[0].type()} ${c} = ${u}`}}class BI extends BO{static type(){return\\\\\\\"null\\\\\\\"}setLines(t){const e=uf.any(this.variableForInput(this._gl_input_name(0))),n=this.io.outputs.namedOutputConnectionPoints()[0],i=`${n.type()} ${this.glVarName(n.name())} = ${e}`;t.addBodyLines(this,[i])}}const zI=new class extends aa{};class UI extends df{constructor(){super(...arguments),this.paramsConfig=zI}static type(){return\\\\\\\"output\\\\\\\"}initializeNode(){super.initializeNode(),this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_add_hook((()=>{var t,e;null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e||e.add_output_inputs(this)}))}setLines(t){t.assembler().set_node_lines_output(this,t)}}class GI{constructor(){this._param_configs=[]}reset(){this._param_configs=[]}push(t){this._param_configs.push(t)}list(){return this._param_configs}}const VI=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"\\\\\\\"),this.type=oa.INTEGER(ko.indexOf(Do.FLOAT),{menu:{entries:ko.map(((t,e)=>({name:t,value:e})))}}),this.asColor=oa.BOOLEAN(0,{visibleIf:{type:ko.indexOf(Do.VEC3)}})}};class HI extends df{constructor(){super(...arguments),this.paramsConfig=VI,this._allow_inputs_created_from_params=!1,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"param\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[ko[this.pv.type]])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}setLines(t){const e=[],n=ko[this.pv.type],i=this.uniform_name();e.push(new Af(this,n,i)),t.addDefinitions(this,e)}paramsGenerating(){return!0}setParamConfigs(){const t=ko[this.pv.type],e=Uo[t];let n=Bo[t];if(this._param_configs_controller=this._param_configs_controller||new GI,this._param_configs_controller.reset(),n==Es.VECTOR3&&this.p.asColor.value&&m.isArray(e)&&3==e.length){const t=new $f(Es.COLOR,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}else{const t=new $f(n,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}}uniform_name(){const t=this.io.outputs.namedOutputConnectionPoints()[0];return this.glVarName(t.name())}set_gl_type(t){const e=ko.indexOf(t);this.p.type.set(e)}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}class jI extends kO{static type(){return\\\\\\\"refract\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"I\\\\\\\",\\\\\\\"N\\\\\\\",\\\\\\\"eta\\\\\\\"][t])),this.io.connection_points.set_output_name_function((t=>\\\\\\\"refract\\\\\\\")),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}gl_method_name(){return\\\\\\\"refract\\\\\\\"}_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.VEC3;return[t,t,Do.FLOAT]}_expected_output_types(){return[this._expected_input_types()[0]]}}const WI=\\\\\\\"SSSModel\\\\\\\";const qI=new class extends aa{constructor(){super(...arguments),this.color=oa.COLOR([1,1,1]),this.thickness=oa.FLOAT(.1),this.power=oa.FLOAT(2),this.scale=oa.FLOAT(16),this.distortion=oa.FLOAT(.1),this.ambient=oa.FLOAT(.4),this.attenuation=oa.FLOAT(.8)}};class XI extends df{constructor(){super(...arguments),this.paramsConfig=qI}static type(){return\\\\\\\"SSSModel\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(WI,Do.SSS_MODEL)])}setLines(t){const e=[],n=this.glVarName(WI);e.push(`SSSModel ${n}`),e.push(`${n}.isActive = true;`),e.push(this._paramLineFloat(n,this.p.color)),e.push(this._paramLineFloat(n,this.p.thickness)),e.push(this._paramLineFloat(n,this.p.power)),e.push(this._paramLineFloat(n,this.p.scale)),e.push(this._paramLineFloat(n,this.p.distortion)),e.push(this._paramLineFloat(n,this.p.ambient)),e.push(this._paramLineFloat(n,this.p.attenuation)),t.addBodyLines(this,e)}_paramLineFloat(t,e){return`${t}.${e.name()} = ${uf.vector3(this.variableForInputParam(e))};`}}class YI extends BO{static type(){return\\\\\\\"quatMult\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"quat0\\\\\\\",\\\\\\\"quat1\\\\\\\"][t])),this.io.connection_points.set_expected_input_types_function((()=>[Do.VEC4,Do.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[Do.VEC4]))}gl_method_name(){return\\\\\\\"quatMult\\\\\\\"}gl_function_definitions(){return[new Tf(this,GR)]}}var $I;!function(t){t.AXIS=\\\\\\\"axis\\\\\\\",t.ANGLE=\\\\\\\"angle\\\\\\\"}($I||($I={}));const JI=[$I.AXIS,$I.ANGLE],ZI={[$I.AXIS]:[0,0,1],[$I.ANGLE]:0};class QI extends zO{static type(){return\\\\\\\"quatFromAxisAngle\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>JI[t])),this.io.connection_points.set_expected_input_types_function((()=>[Do.VEC3,Do.FLOAT])),this.io.connection_points.set_expected_output_types_function((()=>[Do.VEC4]))}paramDefaultValue(t){return ZI[t]}gl_method_name(){return\\\\\\\"quatFromAxisAngle\\\\\\\"}gl_function_definitions(){return[new Tf(this,GR)]}}class KI extends BO{static type(){return\\\\\\\"quatToAngle\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"quat\\\\\\\"][t])),this.io.connection_points.set_expected_input_types_function((()=>[Do.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[Do.FLOAT]))}gl_method_name(){return\\\\\\\"quatToAngle\\\\\\\"}gl_function_definitions(){return[new Tf(this,GR)]}}class tF extends BO{static type(){return\\\\\\\"quatToAxis\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"quat\\\\\\\"][t])),this.io.connection_points.set_expected_input_types_function((()=>[Do.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[Do.VEC3]))}gl_method_name(){return\\\\\\\"quatToAxis\\\\\\\"}gl_function_definitions(){return[new Tf(this,GR)]}}const eF=\\\\\\\"val\\\\\\\";const nF=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"ramp\\\\\\\"),this.input=oa.FLOAT(0)}};class iF extends df{constructor(){super(...arguments),this.paramsConfig=nF}static type(){return\\\\\\\"ramp\\\\\\\"}initializeNode(){super.initializeNode(),this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.io.outputs.setNamedOutputConnectionPoints([new Vo(eF,Do.FLOAT)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}setLines(t){const e=Do.FLOAT,n=this._uniform_name(),i=this.glVarName(eF),r=new Af(this,Do.SAMPLER_2D,n);t.addDefinitions(this,[r]);const s=this.variableForInputParam(this.p.input),o=`${e} ${i} = texture2D(${this._uniform_name()}, vec2(${s}, 0.0)).x`;t.addBodyLines(this,[o])}paramsGenerating(){return!0}setParamConfigs(){this._param_configs_controller=this._param_configs_controller||new GI,this._param_configs_controller.reset();const t=new $f(Es.RAMP,this.pv.name,xo.DEFAULT_VALUE,this._uniform_name());this._param_configs_controller.push(t)}_uniform_name(){return\\\\\\\"ramp_texture_\\\\\\\"+this.glVarName(eF)}}const rF=\\\\\\\"rand\\\\\\\";const sF=new class extends aa{constructor(){super(...arguments),this.seed=oa.VECTOR2([1,1])}};class oF extends df{constructor(){super(...arguments),this.paramsConfig=sF}static type(){return\\\\\\\"random\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(rF,Do.FLOAT)])}setLines(t){const e=this.io.inputs.namedInputConnectionPoints()[0].name(),n=uf.vector2(this.variableForInput(e)),i=`float ${this.glVarName(rF)} = rand(${n})`;t.addBodyLines(this,[i])}}const aF=new class extends aa{constructor(){super(...arguments),this.rgb=oa.VECTOR3([1,1,1])}};class lF extends df{constructor(){super(...arguments),this.paramsConfig=aF}static type(){return\\\\\\\"rgbToHsv\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"hsv\\\\\\\",Do.VEC3)])}setLines(t){const e=[],n=[];e.push(new Tf(this,\\\\\\\"// https://stackoverflow.com/questions/15095909/from-rgb-to-hsv-in-opengl-glsl\\\\nvec3 rgb2hsv(vec3 c)\\\\n{\\\\n\\\\tvec4 K = vec4(0.0, -1.0 / 3.0, 2.0 / 3.0, -1.0);\\\\n\\\\tvec4 p = mix(vec4(c.bg, K.wz), vec4(c.gb, K.xy), step(c.b, c.g));\\\\n\\\\tvec4 q = mix(vec4(p.xyw, c.r), vec4(c.r, p.yzx), step(p.x, c.r));\\\\n\\\\n\\\\tfloat d = q.x - min(q.w, q.y);\\\\n\\\\tfloat e = 1.0e-10;\\\\n\\\\treturn vec3(abs(q.z + (q.w - q.y) / (6.0 * d + e)), d / (q.x + e), q.x);\\\\n}\\\\\\\"));const i=uf.vector3(this.variableForInputParam(this.p.rgb)),r=this.glVarName(\\\\\\\"hsv\\\\\\\");n.push(`vec3 ${r} = rgb2hsv(${i})`),t.addDefinitions(this,e),t.addBodyLines(this,n)}}var cF;!function(t){t[t.AXIS=0]=\\\\\\\"AXIS\\\\\\\",t[t.QUAT=1]=\\\\\\\"QUAT\\\\\\\"}(cF||(cF={}));const uF=[cF.AXIS,cF.QUAT],hF={[cF.AXIS]:\\\\\\\"from axis + angle\\\\\\\",[cF.QUAT]:\\\\\\\"from quaternion\\\\\\\"},dF={[cF.AXIS]:[\\\\\\\"vector\\\\\\\",\\\\\\\"axis\\\\\\\",\\\\\\\"angle\\\\\\\"],[cF.QUAT]:[\\\\\\\"vector\\\\\\\",\\\\\\\"quat\\\\\\\"]},pF={[cF.AXIS]:\\\\\\\"rotateWithAxisAngle\\\\\\\",[cF.QUAT]:\\\\\\\"rotateWithQuat\\\\\\\"},_F={[cF.AXIS]:[Do.VEC3,Do.VEC3,Do.FLOAT],[cF.QUAT]:[Do.VEC3,Do.VEC4]},mF={vector:[0,0,1],axis:[0,1,0]};const fF=new class extends aa{constructor(){super(...arguments),this.signature=oa.INTEGER(cF.AXIS,{menu:{entries:uF.map(((t,e)=>({name:hF[t],value:e})))}})}};class gF extends df{constructor(){super(...arguments),this.paramsConfig=fF}static type(){return\\\\\\\"rotate\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this))}set_signature(t){const e=uF.indexOf(t);this.p.signature.set(e)}_gl_input_name(t){const e=uF[this.pv.signature];return dF[e][t]}paramDefaultValue(t){return mF[t]}gl_method_name(){const t=uF[this.pv.signature];return pF[t]}_expected_input_types(){const t=uF[this.pv.signature];return _F[t]}_expected_output_types(){return[Do.VEC3]}gl_function_definitions(){return[new Tf(this,GR)]}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.io.inputs.namedInputConnectionPoints().map(((t,e)=>{const n=t.name();return uf.any(this.variableForInput(n))})).join(\\\\\\\", \\\\\\\"),i=`${e} ${this.glVarName(this.io.connection_points.output_name(0))} = ${this.gl_method_name()}(${n})`;t.addBodyLines(this,[i]),t.addDefinitions(this,this.gl_function_definitions())}}const vF=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];class yF extends BO{static type(){return\\\\\\\"round\\\\\\\"}setLines(t){const e=this.io.inputs.namedInputConnectionPoints()[0],n=uf.vector2(this.variableForInput(e.name())),i=this.io.outputs.namedOutputConnectionPoints()[0],r=this.glVarName(i.name()),s=[];if(1==Go[i.type()])s.push(`${i.type()} ${r} = ${this._simple_line(n)}`);else{const t=vF.map((t=>this._simple_line(`${n}.${t}`)));s.push(`${i.type()} ${r} = ${i.type()}(${t.join(\\\\\\\",\\\\\\\")})`)}t.addBodyLines(this,s)}_simple_line(t){return`sign(${t})*floor(abs(${t})+0.5)`}}const xF=new class extends aa{constructor(){super(...arguments),this.position=oa.VECTOR3([0,0,0]),this.center=oa.VECTOR3([0,0,0]),this.radius=oa.FLOAT(1),this.feather=oa.FLOAT(.1)}};class bF extends df{constructor(){super(...arguments),this.paramsConfig=xF}static type(){return\\\\\\\"sphere\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"float\\\\\\\",Do.FLOAT)])}setLines(t){const e=uf.vector2(this.variableForInputParam(this.p.position)),n=uf.vector2(this.variableForInputParam(this.p.center)),i=uf.float(this.variableForInputParam(this.p.radius)),r=uf.float(this.variableForInputParam(this.p.feather)),s=`float ${this.glVarName(\\\\\\\"float\\\\\\\")} = disk3d(${e}, ${n}, ${i}, ${r})`;t.addBodyLines(this,[s]),t.addDefinitions(this,[new Tf(this,uP)])}}const wF=new class extends aa{};class TF extends df{constructor(){super(...arguments),this.paramsConfig=wF}static type(){return er.INPUT}initializeNode(){this.io.connection_points.set_output_name_function(this._expected_output_names.bind(this)),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}parent(){return super.parent()}_expected_output_names(t){const e=this.parent();return(null==e?void 0:e.child_expected_input_connection_point_name(t))||`out${t}`}_expected_output_types(){const t=this.parent();return(null==t?void 0:t.child_expected_input_connection_point_types())||[]}setLines(t){const e=this.parent();e&&e.set_lines_block_start(t,this)}}const AF=new class extends aa{};class EF extends df{constructor(){super(...arguments),this.paramsConfig=AF}static type(){return\\\\\\\"switch\\\\\\\"}initializeNode(){this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_gl_input_name(t){return 0==t?EF.INPUT_INDEX:\\\\\\\"in\\\\\\\"+(t-1)}_expected_input_types(){const t=this.io.connection_points.input_connection_type(1)||Do.FLOAT,e=this.io.connections.inputConnections(),n=e?rs.clamp(e.length,2,16):2,i=[Do.INT];for(let e=0;e{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.paramName])}))}))}setLines(t){const e=uf.vector2(this.variableForInputParam(this.p.uv)),n=this.glVarName(SF.OUTPUT_NAME),i=this._uniform_name(),r=new Af(this,Do.SAMPLER_2D,i),s=`vec4 ${n} = texture2D(${i}, ${e})`;t.addDefinitions(this,[r]),t.addBodyLines(this,[s])}paramsGenerating(){return!0}setParamConfigs(){this._param_configs_controller=this._param_configs_controller||new GI,this._param_configs_controller.reset();const t=new $f(Es.OPERATOR_PATH,this.pv.paramName,this.pv.defaultValue,this._uniform_name());this._param_configs_controller.push(t)}_uniform_name(){return this.glVarName(this.pv.paramName)}}var CF;SF.OUTPUT_NAME=\\\\\\\"rgba\\\\\\\",function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.DIR_VEC=\\\\\\\"direction vector\\\\\\\"}(CF||(CF={}));const NF=[CF.POSITION,CF.DIR_VEC];const LF=new class extends aa{constructor(){super(...arguments),this.vec=oa.VECTOR3([0,0,0]),this.interpretation=oa.INTEGER(0,{menu:{entries:NF.map(((t,e)=>({name:t,value:e})))}})}};class OF extends df{constructor(){super(...arguments),this.paramsConfig=LF}static type(){return\\\\\\\"toWorldSpace\\\\\\\"}initializeNode(){this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"interpretation\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"out\\\\\\\",Do.VEC3)])}setLines(t){const e=[],n=uf.vector3(this.variableForInputParam(this.p.vec)),i=this.glVarName(\\\\\\\"out\\\\\\\");switch(NF[this.pv.interpretation]){case CF.POSITION:e.push(`vec3 ${i} = (modelMatrix * vec4( ${n}, 1.0 )).xyz`);break;case CF.DIR_VEC:e.push(`vec3 ${i} = normalize( mat3( modelMatrix[0].xyz, modelMatrix[1].xyz, modelMatrix[2].xyz ) * ${n} )`)}t.addBodyLines(this,e)}}var RF;!function(t){t.CONDITION=\\\\\\\"condition\\\\\\\",t.IF_TRUE=\\\\\\\"ifTrue\\\\\\\",t.IF_FALSE=\\\\\\\"ifFalse\\\\\\\"}(RF||(RF={}));const PF=[RF.CONDITION,RF.IF_TRUE,RF.IF_FALSE];class IF extends _f{static type(){return\\\\\\\"twoWaySwitch\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}_gl_input_name(t){return PF[t]}_gl_output_name(){return\\\\\\\"val\\\\\\\"}_expected_input_types(){const t=this.io.connections.inputConnection(1)||this.io.connections.inputConnection(2),e=t?t.src_connection_point().type():Do.FLOAT;return[Do.BOOL,e,e]}_expected_output_types(){return[this._expected_input_types()[1]]}setLines(t){const e=[],n=this.glVarName(\\\\\\\"val\\\\\\\"),i=uf.bool(this.variableForInput(RF.CONDITION)),r=uf.any(this.variableForInput(RF.IF_TRUE)),s=uf.any(this.variableForInput(RF.IF_FALSE)),o=this._expected_output_types()[0];e.push(`${o} ${n}`),e.push(`if(${i}){`),e.push(`${n} = ${r}`),e.push(\\\\\\\"} else {\\\\\\\"),e.push(`${n} = ${s}`),e.push(\\\\\\\"}\\\\\\\"),t.addBodyLines(this,e)}}const FF=[Do.FLOAT,Do.VEC2,Do.VEC3,Do.VEC4];const DF=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"\\\\\\\"),this.type=oa.INTEGER(0,{menu:{entries:FF.map(((t,e)=>({name:t,value:e})))}})}};class kF extends df{constructor(){super(...arguments),this.paramsConfig=DF,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"varyingRead\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_output_name_function((()=>this.output_name)),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[FF[this.pv.type]])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}get output_name(){return kF.OUTPUT_NAME}setLines(t){if(t.current_shader_name==xf.FRAGMENT){const e=this.pv.name,n=new Ef(this,this.gl_type(),e),i=this.glVarName(kF.OUTPUT_NAME),r=`${this.gl_type()} ${i} = ${e}`;t.addDefinitions(this,[n]),t.addBodyLines(this,[r])}}get attribute_name(){return this.pv.name.trim()}gl_type(){return this.io.outputs.namedOutputConnectionPoints()[0].type()}set_gl_type(t){this.p.type.set(FF.indexOf(t))}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}kF.OUTPUT_NAME=\\\\\\\"fragment\\\\\\\";const BF={start:[0,0,1],end:[1,0,0],up:[0,1,0]};class zF extends(yR(\\\\\\\"vectorAlign\\\\\\\",{in:[\\\\\\\"start\\\\\\\",\\\\\\\"end\\\\\\\",\\\\\\\"up\\\\\\\"],method:\\\\\\\"vectorAlignWithUp\\\\\\\",functions:[GR]})){_expected_input_types(){const t=Do.VEC3;return[t,t,t]}_expected_output_types(){return[Do.VEC4]}paramDefaultValue(t){return BF[t]}}const UF={start:[0,0,1],end:[1,0,0]};class GF extends(uR(\\\\\\\"vectorAngle\\\\\\\",{in:[\\\\\\\"start\\\\\\\",\\\\\\\"end\\\\\\\"],method:\\\\\\\"vectorAngle\\\\\\\",functions:[GR]})){_expected_input_types(){const t=Do.VEC3;return[t,t]}_expected_output_types(){return[Do.FLOAT]}paramDefaultValue(t){return UF[t]}}const VF={only:[`${JP.context()}/${JP.type()}`,`${kP.context()}/${kP.type()}`,`${GP.context()}/${GP.type()}`]};class HF extends ia{static context(){return Ki.JS}initializeBaseNode(){this.uiData.setLayoutHorizontal(),this.io.connection_points.initializeNode()}cook(){console.warn(\\\\\\\"js nodes should never cook\\\\\\\")}_set_function_node_to_recompile(){var t;null===(t=this.function_node)||void 0===t||t.assembler_controller.set_compilation_required_and_dirty(this)}get function_node(){var t;const e=this.parent();if(e)return e.type()==this.type()?null===(t=e)||void 0===t?void 0:t.function_node:e}js_var_name(t){return`v_POLY_${this.name()}_${t}`}variableForInput(t){const e=this.io.inputs.get_input_index(t),n=this.io.connections.inputConnection(e);if(n){const e=n.node_src,i=e.io.outputs.namedOutputConnectionPoints()[n.output_index];if(i){const t=i.name();return e.js_var_name(t)}throw console.warn(`no output called '${t}' for gl node ${e.path()}`),\\\\\\\"variable_for_input ERROR\\\\\\\"}return\\\\\\\"to debug...\\\\\\\"}setLines(t){}reset_code(){var t;null===(t=this._param_configs_controller)||void 0===t||t.reset()}setParamConfigs(){}param_configs(){var t;return null===(t=this._param_configs_controller)||void 0===t?void 0:t.list()}js_input_default_value(t){return null}}new class extends aa{};const jF=[Ho.FLOAT,Ho.VEC2,Ho.VEC3,Ho.VEC4];const WF=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"\\\\\\\"),this.type=oa.INTEGER(0,{menu:{entries:jF.map(((t,e)=>({name:t,value:e})))}})}};class qF extends HF{constructor(){super(...arguments),this.paramsConfig=WF,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"attribute\\\\\\\"}initializeNode(){this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[jF[this.pv.type]]))}get input_name(){return qF.INPUT_NAME}get output_name(){return qF.OUTPUT_NAME}setLines(t){var e;null===(e=this.function_node)||void 0===e||e.assembler_controller.assembler.set_node_lines_attribute(this,t)}get attribute_name(){return this.pv.name.trim()}gl_type(){return this.io.outputs.namedOutputConnectionPoints()[0].type()}set_gl_type(t){this.p.type.set(jF.indexOf(t))}connected_input_node(){return this.io.inputs.named_input(qF.INPUT_NAME)}connected_input_connection_point(){return this.io.inputs.named_input_connection_point(qF.INPUT_NAME)}output_connection_point(){return this.io.outputs.namedOutputConnectionPointsByName(this.input_name)}get is_importing(){return this.io.outputs.used_output_names().length>0}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}qF.INPUT_NAME=\\\\\\\"export\\\\\\\",qF.OUTPUT_NAME=\\\\\\\"val\\\\\\\";const XF=new class extends aa{};class YF extends HF{constructor(){super(...arguments),this.paramsConfig=XF}static type(){return\\\\\\\"globals\\\\\\\"}createParams(){var t;null===(t=this.function_node)||void 0===t||t.assembler_controller.add_globals_outputs(this)}setLines(t){var e,n;null===(n=null===(e=this.function_node)||void 0===e?void 0:e.assembler_controller)||void 0===n||n.assembler.set_node_lines_globals(this,t)}}const $F=new class extends aa{};class JF extends HF{constructor(){super(...arguments),this.paramsConfig=$F}static type(){return\\\\\\\"output\\\\\\\"}initializeNode(){super.initializeNode(),this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_function_node_to_recompile.bind(this))}createParams(){var t;null===(t=this.function_node)||void 0===t||t.assembler_controller.add_output_inputs(this)}setLines(t){var e;null===(e=this.function_node)||void 0===e||e.assembler_controller.assembler.set_node_lines_output(this,t)}}class ZF{constructor(t=[]){this._definitions=t,this._errored=!1}get errored(){return this._errored}get error_message(){return this._error_message}uniq(){const t=new Map,e=[];for(let n of this._definitions)if(!this._errored){const i=n.name(),r=t.get(i);r?r.data_type!=n.data_type&&(this._errored=!0,this._error_message=`attempt to create '${n.name()}' with types '${n.data_type}' by node '${n.node.path()}', when there is already an existing with type ${r.data_type} from node '${r.node.path()}'`,console.warn(\\\\\\\"emitting error message:\\\\\\\",this._error_message)):(t.set(i,n),e.push(i))}const n=[];for(let i of e){const e=t.get(i);e&&n.push(e)}return n}}var QF;!function(t){t.ATTRIBUTE=\\\\\\\"attribute\\\\\\\",t.FUNCTION=\\\\\\\"function\\\\\\\",t.UNIFORM=\\\\\\\"uniform\\\\\\\"}(QF||(QF={}));class KF{constructor(t,e,n,i){this._definition_type=t,this._data_type=e,this._node=n,this._name=i}get definition_type(){return this._definition_type}get data_type(){return this._data_type}get node(){return this._node}name(){return this._name}collection_instance(){return new ZF}}class tD extends KF{constructor(t,e,n){super(QF.UNIFORM,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`uniform ${this.data_type} ${this.name()}`}}class eD extends Yf{constructor(t,e,n,i){super(t,e,n),this._uniform_name=i}get uniform_name(){return this._uniform_name}static uniform_by_type(t){switch(t){case Es.BOOLEAN:case Es.BUTTON:return{value:0};case Es.COLOR:return{value:new D.a(0,0,0)};case Es.FLOAT:case Es.FOLDER:case Es.INTEGER:case Es.OPERATOR_PATH:case Es.NODE_PATH:case Es.PARAM_PATH:return{value:0};case Es.RAMP:case Es.STRING:return{value:null};case Es.VECTOR2:return{value:new d.a(0,0)};case Es.VECTOR3:return{value:new p.a(0,0,0)};case Es.VECTOR4:return{value:new _.a(0,0,0,0)}}ar.unreachable(t)}}const nD=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"\\\\\\\"),this.type=oa.INTEGER(jo.indexOf(Ho.FLOAT),{menu:{entries:jo.map(((t,e)=>({name:t,value:e})))}}),this.asColor=oa.BOOLEAN(0,{visibleIf:{type:jo.indexOf(Ho.VEC3)}})}};class iD extends HF{constructor(){super(...arguments),this.paramsConfig=nD,this._allow_inputs_created_from_params=!1,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"param\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_function_node_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[jo[this.pv.type]]))}setLines(t){const e=[],n=jo[this.pv.type],i=this.uniform_name();e.push(new tD(this,n,i)),t.addDefinitions(this,e)}setParamConfigs(){const t=jo[this.pv.type],e=Xo[t];let n=Wo[t];if(this._param_configs_controller=this._param_configs_controller||new GI,this._param_configs_controller.reset(),n==Es.VECTOR3&&this.p.asColor.value&&m.isArray(e)&&3==e.length){const t=new eD(Es.COLOR,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}else{const t=new eD(n,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}}uniform_name(){const t=this.io.outputs.namedOutputConnectionPoints()[0];return this.js_var_name(t.name())}set_gl_type(t){const e=jo.indexOf(t);this.p.type.set(e)}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}class rD extends ia{constructor(){super(...arguments),this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this)}static context(){return Ki.MAT}initializeBaseNode(){super.initializeBaseNode(),this.nameController.add_post_set_fullPath_hook(this.set_material_name.bind(this)),this.addPostDirtyHook(\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",(()=>{setTimeout(this._cook_main_without_inputs_when_dirty_bound,0)}))}async _cook_main_without_inputs_when_dirty(){await this.cookController.cookMainWithoutInputs()}set_material_name(){this._material&&(this._material.name=this.path())}get material(){return this._material=this._material||this.createMaterial()}setMaterial(t){this._setContainer(t)}}class sD{constructor(t){this.node=t}add_params(){}update(){}get material(){return this.node.material}}const oD={NoBlending:w.ub,NormalBlending:w.xb,AdditiveBlending:w.e,SubtractiveBlending:w.Sc,MultiplyBlending:w.mb},aD=Object.keys(oD);function lD(t){return class extends t{constructor(){super(...arguments),this.doubleSided=oa.BOOLEAN(0),this.front=oa.BOOLEAN(1,{visibleIf:{doubleSided:!1}}),this.overrideShadowSide=oa.BOOLEAN(0),this.shadowDoubleSided=oa.BOOLEAN(0,{visibleIf:{overrideShadowSide:!0}}),this.shadowFront=oa.BOOLEAN(1,{visibleIf:{overrideShadowSide:!0,shadowDoubleSided:!1}}),this.colorWrite=oa.BOOLEAN(1,{separatorBefore:!0,cook:!1,callback:(t,e)=>{cD.update(t)}}),this.depthWrite=oa.BOOLEAN(1,{cook:!1,callback:(t,e)=>{cD.update(t)}}),this.depthTest=oa.BOOLEAN(1,{cook:!1,callback:(t,e)=>{cD.update(t)}}),this.premultipliedAlpha=oa.BOOLEAN(!1,{separatorAfter:!0}),this.blending=oa.INTEGER(w.xb,{menu:{entries:aD.map((t=>({name:t,value:oD[t]})))}}),this.dithering=oa.BOOLEAN(0),this.polygonOffset=oa.BOOLEAN(!1,{separatorBefore:!0}),this.polygonOffsetFactor=oa.INTEGER(0,{range:[0,1e3],visibleIf:{polygonOffset:1}}),this.polygonOffsetUnits=oa.INTEGER(0,{range:[0,1e3],visibleIf:{polygonOffset:1}})}}}lD(aa);class cD extends sD{constructor(t){super(t),this.node=t}initializeNode(){}async update(){const t=this.node.material,e=this.node.pv;this._updateSides(t,e),t.colorWrite=e.colorWrite,t.depthWrite=e.depthWrite,t.depthTest=e.depthTest,t.blending=e.blending,t.premultipliedAlpha=e.premultipliedAlpha,t.dithering=e.dithering,t.polygonOffset=e.polygonOffset,t.polygonOffset&&(t.polygonOffsetFactor=e.polygonOffsetFactor,t.polygonOffsetUnits=e.polygonOffsetUnits,t.needsUpdate=!0)}_updateSides(t,e){const n=e.front?w.H:w.i,i=e.doubleSided?w.z:n;if(i!=t.side&&(t.side=i,t.needsUpdate=!0),e.overrideShadowSide){const t=e.shadowFront?w.H:w.i,n=e.shadowDoubleSided?w.z:t,i=this.node.material;n!=i.shadowSide&&(i.shadowSide=n,i.needsUpdate=!0)}else t.shadowSide=null;const r=t.customMaterials;if(r){const t=Object.keys(r);for(let n of t){const t=r[n];t&&this._updateSides(t,e)}}}static async update(t){t.controllers.advancedCommon.update()}}class uD extends(lD(aa)){constructor(){super(...arguments),this.color=oa.COLOR([1,1,1]),this.lineWidth=oa.FLOAT(1,{range:[1,10],rangeLocked:[!0,!1]})}}const hD=new uD;class dD extends rD{constructor(){super(...arguments),this.paramsConfig=hD,this.controllers={advancedCommon:new cD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"lineBasic\\\\\\\"}createMaterial(){return new wr.a({color:16777215,linewidth:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();this.material.color.copy(this.pv.color),this.material.linewidth=this.pv.lineWidth,this.setMaterial(this.material)}}function pD(t){return class extends t{constructor(){super(...arguments),this.transparent=oa.BOOLEAN(0),this.opacity=oa.FLOAT(1),this.alphaTest=oa.FLOAT(0)}}}pD(aa);class _D extends sD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;this._updateTransparency(e,n)}static _updateTransparency(t,e){t.transparent=e.transparent,this._updateCommon(t,e)}static _updateCommon(t,e){t.uniforms.opacity&&(t.uniforms.opacity.value=e.opacity),t.opacity=e.opacity,t.alphaTest=e.alphaTest;const n=t.customMaterials;if(n){const t=Object.keys(n);for(let i of t){const t=n[i];t&&this._updateCommon(t,e)}}}}class mD extends Xf{constructor(t){super(t),this.node=t}toJSON(){const t=this.node.assemblerController;if(!t)return;const e={},n=this.node.material.customMaterials;if(n){const t=Object.keys(n);for(let i of t){const t=n[i];if(t){const n=this._materialToJson(t,{node:this.node,suffix:i});n&&(e[i]=n)}}}const i=[],r=t.assembler.param_configs();for(let t of r)i.push([t.name(),t.uniform_name]);const s=this._materialToJson(this.node.material,{node:this.node,suffix:\\\\\\\"main\\\\\\\"});s||console.warn(\\\\\\\"failed to save material from node\\\\\\\",this.node.path());return{material:s||{},uniforms_time_dependent:t.assembler.uniformsTimeDependent(),uniforms_resolution_dependent:t.assembler.uniforms_resolution_dependent(),param_uniform_pairs:i,customMaterials:e}}load(t){if(this._material=this._loadMaterial(t.material),this._material){if(this._material.customMaterials=this._material.customMaterials||{},t.customMaterials){const e=Object.keys(t.customMaterials);for(let n of e){const e=t.customMaterials[n],i=this._loadMaterial(e);i&&(this._material.customMaterials[n]=i)}}if(t.uniforms_time_dependent&&this.node.scene().uniformsController.addTimeDependentUniformOwner(this._material.uuid,this._material.uniforms),t.uniforms_resolution_dependent&&this.node.scene().uniformsController.addResolutionDependentUniformOwner(this._material.uuid,this._material.uniforms),t.param_uniform_pairs)for(let e of t.param_uniform_pairs){const t=e[0],n=e[1],i=this.node.params.get(t),r=this._material.uniforms[n],s=Object.keys(this._material.customMaterials);let o;for(let t of s){const e=this._material.customMaterials[t],i=null==e?void 0:e.uniforms[n];i&&(o=o||[],o.push(i))}i&&(r||o)&&i.options.setOption(\\\\\\\"callback\\\\\\\",(()=>{if(r&&$f.callback(i,r),o)for(let t of o)$f.callback(i,t)}))}}}material(){if(ai.playerMode())return this._material}}function fD(t){return class extends t{constructor(){super(...arguments),this.setBuilderNode=oa.BOOLEAN(0,{callback:t=>{gD.PARAM_CALLBACK_setCompileRequired(t)}}),this.builderNode=oa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setBuilderNode:!0},callback:t=>{gD.PARAM_CALLBACK_setCompileRequired(t)}})}}}fD(aa);class gD extends rD{constructor(){super(...arguments),this._children_controller_context=Ki.GL,this.persisted_config=new mD(this)}createMaterial(){var t;let e;return this.persisted_config&&(e=this.persisted_config.material()),e||(e=null===(t=this.assemblerController)||void 0===t?void 0:t.assembler.createMaterial()),e}get assemblerController(){return this._assembler_controller=this._assembler_controller||this._create_assembler_controller()}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}childrenAllowed(){return this.assemblerController?super.childrenAllowed():(this.scene().markAsReadOnly(this),!1)}compileIfRequired(){var t;(null===(t=this.assemblerController)||void 0===t?void 0:t.compileRequired())&&this._compile()}_compile(){const t=this.assemblerController;this.material&&t&&(t.assembler.setGlParentNode(this),this._setAssemblerGlParentNode(t),t.assembler.compileMaterial(this.material),t.post_compile())}_setAssemblerGlParentNode(t){if(!this.pv.setBuilderNode)return;const e=this.pv.builderNode.nodeWithContext(Ki.MAT);if(!e)return;const n=e;n.assemblerController?n.type()==this.type()?t.assembler.setGlParentNode(n):this.states.error.set(`resolved node '${e.path()}' does not have the same type '${e.type()}' as current node '${this.type()}'`):this.states.error.set(`resolved node '${e.path()}' is not a builder node`)}static PARAM_CALLBACK_setCompileRequired(t){t.PARAM_CALLBACK_setCompileRequired()}PARAM_CALLBACK_setCompileRequired(){var t;null===(t=this.assemblerController)||void 0===t||t.setCompilationRequired(!0)}}function vD(t){return class extends t{constructor(){super(...arguments),this.useFog=oa.BOOLEAN(0)}}}vD(aa);class yD extends sD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.fog=n.useFog}}function xD(t){return class extends t{constructor(){super(...arguments),this.default=oa.FOLDER(null)}}}function bD(t){return class extends t{constructor(){super(...arguments),this.advanced=oa.FOLDER(null)}}}class wD extends(vD(lD(fD(bD(pD(xD(aa))))))){constructor(){super(...arguments),this.linewidth=oa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]})}}const TD=new wD;class AD extends gD{constructor(){super(...arguments),this.paramsConfig=TD,this.controllers={advancedCommon:new cD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"lineBasicBuilder\\\\\\\"}usedAssembler(){return Hn.GL_LINE}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_D.update(this),yD.update(this),this.compileIfRequired(),this.material.linewidth=this.pv.linewidth,this.setMaterial(this.material)}}function ED(t){return class extends t{constructor(){super(...arguments),this.color=oa.COLOR([1,1,1],{conversion:so.SRGB_TO_LINEAR}),this.useVertexColors=oa.BOOLEAN(0,{separatorAfter:!0}),this.transparent=oa.BOOLEAN(0),this.opacity=oa.FLOAT(1),this.alphaTest=oa.FLOAT(0)}}}O.a;ED(aa);class MD extends sD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.color.copy(n.color);const i=n.useVertexColors;i!=e.vertexColors&&(e.vertexColors=i,e.needsUpdate=!0),e.opacity=n.opacity,e.transparent=n.transparent,e.alphaTest=n.alphaTest}}function SD(t){return class extends t{constructor(){super(...arguments),this.useFog=oa.BOOLEAN(0)}}}SD(aa);class CD extends sD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.fog=n.useFog}}function ND(t){return{cook:!1,callback:(e,n)=>{t.update(e)}}}function LD(t,e,n){return{visibleIf:{[e]:1},nodeSelection:{context:Ki.COP,types:null==n?void 0:n.types},cook:!1,callback:(e,n)=>{t.update(e)}}}class OD extends sD{constructor(t,e){super(t),this.node=t,this._update_options=e}add_hooks(t,e){t.addPostDirtyHook(\\\\\\\"TextureController\\\\\\\",(()=>{this.update()})),e.addPostDirtyHook(\\\\\\\"TextureController\\\\\\\",(()=>{this.update()}))}static update(t){}async _update(t,e,n,i){if(this._update_options.uniforms){const r=t,s=e;await this._update_texture_on_uniforms(r,s,n,i)}if(this._update_options.directParams){const r=t,s=e;await this._update_texture_on_material(r,s,n,i)}}async _update_texture_on_uniforms(t,e,n,i){this._update_required_attribute(t,t.uniforms,e,n,i,this._apply_texture_on_uniforms.bind(this),this._remove_texture_from_uniforms.bind(this))}_apply_texture_on_uniforms(t,e,n,i){const r=null!=e[n]&&null!=e[n].value;let s=!1;if(r){e[n].value.uuid!=i.uuid&&(s=!0)}if(!r||s){e[n]&&(e[n].value=i),this._apply_texture_on_material(t,t,n,i),t.needsUpdate=!0;const r=t.customMaterials;if(r){const t=Object.keys(r);for(let e of t){const t=r[e];t&&this._apply_texture_on_uniforms(t,t.uniforms,n,i)}}}}_remove_texture_from_uniforms(t,e,n){if(e[n]){if(e[n].value){e[n].value=null,this._remove_texture_from_material(t,t,n),t.needsUpdate=!0;const i=t.customMaterials;if(i){const t=Object.keys(i);for(let e of t){const t=i[e];t&&this._remove_texture_from_uniforms(t,t.uniforms,n)}}}}else ai.warn(`'${n}' uniform not found. existing uniforms are:`,Object.keys(e).sort())}async _update_texture_on_material(t,e,n,i){this._update_required_attribute(t,t,e,n,i,this._apply_texture_on_material.bind(this),this._remove_texture_from_material.bind(this))}_apply_texture_on_material(t,e,n,i){const r=null!=e[n];let s=!1;if(r){e[n].uuid!=i.uuid&&(s=!0)}r&&!s||(e[n]=i,t.needsUpdate=!0)}_remove_texture_from_material(t,e,n){e[n]&&(e[n]=null,t.needsUpdate=!0)}async _update_required_attribute(t,e,n,i,r,s,o){i.isDirty()&&await i.compute();if(i.value){r.isDirty()&&await r.compute();const i=r.value.nodeWithContext(Ki.COP);if(i){const r=(await i.compute()).texture();if(r)return void s(t,e,n,r)}}o(t,e,n)}}function RD(t){return class extends t{constructor(){super(...arguments),this.useMap=oa.BOOLEAN(0,ND(PD)),this.map=oa.NODE_PATH(gi.EMPTY,LD(PD,\\\\\\\"useMap\\\\\\\"))}}}O.a;RD(aa);class PD extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useMap,this.node.p.map)}async update(){this._update(this.node.material,\\\\\\\"map\\\\\\\",this.node.p.useMap,this.node.p.map)}static async update(t){t.controllers.map.update()}}function ID(t){return class extends t{constructor(){super(...arguments),this.useAlphaMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(FD)}),this.alphaMap=oa.NODE_PATH(gi.EMPTY,LD(FD,\\\\\\\"useAlphaMap\\\\\\\"))}}}O.a;ID(aa);class FD extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useAlphaMap,this.node.p.alphaMap)}async update(){this._update(this.node.material,\\\\\\\"alphaMap\\\\\\\",this.node.p.useAlphaMap,this.node.p.alphaMap)}static async update(t){t.controllers.alphaMap.update()}}function DD(t){return class extends t{constructor(){super(...arguments),this.useAOMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(kD)}),this.aoMap=oa.NODE_PATH(gi.EMPTY,LD(kD,\\\\\\\"useAOMap\\\\\\\")),this.aoMapIntensity=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{useAOMap:1}})}}}O.a;DD(aa);class kD extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useAOMap,this.node.p.aoMap)}async update(){if(this._update(this.node.material,\\\\\\\"aoMap\\\\\\\",this.node.p.useAOMap,this.node.p.aoMap),this._update_options.uniforms){this.node.material.uniforms.aoMapIntensity.value=this.node.pv.aoMapIntensity}if(this._update_options.directParams){this.node.material.aoMapIntensity=this.node.pv.aoMapIntensity}}static async update(t){t.controllers.aoMap.update()}}var BD;!function(t){t.MULT=\\\\\\\"mult\\\\\\\",t.ADD=\\\\\\\"add\\\\\\\",t.MIX=\\\\\\\"mix\\\\\\\"}(BD||(BD={}));const zD=[BD.MULT,BD.ADD,BD.MIX],UD={[BD.MULT]:w.nb,[BD.ADD]:w.c,[BD.MIX]:w.lb};function GD(t){return class extends t{constructor(){super(...arguments),this.useEnvMap=oa.BOOLEAN(0,ND(VD)),this.envMap=oa.NODE_PATH(gi.EMPTY,LD(VD,\\\\\\\"useEnvMap\\\\\\\",{types:[Lg.CUBE_CAMERA]})),this.combine=oa.INTEGER(0,{visibleIf:{useEnvMap:1},menu:{entries:zD.map(((t,e)=>({name:t,value:e})))}}),this.reflectivity=oa.FLOAT(1,{visibleIf:{useEnvMap:1}}),this.refractionRatio=oa.FLOAT(.98,{range:[-1,1],rangeLocked:[!1,!1],visibleIf:{useEnvMap:1}})}}}GD(aa);class VD extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useEnvMap,this.node.p.envMap)}async update(){this._update(this.node.material,\\\\\\\"envMap\\\\\\\",this.node.p.useEnvMap,this.node.p.envMap);const t=UD[zD[this.node.pv.combine]];if(this._update_options.uniforms){const t=this.node.material;t.uniforms.reflectivity.value=this.node.pv.reflectivity,t.uniforms.refractionRatio.value=this.node.pv.refractionRatio}if(this._update_options.directParams){const e=this.node.material;e.combine=t,e.reflectivity=this.node.pv.reflectivity,e.refractionRatio=this.node.pv.refractionRatio}}static async update(t){t.controllers.envMap.update()}}function HD(t){return class extends t{constructor(){super(...arguments),this.useLightMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(jD)}),this.lightMap=oa.NODE_PATH(gi.EMPTY,LD(jD,\\\\\\\"useLightMap\\\\\\\")),this.lightMapIntensity=oa.FLOAT(1,{visibleIf:{useLightMap:1}})}}}O.a;HD(aa);class jD extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useLightMap,this.node.p.lightMap)}async update(){if(this._update(this.node.material,\\\\\\\"lightMap\\\\\\\",this.node.p.useLightMap,this.node.p.lightMap),this._update_options.uniforms){this.node.material.uniforms.lightMapIntensity.value=this.node.pv.lightMapIntensity}if(this._update_options.directParams){this.node.material.lightMapIntensity=this.node.pv.lightMapIntensity}}static async update(t){t.controllers.lightMap.update()}}var WD;!function(t){t.ROUND=\\\\\\\"round\\\\\\\",t.BUTT=\\\\\\\"butt\\\\\\\",t.SQUARE=\\\\\\\"square\\\\\\\"}(WD||(WD={}));const qD=[WD.ROUND,WD.BUTT,WD.SQUARE];var XD;!function(t){t.ROUND=\\\\\\\"round\\\\\\\",t.BEVEL=\\\\\\\"bevel\\\\\\\",t.MITER=\\\\\\\"miter\\\\\\\"}(XD||(XD={}));const YD=[XD.ROUND,XD.BEVEL,XD.MITER];function $D(t){return class extends t{constructor(){super(...arguments),this.wireframe=oa.BOOLEAN(0,{separatorBefore:!0}),this.wireframeLinecap=oa.INTEGER(0,{menu:{entries:qD.map(((t,e)=>({name:t,value:e})))},visibleIf:{wireframe:1}}),this.wireframeLinejoin=oa.INTEGER(0,{menu:{entries:YD.map(((t,e)=>({name:t,value:e})))},visibleIf:{wireframe:1}})}}}O.a;$D(aa);class JD extends sD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.wireframe=n.wireframe,e.wireframeLinecap=qD[n.wireframeLinecap],e.wireframeLinejoin=YD[n.wireframeLinejoin],e.needsUpdate=!0}}function ZD(t){return class extends t{constructor(){super(...arguments),this.textures=oa.FOLDER(null)}}}const QD={directParams:!0};class KD extends(SD($D(lD(bD(HD(GD(DD(ID(RD(ZD(ED(xD(aa))))))))))))){}const tk=new KD;class ek extends rD{constructor(){super(...arguments),this.paramsConfig=tk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,QD),aoMap:new kD(this,QD),envMap:new VD(this,QD),lightMap:new jD(this,QD),map:new PD(this,QD)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshBasic\\\\\\\"}createMaterial(){return new at.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();MD.update(this),CD.update(this),JD.update(this),this.setMaterial(this.material)}}function nk(t){return class extends t{constructor(){super(...arguments),this.wireframe=oa.BOOLEAN(0)}}}nk(aa);class ik extends sD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.wireframe=n.wireframe,e.needsUpdate=!0}}const rk={uniforms:!0};class sk extends(vD(nk(lD(fD(bD(GD(DD(ID(RD(ZD(pD(xD(aa))))))))))))){}const ok=new sk;class ak extends gD{constructor(){super(...arguments),this.paramsConfig=ok,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,rk),aoMap:new kD(this,rk),envMap:new VD(this,rk),map:new PD(this,rk)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshBasicBuilder\\\\\\\"}usedAssembler(){return Hn.GL_MESH_BASIC}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_D.update(this),yD.update(this),ik.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function lk(t){return class extends t{constructor(){super(...arguments),this.emissive=oa.COLOR([0,0,0],{separatorBefore:!0}),this.useEmissiveMap=oa.BOOLEAN(0,ND(ck)),this.emissiveMap=oa.NODE_PATH(gi.EMPTY,LD(ck,\\\\\\\"useEmissiveMap\\\\\\\")),this.emissiveIntensity=oa.FLOAT(1)}}}O.a;lk(aa);class ck extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useEmissiveMap,this.node.p.emissiveMap)}async update(){if(this._update(this.node.material,\\\\\\\"emissiveMap\\\\\\\",this.node.p.useEmissiveMap,this.node.p.emissiveMap),this._update_options.uniforms){this.node.material.uniforms.emissive.value.copy(this.node.pv.emissive)}if(this._update_options.directParams){const t=this.node.material;t.emissive.copy(this.node.pv.emissive),t.emissiveIntensity=this.node.pv.emissiveIntensity}}static async update(t){t.controllers.emissiveMap.update()}}const uk={directParams:!0};class hk extends(SD($D(lD(bD(HD(GD(lk(DD(ID(RD(ZD(ED(xD(aa)))))))))))))){}const dk=new hk;class pk extends rD{constructor(){super(...arguments),this.paramsConfig=dk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,uk),aoMap:new kD(this,uk),emissiveMap:new ck(this,uk),envMap:new VD(this,uk),lightMap:new jD(this,uk),map:new PD(this,uk)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshLambert\\\\\\\"}createMaterial(){return new br.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();MD.update(this),CD.update(this),JD.update(this),this.setMaterial(this.material)}}function _k(t){return class extends t{constructor(){super(...arguments),this.shadowPCSS=oa.BOOLEAN(0,{callback:t=>{mk.PARAM_CALLBACK_setRecompileRequired(t)},separatorBefore:!0}),this.shadowPCSSSamplesCount=oa.INTEGER(16,{visibleIf:{shadowPCSS:1},range:[0,128],rangeLocked:[!0,!1]}),this.shadowPCSSFilterSize=oa.FLOAT(1,{visibleIf:{shadowPCSS:1},range:[0,10],rangeLocked:[!0,!1]})}}}_k(aa);class mk extends sD{constructor(t){super(t),this.node=t}initializeNode(){}static filterFragmentShader(t,e){const n=`\\\\n#define NUM_SAMPLES ${uf.integer(t.pv.shadowPCSSSamplesCount)}\\\\n#define PCSS_FILTER_SIZE ${uf.float(t.pv.shadowPCSSFilterSize)}\\\\n#define LIGHT_WORLD_SIZE 0.005\\\\n// #define LIGHT_FRUSTUM_WIDTH 1.0\\\\n// #define PCSS_FILTER_SIZE 1.0\\\\n#define LIGHT_SIZE_UV (PCSS_FILTER_SIZE * LIGHT_WORLD_SIZE)\\\\n#define NEAR_PLANE 9.5\\\\n\\\\n// #define NUM_SAMPLES 32\\\\n#define NUM_RINGS 11\\\\n#define BLOCKER_SEARCH_NUM_SAMPLES NUM_SAMPLES\\\\n#define PCF_NUM_SAMPLES NUM_SAMPLES\\\\n\\\\nvec2 poissonDisk[NUM_SAMPLES];\\\\n\\\\nvoid initPoissonSamples( const in vec2 randomSeed ) {\\\\n\\\\tfloat ANGLE_STEP = PI2 * float( NUM_RINGS ) / float( NUM_SAMPLES );\\\\n\\\\tfloat INV_NUM_SAMPLES = 1.0 / float( NUM_SAMPLES );\\\\n\\\\n\\\\t// jsfiddle that shows sample pattern: https://jsfiddle.net/a16ff1p7/\\\\n\\\\tfloat angle = rand( randomSeed ) * PI2;\\\\n\\\\tfloat radius = INV_NUM_SAMPLES;\\\\n\\\\tfloat radiusStep = radius;\\\\n\\\\n\\\\tfor( int i = 0; i < NUM_SAMPLES; i ++ ) {\\\\n\\\\t\\\\tpoissonDisk[i] = vec2( cos( angle ), sin( angle ) ) * pow( radius, 0.75 );\\\\n\\\\t\\\\tradius += radiusStep;\\\\n\\\\t\\\\tangle += ANGLE_STEP;\\\\n\\\\t}\\\\n}\\\\n\\\\nfloat penumbraSize( const in float zReceiver, const in float zBlocker ) { // Parallel plane estimation\\\\n\\\\treturn (zReceiver - zBlocker) / zBlocker;\\\\n}\\\\n\\\\nfloat findBlocker( sampler2D shadowMap, const in vec2 uv, const in float zReceiver ) {\\\\n\\\\t// This uses similar triangles to compute what\\\\n\\\\t// area of the shadow map we should search\\\\n\\\\tfloat searchRadius = LIGHT_SIZE_UV * ( zReceiver - NEAR_PLANE ) / zReceiver;\\\\n\\\\tfloat blockerDepthSum = 0.0;\\\\n\\\\tint numBlockers = 0;\\\\n\\\\n\\\\tfor( int i = 0; i < BLOCKER_SEARCH_NUM_SAMPLES; i++ ) {\\\\n\\\\t\\\\tfloat shadowMapDepth = unpackRGBAToDepth(texture2D(shadowMap, uv + poissonDisk[i] * searchRadius));\\\\n\\\\t\\\\tif ( shadowMapDepth < zReceiver ) {\\\\n\\\\t\\\\t\\\\tblockerDepthSum += shadowMapDepth;\\\\n\\\\t\\\\t\\\\tnumBlockers ++;\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n\\\\n\\\\tif( numBlockers == 0 ) return -1.0;\\\\n\\\\n\\\\treturn blockerDepthSum / float( numBlockers );\\\\n}\\\\n\\\\nfloat PCF_Filter(sampler2D shadowMap, vec2 uv, float zReceiver, float filterRadius ) {\\\\n\\\\tfloat sum = 0.0;\\\\n\\\\tfor( int i = 0; i < PCF_NUM_SAMPLES; i ++ ) {\\\\n\\\\t\\\\tfloat depth = unpackRGBAToDepth( texture2D( shadowMap, uv + poissonDisk[ i ] * filterRadius ) );\\\\n\\\\t\\\\tif( zReceiver <= depth ) sum += 1.0;\\\\n\\\\t}\\\\n\\\\tfor( int i = 0; i < PCF_NUM_SAMPLES; i ++ ) {\\\\n\\\\t\\\\tfloat depth = unpackRGBAToDepth( texture2D( shadowMap, uv + -poissonDisk[ i ].yx * filterRadius ) );\\\\n\\\\t\\\\tif( zReceiver <= depth ) sum += 1.0;\\\\n\\\\t}\\\\n\\\\treturn sum / ( 2.0 * float( PCF_NUM_SAMPLES ) );\\\\n}\\\\n\\\\nfloat PCSS ( sampler2D shadowMap, vec4 coords ) {\\\\n\\\\tvec2 uv = coords.xy;\\\\n\\\\tfloat zReceiver = coords.z; // Assumed to be eye-space z in this code\\\\n\\\\n\\\\tinitPoissonSamples( uv );\\\\n\\\\t// STEP 1: blocker search\\\\n\\\\tfloat avgBlockerDepth = findBlocker( shadowMap, uv, zReceiver );\\\\n\\\\n\\\\t//There are no occluders so early out (this saves filtering)\\\\n\\\\tif( avgBlockerDepth == -1.0 ) return 1.0;\\\\n\\\\n\\\\t// STEP 2: penumbra size\\\\n\\\\tfloat penumbraRatio = penumbraSize( zReceiver, avgBlockerDepth );\\\\n\\\\tfloat filterRadius = penumbraRatio * LIGHT_SIZE_UV * NEAR_PLANE / zReceiver;\\\\n\\\\n\\\\t// STEP 3: filtering\\\\n\\\\t//return avgBlockerDepth;\\\\n\\\\treturn PCF_Filter( shadowMap, uv, zReceiver, filterRadius );\\\\n}\\\\n`;let i=B;return i=i.replace(\\\\\\\"#ifdef USE_SHADOWMAP\\\\\\\",`#ifdef USE_SHADOWMAP\\\\n${n}\\\\n\\\\t\\\\t\\\\t\\\\t`),i=i.replace(\\\\\\\"#if defined( SHADOWMAP_TYPE_PCF )\\\\\\\",\\\\\\\"\\\\n\\\\t\\\\t\\\\t\\\\treturn PCSS( shadowMap, shadowCoord );\\\\n\\\\t\\\\t\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF )\\\\\\\"),e=e.replace(\\\\\\\"#include \\\\\\\",i)}async update(){const t=this.node;if(!t.assemblerController)return;const e=\\\\\\\"PCSS\\\\\\\";this.node.pv.shadowPCSS?t.assemblerController.addFilterFragmentShaderCallback(e,(t=>mk.filterFragmentShader(this.node,t))):t.assemblerController.removeFilterFragmentShaderCallback(e)}static async update(t){t.controllers.PCSS.update()}static PARAM_CALLBACK_setRecompileRequired(t){t.controllers.PCSS.update()}}const fk={uniforms:!0};class gk extends(_k(vD(nk(lD(fD(bD(HD(GD(lk(DD(ID(RD(ZD(pD(xD(aa)))))))))))))))){}const vk=new gk;class yk extends gD{constructor(){super(...arguments),this.paramsConfig=vk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,fk),aoMap:new kD(this,fk),emissiveMap:new ck(this,fk),envMap:new VD(this,fk),lightMap:new jD(this,fk),map:new PD(this,fk),PCSS:new mk(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshLambertBuilder\\\\\\\"}usedAssembler(){return Hn.GL_MESH_LAMBERT}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_D.update(this),yD.update(this),ik.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function xk(t){return class extends t{constructor(){super(...arguments),this.useBumpMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(bk)}),this.bumpMap=oa.NODE_PATH(\\\\\\\"\\\\\\\",LD(bk,\\\\\\\"useBumpMap\\\\\\\")),this.bumpScale=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...LD(bk,\\\\\\\"useBumpMap\\\\\\\")}),this.bumpBias=oa.FLOAT(0,{range:[0,1],rangeLocked:[!1,!1],...LD(bk,\\\\\\\"useBumpMap\\\\\\\")})}}}O.a;xk(aa);class bk extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useBumpMap,this.node.p.bumpMap)}async update(){if(this._update(this.node.material,\\\\\\\"bumpMap\\\\\\\",this.node.p.useBumpMap,this.node.p.bumpMap),this._update_options.uniforms){this.node.material.uniforms.bumpScale.value=this.node.pv.bumpScale}if(this._update_options.directParams){this.node.material.bumpScale=this.node.pv.bumpScale}}static async update(t){t.controllers.bumpMap.update()}}var wk;!function(t){t.TANGENT=\\\\\\\"tangent\\\\\\\",t.OBJECT=\\\\\\\"object\\\\\\\"}(wk||(wk={}));const Tk=[wk.TANGENT,wk.OBJECT],Ak={[wk.TANGENT]:w.Uc,[wk.OBJECT]:w.zb};function Ek(t){return class extends t{constructor(){super(...arguments),this.useNormalMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(Mk)}),this.normalMap=oa.NODE_PATH(gi.EMPTY,LD(Mk,\\\\\\\"useNormalMap\\\\\\\")),this.normalMapType=oa.INTEGER(0,{visibleIf:{useNormalMap:1},menu:{entries:Tk.map(((t,e)=>({name:t,value:e})))}}),this.normalScale=oa.VECTOR2([1,1],{visibleIf:{useNormalMap:1}})}}}O.a;Ek(aa);class Mk extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useNormalMap,this.node.p.normalMap)}async update(){this._update(this.node.material,\\\\\\\"normalMap\\\\\\\",this.node.p.useNormalMap,this.node.p.normalMap);const t=Ak[Tk[this.node.pv.normalMapType]];if(this._update_options.uniforms){this.node.material.uniforms.normalScale.value.copy(this.node.pv.normalScale)}const e=this.node.material;e.normalMapType=t,this._update_options.directParams&&e.normalScale.copy(this.node.pv.normalScale)}static async update(t){t.controllers.normalMap.update()}}function Sk(t){return class extends t{constructor(){super(...arguments),this.useDisplacementMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(Ck)}),this.displacementMap=oa.NODE_PATH(\\\\\\\"\\\\\\\",LD(Ck,\\\\\\\"useDisplacementMap\\\\\\\")),this.displacementScale=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...LD(Ck,\\\\\\\"useDisplacementMap\\\\\\\")}),this.displacementBias=oa.FLOAT(0,{range:[0,1],rangeLocked:[!1,!1],...LD(Ck,\\\\\\\"useDisplacementMap\\\\\\\")})}}}O.a;Sk(aa);class Ck extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useDisplacementMap,this.node.p.displacementMap)}async update(){if(this._update(this.node.material,\\\\\\\"displacementMap\\\\\\\",this.node.p.useDisplacementMap,this.node.p.displacementMap),this._update_options.uniforms){const t=this.node.material;t.uniforms.displacementScale.value=this.node.pv.displacementScale,t.uniforms.displacementBias.value=this.node.pv.displacementBias}if(this._update_options.directParams){const t=this.node.material;t.displacementScale=this.node.pv.displacementScale,t.displacementBias=this.node.pv.displacementBias}}static async update(t){t.controllers.displacementMap.update()}}function Nk(t){return class extends t{constructor(){super(...arguments),this.useMatcapMap=oa.BOOLEAN(0,ND(Lk)),this.matcapMap=oa.NODE_PATH(gi.EMPTY,LD(Lk,\\\\\\\"useMatcapMap\\\\\\\"))}}}O.a;Nk(aa);class Lk extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useMatcapMap,this.node.p.matcapMap)}async update(){this._update(this.node.material,\\\\\\\"matcap\\\\\\\",this.node.p.useMatcapMap,this.node.p.matcapMap)}static async update(t){t.controllers.matcap.update()}}const Ok={directParams:!0};class Rk extends(SD(lD(bD(Ek(Sk(xk(ID(RD(Nk(ZD(ED(xD(aa))))))))))))){}const Pk=new Rk;class Ik extends rD{constructor(){super(...arguments),this.paramsConfig=Pk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,Ok),bumpMap:new bk(this,Ok),displacementMap:new Ck(this,Ok),map:new PD(this,Ok),matcap:new Lk(this,Ok),normalMap:new Mk(this,Ok)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshMatcap\\\\\\\"}createMaterial(){return new jf({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();MD.update(this),CD.update(this),this.setMaterial(this.material)}}function Fk(t){return class extends t{constructor(){super(...arguments),this.useSpecularMap=oa.BOOLEAN(0,ND(Dk)),this.specularMap=oa.NODE_PATH(gi.EMPTY,LD(Dk,\\\\\\\"useSpecularMap\\\\\\\"))}}}O.a;Fk(aa);class Dk extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useSpecularMap,this.node.p.specularMap)}async update(){this._update(this.node.material,\\\\\\\"specularMap\\\\\\\",this.node.p.useSpecularMap,this.node.p.specularMap)}static async update(t){t.controllers.specularMap.update()}}const kk={directParams:!0};class Bk extends(SD($D(lD(bD(Fk(Ek(HD(GD(lk(Sk(xk(DD(ID(RD(ZD(ED(xD(aa)))))))))))))))))){constructor(){super(...arguments),this.flatShading=oa.BOOLEAN(0)}}const zk=new Bk;class Uk extends rD{constructor(){super(...arguments),this.paramsConfig=zk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,kk),aoMap:new kD(this,kk),bumpMap:new bk(this,kk),displacementMap:new Ck(this,kk),emissiveMap:new ck(this,kk),envMap:new VD(this,kk),lightMap:new jD(this,kk),map:new PD(this,kk),normalMap:new Mk(this,kk),specularMap:new Dk(this,kk)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhong\\\\\\\"}createMaterial(){return new Gf.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();MD.update(this),CD.update(this),JD.update(this),this.material.flatShading!=this.pv.flatShading&&(this.material.flatShading=this.pv.flatShading,this.material.needsUpdate=!0),this.setMaterial(this.material)}}const Gk={uniforms:!0};class Vk extends(_k(vD(nk(lD(fD(bD(Fk(Ek(HD(GD(lk(Sk(xk(DD(ID(RD(ZD(pD(xD(aa)))))))))))))))))))){}const Hk=new Vk;class jk extends gD{constructor(){super(...arguments),this.paramsConfig=Hk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,Gk),aoMap:new kD(this,Gk),bumpMap:new bk(this,Gk),displacementMap:new Ck(this,Gk),emissiveMap:new ck(this,Gk),envMap:new VD(this,Gk),lightMap:new jD(this,Gk),map:new PD(this,Gk),normalMap:new Mk(this,Gk),specularMap:new Dk(this,Gk),PCSS:new mk(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhongBuilder\\\\\\\"}usedAssembler(){return Hn.GL_MESH_PHONG}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_D.update(this),yD.update(this),ik.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function Wk(t){return class extends t{constructor(){super(...arguments),this.useEnvMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(qk)}),this.envMap=oa.NODE_PATH(gi.EMPTY,LD(qk,\\\\\\\"useEnvMap\\\\\\\")),this.envMapIntensity=oa.FLOAT(1,{visibleIf:{useEnvMap:1}}),this.refractionRatio=oa.FLOAT(.98,{range:[-1,1],rangeLocked:[!1,!1],visibleIf:{useEnvMap:1}})}}}Wk(aa);class qk extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useEnvMap,this.node.p.envMap)}async update(){if(this._update(this.node.material,\\\\\\\"envMap\\\\\\\",this.node.p.useEnvMap,this.node.p.envMap),this._update_options.uniforms){const t=this.node.material;t.uniforms.envMapIntensity.value=this.node.pv.envMapIntensity,t.uniforms.refractionRatio.value=this.node.pv.refractionRatio}if(this._update_options.directParams){const t=this.node.material;t.envMapIntensity=this.node.pv.envMapIntensity,t.refractionRatio=this.node.pv.refractionRatio}}static async update(t){t.controllers.envMap.update()}}function Xk(t){return class extends t{constructor(){super(...arguments),this.useMetalnessMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(Yk)}),this.metalnessMap=oa.NODE_PATH(gi.EMPTY,LD(Yk,\\\\\\\"useMetalnessMap\\\\\\\")),this.metalness=oa.FLOAT(1),this.useRoughnessMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(Yk)}),this.roughnessMap=oa.NODE_PATH(gi.EMPTY,LD(Yk,\\\\\\\"useRoughnessMap\\\\\\\")),this.roughness=oa.FLOAT(.5)}}}O.a;Xk(aa);class Yk extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useMetalnessMap,this.node.p.metalnessMap)}async update(){if(this._update(this.node.material,\\\\\\\"metalnessMap\\\\\\\",this.node.p.useMetalnessMap,this.node.p.metalnessMap),this._update_options.uniforms){this.node.material.uniforms.metalness.value=this.node.pv.metalness}if(this._update_options.directParams){this.node.material.metalness=this.node.pv.metalness}if(this._update(this.node.material,\\\\\\\"roughnessMap\\\\\\\",this.node.p.useRoughnessMap,this.node.p.roughnessMap),this._update_options.uniforms){this.node.material.uniforms.roughness.value=this.node.pv.roughness}if(this._update_options.directParams){this.node.material.roughness=this.node.pv.roughness}}static async update(t){t.controllers.metalnessRoughnessMap.update()}}function $k(t){return class extends t{constructor(){super(...arguments),this.clearcoat=oa.FLOAT(0,{separatorBefore:!0}),this.useClearCoatMap=oa.BOOLEAN(0,ND(Jk)),this.clearcoatMap=oa.NODE_PATH(gi.EMPTY,LD(Jk,\\\\\\\"useClearCoatMap\\\\\\\")),this.useClearCoatNormalMap=oa.BOOLEAN(0,ND(Jk)),this.clearcoatNormalMap=oa.NODE_PATH(gi.EMPTY,LD(Jk,\\\\\\\"useClearCoatNormalMap\\\\\\\")),this.clearcoatNormalScale=oa.VECTOR2([1,1],{visibleIf:{useClearCoatNormalMap:1}}),this.clearcoatRoughness=oa.FLOAT(0),this.useClearCoatRoughnessMap=oa.BOOLEAN(0,ND(Jk)),this.clearcoatRoughnessMap=oa.NODE_PATH(gi.EMPTY,LD(Jk,\\\\\\\"useClearCoatRoughnessMap\\\\\\\")),this.useSheen=oa.BOOLEAN(0),this.sheen=oa.FLOAT(0,{range:[0,1],rangeLocked:[!0,!1],visibleIf:{useSheen:1}}),this.sheenRoughness=oa.FLOAT(1,{range:[0,1],rangeLocked:[!0,!1],visibleIf:{useSheen:1}}),this.sheenColor=oa.COLOR([1,1,1],{visibleIf:{useSheen:1}}),this.reflectivity=oa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0]}),this.transmission=oa.FLOAT(0,{range:[0,1]}),this.useTransmissionMap=oa.BOOLEAN(0),this.transmissionMap=oa.NODE_PATH(gi.EMPTY,{visibleIf:{useTransmissionMap:1}}),this.thickness=oa.FLOAT(.01,{range:[0,1],rangeLocked:[!0,!1]}),this.useThicknessMap=oa.BOOLEAN(0),this.thicknessMap=oa.NODE_PATH(gi.EMPTY,{visibleIf:{useThicknessMap:1}}),this.attenuationDistance=oa.FLOAT(0),this.attenuationColor=oa.COLOR([1,1,1])}}}$k(aa);class Jk extends OD{constructor(t,e){super(t,e),this.node=t,this._sheenColorClone=new D.a}initializeNode(){this.add_hooks(this.node.p.useClearCoatMap,this.node.p.clearcoatMap),this.add_hooks(this.node.p.useClearCoatNormalMap,this.node.p.clearcoatNormalMap),this.add_hooks(this.node.p.useClearCoatRoughnessMap,this.node.p.clearcoatRoughnessMap),this.add_hooks(this.node.p.useTransmissionMap,this.node.p.transmissionMap),this.add_hooks(this.node.p.useThicknessMap,this.node.p.thicknessMap)}async update(){this._update(this.node.material,\\\\\\\"clearcoatMap\\\\\\\",this.node.p.useClearCoatMap,this.node.p.clearcoatMap),this._update(this.node.material,\\\\\\\"clearcoatNormalMap\\\\\\\",this.node.p.useClearCoatNormalMap,this.node.p.clearcoatNormalMap),this._update(this.node.material,\\\\\\\"clearcoatRoughnessMap\\\\\\\",this.node.p.useClearCoatRoughnessMap,this.node.p.clearcoatRoughnessMap),this._update(this.node.material,\\\\\\\"transmissionMap\\\\\\\",this.node.p.useTransmissionMap,this.node.p.transmissionMap),this._update(this.node.material,\\\\\\\"thicknessMap\\\\\\\",this.node.p.useThicknessMap,this.node.p.thicknessMap);const t=this.node.pv;if(this._update_options.uniforms){const e=this.node.material;e.uniforms.clearcoat.value=t.clearcoat,e.uniforms.clearcoatNormalScale.value.copy(t.clearcoatNormalScale),e.uniforms.clearcoatRoughness.value=t.clearcoatRoughness,e.uniforms.reflectivity.value=t.reflectivity,e.uniforms.transmission.value=t.transmission,e.uniforms.thickness.value=t.thickness,e.uniforms.attenuationDistance.value=t.attenuationDistance,e.uniforms.attenuationTint.value=t.attenuationColor,t.useSheen?(this._sheenColorClone.copy(t.sheenColor),e.uniforms.sheen.value=t.sheen,e.uniforms.sheenRoughness.value=t.sheenRoughness,e.uniforms.sheenTint.value=this._sheenColorClone):e.uniforms.sheen.value=0}if(this._update_options.directParams){const e=this.node.material;e.clearcoat=t.clearcoat,e.clearcoatNormalScale.copy(t.clearcoatNormalScale),e.clearcoatRoughness=t.clearcoatRoughness,e.reflectivity=t.reflectivity,t.useSheen?(this._sheenColorClone.copy(t.sheenColor),e.sheen=t.sheen,e.sheenRoughness=t.sheenRoughness,e.sheenTint=this._sheenColorClone):e.sheen=0,e.transmission=t.transmission,e.thickness=t.thickness,e.attenuationDistance=t.attenuationDistance,e.attenuationTint=t.attenuationColor}}static async update(t){t.controllers.physical.update()}}const Zk={directParams:!0};class Qk extends(SD($D(lD(bD($k(Xk(Ek(HD(Wk(lk(Sk(xk(DD(ID(RD(ZD(ED(xD(aa))))))))))))))))))){}const Kk=new Qk;class tB extends rD{constructor(){super(...arguments),this.paramsConfig=Kk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,Zk),aoMap:new kD(this,Zk),bumpMap:new bk(this,Zk),displacementMap:new Ck(this,Zk),emissiveMap:new ck(this,Zk),envMap:new qk(this,Zk),lightMap:new jD(this,Zk),map:new PD(this,Zk),metalnessRoughnessMap:new Yk(this,Zk),normalMap:new Mk(this,Zk),physical:new Jk(this,Zk)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhysical\\\\\\\"}createMaterial(){return new Uf.a({vertexColors:!1,side:w.H,color:16777215,opacity:1,metalness:1,roughness:0})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();MD.update(this),CD.update(this),JD.update(this),this.setMaterial(this.material)}}const eB={uniforms:!0};class nB extends(function(t){return class extends(_k(vD(nk(lD(fD(t)))))){}}(bD($k(Xk(Ek(HD(Wk(lk(Sk(xk(DD(ID(RD(ZD(pD(xD(aa))))))))))))))))){}const iB=new nB;class rB extends gD{constructor(){super(...arguments),this.paramsConfig=iB,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,eB),aoMap:new kD(this,eB),bumpMap:new bk(this,eB),displacementMap:new Ck(this,eB),emissiveMap:new ck(this,eB),envMap:new qk(this,eB),lightMap:new jD(this,eB),map:new PD(this,eB),metalnessRoughnessMap:new Yk(this,eB),normalMap:new Mk(this,eB),physical:new Jk(this,eB),PCSS:new mk(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhysicalBuilder\\\\\\\"}usedAssembler(){return Hn.GL_MESH_PHYSICAL}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_D.update(this),yD.update(this),ik.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}const sB={directParams:!0};class oB extends(SD($D(lD(bD(Xk(Ek(HD(Wk(lk(Sk(xk(DD(ID(RD(ZD(ED(xD(aa)))))))))))))))))){}const aB=new oB;class lB extends rD{constructor(){super(...arguments),this.paramsConfig=aB,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,sB),aoMap:new kD(this,sB),bumpMap:new bk(this,sB),displacementMap:new Ck(this,sB),emissiveMap:new ck(this,sB),envMap:new qk(this,sB),lightMap:new jD(this,sB),map:new PD(this,sB),metalnessRoughnessMap:new Yk(this,sB),normalMap:new Mk(this,sB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshStandard\\\\\\\"}createMaterial(){return new xr.a({vertexColors:!1,side:w.H,color:16777215,opacity:1,metalness:1,roughness:0})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();MD.update(this),CD.update(this),JD.update(this),this.setMaterial(this.material)}}const cB={uniforms:!0};class uB extends(_k(vD(nk(lD(fD(bD(Xk(Ek(HD(Wk(lk(Sk(xk(DD(ID(RD(ZD(pD(xD(aa)))))))))))))))))))){}const hB=new uB;class dB extends gD{constructor(){super(...arguments),this.paramsConfig=hB,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,cB),aoMap:new kD(this,cB),bumpMap:new bk(this,cB),displacementMap:new Ck(this,cB),emissiveMap:new ck(this,cB),envMap:new qk(this,cB),lightMap:new jD(this,cB),map:new PD(this,cB),metalnessRoughnessMap:new Yk(this,cB),normalMap:new Mk(this,cB),PCSS:new mk(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshStandardBuilder\\\\\\\"}usedAssembler(){return Hn.GL_MESH_STANDARD}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_D.update(this),yD.update(this),ik.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}const pB=z.meshphong_frag.slice(0,z.meshphong_frag.indexOf(\\\\\\\"void main() {\\\\\\\")),_B=z.meshphong_frag.slice(z.meshphong_frag.indexOf(\\\\\\\"void main() {\\\\\\\")),mB={uniforms:I.merge([V.phong.uniforms,{thicknessMap:{value:null},thicknessColor:{value:new D.a(16777215)},thicknessDistortion:{value:.1},thicknessAmbient:{value:0},thicknessAttenuation:{value:.1},thicknessPower:{value:2},thicknessScale:{value:10}}]),vertexShader:[\\\\\\\"#define USE_UV\\\\\\\",z.meshphong_vert].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"#define USE_UV\\\\\\\",\\\\\\\"#define SUBSURFACE\\\\\\\",pB,\\\\\\\"uniform sampler2D thicknessMap;\\\\\\\",\\\\\\\"uniform float thicknessPower;\\\\\\\",\\\\\\\"uniform float thicknessScale;\\\\\\\",\\\\\\\"uniform float thicknessDistortion;\\\\\\\",\\\\\\\"uniform float thicknessAmbient;\\\\\\\",\\\\\\\"uniform float thicknessAttenuation;\\\\\\\",\\\\\\\"uniform vec3 thicknessColor;\\\\\\\",\\\\\\\"void RE_Direct_Scattering(const in IncidentLight directLight, const in vec2 uv, const in GeometricContext geometry, inout ReflectedLight reflectedLight) {\\\\\\\",\\\\\\\"\\\\tvec3 thickness = thicknessColor * texture2D(thicknessMap, uv).r;\\\\\\\",\\\\\\\"\\\\tvec3 scatteringHalf = normalize(directLight.direction + (geometry.normal * thicknessDistortion));\\\\\\\",\\\\\\\"\\\\tfloat scatteringDot = pow(saturate(dot(geometry.viewDir, -scatteringHalf)), thicknessPower) * thicknessScale;\\\\\\\",\\\\\\\"\\\\tvec3 scatteringIllu = (scatteringDot + thicknessAmbient) * thickness;\\\\\\\",\\\\\\\"\\\\treflectedLight.directDiffuse += scatteringIllu * thicknessAttenuation * directLight.color;\\\\\\\",\\\\\\\"}\\\\\\\",_B.replace(\\\\\\\"#include \\\\\\\",(fB=z.lights_fragment_begin,gB=\\\\\\\"RE_Direct( directLight, geometry, material, reflectedLight );\\\\\\\",vB=[\\\\\\\"RE_Direct( directLight, geometry, material, reflectedLight );\\\\\\\",\\\\\\\"#if defined( SUBSURFACE ) && defined( USE_UV )\\\\\\\",\\\\\\\" RE_Direct_Scattering(directLight, vUv, geometry, reflectedLight);\\\\\\\",\\\\\\\"#endif\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fB.split(gB).join(vB)))].join(\\\\\\\"\\\\n\\\\\\\")};var fB,gB,vB;function yB(t){return{cook:!1,callback:(e,n)=>{AB.PARAM_CALLBACK_update_uniformColor(e,n,t)}}}function xB(t){return{cook:!1,callback:(e,n)=>{AB.PARAM_CALLBACK_update_uniformN(e,n,t)}}}const bB={uniforms:!0};class wB extends(SD(nk(lD(bD(ID(RD(ZD(function(t){return class extends t{constructor(){var t;super(...arguments),this.diffuse=oa.COLOR([1,1,1],{...yB(\\\\\\\"diffuse\\\\\\\")}),this.shininess=oa.FLOAT(1,{range:[0,1e3]}),this.thicknessMap=oa.NODE_PATH(gi.EMPTY,{nodeSelection:{context:Ki.COP},...(t=\\\\\\\"thicknessMap\\\\\\\",{cook:!1,callback:(e,n)=>{AB.PARAM_CALLBACK_update_uniformTexture(e,n,t)}})}),this.thicknessColor=oa.COLOR([.5,.3,0],{...yB(\\\\\\\"thicknessColor\\\\\\\")}),this.thicknessDistortion=oa.FLOAT(.1,{...xB(\\\\\\\"thicknessDistortion\\\\\\\")}),this.thicknessAmbient=oa.FLOAT(.4,{...xB(\\\\\\\"thicknessAmbient\\\\\\\")}),this.thicknessAttenuation=oa.FLOAT(.8,{...xB(\\\\\\\"thicknessAttenuation\\\\\\\")}),this.thicknessPower=oa.FLOAT(2,{range:[0,10],...xB(\\\\\\\"thicknessPower\\\\\\\")}),this.thicknessScale=oa.FLOAT(16,{range:[0,100],...xB(\\\\\\\"thicknessScale\\\\\\\")})}}}(xD(aa)))))))))){}const TB=new wB;class AB extends rD{constructor(){super(...arguments),this.paramsConfig=TB,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,bB),map:new PD(this,bB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshSubsurfaceScattering\\\\\\\"}createMaterial(){const t=I.clone(mB.uniforms),e=new F({uniforms:t,vertexShader:mB.vertexShader,fragmentShader:mB.fragmentShader,lights:!0});return e.extensions.derivatives=!0,e}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();CD.update(this),ik.update(this),this.update_map(this.p.thicknessMap,\\\\\\\"thicknessMap\\\\\\\"),this.material.uniforms.diffuse.value.copy(this.pv.diffuse),this.material.uniforms.shininess.value=this.pv.shininess,this.material.uniforms.thicknessColor.value.copy(this.pv.thicknessColor),this.material.uniforms.thicknessDistortion.value=this.pv.thicknessDistortion,this.material.uniforms.thicknessAmbient.value=this.pv.thicknessAmbient,this.material.uniforms.thicknessAttenuation.value=this.pv.thicknessAttenuation,this.material.uniforms.thicknessPower.value=this.pv.thicknessPower,this.material.uniforms.thicknessScale.value=this.pv.thicknessScale,this.setMaterial(this.material)}static PARAM_CALLBACK_update_uniformN(t,e,n){t.material.uniforms[n].value=e.value}static PARAM_CALLBACK_update_uniformColor(t,e,n){e.parent_param&&t.material.uniforms[n].value.copy(e.parent_param.value)}static PARAM_CALLBACK_update_uniformTexture(t,e,n){t.update_map(e,n)}async update_map(t,e){const n=t.value.nodeWithContext(Ki.COP);n||(this.material.uniforms[e].value=null);const i=n,r=await i.compute();this.material.uniforms[e].value=r.texture()}}function EB(t){return class extends t{constructor(){super(...arguments),this.useGradientMap=oa.BOOLEAN(0,ND(MB)),this.gradientMap=oa.NODE_PATH(gi.EMPTY,LD(MB,\\\\\\\"useGradientMap\\\\\\\"))}}}O.a;EB(aa);class MB extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useGradientMap,this.node.p.gradientMap)}async update(){this._update(this.node.material,\\\\\\\"gradientMap\\\\\\\",this.node.p.useGradientMap,this.node.p.gradientMap)}static async update(t){t.controllers.gradientMap.update()}}const SB={directParams:!0};class CB extends(SD($D(lD(bD(Ek(HD(EB(lk(Sk(xk(DD(ID(RD(ZD(ED(xD(aa))))))))))))))))){}const NB=new CB;class LB extends rD{constructor(){super(...arguments),this.paramsConfig=NB,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,SB),aoMap:new kD(this,SB),bumpMap:new bk(this,SB),displacementMap:new Ck(this,SB),emissiveMap:new ck(this,SB),gradientMap:new MB(this,SB),lightMap:new jD(this,SB),map:new PD(this,SB),normalMap:new Mk(this,SB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshToon\\\\\\\"}createMaterial(){return new Vf({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();MD.update(this),CD.update(this),JD.update(this),this.setMaterial(this.material)}}const OB={directParams:!0};class RB extends(vD(lD(bD(ID(RD(ZD(ED(function(t){return class extends t{constructor(){super(...arguments),this.size=oa.FLOAT(1),this.sizeAttenuation=oa.BOOLEAN(1)}}}(xD(aa)))))))))){}const PB=new RB;class IB extends rD{constructor(){super(...arguments),this.paramsConfig=PB,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,OB),map:new PD(this,OB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"points\\\\\\\"}createMaterial(){return new yr.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();MD.update(this),yD.update(this),this.material.size=this.pv.size,this.material.sizeAttenuation=this.pv.sizeAttenuation,this.setMaterial(this.material)}}class FB extends(vD(lD(fD(bD(pD(xD(aa))))))){}const DB=new FB;class kB extends gD{constructor(){super(...arguments),this.paramsConfig=DB,this.controllers={advancedCommon:new cD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"pointsBuilder\\\\\\\"}usedAssembler(){return Hn.GL_POINTS}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_D.update(this),yD.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}class BB extends(lD(ED(aa))){}const zB=new BB;class UB extends rD{constructor(){super(...arguments),this.paramsConfig=zB,this.controllers={advancedCommon:new cD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"shadow\\\\\\\"}createMaterial(){return new Bf({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();MD.update(this),this.setMaterial(this.material)}}class GB extends k.a{constructor(){const t=GB.SkyShader,e=new F({name:\\\\\\\"SkyShader\\\\\\\",fragmentShader:t.fragmentShader,vertexShader:t.vertexShader,uniforms:I.clone(t.uniforms),side:w.i,depthWrite:!1});super(new N(1,1,1),e)}}GB.prototype.isSky=!0,GB.SkyShader={uniforms:{turbidity:{value:2},rayleigh:{value:1},mieCoefficient:{value:.005},mieDirectionalG:{value:.8},sunPosition:{value:new p.a},up:{value:new p.a(0,1,0)}},vertexShader:\\\\\\\"\\\\n\\\\t\\\\tuniform vec3 sunPosition;\\\\n\\\\t\\\\tuniform float rayleigh;\\\\n\\\\t\\\\tuniform float turbidity;\\\\n\\\\t\\\\tuniform float mieCoefficient;\\\\n\\\\t\\\\tuniform vec3 up;\\\\n\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t\\\\tvarying vec3 vSunDirection;\\\\n\\\\t\\\\tvarying float vSunfade;\\\\n\\\\t\\\\tvarying vec3 vBetaR;\\\\n\\\\t\\\\tvarying vec3 vBetaM;\\\\n\\\\t\\\\tvarying float vSunE;\\\\n\\\\n\\\\t\\\\t// constants for atmospheric scattering\\\\n\\\\t\\\\tconst float e = 2.71828182845904523536028747135266249775724709369995957;\\\\n\\\\t\\\\tconst float pi = 3.141592653589793238462643383279502884197169;\\\\n\\\\n\\\\t\\\\t// wavelength of used primaries, according to preetham\\\\n\\\\t\\\\tconst vec3 lambda = vec3( 680E-9, 550E-9, 450E-9 );\\\\n\\\\t\\\\t// this pre-calcuation replaces older TotalRayleigh(vec3 lambda) function:\\\\n\\\\t\\\\t// (8.0 * pow(pi, 3.0) * pow(pow(n, 2.0) - 1.0, 2.0) * (6.0 + 3.0 * pn)) / (3.0 * N * pow(lambda, vec3(4.0)) * (6.0 - 7.0 * pn))\\\\n\\\\t\\\\tconst vec3 totalRayleigh = vec3( 5.804542996261093E-6, 1.3562911419845635E-5, 3.0265902468824876E-5 );\\\\n\\\\n\\\\t\\\\t// mie stuff\\\\n\\\\t\\\\t// K coefficient for the primaries\\\\n\\\\t\\\\tconst float v = 4.0;\\\\n\\\\t\\\\tconst vec3 K = vec3( 0.686, 0.678, 0.666 );\\\\n\\\\t\\\\t// MieConst = pi * pow( ( 2.0 * pi ) / lambda, vec3( v - 2.0 ) ) * K\\\\n\\\\t\\\\tconst vec3 MieConst = vec3( 1.8399918514433978E14, 2.7798023919660528E14, 4.0790479543861094E14 );\\\\n\\\\n\\\\t\\\\t// earth shadow hack\\\\n\\\\t\\\\t// cutoffAngle = pi / 1.95;\\\\n\\\\t\\\\tconst float cutoffAngle = 1.6110731556870734;\\\\n\\\\t\\\\tconst float steepness = 1.5;\\\\n\\\\t\\\\tconst float EE = 1000.0;\\\\n\\\\n\\\\t\\\\tfloat sunIntensity( float zenithAngleCos ) {\\\\n\\\\t\\\\t\\\\tzenithAngleCos = clamp( zenithAngleCos, -1.0, 1.0 );\\\\n\\\\t\\\\t\\\\treturn EE * max( 0.0, 1.0 - pow( e, -( ( cutoffAngle - acos( zenithAngleCos ) ) / steepness ) ) );\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec3 totalMie( float T ) {\\\\n\\\\t\\\\t\\\\tfloat c = ( 0.2 * T ) * 10E-18;\\\\n\\\\t\\\\t\\\\treturn 0.434 * c * MieConst;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 worldPosition = modelMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\tgl_Position.z = gl_Position.w; // set z to camera.far\\\\n\\\\n\\\\t\\\\t\\\\tvSunDirection = normalize( sunPosition );\\\\n\\\\n\\\\t\\\\t\\\\tvSunE = sunIntensity( dot( vSunDirection, up ) );\\\\n\\\\n\\\\t\\\\t\\\\tvSunfade = 1.0 - clamp( 1.0 - exp( ( sunPosition.y / 450000.0 ) ), 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\tfloat rayleighCoefficient = rayleigh - ( 1.0 * ( 1.0 - vSunfade ) );\\\\n\\\\n\\\\t\\\\t\\\\t// extinction (absorbtion + out scattering)\\\\n\\\\t\\\\t\\\\t// rayleigh coefficients\\\\n\\\\t\\\\t\\\\tvBetaR = totalRayleigh * rayleighCoefficient;\\\\n\\\\n\\\\t\\\\t\\\\t// mie coefficients\\\\n\\\\t\\\\t\\\\tvBetaM = totalMie( turbidity ) * mieCoefficient;\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t\\\\tvarying vec3 vSunDirection;\\\\n\\\\t\\\\tvarying float vSunfade;\\\\n\\\\t\\\\tvarying vec3 vBetaR;\\\\n\\\\t\\\\tvarying vec3 vBetaM;\\\\n\\\\t\\\\tvarying float vSunE;\\\\n\\\\n\\\\t\\\\tuniform float mieDirectionalG;\\\\n\\\\t\\\\tuniform vec3 up;\\\\n\\\\n\\\\t\\\\tconst vec3 cameraPos = vec3( 0.0, 0.0, 0.0 );\\\\n\\\\n\\\\t\\\\t// constants for atmospheric scattering\\\\n\\\\t\\\\tconst float pi = 3.141592653589793238462643383279502884197169;\\\\n\\\\n\\\\t\\\\tconst float n = 1.0003; // refractive index of air\\\\n\\\\t\\\\tconst float N = 2.545E25; // number of molecules per unit volume for air at 288.15K and 1013mb (sea level -45 celsius)\\\\n\\\\n\\\\t\\\\t// optical length at zenith for molecules\\\\n\\\\t\\\\tconst float rayleighZenithLength = 8.4E3;\\\\n\\\\t\\\\tconst float mieZenithLength = 1.25E3;\\\\n\\\\t\\\\t// 66 arc seconds -> degrees, and the cosine of that\\\\n\\\\t\\\\tconst float sunAngularDiameterCos = 0.999956676946448443553574619906976478926848692873900859324;\\\\n\\\\n\\\\t\\\\t// 3.0 / ( 16.0 * pi )\\\\n\\\\t\\\\tconst float THREE_OVER_SIXTEENPI = 0.05968310365946075;\\\\n\\\\t\\\\t// 1.0 / ( 4.0 * pi )\\\\n\\\\t\\\\tconst float ONE_OVER_FOURPI = 0.07957747154594767;\\\\n\\\\n\\\\t\\\\tfloat rayleighPhase( float cosTheta ) {\\\\n\\\\t\\\\t\\\\treturn THREE_OVER_SIXTEENPI * ( 1.0 + pow( cosTheta, 2.0 ) );\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat hgPhase( float cosTheta, float g ) {\\\\n\\\\t\\\\t\\\\tfloat g2 = pow( g, 2.0 );\\\\n\\\\t\\\\t\\\\tfloat inverse = 1.0 / pow( 1.0 - 2.0 * g * cosTheta + g2, 1.5 );\\\\n\\\\t\\\\t\\\\treturn ONE_OVER_FOURPI * ( ( 1.0 - g2 ) * inverse );\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec3 direction = normalize( vWorldPosition - cameraPos );\\\\n\\\\n\\\\t\\\\t\\\\t// optical length\\\\n\\\\t\\\\t\\\\t// cutoff angle at 90 to avoid singularity in next formula.\\\\n\\\\t\\\\t\\\\tfloat zenithAngle = acos( max( 0.0, dot( up, direction ) ) );\\\\n\\\\t\\\\t\\\\tfloat inverse = 1.0 / ( cos( zenithAngle ) + 0.15 * pow( 93.885 - ( ( zenithAngle * 180.0 ) / pi ), -1.253 ) );\\\\n\\\\t\\\\t\\\\tfloat sR = rayleighZenithLength * inverse;\\\\n\\\\t\\\\t\\\\tfloat sM = mieZenithLength * inverse;\\\\n\\\\n\\\\t\\\\t\\\\t// combined extinction factor\\\\n\\\\t\\\\t\\\\tvec3 Fex = exp( -( vBetaR * sR + vBetaM * sM ) );\\\\n\\\\n\\\\t\\\\t\\\\t// in scattering\\\\n\\\\t\\\\t\\\\tfloat cosTheta = dot( direction, vSunDirection );\\\\n\\\\n\\\\t\\\\t\\\\tfloat rPhase = rayleighPhase( cosTheta * 0.5 + 0.5 );\\\\n\\\\t\\\\t\\\\tvec3 betaRTheta = vBetaR * rPhase;\\\\n\\\\n\\\\t\\\\t\\\\tfloat mPhase = hgPhase( cosTheta, mieDirectionalG );\\\\n\\\\t\\\\t\\\\tvec3 betaMTheta = vBetaM * mPhase;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 Lin = pow( vSunE * ( ( betaRTheta + betaMTheta ) / ( vBetaR + vBetaM ) ) * ( 1.0 - Fex ), vec3( 1.5 ) );\\\\n\\\\t\\\\t\\\\tLin *= mix( vec3( 1.0 ), pow( vSunE * ( ( betaRTheta + betaMTheta ) / ( vBetaR + vBetaM ) ) * Fex, vec3( 1.0 / 2.0 ) ), clamp( pow( 1.0 - dot( up, vSunDirection ), 5.0 ), 0.0, 1.0 ) );\\\\n\\\\n\\\\t\\\\t\\\\t// nightsky\\\\n\\\\t\\\\t\\\\tfloat theta = acos( direction.y ); // elevation --\\\\x3e y-axis, [-pi/2, pi/2]\\\\n\\\\t\\\\t\\\\tfloat phi = atan( direction.z, direction.x ); // azimuth --\\\\x3e x-axis [-pi/2, pi/2]\\\\n\\\\t\\\\t\\\\tvec2 uv = vec2( phi, theta ) / vec2( 2.0 * pi, pi ) + vec2( 0.5, 0.0 );\\\\n\\\\t\\\\t\\\\tvec3 L0 = vec3( 0.1 ) * Fex;\\\\n\\\\n\\\\t\\\\t\\\\t// composition + solar disc\\\\n\\\\t\\\\t\\\\tfloat sundisk = smoothstep( sunAngularDiameterCos, sunAngularDiameterCos + 0.00002, cosTheta );\\\\n\\\\t\\\\t\\\\tL0 += ( vSunE * 19000.0 * Fex ) * sundisk;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 texColor = ( Lin + L0 ) * 0.04 + vec3( 0.0, 0.0003, 0.00075 );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 retColor = pow( texColor, vec3( 1.0 / ( 1.2 + ( 1.2 * vSunfade ) ) ) );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( retColor, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const VB=new class extends aa{constructor(){super(...arguments),this.turbidity=oa.FLOAT(2,{range:[0,20]}),this.rayleigh=oa.FLOAT(1,{range:[0,4]}),this.mieCoefficient=oa.FLOAT(.005),this.mieDirectional=oa.FLOAT(.8),this.inclination=oa.FLOAT(.5),this.azimuth=oa.FLOAT(.25),this.up=oa.VECTOR3([0,1,0])}};class HB extends rD{constructor(){super(...arguments),this.paramsConfig=VB}static type(){return\\\\\\\"sky\\\\\\\"}createMaterial(){const t=(new GB).material;return t.depthWrite=!0,t}async cook(){const t=this.material.uniforms;t.turbidity.value=this.pv.turbidity,t.rayleigh.value=this.pv.rayleigh,t.mieCoefficient.value=this.pv.mieCoefficient,t.mieDirectionalG.value=this.pv.mieDirectional,t.up.value.copy(this.pv.up);const e=Math.PI*(this.pv.inclination-.5),n=2*Math.PI*(this.pv.azimuth-.5);t.sunPosition.value.x=Math.cos(n),t.sunPosition.value.y=Math.sin(n)*Math.sin(e),t.sunPosition.value.z=Math.sin(n)*Math.cos(e),this.setMaterial(this.material)}}var jB=\\\\\\\"precision highp float;\\\\nprecision highp int;\\\\n\\\\nvarying vec3 vPw;\\\\n\\\\n#include \\\\n\\\\nvoid main()\\\\t{\\\\n\\\\n\\\\t// start builder body code\\\\n\\\\n\\\\tvPw = position;\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n}\\\\\\\",WB=\\\\\\\"precision highp float;\\\\nprecision highp int;\\\\n\\\\n#include \\\\n\\\\n#define DIR_LIGHTS_COUNT 1\\\\n#define MAX_STEPS_COUNT 4096\\\\n\\\\nuniform vec3 u_Color;\\\\nuniform float u_VolumeDensity;\\\\nuniform float u_ShadowDensity;\\\\nuniform float u_StepSize;\\\\nuniform vec3 u_BoundingBoxMin;\\\\nuniform vec3 u_BoundingBoxMax;\\\\n//const int u_PointsCount = 3;\\\\n//uniform vec3 u_Points[3];\\\\nuniform sampler2D u_Map;\\\\n\\\\n//const int u_DirectionalLightsCount = 1;\\\\nuniform vec3 u_DirectionalLightDirection; //[DIR_LIGHTS_COUNT];\\\\n\\\\nvarying vec3 vPw;\\\\n// varying vec3 vN;\\\\n// varying vec2 vUV;\\\\n//varying vec3 vPCameraSpace;\\\\n// varying vec4 vCd;\\\\n\\\\nvec3 normalize_in_bbox(vec3 point){\\\\n\\\\n\\\\tvec3 min = u_BoundingBoxMin;\\\\n\\\\tvec3 max = u_BoundingBoxMax;\\\\n\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\t(point.x - min.x) / (max.x - min.x),\\\\n\\\\t\\\\t(point.y - min.y) / (max.y - min.y),\\\\n\\\\t\\\\t(point.z - min.z) / (max.z - min.z)\\\\n\\\\t);\\\\n}\\\\n\\\\nbool is_inside_bbox(vec3 Pw){\\\\n\\\\n\\\\tvec3 min = u_BoundingBoxMin;\\\\n\\\\tvec3 max = u_BoundingBoxMax;\\\\n\\\\n\\\\treturn (\\\\n\\\\t\\\\tPw.x > min.x &&\\\\n\\\\t\\\\tPw.y > min.y &&\\\\n\\\\t\\\\tPw.z > min.z &&\\\\n\\\\n\\\\t\\\\tPw.x < max.x &&\\\\n\\\\t\\\\tPw.y < max.y &&\\\\n\\\\t\\\\tPw.z < max.z\\\\n\\\\t\\\\t);\\\\n}\\\\n\\\\nfloat density_to_opacity(float density, float step_size){\\\\n\\\\tfloat curent_density = density;\\\\n\\\\tcurent_density = max(0.0, curent_density);\\\\n\\\\n\\\\tfloat opacity = (1.0-exp(-curent_density * step_size));\\\\n\\\\treturn max(opacity,0.0);\\\\n}\\\\n\\\\nfloat density_function(vec3 position_for_step){\\\\n\\\\tfloat density = 1.0;\\\\n\\\\t// start builder body code\\\\n\\\\n\\\\treturn density;\\\\n}\\\\n\\\\nvec4 raymarch_light(vec3 ray_dir, vec3 start_pos){\\\\n\\\\n\\\\tfloat step_size = u_StepSize;\\\\n\\\\tvec3 step_vector = ray_dir * step_size;\\\\n\\\\n\\\\tvec3 current_pos = start_pos + step_vector*rand(start_pos.x*ray_dir.xy);\\\\n\\\\tfloat opacity = 0.0;\\\\n\\\\tfor(int i=0; i= 0.99){ break; }\\\\n\\\\n\\\\t\\\\tif( is_inside_bbox(current_pos) ){\\\\n\\\\n\\\\t\\\\t\\\\tfloat density = density_function(current_pos) * u_ShadowDensity;\\\\n\\\\t\\\\t\\\\topacity += density_to_opacity(density, step_size);\\\\n\\\\t\\\\t\\\\tcurrent_pos += step_vector;\\\\n\\\\n\\\\t\\\\t}else{\\\\n\\\\t\\\\t\\\\tbreak;\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 light_color = vec3(1.0, 1.0, 1.0) * u_Color;\\\\n\\\\tlight_color *= (1.0-opacity);\\\\n\\\\treturn vec4(light_color, 1.0-opacity);\\\\n}\\\\n\\\\nvec4 raymarch_bbox(vec3 start_pos, vec3 ray_dir){\\\\n\\\\n\\\\tfloat step_size = u_StepSize;\\\\n\\\\tvec3 step_vector = ray_dir * step_size;\\\\n\\\\n\\\\tvec3 current_pos = start_pos - step_vector*rand(ray_dir.xz);\\\\n\\\\tfloat opacity = 0.0;\\\\n\\\\tvec3 color = vec3(0.0, 0.0, 0.0);\\\\n\\\\tfloat steps_count = 0.0;\\\\n\\\\tbool was_inside_bbox = false;\\\\n\\\\tfor(int i=0; i= 0.99){ break; }\\\\n\\\\n\\\\t\\\\tif( i==0 || is_inside_bbox(current_pos) ){\\\\n\\\\t\\\\t\\\\twas_inside_bbox = true;\\\\n\\\\n\\\\t\\\\t\\\\tfloat density = density_function(current_pos) * u_VolumeDensity;\\\\n\\\\t\\\\t\\\\topacity += density_to_opacity(density, step_size);\\\\n\\\\n\\\\t\\\\t\\\\tvec4 light_color = vec4(0.0,0.0,0.0,1.0); //vec4(1.0,1.0,1.0,1.0);\\\\n\\\\t\\\\t\\\\t// vec3 directional_light_direction;\\\\n\\\\t\\\\t\\\\t// for ( int l = 0; l < DIR_LIGHTS_COUNT; l++ ) {\\\\n\\\\t\\\\t\\\\t// directional_light_direction = u_DirectionalLightsDirection[ l ];\\\\n\\\\t\\\\t\\\\tlight_color += raymarch_light(-u_DirectionalLightDirection, current_pos);\\\\n\\\\t\\\\t\\\\t// }\\\\n\\\\t\\\\t\\\\tfloat blend = 1.0-opacity;\\\\n\\\\t\\\\t\\\\tcolor = mix( color.xyz, light_color.xyz, vec3(blend, blend, blend) );\\\\n\\\\t\\\\t\\\\tsteps_count += 1.0;\\\\n\\\\n\\\\t\\\\t}else{\\\\n\\\\t\\\\t\\\\tif (was_inside_bbox) { break; }\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tcurrent_pos += step_vector;\\\\n\\\\t}\\\\n\\\\n\\\\treturn vec4(color, opacity);\\\\n\\\\t// steps_count = steps_count / 5.0;\\\\n\\\\t// return vec4(vec3(steps_count, steps_count, steps_count), 1.0);\\\\n}\\\\n\\\\nvoid main()\\\\t{\\\\n\\\\n\\\\tvec3 eye = normalize(vPw - cameraPosition);\\\\n\\\\t// we can start from the bbox, as we are front facing\\\\n\\\\tvec3 start_pos = vPw;\\\\n\\\\n\\\\tvec4 color = raymarch_bbox(start_pos, eye);\\\\n\\\\tgl_FragColor = color;\\\\n\\\\n}\\\\\\\";const qB={u_Color:{value:new D.a(1,1,1)},u_VolumeDensity:{value:5},u_ShadowDensity:{value:2},u_StepSize:{value:.01},u_BoundingBoxMin:{value:new p.a(-1,-1,-1)},u_BoundingBoxMax:{value:new p.a(1,1,1)},u_DirectionalLightDirection:{value:new p.a(-1,-1,-1)}};var XB=n(16);function YB(t){return class extends t{constructor(){super(...arguments),this.color=oa.COLOR([1,1,1]),this.stepSize=oa.FLOAT(.01),this.density=oa.FLOAT(1),this.shadowDensity=oa.FLOAT(1),this.lightDir=oa.VECTOR3([-1,-1,-1])}}}YB(aa);class $B{constructor(t){this.node=t}static render_hook(t,e,n,i,r,s,o){if(o){this._object_bbox.setFromObject(o);const t=r;t.uniforms.u_BoundingBoxMin.value.copy(this._object_bbox.min),t.uniforms.u_BoundingBoxMax.value.copy(this._object_bbox.max)}}update_uniforms_from_params(){const t=this.node.material.uniforms;t.u_Color.value.copy(this.node.pv.color),t.u_StepSize.value=this.node.pv.stepSize,t.u_VolumeDensity.value=this.node.pv.density,t.u_ShadowDensity.value=this.node.pv.shadowDensity;const e=t.u_DirectionalLightDirection.value,n=this.node.pv.lightDir;e&&(e.x=n.x,e.y=n.y,e.z=n.z)}}$B._object_bbox=new XB.a;class JB extends(YB(aa)){}const ZB=new JB;class QB extends rD{constructor(){super(...arguments),this.paramsConfig=ZB,this._volume_controller=new $B(this)}static type(){return\\\\\\\"volume\\\\\\\"}createMaterial(){const t=new F({vertexShader:jB,fragmentShader:WB,side:w.H,transparent:!0,depthTest:!0,uniforms:I.clone(qB)});return fs.add_user_data_render_hook(t,$B.render_hook.bind($B)),t}initializeNode(){}async cook(){this._volume_controller.update_uniforms_from_params(),this.setMaterial(this.material)}}class KB extends(fD(YB(aa))){}const tz=new KB;class ez extends gD{constructor(){super(...arguments),this.paramsConfig=tz,this._volume_controller=new $B(this)}static type(){return\\\\\\\"volumeBuilder\\\\\\\"}usedAssembler(){return Hn.GL_VOLUME}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){}async cook(){this._volume_controller.update_uniforms_from_params(),this.compileIfRequired(),this.setMaterial(this.material)}}class nz extends ia{static context(){return Ki.MAT}cook(){this.cookController.endCook()}}class iz extends nz{}class rz extends iz{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class sz extends iz{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class oz extends iz{constructor(){super(...arguments),this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class az extends iz{constructor(){super(...arguments),this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class lz extends nz{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class cz extends iz{constructor(){super(...arguments),this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}var uz=n(87);const hz=\\\\\\\"parent object\\\\\\\",dz=[hz,hz,hz,hz];var pz;!function(t){t[t.MANAGER=0]=\\\\\\\"MANAGER\\\\\\\",t[t.CAMERA=2]=\\\\\\\"CAMERA\\\\\\\",t[t.LIGHT=3]=\\\\\\\"LIGHT\\\\\\\"}(pz||(pz={}));class _z extends ia{constructor(){super(...arguments),this.renderOrder=pz.MANAGER,this._children_group=this._create_children_group(),this._attachableToHierarchy=!0,this._used_in_scene=!0}static context(){return Ki.OBJ}static displayedInputNames(){return dz}_create_children_group(){const t=new In.a;return t.matrixAutoUpdate=!1,t}attachableToHierarchy(){return this._attachableToHierarchy}usedInScene(){return this._used_in_scene}addObjectToParent(t){this.attachableToHierarchy()&&t.add(this.object)}removeObjectFromParent(){if(this.attachableToHierarchy()){const t=this.object.parent;t&&t.remove(this.object)}}initializeBaseNode(){this._object=this._create_object_with_attributes(),this.nameController.add_post_set_fullPath_hook(this.set_object_name.bind(this)),this.set_object_name()}get children_group(){return this._children_group}get object(){return this._object}_create_object_with_attributes(){const t=this.createObject();return t.node=this,t.add(this._children_group),t}set_object_name(){this._object&&(this._object.name=this.path(),this._children_group.name=`${this.path()}:parented_outputs`)}createObject(){const t=new Q.a;return t.matrixAutoUpdate=!1,t}isDisplayNodeCooking(){if(this.displayNodeController){const t=this.displayNodeController.displayNode();if(t)return t.cookController.isCooking()}return!1}isDisplayed(){var t,e;return(null===(e=null===(t=this.flags)||void 0===t?void 0:t.display)||void 0===e?void 0:e.active())||!1}}class mz extends _z{constructor(){super(...arguments),this.flags=new Fi(this),this.renderOrder=pz.LIGHT,this._color_with_intensity=new D.a(0),this._used_in_scene=!0,this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this)}get light(){return this._light}initializeBaseNode(){super.initializeBaseNode(),this._light=this.createLight(),this.object.add(this._light),this.flags.display.onUpdate((()=>{this._updateLightAttachment()})),this.dirtyController.addPostDirtyHook(\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",this._cook_main_without_inputs_when_dirty_bound)}async _cook_main_without_inputs_when_dirty(){await this.cookController.cookMainWithoutInputs()}set_object_name(){super.set_object_name(),this._light&&(this._light.name=`${this.path()}:light`)}_updateLightAttachment(){this.flags.display.active()?(this.object.add(this.light),this._cook_main_without_inputs_when_dirty()):this.object.remove(this.light)}cook(){this.updateLightParams(),this.updateShadowParams(),this.cookController.endCook()}updateLightParams(){}updateShadowParams(){}}const fz=new class extends aa{constructor(){super(...arguments),this.color=oa.COLOR([1,1,1],{conversion:so.SRGB_TO_LINEAR}),this.intensity=oa.FLOAT(1)}};class gz extends mz{constructor(){super(...arguments),this.paramsConfig=fz}static type(){return\\\\\\\"ambientLight\\\\\\\"}createLight(){const t=new uz.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.io.inputs.setCount(0,1)}updateLightParams(){this.light.color=this.pv.color,this.light.intensity=this.pv.intensity}}class vz extends nv.a{constructor(t,e,n=10,i=10){super(t,e),this.type=\\\\\\\"RectAreaLight\\\\\\\",this.width=n,this.height=i}get power(){return this.intensity*this.width*this.height*Math.PI}set power(t){this.intensity=t/(this.width*this.height*Math.PI)}copy(t){return super.copy(t),this.width=t.width,this.height=t.height,this}toJSON(t){const e=super.toJSON(t);return e.object.width=this.width,e.object.height=this.height,e}}vz.prototype.isRectAreaLight=!0;var yz,xz=n(60);class bz{static init(){const t=[1,0,0,2e-5,1,0,0,503905e-9,1,0,0,.00201562,1,0,0,.00453516,1,0,0,.00806253,1,0,0,.0125978,1,0,0,.018141,1,0,0,.0246924,1,0,0,.0322525,1,0,0,.0408213,1,0,0,.0503999,1,0,0,.0609894,1,0,0,.0725906,1,0,0,.0852058,1,0,0,.0988363,1,0,0,.113484,1,0,0,.129153,1,0,0,.145839,1,0,0,.163548,1,0,0,.182266,1,0,0,.201942,1,0,0,.222314,1,0,0,.241906,1,0,0,.262314,1,0,0,.285754,1,0,0,.310159,1,0,0,.335426,1,0,0,.361341,1,0,0,.387445,1,0,0,.412784,1,0,0,.438197,1,0,0,.466966,1,0,0,.49559,1,0,0,.523448,1,0,0,.549938,1,0,0,.57979,1,0,0,.608746,1,0,0,.636185,1,0,0,.664748,1,0,0,.69313,1,0,0,.71966,1,0,0,.747662,1,0,0,.774023,1,0,0,.799775,1,0,0,.825274,1,0,0,.849156,1,0,0,.873248,1,0,0,.89532,1,0,0,.917565,1,0,0,.937863,1,0,0,.958139,1,0,0,.976563,1,0,0,.994658,1,0,0,1.0112,1,0,0,1.02712,1,0,0,1.04189,1,0,0,1.05568,1,0,0,1.06877,1,0,0,1.08058,1,0,0,1.09194,1,0,0,1.10191,1,0,0,1.11161,1,0,0,1.1199,1,0,0,1.12813,.999547,-4.48815e-7,.0224417,199902e-10,.999495,-113079e-10,.0224406,503651e-9,.999496,-452317e-10,.0224406,.00201461,.999496,-101772e-9,.0224406,.00453287,.999495,-180928e-9,.0224406,.00805845,.999497,-282702e-9,.0224406,.0125914,.999496,-407096e-9,.0224406,.0181319,.999498,-554114e-9,.0224406,.02468,.999499,-723768e-9,.0224406,.0322363,.999495,-916058e-9,.0224405,.0408009,.999499,-.00113101,.0224408,.050375,.999494,-.00136863,.0224405,.0609586,.999489,-.00162896,.0224401,.0725537,.999489,-.00191201,.0224414,.0851619,.999498,-.00221787,.0224413,.0987867,.999492,-.00254642,.0224409,.113426,.999507,-.00289779,.0224417,.129088,.999494,-.0032716,.0224386,.145767,.999546,-.0036673,.0224424,.163472,.999543,-.00408166,.0224387,.182182,.999499,-.00450056,.0224338,.201843,.999503,-.00483661,.0224203,.222198,.999546,-.00452928,.022315,.241714,.999508,-.00587403,.0224329,.262184,.999509,-.00638806,.0224271,.285609,.999501,-.00691028,.0224166,.309998,.999539,-.00741979,.0223989,.335262,.999454,-.00786282,.0223675,.361154,.999529,-.00811928,.0222828,.387224,.999503,-.00799941,.0221063,.41252,.999561,-.00952753,.0223057,.438006,.999557,-.0099134,.0222065,.466735,.999541,-.0100935,.0220402,.495332,.999562,-.00996821,.0218067,.523197,.999556,-.0105031,.0217096,.550223,.999561,-.0114191,.0217215,.579498,.999588,-.0111818,.0213357,.608416,.999633,-.0107725,.0208689,.635965,.999527,-.0121671,.0210149,.664476,.999508,-.0116005,.020431,.692786,.999568,-.0115604,.0199791,.719709,.999671,-.0121117,.0197415,.74737,.999688,-.0110769,.0188846,.773692,.99962,-.0122368,.0188452,.799534,.999823,-.0110325,.0178001,.825046,.999599,-.0114923,.0174221,.849075,.999619,-.0105923,.0164345,.872999,.999613,-.0105988,.0158227,.895371,.99964,-.00979861,.0148131,.917364,.99977,-.00967238,.0140721,.938002,.999726,-.00869175,.0129543,.957917,.99973,-.00866872,.0122329,.976557,.999773,-.00731956,.0108958,.994459,.999811,-.00756027,.0102715,1.01118,.999862,-.00583732,.00878781,1.02701,.999835,-.00631438,.00827529,1.04186,.999871,-.00450785,.00674583,1.05569,.999867,-.00486079,.00621041,1.06861,.999939,-.00322072,.00478301,1.08064,.999918,-.00318199,.00406395,1.09181,1.00003,-.00193348,.00280682,1.10207,.999928,-.00153729,.00198741,1.11152,.999933,-623666e-9,917714e-9,1.12009,1,-102387e-11,9.07581e-7,1.12813,.997866,-8.96716e-7,.0448334,199584e-10,.997987,-225945e-10,.0448389,502891e-9,.997987,-903781e-10,.0448388,.00201156,.997985,-203351e-9,.0448388,.00452602,.997986,-361514e-9,.0448388,.00804629,.997987,-56487e-8,.0448389,.0125724,.997988,-813423e-9,.0448389,.0181045,.997984,-.00110718,.0448387,.0246427,.997985,-.00144616,.0448388,.0321875,.997987,-.00183038,.044839,.0407392,.997983,-.00225987,.0448387,.0502986,.997991,-.00273467,.0448389,.0608667,.997984,-.00325481,.0448384,.0724444,.998002,-.00382043,.044839,.0850348,.997997,-.00443145,.0448396,.0986372,.998007,-.00508796,.0448397,.113255,.998008,-.00578985,.04484,.128891,.998003,-.00653683,.0448384,.145548,.997983,-.00732713,.0448358,.163221,.997985,-.00815454,.0448358,.181899,.998005,-.00898985,.0448286,.201533,.998026,-.00964404,.0447934,.221821,.998055,-.00922677,.044611,.241282,.99804,-.0117361,.0448245,.261791,.998048,-.0127628,.0448159,.285181,.998088,-.0138055,.0447996,.30954,.998058,-.0148206,.0447669,.334751,.998099,-.0156998,.044697,.36061,.998116,-.0161976,.0445122,.386603,.998195,-.015945,.0441711,.411844,.998168,-.0183947,.0444255,.43773,.998184,-.0197913,.0443809,.466009,.998251,-.0201426,.0440689,.494574,.998305,-.0198847,.0435632,.522405,.998273,-.0210577,.043414,.549967,.998254,-.0227901,.0433943,.578655,.998349,-.0223108,.0426529,.60758,.99843,-.0223088,.042,.635524,.998373,-.0241141,.0418987,.663621,.998425,-.0231446,.0408118,.691906,.998504,-.0233684,.0400565,.719339,.998443,-.0241652,.0394634,.74643,.99848,-.0228715,.0380002,.773086,.998569,-.023519,.0372322,.798988,.998619,-.0223108,.0356468,.824249,.998594,-.0223105,.034523,.848808,.998622,-.0213426,.0328887,.87227,.998669,-.0207912,.0314374,.895157,.998705,-.0198416,.0296925,.916769,.998786,-.0189168,.0279634,.937773,.998888,-.0178811,.0261597,.957431,.99906,-.0166845,.0242159,.976495,.999038,-.0155464,.0222638,.994169,.999237,-.0141349,.0201967,1.01112,.999378,-.0129324,.0181744,1.02692,.999433,-.0113192,.0159898,1.04174,.999439,-.0101244,.0140385,1.05559,.999614,-.00837456,.0117826,1.06852,.999722,-.00721769,.00983745,1.08069,.999817,-.00554067,.00769002,1.09176,.99983,-.00426961,.005782,1.10211,.999964,-.00273904,.00374503,1.11152,1.00001,-.00136739,.00187176,1.12031,.999946,393227e-10,-28919e-9,1.12804,.995847,-13435e-10,.0671785,19916e-9,.995464,-338387e-10,.0671527,501622e-9,.99547,-135355e-9,.0671531,.00200649,.995471,-30455e-8,.0671532,.00451461,.99547,-541423e-9,.0671531,.008026,.995471,-84598e-8,.0671531,.0125407,.99547,-.00121823,.0671531,.0180589,.99547,-.00165817,.0671531,.0245806,.995463,-.00216583,.0671526,.0321062,.995468,-.00274127,.0671527,.0406366,.995474,-.00338447,.0671534,.0501717,.995473,-.00409554,.0671533,.0607131,.995478,-.00487451,.0671531,.0722618,.995476,-.00572148,.0671532,.0848191,.995477,-.00663658,.0671539,.0983882,.995498,-.00761986,.0671541,.112972,.995509,-.00867094,.0671542,.128568,.995509,-.00978951,.0671531,.145183,.995503,-.0109725,.0671491,.162808,.995501,-.012211,.0671465,.181441,.99553,-.0134565,.0671371,.201015,.99555,-.014391,.0670831,.221206,.99558,-.014351,.0668883,.240813,.995577,-.0173997,.0671055,.261257,.995602,-.0191111,.0671178,.284467,.995623,-.0206705,.0670946,.308765,.995658,-.022184,.0670472,.333905,.995705,-.0234832,.0669417,.359677,.995719,-.0241933,.0666714,.385554,.995786,-.0243539,.066266,.410951,.995887,-.0271866,.0664367,.437163,.995944,-.0296012,.0664931,.464842,.996004,-.0301045,.0660105,.49332,.996128,-.0298311,.0652694,.521131,.996253,-.0316426,.0650739,.549167,.996244,-.0339043,.0649433,.57737,.996309,-.033329,.0638926,.606073,.996417,-.0338935,.0630849,.634527,.996372,-.0353104,.0625083,.66256,.996542,-.0348942,.0611986,.690516,.996568,-.0351614,.060069,.718317,.996711,-.0354317,.0588522,.74528,.996671,-.0349513,.0571902,.772061,.996865,-.0345622,.0555321,.798089,.996802,-.0342566,.0537816,.823178,.996992,-.0330862,.0516095,.847949,.996944,-.0324666,.0495537,.871431,.997146,-.0309544,.0470302,.894357,.997189,-.0299372,.0446043,.916142,.997471,-.0281389,.0418812,.937193,.997515,-.0268702,.0391823,.957,.997812,-.0247166,.0361338,.975936,.998027,-.0233525,.0333945,.99391,.998233,-.0209839,.0301917,1.01075,.998481,-.0194309,.027271,1.02669,.998859,-.0169728,.0240162,1.04173,.99894,-.0152322,.0210517,1.05551,.999132,-.0127497,.0178632,1.06856,.999369,-.0108282,.014787,1.08054,.999549,-.00845886,.0116185,1.09185,.999805,-.0063937,.00867209,1.10207,.99985,-.00414582,.00566823,1.1117,.999912,-.00207443,.00277562,1.12022,1.00001,870226e-10,-53766e-9,1.12832,.991943,-178672e-11,.0893382,198384e-10,.991952,-450183e-10,.089339,499849e-9,.991956,-180074e-9,.0893394,.0019994,.991955,-405167e-9,.0893393,.00449867,.991953,-720298e-9,.0893391,.00799764,.991955,-.00112548,.0893393,.0124964,.991957,-.0016207,.0893395,.0179951,.991958,-.00220601,.0893396,.0244939,.991947,-.00288137,.0893385,.0319929,.991962,-.00364693,.0893399,.0404933,.991965,-.00450264,.0893399,.049995,.99198,-.00544862,.0893411,.0604995,.99197,-.00648491,.0893397,.0720074,.991976,-.00761164,.089341,.0845207,.99198,-.00882891,.0893405,.0980413,.991982,-.0101367,.0893396,.112571,.992008,-.011535,.0893415,.128115,.992026,-.0130228,.0893414,.144672,.992064,-.0145966,.0893418,.162241,.992041,-.0162421,.0893359,.180801,.992086,-.0178888,.0893214,.200302,.992157,-.0190368,.0892401,.220332,.992181,-.0195584,.0890525,.240144,.992175,-.0227257,.0892153,.260728,.99221,-.0254195,.089304,.283473,.99222,-.0274883,.0892703,.307673,.992317,-.0294905,.0892027,.332729,.992374,-.0311861,.0890577,.358387,.992505,-.0320656,.0886994,.384102,.992568,-.0329715,.0883198,.409767,.992675,-.036006,.0883602,.436145,.992746,-.0392897,.0884591,.463217,.992873,-.0399337,.0878287,.491557,.992934,-.040231,.0870108,.519516,.993091,-.0422013,.0865857,.547741,.993259,-.0443503,.0861937,.575792,.993455,-.0446368,.0851187,.604233,.993497,-.0454299,.0840576,.632925,.993694,-.0463296,.0829671,.660985,.993718,-.0470619,.0817185,.688714,.993973,-.0468838,.0800294,.716743,.994207,-.046705,.0781286,.74377,.994168,-.0469698,.0763337,.77042,.9945,-.0456816,.0738184,.796659,.994356,-.0455518,.0715545,.821868,.994747,-.0439488,.0686085,.846572,.994937,-.0430056,.065869,.870435,.995142,-.0413414,.0626446,.893272,.995451,-.0396521,.05929,.915376,.995445,-.0378453,.0558503,.936196,.995967,-.0355219,.0520949,.956376,.996094,-.0335146,.048377,.975327,.996622,-.030682,.0442575,.993471,.996938,-.0285504,.0404693,1.01052,.997383,-.0253399,.0360903,1.02637,.997714,-.0231651,.0322176,1.04139,.998249,-.0198138,.0278433,1.05542,.998596,-.0174337,.0238759,1.06846,.998946,-.0141349,.0195944,1.08056,.99928,-.0115603,.0156279,1.09181,.999507,-.00839065,.0114607,1.10213,.999697,-.005666,.00763325,1.11169,.999869,-.00269902,.00364946,1.12042,1.00001,623836e-10,-319288e-10,1.12832,.987221,-222675e-11,.111332,197456e-10,.98739,-561116e-10,.111351,497563e-9,.987448,-224453e-9,.111357,.00199031,.987441,-505019e-9,.111357,.0044782,.987442,-897816e-9,.111357,.00796129,.987442,-.00140284,.111357,.0124396,.987444,-.00202012,.111357,.0179132,.987442,-.00274964,.111357,.0243824,.987446,-.00359147,.111357,.0318474,.987435,-.00454562,.111356,.0403086,.987461,-.00561225,.111358,.0497678,.987458,-.00679125,.111358,.0602239,.987443,-.0080828,.111356,.0716792,.987476,-.0094872,.111358,.0841364,.98749,-.0110044,.111361,.097597,.987508,-.0126344,.111362,.112062,.987494,-.0143767,.111357,.127533,.987526,-.0162307,.111359,.144015,.987558,-.0181912,.111361,.161502,.987602,-.0202393,.111355,.179979,.987692,-.022273,.111346,.199386,.987702,-.0235306,.111215,.219183,.987789,-.0247628,.111061,.239202,.987776,-.0280668,.111171,.259957,.987856,-.0316751,.111327,.282198,.987912,-.0342468,.111282,.306294,.988,-.0367205,.111198,.331219,.988055,-.0387766,.110994,.356708,.988241,-.0397722,.110547,.382234,.988399,-.0416076,.110198,.408227,.988539,-.0448192,.110137,.434662,.988661,-.0483793,.110143,.461442,.988967,-.0495895,.109453,.489318,.989073,-.0506797,.108628,.517516,.989274,-.0526953,.108003,.545844,.989528,-.054578,.107255,.573823,.989709,-.0561503,.106294,.601944,.989991,-.056866,.104896,.630855,.990392,-.0572914,.103336,.658925,.990374,-.0586224,.10189,.686661,.990747,-.0584764,.099783,.714548,.991041,-.0582662,.0974309,.74186,.991236,-.0584118,.0951678,.768422,.991585,-.0573055,.0921581,.794817,.991984,-.0564241,.0891167,.820336,.9921,-.0553608,.085805,.84493,.992749,-.0533816,.0820354,.868961,.99288,-.0518661,.0782181,.891931,.993511,-.0492492,.0738935,.914186,.993617,-.0471956,.0696402,.93532,.99411,-.044216,.0649659,.95543,.994595,-.0416654,.0603177,.974685,.994976,-.0384314,.0553493,.992807,.995579,-.0353491,.0503942,1.00996,.996069,-.0319787,.0452123,1.02606,.996718,-.028472,.0400112,1.04114,.997173,-.0250789,.0349456,1.05517,.997818,-.0213326,.029653,1.0683,.998318,-.0178509,.024549,1.0805,.998853,-.0141118,.0194197,1.09177,.999218,-.0105914,.0143869,1.1022,.999594,-.00693474,.00943517,1.11175,.99975,-.00340478,.00464051,1.12056,1.00001,109172e-9,-112821e-9,1.12853,.983383,-266524e-11,.133358,196534e-10,.981942,-671009e-10,.133162,494804e-9,.981946,-268405e-9,.133163,.00197923,.981944,-603912e-9,.133163,.00445326,.981941,-.00107362,.133162,.00791693,.981946,-.00167755,.133163,.0123703,.981944,-.00241569,.133162,.0178135,.981945,-.00328807,.133163,.0242466,.981945,-.00429472,.133162,.03167,.981955,-.00543573,.133164,.0400846,.981951,-.00671105,.133163,.0494901,.981968,-.00812092,.133165,.0598886,.981979,-.00966541,.133166,.0712811,.981996,-.0113446,.133168,.083669,.982014,-.0131585,.133169,.0970533,.982011,-.0151073,.133167,.111438,.982062,-.0171906,.133172,.126826,.9821,-.0194067,.133175,.143215,.982149,-.0217502,.133176,.160609,.982163,-.0241945,.133173,.178981,.982247,-.0265907,.133148,.198249,.982291,-.027916,.132974,.217795,.982396,-.0299663,.132868,.238042,.982456,-.0334544,.132934,.258901,.982499,-.0378636,.133137,.280639,.982617,-.0409274,.133085,.304604,.98274,-.0438523,.132985,.329376,.982944,-.0462288,.132728,.354697,.98308,-.0475995,.132228,.380102,.983391,-.0501901,.131924,.406256,.983514,-.0535899,.131737,.432735,.98373,-.0571858,.131567,.459359,.984056,-.0592353,.130932,.486637,.984234,-.0610488,.130092,.51509,.984748,-.0630758,.12923,.543461,.985073,-.0647398,.128174,.571376,.985195,-.0671941,.127133,.599414,.985734,-.0681345,.125576,.628134,.986241,-.0686089,.123639,.656399,.986356,-.0698511,.121834,.684258,.986894,-.0700931,.119454,.711818,.987382,-.0698321,.116718,.739511,.988109,-.0693975,.113699,.766267,.988363,-.0689584,.110454,.792456,.989112,-.0672353,.106602,.81813,.989241,-.0662034,.10267,.842889,.990333,-.0638938,.0981381,.867204,.990591,-.0618534,.0935388,.89038,.991106,-.0593117,.088553,.912576,.991919,-.0562676,.0832187,.934118,.992111,-.0534085,.0778302,.954254,.992997,-.0495459,.0720453,.973722,.993317,-.0463707,.0663458,.991949,.994133,-.0421245,.0601883,1.00936,.994705,-.0384977,.0542501,1.02559,.995495,-.0340956,.0479862,1.04083,.996206,-.030105,.041887,1.05497,.996971,-.0256095,.0355355,1.06824,.997796,-.0213932,.0293655,1.08056,.998272,-.0169612,.0232926,1.09182,.998857,-.0126756,.0172786,1.10219,.99939,-.00832486,.0113156,1.11192,.999752,-.00410826,.00557892,1.12075,1,150957e-9,-119101e-9,1.12885,.975169,-309397e-11,.154669,195073e-10,.975439,-779608e-10,.154712,491534e-9,.975464,-311847e-9,.154716,.00196617,.975464,-701656e-9,.154716,.00442387,.975462,-.0012474,.154715,.0078647,.975461,-.00194906,.154715,.0122886,.975464,-.00280667,.154715,.0176959,.975468,-.00382025,.154716,.0240867,.975471,-.00498985,.154716,.0314612,.975472,-.00631541,.154717,.0398199,.975486,-.00779719,.154718,.0491639,.975489,-.00943505,.154718,.0594932,.975509,-.0112295,.154721,.0708113,.97554,-.0131802,.154724,.0831176,.975557,-.0152876,.154726,.096415,.975585,-.0175512,.154728,.110705,.975605,-.0199713,.154729,.125992,.975645,-.0225447,.154729,.142272,.975711,-.0252649,.154735,.159549,.975788,-.0280986,.154736,.177805,.975872,-.0308232,.154704,.196911,.975968,-.0324841,.154525,.216324,.976063,-.0351281,.154432,.236628,.976157,-.0388618,.15446,.257539,.976204,-.0437704,.154665,.278975,.976358,-.047514,.154652,.302606,.976571,-.0508638,.154535,.327204,.976725,-.0534995,.154221,.352276,.977013,-.0555547,.153737,.377696,.977294,-.0586728,.153403,.403855,.977602,-.0622715,.15312,.430333,.977932,-.0658166,.152755,.456855,.978241,-.0689877,.152233,.483668,.978602,-.0712805,.15132,.512097,.979234,-.0732775,.150235,.540455,.97977,-.075163,.148978,.568486,.979995,-.0778026,.147755,.596524,.98078,-.0791854,.146019,.624825,.981628,-.0799666,.143906,.653403,.982067,-.0808532,.141561,.681445,.98271,-.0816024,.139025,.708918,.983734,-.0812511,.135764,.736594,.98431,-.0806201,.132152,.763576,.985071,-.0801605,.12846,.789797,.98618,-.0784208,.124084,.815804,.986886,-.0766643,.1193,.840869,.987485,-.0747744,.114236,.864952,.988431,-.0716701,.108654,.888431,.988886,-.0691609,.102994,.910963,.990024,-.0654048,.0967278,.932629,.990401,-.0619765,.090384,.95313,.991093,-.0579296,.0837885,.972587,.992018,-.0536576,.0770171,.991184,.992536,-.0493719,.0701486,1.00863,.993421,-.0444813,.062953,1.02494,.993928,-.040008,.0560455,1.04017,.994994,-.0347982,.04856,1.05463,.995866,-.0301017,.0416152,1.06807,.996916,-.0248225,.0342597,1.08039,.997766,-.0199229,.0271668,1.09177,.998479,-.0147422,.0201387,1.10235,.99921,-.00980173,.0131944,1.11206,.999652,-.0047426,.00640712,1.12104,.999998,891673e-10,-10379e-8,1.12906,.967868,-351885e-11,.175947,193569e-10,.968001,-886733e-10,.175972,487782e-9,.96801,-354697e-9,.175973,.00195115,.968012,-798063e-9,.175974,.00439006,.968011,-.00141879,.175973,.00780461,.968011,-.00221686,.175973,.0121948,.968016,-.00319231,.175974,.0175607,.968019,-.00434515,.175974,.0239027,.968018,-.00567538,.175974,.0312208,.968033,-.00718308,.175977,.0395158,.968049,-.00886836,.175979,.0487885,.968047,-.0107312,.175978,.0590394,.968072,-.0127719,.175981,.0702705,.968108,-.0149905,.175986,.0824836,.968112,-.0173866,.175985,.0956783,.968173,-.0199611,.175993,.109862,.96827,-.0227128,.176008,.125033,.968292,-.025639,.17601,.141193,.968339,-.0287299,.176007,.158336,.968389,-.0319399,.176001,.176441,.968501,-.034941,.175962,.195359,.968646,-.0370812,.175793,.214686,.968789,-.0402329,.175708,.234973,.96886,-.0442601,.1757,.255871,.969013,-.049398,.175876,.277238,.969242,-.0539932,.17594,.300326,.969419,-.0577299,.175781,.324702,.969763,-.0605643,.175432,.349527,.970093,-.0634488,.174992,.374976,.970361,-.0670589,.174611,.401097,.970825,-.0708246,.174226,.427496,.971214,-.0742871,.173684,.453858,.971622,-.0782608,.173186,.480637,.972175,-.0813151,.172288,.508655,.972944,-.0832678,.170979,.536973,.973595,-.0855964,.169573,.565138,.974345,-.0882163,.168152,.593222,.975233,-.0901671,.166314,.621201,.976239,-.0912111,.163931,.649919,.977289,-.0916959,.161106,.678011,.978076,-.0927061,.158272,.705717,.979533,-.0925562,.15475,.733228,.980335,-.0918159,.150638,.760454,.981808,-.0908508,.146201,.786918,.983061,-.0896172,.141386,.812953,.984148,-.0871588,.135837,.838281,.985047,-.0850624,.130135,.862594,.986219,-.0818541,.123882,.88633,.987043,-.0784523,.117126,.908952,.988107,-.0749601,.110341,.930744,.988955,-.0703548,.102885,.951728,.989426,-.0662798,.0954167,.971166,.990421,-.0610834,.0876331,.989984,.991032,-.0562936,.0797785,1.00765,.992041,-.0508154,.0718166,1.02434,.992794,-.0454045,.0637125,1.03976,.993691,-.0398194,.0555338,1.05418,.994778,-.0341482,.0473388,1.06772,.995915,-.028428,.0391016,1.08028,.997109,-.022642,.0309953,1.09185,.998095,-.0168738,.0230288,1.10247,.998985,-.0111274,.0150722,1.11229,.999581,-.00543881,.00740605,1.12131,1.00003,162239e-9,-105549e-9,1.12946,.959505,-393734e-11,.196876,191893e-10,.959599,-992157e-10,.196895,483544e-9,.959641,-396868e-9,.196903,.0019342,.959599,-892948e-9,.196895,.00435193,.959603,-.00158747,.196896,.0077368,.959604,-.00248042,.196896,.0120888,.959605,-.00357184,.196896,.0174082,.959605,-.00486169,.196896,.0236949,.959613,-.00635008,.196897,.0309497,.959619,-.00803696,.196898,.0391725,.959636,-.00992255,.196901,.0483649,.959634,-.0120067,.1969,.0585266,.959675,-.0142898,.196906,.0696609,.959712,-.0167717,.196911,.0817678,.959752,-.0194524,.196918,.0948494,.959807,-.0223321,.196925,.10891,.959828,-.0254091,.196924,.123947,.959906,-.0286815,.196934,.139968,.960005,-.0321371,.196944,.156968,.960071,-.0357114,.196936,.17491,.960237,-.0389064,.196882,.193597,.960367,-.041623,.196731,.21285,.960562,-.0452655,.196654,.233075,.960735,-.0496207,.196643,.253941,.960913,-.0549379,.196774,.275278,.961121,-.0603414,.196893,.297733,.96139,-.0644244,.196717,.321877,.961818,-.067556,.196314,.346476,.962175,-.0712709,.195917,.371907,.96255,-.0752848,.1955,.397916,.963164,-.0792073,.195026,.424229,.963782,-.0828225,.194424,.450637,.964306,-.0873119,.193831,.477288,.964923,-.0911051,.192973,.504716,.966048,-.093251,.19151,.533053,.967024,-.0958983,.190013,.561366,.968038,-.09835,.188253,.589464,.969152,-.100754,.186257,.617433,.970557,-.102239,.183775,.645801,.972104,-.102767,.180645,.674278,.973203,-.103492,.177242,.702004,.975123,-.103793,.17345,.729529,.97641,-.102839,.168886,.756712,.978313,-.101687,.163892,.783801,.980036,-.100314,.158439,.809671,.981339,-.097836,.152211,.835402,.982794,-.0950006,.145679,.860081,.984123,-.0920994,.138949,.883757,.984918,-.0878641,.131283,.90685,.985999,-.083939,.123464,.928786,.987151,-.0791234,.115324,.94983,.987827,-.0739332,.106854,.96962,.988806,-.0688088,.0982691,.98861,.989588,-.0628962,.0893456,1.00667,.990438,-.0573146,.0805392,1.02344,.991506,-.0509433,.0713725,1.03933,.992492,-.0448724,.0623732,1.05378,.993663,-.0383497,.0530838,1.06747,.994956,-.0319593,.0439512,1.08007,.99634,-.025401,.0347803,1.09182,.99761,-.0189687,.0257954,1.1025,.99863,-.0124441,.0169893,1.11247,.99947,-.00614003,.00829498,1.12151,1.00008,216624e-9,-146107e-9,1.12993,.950129,-434955e-11,.217413,190081e-10,.950264,-10957e-8,.217444,47884e-8,.9503,-438299e-9,.217451,.00191543,.950246,-986124e-9,.21744,.00430951,.950246,-.00175311,.21744,.00766137,.950245,-.00273923,.21744,.011971,.950253,-.00394453,.217441,.0172385,.950258,-.00536897,.217442,.0234641,.950267,-.00701262,.217444,.030648,.950277,-.00887551,.217446,.038791,.950284,-.0109576,.217446,.0478931,.950312,-.0132591,.217451,.0579568,.950334,-.01578,.217454,.0689821,.950378,-.0185204,.217462,.0809714,.950417,-.0214803,.217467,.0939265,.950488,-.0246594,.217479,.10785,.950534,-.0280565,.217483,.122743,.950633,-.0316685,.217498,.138611,.950698,-.0354787,.217499,.155442,.950844,-.0394003,.217507,.173208,.950999,-.0426812,.217419,.191605,.951221,-.0461302,.217317,.21084,.951412,-.0502131,.217238,.230945,.951623,-.0549183,.21722,.251745,.951867,-.0604493,.217306,.273001,.952069,-.0665189,.217466,.294874,.952459,-.0709179,.217266,.318732,.952996,-.0746112,.216891,.34318,.953425,-.0789252,.216503,.36849,.953885,-.0833293,.216042,.394373,.954617,-.087371,.215469,.420505,.955429,-.0914054,.214802,.446907,.956068,-.0961671,.214146,.473522,.957094,-.10048,.213286,.50052,.958372,-.103248,.211796,.528715,.959654,-.106033,.21016,.557065,.961305,-.108384,.208149,.585286,.962785,-.111122,.206024,.613334,.964848,-.112981,.203442,.641334,.966498,-.113717,.19996,.669955,.968678,-.114121,.196105,.698094,.970489,-.114524,.191906,.725643,.972903,-.113792,.186963,.752856,.974701,-.112406,.181343,.780013,.976718,-.110685,.175185,.806268,.978905,-.108468,.168535,.832073,.980267,-.105061,.161106,.857149,.981967,-.101675,.153387,.881145,.983063,-.0974492,.145199,.904255,.984432,-.0925815,.136527,.926686,.985734,-.0877983,.127584,.947901,.986228,-.081884,.118125,.968111,.98719,-.0761208,.108594,.98719,.988228,-.0698196,.0989996,1.00559,.989046,-.0632739,.0890074,1.02246,.990242,-.056522,.0790832,1.03841,.991252,-.0495272,.0689182,1.05347,.992542,-.0425373,.0588592,1.06724,.994096,-.0353198,.0486833,1.08009,.995593,-.028235,.0385977,1.09177,.99711,-.0209511,.0286457,1.10274,.998263,-.0139289,.0188497,1.11262,.999254,-.0067359,.009208,1.12191,.999967,141846e-9,-657764e-10,1.13024,.935608,-474692e-11,.236466,187817e-10,.93996,-11971e-8,.237568,473646e-9,.939959,-478845e-9,.237567,.0018946,.939954,-.0010774,.237566,.00426284,.939956,-.00191538,.237566,.00757842,.939954,-.00299277,.237566,.0118413,.93996,-.00430961,.237567,.0170518,.939969,-.00586589,.237569,.02321,.939982,-.00766166,.237572,.0303164,.939987,-.00969686,.237572,.0383711,.939997,-.0119715,.237574,.0473751,.940031,-.0144858,.237581,.0573298,.940073,-.0172399,.237589,.0682366,.94012,-.0202335,.237598,.080097,.940162,-.0234663,.237604,.0929116,.940237,-.0269387,.237615,.106686,.940328,-.0306489,.237632,.121421,.940419,-.0345917,.237645,.137115,.940522,-.0387481,.237654,.153766,.940702,-.0429906,.237661,.17133,.940871,-.0465089,.237561,.189502,.941103,-.050531,.23748,.208616,.941369,-.0550657,.237423,.228595,.941641,-.0601337,.237399,.249287,.941903,-.0658804,.237443,.270467,.942224,-.0722674,.237597,.292024,.942633,-.0771788,.237419,.315272,.943172,-.0815623,.237068,.339579,.943691,-.0863973,.236682,.364717,.944382,-.0911536,.236213,.390435,.945392,-.0952967,.235562,.416425,.946185,-.0998948,.234832,.442772,.947212,-.104796,.234114,.469347,.948778,-.10928,.233222,.496162,.950149,-.113081,.231845,.523978,.951989,-.115893,.230005,.552295,.953921,-.11846,.227862,.580569,.955624,-.12115,.225439,.608698,.958234,-.123373,.222635,.636696,.960593,-.124519,.219093,.665208,.963201,-.124736,.214749,.693557,.965642,-.125012,.210059,.721334,.968765,-.124661,.204935,.748613,.971753,-.122996,.198661,.776224,.973751,-.120998,.191823,.802461,.976709,-.118583,.184359,.828399,.977956,-.115102,.176437,.853693,.979672,-.111077,.167681,.877962,.981816,-.10688,.158872,.901564,.98238,-.101469,.149398,.924057,.983964,-.0960013,.139436,.945751,.984933,-.0899626,.12943,.966272,.985694,-.0832973,.11894,.985741,.986822,-.0767082,.108349,1.00407,.987725,-.0693614,.0976026,1.02154,.98877,-.06211,.086652,1.03757,.990129,-.0544143,.0756182,1.05296,.991337,-.046744,.0645753,1.06683,.992978,-.0387931,.0534683,1.0798,.994676,-.030973,.0424137,1.09181,.99645,-.0230311,.0314035,1.10286,.997967,-.0152065,.0206869,1.11291,.99922,-.00744837,.010155,1.12237,1.00002,240209e-9,-752767e-10,1.13089,.922948,-515351e-11,.255626,186069e-10,.928785,-129623e-9,.257244,468009e-9,.928761,-51849e-8,.257237,.00187202,.928751,-.0011666,.257235,.00421204,.928751,-.00207395,.257234,.0074881,.928754,-.00324055,.257235,.0117002,.92876,-.00466639,.257236,.0168486,.928763,-.00635149,.257237,.0229334,.928774,-.00829584,.257239,.029955,.928791,-.0104995,.257243,.0379139,.928804,-.0129623,.257245,.0468108,.928847,-.0156846,.257255,.0566473,.92889,-.0186661,.257263,.0674246,.928924,-.0219067,.257268,.0791433,.928989,-.0254066,.257282,.0918076,.92909,-.0291651,.257301,.105419,.92918,-.0331801,.257316,.119978,.92929,-.0374469,.257332,.135491,.929453,-.041939,.257357,.151948,.929586,-.0464612,.257347,.169275,.929858,-.0503426,.257269,.187257,.930125,-.0548409,.257199,.206204,.930403,-.0598063,.257149,.22601,.930726,-.0652437,.257122,.246561,.931098,-.0712376,.257153,.267618,.931396,-.0777506,.257237,.288993,.931947,-.0832374,.257124,.311527,.932579,-.0883955,.25683,.335697,.933194,-.0937037,.256444,.360634,.934013,-.0987292,.255939,.386126,.935307,-.103215,.255282,.412018,.936374,-.108234,.254538,.438292,.93776,-.113234,.253728,.464805,.939599,-.118013,.25275,.491464,.941036,-.122661,.251404,.518751,.94337,-.125477,.249435,.547133,.945318,-.128374,.247113,.575456,.947995,-.130996,.244441,.60372,.950818,-.133438,.241352,.63174,.954378,-.135004,.237849,.659971,.957151,-.135313,.233188,.688478,.960743,-.13521,.228001,.716767,.964352,-.135007,.222249,.744349,.967273,-.133523,.21542,.771786,.969767,-.131155,.208039,.798639,.973195,-.128492,.200076,.824774,.975557,-.125094,.191451,.850222,.977692,-.120578,.18184,.874761,.98026,-.115882,.172102,.898497,.981394,-.110372,.161859,.921636,.982386,-.10415,.15108,.943467,.983783,-.0978128,.140407,.964045,.98422,-.0906171,.129058,.98398,.985447,-.0832921,.117614,1.00276,.986682,-.0754412,.10585,1.02047,.987326,-.0673885,.0940943,1.03678,.988707,-.0592565,.0822093,1.05218,.990185,-.050717,.070192,1.06652,.991866,-.0423486,.0582081,1.07965,.993897,-.0336118,.0460985,1.09188,.995841,-.0252178,.0342737,1.10307,.997605,-.0164893,.0224829,1.11324,.999037,-.00817112,.0110647,1.12262,1.00003,291686e-9,-168673e-9,1.13139,.915304,-552675e-11,.275999,183285e-10,.91668,-139285e-9,.276414,461914e-9,.916664,-55713e-8,.276409,.00184763,.916653,-.00125354,.276406,.00415715,.916651,-.00222851,.276405,.00739053,.916655,-.00348205,.276406,.0115478,.916653,-.00501414,.276405,.0166291,.916667,-.00682478,.276409,.0226346,.91668,-.00891398,.276412,.0295648,.91669,-.0112817,.276413,.0374199,.916727,-.013928,.276422,.0462016,.916759,-.0168528,.276429,.0559101,.916793,-.0200558,.276436,.0665466,.916849,-.0235373,.276448,.0781139,.916964,-.0272973,.276474,.0906156,.917047,-.0313344,.276491,.104051,.917152,-.0356465,.276511,.118424,.917286,-.0402271,.276533,.133736,.917469,-.0450408,.276564,.149978,.917686,-.0497872,.276563,.167057,.917953,-.0540937,.276493,.184846,.918228,-.0590709,.276437,.203614,.918572,-.0644277,.276398,.223212,.918918,-.0702326,.276362,.243584,.919356,-.076484,.276383,.264465,.919842,-.0830808,.276434,.285701,.920451,-.0892972,.276407,.307559,.921113,-.095016,.276128,.331501,.921881,-.100771,.275754,.356207,.923027,-.106029,.275254,.381477,.924364,-.111029,.274595,.40722,.925818,-.116345,.273841,.433385,.92746,-.121424,.272913,.459848,.929167,-.12657,.271837,.486493,.931426,-.131581,.270575,.513432,.934001,-.135038,.268512,.541502,.936296,-.138039,.266135,.569658,.939985,-.140687,.263271,.598375,.943516,-.143247,.260058,.626563,.94782,-.145135,.256138,.654711,.951023,-.145733,.251154,.683285,.955338,-.145554,.245562,.711831,.959629,-.145008,.239265,.739573,.963123,-.144003,.232064,.767027,.966742,-.141289,.224036,.794359,.969991,-.138247,.215305,.820361,.973403,-.134786,.206051,.846548,.975317,-.129966,.195914,.871541,.977647,-.12471,.185184,.895313,.980137,-.119086,.174161,.918398,.981031,-.112297,.162792,.940679,.982037,-.105372,.150952,.961991,.983164,-.097821,.138921,.981913,.983757,-.0897245,.126611,1.00109,.985036,-.0815974,.114228,1.01902,.986289,-.0727725,.101389,1.03604,.987329,-.0639323,.0886476,1.05149,.989193,-.0548109,.0756837,1.06619,.990716,-.045687,.0627581,1.07948,.992769,-.0364315,.0498337,1.09172,.99524,-.0271761,.0370305,1.1033,.997154,-.0179609,.0243959,1.11353,.998845,-.00878063,.0119567,1.12319,1.00002,259038e-9,-108146e-9,1.13177,.903945,-591681e-11,.295126,181226e-10,.903668,-148672e-9,.295037,455367e-9,.903677,-594683e-9,.29504,.00182145,.903673,-.00133805,.295039,.00409831,.903666,-.00237872,.295036,.00728584,.903668,-.00371676,.295037,.0113842,.903679,-.00535212,.29504,.0163936,.903684,-.00728479,.295041,.0223141,.903698,-.00951473,.295044,.0291462,.903718,-.0120419,.295049,.0368904,.903754,-.0148664,.295058,.0455477,.903801,-.017988,.29507,.0551194,.903851,-.0214064,.295082,.0656058,.903921,-.0251219,.295097,.0770109,.904002,-.0291337,.295116,.0893354,.904111,-.033441,.29514,.102583,.904246,-.0380415,.295169,.116755,.904408,-.0429258,.295202,.131853,.904637,-.0480468,.295245,.147869,.904821,-.0529208,.295214,.164658,.905163,-.0577748,.295185,.182274,.905469,-.0631763,.295143,.200828,.905851,-.068917,.295112,.2202,.906322,-.0750861,.295104,.240372,.906761,-.0815855,.295086,.261082,.90735,-.0882138,.295095,.282123,.908087,-.095082,.295139,.303563,.908826,-.101488,.29492,.327028,.909832,-.107577,.294577,.351464,.911393,-.113033,.294115,.376497,.912804,-.118629,.293446,.402115,.914081,-.124232,.292581,.428111,.91637,-.129399,.29166,.454442,.91814,-.134892,.290422,.481024,.921179,-.140069,.289194,.507924,.924544,-.144431,.287421,.535557,.927995,-.147498,.284867,.563984,.931556,-.150197,.281722,.5923,.935777,-.152711,.278207,.620832,.940869,-.154836,.274148,.649069,.945994,-.155912,.269057,.677746,.949634,-.155641,.262799,.706293,.955032,-.154809,.256097,.734278,.95917,-.153678,.248618,.761751,.962931,-.151253,.239794,.789032,.966045,-.147625,.230281,.815422,.96971,-.143964,.220382,.841787,.972747,-.139464,.209846,.867446,.975545,-.133459,.198189,.892004,.978381,-.127424,.186362,.915458,.979935,-.120506,.173964,.937948,.980948,-.11282,.161429,.959732,.982234,-.104941,.148557,.980118,.982767,-.0962905,.135508,.999463,.983544,-.0873625,.122338,1.01756,.984965,-.0783447,.108669,1.03492,.986233,-.0684798,.0949911,1.05087,.987796,-.0590867,.0811386,1.0656,.989885,-.0489145,.0673099,1.0794,.991821,-.0391,.0535665,1.09174,.99448,-.029087,.0397529,1.10341,.996769,-.019114,.0261463,1.11383,.998641,-.00947007,.0128731,1.1237,.999978,446316e-9,-169093e-9,1.13253,.888362,-627064e-11,.312578,178215e-10,.889988,-157791e-9,.313148,448451e-9,.889825,-631076e-9,.313092,.00179356,.88984,-.00141994,.313097,.00403554,.889828,-.0025243,.313092,.00717429,.889831,-.00394421,.313093,.0112099,.889831,-.00567962,.313093,.0161425,.889844,-.00773051,.313096,.0219724,.889858,-.0100968,.3131,.0286999,.889882,-.0127786,.313106,.0363256,.889918,-.0157757,.313116,.0448509,.889967,-.0190878,.313129,.0542758,.89003,-.022715,.313145,.0646032,.890108,-.0266566,.313165,.0758339,.890218,-.0309131,.313193,.0879729,.890351,-.0354819,.313226,.101019,.89051,-.0403613,.313263,.114979,.890672,-.0455385,.313294,.129848,.890882,-.0509444,.313333,.145616,.891189,-.0559657,.313324,.162122,.891457,-.0613123,.313281,.179524,.891856,-.0671488,.313281,.197855,.892312,-.0732732,.313268,.216991,.892819,-.0797865,.313263,.236924,.893369,-.0865269,.313247,.257433,.894045,-.0931592,.313205,.278215,.894884,-.100532,.313276,.299467,.895832,-.107716,.313205,.322276,.897043,-.114099,.312873,.34642,.898515,-.119941,.312331,.371187,.900191,-.126044,.311731,.396656,.90188,-.131808,.310859,.422488,.904359,-.137289,.309857,.448744,.906923,-.142991,.308714,.475239,.910634,-.148253,.307465,.501983,.914502,-.153332,.305774,.529254,.919046,-.156646,.303156,.557709,.923194,-.159612,.299928,.586267,.928858,-.162027,.296245,.614925,.934464,-.164203,.291832,.643187,.939824,-.165602,.286565,.671601,.944582,-.165383,.280073,.700213,.949257,-.164439,.272891,.728432,.954389,-.162953,.264771,.756082,.958595,-.161007,.255927,.78369,.962138,-.157243,.245769,.810769,.966979,-.152872,.235127,.836999,.969566,-.148209,.22347,.862684,.972372,-.142211,.211147,.887847,.975916,-.135458,.198606,.911843,.978026,-.128398,.185498,.934795,.979686,-.120313,.17171,.956787,.980748,-.11166,.158159,.978046,.981622,-.103035,.144399,.997693,.982356,-.0930328,.13001,1.01642,.983308,-.0834627,.115778,1.03366,.985037,-.0732249,.101327,1.05014,.986493,-.0628145,.086554,1.06507,.988484,-.0526556,.0720413,1.07907,.991051,-.0415744,.0571151,1.09189,.993523,-.0314275,.0426643,1.10369,.99628,-.0203603,.0279325,1.11423,.998344,-.0102446,.0138182,1.12421,.999997,42612e-8,-193628e-9,1.1333,.871555,-660007e-11,.329176,174749e-10,.875255,-166579e-9,.330571,441051e-9,.875644,-666394e-9,.330718,.00176441,.875159,-.00149903,.330536,.00396899,.87516,-.00266493,.330536,.007056,.875158,-.00416393,.330535,.0110251,.87516,-.00599598,.330535,.0158764,.875163,-.00816108,.330536,.0216101,.875174,-.0106591,.330538,.0282266,.875199,-.0134899,.330545,.0357266,.875257,-.0166538,.330563,.0441117,.875304,-.0201501,.330575,.0533821,.875373,-.0239785,.330595,.0635395,.875464,-.0281389,.330619,.0745872,.875565,-.0326301,.330645,.0865255,.875691,-.0374516,.330676,.0993599,.875897,-.0425993,.330733,.113093,.876091,-.0480576,.330776,.127722,.876353,-.0537216,.330826,.143227,.876649,-.0589807,.330809,.159462,.877034,-.0647865,.330819,.176642,.877443,-.0709789,.330817,.194702,.877956,-.0774782,.330832,.213577,.878499,-.0843175,.330822,.233246,.879144,-.0912714,.330804,.253512,.879982,-.0980824,.330766,.274137,.88097,-.105823,.330864,.295209,.882051,-.113671,.330896,.317226,.883397,-.120303,.330545,.341068,.884987,-.12667,.330068,.365613,.886789,-.133118,.329418,.390807,.889311,-.139024,.328683,.416494,.891995,-.144971,.327729,.442618,.895106,-.150747,.326521,.469131,.899527,-.156283,.325229,.495921,.90504,-.161707,.32378,.523162,.909875,-.165661,.32122,.55092,.91561,-.168755,.317942,.579928,.921225,-.171193,.313983,.608539,.927308,-.17319,.309636,.636854,.933077,-.174819,.304262,.66523,.938766,-.175002,.297563,.693609,.943667,-.173946,.289613,.722157,.949033,-.172221,.281227,.750021,.953765,-.169869,.271545,.777466,.95804,-.166578,.261034,.804853,.962302,-.161761,.249434,.831569,.966544,-.156636,.237484,.857779,.969372,-.150784,.224395,.883051,.972486,-.143672,.210786,.907864,.975853,-.135772,.196556,.931223,.977975,-.127942,.182307,.954061,.979122,-.118347,.167607,.97531,.980719,-.109112,.152739,.995666,.981223,-.0991789,.137932,1.01475,.98216,-.0883553,.122692,1.03253,.983379,-.0780825,.107493,1.04917,.985434,-.0665646,.0917791,1.06464,.987332,-.0557714,.0764949,1.07896,.990004,-.0442805,.060721,1.09199,.992975,-.0331676,.0452284,1.10393,.995811,-.0219547,.0297934,1.11476,.9982,-.0107613,.0146415,1.12484,1.00002,248678e-9,-14555e-8,1.13413,.859519,-693595e-11,.347264,171673e-10,.859843,-17503e-8,.347394,433219e-9,.859656,-700076e-9,.347319,.00173277,.859671,-.00157517,.347325,.00389875,.859669,-.00280028,.347324,.00693112,.85967,-.0043754,.347324,.01083,.859665,-.00630049,.347321,.0155954,.859685,-.0085755,.347328,.0212278,.859694,-.0112003,.347329,.0277273,.859718,-.0141747,.347336,.0350946,.85976,-.0174988,.347348,.0433314,.85982,-.0211722,.347366,.0524384,.859892,-.0251941,.347387,.0624168,.860006,-.0295649,.347422,.0732708,.860122,-.0342825,.347453,.0849999,.860282,-.0393462,.347499,.0976102,.860482,-.0447513,.347554,.111104,.860719,-.0504775,.347614,.125479,.860998,-.0563577,.347666,.140703,.861322,-.0619473,.347662,.156681,.861724,-.0681277,.347684,.173597,.862198,-.0746567,.347709,.191371,.862733,-.0815234,.347727,.209976,.863371,-.0886643,.347744,.229351,.86414,-.0957908,.347734,.24934,.865138,-.102912,.34772,.269797,.866182,-.110924,.3478,.290654,.867436,-.119223,.347911,.312074,.869087,-.126197,.347649,.335438,.870859,-.133145,.347222,.359732,.872997,-.139869,.346645,.38467,.875939,-.146089,.345935,.41019,.879012,-.152334,.345012,.436218,.883353,-.15821,.343924,.462641,.888362,-.164097,.342636,.489449,.895026,-.169528,.341351,.516629,.900753,-.174408,.339115,.544109,.906814,-.17751,.335809,.572857,.912855,-.180101,.331597,.601554,.919438,-.182116,.32698,.630198,.925962,-.183494,.321449,.658404,.931734,-.184159,.314595,.686625,.93762,-.18304,.306462,.71531,.943858,-.181323,.297514,.744272,.948662,-.178683,.287447,.771462,.953299,-.175379,.276166,.798593,.957346,-.170395,.263758,.8256,.962565,-.165042,.251019,.852575,.966075,-.158655,.237011,.878316,.969048,-.151707,.222518,.90329,.972423,-.143271,.207848,.927745,.975833,-.134824,.192463,.950859,.977629,-.125444,.1768,.972947,.978995,-.114949,.161033,.993263,.980533,-.104936,.145523,1.01337,.980745,-.0935577,.129799,1.03128,.981814,-.0822956,.113486,1.04825,.983943,-.0710082,.0972925,1.06405,.986141,-.0587931,.0808138,1.0785,.988878,-.0472755,.0644915,1.09204,.992132,-.0349128,.0478128,1.10413,.9953,-.0232407,.031621,1.11527,.998117,-.0112713,.0154935,1.12551,1.00003,339743e-9,-195763e-9,1.13504,.845441,-729126e-11,.364305,169208e-10,.843588,-183164e-9,.363506,425067e-9,.843412,-73253e-8,.36343,.00169999,.843401,-.00164818,.363426,.00382495,.843399,-.00293008,.363425,.00679993,.843401,-.00457822,.363425,.010625,.843394,-.00659249,.363421,.0153002,.843398,-.00897282,.363421,.0208258,.843415,-.0117191,.363426,.0272024,.843438,-.0148312,.363432,.0344305,.843483,-.018309,.363447,.0425116,.84356,-.0221521,.363472,.0514471,.843646,-.0263597,.363499,.061238,.843743,-.0309315,.363527,.0718873,.84388,-.0358658,.363569,.0833969,.844079,-.0411624,.363631,.0957742,.844279,-.0468128,.363688,.109015,.844549,-.0527923,.363761,.123124,.844858,-.0588204,.363817,.138044,.84522,-.0647573,.36383,.153755,.845669,-.0713181,.363879,.170394,.846155,-.0781697,.363908,.187861,.846789,-.0853913,.363969,.206176,.847502,-.0928086,.363999,.225244,.8484,-.10005,.363997,.244926,.849461,-.107615,.364008,.265188,.850562,-.115814,.364055,.28587,.851962,-.124334,.364179,.306926,.854326,-.131995,.364233,.329605,.856295,-.139338,.363856,.35359,.858857,-.146346,.363347,.37831,.862428,-.152994,.362807,.403722,.866203,-.159463,.361963,.429537,.871629,-.165623,.36112,.456,.877365,-.171649,.359917,.482773,.883744,-.177151,.35848,.509705,.890693,-.182381,.356523,.537215,.897278,-.186076,.3533,.565493,.903958,-.188602,.349095,.594293,.910908,-.190755,.344215,.623165,.918117,-.192063,.338606,.651573,.924644,-.192758,.331544,.679869,.931054,-.192238,.323163,.708668,.937303,-.190035,.313529,.737201,.943387,-.187162,.303152,.764977,.948494,-.183876,.29146,.792683,.952546,-.178901,.277917,.819228,.958077,-.173173,.264753,.846559,.962462,-.16645,.25002,.872962,.966569,-.159452,.234873,.898729,.969108,-.15074,.218752,.923126,.973072,-.141523,.202673,.947278,.975452,-.132075,.186326,.969938,.977784,-.121257,.169396,.991325,.97899,-.110182,.153044,1.01123,.979777,-.0989634,.136485,1.0299,.980865,-.0865894,.119343,1.04727,.982432,-.0746115,.102452,1.06341,.984935,-.0621822,.0852423,1.07834,.987776,-.0495694,.0678546,1.092,.99103,-.0372386,.0506917,1.1043,.99474,-.0244353,.0333316,1.11576,.997768,-.0121448,.0164348,1.12617,1.00003,31774e-8,-169504e-9,1.13598,.825551,-756799e-11,.378425,165099e-10,.82664,-190922e-9,.378923,416504e-9,.826323,-763495e-9,.378779,.0016656,.826359,-.00171789,.378795,.00374768,.82636,-.00305402,.378795,.00666259,.826368,-.00477185,.378798,.0104104,.826364,-.00687131,.378795,.0149912,.826368,-.00935232,.378795,.0204054,.826376,-.0122146,.378797,.0266532,.826399,-.0154581,.378803,.0337355,.82646,-.0190825,.378824,.0416537,.826525,-.0230873,.378846,.0504091,.826614,-.0274719,.378876,.0600032,.82674,-.0322355,.378917,.0704393,.826888,-.0373766,.378964,.0817195,.827078,-.0428936,.379024,.0938492,.827318,-.0487778,.379099,.106828,.82764,-.0549935,.379199,.120659,.827926,-.0611058,.379227,.13526,.828325,-.0675054,.379275,.150713,.828801,-.0743455,.379332,.167034,.8294,-.0815523,.379415,.184209,.830094,-.0890779,.379495,.202203,.8309,-.096736,.379555,.220945,.831943,-.104135,.379577,.240306,.833037,-.112106,.379604,.260317,.834278,-.120554,.379668,.2808,.836192,-.129128,.3799,.301654,.838671,-.137541,.380109,.323502,.840939,-.14523,.379809,.347176,.844575,-.15248,.379593,.371706,.848379,-.159607,.37909,.39688,.853616,-.166267,.378617,.422702,.858921,-.172698,.377746,.448919,.865324,-.178823,.376749,.475661,.872207,-.184542,.375363,.502599,.880018,-.189836,.373657,.529914,.88694,-.194294,.370673,.557683,.894779,-.197022,.36662,.586848,.902242,-.199108,.36138,.615831,.909914,-.200398,.355434,.644478,.917088,-.20094,.348173,.672905,.923888,-.200671,.339482,.701327,.930495,-.198773,.32956,.730101,.937247,-.195394,.318363,.758383,.943108,-.191956,.306323,.786539,.948296,-.187227,.292576,.813637,.953472,-.181165,.278234,.840793,.958485,-.174119,.263054,.867712,.962714,-.166564,.246756,.893635,.966185,-.158181,.229945,.919028,.970146,-.148275,.212633,.943413,.973491,-.138157,.195229,.966627,.975741,-.127574,.178048,.988817,.977238,-.11554,.160312,1.00924,.978411,-.10364,.142857,1.02845,.979811,-.0913122,.125317,1.04648,.98116,-.0782558,.107627,1.06284,.983543,-.0655957,.0895862,1.07798,.986789,-.0520411,.0713756,1.092,.990292,-.0389727,.053228,1.10484,.994187,-.025808,.0351945,1.11642,.997499,-.0126071,.0173198,1.12703,.999999,275604e-9,-148602e-9,1.13674,.81075,-78735e-10,.394456,161829e-10,.808692,-198293e-9,.393453,407564e-9,.80846,-792877e-9,.39334,.00162965,.808595,-.00178416,.393407,.00366711,.808597,-.00317182,.393408,.00651934,.808598,-.00495589,.393408,.0101866,.808591,-.00713627,.393403,.0146689,.808592,-.00971285,.393402,.0199667,.80861,-.0126855,.393407,.0260803,.808633,-.0160538,.393413,.0330107,.80868,-.0198175,.393429,.0407589,.808748,-.0239758,.393453,.0493264,.808854,-.0285286,.39349,.0587161,.808992,-.0334748,.39354,.0689304,.809141,-.0388116,.393588,.0799707,.809352,-.0445375,.39366,.0918432,.809608,-.0506427,.393742,.104549,.809915,-.0570708,.393834,.118085,.810253,-.0633526,.393885,.132377,.810687,-.0700966,.393953,.147537,.811233,-.0772274,.394047,.163543,.811865,-.0847629,.394148,.180394,.812648,-.0925663,.394265,.198051,.813583,-.100416,.394363,.216443,.814683,-.108119,.394402,.235502,.815948,-.11644,.394489,.255242,.817278,-.125036,.394542,.275441,.819605,-.133655,.39486,.296094,.822256,-.142682,.395248,.317309,.825349,-.150756,.395241,.340516,.829605,-.158392,.395285,.364819,.83391,-.165801,.394922,.389736,.839808,-.172677,.394691,.415409,.845708,-.179448,.394006,.441546,.853025,-.185746,.393279,.46832,.859666,-.191684,.391655,.495302,.86789,-.197146,.390068,.52262,.875845,-.201904,.38727,.550336,.882634,-.205023,.382688,.578825,.891076,-.207098,.377543,.608103,.900589,-.208474,.371752,.63723,.90791,-.209068,.364016,.665769,.915971,-.208655,.355593,.694428,.923455,-.20729,.345439,.723224,.931514,-.203821,.334099,.751925,.937885,-.19986,.321069,.780249,.943136,-.194993,.306571,.8077,.948818,-.189132,.291556,.83497,.954433,-.181617,.275745,.86188,.959078,-.173595,.258695,.888562,.962705,-.164855,.240825,.914008,.966753,-.155129,.22268,.939145,.970704,-.144241,.204542,.963393,.973367,-.133188,.185927,.985983,.975984,-.121146,.167743,1.00704,.976994,-.108366,.149218,1.02715,.978485,-.0956746,.13131,1.0455,.980074,-.0820733,.112513,1.06221,.98225,-.0684061,.0938323,1.07782,.98553,-.0549503,.0749508,1.09199,.989529,-.0407857,.055848,1.10508,.993536,-.0271978,.0368581,1.11684,.997247,-.0132716,.0181845,1.12789,1,431817e-9,-198809e-9,1.13792,.785886,-812608e-11,.405036,157669e-10,.790388,-205278e-9,.407355,398297e-9,.790145,-820824e-9,.407231,.00159263,.790135,-.00184681,.407226,.00358336,.790119,-.00328316,.407218,.00637039,.790126,-.00512988,.40722,.0099539,.79013,-.00738684,.407221,.0143339,.790135,-.0100538,.407221,.0195107,.790134,-.0131306,.407217,.0254848,.79016,-.0166169,.407224,.0322572,.790197,-.020512,.407236,.0398284,.790273,-.0248157,.407263,.0482014,.790381,-.029527,.407304,.0573777,.790521,-.0346446,.407355,.0673602,.790704,-.0401665,.40742,.0781522,.790925,-.0460896,.407499,.0897582,.791195,-.0524017,.407589,.10218,.791522,-.0590121,.407691,.11541,.791878,-.0654876,.407748,.12939,.792361,-.0725207,.407849,.144237,.792942,-.0799844,.407963,.159924,.79362,-.0877896,.408087,.176425,.794529,-.0958451,.408259,.193733,.795521,-.103827,.408362,.211756,.796778,-.111937,.408482,.230524,.798027,-.120521,.408547,.249967,.799813,-.129242,.408721,.269926,.802387,-.138048,.409148,.290338,.805279,-.147301,.409641,.311193,.809251,-.155895,.410154,.333611,.813733,-.163942,.410297,.357615,.819081,-.171666,.410373,.382339,.825427,-.178905,.410348,.407828,.83172,-.185812,.409486,.434034,.83877,-.192318,.408776,.460493,.845817,-.198249,.407176,.487346,.854664,-.204034,.405719,.514832,.863495,-.208908,.403282,.542401,.871883,-.212765,.399293,.570683,.88065,-.214911,.393803,.599947,.89004,-.216214,.387536,.62932,.898476,-.216745,.379846,.658319,.906738,-.216387,.370625,.687138,.914844,-.215053,.360139,.71601,.923877,-.212007,.348849,.745124,.931925,-.207481,.335639,.773366,.938054,-.202418,.320798,.801636,.943895,-.196507,.304772,.829055,.949468,-.189009,.288033,.856097,.955152,-.180539,.270532,.88301,.959403,-.171437,.251639,.909296,.963309,-.161661,.232563,.934868,.967399,-.150425,.213231,.959662,.972009,-.138659,.194247,.98302,.97433,-.126595,.174718,1.00517,.975823,-.113205,.155518,1.02566,.976371,-.0996096,.136709,1.04418,.978705,-.0860754,.117571,1.06146,.981477,-.0714438,.0980046,1.07777,.984263,-.0572304,.0782181,1.09214,.988423,-.0428875,.0584052,1.10553,.993,-.0282442,.038522,1.11758,.99704,-.0140183,.0190148,1.12864,.999913,369494e-9,-145203e-9,1.13901,.777662,-84153e-10,.423844,154403e-10,.770458,-211714e-9,.419915,38845e-8,.770716,-846888e-9,.420055,.00155386,.770982,-.00190567,.420202,.00349653,.770981,-.00338782,.420201,.00621606,.77098,-.00529338,.4202,.00971274,.770983,-.00762223,.4202,.0139867,.770985,-.0103741,.420198,.0190381,.770996,-.0135489,.4202,.0248677,.771029,-.0171461,.420212,.0314764,.771052,-.0211647,.420215,.0388648,.771131,-.0256048,.420245,.047036,.771235,-.0304647,.420284,.0559911,.771383,-.0357436,.420341,.0657346,.771591,-.0414392,.420423,.0762694,.771819,-.0475462,.420506,.0875984,.772123,-.0540506,.420617,.099727,.772464,-.060797,.42072,.112637,.772855,-.0675393,.420799,.126313,.773317,-.0748323,.420893,.140824,.773981,-.0825681,.421058,.15617,.774746,-.0906307,.421226,.172322,.77566,-.0988982,.421397,.189253,.776837,-.106994,.421569,.206912,.778097,-.115528,.421704,.225359,.779588,-.124317,.421849,.24447,.781574,-.133139,.422097,.264156,.784451,-.142179,.422615,.284318,.787682,-.15165,.423269,.304902,.792433,-.160771,.424396,.3265,.797359,-.169166,.424772,.35014,.803986,-.177149,.425475,.374768,.809504,-.184745,.424996,.399928,.815885,-.19173,.424247,.425796,.823513,-.198525,.423515,.452287,.832549,-.204709,.422787,.479321,.841653,-.210447,.421187,.506718,.850401,-.215501,.418519,.53432,.859854,-.219752,.414715,.56242,.869364,-.222305,.409462,.591558,.878837,-.223744,.402926,.621074,.888636,-.224065,.395043,.650538,.898132,-.223742,.38564,.679538,.907181,-.222308,.375378,.708674,.915621,-.219837,.363212,.737714,.9239,-.215233,.349313,.767014,.931644,-.209592,.334162,.795133,.938887,-.203644,.317943,.823228,.945282,-.196349,.300581,.850822,.950758,-.18742,.282195,.877594,.956146,-.177879,.262481,.904564,.960355,-.167643,.242487,.930741,.965256,-.156671,.222668,.955868,.968029,-.144123,.201907,.979869,.97251,-.131305,.18202,1.00291,.974925,-.118335,.161909,1.02392,.975402,-.103714,.142129,1.0433,.976987,-.089415,.122447,1.06089,.979677,-.0748858,.102248,1.07713,.983184,-.0596086,.0814851,1.09218,.987466,-.0447671,.0609484,1.10585,.992348,-.0295217,.0401835,1.11829,.996674,-.0143917,.0198163,1.12966,1.00003,321364e-9,-149983e-9,1.1402,.757901,-869074e-11,.436176,151011e-10,.751195,-217848e-9,.432317,378533e-9,.751178,-871373e-9,.432307,.0015141,.751195,-.00196061,.432317,.0034068,.751198,-.00348552,.432318,.00605659,.751195,-.00544599,.432315,.00946353,.751207,-.00784203,.43232,.013628,.751213,-.0106732,.43232,.0185499,.751221,-.0139393,.432319,.0242302,.751244,-.0176398,.432325,.0306694,.7513,-.0217743,.432348,.0378698,.751358,-.0263412,.432367,.0458321,.751458,-.0313396,.432404,.0545587,.751608,-.0367682,.432464,.0640543,.7518,-.0426246,.43254,.0743222,.752065,-.0489031,.432645,.0853668,.752376,-.0555828,.432762,.0971911,.752715,-.0623861,.432859,.109768,.753137,-.069415,.432958,.123126,.753676,-.0770039,.433099,.137308,.754345,-.084971,.433272,.15229,.755235,-.0932681,.433504,.168075,.756186,-.10171,.433693,.184625,.757363,-.110019,.433857,.201897,.75884,-.11887,.434102,.220014,.760467,-.127881,.434306,.238778,.762969,-.136766,.434751,.258172,.765823,-.14612,.43529,.278062,.769676,-.15566,.436236,.298437,.774909,-.165177,.437754,.319532,.77994,-.17402,.438343,.342505,.785757,-.182201,.438609,.366693,.792487,-.190104,.438762,.391668,.80038,-.197438,.438795,.417494,.808494,-.204365,.438226,.443933,.817695,-.210714,.437283,.470929,.828111,-.216651,.436087,.498569,.837901,-.221804,.433717,.526165,.847813,-.226318,.430133,.554155,.858314,-.229297,.425213,.582822,.868891,-.230999,.418576,.612847,.878941,-.231155,.410405,.642445,.888809,-.230935,.400544,.672024,.898089,-.229343,.389613,.701366,.908081,-.226886,.377197,.730763,.916819,-.222676,.363397,.759642,.924968,-.216835,.347437,.788775,.932906,-.210245,.32995,.817135,.940025,-.202992,.312262,.844912,.946101,-.19436,.293313,.872164,.952835,-.184125,.273638,.899443,.957347,-.173657,.252385,.926389,.961434,-.162204,.231038,.951947,.965522,-.14979,.209834,.976751,.969412,-.136307,.188821,1.00022,.973902,-.122527,.168013,1.02229,.974045,-.108213,.147634,1.04199,.975775,-.0927397,.12705,1.06019,.978383,-.0778212,.106309,1.07711,.98211,-.0621216,.0849279,1.09245,.986517,-.0463847,.0633519,1.10651,.991696,-.0309353,.0419698,1.11903,.996349,-.0150914,.0206272,1.13073,1.00003,442449e-9,-231396e-9,1.14146,.727498,-885074e-11,.441528,145832e-10,.730897,-223525e-9,.443589,368298e-9,.730796,-893996e-9,.443528,.00147303,.730805,-.00201149,.443533,.00331433,.730814,-.00357596,.443538,.00589222,.730815,-.00558734,.443538,.00920678,.730822,-.00804544,.44354,.0132582,.730836,-.0109501,.443545,.0180468,.730848,-.0143008,.443546,.0235732,.730871,-.0180969,.443552,.0298382,.730915,-.022338,.443567,.0368438,.730982,-.0270225,.443591,.044591,.731076,-.0321491,.443627,.0530831,.731245,-.0377166,.443699,.0623243,.73144,-.0437216,.443777,.0723181,.7317,-.0501576,.443881,.0830691,.732034,-.0569942,.444014,.0945809,.732388,-.0638756,.444113,.106825,.732853,-.071203,.444247,.119859,.733473,-.0790076,.444442,.13369,.734195,-.0871937,.444645,.148304,.735069,-.095696,.444877,.163702,.736169,-.10426,.445133,.179861,.73747,-.112853,.44537,.196778,.738991,-.12199,.445651,.214496,.740865,-.131153,.445958,.232913,.743637,-.140245,.446548,.251977,.746797,-.149722,.447246,.271551,.751517,-.159341,.448656,.291774,.756156,-.169106,.449866,.312455,.761519,-.178436,.450919,.334552,.768295,-.186904,.451776,.358491,.776613,-.195117,.452832,.383446,.783966,-.202695,.45249,.408945,.793542,-.20985,.452587,.435364,.803192,-.216403,.451852,.462336,.813892,-.22251,.450708,.48987,.824968,-.227676,.4486,.517697,.835859,-.232443,.445156,.545975,.846825,-.235775,.440351,.574483,.858085,-.237897,.433641,.604246,.868825,-.238074,.425354,.634101,.879638,-.237661,.415383,.664201,.889966,-.236186,.404136,.693918,.899479,-.233599,.390917,.723481,.908769,-.229737,.376352,.75258,.917966,-.223836,.360372,.781764,.926304,-.217067,.342551,.811139,.934626,-.209309,.324238,.839585,.941841,-.20071,.304484,.867044,.94789,-.190602,.283607,.894579,.954196,-.179253,.262205,.921743,.958383,-.167646,.239847,.948026,.963119,-.155073,.218078,.973296,.966941,-.141426,.195899,.998135,.970836,-.126849,.174121,1.02021,.973301,-.112296,.153052,1.04085,.97448,-.0964965,.131733,1.05946,.977045,-.080489,.10997,1.07693,.980751,-.064844,.0881657,1.09254,.985475,-.0481938,.0657987,1.10697,.991089,-.0319185,.0435215,1.12004,.996122,-.0158088,.0214779,1.13173,1.00001,372455e-9,-200295e-9,1.14291,.708622,-907597e-11,.45304,141962e-10,.711162,-228911e-9,.454662,358052e-9,.709812,-914446e-9,.453797,.00143034,.709865,-.00205819,.453834,.00321935,.709864,-.00365894,.453833,.00572331,.709855,-.00571692,.453826,.00894278,.709862,-.00823201,.453828,.012878,.709875,-.011204,.453832,.0175295,.709896,-.0146323,.453839,.0228978,.709925,-.0185163,.453847,.0289839,.709974,-.0228551,.453866,.0357894,.710045,-.0276473,.453892,.0433161,.710133,-.032891,.453924,.0515665,.710292,-.0385851,.453992,.0605458,.710485,-.0447254,.45407,.0702574,.710769,-.0513051,.454192,.0807077,.711106,-.0582733,.454329,.091896,.711516,-.0652866,.45446,.103814,.712071,-.0728426,.454653,.116508,.712676,-.0808307,.45484,.129968,.713476,-.0892216,.455096,.144206,.714377,-.0979047,.455346,.159212,.715579,-.106531,.455647,.174973,.716977,-.115492,.455961,.191504,.71862,-.124821,.456315,.208835,.72084,-.134079,.4568,.226869,.723786,-.143427,.457521,.245582,.727464,-.153061,.458475,.264957,.732771,-.162768,.460239,.284948,.736515,-.172627,.460899,.30522,.743519,-.182487,.463225,.326717,.750041,-.191295,.464027,.350113,.758589,-.199746,.465227,.374782,.767703,-.207584,.465877,.400226,.777484,-.214973,.465996,.426442,.788792,-.221796,.466019,.453688,.800194,-.228038,.465083,.481246,.811234,-.233346,.462506,.509086,.822859,-.238073,.459257,.537338,.835082,-.241764,.454863,.566108,.846332,-.244241,.448163,.595126,.858355,-.244736,.439709,.625574,.87034,-.244278,.429837,.65617,.881027,-.24255,.418002,.686029,.891007,-.239912,.404325,.716039,.900874,-.236133,.389222,.745518,.911072,-.230672,.373269,.775026,.920359,-.22356,.355083,.804521,.928604,-.215591,.335533,.834045,.937175,-.206503,.315278,.861612,.942825,-.196684,.293653,.889131,.949805,-.185116,.271503,.916853,.955535,-.172703,.248821,.943541,.959843,-.159978,.225591,.970132,.964393,-.146375,.202719,.994709,.968008,-.131269,.179928,1.0186,.971013,-.11569,.158007,1.03928,.973334,-.1003,.13624,1.05887,.975775,-.0833352,.1138,1.07652,.979579,-.0668981,.0913141,1.09297,.984323,-.0500902,.0683051,1.10734,.990351,-.0332377,.0451771,1.12084,.995823,-.0161491,.0221705,1.13296,1.0001,234083e-9,-108712e-9,1.14441,.683895,-924677e-11,.46015,137429e-10,.68833,-233383e-9,.463134,346865e-9,.688368,-933547e-9,.463159,.00138748,.688367,-.00210049,.463159,.00312187,.688369,-.00373415,.463159,.00555004,.688377,-.00583449,.463163,.00867216,.688386,-.00840128,.463166,.0124884,.688398,-.0114343,.463169,.0169993,.688418,-.0149329,.463175,.0222054,.688453,-.0188964,.463188,.028108,.688515,-.0233239,.463214,.0347085,.68857,-.0282136,.463231,.0420091,.688679,-.033564,.463276,.0500132,.688854,-.0393733,.463356,.0587255,.689038,-.0456354,.46343,.0681476,.689321,-.0523433,.463553,.0782897,.689662,-.059412,.463693,.0891501,.690188,-.0665736,.4639,.100735,.690755,-.0743106,.464107,.113074,.691405,-.0824722,.464329,.126161,.692198,-.0910484,.464585,.140007,.693196,-.0998778,.464893,.154612,.69454,-.108651,.465285,.169984,.695921,-.117855,.465596,.186106,.697749,-.12734,.466056,.203034,.700375,-.136714,.466771,.220703,.703395,-.146386,.467579,.239062,.707904,-.156096,.469067,.258188,.711673,-.165904,.469851,.277759,.717489,-.175812,.471815,.297935,.724051,-.185931,.47389,.318916,.731965,-.195238,.47587,.341591,.741151,-.204021,.477523,.366062,.751416,-.212113,.478881,.391396,.761848,-.21979,.479226,.417599,.771886,-.2267,.478495,.444401,.783998,-.232991,.477622,.472084,.796523,-.238645,.475833,.500193,.808851,-.243396,.472568,.52865,.821191,-.247226,.467857,.557362,.834261,-.250102,.461871,.586768,.846762,-.251056,.453543,.617085,.859867,-.250604,.443494,.647659,.871948,-.248783,.431711,.678119,.882967,-.245855,.417911,.708399,.892826,-.242168,.401993,.738256,.90332,-.237062,.385371,.767999,.913633,-.22997,.366837,.798191,.922774,-.221687,.346372,.827756,.931371,-.212345,.325682,.856425,.938929,-.20206,.303665,.884299,.944821,-.190981,.280786,.912023,.951792,-.178065,.2573,.939669,.957712,-.164634,.233448,.96655,.961912,-.150863,.209504,.992366,.966382,-.13577,.18597,1.01633,.969588,-.119593,.162905,1.03843,.971777,-.103203,.14053,1.05841,.97433,-.0865888,.117909,1.07632,.978686,-.0690829,.0944101,1.09326,.983281,-.0516568,.0705671,1.10796,.989562,-.034558,.0468592,1.12182,.995465,-.0167808,.0229846,1.1342,.999991,373016e-9,-235606e-9,1.1459,.662251,-939016e-11,.468575,132714e-10,.666634,-237624e-9,.471675,335842e-9,.666411,-950385e-9,.471516,.00134321,.666399,-.00213833,.471509,.00302221,.666386,-.0038014,.471499,.00537283,.666405,-.00593958,.471511,.00839533,.666406,-.00855253,.471508,.0120898,.666428,-.0116401,.471519,.0164569,.666444,-.0152015,.471522,.0214971,.66649,-.0192362,.471543,.027212,.666537,-.0237428,.471558,.033603,.666617,-.0287198,.471591,.0406728,.666718,-.0341647,.471631,.0484238,.666889,-.0400759,.47171,.0568621,.667104,-.0464479,.471805,.0659915,.667374,-.0532677,.471923,.0758178,.667772,-.0603805,.472098,.0863425,.668371,-.0677392,.472363,.0975917,.668971,-.0756028,.472596,.109567,.669696,-.0839293,.472869,.122272,.670481,-.0926683,.473126,.135718,.6715,-.1016,.473442,.149914,.672911,-.110566,.47389,.164882,.674512,-.119984,.474354,.180602,.67651,-.129574,.474922,.19711,.679292,-.139106,.475764,.214371,.682798,-.148993,.476886,.232405,.686955,-.158737,.478179,.251153,.691406,-.168754,.479432,.270436,.697438,-.178703,.481481,.290374,.704761,-.188955,.484143,.311044,.713599,-.198814,.487007,.333003,.723194,-.207869,.488962,.357144,.732601,-.216189,.489815,.382169,.744193,-.22398,.490888,.408227,.754907,-.231156,.490355,.434928,.767403,-.23747,.489548,.462599,.78107,-.243503,.488274,.490908,.793893,-.248114,.484843,.519421,.807296,-.25222,.4803,.548561,.820529,-.255265,.474097,.577772,.833716,-.256741,.466041,.607782,.848403,-.25637,.456547,.638807,.860755,-.254804,.443946,.670058,.874012,-.251834,.430852,.700749,.885619,-.247867,.414903,.731446,.896069,-.242634,.397276,.761191,.906266,-.236093,.378535,.791053,.916759,-.227543,.358038,.821298,.92523,-.21783,.335705,.850747,.93436,-.207534,.313797,.879258,.941631,-.195983,.289671,.907734,.947564,-.183567,.265319,.935206,.953681,-.169345,.240815,.962739,.960008,-.154909,.216119,.989227,.964145,-.140161,.192096,1.01465,.968171,-.123411,.167855,1.03737,.969859,-.106525,.144817,1.05767,.972666,-.0891023,.12149,1.0761,.977055,-.0718094,.0975306,1.09336,.982527,-.0534213,.0730217,1.10878,.989001,-.0355579,.0483366,1.12285,.99512,-.0176383,.023938,1.13548,1.00007,368831e-9,-211581e-9,1.14744,.651047,-960845e-11,.484101,12922e-9,.644145,-241347e-9,.478968,324578e-9,.64396,-965142e-9,.478831,.00129798,.64396,-.00217154,.47883,.00292046,.643968,-.00386049,.478835,.00519202,.643974,-.00603186,.478838,.0081128,.643977,-.0086854,.478836,.011683,.643982,-.0118207,.478834,.0159031,.644024,-.0154374,.478856,.0207743,.644059,-.0195343,.478868,.0262975,.644122,-.0241103,.478896,.0324747,.644207,-.0291638,.478933,.039309,.64432,-.0346919,.478981,.0468029,.644481,-.0406919,.479053,.0549614,.644722,-.047159,.479169,.0637909,.645013,-.0540748,.479302,.0732974,.645503,-.0612001,.479541,.0834898,.646117,-.0687303,.479829,.0943873,.646707,-.0767846,.480061,.105991,.647431,-.0852465,.480343,.11831,.64831,-.0940719,.48066,.131348,.649486,-.103056,.481083,.14514,.650864,-.112261,.481528,.159676,.652604,-.121852,.482102,.174979,.654825,-.131505,.482813,.191079,.657876,-.141189,.483876,.207927,.661339,-.151239,.48499,.225586,.665463,-.161091,.486279,.243947,.670542,-.171235,.487968,.262957,.677361,-.181347,.49053,.282781,.685672,-.191679,.493862,.303311,.694551,-.201781,.49699,.324607,.703753,-.211164,.498884,.347916,.713703,-.219675,.500086,.372628,.725911,-.227836,.501554,.398694,.73862,-.23533,.502193,.425529,.752118,-.241786,.501811,.453209,.76579,-.247865,.500185,.481381,.779568,-.252696,.497159,.51011,.793991,-.256802,.492765,.539322,.808182,-.259942,.486827,.569078,.821698,-.261703,.478386,.598818,.836009,-.262006,.468772,.629762,.849824,-.260333,.456352,.661366,.863888,-.257398,.442533,.69295,.876585,-.253264,.426573,.723608,.888665,-.248026,.408964,.754378,.899537,-.241487,.389677,.784761,.9094,-.233463,.368516,.814688,.920166,-.223397,.346624,.845009,.928899,-.21255,.322717,.874431,.937156,-.200869,.298698,.902922,.943861,-.188387,.273491,.931356,.949557,-.174341,.247866,.958854,.955862,-.158994,.222496,.986098,.961721,-.143664,.197522,1.01229,.965976,-.127412,.17302,1.03571,.968652,-.109798,.148954,1.05699,.971084,-.0916787,.125044,1.07587,.975584,-.0739634,.100577,1.09372,.98122,-.055322,.0753666,1.10948,.988253,-.0366825,.0498899,1.12394,.99482,-.0180389,.024611,1.13694,1.00001,229839e-9,-188283e-9,1.14919,.613867,-964198e-11,.479449,123452e-10,.621485,-244534e-9,.485399,313091e-9,.621429,-978202e-9,.485353,.00125245,.62112,-.00220004,.485114,.00281687,.621119,-.0039111,.485112,.00500783,.621122,-.00611091,.485112,.00782498,.621133,-.00879922,.485117,.0112687,.621152,-.0119756,.485125,.0153394,.621183,-.0156396,.485139,.0200382,.621227,-.0197898,.485158,.0253663,.621298,-.0244253,.485192,.0313261,.621388,-.0295441,.485233,.0379204,.621507,-.0351432,.485286,.0451523,.621693,-.0412198,.485378,.0530277,.621933,-.0477673,.485495,.0615522,.622232,-.0547574,.485635,.0707316,.622809,-.0619417,.485943,.0805883,.623407,-.069625,.486232,.0911267,.62406,-.077796,.486516,.102354,.624835,-.0863731,.486838,.114279,.625758,-.095251,.487188,.126902,.627043,-.104299,.487695,.140285,.628438,-.113724,.488163,.154397,.630325,-.123417,.488858,.169267,.632801,-.133137,.489754,.184941,.635784,-.143052,.490815,.20136,.639406,-.153132,.492048,.218643,.643872,-.163143,.49363,.236615,.6499,-.17333,.496009,.255449,.657201,-.183622,.498994,.275006,.666221,-.194019,.502888,.295354,.674419,-.204192,.505459,.316244,.683729,-.21406,.507771,.33849,.695584,-.222854,.510245,.363166,.708583,-.231315,.512293,.389071,.721233,-.238911,.512747,.415737,.735134,-.245657,.512482,.443331,.750179,-.251879,.511526,.471891,.765073,-.256911,.508935,.500892,.779794,-.261144,.504341,.530294,.794801,-.264316,.498515,.560144,.810339,-.266276,.491015,.590213,.824818,-.266981,.481126,.620865,.839375,-.265778,.468685,.652687,.853043,-.262748,.453925,.684759,.867335,-.258474,.437912,.716209,.88037,-.253187,.419648,.747508,.891711,-.246476,.39982,.77797,.902896,-.238735,.37879,.808586,.913601,-.22885,.355891,.838843,.923019,-.217656,.331773,.869014,.933432,-.205539,.307356,.898512,.939691,-.192595,.281321,.9269,.946938,-.178945,.255441,.955297,.952372,-.163587,.229013,.983231,.95909,-.147214,.203179,1.00971,.963675,-.13064,.17792,1.03438,.968247,-.113121,.152898,1.05625,.97001,-.0945824,.128712,1.07598,.974458,-.0755648,.103349,1.094,.980168,-.0571998,.0776731,1.1104,.987295,-.0377994,.0514445,1.12491,.994432,-.0186417,.025429,1.13851,.999975,542714e-9,-282356e-9,1.15108,.592656,-980249e-11,.486018,119532e-10,.598467,-247275e-9,.490781,301531e-9,.597934,-988317e-9,.490343,.00120517,.597903,-.00222366,.490319,.0027116,.597913,-.00395315,.490327,.00482077,.597919,-.00617653,.490329,.00753264,.597936,-.00889375,.490339,.0108478,.597956,-.0121043,.490347,.0147668,.597992,-.0158073,.490365,.0192905,.598032,-.0200017,.490382,.0244204,.598109,-.0246865,.49042,.0301593,.598215,-.0298594,.490474,.03651,.59833,-.0355167,.490524,.0434757,.598525,-.0416559,.490624,.0510629,.598778,-.0482692,.490753,.0592781,.599135,-.0553114,.49094,.0681304,.599802,-.062542,.491328,.0776467,.600361,-.0703638,.491598,.0878184,.60101,-.0786256,.491882,.0986573,.601811,-.0872962,.492232,.11018,.602861,-.0962284,.492684,.1224,.604167,-.10538,.493213,.135354,.605693,-.114896,.493799,.149034,.607682,-.124654,.494576,.163469,.610672,-.13456,.4959,.178747,.613313,-.144581,.496713,.194723,.617603,-.154703,.498499,.211617,.622174,-.16489,.500188,.229183,.628855,-.175164,.503072,.247786,.636963,-.185565,.506798,.267116,.644866,-.195911,.509719,.28702,.653741,-.206104,.512776,.307763,.664942,-.216447,.516812,.329631,.67633,-.22552,.519181,.353515,.690012,-.234316,.521681,.379226,.704243,-.242032,.523129,.405901,.719396,-.249172,.523768,.433585,.734471,-.255543,.522541,.462085,.750539,-.260697,.520217,.491233,.766365,-.26501,.516293,.521094,.781677,-.268409,.509708,.551014,.797132,-.270399,.501944,.581463,.812655,-.271247,.492025,.612402,.828592,-.270708,.480424,.643798,.844044,-.268085,.465955,.67682,.857305,-.263459,.448425,.708496,.87114,-.258151,.430243,.74046,.884936,-.251171,.410578,.771583,.895772,-.243305,.38862,.802234,.906961,-.234037,.365214,.833179,.917775,-.222714,.34116,.86353,.927883,-.210175,.31572,.893557,.936617,-.196925,.289159,.922976,.943384,-.182788,.261996,.951606,.949713,-.167965,.235324,.979958,.955818,-.151109,.208408,1.00765,.961344,-.133834,.182591,1.03329,.965469,-.115987,.156958,1.0557,.968693,-.09746,.132239,1.07583,.973165,-.0778514,.106195,1.09451,.979387,-.0585067,.0797669,1.11137,.98671,-.0390409,.0530263,1.12643,.994093,-.019408,.0263163,1.14016,1.00002,540029e-9,-194487e-9,1.15299,.574483,-989066e-11,.494533,114896e-10,.574478,-249127e-9,.494528,289403e-9,.574607,-996811e-9,.494637,.00115797,.574396,-.00224241,.494458,.00260498,.574377,-.00398632,.49444,.00463102,.574386,-.00622836,.494445,.00723623,.574401,-.0089683,.494453,.010421,.574419,-.0122056,.49446,.0141859,.574459,-.0159396,.494481,.0185322,.574525,-.0201692,.49452,.0234617,.574587,-.0248924,.494547,.0289762,.574697,-.0301074,.494604,.0350797,.574853,-.0358114,.494688,.0417767,.575027,-.041999,.494772,.0490718,.575294,-.0486618,.494915,.0569728,.575733,-.0557148,.495173,.0654955,.576356,-.0630489,.495537,.0746612,.576944,-.0709285,.495836,.0844615,.57765,-.0792723,.496177,.0949142,.578491,-.0880167,.496563,.10603,.579639,-.0969462,.497096,.117841,.580989,-.10622,.497684,.130367,.582587,-.115861,.498337,.143609,.584951,-.125605,.499414,.157625,.587602,-.135608,.500518,.172413,.59076,-.145742,.501767,.187999,.594992,-.155934,.503542,.20445,.600656,-.166303,.506135,.221764,.607816,-.176681,.509542,.24002,.61522,-.187071,.51263,.258992,.623702,-.197465,.516021,.278773,.634192,-.207816,.520422,.299377,.644936,-.218183,.524073,.320802,.657888,-.2278,.528049,.34384,.670666,-.236747,.52986,.36916,.685626,-.24484,.531892,.395867,.701304,-.252071,.532727,.423488,.717727,-.258714,.532146,.452201,.733914,-.264211,.529883,.481579,.750529,-.26859,.5259,.511558,.76747,-.272046,.51999,.542042,.785189,-.274225,.513083,.572799,.800954,-.275189,.502936,.603816,.816962,-.274946,.490921,.635461,.83336,-.272695,.47684,.6676,.848143,-.268223,.459405,.70051,.861818,-.262768,.440319,.732902,.876828,-.255872,.420123,.765084,.889312,-.247703,.398379,.796391,.900412,-.238381,.374496,.827333,.912251,-.227783,.349874,.858385,.921792,-.214832,.323181,.888652,.931273,-.200949,.296624,.917763,.940295,-.186537,.269211,.947878,.946812,-.171538,.241447,.977016,.953588,-.155254,.213829,1.00501,.958841,-.137156,.186807,1.03179,.963746,-.118699,.160706,1.05502,.966468,-.0998358,.135504,1.07568,.971178,-.0805186,.109131,1.09479,.97831,-.0599348,.0818293,1.1123,.985886,-.0399661,.0545872,1.12771,.994021,-.0198682,.0269405,1.14186,1.00009,271022e-9,-12989e-8,1.15514,.538716,-990918e-11,.486732,109675e-10,.550656,-250642e-9,.497518,277412e-9,.55057,-.00100265,.497441,.00110974,.550903,-.00225672,.497733,.00249779,.550568,-.00401046,.497438,.00443906,.550574,-.00626613,.49744,.00693637,.550591,-.0090226,.497449,.00998921,.550623,-.0122795,.497469,.0135984,.550667,-.0160361,.497495,.0177654,.550724,-.0202908,.497526,.0224915,.550792,-.0250421,.497557,.0277795,.550918,-.0302878,.49763,.0336334,.551058,-.0360241,.497701,.0400573,.551276,-.0422473,.497824,.0470585,.551551,-.0489441,.497977,.0546433,.552074,-.0559596,.498312,.0628367,.552681,-.0633978,.498679,.071646,.553324,-.0713176,.499031,.0810746,.554011,-.0797268,.499365,.091129,.55488,-.0885238,.499779,.101837,.556171,-.0974417,.500444,.113239,.557498,-.106841,.501025,.125316,.559299,-.116533,.501864,.138128,.561647,-.126298,.502967,.151695,.564347,-.136388,.504129,.16604,.567863,-.146576,.505713,.181207,.572569,-.156832,.507953,.197259,.578919,-.167323,.511186,.214258,.585387,-.177712,.514042,.232038,.593134,-.188184,.517484,.250733,.603295,-.198717,.522345,.270454,.613854,-.209177,.526751,.290807,.626092,-.219644,.531595,.312202,.637868,-.229494,.534721,.334435,.652458,-.238718,.538304,.359184,.666985,-.247061,.539875,.385637,.683301,-.254652,.541042,.41328,.69998,-.261376,.540735,.441903,.717824,-.267085,.539139,.471609,.734617,-.271465,.534958,.501446,.753663,-.27528,.53032,.532571,.770512,-.277617,.522134,.563641,.787356,-.278525,.51206,.595067,.806252,-.278512,.50119,.627226,.822061,-.277023,.486791,.659402,.838959,-.273175,.470467,.692874,.85379,-.267238,.450688,.725702,.868268,-.260327,.429741,.75832,.881994,-.251946,.407223,.790189,.893885,-.242432,.383214,.821625,.905118,-.231904,.357297,.853011,.916045,-.219545,.330733,.883773,.927614,-.205378,.303916,.914435,.936005,-.190388,.275941,.944502,.944533,-.1749,.247493,.974439,.950758,-.158588,.218996,1.00286,.957078,-.141027,.191559,1.0304,.962448,-.121507,.164457,1.05466,.964993,-.102068,.138636,1.0761,.970017,-.0822598,.111861,1.09541,.97661,-.062033,.0843438,1.11317,.985073,-.0409832,.0558496,1.12911,.993515,-.020146,.0275331,1.1438,1.00006,27329e-8,-107883e-9,1.15736,.525324,-999341e-11,.498153,105385e-10,.526513,-251605e-9,.499277,265329e-9,.526517,-.00100641,.499282,.0010613,.526588,-.00226466,.499337,.00238823,.526539,-.0040255,.499302,.00424535,.526547,-.00628954,.499306,.00663364,.526561,-.00905628,.499313,.00955337,.526593,-.0123253,.499334,.0130054,.526642,-.0160957,.499365,.0169911,.5267,-.0203661,.499396,.0215122,.526792,-.0251347,.499451,.0265718,.526904,-.0303985,.499511,.0321732,.527079,-.0361554,.499617,.0383231,.527285,-.0423982,.499731,.045026,.527602,-.0491121,.499924,.0522936,.528166,-.0561127,.500306,.0601528,.52879,-.0635988,.5007,.0686059,.529421,-.071581,.501048,.0776518,.530144,-.0799854,.501421,.0873148,.531062,-.0888032,.501884,.0976084,.532374,-.0977643,.50259,.108588,.533828,-.107197,.50329,.120234,.53581,-.116887,.504312,.132602,.538063,-.126755,.505365,.145721,.5409,-.136819,.506668,.159617,.544882,-.147117,.508731,.174369,.550238,-.157446,.511601,.190028,.556038,-.167988,.514431,.206587,.563031,-.178364,.517808,.224046,.571543,-.189007,.521937,.242503,.582255,-.199546,.527415,.261977,.59272,-.210084,.531682,.282162,.605648,-.220448,.537123,.303426,.61785,-.230593,.540664,.325323,.632223,-.240238,.544467,.348993,.648819,-.24887,.547594,.375462,.665825,-.256657,.54912,.403024,.683389,-.263711,.549294,.431773,.701495,-.269666,.547649,.461494,.719197,-.274169,.543786,.491623,.737906,-.278124,.538644,.522994,.756652,-.280632,.531057,.554775,.775279,-.281741,.521972,.586441,.792688,-.281652,.509613,.618596,.811894,-.280345,.496497,.651462,.827938,-.277128,.47968,.684023,.844837,-.271646,.460688,.718024,.859239,-.264397,.438872,.751207,.874088,-.256144,.41577,.784232,.887693,-.246311,.391369,.816191,.899402,-.235497,.365872,.847828,.910973,-.223631,.338618,.87934,.92204,-.209874,.310803,.910325,.930987,-.194265,.281802,.940695,.94,-.178125,.252836,.970958,.948018,-.161479,.224239,1.00078,.955141,-.144038,.195857,1.0288,.960513,-.124915,.168487,1.05371,.963964,-.104284,.141495,1.07596,.968713,-.0838732,.114437,1.09628,.975524,-.0635579,.0863105,1.11448,.98431,-.042291,.0574774,1.13069,.992916,-.0209131,.0284343,1.14568,.999926,743097e-9,-379265e-9,1.15955,.501042,-998428e-11,.498726,100306e-10,.502992,-252112e-9,.500665,253283e-9,.502417,-.00100791,.500092,.00101259,.502965,-.00226919,.500621,.00227978,.502318,-.00403109,.499994,.00405011,.502333,-.00629832,.500005,.00632868,.502362,-.00906907,.500027,.00911446,.502369,-.0123423,.500023,.0124078,.50243,-.0161178,.500066,.016211,.502493,-.0203937,.500103,.0205256,.502592,-.0251684,.500166,.0253548,.502707,-.0304389,.50023,.0307029,.502881,-.0362015,.500335,.0365753,.503124,-.0424507,.500488,.0429798,.503443,-.0491582,.500686,.0499268,.504083,-.0561476,.501155,.0574541,.504668,-.0636846,.501524,.0655408,.505319,-.0716834,.501904,.0742072,.50609,-.0800925,.502321,.0834699,.507122,-.0888425,.502896,.0933603,.508414,-.097855,.503603,.10391,.509955,-.107304,.504416,.115113,.512061,-.116921,.505565,.127054,.514419,-.12689,.506732,.139709,.517529,-.136934,.508338,.153173,.522085,-.147327,.510987,.167528,.526986,-.157612,.513527,.182708,.533122,-.168213,.516717,.198881,.540807,-.178688,.520832,.215986,.550687,-.189511,.52632,.234335,.560567,-.199998,.531009,.253375,.571698,-.210652,.535839,.273499,.584364,-.220917,.541091,.294355,.599066,-.23137,.546875,.316525,.614148,-.241206,.551306,.339671,.631157,-.250379,.555187,.36531,.647919,-.258397,.556595,.392767,.666112,-.265528,.556949,.421397,.686158,-.271827,.556617,.451433,.704838,-.27674,.552975,.482131,.723957,-.280733,.547814,.513458,.74262,-.283359,.53997,.545446,.762009,-.284541,.530422,.57775,.781314,-.284507,.518546,.610434,.799116,-.283309,.504178,.643178,.817604,-.280378,.48843,.676248,.83459,-.275619,.469457,.709698,.850974,-.26856,.447698,.744245,.866747,-.260094,.424791,.777695,.881412,-.249929,.399913,.810392,.8936,-.239137,.37308,.842872,.905943,-.226818,.345705,.874677,.916408,-.213699,.31706,.906257,.927215,-.198428,.288444,.936881,.935625,-.181643,.258329,.96795,.944076,-.164386,.228488,.998216,.951229,-.146339,.199763,1.02689,.958793,-.127709,.172153,1.0535,.963219,-.107244,.144989,1.07646,.967562,-.0857764,.11685,1.09675,.974866,-.0645377,.0880571,1.11576,.983353,-.0431732,.0587352,1.13227,.992503,-.0218356,.0294181,1.1478,1.00003,605203e-9,-231013e-9,1.16207,.482935,-101177e-10,.504695,968142e-11,.477554,-251521e-9,.499071,240676e-9,.477904,-.00100683,.499436,96342e-8,.478368,-.00226636,.499899,.0021687,.477977,-.00402719,.499513,.00385384,.477993,-.00629226,.499525,.0060221,.478011,-.00906011,.499536,.00867289,.478051,-.0123305,.499566,.0118074,.478089,-.016102,.499587,.0154269,.478171,-.0203736,.499645,.0195341,.478254,-.025143,.499692,.0241318,.47839,-.0304071,.499779,.0292247,.478588,-.0361631,.499911,.0348196,.478812,-.0424023,.500046,.0409231,.479208,-.0490724,.500326,.047552,.479841,-.0560722,.500805,.0547377,.480392,-.0636125,.501152,.0624607,.481068,-.0716134,.501561,.0707473,.481898,-.0800062,.502054,.0796118,.483022,-.0886568,.502728,.0890974,.484332,-.0977553,.503479,.0992099,.486126,-.107173,.504546,.10999,.488066,-.11677,.50557,.121476,.490521,-.126725,.506849,.133672,.494232,-.136793,.50911,.146731,.498302,-.147116,.511345,.160577,.503565,-.157446,.514344,.175335,.510902,-.168121,.518824,.191207,.519263,-.178799,.523666,.208058,.528204,-.189407,.528296,.225875,.538854,-.200145,.533724,.244782,.551278,-.210701,.539833,.264753,.565222,-.221303,.546131,.285745,.579403,-.231688,.551496,.307592,.595469,-.241718,.556809,.330582,.610929,-.250992,.559641,.354995,.629433,-.259602,.562379,.382471,.648504,-.267038,.563676,.411126,.66756,-.273388,.562092,.440924,.689143,-.278788,.560807,.472118,.709056,-.282783,.555701,.503774,.729855,-.285836,.548698,.536364,.748954,-.287078,.538544,.56895,.768373,-.287133,.526711,.601991,.78827,-.285839,.512511,.635403,.807465,-.283238,.496323,.668797,.825194,-.27906,.477638,.702584,.842203,-.272286,.456253,.736393,.857749,-.263854,.432412,.77096,.874799,-.253943,.407806,.80489,.887497,-.24237,.38033,.83771,.89966,-.230278,.352446,.870376,.911753,-.21646,.323268,.902256,.923011,-.202071,.294314,.933306,.932375,-.185519,.264104,.965177,.940537,-.167604,.234035,.996303,.948904,-.149068,.20412,1.0261,.955263,-.129539,.175431,1.05304,.960303,-.109932,.148116,1.07617,.965512,-.0880572,.119693,1.09742,.973466,-.0660548,.0901619,1.11721,.98284,-.0439228,.0599875,1.13436,.992216,-.0219588,.0298975,1.15006,.999946,119402e-9,-208547e-10,1.16471,.447827,-100414e-10,.491543,914833e-11,.454778,-251257e-9,.499172,22891e-8,.453519,-.00100342,.497787,914184e-9,.45357,-.00225776,.497847,.00205701,.453578,-.00401371,.497855,.00365705,.45357,-.00627107,.497841,.00571453,.453598,-.00902968,.497864,.00823019,.453627,-.0122888,.497882,.0112049,.453684,-.0160475,.497923,.0146405,.453764,-.0203044,.49798,.0185394,.453866,-.0250576,.498049,.0229054,.453996,-.0303028,.49813,.0277424,.454196,-.0360379,.498267,.0330587,.454457,-.0422521,.498445,.0388613,.454926,-.0488393,.498812,.0451767,.455525,-.0558653,.499272,.0520153,.456074,-.0633772,.499625,.0593754,.456752,-.0713606,.500049,.0672751,.457648,-.07971,.500615,.0757447,.458849,-.0883032,.501399,.0848231,.46029,-.0974095,.502293,.0945135,.462,-.106729,.503301,.104848,.464121,-.116354,.504533,.115884,.466889,-.126214,.506172,.127652,.470744,-.136324,.508667,.14024,.47488,-.146595,.510995,.153673,.480845,-.157027,.514832,.168053,.488262,-.167658,.519506,.183508,.496547,-.178343,.524347,.199948,.506254,-.188916,.52983,.217503,.517961,-.199975,.536357,.236272,.531484,-.210624,.543641,.256096,.545496,-.221227,.550048,.277085,.559497,-.231568,.555076,.298615,.575752,-.241698,.560541,.321547,.591999,-.251172,.564156,.345602,.610654,-.260178,.567607,.371851,.630484,-.268094,.56923,.40076,.651807,-.274661,.569779,.430801,.67239,-.280331,.566791,.461939,.693024,-.284501,.562007,.493854,.715473,-.287852,.555791,.526992,.736323,-.28929,.546345,.560102,.755771,-.289405,.534,.593543,.775424,-.2881,.519114,.627256,.795447,-.285562,.502543,.661464,.815319,-.281416,.484773,.695206,.831769,-.275523,.463445,.729044,.849464,-.267516,.440269,.764069,.866775,-.257584,.415049,.799089,.881252,-.245817,.388049,.831948,.894209,-.233127,.35889,.865526,.906922,-.219579,.329915,.89818,.919686,-.204491,.300441,.930013,.929044,-.188962,.269445,.962061,.938393,-.171079,.238402,.994214,.94661,-.15199,.208204,1.02533,.953095,-.131953,.178653,1.0529,.958644,-.111233,.150684,1.0771,.963925,-.0903098,.122359,1.09855,.971995,-.0680505,.0923342,1.11874,.981658,-.0448512,.0614195,1.13635,.991649,-.0221931,.0303582,1.15238,.999985,393403e-9,-111086e-9,1.16772,.396806,-971563e-11,.457671,842355e-11,.429186,-249421e-9,.495017,21625e-8,.429324,-998052e-9,.495173,865322e-9,.429175,-.00224487,.494999,.00194637,.429129,-.00399041,.494952,.00346004,.429153,-.00623476,.494974,.00540684,.429168,-.0089773,.494983,.00778714,.429207,-.0122175,.495012,.0106022,.429257,-.0159542,.495047,.0138535,.429338,-.0201864,.495106,.0175443,.429431,-.0249104,.495165,.0216774,.429587,-.0301252,.495279,.0262594,.429796,-.0358249,.495432,.0312968,.430065,-.0419972,.495621,.0367985,.430588,-.0485144,.496061,.042798,.43113,-.0555028,.496472,.0492914,.431743,-.0629852,.496904,.0562907,.432448,-.0709256,.497369,.0638056,.433414,-.0791942,.498032,.071885,.434638,-.0877346,.498854,.0805517,.43611,-.0968056,.499812,.0898047,.437859,-.106002,.500891,.0997142,.440017,-.115648,.502198,.110289,.443236,-.125427,.504389,.121644,.44697,-.135492,.506809,.133769,.451689,-.145746,.509858,.146787,.45811,-.156219,.514247,.160793,.465305,-.166834,.518816,.175791,.474085,-.177546,.524331,.191906,.484808,-.188262,.53104,.209199,.49732,-.199346,.538511,.227825,.509693,-.209951,.544554,.247269,.524367,-.220533,.551616,.267978,.539228,-.231082,.557368,.289672,.55644,-.241342,.563782,.31268,.574204,-.250964,.568851,.33651,.593388,-.260306,.57312,.362219,.613358,-.268667,.574916,.390322,.634512,-.275591,.575053,.420478,.65563,-.281328,.572404,.451614,.678265,-.285948,.568893,.484112,.70011,-.289408,.561878,.517348,.723005,-.291328,.55359,.551355,.743744,-.291418,.541099,.585109,.763949,-.290252,.526489,.619487,.784186,-.287648,.509496,.65404,.804304,-.283782,.491484,.688649,.823629,-.278067,.470517,.723133,.84094,-.270588,.44705,.757163,.857852,-.261188,.421252,.792816,.874934,-.249313,.394191,.827248,.888709,-.236492,.365359,.861074,.902589,-.222185,.336016,.894417,.914201,-.207314,.30527,.926825,.925978,-.191146,.274532,.9595,.93512,-.174135,.243393,.991583,.943656,-.155231,.212414,1.02356,.951719,-.134403,.182005,1.05239,.957164,-.113023,.153043,1.07754,.962656,-.0914493,.124186,1.09984,.970695,-.0694179,.0941654,1.12,.980749,-.0466199,.0629671,1.13849,.991205,-.0227032,.0311146,1.15494,.999884,632388e-9,-254483e-9,1.1706,.379821,-957289e-11,.460637,789337e-11,.405188,-247483e-9,.491396,204064e-9,.404796,-989434e-9,.490914,815853e-9,.40483,-.00222607,.490949,.00183559,.40473,-.00395723,.49084,.00326332,.404731,-.00618287,.490836,.00509945,.404768,-.00890258,.490871,.00734463,.404791,-.0121156,.490883,.00999992,.404857,-.0158214,.490938,.0130676,.404943,-.0200178,.491004,.0165503,.405059,-.0247027,.491093,.0204521,.405213,-.0298729,.491205,.0247788,.405399,-.0355226,.491333,.0295373,.405731,-.0416352,.491604,.034741,.406303,-.0480807,.492116,.0404255,.406814,-.0550458,.492506,.0465732,.407404,-.0624652,.492926,.0532058,.408149,-.0702958,.493442,.0603442,.409128,-.0784623,.494136,.0680297,.410408,-.087007,.495054,.0762786,.411813,-.0959639,.495962,.0851046,.413735,-.105075,.497257,.0945878,.416137,-.114646,.498882,.104725,.41934,-.124394,.501132,.11563,.423326,-.134328,.503883,.127325,.428419,-.14458,.50747,.139911,.43484,-.154979,.511964,.153481,.442641,-.165628,.517328,.168114,.452511,-.176365,.524258,.183995,.463473,-.187298,.531248,.200953,.475564,-.198244,.538367,.219176,.488664,-.208938,.545175,.238514,.504073,-.219599,.553227,.259129,.520832,-.230378,.560653,.280997,.538455,-.240703,.567523,.303821,.55709,-.250548,.573287,.327948,.576646,-.259964,.577795,.353362,.596705,-.268721,.580077,.380336,.618053,-.276054,.58018,.4101,.640303,-.282176,.578747,.44161,.662365,-.286931,.574294,.474106,.684542,-.290521,.567035,.507549,.707984,-.292672,.558687,.541853,.730913,-.293189,.547606,.576581,.752948,-.292199,.533471,.61172,.773452,-.289508,.516395,.646339,.794715,-.285716,.497873,.682131,.814251,-.280051,.476845,.716396,.833057,-.272873,.453449,.751503,.84959,-.263982,.427857,.786085,.867022,-.252745,.400335,.821355,.882277,-.239655,.371304,.85646,.895375,-.225386,.340397,.890828,.909347,-.209587,.310005,.923532,.921885,-.193433,.2796,.956419,.932127,-.176135,.247276,.989445,.941869,-.157872,.216186,1.02221,.949735,-.137577,.185602,1.05195,.956617,-.115285,.155767,1.07822,.961974,-.0928418,.126103,1.10149,.96972,-.0700592,.0956758,1.12207,.98012,-.0474671,.0643269,1.1408,.990825,-.0238113,.0320863,1.1577,.999876,381574e-9,-812203e-10,1.17403,.367636,-961342e-11,.469176,753287e-11,.380377,-244772e-9,.485434,191797e-9,.380416,-978857e-9,.485475,767015e-9,.380376,-.00220165,.485435,.00172522,.380419,-.00391408,.485487,.00306734,.380438,-.00611549,.485505,.00479332,.380462,-.00880558,.485525,.00690391,.380496,-.0119837,.485551,.00940039,.38056,-.0156487,.485605,.0122848,.38064,-.0197988,.485666,.0155601,.380767,-.0244324,.48577,.0192313,.380909,-.0295444,.485871,.0233032,.381142,-.0351321,.48606,.0277861,.381472,-.0411535,.486336,.0326939,.382015,-.0475408,.486833,.0380565,.382523,-.0544395,.487231,.0438615,.383129,-.061784,.487683,.0501332,.383952,-.0695085,.488313,.0568996,.38498,-.0775819,.489077,.0641952,.386331,-.0860443,.490113,.0720324,.387788,-.0948406,.491099,.0804379,.389808,-.103899,.492566,.0894899,.39252,-.113313,.494601,.0992098,.395493,-.123007,.496619,.109641,.399826,-.132859,.499912,.120919,.405341,-.143077,.504061,.133107,.411932,-.153465,.508905,.146263,.420591,-.164108,.515482,.160544,.43101,-.174893,.523191,.176123,.441881,-.185839,.53026,.192757,.453919,-.196633,.537295,.210535,.468715,-.207611,.546156,.229886,.485182,-.218517,.555173,.250543,.501926,-.229249,.562728,.27221,.51785,-.239481,.567494,.294892,.536947,-.249395,.573889,.318987,.557115,-.259,.578831,.344348,.577966,-.268075,.582055,.371223,.599489,-.276115,.583307,.399834,.62479,-.282523,.583902,.431415,.647504,-.287663,.57953,.464301,.670601,-.291538,.573103,.498123,.693539,-.293842,.563731,.532662,.717385,-.294681,.553169,.567925,.741533,-.293717,.539908,.603502,.762142,-.291156,.521902,.639074,.783014,-.28719,.502815,.674439,.805158,-.281773,.482598,.710497,.823646,-.274682,.458949,.7456,.841879,-.266184,.433129,.781085,.859515,-.255682,.406064,.816,.875335,-.242849,.376509,.851074,.890147,-.228329,.345502,.886473,.903144,-.212491,.31428,.920751,.916618,-.195695,.282994,.954606,.927953,-.178267,.251091,.988402,.937414,-.159549,.219107,1.02141,.946823,-.140022,.18896,1.05167,.954651,-.118154,.158667,1.07819,.959955,-.0946636,.128808,1.1025,.96858,-.0711792,.0973787,1.12391,.97938,-.0475046,.0650965,1.14322,.990498,-.024059,.0326267,1.16077,.999844,-512408e-10,112444e-9,1.17727,.316912,-934977e-11,.425996,695559e-11,.356423,-241372e-9,.479108,179562e-9,.356272,-965292e-9,.478897,71811e-8,.356262,-.00217182,.478894,.00161574,.356265,-.00386092,.478895,.00287261,.356278,-.0060324,.478905,.00448907,.356293,-.00868565,.478914,.00646572,.356346,-.0118207,.478965,.00880438,.356395,-.0154355,.479001,.0115066,.356484,-.019529,.479075,.0145762,.356609,-.0240991,.47918,.018018,.356766,-.0291413,.479305,.0218379,.357009,-.0346498,.479512,.0260454,.357424,-.0405462,.479909,.0306657,.357899,-.0468825,.480337,.0357054,.358424,-.0536887,.480771,.0411728,.359041,-.0609416,.481242,.0470841,.359903,-.0685239,.481943,.0534831,.360932,-.0764883,.482741,.0603795,.362196,-.0848364,.483688,.0678028,.363847,-.0935002,.484947,.0758086,.365972,-.102471,.486588,.0844173,.368741,-.111751,.488787,.0937199,.372146,-.121334,.491405,.103732,.377114,-.131147,.495604,.114608,.38226,-.141213,.499436,.126345,.389609,-.151632,.505334,.139116,.397925,-.162073,.51168,.152995,.407824,-.172819,.518876,.168071,.420014,-.183929,.527639,.184495,.434266,-.195032,.537588,.20232,.447352,-.205792,.544379,.221189,.463726,-.216704,.553422,.241616,.481406,-.227531,.562074,.263298,.498707,-.238017,.568227,.286116,.518039,-.247936,.574473,.3101,.538277,-.257437,.579191,.335401,.561166,-.266829,.584807,.362246,.583189,-.275329,.586476,.390609,.606024,-.28234,.585578,.420998,.632419,-.287924,.584496,.454357,.656128,-.291972,.577766,.488233,.679953,-.29456,.56875,.523248,.704654,-.295816,.558388,.559168,.729016,-.295157,.544826,.595326,.752062,-.292779,.528273,.631864,.773138,-.288681,.508482,.667793,.794869,-.283358,.487341,.704035,.815101,-.27608,.46354,.739925,.834212,-.26767,.438672,.775539,.852368,-.257397,.411239,.810895,.870207,-.245689,.3829,.846472,.884063,-.231452,.351496,.881788,.898284,-.215561,.31895,.917438,.912964,-.198208,.287367,.952422,.924666,-.180426,.254487,.987551,.934429,-.161525,.222226,1.02142,.943485,-.141197,.191143,1.05218,.9521,-.120085,.161112,1.07937,.957876,-.0975881,.130982,1.10403,.966943,-.0726842,.0990553,1.12616,.978313,-.0483705,.0662818,1.14619,.990048,-.0239072,.0329243,1.16413,.999984,461885e-9,-772859e-10,1.18099,.321287,-935049e-11,.455413,659662e-11,.332595,-237513e-9,.471437,167562e-9,.332729,-949964e-9,.471618,670192e-9,.332305,-.00213618,.471028,.00150712,.332326,-.00379765,.471055,.00267959,.332344,-.00593353,.471072,.00418751,.332356,-.00854349,.471077,.00603172,.332403,-.0116268,.471121,.00821362,.332461,-.0151824,.47117,.0107357,.332552,-.0192088,.471251,.0136014,.332657,-.0237024,.47133,.0168152,.332835,-.0286615,.471487,.0203853,.333083,-.0340765,.471708,.0243212,.333547,-.0398563,.47219,.0286518,.333989,-.0460916,.472587,.0333763,.334532,-.0527897,.473054,.0385084,.335167,-.0599284,.473568,.0440638,.33608,-.0673514,.474362,.0500962,.337146,-.0752237,.475231,.0566022,.338462,-.083418,.476282,.0636272,.34014,-.0919382,.477615,.0712153,.342341,-.100741,.479404,.079417,.345088,-.109905,.481618,.0882631,.349049,-.119369,.485081,.0978851,.353939,-.129033,.489317,.108336,.359893,-.139038,.494309,.119698,.366945,-.149411,.499983,.132024,.375814,-.159843,.507185,.145558,.387112,-.170664,.516392,.160433,.40023,-.181897,.526519,.176648,.412555,-.192785,.53423,.193922,.427023,-.203663,.542741,.212662,.443685,-.214695,.552066,.232944,.461499,-.225561,.560762,.254495,.480975,-.236257,.569421,.277531,.501,-.24639,.576101,.301724,.521691,-.256101,.581493,.327112,.543478,-.265289,.585221,.353917,.566094,-.273938,.587614,.381941,.589578,-.281679,.587991,.41172,.614583,-.287655,.585928,.444148,.641813,-.292228,.582092,.478617,.666189,-.295172,.57398,.51397,.690475,-.29648,.561676,.550118,.715543,-.296203,.548758,.586933,.740405,-.293999,.532792,.62384,.762183,-.28998,.512735,.660723,.786069,-.28478,.492402,.69807,.806812,-.277568,.469058,.734422,.826987,-.268951,.443017,.770946,.844588,-.259049,.415501,.80699,.863725,-.2471,.387328,.842107,.879137,-.234157,.356108,.878078,.894634,-.218719,.324315,.914058,.909162,-.201293,.291813,.949922,.92072,-.18267,.258474,.985337,.93158,-.163212,.225593,1.0205,.941238,-.142771,.193986,1.05273,.949293,-.120956,.163392,1.08075,.956226,-.0985743,.132934,1.10559,.96546,-.075118,.101255,1.12823,.977403,-.0497921,.0675441,1.149,.989648,-.0241574,.0334681,1.16765,1.00001,5762e-7,-184807e-9,1.18519,.303474,-916603e-11,.4542,61243e-10,.308894,-232869e-9,.462306,155592e-9,.309426,-931661e-9,.463093,622499e-9,.308643,-.0020949,.461933,.00139979,.308651,-.0037242,.461941,.00248874,.308662,-.00581873,.46195,.00388933,.308687,-.00837818,.461974,.00560247,.308728,-.0114016,.462011,.00762948,.308789,-.0148884,.462067,.00997326,.308882,-.0188369,.462151,.0126375,.309007,-.0232436,.462263,.0156271,.30918,-.0281054,.462417,.0189498,.309442,-.0334065,.462667,.0226167,.309901,-.0390589,.463162,.0266614,.310331,-.0452042,.463555,.0310715,.310858,-.0517735,.464019,.0358698,.311576,-.0587359,.464669,.0410848,.312436,-.0660383,.465406,.0467453,.313526,-.0737266,.466339,.0528718,.314903,-.0817574,.467504,.0595039,.316814,-.090167,.469226,.0666888,.318965,-.0987555,.470981,.0744658,.322077,-.107792,.473814,.082912,.325947,-.117098,.477241,.0920846,.331008,-.126602,.48184,.102137,.337893,-.136619,.488334,.113135,.345106,-.146838,.494415,.12511,.355111,-.157357,.503275,.138356,.365095,-.167955,.510966,.152686,.378344,-.179157,.521508,.16856,.391599,-.190143,.530455,.18561,.407786,-.20123,.541275,.204308,.425294,-.212456,.551784,.224623,.444021,-.223568,.561493,.246172,.463418,-.234154,.569886,.268979,.484077,-.244546,.577116,.293411,.505513,-.254301,.582914,.318936,.527672,-.263564,.587208,.345856,.550565,-.272332,.589277,.374054,.573656,-.280011,.588426,.403276,.59827,-.286924,.587504,.43474,.624731,-.291994,.583401,.468767,.652396,-.295159,.576997,.504411,.67732,-.296954,.565863,.54114,.703147,-.296877,.552316,.57816,.728715,-.295147,.536773,.616124,.752448,-.291275,.51771,.653885,.775169,-.285905,.496087,.691537,.799307,-.279064,.474232,.729251,.819482,-.270294,.447676,.766267,.837659,-.260032,.419656,.802616,.856903,-.248497,.391328,.838583,.873325,-.235252,.360285,.874711,.889788,-.221126,.329215,.91077,.904486,-.204304,.296392,.94653,.917711,-.185562,.262159,.983828,.928969,-.165635,.229142,1.01955,.939707,-.14442,.19673,1.05317,.948167,-.122147,.165095,1.0823,.955222,-.099098,.13451,1.10791,.964401,-.0755332,.102476,1.1312,.976605,-.0513817,.0689667,1.15218,.989085,-.0258499,.034506,1.17129,.999908,617773e-9,-271268e-9,1.18961,.285803,-905752e-11,.452348,572272e-11,.284689,-22732e-8,.450581,143626e-9,.285263,-910214e-9,.451482,575099e-9,.285302,-.00204784,.451553,.00129395,.285318,-.00364057,.451574,.0023006,.28533,-.00568813,.451585,.00359547,.285361,-.00819001,.451618,.00517934,.285397,-.0111458,.45165,.007054,.285447,-.0145536,.451688,.00922167,.285527,-.0184127,.451758,.0116869,.285688,-.0227207,.451929,.0144555,.28584,-.0274712,.452055,.0175341,.286136,-.0326278,.452369,.0209406,.286574,-.0381792,.452853,.0246965,.287012,-.0441879,.453272,.0287996,.287542,-.0506096,.453752,.033268,.288299,-.0573634,.454488,.0381504,.289186,-.0645458,.455294,.0434447,.290302,-.0720405,.456301,.0491973,.291776,-.0799046,.457648,.0554453,.29372,-.088117,.459483,.0622311,.296052,-.0965328,.461571,.0695992,.299563,-.105409,.465085,.077658,.30335,-.114553,.468506,.0864176,.309167,-.123917,.474423,.0961078,.31529,-.13381,.47995,.106643,.324163,-.144021,.488592,.118322,.333272,-.154382,.496461,.131133,.344224,-.165015,.50562,.145208,.357733,-.176168,.516719,.16073,.373046,-.187468,.528513,.177807,.38788,-.198488,.537713,.196072,.405133,-.209545,.547999,.21605,.423845,-.220724,.55759,.237484,.443777,-.231518,.566246,.26039,.464824,-.242035,.574326,.284835,.486635,-.251898,.58037,.310518,.51012,-.261304,.58568,.337678,.535301,-.270384,.590197,.366242,.559193,-.27841,.590569,.395873,.583544,-.285325,.588161,.426857,.608834,-.291113,.584249,.459477,.635753,-.294882,.57763,.494734,.664367,-.297088,.569479,.532023,.689688,-.297364,.555064,.569629,.715732,-.295949,.539522,.608124,.741307,-.292259,.521613,.646231,.764949,-.287063,.49969,.684938,.788599,-.28012,.476747,.723548,.81048,-.27153,.45116,.761135,.831372,-.261289,.424101,.798916,.850092,-.249559,.39443,.835952,.867777,-.236348,.363849,.871606,.884632,-.221569,.332477,.907843,.90047,-.20618,.300667,.944187,.914524,-.188771,.266552,.981371,.926892,-.168362,.232349,1.01841,.937951,-.146761,.199359,1.05308,.947236,-.123813,.1675,1.0839,.954367,-.099984,.136166,1.11047,.963907,-.0759278,.103808,1.13414,.976218,-.0511367,.0697061,1.15575,.988772,-.0267415,.0352529,1.17531,.999888,-520778e-9,289926e-9,1.19389,.263546,-883274e-11,.441896,526783e-11,.262352,-221849e-9,.439889,132311e-9,.262325,-886683e-9,.439848,528824e-9,.26228,-.00199476,.439765,.00118975,.262372,-.00354671,.439922,.00211568,.26239,-.00554141,.439941,.00330652,.262412,-.00797888,.439961,.00476346,.262453,-.0108584,.440002,.00648818,.262528,-.0141788,.440085,.0084835,.262615,-.017938,.440166,.0107533,.262744,-.0221346,.440291,.0133044,.262939,-.026762,.440493,.0161445,.263277,-.0317573,.440889,.0192974,.26368,-.0371832,.441338,.0227699,.264106,-.0430371,.441753,.0265698,.264624,-.0493035,.442227,.0307178,.265378,-.0558669,.442985,.0352616,.266253,-.0628718,.443795,.0401968,.267478,-.0701569,.445008,.04559,.269062,-.077845,.446599,.0514539,.270926,-.0857941,.448349,.0578382,.273693,-.0940773,.451221,.0648363,.276746,-.102704,.454097,.0724389,.281693,-.111735,.459517,.0808744,.287335,-.121004,.46531,.0901551,.29448,-.130734,.472605,.100371,.30257,-.140777,.480251,.111644,.312465,-.15111,.489444,.124111,.324856,-.16189,.500919,.137979,.33774,-.172946,.511317,.153163,.35255,-.184152,.522684,.169817,.367786,-.19522,.53248,.187886,.385474,-.20632,.543326,.207634,.404976,-.217744,.554109,.229165,.425203,-.228691,.563395,.252068,.446704,-.239299,.571565,.276471,.468951,-.249348,.577935,.302323,.493487,-.258933,.584309,.329882,.517861,-.268009,.58773,.358525,.543309,-.276238,.589612,.388585,.569704,-.28356,.589294,.419787,.594871,-.289497,.585137,.452114,.622555,-.294452,.580356,.486466,.651167,-.296918,.57185,.523079,.677332,-.297647,.558428,.5611,.703718,-.296321,.542232,.599592,.730262,-.293339,.524541,.639138,.754304,-.288036,.502691,.677978,.778051,-.281018,.479212,.716537,.801557,-.272414,.454071,.75586,.822559,-.262419,.425952,.794477,.843051,-.250702,.397313,.832664,.86232,-.237264,.366534,.869876,.879044,-.222716,.334816,.906973,.896362,-.206827,.303143,.943558,.910342,-.189659,.269699,.979759,.924119,-.171108,.236411,1.01718,.935374,-.149579,.202224,1.05289,.944295,-.126295,.16989,1.08496,.952227,-.101511,.138089,1.11256,.962041,-.0766392,.105053,1.1375,.97528,-.0511967,.070329,1.15983,.988476,-.025463,.0351268,1.17987,.999962,286808e-10,145564e-10,1.19901,.227089,-841413e-11,.404216,472707e-11,.239725,-215083e-9,.426708,120833e-9,.239904,-860718e-9,.427028,483555e-9,.239911,-.00193661,.427039,.00108806,.239914,-.00344276,.42704,.00193457,.239933,-.00537907,.427064,.00302363,.239944,-.00774482,.427065,.00435604,.239993,-.01054,.427122,.00593398,.240052,-.0137626,.427179,.00775987,.240148,-.0174115,.427279,.00983854,.240278,-.021484,.42741,.0121763,.240472,-.0259729,.427618,.0147827,.240839,-.0308131,.428086,.0176837,.241201,-.0360893,.428482,.0208775,.241626,-.0417723,.428907,.0243821,.242207,-.0478337,.42952,.0282228,.24298,-.0542199,.430332,.0324333,.243881,-.0610015,.431222,.0370252,.245123,-.0680874,.432512,.0420535,.24667,-.0755482,.434088,.0475414,.248779,-.0832873,.436323,.0535542,.251665,-.0913546,.439509,.0601716,.255305,-.0998489,.443478,.0674282,.260049,-.108576,.448713,.0754673,.266192,-.117754,.455524,.084339,.273158,-.127294,.4627,.0941683,.282131,-.137311,.472068,.10515,.293332,-.147736,.483565,.117402,.304667,-.158357,.493702,.130824,.317785,-.169274,.504708,.145724,.333245,-.180595,.517107,.16215,.349843,-.191892,.528849,.180149,.367944,-.203168,.540301,.199746,.387579,-.214443,.551514,.221047,.408247,-.225624,.560906,.243981,.43014,-.236422,.56959,.268513,.452669,-.24654,.576098,.294409,.476196,-.256157,.580925,.322002,.501157,-.265289,.584839,.351052,.527632,-.273671,.587614,.3812,.555754,-.281254,.589119,.412994,.581682,-.287448,.585204,.445498,.608196,-.292614,.579006,.479505,.635661,-.296068,.571297,.514643,.664999,-.297395,.560855,.552213,.691039,-.296645,.544525,.591365,.7179,-.293785,.526535,.630883,.744059,-.289089,.50545,.670932,.76863,-.282239,.482514,.710904,.793273,-.273688,.457246,.750259,.814731,-.26328,.428872,.78948,.835603,-.251526,.399384,.828597,.85489,-.238339,.368811,.866892,.872828,-.223607,.336617,.90563,.889462,-.207538,.303997,.943538,.904929,-.190297,.270812,.980591,.919101,-.172034,.237453,1.01935,.930536,-.152058,.204431,1.05498,.941223,-.129515,.172495,1.08717,.94982,-.104263,.140175,1.11551,.960592,-.0781944,.106465,1.14098,.974629,-.051688,.0711592,1.16418,.98811,-.0253929,.0354432,1.18465,1.00004,804378e-9,-330876e-9,1.20462,.214668,-821282e-11,.406619,433582e-11,.218053,-208144e-9,.413025,109887e-9,.217987,-832212e-9,.412901,439362e-9,.217971,-.00187246,.412876,988623e-9,.217968,-.00332855,.41286,.00175772,.217985,-.00520055,.412882,.00274729,.218014,-.00748814,.412916,.00395842,.218054,-.0101901,.412957,.00539274,.218106,-.0133057,.413005,.00705348,.218217,-.0168342,.413139,.00894581,.218338,-.0207707,.413258,.0110754,.21855,-.0251001,.413509,.0134551,.218913,-.0297861,.413992,.0161081,.219265,-.0348956,.414383,.0190307,.219696,-.0403909,.414839,.0222458,.220329,-.0462003,.415567,.025792,.220989,-.0524208,.41621,.0296637,.222027,-.058948,.417385,.0339323,.223301,-.0658208,.418779,.0386055,.224988,-.0730347,.420665,.0437355,.227211,-.0805274,.423198,.0493844,.230131,-.088395,.426566,.0556135,.233908,-.0966208,.43091,.0624829,.239092,-.105223,.437148,.0701636,.245315,-.11424,.444302,.0786949,.253166,-.12368,.453262,.0882382,.262374,-.133569,.463211,.0988682,.273145,-.143836,.474271,.110727,.285512,-.154577,.4863,.123945,.299512,-.165501,.498817,.138581,.314287,-.176698,.510341,.154676,.331083,-.188066,.522583,.172459,.349615,-.199597,.534879,.191979,.369318,-.210843,.546083,.21309,.390377,-.222068,.5562,.235998,.412411,-.233059,.564704,.260518,.435715,-.24357,.572314,.286795,.461196,-.253356,.579395,.314559,.485587,-.262362,.581985,.343581,.511908,-.270895,.584347,.374367,.539798,-.278452,.58505,.406015,.567974,-.284877,.583344,.439168,.594303,-.290124,.577348,.473005,.622951,-.294183,.570751,.508534,.652404,-.296389,.561541,.544764,.679291,-.296605,.546426,.582927,.706437,-.294095,.528599,.622681,.734485,-.28978,.508676,.663567,.758841,-.283363,.484768,.704092,.78537,-.275015,.460434,.745101,.807315,-.264689,.432166,.784712,.8271,-.252597,.401807,.824241,.849191,-.239154,.371458,.863803,.867046,-.224451,.338873,.903063,.8852,-.208342,.306175,.942763,.901771,-.190684,.272759,.981559,.915958,-.172105,.239306,1.02048,.928046,-.152214,.206071,1.05765,.939961,-.130247,.17367,1.08999,.948711,-.10672,.142201,1.11829,.959305,-.0808688,.108454,1.14467,.973009,-.0539145,.0728109,1.16839,.987631,-.0262947,.0360625,1.19004,.999978,.00132758,-559424e-9,1.21058,.193925,-793421e-11,.391974,392537e-11,.196746,-200315e-9,.397675,991033e-10,.19667,-801099e-9,.397521,396342e-9,.196633,-.00180246,.397445,891829e-9,.196654,-.00320443,.397482,.00158582,.196659,-.00500647,.39748,.00247867,.196683,-.0072086,.397506,.00357167,.196728,-.00981001,.397562,.00486675,.196792,-.0128096,.397633,.00636707,.19689,-.0162055,.397746,.00807752,.197017,-.0199943,.397884,.0100052,.19729,-.024139,.39827,.0121691,.197583,-.0286671,.398639,.0145755,.197927,-.0335858,.399034,.0172355,.198383,-.0388806,.399554,.0201718,.199002,-.0444736,.400289,.0234194,.199739,-.0504583,.401111,.026984,.200784,-.056729,.402349,.0309217,.202075,-.0633643,.403841,.0352496,.203898,-.0703247,.406076,.0400313,.206199,-.0775565,.408841,.0453282,.209252,-.085184,.41259,.0511794,.213638,-.0931994,.418288,.0577459,.21881,-.101617,.424681,.0650508,.225642,-.11052,.433429,.0732759,.233717,-.119772,.442897,.0824683,.242823,-.129505,.452888,.0927484,.254772,-.139906,.466407,.104417,.266603,-.150402,.477413,.117211,.28073,-.161395,.490519,.131598,.295399,-.172465,.50201,.147407,.312705,-.183982,.515311,.165031,.331335,-.195532,.52786,.184336,.351037,-.206971,.5392,.205361,.372175,-.218117,.54941,.228043,.394548,-.229327,.558642,.25267,.419598,-.240052,.567861,.279071,.443922,-.249937,.573332,.306882,.471495,-.259407,.58013,.33661,.496769,-.267749,.580564,.367328,.524951,-.275524,.581696,.399753,.55318,-.282148,.579885,.433134,.581577,-.287533,.575471,.467534,.609231,-.291612,.567445,.502943,.637478,-.293911,.557657,.53871,.667795,-.295096,.546535,.576568,.694272,-.294073,.529561,.614929,.722937,-.290386,.510561,.655909,.749682,-.284481,.487846,.697663,.774754,-.276188,.462487,.738515,.799301,-.266215,.43481,.779802,.820762,-.254116,.404879,.820045,.843231,-.240393,.374559,.860294,.861857,-.225503,.341582,.900965,.880815,-.209382,.308778,.941727,.89766,-.19155,.275232,.980916,.912926,-.172346,.240938,1.02162,.926391,-.151799,.207223,1.0597,.938429,-.129968,.17484,1.09291,.947834,-.10651,.142984,1.12248,.958432,-.0824098,.109902,1.149,.972402,-.0565242,.0744454,1.1733,.987191,-.028427,.0373794,1.19538,.999975,385685e-10,-4203e-8,1.21676,.178114,-766075e-11,.385418,354027e-11,.176074,-191966e-9,.381002,887135e-10,.17601,-767549e-9,.380861,354715e-9,.17598,-.00172696,.380798,798168e-9,.175994,-.00307012,.380824,.00141928,.176017,-.00479684,.380858,.00221859,.176019,-.00690648,.380839,.00319714,.176072,-.00939888,.380913,.0043572,.176131,-.0122726,.380979,.005702,.176239,-.0155264,.38112,.00723689,.176371,-.0191551,.381272,.00896907,.176638,-.023117,.381669,.0109194,.176912,-.0274633,.382015,.0130903,.177279,-.032173,.382476,.0154949,.17774,-.0372219,.383041,.0181669,.178344,-.0426132,.38378,.0211209,.179153,-.0483309,.384773,.0243899,.180197,-.0543447,.386076,.0280062,.181581,-.0607122,.387809,.032004,.18344,-.0673855,.390205,.036453,.186139,-.0743989,.393944,.0414162,.189432,-.0817731,.39832,.0469394,.193795,-.0895464,.404188,.0531442,.199641,-.0978264,.4121,.0601374,.206679,-.106499,.421425,.0680078,.214865,-.115654,.431504,.076919,.224406,-.125268,.442526,.0868835,.235876,-.135475,.455465,.0981875,.248335,-.146023,.4681,.110759,.262868,-.157016,.482069,.124885,.278962,-.168245,.496182,.140645,.295082,-.17958,.507401,.157838,.313738,-.191227,.520252,.17695,.333573,-.202718,.531708,.197817,.356433,-.214424,.544509,.220785,.378853,-.225492,.55373,.245306,.402717,-.236236,.561348,.271593,.428375,-.246568,.568538,.299776,.454724,-.255941,.573462,.329433,.482291,-.264511,.576356,.360598,.509706,-.272129,.576446,.393204,.538805,-.278979,.575298,.427227,.568919,-.284528,.572154,.462157,.596804,-.288801,.564691,.497997,.625987,-.291334,.555134,.534467,.656414,-.292722,.545051,.571736,.683916,-.292185,.528813,.610158,.711809,-.290043,.51106,.649061,.739547,-.285246,.490103,.690081,.766914,-.277647,.465523,.732554,.791375,-.267603,.437718,.773982,.814772,-.256109,.40882,.81609,.836691,-.242281,.377823,.856849,.856984,-.227155,.34496,.898363,.876332,-.210395,.311335,.939471,.894988,-.192612,.277703,.980799,.911113,-.173236,.243019,1.02215,.924092,-.152258,.209037,1.06139,.936828,-.129575,.175909,1.09635,.946869,-.10594,.143852,1.12707,.958284,-.081318,.110289,1.15419,.972325,-.0556133,.0747232,1.17909,.986878,-.0297899,.0383149,1.20163,.999936,-.00197169,912402e-9,1.22338,.151174,-720365e-11,.351531,309789e-11,.155594,-18279e-8,.361806,78608e-9,.156099,-731569e-9,.362982,314615e-9,.156053,-.00164578,.362869,707845e-9,.156093,-.0029261,.362961,.00125884,.156099,-.00457155,.362959,.00196783,.15612,-.00658224,.362982,.00283622,.156168,-.00895774,.363048,.00386625,.156221,-.0116962,.363101,.00506109,.156324,-.0147973,.363241,.00642675,.156476,-.0182503,.363448,.00797175,.156731,-.0220266,.36384,.00971484,.156994,-.026176,.364179,.0116575,.157341,-.0306701,.36462,.0138207,.157867,-.0354591,.365364,.0162356,.15846,-.0406141,.366111,.0189092,.159308,-.0460519,.367248,.021885,.160426,-.0518096,.368767,.0252004,.161877,-.0578906,.370745,.0288825,.163995,-.0642812,.373831,.0330139,.16655,-.0710067,.377366,.0376283,.170237,-.0781522,.382799,.0428493,.175096,-.0857172,.389915,.0487324,.181069,-.0938025,.398487,.0554214,.188487,-.102363,.408799,.0630189,.197029,-.111343,.419991,.071634,.206684,-.120812,.431455,.0812797,.218698,-.131033,.445746,.0923651,.230726,-.141373,.457471,.104545,.245516,-.152387,.472388,.118449,.261551,-.163628,.486671,.133923,.277437,-.174814,.49762,.150849,.296662,-.186713,.51162,.169924,.31795,-.198513,.525435,.190848,.339422,-.210119,.536267,.213504,.362143,-.221354,.545982,.237947,.387198,-.23224,.555364,.264427,.412349,-.24257,.561489,.292519,.439274,-.252284,.566903,.322561,.466779,-.261023,.569614,.353952,.496011,-.26899,.571589,.387278,.524964,-.275498,.570325,.421356,.556518,-.281449,.568792,.457314,.584363,-.285526,.560268,.493199,.614214,-.28844,.55205,.530276,.645684,-.289777,.541906,.56855,.673446,-.289722,.526464,.606927,.701924,-.287792,.509872,.645945,.73037,-.284315,.490649,.685564,.757405,-.278804,.467964,.726511,.784025,-.269543,.441468,.768601,.808255,-.258117,.41216,.811321,.830739,-.244728,.380606,.853496,.851914,-.229428,.348111,.895374,.872586,-.212508,.314732,.937674,.891581,-.194025,.280338,.979869,.907641,-.174711,.245203,1.02253,.922233,-.153509,.21077,1.06371,.935878,-.130418,.177399,1.09972,.946338,-.105558,.144507,1.13124,.957265,-.080059,.110508,1.15973,.971668,-.0539766,.0742311,1.18515,.9866,-.0277101,.0375224,1.20858,1.00021,-515531e-9,135226e-9,1.23135,.137468,-686011e-11,.345041,273315e-11,.13703,-173378e-9,.343936,690761e-10,.136986,-693048e-9,.34383,276126e-9,.136964,-.00155931,.343761,621337e-9,.137003,-.00277211,.343863,.00110494,.137012,-.00433103,.343868,.00172744,.137043,-.00623606,.343916,.00249022,.13709,-.0084868,.343986,.00339559,.137145,-.0110814,.344045,.00444687,.137242,-.0140187,.344177,.00565007,.137431,-.0172713,.344491,.00701868,.137644,-.0208605,.344805,.00856042,.13791,-.024792,.345172,.0102863,.138295,-.0290461,.345734,.0122185,.138764,-.0335957,.346371,.0143771,.139415,-.038467,.347298,.0167894,.140272,-.0436176,.348527,.0194895,.141457,-.0491016,.350276,.0225043,.14303,-.0548764,.352646,.0258962,.145289,-.0610096,.356206,.0297168,.148502,-.0674777,.361488,.0340562,.152188,-.074345,.367103,.0389534,.157359,-.0817442,.375247,.0445541,.16379,-.0896334,.385064,.0509535,.171376,-.098005,.396082,.0582611,.179901,-.106817,.407418,.06654,.189892,-.116239,.420031,.075994,.201838,-.12627,.434321,.0867239,.214311,-.136701,.447631,.0987517,.228902,-.147616,.462046,.112353,.245107,-.158871,.476942,.127605,.262292,-.170261,.490285,.144469,.281215,-.182017,.503783,.163282,.301058,-.193729,.515505,.183873,.322752,-.205512,.52682,.206466,.347547,-.217214,.539473,.231194,.370969,-.227966,.546625,.257288,.397533,-.238555,.55472,.285789,.42398,-.248278,.559468,.315746,.452928,-.257422,.564095,.347724,.482121,-.265306,.565426,.380922,.510438,-.272043,.563205,.415639,.541188,-.277614,.561087,.451702,.571667,-.281927,.554922,.48845,.602432,-.285015,.546838,.526442,.634126,-.286512,.537415,.564896,.662816,-.286388,.522906,.604037,.692411,-.284734,.507003,.643795,.720946,-.281297,.488398,.68298,.748293,-.276262,.466353,.723466,.776931,-.269978,.443573,.764565,.801065,-.260305,.415279,.805838,.825843,-.247426,.384773,.849985,.84807,-.232437,.352555,.893174,.869122,-.215806,.318642,.936564,.888963,-.197307,.28381,.980253,.905547,-.177203,.247888,1.02463,.918554,-.155542,.212904,1.06714,.931395,-.131948,.1787,1.10451,.941749,-.106723,.145902,1.13694,.954551,-.0804939,.111193,1.1666,.970279,-.0534239,.0744697,1.19249,.986117,-.0257452,.0368788,1.21665,.999938,.00190634,-.0010291,1.23981,.118493,-647439e-11,.32272,23772e-10,.118765,-163023e-9,.323456,598573e-10,.118772,-65212e-8,.323477,239447e-9,.118843,-.00146741,.323657,538881e-9,.118804,-.00260846,.323553,95826e-8,.118826,-.00407576,.323595,.00149845,.118846,-.00586826,.323617,.00216047,.118886,-.00798578,.32367,.00294679,.118947,-.0104273,.323753,.00386124,.119055,-.0131909,.323922,.00490999,.119241,-.0162444,.324251,.00610804,.11944,-.0196339,.324544,.00745805,.119739,-.0233378,.325026,.00897805,.12011,-.0273179,.325586,.0106895,.120571,-.0316143,.326231,.0126073,.12124,-.0361939,.327264,.0147654,.122162,-.0410511,.328733,.0172001,.123378,-.0462233,.330659,.0199375,.125183,-.0517109,.333754,.0230498,.127832,-.0575652,.338507,.026597,.130909,-.0637441,.343666,.0306345,.135221,-.0704302,.351063,.035273,.14082,-.0776364,.360604,.0406137,.146781,-.0852293,.369638,.0466788,.155121,-.0935351,.3827,.0537628,.16398,-.102234,.39522,.0617985,.173926,-.111465,.40793,.07097,.185137,-.121296,.42105,.0813426,.19826,-.13169,.435735,.0931596,.212938,-.142614,.450932,.106547,.229046,-.153884,.465726,.121575,.246246,-.165382,.479461,.138286,.264637,-.176806,.492106,.15666,.284959,-.188793,.504774,.17728,.308157,-.200763,.518805,.19988,.330951,-.21239,.528231,.224293,.3549,-.223521,.536376,.250541,.381502,-.234169,.544846,.278902,.409529,-.244077,.551717,.309227,.437523,-.253363,.55517,.341426,.467624,-.261659,.557772,.37518,.497268,-.268498,.556442,.41007,.528294,-.274018,.553915,.446445,.559053,-.278169,.549153,.483779,.589329,-.281229,.539878,.522249,.622503,-.282902,.53162,.561754,.652382,-.282815,.518119,.601544,.681847,-.281247,.502187,.641574,.712285,-.277986,.484824,.682633,.740094,-.273017,.463483,.723426,.768478,-.266692,.441299,.763747,.794556,-.258358,.415238,.805565,.819408,-.248807,.386912,.847254,.843411,-.236214,.356165,.891091,.862397,-.219794,.320562,.936174,.883113,-.201768,.285322,.982562,.90023,-.181672,.249713,1.02862,.915192,-.159279,.214546,1.07163,.928458,-.134725,.180285,1.10995,.94069,-.10913,.147119,1.14354,.953409,-.0821315,.112492,1.17372,.969537,-.0542677,.0752014,1.20043,.985612,-.0259096,.0370361,1.22528,.999835,.00298198,-.00151801,1.24959,.10097,-602574e-11,.300277,202619e-11,.101577,-152164e-9,.302077,511662e-10,.101572,-608889e-9,.302066,204751e-9,.101566,-.00136997,.302047,460753e-9,.101592,-.00243557,.302114,819497e-9,.101608,-.0038053,.30214,.00128154,.101627,-.00547906,.30216,.0018483,.101669,-.00745647,.302224,.00252223,.101732,-.00973615,.302318,.00330716,.101844,-.0123097,.302513,.00421061,.102025,-.0151681,.30285,.00524481,.102224,-.0183334,.303166,.0064154,.102515,-.0217819,.303654,.00774063,.102886,-.0255067,.304243,.0092398,.103395,-.029514,.305089,.0109339,.104109,-.0337912,.306301,.0128561,.105074,-.0383565,.30798,.0150338,.10654,-.0432132,.310726,.0175228,.108478,-.0484244,.314351,.0203648,.111015,-.0539339,.319032,.0236325,.114682,-.0598885,.32605,.0274188,.11911,-.0663375,.334109,.0317905,.124736,-.0733011,.344013,.0368502,.131479,-.0807744,.355358,.0427104,.139283,-.0888204,.367614,.0494788,.148054,-.0973394,.380072,.0572367,.159037,-.10665,.395678,.0662704,.169794,-.116221,.40795,.0763192,.18314,-.126632,.423546,.087956,.197515,-.137383,.438213,.101042,.213514,-.148641,.453248,.115827,.23065,-.160117,.46688,.132283,.249148,-.171807,.479962,.150644,.270219,-.183695,.494618,.171073,.292338,-.195574,.506937,.193378,.314999,-.207205,.516463,.217585,.340991,-.218955,.528123,.24428,.367982,-.229917,.537025,.272784,.39432,-.239737,.541627,.302742,.423364,-.249048,.546466,.335112,.453751,-.257329,.549466,.369032,.48416,-.264623,.549503,.404577,.515262,-.270411,.547008,.441337,.547036,-.274581,.542249,.479162,.576614,-.277266,.533015,.517904,.611143,-.279144,.525512,.558508,.640989,-.279001,.51154,.598995,.671182,-.277324,.495641,.639935,.700848,-.273908,.477526,.681017,.729862,-.269063,.457955,.722764,.758273,-.262282,.434846,.764349,.784121,-.254281,.409203,.806206,.809798,-.24505,.382694,.848617,.834953,-.233861,.354034,.892445,.856817,-.221308,.321764,.936263,.877609,-.205996,.288118,.982401,.897489,-.186702,.253277,1.02975,.913792,-.164618,.217963,1.07488,.92785,-.140023,.183221,1.11487,.940378,-.11328,.149385,1.14947,.95273,-.0853958,.114152,1.1807,.969059,-.0568698,.0769845,1.20912,.985574,-.0276502,.0381186,1.23498,.999943,.00239052,-.00126861,1.25987,.0852715,-560067e-11,.279021,171162e-11,.0854143,-140871e-9,.279483,430516e-10,.0854191,-563385e-9,.2795,172184e-9,.0854188,-.00126753,.279493,387464e-9,.0854229,-.00225337,.279501,68918e-8,.0854443,-.00352086,.279549,.00107803,.0854697,-.00506962,.279591,.00155536,.0855093,-.00689873,.279652,.00212354,.0855724,-.00900821,.279752,.00278703,.0856991,-.0113799,.280011,.0035551,.085855,-.0140314,.280297,.00443449,.0860682,-.016963,.280682,.00543636,.086344,-.0201438,.281159,.0065788,.0867426,-.0235999,.281886,.00787977,.087239,-.0273069,.282745,.0093606,.0879815,-.031269,.284139,.011056,.0891258,-.035531,.28647,.0130065,.0906909,-.0400947,.289708,.0152495,.0927624,-.0449638,.293904,.0178454,.0958376,-.0502427,.300471,.0208915,.0995827,-.0559514,.30806,.0244247,.104526,-.0622152,.317874,.0285721,.110532,-.0690046,.329332,.0334227,.117385,-.0763068,.341217,.0390466,.12522,-.084184,.353968,.0455786,.134037,-.0925248,.366797,.0530773,.144014,-.101487,.380209,.0617424,.156013,-.111273,.395956,.071777,.168872,-.121431,.41053,.0830905,.183089,-.132105,.425073,.0959341,.198763,-.143286,.439833,.110448,.216159,-.154841,.454507,.126769,.234859,-.166588,.468368,.14495,.255879,-.178626,.482846,.165233,.27677,-.190218,.493489,.187217,.301184,-.202227,.506549,.211659,.325852,-.213764,.5158,.237922,.352824,-.22487,.525442,.26632,.380882,-.235246,.532487,.296691,.410137,-.244847,.537703,.329179,.439787,-.253122,.540361,.363135,.472291,-.260517,.542734,.399222,.501856,-.266519,.538826,.436352,.534816,-.270905,.535152,.474505,.565069,-.273826,.525979,.513988,.597154,-.275333,.516394,.554852,.630473,-.275314,.506206,.596592,.660574,-.273323,.489769,.638117,.692015,-.270008,.472578,.680457,.720647,-.265001,.452134,.723008,.750528,-.258311,.430344,.765954,.777568,-.250046,.405624,.809012,.80387,-.240114,.378339,.852425,.828439,-.228737,.349877,.895346,.851472,-.216632,.318968,.940695,.873906,-.202782,.287489,.987235,.89467,-.187059,.254394,1.03348,.912281,-.168818,.221294,1.07812,.927358,-.146494,.18675,1.11928,.940385,-.120009,.152322,1.15609,.952672,-.0917183,.117514,1.18875,.968496,-.0620321,.0797405,1.21821,.985236,-.0314945,.0402383,1.24523,.99998,-575153e-9,110644e-9,1.27133,.0702429,-512222e-11,.255273,140947e-11,.0702981,-128826e-9,.255469,354488e-10,.0703691,-515562e-9,.255727,141874e-9,.0703805,-.00116,.255754,31929e-8,.0703961,-.00206224,.255813,567999e-9,.0704102,-.00322223,.255839,88871e-8,.0704298,-.00463928,.255863,.00128272,.0704759,-.00631375,.255953,.00175283,.0705434,-.00824317,.256079,.00230342,.0706693,-.010412,.25636,.0029443,.0708189,-.0128439,.256647,.00368031,.0710364,-.0155177,.257084,.00452614,.0713223,-.0184374,.257637,.00549706,.0717182,-.0216002,.258416,.00661246,.072321,-.0249966,.259699,.00790147,.0731446,-.0286566,.261475,.0093884,.0743352,-.0325888,.264132,.0111186,.0760676,-.036843,.26815,.013145,.078454,-.0414292,.273636,.0155251,.0818618,-.0464634,.281653,.0183525,.0857382,-.0519478,.289992,.0216642,.0908131,-.0579836,.30066,.0255956,.0967512,-.0645124,.312204,.0301954,.103717,-.0716505,.325001,.0356017,.111596,-.0793232,.338129,.041896,.120933,-.087645,.352853,.0492447,.130787,-.096492,.366192,.0576749,.142311,-.105973,.380864,.0673969,.155344,-.116182,.396575,.0785899,.169535,-.126815,.411443,.0912377,.185173,-.138015,.426256,.105607,.201755,-.149325,.439607,.121551,.221334,-.161207,.455467,.139608,.241461,-.173162,.469096,.159591,.26294,-.18504,.481014,.18156,.286776,-.196881,.493291,.205781,.311596,-.208311,.503556,.231819,.338667,-.219671,.513268,.260274,.366021,-.230451,.519414,.290862,.395875,-.240131,.526766,.323196,.425564,-.248566,.52905,.357071,.457094,-.256195,.530796,.393262,.488286,-.262331,.528703,.430797,.522291,-.267141,.52727,.470231,.554172,-.270411,.519848,.510477,.586427,-.271986,.510307,.551594,.619638,-.27192,.499158,.593849,.650656,-.269817,.483852,.636314,.68284,-.266267,.467515,.679679,.714356,-.26113,.44931,.723884,.742717,-.254067,.425789,.767245,.770894,-.245652,.401144,.811819,.797358,-.235554,.374224,.856315,.823377,-.223896,.346167,.901077,.847456,-.210865,.316056,.946502,.870697,-.196574,.284503,.993711,.891068,-.180814,.251628,1.04134,.909267,-.163314,.219065,1.08609,.925653,-.143304,.186446,1.12702,.940017,-.121322,.153416,1.16371,.952398,-.0973872,.120334,1.19712,.967568,-.0698785,.08352,1.22791,.984772,-.0390031,.0439209,1.25672,1.00026,-.0070087,.00315668,1.28428,.0556653,-459654e-11,.227325,112556e-11,.0565238,-116382e-9,.230826,284985e-10,.0565717,-465666e-9,.231026,114036e-9,.0565859,-.00104773,.231079,256656e-9,.0565761,-.00186255,.231025,45663e-8,.0565913,-.00291002,.231058,714664e-9,.0566108,-.00418998,.231085,.00103224,.0566532,-.00570206,.231169,.00141202,.0567473,-.00743666,.231417,.00186018,.0568567,-.00940298,.231661,.00238264,.0569859,-.0115991,.231895,.00298699,.0572221,-.0140096,.232456,.00368957,.057519,-.0166508,.233096,.00450303,.0579534,-.01951,.234094,.00544945,.0585922,-.0225991,.235629,.00655564,.0595647,-.0259416,.238106,.00785724,.0609109,-.0295661,.241557,.00939127,.0628751,-.0335126,.246652,.0112198,.0656908,-.0378604,.254091,.0134168,.0691347,-.0426543,.262666,.0160374,.0732165,-.0478967,.272029,.0191514,.0782863,-.0536716,.283007,.0228597,.0843973,-.0600683,.295732,.0272829,.0913598,-.0670095,.308779,.032484,.0994407,-.0745516,.322886,.0385886,.108189,-.082712,.336408,.0457133,.118574,-.0914927,.351692,.0539832,.129989,-.100854,.366502,.0635162,.142722,-.110837,.381675,.0744386,.156654,-.121353,.3963,.0868483,.172151,-.132414,.411477,.100963,.188712,-.143809,.42508,.116795,.208093,-.155765,.441328,.134715,.227936,-.167608,.454328,.154396,.249495,-.179579,.467235,.176179,.27362,-.191488,.480248,.200193,.296371,-.202618,.487886,.225775,.324234,-.214133,.499632,.25441,.353049,-.225212,.509532,.285077,.381785,-.234875,.514265,.317047,.414038,-.244205,.521282,.351874,.445251,-.252145,.522931,.388279,.476819,-.258433,.520947,.425825,.509209,-.263411,.517669,.465104,.542759,-.266732,.512841,.505741,.574822,-.268263,.503317,.547611,.609324,-.268489,.493035,.590953,.641772,-.266941,.478816,.63488,.674049,-.263297,.462863,.679072,.705071,-.257618,.442931,.723487,.734709,-.250625,.421299,.768708,.763704,-.24179,.397085,.814375,.791818,-.231115,.370577,.859907,.817439,-.21922,.34232,.906715,.843202,-.205658,.312627,.953943,.866639,-.190563,.280933,1.00185,.888129,-.173978,.248393,1.05105,.907239,-.155485,.216007,1.09704,.923893,-.134782,.183233,1.13857,.938882,-.11249,.150376,1.17539,.952464,-.0890706,.117177,1.20924,.968529,-.0646523,.0813095,1.24055,.984763,-.038606,.0439378,1.27018,1.00053,-.01238,.00598668,1.29873,.0437928,-409594e-11,.204012,8.79224e-7,.0440166,-103395e-9,.205049,221946e-10,.0440529,-413633e-9,.205225,887981e-10,.0440493,-930594e-9,.2052,199858e-9,.0439884,-.00165352,.204901,355495e-9,.0440716,-.0025849,.205255,556983e-9,.0440968,-.00372222,.205311,805326e-9,.0441359,-.00506478,.205391,.00110333,.0442231,-.00660384,.205638,.00145768,.0443254,-.00835246,.205877,.00187275,.0444832,-.0102992,.20627,.00235938,.0447001,-.0124449,.206796,.0029299,.0450168,-.0147935,.207593,.0036005,.0454816,-.017336,.208819,.00439246,.0462446,-.0201156,.211036,.00533864,.0473694,-.0231568,.214388,.00646984,.0490191,-.0264941,.219357,.00783856,.0512776,-.030184,.226061,.00950182,.0541279,-.0342661,.234094,.0115156,.0578989,-.0388539,.244297,.0139687,.0620835,-.0438735,.254457,.0169015,.0673497,-.04951,.266706,.0204554,.0731759,-.0556263,.278753,.0246606,.0803937,-.0624585,.29309,.0297126,.0879287,-.0697556,.305856,.0355868,.0970669,-.0778795,.321059,.0425768,.106508,-.0863541,.333873,.05056,.11776,-.0955935,.349008,.0598972,.130081,-.105438,.363776,.0706314,.144454,-.115899,.380112,.0828822,.1596,-.126827,.394843,.0967611,.176097,-.138161,.409033,.112381,.194726,-.149904,.424257,.129952,.213944,-.161675,.436945,.149333,.235516,-.173659,.450176,.170892,.260564,-.185963,.466305,.194984,.285183,-.197582,.477328,.220805,.311095,-.208697,.486566,.248694,.338924,-.219519,.494811,.279015,.369757,-.229766,.504065,.311725,.3996,-.238879,.507909,.345844,.430484,-.246802,.509805,.381749,.46413,-.253924,.511436,.420251,.497077,-.259319,.508787,.459957,.530434,-.263297,.50394,.501356,.565725,-.265619,.49804,.544252,.599254,-.265842,.487346,.587856,.631251,-.263978,.472975,.631969,.663972,-.26043,.457135,.677471,.697724,-.255358,.439844,.723744,.727725,-.248308,.417872,.770653,.756417,-.239181,.39273,.817357,.785419,-.22814,.367839,.864221,.81266,-.215681,.339449,.912701,.839391,-.201623,.309279,.962419,.86366,-.185624,.278029,1.0122,.885028,-.16797,.245294,1.06186,.904639,-.148336,.212689,1.10934,.922048,-.12637,.179616,1.15063,.936952,-.102928,.146749,1.18885,.951895,-.0785268,.112733,1.22352,.967198,-.0530153,.0760056,1.25681,.984405,-.02649,.0383183,1.28762,1.00021,70019e-8,-20039e-8,1.31656,.0325964,-355447e-11,.176706,6.55682e-7,.0329333,-899174e-10,.178527,165869e-10,.0329181,-359637e-9,.178453,663498e-10,.0329085,-808991e-9,.178383,149332e-9,.0329181,-.00143826,.178394,265873e-9,.0329425,-.00224678,.178517,416597e-9,.0329511,-.00323575,.17849,603299e-9,.033011,-.00439875,.178695,829422e-9,.0330733,-.00574059,.178843,.00109908,.0331857,-.00725896,.179176,.00141933,.0333445,-.00895289,.179618,.0017999,.0335674,-.0108219,.180238,.00225316,.033939,-.0128687,.181417,.00279765,.0345239,-.015114,.183395,.0034564,.0354458,-.017596,.186616,.00425864,.0368313,-.0203524,.191547,.00524936,.0386115,-.0234105,.197508,.00647033,.0410303,-.0268509,.205395,.00798121,.0442245,-.0307481,.215365,.0098557,.0478659,-.0350863,.225595,.0121417,.0522416,-.0399506,.236946,.0149385,.0574513,-.045357,.249442,.0183189,.0631208,-.0512863,.261222,.0223644,.0701124,-.0579273,.275418,.0272418,.0777331,-.0650652,.288989,.0329458,.0862709,-.0728813,.302546,.0396819,.096103,-.081363,.317164,.04757,.106976,-.0904463,.331733,.0567012,.119175,-.100105,.34661,.067202,.132919,-.110375,.362249,.0792588,.147727,-.121115,.376978,.0928672,.163618,-.132299,.390681,.108228,.182234,-.143887,.406571,.125502,.201809,-.155827,.42042,.144836,.225041,-.168357,.438411,.166706,.247621,-.18004,.450368,.189909,.27097,-.191536,.460083,.215251,.296658,-.203024,.469765,.243164,.325892,-.214056,.481837,.273388,.35406,-.224104,.487474,.305344,.384372,-.233489,.492773,.339741,.41749,-.241874,.498451,.376287,.45013,-.248834,.499632,.414195,.481285,-.254658,.495233,.454077,.519183,-.259367,.496401,.496352,.551544,-.261818,.487686,.538798,.587349,-.262964,.479453,.583626,.621679,-.262128,.467709,.629451,.654991,-.258998,.452123,.67566,.686873,-.254119,.433495,.723248,.719801,-.246946,.413657,.771156,.750355,-.237709,.390366,.81989,.780033,-.226549,.364947,.868601,.809254,-.214186,.337256,.920034,.836576,-.199639,.307395,.971706,.861774,-.183169,.275431,1.02479,.885707,-.165111,.243431,1.07837,.904742,-.144363,.210921,1.12783,.915604,-.121305,.17647,1.17254,.930959,-.0962119,.143106,1.21012,.948404,-.069969,.108112,1.24474,.967012,-.0427586,.0708478,1.27718,.984183,-.0147043,.032335,1.3083,.999577,.0142165,-.00726867,1.3382,.0229227,-299799e-11,.148623,4.62391e-7,.0232194,-758796e-10,.15054,117033e-10,.0232315,-303636e-9,.15063,468397e-10,.0232354,-683189e-9,.150624,105472e-9,.0232092,-.0012136,.150445,187744e-9,.0232523,-.00189765,.150679,294847e-9,.0232828,-.00273247,.150789,428013e-9,.0233371,-.00371287,.150995,591134e-9,.0234015,-.00484794,.15118,787642e-9,.023514,-.00612877,.151562,.00102547,.023679,-.00756125,.152116,.00131351,.0239559,-.00914651,.153162,.00166594,.0244334,-.010904,.155133,.00210182,.025139,-.0128615,.158035,.00264406,.0262598,-.0150628,.162751,.00332923,.0277875,-.0175532,.168944,.00419773,.0298472,-.0203981,.176835,.00530034,.0325444,-.023655,.186686,.00669777,.0355581,-.0272982,.196248,.00842661,.0392841,-.0314457,.207352,.0105854,.0436815,-.0361157,.219279,.0132458,.0485272,-.0412932,.230728,.0164736,.0541574,-.0470337,.242994,.0203715,.0609479,-.0535002,.257042,.0250953,.0685228,-.0605409,.27102,.0306856,.0768042,-.0680553,.28406,.037193,.0864844,-.0765011,.299186,.0449795,.0969415,-.0852674,.3132,.0538316,.108478,-.0947333,.327138,.0641149,.121705,-.10481,.342345,.0759185,.136743,-.115474,.358472,.0894116,.152986,-.126536,.374067,.104562,.170397,-.138061,.388267,.121632,.191392,-.150203,.406467,.140996,.211566,-.161751,.418641,.161696,.233567,-.173407,.430418,.184557,.257769,-.185397,.44277,.210092,.28531,-.197048,.457191,.237827,.311726,-.20784,.464712,.267253,.340537,-.218345,.472539,.299332,.372921,-.228306,.482331,.333988,.402924,-.236665,.484378,.369722,.434475,-.244097,.484717,.407836,.469736,-.250547,.487093,.448465,.505045,-.25511,.485575,.490263,.540262,-.258444,.481225,.534495,.576347,-.259903,.473481,.579451,.608656,-.259572,.4603,.625604,.646679,-.257908,.450341,.674511,.679902,-.253663,.431561,.723269,.714159,-.247419,.412684,.773263,.745345,-.239122,.389388,.824182,.778248,-.228837,.365361,.876634,.807208,-.216197,.337667,.92945,.835019,-.201772,.307197,.985261,.860261,-.185291,.274205,1.04299,.877601,-.165809,.240178,1.09816,.898211,-.143897,.207571,1.14694,.915789,-.119513,.174904,1.19008,.931831,-.0932919,.141423,1.2297,.949244,-.0656528,.105603,1.26553,.967527,-.0370262,.0679551,1.29986,.984139,-.00730117,.0283133,1.33252,.999713,.0234648,-.0121785,1.36397,.0152135,-245447e-11,.122795,3.04092e-7,.0151652,-615778e-10,.122399,76292e-10,.0151181,-245948e-9,.122023,304802e-10,.0151203,-553394e-9,.12203,686634e-10,.015125,-983841e-9,.122037,122463e-9,.0151427,-.00153774,.12214,192706e-9,.0151708,-.0022103,.122237,281219e-9,.0152115,-.00300741,.12238,390804e-9,.0152877,-.00392494,.1227,526317e-9,.015412,-.00496597,.123244,69443e-8,.0156201,-.00613314,.124228,90547e-8,.0159658,-.00744113,.125945,.0011732,.0165674,-.00892546,.129098,.00151888,.017487,-.010627,.133865,.00197007,.018839,-.0126043,.140682,.0025637,.020554,-.0148814,.148534,.00333637,.0226727,-.0175123,.157381,.00433738,.0251879,-.0205266,.166685,.00561664,.0283635,-.0240319,.177796,.00725563,.0318694,-.0279432,.188251,.00928811,.0361044,-.0324313,.200038,.011835,.0406656,-.0373527,.210685,.0149146,.0463846,-.0430132,.224182,.0187254,.0525696,-.0491013,.23634,.0232283,.0598083,-.0559175,.250013,.0286521,.0679437,-.0633657,.263981,.0350634,.0771181,-.0714602,.278072,.0425882,.0881273,-.0803502,.29511,.0514487,.0996628,-.0896903,.309976,.0615766,.112702,-.099644,.325611,.0732139,.126488,-.109829,.339321,.0862324,.142625,-.120859,.35574,.101275,.15953,-.131956,.369845,.117892,.176991,-.143145,.38146,.136205,.199715,-.155292,.40052,.157252,.220787,-.167066,.412055,.179966,.243697,-.178396,.423133,.204418,.272106,-.190433,.439524,.232141,.297637,-.201265,.447041,.261109,.325273,-.211834,.454488,.292627,.357219,-.221889,.465004,.326669,.387362,-.230729,.468527,.362426,.423131,-.23924,.475836,.401533,.45543,-.246067,.475017,.441902,.493393,-.251557,.478017,.484239,.526253,-.255571,.4709,.528586,.560554,-.257752,.463167,.574346,.599306,-.258076,.456452,.621655,.634541,-.256471,.443725,.670492,.668907,-.253283,.428719,.721943,.705619,-.247562,.411348,.772477,.739034,-.240626,.388939,.8264,.771408,-.231493,.36425,.881702,.803312,-.220125,.337321,.9385,.828457,-.206645,.305364,.997437,.854819,-.190664,.273715,1.05693,.878666,-.171429,.242218,1.11251,.898404,-.149235,.209556,1.16398,.917416,-.12435,.176863,1.21014,.933133,-.0972703,.142775,1.25178,.95066,-.0683607,.106735,1.29028,.968589,-.0378724,.0681609,1.32703,.984776,-.00605712,.0273966,1.36158,.99994,.0263276,-.0138124,1.3943,.00867437,-186005e-11,.0928979,1.73682e-7,.00864003,-466389e-10,.0925237,435505e-11,.00864593,-186594e-9,.0925806,174322e-10,.00864095,-419639e-9,.0924903,392862e-10,.00863851,-746272e-9,.0924589,702598e-10,.00868531,-.00116456,.0929,111188e-9,.00869667,-.00167711,.0928529,163867e-9,.00874332,-.00228051,.0930914,23104e-8,.00882709,-.00297864,.0935679,31741e-8,.00898874,-.00377557,.0946165,430186e-9,.00929346,-.00469247,.0967406,580383e-9,.00978271,-.00575491,.100084,783529e-9,.0105746,-.00701514,.105447,.00106304,.0116949,-.00851797,.112494,.00144685,.0130419,-.0102757,.119876,.00196439,.0148375,-.012381,.129034,.00266433,.0168725,-.01482,.137812,.00358364,.0193689,-.0176563,.147696,.00478132,.0222691,-.0209211,.157795,.00631721,.0256891,-.0246655,.168431,.00826346,.0294686,-.0288597,.178587,.0106714,.0340412,-.0336441,.190251,.0136629,.0393918,-.039033,.202999,.0173272,.0453947,-.0450087,.215655,.0217448,.0521936,-.0515461,.228686,.0269941,.0600279,-.058817,.242838,.033272,.0692398,-.0667228,.258145,.0406457,.0793832,-.0752401,.273565,.0492239,.0902297,-.0841851,.287735,.0590105,.102014,-.0936479,.301161,.0702021,.116054,-.103967,.317438,.0832001,.13191,-.114622,.334166,.0977951,.148239,-.125452,.348192,.113985,.165809,-.136453,.361094,.131928,.184616,-.147648,.373534,.151811,.207491,-.159607,.39101,.174476,.230106,-.171119,.402504,.198798,.257036,-.182906,.418032,.225796,.281172,-.193605,.425468,.254027,.312034,-.204771,.440379,.285713,.340402,-.214988,.445406,.319196,.370231,-.224711,.44968,.35537,.407105,-.233516,.460747,.393838,.439037,-.240801,.460624,.433747,.47781,-.24762,.465957,.477234,.510655,-.251823,.460054,.52044,.550584,-.255552,.459172,.567853,.585872,-.257036,.450311,.615943,.620466,-.257535,.437763,.667693,.660496,-.255248,.426639,.718988,.695578,-.251141,.409185,.772503,.732176,-.244718,.39015,.827023,.760782,-.236782,.362594,.885651,.79422,-.225923,.33711,.943756,.824521,-.213855,.308272,1.00874,.854964,-.197723,.278529,1.06764,.878065,-.179209,.246208,1.12836,.899834,-.157569,.21329,1.18318,.918815,-.133206,.181038,1.23161,.934934,-.106545,.146993,1.27644,.952115,-.0780574,.111175,1.31842,.96906,-.0478279,.0728553,1.35839,.985178,-.0160014,.032579,1.39697,1.00039,.0173126,-.0095256,1.43312,.00384146,-124311e-11,.0613583,7.78271e-8,.00390023,-314043e-10,.0622919,196626e-11,.00389971,-125622e-9,.0622632,787379e-11,.00389491,-282352e-9,.0620659,1778e-8,.00391618,-502512e-9,.0624687,320918e-10,.00392662,-784458e-9,.0625113,515573e-10,.00396053,-.00112907,.0628175,778668e-10,.00401911,-.00153821,.0633286,113811e-9,.00414994,-.0020208,.0646443,16445e-8,.00441223,-.00260007,.0673886,237734e-9,.00484427,-.0033097,.0716528,345929e-9,.00549109,-.00418966,.0774998,505987e-9,.00636293,-.00527331,.0844758,739208e-9,.00746566,-.00660428,.0921325,.00107347,.00876625,-.00818826,.0997067,.00153691,.0103125,-.0100811,.107433,.00217153,.0123309,-.0123643,.117088,.00303427,.0146274,-.0150007,.126438,.00416018,.0172295,-.0180531,.135672,.00561513,.0204248,-.0215962,.146244,.007478,.0241597,-.0256234,.157481,.00981046,.0284693,-.0302209,.169125,.0127148,.033445,-.0353333,.181659,.0162453,.0391251,-.0410845,.1944,.0205417,.0454721,-.0473451,.207082,.0256333,.0530983,-.0542858,.221656,.0317036,.0615356,-.0618384,.236036,.0388319,.0703363,-.0697631,.248398,.046974,.0810391,-.0784757,.263611,.0565246,.0920144,-.0873488,.275857,.0671724,.105584,-.0973652,.292555,.0798105,.119506,-.107271,.306333,.0935945,.134434,-.117608,.318888,.109106,.153399,-.128938,.337552,.127074,.171258,-.139944,.349955,.14643,.191059,-.151288,.361545,.168,.215069,-.163018,.378421,.192082,.237838,-.174226,.38879,.217838,.266965,-.186063,.405857,.246931,.292827,-.196909,.414146,.277505,.324352,-.207473,.426955,.310711,.354427,-.217713,.433429,.346794,.389854,-.227183,.443966,.385237,.420749,-.235131,.44471,.424955,.459597,-.242786,.451729,.468446,.495316,-.248767,.45072,.513422,.534903,-.253351,.450924,.560618,.572369,-.256277,.445266,.609677,.612383,-.2576,.438798,.660995,.644037,-.256931,.421693,.713807,.686749,-.254036,.4109,.767616,.719814,-.249785,.390151,.82533,.754719,-.244283,.367847,.888311,.792022,-.235076,.345013,.948177,.822404,-.225061,.316193,1.01661,.853084,-.211113,.287013,1.08075,.879871,-.19449,.255424,1.14501,.901655,-.174023,.222879,1.20203,.919957,-.1509,.18989,1.25698,.938412,-.124923,.15606,1.30588,.953471,-.0968139,.120512,1.3529,.970451,-.066734,.0828515,1.3986,.985522,-.034734,.0424458,1.44148,1.00099,-.00102222,678929e-9,1.48398,965494e-9,-6.27338e-7,.0306409,1.97672e-8,99168e-8,-158573e-10,.0314638,4.99803e-7,991068e-9,-634012e-10,.031363,200682e-11,974567e-9,-14144e-8,.03036,457312e-11,998079e-9,-252812e-9,.031496,860131e-11,.00102243,-396506e-9,.0319955,148288e-10,.00107877,-577593e-9,.0331376,249141e-10,.00121622,-816816e-9,.0359396,423011e-10,.0014455,-.00113761,.0399652,724613e-10,.00178791,-.00156959,.0450556,123929e-9,.00225668,-.00214064,.0508025,208531e-9,.00285627,-.00287655,.0568443,341969e-9,.0035991,-.00380271,.0630892,544158e-9,.00455524,-.00496264,.0702204,842423e-9,.00569143,-.0063793,.0773426,.00126704,.00716928,-.00813531,.0860839,.00186642,.00885307,-.0101946,.0944079,.00267014,.0109316,-.0126386,.103951,.00374033,.0133704,-.0154876,.113786,.0051304,.0161525,-.0187317,.123477,.00688858,.0194267,-.0224652,.133986,.00910557,.0230967,-.0265976,.143979,.0118074,.0273627,-.0312848,.154645,.0151266,.0323898,-.0365949,.166765,.0191791,.0379225,-.0422914,.177932,.0239236,.0447501,-.0487469,.19167,.0296568,.0519391,-.0556398,.203224,.0362924,.0599464,-.0631646,.215652,.0440585,.0702427,-.0714308,.232089,.0531619,.0806902,-.0800605,.245258,.0634564,.0923194,-.0892815,.258609,.0752481,.106938,-.09931,.276654,.0888914,.121238,-.109575,.289847,.104055,.138817,-.120461,.307566,.121266,.15595,-.131209,.320117,.139944,.178418,-.143049,.339677,.161591,.197875,-.154074,.349886,.184303,.224368,-.166307,.369352,.210669,.252213,-.178051,.386242,.238895,.277321,-.189335,.395294,.269182,.310332,-.200683,.412148,.302508,.338809,-.210856,.418266,.337264,.372678,-.220655,.428723,.374881,.405632,-.230053,.433887,.415656,.442293,-.237993,.439911,.457982,.477256,-.244897,.440175,.502831,.515592,-.250657,.441079,.550277,.550969,-.255459,.435219,.601102,.592883,-.257696,.432882,.651785,.629092,-.259894,.421054,.708961,.672033,-.258592,.41177,.763806,.709147,-.256525,.395267,.824249,.745367,-.254677,.375013,.8951,.784715,-.247892,.353906,.959317,.818107,-.240162,.327801,1.03153,.847895,-.229741,.298821,1.10601,.879603,-.213084,.269115,1.164,.902605,-.195242,.236606,1.22854,.922788,-.174505,.203442,1.29017,.944831,-.150169,.169594,1.34157,.959656,-.124099,.135909,1.3956,.972399,-.0960626,.0990563,1.45128,.986549,-.0657097,.0602348,1.50312,1.00013,-.0333558,.0186694,1.55364,619747e-11,-1e-7,.00778326,796756e-16,2.37499e-8,-9.99999e-8,282592e-10,1.14596e-10,100292e-11,-166369e-11,250354e-9,6.77492e-9,350752e-11,-637769e-11,357289e-9,6.31655e-8,826445e-11,-174689e-10,516179e-9,3.1851e-7,242481e-10,-450868e-10,.0010223,130577e-11,455631e-10,-89044e-9,.00144302,374587e-11,971222e-10,-178311e-9,.00241912,102584e-10,171403e-9,-313976e-9,.00354938,236481e-10,292747e-9,-520026e-9,.00513765,496014e-10,789827e-9,-.00118187,.0238621,139056e-9,.00114093,-.00171827,.0286691,244093e-9,.00176119,-.00249667,.0368565,420623e-9,.0022233,-.00333742,.0400469,65673e-8,.00343382,-.00481976,.0535751,.00109323,.00427602,-.00600755,.057099,.00155268,.00461435,-.00737637,.0551084,.00215031,.00695698,-.00971401,.0715767,.00316529,.00867619,-.0120943,.0793314,.00436995,.0106694,-.0148202,.0869391,.0058959,.0140351,-.0183501,.101572,.00798757,.0168939,-.022006,.11018,.0104233,.020197,-.0261568,.119041,.0134167,.0254702,-.0312778,.135404,.0173009,.0298384,-.0362469,.1437,.0215428,.035159,-.042237,.15512,.0268882,.0427685,-.0488711,.17128,.033235,.0494848,-.0557997,.181813,.0404443,.0592394,-.0635578,.198745,.0490043,.0681463,-.071838,.210497,.0588239,.0804753,-.0809297,.228864,.0702835,.0942205,-.0906488,.247008,.0834012,.106777,-.100216,.258812,.0975952,.124471,-.110827,.278617,.114162,.138389,-.121193,.287049,.131983,.159543,-.13253,.307151,.152541,.176432,-.143611,.31564,.174673,.201723,-.15548,.33538,.199842,.229721,-.167166,.355256,.227097,.250206,-.178238,.360047,.256014,.282118,-.189905,.378761,.28855,.312821,-.201033,.39181,.323348,.341482,-.211584,.397716,.360564,.377368,-.221314,.410141,.400004,.418229,-.230474,.423485,.442371,.444881,-.239443,.418874,.488796,.488899,-.245987,.427545,.535012,.520317,-.253948,.422147,.589678,.568566,-.256616,.42719,.637683,.599607,-.26376,.415114,.703363,.64222,-.268687,.408715,.771363,.685698,-.2694,.399722,.83574,.732327,-.266642,.388651,.897764,.769873,-.267712,.369198,.983312,.806733,-.263479,.346802,1.06222,.843466,-.254575,.321368,1.13477,.873008,-.242749,.29211,1.20712,.908438,-.22725,.262143,1.27465,.936321,-.207621,.228876,1.33203,.950353,-.187932,.19484,1.40439,.96442,-.165154,.163178,1.4732,.979856,-.139302,.127531,1.53574,.982561,-.11134,.0903457,1.59982,.996389,-.0808124,.0489007,1.6577],e=[1,0,0,0,1,791421e-36,0,0,1,104392e-29,0,0,1,349405e-26,0,0,1,109923e-23,0,0,1,947414e-22,0,0,1,359627e-20,0,0,1,772053e-19,0,0,1,108799e-17,0,0,1,110655e-16,0,0,1,865818e-16,0,0,.999998,5.45037e-10,0,0,.999994,2.85095e-9,0,0,.999989,1.26931e-8,0,0,.999973,4.89938e-8,0,0,.999947,1.66347e-7,0,0,.999894,5.02694e-7,0,0,.999798,136532e-11,0,0,.999617,335898e-11,0,0,.999234,752126e-11,0,0,.998258,152586e-10,0,0,.99504,266207e-10,0,0,.980816,236802e-10,0,0,.967553,207684e-11,0,0,.966877,403733e-11,0,0,.965752,741174e-11,0,0,.96382,127746e-10,0,0,.960306,202792e-10,0,0,.953619,280232e-10,0,0,.941103,278816e-10,0,0,.926619,160221e-10,0,0,.920983,235164e-10,0,0,.912293,311924e-10,0,.0158731,.899277,348118e-10,0,.0476191,.880884,26041e-9,0,.0793651,.870399,338726e-10,0,.111111,.856138,392906e-10,0,.142857,.837436,372874e-10,0,.174603,.820973,392558e-10,0,.206349,.803583,434658e-10,0,.238095,.782168,40256e-9,0,.269841,.764107,448159e-10,0,.301587,.743092,457627e-10,0,.333333,.721626,455314e-10,0,.365079,.700375,477335e-10,0,.396825,.677334,461072e-10,0,.428571,.655702,484393e-10,0,.460317,.632059,464583e-10,0,.492064,.610125,483923e-10,0,.52381,.58653,464342e-10,0,.555556,.564508,477033e-10,0,.587302,.541405,459263e-10,0,.619048,.519556,46412e-9,0,.650794,.497292,448913e-10,0,.68254,.475898,445789e-10,0,.714286,.454722,433496e-10,0,.746032,.434042,423054e-10,0,.777778,.414126,413737e-10,0,.809524,.394387,397265e-10,0,.84127,.375841,390709e-10,0,.873016,.357219,369938e-10,0,.904762,.340084,365618e-10,0,.936508,.322714,342533e-10,0,.968254,.306974,339596e-10,0,1,1,101524e-23,0,0,1,10292e-22,0,0,1,130908e-23,0,0,1,473331e-23,0,0,1,625319e-22,0,0,1,107932e-20,0,0,1,163779e-19,0,0,1,203198e-18,0,0,1,204717e-17,0,0,.999999,168995e-16,0,0,.999998,1.15855e-10,0,0,.999996,6.6947e-10,0,0,.999991,3.30863e-9,0,0,.999983,1.41737e-8,0,0,.999968,5.32626e-8,0,0,.99994,1.77431e-7,0,0,.999891,5.28835e-7,0,0,.999797,142169e-11,0,0,.999617,347057e-11,0,0,.999227,77231e-10,0,0,.998239,155753e-10,0,0,.994937,268495e-10,0,0,.980225,213742e-10,0,0,.967549,21631e-10,0,0,.966865,417989e-11,0,0,.965739,763341e-11,0,0,.963794,130892e-10,0,0,.960244,206456e-10,0,0,.953495,282016e-10,0,148105e-9,.940876,271581e-10,0,.002454,.926569,164159e-10,0,.00867491,.920905,239521e-10,0,.01956,.912169,315127e-10,0,.035433,.899095,346626e-10,0,.056294,.882209,290223e-10,0,.0818191,.870272,342992e-10,0,.111259,.855977,394164e-10,0,.142857,.837431,372343e-10,0,.174603,.820826,396691e-10,0,.206349,.803408,435395e-10,0,.238095,.782838,419579e-10,0,.269841,.763941,450953e-10,0,.301587,.742904,455847e-10,0,.333333,.721463,458833e-10,0,.365079,.700197,477159e-10,0,.396825,.677501,470641e-10,0,.428571,.655527,484732e-10,0,.460317,.6324,476834e-10,0,.492064,.609964,484213e-10,0,.52381,.586839,475541e-10,0,.555556,.564353,476951e-10,0,.587302,.541589,467611e-10,0,.619048,.519413,463493e-10,0,.650794,.497337,453994e-10,0,.68254,.475797,445308e-10,0,.714286,.454659,435787e-10,0,.746032,.434065,424839e-10,0,.777778,.414018,41436e-9,0,.809524,.39455,401902e-10,0,.84127,.375742,390813e-10,0,.873016,.357501,377116e-10,0,.904762,.339996,36535e-9,0,.936508,.323069,351265e-10,0,.968254,.306897,339112e-10,0,1,1,10396e-19,0,0,1,104326e-20,0,0,1,110153e-20,0,0,1,144668e-20,0,0,1,34528e-19,0,0,1,175958e-19,0,0,1,12627e-17,0,0,1,936074e-18,0,0,1,645742e-17,0,0,.999998,401228e-16,0,0,.999997,2.22338e-10,0,0,.999995,1.0967e-9,0,0,.999991,4.82132e-9,0,0,.999981,1.89434e-8,0,0,.999967,6.67716e-8,0,0,.999938,2.12066e-7,0,0,.999886,6.0977e-7,0,0,.999792,159504e-11,0,0,.999608,381191e-11,0,0,.999209,833727e-11,0,0,.998179,165288e-10,0,0,.994605,274387e-10,0,0,.979468,167316e-10,0,0,.967529,242877e-11,0,0,.966836,461696e-11,0,0,.96569,830977e-11,0,0,.963706,140427e-10,0,244659e-11,.960063,217353e-10,0,760774e-9,.953113,286606e-10,0,.00367261,.940192,247691e-10,0,.00940263,.927731,195814e-10,0,.018333,.920669,252531e-10,0,.0306825,.911799,324277e-10,0,.0465556,.89857,340982e-10,0,.0659521,.883283,319622e-10,0,.0887677,.86989,35548e-9,0,.114784,.855483,397143e-10,0,.143618,.837987,391665e-10,0,.174606,.820546,411306e-10,0,.206349,.802878,436753e-10,0,.238095,.783402,444e-7,0,.269841,.763439,458726e-10,0,.301587,.742925,467097e-10,0,.333333,.721633,478887e-10,0,.365079,.69985,481251e-10,0,.396825,.67783,491811e-10,0,.428571,.655126,488199e-10,0,.460318,.632697,496025e-10,0,.492064,.609613,48829e-9,0,.52381,.587098,492754e-10,0,.555556,.564119,482625e-10,0,.587302,.541813,482807e-10,0,.619048,.519342,471552e-10,0,.650794,.497514,466765e-10,0,.68254,.475879,455582e-10,0,.714286,.454789,446007e-10,0,.746032,.434217,435382e-10,0,.777778,.414086,421753e-10,0,.809524,.394744,412093e-10,0,.84127,.375782,396634e-10,0,.873016,.357707,386419e-10,0,.904762,.340038,370345e-10,0,.936508,.323284,359725e-10,0,.968254,.306954,3436e-8,0,1,1,599567e-19,0,0,1,600497e-19,0,0,1,614839e-19,0,0,1,686641e-19,0,0,1,972658e-19,0,0,1,221271e-18,0,0,1,833195e-18,0,0,1,403601e-17,0,0,.999999,206001e-16,0,0,.999998,1.01739e-10,0,0,.999997,4.70132e-10,0,0,.999993,2.00436e-9,0,0,.999988,7.83682e-9,0,0,.999979,2.80338e-8,0,0,.999962,9.17033e-8,0,0,.999933,2.74514e-7,0,0,.999881,7.53201e-7,0,0,.999783,189826e-11,0,0,.999594,440279e-11,0,0,.999178,93898e-10,0,0,.998073,181265e-10,0,0,.993993,280487e-10,0,0,.979982,149422e-10,0,0,.968145,378481e-11,0,0,.966786,53771e-10,0,0,.965611,947508e-11,0,388934e-10,.963557,156616e-10,0,9693e-7,.959752,235144e-10,0,.00370329,.952461,291568e-10,0,.00868428,.940193,240102e-10,0,.0161889,.929042,231235e-10,0,.0263948,.920266,273968e-10,0,.0394088,.911178,337915e-10,0,.0552818,.897873,333629e-10,0,.0740138,.884053,351405e-10,0,.0955539,.869455,378034e-10,0,.119795,.854655,399378e-10,0,.14656,.838347,419108e-10,0,.175573,.820693,440831e-10,0,.206388,.802277,445599e-10,0,.238095,.783634,472691e-10,0,.269841,.763159,476984e-10,0,.301587,.742914,491487e-10,0,.333333,.721662,502312e-10,0,.365079,.699668,502817e-10,0,.396825,.677839,51406e-9,0,.428571,.655091,511095e-10,0,.460317,.632665,516067e-10,0,.492064,.609734,512255e-10,0,.52381,.587043,510263e-10,0,.555556,.564298,50565e-9,0,.587302,.541769,497951e-10,0,.619048,.519529,492698e-10,0,.650794,.497574,482066e-10,0,.68254,.476028,473689e-10,0,.714286,.454961,461941e-10,0,.746032,.434341,450618e-10,0,.777778,.414364,438355e-10,0,.809524,.394832,424196e-10,0,.84127,.376109,412563e-10,0,.873016,.35779,396226e-10,0,.904762,.340379,384886e-10,0,.936508,.323385,368214e-10,0,.968254,.307295,356636e-10,0,1,1,106465e-17,0,0,1,106555e-17,0,0,1,107966e-17,0,0,1,114601e-17,0,0,1,137123e-17,0,0,1,21243e-16,0,0,.999999,489653e-17,0,0,.999999,160283e-16,0,0,.999998,62269e-15,0,0,.999997,2.51859e-10,0,0,.999996,9.96192e-10,0,0,.999992,3.74531e-9,0,0,.999986,1.32022e-8,0,0,.999975,4.33315e-8,0,0,.999959,1.31956e-7,0,0,.999927,3.72249e-7,0,0,.999871,9.72461e-7,0,0,.999771,235343e-11,0,0,.999572,52768e-10,0,0,.999133,109237e-10,0,0,.997912,203675e-10,0,0,.993008,279396e-10,0,0,.980645,139604e-10,0,0,.970057,646596e-11,0,0,.966717,65089e-10,0,474145e-10,.965497,111863e-10,0,89544e-8,.96334,179857e-10,0,.0032647,.959294,259045e-10,0,.0075144,.951519,292327e-10,0,.0138734,.940517,249769e-10,0,.0224952,.93014,26803e-9,0,.0334828,.91972,303656e-10,0,.0468973,.910294,353323e-10,0,.0627703,.897701,351002e-10,0,.0811019,.884522,388104e-10,0,.10186,.869489,412932e-10,0,.124985,.853983,415781e-10,0,.150372,.838425,454066e-10,0,.177868,.820656,471624e-10,0,.207245,.801875,475243e-10,0,.238143,.783521,505621e-10,0,.269841,.763131,50721e-9,0,.301587,.74261,523293e-10,0,.333333,.72148,528699e-10,0,.365079,.699696,538677e-10,0,.396825,.677592,539255e-10,0,.428571,.65525,546367e-10,0,.460317,.632452,541348e-10,0,.492064,.609903,544976e-10,0,.52381,.586928,536201e-10,0,.555556,.564464,535185e-10,0,.587302,.541801,524949e-10,0,.619048,.519681,51812e-9,0,.650794,.497685,507687e-10,0,.68254,.47622,496243e-10,0,.714286,.455135,485714e-10,0,.746032,.4346,471847e-10,0,.777778,.414564,459294e-10,0,.809524,.395165,444705e-10,0,.84127,.376333,430772e-10,0,.873016,.358197,416229e-10,0,.904762,.34064,401019e-10,0,.936508,.323816,386623e-10,0,.968254,.307581,370933e-10,0,1,1,991541e-17,0,0,1,992077e-17,0,0,1,100041e-16,0,0,1,10385e-15,0,0,1,115777e-16,0,0,1,150215e-16,0,0,.999999,254738e-16,0,0,.999999,598822e-16,0,0,.999998,1.79597e-10,0,0,.999997,6.02367e-10,0,0,.999994,2.06835e-9,0,0,.99999,6.94952e-9,0,0,.999984,2.23363e-8,0,0,.999972,6.78578e-8,0,0,.999952,1.93571e-7,0,0,.999919,5.16594e-7,0,0,.99986,128739e-11,0,0,.999753,299298e-11,0,0,.999546,648258e-11,0,0,.999074,129985e-10,0,0,.997671,232176e-10,0,0,.991504,256701e-10,0,0,.981148,131141e-10,0,0,.971965,869048e-11,0,280182e-10,.966624,808301e-11,0,695475e-9,.965344,135235e-10,0,.00265522,.963048,210592e-10,0,.00622975,.958673,287473e-10,0,.0116234,.950262,281379e-10,0,.018976,.940836,271089e-10,0,.0283844,.930996,30926e-9,0,.0399151,.919848,348359e-10,0,.0536063,.909136,366092e-10,0,.0694793,.897554,384162e-10,0,.0875342,.884691,430971e-10,0,.107749,.869414,447803e-10,0,.130087,.853462,452858e-10,0,.154481,.838187,495769e-10,0,.180833,.820381,502709e-10,0,.209005,.801844,522713e-10,0,.238791,.783061,541505e-10,0,.269869,.763205,553712e-10,0,.301587,.742362,564909e-10,0,.333333,.721393,572646e-10,0,.365079,.699676,581012e-10,0,.396825,.677395,58096e-9,0,.428571,.655208,585766e-10,0,.460317,.632451,583602e-10,0,.492064,.609839,580234e-10,0,.52381,.587093,577161e-10,0,.555556,.564467,568447e-10,0,.587302,.542043,563166e-10,0,.619048,.519826,55156e-9,0,.650794,.497952,541682e-10,0,.68254,.476477,528971e-10,0,.714286,.455412,514952e-10,0,.746032,.434926,502222e-10,0,.777778,.4149,485779e-10,0,.809524,.395552,472242e-10,0,.84127,.376712,454891e-10,0,.873016,.358622,440924e-10,0,.904762,.341048,422984e-10,0,.936508,.324262,408582e-10,0,.968254,.308013,390839e-10,0,1,1,613913e-16,0,0,1,614145e-16,0,0,1,617708e-16,0,0,1,633717e-16,0,0,1,681648e-16,0,0,1,808291e-16,0,0,1,1.14608e-10,0,0,.999998,2.10507e-10,0,0,.999997,4.99595e-10,0,0,.999995,1.39897e-9,0,0,.999994,4.19818e-9,0,0,.999988,1.27042e-8,0,0,.999979,3.75153e-8,0,0,.999965,1.06206e-7,0,0,.999945,2.85381e-7,0,0,.999908,7.23611e-7,0,0,.999846,17255e-10,0,0,.999733,386104e-11,0,0,.999511,808493e-11,0,0,.998993,156884e-10,0,0,.997326,265538e-10,0,0,.989706,206466e-10,0,0,.981713,130756e-10,0,70005e-10,.973636,106473e-10,0,464797e-9,.966509,10194e-9,0,.00201743,.965149,165881e-10,0,.00497549,.962669,249147e-10,0,.00953262,.95786,317449e-10,0,.0158211,.949334,281045e-10,0,.0239343,.941041,303263e-10,0,.0339372,.931575,356754e-10,0,.0458738,.920102,397075e-10,0,.059772,.908002,384886e-10,0,.075645,.897269,43027e-9,0,.0934929,.884559,479925e-10,0,.113302,.869161,48246e-9,0,.135045,.853342,509505e-10,0,.158678,.837633,542846e-10,0,.184136,.820252,554139e-10,0,.211325,.801872,581412e-10,0,.240113,.782418,585535e-10,0,.270306,.7631,610923e-10,0,.301594,.742183,613678e-10,0,.333333,.721098,627275e-10,0,.365079,.699512,629413e-10,0,.396825,.677372,636351e-10,0,.428571,.655059,633555e-10,0,.460317,.632567,636513e-10,0,.492064,.609784,628965e-10,0,.52381,.587237,625546e-10,0,.555556,.564525,615825e-10,0,.587302,.542181,605048e-10,0,.619048,.520017,596329e-10,0,.650794,.498204,581516e-10,0,.68254,.476742,569186e-10,0,.714286,.455803,553833e-10,0,.746032,.435251,537807e-10,0,.777778,.415374,522025e-10,0,.809524,.395921,503421e-10,0,.84127,.377253,488211e-10,0,.873016,.359021,468234e-10,0,.904762,.341637,453269e-10,0,.936508,.3247,433014e-10,0,.968254,.308625,418007e-10,0,1,1,2.86798e-10,0,0,1,2.86877e-10,0,0,1,2.88094e-10,0,0,1,2.93506e-10,0,0,1,3.09262e-10,0,0,.999999,3.48593e-10,0,0,.999999,4.44582e-10,0,0,.999998,6.88591e-10,0,0,.999996,1.34391e-9,0,0,.999993,3.17438e-9,0,0,.999989,8.35609e-9,0,0,.999983,2.28677e-8,0,0,.999974,6.23361e-8,0,0,.999959,1.65225e-7,0,0,.999936,4.19983e-7,0,0,.999896,101546e-11,0,0,.99983,232376e-11,0,0,.999709,50156e-10,0,0,.999469,10167e-9,0,0,.998886,190775e-10,0,0,.996819,300511e-10,0,0,.988837,185092e-10,0,1.68222e-7,.982178,134622e-10,0,259622e-9,.975017,125961e-10,0,.00142595,.967101,13507e-9,0,.00382273,.964905,205003e-10,0,.00764164,.96218,29546e-9,0,.0130121,.956821,343738e-10,0,.0200253,.948829,305063e-10,0,.0287452,.941092,346487e-10,0,.039218,.931883,412061e-10,0,.0514748,.920211,444651e-10,0,.0655351,.907307,431252e-10,0,.0814082,.89684,490382e-10,0,.0990939,.884119,53334e-9,0,.118583,.869148,54114e-9,0,.139856,.853377,578536e-10,0,.162882,.836753,592285e-10,0,.187615,.820063,622787e-10,0,.213991,.801694,645492e-10,0,.241918,.782116,65353e-9,0,.271267,.762673,674344e-10,0,.301847,.742133,682788e-10,0,.333333,.720779,691959e-10,0,.365079,.699386,696817e-10,0,.396826,.67732,699583e-10,0,.428572,.654888,698447e-10,0,.460318,.632499,694063e-10,0,.492064,.609825,691612e-10,0,.52381,.587287,681576e-10,0,.555556,.564743,674138e-10,0,.587302,.542409,661617e-10,0,.619048,.520282,647785e-10,0,.650794,.498506,633836e-10,0,.68254,.477102,615905e-10,0,.714286,.456167,601013e-10,0,.746032,.435728,581457e-10,0,.777778,.415809,564215e-10,0,.809524,.396517,544997e-10,0,.84127,.377737,525061e-10,0,.873016,.359698,506831e-10,0,.904762,.342164,48568e-9,0,.936508,.325417,467826e-10,0,.968254,.309186,446736e-10,0,1,1,1.09018e-9,0,0,1,1.0904e-9,0,0,1,1.09393e-9,0,0,1,1.1095e-9,0,0,1,1.154e-9,0,0,1,1.26089e-9,0,0,.999999,1.5059e-9,0,0,.999997,2.07899e-9,0,0,.999994,3.48164e-9,0,0,.999993,7.05728e-9,0,0,.999987,1.63692e-8,0,0,.999981,4.06033e-8,0,0,.999969,1.0245e-7,0,0,.999953,2.55023e-7,0,0,.999925,6.1511e-7,0,0,.999881,142218e-11,0,0,.99981,313086e-11,0,0,.99968,653119e-11,0,0,.999418,12832e-9,0,0,.998748,232497e-10,0,0,.996066,329522e-10,0,0,.988379,179613e-10,0,108799e-9,.982567,143715e-10,0,921302e-9,.976097,148096e-10,0,.00280738,.968475,178905e-10,0,.00596622,.964606,253921e-10,0,.0105284,.961564,348623e-10,0,.0165848,.955517,357612e-10,0,.0242,.948381,343493e-10,0,.03342,.941095,405849e-10,0,.0442777,.931923,475394e-10,0,.0567958,.91996,484328e-10,0,.0709879,.907419,502146e-10,0,.086861,.89618,561654e-10,0,.104415,.88337,587612e-10,0,.123643,.869046,618057e-10,0,.144531,.853278,657392e-10,0,.167057,.836091,66303e-9,0,.191188,.819644,704445e-10,0,.216878,.801246,714071e-10,0,.244062,.782031,740093e-10,0,.272649,.762066,74685e-9,0,.302509,.741964,766647e-10,0,.333442,.720554,766328e-10,0,.365079,.699098,777857e-10,0,.396826,.677189,774633e-10,0,.428572,.65484,776235e-10,0,.460318,.632496,770316e-10,0,.492064,.609908,762669e-10,0,.52381,.587312,753972e-10,0,.555556,.564938,739994e-10,0,.587302,.542577,728382e-10,0,.619048,.52062,71112e-9,0,.650794,.498819,694004e-10,0,.68254,.477555,675575e-10,0,.714286,.456568,653449e-10,0,.746032,.436278,636068e-10,0,.777778,.41637,613466e-10,0,.809524,.397144,594177e-10,0,.84127,.378412,570987e-10,0,.873016,.360376,550419e-10,0,.904762,.342906,527422e-10,0,.936508,.326136,506544e-10,0,.968254,.30997,484307e-10,0,1,1,3.54014e-9,0,0,1,3.54073e-9,0,0,1,3.54972e-9,0,0,1,3.58929e-9,0,0,1,3.70093e-9,0,0,.999999,3.96194e-9,0,0,.999998,4.53352e-9,0,0,.999997,5.78828e-9,0,0,.999994,8.63812e-9,0,0,.999991,1.53622e-8,0,0,.999985,3.16356e-8,0,0,.999977,7.12781e-8,0,0,.999964,1.66725e-7,0,0,.999945,3.90501e-7,0,0,.999912,8.95622e-7,0,0,.999866,198428e-11,0,0,.999786,421038e-11,0,0,.999647,850239e-11,0,0,.999356,162059e-10,0,0,.998563,282652e-10,0,0,.994928,336309e-10,0,244244e-10,.987999,178458e-10,0,523891e-9,.982893,159162e-10,0,.00194729,.977044,178056e-10,0,.00451099,.969972,230624e-10,0,.00835132,.964237,313922e-10,0,.013561,.960791,406145e-10,0,.0202056,.954292,372796e-10,0,.0283321,.948052,403199e-10,0,.0379739,.940938,479537e-10,0,.0491551,.931689,545292e-10,0,.0618918,.91987,54038e-9,0,.0761941,.907665,589909e-10,0,.0920672,.895281,642651e-10,0,.109511,.882621,659707e-10,0,.12852,.86873,709973e-10,0,.149085,.853008,742221e-10,0,.171189,.835944,761754e-10,0,.194809,.818949,797052e-10,0,.21991,.800951,812434e-10,0,.246447,.781847,838075e-10,0,.274352,.761649,84501e-9,0,.303535,.74152,860258e-10,0,.333857,.720495,866233e-10,0,.365104,.698742,868326e-10,0,.396826,.677096,87133e-9,0,.428572,.654782,863497e-10,0,.460318,.632335,860206e-10,0,.492064,.610031,849337e-10,0,.52381,.587457,838279e-10,0,.555556,.56513,82309e-9,0,.587302,.542877,803542e-10,0,.619048,.5209,786928e-10,0,.650794,.499291,765171e-10,0,.68254,.477971,744753e-10,0,.714286,.457221,72209e-9,0,.746032,.436803,697448e-10,0,.777778,.417083,675333e-10,0,.809524,.397749,648058e-10,0,.84127,.379177,625759e-10,0,.873016,.361061,598584e-10,0,.904762,.343713,575797e-10,0,.936508,.326894,549999e-10,0,.968254,.310816,527482e-10,0,1,1,1.0153e-8,0,0,1,1.01544e-8,0,0,1,1.01751e-8,0,0,1,1.02662e-8,0,0,1,1.0521e-8,0,0,.999999,1.11049e-8,0,0,.999999,1.23408e-8,0,0,.999996,1.4924e-8,0,0,.999992,2.04471e-8,0,0,.999989,3.26539e-8,0,0,.99998,6.03559e-8,0,0,.999971,1.23936e-7,0,0,.999955,2.69058e-7,0,0,.999933,5.93604e-7,0,0,.999901,129633e-11,0,0,.999847,275621e-11,0,0,.999761,564494e-11,0,0,.999607,110485e-10,0,0,.999282,204388e-10,0,0,.99831,341084e-10,0,2.2038e-7,.993288,294949e-10,0,242388e-9,.987855,192736e-10,0,.0012503,.983167,182383e-10,0,.0032745,.977908,218633e-10,0,.00646321,.971194,290662e-10,0,.0109133,.963867,386401e-10,0,.0166927,.95982,462827e-10,0,.0238494,.953497,420705e-10,0,.0324178,.947621,477743e-10,0,.0424225,.940611,568258e-10,0,.0538808,.931174,618061e-10,0,.0668047,.919919,627098e-10,0,.0812014,.907856,694714e-10,0,.0970745,.894509,735008e-10,0,.114424,.881954,763369e-10,0,.133246,.868309,821896e-10,0,.153534,.852511,83769e-9,0,.175275,.835821,881615e-10,0,.198453,.817981,896368e-10,0,.223042,.800504,930906e-10,0,.249009,.78141,945056e-10,0,.276304,.761427,963605e-10,0,.304862,.74094,968088e-10,0,.334584,.720233,981481e-10,0,.365322,.698592,979122e-10,0,.396826,.676763,981057e-10,0,.428571,.654808,973956e-10,0,.460318,.632326,962619e-10,0,.492064,.610049,952996e-10,0,.52381,.58763,933334e-10,0,.555556,.565261,917573e-10,0,.587302,.543244,896636e-10,0,.619048,.521273,873304e-10,0,.650794,.499818,852648e-10,0,.68254,.478536,823961e-10,0,.714286,.457826,79939e-9,0,.746032,.437549,77126e-9,0,.777778,.41776,743043e-10,0,.809524,.39863,716426e-10,0,.84127,.379954,686456e-10,0,.873016,.362025,660514e-10,0,.904762,.344581,630755e-10,0,.936508,.327909,605439e-10,0,.968254,.311736,576345e-10,0,1,1,2.63344e-8,0,0,1,2.63373e-8,0,0,1,2.63815e-8,0,0,1,2.65753e-8,0,0,1,2.71132e-8,0,0,.999999,2.83279e-8,0,0,.999997,3.0833e-8,0,0,.999995,3.58711e-8,0,0,.999992,4.61266e-8,0,0,.999985,6.7574e-8,0,0,.999977,1.1358e-7,0,0,.999966,2.13657e-7,0,0,.999948,4.31151e-7,0,0,.999923,8.96656e-7,0,0,.999884,186603e-11,0,0,.999826,381115e-11,0,0,.999732,754184e-11,0,0,.999561,143192e-10,0,0,.999191,257061e-10,0,0,.997955,405724e-10,0,744132e-10,.992228,276537e-10,0,716477e-9,.987638,208885e-10,0,.0022524,.983395,215226e-10,0,.00484816,.978614,270795e-10,0,.00860962,.972389,365282e-10,0,.0136083,.964392,474747e-10,0,.0198941,.95861,509141e-10,0,.0275023,.952806,48963e-9,0,.0364584,.94712,571119e-10,0,.04678,.940104,671704e-10,0,.0584799,.930398,687586e-10,0,.0715665,.919866,738161e-10,0,.086045,.907853,813235e-10,0,.101918,.894078,834582e-10,0,.119186,.881177,892093e-10,0,.137845,.867575,944548e-10,0,.157891,.852107,969607e-10,0,.179316,.835502,101456e-9,0,.202106,.81756,103256e-9,0,.226243,.79984,106954e-9,0,.251704,.780998,108066e-9,0,.278451,.761132,110111e-9,0,.306436,.740429,110459e-9,0,.335586,.719836,111219e-9,0,.365796,.698467,11145e-8,0,.3969,.676446,110393e-9,0,.428571,.654635,110035e-9,0,.460318,.632411,108548e-9,0,.492064,.609986,106963e-9,0,.52381,.587872,105238e-9,0,.555556,.565528,102665e-9,0,.587302,.543563,100543e-9,0,.619048,.52176,976182e-10,0,.650794,.500188,947099e-10,0,.68254,.479204,919929e-10,0,.714286,.458413,886139e-10,0,.746032,.438314,857839e-10,0,.777778,.418573,82411e-9,0,.809524,.39947,792211e-10,0,.84127,.380892,759546e-10,0,.873016,.362953,727571e-10,0,.904762,.345601,695738e-10,0,.936508,.328895,664907e-10,0,.968254,.312808,634277e-10,0,1,1,6.28647e-8,0,0,1,6.28705e-8,0,0,1,6.29587e-8,0,0,1,6.33441e-8,0,0,.999999,6.44087e-8,0,0,.999998,6.67856e-8,0,0,.999997,7.15889e-8,0,0,.999995,8.09577e-8,0,0,.999989,9.92764e-8,0,0,.999983,1.35834e-7,0,0,.999974,2.10482e-7,0,0,.999959,3.65215e-7,0,0,.999939,6.86693e-7,0,0,.999911,13472e-10,0,0,.999868,26731e-10,0,0,.999804,524756e-11,0,0,.9997,100403e-10,0,0,.99951,185019e-10,0,0,.999078,322036e-10,0,620676e-11,.997428,470002e-10,0,341552e-9,.99162,287123e-10,0,.00143727,.987479,234706e-10,0,.00349201,.983582,260083e-10,0,.0066242,.979186,337927e-10,0,.0109113,.97325,454689e-10,0,.0164064,.965221,573759e-10,0,.0231463,.957262,544114e-10,0,.0311571,.952211,587006e-10,0,.0404572,.946631,692256e-10,0,.0510592,.939391,787819e-10,0,.0629723,.929795,792368e-10,0,.0762025,.91965,875075e-10,0,.090753,.907737,950903e-10,0,.106626,.893899,972963e-10,0,.123822,.880239,10459e-8,0,.142337,.866562,107689e-9,0,.16217,.85164,113081e-9,0,.183314,.835021,116636e-9,0,.20576,.817311,120074e-9,0,.229496,.798845,121921e-9,0,.254502,.780479,12475e-8,0,.280753,.760694,125255e-9,0,.308212,.740142,126719e-9,0,.336825,.719248,12636e-8,0,.366517,.698209,126712e-9,0,.397167,.676398,125769e-9,0,.428578,.654378,124432e-9,0,.460318,.632484,123272e-9,0,.492064,.610113,12085e-8,0,.52381,.587931,118411e-9,0,.555556,.565872,11569e-8,0,.587302,.543814,112521e-9,0,.619048,.522265,109737e-9,0,.650794,.500835,106228e-9,0,.68254,.479818,102591e-9,0,.714286,.459258,991288e-10,0,.746032,.439061,952325e-10,0,.777778,.419552,91895e-9,0,.809524,.400399,879051e-10,0,.84127,.381976,844775e-10,0,.873016,.364009,806316e-10,0,.904762,.346761,771848e-10,0,.936508,.330049,735429e-10,0,.968254,.314018,702103e-10,0,1,1,1.39968e-7,0,0,1,1.39979e-7,0,0,1,1.40145e-7,0,0,1,1.4087e-7,0,0,.999999,1.42865e-7,0,0,.999998,1.47279e-7,0,0,.999997,1.56057e-7,0,0,.999992,1.7276e-7,0,0,.999989,2.04352e-7,0,0,.99998,2.6494e-7,0,0,.999969,3.83435e-7,0,0,.999953,6.18641e-7,0,0,.999929,108755e-11,0,0,.999898,201497e-11,0,0,.999849,381346e-11,0,0,.999778,719815e-11,0,0,.999661,133215e-10,0,0,.999451,238313e-10,0,0,.998936,401343e-10,0,113724e-9,.99662,517346e-10,0,820171e-9,.991094,304323e-10,0,.00238143,.987487,281757e-10,0,.00493527,.983731,320048e-10,0,.00856859,.979647,423905e-10,0,.0133393,.973837,562935e-10,0,.0192863,.96584,677442e-10,0,.0264369,.956309,623073e-10,0,.03481,.951523,704131e-10,0,.0444184,.946003,836594e-10,0,.0552713,.938454,911736e-10,0,.0673749,.929279,938264e-10,0,.0807329,.919239,103754e-9,0,.0953479,.907293,109928e-9,0,.111221,.893936,115257e-9,0,.128352,.879674,122265e-9,0,.14674,.865668,125733e-9,0,.166382,.850998,132305e-9,0,.187276,.834498,134844e-9,0,.209413,.816903,139276e-9,0,.232786,.798235,140984e-9,0,.257382,.779724,14378e-8,0,.283181,.760251,144623e-9,0,.310156,.739808,145228e-9,0,.338269,.718762,14539e-8,0,.367461,.697815,144432e-9,0,.397646,.67631,143893e-9,0,.428685,.654278,141846e-9,0,.460318,.632347,13935e-8,0,.492064,.610296,137138e-9,0,.52381,.588039,133806e-9,0,.555556,.566218,130755e-9,0,.587302,.544346,127128e-9,0,.619048,.522701,123002e-9,0,.650794,.501542,119443e-9,0,.68254,.480508,115055e-9,0,.714286,.460092,111032e-9,0,.746032,.440021,106635e-9,0,.777778,.420446,102162e-9,0,.809524,.401512,98184e-9,0,.84127,.38299,936497e-10,0,.873016,.365232,89813e-9,0,.904762,.347865,853073e-10,0,.936508,.331342,817068e-10,0,.968254,.315202,773818e-10,0,1,1,2.9368e-7,0,0,1,2.937e-7,0,0,1,2.93998e-7,0,0,1,2.95298e-7,0,0,.999999,2.98865e-7,0,0,.999998,3.067e-7,0,0,.999995,3.22082e-7,0,0,.999992,3.50767e-7,0,0,.999986,4.03538e-7,0,0,.999976,5.01372e-7,0,0,.999964,6.8562e-7,0,0,.999945,10374e-10,0,0,.999919,171269e-11,0,0,.999882,300175e-11,0,0,.999829,542144e-11,0,0,.999749,984182e-11,0,0,.99962,176213e-10,0,0,.999382,305995e-10,0,138418e-10,.998751,496686e-10,0,389844e-9,.995344,510733e-10,0,.00150343,.990768,345829e-10,0,.00352451,.987464,342841e-10,0,.00655379,.983846,399072e-10,0,.0106554,.980007,533219e-10,0,.0158723,.974494,696992e-10,0,.0222333,.96622,776754e-10,0,.029758,.956273,747718e-10,0,.0384596,.950952,864611e-10,0,.0483473,.945215,100464e-9,0,.0594266,.937287,103729e-9,0,.0717019,.928649,111665e-9,0,.0851752,.918791,12353e-8,0,.0998479,.906685,127115e-9,0,.115721,.893706,13628e-8,0,.132794,.879248,142427e-9,0,.151067,.864685,148091e-9,0,.170538,.850032,153517e-9,0,.191204,.833853,157322e-9,0,.213063,.816353,161086e-9,0,.236107,.797834,164111e-9,0,.260329,.778831,165446e-9,0,.285714,.759756,167492e-9,0,.312243,.739419,166928e-9,0,.339887,.718491,167e-6,0,.368604,.697392,165674e-9,0,.398329,.676102,163815e-9,0,.428961,.654243,162003e-9,0,.460331,.632176,158831e-9,0,.492064,.610407,155463e-9,0,.52381,.588394,152062e-9,0,.555556,.56645,147665e-9,0,.587302,.5449,14375e-8,0,.619048,.523276,138905e-9,0,.650794,.502179,134189e-9,0,.68254,.481359,129392e-9,0,.714286,.46092,124556e-9,0,.746032,.441084,11957e-8,0,.777778,.421517,114652e-9,0,.809524,.402721,109688e-9,0,.84127,.384222,104667e-9,0,.873016,.366534,999633e-10,0,.904762,.349205,950177e-10,0,.936508,.332702,907301e-10,0,.968254,.316599,859769e-10,0,1,1,5.85473e-7,0,0,1,5.85507e-7,0,0,1,5.8602e-7,0,0,.999999,5.88259e-7,0,0,.999999,5.94381e-7,0,0,.999998,6.07754e-7,0,0,.999995,6.33729e-7,0,0,.99999,6.8137e-7,0,0,.999984,7.67003e-7,0,0,.999973,9.21212e-7,0,0,.999959,120218e-11,0,0,.999936,172024e-11,0,0,.999907,268088e-11,0,0,.999866,445512e-11,0,0,.999806,768481e-11,0,0,.999716,1342e-8,0,0,.999576,232473e-10,0,0,.9993,391694e-10,0,129917e-9,.998498,608429e-10,0,845035e-9,.994132,489743e-10,0,.00237616,.99031,384644e-10,0,.00484456,.987409,421768e-10,0,.00832472,.983981,504854e-10,0,.0128643,.980268,671028e-10,0,.0184947,.974875,852749e-10,0,.025237,.966063,85531e-9,0,.0331046,.956779,900588e-10,0,.0421067,.950259,10577e-8,0,.0522487,.944239,119458e-9,0,.0635343,.936341,122164e-9,0,.0759654,.928047,134929e-9,0,.0895434,.918065,145544e-9,0,.104269,.906267,150531e-9,0,.120142,.893419,161652e-9,0,.137163,.878758,16593e-8,0,.15533,.863699,174014e-9,0,.174645,.848876,177877e-9,0,.195106,.833032,184049e-9,0,.21671,.815557,186088e-9,0,.239454,.797323,19054e-8,0,.263332,.778124,191765e-9,0,.288336,.758929,192535e-9,0,.314451,.738979,192688e-9,0,.341658,.718213,191522e-9,0,.369924,.696947,190491e-9,0,.399202,.675807,187913e-9,0,.429416,.654147,184451e-9,0,.460447,.63229,181442e-9,0,.492064,.610499,177139e-9,0,.523809,.588747,172596e-9,0,.555555,.566783,167457e-9,0,.587301,.545359,162518e-9,0,.619048,.523984,156818e-9,0,.650794,.502917,151884e-9,0,.68254,.482294,145514e-9,0,.714286,.461945,140199e-9,0,.746032,.442133,134101e-9,0,.777778,.422705,128374e-9,0,.809524,.403916,122996e-9,0,.84127,.38554,116808e-9,0,.873016,.367909,111973e-9,0,.904762,.350651,105938e-9,0,.936508,.334208,101355e-9,0,.968254,.318123,957629e-10,0,1,1,111633e-11,0,0,1,111639e-11,0,0,1,111725e-11,0,0,1,112096e-11,0,0,.999999,11311e-10,0,0,.999997,115315e-11,0,0,.999995,11956e-10,0,0,.999989,127239e-11,0,0,.999981,140772e-11,0,0,.999969,164541e-11,0,0,.999952,206607e-11,0,0,.999928,281783e-11,0,0,.999895,416835e-11,0,0,.999848,658728e-11,0,0,.999781,108648e-10,0,0,.999682,182579e-10,0,0,.999523,306003e-10,0,159122e-10,.999205,499862e-10,0,391184e-9,.998131,73306e-9,0,.00147534,.993334,513229e-10,0,.0034227,.99016,467783e-10,0,.00632232,.987321,523413e-10,0,.0102295,.984099,64267e-9,0,.0151794,.980432,843042e-10,0,.0211947,.974976,102819e-9,0,.0282899,.966429,996234e-10,0,.0364739,.957633,111074e-9,0,.0457522,.949422,128644e-9,0,.0561278,.943045,140076e-9,0,.0676023,.935448,146349e-9,0,.0801762,.927225,161854e-9,0,.0938499,.917033,169135e-9,0,.108623,.905762,179987e-9,0,.124496,.892879,189832e-9,0,.141469,.878435,195881e-9,0,.159541,.863114,20466e-8,0,.178713,.84776,209473e-9,0,.198985,.832084,214861e-9,0,.220355,.814915,217695e-9,0,.242823,.796711,220313e-9,0,.266385,.777603,22313e-8,0,.291036,.757991,222471e-9,0,.316767,.738371,222869e-9,0,.343563,.717872,221243e-9,0,.371402,.696619,218089e-9,0,.400248,.675379,21562e-8,0,.430047,.65411,21169e-8,0,.460709,.63241,206947e-9,0,.492079,.61046,201709e-9,0,.52381,.58903,196753e-9,0,.555556,.567267,189637e-9,0,.587302,.545886,184735e-9,0,.619048,.524714,177257e-9,0,.650794,.503789,171424e-9,0,.68254,.483204,164688e-9,0,.714286,.462976,157172e-9,0,.746032,.443294,151341e-9,0,.777778,.423988,143737e-9,0,.809524,.405325,138098e-9,0,.84127,.386981,130698e-9,0,.873016,.369436,125276e-9,0,.904762,.35219,118349e-9,0,.936508,.335804,11312e-8,0,.968254,.319749,106687e-9,0,1,1,204685e-11,0,0,1,204694e-11,0,0,1,204831e-11,0,0,.999999,205428e-11,0,0,.999999,207056e-11,0,0,.999997,210581e-11,0,0,.999993,21732e-10,0,0,.999987,229365e-11,0,0,.999979,250243e-11,0,0,.999965,286127e-11,0,0,.999947,348028e-11,0,0,.999918,455588e-11,0,0,.999881,643303e-11,0,0,.999828,970064e-11,0,0,.999753,153233e-10,0,0,.999642,24793e-9,0,0,.999464,402032e-10,0,122947e-9,.999089,635852e-10,0,807414e-9,.997567,857026e-10,0,.00227206,.992903,594912e-10,0,.00462812,.990011,578515e-10,0,.00794162,.987192,65399e-9,0,.0122534,.98418,819675e-10,0,.0175888,.980491,105514e-9,0,.0239635,.974779,121532e-9,0,.031387,.96675,119144e-9,0,.0398644,.958248,136125e-9,0,.0493982,.948884,155408e-9,0,.0599896,.941673,162281e-9,0,.0716382,.934521,176754e-9,0,.0843437,.926205,192873e-9,0,.0981056,.916089,200038e-9,0,.112923,.904963,213624e-9,0,.128796,.892089,221834e-9,0,.145725,.878028,232619e-9,0,.163709,.86249,238632e-9,0,.182749,.846587,247002e-9,0,.202847,.830988,250702e-9,0,.224001,.814165,255562e-9,0,.246214,.796135,257505e-9,0,.269482,.777052,258625e-9,0,.293805,.757201,258398e-9,0,.319176,.737655,256714e-9,0,.345587,.717477,255187e-9,0,.373021,.696433,251792e-9,0,.401454,.675084,247223e-9,0,.430844,.653907,242213e-9,0,.461125,.632561,237397e-9,0,.492187,.610658,229313e-9,0,.52381,.589322,224402e-9,0,.555556,.567857,216116e-9,0,.587302,.54652,209124e-9,0,.619048,.525433,201601e-9,0,.650794,.504679,192957e-9,0,.68254,.484203,186052e-9,0,.714286,.464203,177672e-9,0,.746032,.444549,170005e-9,0,.777778,.425346,162401e-9,0,.809524,.406706,1544e-7,0,.84127,.388576,147437e-9,0,.873016,.37094,139493e-9,0,.904762,.353996,133219e-9,0,.936508,.337391,125573e-9,0,.968254,.321648,119867e-9,0,1,1,362511e-11,0,0,1,362525e-11,0,0,1,362739e-11,0,0,.999999,363673e-11,0,0,.999998,366214e-11,0,0,.999996,371698e-11,0,0,.999992,382116e-11,0,0,.999986,400554e-11,0,0,.999976,432058e-11,0,0,.999961,485194e-11,0,0,.999938,574808e-11,0,0,.999908,726643e-11,0,0,.999865,984707e-11,0,0,.999807,142217e-10,0,0,.999723,215581e-10,0,0,.999602,336114e-10,0,119113e-10,.999398,527353e-10,0,355813e-9,.998946,805809e-10,0,.00137768,.996647,942908e-10,0,.00322469,.992298,668733e-10,0,.00597897,.989802,716564e-10,0,.00968903,.987019,821355e-10,0,.0143845,.984219,104555e-9,0,.0200831,.980425,131245e-9,0,.0267948,.974241,139613e-9,0,.034525,.967006,145931e-9,0,.0432757,.95893,167153e-9,0,.0530471,.949157,188146e-9,0,.0638386,.94062,194625e-9,0,.0756487,.933509,213721e-9,0,.0884762,.925088,229616e-9,0,.10232,.915178,239638e-9,0,.117178,.904093,254814e-9,0,.133051,.891337,263685e-9,0,.149939,.877326,274789e-9,0,.167841,.861794,280534e-9,0,.18676,.845758,289534e-9,0,.206696,.829792,294446e-9,0,.22765,.813037,296877e-9,0,.249625,.795285,300217e-9,0,.27262,.776323,299826e-9,0,.296636,.756673,299787e-9,0,.321671,.736856,297867e-9,0,.347718,.716883,294052e-9,0,.374768,.696089,289462e-9,0,.402804,.67505,285212e-9,0,.431796,.653509,27653e-8,0,.461695,.63258,271759e-9,0,.49242,.61104,262811e-9,0,.523822,.589567,255151e-9,0,.555556,.568322,246434e-9,0,.587302,.547235,237061e-9,0,.619048,.52616,228343e-9,0,.650794,.505716,219236e-9,0,.68254,.485274,209595e-9,0,.714286,.465411,201011e-9,0,.746032,.445854,19109e-8,0,.777778,.426911,182897e-9,0,.809524,.408222,173569e-9,0,.84127,.390307,165496e-9,0,.873016,.372624,156799e-9,0,.904762,.355804,14917e-8,0,.936508,.33924,140907e-9,0,.968254,.323534,134062e-9,0,1,1,622487e-11,0,0,1,62251e-10,0,0,1,622837e-11,0,0,.999999,624259e-11,0,0,.999998,628127e-11,0,0,.999996,636451e-11,0,0,.999991,65218e-10,0,0,.999984,679782e-11,0,0,.999973,726361e-11,0,0,.999955,803644e-11,0,0,.999931,931397e-11,0,0,.999896,114299e-10,0,0,.999847,149402e-10,0,0,.999784,207461e-10,0,0,.999692,302493e-10,0,0,.999554,454957e-10,0,997275e-10,.999326,690762e-10,0,724813e-9,.998757,101605e-9,0,.0020972,.995367,958745e-10,0,.00432324,.99209,832808e-10,0,.00746347,.989517,887601e-10,0,.0115534,.987008,10564e-8,0,.0166134,.98421,133179e-9,0,.0226552,.98021,161746e-9,0,.0296838,.973676,161821e-9,0,.0377016,.967052,178635e-9,0,.0467079,.959385,206765e-9,0,.0567013,.949461,22476e-8,0,.0676796,.939578,23574e-8,0,.0796403,.932416,25893e-8,0,.0925812,.923759,271228e-9,0,.106501,.914223,289165e-9,0,.121397,.902942,301156e-9,0,.13727,.890419,313852e-9,0,.15412,.876639,324408e-9,0,.171946,.861316,33249e-8,0,.190751,.84496,338497e-9,0,.210537,.828427,345861e-9,0,.231305,.811871,347863e-9,0,.253057,.794397,350225e-9,0,.275797,.775726,349915e-9,0,.299525,.75617,347297e-9,0,.324242,.736091,344232e-9,0,.349947,.716213,340835e-9,0,.376633,.695736,332369e-9,0,.404289,.674961,327943e-9,0,.432895,.653518,318533e-9,0,.462415,.632574,310391e-9,0,.492788,.61134,300755e-9,0,.523909,.590017,290506e-9,0,.555556,.568752,280446e-9,0,.587302,.548061,269902e-9,0,.619048,.52711,258815e-9,0,.650794,.506682,248481e-9,0,.68254,.486524,237141e-9,0,.714286,.466812,226872e-9,0,.746032,.44732,216037e-9,0,.777778,.428473,205629e-9,0,.809524,.409921,195691e-9,0,.84127,.392028,185457e-9,0,.873016,.374606,176436e-9,0,.904762,.357601,166508e-9,0,.936508,.341348,158385e-9,0,.968254,.32542,149203e-9,0,1,1,103967e-10,0,0,1,10397e-9,0,0,1,104019e-10,0,0,.999999,104231e-10,0,0,.999998,104806e-10,0,0,.999995,106042e-10,0,0,.999991,108366e-10,0,0,.999982,112415e-10,0,0,.999968,119174e-10,0,0,.99995,130227e-10,0,0,.999922,148176e-10,0,0,.999884,177303e-10,0,0,.99983,224564e-10,0,0,.999758,300966e-10,0,0,.999654,423193e-10,0,549083e-11,.999503,614848e-10,0,296087e-9,.999237,903576e-10,0,.00123144,.998491,1271e-7,0,.00295954,.994594,107754e-9,0,.00555829,.99178,103025e-9,0,.00907209,.989265,11154e-8,0,.0135257,.986998,136296e-9,0,.0189327,.984137,169154e-9,0,.0252993,.979798,196671e-9,0,.0326272,.97337,196678e-9,0,.0409157,.967239,223121e-9,0,.0501623,.959543,253809e-9,0,.0603638,.949466,265972e-9,0,.0715171,.939074,288372e-9,0,.0836187,.931118,310983e-9,0,.0966657,.922525,325561e-9,0,.110656,.912983,345725e-9,0,.125588,.901617,3556e-7,0,.141461,.889487,374012e-9,0,.158275,.875787,383445e-9,0,.176031,.860654,393972e-9,0,.19473,.844417,400311e-9,0,.214374,.82741,405004e-9,0,.234967,.810545,407378e-9,0,.256512,.793312,407351e-9,0,.279011,.774847,406563e-9,0,.302468,.755621,404903e-9,0,.326887,.735511,397486e-9,0,.352266,.715435,39357e-8,0,.378605,.695403,384739e-9,0,.405897,.674681,376108e-9,0,.43413,.65359,365997e-9,0,.463277,.632471,354957e-9,0,.493295,.61151,343593e-9,0,.524106,.59064,331841e-9,0,.555561,.569386,318891e-9,0,.587302,.548785,3072e-7,0,.619048,.528146,29361e-8,0,.650794,.507872,281709e-9,0,.68254,.487805,268627e-9,0,.714286,.468196,255887e-9,0,.746032,.448922,243997e-9,0,.777778,.430093,231662e-9,0,.809524,.411845,220339e-9,0,.84127,.393808,208694e-9,0,.873016,.376615,198045e-9,0,.904762,.359655,187375e-9,0,.936508,.343452,177371e-9,0,.968254,.32765,167525e-9,0,1,1,169351e-10,0,0,1,169356e-10,0,0,1,169427e-10,0,0,.999999,169736e-10,0,0,.999998,170575e-10,0,0,.999995,172372e-10,0,0,.99999,175739e-10,0,0,.999979,181568e-10,0,0,.999966,191206e-10,0,0,.999944,20677e-9,0,0,.999912,231644e-10,0,0,.999869,271268e-10,0,0,.999811,334272e-10,0,0,.99973,433979e-10,0,0,.999617,590083e-10,0,680315e-10,.999445,829497e-10,0,612796e-9,.999138,118019e-9,0,.00187408,.998095,156712e-9,0,.00395791,.993919,125054e-9,0,.00692144,.991333,126091e-9,0,.0107962,.989226,144912e-9,0,.0155986,.986954,175737e-9,0,.0213364,.983982,213883e-9,0,.0280114,.979128,234526e-9,0,.0356226,.973327,243725e-9,0,.0441668,.967416,2773e-7,0,.0536399,.959729,308799e-9,0,.0640376,.949758,322447e-9,0,.0753554,.939173,350021e-9,0,.0875893,.9296,370089e-9,0,.100736,.921181,391365e-9,0,.114793,.91164,413636e-9,0,.129759,.900435,427068e-9,0,.145632,.888183,441046e-9,0,.162412,.874772,454968e-9,0,.180101,.859566,461882e-9,0,.1987,.843579,471556e-9,0,.218213,.826453,474335e-9,0,.238641,.809164,477078e-9,0,.259989,.792179,47755e-8,0,.282262,.773866,472573e-9,0,.305464,.754944,469765e-9,0,.329599,.735133,462371e-9,0,.35467,.714858,453674e-9,0,.380678,.694829,443888e-9,0,.407622,.674453,432052e-9,0,.435493,.653685,420315e-9,0,.464275,.632666,406829e-9,0,.493938,.611676,392234e-9,0,.524422,.591193,379208e-9,0,.555624,.570145,36319e-8,0,.587302,.549566,349111e-9,0,.619048,.529278,334166e-9,0,.650794,.509026,318456e-9,0,.68254,.489186,30449e-8,0,.714286,.469662,289051e-9,0,.746032,.450691,275494e-9,0,.777778,.431841,261437e-9,0,.809524,.413752,247846e-9,0,.84127,.395951,235085e-9,0,.873016,.378633,222245e-9,0,.904762,.36194,210533e-9,0,.936508,.345599,198494e-9,0,.968254,.329999,188133e-9,0,1,1,269663e-10,0,0,1,26967e-9,0,0,1,269772e-10,0,0,.999999,270214e-10,0,0,.999998,271415e-10,0,0,.999994,27398e-9,0,0,.999988,278771e-10,0,0,.999977,287019e-10,0,0,.999961,300544e-10,0,0,.999937,322138e-10,0,0,.999904,356163e-10,0,0,.999854,409465e-10,0,0,.99979,492651e-10,0,0,.999699,621722e-10,0,8.8288e-7,.999572,819715e-10,0,223369e-9,.999381,111689e-9,0,.00105414,.999016,153862e-9,0,.0026493,.997437,187667e-9,0,.00508608,.993545,155672e-9,0,.00840554,.991135,161455e-9,0,.012629,.989157,188241e-9,0,.0177661,.986874,226229e-9,0,.0238198,.983714,268668e-9,0,.0307887,.978301,277109e-9,0,.0386688,.973227,303446e-9,0,.0474554,.967317,341851e-9,0,.0571428,.959477,370885e-9,0,.0677256,.950012,392753e-9,0,.0791988,.939484,42781e-8,0,.0915576,.928135,443866e-9,0,.104798,.919819,472959e-9,0,.118918,.910049,491551e-9,0,.133915,.899181,512616e-9,0,.149788,.886881,523563e-9,0,.166537,.87359,540183e-9,0,.184164,.858613,547386e-9,0,.202669,.842809,554809e-9,0,.222056,.825727,558316e-9,0,.242329,.808086,557824e-9,0,.263492,.790728,556346e-9,0,.285551,.772987,552672e-9,0,.30851,.7541,543738e-9,0,.332376,.734669,536107e-9,0,.357153,.714411,523342e-9,0,.382845,.694196,512238e-9,0,.409454,.674252,497465e-9,0,.436977,.65357,481096e-9,0,.465404,.632999,467054e-9,0,.494713,.611994,448771e-9,0,.524864,.591604,431889e-9,0,.555779,.571134,415238e-9,0,.587302,.550528,396369e-9,0,.619048,.530292,379477e-9,0,.650794,.510364,361488e-9,0,.68254,.490749,343787e-9,0,.714286,.471266,327822e-9,0,.746032,.452462,310626e-9,0,.777778,.433907,295352e-9,0,.809524,.415659,279179e-9,0,.84127,.398138,264685e-9,0,.873016,.380833,249905e-9,0,.904762,.364247,236282e-9,0,.936508,.348041,222905e-9,0,.968254,.332389,210522e-9,0,1,1,420604e-10,0,0,1,420614e-10,0,0,1,420757e-10,0,0,.999999,42138e-9,0,0,.999997,423067e-10,0,0,.999993,426668e-10,0,0,.999986,433372e-10,0,0,.999974,444857e-10,0,0,.999956,463554e-10,0,0,.99993,493105e-10,0,0,.999892,539077e-10,0,0,.999838,610005e-10,0,0,.999767,718822e-10,0,0,.999666,884581e-10,0,365471e-10,.999525,113398e-9,0,485623e-9,.999311,150043e-9,0,.00162096,.998865,200063e-9,0,.00355319,.996278,211014e-9,0,.00633818,.992956,189672e-9,0,.0100043,.991017,210262e-9,0,.0145648,.989055,244292e-9,0,.0200237,.986741,290481e-9,0,.0263798,.983288,334303e-9,0,.033629,.977784,340307e-9,0,.0417652,.973037,377864e-9,0,.0507821,.967181,4239e-7,0,.060673,.958971,443854e-9,0,.0714314,.950093,483039e-9,0,.0830518,.939552,517934e-9,0,.0955288,.927678,539449e-9,0,.108859,.918278,568604e-9,0,.123038,.908449,588505e-9,0,.138065,.897713,612473e-9,0,.153938,.885533,625575e-9,0,.170657,.872131,63854e-8,0,.188224,.857517,647034e-9,0,.20664,.841796,65209e-8,0,.225909,.824726,6544e-7,0,.246035,.807297,655744e-9,0,.267022,.789058,646716e-9,0,.288878,.77189,643898e-9,0,.311607,.753082,629973e-9,0,.335216,.7341,621564e-9,0,.359713,.714094,605171e-9,0,.385103,.693839,588752e-9,0,.41139,.673891,573294e-9,0,.438576,.653565,552682e-9,0,.466656,.633326,533446e-9,0,.495617,.612582,514635e-9,0,.525431,.59205,49303e-8,0,.556041,.571918,471842e-9,0,.587338,.551572,451713e-9,0,.619048,.531553,430049e-9,0,.650794,.51175,410445e-9,0,.68254,.49238,390098e-9,0,.714286,.473143,370033e-9,0,.746032,.45423,351205e-9,0,.777778,.435963,332049e-9,0,.809524,.41787,315021e-9,0,.84127,.400387,297315e-9,0,.873016,.383332,281385e-9,0,.904762,.366665,265397e-9,0,.936508,.350633,250601e-9,0,.968254,.334964,23589e-8,0,1,1,643736e-10,0,0,1,64375e-9,0,0,1,643947e-10,0,0,.999999,64481e-9,0,0,.999997,647143e-10,0,0,.999994,652119e-10,0,0,.999985,661359e-10,0,0,.999972,677116e-10,0,0,.999952,702599e-10,0,0,.999922,742517e-10,0,0,.99988,803906e-10,0,0,.99982,897315e-10,0,0,.999741,103838e-9,0,0,.999629,12496e-8,0,149024e-9,.999474,156161e-9,0,861027e-9,.999229,201034e-9,0,.00231198,.998662,259069e-9,0,.00458147,.995299,245439e-9,0,.00770895,.992732,24498e-8,0,.0117126,.990847,273211e-9,0,.0165989,.988911,316492e-9,0,.0223674,.98654,37161e-8,0,.0290135,.982636,410352e-9,0,.0365309,.977346,421756e-9,0,.0449117,.972909,475578e-9,0,.0541481,.966821,522482e-9,0,.0642326,.958686,545008e-9,0,.075158,.949754,589286e-9,0,.0869181,.939184,619995e-9,0,.0995074,.927505,654266e-9,0,.112922,.916606,682362e-9,0,.127157,.906707,704286e-9,0,.142212,.895937,725909e-9,0,.158085,.883913,743939e-9,0,.174776,.870642,755157e-9,0,.192287,.856241,764387e-9,0,.210619,.84069,771032e-9,0,.229775,.823728,765906e-9,0,.249761,.806481,767604e-9,0,.270582,.787924,754385e-9,0,.292243,.770588,749668e-9,0,.314753,.751991,731613e-9,0,.338118,.733407,717655e-9,0,.362347,.713688,700604e-9,0,.387447,.693595,678765e-9,0,.413424,.673426,657042e-9,0,.440284,.65359,635892e-9,0,.468027,.633576,611569e-9,0,.496645,.613144,586011e-9,0,.526122,.592711,563111e-9,0,.556417,.572722,537699e-9,0,.587451,.552762,512556e-9,0,.619048,.532985,489757e-9,0,.650794,.513219,464139e-9,0,.68254,.493992,442193e-9,0,.714286,.47509,418629e-9,0,.746032,.456287,397045e-9,0,.777778,.438152,375504e-9,0,.809524,.420294,35492e-8,0,.84127,.402749,335327e-9,0,.873016,.385879,316422e-9,0,.904762,.369352,298333e-9,0,.936508,.353301,281417e-9,0,.968254,.337781,265203e-9,0,1,1,968267e-10,0,0,1,968284e-10,0,0,1,968556e-10,0,0,.999999,969733e-10,0,0,.999997,972913e-10,0,0,.999993,979688e-10,0,0,.999984,992239e-10,0,0,.999969,101356e-9,0,0,.999946,104784e-9,0,0,.999913,110111e-9,0,0,.999868,118217e-9,0,0,.999801,130396e-9,0,0,.999712,148523e-9,0,124907e-10,.999589,175233e-9,0,355405e-9,.999416,213999e-9,0,.0013528,.999136,268529e-9,0,.00312557,.998367,333088e-9,0,.00573045,.994701,304757e-9,0,.00919397,.992497,318031e-9,0,.0135261,.990608,353863e-9,0,.0187278,.988715,409044e-9,0,.0247947,.986241,472967e-9,0,.0317196,.981696,495104e-9,0,.039494,.977097,532873e-9,0,.0481087,.972583,594447e-9,0,.0575549,.966142,636867e-9,0,.0678242,.95823,669899e-9,0,.0789089,.949677,719499e-9,0,.0908023,.939226,750584e-9,0,.103499,.927501,793183e-9,0,.116993,.915199,81995e-8,0,.131282,.90498,847654e-9,0,.146364,.894243,868929e-9,0,.162237,.882154,884278e-9,0,.178902,.869161,898108e-9,0,.196358,.854751,901254e-9,0,.21461,.839368,90679e-8,0,.23366,.822874,901541e-9,0,.253512,.805514,897297e-9,0,.274174,.78716,881856e-9,0,.29565,.769061,870032e-9,0,.31795,.751,851719e-9,0,.341081,.732614,830671e-9,0,.365053,.713171,806569e-9,0,.389874,.693472,78338e-8,0,.415553,.673528,756404e-9,0,.442098,.653397,726872e-9,0,.469512,.633781,700494e-9,0,.497794,.613877,67105e-8,0,.526935,.593506,640361e-9,0,.556908,.573667,613502e-9,0,.587657,.553932,583177e-9,0,.61906,.534345,554375e-9,0,.650794,.515042,527811e-9,0,.68254,.495674,499367e-9,0,.714286,.477132,47429e-8,0,.746032,.458609,447726e-9,0,.777778,.440354,424205e-9,0,.809524,.422765,399549e-9,0,.84127,.405472,378315e-9,0,.873016,.388482,355327e-9,0,.904762,.372191,336122e-9,0,.936508,.356099,315247e-9,0,.968254,.340737,29794e-8,0,1,1,143327e-9,0,0,1,14333e-8,0,0,1,143366e-9,0,0,.999999,143524e-9,0,0,.999996,143952e-9,0,0,.999991,144862e-9,0,0,.999981,146544e-9,0,0,.999966,149391e-9,0,0,.999941,153946e-9,0,0,.999905,160971e-9,0,0,.999852,171562e-9,0,0,.99978,18729e-8,0,0,.999681,210386e-9,0,826239e-10,.999546,243906e-9,0,664807e-9,.999352,291739e-9,0,.00196192,.999027,357419e-9,0,.00405941,.997886,422349e-9,0,.00699664,.99419,385008e-9,0,.0107896,.99214,409775e-9,0,.0154415,.990274,456418e-9,0,.0209488,.988455,527008e-9,0,.0273037,.985804,597685e-9,0,.0344969,.98103,613124e-9,0,.0425183,.976674,668321e-9,0,.0513575,.972021,736985e-9,0,.0610046,.965274,773789e-9,0,.0714508,.958046,830852e-9,0,.0826877,.949333,875766e-9,0,.0947085,.939135,917088e-9,0,.107507,.927119,952244e-9,0,.121078,.91469,990626e-9,0,.135419,.903006,.00101304,0,.150526,.892368,.00103834,0,.166399,.880231,.00105002,0,.183038,.867432,.00106331,0,.200443,.853208,.00106783,0,.218618,.837956,.00106458,0,.237566,.821772,.00105945,0,.257291,.804328,.00104685,0,.2778,.786465,.00103178,0,.2991,.768004,.00101077,0,.321199,.74972,985504e-9,0,.344106,.731682,962893e-9,0,.36783,.712813,932146e-9,0,.392383,.693139,89871e-8,0,.417774,.673566,869678e-9,0,.444013,.653483,835525e-9,0,.471107,.633891,799853e-9,0,.49906,.614433,766838e-9,0,.527869,.594586,732227e-9,0,.557517,.574769,696442e-9,0,.587966,.555149,663935e-9,0,.61913,.535898,629826e-9,0,.650794,.516753,596486e-9,0,.68254,.497816,567078e-9,0,.714286,.479034,534399e-9,0,.746032,.460975,507013e-9,0,.777778,.442935,477421e-9,0,.809524,.425263,451101e-9,0,.84127,.408248,424964e-9,0,.873016,.391339,39993e-8,0,.904762,.37513,377619e-9,0,.936508,.359172,354418e-9,0,.968254,.343876,334823e-9,0,1,1,209042e-9,0,0,1,209045e-9,0,0,1,209093e-9,0,0,.999999,209304e-9,0,0,.999996,209871e-9,0,0,.999991,211078e-9,0,0,.999979,213304e-9,0,0,.999963,217061e-9,0,0,.999933,223042e-9,0,0,.999894,232206e-9,0,0,.999837,245901e-9,0,0,.999756,266023e-9,0,102927e-11,.999648,295204e-9,0,233468e-9,.999499,336958e-9,0,.00108237,.999283,395563e-9,0,.00268832,.998896,473785e-9,0,.00511138,.997006,520008e-9,0,.00837705,.993819,497261e-9,0,.0124928,.991632,523722e-9,0,.0174561,.989875,587258e-9,0,.0232596,.988109,676329e-9,0,.0298932,.985155,747701e-9,0,.0373453,.980479,768803e-9,0,.0456045,.976271,841054e-9,0,.0546593,.971347,911469e-9,0,.0644994,.964528,953057e-9,0,.0751152,.957632,.00102221,0,.0864981,.948681,.00106122,0,.0986407,.938716,.00111857,0,.111537,.926629,.00114762,0,.125182,.914025,.00118995,0,.139571,.901026,.00121228,0,.154703,.890358,.00123946,0,.170576,.878283,.0012527,0,.18719,.865459,.00125536,0,.204547,.851407,.00126134,0,.222648,.836276,.00124759,0,.241498,.820436,.00124443,0,.261101,.803253,.00122071,0,.281465,.785562,.00120107,0,.302595,.76718,.00117762,0,.324501,.748551,.00114289,0,.347192,.730564,.00110872,0,.370679,.712253,.00107636,0,.394973,.692867,.00103646,0,.420085,.673695,996793e-9,0,.446027,.653912,95675e-8,0,.47281,.634129,916739e-9,0,.500441,.615004,874401e-9,0,.528921,.595587,833411e-9,0,.558244,.575965,794556e-9,0,.588384,.5566,75196e-8,0,.619281,.537428,716381e-9,0,.650795,.518623,676558e-9,0,.68254,.499964,64074e-8,0,.714286,.481356,605984e-9,0,.746032,.463279,570256e-9,0,.777778,.445673,540138e-9,0,.809524,.428032,507299e-9,0,.84127,.411112,479553e-9,0,.873016,.394444,450737e-9,0,.904762,.378247,424269e-9,0,.936508,.362415,399111e-9,0,.968254,.347103,375274e-9,0,1,1,300729e-9,0,0,1,300733e-9,0,0,1,300797e-9,0,0,.999998,301072e-9,0,0,.999996,301817e-9,0,0,.999989,303398e-9,0,0,.999977,306309e-9,0,0,.999958,311209e-9,0,0,.999927,318975e-9,0,0,.999884,330804e-9,0,0,.99982,34834e-8,0,0,.999733,373854e-9,0,326995e-10,.999613,410424e-9,0,477174e-9,.999447,462047e-9,0,.00161099,.999204,533322e-9,0,.00353153,.998725,624964e-9,0,.00627965,.995871,631786e-9,0,.0098693,.993194,632017e-9,0,.0143011,.991541,68923e-8,0,.019568,.989773,766892e-9,0,.0256593,.987647,863668e-9,0,.0325625,.984193,922089e-9,0,.0402647,.980016,970749e-9,0,.0487532,.975859,.00106027,0,.058016,.970514,.00112239,0,.0680419,.963625,.00117212,0,.0788208,.956959,.00125211,0,.0903439,.947956,.00129411,0,.102604,.93809,.00135879,0,.115594,.92659,.00139309,0,.129309,.913829,.00143253,0,.143745,.90005,.00145809,0,.158901,.888129,.0014748,0,.174774,.87607,.00148756,0,.191365,.863461,.00148714,0,.208674,.849594,.00148892,0,.226705,.834531,.00146496,0,.245461,.81903,.0014579,0,.264947,.802122,.00143039,0,.28517,.78445,.00139717,0,.306137,.766434,.00136312,0,.327857,.747816,.00132597,0,.350341,.729519,.00128323,0,.373598,.711454,.00123803,0,.397642,.692699,.00119097,0,.422485,.673723,.00114565,0,.448139,.654386,.00109552,0,.474619,.634673,.00104553,0,.501933,.615554,99985e-8,0,.530089,.596462,948207e-9,0,.559087,.577385,902299e-9,0,.588913,.558257,856448e-9,0,.619525,.5392,810395e-9,0,.650826,.520543,768558e-9,0,.68254,.502206,7239e-7,0,.714286,.48402,685794e-9,0,.746032,.465779,64471e-8,0,.777778,.448455,609583e-9,0,.809524,.431091,57227e-8,0,.84127,.414147,54042e-8,0,.873016,.39765,506545e-9,0,.904762,.381576,477635e-9,0,.936508,.365881,448446e-9,0,.968254,.350582,421424e-9,0,1,1,427144e-9,0,0,1,427151e-9,0,0,1,427232e-9,0,0,.999998,42759e-8,0,0,.999995,428555e-9,0,0,.999988,430603e-9,0,0,.999976,434368e-9,0,0,.999952,440688e-9,0,0,.999919,450667e-9,0,0,.999871,46578e-8,0,0,.999801,488024e-9,0,0,.999704,520092e-9,0,129791e-9,.999572,565553e-9,0,821056e-9,.999389,628906e-9,0,.00225241,.999114,714911e-9,0,.00449109,.998488,819218e-9,0,.00756249,.995234,80415e-8,0,.0114716,.993021,830181e-9,0,.0162131,.991407,902645e-9,0,.021776,.989625,996934e-9,0,.0281471,.987064,.00109707,0,.0353118,.983265,.00114353,0,.0432562,.979535,.0012272,0,.0519665,.975224,.00132642,0,.0614298,.969574,.00138092,0,.0716348,.963021,.00145896,0,.0825709,.956046,.00152834,0,.094229,.947136,.00158217,0,.106602,.937313,.0016347,0,.119682,.926073,.00168383,0,.133465,.913121,.00171627,0,.147947,.899165,.00174229,0,.163125,.885891,.00176137,0,.178998,.873783,.00176406,0,.195566,.861331,.00176156,0,.21283,.847569,.00175346,0,.230793,.832785,.00172753,0,.249459,.817442,.00170204,0,.268832,.800613,.00166576,0,.28892,.783597,.00162909,0,.30973,.76571,.0015826,0,.331271,.747021,.00153106,0,.353554,.728593,.00148036,0,.37659,.710661,.00142808,0,.400391,.692426,.00136906,0,.424973,.673623,.00131066,0,.450347,.65494,.00125569,0,.476531,.635448,.00119517,0,.503535,.616221,.00113828,0,.531372,.597531,.0010816,0,.560047,.578795,.00102673,0,.589554,.559892,970985e-9,0,.619869,.541307,919773e-9,0,.650923,.522608,868479e-9,0,.68254,.504484,82137e-8,0,.714286,.486603,772916e-9,0,.746032,.468802,730353e-9,0,.777778,.451172,684955e-9,0,.809524,.434348,647565e-9,0,.84127,.417445,605863e-9,0,.873016,.401077,571885e-9,0,.904762,.385039,536034e-9,0,.936508,.369483,504227e-9,0,.968254,.354272,473165e-9,0,1,1,599525e-9,0,0,1,599533e-9,0,0,1,599639e-9,0,0,.999998,600097e-9,0,0,.999994,601336e-9,0,0,.999987,603958e-9,0,0,.999972,608775e-9,0,0,.999949,616842e-9,0,0,.999912,629534e-9,0,0,.999857,648658e-9,0,0,.999781,676615e-9,0,538873e-11,.999674,716574e-9,0,308602e-9,.999528,772641e-9,0,.00127003,.999326,849806e-9,0,.00300783,.999009,952682e-9,0,.00556637,.998112,.00106394,0,.00895889,.994496,.00102228,0,.0131827,.992806,.00108586,0,.0182277,.991211,.0011759,0,.0240795,.989415,.00128955,0,.030723,.986499,.00139038,0,.0381418,.982679,.00144539,0,.046321,.978839,.00153954,0,.0552459,.974295,.00164417,0,.0649034,.968784,.00171517,0,.0752814,.962324,.00180282,0,.0863693,.954956,.00186387,0,.0981578,.94624,.00193817,0,.110639,.936517,.00198156,0,.123806,.925186,.00203042,0,.137655,.91252,.0020664,0,.15218,.898441,.00207822,0,.16738,.884394,.0020992,0,.183253,.871273,.00208748,0,.199799,.859057,.00208686,0,.21702,.845243,.00205519,0,.234918,.830723,.00202868,0,.253496,.815801,.00199501,0,.272761,.79914,.00194193,0,.292719,.782372,.00188824,0,.313377,.76482,.00183695,0,.334745,.746586,.00177418,0,.356833,.7281,.00170628,0,.379654,.709842,.00164063,0,.403221,.692019,.00157355,0,.427548,.67364,.00150262,0,.452651,.655277,.00143473,0,.478545,.636438,.00136371,0,.505246,.617364,.00129911,0,.532768,.598603,.00123014,0,.561122,.580195,.00116587,0,.590309,.561786,.00110398,0,.620318,.543377,.00104148,0,.651102,.525093,983984e-9,0,.682545,.506791,92667e-8,0,.714286,.489291,874326e-9,0,.746032,.471811,821734e-9,0,.777778,.454435,774698e-9,0,.809524,.437493,727302e-9,0,.84127,.420977,684039e-9,0,.873016,.404729,64373e-8,0,.904762,.388756,60285e-8,0,.936508,.373344,56765e-8,0,.968254,.358191,531929e-9,0,1,1,832169e-9,0,0,1,832178e-9,0,0,1,83231e-8,0,0,.999998,832893e-9,0,0,.999995,834465e-9,0,0,.999985,837791e-9,0,0,.999969,843893e-9,0,0,.999944,854086e-9,0,0,.999903,870071e-9,0,0,.999843,894042e-9,0,0,.999759,928865e-9,0,531805e-10,.999643,978242e-9,0,579365e-9,.99948,.00104684,0,.00182774,.999255,.00114012,0,.00387804,.998885,.00126188,0,.00675709,.997405,.00135888,0,.010468,.99424,.00133626,0,.0150018,.992458,.00140905,0,.0203443,.990929,.00152305,0,.0264786,.989116,.00165882,0,.0333875,.985624,.00174128,0,.0410536,.982003,.00182108,0,.0494609,.978336,.00194498,0,.0585941,.973184,.00202708,0,.0684396,.9678,.00212166,0,.0789851,.961348,.00221366,0,.0902199,.953841,.00228219,0,.102134,.94534,.00235662,0,.114721,.935552,.00240572,0,.127972,.924064,.00244405,0,.141884,.911827,.00247557,0,.156451,.897731,.00248374,0,.171672,.883409,.00249863,0,.187545,.868625,.00246688,0,.20407,.856529,.00246523,0,.221249,.842999,.00242368,0,.239083,.828505,.00237354,0,.257578,.813825,.00232588,0,.276738,.797813,.00226731,0,.296569,.781097,.00219704,0,.31708,.764038,.00212394,0,.338281,.746067,.00204786,0,.360181,.727687,.00196728,0,.382794,.709571,.00188779,0,.406133,.691503,.00180532,0,.430213,.673673,.00171849,0,.45505,.655732,.00164147,0,.480662,.637399,.00155858,0,.507065,.618616,.00147641,0,.534278,.60005,.00140125,0,.562313,.581713,.00132441,0,.59118,.563546,.00125014,0,.620875,.545605,.00118249,0,.651373,.527559,.0011116,0,.682593,.509764,.00104979,0,.714286,.49193,985977e-9,0,.746032,.475011,928592e-9,0,.777778,.457878,873466e-9,0,.809524,.440979,819585e-9,0,.84127,.424613,772365e-9,0,.873016,.408549,722195e-9,0,.904762,.392771,680014e-9,0,.936508,.377317,636797e-9,0,.968254,.362352,598318e-9,0,1,1,.00114313,0,0,1,.00114314,0,0,.999999,.00114331,0,0,.999998,.00114404,0,0,.999994,.00114601,0,0,.999984,.00115019,0,0,.999967,.00115784,0,0,.999937,.0011706,0,0,.999894,.00119054,0,0,.999828,.00122031,0,0,.999735,.00126331,0,169263e-9,.999606,.00132382,0,949167e-9,.999426,.0014071,0,.00249668,.999173,.00151895,0,.00486392,.99873,.00166102,0,.00806323,.996243,.0017023,0,.0120895,.993779,.00172782,0,.0169288,.9919,.0018108,0,.0225633,.990524,.00196028,0,.028974,.98868,.00212014,0,.036142,.984663,.00217598,0,.044049,.981457,.00230563,0,.0526781,.977608,.00243966,0,.0620137,.972215,.00251336,0,.0720418,.966798,.0026285,0,.0827499,.960241,.00271409,0,.0941271,.952489,.00278381,0,.106164,.944127,.00285399,0,.118852,.934282,.00290994,0,.132185,.923271,.00294558,0,.146157,.910803,.00296269,0,.160766,.896705,.00296803,0,.176007,.88238,.00296637,0,.19188,.867116,.00293163,0,.208385,.853636,.00289418,0,.225523,.840469,.00284663,0,.243296,.82639,.00278594,0,.261709,.811759,.00271618,0,.280767,.796113,.00263187,0,.300476,.779518,.00254589,0,.320845,.763142,.00246003,0,.341883,.745464,.00236529,0,.363601,.727491,.00226536,0,.386011,.709414,.00216375,0,.409128,.691396,.00207127,0,.432967,.67368,.00197106,0,.457545,.656049,.00187022,0,.482881,.638188,.00177605,0,.508992,.620177,.00168482,0,.535899,.601506,.00158909,0,.563619,.58362,.00150583,0,.592165,.565496,.00141791,0,.621544,.54789,.00133693,0,.651743,.530323,.00126038,0,.682709,.512795,.00118556,0,.714286,.495199,.00111527,0,.746032,.478101,.0010489,0,.777778,.461511,984264e-9,0,.809524,.444879,92591e-8,0,.84127,.428424,866582e-9,0,.873016,.412495,814463e-9,0,.904762,.396975,764498e-9,0,.936508,.381614,715967e-9,0,.968254,.366732,672483e-9,0,1,1,.00155501,0,0,1,.00155503,0,0,1,.00155524,0,0,.999998,.00155615,0,0,.999994,.0015586,0,0,.999983,.00156379,0,0,.999963,.0015733,0,0,.999932,.00158911,0,0,.999882,.00161376,0,0,.99981,.00165041,0,100875e-10,.999708,.00170304,0,367658e-9,.999565,.00177658,0,.0014234,.999368,.00187688,0,.00327939,.999081,.00200989,0,.00596629,.99852,.00217177,0,.0094852,.99549,.0021745,0,.013824,.993252,.00222357,0,.0189642,.991727,.00235022,0,.0248856,.989951,.00250561,0,.0315669,.988029,.00268829,0,.0389882,.984029,.0027496,0,.0471302,.980683,.00289793,0,.0559754,.976554,.00303315,0,.0655081,.97139,.00313257,0,.0757138,.965544,.00323656,0,.08658,.95912,.00333432,0,.0980954,.951183,.0034039,0,.110251,.942974,.00347515,0,.123038,.932642,.00350381,0,.13645,.922158,.00354519,0,.150482,.909404,.00353851,0,.165129,.896071,.0035435,0,.18039,.881206,.00349936,0,.196263,.866077,.00347256,0,.212748,.85093,.003415,0,.229847,.837703,.00333367,0,.247561,.823878,.003249,0,.265895,.809449,.00316347,0,.284854,.794379,.00306351,0,.304445,.778138,.0029499,0,.324675,.761997,.00284099,0,.345555,.744938,.00272104,0,.367095,.727212,.00260715,0,.389309,.709549,.00248855,0,.41221,.691704,.00236783,0,.435814,.673689,.00225178,0,.460138,.656453,.00213765,0,.485203,.639128,.00202178,0,.511028,.621512,.00191443,0,.537634,.603598,.00180977,0,.565041,.58559,.00170456,0,.593268,.567852,.00160927,0,.622327,.5503,.00151395,0,.652217,.533033,.00142499,0,.682907,.515942,.00133955,0,.714296,.498814,.0012602,0,.746032,.481595,.00118188,0,.777778,.465117,.00111171,0,.809524,.448865,.00104091,0,.84127,.432711,976618e-9,0,.873016,.416822,91859e-8,0,.904762,.401272,857704e-9,0,.936508,.386226,807172e-9,0,.968254,.371321,75464e-8,0,1,1,.00209596,0,0,1,.00209598,0,0,1,.00209624,0,0,.999997,.00209736,0,0,.999991,.00210039,0,0,.999979,.00210678,0,0,.999959,.00211847,0,0,.999925,.0021379,0,0,.99987,.00216809,0,0,.999791,.00221281,0,681487e-10,.999677,.00227669,0,658161e-9,.999521,.00236533,0,.00200635,.999301,.00248514,0,.0041779,.998977,.00264185,0,.00718648,.998191,.00281695,0,.0110239,.994801,.00278518,0,.015672,.993091,.00288774,0,.0211091,.991571,.00303931,0,.0273123,.9897,.00321643,0,.034259,.987023,.00337332,0,.0419282,.983289,.00346146,0,.0502998,.979892,.00363704,0,.0593562,.975111,.00373601,0,.069081,.970351,.0038842,0,.0794598,.964131,.00397053,0,.0904798,.957747,.00408078,0,.10213,.949536,.00413533,0,.1144,.941372,.00420305,0,.127284,.931049,.00422815,0,.140772,.920647,.00425048,0,.154862,.908033,.0042281,0,.169548,.895028,.00422026,0,.184828,.879968,.00415042,0,.200701,.864875,.00408821,0,.217167,.84918,.00400909,0,.234227,.834934,.00391178,0,.251884,.821397,.00380066,0,.270141,.807135,.00367974,0,.289004,.792363,.00355172,0,.308479,.776661,.003411,0,.328575,.760705,.00328123,0,.349301,.744408,.00314003,0,.370668,.726994,.0029906,0,.392689,.709598,.00285034,0,.415379,.692112,.00271179,0,.438754,.674435,.00257185,0,.46283,.65676,.00243425,0,.48763,.639982,.00230351,0,.513173,.622983,.0021777,0,.539482,.605471,.00204991,0,.566579,.58796,.00193759,0,.594488,.570463,.00181976,0,.623226,.553058,.00171497,0,.6528,.535894,.00161109,0,.683198,.519089,.00151394,0,.714354,.502454,.00142122,0,.746032,.485681,.00133488,0,.777778,.468935,.00124975,0,.809524,.452951,.00117309,0,.84127,.437139,.00110155,0,.873016,.421446,.00103124,0,.904762,.405951,966387e-9,0,.936508,.391003,908119e-9,0,.968254,.376198,848057e-9,0,1,1,.00280076,0,0,1,.00280078,0,0,.999999,.00280109,0,0,.999997,.00280246,0,0,.999992,.00280616,0,0,.999979,.00281396,0,0,.999956,.00282822,0,0,.999916,.00285186,0,0,.999857,.0028885,0,0,.999768,.00294259,0,196026e-9,.999645,.00301946,0,.00104842,.99947,.00312541,0,.00270199,.999229,.00326733,0,.00519449,.998852,.00344992,0,.00852602,.997558,.00361052,0,.0126804,.994417,.0035898,0,.017635,.992824,.00372393,0,.023365,.991344,.00390695,0,.0298456,.989337,.00410392,0,.0370529,.985811,.00420987,0,.0449651,.982772,.00437488,0,.0535615,.979001,.00455069,0,.0628243,.974102,.00464462,0,.0727368,.969197,.00480577,0,.0832844,.962759,.00487818,0,.0944545,.956207,.00498176,0,.106236,.947909,.00503392,0,.118619,.939596,.00507474,0,.131595,.929642,.00509798,0,.145159,.918807,.00508476,0,.159305,.906921,.00505634,0,.174028,.893312,.00498845,0,.189327,.878933,.0049133,0,.2052,.863986,.0048259,0,.221647,.847936,.00470848,0,.23867,.832253,.00456889,0,.25627,.818619,.00442726,0,.274453,.804788,.00427677,0,.293222,.790241,.00411906,0,.312585,.775162,.00394833,0,.33255,.759463,.00377366,0,.353126,.743598,.00361026,0,.374324,.72697,.00343627,0,.396158,.709646,.00326422,0,.418641,.69277,.00309717,0,.44179,.675371,.0029356,0,.465624,.657863,.00277712,0,.490163,.640772,.00261738,0,.515429,.624441,.0024737,0,.541445,.607497,.00233125,0,.568236,.590438,.00218994,0,.595828,.573224,.0020664,0,.624242,.556168,.00193526,0,.653496,.539232,.00182463,0,.683588,.522352,.00170735,0,.714482,.506172,.00160555,0,.746032,.489842,.00150451,0,.777778,.473463,.00140938,0,.809524,.457266,.00132568,0,.84127,.441609,.0012376,0,.873016,.426348,.00116265,0,.904762,.411002,.00108935,0,.936508,.396045,.00101946,0,.968254,.381448,955665e-9,0,1,1,.0037121,0,0,1,.00371213,0,0,1,.00371251,0,0,.999997,.00371417,0,0,.99999,.00371863,0,0,.999977,.00372807,0,0,.99995,.00374529,0,0,.999908,.0037738,0,0,.999843,.00381789,0,123596e-10,.999745,.00388273,0,407442e-9,.999608,.00397443,0,.0015447,.999415,.00409998,0,.00351385,.999143,.00426662,0,.0063316,.9987,.00447625,0,.00998679,.996363,.00455323,0,.0144569,.994021,.00461052,0,.0197151,.992372,.00476359,0,.0257344,.991007,.00499101,0,.0324882,.988767,.0051972,0,.0399517,.984872,.00528407,0,.0481022,.982004,.00548926,0,.0569191,.977714,.00564385,0,.0663839,.973076,.0057693,0,.0764801,.967565,.0058924,0,.0871928,.961384,.00599629,0,.0985095,.954435,.00605998,0,.110419,.946303,.0061133,0,.122912,.937662,.00612028,0,.13598,.927867,.00612209,0,.149617,.916475,.00604813,0,.163817,.90541,.00603088,0,.178577,.891591,.00592218,0,.193894,.877573,.00578854,0,.209767,.862511,.00566648,0,.226196,.846861,.00551481,0,.243182,.83068,.00533754,0,.260728,.815725,.00515487,0,.278837,.802321,.0049655,0,.297515,.787826,.00475421,0,.316768,.773454,.00456002,0,.336605,.758224,.00434727,0,.357034,.74265,.00414444,0,.378067,.726729,.00393738,0,.399717,.710155,.00373575,0,.421998,.693312,.00353736,0,.444928,.67653,.00334368,0,.468523,.659444,.00315981,0,.492806,.642051,.00297809,0,.517798,.625758,.00280592,0,.543525,.609615,.00264254,0,.570012,.592919,.00248459,0,.597288,.576298,.00233327,0,.625379,.559489,.00219519,0,.654307,.542891,.00205441,0,.684084,.526255,.00193385,0,.714693,.509853,.00180745,0,.746044,.494131,.00169817,0,.777778,.478114,.0015913,0,.809524,.462274,.00148981,0,.84127,.446412,.00139537,0,.873016,.431274,.00130984,0,.904762,.41635,.00122403,0,.936508,.401476,.00114809,0,.968254,.386993,.00107563,0,1,1,.00488216,0,0,1,.0048822,0,0,1,.00488265,0,0,.999997,.00488463,0,0,.999988,.00488999,0,0,.999974,.00490129,0,0,.999946,.00492191,0,0,.999897,.00495598,0,0,.999825,.00500855,0,744791e-10,.999718,.00508559,0,712744e-9,.999565,.005194,0,.00215249,.999352,.00534147,0,.00444576,.999046,.00553523,0,.00759218,.998492,.00577016,0,.0115714,.995564,.00578487,0,.0163557,.993339,.00586414,0,.021915,.991834,.00606002,0,.0282201,.990496,.00633312,0,.0352433,.987826,.00651941,0,.042959,.98383,.00660842,0,.0513439,.98109,.00685523,0,.0603772,.976131,.00695778,0,.0700402,.971922,.00714236,0,.0803163,.965901,.00721437,0,.0911908,.959606,.00732017,0,.102651,.952504,.00735788,0,.114686,.944365,.00738493,0,.127286,.935652,.00737969,0,.140443,.925813,.00733612,0,.154151,.914397,.00723094,0,.168405,.903257,.00714002,0,.183201,.890015,.00700149,0,.198536,.876014,.00682813,0,.214409,.861436,.00665567,0,.23082,.845752,.00644526,0,.24777,.829169,.00621635,0,.265263,.813435,.00597789,0,.283301,.799701,.00575694,0,.301889,.785726,.00549866,0,.321035,.77152,.0052503,0,.340746,.75683,.00499619,0,.361032,.741951,.0047543,0,.381904,.726367,.0045084,0,.403374,.710537,.00426784,0,.425457,.693965,.00403487,0,.448169,.677724,.0038075,0,.47153,.66117,.00359431,0,.495561,.644274,.00338354,0,.520284,.627449,.00318163,0,.545725,.611645,.00299672,0,.571911,.595614,.00281016,0,.598873,.579426,.00264252,0,.62664,.563016,.00247509,0,.655239,.546728,.00232647,0,.684692,.530539,.00217803,0,.714999,.514164,.00204216,0,.746106,.498344,.00191403,0,.777778,.482957,.00179203,0,.809524,.467336,.00167695,0,.84127,.451994,.00157567,0,.873016,.436514,.00147113,0,.904762,.42178,.00138034,0,.936508,.407271,.00129219,0,.968254,.392822,.0012098,0,1,1,.00637427,0,0,1,.00637431,0,0,.999999,.00637485,0,0,.999996,.00637721,0,0,.999987,.00638357,0,0,.999971,.006397,0,0,.999939,.00642142,0,0,.999888,.00646177,0,0,.999807,.00652387,0,207916e-9,.999689,.00661454,0,.00112051,.99952,.00674155,0,.00287719,.999283,.00691313,0,.00550145,.998936,.00713598,0,.00897928,.998165,.00738501,0,.0132829,.994847,.00734388,0,.01838,.993182,.00749991,0,.0242381,.991665,.0077246,0,.030826,.989708,.00797579,0,.0381152,.986663,.00813011,0,.0460794,.983288,.00830365,0,.0546951,.980104,.00853496,0,.0639411,.974855,.00861045,0,.0737988,.97045,.00879133,0,.0842516,.964509,.00886377,0,.0952848,.957594,.00890346,0,.106886,.950546,.00893289,0,.119044,.942225,.00890074,0,.131749,.933365,.00886826,0,.144994,.923202,.0087316,0,.158772,.912605,.00863082,0,.173078,.901099,.00847403,0,.187908,.888177,.00825838,0,.203261,.873955,.00801834,0,.219134,.860091,.00779026,0,.235527,.84434,.00752478,0,.252443,.828517,.00724074,0,.269883,.81239,.00693769,0,.287851,.79721,.00664817,0,.306352,.783489,.00634763,0,.325393,.769514,.00604221,0,.344981,.755419,.00573568,0,.365126,.741083,.00544359,0,.385839,.726059,.00515515,0,.407132,.710809,.00487139,0,.42902,.695052,.00459846,0,.45152,.678886,.00433412,0,.474651,.663042,.00407981,0,.498433,.646634,.00384264,0,.52289,.630117,.00360897,0,.548048,.613804,.00338863,0,.573936,.598338,.00318486,0,.600584,.582687,.00298377,0,.628027,.566809,.00280082,0,.656295,.550817,.00262255,0,.685417,.534937,.00245835,0,.715406,.519151,.00230574,0,.74624,.503118,.0021549,0,.777778,.487723,.00202008,0,.809524,.472725,.00189355,0,.84127,.457599,.00177108,0,.873016,.442558,.00165843,0,.904762,.427624,.00155494,0,.936508,.413171,.00145273,0,.968254,.399122,.00136454,0,1,1,.00826496,0,0,1,.00826499,0,0,1,.00826564,0,0,.999996,.00826842,0,0,.999987,.00827589,0,0,.999967,.00829167,0,0,.999933,.00832037,0,0,.999876,.00836768,0,109338e-10,.999786,.00844031,0,427145e-9,.999655,.00854603,0,.0016384,.999468,.00869337,0,.00372392,.999203,.008891,0,.00668513,.998803,.00914387,0,.0104968,.99748,.00935838,0,.015125,.994446,.00933309,0,.0205338,.99292,.00953084,0,.0266884,.991414,.0097893,0,.0335565,.989049,.0100228,0,.0411086,.98582,.0101664,0,.0493181,.982441,.0103582,0,.0581613,.978595,.0105292,0,.0676169,.973495,.0106274,0,.0776661,.968405,.0107261,0,.0882926,.962717,.0108234,0,.0994817,.955478,.0108102,0,.111221,.948275,.0107914,0,.123499,.940006,.0107161,0,.136308,.930831,.0106309,0,.149639,.920648,.0104083,0,.163485,.910205,.0102312,0,.177843,.898445,.0100051,0,.192707,.885986,.00971928,0,.208077,.872204,.00940747,0,.22395,.858436,.0091085,0,.240326,.843454,.00876595,0,.257208,.827437,.00839794,0,.274596,.811488,.00803692,0,.292496,.796039,.00767352,0,.310911,.781083,.0073097,0,.329849,.767642,.00694032,0,.349316,.753901,.00657476,0,.369323,.740131,.00622699,0,.38988,.725845,.0058838,0,.410999,.710991,.00555586,0,.432696,.696002,.00523089,0,.454987,.680461,.00492494,0,.47789,.664875,.00463464,0,.501426,.649273,.00435422,0,.52562,.63302,.0040875,0,.550498,.61705,.00384075,0,.576089,.601154,.00359557,0,.602427,.586008,.00337636,0,.629544,.570699,.00316019,0,.657479,.555166,.00296033,0,.686264,.539645,.00277552,0,.715924,.524159,.00259499,0,.746459,.508682,.00243257,0,.777789,.493163,.00227851,0,.809524,.478004,.00213083,0,.84127,.46347,.00199502,0,.873016,.448778,.00186967,0,.904762,.434105,.00174732,0,.936508,.419576,.00163861,0,.968254,.405541,.00153341,0,1,1,.0106462,0,0,1,.0106462,0,0,.999999,.010647,0,0,.999995,.0106502,0,0,.999985,.0106589,0,0,.999964,.0106773,0,0,.999925,.0107106,0,0,.999861,.0107655,0,712986e-10,.999763,.0108497,0,743959e-9,.999616,.0109716,0,.00227361,.999408,.0111408,0,.0046983,.999112,.0113659,0,.00800158,.998637,.0116475,0,.0121493,.996223,.0117231,0,.0171023,.994006,.0118064,0,.0228218,.992444,.0120254,0,.0292711,.991028,.0123314,0,.036417,.98803,.0124954,0,.0442295,.984816,.0126538,0,.0526815,.981399,.0128537,0,.0617492,.977085,.0129694,0,.0714114,.972154,.013091,0,.0816495,.966617,.0131166,0,.0924472,.960628,.0131583,0,.10379,.953295,.0131094,0,.115665,.94575,.0129966,0,.128062,.937654,.0128796,0,.140972,.927716,.0126477,0,.154387,.917932,.0123889,0,.168301,.907719,.012131,0,.182709,.89584,.0118013,0,.197608,.883526,.0114145,0,.212994,.870301,.0110075,0,.228867,.856272,.0106019,0,.245227,.842251,.0101938,0,.262074,.826466,.00973254,0,.279412,.810859,.0092846,0,.297244,.795051,.00883304,0,.315575,.780053,.00840272,0,.334412,.76575,.00796438,0,.35376,.752298,.00752526,0,.373631,.739153,.00711486,0,.394034,.725514,.00670361,0,.414983,.711473,.00632656,0,.436491,.696936,.00595206,0,.458575,.682126,.00559191,0,.481253,.667027,.00525362,0,.504547,.651875,.00493805,0,.528481,.636463,.00462848,0,.553081,.620641,.00433936,0,.578377,.604931,.00407,0,.604404,.589549,.00380864,0,.631197,.574712,.00357049,0,.658795,.559775,.00334466,0,.687238,.544514,.00312505,0,.716559,.529555,.00293199,0,.746776,.514402,.00274204,0,.777849,.499302,.00256647,0,.809524,.484114,.00239901,0,.84127,.469308,.00225148,0,.873016,.455133,.00210178,0,.904762,.440939,.0019727,0,.936508,.426627,.00184382,0,.968254,.412509,.00172548,0,1,1,.013628,0,0,1,.0136281,0,0,.999999,.0136289,0,0,.999995,.0136327,0,0,.999983,.0136427,0,0,.99996,.0136638,0,0,.999917,.0137022,0,0,.999846,.0137652,0,204597e-9,.999736,.0138615,0,.00116837,.999573,.0140007,0,.00303325,.99934,.0141927,0,.00580613,.999004,.0144457,0,.00945626,.998407,.0147489,0,.0139421,.995464,.014731,0,.0192202,.993328,.0148283,0,.0252495,.991799,.0150797,0,.0319921,.990397,.0154316,0,.0394138,.986835,.0155005,0,.0474843,.983938,.0157308,0,.0561763,.980154,.0158753,0,.0654661,.975659,.0159581,0,.0753326,.970171,.0159832,0,.0857571,.964803,.0160084,0,.0967236,.958366,.0159484,0,.108218,.950613,.0158001,0,.120227,.942874,.0155845,0,.132741,.935005,.0154292,0,.145751,.924991,.0150742,0,.159249,.914814,.0146757,0,.17323,.904743,.0143097,0,.187687,.893216,.0138695,0,.202619,.880769,.0133706,0,.218021,.868136,.0128606,0,.233894,.85469,.0123403,0,.250238,.840593,.0118091,0,.267052,.825808,.011253,0,.284341,.81009,.0107099,0,.302106,.79504,.0101636,0,.320354,.779757,.00964041,0,.33909,.764697,.00911896,0,.358322,.750913,.00859533,0,.378059,.738175,.00811592,0,.398311,.725242,.00764504,0,.41909,.711864,.00718885,0,.440412,.698009,.00675843,0,.462292,.683841,.00634984,0,.484748,.669391,.00595502,0,.507802,.654731,.00558671,0,.531477,.639805,.00523578,0,.555802,.624789,.00490834,0,.580805,.609325,.00459448,0,.606522,.593975,.00430342,0,.63299,.578983,.00403019,0,.66025,.564442,.0037707,0,.688346,.549835,.0035316,0,.717319,.535039,.00330255,0,.7472,.520403,.00308932,0,.777982,.505687,.00289335,0,.809524,.490939,.00270818,0,.84127,.476233,.0025343,0,.873016,.461624,.00237097,0,.904762,.447833,.00222065,0,.936508,.433992,.00207561,0,.968254,.420147,.00194955,0,1,1,.0173415,0,0,1,.0173416,0,0,.999999,.0173426,0,0,.999995,.0173468,0,0,.999983,.0173582,0,0,.999954,.0173822,0,0,.999908,.0174258,0,669501e-11,.999828,.0174973,0,427399e-9,.999705,.0176063,0,.00171019,.999524,.0177631,0,.0039248,.999263,.0179781,0,.00705382,.998878,.018258,0,.0110552,.998012,.0185551,0,.0158812,.994614,.0184264,0,.0214852,.993132,.0186385,0,.0278239,.991563,.0189067,0,.0348585,.989298,.0191577,0,.0425544,.986036,.0192522,0,.050881,.982558,.0194063,0,.059811,.978531,.019486,0,.0693209,.974198,.0195847,0,.0793895,.968148,.0194749,0,.0899984,.962565,.0194277,0,.101132,.956041,.0192991,0,.112775,.947749,.0189893,0,.124917,.94018,.018704,0,.137547,.93165,.0183458,0,.150655,.921798,.0178775,0,.164236,.911573,.0173618,0,.178281,.901569,.0168482,0,.192788,.890341,.016265,0,.207752,.877835,.0156199,0,.223171,.865472,.0149516,0,.239044,.852905,.0143274,0,.255371,.838906,.0136643,0,.272153,.824888,.0129903,0,.289393,.809977,.0123218,0,.307093,.794697,.0116572,0,.325259,.780028,.0110307,0,.343896,.765124,.0104236,0,.363012,.750411,.0098219,0,.382617,.737264,.00924397,0,.402719,.724799,.00868719,0,.423332,.712253,.00816476,0,.444469,.699267,.00767262,0,.466146,.685618,.00719746,0,.488383,.671736,.00673916,0,.511199,.657777,.00631937,0,.534618,.643497,.00592411,0,.558668,.62889,.00553928,0,.58338,.614299,.0051934,0,.608787,.599197,.00485985,0,.634929,.584175,.00454357,0,.661849,.569541,.00425787,0,.689594,.555193,.00397905,0,.718211,.540947,.00372364,0,.747742,.526593,.00348599,0,.778205,.512335,.00326103,0,.80953,.498017,.00305137,0,.84127,.483609,.00285485,0,.873016,.469368,.00267472,0,.904762,.455037,.00249945,0,.936508,.441493,.00234792,0,.968254,.428147,.00219936,0,1,1,.0219422,0,0,1,.0219423,0,0,.999998,.0219434,0,0,.999993,.0219481,0,0,.999981,.021961,0,0,.999949,.0219879,0,0,.999896,.0220367,0,593194e-10,.999808,.0221167,0,75364e-8,.99967,.0222383,0,.00237884,.999466,.0224125,0,.00495612,.999174,.0226495,0,.00844887,.998725,.0229525,0,.0128058,.996979,.0231123,0,.0179742,.994317,.0230742,0,.0239047,.992781,.0232895,0,.0305526,.991191,.0235734,0,.0378786,.987787,.0236152,0,.0458475,.985092,.0237994,0,.0544287,.981121,.0238553,0,.0635952,.976924,.0238706,0,.0733233,.97218,.0238704,0,.0835922,.965956,.0236598,0,.0943839,.959998,.0234735,0,.105682,.953245,.0232277,0,.117474,.944445,.0226973,0,.129747,.937087,.0223527,0,.142491,.928341,.0218144,0,.155697,.9184,.0211516,0,.169358,.907959,.0204553,0,.183469,.89808,.0197673,0,.198024,.887047,.0189915,0,.21302,.875221,.0182082,0,.228455,.86269,.0173584,0,.244329,.850735,.0165718,0,.260639,.837545,.0157524,0,.277389,.823639,.0149482,0,.29458,.809699,.0141431,0,.312216,.794797,.0133527,0,.3303,.780578,.0126193,0,.34884,.766019,.0118914,0,.367842,.751447,.0111839,0,.387315,.737275,.010514,0,.40727,.724545,.00987277,0,.427717,.712644,.00926569,0,.448671,.700432,.00869029,0,.470149,.687664,.00814691,0,.492167,.674288,.00763012,0,.514746,.660966,.00714437,0,.537911,.647264,.00668457,0,.561688,.633431,.00626581,0,.586108,.619133,.00585593,0,.611206,.604935,.00548188,0,.637022,.590236,.00513288,0,.663599,.575473,.0047906,0,.690989,.561228,.00448895,0,.719242,.547054,.00420233,0,.748411,.533175,.00392869,0,.778531,.519163,.00367445,0,.809583,.505328,.00344097,0,.84127,.491446,.00322003,0,.873016,.477356,.00301283,0,.904762,.46356,.00282592,0,.936508,.449623,.00264956,0,.968254,.436068,.00246956,0,1,1,.0276135,0,0,1,.0276136,0,0,.999998,.0276148,0,0,.999993,.0276201,0,0,.999976,.0276342,0,0,.999945,.027664,0,0,.999884,.0277179,0,18679e-8,.999784,.027806,0,.00119607,.99963,.0279394,0,.00318407,.999401,.0281295,0,.00613601,.999066,.0283858,0,.00999963,.998524,.0287027,0,.0147164,.995702,.0286256,0,.0202295,.993593,.0286733,0,.0264876,.992067,.0288989,0,.0334452,.990548,.0292135,0,.0410621,.986775,.0291296,0,.0493032,.984054,.0293099,0,.0581381,.979481,.0291881,0,.0675397,.975297,.0291598,0,.0774848,.96981,.028954,0,.0879528,.963524,.028628,0,.0989258,.957398,.0283135,0,.110388,.950088,.0278469,0,.122327,.941538,.0271798,0,.134729,.933332,.0265388,0,.147587,.924392,.0257776,0,.160889,.914581,.024916,0,.174631,.904347,.0240242,0,.188806,.894324,.0231229,0,.203409,.883724,.022153,0,.218437,.872207,.0211355,0,.233888,.859927,.0201048,0,.249761,.848373,.0191263,0,.266056,.836023,.0181306,0,.282774,.82289,.0171718,0,.299917,.809324,.0162196,0,.317488,.795361,.0152622,0,.335493,.781253,.01439,0,.353936,.767338,.013533,0,.372825,.753156,.0127244,0,.392168,.739122,.0119454,0,.411976,.725358,.0112054,0,.432259,.712949,.010487,0,.453032,.701621,.00984032,0,.47431,.689703,.00921495,0,.496111,.677216,.00862492,0,.518456,.664217,.00806882,0,.541367,.65137,.00755922,0,.564872,.638,.00705705,0,.589001,.62453,.00661266,0,.613789,.610601,.00618432,0,.639277,.59676,.00578033,0,.66551,.582433,.00540927,0,.692539,.568026,.00506104,0,.720422,.55414,.0047353,0,.749216,.540178,.00442889,0,.778974,.526513,.00414363,0,.809711,.512954,.00388237,0,.84127,.499403,.00362875,0,.873016,.486026,.00340827,0,.904762,.472345,.00318598,0,.936508,.458828,.00297635,0,.968254,.445379,.00279447,0,1,1,.0345716,0,0,1,.0345717,0,0,.999999,.034573,0,0,.999991,.0345787,0,0,.999974,.0345941,0,0,.999937,.0346263,0,188589e-11,.999869,.0346847,0,409238e-9,.999757,.0347798,0,.0017674,.999582,.0349233,0,.00413658,.999322,.0351265,0,.00747408,.998939,.0353967,0,.0117157,.998219,.0357018,0,.0167966,.994974,.0354726,0,.0226572,.993201,.0355621,0,.0292445,.991573,.0357641,0,.0365123,.989301,.0359252,0,.0444203,.985712,.0358017,0,.0529334,.982411,.0358353,0,.0620214,.977827,.035617,0,.0716574,.973278,.0354398,0,.0818186,.967397,.0350483,0,.0924846,.960696,.0344795,0,.103638,.954349,.0339861,0,.115263,.946066,.0331323,0,.127348,.938012,.032359,0,.13988,.929413,.0314413,0,.152849,.920355,.0304103,0,.166248,.910586,.0292785,0,.18007,.900609,.0281391,0,.194308,.890093,.0269103,0,.208958,.880013,.0257269,0,.224018,.869001,.0244671,0,.239485,.85751,.0232252,0,.255359,.84582,.0220117,0,.271638,.834383,.0208274,0,.288324,.822158,.0196628,0,.305419,.809056,.0185306,0,.322927,.795832,.0174174,0,.340851,.782547,.0163758,0,.359199,.7689,.015391,0,.377975,.755526,.0144488,0,.397189,.741681,.0135372,0,.416851,.728178,.0126957,0,.436971,.714642,.0118812,0,.457564,.702756,.0111165,0,.478644,.69175,.0104145,0,.500229,.680159,.00974439,0,.522339,.668073,.00911926,0,.544997,.655405,.00851393,0,.56823,.642921,.00797637,0,.592068,.629993,.00745119,0,.616546,.616828,.00696972,0,.641705,.603305,.00652425,0,.66759,.589833,.00610188,0,.694255,.575945,.00570834,0,.72176,.561745,.00533384,0,.750168,.548277,.00500001,0,.779545,.534467,.00467582,0,.809933,.521032,.00438092,0,.841272,.507877,.00410348,0,.873016,.494654,.00383618,0,.904762,.481592,.00358699,0,.936508,.468509,.00337281,0,.968254,.455293,.00316196,0,1,1,.0430698,0,0,1,.0430699,0,0,.999998,.0430713,0,0,.999991,.0430773,0,0,.99997,.0430936,0,0,.999928,.0431277,0,406396e-10,.999852,.0431893,0,744376e-9,.999724,.0432895,0,.0024806,.999527,.0434397,0,.00524779,.99923,.0436507,0,.00898164,.998783,.0439255,0,.0136083,.997507,.0441104,0,.0190582,.994418,.0438225,0,.0252694,.992864,.0439396,0,.0321879,.991127,.0440962,0,.039767,.987331,.0438408,0,.0479667,.984819,.0438991,0,.056752,.980384,.0435906,0,.0660929,.975846,.0432543,0,.075963,.970748,.0428293,0,.0863398,.964303,.042153,0,.0972035,.95772,.0414111,0,.108537,.950747,.0405893,0,.120325,.942533,.0394887,0,.132554,.934045,.0383544,0,.145215,.924942,.037057,0,.158296,.915811,.0356993,0,.17179,.90612,.0342401,0,.185691,.896434,.0328078,0,.199993,.886021,.031288,0,.214691,.876081,.0297776,0,.229782,.865608,.0282334,0,.245265,.854924,.026749,0,.261138,.843607,.02526,0,.277401,.832456,.0238214,0,.294056,.821342,.0224682,0,.311104,.809303,.0211297,0,.328548,.796468,.0198387,0,.346394,.784046,.0186227,0,.364645,.771262,.0174561,0,.38331,.758118,.0163806,0,.402396,.745075,.0153287,0,.421912,.731926,.0143647,0,.44187,.71863,.0134363,0,.462283,.705414,.0125603,0,.483165,.693792,.0117508,0,.504535,.683108,.0110016,0,.52641,.67183,.0102757,0,.548816,.66015,.00962044,0,.571776,.647907,.00898031,0,.595323,.635734,.00840811,0,.619489,.623208,.00786211,0,.644317,.610438,.00734953,0,.669852,.597345,.00687688,0,.696148,.584138,.00643469,0,.723267,.5707,.00602236,0,.75128,.556966,.0056324,0,.780258,.543607,.00528277,0,.810268,.530213,.00493999,0,.841311,.516912,.00462265,0,.873016,.503916,.0043307,0,.904762,.491146,.00406858,0,.936508,.478439,.00381436,0,.968254,.465834,.00358003,0,1,1,.0534039,0,0,1,.053404,0,0,.999998,.0534055,0,0,.999989,.0534116,0,0,.999968,.0534283,0,0,.999918,.0534633,0,155895e-9,.99983,.0535262,0,.00120914,.999685,.0536281,0,.00334944,.999461,.0537799,0,.00653077,.999119,.0539902,0,.0106718,.998582,.0542524,0,.0156907,.995919,.0540318,0,.0215147,.993735,.0538914,0,.0280801,.992126,.0539557,0,.0353323,.990266,.0540401,0,.0432247,.986317,.0536064,0,.0517172,.983213,.0534425,0,.0607754,.978303,.0528622,0,.0703698,.973665,.0523363,0,.0804742,.968091,.0516165,0,.0910667,.961026,.0505434,0,.102128,.954333,.049523,0,.113641,.946372,.0481698,0,.125591,.938254,.0467674,0,.137965,.929516,.0452341,0,.150754,.920106,.0435083,0,.163947,.910899,.0417399,0,.177537,.901532,.0399389,0,.191516,.891919,.0380901,0,.205881,.882006,.0362341,0,.220626,.871965,.0343444,0,.235749,.862145,.0324832,0,.251248,.852058,.0306681,0,.267121,.84161,.0289097,0,.283368,.830806,.0272079,0,.299992,.820476,.0256089,0,.316992,.809514,.0240394,0,.334374,.797865,.0225379,0,.35214,.785621,.0211235,0,.370296,.773765,.0197908,0,.388849,.761629,.0185235,0,.407807,.748891,.0173358,0,.427178,.736437,.0162305,0,.446974,.723707,.0151778,0,.467207,.710606,.0141791,0,.487892,.698019,.0132592,0,.509046,.686203,.0123887,0,.530687,.675692,.0115976,0,.552839,.664826,.0108325,0,.575527,.65349,.0101348,0,.59878,.641774,.00947756,0,.622634,.629794,.00886058,0,.647128,.617647,.00828526,0,.672308,.60534,.00775312,0,.698231,.592718,.00726033,0,.724958,.579746,.00679731,0,.752563,.566763,.00636111,0,.781127,.553515,.00595228,0,.810733,.540118,.00556876,0,.841426,.527325,.00523051,0,.873016,.514265,.00490712,0,.904762,.501406,.00460297,0,.936508,.488922,.00431247,0,.968254,.476541,.0040472,0,1,1,.0659184,0,0,1,.0659185,0,0,.999998,.06592,0,0,.999988,.0659259,0,0,.999963,.0659423,0,0,.999907,.0659764,0,374198e-9,.999806,.0660376,0,.00182071,.999639,.0661361,0,.0043894,.999378,.0662814,0,.00800055,.998985,.0664779,0,.0125594,.998285,.0666914,0,.0179786,.995071,.0661989,0,.0241822,.993172,.0660454,0,.031106,.991438,.0660105,0,.0386952,.988428,.0656875,0,.0469032,.985218,.0652913,0,.0556905,.981128,.0647107,0,.065023,.976015,.0638491,0,.0748717,.97097,.062993,0,.0852112,.964582,.0617927,0,.0960199,.957383,.0603626,0,.107279,.949969,.0588128,0,.118971,.941843,.0570274,0,.131084,.933624,.0551885,0,.143604,.924543,.053122,0,.156521,.914919,.0508897,0,.169825,.905773,.0486418,0,.18351,.896434,.0463364,0,.197569,.887195,.0440623,0,.211997,.877706,.0417799,0,.226789,.867719,.03945,0,.241944,.858587,.037243,0,.257458,.849317,.0350956,0,.273331,.839585,.0329852,0,.289563,.829856,.0310028,0,.306154,.819589,.0290953,0,.323108,.809714,.0272738,0,.340426,.79934,.0255631,0,.358113,.788224,.0239175,0,.376175,.776619,.0223831,0,.394616,.76521,.0209298,0,.413445,.753716,.0195786,0,.432671,.741564,.0183001,0,.452305,.729413,.0171259,0,.472358,.717146,.0159933,0,.492845,.70436,.0149495,0,.513783,.69219,.0139681,0,.535189,.680289,.0130577,0,.557087,.669611,.0122198,0,.5795,.659113,.0114174,0,.602459,.648148,.0106729,0,.625997,.636905,.00998997,0,.650154,.625154,.00934313,0,.674976,.613481,.00874839,0,.700518,.60154,.00818265,0,.726845,.58943,.00766889,0,.754032,.576828,.00717153,0,.782167,.564194,.00672696,0,.811344,.551501,.00630863,0,.841644,.538635,.00592177,0,.873016,.525724,.00554888,0,.904762,.513209,.00520225,0,.936508,.500457,.00488231,0,.968254,.48799,.00457153,0,1,1,.0810131,0,0,1,.0810133,0,0,.999997,.0810145,0,0,.999985,.08102,0,0,.999956,.0810347,0,195026e-10,.999893,.0810656,0,719316e-9,.999777,.0811205,0,.00259774,.999583,.081208,0,.00561807,.999281,.0813343,0,.00967472,.998813,.0814969,0,.0146627,.997597,.0815217,0,.0204902,.994379,.0808502,0,.0270802,.992744,.0806792,0,.0343674,.990745,.0804589,0,.0422974,.986646,.0796107,0,.0508242,.983611,.0790913,0,.0599087,.978869,.0780746,0,.0695175,.973475,.0768218,0,.0796223,.967845,.0754926,0,.0901983,.960778,.0737063,0,.101224,.953333,.0718052,0,.112682,.945274,.0695946,0,.124555,.936955,.0672492,0,.136831,.928319,.0647732,0,.149496,.919075,.0620947,0,.162542,.909114,.0591816,0,.175958,.900137,.0563917,0,.189739,.891069,.0535392,0,.203877,.882262,.0507642,0,.218368,.873232,.0479793,0,.233208,.864042,.045226,0,.248393,.855002,.0425413,0,.263923,.846569,.0400126,0,.279796,.837714,.0375269,0,.296012,.828918,.0352027,0,.312573,.819783,.0330011,0,.329479,.810129,.0308908,0,.346734,.800866,.0289112,0,.364342,.79093,.0270255,0,.382307,.780593,.0252758,0,.400637,.769511,.0236178,0,.419337,.758558,.0220652,0,.438418,.747632,.0206289,0,.457889,.736146,.0192873,0,.477761,.724093,.0180333,0,.49805,.71234,.0168264,0,.51877,.700201,.015746,0,.53994,.687949,.0147027,0,.561581,.676163,.0137512,0,.583718,.665001,.0128655,0,.60638,.65472,.0120366,0,.629599,.644213,.0112604,0,.653415,.633382,.0105413,0,.677874,.62212,.00986498,0,.70303,.610631,.00923308,0,.728948,.599078,.00864206,0,.755706,.587519,.00811784,0,.783396,.575505,.00761237,0,.812121,.563148,.00713949,0,.841989,.550828,.00668379,0,.873035,.538458,.00627715,0,.904762,.525905,.00588336,0,.936508,.513517,.00552687,0,.968254,.501395,.00519681,0,1,1,.0991506,0,0,1,.0991504,0,0,.999996,.0991515,0,0,.999984,.0991558,0,0,.999947,.0991672,0,114389e-9,.999874,.0991912,0,.00121503,.999739,.0992331,0,.00356108,.999514,.0992983,0,.00705578,.999159,.0993877,0,.011574,.998586,.0994837,0,.017003,.995731,.0988425,0,.0232484,.993384,.098276,0,.0302318,.991615,.0979269,0,.0378884,.989029,.0973432,0,.0461641,.985373,.0963539,0,.0550136,.981278,.0952306,0,.0643988,.975777,.0936233,0,.0742868,.970526,.0920219,0,.0846501,.963755,.0898912,0,.0954644,.956676,.0876064,0,.106709,.948099,.0847751,0,.118367,.939718,.0818638,0,.130423,.931305,.078857,0,.142862,.922342,.0756127,0,.155674,.912842,.0721473,0,.168849,.903304,.0686195,0,.182378,.89411,.0650589,0,.196255,.885512,.0616022,0,.210473,.877193,.0582434,0,.225027,.86877,.0548979,0,.239915,.860267,.0516095,0,.255132,.851915,.048468,0,.270678,.843912,.0454447,0,.286551,.83604,.0425612,0,.302751,.828245,.0398752,0,.31928,.820159,.0373198,0,.336138,.81167,.034916,0,.35333,.802659,.0326402,0,.370858,.793921,.0304901,0,.388728,.784713,.0284857,0,.406944,.774946,.0266186,0,.425515,.76448,.0248593,0,.444449,.753793,.0232114,0,.463756,.743506,.0217039,0,.483447,.732555,.0202841,0,.503535,.720965,.0189648,0,.524036,.709422,.0177189,0,.544968,.697756,.0165626,0,.56635,.685565,.015483,0,.588208,.673987,.0144892,0,.610569,.66244,.0135607,0,.633466,.651675,.0126956,0,.656936,.641598,.0118788,0,.681025,.63121,.0111261,0,.705788,.620514,.010437,0,.731289,.609366,.00978747,0,.757606,.598137,.00917257,0,.784834,.586966,.00859778,0,.813085,.575549,.00806803,0,.842485,.563797,.00757294,0,.87313,.551758,.00710592,0,.904762,.539894,.0066841,0,.936508,.527901,.00627901,0,.968254,.515819,.00590506,0,1,1,.120864,0,0,1,.120864,0,0,.999996,.120864,0,0,.99998,.120867,0,0,.99994,.120872,0,323781e-9,.999852,.120884,0,.00188693,.999693,.120903,0,.00473489,.999426,.120929,0,.00872704,.999002,.120955,0,.0137237,.998235,.120918,0,.0196068,.994608,.119764,0,.0262803,.992997,.119265,0,.0336657,.990968,.11863,0,.0416987,.987002,.117261,0,.0503261,.983524,.116009,0,.0595035,.97875,.114252,0,.0691935,.972652,.11193,0,.0793645,.966613,.109555,0,.0899894,.959275,.106612,0,.101045,.951272,.103375,0,.112512,.942323,.0996594,0,.124372,.933679,.0958841,0,.136611,.924822,.0919265,0,.149216,.915742,.0878061,0,.162176,.906348,.0834894,0,.175482,.896883,.079085,0,.189125,.88774,.0746745,0,.203098,.87986,.0705773,0,.217396,.871998,.0665005,0,.232015,.864325,.0625413,0,.24695,.856685,.0586781,0,.2622,.84925,.0550063,0,.277761,.841719,.0514727,0,.293634,.834755,.0481398,0,.309819,.827853,.0450172,0,.326315,.820888,.0420969,0,.343126,.813616,.0393702,0,.360254,.805767,.0367771,0,.377701,.797338,.0343274,0,.395474,.789122,.0320529,0,.413577,.780601,.0299485,0,.432018,.771424,.0279812,0,.450804,.761502,.0261054,0,.469944,.751166,.0243942,0,.489451,.741276,.0228087,0,.509337,.730898,.0213265,0,.529617,.719878,.0199307,0,.550307,.708379,.0186574,0,.571428,.697165,.0174446,0,.593003,.685554,.0163144,0,.615059,.673631,.015276,0,.637628,.662385,.0143003,0,.660746,.651059,.0134112,0,.68446,.640451,.0125794,0,.70882,.630536,.011793,0,.733893,.620316,.0110547,0,.759756,.609722,.0103668,0,.786505,.598804,.00973009,0,.814259,.587871,.00912812,0,.843157,.577121,.00858916,0,.87334,.566019,.00807333,0,.904762,.554664,.00759687,0,.936508,.543101,.00714759,0,.968254,.531558,.00673418,0,1,1,.146767,0,0,1,.146767,0,0,.999997,.146767,0,0,.999977,.146765,0,320658e-11,.999929,.146762,0,682576e-9,.999823,.146753,0,.00276402,.999633,.146735,0,.00614771,.999314,.146699,0,.0106613,.998796,.14662,0,.0161546,.997124,.146107,0,.0225063,.994062,.144857,0,.0296198,.992154,.144011,0,.037417,.989186,.142712,0,.0458348,.985279,.140926,0,.0548211,.980826,.13885,0,.0643326,.975056,.136168,0,.074333,.969005,.133217,0,.0847917,.961554,.12959,0,.0956828,.954206,.125886,0,.106984,.945046,.121335,0,.118675,.935678,.116492,0,.130741,.926748,.111635,0,.143166,.917764,.106625,0,.155939,.908358,.101325,0,.169049,.899219,.0960249,0,.182487,.890089,.0906527,0,.196245,.881488,.0853905,0,.210317,.874031,.0804177,0,.224697,.866932,.0756005,0,.23938,.859976,.0709019,0,.254364,.853375,.0664391,0,.269646,.846971,.0622012,0,.285223,.840483,.058129,0,.301096,.833969,.0542762,0,.317265,.82806,.0507042,0,.333729,.822128,.047368,0,.350491,.815989,.044272,0,.367554,.809336,.0413444,0,.38492,.802177,.038601,0,.402594,.79441,.0360227,0,.420582,.786573,.0336383,0,.438891,.778619,.0314321,0,.457527,.77,.029362,0,.476499,.760698,.0274102,0,.49582,.750932,.0256146,0,.5155,.740993,.023974,0,.535555,.731159,.0224182,0,.556,.720836,.0209889,0,.576855,.709913,.0196411,0,.598143,.698415,.0183824,0,.619888,.68745,.0172222,0,.642123,.676154,.0161509,0,.664883,.664383,.0151397,0,.688211,.6533,.0141873,0,.71216,.642072,.0133105,0,.736792,.631412,.0124932,0,.762186,.621622,.0117408,0,.788439,.611681,.0110358,0,.815672,.60142,.0103775,0,.844034,.59083,.00975623,0,.873699,.580254,.00918084,0,.904765,.569841,.00864721,0,.936508,.559224,.00815731,0,.968254,.548315,.00767924,0,1,1,.177563,0,0,1,.177563,0,0,.999994,.177562,0,0,.999972,.177555,0,664171e-10,.999914,.177536,0,.0012276,.999787,.177496,0,.00388025,.999556,.17742,0,.00783463,.999165,.177285,0,.0128953,.9985,.177037,0,.0189053,.995388,.175634,0,.025742,.993102,.174375,0,.033309,.990992,.173121,0,.0415298,.986932,.170896,0,.0503425,.982786,.16847,0,.0596964,.977592,.165455,0,.0695498,.971075,.161676,0,.0798676,.963967,.157458,0,.0906201,.956397,.152836,0,.101783,.947489,.147467,0,.113333,.937564,.14145,0,.125254,.928182,.135383,0,.137529,.919027,.129212,0,.150144,.909618,.12276,0,.163088,.900492,.116273,0,.176351,.891671,.1098,0,.189924,.883146,.103362,0,.203799,.875151,.0970799,0,.21797,.868338,.0911732,0,.232433,.862033,.0854966,0,.247182,.856107,.0800691,0,.262216,.850644,.0749618,0,.27753,.845261,.070079,0,.293124,.839885,.0654321,0,.308997,.834609,.0610975,0,.325149,.829083,.0569741,0,.341581,.82404,.0531736,0,.358294,.818968,.049665,0,.37529,.813496,.0463856,0,.392573,.807533,.0433217,0,.410148,.80099,.0404402,0,.428019,.793891,.0377578,0,.446192,.786281,.0352616,0,.464676,.778773,.0329577,0,.483478,.770737,.030808,0,.502608,.762094,.0287964,0,.522079,.752898,.0269254,0,.541905,.743306,.0251926,0,.5621,.733416,.023595,0,.582684,.723742,.0221155,0,.603677,.713542,.0207435,0,.625106,.702755,.019434,0,.646998,.691484,.0182046,0,.66939,.680531,.0170771,0,.692324,.66953,.0160339,0,.715849,.658126,.0150677,0,.740028,.646933,.0141551,0,.764937,.636107,.0133179,0,.790673,.625271,.0125284,0,.817358,.615225,.0117937,0,.84515,.605678,.0111181,0,.874244,.59583,.0104759,0,.904828,.585704,.00986672,0,.936508,.575413,.00929712,0,.968254,.565373,.00876713,0,1,1,.214058,0,0,.999999,.214058,0,0,.999994,.214055,0,0,.999966,.214039,0,259642e-9,.999893,.213998,0,.00200075,.999737,.21391,0,.00527775,.999449,.213745,0,.00983959,.99896,.213458,0,.0154755,.9979,.212855,0,.0220249,.994278,.210779,0,.0293654,.992254,.20926,0,.0374021,.98881,.206908,0,.0460604,.984715,.204009,0,.0552802,.979738,.200471,0,.0650127,.972884,.195813,0,.0752175,.965996,.190856,0,.0858612,.957974,.185077,0,.0969155,.949155,.17868,0,.108356,.939288,.171513,0,.120163,.928996,.163838,0,.132319,.919563,.156246,0,.144808,.910004,.148359,0,.157618,.900791,.140417,0,.170737,.892135,.132569,0,.184155,.883803,.124741,0,.197866,.876034,.117091,0,.211861,.869219,.109835,0,.226134,.863062,.102859,0,.240682,.857795,.0962928,0,.255499,.853009,.0900725,0,.270583,.848603,.0842101,0,.285931,.844335,.0786527,0,.301542,.840208,.0734397,0,.317415,.836035,.0685334,0,.33355,.83172,.0639275,0,.349948,.827135,.0595909,0,.36661,.822797,.0556204,0,.383539,.818387,.0519394,0,.400738,.813565,.0485317,0,.41821,.808142,.0453138,0,.435961,.802212,.0423354,0,.453997,.79573,.0395553,0,.472324,.788741,.036988,0,.490951,.781093,.0345688,0,.509887,.773597,.0323297,0,.529144,.765622,.0302719,0,.548735,.757083,.0283477,0,.568674,.747992,.0265562,0,.588979,.738591,.0248844,0,.609671,.728719,.0233342,0,.630773,.719146,.0219081,0,.652314,.709165,.0205711,0,.674328,.69875,.0193248,0,.696854,.687884,.0181582,0,.719942,.676818,.0170746,0,.743651,.666247,.0160718,0,.768057,.655284,.0151262,0,.793253,.64401,.0142561,0,.819363,.633353,.0134327,0,.846547,.622674,.012653,0,.875017,.612265,.0119354,0,.905021,.602455,.0112533,0,.936508,.593147,.0106234,0,.968254,.583592,.0100213,0,1,1,.25717,0,0,1,.25717,0,0,.999992,.257164,0,0,.999958,.257135,0,641715e-9,.999864,.25706,0,.00305314,.999666,.256897,0,.00700975,.999302,.256596,0,.0122194,.998663,.25607,0,.0184622,.995607,.254123,0,.0255773,.993094,.252081,0,.0334439,.9907,.249867,0,.0419696,.98594,.246118,0,.0510823,.981214,.242049,0,.0607242,.974966,.236869,0,.0708486,.967589,.230724,0,.081417,.95915,.223635,0,.0923974,.950257,.21596,0,.103763,.940165,.207296,0,.115491,.929396,.197901,0,.127562,.919288,.188437,0,.13996,.909428,.178762,0,.15267,.900105,.169072,0,.165679,.891418,.159478,0,.178979,.883347,.15002,0,.192558,.875992,.140813,0,.20641,.869466,.13196,0,.220529,.863699,.123501,0,.234907,.858553,.115436,0,.249542,.854379,.107901,0,.264428,.850894,.10088,0,.279564,.847632,.0942296,0,.294947,.844571,.0879861,0,.310575,.84163,.0821534,0,.326448,.838542,.0766409,0,.342566,.835412,.0715322,0,.358929,.831899,.0666883,0,.37554,.828177,.0622175,0,.392399,.82416,.0580452,0,.409511,.820393,.054267,0,.426878,.816068,.0507172,0,.444506,.811201,.0474041,0,.4624,.805785,.0443174,0,.480566,.799878,.0414562,0,.499013,.793469,.0388147,0,.517749,.786473,.0363453,0,.536785,.778874,.0340225,0,.556134,.771277,.0318599,0,.575809,.763426,.0298859,0,.595827,.755044,.0280357,0,.616207,.746161,.0262979,0,.636973,.737124,.0247295,0,.65815,.72761,.0232514,0,.679772,.717822,.0218755,0,.701876,.708279,.0205942,0,.724509,.698333,.0193947,0,.74773,.68802,.0182717,0,.771609,.677321,.0172044,0,.79624,.666504,.0162122,0,.821743,.656184,.0152924,0,.84828,.64556,.0144326,0,.876069,.634636,.0136157,0,.905404,.624124,.0128612,0,.936508,.613914,.0121435,0,.968254,.603589,.0114887,0,1,1,.307946,0,0,.999999,.307945,0,0,.999988,.307934,0,204479e-10,.999944,.307886,0,.00127833,.999824,.307756,0,.00445047,.999565,.30748,0,.00914673,.999085,.306966,0,.0150498,.998103,.306004,0,.0219367,.994249,.303028,0,.0296485,.991807,.300435,0,.038068,.987773,.296554,0,.0471062,.982673,.2916,0,.0566942,.976623,.285641,0,.0667768,.968757,.27815,0,.0773099,.959849,.269529,0,.088257,.950663,.260248,0,.0995879,.940129,.249704,0,.111277,.92895,.238291,0,.123304,.917996,.226501,0,.13565,.907813,.214669,0,.148299,.898305,.202835,0,.161237,.889626,.191158,0,.174455,.88175,.179695,0,.187941,.874715,.168548,0,.201687,.868746,.15792,0,.215687,.863703,.147807,0,.229933,.859315,.138149,0,.24442,.855538,.128993,0,.259145,.852428,.120414,0,.274103,.850168,.112498,0,.289293,.848132,.105054,0,.304711,.846291,.0981087,0,.320357,.844431,.0915942,0,.33623,.842493,.0855056,0,.35233,.840368,.0798204,0,.368658,.83798,.0745097,0,.385214,.83523,.0695424,0,.402002,.832091,.0649092,0,.419023,.828667,.0606291,0,.436282,.824805,.0566523,0,.453782,.820988,.0530229,0,.471529,.816635,.0496364,0,.489528,.811725,.0464658,0,.507788,.806316,.0435082,0,.526317,.800469,.0407873,0,.545124,.794107,.038255,0,.564221,.787218,.0358825,0,.583621,.779872,.0336785,0,.603341,.772097,.0316379,0,.623397,.764484,.0297379,0,.643812,.756428,.0279581,0,.664611,.748022,.0263153,0,.685824,.739268,.0247799,0,.707488,.73024,.0233385,0,.729646,.720893,.0220035,0,.752354,.71119,.0207555,0,.77568,.701791,.0195843,0,.799715,.692184,.0184891,0,.824574,.682258,.0174541,0,.850417,.67206,.0164873,0,.877466,.661717,.0155959,0,.90604,.651462,.0147519,0,.936528,.641467,.0139727,0,.968254,.631229,.0132363,0,1,1,.367573,0,0,.999999,.367571,0,0,.999984,.367553,0,183382e-9,.999925,.367473,0,.00225254,.999759,.367259,0,.00628165,.99941,.366801,0,.0117858,.998739,.365946,0,.0184359,.995529,.363191,0,.0260114,.992875,.360171,0,.0343581,.989135,.355981,0,.0433637,.984166,.350401,0,.0529438,.977871,.343348,0,.0630334,.96951,.334341,0,.0735805,.959964,.323862,0,.0845437,.950162,.312521,0,.095889,.938882,.299577,0,.107588,.926992,.285573,0,.119617,.915589,.271212,0,.131957,.904791,.256611,0,.144591,.895177,.242224,0,.157503,.886403,.227952,0,.170682,.878957,.214192,0,.184117,.872418,.200795,0,.197799,.867029,.188015,0,.21172,.862835,.175975,0,.225873,.859411,.164526,0,.240253,.856655,.153693,0,.254854,.854519,.14352,0,.269673,.852828,.13397,0,.284707,.851412,.124984,0,.299953,.850609,.116748,0,.315408,.849855,.10905,0,.331073,.849017,.101839,0,.346946,.848079,.0951359,0,.363028,.846911,.0888774,0,.379318,.845445,.0830375,0,.395818,.84362,.0775844,0,.41253,.841411,.0725054,0,.429457,.838768,.0677691,0,.446602,.835801,.0634016,0,.463968,.832341,.0593095,0,.481561,.828424,.0555121,0,.499386,.824312,.052024,0,.51745,.819918,.0487865,0,.535761,.815072,.0457801,0,.554328,.809863,.0430184,0,.573162,.804164,.0404245,0,.592275,.798034,.0380146,0,.611681,.791436,.0357436,0,.631398,.784498,.0336475,0,.651445,.777125,.0316666,0,.671845,.769365,.0298122,0,.692628,.761579,.0281001,0,.713827,.753746,.0265049,0,.735484,.745573,.0250067,0,.75765,.737083,.0236026,0,.78039,.728545,.0223302,0,.803789,.719691,.0211243,0,.82796,.710569,.0199983,0,.853056,.701216,.0189569,0,.879298,.692094,.0179702,0,.907014,.682909,.0170418,0,.936691,.673509,.0161732,0,.968254,.663863,.0153406,0,1,1,.437395,0,0,.999998,.437394,0,0,.99998,.437363,0,616704e-9,.999891,.437232,0,.00367925,.999656,.436877,0,.00867446,.999148,.436121,0,.0150679,.997959,.434564,0,.022531,.993464,.430134,0,.0308507,.990606,.426077,0,.0398805,.985027,.419397,0,.0495148,.978491,.41118,0,.0596749,.969643,.40048,0,.0703001,.959189,.38769,0,.0813427,.948223,.373575,0,.0927641,.935955,.357622,0,.104533,.923237,.34043,0,.116624,.911074,.322735,0,.129015,.899724,.30479,0,.141687,.890189,.287392,0,.154626,.881796,.270248,0,.167818,.874781,.253659,0,.181252,.869166,.237786,0,.194918,.864725,.222618,0,.208807,.861565,.208356,0,.222913,.859284,.194867,0,.237229,.857677,.18212,0,.25175,.856714,.17018,0,.266473,.856155,.158969,0,.281392,.8558,.148413,0,.296505,.855672,.138578,0,.311811,.855538,.129345,0,.327306,.855689,.120861,0,.342991,.855767,.112969,0,.358864,.855618,.105593,0,.374925,.85525,.0987451,0,.391176,.854583,.0923727,0,.407616,.853534,.0864143,0,.424249,.852061,.0808338,0,.441076,.850253,.0756771,0,.4581,.848004,.0708612,0,.475324,.845333,.0663784,0,.492754,.842376,.0622631,0,.510394,.838956,.0584112,0,.528251,.835121,.0548328,0,.546331,.830842,.0514838,0,.564644,.826212,.048355,0,.583198,.821522,.0454714,0,.602005,.816551,.0428263,0,.621078,.811211,.0403612,0,.640434,.805479,.038039,0,.660089,.799409,.0358739,0,.680066,.79306,.0338727,0,.70039,.786395,.0319985,0,.721094,.779416,.030241,0,.742215,.77214,.0285951,0,.7638,.764636,.0270747,0,.785912,.756836,.0256354,0,.808628,.749315,.0243027,0,.832055,.741561,.0230497,0,.856338,.733589,.0218801,0,.88169,.725479,.020784,0,.908441,.717255,.0197702,0,.937125,.708829,.0188168,0,.968254,.700191,.0179113,0,1,1,.518937,0,0,.999998,.518933,0,0,.999967,.518883,0,.00147741,.999832,.51866,0,.00573221,.999466,.518057,0,.011826,.998644,.516752,0,.0192116,.994458,.512347,0,.027573,.991223,.507675,0,.0367099,.985515,.500188,0,.046487,.978308,.490408,0,.0568071,.968359,.477357,0,.0675984,.95682,.461752,0,.0788059,.943929,.443796,0,.090386,.930224,.423893,0,.102304,.916514,.402682,0,.114532,.903653,.380914,0,.127047,.892315,.359212,0,.139828,.882942,.338102,0,.152861,.875438,.31773,0,.16613,.869642,.298186,0,.179624,.865304,.279491,0,.193332,.862382,.261804,0,.207247,.860666,.245146,0,.22136,.859788,.229406,0,.235666,.859608,.214605,0,.250158,.859912,.200691,0,.264832,.86053,.187623,0,.279684,.861368,.17539,0,.294711,.862237,.163901,0,.309911,.863127,.153175,0,.32528,.863923,.143147,0,.340819,.864567,.133781,0,.356524,.865013,.125042,0,.372397,.86539,.116952,0,.388438,.865591,.109476,0,.404645,.865517,.102542,0,.421022,.865084,.0960688,0,.437569,.864309,.0900499,0,.454287,.863151,.0844328,0,.471181,.861649,.0792218,0,.488253,.859742,.0743482,0,.505507,.857446,.0697963,0,.522947,.854757,.0655364,0,.54058,.851783,.061608,0,.558412,.848516,.0579701,0,.576449,.844897,.0545742,0,.594701,.840956,.0514167,0,.613178,.836676,.0484598,0,.631892,.832075,.0456934,0,.650856,.827191,.0431178,0,.670088,.822295,.0407718,0,.689606,.817294,.0386032,0,.709434,.812013,.0365675,0,.7296,.806465,.0346547,0,.750138,.800691,.0328717,0,.771093,.794709,.031211,0,.792519,.788493,.0296504,0,.814488,.782049,.0281782,0,.837097,.775403,.0267965,0,.860481,.76857,.0255002,0,.884842,.761536,.0242759,0,.910494,.754303,.0231142,0,.937985,.74692,.0220305,0,.968254,.739745,.0210192,0,1,1,.613914,0,0,.999996,.613907,0,963597e-10,.999942,.613814,0,.00301247,.999704,.613407,0,.00870385,.999046,.612302,0,.0160714,.995516,.608266,0,.0245899,.991726,.602863,0,.0339681,.985157,.593956,0,.0440254,.97642,.581748,0,.0546409,.964404,.565183,0,.0657284,.950601,.545273,0,.0772246,.935158,.522129,0,.0890812,.919364,.496782,0,.10126,.904754,.470571,0,.113731,.89176,.444037,0,.126469,.881492,.418322,0,.139454,.873656,.393522,0,.15267,.868053,.369795,0,.166101,.864336,.347171,0,.179736,.862259,.325737,0,.193565,.861556,.305532,0,.207578,.861776,.286416,0,.221769,.862661,.268355,0,.23613,.864015,.251334,0,.250656,.865711,.235352,0,.265343,.867519,.220302,0,.280187,.869351,.206161,0,.295183,.871144,.192908,0,.31033,.872839,.180505,0,.325624,.874307,.168848,0,.341065,.875667,.158021,0,.35665,.876758,.147877,0,.37238,.87764,.138441,0,.388253,.878237,.129627,0,.404269,.878563,.121415,0,.42043,.878572,.113741,0,.436735,.87842,.106652,0,.453187,.878057,.100097,0,.469786,.877413,.0940128,0,.486536,.87646,.0883462,0,.503439,.875233,.0830924,0,.520498,.8737,.0781975,0,.537717,.871873,.07364,0,.555102,.86978,.0694103,0,.572657,.867405,.0654696,0,.59039,.864751,.0617914,0,.608307,.861818,.0583491,0,.626419,.858645,.0551443,0,.644733,.855307,.0521894,0,.663264,.851736,.0494334,0,.682025,.847927,.0468504,0,.701032,.843888,.0444261,0,.720308,.839629,.0421497,0,.739875,.835158,.0400082,0,.759764,.830509,.0380076,0,.780014,.825714,.0361488,0,.800673,.820729,.0343956,0,.821803,.815751,.0327781,0,.843492,.810752,.031275,0,.86586,.805587,.0298542,0,.889087,.800317,.0285397,0,.913466,.79489,.0272948,0,.93952,.789314,.0261139,0,.96835,.783593,.0249938,0,1,1,.724258,0,0,.999992,.724243,0,726889e-9,.99987,.724044,0,.00569574,.999336,.72317,0,.0131702,.996271,.719432,0,.0220738,.991159,.712576,0,.0319405,.982465,.700927,0,.0425202,.97049,.684297,0,.0536599,.953973,.661244,0,.065258,.935546,.633804,0,.0772427,.916596,.603071,0,.0895616,.899353,.57105,0,.102175,.885216,.539206,0,.11505,.875076,.508714,0,.128164,.868334,.479571,0,.141495,.864414,.451796,0,.155026,.862678,.425328,0,.168745,.862835,.400352,0,.182639,.864067,.376532,0,.196699,.866086,.35391,0,.210915,.868557,.332424,0,.225282,.871271,.312053,0,.239792,.874058,.292764,0,.25444,.8768,.27453,0,.269223,.87939,.257297,0,.284135,.8819,.24114,0,.299174,.884187,.225934,0,.314337,.886262,.211669,0,.329622,.888119,.198311,0,.345026,.889709,.185783,0,.360549,.891054,.174063,0,.376189,.892196,.163143,0,.391946,.893101,.152952,0,.407819,.893803,.143475,0,.423808,.894277,.134647,0,.439914,.894532,.126434,0,.456137,.894576,.1188,0,.472479,.894393,.111694,0,.48894,.893976,.105069,0,.505523,.893346,.0989077,0,.52223,.892502,.0931724,0,.539064,.891441,.0878276,0,.556028,.890276,.082903,0,.573125,.888972,.0783505,0,.590361,.887469,.0741083,0,.607741,.885785,.0701633,0,.62527,.883914,.0664835,0,.642957,.881872,.0630567,0,.660809,.879651,.0598527,0,.678836,.877267,.0568615,0,.69705,.874717,.05406,0,.715465,.872012,.0514378,0,.734098,.869157,.0489805,0,.752968,.866155,.0466727,0,.772101,.863014,.0445056,0,.791529,.859748,.0424733,0,.81129,.856416,.0405957,0,.831438,.852958,.0388273,0,.852044,.849382,.0371619,0,.87321,.845694,.0355959,0,.89509,.841893,.0341155,0,.917932,.837981,.0327141,0,.942204,.833963,.0313856,0,.968981,.829847,.0301275,0,1,1,.85214,0,0,.999969,.852095,0,.00279627,.999483,.851408,0,.0107635,.994545,.84579,0,.0206454,.986188,.835231,0,.0315756,.969847,.814687,0,.0432021,.945951,.783735,0,.0553396,.91917,.746074,0,.0678766,.895488,.706938,0,.0807395,.878232,.669534,0,.0938767,.868252,.635168,0,.10725,.863873,.603069,0,.120832,.863369,.572514,0,.134598,.86545,.543169,0,.148533,.868803,.514578,0,.16262,.872794,.486762,0,.176849,.87702,.459811,0,.19121,.881054,.433654,0,.205694,.884974,.408574,0,.220294,.888587,.384525,0,.235005,.891877,.36156,0,.24982,.894793,.339661,0,.264737,.89743,.318913,0,.279751,.899796,.299302,0,.294859,.901943,.280843,0,.310058,.903858,.263481,0,.325346,.905574,.247197,0,.340721,.907069,.231915,0,.356181,.908379,.217614,0,.371725,.90952,.20425,0,.387353,.910483,.191758,0,.403063,.91128,.180092,0,.418854,.911936,.169222,0,.434727,.912454,.159098,0,.450682,.912835,.149668,0,.466718,.913078,.140884,0,.482837,.913192,.132709,0,.499038,.913175,.125095,0,.515324,.91304,.118012,0,.531695,.912781,.111417,0,.548153,.91241,.105281,0,.5647,.911924,.0995691,0,.581338,.911331,.0942531,0,.59807,.910637,.0893076,0,.6149,.90984,.0846998,0,.63183,.908941,.0804044,0,.648865,.907944,.0763984,0,.666011,.906857,.0726638,0,.683273,.90568,.0691783,0,.700659,.904416,.0659222,0,.718176,.903067,.0628782,0,.735834,.901637,.0600307,0,.753646,.900128,.0573647,0,.771625,.898544,.0548668,0,.78979,.89689,.052527,0,.808162,.895165,.0503306,0,.826771,.893371,.0482668,0,.845654,.891572,.0463605,0,.864863,.889763,.0445998,0,.884472,.887894,.0429451,0,.904592,.885967,.0413884,0,.925407,.883984,.0399225,0,.947271,.881945,.0385405,0,.97105,.879854,.0372362,0,1,.999804,.995833,0,0,.938155,.933611,0,.0158731,.864755,.854311,0,.0317461,.888594,.865264,0,.0476191,.905575,.863922,0,.0634921,.915125,.850558,0,.0793651,.920665,.829254,0,.0952381,.924073,.802578,0,.111111,.926304,.772211,0,.126984,.927829,.739366,0,.142857,.928924,.705033,0,.15873,.92973,.670019,0,.174603,.930339,.634993,0,.190476,.930811,.600485,0,.206349,.931191,.566897,0,.222222,.93149,.534485,0,.238095,.931737,.503429,0,.253968,.931939,.473811,0,.269841,.932108,.445668,0,.285714,.93225,.418993,0,.301587,.932371,.393762,0,.31746,.932474,.369939,0,.333333,.932562,.347479,0,.349206,.932638,.326336,0,.365079,.932703,.306462,0,.380952,.93276,.287805,0,.396825,.932809,.270313,0,.412698,.932851,.253933,0,.428571,.932887,.23861,0,.444444,.932917,.224289,0,.460317,.932943,.210917,0,.47619,.932965,.19844,0,.492063,.932982,.186807,0,.507937,.932995,.175966,0,.52381,.933005,.165869,0,.539683,.933011,.156468,0,.555556,.933013,.147719,0,.571429,.933013,.139579,0,.587302,.93301,.132007,0,.603175,.933004,.124965,0,.619048,.932994,.118416,0,.634921,.932982,.112326,0,.650794,.932968,.106663,0,.666667,.93295,.101397,0,.68254,.932931,.0964993,0,.698413,.932908,.0919438,0,.714286,.932883,.0877057,0,.730159,.932856,.0837623,0,.746032,.932827,.0800921,0,.761905,.932796,.0766754,0,.777778,.932762,.0734936,0,.793651,.932727,.0705296,0,.809524,.932689,.0677676,0,.825397,.93265,.0651929,0,.84127,.932609,.0627917,0,.857143,.932565,.0605515,0,.873016,.932521,.0584606,0,.888889,.932474,.0565082,0,.904762,.932427,.0546841,0,.920635,.932377,.0529793,0,.936508,.932326,.0513851,0,.952381,.932274,.0498936,0,.968254,.93222,.0484975,0,.984127,.932164,.0471899,0,1],n=new Float32Array(t),i=new Float32Array(e);G.LTC_FLOAT_1=new mo.a(n,64,64,w.Ib,w.G,w.Yc,w.n,w.n,w.V,w.ob,1),G.LTC_FLOAT_2=new mo.a(i,64,64,w.Ib,w.G,w.Yc,w.n,w.n,w.V,w.ob,1);const r=new Uint16Array(t.length);t.forEach((function(t,e){r[e]=xz.a.toHalfFloat(t)}));const s=new Uint16Array(e.length);e.forEach((function(t,e){s[e]=xz.a.toHalfFloat(t)})),G.LTC_HALF_1=new mo.a(r,64,64,w.Ib,w.M,w.Yc,w.n,w.n,w.V,w.ob,1),G.LTC_HALF_2=new mo.a(s,64,64,w.Ib,w.M,w.Yc,w.n,w.n,w.V,w.ob,1)}}!function(t){t.OBJECTS=\\\\\\\"objects\\\\\\\",t.GEOMETRIES=\\\\\\\"geometries\\\\\\\"}(yz||(yz={}));const wz=[yz.GEOMETRIES,yz.OBJECTS];var Tz;!function(t){t.XYZ=\\\\\\\"XYZ\\\\\\\",t.XZY=\\\\\\\"XZY\\\\\\\",t.YXZ=\\\\\\\"YXZ\\\\\\\",t.YZX=\\\\\\\"YZX\\\\\\\",t.ZYX=\\\\\\\"ZYX\\\\\\\",t.ZXY=\\\\\\\"ZXY\\\\\\\"}(Tz||(Tz={}));const Az=[Tz.XYZ,Tz.XZY,Tz.YXZ,Tz.YZX,Tz.ZXY,Tz.ZYX],Ez=Tz.XYZ;class Mz{constructor(){this._translation_matrix=new A.a,this._translation_matrix_q=new au.a,this._translation_matrix_s=new p.a(1,1,1),this._matrix=(new A.a).identity(),this._matrix_q=new au.a,this._matrix_euler=new Wv.a,this._matrix_s=new p.a,this._rotate_geometry_m=new A.a,this._rotate_geometry_q=new au.a,this._rotate_geometry_vec_dest=new p.a}static set_params_from_matrix(t,e,n={}){let i=n.scale;null==i&&(i=!0),t.decompose(this.set_params_from_matrix_position,this.set_params_from_matrix_quaternion,this.set_params_from_matrix_scale),this.set_params_from_matrix_euler.setFromQuaternion(this.set_params_from_matrix_quaternion),this.set_params_from_matrix_euler.toVector3(this.set_params_from_matrix_rotation),this.set_params_from_matrix_rotation.divideScalar(Math.PI/180),this.set_params_from_matrix_position.toArray(this.set_params_from_matrix_t),this.set_params_from_matrix_rotation.toArray(this.set_params_from_matrix_r),this.set_params_from_matrix_scale.toArray(this.set_params_from_matrix_s),e.scene().batchUpdates((()=>{e.params.set_vector3(\\\\\\\"t\\\\\\\",this.set_params_from_matrix_t),e.params.set_vector3(\\\\\\\"r\\\\\\\",this.set_params_from_matrix_r),e.params.set_vector3(\\\\\\\"s\\\\\\\",this.set_params_from_matrix_s),i&&e.params.set_float(\\\\\\\"scale\\\\\\\",1)}))}static set_params_from_object(t,e){t.position.toArray(this.set_params_from_object_position_array),t.rotation.toArray(this.set_params_from_object_rotation_array),this.set_params_from_object_rotation_deg.fromArray(this.set_params_from_object_rotation_array),this.set_params_from_object_rotation_deg.multiplyScalar(180/Math.PI),this.set_params_from_object_rotation_deg.toArray(this.set_params_from_object_rotation_array),e.scene().batchUpdates((()=>{e.params.set_vector3(\\\\\\\"t\\\\\\\",this.set_params_from_object_position_array),e.params.set_vector3(\\\\\\\"r\\\\\\\",this.set_params_from_object_rotation_array)}))}translation_matrix(t){return this._translation_matrix.compose(t,this._translation_matrix_q,this._translation_matrix_s),this._translation_matrix}matrix(t,e,n,i,r){return this._matrix_euler.set(Object(Ln.e)(e.x),Object(Ln.e)(e.y),Object(Ln.e)(e.z),r),this._matrix_q.setFromEuler(this._matrix_euler),this._matrix_s.copy(n).multiplyScalar(i),this._matrix.compose(t,this._matrix_q,this._matrix_s),this._matrix}rotate_geometry(t,e,n){this._rotate_geometry_vec_dest.copy(n),this._rotate_geometry_vec_dest.normalize(),this._rotate_geometry_q.setFromUnitVectors(e,this._rotate_geometry_vec_dest),this._rotate_geometry_m.makeRotationFromQuaternion(this._rotate_geometry_q),t.applyMatrix4(this._rotate_geometry_m)}static decompose_matrix(t){t.matrix.decompose(t.position,t.quaternion,t.scale)}}function Sz(t,e){const n=(null==e?void 0:e.matrixAutoUpdate)||!1;return class extends t{constructor(){super(...arguments),this.transform=oa.FOLDER(),this.keepPosWhenParenting=oa.BOOLEAN(0),this.rotationOrder=oa.INTEGER(Az.indexOf(Tz.XYZ),{menu:{entries:Az.map(((t,e)=>({name:t,value:e})))}}),this.t=oa.VECTOR3([0,0,0]),this.r=oa.VECTOR3([0,0,0]),this.s=oa.VECTOR3([1,1,1]),this.scale=oa.FLOAT(1),this.matrixAutoUpdate=oa.BOOLEAN(n?1:0),this.updateTransformFromObject=oa.BUTTON(null,{callback:t=>{Nz.PARAM_CALLBACK_update_transform_from_object(t)}})}}}Mz.set_params_from_matrix_position=new p.a,Mz.set_params_from_matrix_quaternion=new au.a,Mz.set_params_from_matrix_scale=new p.a,Mz.set_params_from_matrix_euler=new Wv.a,Mz.set_params_from_matrix_rotation=new p.a,Mz.set_params_from_matrix_t=[0,0,0],Mz.set_params_from_matrix_r=[0,0,0],Mz.set_params_from_matrix_s=[0,0,0],Mz.set_params_from_object_position_array=[0,0,0],Mz.set_params_from_object_rotation_deg=new p.a,Mz.set_params_from_object_rotation_array=[0,0,0];Sz(aa);const Cz=\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\";class Nz{constructor(t){this.node=t,this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this),this._core_transform=new Mz,this._keep_pos_when_parenting_m_object=new A.a,this._keep_pos_when_parenting_m_new_parent_inv=new A.a}initializeNode(){this.node.dirtyController.hasHook(Cz)||this.node.dirtyController.addPostDirtyHook(Cz,this._cook_main_without_inputs_when_dirty_bound)}async _cook_main_without_inputs_when_dirty(){await this.node.cookController.cookMainWithoutInputs()}update(){this.update_transform_with_matrix();this.node.object.matrixAutoUpdate=this.node.pv.matrixAutoUpdate}update_transform_with_matrix(t){const e=this.node.object;null==t||t.equals(e.matrix)?this._update_matrix_from_params_with_core_transform():(e.matrix.copy(t),e.dispatchEvent({type:\\\\\\\"change\\\\\\\"}))}_update_matrix_from_params_with_core_transform(){const t=this.node.object;let e=t.matrixAutoUpdate;e&&(t.matrixAutoUpdate=!1);const n=this._core_transform.matrix(this.node.pv.t,this.node.pv.r,this.node.pv.s,this.node.pv.scale,Az[this.node.pv.rotationOrder]);t.matrix.identity(),t.applyMatrix4(n),this._apply_look_at(),t.updateMatrix(),e&&(t.matrixAutoUpdate=!0),t.dispatchEvent({type:\\\\\\\"change\\\\\\\"})}_apply_look_at(){}set_params_from_matrix(t,e={}){Mz.set_params_from_matrix(t,this.node,e)}static update_node_transform_params_if_required(t,e){t.transformController.update_node_transform_params_if_required(e)}update_node_transform_params_if_required(t){if(!this.node.pv.keepPosWhenParenting)return;if(!this.node.scene().loadingController.loaded())return;if(t==this.node.object.parent)return;const e=this.node.object;e.updateMatrixWorld(!0),t.updateMatrixWorld(!0),this._keep_pos_when_parenting_m_object.copy(e.matrixWorld),this._keep_pos_when_parenting_m_new_parent_inv.copy(t.matrixWorld),this._keep_pos_when_parenting_m_new_parent_inv.invert(),this._keep_pos_when_parenting_m_object.premultiply(this._keep_pos_when_parenting_m_new_parent_inv),Mz.set_params_from_matrix(this._keep_pos_when_parenting_m_object,this.node,{scale:!0})}update_node_transform_params_from_object(t=!1){const e=this.node.object;t&&e.updateMatrix(),Mz.set_params_from_matrix(e.matrix,this.node,{scale:!0})}static PARAM_CALLBACK_update_transform_from_object(t){t.transformController.update_node_transform_params_from_object()}}class Lz{constructor(t){this.node=t}initializeNode(){this.node.io.inputs.setCount(0,1),this.node.io.inputs.set_depends_on_inputs(!1),this.node.io.outputs.setHasOneOutput(),this.node.io.inputs.add_on_set_input_hook(\\\\\\\"on_input_updated:update_parent\\\\\\\",(()=>{this.on_input_updated()}))}static on_input_updated(t){const e=t.root().getParentForNode(t);t.transformController&&e&&Nz.update_node_transform_params_if_required(t,e),null!=t.io.inputs.input(0)?t.root().addToParentTransform(t):t.root().removeFromParentTransform(t)}on_input_updated(){Lz.on_input_updated(this.node)}}Sz(aa);class Oz extends mz{constructor(){super(...arguments),this.flags=new Fi(this),this.hierarchyController=new Lz(this),this.transformController=new Nz(this)}initializeBaseNode(){super.initializeBaseNode(),this.hierarchyController.initializeNode(),this.transformController.initializeNode()}cook(){this.transformController.update(),this.updateLightParams(),this.updateShadowParams(),this.cookController.endCook()}}class Rz{constructor(t,e,n){this.node=t,this._helperConstructor=e,this._name=n}initializeNode(){this.node.flags.display.onUpdate((()=>{this.update()}))}visible(){return this.node.flags.display.active()&&this.node.pv.showHelper}_createHelper(){const t=new this._helperConstructor(this.node,this._name);return t.build(),t}update(){this.visible()?(this._helper||(this._helper=this._createHelper()),this._helper&&(this.node.light.add(this._helper.object),this._helper.update())):this._helper&&this.node.light.remove(this._helper.object)}}var Pz=n(41);class Iz extends Pz.a{constructor(t,e){const n=new S.a;n.setAttribute(\\\\\\\"position\\\\\\\",new C.c([1,1,0,-1,1,0,-1,-1,0,1,-1,0,1,1,0],3)),n.computeBoundingSphere();super(n,new wr.a({fog:!1})),this.light=t,this.color=e,this.type=\\\\\\\"RectAreaLightHelper\\\\\\\";const i=new S.a;i.setAttribute(\\\\\\\"position\\\\\\\",new C.c([1,1,0,-1,1,0,-1,-1,0,1,1,0,-1,-1,0,1,-1,0],3)),i.computeBoundingSphere(),this.add(new k.a(i,new at.a({side:w.i,fog:!1})))}updateMatrixWorld(){if(this.scale.set(.5*this.light.width,.5*this.light.height,1),void 0!==this.color)this.material.color.set(this.color),this.children[0].material.color.set(this.color);else{this.material.color.copy(this.light.color).multiplyScalar(this.light.intensity);const t=this.material.color,e=Math.max(t.r,t.g,t.b);e>1&&t.multiplyScalar(1/e),this.children[0].material.color.copy(this.material.color)}this.matrixWorld.extractRotation(this.light.matrixWorld).scale(this.scale).copyPosition(this.light.matrixWorld),this.children[0].matrixWorld.copy(this.matrixWorld)}dispose(){this.geometry.dispose(),this.material.dispose(),this.children[0].geometry.dispose(),this.children[0].material.dispose()}}Fz=aa;var Fz;class Dz{constructor(t,e){this.node=t,this._name=e,this._object=this.createObject(),this._material=new at.a({wireframe:!0,fog:!1})}build(){this._object.matrixAutoUpdate=!1,this._object.name=this._name,this.buildHelper()}get object(){return this._object}}class kz extends Dz{createObject(){return new Iz(this.node.light)}buildHelper(){}update(){this._object.updateMatrixWorld()}}class Bz extends(function(t){return class extends t{constructor(){super(...arguments),this.light=oa.FOLDER(),this.color=oa.COLOR([1,1,1],{conversion:so.SRGB_TO_LINEAR}),this.intensity=oa.FLOAT(1,{range:[0,10]}),this.width=oa.FLOAT(1,{range:[0,10]}),this.height=oa.FLOAT(1,{range:[0,10]}),this.showHelper=oa.BOOLEAN(0)}}}(Sz(aa))){}const zz=new Bz;class Uz extends Oz{constructor(){super(...arguments),this.paramsConfig=zz,this._helperController=new Rz(this,kz,\\\\\\\"RectAreaLightObjNodeHelper\\\\\\\")}static type(){return\\\\\\\"areaLight\\\\\\\"}initializeNode(){this._helperController.initializeNode()}createLight(){const t=new vz(16777215,1,1,1);return t.matrixAutoUpdate=!1,bz.initialized||(bz.init(),bz.initialized=!0),t}updateLightParams(){this.light.color=this.pv.color,this.light.intensity=this.pv.intensity,this.light.width=this.pv.width,this.light.height=this.pv.height,this._helperController.update()}}var Gz=n(72);const Vz=new p.a,Hz=new tf.a;class jz extends Tr.a{constructor(t){const e=new S.a,n=new wr.a({color:16777215,vertexColors:!0,toneMapped:!1}),i=[],r=[],s={},o=new D.a(16755200),a=new D.a(16711680),l=new D.a(43775),c=new D.a(16777215),u=new D.a(3355443);function h(t,e,n){d(t,n),d(e,n)}function d(t,e){i.push(0,0,0),r.push(e.r,e.g,e.b),void 0===s[t]&&(s[t]=[]),s[t].push(i.length/3-1)}h(\\\\\\\"n1\\\\\\\",\\\\\\\"n2\\\\\\\",o),h(\\\\\\\"n2\\\\\\\",\\\\\\\"n4\\\\\\\",o),h(\\\\\\\"n4\\\\\\\",\\\\\\\"n3\\\\\\\",o),h(\\\\\\\"n3\\\\\\\",\\\\\\\"n1\\\\\\\",o),h(\\\\\\\"f1\\\\\\\",\\\\\\\"f2\\\\\\\",o),h(\\\\\\\"f2\\\\\\\",\\\\\\\"f4\\\\\\\",o),h(\\\\\\\"f4\\\\\\\",\\\\\\\"f3\\\\\\\",o),h(\\\\\\\"f3\\\\\\\",\\\\\\\"f1\\\\\\\",o),h(\\\\\\\"n1\\\\\\\",\\\\\\\"f1\\\\\\\",o),h(\\\\\\\"n2\\\\\\\",\\\\\\\"f2\\\\\\\",o),h(\\\\\\\"n3\\\\\\\",\\\\\\\"f3\\\\\\\",o),h(\\\\\\\"n4\\\\\\\",\\\\\\\"f4\\\\\\\",o),h(\\\\\\\"p\\\\\\\",\\\\\\\"n1\\\\\\\",a),h(\\\\\\\"p\\\\\\\",\\\\\\\"n2\\\\\\\",a),h(\\\\\\\"p\\\\\\\",\\\\\\\"n3\\\\\\\",a),h(\\\\\\\"p\\\\\\\",\\\\\\\"n4\\\\\\\",a),h(\\\\\\\"u1\\\\\\\",\\\\\\\"u2\\\\\\\",l),h(\\\\\\\"u2\\\\\\\",\\\\\\\"u3\\\\\\\",l),h(\\\\\\\"u3\\\\\\\",\\\\\\\"u1\\\\\\\",l),h(\\\\\\\"c\\\\\\\",\\\\\\\"t\\\\\\\",c),h(\\\\\\\"p\\\\\\\",\\\\\\\"c\\\\\\\",u),h(\\\\\\\"cn1\\\\\\\",\\\\\\\"cn2\\\\\\\",u),h(\\\\\\\"cn3\\\\\\\",\\\\\\\"cn4\\\\\\\",u),h(\\\\\\\"cf1\\\\\\\",\\\\\\\"cf2\\\\\\\",u),h(\\\\\\\"cf3\\\\\\\",\\\\\\\"cf4\\\\\\\",u),e.setAttribute(\\\\\\\"position\\\\\\\",new C.c(i,3)),e.setAttribute(\\\\\\\"color\\\\\\\",new C.c(r,3)),super(e,n),this.type=\\\\\\\"CameraHelper\\\\\\\",this.camera=t,this.camera.updateProjectionMatrix&&this.camera.updateProjectionMatrix(),this.matrixAutoUpdate=!1,this.pointMap=s,this.update()}update(){const t=this.geometry,e=this.pointMap;Hz.projectionMatrixInverse.copy(this.camera.projectionMatrixInverse),Wz(\\\\\\\"c\\\\\\\",e,t,Hz,0,0,-1),Wz(\\\\\\\"t\\\\\\\",e,t,Hz,0,0,1),Wz(\\\\\\\"n1\\\\\\\",e,t,Hz,-1,-1,-1),Wz(\\\\\\\"n2\\\\\\\",e,t,Hz,1,-1,-1),Wz(\\\\\\\"n3\\\\\\\",e,t,Hz,-1,1,-1),Wz(\\\\\\\"n4\\\\\\\",e,t,Hz,1,1,-1),Wz(\\\\\\\"f1\\\\\\\",e,t,Hz,-1,-1,1),Wz(\\\\\\\"f2\\\\\\\",e,t,Hz,1,-1,1),Wz(\\\\\\\"f3\\\\\\\",e,t,Hz,-1,1,1),Wz(\\\\\\\"f4\\\\\\\",e,t,Hz,1,1,1),Wz(\\\\\\\"u1\\\\\\\",e,t,Hz,.7,1.1,-1),Wz(\\\\\\\"u2\\\\\\\",e,t,Hz,-.7,1.1,-1),Wz(\\\\\\\"u3\\\\\\\",e,t,Hz,0,2,-1),Wz(\\\\\\\"cf1\\\\\\\",e,t,Hz,-1,0,1),Wz(\\\\\\\"cf2\\\\\\\",e,t,Hz,1,0,1),Wz(\\\\\\\"cf3\\\\\\\",e,t,Hz,0,-1,1),Wz(\\\\\\\"cf4\\\\\\\",e,t,Hz,0,1,1),Wz(\\\\\\\"cn1\\\\\\\",e,t,Hz,-1,0,-1),Wz(\\\\\\\"cn2\\\\\\\",e,t,Hz,1,0,-1),Wz(\\\\\\\"cn3\\\\\\\",e,t,Hz,0,-1,-1),Wz(\\\\\\\"cn4\\\\\\\",e,t,Hz,0,1,-1),t.getAttribute(\\\\\\\"position\\\\\\\").needsUpdate=!0}}function Wz(t,e,n,i,r,s,o){Vz.set(r,s,o).unproject(i);const a=e[t];if(void 0!==a){const t=n.getAttribute(\\\\\\\"position\\\\\\\");for(let e=0,n=a.length;e.9&&o<.1&&(e<.2&&(s[t+0]+=1),n<.2&&(s[t+2]+=1),i<.2&&(s[t+4]+=1))}}()}(),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(r,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new C.c(r.slice(),3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(s,2)),0===i?this.computeVertexNormals():this.normalizeNormals()}static fromJSON(t){return new Kz(t.vertices,t.indices,t.radius,t.details)}}class tU extends Kz{constructor(t=1,e=0){super([1,0,0,-1,0,0,0,1,0,0,-1,0,0,0,1,0,0,-1],[0,2,4,0,4,3,0,3,5,0,5,2,1,2,5,1,5,3,1,3,4,1,4,2],t,e),this.type=\\\\\\\"OctahedronGeometry\\\\\\\",this.parameters={radius:t,detail:e}}static fromJSON(t){return new tU(t.radius,t.detail)}}class eU extends Dz{constructor(){super(...arguments),this._geometry=new tU(1),this._quat=new au.a,this._default_position=new p.a(0,1,0),this._color1=new D.a,this._color2=new D.a}createObject(){return new k.a}buildHelper(){this._geometry.rotateZ(.5*Math.PI),this._material.vertexColors=!0;const t=this._geometry.getAttribute(\\\\\\\"position\\\\\\\"),e=new Float32Array(3*t.count);this._geometry.setAttribute(\\\\\\\"color\\\\\\\",new C.a(e,3)),this._object.geometry=this._geometry,this._object.material=this._material,this._object.matrixAutoUpdate=!1}update(){if(!this.node.pv.position)return;this._object.position.copy(this.node.pv.position).multiplyScalar(-1),this._quat.setFromUnitVectors(this._default_position,this.node.pv.position),this._object.setRotationFromQuaternion(this._quat),this._object.scale.setScalar(this.node.pv.helperSize),this._object.updateMatrix();const t=this._geometry.getAttribute(\\\\\\\"color\\\\\\\");this._color1.copy(this.node.light.color),this._color2.copy(this.node.light.groundColor);for(let e=0,n=t.count;e0)&&d.push(e,r,l),(t!==n-1||a0&&y(!0),e>0&&y(!1)),this.setIndex(c),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(u,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new C.c(h,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(_,2))}static fromJSON(t){return new pU(t.radiusTop,t.radiusBottom,t.height,t.radialSegments,t.heightSegments,t.openEnded,t.thetaStart,t.thetaLength)}}class _U extends pU{constructor(t=1,e=1,n=8,i=1,r=!1,s=0,o=2*Math.PI){super(0,t,e,n,i,r,s,o),this.type=\\\\\\\"ConeGeometry\\\\\\\",this.parameters={radius:t,height:e,radialSegments:n,heightSegments:i,openEnded:r,thetaStart:s,thetaLength:o}}static fromJSON(t){return new _U(t.radius,t.height,t.radialSegments,t.heightSegments,t.openEnded,t.thetaStart,t.thetaLength)}}class mU{constructor(t){this.node=t}update(){const t=this.node.pv;if(t.tvolumetric){const e=this.object(),n=this.node.light;dU.updateConeObject(e,{sizeMult:t.helperSize,distance:n.distance,angle:n.angle});const i=e.material.uniforms;i.lightColor.value.copy(n.color),i.attenuation.value=t.volAttenuation,i.anglePower.value=t.volAnglePower,this.node.light.add(e)}else this._mesh&&this.node.light.remove(this._mesh)}object(){return this._mesh=this._mesh||this._createMesh()}_createMesh(){const t=new _U(1,1,256,1);t.applyMatrix4((new A.a).makeTranslation(0,-.5,0)),t.applyMatrix4((new A.a).makeRotationX(-Math.PI/2));const e=this._createMaterial(),n=new k.a(t,e);return n.matrixAutoUpdate=!1,n.name=\\\\\\\"Volumetric\\\\\\\",e.uniforms.lightColor.value.set(\\\\\\\"white\\\\\\\"),n}_createMaterial(){return new F({uniforms:{attenuation:{value:5},anglePower:{value:1.2},lightColor:{value:new D.a(\\\\\\\"cyan\\\\\\\")}},vertexShader:\\\\\\\"varying vec3 vNormal;\\\\nvarying vec3 vWorldPosition;\\\\nvarying vec3 vWorldOrigin;\\\\n\\\\nvoid main(){\\\\n\\\\t// compute intensity\\\\n\\\\tvNormal\\\\t\\\\t= normalize( normalMatrix * normal );\\\\n\\\\n\\\\tvec4 worldPosition\\\\t= modelMatrix * vec4( position, 1.0 );\\\\n\\\\tvWorldPosition\\\\t\\\\t= worldPosition.xyz;\\\\n\\\\n\\\\tvec4 worldOrigin\\\\t= modelMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\tvWorldOrigin\\\\t\\\\t= worldOrigin.xyz;\\\\n\\\\n\\\\t// set gl_Position\\\\n\\\\tgl_Position\\\\t= projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n}\\\\\\\",fragmentShader:\\\\\\\"varying vec3 vNormal;\\\\nvarying vec3 vWorldPosition;\\\\nvarying vec3 vWorldOrigin;\\\\n\\\\nuniform vec3 lightColor;\\\\n\\\\n// uniform vec3 spotPosition;\\\\n\\\\nuniform float attenuation;\\\\nuniform float anglePower;\\\\n\\\\nvoid main(){\\\\n\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\t// distance attenuation //\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\tfloat intensity = distance(vWorldPosition, vWorldOrigin) / attenuation;\\\\n\\\\tintensity = 1.0 - clamp(intensity, 0.0, 1.0);\\\\n\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\t// intensity on angle //\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\tvec3 normal = vec3(vNormal.x, vNormal.y, abs(vNormal.z));\\\\n\\\\tfloat angleIntensity = pow( dot(normal, vec3(0.0, 0.0, 1.0)), anglePower );\\\\n\\\\tintensity = intensity * angleIntensity;\\\\n\\\\t// 'gl_FragColor = vec4( lightColor, intensity );\\\\n\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\t// final color //\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\n\\\\t// set the final color\\\\n\\\\tgl_FragColor = vec4( lightColor, intensity);\\\\n}\\\\\\\",transparent:!0,depthWrite:!1})}}class fU extends(Sz(aa)){constructor(){super(...arguments),this.light=oa.FOLDER(),this.color=oa.COLOR([1,1,1],{conversion:so.SRGB_TO_LINEAR}),this.intensity=oa.FLOAT(1),this.angle=oa.FLOAT(45,{range:[0,180]}),this.penumbra=oa.FLOAT(.1),this.decay=oa.FLOAT(.1,{range:[0,1]}),this.distance=oa.FLOAT(100,{range:[0,100]}),this.showHelper=oa.BOOLEAN(0),this.helperSize=oa.FLOAT(1,{visibleIf:{showHelper:1}}),this.shadow=oa.FOLDER(),this.castShadow=oa.BOOLEAN(1),this.shadowAutoUpdate=oa.BOOLEAN(1,{visibleIf:{castShadow:1}}),this.shadowUpdateOnNextRender=oa.BOOLEAN(0,{visibleIf:{castShadow:1,shadowAutoUpdate:0}}),this.shadowRes=oa.VECTOR2([256,256],{visibleIf:{castShadow:1}}),this.shadowBias=oa.FLOAT(.001,{visibleIf:{castShadow:1},range:[-.01,.01],rangeLocked:[!1,!1]}),this.shadowNear=oa.FLOAT(.1,{visibleIf:{castShadow:1},range:[0,100],rangeLocked:[!0,!1]}),this.shadowFar=oa.FLOAT(100,{visibleIf:{castShadow:1},range:[0,100],rangeLocked:[!0,!1]}),this.shadowRadius=oa.FLOAT(0,{visibleIf:{castShadow:1},range:[0,10],rangeLocked:[!0,!1]}),this.volumetric=oa.FOLDER(),this.tvolumetric=oa.BOOLEAN(0),this.volAttenuation=oa.FLOAT(5,{range:[0,10],rangeLocked:[!0,!1]}),this.volAnglePower=oa.FLOAT(10,{range:[0,20],rangeLocked:[!0,!1]})}}const gU=new fU;class vU extends Oz{constructor(){super(...arguments),this.paramsConfig=gU,this._helperController=new Rz(this,dU,\\\\\\\"SpotLightHelper\\\\\\\"),this._volumetricController=new mU(this)}static type(){return Xz.SPOT}initializeNode(){this._helperController.initializeNode()}createLight(){const t=new hU.a;return t.matrixAutoUpdate=!1,t.castShadow=!0,t.shadow.bias=-.001,t.shadow.mapSize.x=256,t.shadow.mapSize.y=256,t.shadow.camera.near=.1,this._target_target=t.target,this._target_target.name=\\\\\\\"SpotLight Default Target\\\\\\\",this._target_target.matrixAutoUpdate=!1,this.object.add(this._target_target),t}updateLightParams(){this.light.color=this.pv.color,this.light.intensity=this.pv.intensity,this.light.angle=this.pv.angle*(Math.PI/180),this.light.penumbra=this.pv.penumbra,this.light.decay=this.pv.decay,this.light.distance=this.pv.distance,this._helperController.update(),this._volumetricController.update()}updateShadowParams(){this.light.castShadow=this.pv.castShadow,this.light.shadow.autoUpdate=this.pv.shadowAutoUpdate,this.light.shadow.needsUpdate=this.pv.shadowUpdateOnNextRender,this.light.shadow.mapSize.copy(this.pv.shadowRes),this.light.shadow.camera.near=this.pv.shadowNear,this.light.shadow.camera.far=this.pv.shadowFar,this.light.shadow.bias=this.pv.shadowBias,this.light.shadow.radius=this.pv.shadowRadius}}let yU;const xU=function(){return void 0===yU&&(yU=new(window.AudioContext||window.webkitAudioContext)),yU},bU=new p.a,wU=new au.a,TU=new p.a,AU=new p.a;class EU extends Q.a{constructor(){super(),this.type=\\\\\\\"AudioListener\\\\\\\",this.context=xU(),this.gain=this.context.createGain(),this.gain.connect(this.context.destination),this.filter=null,this.timeDelta=0,this._clock=new Om}getInput(){return this.gain}removeFilter(){return null!==this.filter&&(this.gain.disconnect(this.filter),this.filter.disconnect(this.context.destination),this.gain.connect(this.context.destination),this.filter=null),this}getFilter(){return this.filter}setFilter(t){return null!==this.filter?(this.gain.disconnect(this.filter),this.filter.disconnect(this.context.destination)):this.gain.disconnect(this.context.destination),this.filter=t,this.gain.connect(this.filter),this.filter.connect(this.context.destination),this}getMasterVolume(){return this.gain.gain.value}setMasterVolume(t){return this.gain.gain.setTargetAtTime(t,this.context.currentTime,.01),this}updateMatrixWorld(t){super.updateMatrixWorld(t);const e=this.context.listener,n=this.up;if(this.timeDelta=this._clock.getDelta(),this.matrixWorld.decompose(bU,wU,TU),AU.set(0,0,-1).applyQuaternion(wU),e.positionX){const t=this.context.currentTime+this.timeDelta;e.positionX.linearRampToValueAtTime(bU.x,t),e.positionY.linearRampToValueAtTime(bU.y,t),e.positionZ.linearRampToValueAtTime(bU.z,t),e.forwardX.linearRampToValueAtTime(AU.x,t),e.forwardY.linearRampToValueAtTime(AU.y,t),e.forwardZ.linearRampToValueAtTime(AU.z,t),e.upX.linearRampToValueAtTime(n.x,t),e.upY.linearRampToValueAtTime(n.y,t),e.upZ.linearRampToValueAtTime(n.z,t)}else e.setPosition(bU.x,bU.y,bU.z),e.setOrientation(AU.x,AU.y,AU.z,n.x,n.y,n.z)}}class MU extends(Sz(aa)){}const SU=new MU;class CU extends _z{constructor(){super(...arguments),this.paramsConfig=SU,this.hierarchyController=new Lz(this),this.transformController=new Nz(this),this.flags=new Fi(this)}static type(){return Ng.AUDIO_LISTENER}createObject(){const t=new EU;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode()}cook(){this.transformController.update(),this.cookController.endCook()}}class NU extends Tr.a{constructor(t=1){const e=[0,0,0,t,0,0,0,0,0,0,t,0,0,0,0,0,0,t],n=new S.a;n.setAttribute(\\\\\\\"position\\\\\\\",new C.c(e,3)),n.setAttribute(\\\\\\\"color\\\\\\\",new C.c([1,0,0,1,.6,0,0,1,0,.6,1,0,0,0,1,0,.6,1],3));super(n,new wr.a({vertexColors:!0,toneMapped:!1})),this.type=\\\\\\\"AxesHelper\\\\\\\"}setColors(t,e,n){const i=new D.a,r=this.geometry.attributes.color.array;return i.set(t),i.toArray(r,0),i.toArray(r,3),i.set(e),i.toArray(r,6),i.toArray(r,9),i.set(n),i.toArray(r,12),i.toArray(r,15),this.geometry.attributes.color.needsUpdate=!0,this}dispose(){this.geometry.dispose(),this.material.dispose()}}var LU;!function(t){t.TOGETHER=\\\\\\\"translate + rotate together\\\\\\\",t.SEPARATELY=\\\\\\\"translate + rotate separately\\\\\\\"}(LU||(LU={}));const OU=[LU.TOGETHER,LU.SEPARATELY];const RU=new class extends aa{constructor(){super(...arguments),this.object0=oa.OPERATOR_PATH(\\\\\\\"/geo1\\\\\\\",{nodeSelection:{context:Ki.OBJ}}),this.object1=oa.OPERATOR_PATH(\\\\\\\"/geo2\\\\\\\",{nodeSelection:{context:Ki.OBJ}}),this.mode=oa.INTEGER(OU.indexOf(LU.TOGETHER),{menu:{entries:OU.map(((t,e)=>({name:t,value:e})))}}),this.blend=oa.FLOAT(0,{visibleIf:{mode:OU.indexOf(LU.TOGETHER)},range:[0,1],rangeLocked:[!1,!1]}),this.blendT=oa.FLOAT(0,{visibleIf:{mode:OU.indexOf(LU.SEPARATELY)},range:[0,1],rangeLocked:[!1,!1]}),this.blendR=oa.FLOAT(0,{visibleIf:{mode:OU.indexOf(LU.SEPARATELY)},range:[0,1],rangeLocked:[!1,!1]})}};class PU extends _z{constructor(){super(...arguments),this.paramsConfig=RU,this.hierarchyController=new Lz(this),this.flags=new Fi(this),this._helper=new NU(1),this._t0=new p.a,this._q0=new au.a,this._s0=new p.a,this._t1=new p.a,this._q1=new au.a,this._s1=new p.a}static type(){return\\\\\\\"blend\\\\\\\"}createObject(){const t=new In.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.io.inputs.setCount(0),this.addPostDirtyHook(\\\\\\\"blend_on_dirty\\\\\\\",(()=>{this.cookController.cookMainWithoutInputs()})),this._updateHelperHierarchy(),this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this.flags.display.active()?this.object.add(this._helper):this.object.remove(this._helper)}cook(){const t=this.p.object0.found_node_with_context(Ki.OBJ),e=this.p.object1.found_node_with_context(Ki.OBJ);t&&e&&this._blend(t.object,e.object),this.cookController.endCook()}_blend(t,e){const n=OU[this.pv.mode];switch(n){case LU.TOGETHER:return this._blend_together(t,e);case LU.SEPARATELY:return this._blend_separately(t,e)}ar.unreachable(n)}_blend_together(t,e){this._decompose_matrices(t,e),this._object.position.copy(this._t0).lerp(this._t1,this.pv.blend),this._object.quaternion.copy(this._q0).slerp(this._q1,this.pv.blend),this._object.matrixAutoUpdate||this._object.updateMatrix()}_blend_separately(t,e){this._decompose_matrices(t,e),this._object.position.copy(this._t0).lerp(this._t1,this.pv.blendT),this._object.quaternion.copy(this._q0).slerp(this._q1,this.pv.blendR),this._object.matrixAutoUpdate||this._object.updateMatrix()}_decompose_matrices(t,e){t.matrixWorld.decompose(this._t0,this._q0,this._s0),e.matrixWorld.decompose(this._t1,this._q1,this._s1)}}var IU={uniforms:{tDiffuse:{value:null},h:{value:1/512}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float h;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 sum = vec4( 0.0 );\\\\n\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 4.0 * h, vUv.y ) ) * 0.051;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 3.0 * h, vUv.y ) ) * 0.0918;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 2.0 * h, vUv.y ) ) * 0.12245;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 1.0 * h, vUv.y ) ) * 0.1531;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y ) ) * 0.1633;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 1.0 * h, vUv.y ) ) * 0.1531;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 2.0 * h, vUv.y ) ) * 0.12245;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 3.0 * h, vUv.y ) ) * 0.0918;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 4.0 * h, vUv.y ) ) * 0.051;\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = sum;\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const FU={uniforms:{tDiffuse:{value:null},v:{value:1/512}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float v;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 sum = vec4( 0.0 );\\\\n\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 4.0 * v ) ) * 0.051;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 3.0 * v ) ) * 0.0918;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 2.0 * v ) ) * 0.12245;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 1.0 * v ) ) * 0.1531;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y ) ) * 0.1633;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 1.0 * v ) ) * 0.1531;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 2.0 * v ) ) * 0.12245;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 3.0 * v ) ) * 0.0918;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 4.0 * v ) ) * 0.051;\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = sum;\\\\n\\\\n\\\\t\\\\t}\\\\\\\"},DU=1/256e3;class kU{constructor(t){this._renderTargetBlur=this._createRenderTarget(t),this._camera=this._createCamera(),this._blurPlane=this._createBlurPlane(),this._horizontalBlurMaterial=new F(IU),this._horizontalBlurMaterial.depthTest=!1,this._verticalBlurMaterial=new F(FU),this._verticalBlurMaterial.depthTest=!1}setSize(t,e){this._renderTargetBlur.setSize(t,e)}_createRenderTarget(t){const e=new Z(t.x,t.y);return e.texture.generateMipmaps=!1,e}_createCamera(){const t=new st.a(-.5,.5,.5,-.5,0,1);return t.position.z=.5,t}_createBlurPlane(){const t=new L(1,1);return new k.a(t)}applyBlur(t,e,n,i){const r=Math.max(this._renderTargetBlur.width,this._renderTargetBlur.height);this._horizontalBlurMaterial.uniforms.tDiffuse.value=t.texture,this._horizontalBlurMaterial.uniforms.h.value=n*r*DU,this._blurPlane.material=this._horizontalBlurMaterial,e.setRenderTarget(this._renderTargetBlur),e.render(this._blurPlane,this._camera),this._verticalBlurMaterial.uniforms.tDiffuse.value=this._renderTargetBlur.texture,this._verticalBlurMaterial.uniforms.v.value=i*r*DU,this._blurPlane.material=this._verticalBlurMaterial,e.setRenderTarget(t),e.render(this._blurPlane,this._camera)}}var BU;!function(t){t.ON_RENDER=\\\\\\\"On Every Render\\\\\\\",t.MANUAL=\\\\\\\"Manual\\\\\\\"}(BU||(BU={}));const zU=[BU.ON_RENDER,BU.MANUAL];class UU extends(Sz(aa)){constructor(){super(...arguments),this.shadow=oa.FOLDER(),this.dist=oa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]}),this.planeSize=oa.VECTOR2([1,1]),this.shadowRes=oa.VECTOR2([256,256]),this.blur=oa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]}),this.tblur2=oa.BOOLEAN(1),this.blur2=oa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1],visibleIf:{tblur2:1}}),this.darkness=oa.FLOAT(1),this.opacity=oa.FLOAT(1),this.showHelper=oa.BOOLEAN(0),this.updateMode=oa.INTEGER(zU.indexOf(BU.ON_RENDER),{callback:t=>{HU.PARAM_CALLBACK_update_updateMode(t)},menu:{entries:zU.map(((t,e)=>({name:t,value:e})))}}),this.update=oa.BUTTON(null,{callback:t=>{HU.PARAM_CALLBACK_updateManual(t)},visibleIf:{updateMode:zU.indexOf(BU.MANUAL)}}),this.scene=oa.FOLDER(),this.include=oa.STRING(\\\\\\\"\\\\\\\"),this.exclude=oa.STRING(\\\\\\\"\\\\\\\"),this.updateObjectsList=oa.BUTTON(null,{callback:t=>{HU.PARAM_CALLBACK_updateObjectsList(t)}}),this.printResolveObjectsList=oa.BUTTON(null,{callback:t=>{HU.PARAM_CALLBACK_printResolveObjectsList(t)}})}}const GU=new UU,VU=new d.a(256,256);class HU extends _z{constructor(){super(...arguments),this.paramsConfig=GU,this.hierarchyController=new Lz(this),this.flags=new Fi(this),this._renderTarget=this._createRenderTarget(VU),this._coreRenderBlur=this._createCoreRenderBlur(VU),this._includedObjects=[],this._includedAncestors=[],this._excludedObjects=[],this.transformController=new Nz(this),this._darknessUniform={value:1},this._emptyOnBeforeRender=()=>{},this._emptyRenderHook=()=>{},this._on_object_before_render_bound=this._update.bind(this),this._initialVisibilityState=new WeakMap}static type(){return\\\\\\\"contactShadow\\\\\\\"}_createRenderTarget(t){const e=new Z(t.x,t.y);return e.texture.generateMipmaps=!1,e}_createCoreRenderBlur(t){return new kU(t)}createObject(){const t=new In.a;this._shadowGroup=new In.a,t.add(this._shadowGroup),this._shadowGroup.name=\\\\\\\"shadowGroup\\\\\\\";const e=new L(1,1).rotateX(-Math.PI/2),n=e.getAttribute(\\\\\\\"uv\\\\\\\").array;for(let t of[1,3,5,7])n[t]=1-n[t];return this._planeMaterial=new at.a({map:this._renderTarget.texture,opacity:1,transparent:!0,depthWrite:!1}),this._plane=new k.a(e,this._planeMaterial),this._plane.renderOrder=1,this._plane.matrixAutoUpdate=!1,this._shadowGroup.add(this._plane),this._createDepthCamera(this._shadowGroup),this._createMaterials(),t}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this._updateShadowGroupVisibility(),this._updateHelperVisibility(),this.flags.display.onUpdate((()=>{this._updateShadowGroupVisibility(),this._updateHelperVisibility()}))}async cook(){this.transformController.update(),this._updateRenderHook(),this._updateHelperVisibility(),this._updateObjectsList(),this._planeMaterial&&(this._planeMaterial.opacity=this.pv.opacity),this._darknessUniform.value=this.pv.darkness,this._plane&&this._shadowCamera&&this._helper&&(this._plane.scale.x=this.pv.planeSize.x,this._plane.scale.z=this.pv.planeSize.y,this._plane.updateMatrix(),this._shadowCamera.left=-this.pv.planeSize.x/2,this._shadowCamera.right=this.pv.planeSize.x/2,this._shadowCamera.bottom=-this.pv.planeSize.y/2,this._shadowCamera.top=this.pv.planeSize.y/2,this._shadowCamera.far=this.pv.dist,this._shadowCamera.updateProjectionMatrix(),this._helper.update()),this._renderTarget.width==this.pv.shadowRes.x&&this._renderTarget.height==this.pv.shadowRes.y||this._planeMaterial&&(this._renderTarget=this._createRenderTarget(this.pv.shadowRes),this._coreRenderBlur=this._createCoreRenderBlur(this.pv.shadowRes),this._planeMaterial.map=this._renderTarget.texture),this.cookController.endCook()}_createDepthCamera(t){this._shadowCamera=new st.a(-.5,.5,.5,-.5,0,1),this._shadowCamera.rotation.x=Math.PI/2,t.add(this._shadowCamera),this._helper=new jz(this._shadowCamera),this._helper.visible=!1,this._shadowCamera.add(this._helper)}_createMaterials(){this._depthMaterial=new Mn,this._depthMaterial.onBeforeCompile=t=>{t.uniforms.darkness=this._darknessUniform,t.fragmentShader=`\\\\n\\\\t\\\\t\\\\tuniform float darkness;\\\\n\\\\t\\\\t\\\\t${t.fragmentShader.replace(\\\\\\\"gl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\\\\\\\",\\\\\\\"gl_FragColor = vec4( vec3( 0.0 ), ( 1.0 - fragCoordZ ) * darkness );\\\\\\\")}\\\\n\\\\t\\\\t`},this._depthMaterial.depthTest=!1,this._depthMaterial.depthWrite=!1}_renderShadow(t,e){if(!this._helper)return;if(!this._depthMaterial)return;if(!this._shadowCamera)return;if(!this._helper)return;if(!this._plane)return;const n=this._plane.onBeforeRender,i=e.background,r=this._helper.visible;e.background=null,this._plane.onBeforeRender=this._emptyOnBeforeRender,this._helper.visible=!1,e.overrideMaterial=this._depthMaterial,this._initVisibility(e),t.setRenderTarget(this._renderTarget),t.render(e,this._shadowCamera),this._coreRenderBlur.applyBlur(this._renderTarget,t,this.pv.blur,this.pv.blur),this.pv.tblur2&&this._coreRenderBlur.applyBlur(this._renderTarget,t,this.pv.blur2,this.pv.blur2),this._restoreVisibility(e),e.overrideMaterial=null,this._helper.visible=r,t.setRenderTarget(null),e.background=i,this._plane.onBeforeRender=n}_updateShadowGroupVisibility(){this._shadowGroup&&(this.flags.display.active()?this._shadowGroup.visible=!0:this._shadowGroup.visible=!1)}_updateHelperVisibility(){this._helper&&(this.flags.display.active()&&this.pv.showHelper?this._helper.visible=!0:this._helper.visible=!1)}_updateRenderHook(){const t=zU[this.pv.updateMode];switch(t){case BU.ON_RENDER:return this._addRenderHook();case BU.MANUAL:return this._removeRenderHook()}ar.unreachable(t)}_addRenderHook(){this._plane&&this._plane.onBeforeRender!=this._on_object_before_render_bound&&(this._plane.onBeforeRender=this._on_object_before_render_bound)}_removeRenderHook(){this._plane&&this._plane.onBeforeRender!=this._emptyRenderHook&&(this._plane.onBeforeRender=this._emptyRenderHook)}_update(t,e,n,i,r,s){t&&e?this._renderShadow(t,e):console.log(\\\\\\\"no renderer or scene\\\\\\\")}_updateManual(){const t=ai.renderersController.firstRenderer();if(!t)return void console.log(\\\\\\\"no renderer found\\\\\\\");const e=this.scene().threejsScene();this._renderShadow(t,e)}static PARAM_CALLBACK_update_updateMode(t){t._updateRenderHook()}static PARAM_CALLBACK_updateManual(t){t._updateManual()}static PARAM_CALLBACK_updateObjectsList(t){t._updateObjectsList()}_updateObjectsList(){\\\\\\\"\\\\\\\"!=this.pv.include?this._includedObjects=this.scene().objectsByMask(this.pv.include):this._includedObjects=[];const t=new Map;for(let e of this._includedObjects)e.traverseAncestors((e=>{t.set(e.uuid,e)}));this._includedAncestors=[],t.forEach(((t,e)=>{this._includedAncestors.push(t)})),\\\\\\\"\\\\\\\"!=this.pv.exclude?this._excludedObjects=this.scene().objectsByMask(this.pv.exclude):this._excludedObjects=[]}static PARAM_CALLBACK_printResolveObjectsList(t){t._printResolveObjectsList()}_printResolveObjectsList(){console.log(\\\\\\\"included objects:\\\\\\\"),console.log(this._includedObjects),console.log(\\\\\\\"included parents:\\\\\\\"),console.log(this._includedAncestors),console.log(\\\\\\\"excluded objects:\\\\\\\"),console.log(this._excludedObjects)}_initVisibility(t){this._includedObjects.length>0?t.traverse((t=>{this._initialVisibilityState.set(t,t.visible),t.visible=!1})):(this._storeObjectsVisibility(this._includedObjects),this._storeObjectsVisibility(this._includedAncestors),this._storeObjectsVisibility(this._excludedObjects)),this._setObjectsVisibility(this._includedObjects,!0),this._setObjectsVisibility(this._includedAncestors,!0),this._setObjectsVisibility(this._excludedObjects,!1)}_storeObjectsVisibility(t){for(let e of t)this._initialVisibilityState.set(e,e.visible)}_setObjectsVisibility(t,e){for(let n of t)n.visible=e}_restoreVisibility(t){this._includedObjects.length>0?t.traverse((t=>{const e=this._initialVisibilityState.get(t);e&&(t.visible=e)})):(this._restoreObjectsVisibility(this._includedObjects),this._restoreObjectsVisibility(this._includedAncestors),this._restoreObjectsVisibility(this._excludedObjects))}_restoreObjectsVisibility(t){for(let e of t){const t=this._initialVisibilityState.get(e);t&&(e.visible=t)}}}const jU=\\\\\\\"display\\\\\\\";class WU{constructor(t){this.node=t,this._children_uuids_dict=new Map,this._children_length=0,this._sop_group=this._create_sop_group()}_create_sop_group(){const t=new In.a;return t.matrixAutoUpdate=!1,t}sopGroup(){return this._sop_group}set_sop_group_name(){this._sop_group.name=`${this.node.name()}:sop_group`}displayNodeControllerCallbacks(){return{onDisplayNodeRemove:()=>{this.remove_children()},onDisplayNodeSet:()=>{setTimeout((()=>{this.request_display_node_container()}),0)},onDisplayNodeUpdate:()=>{this.request_display_node_container()}}}initializeNode(){var t;this.node.object.add(this.sopGroup()),this.node.nameController.add_post_set_fullPath_hook(this.set_sop_group_name.bind(this)),this._create_sop_group();const e=null===(t=this.node.flags)||void 0===t?void 0:t.display;e&&e.onUpdate((()=>{this._updateSopGroupHierarchy(),e.active()&&this.request_display_node_container()}))}_updateSopGroupHierarchy(){var t;if(null===(t=this.node.flags)||void 0===t?void 0:t.display){const t=this.sopGroup();this.usedInScene()?(t.visible=!0,this.node.object.add(t),t.updateMatrix()):(t.visible=!1,this.node.object.remove(t))}}usedInScene(){var t,e;const n=this.node.params.has(jU),i=this.node.params.boolean(jU),r=this.node.usedInScene(),s=(null===(e=null===(t=this.node.flags)||void 0===t?void 0:t.display)||void 0===e?void 0:e.active())||!1;return r&&s&&(!n||i)}async request_display_node_container(){this.node.scene().loadingController.loaded()&&this.usedInScene()&&await this._set_content_under_sop_group()}remove_children(){if(0==this._sop_group.children.length)return;let t;for(;t=this._sop_group.children[0];)this._sop_group.remove(t);this._children_uuids_dict.clear(),this._children_length=0}async _set_content_under_sop_group(){var t;const e=this.node.displayNodeController.displayNode();if(e&&(null===(t=e.parent())||void 0===t?void 0:t.graphNodeId())==this.node.graphNodeId()){const t=(await e.compute()).coreContent();if(t){const e=t.objects();let n=e.length!=this._children_length;if(!n)for(let t of e)this._children_uuids_dict.get(t.uuid)||(n=!0);if(n){this.remove_children();for(let t of e)this._sop_group.add(t),t.updateMatrix(),this._children_uuids_dict.set(t.uuid,!0);this._children_length=e.length}return}}this.remove_children()}}class qU extends(Sz(aa)){constructor(){super(...arguments),this.display=oa.BOOLEAN(1),this.renderOrder=oa.INTEGER(0,{range:[0,10],rangeLocked:[!0,!1]})}}const XU=new qU;class YU extends _z{constructor(){super(...arguments),this.paramsConfig=XU,this.hierarchyController=new Lz(this),this.transformController=new Nz(this),this.flags=new Fi(this),this.childrenDisplayController=new WU(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.SOP,this._onChildAddBound=this._onChildAdd.bind(this)}static type(){return Ng.GEO}createObject(){const t=new In.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.lifecycle.add_on_child_add_hook(this._onChildAddBound),this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this.childrenDisplayController.initializeNode()}isDisplayNodeCooking(){if(this.flags.display.active()){const t=this.displayNodeController.displayNode();return!!t&&t.isDirty()}return!1}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}_onChildAdd(t){var e,n;this.scene().loadingController.loaded()&&1==this.children().length&&(null===(n=null===(e=t.flags)||void 0===e?void 0:e.display)||void 0===n||n.set(!0))}cook(){this.transformController.update(),this.object.visible=this.pv.display,this.object.renderOrder=this.pv.renderOrder,this.cookController.endCook()}}class $U extends(Sz(aa)){}const JU=new $U;class ZU extends _z{constructor(){super(...arguments),this.paramsConfig=JU,this.hierarchyController=new Lz(this),this.transformController=new Nz(this),this.flags=new Fi(this),this._helper=new NU(1)}static type(){return\\\\\\\"null\\\\\\\"}createObject(){const t=new In.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this._updateHelperHierarchy(),this._helper.matrixAutoUpdate=!1,this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this.flags.display.active()?(this.object.add(this._helper),this._helper.updateMatrix()):this.object.remove(this._helper)}cook(){this.transformController.update(),this.cookController.endCook()}}const QU=new class extends aa{constructor(){super(...arguments),this.center=oa.VECTOR3([0,0,0]),this.longitude=oa.FLOAT(0,{range:[0,360]}),this.latitude=oa.FLOAT(0,{range:[-180,180]}),this.depth=oa.FLOAT(1,{range:[0,10]})}},KU=\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",tG=new p.a(0,1,0),eG=new p.a(-1,0,0);class nG extends _z{constructor(){super(...arguments),this.paramsConfig=QU,this.hierarchyController=new Lz(this),this.flags=new Fi(this),this._helper=new NU(1),this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this),this._centerMatrix=new A.a,this._longitudeMatrix=new A.a,this._latitudeMatrix=new A.a,this._depthMatrix=new A.a,this._fullMatrix=new A.a,this._decomposed={t:new p.a,q:new au.a,s:new p.a}}static type(){return\\\\\\\"polarTransform\\\\\\\"}createObject(){const t=new In.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.dirtyController.hasHook(KU)||this.dirtyController.addPostDirtyHook(KU,this._cook_main_without_inputs_when_dirty_bound),this._updateHelperHierarchy(),this._helper.matrixAutoUpdate=!1,this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this.flags.display.active()?(this.object.add(this._helper),this._helper.updateMatrix()):this.object.remove(this._helper)}async _cook_main_without_inputs_when_dirty(){await this.cookController.cookMainWithoutInputs()}cook(){const t=this.object;this._centerMatrix.identity(),this._longitudeMatrix.identity(),this._latitudeMatrix.identity(),this._depthMatrix.identity(),this._centerMatrix.makeTranslation(this.pv.center.x,this.pv.center.y,this.pv.center.z),this._longitudeMatrix.makeRotationAxis(tG,Object(Ln.e)(this.pv.longitude)),this._latitudeMatrix.makeRotationAxis(eG,Object(Ln.e)(this.pv.latitude)),this._depthMatrix.makeTranslation(0,0,this.pv.depth),this._fullMatrix.copy(this._centerMatrix).multiply(this._longitudeMatrix).multiply(this._latitudeMatrix).multiply(this._depthMatrix),this._fullMatrix.decompose(this._decomposed.t,this._decomposed.q,this._decomposed.s),t.position.copy(this._decomposed.t),t.quaternion.copy(this._decomposed.q),t.scale.copy(this._decomposed.s),t.updateMatrix(),this.cookController.endCook()}}class iG{constructor(t){this._scene=t,this._data={}}data(t){this._scene.nodesController.reset_node_context_signatures();const e=hG.dispatch_node(this._scene.root()),n=e.data(),i=e.ui_data();return this._data={properties:{frame:this._scene.frame()||Ml.START_FRAME,maxFrame:this._scene.maxFrame(),maxFrameLocked:this._scene.timeController.maxFrameLocked(),realtimeState:this._scene.timeController.realtimeState(),mainCameraNodePath:this._scene.camerasController.mainCameraNodePath(),versions:t},root:n,ui:i},this._data}static sanitize_string(t){return t=t.replace(/'/g,\\\\\\\"'\\\\\\\"),t=sr.escapeLineBreaks(t)}}class rG{constructor(t){this._node=t}data(t={}){var e,n,i,r,s,o,a;this.is_root()||this._node.scene().nodesController.register_node_context_signature(this._node),this._data={type:this._node.type()};const l=this.nodes_data(t);Object.keys(l).length>0&&(this._data.nodes=l);const c=this.params_data();if(Object.keys(c).length>0&&(this._data.params=c),!this.is_root()){this._node.io.inputs.maxInputsCountOverriden()&&(this._data.maxInputsCount=this._node.io.inputs.maxInputsCount());const t=this.inputs_data();t.length>0&&(this._data.inputs=t);const e=this.connection_points_data();e&&(this._data.connection_points=e)}if(this._node.flags){const t={};(this._node.flags.hasBypass()||this._node.flags.hasDisplay()||this._node.flags.hasOptimize())&&(this._node.flags.hasBypass()&&(null===(e=this._node.flags.bypass)||void 0===e?void 0:e.active())&&(t.bypass=this._node.flags.bypass.active()),this._node.flags.hasDisplay()&&(!(null===(n=this._node.flags.display)||void 0===n?void 0:n.active())&&(null===(i=this._node.parent())||void 0===i?void 0:i.displayNodeController)||(t.display=null===(r=this._node.flags.display)||void 0===r?void 0:r.active())),this._node.flags.hasOptimize()&&(null===(s=this._node.flags.optimize)||void 0===s?void 0:s.active())&&(t.optimize=null===(o=this._node.flags.optimize)||void 0===o?void 0:o.active())),Object.keys(t).length>0&&(this._data.flags=t)}if(this._node.childrenAllowed()){const t=null===(a=this._node.childrenController)||void 0===a?void 0:a.selection;if(t&&this._node.children().length>0){const e=[],n={};for(let e of t.nodes())n[e.graphNodeId()]=!0;for(let t of this._node.children())t.graphNodeId()in n&&e.push(t);const i=e.map((t=>t.name()));i.length>0&&(this._data.selection=i)}}if(this._node.io.inputs.overrideClonedStateAllowed()){const t=this._node.io.inputs.clonedStateOverriden();t&&(this._data.cloned_state_overriden=t)}if(this._node.persisted_config){const t=this._node.persisted_config.toJSON();t&&(this._data.persisted_config=t)}return this.add_custom(),this._data}ui_data(t={}){const e=this.ui_data_without_children(),n=this._node.children();return n.length>0&&(e.nodes={},n.forEach((n=>{const i=hG.dispatch_node(n);e.nodes[n.name()]=i.ui_data(t)}))),e}ui_data_without_children(){const t={};if(!this.is_root()){const e=this._node.uiData;t.pos=e.position().toArray();const n=e.comment();n&&(t.comment=iG.sanitize_string(n))}return t}is_root(){return null===this._node.parent()&&this._node.graphNodeId()==this._node.root().graphNodeId()}inputs_data(){const t=[];return this._node.io.inputs.inputs().forEach(((e,n)=>{var i;if(e){const r=this._node.io.connections.inputConnection(n);if(this._node.io.inputs.hasNamedInputs()){const s=r.output_index,o=null===(i=e.io.outputs.namedOutputConnectionPoints()[s])||void 0===i?void 0:i.name();o&&(t[n]={index:n,node:e.name(),output:o})}else t[n]=e.name()}})),t}connection_points_data(){if(this._node.io.has_connection_points_controller&&this._node.io.connection_points.initialized()&&(this._node.io.inputs.hasNamedInputs()||this._node.io.outputs.hasNamedOutputs())){const t={};if(this._node.io.inputs.hasNamedInputs()){t.in=[];for(let e of this._node.io.inputs.namedInputConnectionPoints())e&&t.in.push(e.toJSON())}if(this._node.io.outputs.hasNamedOutputs()){t.out=[];for(let e of this._node.io.outputs.namedOutputConnectionPoints())e&&t.out.push(e.toJSON())}return t}}params_data(){const t={};for(let e of this._node.params.names){const n=this._node.params.get(e);if(n&&!n.parent_param){const e=hG.dispatch_param(n);if(e.required()){const i=e.data();t[n.name()]=i}}}return t}nodes_data(t={}){const e={};for(let n of this._node.children()){const i=hG.dispatch_node(n);e[n.name()]=i.data(t)}return e}add_custom(){}}class sG{constructor(t){this._param=t,this._complex_data={}}required(){const t=this._param.options.isSpare()&&!this._param.parent_param,e=!this._param.isDefault();return t||e||this._param.options.hasOptionsOverridden()}data(){if(this._param.parent_param)throw console.warn(\\\\\\\"no component should be saved\\\\\\\"),\\\\\\\"no component should be saved\\\\\\\";return this._require_data_complex()?this._data_complex():this._data_simple()}_data_simple(){return this._param.rawInputSerialized()}_data_complex(){if(this._complex_data={},this._param.options.isSpare()&&!this._param.parent_param&&(this._complex_data.type=this._param.type(),this._complex_data.default_value=this._param.defaultValueSerialized(),this._complex_data.options=this._param.options.current()),this._param.isDefault()||(this._complex_data.raw_input=this._param.rawInputSerialized()),this._param.options.hasOptionsOverridden()){const t={},e=this._param.options.overriddenOptions();for(let n of Object.keys(e)){const i=e[n];m.isString(i)||m.isNumber(i)?t[n]=i:t[n]=JSON.stringify(i)}this._complex_data.overriden_options=t}return this._complex_data}_require_data_complex(){return!!this._param.options.isSpare()||!!this._param.options.hasOptionsOverridden()}add_main(){}}class oG extends sG{add_main(){if(!this._require_data_complex())return this._param.rawInputSerialized();this._complex_data.raw_input=this._param.rawInputSerialized()}}class aG extends sG{add_main(){let t=this._param.rawInput();if(t=iG.sanitize_string(t),!this._require_data_complex())return t;this._complex_data.raw_input=t}}class lG extends sG{add_main(){let t=this._param.rawInput();if(t=iG.sanitize_string(t),!this._require_data_complex())return t;this._complex_data.raw_input=t}}class cG extends sG{add_main(){if(!this._require_data_complex())return this._param.rawInputSerialized();this._complex_data.raw_input=this._param.rawInputSerialized()}}class uG extends rG{nodes_data(t={}){return t.showPolyNodesData?super.nodes_data(t):{}}ui_data(t={}){return t.showPolyNodesData?super.ui_data(t):this.ui_data_without_children()}}class hG{static dispatch_node(t){return t.polyNodeController?new uG(t):new rG(t)}static dispatch_param(t){return t instanceof no?new oG(t):t instanceof po?new aG(t):t instanceof bo?new lG(t):t instanceof xo?new cG(t):new sG(t)}}class dG{constructor(){this._objects=[],this._objects_with_geo=[],this.touch()}timestamp(){return this._timestamp}touch(){const t=ai.performance.performanceManager();this._timestamp=t.now(),this.reset()}reset(){this._bounding_box=void 0,this._core_geometries=void 0,this._core_objects=void 0}clone(){const t=new dG;if(this._objects){const e=[];for(let t of this._objects)e.push(vs.clone(t));t.setObjects(e)}return t}setObjects(t){this._objects=t,this._objects_with_geo=t.filter((t=>null!=t.geometry)),this.touch()}objects(){return this._objects}objectsWithGeo(){return this._objects_with_geo}coreObjects(){return this._core_objects=this._core_objects||this._create_core_objects()}_create_core_objects(){return this._objects?this._objects.map(((t,e)=>new vs(t,e))):[]}objectsData(){return this._objects?this._objects.map((t=>this._objectData(t))):[]}_objectData(t){let e=0;return t.geometry&&(e=ps.pointsCount(t.geometry)),{type:Nr(t.constructor),name:t.name,children_count:t.children.length,points_count:e}}geometries(){const t=[];for(let e of this.coreObjects()){const n=e.object().geometry;n&&t.push(n)}return t}coreGeometries(){return this._core_geometries=this._core_geometries||this._createCoreGeometries()}_createCoreGeometries(){const t=[];for(let e of this.geometries())t.push(new ps(e));return t}static geometryFromObject(t){return t.isMesh||t.isLine||t.isPoints?t.geometry:null}faces(){const t=[];for(let e of this.objectsWithGeo())if(e.geometry){const n=new ps(e.geometry).faces();for(let i of n)i.applyMatrix4(e.matrix),t.push(i)}return t}points(){return this.coreGeometries().map((t=>t.points())).flat()}pointsCount(){return f.sum(this.coreGeometries().map((t=>t.pointsCount())))}totalPointsCount(){if(this._objects){let t=0;for(let e of this._objects)e.traverse((e=>{const n=e.geometry;n&&(t+=ps.pointsCount(n))}));return t}return 0}pointsFromGroup(t){if(t){const e=sr.indices(t),n=this.points();return f.compact(e.map((t=>n[t])))}return this.points()}static _fromObjects(t){const e=new dG;return e.setObjects(t),e}objectsFromGroup(t){return this.coreObjectsFromGroup(t).map((t=>t.object()))}coreObjectsFromGroup(t){if(\\\\\\\"\\\\\\\"!==(t=t.trim())){const e=parseInt(t);return m.isNaN(e)?this.coreObjects().filter((e=>sr.matchMask(t,e.name()))):f.compact([this.coreObjects()[e]])}return this.coreObjects()}boundingBox(){return this._bounding_box=this._bounding_box||this._compute_bounding_box()}center(){const t=new p.a;return this.boundingBox().getCenter(t),t}size(){const t=new p.a;return this.boundingBox().getSize(t),t}_compute_bounding_box(){let t;if(this._objects)for(let e of this._objects){const n=e.geometry;n&&(n.computeBoundingBox(),t?t.expandByObject(e):n.boundingBox&&(t=n.boundingBox.clone()))}return t=t||new XB.a(new p.a(-1,-1,-1),new p.a(1,1,1)),t}computeVertexNormals(){for(let t of this.coreObjects())t.computeVertexNormals()}hasAttrib(t){let e;return null!=(e=this.coreGeometries()[0])&&e.hasAttrib(t)}attribType(t){const e=this.coreGeometries()[0];return null!=e?e.attribType(t):null}objectAttribType(t){const e=this.coreObjects()[0];return null!=e?e.attribType(t):null}renameAttrib(t,e,n){switch(n){case Vr.ATTRIB_CLASS.VERTEX:if(this.hasAttrib(t)&&this._objects)for(let n of this._objects)n.traverse((n=>{const i=dG.geometryFromObject(n);if(i){new ps(i).renameAttrib(t,e)}}));break;case Vr.ATTRIB_CLASS.OBJECT:if(this.hasAttrib(t)&&this._objects)for(let n of this._objects)n.traverse((n=>{new vs(n,0).renameAttrib(t,e)}))}}attribNames(){let t;return null!=(t=this.coreGeometries()[0])?t.attribNames():[]}objectAttribNames(){let t;return null!=(t=this.coreObjects()[0])?t.attribNames():[]}attribNamesMatchingMask(t){const e=sr.attribNames(t),n=[];for(let t of this.attribNames())for(let i of e)if(sr.matchMask(t,i))n.push(t);else{t==Wr.remapName(i)&&n.push(t)}return f.uniq(n)}attribSizes(){let t;return null!=(t=this.coreGeometries()[0])?t.attribSizes():{}}objectAttribSizes(){let t;return null!=(t=this.coreObjects()[0])?t.attribSizes():{}}attribSize(t){let e;return null!=(e=this.coreGeometries()[0])?e.attribSize(t):0}addNumericVertexAttrib(t,e,n){null==n&&(n=Wr.default_value(e));for(let i of this.coreGeometries())i.addNumericAttrib(t,e,n)}static clone(t){const e=new In.a;return t.children.forEach((t=>{const n=vs.clone(t);e.add(n)})),e}}class pG extends Fl{static context(){return Ki.SOP}cook(t,e){}createCoreGroupFromObjects(t){const e=new dG;return e.setObjects(t),e}createCoreGroupFromGeometry(t,e=Sr.MESH){const n=pG.createObject(t,e);return this.createCoreGroupFromObjects([n])}createObject(t,e,n){return pG.createObject(t,e,n)}static createObject(t,e,n){this.createIndexIfNone(t);const i=new(0,Cr[e])(t,n=n||Vr.MATERIALS[e].clone());return i.castShadow=!0,i.receiveShadow=!0,i.frustumCulled=!1,i.matrixAutoUpdate=!1,i}createIndexIfNone(t){pG.createIndexIfNone(t)}static createIndexIfNone(t){hs.createIndexIfNone(t)}}var _G;!function(t){t.FROM_SET_CORE_GROUP=\\\\\\\"from set_core_group\\\\\\\",t.FROM_SET_GROUP=\\\\\\\"from set_group\\\\\\\",t.FROM_SET_OBJECTS=\\\\\\\"from set_objects\\\\\\\",t.FROM_SET_OBJECT=\\\\\\\"from set_object\\\\\\\",t.FROM_SET_GEOMETRIES=\\\\\\\"from set_geometries\\\\\\\",t.FROM_SET_GEOMETRY=\\\\\\\"from set_geometry\\\\\\\"}(_G||(_G={}));const mG=\\\\\\\"input geometry\\\\\\\",fG=[mG,mG,mG,mG];class gG extends ia{constructor(){super(...arguments),this.flags=new zi(this)}static context(){return Ki.SOP}static displayedInputNames(){return fG}initializeBaseNode(){this.flags.display.set(!1),this.flags.display.onUpdate((()=>{if(this.flags.display.active()){const t=this.parent();t&&t.displayNodeController&&t.displayNodeController.setDisplayNode(this)}})),this.io.outputs.setHasOneOutput()}setCoreGroup(t){this._setContainer(t,_G.FROM_SET_CORE_GROUP)}setObject(t){this._setContainerObjects([t],_G.FROM_SET_OBJECT)}setObjects(t){this._setContainerObjects(t,_G.FROM_SET_OBJECTS)}setGeometry(t,e=Sr.MESH){const n=this.createObject(t,e);this._setContainerObjects([n],_G.FROM_SET_GEOMETRY)}setGeometries(t,e=Sr.MESH){const n=[];let i;for(let r of t)i=this.createObject(r,e),n.push(i);this._setContainerObjects(n,_G.FROM_SET_GEOMETRIES)}_setContainerObjects(t,e){const n=this.containerController.container().coreContent()||new dG;n.setObjects(t),n.touch(),this._setContainer(n)}static createObject(t,e,n){return pG.createObject(t,e,n)}createObject(t,e,n){return gG.createObject(t,e,n)}static createIndexIfNone(t){pG.createIndexIfNone(t)}_createIndexIfNone(t){gG.createIndexIfNone(t)}}const vG=new class extends aa{};class yG extends gG{constructor(){super(...arguments),this.paramsConfig=vG}static type(){return er.OUTPUT}initializeNode(){this.io.inputs.setCount(1),this.io.outputs.setHasNoOutput(),this.io.inputs.initInputsClonedState(Qi.NEVER)}cook(t){this.setCoreGroup(t[0])}}class xG extends gG{constructor(){super(...arguments),this.childrenDisplayController=new wG(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.SOP}initializeBaseNode(){super.initializeBaseNode(),this.childrenDisplayController.initializeNode(),this.cookController.disallowInputsEvaluation()}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}async cook(t){const e=this.childrenDisplayController.output_node();if(e){const t=(await e.compute()).coreContent();t?this.setCoreGroup(t):e.states.error.active()?this.states.error.set(e.states.error.message()):this.setObjects([])}else this.states.error.set(\\\\\\\"no output node found inside subnet\\\\\\\")}}const bG={dependsOnDisplayNode:!0};class wG{constructor(t,e=bG){this.node=t,this.options=e,this._output_node_needs_update=!0}dispose(){var t;null===(t=this._graph_node)||void 0===t||t.dispose()}displayNodeControllerCallbacks(){return{onDisplayNodeRemove:()=>{this.node.setDirty()},onDisplayNodeSet:()=>{this.node.setDirty()},onDisplayNodeUpdate:()=>{this.node.setDirty()}}}output_node(){return this._output_node_needs_update&&this._update_output_node(),this._output_node}initializeNode(){var t;const e=null===(t=this.node.flags)||void 0===t?void 0:t.display;e&&e.onUpdate((()=>{e.active()&&this.node.setDirty()})),this.node.lifecycle.add_on_child_add_hook((()=>{this._output_node_needs_update=!0,this.node.setDirty()})),this.node.lifecycle.add_on_child_remove_hook((()=>{this._output_node_needs_update=!0,this.node.setDirty()}))}_update_output_node(){const t=this.node.nodesByType(yG.type())[0];null!=this._output_node&&null!=t&&this._output_node.graphNodeId()==t.graphNodeId()||(this._graph_node&&this._output_node&&this._graph_node.removeGraphInput(this._output_node),this._output_node=t,this._output_node&&this.options.dependsOnDisplayNode&&(this._graph_node=this._graph_node||this._create_graph_node(),this._graph_node.addGraphInput(this._output_node)))}_create_graph_node(){const t=new Ai(this.node.scene(),\\\\\\\"subnetChildrenDisplayController\\\\\\\");return t.addPostDirtyHook(\\\\\\\"subnetChildrenDisplayController\\\\\\\",(()=>{this.node.setDirty()})),t}}function TG(t,e){const n=new class extends aa{constructor(){super(...arguments),this.template=oa.OPERATOR_PATH(\\\\\\\"../template\\\\\\\"),this.debug=oa.BUTTON(null,{callback:t=>{i.PARAM_CALLBACK_debug(t)}})}};class i extends xG{constructor(){super(...arguments),this.paramsConfig=n,this.polyNodeController=new MG(this,e)}static type(){return t}static PARAM_CALLBACK_debug(t){t._debug()}_debug(){this.polyNodeController.debug(this.p.template)}}return i}const AG=TG(\\\\\\\"poly\\\\\\\",{nodeContext:Ki.SOP,inputs:[0,4]});class EG extends AG{}class MG{constructor(t,e){this.node=t,this._definition=e}initializeNode(){this.init_inputs(),this.node.params.onParamsCreated(\\\\\\\"poly_node_init\\\\\\\",(()=>{this.create_params_from_definition()})),this.node.lifecycle.add_on_create_hook((()=>{this.create_params_from_definition(),this.createChildNodesFromDefinition()}))}init_inputs(){const t=this._definition.inputs;t&&this.node.io.inputs.setCount(t[0],t[1])}create_params_from_definition(){const t=this._definition.params;if(t){for(let e of t)e.options=e.options||{},e.options.spare=!0;this.node.params.updateParams({toAdd:t})}}createChildNodesFromDefinition(){const t=this._definition.nodes;if(!t)return;const e=this.node.scene().loadingController.loaded();e&&this.node.scene().loadingController.markAsLoading();const n=new Xl({}),i=new zl(this.node);i.create_nodes(n,t);const r=this._definition.ui;r&&i.process_nodes_ui_data(n,r),e&&this.node.scene().loadingController.markAsLoaded()}debug(t){const e=t.found_node();if(e){const t=hG.dispatch_node(e),n=t.data({showPolyNodesData:!0}),i=t.ui_data({showPolyNodesData:!0}),r={nodeContext:e.context(),inputs:[0,0],params:[],nodes:n.nodes,ui:i.nodes};console.log(JSON.stringify(r))}}static createNodeClass(t,e,n){switch(e){case Ki.SOP:return TG(t,n);case Ki.OBJ:return SG(t,n)}}}function SG(t,e){const n=new class extends aa{constructor(){super(...arguments),this.display=oa.BOOLEAN(1),this.template=oa.OPERATOR_PATH(\\\\\\\"../template\\\\\\\"),this.debug=oa.BUTTON(null,{callback:t=>{i.PARAM_CALLBACK_debug(t)}})}};class i extends _z{constructor(){super(...arguments),this.paramsConfig=n,this.hierarchyController=new Lz(this),this.flags=new Fi(this),this.childrenDisplayController=new WU(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.SOP,this.polyNodeController=new MG(this,e)}static type(){return t}createObject(){const t=new In.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.childrenDisplayController.initializeNode()}isDisplayNodeCooking(){if(this.flags.display.active()){const t=this.displayNodeController.displayNode();return!!t&&t.isDirty()}return!1}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}cook(){this.object.visible=this.pv.display,this.cookController.endCook()}static PARAM_CALLBACK_debug(t){t._debug()}_debug(){this.polyNodeController.debug(this.p.template)}}return i}const CG=SG(\\\\\\\"poly\\\\\\\",{nodeContext:Ki.OBJ});class NG extends CG{}class LG extends Q.a{constructor(t){super(),this.type=\\\\\\\"Audio\\\\\\\",this.listener=t,this.context=t.context,this.gain=this.context.createGain(),this.gain.connect(t.getInput()),this.autoplay=!1,this.buffer=null,this.detune=0,this.loop=!1,this.loopStart=0,this.loopEnd=0,this.offset=0,this.duration=void 0,this.playbackRate=1,this.isPlaying=!1,this.hasPlaybackControl=!0,this.source=null,this.sourceType=\\\\\\\"empty\\\\\\\",this._startedAt=0,this._progress=0,this._connected=!1,this.filters=[]}getOutput(){return this.gain}setNodeSource(t){return this.hasPlaybackControl=!1,this.sourceType=\\\\\\\"audioNode\\\\\\\",this.source=t,this.connect(),this}setMediaElementSource(t){return this.hasPlaybackControl=!1,this.sourceType=\\\\\\\"mediaNode\\\\\\\",this.source=this.context.createMediaElementSource(t),this.connect(),this}setMediaStreamSource(t){return this.hasPlaybackControl=!1,this.sourceType=\\\\\\\"mediaStreamNode\\\\\\\",this.source=this.context.createMediaStreamSource(t),this.connect(),this}setBuffer(t){return this.buffer=t,this.sourceType=\\\\\\\"buffer\\\\\\\",this.autoplay&&this.play(),this}play(t=0){if(!0===this.isPlaying)return void console.warn(\\\\\\\"THREE.Audio: Audio is already playing.\\\\\\\");if(!1===this.hasPlaybackControl)return void console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\");this._startedAt=this.context.currentTime+t;const e=this.context.createBufferSource();return e.buffer=this.buffer,e.loop=this.loop,e.loopStart=this.loopStart,e.loopEnd=this.loopEnd,e.onended=this.onEnded.bind(this),e.start(this._startedAt,this._progress+this.offset,this.duration),this.isPlaying=!0,this.source=e,this.setDetune(this.detune),this.setPlaybackRate(this.playbackRate),this.connect()}pause(){if(!1!==this.hasPlaybackControl)return!0===this.isPlaying&&(this._progress+=Math.max(this.context.currentTime-this._startedAt,0)*this.playbackRate,!0===this.loop&&(this._progress=this._progress%(this.duration||this.buffer.duration)),this.source.stop(),this.source.onended=null,this.isPlaying=!1),this;console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\")}stop(){if(!1!==this.hasPlaybackControl)return this._progress=0,this.source.stop(),this.source.onended=null,this.isPlaying=!1,this;console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\")}connect(){if(this.filters.length>0){this.source.connect(this.filters[0]);for(let t=1,e=this.filters.length;t0){this.source.disconnect(this.filters[0]);for(let t=1,e=this.filters.length;t{t.load(e,(function(t){n(t)}))}))}}var UG;!function(t){t.LINEAR=\\\\\\\"linear\\\\\\\",t.INVERSE=\\\\\\\"inverse\\\\\\\",t.EXPONENTIAL=\\\\\\\"exponential\\\\\\\"}(UG||(UG={}));const GG=[UG.LINEAR,UG.INVERSE,UG.EXPONENTIAL];class VG extends(Sz(aa)){constructor(){super(...arguments),this.audio=oa.FOLDER(),this.listener=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.OBJ,types:[Ng.AUDIO_LISTENER]}}),this.url=oa.STRING(\\\\\\\"\\\\\\\",{fileBrowse:{type:[Ls.AUDIO]}}),this.volume=oa.FLOAT(1),this.loop=oa.BOOLEAN(1,{separatorBefore:!0}),this.loopStart=oa.FLOAT(0,{visibleIf:{loop:1}}),this.loopEnd=oa.FLOAT(0,{visibleIf:{loop:1},separatorAfter:!0}),this.refDistance=oa.FLOAT(10,{range:[0,10],rangeLocked:[!0,!1]}),this.rolloffFactor=oa.FLOAT(10,{range:[0,10],rangeLocked:[!0,!1]}),this.maxDistance=oa.FLOAT(100,{range:[.001,100],rangeLocked:[!0,!1]}),this.distanceModel=oa.INTEGER(GG.indexOf(UG.LINEAR),{menu:{entries:GG.map(((t,e)=>({name:t,value:e})))}}),this.coneInnerAngle=oa.FLOAT(180,{range:[0,360],rangeLocked:[!0,!0]}),this.coneOuterAngle=oa.FLOAT(230,{range:[0,360],rangeLocked:[!0,!0]}),this.coneOuterGain=oa.FLOAT(.1,{range:[0,1],rangeLocked:[!0,!0]}),this.autoplay=oa.BOOLEAN(1),this.showHelper=oa.BOOLEAN(0),this.play=oa.BUTTON(null,{callback:t=>{jG.PARAM_CALLBACK_play(t)}}),this.pause=oa.BUTTON(null,{callback:t=>{jG.PARAM_CALLBACK_pause(t)}})}}const HG=new VG;class jG extends _z{constructor(){super(...arguments),this.paramsConfig=HG,this.hierarchyController=new Lz(this),this.transformController=new Nz(this),this.flags=new Fi(this)}static type(){return Ng.POSITIONAL_AUDIO}createObject(){const t=new In.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this._updateHelperHierarchy(),this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this._helper&&(this.flags.display.active()?this.object.add(this._helper):this.object.remove(this._helper))}cook(){this.transformController.update(),this._updatePositionalAudio(),this.cookController.endCook()}async _updatePositionalAudio(){this.p.listener.isDirty()&&await this.p.listener.compute();const t=this.pv.url;if(this._loadedUrl!=t)try{await this._createPositionalAudio()}catch(t){this.states.error.set(`error when creating audio: ${t}`)}this._positionalAudio&&(this._positionalAudio.setVolume(this.pv.volume),this._positionalAudio.setLoop(this.pv.loop),this._positionalAudio.setLoopStart(this.pv.loopStart),this._positionalAudio.setLoopEnd(this.pv.loopEnd),this._positionalAudio.setRefDistance(this.pv.refDistance),this._positionalAudio.setRolloffFactor(this.pv.rolloffFactor),this._positionalAudio.setMaxDistance(this.pv.maxDistance),this._positionalAudio.setDistanceModel(GG[this.pv.distanceModel]),this._positionalAudio.setDirectionalCone(this.pv.coneInnerAngle,this.pv.coneOuterAngle,this.pv.coneOuterGain),this.pv.showHelper&&(this._helper=this._helper||this._createHelper(this._positionalAudio),this.object.add(this._helper)),this._helper&&(this._helper.visible=this.pv.showHelper,this._helper.update()))}_createHelper(t){const e=new DG(t);return e.matrixAutoUpdate=!1,e}async _createPositionalAudio(){const t=this.pv.listener.nodeWithContext(Ki.OBJ);if(!t)return;const e=t.object;this._positionalAudio&&(this._positionalAudio.source&&(this._positionalAudio.stop(),this._positionalAudio.disconnect()),this.object.remove(this._positionalAudio),this._positionalAudio=void 0),this._helper&&(this._helper.dispose(),this._helper=void 0),this._positionalAudio=new FG(e),this._positionalAudio.matrixAutoUpdate=!1;const n=new zG(this.pv.url,this.scene(),this),i=await n.load();this._loadedUrl=this.pv.url,this._positionalAudio.autoplay=this.pv.autoplay,this._positionalAudio.setBuffer(i),this.object.add(this._positionalAudio)}isPlaying(){return!!this._positionalAudio&&this._positionalAudio.isPlaying}static PARAM_CALLBACK_play(t){t.PARAM_CALLBACK_play()}static PARAM_CALLBACK_pause(t){t.PARAM_CALLBACK_pause()}PARAM_CALLBACK_play(){this._positionalAudio&&(this.isPlaying()||this._positionalAudio.play())}PARAM_CALLBACK_pause(){this._positionalAudio&&this.isPlaying()&&this._positionalAudio.pause()}}var WG;!function(t){t.ON_RENDER=\\\\\\\"On Every Render\\\\\\\",t.MANUAL=\\\\\\\"Manual\\\\\\\"}(WG||(WG={}));const qG=[WG.ON_RENDER,WG.MANUAL];const XG=new class extends aa{constructor(){super(...arguments),this.object=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.OBJ},dependentOnFoundNode:!1,computeOnDirty:!0,callback:t=>{YG.PARAM_CALLBACK_update_resolved_object(t)}}),this.pointIndex=oa.INTEGER(0,{range:[0,100]}),this.updateMode=oa.INTEGER(qG.indexOf(WG.ON_RENDER),{callback:t=>{YG.PARAM_CALLBACK_update_updateMode(t)},menu:{entries:qG.map(((t,e)=>({name:t,value:e})))}}),this.update=oa.BUTTON(null,{callback:t=>{YG.PARAM_CALLBACK_update(t)},visibleIf:{updateMode:qG.indexOf(WG.MANUAL)}})}};class YG extends _z{constructor(){super(...arguments),this.paramsConfig=XG,this.hierarchyController=new Lz(this),this.flags=new Fi(this),this._helper=new NU(1),this._found_point_post=new p.a,this._on_object_before_render_bound=this._update.bind(this)}static type(){return\\\\\\\"rivet\\\\\\\"}createObject(){const t=new k.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.addPostDirtyHook(\\\\\\\"rivet_on_dirty\\\\\\\",(()=>{this.cookController.cookMainWithoutInputs()})),this._updateHelperHierarchy(),this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this.flags.display.active()?this.object.add(this._helper):this.object.remove(this._helper)}async cook(){await this._update_resolved_object(),this._update_render_hook(),this.cookController.endCook()}_update_render_hook(){const t=qG[this.pv.updateMode];switch(t){case WG.ON_RENDER:return this._add_render_hook();case WG.MANUAL:return this._remove_render_hook()}ar.unreachable(t)}_add_render_hook(){this.object.onBeforeRender=this._on_object_before_render_bound,this.object.frustumCulled=!1}_remove_render_hook(){this.object.onBeforeRender=()=>{}}_update(t,e,n,i,r,s){const o=this._resolved_object();if(o){const t=o.geometry;if(t){const e=t.attributes.position;if(e){const t=e.array;this._found_point_post.fromArray(t,3*this.pv.pointIndex),o.updateWorldMatrix(!0,!1),o.localToWorld(this._found_point_post),this.object.matrix.makeTranslation(this._found_point_post.x,this._found_point_post.y,this._found_point_post.z)}}}}static PARAM_CALLBACK_update_resolved_object(t){t._update_resolved_object()}async _update_resolved_object(){this.p.object.isDirty()&&await this.p.object.compute();const t=this.p.object.found_node();if(t)if(t.context()==Ki.OBJ&&t.type()==YU.type()){const e=t;this._resolved_sop_group=e.childrenDisplayController.sopGroup()}else this.states.error.set(\\\\\\\"found node is not a geo node\\\\\\\")}_resolved_object(){if(!this._resolved_sop_group)return;const t=this._resolved_sop_group.children[0];return t||void 0}static PARAM_CALLBACK_update_updateMode(t){t._update_render_hook()}static PARAM_CALLBACK_update(t){t._update()}}class $G extends(Ca(Ea(va(ma(ua(aa)))))){}const JG=new $G;class ZG extends _z{constructor(){super(...arguments),this.paramsConfig=JG,this.hierarchyController=new Lz(this),this.SceneAutoUpdateController=new ha(this),this.sceneBackgroundController=new fa(this),this.SceneEnvController=new ya(this),this.sceneFogController=new Ma(this),this.sceneMaterialOverrideController=new Na(this)}static type(){return\\\\\\\"scene\\\\\\\"}createObject(){const t=new fr;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode()}cook(){this.SceneAutoUpdateController.update(),this.sceneBackgroundController.update(),this.SceneEnvController.update(),this.sceneFogController.update(),this.sceneMaterialOverrideController.update(),this.cookController.endCook()}}class QG{constructor(t,e,n){this._camera_node_id=t,this._controls_node=e,this._controls=n,this._update_required=this._controls_node.update_required()}update_required(){return this._update_required}get camera_node_id(){return this._camera_node_id}get controls(){return this._controls}get controls_node(){return this._controls_node}is_equal(t){return t.camera_node_id==this._camera_node_id&&t.controls_node.graphNodeId()==this._controls_node.graphNodeId()}}const KG=\\\\\\\"controls\\\\\\\";class tV{constructor(t){this.node=t,this._applied_controls_by_element_id=new Map,this._controls_node=null}controls_param(){return this.node.params.has(KG)?this.node.params.get(KG):null}async controls_node(){const t=this.node.p.controls,e=t.rawInput();if(e&&\\\\\\\"\\\\\\\"!=e){t.isDirty()&&await t.compute();const e=t.value.node();if(e){if(pr.includes(e.type()))return e;this.node.states.error.set(\\\\\\\"found node is not of a camera control type\\\\\\\")}else this.node.states.error.set(\\\\\\\"no node has been found\\\\\\\")}return null}async update_controls(){const t=await this.controls_node();t&&this._controls_node!=t&&this._dispose_control_refs(),this._controls_node=t}async apply_controls(t){const e=t.canvas();if(!e)return;const n=await this.controls_node();if(n){this._controlsEndEventName=n.endEventName();const i=n.controls_id();let r=!1,s=this._applied_controls_by_element_id.get(e.id);if(s&&s.get(i)&&(r=!0),!r){s=new Map,this._applied_controls_by_element_id.set(e.id,s),s.set(i,n);const r=await n.apply_controls(this.node.object,t);if(!r)return;const o=new QG(this.node.graphNodeId(),n,r);return this.set_controls_events(r),o}}}_dispose_control_refs(){this._applied_controls_by_element_id.forEach(((t,e)=>{this._dispose_controls_for_element_id(e)})),this._applied_controls_by_element_id.clear(),this._controlsEndEventName=void 0}_dispose_controls_for_element_id(t){const e=this._applied_controls_by_element_id.get(t);e&&e.forEach(((e,n)=>{e.dispose_controls_for_html_element_id(t)})),this._applied_controls_by_element_id.delete(t)}async dispose_controls(t){this._dispose_controls_for_element_id(t.id)}set_controls_events(t){const e=qV[this.node.pv.updateFromControlsMode];switch(e){case WV.ON_END:return this._set_controls_events_to_update_on_end(t);case WV.ALWAYS:return this._set_controls_events_to_update_always(t);case WV.NEVER:return this._reset(t)}ar.unreachable(e)}_reset(t){this.controls_change_listener&&(t.removeEventListener(\\\\\\\"change\\\\\\\",this.controls_change_listener),this.controls_change_listener=void 0),this.controls_end_listener&&this._controlsEndEventName&&(t.removeEventListener(this._controlsEndEventName,this.controls_end_listener),this.controls_end_listener=void 0)}_set_controls_events_to_update_on_end(t){this._reset(t),this._controlsEndEventName&&(this.controls_end_listener=()=>{this.node.update_transform_params_from_object()},t.addEventListener(this._controlsEndEventName,this.controls_end_listener))}_set_controls_events_to_update_always(t){this._reset(t),this.controls_change_listener=()=>{this.node.update_transform_params_from_object()},t.addEventListener(\\\\\\\"change\\\\\\\",this.controls_change_listener)}}function eV(t){return class extends t{constructor(){super(...arguments),this.layer=oa.INTEGER(0,{range:[0,31],rangeLocked:[!0,!0]})}}}class nV{constructor(t){this.node=t}update(){const t=this.node.object;t.layers.set(0),t.layers.enable(this.node.params.integer(\\\\\\\"layer\\\\\\\"))}}const iV={callback:t=>{eH.PARAM_CALLBACK_reset_effects_composer(t)}};function rV(t){return class extends t{constructor(){super(...arguments),this.doPostProcess=oa.BOOLEAN(0),this.postProcessNode=oa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{doPostProcess:1},nodeSelection:{types:[tr.POST]},...iV})}}}class sV{constructor(t){this.node=t,this._composers_by_canvas_id={},this.node.p.postProcessNode?this._add_param_dirty_hook():this.node.params.onParamsCreated(\\\\\\\"post process add param dirty hook\\\\\\\",(()=>{this._add_param_dirty_hook()}))}_add_param_dirty_hook(){this.node.p.postProcessNode.addPostDirtyHook(\\\\\\\"on_post_node_dirty\\\\\\\",(()=>{this.reset()}))}render(t,e){const n=this.composer(t);n&&(e&&n.setSize(e.x,e.y),n.render())}reset(){const t=Object.keys(this._composers_by_canvas_id);for(let e of t)delete this._composers_by_canvas_id[e]}composer(t){return this._composers_by_canvas_id[t.id]=this._composers_by_canvas_id[t.id]||this._create_composer(t)}_create_composer(t){const e=this.node.renderController.renderer(t);if(e){const n=this.node.renderController.resolved_scene||this.node.scene().threejsScene(),i=this.node.object,r=this.node.p.postProcessNode.value.node();if(r){if(r.type()==tr.POST){const s=r,o=this.node.renderController.canvas_resolution(t);return s.effectsComposerController.createEffectsComposer({renderer:e,scene:n,camera:i,resolution:o,requester:this.node,camera_node:this.node})}this.node.states.error.set(\\\\\\\"found node is not a post process node\\\\\\\")}else this.node.states.error.set(\\\\\\\"no post node found\\\\\\\")}}}class oV extends ia{constructor(){super(...arguments),this.flags=new Oi(this)}static context(){return Ki.ROP}initializeBaseNode(){this.dirtyController.addPostDirtyHook(\\\\\\\"cook_immediately\\\\\\\",(()=>{this.cookController.cookMainWithoutInputs()}))}cook(){this.cookController.endCook()}}var aV,lV,cV,uV;!function(t){t.CSS2D=\\\\\\\"CSS2DRenderer\\\\\\\",t.CSS3D=\\\\\\\"CSS3DRenderer\\\\\\\",t.WEBGL=\\\\\\\"WebGLRenderer\\\\\\\"}(aV||(aV={})),function(t){t.Linear=\\\\\\\"Linear\\\\\\\",t.sRGB=\\\\\\\"sRGB\\\\\\\",t.Gamma=\\\\\\\"Gamma\\\\\\\",t.RGBE=\\\\\\\"RGBE\\\\\\\",t.LogLuv=\\\\\\\"LogLuv\\\\\\\",t.RGBM7=\\\\\\\"RGBM7\\\\\\\",t.RGBM16=\\\\\\\"RGBM16\\\\\\\",t.RGBD=\\\\\\\"RGBD\\\\\\\"}(lV||(lV={})),(uV=cV||(cV={}))[uV.Linear=w.U]=\\\\\\\"Linear\\\\\\\",uV[uV.sRGB=w.ld]=\\\\\\\"sRGB\\\\\\\",uV[uV.Gamma=w.J]=\\\\\\\"Gamma\\\\\\\",uV[uV.RGBE=w.gc]=\\\\\\\"RGBE\\\\\\\",uV[uV.LogLuv=w.bb]=\\\\\\\"LogLuv\\\\\\\",uV[uV.RGBM7=w.lc]=\\\\\\\"RGBM7\\\\\\\",uV[uV.RGBM16=w.kc]=\\\\\\\"RGBM16\\\\\\\",uV[uV.RGBD=w.fc]=\\\\\\\"RGBD\\\\\\\";const hV=[lV.Linear,lV.sRGB,lV.Gamma,lV.RGBE,lV.LogLuv,lV.RGBM7,lV.RGBM16,lV.RGBD],dV=[cV.Linear,cV.sRGB,cV.Gamma,cV.RGBE,cV.LogLuv,cV.RGBM7,cV.RGBM16,cV.RGBD],pV=cV.sRGB;var _V,mV,fV;!function(t){t.No=\\\\\\\"No\\\\\\\",t.Linear=\\\\\\\"Linear\\\\\\\",t.Reinhard=\\\\\\\"Reinhard\\\\\\\",t.Cineon=\\\\\\\"Cineon\\\\\\\",t.ACESFilmic=\\\\\\\"ACESFilmic\\\\\\\"}(_V||(_V={})),(fV=mV||(mV={}))[fV.No=w.vb]=\\\\\\\"No\\\\\\\",fV[fV.Linear=w.ab]=\\\\\\\"Linear\\\\\\\",fV[fV.Reinhard=w.vc]=\\\\\\\"Reinhard\\\\\\\",fV[fV.Cineon=w.m]=\\\\\\\"Cineon\\\\\\\",fV[fV.ACESFilmic=w.a]=\\\\\\\"ACESFilmic\\\\\\\";const gV=[_V.No,_V.Linear,_V.Reinhard,_V.Cineon,_V.ACESFilmic],vV=[mV.No,mV.Linear,mV.Reinhard,mV.Cineon,mV.ACESFilmic],yV=mV.ACESFilmic,xV=gV.map(((t,e)=>({name:t,value:vV[e]})));var bV;!function(t){t.HIGH=\\\\\\\"highp\\\\\\\",t.MEDIUM=\\\\\\\"mediump\\\\\\\",t.LOW=\\\\\\\"lowp\\\\\\\"}(bV||(bV={}));const wV=[bV.HIGH,bV.MEDIUM,bV.LOW];var TV;!function(t){t.HIGH=\\\\\\\"high-performance\\\\\\\",t.LOW=\\\\\\\"low-power\\\\\\\",t.DEFAULT=\\\\\\\"default\\\\\\\"}(TV||(TV={}));const AV=[TV.HIGH,TV.LOW,TV.DEFAULT];var EV,MV,SV;!function(t){t.Basic=\\\\\\\"Basic\\\\\\\",t.PCF=\\\\\\\"PCF\\\\\\\",t.PCFSoft=\\\\\\\"PCFSoft\\\\\\\",t.VSM=\\\\\\\"VSM\\\\\\\"}(EV||(EV={})),(SV=MV||(MV={}))[SV.Basic=w.k]=\\\\\\\"Basic\\\\\\\",SV[SV.PCF=w.Fb]=\\\\\\\"PCF\\\\\\\",SV[SV.PCFSoft=w.Gb]=\\\\\\\"PCFSoft\\\\\\\",SV[SV.VSM=w.gd]=\\\\\\\"VSM\\\\\\\";const CV=[EV.Basic,EV.PCF,EV.PCFSoft,EV.VSM],NV=[MV.Basic,MV.PCF,MV.PCFSoft,MV.VSM],LV=(w.k,w.Fb,w.Gb,w.gd,MV.PCFSoft),OV={alpha:!1,precision:bV.HIGH,premultipliedAlpha:!0,antialias:!1,stencil:!0,preserveDrawingBuffer:!1,powerPreference:TV.DEFAULT,depth:!0,logarithmicDepthBuffer:!1};const RV=new class extends aa{constructor(){super(...arguments),this.tprecision=oa.BOOLEAN(0),this.precision=oa.INTEGER(wV.indexOf(bV.HIGH),{visibleIf:{tprecision:1},menu:{entries:wV.map(((t,e)=>({value:e,name:t})))}}),this.tpowerPreference=oa.BOOLEAN(0),this.powerPreference=oa.INTEGER(AV.indexOf(TV.DEFAULT),{visibleIf:{tpowerPreference:1},menu:{entries:AV.map(((t,e)=>({value:e,name:t})))}}),this.alpha=oa.BOOLEAN(1),this.premultipliedAlpha=oa.BOOLEAN(1),this.antialias=oa.BOOLEAN(1),this.stencil=oa.BOOLEAN(1),this.depth=oa.BOOLEAN(1),this.logarithmicDepthBuffer=oa.BOOLEAN(0),this.toneMapping=oa.INTEGER(yV,{menu:{entries:xV}}),this.toneMappingExposure=oa.FLOAT(1,{range:[0,2]}),this.outputEncoding=oa.INTEGER(pV,{menu:{entries:hV.map(((t,e)=>({name:t,value:dV[e]})))}}),this.physicallyCorrectLights=oa.BOOLEAN(1),this.sortObjects=oa.BOOLEAN(1),this.tpixelRatio=oa.BOOLEAN(0),this.pixelRatio=oa.INTEGER(2,{visibleIf:{tpixelRatio:!0},range:[1,4],rangeLocked:[!0,!1]}),this.tshadowMap=oa.BOOLEAN(1),this.shadowMapAutoUpdate=oa.BOOLEAN(1,{visibleIf:{tshadowMap:1}}),this.shadowMapNeedsUpdate=oa.BOOLEAN(0,{visibleIf:{tshadowMap:1}}),this.shadowMapType=oa.INTEGER(LV,{visibleIf:{tshadowMap:1},menu:{entries:CV.map(((t,e)=>({name:t,value:NV[e]})))}})}};class PV extends oV{constructor(){super(...arguments),this.paramsConfig=RV,this._renderers_by_canvas_id={}}static type(){return aV.WEBGL}createRenderer(t,e){const n={},i=Object.keys(OV);let r;for(r of i)n[r]=OV[r];if(this.pv.tprecision){const t=wV[this.pv.precision];n.precision=t}if(this.pv.tpowerPreference){const t=AV[this.pv.powerPreference];n.powerPreference=t}n.antialias=this.pv.antialias,n.antialias=this.pv.antialias,n.alpha=this.pv.alpha,n.premultipliedAlpha=this.pv.premultipliedAlpha,n.depth=this.pv.depth,n.stencil=this.pv.stencil,n.logarithmicDepthBuffer=this.pv.logarithmicDepthBuffer,n.canvas=t,n.context=e;const s=ai.renderersController.createWebGLRenderer(n);return ai.renderersController.printDebug()&&(ai.renderersController.printDebugMessage(`create renderer from node '${this.path()}'`),ai.renderersController.printDebugMessage({params:n})),this._update_renderer(s),this._renderers_by_canvas_id[t.id]=s,s}cook(){const t=Object.keys(this._renderers_by_canvas_id);for(let e of t){const t=this._renderers_by_canvas_id[e];this._update_renderer(t)}this._traverse_scene_and_update_materials(),this.cookController.endCook()}_update_renderer(t){t.physicallyCorrectLights=this.pv.physicallyCorrectLights,t.outputEncoding=this.pv.outputEncoding,t.toneMapping=this.pv.toneMapping,t.toneMappingExposure=this.pv.toneMappingExposure,t.shadowMap.enabled=this.pv.tshadowMap,t.shadowMap.autoUpdate=this.pv.shadowMapAutoUpdate,t.shadowMap.needsUpdate=this.pv.shadowMapNeedsUpdate,t.shadowMap.type=this.pv.shadowMapType,t.sortObjects=this.pv.sortObjects;const e=this.pv.tpixelRatio?this.pv.pixelRatio:FV.defaultPixelRatio();ai.renderersController.printDebug()&&(ai.renderersController.printDebugMessage(`set renderer pixelRatio from '${this.path()}'`),ai.renderersController.printDebugMessage({pixelRatio:e})),t.setPixelRatio(e)}_traverse_scene_and_update_materials(){this.scene().threejsScene().traverse((t=>{const e=t.material;if(e)if(m.isArray(e))for(let t of e)t.needsUpdate=!0;else e.needsUpdate=!0}))}}function IV(t){return class extends t{constructor(){super(...arguments),this.render=oa.FOLDER(),this.setScene=oa.BOOLEAN(0),this.scene=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setScene:1},nodeSelection:{context:Ki.OBJ,types:[ZG.type()]}}),this.setRenderer=oa.BOOLEAN(0),this.renderer=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setRenderer:1},nodeSelection:{context:Ki.ROP,types:[PV.type()]}}),this.setCSSRenderer=oa.BOOLEAN(0),this.CSSRenderer=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setCSSRenderer:1},nodeSelection:{context:Ki.ROP,types:[aV.CSS2D,aV.CSS3D]}})}}}class FV{constructor(t){this.node=t,this._renderers_by_canvas_id={},this._resolution_by_canvas_id={},this._super_sampling_size=new d.a}render(t,e,n){if(this.node.pv.doPostProcess?this.node.postProcessController.render(t,e):this.render_with_renderer(t),this._resolved_cssRenderer_rop&&this._resolved_scene&&this.node.pv.setCSSRenderer){const e=this.cssRenderer(t);e&&e.render(this._resolved_scene,this.node.object)}}render_with_renderer(t){const e=this.renderer(t);e&&this._resolved_scene&&e.render(this._resolved_scene,this.node.object)}async update(){this.update_scene(),this.update_renderer(),this.update_cssRenderer()}get resolved_scene(){return this._resolved_scene}update_scene(){if(this.node.pv.setScene){const t=this.node.p.scene;t.isDirty()&&t.find_target();const e=t.found_node_with_context_and_type(Ki.OBJ,ZG.type());e&&(e.isDirty()&&e.cookController.cookMainWithoutInputs(),this._resolved_scene=e.object)}else this._resolved_scene=this.node.scene().threejsScene()}update_renderer(){if(this.node.pv.setRenderer){const t=this.node.p.renderer;t.isDirty()&&t.find_target(),this._resolved_renderer_rop=t.found_node_with_context_and_type(Ki.ROP,aV.WEBGL)}else this._resolved_renderer_rop=void 0}update_cssRenderer(){if(this.node.pv.setCSSRenderer){const t=this.node.p.CSSRenderer;t.isDirty()&&t.find_target(),this._resolved_cssRenderer_rop=t.found_node_with_context_and_type(Ki.ROP,[aV.CSS2D,aV.CSS3D])}else this._resolved_cssRenderer_rop,this._resolved_cssRenderer_rop=void 0}renderer(t){return this._renderers_by_canvas_id[t.id]}cssRenderer(t){if(this._resolved_cssRenderer_rop&&this.node.pv.setCSSRenderer)return this._resolved_cssRenderer_rop.renderer(t)}createRenderer(t,e){const n=ai.renderersController.createRenderingContext(t);if(!n)return void console.error(\\\\\\\"failed to create webgl context\\\\\\\");let i;return this.node.pv.setRenderer&&(this.update_renderer(),this._resolved_renderer_rop&&(i=this._resolved_renderer_rop.createRenderer(t,n))),i||(i=FV._createDefaultRenderer(t,n)),ai.renderersController.registerRenderer(i),this._renderers_by_canvas_id[t.id]=i,this._super_sampling_size.copy(e),this.set_renderer_size(t,this._super_sampling_size),i}static defaultPixelRatio(){return Zf.isMobile()?1:Math.max(2,window.devicePixelRatio)}static _createDefaultRenderer(t,e){const n={canvas:t,antialias:!1,alpha:!1,context:e},i=ai.renderersController.createWebGLRenderer(n),r=this.defaultPixelRatio();return i.setPixelRatio(r),i.shadowMap.enabled=!0,i.shadowMap.type=LV,i.physicallyCorrectLights=!0,i.toneMapping=yV,i.toneMappingExposure=1,i.outputEncoding=pV,ai.renderersController.printDebug()&&(ai.renderersController.printDebugMessage(\\\\\\\"create default renderer\\\\\\\"),ai.renderersController.printDebugMessage({params:n,pixelRatio:r})),i}delete_renderer(t){const e=this.renderer(t);e&&ai.renderersController.deregisterRenderer(e)}canvas_resolution(t){return this._resolution_by_canvas_id[t.id]}set_renderer_size(t,e){this._resolution_by_canvas_id[t.id]=this._resolution_by_canvas_id[t.id]||new d.a,this._resolution_by_canvas_id[t.id].copy(e);const n=this.renderer(t);if(n){const t=!1;n.setSize(e.x,e.y,t)}if(this._resolved_cssRenderer_rop){const n=this.cssRenderer(t);n&&n.setSize(e.x,e.y)}}}class DV{constructor(t){this.viewer=t,this._active=!1,this._controls=null,this._bound_on_controls_start=this._on_controls_start.bind(this),this._bound_on_controls_end=this._on_controls_end.bind(this),this._update_graph_node()}controls(){return this._controls}async create_controls(){var t;this.dispose_controls();this.viewer.canvas()&&(this._config=await(null===(t=this.viewer.cameraControlsController)||void 0===t?void 0:t.apply_controls(this.viewer)),this._config&&(this._controls=this._config.controls,this._controls&&(this.viewer.active()?(this._controls.addEventListener(\\\\\\\"start\\\\\\\",this._bound_on_controls_start),this._controls.addEventListener(\\\\\\\"end\\\\\\\",this._bound_on_controls_end)):this.dispose_controls())))}update(){this._config&&this._controls&&this._config.update_required()&&this._controls.update()}dispose(){var t;null===(t=this._graph_node)||void 0===t||t.graphDisconnectPredecessors(),this.dispose_controls()}dispose_controls(){var t;if(this._controls){const e=this.viewer.canvas();e&&(null===(t=this.viewer)||void 0===t||t.cameraControlsController.dispose_controls(e)),this._bound_on_controls_start&&this._controls.removeEventListener(\\\\\\\"start\\\\\\\",this._bound_on_controls_start),this._bound_on_controls_end&&this._controls.removeEventListener(\\\\\\\"end\\\\\\\",this._bound_on_controls_end),this._controls.dispose(),this._controls=null}}_on_controls_start(){this._active=!0}_on_controls_end(){this._active=!1}_update_graph_node(){const t=this.viewer.cameraNode().p.controls;this._graph_node=this._graph_node||this._create_graph_node(),this._graph_node&&(this._graph_node.graphDisconnectPredecessors(),this._graph_node.addGraphInput(t))}_create_graph_node(){const t=new Ai(this.viewer.cameraNode().scene(),\\\\\\\"viewer-controls\\\\\\\");return t.addPostDirtyHook(\\\\\\\"this.viewer.controls_controller\\\\\\\",(async()=>{await this.viewer.controlsController.create_controls()})),t}}class kV{constructor(t){this._viewer=t,this._size=new d.a(100,100),this._aspect=1}cameraNode(){return this._viewer.cameraNode()}get size(){return this._size}get aspect(){return this._aspect}computeSizeAndAspect(){this._updateSize(),this.cameraNode().scene().uniformsController.updateResolutionDependentUniformOwners(this._size),this._aspect=this._getAspect()}_updateSize(){this._size.x=this._viewer.domElement().offsetWidth,this._size.y=this._viewer.domElement().offsetHeight}_getAspect(){return this._size.x/this._size.y}updateCameraAspect(){this.cameraNode().setupForAspectRatio(this._aspect)}async prepareCurrentCamera(){await this.cameraNode().compute(),await this._updateFromCameraContainer()}async _updateFromCameraContainer(){var t;this.updateCameraAspect(),await(null===(t=this._viewer.controlsController)||void 0===t?void 0:t.create_controls())}}class BV{constructor(t){this.viewer=t}init(){const t=this.viewer.canvas();t&&(t.onwebglcontextlost=this._on_webglcontextlost.bind(this),t.onwebglcontextrestored=this._on_webglcontextrestored.bind(this))}_on_webglcontextlost(){console.warn(\\\\\\\"context lost at frame\\\\\\\",this.viewer.scene().frame()),this.request_animation_frame_id?cancelAnimationFrame(this.request_animation_frame_id):console.warn(\\\\\\\"request_animation_frame_id not initialized\\\\\\\"),console.warn(\\\\\\\"not canceled\\\\\\\",this.request_animation_frame_id)}_on_webglcontextrestored(){console.log(\\\\\\\"context restored\\\\\\\")}}const zV=\\\\\\\"hovered\\\\\\\";class UV{constructor(t,e,n){this._container=t,this._scene=e,this._camera_node=n,this._active=!1,this._id=UV._next_viewer_id++,this._scene.viewersRegister.registerViewer(this)}active(){return this._active}activate(){this._active=!0}deactivate(){this._active=!1}get camerasController(){return this._cameras_controller=this._cameras_controller||new kV(this)}get controlsController(){return this._controls_controller}get eventsController(){return this._events_controller=this._events_controller||new Ga(this)}get webglController(){return this._webgl_controller=this._webgl_controller||new BV(this)}domElement(){return this._container}scene(){return this._scene}canvas(){return this._canvas}cameraNode(){return this._camera_node}get cameraControlsController(){}id(){return this._id}dispose(){let t;for(this._scene.viewersRegister.unregisterViewer(this),this.eventsController.dispose();t=this._container.children[0];)this._container.removeChild(t)}resetContainerClass(){this.domElement().classList.remove(zV)}setContainerClassHovered(){this.domElement().classList.add(zV)}registerOnBeforeTick(t,e){this._onBeforeTickCallbackNames=this._onBeforeTickCallbackNames||[],this._onBeforeTickCallbacks=this._onBeforeTickCallbacks||[],this._registerCallback(t,e,this._onBeforeTickCallbackNames,this._onBeforeTickCallbacks)}unRegisterOnBeforeTick(t){this._unregisterCallback(t,this._onBeforeTickCallbackNames,this._onBeforeTickCallbacks)}registeredBeforeTickCallbackNames(){return this._onBeforeTickCallbackNames}registerOnAfterTick(t,e){this._onAfterTickCallbacks=this._onAfterTickCallbacks||[],this._onAfterTickCallbackNames=this._onAfterTickCallbackNames||[],this._registerCallback(t,e,this._onAfterTickCallbackNames,this._onAfterTickCallbacks)}unRegisterOnAfterTick(t){this._unregisterCallback(t,this._onAfterTickCallbackNames,this._onAfterTickCallbacks)}registeredAfterTickCallbackNames(){return this._onAfterTickCallbackNames}registerOnBeforeRender(t,e){this._onBeforeRenderCallbackNames=this._onBeforeRenderCallbackNames||[],this._onBeforeRenderCallbacks=this._onBeforeRenderCallbacks||[],this._registerCallback(t,e,this._onBeforeRenderCallbackNames,this._onBeforeRenderCallbacks)}unRegisterOnBeforeRender(t){this._unregisterCallback(t,this._onBeforeRenderCallbackNames,this._onBeforeRenderCallbacks)}registeredBeforeRenderCallbackNames(){return this._onBeforeRenderCallbackNames}registerOnAfterRender(t,e){this._onAfterRenderCallbackNames=this._onAfterRenderCallbackNames||[],this._onAfterRenderCallbacks=this._onAfterRenderCallbacks||[],this._registerCallback(t,e,this._onAfterRenderCallbackNames,this._onAfterRenderCallbacks)}unRegisterOnAfterRender(t){this._unregisterCallback(t,this._onAfterRenderCallbackNames,this._onAfterRenderCallbacks)}registeredAfterRenderCallbackNames(){return this._onAfterRenderCallbackNames}_registerCallback(t,e,n,i){(null==n?void 0:n.includes(t))?console.warn(`callback ${t} already registered`):(i.push(e),n.push(t))}_unregisterCallback(t,e,n){if(!e||!n)return;const i=e.indexOf(t);e.splice(i,1),n.splice(i,1)}}UV._next_viewer_id=0;class GV extends UV{constructor(t,e,n,i){super(t,e,n),this._scene=e,this._camera_node=n,this._properties=i,this._do_render=!0,this._animate_method=this.animate.bind(this),this._onResizeBound=this.onResize.bind(this),this._do_render=null==this._properties||this._properties.autoRender,this._canvas=document.createElement(\\\\\\\"canvas\\\\\\\"),this._canvas.id=`canvas_id_${Math.random()}`.replace(\\\\\\\".\\\\\\\",\\\\\\\"_\\\\\\\"),this._canvas.style.display=\\\\\\\"block\\\\\\\",this._canvas.style.outline=\\\\\\\"none\\\\\\\",this._container.appendChild(this._canvas),this._container.classList.add(\\\\\\\"CoreThreejsViewer\\\\\\\"),this._build(),this._setEvents()}get controlsController(){return this._controls_controller=this._controls_controller||new DV(this)}_build(){this._init_display(),this.activate()}dispose(){this._cancel_animate(),this.controlsController.dispose(),this._disposeEvents(),super.dispose()}get cameraControlsController(){return this._camera_node.controls_controller}_setEvents(){this.eventsController.init(),this.webglController.init(),window.addEventListener(\\\\\\\"resize\\\\\\\",this._onResizeBound.bind(this),!1)}_disposeEvents(){window.removeEventListener(\\\\\\\"resize\\\\\\\",this._onResizeBound.bind(this),!1)}onResize(){const t=this.canvas();t&&(this.camerasController.computeSizeAndAspect(),this._camera_node.renderController.set_renderer_size(t,this.camerasController.size),this.camerasController.updateCameraAspect())}_init_display(){if(!this._canvas)return void console.warn(\\\\\\\"no canvas found for viewer\\\\\\\");this.camerasController.computeSizeAndAspect();const t=this.camerasController.size;this._camera_node.renderController.createRenderer(this._canvas,t),this.camerasController.prepareCurrentCamera(),this.animate()}setAutoRender(t=!0){this._do_render=t,this._do_render&&this.animate()}animate(){var t;if(this._do_render){if(this._request_animation_frame_id=requestAnimationFrame(this._animate_method),this._onBeforeTickCallbacks)for(let t of this._onBeforeTickCallbacks)t();if(this._scene.timeController.incrementTimeIfPlaying(),this._onAfterTickCallbacks)for(let t of this._onAfterTickCallbacks)t();this.render(),null===(t=this._controls_controller)||void 0===t||t.update()}}_cancel_animate(){this._do_render=!1,this._request_animation_frame_id&&cancelAnimationFrame(this._request_animation_frame_id),this._canvas&&this._camera_node.renderController.delete_renderer(this._canvas)}render(){if(this.camerasController.cameraNode()&&this._canvas){if(this._onBeforeRenderCallbacks)for(let t of this._onBeforeRenderCallbacks)t();const t=this.camerasController.size,e=this.camerasController.aspect;if(this._camera_node.renderController.render(this._canvas,t,e),this._onAfterRenderCallbacks)for(let t of this._onAfterRenderCallbacks)t()}else console.warn(\\\\\\\"no camera to render with\\\\\\\")}renderer(){if(this._canvas)return this._camera_node.renderController.renderer(this._canvas)}}const VV={type:\\\\\\\"change\\\\\\\"},HV=1,jV=100;var WV;!function(t){t.ON_END=\\\\\\\"on move end\\\\\\\",t.ALWAYS=\\\\\\\"always\\\\\\\",t.NEVER=\\\\\\\"never\\\\\\\"}(WV||(WV={}));const qV=[WV.ON_END,WV.ALWAYS,WV.NEVER];function XV(t){return class extends t{constructor(){super(...arguments),this.setMainCamera=oa.BUTTON(null,{callback:(t,e)=>{tH.PARAM_CALLBACK_setMasterCamera(t)}})}}}function YV(t){return class extends t{constructor(){super(...arguments),this.camera=oa.FOLDER(),this.controls=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.EVENT}}),this.updateFromControlsMode=oa.INTEGER(qV.indexOf(WV.ON_END),{menu:{entries:qV.map(((t,e)=>({name:t,value:e})))}}),this.near=oa.FLOAT(HV,{range:[0,100],cook:!1,computeOnDirty:!0,callback:(t,e)=>{eH.PARAM_CALLBACK_update_near_far_from_param(t,e)}}),this.far=oa.FLOAT(jV,{range:[0,100],cook:!1,computeOnDirty:!0,callback:(t,e)=>{eH.PARAM_CALLBACK_update_near_far_from_param(t,e)}}),this.display=oa.BOOLEAN(1),this.showHelper=oa.BOOLEAN(0)}}}var $V;!function(t){t.DEFAULT=\\\\\\\"default\\\\\\\",t.COVER=\\\\\\\"cover\\\\\\\",t.CONTAIN=\\\\\\\"contain\\\\\\\"}($V||($V={}));const JV=[$V.DEFAULT,$V.COVER,$V.CONTAIN];function ZV(t){return class extends t{constructor(){super(...arguments),this.fovAdjustMode=oa.INTEGER(JV.indexOf($V.DEFAULT),{menu:{entries:JV.map(((t,e)=>({name:t,value:e})))}}),this.expectedAspectRatio=oa.FLOAT(\\\\\\\"16/9\\\\\\\",{visibleIf:[{fovAdjustMode:JV.indexOf($V.COVER)},{fovAdjustMode:JV.indexOf($V.CONTAIN)}],range:[0,2],rangeLocked:[!0,!1]})}}}XV(aa);rV(IV(Sz(eV(YV(XV(aa))))));class QV extends _z{constructor(){super(...arguments),this.renderOrder=pz.CAMERA,this._aspect=-1}get object(){return this._object}async cook(){this.updateCamera(),this._object.dispatchEvent(VV),this.cookController.endCook()}on_create(){}on_delete(){}prepareRaycaster(t,e){}camera(){return this._object}updateCamera(){}static PARAM_CALLBACK_setMasterCamera(t){t.set_as_master_camera()}set_as_master_camera(){this.scene().camerasController.setMainCameraNodePath(this.path())}setupForAspectRatio(t){}_updateForAspectRatio(){}update_transform_params_from_object(){Mz.set_params_from_object(this._object,this)}static PARAM_CALLBACK_update_from_param(t,e){t.object[e.name()]=t.pv[e.name()]}}class KV extends QV{constructor(){super(...arguments),this.flags=new Fi(this),this.hierarchyController=new Lz(this),this.transformController=new Nz(this),this.childrenDisplayController=new WU(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.SOP}get controls_controller(){return this._controls_controller=this._controls_controller||new tV(this)}get layers_controller(){return this._layers_controller=this._layers_controller||new nV(this)}get renderController(){return this._render_controller=this._render_controller||new FV(this)}get postProcessController(){return this._post_process_controller=this._post_process_controller||new sV(this)}initializeBaseNode(){super.initializeBaseNode(),this.io.outputs.setHasOneOutput(),this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this.childrenDisplayController.initializeNode(),this.initHelperHook()}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}prepareRaycaster(t,e){e.setFromCamera(t,this._object)}async cook(){this.transformController.update(),this.layers_controller.update(),this.updateNearFar(),this.renderController.update(),this.updateCamera(),this._updateHelper(),this.controls_controller.update_controls(),this._object.dispatchEvent(VV),this.cookController.endCook()}static PARAM_CALLBACK_update_near_far_from_param(t,e){t.updateNearFar()}updateNearFar(){this._object.near==this.pv.near&&this._object.far==this.pv.far||(this._object.near=this.pv.near,this._object.far=this.pv.far,this._object.updateProjectionMatrix(),this._updateHelper())}setupForAspectRatio(t){m.isNaN(t)||t&&this._aspect!=t&&(this._aspect=t,this._updateForAspectRatio())}createViewer(t,e){return new GV(t,this.scene(),this,e)}static PARAM_CALLBACK_reset_effects_composer(t){t.postProcessController.reset()}initHelperHook(){this.flags.display.onUpdate((()=>{this._updateHelper()}))}helperVisible(){return this.flags.display.active()&&this.pv.showHelper}_createHelper(){const t=new jz(this.object);return t.update(),t}_updateHelper(){this.helperVisible()?(this._helper||(this._helper=this._createHelper()),this._helper&&(this.object.add(this._helper),this._helper.update())):this._helper&&this.object.remove(this._helper)}}class tH extends QV{}class eH extends KV{PARAM_CALLBACK_update_effects_composer(t){}}const nH=-.5,iH=.5,rH=.5,sH=-.5;class oH extends(rV(IV(eV(XV(ZV(function(t){return class extends t{constructor(){super(...arguments),this.size=oa.FLOAT(1)}}}(YV(Sz(aa,{matrixAutoUpdate:!0}))))))))){}const aH=new oH;class lH extends KV{constructor(){super(...arguments),this.paramsConfig=aH}static type(){return nr.ORTHOGRAPHIC}createObject(){return new st.a(2*nH,2*iH,2*rH,2*sH,HV,jV)}updateCamera(){this._updateForAspectRatio()}_updateForAspectRatio(){this._aspect&&(this._adjustFOVFromMode(),this._object.updateProjectionMatrix())}_adjustFOVFromMode(){const t=JV[this.pv.fovAdjustMode];switch(t){case $V.DEFAULT:return this._adjustFOVFromModeDefault();case $V.COVER:return this._adjustFOVFromModeCover();case $V.CONTAIN:return this._adjustFOVFromModeContain()}ar.unreachable(t)}_adjustFOVFromModeDefault(){this._adjustFOVFromSize(this.pv.size||1)}_adjustFOVFromModeCover(){const t=this.pv.size||1;this._aspect>this.pv.expectedAspectRatio?this._adjustFOVFromSize(this.pv.expectedAspectRatio*t/this._aspect):this._adjustFOVFromSize(t)}_adjustFOVFromModeContain(){const t=this.pv.size||1;this._aspect>this.pv.expectedAspectRatio?this._adjustFOVFromSize(t):this._adjustFOVFromSize(this.pv.expectedAspectRatio*t/this._aspect)}_adjustFOVFromSize(t){const e=t*this._aspect;this._object.left=nH*e*1,this._object.right=iH*e*1,this._object.top=rH*t*1,this._object.bottom=sH*t*1}}const cH=50;class uH extends(rV(IV(eV(XV(ZV(function(t){return class extends t{constructor(){super(...arguments),this.fov=oa.FLOAT(cH,{range:[0,100]})}}}(YV(Sz(aa,{matrixAutoUpdate:!0}))))))))){}const hH=new uH;class dH extends KV{constructor(){super(...arguments),this.paramsConfig=hH}static type(){return nr.PERSPECTIVE}createObject(){return new K.a(cH,1,HV,jV)}updateCamera(){this._object.fov!=this.pv.fov&&(this._object.fov=this.pv.fov,this._object.updateProjectionMatrix()),this._updateForAspectRatio()}_updateForAspectRatio(){this._aspect&&(this._object.aspect=this._aspect,this._adjustFOVFromMode(),this._object.updateProjectionMatrix())}_adjustFOVFromMode(){const t=JV[this.pv.fovAdjustMode];switch(t){case $V.DEFAULT:return this._adjustFOVFromModeDefault();case $V.COVER:return this._adjustFOVFromModeCover();case $V.CONTAIN:return this._adjustFOVFromModeContain()}ar.unreachable(t)}_adjustFOVFromModeDefault(){this._object.fov=this.pv.fov}_adjustFOVFromModeCover(){if(this._object.aspect>this.pv.expectedAspectRatio){const t=Math.tan(Object(Ln.e)(this.pv.fov/2))/(this._object.aspect/this.pv.expectedAspectRatio);this._object.fov=2*Object(Ln.k)(Math.atan(t))}else this._object.fov=this.pv.fov}_adjustFOVFromModeContain(){if(this._object.aspect>this.pv.expectedAspectRatio)this._object.fov=this.pv.fov;else{const t=Math.tan(Object(Ln.e)(this.pv.fov/2))/(this._object.aspect/this.pv.expectedAspectRatio);this._object.fov=2*Object(Ln.k)(Math.atan(t))}}}class pH extends(function(t){return class extends t{constructor(){super(...arguments),this.main=oa.FOLDER(),this.resolution=oa.INTEGER(256),this.excludedObjects=oa.STRING(\\\\\\\"*`$OS`\\\\\\\"),this.printResolve=oa.BUTTON(null,{callback:t=>{mH.PARAM_CALLBACK_printResolve(t)}}),this.near=oa.FLOAT(1),this.far=oa.FLOAT(100),this.render=oa.BUTTON(null,{callback:t=>{mH.PARAM_CALLBACK_render(t)}}),this.renderTarget=oa.FOLDER(),this.tencoding=oa.BOOLEAN(0),this.encoding=oa.INTEGER(w.ld,{visibleIf:{tencoding:1},menu:{entries:eg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.tminFilter=oa.BOOLEAN(0),this.minFilter=oa.INTEGER(Xm,{visibleIf:{tminFilter:1},menu:{entries:$m}}),this.tmagFilter=oa.BOOLEAN(0),this.magFilter=oa.INTEGER(qm,{visibleIf:{tmagFilter:1},menu:{entries:Ym}})}}}(Sz(aa))){}const _H=new pH;class mH extends _z{constructor(){super(...arguments),this.paramsConfig=_H,this.hierarchyController=new Lz(this),this.transformController=new Nz(this),this.flags=new Fi(this),this._excludedObjects=[],this._previousVisibleStateByUuid=new Map,this._helper=new NU(1)}static type(){return Ng.CUBE_CAMERA}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this._updateHelperHierarchy(),this._helper.matrixAutoUpdate=!1,this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()})),this.io.inputs.setCount(0,1)}createObject(){const t=new In.a;return t.matrixAutoUpdate=!0,t}cook(){this.transformController.update(),this._resolveObjects();const t=this._setupCubeCamera();this._cubeCamera&&!t||this._createCubeCamera(),this.cookController.endCook()}_updateHelperHierarchy(){this.flags.display.active()?this.object.add(this._helper):this.object.remove(this._helper)}_setupCubeCamera(){let t=!1;if(this._cubeCamera){const e=this._cubeCamera.children[0],n=e.near,i=e.far,r=this._cubeCamera.renderTarget.width;n==this.pv.near&&i==this.pv.far&&r==this.pv.resolution||(t=!0),t&&this.object.remove(this._cubeCamera)}return t}_createCubeCamera(){const t=new it(this.pv.resolution,{encoding:this.pv.tencoding?this.pv.encoding:w.ld,minFilter:this.pv.tminFilter?this.pv.minFilter:void 0,magFilter:this.pv.tmagFilter?this.pv.magFilter:void 0});this._cubeCamera=new et(this.pv.near,this.pv.far,t),this._cubeCamera.matrixAutoUpdate=!0,this.object.add(this._cubeCamera)}renderTarget(){if(this._cubeCamera)return this._cubeCamera.renderTarget}render(){const t=ai.renderersController.firstRenderer();if(t)if(this._cubeCamera){for(let t of this._excludedObjects)this._previousVisibleStateByUuid.set(t.uuid,t.visible),t.visible=!1;this._cubeCamera.update(t,this.scene().threejsScene());for(let t of this._excludedObjects){const e=this._previousVisibleStateByUuid.get(t.uuid);e&&(t.visible=e)}this._previousVisibleStateByUuid.clear()}else console.warn(`no cubeCamera for ${this.path()}`);else console.warn(`no renderer found for ${this.path()}`)}_resolveObjects(){const t=this.scene().objectsByMask(this.pv.excludedObjects),e=new Map;for(let n of t)e.set(n.uuid,n);this._excludedObjects=[];for(let n of t){const t=n.parent;t&&(e.get(t.uuid)||this._excludedObjects.push(n))}}static PARAM_CALLBACK_printResolve(t){t.param_callback_printResolve()}param_callback_printResolve(){this._resolveObjects(),console.log(this._excludedObjects)}static PARAM_CALLBACK_render(t){t.param_callback_render()}param_callback_render(){this.render()}}class fH extends _z{constructor(){super(...arguments),this._attachableToHierarchy=!1}createObject(){const t=new In.a;return t.matrixAutoUpdate=!1,t}cook(){this.cookController.endCook()}}class gH extends fH{}class vH extends gH{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class yH extends vH{constructor(){super(...arguments),this.renderOrder=pz.MANAGER}}class xH extends gH{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class bH extends gH{constructor(){super(...arguments),this.renderOrder=pz.MANAGER,this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class wH extends gH{constructor(){super(...arguments),this.renderOrder=pz.MANAGER,this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class TH extends fH{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class AH extends gH{constructor(){super(...arguments),this.renderOrder=pz.MANAGER,this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}const EH=[\\\\\\\"input pass\\\\\\\"];const MH={cook:!1,callback:function(t,e){SH.PARAM_CALLBACK_updatePasses(t)},computeOnDirty:!0};class SH extends ia{constructor(){super(...arguments),this.flags=new ki(this),this._passes_by_requester_id=new Map,this._update_pass_bound=this.updatePass.bind(this)}static context(){return Ki.POST}static displayedInputNames(){return EH}initializeNode(){this.flags.display.set(!1),this.flags.display.onUpdate((()=>{if(this.flags.display.active()){const t=this.parent();t&&t.displayNodeController&&t.displayNodeController.setDisplayNode(this)}})),this.io.inputs.setCount(0,1),this.io.outputs.setHasOneOutput()}cook(){this.cookController.endCook()}setupComposer(t){if(this._addPassFromInput(0,t),!this.flags.bypass.active()){let e=this._passes_by_requester_id.get(t.requester.graphNodeId());e||(e=this._createPass(t),e&&this._passes_by_requester_id.set(t.requester.graphNodeId(),e)),e&&t.composer.addPass(e)}}_addPassFromInput(t,e){const n=this.io.inputs.input(t);n&&n.setupComposer(e)}_createPass(t){}static PARAM_CALLBACK_updatePasses(t){t._updatePasses()}_updatePasses(){this._passes_by_requester_id.forEach(this._update_pass_bound)}updatePass(t){}}const CH={uniforms:{tDiffuse:{value:null}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tfloat l = linearToRelativeLuminance( texel.rgb );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( l, l, l, texel.w );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};var NH={uniforms:{tDiffuse:{value:null},averageLuminance:{value:1},luminanceMap:{value:null},maxLuminance:{value:16},minLuminance:{value:.01},middleGrey:{value:.6}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tuniform float middleGrey;\\\\n\\\\t\\\\tuniform float minLuminance;\\\\n\\\\t\\\\tuniform float maxLuminance;\\\\n\\\\t\\\\t#ifdef ADAPTED_LUMINANCE\\\\n\\\\t\\\\t\\\\tuniform sampler2D luminanceMap;\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tuniform float averageLuminance;\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tvec3 ToneMap( vec3 vColor ) {\\\\n\\\\t\\\\t\\\\t#ifdef ADAPTED_LUMINANCE\\\\n\\\\t\\\\t\\\\t\\\\t// Get the calculated average luminance\\\\n\\\\t\\\\t\\\\t\\\\tfloat fLumAvg = texture2D(luminanceMap, vec2(0.5, 0.5)).r;\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\tfloat fLumAvg = averageLuminance;\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t\\\\t// Calculate the luminance of the current pixel\\\\n\\\\t\\\\t\\\\tfloat fLumPixel = linearToRelativeLuminance( vColor );\\\\n\\\\n\\\\t\\\\t\\\\t// Apply the modified operator (Eq. 4)\\\\n\\\\t\\\\t\\\\tfloat fLumScaled = (fLumPixel * middleGrey) / max( minLuminance, fLumAvg );\\\\n\\\\n\\\\t\\\\t\\\\tfloat fLumCompressed = (fLumScaled * (1.0 + (fLumScaled / (maxLuminance * maxLuminance)))) / (1.0 + fLumScaled);\\\\n\\\\t\\\\t\\\\treturn fLumCompressed * vColor;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( ToneMap( texel.xyz ), texel.w );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class LH extends Im{constructor(t,e){super(),this.resolution=void 0!==e?e:256,this.needsInit=!0,this.adaptive=void 0===t||!!t,this.luminanceRT=null,this.previousLuminanceRT=null,this.currentLuminanceRT=null,void 0===Pm&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on CopyShader\\\\\\\");const n=Pm;this.copyUniforms=I.clone(n.uniforms),this.materialCopy=new F({uniforms:this.copyUniforms,vertexShader:n.vertexShader,fragmentShader:n.fragmentShader,blending:w.ub,depthTest:!1}),void 0===CH&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on LuminosityShader\\\\\\\"),this.materialLuminance=new F({uniforms:I.clone(CH.uniforms),vertexShader:CH.vertexShader,fragmentShader:CH.fragmentShader,blending:w.ub}),this.adaptLuminanceShader={defines:{MIP_LEVEL_1X1:(Math.log(this.resolution)/Math.log(2)).toFixed(1)},uniforms:{lastLum:{value:null},currentLum:{value:null},minLuminance:{value:.01},delta:{value:.016},tau:{value:1}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D lastLum;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D currentLum;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float minLuminance;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float delta;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float tau;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 lastLum = texture2D( lastLum, vUv, MIP_LEVEL_1X1 );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 currentLum = texture2D( currentLum, vUv, MIP_LEVEL_1X1 );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat fLastLum = max( minLuminance, lastLum.r );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat fCurrentLum = max( minLuminance, currentLum.r );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t//The adaption seems to work better in extreme lighting differences\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t//if the input luminance is squared.\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfCurrentLum *= fCurrentLum;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t// Adapt the luminance using Pattanaik's technique\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat fAdaptedLum = fLastLum + (fCurrentLum - fLastLum) * (1.0 - exp(-delta * tau));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t// \\\\\\\\\\\"fAdaptedLum = sqrt(fAdaptedLum);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor.r = fAdaptedLum;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"},this.materialAdaptiveLum=new F({uniforms:I.clone(this.adaptLuminanceShader.uniforms),vertexShader:this.adaptLuminanceShader.vertexShader,fragmentShader:this.adaptLuminanceShader.fragmentShader,defines:Object.assign({},this.adaptLuminanceShader.defines),blending:w.ub}),void 0===NH&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on ToneMapShader\\\\\\\"),this.materialToneMap=new F({uniforms:I.clone(NH.uniforms),vertexShader:NH.vertexShader,fragmentShader:NH.fragmentShader,blending:w.ub}),this.fsQuad=new km(null)}render(t,e,n,i){this.needsInit&&(this.reset(t),this.luminanceRT.texture.type=n.texture.type,this.previousLuminanceRT.texture.type=n.texture.type,this.currentLuminanceRT.texture.type=n.texture.type,this.needsInit=!1),this.adaptive&&(this.fsQuad.material=this.materialLuminance,this.materialLuminance.uniforms.tDiffuse.value=n.texture,t.setRenderTarget(this.currentLuminanceRT),this.fsQuad.render(t),this.fsQuad.material=this.materialAdaptiveLum,this.materialAdaptiveLum.uniforms.delta.value=i,this.materialAdaptiveLum.uniforms.lastLum.value=this.previousLuminanceRT.texture,this.materialAdaptiveLum.uniforms.currentLum.value=this.currentLuminanceRT.texture,t.setRenderTarget(this.luminanceRT),this.fsQuad.render(t),this.fsQuad.material=this.materialCopy,this.copyUniforms.tDiffuse.value=this.luminanceRT.texture,t.setRenderTarget(this.previousLuminanceRT),this.fsQuad.render(t)),this.fsQuad.material=this.materialToneMap,this.materialToneMap.uniforms.tDiffuse.value=n.texture,this.renderToScreen?(t.setRenderTarget(null),this.fsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(),this.fsQuad.render(t))}reset(){this.luminanceRT&&this.luminanceRT.dispose(),this.currentLuminanceRT&&this.currentLuminanceRT.dispose(),this.previousLuminanceRT&&this.previousLuminanceRT.dispose();const t={minFilter:w.V,magFilter:w.V,format:w.Ib};this.luminanceRT=new Z(this.resolution,this.resolution,t),this.luminanceRT.texture.name=\\\\\\\"AdaptiveToneMappingPass.l\\\\\\\",this.luminanceRT.texture.generateMipmaps=!1,this.previousLuminanceRT=new Z(this.resolution,this.resolution,t),this.previousLuminanceRT.texture.name=\\\\\\\"AdaptiveToneMappingPass.pl\\\\\\\",this.previousLuminanceRT.texture.generateMipmaps=!1,t.minFilter=w.Y,t.generateMipmaps=!0,this.currentLuminanceRT=new Z(this.resolution,this.resolution,t),this.currentLuminanceRT.texture.name=\\\\\\\"AdaptiveToneMappingPass.cl\\\\\\\",this.adaptive&&(this.materialToneMap.defines.ADAPTED_LUMINANCE=\\\\\\\"\\\\\\\",this.materialToneMap.uniforms.luminanceMap.value=this.luminanceRT.texture),this.fsQuad.material=new at.a({color:7829367}),this.materialLuminance.needsUpdate=!0,this.materialAdaptiveLum.needsUpdate=!0,this.materialToneMap.needsUpdate=!0}setAdaptive(t){t?(this.adaptive=!0,this.materialToneMap.defines.ADAPTED_LUMINANCE=\\\\\\\"\\\\\\\",this.materialToneMap.uniforms.luminanceMap.value=this.luminanceRT.texture):(this.adaptive=!1,delete this.materialToneMap.defines.ADAPTED_LUMINANCE,this.materialToneMap.uniforms.luminanceMap.value=null),this.materialToneMap.needsUpdate=!0}setAdaptionRate(t){t&&(this.materialAdaptiveLum.uniforms.tau.value=Math.abs(t))}setMinLuminance(t){t&&(this.materialToneMap.uniforms.minLuminance.value=t,this.materialAdaptiveLum.uniforms.minLuminance.value=t)}setMaxLuminance(t){t&&(this.materialToneMap.uniforms.maxLuminance.value=t)}setAverageLuminance(t){t&&(this.materialToneMap.uniforms.averageLuminance.value=t)}setMiddleGrey(t){t&&(this.materialToneMap.uniforms.middleGrey.value=t)}dispose(){this.luminanceRT&&this.luminanceRT.dispose(),this.previousLuminanceRT&&this.previousLuminanceRT.dispose(),this.currentLuminanceRT&&this.currentLuminanceRT.dispose(),this.materialLuminance&&this.materialLuminance.dispose(),this.materialAdaptiveLum&&this.materialAdaptiveLum.dispose(),this.materialCopy&&this.materialCopy.dispose(),this.materialToneMap&&this.materialToneMap.dispose()}}const OH=new class extends aa{constructor(){super(...arguments),this.adaptive=oa.BOOLEAN(1,{...MH}),this.averageLuminance=oa.FLOAT(.7,{...MH}),this.midGrey=oa.FLOAT(.04,{...MH}),this.maxLuminance=oa.FLOAT(16,{range:[0,20],...MH}),this.adaptiveRange=oa.FLOAT(2,{range:[0,10],...MH})}};class RH extends SH{constructor(){super(...arguments),this.paramsConfig=OH}static type(){return\\\\\\\"adaptiveToneMapping\\\\\\\"}_createPass(t){const e=new LH(this.pv.adaptive,t.resolution.x);return this.updatePass(e),e}updatePass(t){t.setMaxLuminance(this.pv.maxLuminance),t.setMiddleGrey(this.pv.midGrey),t.setAverageLuminance(this.pv.averageLuminance)}}const PH={uniforms:{damp:{value:.96},tOld:{value:null},tNew:{value:null}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float damp;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tOld;\\\\n\\\\t\\\\tuniform sampler2D tNew;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvec4 when_gt( vec4 x, float y ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn max( sign( x - y ), 0.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texelOld = texture2D( tOld, vUv );\\\\n\\\\t\\\\t\\\\tvec4 texelNew = texture2D( tNew, vUv );\\\\n\\\\n\\\\t\\\\t\\\\ttexelOld *= damp * when_gt( texelOld, 0.1 );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = max(texelNew, texelOld);\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class IH extends Im{constructor(t=.96){super(),void 0===PH&&console.error(\\\\\\\"THREE.AfterimagePass relies on AfterimageShader\\\\\\\"),this.shader=PH,this.uniforms=I.clone(this.shader.uniforms),this.uniforms.damp.value=t,this.textureComp=new Z(window.innerWidth,window.innerHeight,{minFilter:w.V,magFilter:w.ob,format:w.Ib}),this.textureOld=new Z(window.innerWidth,window.innerHeight,{minFilter:w.V,magFilter:w.ob,format:w.Ib}),this.shaderMaterial=new F({uniforms:this.uniforms,vertexShader:this.shader.vertexShader,fragmentShader:this.shader.fragmentShader}),this.compFsQuad=new km(this.shaderMaterial);const e=new at.a;this.copyFsQuad=new km(e)}render(t,e,n){this.uniforms.tOld.value=this.textureOld.texture,this.uniforms.tNew.value=n.texture,t.setRenderTarget(this.textureComp),this.compFsQuad.render(t),this.copyFsQuad.material.map=this.textureComp.texture,this.renderToScreen?(t.setRenderTarget(null),this.copyFsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(),this.copyFsQuad.render(t));const i=this.textureOld;this.textureOld=this.textureComp,this.textureComp=i}setSize(t,e){this.textureComp.setSize(t,e),this.textureOld.setSize(t,e)}}const FH=new class extends aa{constructor(){super(...arguments),this.damp=oa.FLOAT(.96,{range:[0,1],rangeLocked:[!0,!0],...MH})}};class DH extends SH{constructor(){super(...arguments),this.paramsConfig=FH}static type(){return\\\\\\\"afterImage\\\\\\\"}_createPass(t){const e=new IH;return this.updatePass(e),e}updatePass(t){t.uniforms.damp.value=this.pv.damp}}const kH={uniforms:{tDiffuse:{value:null},opacity:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float opacity;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 base = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 lumCoeff = vec3( 0.25, 0.65, 0.1 );\\\\n\\\\t\\\\t\\\\tfloat lum = dot( lumCoeff, base.rgb );\\\\n\\\\t\\\\t\\\\tvec3 blend = vec3( lum );\\\\n\\\\n\\\\t\\\\t\\\\tfloat L = min( 1.0, max( 0.0, 10.0 * ( lum - 0.45 ) ) );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 result1 = 2.0 * base.rgb * blend;\\\\n\\\\t\\\\t\\\\tvec3 result2 = 1.0 - 2.0 * ( 1.0 - blend ) * ( 1.0 - base.rgb );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 newColor = mix( result1, result2, L );\\\\n\\\\n\\\\t\\\\t\\\\tfloat A2 = opacity * base.a;\\\\n\\\\t\\\\t\\\\tvec3 mixRGB = A2 * newColor.rgb;\\\\n\\\\t\\\\t\\\\tmixRGB += ( ( 1.0 - A2 ) * base.rgb );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( mixRGB, base.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const BH=new class extends aa{constructor(){super(...arguments),this.opacity=oa.FLOAT(.95,{range:[-5,5],rangeLocked:[!0,!0],...MH})}};class zH extends SH{constructor(){super(...arguments),this.paramsConfig=BH}static type(){return\\\\\\\"bleach\\\\\\\"}_createPass(t){const e=new Bm(kH);return this.updatePass(e),e}updatePass(t){t.uniforms.opacity.value=this.pv.opacity}}const UH={uniforms:{tDiffuse:{value:null},brightness:{value:0},contrast:{value:0}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float brightness;\\\\n\\\\t\\\\tuniform float contrast;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor.rgb += brightness;\\\\n\\\\n\\\\t\\\\t\\\\tif (contrast > 0.0) {\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = (gl_FragColor.rgb - 0.5) / (1.0 - contrast) + 0.5;\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = (gl_FragColor.rgb - 0.5) * (1.0 + contrast) + 0.5;\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const GH=new class extends aa{constructor(){super(...arguments),this.brightness=oa.FLOAT(0,{range:[-1,1],rangeLocked:[!1,!1],...MH}),this.contrast=oa.FLOAT(0,{range:[-1,1],rangeLocked:[!1,!1],...MH}),this.transparent=oa.BOOLEAN(1,MH)}};class VH extends SH{constructor(){super(...arguments),this.paramsConfig=GH}static type(){return\\\\\\\"brightnessContrast\\\\\\\"}_createPass(t){const e=new Bm(UH);return console.log(\\\\\\\"brightness\\\\\\\",e),e.fsQuad.material.transparent=!0,this.updatePass(e),e}updatePass(t){t.uniforms.brightness.value=this.pv.brightness,t.uniforms.contrast.value=this.pv.contrast,t.material.transparent=this.pv.transparent}}class HH extends Im{constructor(t,e){super(),this.needsSwap=!1,this.clearColor=void 0!==t?t:0,this.clearAlpha=void 0!==e?e:0,this._oldClearColor=new D.a}render(t,e,n){let i;this.clearColor&&(t.getClearColor(this._oldClearColor),i=t.getClearAlpha(),t.setClearColor(this.clearColor,this.clearAlpha)),t.setRenderTarget(this.renderToScreen?null:n),t.clear(),this.clearColor&&t.setClearColor(this._oldClearColor,i)}}const jH=new class extends aa{};class WH extends SH{constructor(){super(...arguments),this.paramsConfig=jH}static type(){return\\\\\\\"clear\\\\\\\"}_createPass(t){const e=new HH;return this.updatePass(e),e}updatePass(t){}}const qH=new class extends aa{};class XH extends SH{constructor(){super(...arguments),this.paramsConfig=qH}static type(){return\\\\\\\"clearMask\\\\\\\"}_createPass(t){const e=new Um;return this.updatePass(e),e}updatePass(t){}}const YH={uniforms:{tDiffuse:{value:null},powRGB:{value:new p.a(2,2,2)},mulRGB:{value:new p.a(1,1,1)},addRGB:{value:new p.a(0,0,0)}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform vec3 powRGB;\\\\n\\\\t\\\\tuniform vec3 mulRGB;\\\\n\\\\t\\\\tuniform vec3 addRGB;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = texture2D( tDiffuse, vUv );\\\\n\\\\t\\\\t\\\\tgl_FragColor.rgb = mulRGB * pow( ( gl_FragColor.rgb + addRGB ), powRGB );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const $H=new class extends aa{constructor(){super(...arguments),this.pow=oa.VECTOR3([2,2,2],{...MH}),this.mult=oa.COLOR([1,1,1],{...MH}),this.add=oa.COLOR([0,0,0],{...MH})}};class JH extends SH{constructor(){super(...arguments),this.paramsConfig=$H}static type(){return\\\\\\\"colorCorrection\\\\\\\"}_createPass(t){const e=new Bm(YH);return this.updatePass(e),e}updatePass(t){t.uniforms.powRGB.value.copy(this.pv.pow),t.uniforms.mulRGB.value.set(this.pv.mult.r,this.pv.mult.g,this.pv.mult.b),t.uniforms.addRGB.value.set(this.pv.add.r,this.pv.add.g,this.pv.add.b)}}const ZH=new class extends aa{constructor(){super(...arguments),this.opacity=oa.FLOAT(1,{range:[0,1],rangeLocked:[!0,!0],...MH}),this.transparent=oa.BOOLEAN(1,MH)}};class QH extends SH{constructor(){super(...arguments),this.paramsConfig=ZH}static type(){return\\\\\\\"copy\\\\\\\"}_createPass(t){const e=new Bm(Pm);return this.updatePass(e),e}updatePass(t){t.uniforms.opacity.value=this.pv.opacity,t.material.transparent=this.pv.transparent}}const KH={uniforms:{textureWidth:{value:1},textureHeight:{value:1},focalDepth:{value:1},focalLength:{value:24},fstop:{value:.9},tColor:{value:null},tDepth:{value:null},maxblur:{value:1},showFocus:{value:0},manualdof:{value:0},vignetting:{value:0},depthblur:{value:0},threshold:{value:.5},gain:{value:2},bias:{value:.5},fringe:{value:.7},znear:{value:.1},zfar:{value:100},noise:{value:1},dithering:{value:1e-4},pentagon:{value:0},shaderFocus:{value:1},focusCoords:{value:new d.a}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tColor;\\\\n\\\\t\\\\tuniform sampler2D tDepth;\\\\n\\\\t\\\\tuniform float textureWidth;\\\\n\\\\t\\\\tuniform float textureHeight;\\\\n\\\\n\\\\t\\\\tuniform float focalDepth; //focal distance value in meters, but you may use autofocus option below\\\\n\\\\t\\\\tuniform float focalLength; //focal length in mm\\\\n\\\\t\\\\tuniform float fstop; //f-stop value\\\\n\\\\t\\\\tuniform bool showFocus; //show debug focus point and focal range (red = focal point, green = focal range)\\\\n\\\\n\\\\t\\\\t/*\\\\n\\\\t\\\\tmake sure that these two values are the same for your camera, otherwise distances will be wrong.\\\\n\\\\t\\\\t*/\\\\n\\\\n\\\\t\\\\tuniform float znear; // camera clipping start\\\\n\\\\t\\\\tuniform float zfar; // camera clipping end\\\\n\\\\n\\\\t\\\\t//------------------------------------------\\\\n\\\\t\\\\t//user variables\\\\n\\\\n\\\\t\\\\tconst int samples = SAMPLES; //samples on the first ring\\\\n\\\\t\\\\tconst int rings = RINGS; //ring count\\\\n\\\\n\\\\t\\\\tconst int maxringsamples = rings * samples;\\\\n\\\\n\\\\t\\\\tuniform bool manualdof; // manual dof calculation\\\\n\\\\t\\\\tfloat ndofstart = 1.0; // near dof blur start\\\\n\\\\t\\\\tfloat ndofdist = 2.0; // near dof blur falloff distance\\\\n\\\\t\\\\tfloat fdofstart = 1.0; // far dof blur start\\\\n\\\\t\\\\tfloat fdofdist = 3.0; // far dof blur falloff distance\\\\n\\\\n\\\\t\\\\tfloat CoC = 0.03; //circle of confusion size in mm (35mm film = 0.03mm)\\\\n\\\\n\\\\t\\\\tuniform bool vignetting; // use optical lens vignetting\\\\n\\\\n\\\\t\\\\tfloat vignout = 1.3; // vignetting outer border\\\\n\\\\t\\\\tfloat vignin = 0.0; // vignetting inner border\\\\n\\\\t\\\\tfloat vignfade = 22.0; // f-stops till vignete fades\\\\n\\\\n\\\\t\\\\tuniform bool shaderFocus;\\\\n\\\\t\\\\t// disable if you use external focalDepth value\\\\n\\\\n\\\\t\\\\tuniform vec2 focusCoords;\\\\n\\\\t\\\\t// autofocus point on screen (0.0,0.0 - left lower corner, 1.0,1.0 - upper right)\\\\n\\\\t\\\\t// if center of screen use vec2(0.5, 0.5);\\\\n\\\\n\\\\t\\\\tuniform float maxblur;\\\\n\\\\t\\\\t//clamp value of max blur (0.0 = no blur, 1.0 default)\\\\n\\\\n\\\\t\\\\tuniform float threshold; // highlight threshold;\\\\n\\\\t\\\\tuniform float gain; // highlight gain;\\\\n\\\\n\\\\t\\\\tuniform float bias; // bokeh edge bias\\\\n\\\\t\\\\tuniform float fringe; // bokeh chromatic aberration / fringing\\\\n\\\\n\\\\t\\\\tuniform bool noise; //use noise instead of pattern for sample dithering\\\\n\\\\n\\\\t\\\\tuniform float dithering;\\\\n\\\\n\\\\t\\\\tuniform bool depthblur; // blur the depth buffer\\\\n\\\\t\\\\tfloat dbsize = 1.25; // depth blur size\\\\n\\\\n\\\\t\\\\t/*\\\\n\\\\t\\\\tnext part is experimental\\\\n\\\\t\\\\tnot looking good with small sample and ring count\\\\n\\\\t\\\\tlooks okay starting from samples = 4, rings = 4\\\\n\\\\t\\\\t*/\\\\n\\\\n\\\\t\\\\tuniform bool pentagon; //use pentagon as bokeh shape?\\\\n\\\\t\\\\tfloat feather = 0.4; //pentagon shape feather\\\\n\\\\n\\\\t\\\\t//------------------------------------------\\\\n\\\\n\\\\t\\\\tfloat penta(vec2 coords) {\\\\n\\\\t\\\\t\\\\t//pentagonal shape\\\\n\\\\t\\\\t\\\\tfloat scale = float(rings) - 1.3;\\\\n\\\\t\\\\t\\\\tvec4 HS0 = vec4( 1.0, 0.0, 0.0, 1.0);\\\\n\\\\t\\\\t\\\\tvec4 HS1 = vec4( 0.309016994, 0.951056516, 0.0, 1.0);\\\\n\\\\t\\\\t\\\\tvec4 HS2 = vec4(-0.809016994, 0.587785252, 0.0, 1.0);\\\\n\\\\t\\\\t\\\\tvec4 HS3 = vec4(-0.809016994,-0.587785252, 0.0, 1.0);\\\\n\\\\t\\\\t\\\\tvec4 HS4 = vec4( 0.309016994,-0.951056516, 0.0, 1.0);\\\\n\\\\t\\\\t\\\\tvec4 HS5 = vec4( 0.0 ,0.0 , 1.0, 1.0);\\\\n\\\\n\\\\t\\\\t\\\\tvec4 one = vec4( 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 P = vec4((coords),vec2(scale, scale));\\\\n\\\\n\\\\t\\\\t\\\\tvec4 dist = vec4(0.0);\\\\n\\\\t\\\\t\\\\tfloat inorout = -4.0;\\\\n\\\\n\\\\t\\\\t\\\\tdist.x = dot( P, HS0 );\\\\n\\\\t\\\\t\\\\tdist.y = dot( P, HS1 );\\\\n\\\\t\\\\t\\\\tdist.z = dot( P, HS2 );\\\\n\\\\t\\\\t\\\\tdist.w = dot( P, HS3 );\\\\n\\\\n\\\\t\\\\t\\\\tdist = smoothstep( -feather, feather, dist );\\\\n\\\\n\\\\t\\\\t\\\\tinorout += dot( dist, one );\\\\n\\\\n\\\\t\\\\t\\\\tdist.x = dot( P, HS4 );\\\\n\\\\t\\\\t\\\\tdist.y = HS5.w - abs( P.z );\\\\n\\\\n\\\\t\\\\t\\\\tdist = smoothstep( -feather, feather, dist );\\\\n\\\\t\\\\t\\\\tinorout += dist.x;\\\\n\\\\n\\\\t\\\\t\\\\treturn clamp( inorout, 0.0, 1.0 );\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat bdepth(vec2 coords) {\\\\n\\\\t\\\\t\\\\t// Depth buffer blur\\\\n\\\\t\\\\t\\\\tfloat d = 0.0;\\\\n\\\\t\\\\t\\\\tfloat kernel[9];\\\\n\\\\t\\\\t\\\\tvec2 offset[9];\\\\n\\\\n\\\\t\\\\t\\\\tvec2 wh = vec2(1.0/textureWidth,1.0/textureHeight) * dbsize;\\\\n\\\\n\\\\t\\\\t\\\\toffset[0] = vec2(-wh.x,-wh.y);\\\\n\\\\t\\\\t\\\\toffset[1] = vec2( 0.0, -wh.y);\\\\n\\\\t\\\\t\\\\toffset[2] = vec2( wh.x -wh.y);\\\\n\\\\n\\\\t\\\\t\\\\toffset[3] = vec2(-wh.x, 0.0);\\\\n\\\\t\\\\t\\\\toffset[4] = vec2( 0.0, 0.0);\\\\n\\\\t\\\\t\\\\toffset[5] = vec2( wh.x, 0.0);\\\\n\\\\n\\\\t\\\\t\\\\toffset[6] = vec2(-wh.x, wh.y);\\\\n\\\\t\\\\t\\\\toffset[7] = vec2( 0.0, wh.y);\\\\n\\\\t\\\\t\\\\toffset[8] = vec2( wh.x, wh.y);\\\\n\\\\n\\\\t\\\\t\\\\tkernel[0] = 1.0/16.0; kernel[1] = 2.0/16.0; kernel[2] = 1.0/16.0;\\\\n\\\\t\\\\t\\\\tkernel[3] = 2.0/16.0; kernel[4] = 4.0/16.0; kernel[5] = 2.0/16.0;\\\\n\\\\t\\\\t\\\\tkernel[6] = 1.0/16.0; kernel[7] = 2.0/16.0; kernel[8] = 1.0/16.0;\\\\n\\\\n\\\\n\\\\t\\\\t\\\\tfor( int i=0; i<9; i++ ) {\\\\n\\\\t\\\\t\\\\t\\\\tfloat tmp = texture2D(tDepth, coords + offset[i]).r;\\\\n\\\\t\\\\t\\\\t\\\\td += tmp * kernel[i];\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\treturn d;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\n\\\\t\\\\tvec3 color(vec2 coords,float blur) {\\\\n\\\\t\\\\t\\\\t//processing the sample\\\\n\\\\n\\\\t\\\\t\\\\tvec3 col = vec3(0.0);\\\\n\\\\t\\\\t\\\\tvec2 texel = vec2(1.0/textureWidth,1.0/textureHeight);\\\\n\\\\n\\\\t\\\\t\\\\tcol.r = texture2D(tColor,coords + vec2(0.0,1.0)*texel*fringe*blur).r;\\\\n\\\\t\\\\t\\\\tcol.g = texture2D(tColor,coords + vec2(-0.866,-0.5)*texel*fringe*blur).g;\\\\n\\\\t\\\\t\\\\tcol.b = texture2D(tColor,coords + vec2(0.866,-0.5)*texel*fringe*blur).b;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 lumcoeff = vec3(0.299,0.587,0.114);\\\\n\\\\t\\\\t\\\\tfloat lum = dot(col.rgb, lumcoeff);\\\\n\\\\t\\\\t\\\\tfloat thresh = max((lum-threshold)*gain, 0.0);\\\\n\\\\t\\\\t\\\\treturn col+mix(vec3(0.0),col,thresh*blur);\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec3 debugFocus(vec3 col, float blur, float depth) {\\\\n\\\\t\\\\t\\\\tfloat edge = 0.002*depth; //distance based edge smoothing\\\\n\\\\t\\\\t\\\\tfloat m = clamp(smoothstep(0.0,edge,blur),0.0,1.0);\\\\n\\\\t\\\\t\\\\tfloat e = clamp(smoothstep(1.0-edge,1.0,blur),0.0,1.0);\\\\n\\\\n\\\\t\\\\t\\\\tcol = mix(col,vec3(1.0,0.5,0.0),(1.0-m)*0.6);\\\\n\\\\t\\\\t\\\\tcol = mix(col,vec3(0.0,0.5,1.0),((1.0-e)-(1.0-m))*0.2);\\\\n\\\\n\\\\t\\\\t\\\\treturn col;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat linearize(float depth) {\\\\n\\\\t\\\\t\\\\treturn -zfar * znear / (depth * (zfar - znear) - zfar);\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat vignette() {\\\\n\\\\t\\\\t\\\\tfloat dist = distance(vUv.xy, vec2(0.5,0.5));\\\\n\\\\t\\\\t\\\\tdist = smoothstep(vignout+(fstop/vignfade), vignin+(fstop/vignfade), dist);\\\\n\\\\t\\\\t\\\\treturn clamp(dist,0.0,1.0);\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat gather(float i, float j, int ringsamples, inout vec3 col, float w, float h, float blur) {\\\\n\\\\t\\\\t\\\\tfloat rings2 = float(rings);\\\\n\\\\t\\\\t\\\\tfloat step = PI*2.0 / float(ringsamples);\\\\n\\\\t\\\\t\\\\tfloat pw = cos(j*step)*i;\\\\n\\\\t\\\\t\\\\tfloat ph = sin(j*step)*i;\\\\n\\\\t\\\\t\\\\tfloat p = 1.0;\\\\n\\\\t\\\\t\\\\tif (pentagon) {\\\\n\\\\t\\\\t\\\\t\\\\tp = penta(vec2(pw,ph));\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\tcol += color(vUv.xy + vec2(pw*w,ph*h), blur) * mix(1.0, i/rings2, bias) * p;\\\\n\\\\t\\\\t\\\\treturn 1.0 * mix(1.0, i /rings2, bias) * p;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t//scene depth calculation\\\\n\\\\n\\\\t\\\\t\\\\tfloat depth = linearize(texture2D(tDepth,vUv.xy).x);\\\\n\\\\n\\\\t\\\\t\\\\t// Blur depth?\\\\n\\\\t\\\\t\\\\tif ( depthblur ) {\\\\n\\\\t\\\\t\\\\t\\\\tdepth = linearize(bdepth(vUv.xy));\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t//focal plane calculation\\\\n\\\\n\\\\t\\\\t\\\\tfloat fDepth = focalDepth;\\\\n\\\\n\\\\t\\\\t\\\\tif (shaderFocus) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfDepth = linearize(texture2D(tDepth,focusCoords).x);\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t// dof blur factor calculation\\\\n\\\\n\\\\t\\\\t\\\\tfloat blur = 0.0;\\\\n\\\\n\\\\t\\\\t\\\\tif (manualdof) {\\\\n\\\\t\\\\t\\\\t\\\\tfloat a = depth-fDepth; // Focal plane\\\\n\\\\t\\\\t\\\\t\\\\tfloat b = (a-fdofstart)/fdofdist; // Far DoF\\\\n\\\\t\\\\t\\\\t\\\\tfloat c = (-a-ndofstart)/ndofdist; // Near Dof\\\\n\\\\t\\\\t\\\\t\\\\tblur = (a>0.0) ? b : c;\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\tfloat f = focalLength; // focal length in mm\\\\n\\\\t\\\\t\\\\t\\\\tfloat d = fDepth*1000.0; // focal plane in mm\\\\n\\\\t\\\\t\\\\t\\\\tfloat o = depth*1000.0; // depth in mm\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat a = (o*f)/(o-f);\\\\n\\\\t\\\\t\\\\t\\\\tfloat b = (d*f)/(d-f);\\\\n\\\\t\\\\t\\\\t\\\\tfloat c = (d-f)/(d*fstop*CoC);\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tblur = abs(a-b)*c;\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tblur = clamp(blur,0.0,1.0);\\\\n\\\\n\\\\t\\\\t\\\\t// calculation of pattern for dithering\\\\n\\\\n\\\\t\\\\t\\\\tvec2 noise = vec2(rand(vUv.xy), rand( vUv.xy + vec2( 0.4, 0.6 ) ) )*dithering*blur;\\\\n\\\\n\\\\t\\\\t\\\\t// getting blur x and y step factor\\\\n\\\\n\\\\t\\\\t\\\\tfloat w = (1.0/textureWidth)*blur*maxblur+noise.x;\\\\n\\\\t\\\\t\\\\tfloat h = (1.0/textureHeight)*blur*maxblur+noise.y;\\\\n\\\\n\\\\t\\\\t\\\\t// calculation of final color\\\\n\\\\n\\\\t\\\\t\\\\tvec3 col = vec3(0.0);\\\\n\\\\n\\\\t\\\\t\\\\tif(blur < 0.05) {\\\\n\\\\t\\\\t\\\\t\\\\t//some optimization thingy\\\\n\\\\t\\\\t\\\\t\\\\tcol = texture2D(tColor, vUv.xy).rgb;\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\tcol = texture2D(tColor, vUv.xy).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tfloat s = 1.0;\\\\n\\\\t\\\\t\\\\t\\\\tint ringsamples;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfor (int i = 1; i <= rings; i++) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t/*unboxstart*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tringsamples = i * samples;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfor (int j = 0 ; j < maxringsamples ; j++) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif (j >= ringsamples) break;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ts += gather(float(i), float(j), ringsamples, col, w, h, blur);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t/*unboxend*/\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tcol /= s; //divide by sample count\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tif (showFocus) {\\\\n\\\\t\\\\t\\\\t\\\\tcol = debugFocus(col, blur, depth);\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tif (vignetting) {\\\\n\\\\t\\\\t\\\\t\\\\tcol *= vignette();\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor.rgb = col;\\\\n\\\\t\\\\t\\\\tgl_FragColor.a = 1.0;\\\\n\\\\t\\\\t}\\\\\\\"},tj={uniforms:{mNear:{value:1},mFar:{value:1e3}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying float vViewZDepth;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\tvViewZDepth = - mvPosition.z;\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float mNear;\\\\n\\\\t\\\\tuniform float mFar;\\\\n\\\\n\\\\t\\\\tvarying float vViewZDepth;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tfloat color = 1.0 - smoothstep( mNear, mFar, vViewZDepth );\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( vec3( color ), 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class ej{constructor(t){this._scene=t}scene(){return this._scene}with_overriden_material(t,e,n,i){const r={};let s;this._scene.traverse((i=>{const o=i;if(o.material){const i=o.geometry;if(i){const a=o.customDepthDOFMaterial;if(a){if(s=a,s.uniforms)for(let t of Object.keys(n))s.uniforms[t].value=n[t].value}else s=ps.markedAsInstance(i)?e:t;s&&(r[o.uuid]=o.material,o.material=s)}}})),i(),this._scene.traverse((t=>{const e=t;if(e.material){e.geometry&&(e.material=r[e.uuid])}}));for(let t of Object.keys(r))delete r[t]}}class nj{constructor(t,e,n,i){this._depth_of_field_node=t,this._scene=e,this._camera=n,this._resolution=i,this._camera_uniforms={mNear:{value:0},mFar:{value:0}},this.enabled=!0,this.needsSwap=!0,this.clear=!0,this.renderToScreen=!0,this._processing_scene=new fr,this.clear_color=new D.a(1,1,1),this._prev_clear_color=new D.a,this._core_scene=new ej(this._scene);const r=3,s=4;this._processing_camera=new st.a(this._resolution.x/-2,this._resolution.x/2,this._resolution.y/2,this._resolution.y/-2,-1e4,1e4),this._processing_camera.position.z=100,this._processing_scene.add(this._processing_camera);var o={minFilter:w.V,magFilter:w.V,format:w.ic};this._rtTextureDepth=new Z(this._resolution.x,this._resolution.y,o),this._rtTextureColor=new Z(this._resolution.x,this._resolution.y,o);var a=KH;a||console.error(\\\\\\\"BokehPass relies on BokehShader\\\\\\\"),this.bokeh_uniforms=I.clone(a.uniforms),this.bokeh_uniforms.tColor.value=this._rtTextureColor.texture,this.bokeh_uniforms.tDepth.value=this._rtTextureDepth.texture,this.bokeh_uniforms.textureWidth.value=this._resolution.x,this.bokeh_uniforms.textureHeight.value=this._resolution.y,this.bokeh_material=new F({uniforms:this.bokeh_uniforms,vertexShader:a.vertexShader,fragmentShader:a.fragmentShader,defines:{RINGS:r,SAMPLES:s}}),this._quad=new k.a(new L(this._resolution.x,this._resolution.y),this.bokeh_material),this._quad.position.z=-500,this._processing_scene.add(this._quad);var l=tj;l||console.error(\\\\\\\"BokehPass relies on BokehDepthShader\\\\\\\"),this.materialDepth=new F({uniforms:l.uniforms,vertexShader:l.vertexShader,fragmentShader:l.fragmentShader}),this.materialDepthInstance=new F({uniforms:l.uniforms,vertexShader:\\\\\\\"#include \\\\n\\\\nvec3 rotate_with_quat( vec3 v, vec4 q )\\\\n{\\\\n\\\\treturn v + 2.0 * cross( q.xyz, cross( q.xyz, v ) + q.w * v );\\\\n}\\\\n\\\\n\\\\nattribute vec4 instanceOrientation;\\\\nattribute vec3 instancePosition;\\\\nattribute vec3 instanceScale;\\\\nvarying float vViewZDepth;\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec3 v_POLYGON_instance_transform1_position = vec3(position);\\\\n\\\\tv_POLYGON_instance_transform1_position *= instanceScale;\\\\n\\\\tv_POLYGON_instance_transform1_position = rotate_with_quat( v_POLYGON_instance_transform1_position, instanceOrientation );\\\\n\\\\tv_POLYGON_instance_transform1_position += instancePosition;\\\\n\\\\t\\\\n\\\\t// replaces #include \\\\n\\\\tvec3 transformed = v_POLYGON_instance_transform1_position;\\\\n\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvViewZDepth = - mvPosition.z;\\\\n}\\\\\\\",fragmentShader:l.fragmentShader}),this.update_camera_uniforms_with_node(this._depth_of_field_node,this._camera)}setSize(t,e){this._rtTextureDepth.setSize(t,e),this._rtTextureColor.setSize(t,e),this.bokeh_uniforms.textureWidth.value=t,this.bokeh_uniforms.textureHeight.value=e}dispose(){this._rtTextureDepth.dispose(),this._rtTextureColor.dispose()}render(t,e,n){t.getClearColor(this._prev_clear_color),t.setClearColor(this.clear_color),t.clear(),t.setRenderTarget(this._rtTextureColor),t.clear(),t.render(this._scene,this._camera),t.setClearColor(0),this._core_scene.with_overriden_material(this.materialDepth,this.materialDepthInstance,this._camera_uniforms,(()=>{t.setRenderTarget(this._rtTextureDepth),t.clear(),t.render(this._scene,this._camera)})),t.setRenderTarget(null),t.clear(),t.render(this._processing_scene,this._processing_camera),t.setClearColor(this._prev_clear_color)}update_camera_uniforms_with_node(t,e){this.bokeh_uniforms.focalLength.value=e.getFocalLength(),this.bokeh_uniforms.znear.value=e.near,this.bokeh_uniforms.zfar.value=e.far;var n=rj.smoothstep(e.near,e.far,t.pv.focalDepth),i=rj.linearize(1-n,e.near,e.far);this.bokeh_uniforms.focalDepth.value=i,this._camera_uniforms={mNear:{value:e.near},mFar:{value:e.far}};for(let t of[this.materialDepth,this.materialDepthInstance])t.uniforms.mNear.value=this._camera_uniforms.mNear.value,t.uniforms.mFar.value=this._camera_uniforms.mFar.value}}const ij=new class extends aa{constructor(){super(...arguments),this.focalDepth=oa.FLOAT(10,{range:[0,50],rangeLocked:[!0,!1],step:.001,...MH}),this.fStep=oa.FLOAT(10,{range:[.1,22],rangeLocked:[!0,!0],...MH}),this.maxBlur=oa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],...MH}),this.vignetting=oa.BOOLEAN(0,{...MH}),this.depthBlur=oa.BOOLEAN(0,{...MH}),this.threshold=oa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0],step:.001,...MH}),this.gain=oa.FLOAT(1,{range:[0,100],rangeLocked:[!0,!0],step:.001,...MH}),this.bias=oa.FLOAT(1,{range:[0,3],rangeLocked:[!0,!0],step:.001,...MH}),this.fringe=oa.FLOAT(.7,{range:[0,5],rangeLocked:[!0,!1],step:.001,...MH}),this.noise=oa.BOOLEAN(0,{...MH}),this.dithering=oa.FLOAT(0,{range:[0,.001],rangeLocked:[!0,!0],step:1e-4,...MH}),this.pentagon=oa.BOOLEAN(0,{...MH}),this.rings=oa.INTEGER(3,{range:[1,8],rangeLocked:[!0,!0],...MH}),this.samples=oa.INTEGER(4,{range:[1,13],rangeLocked:[!0,!0],...MH}),this.clearColor=oa.COLOR([1,1,1],{...MH})}};class rj extends SH{constructor(){super(...arguments),this.paramsConfig=ij}static type(){return\\\\\\\"depthOfField\\\\\\\"}static saturate(t){return Math.max(0,Math.min(1,t))}static linearize(t,e,n){return-n*e/(t*(n-e)-n)}static smoothstep(t,e,n){var i=this.saturate((n-t)/(e-t));return i*i*(3-2*i)}_createPass(t){if(t.camera.isPerspectiveCamera){const e=t.camera_node;if(e){const n=new nj(this,t.scene,e.object,t.resolution);this.updatePass(n);const i=new Ai(this.scene(),\\\\\\\"DOF\\\\\\\");return i.addGraphInput(e.p.near),i.addGraphInput(e.p.far),i.addGraphInput(e.p.fov),i.addGraphInput(this.p.focalDepth),i.addPostDirtyHook(\\\\\\\"post/DOF\\\\\\\",(()=>{this.update_pass_from_camera_node(n,e)})),n}}}update_pass_from_camera_node(t,e){t.update_camera_uniforms_with_node(this,e.object)}updatePass(t){t.bokeh_uniforms.fstop.value=this.pv.fStep,t.bokeh_uniforms.maxblur.value=this.pv.maxBlur,t.bokeh_uniforms.threshold.value=this.pv.threshold,t.bokeh_uniforms.gain.value=this.pv.gain,t.bokeh_uniforms.bias.value=this.pv.bias,t.bokeh_uniforms.fringe.value=this.pv.fringe,t.bokeh_uniforms.dithering.value=this.pv.dithering,t.bokeh_uniforms.noise.value=this.pv.noise?1:0,t.bokeh_uniforms.pentagon.value=this.pv.pentagon?1:0,t.bokeh_uniforms.vignetting.value=this.pv.vignetting?1:0,t.bokeh_uniforms.depthblur.value=this.pv.depthBlur?1:0,t.bokeh_uniforms.shaderFocus.value=0,t.bokeh_uniforms.showFocus.value=0,t.bokeh_uniforms.manualdof.value=0,t.bokeh_uniforms.focusCoords.value.set(.5,.5),t.bokeh_material.defines.RINGS=this.pv.rings,t.bokeh_material.defines.SAMPLES=this.pv.samples,t.bokeh_material.needsUpdate=!0,t.clear_color.copy(this.pv.clearColor)}}const sj={uniforms:{tDiffuse:{value:null},tSize:{value:new d.a(256,256)},center:{value:new d.a(.5,.5)},angle:{value:1.57},scale:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform vec2 center;\\\\n\\\\t\\\\tuniform float angle;\\\\n\\\\t\\\\tuniform float scale;\\\\n\\\\t\\\\tuniform vec2 tSize;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tfloat pattern() {\\\\n\\\\n\\\\t\\\\t\\\\tfloat s = sin( angle ), c = cos( angle );\\\\n\\\\n\\\\t\\\\t\\\\tvec2 tex = vUv * tSize - center;\\\\n\\\\t\\\\t\\\\tvec2 point = vec2( c * tex.x - s * tex.y, s * tex.x + c * tex.y ) * scale;\\\\n\\\\n\\\\t\\\\t\\\\treturn ( sin( point.x ) * sin( point.y ) ) * 4.0;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 color = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tfloat average = ( color.r + color.g + color.b ) / 3.0;\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( vec3( average * 10.0 - 5.0 + pattern() ), color.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const oj=new class extends aa{constructor(){super(...arguments),this.center=oa.VECTOR2([.5,.5],{...MH}),this.angle=oa.FLOAT(\\\\\\\"$PI*0.5\\\\\\\",{range:[0,10],rangeLocked:[!1,!1],...MH}),this.scale=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...MH})}};class aj extends SH{constructor(){super(...arguments),this.paramsConfig=oj}static type(){return\\\\\\\"dotScreen\\\\\\\"}_createPass(t){const e=new Bm(sj);return this.updatePass(e),e}updatePass(t){t.uniforms.center.value=this.pv.center,t.uniforms.angle.value=this.pv.angle,t.uniforms.scale.value=this.pv.scale}}const lj={uniforms:{tDiffuse:{value:null},time:{value:0},nIntensity:{value:.5},sIntensity:{value:.05},sCount:{value:4096},grayscale:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t// control parameter\\\\n\\\\t\\\\tuniform float time;\\\\n\\\\n\\\\t\\\\tuniform bool grayscale;\\\\n\\\\n\\\\t\\\\t// noise effect intensity value (0 = no effect, 1 = full effect)\\\\n\\\\t\\\\tuniform float nIntensity;\\\\n\\\\n\\\\t\\\\t// scanlines effect intensity value (0 = no effect, 1 = full effect)\\\\n\\\\t\\\\tuniform float sIntensity;\\\\n\\\\n\\\\t\\\\t// scanlines effect count value (0 = no effect, 4096 = full effect)\\\\n\\\\t\\\\tuniform float sCount;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t// sample the source\\\\n\\\\t\\\\t\\\\tvec4 cTextureScreen = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t// make some noise\\\\n\\\\t\\\\t\\\\tfloat dx = rand( vUv + time );\\\\n\\\\n\\\\t\\\\t// add noise\\\\n\\\\t\\\\t\\\\tvec3 cResult = cTextureScreen.rgb + cTextureScreen.rgb * clamp( 0.1 + dx, 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t// get us a sine and cosine\\\\n\\\\t\\\\t\\\\tvec2 sc = vec2( sin( vUv.y * sCount ), cos( vUv.y * sCount ) );\\\\n\\\\n\\\\t\\\\t// add scanlines\\\\n\\\\t\\\\t\\\\tcResult += cTextureScreen.rgb * vec3( sc.x, sc.y, sc.x ) * sIntensity;\\\\n\\\\n\\\\t\\\\t// interpolate between source and result by intensity\\\\n\\\\t\\\\t\\\\tcResult = cTextureScreen.rgb + clamp( nIntensity, 0.0,1.0 ) * ( cResult - cTextureScreen.rgb );\\\\n\\\\n\\\\t\\\\t// convert to grayscale if desired\\\\n\\\\t\\\\t\\\\tif( grayscale ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tcResult = vec3( cResult.r * 0.3 + cResult.g * 0.59 + cResult.b * 0.11 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( cResult, cTextureScreen.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class cj extends Im{constructor(t,e,n,i){super(),void 0===lj&&console.error(\\\\\\\"THREE.FilmPass relies on FilmShader\\\\\\\");const r=lj;this.uniforms=I.clone(r.uniforms),this.material=new F({uniforms:this.uniforms,vertexShader:r.vertexShader,fragmentShader:r.fragmentShader}),void 0!==i&&(this.uniforms.grayscale.value=i),void 0!==t&&(this.uniforms.nIntensity.value=t),void 0!==e&&(this.uniforms.sIntensity.value=e),void 0!==n&&(this.uniforms.sCount.value=n),this.fsQuad=new km(this.material)}render(t,e,n,i){this.uniforms.tDiffuse.value=n.texture,this.uniforms.time.value+=i,this.renderToScreen?(t.setRenderTarget(null),this.fsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(),this.fsQuad.render(t))}}const uj=new class extends aa{constructor(){super(...arguments),this.noiseIntensity=oa.FLOAT(.5,{range:[0,1],rangeLocked:[!1,!1],...MH}),this.scanlinesIntensity=oa.FLOAT(.05,{range:[0,1],rangeLocked:[!0,!1],...MH}),this.scanlinesCount=oa.FLOAT(4096,{range:[0,4096],rangeLocked:[!0,!1],...MH}),this.grayscale=oa.BOOLEAN(1,{...MH})}};class hj extends SH{constructor(){super(...arguments),this.paramsConfig=uj}static type(){return\\\\\\\"film\\\\\\\"}_createPass(t){const e=new cj(this.pv.noiseIntensity,this.pv.scanlinesIntensity,this.pv.scanlinesCount,this.pv.grayscale?1:0);return this.updatePass(e),e}updatePass(t){t.uniforms.nIntensity.value=this.pv.noiseIntensity,t.uniforms.sIntensity.value=this.pv.scanlinesIntensity,t.uniforms.sCount.value=this.pv.scanlinesCount,t.uniforms.grayscale.value=this.pv.grayscale?1:0}}const dj={uniforms:{tDiffuse:{value:null},resolution:{value:new d.a(1/1024,1/512)}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:'\\\\n\\\\n\\\\t\\\\tprecision highp float;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tuniform vec2 resolution;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\t#define FXAA_PC 1\\\\n\\\\t\\\\t#define FXAA_GLSL_100 1\\\\n\\\\t\\\\t#define FXAA_QUALITY_PRESET 12\\\\n\\\\n\\\\t\\\\t#define FXAA_GREEN_AS_LUMA 1\\\\n\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_PC_CONSOLE\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// The console algorithm for PC is included\\\\n\\\\t\\\\t\\\\t\\\\t// for developers targeting really low spec machines.\\\\n\\\\t\\\\t\\\\t\\\\t// Likely better to just run FXAA_PC, and use a really low preset.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_PC_CONSOLE 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_GLSL_120\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_GLSL_120 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_GLSL_130\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_GLSL_130 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_HLSL_3\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_HLSL_3 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_HLSL_4\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_HLSL_4 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_HLSL_5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_HLSL_5 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*==========================================================================*/\\\\n\\\\t\\\\t#ifndef FXAA_GREEN_AS_LUMA\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// For those using non-linear color,\\\\n\\\\t\\\\t\\\\t\\\\t// and either not able to get luma in alpha, or not wanting to,\\\\n\\\\t\\\\t\\\\t\\\\t// this enables FXAA to run using green as a proxy for luma.\\\\n\\\\t\\\\t\\\\t\\\\t// So with this enabled, no need to pack luma in alpha.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// This will turn off AA on anything which lacks some amount of green.\\\\n\\\\t\\\\t\\\\t\\\\t// Pure red and blue or combination of only R and B, will get no AA.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Might want to lower the settings for both,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\tfxaaConsoleEdgeThresholdMin\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\tfxaaQualityEdgeThresholdMin\\\\n\\\\t\\\\t\\\\t\\\\t// In order to insure AA does not get turned off on colors\\\\n\\\\t\\\\t\\\\t\\\\t// which contain a minor amount of green.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = On.\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = Off.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_GREEN_AS_LUMA 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_EARLY_EXIT\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Controls algorithm\\\\'s early exit path.\\\\n\\\\t\\\\t\\\\t\\\\t// On PS3 turning this ON adds 2 cycles to the shader.\\\\n\\\\t\\\\t\\\\t\\\\t// On 360 turning this OFF adds 10ths of a millisecond to the shader.\\\\n\\\\t\\\\t\\\\t\\\\t// Turning this off on console will result in a more blurry image.\\\\n\\\\t\\\\t\\\\t\\\\t// So this defaults to on.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = On.\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = Off.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_EARLY_EXIT 1\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_DISCARD\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only valid for PC OpenGL currently.\\\\n\\\\t\\\\t\\\\t\\\\t// Probably will not work when FXAA_GREEN_AS_LUMA = 1.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = Use discard on pixels which don\\\\'t need AA.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\t For APIs which enable concurrent TEX+ROP from same surface.\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = Return unchanged color on pixels which don\\\\'t need AA.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_DISCARD 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_FAST_PIXEL_OFFSET\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Used for GLSL 120 only.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = GL API supports fast pixel offsets\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = do not use fast pixel offsets\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_EXT_gpu_shader4\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_FAST_PIXEL_OFFSET 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_NV_gpu_shader5\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_FAST_PIXEL_OFFSET 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_ARB_gpu_shader5\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_FAST_PIXEL_OFFSET 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifndef FXAA_FAST_PIXEL_OFFSET\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_FAST_PIXEL_OFFSET 0\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_GATHER4_ALPHA\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = API supports gather4 on alpha channel.\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = API does not support gather4 on alpha channel.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_HLSL_5 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_GATHER4_ALPHA 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_ARB_gpu_shader5\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_GATHER4_ALPHA 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_NV_gpu_shader5\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_GATHER4_ALPHA 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifndef FXAA_GATHER4_ALPHA\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_GATHER4_ALPHA 0\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFXAA QUALITY - TUNING KNOBS\\\\n\\\\t\\\\t------------------------------------------------------------------------------\\\\n\\\\t\\\\tNOTE the other tuning knobs are now in the shader function inputs!\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#ifndef FXAA_QUALITY_PRESET\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Choose the quality preset.\\\\n\\\\t\\\\t\\\\t\\\\t// This needs to be compiled into the shader as it effects code.\\\\n\\\\t\\\\t\\\\t\\\\t// Best option to include multiple presets is to\\\\n\\\\t\\\\t\\\\t\\\\t// in each shader define the preset, then include this file.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// OPTIONS\\\\n\\\\t\\\\t\\\\t\\\\t// -----------------------------------------------------------------------\\\\n\\\\t\\\\t\\\\t\\\\t// 10 to 15 - default medium dither (10=fastest, 15=highest quality)\\\\n\\\\t\\\\t\\\\t\\\\t// 20 to 29 - less dither, more expensive (20=fastest, 29=highest quality)\\\\n\\\\t\\\\t\\\\t\\\\t// 39\\\\t\\\\t\\\\t - no dither, very expensive\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// NOTES\\\\n\\\\t\\\\t\\\\t\\\\t// -----------------------------------------------------------------------\\\\n\\\\t\\\\t\\\\t\\\\t// 12 = slightly faster then FXAA 3.9 and higher edge quality (default)\\\\n\\\\t\\\\t\\\\t\\\\t// 13 = about same speed as FXAA 3.9 and better than 12\\\\n\\\\t\\\\t\\\\t\\\\t// 23 = closest to FXAA 3.9 visually and performance wise\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t_ = the lowest digit is directly related to performance\\\\n\\\\t\\\\t\\\\t\\\\t// _\\\\t= the highest digit is directly related to style\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PRESET 12\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA QUALITY - PRESETS\\\\n\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA QUALITY - MEDIUM DITHER PRESETS\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 10)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 3\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 3.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 11)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 4\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 3.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 12)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 13)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 6\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 14)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 7\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 15)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 8\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA QUALITY - LOW DITHER PRESETS\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 20)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 3\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 21)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 4\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 22)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 23)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 6\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 24)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 7\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 3.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 25)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 8\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 26)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 9\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 27)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 10\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P9 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 28)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 11\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P9 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P10 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 29)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 12\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P9 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P10 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P11 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA QUALITY - EXTREME QUALITY\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 39)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 12\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P9 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P10 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P11 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tAPI PORTING\\\\n\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_GLSL_100 == 1) || (FXAA_GLSL_120 == 1) || (FXAA_GLSL_130 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaBool bool\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaDiscard discard\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat float\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat2 vec2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat3 vec3\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat4 vec4\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf float\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf2 vec2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf3 vec3\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf4 vec4\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaInt2 ivec2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaSat(x) clamp(x, 0.0, 1.0)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTex sampler2D\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaBool bool\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaDiscard clip(-1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat float\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat2 float2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat3 float3\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat4 float4\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf half\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf2 half2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf3 half3\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf4 half4\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaSat(x) saturate(x)\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_GLSL_100 == 1)\\\\n\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) texture2D(t, p, 0.0)\\\\n\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) texture2D(t, p + (o * r), 0.0)\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_GLSL_120 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t// Requires,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t#version 120\\\\n\\\\t\\\\t\\\\t\\\\t// And at least,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t#extension GL_EXT_gpu_shader4 : enable\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t(or set FXAA_FAST_PIXEL_OFFSET 1 to work like DX9)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) texture2DLod(t, p, 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_FAST_PIXEL_OFFSET == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) texture2DLodOffset(t, p, 0.0, o)\\\\n\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) texture2DLod(t, p + (o * r), 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_GATHER4_ALPHA == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t// use #extension GL_ARB_gpu_shader5 : enable\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexAlpha4(t, p) textureGather(t, p, 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffAlpha4(t, p, o) textureGatherOffset(t, p, o, 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexGreen4(t, p) textureGather(t, p, 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffGreen4(t, p, o) textureGatherOffset(t, p, o, 1)\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_GLSL_130 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t// Requires \\\\\\\"#version 130\\\\\\\" or better\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) textureLod(t, p, 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) textureLodOffset(t, p, 0.0, o)\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_GATHER4_ALPHA == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t// use #extension GL_ARB_gpu_shader5 : enable\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexAlpha4(t, p) textureGather(t, p, 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffAlpha4(t, p, o) textureGatherOffset(t, p, o, 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexGreen4(t, p) textureGather(t, p, 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffGreen4(t, p, o) textureGatherOffset(t, p, o, 1)\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_HLSL_3 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaInt2 float2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTex sampler2D\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) tex2Dlod(t, float4(p, 0.0, 0.0))\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) tex2Dlod(t, float4(p + (o * r), 0, 0))\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_HLSL_4 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaInt2 int2\\\\n\\\\t\\\\t\\\\t\\\\tstruct FxaaTex { SamplerState smpl; Texture2D tex; };\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) t.tex.SampleLevel(t.smpl, p, 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) t.tex.SampleLevel(t.smpl, p, 0.0, o)\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_HLSL_5 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaInt2 int2\\\\n\\\\t\\\\t\\\\t\\\\tstruct FxaaTex { SamplerState smpl; Texture2D tex; };\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) t.tex.SampleLevel(t.smpl, p, 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) t.tex.SampleLevel(t.smpl, p, 0.0, o)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexAlpha4(t, p) t.tex.GatherAlpha(t.smpl, p)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffAlpha4(t, p, o) t.tex.GatherAlpha(t.smpl, p, o)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexGreen4(t, p) t.tex.GatherGreen(t.smpl, p)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffGreen4(t, p, o) t.tex.GatherGreen(t.smpl, p, o)\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t GREEN AS LUMA OPTION SUPPORT FUNCTION\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat FxaaLuma(FxaaFloat4 rgba) { return rgba.w; }\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat FxaaLuma(FxaaFloat4 rgba) { return rgba.y; }\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA3 QUALITY - PC\\\\n\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_PC == 1)\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\tFxaaFloat4 FxaaPixelShader(\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Use noperspective interpolation here (turn off perspective interpolation).\\\\n\\\\t\\\\t\\\\t\\\\t// {xy} = center of pixel\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 pos,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Used only for FXAA Console, and not used on the 360 version.\\\\n\\\\t\\\\t\\\\t\\\\t// Use noperspective interpolation here (turn off perspective interpolation).\\\\n\\\\t\\\\t\\\\t\\\\t// {xy_} = upper left of pixel\\\\n\\\\t\\\\t\\\\t\\\\t// {_zw} = lower right of pixel\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsolePosPos,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Input color texture.\\\\n\\\\t\\\\t\\\\t\\\\t// {rgb_} = color in linear or perceptual color space\\\\n\\\\t\\\\t\\\\t\\\\t// if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\t {__a} = luma in perceptual color space (not linear)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaTex tex,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on the optimized 360 version of FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// For everything but 360, just use the same input here as for \\\\\\\"tex\\\\\\\".\\\\n\\\\t\\\\t\\\\t\\\\t// For 360, same texture, just alias with a 2nd sampler.\\\\n\\\\t\\\\t\\\\t\\\\t// This sampler needs to have an exponent bias of -1.\\\\n\\\\t\\\\t\\\\t\\\\tFxaaTex fxaaConsole360TexExpBiasNegOne,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on the optimized 360 version of FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// For everything but 360, just use the same input here as for \\\\\\\"tex\\\\\\\".\\\\n\\\\t\\\\t\\\\t\\\\t// For 360, same texture, just alias with a 3nd sampler.\\\\n\\\\t\\\\t\\\\t\\\\t// This sampler needs to have an exponent bias of -2.\\\\n\\\\t\\\\t\\\\t\\\\tFxaaTex fxaaConsole360TexExpBiasNegTwo,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Quality.\\\\n\\\\t\\\\t\\\\t\\\\t// This must be from a constant/uniform.\\\\n\\\\t\\\\t\\\\t\\\\t// {x_} = 1.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_y} = 1.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 fxaaQualityRcpFrame,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// This must be from a constant/uniform.\\\\n\\\\t\\\\t\\\\t\\\\t// This effects sub-pixel AA quality and inversely sharpness.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Where N ranges between,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\t N = 0.50 (default)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\t N = 0.33 (sharper)\\\\n\\\\t\\\\t\\\\t\\\\t// {x__} = -N/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_y_} = -N/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_z_} =\\\\tN/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {__w} =\\\\tN/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsoleRcpFrameOpt,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// Not used on 360, but used on PS3 and PC.\\\\n\\\\t\\\\t\\\\t\\\\t// This must be from a constant/uniform.\\\\n\\\\t\\\\t\\\\t\\\\t// {x__} = -2.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_y_} = -2.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_z_} =\\\\t2.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {__w} =\\\\t2.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsoleRcpFrameOpt2,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on 360 in place of fxaaConsoleRcpFrameOpt2.\\\\n\\\\t\\\\t\\\\t\\\\t// This must be from a constant/uniform.\\\\n\\\\t\\\\t\\\\t\\\\t// {x__} =\\\\t8.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_y_} =\\\\t8.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_z_} = -4.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {__w} = -4.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsole360RcpFrameOpt2,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Quality.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_QUALITY_SUBPIX define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// Choose the amount of sub-pixel aliasing removal.\\\\n\\\\t\\\\t\\\\t\\\\t// This can effect sharpness.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 1.00 - upper limit (softer)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.75 - default amount of filtering\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.50 - lower limit (sharper, less sub-pixel aliasing removal)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.25 - almost off\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.00 - completely off\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaQualitySubpix,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Quality.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_QUALITY_EDGE_THRESHOLD define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// The minimum amount of local contrast required to apply algorithm.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.333 - too little (faster)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.250 - low quality\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.166 - default\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.125 - high quality\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.063 - overkill (slower)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaQualityEdgeThreshold,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Quality.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_QUALITY_EDGE_THRESHOLD_MIN define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// Trims the algorithm from processing darks.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.0833 - upper limit (default, the start of visible unfiltered edges)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.0625 - high quality (faster)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.0312 - visible limit (slower)\\\\n\\\\t\\\\t\\\\t\\\\t// Special notes when using FXAA_GREEN_AS_LUMA,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Likely want to set this to zero.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t As colors that are mostly not-green\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t will appear very dark in the green channel!\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Tune by looking at mostly non-green content,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t then start at zero and increase until aliasing is a problem.\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaQualityEdgeThresholdMin,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_CONSOLE_EDGE_SHARPNESS define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// This does not effect PS3, as this needs to be compiled in.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Use FXAA_CONSOLE_PS3_EDGE_SHARPNESS for PS3.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Due to the PS3 being ALU bound,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t there are only three safe values here: 2 and 4 and 8.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t These options use the shaders ability to a free *|/ by 2|4|8.\\\\n\\\\t\\\\t\\\\t\\\\t// For all other platforms can be a non-power of two.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 8.0 is sharper (default!!!)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 4.0 is softer\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 2.0 is really soft (good only for vector graphics inputs)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaConsoleEdgeSharpness,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_CONSOLE_EDGE_THRESHOLD define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// This does not effect PS3, as this needs to be compiled in.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Use FXAA_CONSOLE_PS3_EDGE_THRESHOLD for PS3.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Due to the PS3 being ALU bound,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t there are only two safe values here: 1/4 and 1/8.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t These options use the shaders ability to a free *|/ by 2|4|8.\\\\n\\\\t\\\\t\\\\t\\\\t// The console setting has a different mapping than the quality setting.\\\\n\\\\t\\\\t\\\\t\\\\t// Other platforms can use other values.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.125 leaves less aliasing, but is softer (default!!!)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.25 leaves more aliasing, and is sharper\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaConsoleEdgeThreshold,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_CONSOLE_EDGE_THRESHOLD_MIN define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// Trims the algorithm from processing darks.\\\\n\\\\t\\\\t\\\\t\\\\t// The console setting has a different mapping than the quality setting.\\\\n\\\\t\\\\t\\\\t\\\\t// This only applies when FXAA_EARLY_EXIT is 1.\\\\n\\\\t\\\\t\\\\t\\\\t// This does not apply to PS3,\\\\n\\\\t\\\\t\\\\t\\\\t// PS3 was simplified to avoid more shader instructions.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.06 - faster but more aliasing in darks\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.05 - default\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.04 - slower and less aliasing in darks\\\\n\\\\t\\\\t\\\\t\\\\t// Special notes when using FXAA_GREEN_AS_LUMA,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Likely want to set this to zero.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t As colors that are mostly not-green\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t will appear very dark in the green channel!\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Tune by looking at mostly non-green content,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t then start at zero and increase until aliasing is a problem.\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaConsoleEdgeThresholdMin,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Extra constants for 360 FXAA Console only.\\\\n\\\\t\\\\t\\\\t\\\\t// Use zeros or anything else for other platforms.\\\\n\\\\t\\\\t\\\\t\\\\t// These must be in physical constant registers and NOT immediates.\\\\n\\\\t\\\\t\\\\t\\\\t// Immediates will result in compiler un-optimizing.\\\\n\\\\t\\\\t\\\\t\\\\t// {xyzw} = float4(1.0, -1.0, 0.25, -0.25)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsole360ConstDir\\\\n\\\\t\\\\t) {\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 posM;\\\\n\\\\t\\\\t\\\\t\\\\tposM.x = pos.x;\\\\n\\\\t\\\\t\\\\t\\\\tposM.y = pos.y;\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_GATHER4_ALPHA == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_DISCARD == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 rgbyM = FxaaTexTop(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM rgbyM.w\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM rgbyM.y\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 luma4A = FxaaTexAlpha4(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 luma4B = FxaaTexOffAlpha4(tex, posM, FxaaInt2(-1, -1));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 luma4A = FxaaTexGreen4(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 luma4B = FxaaTexOffGreen4(tex, posM, FxaaInt2(-1, -1));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_DISCARD == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM luma4A.w\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaE luma4A.z\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaS luma4A.x\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaSE luma4A.y\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaNW luma4B.w\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaN luma4B.z\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaW luma4B.x\\\\n\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 rgbyM = FxaaTexTop(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM rgbyM.w\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM rgbyM.y\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GLSL_100 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaS = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 0.0, 1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaE = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 1.0, 0.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaN = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 0.0,-1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaW = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2(-1.0, 0.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaS = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 0, 1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 1, 0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaN = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 0,-1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1, 0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat maxSM = max(lumaS, lumaM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat minSM = min(lumaS, lumaM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat maxESM = max(lumaE, maxSM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat minESM = min(lumaE, minSM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat maxWN = max(lumaN, lumaW);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat minWN = min(lumaN, lumaW);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat rangeMax = max(maxWN, maxESM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat rangeMin = min(minWN, minESM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat rangeMaxScaled = rangeMax * fxaaQualityEdgeThreshold;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat range = rangeMax - rangeMin;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat rangeMaxClamped = max(fxaaQualityEdgeThresholdMin, rangeMaxScaled);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool earlyExit = range < rangeMaxClamped;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tif(earlyExit)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_DISCARD == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaDiscard;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treturn rgbyM;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_GATHER4_ALPHA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GLSL_100 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNW = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2(-1.0,-1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSE = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 1.0, 1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNE = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 1.0,-1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSW = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2(-1.0, 1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1,-1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 1, 1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 1,-1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1, 1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(1, -1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1, 1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNS = lumaN + lumaS;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaWE = lumaW + lumaE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixRcpRange = 1.0/range;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixNSWE = lumaNS + lumaWE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz1 = (-2.0 * lumaM) + lumaNS;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert1 = (-2.0 * lumaM) + lumaWE;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNESE = lumaNE + lumaSE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNWNE = lumaNW + lumaNE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz2 = (-2.0 * lumaE) + lumaNESE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert2 = (-2.0 * lumaN) + lumaNWNE;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNWSW = lumaNW + lumaSW;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSWSE = lumaSW + lumaSE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz4 = (abs(edgeHorz1) * 2.0) + abs(edgeHorz2);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert4 = (abs(edgeVert1) * 2.0) + abs(edgeVert2);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz3 = (-2.0 * lumaW) + lumaNWSW;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert3 = (-2.0 * lumaS) + lumaSWSE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz = abs(edgeHorz3) + edgeHorz4;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert = abs(edgeVert3) + edgeVert4;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixNWSWNESE = lumaNWSW + lumaNESE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lengthSign = fxaaQualityRcpFrame.x;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool horzSpan = edgeHorz >= edgeVert;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixA = subpixNSWE * 2.0 + subpixNWSWNESE;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) lumaN = lumaW;\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) lumaS = lumaE;\\\\n\\\\t\\\\t\\\\t\\\\tif(horzSpan) lengthSign = fxaaQualityRcpFrame.y;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixB = (subpixA * (1.0/12.0)) - lumaM;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat gradientN = lumaN - lumaM;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat gradientS = lumaS - lumaM;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNN = lumaN + lumaM;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSS = lumaS + lumaM;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool pairN = abs(gradientN) >= abs(gradientS);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat gradient = max(abs(gradientN), abs(gradientS));\\\\n\\\\t\\\\t\\\\t\\\\tif(pairN) lengthSign = -lengthSign;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixC = FxaaSat(abs(subpixB) * subpixRcpRange);\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 posB;\\\\n\\\\t\\\\t\\\\t\\\\tposB.x = posM.x;\\\\n\\\\t\\\\t\\\\t\\\\tposB.y = posM.y;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 offNP;\\\\n\\\\t\\\\t\\\\t\\\\toffNP.x = (!horzSpan) ? 0.0 : fxaaQualityRcpFrame.x;\\\\n\\\\t\\\\t\\\\t\\\\toffNP.y = ( horzSpan) ? 0.0 : fxaaQualityRcpFrame.y;\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) posB.x += lengthSign * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tif( horzSpan) posB.y += lengthSign * 0.5;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 posN;\\\\n\\\\t\\\\t\\\\t\\\\tposN.x = posB.x - offNP.x * FXAA_QUALITY_P0;\\\\n\\\\t\\\\t\\\\t\\\\tposN.y = posB.y - offNP.y * FXAA_QUALITY_P0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 posP;\\\\n\\\\t\\\\t\\\\t\\\\tposP.x = posB.x + offNP.x * FXAA_QUALITY_P0;\\\\n\\\\t\\\\t\\\\t\\\\tposP.y = posB.y + offNP.y * FXAA_QUALITY_P0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixD = ((-2.0)*subpixC) + 3.0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaEndN = FxaaLuma(FxaaTexTop(tex, posN));\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixE = subpixC * subpixC;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaEndP = FxaaLuma(FxaaTexTop(tex, posP));\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tif(!pairN) lumaNN = lumaSS;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat gradientScaled = gradient * 1.0/4.0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaMM = lumaM - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixF = subpixD * subpixE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool lumaMLTZero = lumaMM < 0.0;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tlumaEndN -= lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tlumaEndP -= lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool doneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool doneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P1;\\\\n\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P1;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool doneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P1;\\\\n\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P1;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P2;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P2;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P2;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P2;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P3;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P3;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P3;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P3;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 4)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P4;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P4;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P4;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P4;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 5)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P5;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 6)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P6;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P6;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P6;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P6;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 7)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P7;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P7;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P7;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P7;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 8)\\\\n\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P8;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P8;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P8;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P8;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 9)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P9;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P9;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P9;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P9;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 10)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P10;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P10;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P10;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P10;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 11)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P11;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P11;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P11;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P11;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 12)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P12;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P12;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P12;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P12;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat dstN = posM.x - posN.x;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat dstP = posP.x - posM.x;\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) dstN = posM.y - posN.y;\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) dstP = posP.y - posM.y;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool goodSpanN = (lumaEndN < 0.0) != lumaMLTZero;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat spanLength = (dstP + dstN);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool goodSpanP = (lumaEndP < 0.0) != lumaMLTZero;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat spanLengthRcp = 1.0/spanLength;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool directionN = dstN < dstP;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat dst = min(dstN, dstP);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool goodSpan = directionN ? goodSpanN : goodSpanP;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixG = subpixF * subpixF;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat pixelOffset = (dst * (-spanLengthRcp)) + 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixH = subpixG * fxaaQualitySubpix;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat pixelOffsetGood = goodSpan ? pixelOffset : 0.0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat pixelOffsetSubpix = max(pixelOffsetGood, subpixH);\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) posM.x += pixelOffsetSubpix * lengthSign;\\\\n\\\\t\\\\t\\\\t\\\\tif( horzSpan) posM.y += pixelOffsetSubpix * lengthSign;\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_DISCARD == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treturn FxaaTexTop(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treturn FxaaFloat4(FxaaTexTop(tex, posM).xyz, lumaM);\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t/*==========================================================================*/\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\tgl_FragColor = FxaaPixelShader(\\\\n\\\\t\\\\t\\\\t\\\\tvUv,\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0),\\\\n\\\\t\\\\t\\\\t\\\\ttDiffuse,\\\\n\\\\t\\\\t\\\\t\\\\ttDiffuse,\\\\n\\\\t\\\\t\\\\t\\\\ttDiffuse,\\\\n\\\\t\\\\t\\\\t\\\\tresolution,\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0),\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0),\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0),\\\\n\\\\t\\\\t\\\\t\\\\t0.75,\\\\n\\\\t\\\\t\\\\t\\\\t0.166,\\\\n\\\\t\\\\t\\\\t\\\\t0.0833,\\\\n\\\\t\\\\t\\\\t\\\\t0.0,\\\\n\\\\t\\\\t\\\\t\\\\t0.0,\\\\n\\\\t\\\\t\\\\t\\\\t0.0,\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0)\\\\n\\\\t\\\\t\\\\t);\\\\n\\\\n\\\\t\\\\t\\\\t// TODO avoid querying texture twice for same texel\\\\n\\\\t\\\\t\\\\tgl_FragColor.a = texture2D(tDiffuse, vUv).a;\\\\n\\\\t\\\\t}'};const pj=new class extends aa{constructor(){super(...arguments),this.transparent=oa.BOOLEAN(1,MH)}};class _j extends SH{constructor(){super(...arguments),this.paramsConfig=pj}static type(){return\\\\\\\"FXAA\\\\\\\"}_createPass(t){const e=new Bm(dj);return e.uniforms.resolution.value.set(1/t.resolution.x,1/t.resolution.y),e.material.transparent=!0,this.updatePass(e),e}updatePass(t){t.material.transparent=this.pv.transparent}}const mj={uniforms:{tDiffuse:{value:null}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 tex = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = LinearTosRGB( tex ); // optional: LinearToGamma( tex, float( GAMMA_FACTOR ) );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const fj=new class extends aa{};class gj extends SH{constructor(){super(...arguments),this.paramsConfig=fj}static type(){return\\\\\\\"gammaCorrection\\\\\\\"}_createPass(t){const e=new Bm(mj);return this.updatePass(e),e}updatePass(t){}}const vj=new class extends aa{constructor(){super(...arguments),this.amount=oa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],step:.01,...MH}),this.transparent=oa.BOOLEAN(1,MH)}};class yj extends SH{constructor(){super(...arguments),this.paramsConfig=vj}static type(){return\\\\\\\"horizontalBlur\\\\\\\"}_createPass(t){const e=new Bm(IU);return e.resolution_x=t.resolution.x,this.updatePass(e),e}updatePass(t){t.uniforms.h.value=this.pv.amount/(t.resolution_x*window.devicePixelRatio),t.material.transparent=this.pv.transparent}}const xj=new class extends aa{constructor(){super(...arguments),this.map=oa.OPERATOR_PATH(gi.UV,{nodeSelection:{context:Ki.COP},...MH}),this.darkness=oa.FLOAT(0,{range:[0,2],rangeLocked:[!0,!1],...MH}),this.offset=oa.FLOAT(0,{range:[0,2],rangeLocked:[!0,!1],...MH})}};class bj extends SH{constructor(){super(...arguments),this.paramsConfig=xj}static type(){return\\\\\\\"image\\\\\\\"}static _create_shader(){return{uniforms:{tDiffuse:{value:null},map:{value:null},offset:{value:1},darkness:{value:1}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\nvoid main() {\\\\n\\\\tvUv = uv;\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n}\\\\\\\",fragmentShader:\\\\\\\"uniform float offset;\\\\nuniform float darkness;\\\\nuniform sampler2D tDiffuse;\\\\nuniform sampler2D map;\\\\nvarying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\tvec4 map_val = texture2D( map, vUv );\\\\n\\\\tvec2 uv = ( vUv - vec2( 0.5 ) ) * vec2( offset );\\\\n\\\\t// gl_FragColor = vec4( mix( texel.rgb, vec3( 1.0 - darkness ), dot( uv, uv ) ), texel.a );\\\\n\\\\tgl_FragColor = vec4( mix( texel.rgb, map_val.rgb, map_val.a ), texel.a );\\\\n\\\\n}\\\\n\\\\\\\"}}_createPass(t){const e=new Bm(bj._create_shader());return this.updatePass(e),e}updatePass(t){t.uniforms.darkness.value=this.pv.darkness,t.uniforms.offset.value=this.pv.offset,this._update_map(t)}async _update_map(t){this.p.map.isDirty()&&await this.p.map.compute();const e=this.p.map.found_node();if(e)if(e.context()==Ki.COP){const n=e,i=(await n.compute()).coreContent();t.uniforms.map.value=i}else this.states.error.set(\\\\\\\"node is not COP\\\\\\\");else this.states.error.set(\\\\\\\"no map found\\\\\\\")}}const wj={tDiffuse:{value:null},texture1:{value:null},texture2:{value:null},h:{value:1/512}},Tj=\\\\\\\"varying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvUv = uv;\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n}\\\\\\\",Aj=\\\\\\\"uniform sampler2D texture1;\\\\nuniform sampler2D texture2;\\\\nvarying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 t1 = texture2D( texture1, vUv);\\\\n\\\\tvec4 t2 = texture2D( texture2, vUv);\\\\n\\\\n\\\\tvec3 c1 = t1.rgb * t1.a * (1.0-t2.a);\\\\n\\\\tvec3 c2 = t2.rgb * t2.a;\\\\n\\\\tfloat a = t2.a + t1.a;\\\\n\\\\tvec3 c = max(c1,c2);\\\\n\\\\n\\\\tgl_FragColor = vec4(c,a);\\\\n\\\\n}\\\\\\\";class Ej extends Im{constructor(t,e){super(),this._composer1=t,this._composer2=e,this.uniforms=I.clone(wj),this.material=new F({uniforms:this.uniforms,vertexShader:Tj,fragmentShader:Aj,transparent:!0}),this.fsQuad=new km(this.material)}render(t,e){this._composer1.render(),this._composer2.render(),this.uniforms.texture1.value=this._composer1.readBuffer.texture,this.uniforms.texture2.value=this._composer2.readBuffer.texture,this.renderToScreen?(t.setRenderTarget(null),this.fsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(t.autoClearColor,t.autoClearDepth,t.autoClearStencil),this.fsQuad.render(t))}}const Mj=new class extends aa{};class Sj extends SH{constructor(){super(...arguments),this.paramsConfig=Mj}static type(){return\\\\\\\"layer\\\\\\\"}initializeNode(){super.initializeNode(),this.io.inputs.setCount(2)}setupComposer(t){const e=t.composer.renderer,n={minFilter:w.V,magFilter:w.V,format:w.Ib,stencilBuffer:!0},i=ai.renderersController.renderTarget(e.domElement.offsetWidth,e.domElement.offsetHeight,n),r=ai.renderersController.renderTarget(e.domElement.offsetWidth,e.domElement.offsetHeight,n),s=new Gm(e,i),o=new Gm(e,r);s.renderToScreen=!1,o.renderToScreen=!1;const a={...t},l={...t};a.composer=s,l.composer=o,this._addPassFromInput(0,a),this._addPassFromInput(1,l);const c=new Ej(s,o);this.updatePass(c),t.composer.addPass(c)}updatePass(t){}}const Cj=new class extends aa{constructor(){super(...arguments),this.overrideScene=oa.BOOLEAN(0,MH),this.scene=oa.OPERATOR_PATH(\\\\\\\"/scene1\\\\\\\",{visibleIf:{overrideScene:1},nodeSelection:{context:Ki.OBJ,types:[ZG.type()]},...MH}),this.overrideCamera=oa.BOOLEAN(0,MH),this.camera=oa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{overrideCamera:1},nodeSelection:{context:Ki.OBJ},...MH}),this.inverse=oa.BOOLEAN(0,MH)}};class Nj extends SH{constructor(){super(...arguments),this.paramsConfig=Cj}static type(){return\\\\\\\"mask\\\\\\\"}_createPass(t){const e=new zm(t.scene,t.camera);return e.context={scene:t.scene,camera:t.camera},this.updatePass(e),e}updatePass(t){t.inverse=this.pv.inverse,this._update_scene(t),this._updateCamera(t)}async _update_scene(t){if(this.pv.overrideScene){this.p.scene.isDirty()&&await this.p.scene.compute();const e=this.p.scene.found_node_with_expected_type();if(e)return void(t.scene=e.object)}t.scene=t.context.scene}async _updateCamera(t){if(this.pv.overrideCamera){this.p.camera.isDirty()&&await this.p.camera.compute();const e=this.p.camera.found_node_with_expected_type();if(e)return void(t.camera=e.object)}t.camera=t.context.camera}}const Lj=new class extends aa{};class Oj extends SH{constructor(){super(...arguments),this.paramsConfig=Lj}static type(){return\\\\\\\"null\\\\\\\"}}class Rj extends Im{constructor(t,e,n,i){super(),this.renderScene=e,this.renderCamera=n,this.selectedObjects=void 0!==i?i:[],this.visibleEdgeColor=new D.a(1,1,1),this.hiddenEdgeColor=new D.a(.1,.04,.02),this.edgeGlow=0,this.usePatternTexture=!1,this.edgeThickness=1,this.edgeStrength=3,this.downSampleRatio=2,this.pulsePeriod=0,this._visibilityCache=new Map,this.resolution=void 0!==t?new d.a(t.x,t.y):new d.a(256,256);const r={minFilter:w.V,magFilter:w.V,format:w.Ib},s=Math.round(this.resolution.x/this.downSampleRatio),o=Math.round(this.resolution.y/this.downSampleRatio);this.maskBufferMaterial=new at.a({color:16777215}),this.maskBufferMaterial.side=w.z,this.renderTargetMaskBuffer=new Z(this.resolution.x,this.resolution.y,r),this.renderTargetMaskBuffer.texture.name=\\\\\\\"OutlinePass.mask\\\\\\\",this.renderTargetMaskBuffer.texture.generateMipmaps=!1,this.depthMaterial=new Mn,this.depthMaterial.side=w.z,this.depthMaterial.depthPacking=w.Hb,this.depthMaterial.blending=w.ub,this.prepareMaskMaterial=this.getPrepareMaskMaterial(),this.prepareMaskMaterial.side=w.z,this.prepareMaskMaterial.fragmentShader=function(t,e){var n=e.isPerspectiveCamera?\\\\\\\"perspective\\\\\\\":\\\\\\\"orthographic\\\\\\\";return t.replace(/DEPTH_TO_VIEW_Z/g,n+\\\\\\\"DepthToViewZ\\\\\\\")}(this.prepareMaskMaterial.fragmentShader,this.renderCamera),this.renderTargetDepthBuffer=new Z(this.resolution.x,this.resolution.y,r),this.renderTargetDepthBuffer.texture.name=\\\\\\\"OutlinePass.depth\\\\\\\",this.renderTargetDepthBuffer.texture.generateMipmaps=!1,this.renderTargetMaskDownSampleBuffer=new Z(s,o,r),this.renderTargetMaskDownSampleBuffer.texture.name=\\\\\\\"OutlinePass.depthDownSample\\\\\\\",this.renderTargetMaskDownSampleBuffer.texture.generateMipmaps=!1,this.renderTargetBlurBuffer1=new Z(s,o,r),this.renderTargetBlurBuffer1.texture.name=\\\\\\\"OutlinePass.blur1\\\\\\\",this.renderTargetBlurBuffer1.texture.generateMipmaps=!1,this.renderTargetBlurBuffer2=new Z(Math.round(s/2),Math.round(o/2),r),this.renderTargetBlurBuffer2.texture.name=\\\\\\\"OutlinePass.blur2\\\\\\\",this.renderTargetBlurBuffer2.texture.generateMipmaps=!1,this.edgeDetectionMaterial=this.getEdgeDetectionMaterial(),this.renderTargetEdgeBuffer1=new Z(s,o,r),this.renderTargetEdgeBuffer1.texture.name=\\\\\\\"OutlinePass.edge1\\\\\\\",this.renderTargetEdgeBuffer1.texture.generateMipmaps=!1,this.renderTargetEdgeBuffer2=new Z(Math.round(s/2),Math.round(o/2),r),this.renderTargetEdgeBuffer2.texture.name=\\\\\\\"OutlinePass.edge2\\\\\\\",this.renderTargetEdgeBuffer2.texture.generateMipmaps=!1;this.separableBlurMaterial1=this.getSeperableBlurMaterial(4),this.separableBlurMaterial1.uniforms.texSize.value.set(s,o),this.separableBlurMaterial1.uniforms.kernelRadius.value=1,this.separableBlurMaterial2=this.getSeperableBlurMaterial(4),this.separableBlurMaterial2.uniforms.texSize.value.set(Math.round(s/2),Math.round(o/2)),this.separableBlurMaterial2.uniforms.kernelRadius.value=4,this.overlayMaterial=this.getOverlayMaterial(),void 0===Pm&&console.error(\\\\\\\"THREE.OutlinePass relies on CopyShader\\\\\\\");const a=Pm;this.copyUniforms=I.clone(a.uniforms),this.copyUniforms.opacity.value=1,this.materialCopy=new F({uniforms:this.copyUniforms,vertexShader:a.vertexShader,fragmentShader:a.fragmentShader,blending:w.ub,depthTest:!1,depthWrite:!1,transparent:!0}),this.enabled=!0,this.needsSwap=!1,this._oldClearColor=new D.a,this.oldClearAlpha=1,this.fsQuad=new km(null),this.tempPulseColor1=new D.a,this.tempPulseColor2=new D.a,this.textureMatrix=new A.a}dispose(){this.renderTargetMaskBuffer.dispose(),this.renderTargetDepthBuffer.dispose(),this.renderTargetMaskDownSampleBuffer.dispose(),this.renderTargetBlurBuffer1.dispose(),this.renderTargetBlurBuffer2.dispose(),this.renderTargetEdgeBuffer1.dispose(),this.renderTargetEdgeBuffer2.dispose()}setSize(t,e){this.renderTargetMaskBuffer.setSize(t,e),this.renderTargetDepthBuffer.setSize(t,e);let n=Math.round(t/this.downSampleRatio),i=Math.round(e/this.downSampleRatio);this.renderTargetMaskDownSampleBuffer.setSize(n,i),this.renderTargetBlurBuffer1.setSize(n,i),this.renderTargetEdgeBuffer1.setSize(n,i),this.separableBlurMaterial1.uniforms.texSize.value.set(n,i),n=Math.round(n/2),i=Math.round(i/2),this.renderTargetBlurBuffer2.setSize(n,i),this.renderTargetEdgeBuffer2.setSize(n,i),this.separableBlurMaterial2.uniforms.texSize.value.set(n,i)}changeVisibilityOfSelectedObjects(t){const e=this._visibilityCache;function n(n){n.isMesh&&(!0===t?n.visible=e.get(n):(e.set(n,n.visible),n.visible=t))}for(let t=0;t0){t.getClearColor(this._oldClearColor),this.oldClearAlpha=t.getClearAlpha();const e=t.autoClear;t.autoClear=!1,r&&t.state.buffers.stencil.setTest(!1),t.setClearColor(16777215,1),this.changeVisibilityOfSelectedObjects(!1);const i=this.renderScene.background;if(this.renderScene.background=null,this.renderScene.overrideMaterial=this.depthMaterial,t.setRenderTarget(this.renderTargetDepthBuffer),t.clear(),t.render(this.renderScene,this.renderCamera),this.changeVisibilityOfSelectedObjects(!0),this._visibilityCache.clear(),this.updateTextureMatrix(),this.changeVisibilityOfNonSelectedObjects(!1),this.renderScene.overrideMaterial=this.prepareMaskMaterial,this.prepareMaskMaterial.uniforms.cameraNearFar.value.set(this.renderCamera.near,this.renderCamera.far),this.prepareMaskMaterial.uniforms.depthTexture.value=this.renderTargetDepthBuffer.texture,this.prepareMaskMaterial.uniforms.textureMatrix.value=this.textureMatrix,t.setRenderTarget(this.renderTargetMaskBuffer),t.clear(),t.render(this.renderScene,this.renderCamera),this.renderScene.overrideMaterial=null,this.changeVisibilityOfNonSelectedObjects(!0),this._visibilityCache.clear(),this.renderScene.background=i,this.fsQuad.material=this.materialCopy,this.copyUniforms.tDiffuse.value=this.renderTargetMaskBuffer.texture,t.setRenderTarget(this.renderTargetMaskDownSampleBuffer),t.clear(),this.fsQuad.render(t),this.tempPulseColor1.copy(this.visibleEdgeColor),this.tempPulseColor2.copy(this.hiddenEdgeColor),this.pulsePeriod>0){const t=.625+.75*Math.cos(.01*performance.now()/this.pulsePeriod)/2;this.tempPulseColor1.multiplyScalar(t),this.tempPulseColor2.multiplyScalar(t)}this.fsQuad.material=this.edgeDetectionMaterial,this.edgeDetectionMaterial.uniforms.maskTexture.value=this.renderTargetMaskDownSampleBuffer.texture,this.edgeDetectionMaterial.uniforms.texSize.value.set(this.renderTargetMaskDownSampleBuffer.width,this.renderTargetMaskDownSampleBuffer.height),this.edgeDetectionMaterial.uniforms.visibleEdgeColor.value=this.tempPulseColor1,this.edgeDetectionMaterial.uniforms.hiddenEdgeColor.value=this.tempPulseColor2,t.setRenderTarget(this.renderTargetEdgeBuffer1),t.clear(),this.fsQuad.render(t),this.fsQuad.material=this.separableBlurMaterial1,this.separableBlurMaterial1.uniforms.colorTexture.value=this.renderTargetEdgeBuffer1.texture,this.separableBlurMaterial1.uniforms.direction.value=Rj.BlurDirectionX,this.separableBlurMaterial1.uniforms.kernelRadius.value=this.edgeThickness,t.setRenderTarget(this.renderTargetBlurBuffer1),t.clear(),this.fsQuad.render(t),this.separableBlurMaterial1.uniforms.colorTexture.value=this.renderTargetBlurBuffer1.texture,this.separableBlurMaterial1.uniforms.direction.value=Rj.BlurDirectionY,t.setRenderTarget(this.renderTargetEdgeBuffer1),t.clear(),this.fsQuad.render(t),this.fsQuad.material=this.separableBlurMaterial2,this.separableBlurMaterial2.uniforms.colorTexture.value=this.renderTargetEdgeBuffer1.texture,this.separableBlurMaterial2.uniforms.direction.value=Rj.BlurDirectionX,t.setRenderTarget(this.renderTargetBlurBuffer2),t.clear(),this.fsQuad.render(t),this.separableBlurMaterial2.uniforms.colorTexture.value=this.renderTargetBlurBuffer2.texture,this.separableBlurMaterial2.uniforms.direction.value=Rj.BlurDirectionY,t.setRenderTarget(this.renderTargetEdgeBuffer2),t.clear(),this.fsQuad.render(t),this.fsQuad.material=this.overlayMaterial,this.overlayMaterial.uniforms.maskTexture.value=this.renderTargetMaskBuffer.texture,this.overlayMaterial.uniforms.edgeTexture1.value=this.renderTargetEdgeBuffer1.texture,this.overlayMaterial.uniforms.edgeTexture2.value=this.renderTargetEdgeBuffer2.texture,this.overlayMaterial.uniforms.patternTexture.value=this.patternTexture,this.overlayMaterial.uniforms.edgeStrength.value=this.edgeStrength,this.overlayMaterial.uniforms.edgeGlow.value=this.edgeGlow,this.overlayMaterial.uniforms.usePatternTexture.value=this.usePatternTexture,r&&t.state.buffers.stencil.setTest(!0),t.setRenderTarget(n),this.fsQuad.render(t),t.setClearColor(this._oldClearColor,this.oldClearAlpha),t.autoClear=e}this.renderToScreen&&(this.fsQuad.material=this.materialCopy,this.copyUniforms.tDiffuse.value=n.texture,t.setRenderTarget(null),this.fsQuad.render(t))}getPrepareMaskMaterial(){return new F({uniforms:{depthTexture:{value:null},cameraNearFar:{value:new d.a(.5,.5)},textureMatrix:{value:null}},vertexShader:\\\\\\\"#include \\\\n\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec4 projTexCoord;\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec4 vPosition;\\\\n\\\\t\\\\t\\\\t\\\\tuniform mat4 textureMatrix;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvPosition = mvPosition;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 worldPosition = modelMatrix * vec4( transformed, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tprojTexCoord = textureMatrix * worldPosition;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"#include \\\\n\\\\t\\\\t\\\\t\\\\tvarying vec4 vPosition;\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec4 projTexCoord;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D depthTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 cameraNearFar;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat depth = unpackRGBAToDepth(texture2DProj( depthTexture, projTexCoord ));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat viewZ = - DEPTH_TO_VIEW_Z( depth, cameraNearFar.x, cameraNearFar.y );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat depthTest = (-vPosition.z > viewZ) ? 1.0 : 0.0;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4(0.0, depthTest, 1.0, 1.0);\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}getEdgeDetectionMaterial(){return new F({uniforms:{maskTexture:{value:null},texSize:{value:new d.a(.5,.5)},visibleEdgeColor:{value:new p.a(1,1,1)},hiddenEdgeColor:{value:new p.a(1,1,1)}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D maskTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 texSize;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec3 visibleEdgeColor;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec3 hiddenEdgeColor;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 invSize = 1.0 / texSize;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 uvOffset = vec4(1.0, 0.0, 0.0, 1.0) * vec4(invSize, invSize);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 c1 = texture2D( maskTexture, vUv + uvOffset.xy);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 c2 = texture2D( maskTexture, vUv - uvOffset.xy);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 c3 = texture2D( maskTexture, vUv + uvOffset.yw);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 c4 = texture2D( maskTexture, vUv - uvOffset.yw);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat diff1 = (c1.r - c2.r)*0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat diff2 = (c3.r - c4.r)*0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat d = length( vec2(diff1, diff2) );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat a1 = min(c1.g, c2.g);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat a2 = min(c3.g, c4.g);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat visibilityFactor = min(a1, a2);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec3 edgeColor = 1.0 - visibilityFactor > 0.001 ? visibleEdgeColor : hiddenEdgeColor;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4(edgeColor, 1.0) * vec4(d);\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}getSeperableBlurMaterial(t){return new F({defines:{MAX_RADIUS:t},uniforms:{colorTexture:{value:null},texSize:{value:new d.a(.5,.5)},direction:{value:new d.a(.5,.5)},kernelRadius:{value:1}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"#include \\\\n\\\\t\\\\t\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D colorTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 texSize;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 direction;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float kernelRadius;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat gaussianPdf(in float x, in float sigma) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treturn 0.39894 * exp( -0.5 * x * x/( sigma * sigma))/sigma;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 invSize = 1.0 / texSize;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat weightSum = gaussianPdf(0.0, kernelRadius);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 diffuseSum = texture2D( colorTexture, vUv) * weightSum;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 delta = direction * invSize * kernelRadius/float(MAX_RADIUS);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 uvOffset = delta;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfor( int i = 1; i <= MAX_RADIUS; i ++ ) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfloat w = gaussianPdf(uvOffset.x, kernelRadius);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec4 sample1 = texture2D( colorTexture, vUv + uvOffset);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec4 sample2 = texture2D( colorTexture, vUv - uvOffset);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdiffuseSum += ((sample1 + sample2) * w);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tweightSum += (2.0 * w);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tuvOffset += delta;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = diffuseSum/weightSum;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}getOverlayMaterial(){return new F({uniforms:{maskTexture:{value:null},edgeTexture1:{value:null},edgeTexture2:{value:null},patternTexture:{value:null},edgeStrength:{value:1},edgeGlow:{value:1},usePatternTexture:{value:0}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D maskTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D edgeTexture1;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D edgeTexture2;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D patternTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float edgeStrength;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float edgeGlow;\\\\n\\\\t\\\\t\\\\t\\\\tuniform bool usePatternTexture;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 edgeValue1 = texture2D(edgeTexture1, vUv);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 edgeValue2 = texture2D(edgeTexture2, vUv);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 maskColor = texture2D(maskTexture, vUv);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 patternColor = texture2D(patternTexture, 6.0 * vUv);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat visibilityFactor = 1.0 - maskColor.g > 0.0 ? 1.0 : 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 edgeValue = edgeValue1 + edgeValue2 * edgeGlow;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 finalColor = edgeStrength * maskColor.r * edgeValue;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tif(usePatternTexture)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfinalColor += + visibilityFactor * (1.0 - maskColor.r) * (1.0 - patternColor.r);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = finalColor;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",blending:w.e,depthTest:!1,depthWrite:!1,transparent:!0})}}Rj.BlurDirectionX=new d.a(1,0),Rj.BlurDirectionY=new d.a(0,1);const Pj=new class extends aa{constructor(){super(...arguments),this.objectsMask=oa.STRING(\\\\\\\"*outlined*\\\\\\\",{...MH}),this.refreshObjects=oa.BUTTON(null,{...MH}),this.printObjects=oa.BUTTON(null,{cook:!1,callback:t=>{Ij.PARAM_CALLBACK_printResolve(t)}}),this.edgeStrength=oa.FLOAT(3,{range:[0,10],rangeLocked:[!0,!1],...MH}),this.edgeThickness=oa.FLOAT(1,{range:[0,4],rangeLocked:[!0,!1],...MH}),this.edgeGlow=oa.FLOAT(0,{range:[0,1],rangeLocked:[!0,!1],...MH}),this.pulsePeriod=oa.FLOAT(0,{range:[0,5],rangeLocked:[!0,!1],...MH}),this.visibleEdgeColor=oa.COLOR([1,1,1],{...MH}),this.hiddenEdgeColor=oa.COLOR([.2,.1,.4],{...MH})}};class Ij extends SH{constructor(){super(...arguments),this.paramsConfig=Pj,this._resolvedObjects=[],this._map=new Map}static type(){return\\\\\\\"outline\\\\\\\"}_createPass(t){const e=new Rj(new d.a(t.resolution.x,t.resolution.y),t.scene,t.camera,t.scene.children);return this.updatePass(e),e}updatePass(t){t.edgeStrength=this.pv.edgeStrength,t.edgeThickness=this.pv.edgeThickness,t.edgeGlow=this.pv.edgeGlow,t.pulsePeriod=this.pv.pulsePeriod,t.visibleEdgeColor=this.pv.visibleEdgeColor,t.hiddenEdgeColor=this.pv.hiddenEdgeColor,this._setSelectedObjects(t)}_setSelectedObjects(t){const e=this.scene().objectsByMask(this.pv.objectsMask);this._map.clear();for(let t of e)this._map.set(t.uuid,t);this._resolvedObjects=e.filter((t=>{let e=!1;return t.traverseAncestors((t=>{this._map.has(t.uuid)&&(e=!0)})),!e})),t.selectedObjects=this._resolvedObjects}static PARAM_CALLBACK_printResolve(t){t.printResolve()}printResolve(){console.log(this._resolvedObjects)}}const Fj={uniforms:{tDiffuse:{value:null},resolution:{value:null},pixelSize:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying highp vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float pixelSize;\\\\n\\\\t\\\\tuniform vec2 resolution;\\\\n\\\\n\\\\t\\\\tvarying highp vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main(){\\\\n\\\\n\\\\t\\\\t\\\\tvec2 dxy = pixelSize / resolution;\\\\n\\\\t\\\\t\\\\tvec2 coord = dxy * floor( vUv / dxy );\\\\n\\\\t\\\\t\\\\tgl_FragColor = texture2D(tDiffuse, coord);\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const Dj=new class extends aa{constructor(){super(...arguments),this.pixelSize=oa.INTEGER(16,{range:[1,50],rangeLocked:[!0,!1],...MH})}};class kj extends SH{constructor(){super(...arguments),this.paramsConfig=Dj}static type(){return\\\\\\\"pixel\\\\\\\"}_createPass(t){const e=new Bm(Fj);return e.uniforms.resolution.value=t.resolution,e.uniforms.resolution.value.multiplyScalar(window.devicePixelRatio),this.updatePass(e),e}updatePass(t){t.uniforms.pixelSize.value=this.pv.pixelSize}}const Bj=new class extends aa{constructor(){super(...arguments),this.overrideScene=oa.BOOLEAN(0,MH),this.scene=oa.OPERATOR_PATH(\\\\\\\"/scene1\\\\\\\",{visibleIf:{overrideScene:1},nodeSelection:{context:Ki.OBJ,types:[ZG.type()]},...MH}),this.overrideCamera=oa.BOOLEAN(0,MH),this.camera=oa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{overrideCamera:1},nodeSelection:{context:Ki.OBJ},...MH})}};class zj extends SH{constructor(){super(...arguments),this.paramsConfig=Bj}static type(){return\\\\\\\"render\\\\\\\"}_createPass(t){const e=new Hm(t.scene,t.camera);return e.context={camera:t.camera,scene:t.scene},this.updatePass(e),e}updatePass(t){this._updateCamera(t),this._update_scene(t)}async _updateCamera(t){if(this.pv.overrideCamera){this.p.camera.isDirty()&&await this.p.camera.compute();const e=this.p.camera.found_node_with_context(Ki.OBJ);if(e&&(e.type()==nr.PERSPECTIVE||e.type()==nr.ORTHOGRAPHIC)){const n=e.object;t.camera=n}}else t.camera=t.context.camera}async _update_scene(t){if(this.pv.overrideScene){this.p.camera.isDirty()&&await this.p.scene.compute();const e=this.p.scene.found_node_with_context(Ki.OBJ);if(e&&e.type()==ZG.type()){const n=e.object;t.scene=n}}else t.scene=t.context.scene}}const Uj={uniforms:{tDiffuse:{value:null},amount:{value:.005},angle:{value:0}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float amount;\\\\n\\\\t\\\\tuniform float angle;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec2 offset = amount * vec2( cos(angle), sin(angle));\\\\n\\\\t\\\\t\\\\tvec4 cr = texture2D(tDiffuse, vUv + offset);\\\\n\\\\t\\\\t\\\\tvec4 cga = texture2D(tDiffuse, vUv);\\\\n\\\\t\\\\t\\\\tvec4 cb = texture2D(tDiffuse, vUv - offset);\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4(cr.r, cga.g, cb.b, cga.a);\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const Gj=new class extends aa{constructor(){super(...arguments),this.amount=oa.FLOAT(.005,{range:[0,1],rangeLocked:[!0,!1],...MH}),this.angle=oa.FLOAT(0,{range:[0,10],rangeLocked:[!0,!1],...MH})}};class Vj extends SH{constructor(){super(...arguments),this.paramsConfig=Gj}static type(){return\\\\\\\"RGBShift\\\\\\\"}_createPass(t){const e=new Bm(Uj);return this.updatePass(e),e}updatePass(t){t.uniforms.amount.value=this.pv.amount,t.uniforms.angle.value=this.pv.angle}}const Hj={uniforms:{tDiffuse:{value:null},amount:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float amount;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 color = texture2D( tDiffuse, vUv );\\\\n\\\\t\\\\t\\\\tvec3 c = color.rgb;\\\\n\\\\n\\\\t\\\\t\\\\tcolor.r = dot( c, vec3( 1.0 - 0.607 * amount, 0.769 * amount, 0.189 * amount ) );\\\\n\\\\t\\\\t\\\\tcolor.g = dot( c, vec3( 0.349 * amount, 1.0 - 0.314 * amount, 0.168 * amount ) );\\\\n\\\\t\\\\t\\\\tcolor.b = dot( c, vec3( 0.272 * amount, 0.534 * amount, 1.0 - 0.869 * amount ) );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( min( vec3( 1.0 ), color.rgb ), color.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const jj=new class extends aa{constructor(){super(...arguments),this.amount=oa.FLOAT(.5,{range:[0,2],rangeLocked:[!1,!1],...MH})}};class Wj extends SH{constructor(){super(...arguments),this.paramsConfig=jj}static type(){return\\\\\\\"sepia\\\\\\\"}_createPass(t){const e=new Bm(Hj);return this.updatePass(e),e}updatePass(t){t.uniforms.amount.value=this.pv.amount}}const qj=new class extends aa{};class Xj extends SH{constructor(){super(...arguments),this.paramsConfig=qj}static type(){return\\\\\\\"sequence\\\\\\\"}initializeNode(){super.initializeNode(),this.io.inputs.setCount(0,4)}setupComposer(t){this._addPassFromInput(0,t),this._addPassFromInput(1,t),this._addPassFromInput(2,t),this._addPassFromInput(3,t)}}const Yj=I.clone(IU.uniforms);Yj.delta={value:new d.a};const $j={uniforms:Yj,vertexShader:IU.vertexShader,fragmentShader:\\\\\\\"\\\\n#include \\\\n#define ITERATIONS 10.0\\\\nuniform sampler2D tDiffuse;\\\\nuniform vec2 delta;\\\\nvarying vec2 vUv;\\\\nvoid main() {\\\\n\\\\tvec4 color = vec4( 0.0 );\\\\n\\\\tfloat total = 0.0;\\\\n\\\\tfloat offset = rand( vUv );\\\\n\\\\tfor ( float t = -ITERATIONS; t <= ITERATIONS; t ++ ) {\\\\n\\\\t\\\\tfloat percent = ( t + offset - 0.5 ) / ITERATIONS;\\\\n\\\\t\\\\tfloat weight = 1.0 - abs( percent );\\\\n\\\\t\\\\tcolor += texture2D( tDiffuse, vUv + delta * percent ) * weight;\\\\n\\\\t\\\\ttotal += weight;\\\\n\\\\t}\\\\n\\\\tgl_FragColor = color / total;\\\\n}\\\\\\\"};const Jj=new class extends aa{constructor(){super(...arguments),this.delta=oa.VECTOR2([2,2],{...MH})}};class Zj extends SH{constructor(){super(...arguments),this.paramsConfig=Jj}static type(){return\\\\\\\"triangleBlur\\\\\\\"}_createPass(t){const e=new Bm($j);return e.resolution=t.resolution.clone(),this.updatePass(e),e}updatePass(t){t.uniforms.delta.value.copy(this.pv.delta).divide(t.resolution).multiplyScalar(window.devicePixelRatio)}}const Qj={shaderID:\\\\\\\"luminosityHighPass\\\\\\\",uniforms:{tDiffuse:{value:null},luminosityThreshold:{value:1},smoothWidth:{value:1},defaultColor:{value:new D.a(0)},defaultOpacity:{value:0}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform vec3 defaultColor;\\\\n\\\\t\\\\tuniform float defaultOpacity;\\\\n\\\\t\\\\tuniform float luminosityThreshold;\\\\n\\\\t\\\\tuniform float smoothWidth;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 luma = vec3( 0.299, 0.587, 0.114 );\\\\n\\\\n\\\\t\\\\t\\\\tfloat v = dot( texel.xyz, luma );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 outputColor = vec4( defaultColor.rgb, defaultOpacity );\\\\n\\\\n\\\\t\\\\t\\\\tfloat alpha = smoothstep( luminosityThreshold, luminosityThreshold + smoothWidth, v );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = mix( outputColor, texel, alpha );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class Kj extends Im{constructor(t,e,n,i){super(),this.strength=void 0!==e?e:1,this.radius=n,this.threshold=i,this.resolution=void 0!==t?new d.a(t.x,t.y):new d.a(256,256),this.clearColor=new D.a(0,0,0);const r={minFilter:w.V,magFilter:w.V,format:w.Ib};this.renderTargetsHorizontal=[],this.renderTargetsVertical=[],this.nMips=5;let s=Math.round(this.resolution.x/2),o=Math.round(this.resolution.y/2);this.renderTargetBright=new Z(s,o,r),this.renderTargetBright.texture.name=\\\\\\\"UnrealBloomPass.bright\\\\\\\",this.renderTargetBright.texture.generateMipmaps=!1;for(let t=0;t\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D colorTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 texSize;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 direction;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat gaussianPdf(in float x, in float sigma) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treturn 0.39894 * exp( -0.5 * x * x/( sigma * sigma))/sigma;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 invSize = 1.0 / texSize;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat fSigma = float(SIGMA);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat weightSum = gaussianPdf(0.0, fSigma);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec3 diffuseSum = texture2D( colorTexture, vUv).rgb * weightSum;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfor( int i = 1; i < KERNEL_RADIUS; i ++ ) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfloat x = float(i);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfloat w = gaussianPdf(x, fSigma);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec2 uvOffset = direction * invSize * x;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec3 sample1 = texture2D( colorTexture, vUv + uvOffset).rgb;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec3 sample2 = texture2D( colorTexture, vUv - uvOffset).rgb;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdiffuseSum += (sample1 + sample2) * w;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tweightSum += 2.0 * w;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4(diffuseSum/weightSum, 1.0);\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}getCompositeMaterial(t){return new F({defines:{NUM_MIPS:t},uniforms:{blurTexture1:{value:null},blurTexture2:{value:null},blurTexture3:{value:null},blurTexture4:{value:null},blurTexture5:{value:null},dirtTexture:{value:null},bloomStrength:{value:1},bloomFactors:{value:null},bloomTintColors:{value:null},bloomRadius:{value:0}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture1;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture2;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture3;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture4;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture5;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D dirtTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float bloomStrength;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float bloomRadius;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float bloomFactors[NUM_MIPS];\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec3 bloomTintColors[NUM_MIPS];\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat lerpBloomFactor(const in float factor) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat mirrorFactor = 1.2 - factor;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treturn mix(factor, mirrorFactor, bloomRadius);\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = bloomStrength * ( lerpBloomFactor(bloomFactors[0]) * vec4(bloomTintColors[0], 1.0) * texture2D(blurTexture1, vUv) +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tlerpBloomFactor(bloomFactors[1]) * vec4(bloomTintColors[1], 1.0) * texture2D(blurTexture2, vUv) +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tlerpBloomFactor(bloomFactors[2]) * vec4(bloomTintColors[2], 1.0) * texture2D(blurTexture3, vUv) +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tlerpBloomFactor(bloomFactors[3]) * vec4(bloomTintColors[3], 1.0) * texture2D(blurTexture4, vUv) +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tlerpBloomFactor(bloomFactors[4]) * vec4(bloomTintColors[4], 1.0) * texture2D(blurTexture5, vUv) );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}}Kj.BlurDirectionX=new d.a(1,0),Kj.BlurDirectionY=new d.a(0,1);const tW=new class extends aa{constructor(){super(...arguments),this.strength=oa.FLOAT(1.5,{range:[0,3],rangeLocked:[!0,!1],...MH}),this.radius=oa.FLOAT(1,{...MH}),this.threshold=oa.FLOAT(0,{...MH})}};class eW extends SH{constructor(){super(...arguments),this.paramsConfig=tW}static type(){return\\\\\\\"unrealBloom\\\\\\\"}_createPass(t){return new Kj(new d.a(t.resolution.x,t.resolution.y),this.pv.strength,this.pv.radius,this.pv.threshold)}updatePass(t){t.strength=this.pv.strength,t.radius=this.pv.radius,t.threshold=this.pv.threshold}}const nW=new class extends aa{constructor(){super(...arguments),this.amount=oa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],step:.01,...MH}),this.transparent=oa.BOOLEAN(1,MH)}};class iW extends SH{constructor(){super(...arguments),this.paramsConfig=nW}static type(){return\\\\\\\"verticalBlur\\\\\\\"}_createPass(t){const e=new Bm(FU);return e.resolution_y=t.resolution.y,this.updatePass(e),e}updatePass(t){t.uniforms.v.value=this.pv.amount/(t.resolution_y*window.devicePixelRatio),t.material.transparent=this.pv.transparent}}const rW={uniforms:{tDiffuse:{value:null},offset:{value:1},darkness:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float offset;\\\\n\\\\t\\\\tuniform float darkness;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t// Eskil's vignette\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\t\\\\t\\\\tvec2 uv = ( vUv - vec2( 0.5 ) ) * vec2( offset );\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( mix( texel.rgb, vec3( 1.0 - darkness ), dot( uv, uv ) ), texel.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const sW=new class extends aa{constructor(){super(...arguments),this.offset=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...MH}),this.darkness=oa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1],...MH})}};class oW extends SH{constructor(){super(...arguments),this.paramsConfig=sW}static type(){return\\\\\\\"vignette\\\\\\\"}_createPass(t){const e=new Bm(rW);return this.updatePass(e),e}updatePass(t){t.uniforms.offset.value=this.pv.offset,t.uniforms.darkness.value=this.pv.darkness}}class aW extends ia{static context(){return Ki.POST}cook(){this.cookController.endCook()}}class lW extends aW{}class cW extends lW{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class uW extends lW{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class hW extends lW{constructor(){super(...arguments),this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class dW extends lW{constructor(){super(...arguments),this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class pW extends aW{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class _W extends lW{constructor(){super(...arguments),this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class mW extends ia{static context(){return Ki.ROP}cook(){this.cookController.endCook()}}class fW extends mW{}class gW extends fW{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class vW extends Q.a{constructor(t){super(),this._element=t,this._element.style.position=\\\\\\\"absolute\\\\\\\",this.addEventListener(\\\\\\\"removed\\\\\\\",this._on_removed.bind(this))}_on_removed(){this.traverse((function(t){t instanceof vW&&t.element instanceof Element&&null!==t.element.parentNode&&t.element.parentNode.removeChild(t.element)}))}get element(){return this._element}copy(t,e){return Q.a.prototype.copy.call(this,t,e),this._element=t.element.cloneNode(!0),this.matrixAutoUpdate=t.matrixAutoUpdate,this}}class yW{constructor(){this._width=0,this._height=0,this._widthHalf=0,this._heightHalf=0,this.vector=new p.a,this.viewMatrix=new A.a,this.viewProjectionMatrix=new A.a,this.cache_distanceToCameraSquared=new WeakMap,this.domElement=document.createElement(\\\\\\\"div\\\\\\\"),this._sort_objects=!1,this._use_fog=!1,this._fog_near=1,this._fog_far=100,this.a=new p.a,this.b=new p.a,this.domElement.classList.add(\\\\\\\"polygonjs-CSS2DRenderer\\\\\\\")}getSize(){return{width:this._width,height:this._height}}setSize(t,e){this._width=t,this._height=e,this._widthHalf=this._width/2,this._heightHalf=this._height/2,this.domElement.style.width=t+\\\\\\\"px\\\\\\\",this.domElement.style.height=e+\\\\\\\"px\\\\\\\"}renderObject(t,e,n){if(t instanceof vW){this.vector.setFromMatrixPosition(t.matrixWorld),this.vector.applyMatrix4(this.viewProjectionMatrix);var i=t.element,r=\\\\\\\"translate(-50%,-50%) translate(\\\\\\\"+(this.vector.x*this._widthHalf+this._widthHalf)+\\\\\\\"px,\\\\\\\"+(-this.vector.y*this._heightHalf+this._heightHalf)+\\\\\\\"px)\\\\\\\";if(i.style.webkitTransform=r,i.style.transform=r,i.style.display=t.visible&&this.vector.z>=-1&&this.vector.z<=1?\\\\\\\"\\\\\\\":\\\\\\\"none\\\\\\\",this._sort_objects||this._use_fog){const e=this.getDistanceToSquared(n,t);if(this._use_fog){const t=Math.sqrt(e),n=rs.fit(t,this._fog_near,this._fog_far,0,1),r=rs.clamp(1-n,0,1);i.style.opacity=`${r}`,0==r&&(i.style.display=\\\\\\\"none\\\\\\\")}this.cache_distanceToCameraSquared.set(t,e)}i.parentNode!==this.domElement&&this.domElement.appendChild(i)}for(var s=0,o=t.children.length;s{const n=this.cache_distanceToCameraSquared.get(t),i=this.cache_distanceToCameraSquared.get(e);return null!=n&&null!=i?n-i:0})),n=e.length;for(let t=0,i=e.length;t{this._update_renderer(t)})),this.cookController.endCook()}_update_renderer(t){t.set_sorting(this.pv.sortObjects),t.set_use_fog(this.pv.useFog),t.set_fog_range(this.pv.fogNear,this.pv.fogFar)}_update_css(){this.css_element().innerHTML=this.pv.css}css_element(){return this._css_element=this._css_element||this._find_element()||this._create_element()}_find_element(){return document.getElementById(this._css_element_id())}_create_element(){const t=document.createElement(\\\\\\\"style\\\\\\\");return t.appendChild(document.createTextNode(\\\\\\\"\\\\\\\")),document.head.appendChild(t),t.id=this._css_element_id(),t}_css_element_id(){return`css_2d_renderer-${this.graphNodeId()}`}}class wW extends fW{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class TW extends fW{constructor(){super(...arguments),this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class AW extends fW{constructor(){super(...arguments),this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class EW extends mW{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class MW extends fW{constructor(){super(...arguments),this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class SW extends pG{static type(){return\\\\\\\"add\\\\\\\"}cook(t,e){const n=[];return this._create_point(n,e),this._create_polygon(t[0],n,e),this.createCoreGroupFromObjects(n)}_create_point(t,e){if(!e.createPoint)return;const n=new S.a,i=[];for(let t=0;t0&&this._create_polygon_open(t,e,n)}_create_polygon_open(t,e,n){const i=t.points();let r=[];const s=[];let o;for(let t=0;t0&&(s.push(t-1),s.push(t));if(i.length>2&&n.connectToLastPoint){i[0].position().toArray(r,r.length);const t=s[s.length-1];s.push(t),s.push(0)}const a=new S.a;a.setAttribute(\\\\\\\"position\\\\\\\",new C.c(r,3)),a.setIndex(s);const l=this.createObject(a,Sr.LINE_SEGMENTS);e.push(l)}}SW.DEFAULT_PARAMS={createPoint:!0,pointsCount:1,position:new p.a(0,0,0),connectInputPoints:!1,connectToLastPoint:!1};const CW=SW.DEFAULT_PARAMS;const NW=new class extends aa{constructor(){super(...arguments),this.createPoint=oa.BOOLEAN(CW.createPoint),this.pointsCount=oa.INTEGER(CW.pointsCount,{range:[1,100],rangeLocked:[!0,!1],visibleIf:{createPoint:!0}}),this.position=oa.VECTOR3(CW.position,{visibleIf:{createPoint:!0}}),this.connectInputPoints=oa.BOOLEAN(CW.connectInputPoints),this.connectToLastPoint=oa.BOOLEAN(CW.connectToLastPoint)}};class LW extends gG{constructor(){super(...arguments),this.paramsConfig=NW}static type(){return\\\\\\\"add\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create polygons from (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1)}cook(t){this._operation=this._operation||new SW(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const OW=new class extends aa{};class RW extends gG{constructor(){super(...arguments),this.paramsConfig=OW}static type(){return\\\\\\\"animationCopy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to copy animation to\\\\\\\",\\\\\\\"geometry to copy animation from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState([Qi.FROM_NODE,Qi.NEVER])}cook(t){const e=t[0],n=t[1].objects()[0],i=e.objects()[0],r=n.animations;r?(i.animations=r.map((t=>t.clone())),this.setCoreGroup(e)):this.states.error.set(\\\\\\\"no animation found\\\\\\\")}}class PW{constructor(t,e,n=null,i=e.blendMode){this._mixer=t,this._clip=e,this._localRoot=n,this.blendMode=i;const r=e.tracks,s=r.length,o=new Array(s),a={endingStart:w.id,endingEnd:w.id};for(let t=0;t!==s;++t){const e=r[t].createInterpolant(null);o[t]=e,e.settings=a}this._interpolantSettings=a,this._interpolants=o,this._propertyBindings=new Array(s),this._cacheIndex=null,this._byClipCacheIndex=null,this._timeScaleInterpolant=null,this._weightInterpolant=null,this.loop=w.eb,this._loopCount=-1,this._startTime=null,this.time=0,this.timeScale=1,this._effectiveTimeScale=1,this.weight=1,this._effectiveWeight=1,this.repetitions=1/0,this.paused=!1,this.enabled=!0,this.clampWhenFinished=!1,this.zeroSlopeAtStart=!0,this.zeroSlopeAtEnd=!0}play(){return this._mixer._activateAction(this),this}stop(){return this._mixer._deactivateAction(this),this.reset()}reset(){return this.paused=!1,this.enabled=!0,this.time=0,this._loopCount=-1,this._startTime=null,this.stopFading().stopWarping()}isRunning(){return this.enabled&&!this.paused&&0!==this.timeScale&&null===this._startTime&&this._mixer._isActiveAction(this)}isScheduled(){return this._mixer._isActiveAction(this)}startAt(t){return this._startTime=t,this}setLoop(t,e){return this.loop=t,this.repetitions=e,this}setEffectiveWeight(t){return this.weight=t,this._effectiveWeight=this.enabled?t:0,this.stopFading()}getEffectiveWeight(){return this._effectiveWeight}fadeIn(t){return this._scheduleFading(t,0,1)}fadeOut(t){return this._scheduleFading(t,1,0)}crossFadeFrom(t,e,n){if(t.fadeOut(e),this.fadeIn(e),n){const n=this._clip.duration,i=t._clip.duration,r=i/n,s=n/i;t.warp(1,r,e),this.warp(s,1,e)}return this}crossFadeTo(t,e,n){return t.crossFadeFrom(this,e,n)}stopFading(){const t=this._weightInterpolant;return null!==t&&(this._weightInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}setEffectiveTimeScale(t){return this.timeScale=t,this._effectiveTimeScale=this.paused?0:t,this.stopWarping()}getEffectiveTimeScale(){return this._effectiveTimeScale}setDuration(t){return this.timeScale=this._clip.duration/t,this.stopWarping()}syncWith(t){return this.time=t.time,this.timeScale=t.timeScale,this.stopWarping()}halt(t){return this.warp(this._effectiveTimeScale,0,t)}warp(t,e,n){const i=this._mixer,r=i.time,s=this.timeScale;let o=this._timeScaleInterpolant;null===o&&(o=i._lendControlInterpolant(),this._timeScaleInterpolant=o);const a=o.parameterPositions,l=o.sampleValues;return a[0]=r,a[1]=r+n,l[0]=t/s,l[1]=e/s,this}stopWarping(){const t=this._timeScaleInterpolant;return null!==t&&(this._timeScaleInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}getMixer(){return this._mixer}getClip(){return this._clip}getRoot(){return this._localRoot||this._mixer._root}_update(t,e,n,i){if(!this.enabled)return void this._updateWeight(t);const r=this._startTime;if(null!==r){const i=(t-r)*n;if(i<0||0===n)return;this._startTime=null,e=n*i}e*=this._updateTimeScale(t);const s=this._updateTime(e),o=this._updateWeight(t);if(o>0){const t=this._interpolants,e=this._propertyBindings;switch(this.blendMode){case w.d:for(let n=0,i=t.length;n!==i;++n)t[n].evaluate(s),e[n].accumulateAdditive(o);break;case w.wb:default:for(let n=0,r=t.length;n!==r;++n)t[n].evaluate(s),e[n].accumulate(i,o)}}}_updateWeight(t){let e=0;if(this.enabled){e=this.weight;const n=this._weightInterpolant;if(null!==n){const i=n.evaluate(t)[0];e*=i,t>n.parameterPositions[1]&&(this.stopFading(),0===i&&(this.enabled=!1))}}return this._effectiveWeight=e,e}_updateTimeScale(t){let e=0;if(!this.paused){e=this.timeScale;const n=this._timeScaleInterpolant;if(null!==n){e*=n.evaluate(t)[0],t>n.parameterPositions[1]&&(this.stopWarping(),0===e?this.paused=!0:this.timeScale=e)}}return this._effectiveTimeScale=e,e}_updateTime(t){const e=this._clip.duration,n=this.loop;let i=this.time+t,r=this._loopCount;const s=n===w.db;if(0===t)return-1===r?i:s&&1==(1&r)?e-i:i;if(n===w.cb){-1===r&&(this._loopCount=0,this._setEndings(!0,!0,!1));t:{if(i>=e)i=e;else{if(!(i<0)){this.time=i;break t}i=0}this.clampWhenFinished?this.paused=!0:this.enabled=!1,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:t<0?-1:1})}}else{if(-1===r&&(t>=0?(r=0,this._setEndings(!0,0===this.repetitions,s)):this._setEndings(0===this.repetitions,!0,s)),i>=e||i<0){const n=Math.floor(i/e);i-=e*n,r+=Math.abs(n);const o=this.repetitions-r;if(o<=0)this.clampWhenFinished?this.paused=!0:this.enabled=!1,i=t>0?e:0,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:t>0?1:-1});else{if(1===o){const e=t<0;this._setEndings(e,!e,s)}else this._setEndings(!1,!1,s);this._loopCount=r,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"loop\\\\\\\",action:this,loopDelta:n})}}else this.time=i;if(s&&1==(1&r))return e-i}return i}_setEndings(t,e,n){const i=this._interpolantSettings;n?(i.endingStart=w.kd,i.endingEnd=w.kd):(i.endingStart=t?this.zeroSlopeAtStart?w.kd:w.id:w.hd,i.endingEnd=e?this.zeroSlopeAtEnd?w.kd:w.id:w.hd)}_scheduleFading(t,e,n){const i=this._mixer,r=i.time;let s=this._weightInterpolant;null===s&&(s=i._lendControlInterpolant(),this._weightInterpolant=s);const o=s.parameterPositions,a=s.sampleValues;return o[0]=r,a[0]=e,o[1]=r+t,a[1]=n,this}}var IW=n(71),FW=n(66);class DW{constructor(t,e,n){let i,r,s;switch(this.binding=t,this.valueSize=n,e){case\\\\\\\"quaternion\\\\\\\":i=this._slerp,r=this._slerpAdditive,s=this._setAdditiveIdentityQuaternion,this.buffer=new Float64Array(6*n),this._workIndex=5;break;case\\\\\\\"string\\\\\\\":case\\\\\\\"bool\\\\\\\":i=this._select,r=this._select,s=this._setAdditiveIdentityOther,this.buffer=new Array(5*n);break;default:i=this._lerp,r=this._lerpAdditive,s=this._setAdditiveIdentityNumeric,this.buffer=new Float64Array(5*n)}this._mixBufferRegion=i,this._mixBufferRegionAdditive=r,this._setIdentity=s,this._origIndex=3,this._addIndex=4,this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,this.useCount=0,this.referenceCount=0}accumulate(t,e){const n=this.buffer,i=this.valueSize,r=t*i+i;let s=this.cumulativeWeight;if(0===s){for(let t=0;t!==i;++t)n[r+t]=n[t];s=e}else{s+=e;const t=e/s;this._mixBufferRegion(n,r,0,t,i)}this.cumulativeWeight=s}accumulateAdditive(t){const e=this.buffer,n=this.valueSize,i=n*this._addIndex;0===this.cumulativeWeightAdditive&&this._setIdentity(),this._mixBufferRegionAdditive(e,i,0,t,n),this.cumulativeWeightAdditive+=t}apply(t){const e=this.valueSize,n=this.buffer,i=t*e+e,r=this.cumulativeWeight,s=this.cumulativeWeightAdditive,o=this.binding;if(this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,r<1){const t=e*this._origIndex;this._mixBufferRegion(n,i,t,1-r,e)}s>0&&this._mixBufferRegionAdditive(n,i,this._addIndex*e,1,e);for(let t=e,r=e+e;t!==r;++t)if(n[t]!==n[t+e]){o.setValue(n,i);break}}saveOriginalState(){const t=this.binding,e=this.buffer,n=this.valueSize,i=n*this._origIndex;t.getValue(e,i);for(let t=n,r=i;t!==r;++t)e[t]=e[i+t%n];this._setIdentity(),this.cumulativeWeight=0,this.cumulativeWeightAdditive=0}restoreOriginalState(){const t=3*this.valueSize;this.binding.setValue(this.buffer,t)}_setAdditiveIdentityNumeric(){const t=this._addIndex*this.valueSize,e=t+this.valueSize;for(let n=t;n=.5)for(let i=0;i!==r;++i)t[e+i]=t[n+i]}_slerp(t,e,n,i){au.a.slerpFlat(t,e,t,e,t,n,i)}_slerpAdditive(t,e,n,i,r){const s=this._workIndex*r;au.a.multiplyQuaternionsFlat(t,s,t,e,t,n),au.a.slerpFlat(t,e,t,e,t,s,i)}_lerp(t,e,n,i,r){const s=1-i;for(let o=0;o!==r;++o){const r=e+o;t[r]=t[r]*s+t[n+o]*i}}_lerpAdditive(t,e,n,i,r){for(let s=0;s!==r;++s){const r=e+s;t[r]=t[r]+t[n+s]*i}}}var kW=n(63);class BW extends $.a{constructor(t){super(),this._root=t,this._initMemoryManager(),this._accuIndex=0,this.time=0,this.timeScale=1}_bindAction(t,e){const n=t._localRoot||this._root,i=t._clip.tracks,r=i.length,s=t._propertyBindings,o=t._interpolants,a=n.uuid,l=this._bindingsByRootAndName;let c=l[a];void 0===c&&(c={},l[a]=c);for(let t=0;t!==r;++t){const r=i[t],l=r.name;let u=c[l];if(void 0!==u)s[t]=u;else{if(u=s[t],void 0!==u){null===u._cacheIndex&&(++u.referenceCount,this._addInactiveBinding(u,a,l));continue}const i=e&&e._propertyBindings[t].binding.parsedPath;u=new DW(FW.a.create(n,l,i),r.ValueTypeName,r.getValueSize()),++u.referenceCount,this._addInactiveBinding(u,a,l),s[t]=u}o[t].resultBuffer=u.buffer}}_activateAction(t){if(!this._isActiveAction(t)){if(null===t._cacheIndex){const e=(t._localRoot||this._root).uuid,n=t._clip.uuid,i=this._actionsByClip[n];this._bindAction(t,i&&i.knownActions[0]),this._addInactiveAction(t,n,e)}const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==n.useCount++&&(this._lendBinding(n),n.saveOriginalState())}this._lendAction(t)}}_deactivateAction(t){if(this._isActiveAction(t)){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.useCount&&(n.restoreOriginalState(),this._takeBackBinding(n))}this._takeBackAction(t)}}_initMemoryManager(){this._actions=[],this._nActiveActions=0,this._actionsByClip={},this._bindings=[],this._nActiveBindings=0,this._bindingsByRootAndName={},this._controlInterpolants=[],this._nActiveControlInterpolants=0;const t=this;this.stats={actions:{get total(){return t._actions.length},get inUse(){return t._nActiveActions}},bindings:{get total(){return t._bindings.length},get inUse(){return t._nActiveBindings}},controlInterpolants:{get total(){return t._controlInterpolants.length},get inUse(){return t._nActiveControlInterpolants}}}}_isActiveAction(t){const e=t._cacheIndex;return null!==e&&e=0;--e)t[e].stop();return this}update(t){t*=this.timeScale;const e=this._actions,n=this._nActiveActions,i=this.time+=t,r=Math.sign(t),s=this._accuIndex^=1;for(let o=0;o!==n;++o){e[o]._update(i,t,r,s)}const o=this._bindings,a=this._nActiveBindings;for(let t=0;t!==a;++t)o[t].apply(s);return this}setTime(t){this.time=0;for(let t=0;t{UW.PARAM_CALLBACK_reset(t,e)}})}};class UW extends gG{constructor(){super(...arguments),this.paramsConfig=zW}static type(){return\\\\\\\"animationMixer\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to be animated\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.NEVER)}async cook(t){const e=t[0].objects()[0];e&&(await this.create_mixer_if_required(e),this._update_mixer()),this.setObjects([e])}async create_mixer_if_required(t){if(!this._mixer){const e=await this._create_mixer(t);e&&(this._mixer=e)}}async _create_mixer(t){this.p.clip.isDirty()&&await this.p.clip.compute();if(this.p.clip.found_node_with_context(Ki.ANIM)){return new BW(t)}}_update_mixer(){this._set_mixer_time()}_set_mixer_time(){this.pv.time!=this._previous_time&&(this._mixer&&this._mixer.setTime(this.pv.time),this._previous_time=this.pv.time)}static PARAM_CALLBACK_reset(t,e){e.setDirty(),t.reset_animation_mixer()}async reset_animation_mixer(){this._mixer=void 0,this._previous_time=void 0,this.setDirty()}}class GW extends pG{static type(){return\\\\\\\"attribAddMult\\\\\\\"}cook(t,e){const n=t[0],i=n.attribNamesMatchingMask(e.name);for(let t of i){const i=n.geometries();for(let n of i)this._update_attrib(t,n,e)}return n}_update_attrib(t,e,n){const i=e.getAttribute(t);if(i){const t=i.array,e=n.preAdd,r=n.mult,s=n.postAdd;for(let n=0;n({name:t,value:e})))}})}};class QW extends gG{constructor(){super(...arguments),this.paramsConfig=ZW}static type(){return\\\\\\\"attribCast\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState($W.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.type],(()=>qW[this.pv.type]))}))}))}cook(t){this._operation=this._operation||new $W(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class KW extends pG{static type(){return\\\\\\\"attribCopy\\\\\\\"}cook(t,e){const n=t[0],i=t[1]||n,r=i.attribNamesMatchingMask(e.name);for(let t of r)this.copy_vertex_attribute_between_core_groups(n,i,t,e);return n}copy_vertex_attribute_between_core_groups(t,e,n,i){var r;const s=e.objectsWithGeo(),o=t.objectsWithGeo();if(o.length>s.length)null===(r=this.states)||void 0===r||r.error.set(\\\\\\\"second input does not have enough objects to copy attributes from\\\\\\\");else for(let t=0;ta&&(null===(r=this.states)||void 0===r||r.error.set(\\\\\\\"not enough points in second input\\\\\\\"));const c=i.tnewName?i.newName:n;let u=t.getAttribute(c);if(u)this._fill_dest_array(u,o,i),u.needsUpdate=!0;else{const e=o.array.slice(0,l*s);t.setAttribute(c,new C.c(e,s))}}else null===(s=this.states)||void 0===s||s.error.set(`attribute '${n}' does not exist on second input`)}_fill_dest_array(t,e,n){const i=t.array,r=e.array,s=i.length,o=t.itemSize,a=e.itemSize,l=n.srcOffset,c=n.destOffset;if(t.itemSize==e.itemSize){t.copyArray(e.array);for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name,this.p.tnewName,this.p.newName],(()=>this.pv.tnewName?`${this.pv.name} -> ${this.pv.newName}`:this.pv.name))}))}))}cook(t){this._operation=this._operation||new KW(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class iq extends pG{static type(){return\\\\\\\"attribCreate\\\\\\\"}cook(t,e){var n;const i=t[0];return e.name&&\\\\\\\"\\\\\\\"!=e.name.trim()?this._add_attribute(Ir[e.class],i,e):null===(n=this.states)||void 0===n||n.error.set(\\\\\\\"attribute name is not valid\\\\\\\"),i}async _add_attribute(t,e,n){const i=kr[n.type];switch(t){case Pr.VERTEX:return void await this.add_point_attribute(i,e,n);case Pr.OBJECT:return void await this.add_object_attribute(i,e,n)}ar.unreachable(t)}async add_point_attribute(t,e,n){const i=e.coreObjects();switch(t){case Dr.NUMERIC:for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}cook(t){if(this._is_using_expression())this.pv.name&&\\\\\\\"\\\\\\\"!=this.pv.name.trim()?this._add_attribute(Ir[this.pv.class],t[0]):this.states.error.set(\\\\\\\"attribute name is not valid\\\\\\\");else{this._operation=this._operation||new iq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}async _add_attribute(t,e){const n=kr[this.pv.type];switch(t){case Pr.VERTEX:return await this.add_point_attribute(n,e),this.setCoreGroup(e);case Pr.OBJECT:return await this.add_object_attribute(n,e),this.setCoreGroup(e)}ar.unreachable(t)}async add_point_attribute(t,e){const n=e.coreObjects();switch(t){case Dr.NUMERIC:for(let t=0;t{r[t.index()*this.pv.size+0]=e}));else{let e=[this.p.value2,this.p.value3,this.p.value4][this.pv.size-2].components;const i=new Array(e.length);let s;const o=[this._x_arrays_by_geometry_uuid,this._y_arrays_by_geometry_uuid,this._z_arrays_by_geometry_uuid,this._w_arrays_by_geometry_uuid];for(let a=0;a{i[a][t.index()]=e}));else{const t=s.value;for(let e of n)r[e.index()*this.pv.size+a]=t}for(let t=0;t{t.setAttribValue(this.pv.name,e)}));else{let e=[this.p.value2,this.p.value3,this.p.value4][this.pv.size-2].components,n={};const i=this._vector_by_attrib_size(this.pv.size);if(i){for(let e of t)n[e.index()]=i;for(let i=0;i{n[t.index()][s]=e}));else for(let e of t){n[e.index()][s]=r.value}}for(let e=0;e{i[t.index()]=e}));const r=Wr.arrayToIndexedArrays(i),s=t.coreGeometry();s&&s.setIndexedAttribute(this.pv.name,r.values,r.indices)}async add_string_attribute_to_object(t){const e=this.p.string;e.hasExpression()&&e.expressionController&&await e.expressionController.compute_expression_for_objects(t,((t,e)=>{t.setAttribValue(this.pv.name,e)}))}_init_array_if_required(t,e,n){const i=t.uuid,r=e[i];return r?r.length{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}cook(t){const e=t[0],n=e.attribNamesMatchingMask(this.pv.name);for(let t of n)switch(this.pv.class){case Pr.VERTEX:this.delete_vertex_attribute(e,t);case Pr.OBJECT:this.delete_object_attribute(e,t)}this.setCoreGroup(e)}delete_vertex_attribute(t,e){for(let n of t.objects())n.traverse((t=>{const n=t;if(n.geometry){new ps(n.geometry).deleteAttribute(e)}}))}delete_object_attribute(t,e){for(let n of t.objects()){let t=0;n.traverse((n=>{new vs(n,t).deleteAttribute(e),t++}))}}}class uq{set_attrib(t){const e=t.geometry,n=t.targetAttribSize;if(n<1||n>4)return;const i=t.add,r=t.mult,s=this._data_from_texture(t.texture);if(!s)return;const{data:o,resx:a,resy:l}=s,c=o.length/(a*l),u=e.getAttribute(t.uvAttribName).array,h=u.length/2,d=new Array(h*n);let p,_,m,f,g,v,y,x,b;const w=rs.clamp;for(v=0;v{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.attrib])}))}))}async cook(t){this._operation=this._operation||new hq(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var mq;!function(t){t.MIN_MAX_TO_01=\\\\\\\"min/max to 0/1\\\\\\\",t.VECTOR_TO_LENGTH_1=\\\\\\\"vectors to length 1\\\\\\\"}(mq||(mq={}));const fq=[mq.MIN_MAX_TO_01,mq.VECTOR_TO_LENGTH_1];class gq extends pG{constructor(){super(...arguments),this.min3=new p.a,this.max3=new p.a,this._vec=new p.a}static type(){return\\\\\\\"attribNormalize\\\\\\\"}cook(t,e){const n=t[0],i=t[0].objectsWithGeo(),r=sr.attribNames(e.name);for(let t of i){const n=t.geometry;for(let t of r){const i=n.getAttribute(t);if(i){let t=i;e.changeName&&\\\\\\\"\\\\\\\"!=e.newName&&(t=n.getAttribute(e.newName),t&&(t.needsUpdate=!0),t=t||i.clone()),this._normalize_attribute(i,t,e)}}}return n}_normalize_attribute(t,e,n){switch(fq[n.mode]){case mq.MIN_MAX_TO_01:return this._normalize_from_min_max_to_01(t,e);case mq.VECTOR_TO_LENGTH_1:return this._normalize_vectors(t,e)}}_normalize_from_min_max_to_01(t,e){const n=t.itemSize,i=t.array,r=e.array;switch(n){case 1:{const t=Math.min(...i),e=Math.max(...i);for(let n=0;n({name:t,value:e})))}}),this.name=oa.STRING(vq.name),this.changeName=oa.BOOLEAN(vq.changeName),this.newName=oa.STRING(vq.newName,{visibleIf:{changeName:1}})}};class xq extends gG{constructor(){super(...arguments),this.paramsConfig=yq}static type(){return\\\\\\\"attribNormalize\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(gq.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}set_mode(t){this.p.mode.set(fq.indexOf(t))}cook(t){this._operation=this._operation||new gq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var bq;!function(t){t[t.MIN=0]=\\\\\\\"MIN\\\\\\\",t[t.MAX=1]=\\\\\\\"MAX\\\\\\\",t[t.FIRST_FOUND=2]=\\\\\\\"FIRST_FOUND\\\\\\\"}(bq||(bq={}));class wq extends pG{constructor(){super(...arguments),this._values_per_attrib_name={},this._filtered_values_per_attrib_name={}}static type(){return\\\\\\\"attribPromote\\\\\\\"}cook(t,e){this._core_group=t[0],this._values_per_attrib_name={},this._filtered_values_per_attrib_name={};for(let t of this._core_group.coreObjects())this._core_object=t,this.find_values(e),this.filter_values(e),this.set_values(e);return this._core_group}find_values(t){const e=sr.attribNames(t.name);for(let n of e)this._find_values_for_attrib_name(n,t)}_find_values_for_attrib_name(t,e){switch(e.classFrom){case Pr.VERTEX:return this.find_values_from_points(t,e);case Pr.OBJECT:return this.find_values_from_object(t,e)}}find_values_from_points(t,e){if(this._core_object){const e=this._core_object.points(),n=e[0];if(n&&!n.isAttribIndexed(t)){const n=new Array(e.length);let i;for(let r=0;r{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name,this.p.classFrom,this.p.classTo],(()=>{if(\\\\\\\"\\\\\\\"!=this.pv.name){const t=Fr.filter((t=>t.value==this.pv.classFrom))[0].name,e=Fr.filter((t=>t.value==this.pv.classTo))[0].name;return`${this.pv.name} (${t} -> ${e})`}return\\\\\\\"\\\\\\\"}))}))}))}cook(t){this._operation=this._operation||new wq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const Sq=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(),this.ramp=oa.RAMP(),this.changeName=oa.BOOLEAN(0),this.newName=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{changeName:1}})}};class Cq extends gG{constructor(){super(...arguments),this.paramsConfig=Sq}static type(){return\\\\\\\"attribRemap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}cook(t){const e=t[0];this._remap_attribute(e),this.setCoreGroup(e)}_remap_attribute(t){const e=t.points();if(0===e.length)return;if(\\\\\\\"\\\\\\\"===this.pv.name)return;const n=e[0].attribSize(this.pv.name),i=e.map((t=>t.attribValue(this.pv.name)));let r=new Array(e.length);this._get_remaped_values(n,i,r);let s=this.pv.name;this.pv.changeName&&(s=this.pv.newName,t.hasAttrib(s)||t.addNumericVertexAttrib(s,n,0));let o=0;for(let t of r){e[o].setAttribValue(s,t),o++}}_get_remaped_values(t,e,n){switch(t){case zr.FLOAT:return this._get_normalized_float(e,n);case zr.VECTOR2:return this._get_normalized_vector2(e,n);case zr.VECTOR3:return this._get_normalized_vector3(e,n);case zr.VECTOR4:return this._get_normalized_vector4(e,n)}ar.unreachable(t)}_get_normalized_float(t,e){const n=t,i=this.p.ramp;for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.oldName,this.p.newName],(()=>\\\\\\\"\\\\\\\"!=this.pv.oldName&&\\\\\\\"\\\\\\\"!=this.pv.newName?`${this.pv.oldName} -> ${this.pv.newName}`:\\\\\\\"\\\\\\\"))}))}))}cook(t){const e=t[0];e.renameAttrib(this.pv.oldName,this.pv.newName,this.pv.class),this.setCoreGroup(e)}}var Oq=n(18);class Rq{constructor(t,e=0){this._bbox=t,this._level=e,this._leaves_by_octant={},this._points_by_octant_id={},this._leaves=[],this._bounding_boxes_by_octant={},this._bounding_boxes_by_octant_prepared=!1,this._center=this._bbox.max.clone().add(this._bbox.min).multiplyScalar(.5)}level(){return this._level}traverse(t){t(this);Object.values(this._leaves_by_octant).forEach((e=>{e.traverse(t)}))}intersects_sphere(t){return!!this._bbox&&this._bbox.intersectsSphere(t)}points_in_sphere(t,e){if(0==this._leaves.length){Object.values(this._points_by_octant_id).flat().filter((e=>t.containsPoint(e.position()))).forEach((t=>{e.push(t)}))}else{this._leaves.filter((e=>e.intersects_sphere(t))).forEach((n=>n.points_in_sphere(t,e)))}}bounding_box(){return this._bbox}set_points(t){this._points_by_octant_id={};for(let e of t)this.add_point(e);const e=Object.keys(this._points_by_octant_id);e.length>1&&e.forEach((t=>{this.create_leaf(t)}))}create_leaf(t){const e=this._leaf_bbox(t),n=new Rq(e,this._level+1);this._leaves_by_octant[t]=n,this._leaves.push(n),n.set_points(this._points_by_octant_id[t])}add_point(t){const e=this._octant_id(t.position());null==this._points_by_octant_id[e]&&(this._points_by_octant_id[e]=[]),this._points_by_octant_id[e].push(t)}_octant_id(t){return`${t.x>this._center.x?1:0}${t.y>this._center.y?1:0}${t.z>this._center.z?1:0}`}_leaf_bbox(t){return this._bounding_boxes_by_octant_prepared||(this._prepare_leaves_bboxes(),this._bounding_boxes_by_octant_prepared=!0),this._bounding_boxes_by_octant[t]}_bbox_center(t,e,n){const i=this._bbox.min.clone();return t&&(i.x=this._bbox.max.x),e&&(i.y=this._bbox.max.y),n&&(i.z=this._bbox.max.z),i.clone().add(this._center).multiplyScalar(.5)}_prepare_leaves_bboxes(){const t=[];t.push(this._bbox_center(0,0,0)),t.push(this._bbox_center(0,0,1)),t.push(this._bbox_center(0,1,0)),t.push(this._bbox_center(0,1,1)),t.push(this._bbox_center(1,0,0)),t.push(this._bbox_center(1,0,1)),t.push(this._bbox_center(1,1,0)),t.push(this._bbox_center(1,1,1));const e=this._bbox.max.clone().sub(this._bbox.min).multiplyScalar(.25);for(let n of t){const t=this._octant_id(n),i=new XB.a(n.clone().sub(e),n.clone().add(e));this._bounding_boxes_by_octant[t]=i}}}class Pq{constructor(t){this._root=new Rq(t)}set_points(t){this._root.set_points(t)}traverse(t){this._root.traverse(t)}find_points(t,e,n){const i=new Oq.a(t,e);let r=[];return this._root.intersects_sphere(i)&&this._root.points_in_sphere(i,r),null==n||r.length>n&&(r=f.sortBy(r,(e=>e.position().distanceTo(t))),r=r.slice(0,n)),r}}class Iq{constructor(t={}){this._array_index=0,this._count=0,this._current_count_index=0,this._resolve=null,this._max_time_per_chunk=t.max_time_per_chunk||10,this._check_every_interations=t.check_every_interations||100}async startWithCount(t,e){if(this._count=t,this._current_count_index=0,this._iteratee_method_count=e,this._bound_next_with_count=this.nextWithCount.bind(this),this._resolve)throw\\\\\\\"an iterator cannot be started twice\\\\\\\";return new Promise(((t,e)=>{this._resolve=t,this.nextWithCount()}))}nextWithCount(){const t=ai.performance.performanceManager(),e=t.now();if(this._iteratee_method_count&&this._bound_next_with_count)for(;this._current_count_indexthis._max_time_per_chunk){setTimeout(this._bound_next_with_count,1);break}this._current_count_index>=this._count&&this._resolve&&this._resolve()}async startWithArray(t,e){if(this._array=t,this._array_index=0,this._iteratee_method_array=e,this._bound_next_with_array=this.nextWithArray.bind(this),this._resolve)throw\\\\\\\"an iterator cannot be started twice\\\\\\\";return new Promise(((t,e)=>{this._resolve=t,this.nextWithArray()}))}nextWithArray(){const t=ai.performance.performanceManager(),e=t.now();if(this._iteratee_method_array&&this._bound_next_with_array&&this._array)for(;this._current_array_element=this._array[this._array_index];)if(this._iteratee_method_array(this._current_array_element,this._array_index),this._array_index++,this._array_index%this._check_every_interations==0&&t.now()-e>this._max_time_per_chunk){setTimeout(this._bound_next_with_array,1);break}void 0===this._current_array_element&&this._resolve&&this._resolve()}}const Fq=new class extends aa{constructor(){super(...arguments),this.srcGroup=oa.STRING(),this.destGroup=oa.STRING(),this.name=oa.STRING(),this.maxSamplesCount=oa.INTEGER(1,{range:[1,10],rangeLocked:[!0,!1]}),this.distanceThreshold=oa.FLOAT(1),this.blendWidth=oa.FLOAT(0)}};class Dq extends gG{constructor(){super(...arguments),this.paramsConfig=Fq}static type(){return\\\\\\\"attribTransfer\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to transfer attributes to\\\\\\\",\\\\\\\"geometry to transfer attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState([Qi.FROM_NODE,Qi.NEVER])}async cook(t){this._core_group_dest=t[0];const e=this._core_group_dest.pointsFromGroup(this.pv.destGroup);this._core_group_src=t[1],this._attrib_names=this._core_group_src.attribNamesMatchingMask(this.pv.name),this._error_if_attribute_not_found_on_second_input(),this._build_octree_if_required(this._core_group_src),this._add_attribute_if_required(),await this._transfer_attributes(e),this.setCoreGroup(this._core_group_dest)}_error_if_attribute_not_found_on_second_input(){for(let t of this._attrib_names)this._core_group_src.hasAttrib(t)||this.states.error.set(`attribute '${t}' not found on second input`)}_build_octree_if_required(t){const e=null==this._octree_timestamp||this._octree_timestamp!==t.timestamp();if(this._prev_param_srcGroup!==this.pv.srcGroup||e){this._octree_timestamp=t.timestamp(),this._prev_param_srcGroup=this.pv.srcGroup;const e=this._core_group_src.pointsFromGroup(this.pv.srcGroup);this._octree=new Pq(this._core_group_src.boundingBox()),this._octree.set_points(e)}}_add_attribute_if_required(){for(let t of this._attrib_names)if(!this._core_group_dest.hasAttrib(t)){const e=this._core_group_src.attribSize(t);this._core_group_dest.addNumericVertexAttrib(t,e,0)}}async _transfer_attributes(t){const e=new Iq;await e.startWithArray(t,this._transfer_attributes_for_point.bind(this))}_transfer_attributes_for_point(t){var e;const n=this.pv.distanceThreshold+this.pv.blendWidth,i=(null===(e=this._octree)||void 0===e?void 0:e.find_points(t.position(),n,this.pv.maxSamplesCount))||[];for(let e of this._attrib_names)this._interpolate_points(t,i,e)}_interpolate_points(t,e,n){let i;i=class{static perform(t,e,n,i,r){switch(e.length){case 0:return t.attribValue(n);case 1:return this._interpolate_with_1_point(t,e[0],n,i,r);default:return this._interpolate_with_multiple_points(t,e,n,i,r)}}static _interpolate_with_1_point(t,e,n,i,r){const s=t.position(),o=e.position(),a=s.distanceTo(o),l=e.attribValue(n);return m.isNumber(l)?this._weighted_value_from_distance(t,l,n,a,i,r):(console.warn(\\\\\\\"value is not a number\\\\\\\",l),0)}static _weight_from_distance(t,e,n){return(t-e)/n}static _weighted_value_from_distance(t,e,n,i,r,s){if(i<=r)return e;{const o=t.attribValue(n);if(m.isNumber(o)){const t=this._weight_from_distance(i,r,s);return t*o+(1-t)*e}return console.warn(\\\\\\\"value is not a number\\\\\\\",o),0}}static _interpolate_with_multiple_points(t,e,n,i,r){const s=e.map((e=>this._interpolate_with_1_point(t,e,n,i,r)));return f.max(s)||0}static weights(t,e){switch(e.length){case 1:return 1;case 2:return this._weights_from_2(t,e);default:return e=e.slice(0,3),this._weights_from_3(t,e)}}static _weights_from_2(t,e){const n=e.map((e=>t.distanceTo(e))),i=f.sum(n);return[n[1]/i,n[0]/i]}static _weights_from_3(t,e){const n=e.map((e=>t.distanceTo(e))),i=f.sum([n[0]*n[1],n[0]*n[2],n[1]*n[2]]);return[n[1]*n[2]/i,n[0]*n[2]/i,n[0]*n[1]/i]}}.perform(t,e,n,this.pv.distanceThreshold,this.pv.blendWidth),null!=i&&t.setAttribValue(n,i)}}const kq=new class extends aa{constructor(){super(...arguments),this.stepSize=oa.FLOAT(.1)}};class Bq extends gG{constructor(){super(...arguments),this.paramsConfig=kq}static type(){return\\\\\\\"bboxScatter\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create points from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1)}cook(t){const e=t[0],n=this.pv.stepSize,i=e.boundingBox(),r=i.min,s=i.max,o=[];for(let t=r.x;t<=s.x;t+=n)for(let e=r.y;e<=s.y;e+=n)for(let i=r.z;i<=s.z;i+=n)o.push(t),o.push(e),o.push(i);const a=new S.a;a.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(o),3)),this.setGeometry(a,Sr.POINTS)}}const zq=new class extends aa{constructor(){super(...arguments),this.attribName=oa.STRING(\\\\\\\"position\\\\\\\"),this.blend=oa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0]})}};class Uq extends gG{constructor(){super(...arguments),this.paramsConfig=zq}static type(){return\\\\\\\"blend\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to blend from\\\\\\\",\\\\\\\"geometry to blend to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState([Qi.FROM_NODE,Qi.NEVER])}cook(t){const e=t[0],n=t[1],i=e.objects(),r=n.objects();let s,o;for(let t=0;tt.flip())),t}}Gq.fromPolygons=function(t){let e=new Gq;return e.polygons=t,e};class Vq{constructor(t=0,e=0,n=0){this.x=t,this.y=e,this.z=n}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this}clone(){return new Vq(this.x,this.y,this.z)}negate(){return this.x*=-1,this.y*=-1,this.z*=-1,this}add(t){return this.x+=t.x,this.y+=t.y,this.z+=t.z,this}sub(t){return this.x-=t.x,this.y-=t.y,this.z-=t.z,this}times(t){return this.x*=t,this.y*=t,this.z*=t,this}dividedBy(t){return this.x/=t,this.y/=t,this.z/=t,this}lerp(t,e){return this.add(Hq.copy(t).sub(this).times(e))}unit(){return this.dividedBy(this.length())}length(){return Math.sqrt(this.x**2+this.y**2+this.z**2)}normalize(){return this.unit()}cross(t){let e=this;const n=e.x,i=e.y,r=e.z,s=t.x,o=t.y,a=t.z;return this.x=i*a-r*o,this.y=r*s-n*a,this.z=n*o-i*s,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z}}let Hq=new Vq,jq=new Vq;class Wq{constructor(t,e,n,i){this.pos=(new Vq).copy(t),this.normal=(new Vq).copy(e),this.uv=(new Vq).copy(n),this.uv.z=0,i&&(this.color=(new Vq).copy(i))}clone(){return new Wq(this.pos,this.normal,this.uv,this.color)}flip(){this.normal.negate()}interpolate(t,e){return new Wq(this.pos.clone().lerp(t.pos,e),this.normal.clone().lerp(t.normal,e),this.uv.clone().lerp(t.uv,e),this.color&&t.color&&this.color.clone().lerp(t.color,e))}}class qq{constructor(t,e){this.normal=t,this.w=e}clone(){return new qq(this.normal.clone(),this.w)}flip(){this.normal.negate(),this.w=-this.w}splitPolygon(t,e,n,i,r){let s=0,o=[];for(let e=0;eqq.EPSILON?1:0;s|=i,o.push(i)}switch(s){case 0:(this.normal.dot(t.plane.normal)>0?e:n).push(t);break;case 1:i.push(t);break;case 2:r.push(t);break;case 3:let s=[],a=[];for(let e=0;e=3&&i.push(new Xq(s,t.shared)),a.length>=3&&r.push(new Xq(a,t.shared))}}}qq.EPSILON=1e-5,qq.fromPoints=function(t,e,n){let i=Hq.copy(e).sub(t).cross(jq.copy(n).sub(t)).normalize();return new qq(i.clone(),i.dot(t))};class Xq{constructor(t,e){this.vertices=t,this.shared=e,this.plane=qq.fromPoints(t[0].pos,t[1].pos,t[2].pos)}clone(){return new Xq(this.vertices.map((t=>t.clone())),this.shared)}flip(){this.vertices.reverse().map((t=>t.flip())),this.plane.flip()}}class Yq{constructor(t){this.plane=null,this.front=null,this.back=null,this.polygons=[],t&&this.build(t)}clone(){let t=new Yq;return t.plane=this.plane&&this.plane.clone(),t.front=this.front&&this.front.clone(),t.back=this.back&&this.back.clone(),t.polygons=this.polygons.map((t=>t.clone())),t}invert(){for(let t=0;tnew Xq(t.vertices.map((t=>new Wq(t.pos,t.normal,t.uv))),t.shared))))},Gq.fromGeometry=function(t,e){let n=[];if(t.isGeometry){let i=t.faces,r=t.vertices,s=[\\\\\\\"a\\\\\\\",\\\\\\\"b\\\\\\\",\\\\\\\"c\\\\\\\"];for(let o=0;o({top:0,array:new Float32Array(t),write:function(t){this.array[this.top++]=t.x,this.array[this.top++]=t.y,this.array[this.top++]=t.z}}),Qq=t=>({top:0,array:new Float32Array(t),write:function(t){this.array[this.top++]=t.x,this.array[this.top++]=t.y}});var Kq;Gq.toMesh=function(t,e,n){let i,r,s=t.polygons;{let t=0;s.forEach((e=>t+=e.vertices.length-2)),i=new S.a;let e,n=Zq(3*t*3),o=Zq(3*t*3),a=Qq(2*t*3),l=[];if(s.forEach((i=>{let r=i.vertices,s=r.length;void 0!==i.shared&&(l[i.shared]||(l[i.shared]=[])),s&&void 0!==r[0].color&&(e||(e=Zq(3*t*3)));for(let t=3;t<=s;t++)void 0!==i.shared&&l[i.shared].push(n.top/3,n.top/3+1,n.top/3+2),n.write(r[0].pos),n.write(r[t-2].pos),n.write(r[t-1].pos),o.write(r[0].normal),o.write(r[t-2].normal),o.write(r[t-1].normal),a.write(r[0].uv),a.write(r[t-2].uv),a.write(r[t-1].uv),e&&(e.write(r[0].color)||e.write(r[t-2].color)||e.write(r[t-1].color))})),i.setAttribute(\\\\\\\"position\\\\\\\",new C.a(n.array,3)),i.setAttribute(\\\\\\\"normal\\\\\\\",new C.a(o.array,3)),i.setAttribute(\\\\\\\"uv\\\\\\\",new C.a(a.array,2)),e&&i.setAttribute(\\\\\\\"color\\\\\\\",new C.a(e.array,3)),l.length){let t=[],e=0;for(let n=0;n({name:t,value:e})))}}),this.useBothMaterials=oa.BOOLEAN(nX.useBothMaterials)}};class rX extends gG{constructor(){super(...arguments),this.paramsConfig=iX}static type(){return\\\\\\\"boolean\\\\\\\"}initializeNode(){super.initializeNode(),this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState(eX.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.operation],(()=>tX[this.pv.operation]))}))}))}setOperation(t){this.p.operation.set(tX.indexOf(t))}async cook(t){this._operation=this._operation||new eX(this.scene(),this.states,this);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class sX extends pG{constructor(){super(...arguments),this._core_transform=new Mz}static type(){return\\\\\\\"box\\\\\\\"}cook(t,e){const n=t[0],i=n?this._cook_with_input(n,e):this._cook_without_input(e);return this.createCoreGroupFromGeometry(i)}_cook_without_input(t){const e=t.divisions,n=t.size,i=new N(n,n,n,e,e,e);return i.translate(t.center.x,t.center.y,t.center.z),i.computeVertexNormals(),i}_cook_with_input(t,e){const n=e.divisions,i=t.boundingBox(),r=i.max.clone().sub(i.min),s=i.max.clone().add(i.min).multiplyScalar(.5),o=new N(r.x,r.y,r.z,n,n,n),a=this._core_transform.translation_matrix(s);return o.applyMatrix4(a),o}}sX.DEFAULT_PARAMS={size:1,divisions:1,center:new p.a(0,0,0)},sX.INPUT_CLONED_STATE=Qi.NEVER;const oX=sX.DEFAULT_PARAMS;const aX=new class extends aa{constructor(){super(...arguments),this.size=oa.FLOAT(oX.size),this.divisions=oa.INTEGER(oX.divisions,{range:[1,10],rangeLocked:[!0,!1]}),this.center=oa.VECTOR3(oX.center)}};class lX extends gG{constructor(){super(...arguments),this.paramsConfig=aX}static type(){return\\\\\\\"box\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create bounding box from (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(sX.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new sX(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class cX extends C.a{constructor(t,e,n,i=1){\\\\\\\"number\\\\\\\"==typeof n&&(i=n,n=!1,console.error(\\\\\\\"THREE.InstancedBufferAttribute: The constructor now expects normalized as the third argument.\\\\\\\")),super(t,e,n),this.meshPerAttribute=i}copy(t){return super.copy(t),this.meshPerAttribute=t.meshPerAttribute,this}toJSON(){const t=super.toJSON();return t.meshPerAttribute=this.meshPerAttribute,t.isInstancedBufferAttribute=!0,t}}cX.prototype.isInstancedBufferAttribute=!0;const uX=new A.a,hX=new A.a,dX=[],pX=new k.a;class _X extends k.a{constructor(t,e,n){super(t,e),this.instanceMatrix=new cX(new Float32Array(16*n),16),this.instanceColor=null,this.count=n,this.frustumCulled=!1}copy(t){return super.copy(t),this.instanceMatrix.copy(t.instanceMatrix),null!==t.instanceColor&&(this.instanceColor=t.instanceColor.clone()),this.count=t.count,this}getColorAt(t,e){e.fromArray(this.instanceColor.array,3*t)}getMatrixAt(t,e){e.fromArray(this.instanceMatrix.array,16*t)}raycast(t,e){const n=this.matrixWorld,i=this.count;if(pX.geometry=this.geometry,pX.material=this.material,void 0!==pX.material)for(let r=0;rt.far||e.push({distance:a,point:fX.clone(),uv:Qr.a.getUV(fX,wX,TX,AX,EX,MX,SX,new d.a),face:null,object:this})}copy(t){return super.copy(t),void 0!==t.center&&this.center.copy(t.center),this.material=t.material,this}}function NX(t,e,n,i,r,s){yX.subVectors(t,n).addScalar(.5).multiply(i),void 0!==r?(xX.x=s*yX.x-r*yX.y,xX.y=r*yX.x+s*yX.y):xX.copy(yX),t.copy(e),t.x+=xX.x,t.y+=xX.y,t.applyMatrix4(bX)}CX.prototype.isSprite=!0;var LX=n(94),OX=n(81),RX=n(46);class PX{constructor(){this.coefficients=[];for(let t=0;t<9;t++)this.coefficients.push(new p.a)}set(t){for(let e=0;e<9;e++)this.coefficients[e].copy(t[e]);return this}zero(){for(let t=0;t<9;t++)this.coefficients[t].set(0,0,0);return this}getAt(t,e){const n=t.x,i=t.y,r=t.z,s=this.coefficients;return e.copy(s[0]).multiplyScalar(.282095),e.addScaledVector(s[1],.488603*i),e.addScaledVector(s[2],.488603*r),e.addScaledVector(s[3],.488603*n),e.addScaledVector(s[4],n*i*1.092548),e.addScaledVector(s[5],i*r*1.092548),e.addScaledVector(s[6],.315392*(3*r*r-1)),e.addScaledVector(s[7],n*r*1.092548),e.addScaledVector(s[8],.546274*(n*n-i*i)),e}getIrradianceAt(t,e){const n=t.x,i=t.y,r=t.z,s=this.coefficients;return e.copy(s[0]).multiplyScalar(.886227),e.addScaledVector(s[1],1.023328*i),e.addScaledVector(s[2],1.023328*r),e.addScaledVector(s[3],1.023328*n),e.addScaledVector(s[4],.858086*n*i),e.addScaledVector(s[5],.858086*i*r),e.addScaledVector(s[6],.743125*r*r-.247708),e.addScaledVector(s[7],.858086*n*r),e.addScaledVector(s[8],.429043*(n*n-i*i)),e}add(t){for(let e=0;e<9;e++)this.coefficients[e].add(t.coefficients[e]);return this}addScaledSH(t,e){for(let n=0;n<9;n++)this.coefficients[n].addScaledVector(t.coefficients[n],e);return this}scale(t){for(let e=0;e<9;e++)this.coefficients[e].multiplyScalar(t);return this}lerp(t,e){for(let n=0;n<9;n++)this.coefficients[n].lerp(t.coefficients[n],e);return this}equals(t){for(let e=0;e<9;e++)if(!this.coefficients[e].equals(t.coefficients[e]))return!1;return!0}copy(t){return this.set(t.coefficients)}clone(){return(new this.constructor).copy(this)}fromArray(t,e=0){const n=this.coefficients;for(let i=0;i<9;i++)n[i].fromArray(t,e+3*i);return this}toArray(t=[],e=0){const n=this.coefficients;for(let i=0;i<9;i++)n[i].toArray(t,e+3*i);return t}static getBasisAt(t,e){const n=t.x,i=t.y,r=t.z;e[0]=.282095,e[1]=.488603*i,e[2]=.488603*r,e[3]=.488603*n,e[4]=1.092548*n*i,e[5]=1.092548*i*r,e[6]=.315392*(3*r*r-1),e[7]=1.092548*n*r,e[8]=.546274*(n*n-i*i)}}PX.prototype.isSphericalHarmonics3=!0;class IX extends nv.a{constructor(t=new PX,e=1){super(void 0,e),this.sh=t}copy(t){return super.copy(t),this.sh.copy(t.sh),this}fromJSON(t){return this.intensity=t.intensity,this.sh.fromArray(t.sh),this}toJSON(t){const e=super.toJSON(t);return e.object.sh=this.sh.toArray(),e}}IX.prototype.isLightProbe=!0;var FX=n(62),DX=n(44);class kX extends S.a{constructor(){super(),this.type=\\\\\\\"InstancedBufferGeometry\\\\\\\",this.instanceCount=1/0}copy(t){return super.copy(t),this.instanceCount=t.instanceCount,this}clone(){return(new this.constructor).copy(this)}toJSON(){const t=super.toJSON(this);return t.instanceCount=this.instanceCount,t.isInstancedBufferGeometry=!0,t}}kX.prototype.isInstancedBufferGeometry=!0;class BX extends kf.a{constructor(t){super(t)}load(t,e,n,i){const r=this,s=new Df.a(r.manager);s.setPath(r.path),s.setRequestHeader(r.requestHeader),s.setWithCredentials(r.withCredentials),s.load(t,(function(n){try{e(r.parse(JSON.parse(n)))}catch(e){i?i(e):console.error(e),r.manager.itemError(t)}}),n,i)}parse(t){const e={},n={};function i(t,i){if(void 0!==e[i])return e[i];const r=t.interleavedBuffers[i],s=function(t,e){if(void 0!==n[e])return n[e];const i=t.arrayBuffers[e],r=new Uint32Array(i).buffer;return n[e]=r,r}(t,r.buffer),o=Object(Pt.c)(r.type,s),a=new as.a(o,r.stride);return a.uuid=r.uuid,e[i]=a,a}const r=t.isInstancedBufferGeometry?new kX:new S.a,s=t.data.index;if(void 0!==s){const t=Object(Pt.c)(s.type,s.array);r.setIndex(new C.a(t,1))}const o=t.data.attributes;for(const e in o){const n=o[e];let s;if(n.isInterleavedBufferAttribute){const e=i(t.data,n.data);s=new ls.a(e,n.itemSize,n.offset,n.normalized)}else{const t=Object(Pt.c)(n.type,n.array);s=new(n.isInstancedBufferAttribute?cX:C.a)(t,n.itemSize,n.normalized)}void 0!==n.name&&(s.name=n.name),void 0!==n.usage&&s.setUsage(n.usage),void 0!==n.updateRange&&(s.updateRange.offset=n.updateRange.offset,s.updateRange.count=n.updateRange.count),r.setAttribute(e,s)}const a=t.data.morphAttributes;if(a)for(const e in a){const n=a[e],s=[];for(let e=0,r=n.length;eNumber.EPSILON){const h=Math.sqrt(u),p=Math.sqrt(l*l+c*c),_=e.x-a/h,m=e.y+o/h,f=((n.x-c/p-_)*c-(n.y+l/p-m)*l)/(o*c-a*l);i=_+o*f-t.x,r=m+a*f-t.y;const g=i*i+r*r;if(g<=2)return new d.a(i,r);s=Math.sqrt(g/2)}else{let t=!1;o>Number.EPSILON?l>Number.EPSILON&&(t=!0):o<-Number.EPSILON?l<-Number.EPSILON&&(t=!0):Math.sign(a)===Math.sign(c)&&(t=!0),t?(i=-a,r=o,s=Math.sqrt(u)):(i=o,r=a,s=Math.sqrt(u/2))}return new d.a(i/s,r/s)}const P=[];for(let t=0,e=C.length,n=e-1,i=t+1;t=0;t--){const e=t/m,n=u*Math.cos(e*Math.PI/2),i=h*Math.sin(e*Math.PI/2)+_;for(let t=0,e=C.length;t=0;){const i=n;let r=n-1;r<0&&(r=t.length-1);for(let t=0,n=a+2*m;t0){const n=new Vg.b(e);r=new FX.a(n),r.setCrossOrigin(this.crossOrigin);for(let e=0,n=t.length;e0){i=new FX.a(this.manager),i.setCrossOrigin(this.crossOrigin);for(let e=0,i=t.length;e{dY.PARAM_CALLBACK_reset(t,e)}})}};class dY extends gG{constructor(){super(...arguments),this.paramsConfig=hY}static type(){return\\\\\\\"cache\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to cache\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1)}cook(t){const e=\\\\\\\"\\\\\\\"==this.pv.cache||null==this.pv.cache,n=t[0];if(e&&n){const t=[];for(let e of n.objects())t.push(e.toJSON());this.setCoreGroup(n),this.p.cache.set(JSON.stringify(t))}else if(this.pv.cache){const t=new aY,e=JSON.parse(this.pv.cache),n=[];for(let i of e){const e=t.parse(i);n.push(e)}this.setObjects(n)}else this.setObjects([])}static PARAM_CALLBACK_reset(t,e){t.param_callback_PARAM_CALLBACK_reset()}async param_callback_PARAM_CALLBACK_reset(){this.p.cache.set(\\\\\\\"\\\\\\\"),this.compute()}}const pY=[nr.ORTHOGRAPHIC,nr.PERSPECTIVE],_Y={direction:new p.a(0,1,0)},mY=[new d.a(-1,-1),new d.a(-1,1),new d.a(1,1),new d.a(1,-1)],fY=new p.a(0,0,1);const gY=new class extends aa{constructor(){super(...arguments),this.camera=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.OBJ,types:pY}}),this.direction=oa.VECTOR3(_Y.direction),this.offset=oa.FLOAT(0,{range:[-10,10],rangeLocked:[!1,!1]}),this.useSegmentsCount=oa.BOOLEAN(!0),this.stepSize=oa.FLOAT(1,{range:[.001,1],rangeLocked:[!1,!1],visibleIf:{useSegmentsCount:0}}),this.segments=oa.VECTOR2([10,10],{visibleIf:{useSegmentsCount:1}}),this.sizeMult=oa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1]}),this.updateOnWindowResize=oa.BOOLEAN(1),this.update=oa.BUTTON(null,{callback:t=>{vY.PARAM_CALLBACK_update(t)}})}};class vY extends gG{constructor(){super(...arguments),this.paramsConfig=gY,this._plane=new X.a,this._raycaster=new uL,this._planeCorners=[new p.a,new p.a,new p.a,new p.a],this._planeCenter=new p.a,this._core_transform=new Mz,this.segments_count=new d.a(1,1),this.planeSize=new d.a}static type(){return\\\\\\\"cameraPlane\\\\\\\"}cook(){this._updateWindowControllerDependency();const t=this.pv.camera.nodeWithContext(Ki.OBJ);if(!t)return this.states.error.set(\\\\\\\"no camera found\\\\\\\"),void this.cookController.endCook();if(!pY.includes(t.type()))return this.states.error.set(\\\\\\\"node found is not a camera\\\\\\\"),void this.cookController.endCook();const e=t.object;this._computePlaneParams(e)}_updateWindowControllerDependency(){this.pv.updateOnWindowResize?this.addGraphInput(this.scene().windowController.graphNode()):this.removeGraphInput(this.scene().windowController.graphNode())}_computePlaneParams(t){this._plane.normal.copy(this.pv.direction),this._plane.constant=this.pv.offset;let e=0;this._planeCenter.set(0,0,0);for(let n of mY){this._raycaster.setFromCamera(n,t);const i=this._planeCorners[e];this._raycaster.ray.intersectPlane(this._plane,i),this._planeCenter.add(i),e++}this._planeCenter.multiplyScalar(.25);const n=this._planeCorners[1].distanceTo(this._planeCorners[2]),i=this._planeCorners[0].distanceTo(this._planeCorners[3]),r=this._planeCorners[0].distanceTo(this._planeCorners[1]),s=this._planeCorners[2].distanceTo(this._planeCorners[3]),o=Math.max(n,i)*this.pv.sizeMult,a=Math.max(r,s)*this.pv.sizeMult;this.planeSize.set(o,a);const l=this._createPlane(this.planeSize);this._core_transform.rotate_geometry(l,fY,this.pv.direction);const c=this._core_transform.translation_matrix(this._planeCenter);l.applyMatrix4(c),this.setGeometry(l)}_createPlane(t){return t=t.clone(),this.pv.useSegmentsCount?(this.segments_count.x=Math.floor(this.pv.segments.x),this.segments_count.y=Math.floor(this.pv.segments.y)):this.pv.stepSize>0&&(this.segments_count.x=Math.floor(t.x/this.pv.stepSize),this.segments_count.y=Math.floor(t.y/this.pv.stepSize),t.x=this.segments_count.x*this.pv.stepSize,t.y=this.segments_count.y*this.pv.stepSize),new L(t.x,t.y,this.segments_count.x,this.segments_count.y)}static PARAM_CALLBACK_update(t){t._paramCallbackUpdate()}_paramCallbackUpdate(){this.setDirty()}}class yY extends pG{constructor(){super(...arguments),this._geo_center=new p.a}static type(){return\\\\\\\"center\\\\\\\"}cook(t,e){var n;const i=t[0].objectsWithGeo(),r=new Array(3*i.length);r.fill(0);for(let t=0;t0&&(s.push(t-1),s.push(t));s.push(e-1),s.push(0);const a=new S.a;return a.setAttribute(\\\\\\\"position\\\\\\\",new C.c(r,3)),a.setIndex(s),a}}const TY=new p.a(0,0,1);class AY extends pG{constructor(){super(...arguments),this._core_transform=new Mz}static type(){return\\\\\\\"circle\\\\\\\"}cook(t,e){return e.open?this._create_circle(e):this._create_disk(e)}_create_circle(t){const e=wY.create(t.radius,t.segments,t.arcAngle);return this._core_transform.rotate_geometry(e,TY,t.direction),this.createCoreGroupFromGeometry(e,Sr.LINE_SEGMENTS)}_create_disk(t){const e=new zX(t.radius,t.segments);return this._core_transform.rotate_geometry(e,TY,t.direction),this.createCoreGroupFromGeometry(e)}}AY.DEFAULT_PARAMS={radius:1,segments:12,open:!0,arcAngle:360,direction:new p.a(0,1,0)};const EY=AY.DEFAULT_PARAMS;const MY=new class extends aa{constructor(){super(...arguments),this.radius=oa.FLOAT(EY.radius),this.segments=oa.INTEGER(EY.segments,{range:[1,50],rangeLocked:[!0,!1]}),this.open=oa.BOOLEAN(EY.open),this.arcAngle=oa.FLOAT(EY.arcAngle,{range:[0,360],rangeLocked:[!1,!1],visibleIf:{open:1}}),this.direction=oa.VECTOR3(EY.direction)}};class SY extends gG{constructor(){super(...arguments),this.paramsConfig=MY}static type(){return\\\\\\\"circle\\\\\\\"}initializeNode(){}cook(){this._operation=this._operation||new AY(this._scene,this.states);const t=this._operation.cook([],this.pv);this.setCoreGroup(t)}}var CY;!function(t){t.SEGMENTS_COUNT=\\\\\\\"segments count\\\\\\\",t.SEGMENTS_LENGTH=\\\\\\\"segments length\\\\\\\"}(CY||(CY={}));const NY=[CY.SEGMENTS_COUNT,CY.SEGMENTS_LENGTH];var LY;!function(t){t.ABC=\\\\\\\"abc\\\\\\\",t.ACB=\\\\\\\"acb\\\\\\\",t.AB=\\\\\\\"ab\\\\\\\",t.BC=\\\\\\\"bc\\\\\\\",t.AC=\\\\\\\"ac\\\\\\\"}(LY||(LY={}));const OY=[LY.ABC,LY.ACB,LY.AB,LY.AC,LY.BC];class RY{constructor(t){this.params=t,this.a=new p.a,this.b=new p.a,this.c=new p.a,this.an=new p.a,this.bn=new p.a,this.cn=new p.a,this.ac=new p.a,this.ab=new p.a,this.ab_x_ac=new p.a,this.part0=new p.a,this.part1=new p.a,this.divider=1,this.a_center=new p.a,this.center=new p.a,this.normal=new p.a,this.radius=1,this.x=new p.a,this.y=new p.a,this.z=new p.a,this.angle_ab=1,this.angle_ac=1,this.angle_bc=1,this.angle=2*Math.PI,this.x_rotated=new p.a,this._created_geometries={}}created_geometries(){return this._created_geometries}create(t,e,n){this.a.copy(t),this.b.copy(e),this.c.copy(n),this._compute_axis(),this._create_arc(),this._create_center()}_create_arc(){this._compute_angle();const t=this._points_count(),e=new Array(3*t),n=new Array(t),i=this.angle/(t-1);this.x_rotated.copy(this.x).multiplyScalar(this.radius);let r=0;for(r=0;r0&&(n[2*(r-1)]=r-1,n[2*(r-1)+1]=r);this.params.full&&(n.push(r-1),n.push(0));const s=new S.a;if(s.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(e),3)),s.setIndex(n),this.params.addIdAttribute||this.params.addIdnAttribute){const e=new Array(t);for(let t=0;te/(t-1)));this.params.addIdnAttribute&&s.setAttribute(\\\\\\\"idn\\\\\\\",new C.a(new Float32Array(n),1))}this._created_geometries.arc=s}_create_center(){if(!this.params.center)return;const t=new S.a,e=[this.center.x,this.center.y,this.center.z];t.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(e),3)),this._created_geometries.center=t}_compute_axis(){this.ac.copy(this.c).sub(this.a),this.ab.copy(this.b).sub(this.a),this.ab_x_ac.copy(this.ab).cross(this.ac),this.divider=2*this.ab_x_ac.lengthSq(),this.part0.copy(this.ab_x_ac).cross(this.ab).multiplyScalar(this.ac.lengthSq()),this.part1.copy(this.ac).cross(this.ab_x_ac).multiplyScalar(this.ab.lengthSq()),this.a_center.copy(this.part0).add(this.part1).divideScalar(this.divider),this.radius=this.a_center.length(),this.normal.copy(this.ab_x_ac).normalize(),this.center.copy(this.a).add(this.a_center)}_compute_angle(){this.params.arc&&(this.params.full?(this.x.copy(this.a).sub(this.center).normalize(),this.angle=2*Math.PI):(this.an.copy(this.a).sub(this.center).normalize(),this.bn.copy(this.b).sub(this.center).normalize(),this.cn.copy(this.c).sub(this.center).normalize(),this._set_x_from_joinMode(),this.y.copy(this.normal),this.z.copy(this.x).cross(this.y).normalize(),this.angle_ab=this.an.angleTo(this.bn),this.angle_ac=this.an.angleTo(this.cn),this.angle_bc=this.bn.angleTo(this.cn),this._set_angle_from_joinMode()))}_points_count(){const t=this.params.pointsCountMode;switch(t){case CY.SEGMENTS_COUNT:return this.params.segmentsCount+1;case CY.SEGMENTS_LENGTH:{let t=Math.PI*this.radius*this.radius;return this.params.full||(t*=Math.abs(this.angle)/(2*Math.PI)),Math.ceil(t/this.params.segmentsLength)}}ar.unreachable(t)}_set_x_from_joinMode(){const t=this.params.joinMode;switch(this.x.copy(this.a).sub(this.center).normalize(),t){case LY.ABC:case LY.ACB:case LY.AB:case LY.AC:return this.x.copy(this.an);case LY.BC:return this.x.copy(this.bn)}ar.unreachable(t)}_set_angle_from_joinMode(){const t=this.params.joinMode;switch(t){case LY.ABC:return void(this.angle=this.angle_ab+this.angle_bc);case LY.ACB:return this.angle=this.angle_ac+this.angle_bc,void(this.angle*=-1);case LY.AB:return void(this.angle=this.angle_ab);case LY.AC:return this.angle=this.angle_ac,void(this.angle*=-1);case LY.BC:return void(this.angle=this.angle_bc)}ar.unreachable(t)}}const PY=new class extends aa{constructor(){super(...arguments),this.arc=oa.BOOLEAN(1),this.pointsCountMode=oa.INTEGER(NY.indexOf(CY.SEGMENTS_COUNT),{visibleIf:{arc:1},menu:{entries:NY.map(((t,e)=>({value:e,name:t})))}}),this.segmentsLength=oa.FLOAT(.1,{visibleIf:{arc:1,pointsCountMode:NY.indexOf(CY.SEGMENTS_LENGTH)},range:[0,1],rangeLocked:[!0,!1]}),this.segmentsCount=oa.INTEGER(100,{visibleIf:{arc:1,pointsCountMode:NY.indexOf(CY.SEGMENTS_COUNT)},range:[1,100],rangeLocked:[!0,!1]}),this.full=oa.BOOLEAN(1,{visibleIf:{arc:1}}),this.joinMode=oa.INTEGER(OY.indexOf(LY.ABC),{visibleIf:{arc:1,full:0},menu:{entries:OY.map(((t,e)=>({value:e,name:t})))}}),this.addIdAttribute=oa.BOOLEAN(1),this.addIdnAttribute=oa.BOOLEAN(1),this.center=oa.BOOLEAN(0)}};class IY extends gG{constructor(){super(...arguments),this.paramsConfig=PY,this.a=new p.a,this.b=new p.a,this.c=new p.a}static type(){return\\\\\\\"circle3Points\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Qi.NEVER])}cook(t){const e=t[0].points();e.length<3?this.states.error.set(`only ${e.length} points found, when 3 are required`):this._create_circle(e)}_create_circle(t){const e=new RY({arc:this.pv.arc,center:this.pv.center,pointsCountMode:NY[this.pv.pointsCountMode],segmentsLength:this.pv.segmentsLength,segmentsCount:this.pv.segmentsCount,full:this.pv.full,joinMode:OY[this.pv.joinMode],addIdAttribute:this.pv.addIdAttribute,addIdnAttribute:this.pv.addIdnAttribute});t[0].getPosition(this.a),t[1].getPosition(this.b),t[2].getPosition(this.c),e.create(this.a,this.b,this.c);const n=[],i=e.created_geometries();i.arc&&n.push(this.createObject(i.arc,Sr.LINE_SEGMENTS)),i.center&&n.push(this.createObject(i.center,Sr.POINTS)),this.setObjects(n)}}class FY extends pG{static type(){return\\\\\\\"color\\\\\\\"}cook(t,e){}}FY.DEFAULT_PARAMS={fromAttribute:!1,attribName:\\\\\\\"\\\\\\\",color:new D.a(1,1,1),asHsv:!1};const DY=new D.a(1,1,1),kY=\\\\\\\"color\\\\\\\",BY=FY.DEFAULT_PARAMS;const zY=new class extends aa{constructor(){super(...arguments),this.fromAttribute=oa.BOOLEAN(BY.fromAttribute),this.attribName=oa.STRING(BY.attribName,{visibleIf:{fromAttribute:1}}),this.color=oa.COLOR(BY.color,{visibleIf:{fromAttribute:0},expression:{forEntities:!0}}),this.asHsv=oa.BOOLEAN(BY.asHsv,{visibleIf:{fromAttribute:0}})}};class UY extends gG{constructor(){super(...arguments),this.paramsConfig=zY,this._r_arrays_by_geometry_uuid={},this._g_arrays_by_geometry_uuid={},this._b_arrays_by_geometry_uuid={}}static type(){return\\\\\\\"color\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to update color of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}async cook(t){const e=t[0],n=e.coreObjects();for(let t of n)if(this.pv.fromAttribute)this._set_fromAttribute(t);else{this.p.color.hasExpression()?await this._eval_expressions(t):this._eval_simple_values(t)}if(!this.io.inputs.cloneRequired(0)){const t=e.geometries();for(let e of t)e.getAttribute(kY).needsUpdate=!0}this.setCoreGroup(e)}_set_fromAttribute(t){const e=t.coreGeometry();if(!e)return;this._create_init_color(e,DY);const n=e.points(),i=e.attribSize(this.pv.attribName),r=e.geometry(),s=r.getAttribute(this.pv.attribName).array,o=r.getAttribute(kY).array;switch(i){case 1:for(let t=0;t{a[t.index()]=e}));else for(let t of n)e[3*t.index()+i]=s;return a}_init_array_if_required(t,e,n){const i=t.uuid,r=e[i];return r?r.length{const e=new A.a;return this._matrix_from_point(t,e),e}))}_matrix_from_point(t,e){const n=t.position();this._is_scale_present?t.attribValue(\\\\\\\"scale\\\\\\\",this._point_scale):this._point_scale.copy(jY.SCALE);const i=this._is_pscale_present?t.attribValue(\\\\\\\"pscale\\\\\\\"):jY.PSCALE;this._point_scale.multiplyScalar(i);const r=this._matrices.scale;r.makeScale(this._point_scale.x,this._point_scale.y,this._point_scale.z);const s=this._matrices.translate;if(s.makeTranslation(n.x,n.y,n.z),e.multiply(s),this._do_rotate_matrices){const n=this._matrices.rotate,i=jY.EYE;t.attribValue(\\\\\\\"normal\\\\\\\",this._point_normal),this._point_normal.multiplyScalar(-1),this._is_up_present?t.attribValue(\\\\\\\"up\\\\\\\",this._point_up):this._point_up.copy(jY.UP),this._point_up.normalize(),n.lookAt(i,this._point_normal,this._point_up),e.multiply(n)}e.multiply(r)}static create_instance_buffer_geo(t,e,n){const i=e.points(),r=new kX;r.copy(t),r.instanceCount=1/0;const s=i.length,o=new Float32Array(3*s),a=new Float32Array(3*s),l=new Float32Array(3*s),c=new Float32Array(4*s),u=e.hasAttrib(XY),h=new p.a(0,0,0),d=new au.a,_=new p.a(1,1,1),f=new JY(e).matrices();i.forEach(((t,e)=>{const n=3*e,i=4*e;f[e].decompose(h,d,_),h.toArray(o,n),d.toArray(c,i),_.toArray(l,n);(u?t.attribValue(XY,this._point_color):WY).toArray(a,n)}));const g=e.hasAttrib(\\\\\\\"uv\\\\\\\");if(g){const t=new Float32Array(2*s);i.forEach(((e,n)=>{const i=2*n;(g?e.attribValue(\\\\\\\"uv\\\\\\\",this._point_uv):qY).toArray(t,i)})),r.setAttribute(YY.UV,new cX(t,2))}r.setAttribute(YY.POSITION,new cX(o,3)),r.setAttribute(YY.SCALE,new cX(l,3)),r.setAttribute(YY.ORIENTATION,new cX(c,4)),r.setAttribute(YY.COLOR,new cX(a,3));e.attribNamesMatchingMask(n).forEach((t=>{const n=e.attribSize(t),o=new Float32Array(s*n);i.forEach(((e,i)=>{const r=e.attribValue(t);m.isNumber(r)?o[i]=r:r.toArray(o,i*n)})),r.setAttribute(t,new cX(o,n))}));return new ps(r).markAsInstance(),r}}JY._point_color=new p.a,JY._point_uv=new d.a;class ZY extends ru{set_point(t){this._point=t,this.setDirty(),this.removeDirtyState()}value(t){return this._point?t?this._point.attribValue(t):this._point.index():this._global_index}}!function(t){t[t.OBJECT=0]=\\\\\\\"OBJECT\\\\\\\",t[t.GEOMETRY=1]=\\\\\\\"GEOMETRY\\\\\\\"}($Y||($Y={}));const QY=[$Y.OBJECT,$Y.GEOMETRY],KY=[{name:\\\\\\\"object\\\\\\\",value:$Y.OBJECT},{name:\\\\\\\"geometry\\\\\\\",value:$Y.GEOMETRY}];const t$=new class extends aa{constructor(){super(...arguments),this.count=oa.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1]}),this.transformOnly=oa.BOOLEAN(0),this.transformMode=oa.INTEGER(0,{menu:{entries:KY}}),this.copyAttributes=oa.BOOLEAN(0),this.attributesToCopy=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{copyAttributes:!0}}),this.useCopyExpr=oa.BOOLEAN(0)}};class e$ extends gG{constructor(){super(...arguments),this.paramsConfig=t$,this._attribute_names_to_copy=[],this._objects=[],this._object_position=new p.a}static type(){return\\\\\\\"copy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to be copied\\\\\\\",\\\\\\\"points to copy to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState([Qi.ALWAYS,Qi.NEVER])}async cook(t){const e=t[0];if(!this.io.inputs.has_input(1))return void await this.cook_without_template(e);const n=t[1];n?await this.cook_with_template(e,n):this.states.error.set(\\\\\\\"second input invalid\\\\\\\")}async cook_with_template(t,e){this._objects=[];const n=e.points();let i=new JY(e).matrices();const r=new p.a,s=new au.a,o=new p.a;i[0].decompose(r,s,o),this._attribute_names_to_copy=sr.attribNames(this.pv.attributesToCopy).filter((t=>e.hasAttrib(t))),await this._copy_moved_objects_on_template_points(t,i,n),this.setObjects(this._objects)}async _copy_moved_objects_on_template_points(t,e,n){for(let i=0;i{const e=vs.clone(t);this._objects.push(e)}))}async cook_without_template(t){this._objects=[],await this._copy_moved_objects_for_each_instance(t),this.setObjects(this._objects)}_copyAttributes_from_template(t,e){this._attribute_names_to_copy.forEach(((n,i)=>{const r=e.attribValue(n);new vs(t,i).addAttribute(n,r)}))}stamp_value(t){return this.stamp_node.value(t)}get stamp_node(){return this._stamp_node=this._stamp_node||this.create_stamp_node()}create_stamp_node(){const t=new ZY(this.scene());return this.dirtyController.setForbiddenTriggerNodes([t]),t}dispose(){super.dispose(),this._stamp_node&&this._stamp_node.dispose()}}const n$=\\\\\\\"id\\\\\\\",i$=\\\\\\\"class\\\\\\\",r$=\\\\\\\"html\\\\\\\";class s$ extends pG{static type(){return\\\\\\\"CSS2DObject\\\\\\\"}cook(t,e){const n=t[0];if(n){const t=this._create_objects_from_input_points(n,e);return this.createCoreGroupFromObjects(t)}{const t=this._create_object_from_scratch(e);return this.createCoreGroupFromObjects([t])}}_create_objects_from_input_points(t,e){const n=t.points(),i=[];for(let t of n){const n=e.useIdAttrib?t.attribValue(n$):e.className,r=e.useClassAttrib?t.attribValue(i$):e.className,s=e.useHtmlAttrib?t.attribValue(r$):e.html,o=s$.create_css_object({id:n,className:r,html:s}),a=o.element;if(e.copyAttributes){const n=sr.attribNames(e.attributesToCopy);for(let e of n){const n=t.attribValue(e);m.isString(n)?a.setAttribute(e,n):m.isNumber(n)&&a.setAttribute(e,`${n}`)}}o.position.copy(t.position()),o.updateMatrix(),i.push(o)}return i}_create_object_from_scratch(t){return s$.create_css_object({id:t.id,className:t.className,html:t.html})}static create_css_object(t){const e=document.createElement(\\\\\\\"div\\\\\\\");e.id=t.id,e.className=t.className,e.innerHTML=t.html;const n=new vW(e);return n.matrixAutoUpdate=!1,n}}s$.DEFAULT_PARAMS={useIdAttrib:!1,id:\\\\\\\"my_css_object\\\\\\\",useClassAttrib:!1,className:\\\\\\\"CSS2DObject\\\\\\\",useHtmlAttrib:!1,html:\\\\\\\"
default html
\\\\\\\",copyAttributes:!1,attributesToCopy:\\\\\\\"\\\\\\\"},s$.INPUT_CLONED_STATE=Qi.FROM_NODE;const o$=s$.DEFAULT_PARAMS;const a$=new class extends aa{constructor(){super(...arguments),this.useIdAttrib=oa.BOOLEAN(o$.useIdAttrib),this.id=oa.STRING(o$.id,{visibleIf:{useIdAttrib:0}}),this.useClassAttrib=oa.BOOLEAN(o$.useClassAttrib),this.className=oa.STRING(o$.className,{visibleIf:{useClassAttrib:0}}),this.useHtmlAttrib=oa.BOOLEAN(o$.useHtmlAttrib),this.html=oa.STRING(o$.html,{visibleIf:{useHtmlAttrib:0},multiline:!0}),this.copyAttributes=oa.BOOLEAN(o$.copyAttributes),this.attributesToCopy=oa.STRING(o$.attributesToCopy,{visibleIf:{copyAttributes:!0}})}};class l$ extends gG{constructor(){super(...arguments),this.paramsConfig=a$}static type(){return\\\\\\\"CSS2DObject\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1)}cook(t){this._operation=this._operation||new s$(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class c${constructor(t,e){this._size=t,this._type=e}size(){return this._size}type(){return this._type}static from_value(t){const e=m.isString(t)?Dr.STRING:Dr.NUMERIC;return new this(m.isArray(t)?t.length:1,e)}}class u${constructor(t={}){this._attribute_datas_by_name={},this._options={},this._options.dataKeysPrefix=t.dataKeysPrefix,this._options.skipEntries=t.skipEntries,this._options.doConvert=t.doConvert||!1,this._options.convertToNumeric=t.convertToNumeric}dataKeysPrefix(){return this._options.dataKeysPrefix}get_prefixed_json(t,e){if(0==e.length)return t;{const n=e.shift();if(n)return this.get_prefixed_json(t[n],e)}return[]}setJSON(t){return this._json=t}createObject(){const t=new S.a,e=new ps(t);if(null!=this._json){const n=this._json.length;e.initPositionAttribute(n),this._find_attributes();const i=sr.attribNames(this._options.convertToNumeric||\\\\\\\"\\\\\\\");for(let n of Object.keys(this._attribute_datas_by_name)){const r=Wr.remapName(n);let s=this._attribute_values_for_name(n).flat();const o=this._attribute_datas_by_name[n],a=o.size();if(o.type()===Dr.STRING)if(this._options.doConvert&&sr.matchesOneMask(n,i)){const e=s.map((t=>m.isString(t)?parseFloat(t)||0:t));t.setAttribute(r,new C.c(e,a))}else{const t=Wr.arrayToIndexedArrays(s);e.setIndexedAttribute(r,t.values,t.indices)}else{const e=s;t.setAttribute(r,new C.c(e,a))}}}return t}_find_attributes(){let t;const e=sr.attribNames(this._options.skipEntries||\\\\\\\"\\\\\\\");if(this._json&&null!=(t=this._json[0]))for(let n of Object.keys(t)){const i=t[n];if(this._value_has_subentries(i))for(let t of Object.keys(i)){const r=[n,t].join(\\\\\\\":\\\\\\\"),s=i[n];sr.matchesOneMask(r,e)||(this._attribute_datas_by_name[r]=c$.from_value(s))}else sr.matchesOneMask(n,e)||(this._attribute_datas_by_name[n]=c$.from_value(i))}}_attribute_values_for_name(t){return this._json?this._json.map((e=>{const n=t.split(\\\\\\\":\\\\\\\")[0],i=e[n];if(this._value_has_subentries(i)){return i[t.substring(n.length+1)]||0}return i||0})):[]}_value_has_subentries(t){return m.isObject(t)&&!m.isArray(t)}}const h$=JSON.stringify([{value:-40},{value:-30},{value:-20},{value:-10},{value:0},{value:10},{value:20},{value:30},{value:40},{value:50},{value:60},{value:70},{value:80}]);const d$=new class extends aa{constructor(){super(...arguments),this.data=oa.STRING(h$)}};class p$ extends gG{constructor(){super(...arguments),this.paramsConfig=d$}static type(){return\\\\\\\"data\\\\\\\"}cook(){let t=null;try{t=JSON.parse(this.pv.data)}catch(t){this.states.error.set(\\\\\\\"could not parse json\\\\\\\")}if(t)try{const e=new u$;e.setJSON(t);const n=e.createObject();this.setGeometry(n,Sr.POINTS)}catch(t){this.states.error.set(\\\\\\\"could not build geometry from json\\\\\\\")}else this.cookController.endCook()}}class _$ extends jg{constructor(t,e,n={},i){super(t,e,i),this._node=i,this._parser=new u$(n)}async load(t,e,n){const i=await this._urlToLoad();fetch(i).then((async e=>{let n=await e.json();const i=this._parser.dataKeysPrefix();null!=i&&\\\\\\\"\\\\\\\"!=i&&(n=this._parser.get_prefixed_json(n,i.split(\\\\\\\".\\\\\\\"))),this._parser.setJSON(n);const r=this._parser.createObject();t(r)})).catch((t=>{ai.error(\\\\\\\"error\\\\\\\",t),n(t)}))}}const m$=\\\\\\\"position\\\\\\\";class f${constructor(t){this.attribute_names=t,this.attribute_names_from_first_line=!1,this.lines=[],this.points_count=0,this.attribute_values_by_name={},this.attribute_data_by_name={},this._loading=!1,this.attribute_names||(this.attribute_names_from_first_line=!0)}async load(t){if(this._loading)return void console.warn(\\\\\\\"is already loading\\\\\\\");this._loading=!0,this.points_count=0,await this.load_data(t),this.infer_types(),this.read_values();return this.create_points()}async load_data(t){const e=await fetch(t),n=await e.text();this.lines=n.split(\\\\\\\"\\\\n\\\\\\\"),this.attribute_names||(this.attribute_names=this.lines[0].split(f$.SEPARATOR)),this.attribute_names=this.attribute_names.map((t=>Wr.remapName(t)));for(let t of this.attribute_names)this.attribute_values_by_name[t]=[]}infer_types(){const t=this.attribute_names_from_first_line?1:0;let e=this.lines[t].split(f$.SEPARATOR);for(let t=0;tparseFloat(t)))}return t}return t}read_values(){if(!this.attribute_names)return;let t;for(let e=this.attribute_names_from_first_line?1:0;e=this.attribute_names.length){for(let t=0;t({name:t,value:e})))}}),this.url=oa.STRING(y$,{fileBrowse:{type:[Ls.JSON]}}),this.jsonDataKeysPrefix=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:v$.indexOf(g$.JSON)}}),this.skipEntries=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:v$.indexOf(g$.JSON)}}),this.convert=oa.BOOLEAN(0,{visibleIf:{dataType:v$.indexOf(g$.JSON)}}),this.convertToNumeric=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:v$.indexOf(g$.JSON),convert:1}}),this.readAttribNamesFromFile=oa.BOOLEAN(1,{visibleIf:{dataType:v$.indexOf(g$.CSV)}}),this.attribNames=oa.STRING(\\\\\\\"height scale\\\\\\\",{visibleIf:{dataType:v$.indexOf(g$.CSV),readAttribNamesFromFile:0}}),this.reload=oa.BUTTON(null,{callback:(t,e)=>{b$.PARAM_CALLBACK_reload(t,e)}})}};class b$ extends gG{constructor(){super(...arguments),this.paramsConfig=x$}static type(){return\\\\\\\"dataUrl\\\\\\\"}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\");return e[e.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(){switch(v$[this.pv.dataType]){case g$.JSON:return this._load_json();case g$.CSV:return this._load_csv()}}_url(){const t=this.scene().assets.root();return t?`${t}${this.pv.url}`:this.pv.url}_load_json(){new _$(this._url(),this.scene(),{dataKeysPrefix:this.pv.jsonDataKeysPrefix,skipEntries:this.pv.skipEntries,doConvert:this.pv.convert,convertToNumeric:this.pv.convertToNumeric},this).load(this._on_load.bind(this),void 0,this._on_error.bind(this))}_on_load(t){this.setGeometry(t,Sr.POINTS)}_on_error(t){this.states.error.set(`could not load geometry from ${this._url()} (${t})`),this.cookController.endCook()}async _load_csv(){const t=this.pv.readAttribNamesFromFile?void 0:this.pv.attribNames.split(\\\\\\\" \\\\\\\"),e=new f$(t),n=await e.load(this._url());n?this.setGeometry(n,Sr.POINTS):this.states.error.set(\\\\\\\"could not generate points\\\\\\\")}static PARAM_CALLBACK_reload(t,e){t.param_callback_reload()}param_callback_reload(){this.p.url.setDirty()}}class w$ extends S.a{constructor(t,e,n,i){super();const r=[],s=[],o=[],a=new p.a,l=new A.a;l.makeRotationFromEuler(n),l.setPosition(e);const c=new A.a;function u(e,n,i){n.applyMatrix4(t.matrixWorld),n.applyMatrix4(c),i.transformDirection(t.matrixWorld),e.push(new T$(n.clone(),i.clone()))}function h(t,e){const n=[],r=.5*Math.abs(i.dot(e));for(let i=0;i0,h=t[i+1].position.dot(e)-r>0,p=t[i+2].position.dot(e)-r>0;switch(c=(u?1:0)+(h?1:0)+(p?1:0),c){case 0:n.push(t[i]),n.push(t[i+1]),n.push(t[i+2]);break;case 1:if(u&&(s=t[i+1],o=t[i+2],a=d(t[i],s,e,r),l=d(t[i],o,e,r)),h){s=t[i],o=t[i+2],a=d(t[i+1],s,e,r),l=d(t[i+1],o,e,r),n.push(a),n.push(o.clone()),n.push(s.clone()),n.push(o.clone()),n.push(a.clone()),n.push(l);break}p&&(s=t[i],o=t[i+1],a=d(t[i+2],s,e,r),l=d(t[i+2],o,e,r)),n.push(s.clone()),n.push(o.clone()),n.push(a),n.push(l),n.push(a.clone()),n.push(o.clone());break;case 2:u||(s=t[i].clone(),o=d(s,t[i+1],e,r),a=d(s,t[i+2],e,r),n.push(s),n.push(o),n.push(a)),h||(s=t[i+1].clone(),o=d(s,t[i+2],e,r),a=d(s,t[i],e,r),n.push(s),n.push(o),n.push(a)),p||(s=t[i+2].clone(),o=d(s,t[i],e,r),a=d(s,t[i+1],e,r),n.push(s),n.push(o),n.push(a))}}return n}function d(t,e,n,i){const r=t.position.dot(n)-i,s=r/(r-(e.position.dot(n)-i));return new T$(new p.a(t.position.x+s*(e.position.x-t.position.x),t.position.y+s*(e.position.y-t.position.y),t.position.z+s*(e.position.z-t.position.z)),new p.a(t.normal.x+s*(e.normal.x-t.normal.x),t.normal.y+s*(e.normal.y-t.normal.y),t.normal.z+s*(e.normal.z-t.normal.z)))}c.copy(l).invert(),function(){let e=[];const n=new p.a,c=new p.a;if(!0===t.geometry.isGeometry)return void console.error(\\\\\\\"THREE.DecalGeometry no longer supports THREE.Geometry. Use BufferGeometry instead.\\\\\\\");const d=t.geometry,_=d.attributes.position,m=d.attributes.normal;if(null!==d.index){const t=d.index;for(let i=0;i{this.setCoreGroup(e)}),Math.max(this.pv.duration,0))}}class L${constructor(t){this.node=t,this.selected_state=new Map,this._entities_count=0,this._selected_entities_count=0}init(t){this.selected_state.clear();for(let e of t)this.selected_state.set(e,!1);this._entities_count=t.length,this._selected_entities_count=0}select(t){const e=this.selected_state.get(t);null!=e&&0==e&&(this.selected_state.set(t,!0),this._selected_entities_count++)}entities_to_keep(){return this._entities_for_state(this.node.pv.invert)}entities_to_delete(){return this._entities_for_state(!this.node.pv.invert)}_entities_for_state(t){const e=!!t,n=t?this._selected_entities_count:this._entities_count-this._selected_entities_count;if(0==n)return[];{const t=new Array(n);let i=0;return this.selected_state.forEach(((n,r)=>{n==e&&(t[i]=r,i++)})),t}}}var O$;!function(t){t.EQUAL=\\\\\\\"==\\\\\\\",t.LESS_THAN=\\\\\\\"<\\\\\\\",t.EQUAL_OR_LESS_THAN=\\\\\\\"<=\\\\\\\",t.EQUAL_OR_GREATER_THAN=\\\\\\\">=\\\\\\\",t.GREATER_THAN=\\\\\\\">\\\\\\\",t.DIFFERENT=\\\\\\\"!=\\\\\\\"}(O$||(O$={}));const R$=[O$.EQUAL,O$.LESS_THAN,O$.EQUAL_OR_LESS_THAN,O$.EQUAL_OR_GREATER_THAN,O$.GREATER_THAN,O$.DIFFERENT],P$={[O$.EQUAL]:(t,e)=>t==e,[O$.LESS_THAN]:(t,e)=>tt<=e,[O$.EQUAL_OR_GREATER_THAN]:(t,e)=>t>=e,[O$.GREATER_THAN]:(t,e)=>t>e,[O$.DIFFERENT]:(t,e)=>t!=e},I$=R$.map(((t,e)=>({name:t,value:e})));class F${constructor(t){this.node=t}evalForEntities(t){const e=kr[this.node.pv.attribType];switch(e){case Dr.NUMERIC:return void this._eval_for_numeric(t);case Dr.STRING:return void this._eval_for_string(t)}ar.unreachable(e)}_eval_for_string(t){let e;for(let n of t)e=n.stringAttribValue(this.node.pv.attribName),e==this.node.pv.attrib_string&&this.node.entitySelectionHelper.select(n)}_eval_for_numeric(t){const e=Ur[this.node.pv.attribSize-1];switch(e){case zr.FLOAT:return this._eval_for_points_numeric_float(t);case zr.VECTOR2:return this._eval_for_points_numeric_vector2(t);case zr.VECTOR3:return this._eval_for_points_numeric_vector3(t);case zr.VECTOR4:return this._eval_for_points_numeric_vector4(t)}ar.unreachable(e)}_eval_for_points_numeric_float(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue1;let i;const r=R$[this.node.pv.attribComparisonOperator],s=P$[r];for(let r of t)i=r.attribValue(e),s(i,n)&&this.node.entitySelectionHelper.select(r)}_eval_for_points_numeric_vector2(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue2;let i=new d.a;for(let r of t){const t=r.attribValue(e,i);n.equals(t)&&this.node.entitySelectionHelper.select(r)}}_eval_for_points_numeric_vector3(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue3;let i=new p.a;for(let r of t){const t=r.attribValue(e,i);n.equals(t)&&this.node.entitySelectionHelper.select(r)}}_eval_for_points_numeric_vector4(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue4;let i=new _.a;for(let r of t){const t=r.attribValue(e,i);n.equals(t)&&this.node.entitySelectionHelper.select(r)}}}class D${constructor(t){this.node=t}async evalForEntities(t){const e=this.node.p.expression;this.node.p.expression.hasExpression()&&e.expressionController?await this.eval_expressions_for_points_with_expression(t):this.eval_expressions_without_expression(t)}async eval_expressions_for_points_with_expression(t){const e=this.node.p.expression;e.expressionController&&await e.expressionController.compute_expression_for_entities(t,((t,e)=>{e&&this.node.entitySelectionHelper.select(t)}))}eval_expressions_without_expression(t){if(this.node.pv.expression)for(let e of t)this.node.entitySelectionHelper.select(e)}}class k${constructor(t){this.node=t,this._point_position=new p.a}evalForPoints(t){const e=this._createBbox();for(let n of t){e.containsPoint(n.getPosition(this._point_position))&&this.node.entitySelectionHelper.select(n)}}_createBbox(){return new XB.a(this.node.pv.bboxCenter.clone().sub(this.node.pv.bboxSize.clone().multiplyScalar(.5)),this.node.pv.bboxCenter.clone().add(this.node.pv.bboxSize.clone().multiplyScalar(.5)))}}class B${constructor(t){this.node=t}eval_for_objects(t){const e=Lr[this.node.pv.objectType];for(let n of t){Nr(n.object().constructor)==e&&this.node.entitySelectionHelper.select(n)}}}class z${constructor(){this._sidePropertyByMaterial=new WeakMap,this._bound_setMat=this._setObjectMaterialDoubleSided.bind(this),this._bound_restoreMat=this._restoreObjectMaterialSide.bind(this)}setCoreGroupMaterialDoubleSided(t){const e=t.objects();for(let t of e)t.traverse(this._bound_setMat)}restoreMaterialSideProperty(t){const e=t.objects();for(let t of e)t.traverse(this._bound_restoreMat)}_setObjectMaterialDoubleSided(t){const e=t.material;if(e)if(m.isArray(e))for(let t of e)this._setMaterialDoubleSided(t);else this._setMaterialDoubleSided(e)}_restoreObjectMaterialSide(t){const e=t.material;if(e)if(m.isArray(e))for(let t of e)this._restoreMaterialDoubleSided(t);else this._restoreMaterialDoubleSided(e)}_setMaterialDoubleSided(t){this._sidePropertyByMaterial.set(t,t.side),t.side=w.z}_restoreMaterialDoubleSided(t){t.side=this._sidePropertyByMaterial.get(t)||w.z}}const U$=new p.a(0,1,0),G$=new p.a(0,-1,0);class V${constructor(t){this.node=t,this._matDoubleSideTmpSetter=new z$,this._point_position=new p.a,this._raycaster=new uL,this._intersections=[]}evalForPoints(t,e){if(!e)return;const n=null==e?void 0:e.objectsWithGeo()[0];if(!n)return;const i=n;if(!i.isMesh)return;this._matDoubleSideTmpSetter.setCoreGroupMaterialDoubleSided(e);const r=n.geometry;r.computeBoundingBox();const s=r.boundingBox;for(let e of t)e.getPosition(this._point_position),s.containsPoint(this._point_position)?this._isPositionInObject(this._point_position,i,U$)&&this._isPositionInObject(this._point_position,i,G$)&&this.node.entitySelectionHelper.select(e):this.node.entitySelectionHelper.select(e);this._matDoubleSideTmpSetter.restoreMaterialSideProperty(e)}_isPositionInObject(t,e,n){var i;this._raycaster.ray.direction.copy(n),this._raycaster.ray.origin.copy(t),this._intersections.length=0;const r=this._raycaster.intersectObject(e,!1,this._intersections);if(!r)return!1;if(0==r.length)return!1;const s=null===(i=r[0].face)||void 0===i?void 0:i.normal;if(!s)return!1;return this._raycaster.ray.direction.dot(s)>=0}}const H$=new class extends aa{constructor(){super(...arguments),this.class=oa.INTEGER(Ir.indexOf(Pr.VERTEX),{menu:{entries:Fr}}),this.invert=oa.BOOLEAN(0),this.byObjectType=oa.BOOLEAN(0,{visibleIf:{class:Ir.indexOf(Pr.OBJECT)}}),this.objectType=oa.INTEGER(Lr.indexOf(Sr.MESH),{menu:{entries:Or},visibleIf:{class:Ir.indexOf(Pr.OBJECT),byObjectType:!0},separatorAfter:!0}),this.byExpression=oa.BOOLEAN(0),this.expression=oa.BOOLEAN(\\\\\\\"@ptnum==0\\\\\\\",{visibleIf:{byExpression:!0},expression:{forEntities:!0},separatorAfter:!0}),this.byAttrib=oa.BOOLEAN(0),this.attribType=oa.INTEGER(kr.indexOf(Dr.NUMERIC),{menu:{entries:Br},visibleIf:{byAttrib:1}}),this.attribName=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{byAttrib:1}}),this.attribSize=oa.INTEGER(1,{range:Gr,rangeLocked:[!0,!0],visibleIf:{byAttrib:1,attribType:kr.indexOf(Dr.NUMERIC)}}),this.attribComparisonOperator=oa.INTEGER(R$.indexOf(O$.EQUAL),{menu:{entries:I$},visibleIf:{byAttrib:!0,attribType:kr.indexOf(Dr.NUMERIC),attribSize:zr.FLOAT}}),this.attribValue1=oa.FLOAT(0,{visibleIf:{byAttrib:1,attribType:kr.indexOf(Dr.NUMERIC),attribSize:1}}),this.attribValue2=oa.VECTOR2([0,0],{visibleIf:{byAttrib:1,attribType:kr.indexOf(Dr.NUMERIC),attribSize:2}}),this.attribValue3=oa.VECTOR3([0,0,0],{visibleIf:{byAttrib:1,attribType:kr.indexOf(Dr.NUMERIC),attribSize:3}}),this.attribValue4=oa.VECTOR4([0,0,0,0],{visibleIf:{byAttrib:1,attribType:kr.indexOf(Dr.NUMERIC),attribSize:4}}),this.attribString=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{byAttrib:1,attribType:kr.indexOf(Dr.STRING)},separatorAfter:!0}),this.byBbox=oa.BOOLEAN(0,{visibleIf:{class:Ir.indexOf(Pr.VERTEX)}}),this.bboxSize=oa.VECTOR3([1,1,1],{visibleIf:{class:Ir.indexOf(Pr.VERTEX),byBbox:!0}}),this.bboxCenter=oa.VECTOR3([0,0,0],{visibleIf:{class:Ir.indexOf(Pr.VERTEX),byBbox:!0},separatorAfter:!0}),this.byBoundingObject=oa.BOOLEAN(0,{visibleIf:{class:Ir.indexOf(Pr.VERTEX)}}),this.keepPoints=oa.BOOLEAN(0,{visibleIf:{class:Ir.indexOf(Pr.OBJECT)}})}};class j$ extends gG{constructor(){super(...arguments),this.paramsConfig=H$,this._marked_for_deletion_per_object_index=new Map,this.entitySelectionHelper=new L$(this),this.byExpressionHelper=new D$(this),this.byAttributeHelper=new F$(this),this.byObjectTypeHelper=new B$(this),this.byBboxHelper=new k$(this),this.byBoundingObjectHelper=new V$(this)}static type(){return\\\\\\\"delete\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to delete from\\\\\\\",\\\\\\\"points inside this geometry will be deleted (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}async cook(t){const e=t[0],n=t[1];switch(this.pv.class){case Pr.VERTEX:await this._eval_for_points(e,n);break;case Pr.OBJECT:await this._eval_for_objects(e)}}set_class(t){this.p.class.set(t)}async _eval_for_objects(t){const e=t.coreObjects();this.entitySelectionHelper.init(e),this._marked_for_deletion_per_object_index=new Map;for(let t of e)this._marked_for_deletion_per_object_index.set(t.index(),!1);this.pv.byExpression&&await this.byExpressionHelper.evalForEntities(e),this.pv.byObjectType&&this.byObjectTypeHelper.eval_for_objects(e),this.pv.byAttrib&&\\\\\\\"\\\\\\\"!=this.pv.attribName&&this.byAttributeHelper.evalForEntities(e);const n=this.entitySelectionHelper.entities_to_keep().map((t=>t.object()));if(this.pv.keepPoints){const t=this.entitySelectionHelper.entities_to_delete();for(let e of t){const t=this._point_object(e);t&&n.push(t)}}this.setObjects(n)}async _eval_for_points(t,e){const n=t.coreObjects();let i,r=[];for(let t=0;t0){const e=ps.geometryFromPoints(a,Nr(t.constructor));e&&(t.geometry=e,r.push(t))}}}this.setObjects(r)}_point_object(t){const e=t.points(),n=ps.geometryFromPoints(e,Sr.POINTS);if(n)return this.createObject(n,Sr.POINTS)}}const W$=new class extends aa{constructor(){super(...arguments),this.start=oa.INTEGER(0,{range:[0,100],rangeLocked:[!0,!1]}),this.useCount=oa.BOOLEAN(0),this.count=oa.INTEGER(0,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useCount:1}})}};class q$ extends gG{constructor(){super(...arguments),this.paramsConfig=W$}static type(){return\\\\\\\"drawRange\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}cook(t){const e=t[0],n=e.objects();for(let t of n){const e=t.geometry;if(e){const t=e.drawRange;t.start=this.pv.start,this.pv.useCount?t.count=this.pv.count:t.count=1/0}}this.setCoreGroup(e)}}class X${constructor(){this.pluginCallbacks=[],this.register((function(t){return new bJ(t)})),this.register((function(t){return new wJ(t)})),this.register((function(t){return new TJ(t)})),this.register((function(t){return new AJ(t)})),this.register((function(t){return new EJ(t)}))}register(t){return-1===this.pluginCallbacks.indexOf(t)&&this.pluginCallbacks.push(t),this}unregister(t){return-1!==this.pluginCallbacks.indexOf(t)&&this.pluginCallbacks.splice(this.pluginCallbacks.indexOf(t),1),this}parse(t,e,n){const i=new xJ,r=[];for(let t=0,e=this.pluginCallbacks.length;t0&&(this.options.trs=!0),this.processInput(t);const i=this;Promise.all(this.pending).then((function(){const t=i.buffers,n=i.json,r=i.options,s=i.extensionsUsed,o=new Blob(t,{type:\\\\\\\"application/octet-stream\\\\\\\"}),a=Object.keys(s);if(a.length>0&&(n.extensionsUsed=a),n.buffers&&n.buffers.length>0&&(n.buffers[0].byteLength=o.size),!0===r.binary){const t=new window.FileReader;t.readAsArrayBuffer(o),t.onloadend=function(){const i=vJ(t.result),r=new DataView(new ArrayBuffer(8));r.setUint32(0,i.byteLength,!0),r.setUint32(4,5130562,!0);const s=vJ(function(t){if(void 0!==window.TextEncoder)return(new TextEncoder).encode(t).buffer;const e=new Uint8Array(new ArrayBuffer(t.length));for(let n=0,i=t.length;n255?32:i}return e.buffer}(JSON.stringify(n)),32),o=new DataView(new ArrayBuffer(8));o.setUint32(0,s.byteLength,!0),o.setUint32(4,1313821514,!0);const a=new ArrayBuffer(12),l=new DataView(a);l.setUint32(0,1179937895,!0),l.setUint32(4,2,!0);const c=12+o.byteLength+s.byteLength+r.byteLength+i.byteLength;l.setUint32(8,c,!0);const u=new Blob([a,o,s,r,i],{type:\\\\\\\"application/octet-stream\\\\\\\"}),h=new window.FileReader;h.readAsArrayBuffer(u),h.onloadend=function(){e(h.result)}}}else if(n.buffers&&n.buffers.length>0){const t=new window.FileReader;t.readAsDataURL(o),t.onloadend=function(){const i=t.result;n.buffers[0].uri=i,e(n)}}else e(n)}))}serializeUserData(t,e){if(0===Object.keys(t.userData).length)return;const n=this.options,i=this.extensionsUsed;try{const r=JSON.parse(JSON.stringify(t.userData));if(n.includeCustomExtensions&&r.gltfExtensions){void 0===e.extensions&&(e.extensions={});for(const t in r.gltfExtensions)e.extensions[t]=r.gltfExtensions[t],i[t]=!0;delete r.gltfExtensions}Object.keys(r).length>0&&(e.extras=r)}catch(e){console.warn(\\\\\\\"THREE.GLTFExporter: userData of '\\\\\\\"+t.name+\\\\\\\"' won't be serialized because of JSON.stringify error - \\\\\\\"+e.message)}}getUID(t){return this.uids.has(t)||this.uids.set(t,this.uid++),this.uids.get(t)}isNormalizedNormalAttribute(t){if(this.cache.attributesNormalized.has(t))return!1;const e=new Nx;for(let n=0,i=t.count;n5e-4)return!1;return!0}createNormalizedNormalAttribute(t){const e=this.cache;if(e.attributesNormalized.has(t))return e.attributesNormalized.get(t);const n=t.clone(),i=new Nx;for(let t=0,e=n.count;t4?i=t.array[r*t.itemSize+n]:0===n?i=t.getX(r):1===n?i=t.getY(r):2===n?i=t.getZ(r):3===n&&(i=t.getW(r)),e===eJ?l.setFloat32(c,i,!0):e===nJ?l.setUint32(c,i,!0):e===tJ?l.setUint16(c,i,!0):e===K$&&l.setUint8(c,i),c+=o}const u={buffer:this.processBuffer(l.buffer),byteOffset:this.byteOffset,byteLength:a};void 0!==r&&(u.target=r),r===iJ&&(u.byteStride=t.itemSize*o),this.byteOffset+=a,s.bufferViews.push(u);return{id:s.bufferViews.length-1,byteLength:0}}processBufferViewImage(t){const e=this,n=e.json;return n.bufferViews||(n.bufferViews=[]),new Promise((function(i){const r=new window.FileReader;r.readAsArrayBuffer(t),r.onloadend=function(){const t=vJ(r.result),s={buffer:e.processBuffer(t),byteOffset:e.byteOffset,byteLength:t.byteLength};e.byteOffset+=t.byteLength,i(n.bufferViews.push(s)-1)}}))}processAccessor(t,e,n,i){const r=this.options,s=this.json;let o;if(t.array.constructor===Float32Array)o=eJ;else if(t.array.constructor===Uint32Array)o=nJ;else if(t.array.constructor===Uint16Array)o=tJ;else{if(t.array.constructor!==Uint8Array)throw new Error(\\\\\\\"THREE.GLTFExporter: Unsupported bufferAttribute component type.\\\\\\\");o=K$}if(void 0===n&&(n=0),void 0===i&&(i=t.count),r.truncateDrawRange&&void 0!==e&&null===e.index){const r=n+i,s=e.drawRange.count===1/0?t.count:e.drawRange.start+e.drawRange.count;n=Math.max(n,e.drawRange.start),(i=Math.min(r,s)-n)<0&&(i=0)}if(0===i)return null;const a=function(t,e,n){const i={min:new Array(t.itemSize).fill(Number.POSITIVE_INFINITY),max:new Array(t.itemSize).fill(Number.NEGATIVE_INFINITY)};for(let r=e;r4?n=t.array[r*t.itemSize+e]:0===e?n=t.getX(r):1===e?n=t.getY(r):2===e?n=t.getZ(r):3===e&&(n=t.getW(r)),i.min[e]=Math.min(i.min[e],n),i.max[e]=Math.max(i.max[e],n)}return i}(t,n,i);let l;void 0!==e&&(l=t===e.index?rJ:iJ);const c=this.processBufferView(t,o,n,i,l),u={bufferView:c.id,byteOffset:c.byteOffset,componentType:o,count:i,max:a.max,min:a.min,type:{1:\\\\\\\"SCALAR\\\\\\\",2:\\\\\\\"VEC2\\\\\\\",3:\\\\\\\"VEC3\\\\\\\",4:\\\\\\\"VEC4\\\\\\\",16:\\\\\\\"MAT4\\\\\\\"}[t.itemSize]};return!0===t.normalized&&(u.normalized=!0),s.accessors||(s.accessors=[]),s.accessors.push(u)-1}processImage(t,e,n){const i=this,r=i.cache,s=i.json,o=i.options,a=i.pending;r.images.has(t)||r.images.set(t,{});const l=r.images.get(t),c=e===By?\\\\\\\"image/png\\\\\\\":\\\\\\\"image/jpeg\\\\\\\",u=c+\\\\\\\":flipY/\\\\\\\"+n.toString();if(void 0!==l[u])return l[u];s.images||(s.images=[]);const h={mimeType:c};if(o.embedImages){const r=yJ=yJ||document.createElement(\\\\\\\"canvas\\\\\\\");r.width=Math.min(t.width,o.maxTextureSize),r.height=Math.min(t.height,o.maxTextureSize);const s=r.getContext(\\\\\\\"2d\\\\\\\");if(!0===n&&(s.translate(0,r.height),s.scale(1,-1)),\\\\\\\"undefined\\\\\\\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\\\\\\\"undefined\\\\\\\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\\\\\\\"undefined\\\\\\\"!=typeof OffscreenCanvas&&t instanceof OffscreenCanvas||\\\\\\\"undefined\\\\\\\"!=typeof ImageBitmap&&t instanceof ImageBitmap)s.drawImage(t,0,0,r.width,r.height);else{e!==By&&e!==ky&&console.error(\\\\\\\"GLTFExporter: Only RGB and RGBA formats are supported.\\\\\\\"),(t.width>o.maxTextureSize||t.height>o.maxTextureSize)&&console.warn(\\\\\\\"GLTFExporter: Image size is bigger than maxTextureSize\\\\\\\",t);const n=new Uint8ClampedArray(t.height*t.width*4);if(e===By)for(let e=0;e1&&(e.multiplyScalar(1/n),console.warn(\\\\\\\"THREE.GLTFExporter: Some emissive components exceed 1; emissive has been limited\\\\\\\")),n>0&&(i.emissiveFactor=e.toArray()),t.emissiveMap){const e={index:this.processTexture(t.emissiveMap)};this.applyTextureTransform(e,t.emissiveMap),i.emissiveTexture=e}}if(t.normalMap){const e={index:this.processTexture(t.normalMap)};t.normalScale&&1!==t.normalScale.x&&(e.scale=t.normalScale.x),this.applyTextureTransform(e,t.normalMap),i.normalTexture=e}if(t.aoMap){const e={index:this.processTexture(t.aoMap),texCoord:1};1!==t.aoMapIntensity&&(e.strength=t.aoMapIntensity),this.applyTextureTransform(e,t.aoMap),i.occlusionTexture=e}t.transparent?i.alphaMode=\\\\\\\"BLEND\\\\\\\":t.alphaTest>0&&(i.alphaMode=\\\\\\\"MASK\\\\\\\",i.alphaCutoff=t.alphaTest),2===t.side&&(i.doubleSided=!0),\\\\\\\"\\\\\\\"!==t.name&&(i.name=t.name),this.serializeUserData(t,i),this._invokeAll((function(e){e.writeMaterial&&e.writeMaterial(t,i)}));const s=n.materials.push(i)-1;return e.materials.set(t,s),s}processMesh(t){const e=this.cache,n=this.json,i=[t.geometry.uuid];if(Array.isArray(t.material))for(let e=0,n=t.material.length;e0){const n=[],i=[],r={};if(void 0!==t.morphTargetDictionary)for(const e in t.morphTargetDictionary)r[t.morphTargetDictionary[e]]=e;for(let o=0;o0&&(a.extras={},a.extras.targetNames=i)}const _=Array.isArray(t.material);if(_&&0===s.groups.length)return null;const m=_?t.material:[t.material],f=_?s.groups:[{materialIndex:0,start:void 0,count:void 0}];for(let t=0,n=f.length;t0&&(n.targets=u),null!==s.index){let i=this.getUID(s.index);void 0===f[t].start&&void 0===f[t].count||(i+=\\\\\\\":\\\\\\\"+f[t].start+\\\\\\\":\\\\\\\"+f[t].count),e.attributes.has(i)?n.indices=e.attributes.get(i):(n.indices=this.processAccessor(s.index,s,f[t].start,f[t].count),e.attributes.set(i,n.indices)),null===n.indices&&delete n.indices}const i=this.processMaterial(m[f[t].materialIndex]);null!==i&&(n.material=i),c.push(n)}a.primitives=c,n.meshes||(n.meshes=[]),this._invokeAll((function(e){e.writeMesh&&e.writeMesh(t,a)}));const g=n.meshes.push(a)-1;return e.meshes.set(r,g),g}processCamera(t){const e=this.json;e.cameras||(e.cameras=[]);const n=t.isOrthographicCamera,i={type:n?\\\\\\\"orthographic\\\\\\\":\\\\\\\"perspective\\\\\\\"};return n?i.orthographic={xmag:2*t.right,ymag:2*t.top,zfar:t.far<=0?.001:t.far,znear:t.near<0?0:t.near}:i.perspective={aspectRatio:t.aspect,yfov:mx.degToRad(t.fov),zfar:t.far<=0?.001:t.far,znear:t.near<0?0:t.near},\\\\\\\"\\\\\\\"!==t.name&&(i.name=t.type),e.cameras.push(i)-1}processAnimation(t,e){const n=this.json,i=this.nodeMap;n.animations||(n.animations=[]);const r=(t=X$.Utils.mergeMorphTargetTracks(t.clone(),e)).tracks,s=[],o=[];for(let t=0;t0){const e=[];for(let i=0,r=t.children.length;i0&&(r.children=e)}this._invokeAll((function(e){e.writeNode&&e.writeNode(t,r)}));const s=e.nodes.push(r)-1;return i.set(t,s),s}processScene(t){const e=this.json,n=this.options;e.scenes||(e.scenes=[],e.scene=0);const i={};\\\\\\\"\\\\\\\"!==t.name&&(i.name=t.name),e.scenes.push(i);const r=[];for(let e=0,i=t.children.length;e0&&(i.nodes=r),this.serializeUserData(t,i)}processObjects(t){const e=new UE;e.name=\\\\\\\"AuxScene\\\\\\\";for(let n=0;n0&&this.processObjects(n);for(let t=0;t0&&(s.range=t.distance)):t.isSpotLight&&(s.type=\\\\\\\"spot\\\\\\\",t.distance>0&&(s.range=t.distance),s.spot={},s.spot.innerConeAngle=(t.penumbra-1)*t.angle*-1,s.spot.outerConeAngle=t.angle),void 0!==t.decay&&2!==t.decay&&console.warn(\\\\\\\"THREE.GLTFExporter: Light decay may be lost. glTF is physically-based, and expects light.decay=2.\\\\\\\"),!t.target||t.target.parent===t&&0===t.target.position.x&&0===t.target.position.y&&-1===t.target.position.z||console.warn(\\\\\\\"THREE.GLTFExporter: Light direction may be lost. For best results, make light.target a child of the light with position 0,0,-1.\\\\\\\"),r[this.name]||(i.extensions=i.extensions||{},i.extensions[this.name]={lights:[]},r[this.name]=!0);const o=i.extensions[this.name].lights;o.push(s),e.extensions=e.extensions||{},e.extensions[this.name]={light:o.length-1}}}class wJ{constructor(t){this.writer=t,this.name=\\\\\\\"KHR_materials_unlit\\\\\\\"}writeMaterial(t,e){if(!t.isMeshBasicMaterial)return;const n=this.writer.extensionsUsed;e.extensions=e.extensions||{},e.extensions[this.name]={},n[this.name]=!0,e.pbrMetallicRoughness.metallicFactor=0,e.pbrMetallicRoughness.roughnessFactor=.9}}class TJ{constructor(t){this.writer=t,this.name=\\\\\\\"KHR_materials_pbrSpecularGlossiness\\\\\\\"}writeMaterial(t,e){if(!t.isGLTFSpecularGlossinessMaterial)return;const n=this.writer,i=n.extensionsUsed,r={};e.pbrMetallicRoughness.baseColorFactor&&(r.diffuseFactor=e.pbrMetallicRoughness.baseColorFactor);const s=[1,1,1];if(t.specular.toArray(s,0),r.specularFactor=s,r.glossinessFactor=t.glossiness,e.pbrMetallicRoughness.baseColorTexture&&(r.diffuseTexture=e.pbrMetallicRoughness.baseColorTexture),t.specularMap){const e={index:n.processTexture(t.specularMap)};n.applyTextureTransform(e,t.specularMap),r.specularGlossinessTexture=e}e.extensions=e.extensions||{},e.extensions[this.name]=r,i[this.name]=!0}}class AJ{constructor(t){this.writer=t,this.name=\\\\\\\"KHR_materials_transmission\\\\\\\"}writeMaterial(t,e){if(!t.isMeshPhysicalMaterial||0===t.transmission)return;const n=this.writer,i=n.extensionsUsed,r={};if(r.transmissionFactor=t.transmission,t.transmissionMap){const e={index:n.processTexture(t.transmissionMap)};n.applyTextureTransform(e,t.transmissionMap),r.transmissionTexture=e}e.extensions=e.extensions||{},e.extensions[this.name]=r,i[this.name]=!0}}class EJ{constructor(t){this.writer=t,this.name=\\\\\\\"KHR_materials_volume\\\\\\\"}writeMaterial(t,e){if(!t.isMeshPhysicalMaterial||0===t.thickness)return;const n=this.writer,i=n.extensionsUsed,r={};if(r.thicknessFactor=t.thickness,t.thicknessMap){const e={index:n.processTexture(t.thicknessMap)};n.applyTextureTransform(e,t.thicknessMap),r.thicknessTexture=e}r.attenuationDistance=t.attenuationDistance,r.attenuationColor=t.attenuationTint.toArray(),e.extensions=e.extensions||{},e.extensions[this.name]=r,i[this.name]=!0}}function MJ(t,e){const n=document.createElement(\\\\\\\"a\\\\\\\");n.style.display=\\\\\\\"none\\\\\\\",document.body.appendChild(n),n.href=URL.createObjectURL(t),n.download=e,n.click(),setTimeout((()=>{document.body.removeChild(n)}),10)}X$.Utils={insertKeyframe:function(t,e){const n=.001,i=t.getValueSize(),r=new t.TimeBufferType(t.times.length+1),s=new t.ValueBufferType(t.values.length+i),o=t.createInterpolant(new t.ValueBufferType(i));let a;if(0===t.times.length){r[0]=e;for(let t=0;tt.times[t.times.length-1]){if(Math.abs(t.times[t.times.length-1]-e)e){r.set(t.times.slice(0,l+1),0),r[l+1]=e,r.set(t.times.slice(l+1),l+2),s.set(t.values.slice(0,(l+1)*i),0),s.set(o.evaluate(e),(l+1)*i),s.set(t.values.slice((l+1)*i),(l+2)*i),a=l+1;break}}return t.times=r,t.values=s,a},mergeMorphTargetTracks:function(t,e){const n=[],i={},r=t.tracks;for(let t=0;t{CJ.PARAM_CALLBACK_export(t)}})}};class CJ extends gG{constructor(){super(...arguments),this.paramsConfig=SJ}static type(){return\\\\\\\"exporter\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.NEVER)}async cook(t){this.setCoreGroup(t[0])}static PARAM_CALLBACK_export(t){t._paramCallbackExport()}async _paramCallbackExport(){const t=(await this.compute()).coreContent();if(!t)return void console.error(\\\\\\\"input invalid\\\\\\\");const e=new WeakMap,n=t.objects();for(let t of n)e.set(t,t.parent);const i=new fr;for(let t of n)i.add(t);(new X$).parse(i,(t=>{if(t instanceof ArrayBuffer)i=\\\\\\\"scene.glb\\\\\\\",MJ(new Blob([t],{type:\\\\\\\"application/octet-stream\\\\\\\"}),i);else{!function(t,e){MJ(new Blob([t],{type:\\\\\\\"text/plain\\\\\\\"}),e)}(JSON.stringify(t,null,2),\\\\\\\"scene.gltf\\\\\\\")}var i;for(let t of n){const n=e.get(t);n&&n.add(t)}}),{embedImages:!0})}}const NJ=new class extends aa{constructor(){super(...arguments),this.makeFacesUnique=oa.BOOLEAN(0),this.addFaceCenterAttribute=oa.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.addFaceId=oa.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.transform=oa.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.scale=oa.FLOAT(1,{visibleIf:{makeFacesUnique:1,transform:1}})}};class LJ extends gG{constructor(){super(...arguments),this.paramsConfig=NJ}static type(){return\\\\\\\"face\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}cook(t){const e=t[0];this.pv.makeFacesUnique&&(this._makeFacesUnique(e),this.pv.addFaceCenterAttribute&&this._addFaceCenterAttribute(e),this.pv.addFaceId&&this._addFaceId(e),this.pv.transform&&this._transform_faces(e)),this.setCoreGroup(e)}_makeFacesUnique(t){var e;for(let n of t.objects())if(n.isMesh){const t=n.geometry,i=f.chunk((null===(e=t.index)||void 0===e?void 0:e.array)||[],3),r=3*i.length;for(let e of Object.keys(t.attributes)){const n=t.attributes[e],s=n.itemSize,o=new Float32Array(r*s);let a=0;i.forEach((t=>{t.forEach((t=>{for(let e=0;e{const a=t.object(),l=t.coreGeometry();if(a.isMesh&&l){i=l.faces(),l.hasAttrib(e)||l.addNumericAttrib(e,3,-1);for(let t=0;t{const n=t.object(),i=t.coreGeometry();if(n.isMesh&&i){const t=i.faces();i.hasAttrib(e)||i.addNumericAttrib(e,1,-1);for(let n=0;n{const c=t.object(),u=t.coreGeometry();if(c.isMesh&&u){s=u.faces(),u.hasAttrib(e)||u.addNumericAttrib(e,3,-1);for(let t=0;t{t(e)})).catch((t=>{e(t)}))}_load(){return new Promise((async(t,e)=>{const n=await this._urlToLoad(),i=this.extension();if(i==OJ.JSON&&this._options.format==OJ.AUTO)IJ.increment_in_progress_loads_count(),await IJ.wait_for_max_concurrent_loads_queue_freed(),fetch(n).then((async e=>{const n=await e.json();new aY(this.loadingManager).parse(n,(e=>{IJ.decrement_in_progress_loads_count(),t(this.on_load_success(e.children[0]))}))})).catch((t=>{IJ.decrement_in_progress_loads_count(),e(t)}));else{const r=await this._loaderForFormat();if(r)IJ.increment_in_progress_loads_count(),await IJ.wait_for_max_concurrent_loads_queue_freed(),r.load(n,(e=>{this.on_load_success(e).then((e=>{IJ.decrement_in_progress_loads_count(),t(e)}))}),void 0,(t=>{ai.warn(\\\\\\\"error loading\\\\\\\",n,t),IJ.decrement_in_progress_loads_count(),e(t)}));else{e(`format not supported (${i})`)}}}))}async on_load_success(t){const e=this.extension();if(e==OJ.JSON)return[t];const n=t;if(n.isObject3D)switch(e){case PJ.PDB:return this.on_load_succes_pdb(t);case PJ.OBJ:default:return[n]}const i=t;if(i.isBufferGeometry)switch(e){case PJ.DRC:return this.on_load_succes_drc(i);default:return[new k.a(i)]}const r=t;if(null!=r.scene)switch(e){case PJ.GLTF:case PJ.GLB:return this.on_load_succes_gltf(r);default:return[n]}const s=t;if(s.geometryAtoms||s.geometryBonds)switch(e){case PJ.PDB:return this.on_load_succes_pdb(s);default:return[]}return[]}on_load_succes_drc(t){return[new k.a(t,IJ._default_mat_mesh)]}on_load_succes_gltf(t){const e=t.scene;return e.animations=t.animations,[e]}on_load_succes_pdb(t){return[new gr.a(t.geometryAtoms,IJ._default_mat_point),new Tr.a(t.geometryBonds,IJ._default_mat_line)]}static moduleNamesFromFormat(t,e){switch(t){case OJ.AUTO:return this.moduleNamesFromExt(e);case OJ.DRC:return[Vn.DRACOLoader];case OJ.FBX:return[Vn.FBXLoader];case OJ.JSON:return[];case OJ.GLTF:return[Vn.GLTFLoader];case OJ.GLTF_WITH_DRACO:return[Vn.GLTFLoader,Vn.DRACOLoader];case OJ.OBJ:return[Vn.OBJLoader];case OJ.PDB:return[Vn.PDBLoader];case OJ.PLY:return[Vn.PLYLoader];case OJ.STL:return[Vn.STLLoader]}ar.unreachable(t)}static moduleNamesFromExt(t){switch(t){case PJ.DRC:return[Vn.DRACOLoader];case PJ.FBX:return[Vn.FBXLoader];case PJ.GLTF:return[Vn.GLTFLoader];case PJ.GLB:return[Vn.GLTFLoader,Vn.DRACOLoader];case PJ.OBJ:return[Vn.OBJLoader];case PJ.PDB:return[Vn.PDBLoader];case PJ.PLY:return[Vn.PLYLoader];case PJ.STL:return[Vn.STLLoader]}}async _loaderForFormat(){const t=this._options.format;switch(t){case OJ.AUTO:return this._loaderForExt();case OJ.DRC:return this.loader_for_drc(this._node);case OJ.FBX:return this.loader_for_fbx();case OJ.JSON:return;case OJ.GLTF:return this.loader_for_gltf();case OJ.GLTF_WITH_DRACO:return this.loader_for_glb(this._node);case OJ.OBJ:return this.loader_for_obj();case OJ.PDB:return this.loader_for_pdb();case OJ.PLY:return this.loader_for_ply();case OJ.STL:return this.loader_for_stl()}ar.unreachable(t)}async _loaderForExt(){switch(this.extension().toLowerCase()){case PJ.DRC:return this.loader_for_drc(this._node);case PJ.FBX:return this.loader_for_fbx();case PJ.GLTF:return this.loader_for_gltf();case PJ.GLB:return this.loader_for_glb(this._node);case PJ.OBJ:return this.loader_for_obj();case PJ.PDB:return this.loader_for_pdb();case PJ.PLY:return this.loader_for_ply();case PJ.STL:return this.loader_for_stl()}}loader_for_fbx(){const t=ai.modulesRegister.module(Vn.FBXLoader);if(t)return new t(this.loadingManager)}loader_for_gltf(){const t=ai.modulesRegister.module(Vn.GLTFLoader);if(t)return new t(this.loadingManager)}static async loader_for_drc(t){const e=ai.modulesRegister.module(Vn.DRACOLoader);if(e){const n=new e(this.loadingManager),i=ai.libs.root(),r=ai.libs.DRACOPath();if(i||r){const e=`${i||\\\\\\\"\\\\\\\"}${r||\\\\\\\"\\\\\\\"}/`;if(t){const n=[\\\\\\\"draco_decoder.js\\\\\\\",\\\\\\\"draco_decoder.wasm\\\\\\\",\\\\\\\"draco_wasm_wrapper.js\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((t=>({storedUrl:`${r}/${t}`,fullUrl:`${e}${t}`}))),node:t,error:\\\\\\\"failed to load draco libraries. Make sure to install them to load .glb files\\\\\\\"})}n.setDecoderPath(e)}else n.setDecoderPath(void 0);return n.setDecoderConfig({type:\\\\\\\"js\\\\\\\"}),n}}loader_for_drc(t){return IJ.loader_for_drc(t)}static async loader_for_glb(t){const e=ai.modulesRegister.module(Vn.GLTFLoader),n=ai.modulesRegister.module(Vn.DRACOLoader);if(e&&n){this.gltf_loader=this.gltf_loader||new e(this.loadingManager),this.draco_loader=this.draco_loader||new n(this.loadingManager);const i=ai.libs.root(),r=ai.libs.DRACOGLTFPath();if(i||r){const e=`${i||\\\\\\\"\\\\\\\"}${r||\\\\\\\"\\\\\\\"}/`;if(t){const n=[\\\\\\\"draco_decoder.js\\\\\\\",\\\\\\\"draco_decoder.wasm\\\\\\\",\\\\\\\"draco_wasm_wrapper.js\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((t=>({storedUrl:`${r}/${t}`,fullUrl:`${e}${t}`}))),node:t,error:\\\\\\\"failed to load draco libraries. Make sure to install them to load .glb files\\\\\\\"})}this.draco_loader.setDecoderPath(e)}else this.draco_loader.setDecoderPath(void 0);return this.gltf_loader.setDRACOLoader(this.draco_loader),this.gltf_loader}}loader_for_glb(t){return IJ.loader_for_glb(t)}loader_for_obj(){const t=ai.modulesRegister.module(Vn.OBJLoader);if(t)return new t(this.loadingManager)}loader_for_pdb(){const t=ai.modulesRegister.module(Vn.PDBLoader);if(t)return new t(this.loadingManager)}loader_for_ply(){const t=ai.modulesRegister.module(Vn.PLYLoader);if(t)return new t(this.loadingManager)}loader_for_stl(){const t=ai.modulesRegister.module(Vn.STLLoader);if(t)return new t(this.loadingManager)}static setMaxConcurrentLoadsCount(t){this._maxConcurrentLoadsCountMethod=t}static _init_max_concurrent_loads_count(){return this._maxConcurrentLoadsCountMethod?this._maxConcurrentLoadsCountMethod():Zf.isChrome()?4:1}static _init_concurrent_loads_delay(){return Zf.isChrome()?1:10}static increment_in_progress_loads_count(){this.in_progress_loads_count++}static decrement_in_progress_loads_count(){this.in_progress_loads_count--;const t=this._queue.pop();if(t){const e=this.CONCURRENT_LOADS_DELAY;setTimeout((()=>{t()}),e)}}static async wait_for_max_concurrent_loads_queue_freed(){return this.in_progress_loads_count<=this.MAX_CONCURRENT_LOADS_COUNT?void 0:new Promise((t=>{this._queue.push(t)}))}}IJ._default_mat_mesh=new br.a,IJ._default_mat_point=new yr.a,IJ._default_mat_line=new wr.a,IJ.MAX_CONCURRENT_LOADS_COUNT=IJ._init_max_concurrent_loads_count(),IJ.CONCURRENT_LOADS_DELAY=IJ._init_concurrent_loads_delay(),IJ.in_progress_loads_count=0,IJ._queue=[];const FJ=`${Gg}/models/wolf.obj`;class DJ extends pG{static type(){return\\\\\\\"file\\\\\\\"}cook(t,e){const n=new IJ({url:e.url,format:e.format},this.scene(),this._node);return new Promise((t=>{n.load((e=>{const n=this._on_load(e);t(this.createCoreGroupFromObjects(n))}),(t=>{this._on_error(t,e)}))}))}_on_load(t){t=t.flat();for(let e of t)e.traverse((t=>{this._ensure_geometry_has_index(t),t.matrixAutoUpdate=!1}));return t}_on_error(t,e){var n;null===(n=this.states)||void 0===n||n.error.set(`could not load geometry from ${e.url} (${t})`)}_ensure_geometry_has_index(t){const e=t.geometry;e&&this.createIndexIfNone(e)}}DJ.DEFAULT_PARAMS={url:FJ,format:OJ.AUTO};const kJ=DJ.DEFAULT_PARAMS;const BJ=new class extends aa{constructor(){super(...arguments),this.url=oa.STRING(kJ.url,{fileBrowse:{type:[Ls.GEOMETRY]}}),this.format=oa.STRING(kJ.format,{menuString:{entries:RJ.map((t=>({name:t,value:t})))}}),this.reload=oa.BUTTON(null,{callback:t=>{zJ.PARAM_CALLBACK_reload(t)}})}};class zJ extends gG{constructor(){super(...arguments),this.paramsConfig=BJ}static type(){return\\\\\\\"file\\\\\\\"}async requiredModules(){for(let t of[this.p.url,this.p.format])t.isDirty()&&await t.compute();const t=jg.extension(this.pv.url||\\\\\\\"\\\\\\\"),e=this.pv.format;return IJ.moduleNamesFromFormat(e,t)}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\");return e[e.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){this._operation=this._operation||new DJ(this.scene(),this.states,this);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}static PARAM_CALLBACK_reload(t){t._paramCallbackReload()}_paramCallbackReload(){this.p.url.setDirty()}}const UJ=new class extends aa{constructor(){super(...arguments),this.dist=oa.FLOAT(.1,{range:[0,1],rangeLocked:[!0,!1]})}};class GJ extends gG{constructor(){super(...arguments),this.paramsConfig=UJ}static type(){return\\\\\\\"fuse\\\\\\\"}static displayedInputNames(){return[\\\\\\\"points to fuse together\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}cook(t){const e=t[0],n=[];let i;for(let t of e.coreObjects())i=this._fuse_core_object(t),i&&n.push(i);this.setObjects(n)}_fuse_core_object(t){const e=t.object();if(!e)return;const n=t.points(),i=this.pv.dist,r={};for(let t of n){const e=t.position(),n=new p.a(Math.round(e.x/i),Math.round(e.y/i),Math.round(e.z/i)).toArray().join(\\\\\\\"-\\\\\\\");r[n]=r[n]||[],r[n].push(t)}const s=[];if(Object.keys(r).forEach((t=>{s.push(r[t][0])})),e.geometry.dispose(),s.length>0){const t=ps.geometryFromPoints(s,Nr(e.constructor));return t&&(e.geometry=t),e}}}class VJ{constructor(t,e,n){this._param_size=t,this._param_hexagon_radius=e,this._param_points_only=n}process(){const t=this._param_hexagon_radius,e=.5*t,n=t,i=Math.cos(Math.PI/6)*this._param_hexagon_radius,r=Math.floor(this._param_size.x/n),s=Math.floor(this._param_size.y/i);let o=[],a=[];for(let t=0;t=1&&(0==s||s==r-1?0==s?a.push([s+1+(t-1)*r,s+(t-1)*r,s+t*r]):a.push([s+t*r,s+(t-1)*r,s-1+t*r]):(a.push([s+t*r,s+(t-1)*r,s-1+t*r]),a.push([s+t*r,s+1+(t-1)*r,s+(t-1)*r])));const l=new S.a;return l.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(o.flat()),3)),this._param_points_only||(l.setIndex(a.flat()),l.computeVertexNormals()),l}}const HJ=new p.a(0,1,0);const jJ=new class extends aa{constructor(){super(...arguments),this.size=oa.VECTOR2([1,1]),this.hexagonRadius=oa.FLOAT(.1,{range:[.001,1],rangeLocked:[!1,!1]}),this.direction=oa.VECTOR3([0,1,0]),this.pointsOnly=oa.BOOLEAN(0)}};class WJ extends gG{constructor(){super(...arguments),this.paramsConfig=jJ,this._core_transform=new Mz}static type(){return\\\\\\\"hexagons\\\\\\\"}initializeNode(){}cook(){if(this.pv.hexagonRadius>0){const t=new VJ(this.pv.size,this.pv.hexagonRadius,this.pv.pointsOnly).process();this._core_transform.rotate_geometry(t,HJ,this.pv.direction),this.pv.pointsOnly?this.setGeometry(t,Sr.POINTS):this.setGeometry(t)}else this.setObjects([])}}var qJ;!function(t){t.ADD_PARENT=\\\\\\\"add_parent\\\\\\\",t.REMOVE_PARENT=\\\\\\\"remove_parent\\\\\\\",t.ADD_CHILD=\\\\\\\"add_child\\\\\\\"}(qJ||(qJ={}));const XJ=[qJ.ADD_PARENT,qJ.REMOVE_PARENT,qJ.ADD_CHILD];class YJ extends pG{static type(){return\\\\\\\"hierarchy\\\\\\\"}cook(t,e){const n=t[0],i=XJ[e.mode];switch(i){case qJ.ADD_PARENT:{const t=this._add_parent_to_core_group(n,e);return this.createCoreGroupFromObjects(t)}case qJ.REMOVE_PARENT:{const t=this._remove_parent_from_core_group(n,e);return this.createCoreGroupFromObjects(t)}case qJ.ADD_CHILD:{const i=this._add_child_to_core_group(n,t[1],e);return this.createCoreGroupFromObjects(i)}}ar.unreachable(i)}_add_parent_to_core_group(t,e){if(0==e.levels)return t.objects();return[this._add_parent_to_object(t.objects(),e)]}_add_parent_to_object(t,e){let n=new In.a;if(n.matrixAutoUpdate=!1,n.add(...t),e.levels>0)for(let t=0;t({name:t,value:e})))}}),this.levels=oa.INTEGER(JJ.levels,{range:[0,5],visibleIf:[{mode:XJ.indexOf(qJ.ADD_PARENT)},{mode:XJ.indexOf(qJ.REMOVE_PARENT)}]}),this.objectMask=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{mode:XJ.indexOf(qJ.ADD_CHILD)}}),this.debugObjectMask=oa.BOOLEAN(0,{visibleIf:{mode:XJ.indexOf(qJ.ADD_CHILD)}})}};class QJ extends gG{constructor(){super(...arguments),this.paramsConfig=ZJ}static type(){return\\\\\\\"hierarchy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to add or remove parents to/from\\\\\\\",\\\\\\\"objects to use as parent or children (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState(YJ.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.mode,this.p.levels,this.p.objectMask],(()=>{const t=XJ[this.pv.mode];return $J.includes(t)?`${t} ${this.pv.levels}`:`${t} (with mask: ${this.pv.objectMask})`}))}))}))}cook(t){this._operation=this._operation||new YJ(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const KJ=new class extends aa{constructor(){super(...arguments),this.texture=oa.OPERATOR_PATH(gi.UV,{nodeSelection:{context:Ki.COP}}),this.mult=oa.FLOAT(1)}};class tZ extends gG{constructor(){super(...arguments),this.paramsConfig=KJ}static type(){return\\\\\\\"heightMap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}async cook(t){const e=t[0],n=this.p.texture.found_node();if(n){if(n.context()==Ki.COP){const t=n,i=(await t.compute()).texture();for(let t of e.coreObjects())this._set_position_from_data_texture(t,i)}else this.states.error.set(\\\\\\\"found node is not a texture\\\\\\\")}e.computeVertexNormals(),this.setCoreGroup(e)}_set_position_from_data_texture(t,e){var n;const i=this._data_from_texture(e);if(!i)return;const{data:r,resx:s,resy:o}=i,a=r.length/(s*o),l=null===(n=t.coreGeometry())||void 0===n?void 0:n.geometry();if(!l)return;const c=l.getAttribute(\\\\\\\"position\\\\\\\").array,u=l.getAttribute(\\\\\\\"uv\\\\\\\"),h=l.getAttribute(\\\\\\\"normal\\\\\\\");if(null==u)return void this.states.error.set(\\\\\\\"uvs are required\\\\\\\");if(null==h)return void this.states.error.set(\\\\\\\"normals are required\\\\\\\");const d=u.array,p=h.array,_=c.length/3;let m,f,g,v,y,x,b,w=0;for(let t=0;t<_;t++)m=2*t,f=d[m],g=d[m+1],v=Math.floor((s-1)*f),y=Math.floor((o-1)*(1-g)),x=y*s+v,b=r[a*x],w=3*t,c[w+0]+=p[w+0]*b*this.pv.mult,c[w+1]+=p[w+1]*b*this.pv.mult,c[w+2]+=p[w+2]*b*this.pv.mult}_data_from_texture(t){if(t.image)return t.image.data?this._data_from_data_texture(t):this._data_from_default_texture(t)}_data_from_default_texture(t){const e=t.image.width,n=t.image.height;return{data:Rf.data_from_image(t.image).data,resx:e,resy:n}}_data_from_data_texture(t){return{data:t.image.data,resx:t.image.width,resy:t.image.height}}}function eZ(t){return Math.atan2(-t.y,Math.sqrt(t.x*t.x+t.z*t.z))}class nZ extends S.a{constructor(t,e,n,i,r){super(),this.type=\\\\\\\"PolyhedronBufferGeometry\\\\\\\",this.parameters={vertices:t,indices:e,radius:n,detail:i},n=n||1,i=i||0;const s=[],o=[],a=new Map;function l(t,e,n,i){const r=i+1,s=[];for(let i=0;i<=r;i++){s[i]=[];const o=t.clone().lerp(n,i/r),a=e.clone().lerp(n,i/r),l=r-i;for(let t=0;t<=l;t++)s[i][t]=0===t&&i===r?o:o.clone().lerp(a,t/l)}for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.layer])}))}))}cook(t){const e=t[0];for(let t of e.objects())t.layers.set(this.pv.layer);this.setCoreGroup(e)}}const xZ=new class extends aa{constructor(){super(...arguments),this.length=oa.FLOAT(1,{range:[0,10]}),this.pointsCount=oa.INTEGER(1,{range:[2,100],rangeLocked:[!0,!1]}),this.origin=oa.VECTOR3([0,0,0]),this.direction=oa.VECTOR3([0,1,0])}};class bZ extends gG{constructor(){super(...arguments),this.paramsConfig=xZ}static type(){return\\\\\\\"line\\\\\\\"}initializeNode(){}cook(){const t=Math.max(2,this.pv.pointsCount),e=new Array(3*t),n=new Array(t),i=this.pv.direction.clone().normalize().multiplyScalar(this.pv.length);for(let r=0;r0&&(n[2*(r-1)]=r-1,n[2*(r-1)+1]=r)}const r=new S.a;r.setAttribute(\\\\\\\"position\\\\\\\",new C.c(e,3)),r.setIndex(n),this.setGeometry(r,Sr.LINE_SEGMENTS)}}const wZ=new class extends aa{constructor(){super(...arguments),this.distance0=oa.FLOAT(1),this.distance1=oa.FLOAT(2),this.autoUpdate=oa.BOOLEAN(1),this.update=oa.BUTTON(null,{callback:t=>{TZ.PARAM_CALLBACK_update(t)}}),this.camera=oa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{autoUpdate:0},dependentOnFoundNode:!1})}};class TZ extends gG{constructor(){super(...arguments),this.paramsConfig=wZ,this._lod=this._create_LOD()}static type(){return\\\\\\\"lod\\\\\\\"}static displayedInputNames(){return[\\\\\\\"high res\\\\\\\",\\\\\\\"mid res\\\\\\\",\\\\\\\"low res\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,3),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}_create_LOD(){const t=new Mr;return t.matrixAutoUpdate=!1,t}cook(t){this._clear_lod(),this._add_level(t[0],0),this._add_level(t[1],this.pv.distance0),this._add_level(t[2],this.pv.distance1),this._lod.autoUpdate=this.pv.autoUpdate,this.setObject(this._lod)}_add_level(t,e){if(t){const n=t.objects();let i;for(let t=0;t{this._apply_material(t,n,e)}));else for(let i of t.objectsFromGroup(e.group))this._apply_material(i,n,e);return t}null===(i=this.states)||void 0===i||i.error.set(`material invalid. (error: '${s.states.error.message()}')`)}else null===(r=this.states)||void 0===r||r.error.set(\\\\\\\"no material node found\\\\\\\")}_swap_textures(t,e){if(e.swapCurrentTex){this._materials_by_uuid.clear();for(let n of t.objectsFromGroup(e.group))if(e.applyToChildren)n.traverse((t=>{const e=n.material;this._materials_by_uuid.set(e.uuid,e)}));else{const t=n.material;this._materials_by_uuid.set(t.uuid,t)}this._materials_by_uuid.forEach(((t,n)=>{this._swap_texture(t,e)}))}}_apply_material(t,e,n){if(n.group&&!vs.isInGroup(n.group,t))return;const i=n.cloneMat?fs.clone(e):e;if(e instanceof F&&i instanceof F)for(let t in e.uniforms)i.uniforms[t]=e.uniforms[t];const r=t;this._old_mat_by_old_new_id.set(i.uuid,r.material),r.material=i,fs.apply_render_hook(t,i),fs.applyCustomMaterials(t,i)}_swap_texture(t,e){if(\\\\\\\"\\\\\\\"==e.texSrc0||\\\\\\\"\\\\\\\"==e.texDest0)return;let n=this._old_mat_by_old_new_id.get(t.uuid);n=n||t;const i=n[e.texSrc0];if(i){t[e.texDest0]=i;const n=t.uniforms;if(n){n[e.texDest0]&&(n[e.texDest0]={value:i})}}}}AZ.DEFAULT_PARAMS={group:\\\\\\\"\\\\\\\",assignMat:!0,material:new vi(\\\\\\\"\\\\\\\"),applyToChildren:!0,cloneMat:!1,shareUniforms:!0,swapCurrentTex:!1,texSrc0:\\\\\\\"emissiveMap\\\\\\\",texDest0:\\\\\\\"map\\\\\\\"},AZ.INPUT_CLONED_STATE=Qi.FROM_NODE;const EZ=AZ.DEFAULT_PARAMS;const MZ=new class extends aa{constructor(){super(...arguments),this.group=oa.STRING(EZ.group),this.assignMat=oa.BOOLEAN(EZ.assignMat),this.material=oa.NODE_PATH(EZ.material.path(),{nodeSelection:{context:Ki.MAT},dependentOnFoundNode:!1,visibleIf:{assignMat:1}}),this.applyToChildren=oa.BOOLEAN(EZ.applyToChildren,{visibleIf:{assignMat:1}}),this.cloneMat=oa.BOOLEAN(EZ.cloneMat,{visibleIf:{assignMat:1}}),this.shareUniforms=oa.BOOLEAN(EZ.shareUniforms,{visibleIf:{assignMat:1,cloneMat:1}}),this.swapCurrentTex=oa.BOOLEAN(EZ.swapCurrentTex),this.texSrc0=oa.STRING(EZ.texSrc0,{visibleIf:{swapCurrentTex:1}}),this.texDest0=oa.STRING(EZ.texDest0,{visibleIf:{swapCurrentTex:1}})}};class SZ extends gG{constructor(){super(...arguments),this.paramsConfig=MZ}static type(){return\\\\\\\"material\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to assign material to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(AZ.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.material],(()=>this.p.material.rawInput()))}))}))}async cook(t){this._operation=this._operation||new AZ(this._scene,this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var CZ=n(88);class NZ{static sleep(t){return new Promise(((e,n)=>{setTimeout((()=>{e()}),t)}))}}const LZ=[Lg.VIDEO,Lg.WEB_CAM];const OZ=new class extends aa{constructor(){super(...arguments),this.webcam=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.COP,types:LZ}}),this.scale=oa.FLOAT(2,{range:[0,2],rangeLocked:[!0,!1]}),this.selfieMode=oa.BOOLEAN(0),this.minDetectionConfidence=oa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0]}),this.maxDetectionConfidence=oa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0]}),this.frames=oa.INTEGER(1,{range:[1,100],rangeLocked:[!0,!1]}),this.fps=oa.INTEGER(24,{range:[1,60],rangeLocked:[!0,!1]}),this.capture=oa.BUTTON(null,{callback:t=>{RZ.PARAM_CALLBACK_capture(t)}}),this.showAllMeshes=oa.BOOLEAN(1)}};class RZ extends gG{constructor(){super(...arguments),this.paramsConfig=OZ,this._faceMesh=this._createFaceMesh(),this._currentCapturedFrame=9999999,this._webcamSnapshotCanvases=[],this._webcamSnapshots=[],this._faceMeshObjects=[]}static type(){return\\\\\\\"mediapipeFaceMesh\\\\\\\"}initializeNode(){this._forceTimeDependent()}_forceTimeDependent(){this._graph_node||(this._graph_node=new Ai(this.scene(),\\\\\\\"facemesh_update_object\\\\\\\"),this._graph_node.addGraphInput(this.scene().timeController.graphNode),this._graph_node.addPostDirtyHook(\\\\\\\"on_time_change\\\\\\\",this.setDirty.bind(this)))}async cook(){if(this.pv.showAllMeshes)this.setObjects(this._faceMeshObjects);else{const t=this.scene().frame()%this._faceMeshObjects.length,e=this._faceMeshObjects[t];e?this.setObject(e):this.setObjects([])}}_createFaceMesh(){const t=new CZ.FaceMesh({locateFile:t=>`https://cdn.jsdelivr.net/npm/@mediapipe/face_mesh@0.4/${t}`});return t.onResults(this._onResults.bind(this)),t}async _getHTMLVideoElement(){const t=this.pv.webcam.nodeWithContext(Ki.COP);if(!t)return this.states.error.set(\\\\\\\"node is not a COP node\\\\\\\"),void this.cookController.endCook();if(!LZ.includes(t.type()))return this.states.error.set(`node type '${t.type()}' is not accepted by MediapipeFaceMesh (${LZ.join(\\\\\\\", \\\\\\\")})`),void this.cookController.endCook();const e=t;await e.compute();const n=e.HTMLVideoElement();if(n)return{videoElement:n};console.log(\\\\\\\"no video element found\\\\\\\")}_videoSnapshotCanvas(t){const e=document.createElement(\\\\\\\"canvas\\\\\\\");e.width=t.videoWidth,e.height=t.videoHeight;return e.getContext(\\\\\\\"2d\\\\\\\").drawImage(t,0,0,e.width,e.height),e}static _canvasToTexture(t,e){return new Promise((n=>{const i=t.toDataURL(\\\\\\\"image/png\\\\\\\"),r=new Image;console.log(`start ${e}`),r.onload=()=>{const t=new J.a(r);t.name=`facemesh-texture-${e}`,t.encoding=w.ld,t.needsUpdate=!0,console.log(`done ${e}`),n({texture:t,image:r})},r.src=i}))}async _initActiveHTMLVideoElement(){const t=await this._getHTMLVideoElement();if(!t)return;const{videoElement:e}=t;for(;e.paused;)console.log(\\\\\\\"video is paused\\\\\\\"),await NZ.sleep(500);this._activeHTMLVideoElement=e}_captureAllowed(){return this._currentCapturedFrame{this._webcamSnapshotCanvases=[],this._webcamSnapshots=[],this._captureSingleWebCamSnapshot(t)}))}async _captureSingleWebCamSnapshot(t){if(this._activeHTMLVideoElement)if(this._captureAllowed()){const e=this._videoSnapshotCanvas(this._activeHTMLVideoElement);this._webcamSnapshotCanvases.push(e),this._currentCapturedFrame++,console.log(\\\\\\\"capture\\\\\\\",this._currentCapturedFrame),setTimeout((()=>{this._captureSingleWebCamSnapshot(t)}),1e3/this.pv.fps)}else{console.log(\\\\\\\"converting snapshots to images and textures...\\\\\\\");let e=0;for(let t of this._webcamSnapshotCanvases){const n=await RZ._canvasToTexture(t,e);this._webcamSnapshots.push(n),e++}t()}else console.log(\\\\\\\"no video found\\\\\\\")}_initFaceMeshObjects(){this._faceMeshObjects=[];const t=this.pv.frames;for(let e=0;e{this._onSendToFaceMeshSingleResolve=e,this._faceMesh.send({image:t.image})}))}_onResults(t){if(!this._captureAllowed())return;console.log(this._currentCapturedFrame);const e=t.multiFaceLandmarks[0];if(!e)return console.error(`no landmark found (${this._currentCapturedFrame})`),void console.log(\\\\\\\"results\\\\\\\",t);const n=this._faceMeshObjects[this._currentCapturedFrame];let i=0;const r=n.geometry.getAttribute(\\\\\\\"position\\\\\\\"),s=n.geometry.getAttribute(\\\\\\\"uv\\\\\\\"),o=r.array,a=s.array,l=this.pv.scale;for(let t of e)o[3*i+0]=(1-t.x)*l,o[3*i+1]=(1-t.y)*l,o[3*i+2]=t.z*l,a[2*i+0]=t.x,a[2*i+1]=1-t.y,i++;r.needsUpdate=!0,s.needsUpdate=!0,this._updateMaterial(this._currentCapturedFrame),this._currentCapturedFrame++,this._onSendToFaceMeshSingleResolve&&this._onSendToFaceMeshSingleResolve()}_updateMaterial(t){const e=this._faceMeshObjects[t].material;if(!m.isArray(e)){const n=e,i=this._webcamSnapshots[t].texture;n.map=i,n.needsUpdate=!0}}_createFaceMeshObject(t){const e=new S.a,n=[],i=[];for(let t=0;t<468;t++)n.push(t),n.push(t),n.push(t),i.push(t),i.push(t);const r=[],s=CZ.FACEMESH_TESSELATION.length/3;for(let t=0;t{n.push(t)}));else for(let t of i.objects())n.push(t)}e.compact&&(n=this._make_compact(n));for(let t of n)t.traverse((t=>{t.matrixAutoUpdate=!1}));return this.createCoreGroupFromObjects(n)}_make_compact(t){const e=new Map,n=new Map,i=[];for(let r of t)r.traverse((t=>{if(t instanceof In.a)return;const r=t;if(r.geometry){const t=Nr(r.constructor);if(i.includes(t)||i.push(t),t){e.get(t)||e.set(t,r.material),u.pushOnArrayAtEntry(n,t,r)}}}));const r=[];return i.forEach((t=>{var i,s;const o=n.get(t);if(o){const n=[];for(let t of o){const e=t.geometry;e.applyMatrix4(t.matrix),n.push(e)}try{const s=ps.mergeGeometries(n);if(s){const n=e.get(t),i=this.createObject(s,t,n);r.push(i)}else null===(i=this.states)||void 0===i||i.error.set(\\\\\\\"merge failed, check that input geometries have the same attributes\\\\\\\")}catch(t){null===(s=this.states)||void 0===s||s.error.set(t.message)}}})),r}}PZ.DEFAULT_PARAMS={compact:!1},PZ.INPUT_CLONED_STATE=Qi.FROM_NODE;const IZ=\\\\\\\"geometry to merge\\\\\\\",FZ=PZ.DEFAULT_PARAMS;const DZ=new class extends aa{constructor(){super(...arguments),this.compact=oa.BOOLEAN(FZ.compact),this.inputsCount=oa.INTEGER(4,{range:[1,32],rangeLocked:[!0,!1],callback:t=>{kZ.PARAM_CALLBACK_setInputsCount(t)}})}};class kZ extends gG{constructor(){super(...arguments),this.paramsConfig=DZ}static type(){return\\\\\\\"merge\\\\\\\"}static displayedInputNames(){return[IZ,IZ,IZ,IZ]}setCompactMode(t){this.p.compact.set(t)}initializeNode(){this.io.inputs.setCount(1,4),this.io.inputs.initInputsClonedState(PZ.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.compact],(()=>this.pv.compact?\\\\\\\"compact\\\\\\\":\\\\\\\"separate objects\\\\\\\"))})),this.params.addOnSceneLoadHook(\\\\\\\"update inputs\\\\\\\",(()=>{this._callbackUpdateInputsCount()}))}))}cook(t){this._operation=this._operation||new PZ(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}_callbackUpdateInputsCount(){this.io.inputs.setCount(1,this.pv.inputsCount),this.emit(Ei.INPUTS_UPDATED)}static PARAM_CALLBACK_setInputsCount(t){t._callbackUpdateInputsCount()}}class BZ{constructor(t=Math){this.grad3=[[1,1,0],[-1,1,0],[1,-1,0],[-1,-1,0],[1,0,1],[-1,0,1],[1,0,-1],[-1,0,-1],[0,1,1],[0,-1,1],[0,1,-1],[0,-1,-1]],this.grad4=[[0,1,1,1],[0,1,1,-1],[0,1,-1,1],[0,1,-1,-1],[0,-1,1,1],[0,-1,1,-1],[0,-1,-1,1],[0,-1,-1,-1],[1,0,1,1],[1,0,1,-1],[1,0,-1,1],[1,0,-1,-1],[-1,0,1,1],[-1,0,1,-1],[-1,0,-1,1],[-1,0,-1,-1],[1,1,0,1],[1,1,0,-1],[1,-1,0,1],[1,-1,0,-1],[-1,1,0,1],[-1,1,0,-1],[-1,-1,0,1],[-1,-1,0,-1],[1,1,1,0],[1,1,-1,0],[1,-1,1,0],[1,-1,-1,0],[-1,1,1,0],[-1,1,-1,0],[-1,-1,1,0],[-1,-1,-1,0]],this.p=[];for(let e=0;e<256;e++)this.p[e]=Math.floor(256*t.random());this.perm=[];for(let t=0;t<512;t++)this.perm[t]=this.p[255&t];this.simplex=[[0,1,2,3],[0,1,3,2],[0,0,0,0],[0,2,3,1],[0,0,0,0],[0,0,0,0],[0,0,0,0],[1,2,3,0],[0,2,1,3],[0,0,0,0],[0,3,1,2],[0,3,2,1],[0,0,0,0],[0,0,0,0],[0,0,0,0],[1,3,2,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[1,2,0,3],[0,0,0,0],[1,3,0,2],[0,0,0,0],[0,0,0,0],[0,0,0,0],[2,3,0,1],[2,3,1,0],[1,0,2,3],[1,0,3,2],[0,0,0,0],[0,0,0,0],[0,0,0,0],[2,0,3,1],[0,0,0,0],[2,1,3,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[2,0,1,3],[0,0,0,0],[0,0,0,0],[0,0,0,0],[3,0,1,2],[3,0,2,1],[0,0,0,0],[3,1,2,0],[2,1,0,3],[0,0,0,0],[0,0,0,0],[0,0,0,0],[3,1,0,2],[0,0,0,0],[3,2,0,1],[3,2,1,0]]}dot(t,e,n){return t[0]*e+t[1]*n}dot3(t,e,n,i){return t[0]*e+t[1]*n+t[2]*i}dot4(t,e,n,i,r){return t[0]*e+t[1]*n+t[2]*i+t[3]*r}noise(t,e){let n,i,r;const s=(t+e)*(.5*(Math.sqrt(3)-1)),o=Math.floor(t+s),a=Math.floor(e+s),l=(3-Math.sqrt(3))/6,c=(o+a)*l,u=t-(o-c),h=e-(a-c);let d,p;u>h?(d=1,p=0):(d=0,p=1);const _=u-d+l,m=h-p+l,f=u-1+2*l,g=h-1+2*l,v=255&o,y=255&a,x=this.perm[v+this.perm[y]]%12,b=this.perm[v+d+this.perm[y+p]]%12,w=this.perm[v+1+this.perm[y+1]]%12;let T=.5-u*u-h*h;T<0?n=0:(T*=T,n=T*T*this.dot(this.grad3[x],u,h));let A=.5-_*_-m*m;A<0?i=0:(A*=A,i=A*A*this.dot(this.grad3[b],_,m));let E=.5-f*f-g*g;return E<0?r=0:(E*=E,r=E*E*this.dot(this.grad3[w],f,g)),70*(n+i+r)}noise3d(t,e,n){let i,r,s,o;const a=(t+e+n)*(1/3),l=Math.floor(t+a),c=Math.floor(e+a),u=Math.floor(n+a),h=1/6,d=(l+c+u)*h,p=t-(l-d),_=e-(c-d),m=n-(u-d);let f,g,v,y,x,b;p>=_?_>=m?(f=1,g=0,v=0,y=1,x=1,b=0):p>=m?(f=1,g=0,v=0,y=1,x=0,b=1):(f=0,g=0,v=1,y=1,x=0,b=1):_b?32:0)+(x>w?16:0)+(b>w?8:0)+(x>T?4:0)+(b>T?2:0)+(w>T?1:0),E=s[A][0]>=3?1:0,M=s[A][1]>=3?1:0,S=s[A][2]>=3?1:0,C=s[A][3]>=3?1:0,N=s[A][0]>=2?1:0,L=s[A][1]>=2?1:0,O=s[A][2]>=2?1:0,R=s[A][3]>=2?1:0,P=s[A][0]>=1?1:0,I=s[A][1]>=1?1:0,F=s[A][2]>=1?1:0,D=s[A][3]>=1?1:0,k=x-E+l,B=b-M+l,z=w-S+l,U=T-C+l,G=x-N+2*l,V=b-L+2*l,H=w-O+2*l,j=T-R+2*l,W=x-P+3*l,q=b-I+3*l,X=w-F+3*l,Y=T-D+3*l,$=x-1+4*l,J=b-1+4*l,Z=w-1+4*l,Q=T-1+4*l,K=255&m,tt=255&f,et=255&g,nt=255&v,it=o[K+o[tt+o[et+o[nt]]]]%32,rt=o[K+E+o[tt+M+o[et+S+o[nt+C]]]]%32,st=o[K+N+o[tt+L+o[et+O+o[nt+R]]]]%32,ot=o[K+P+o[tt+I+o[et+F+o[nt+D]]]]%32,at=o[K+1+o[tt+1+o[et+1+o[nt+1]]]]%32;let lt=.6-x*x-b*b-w*w-T*T;lt<0?c=0:(lt*=lt,c=lt*lt*this.dot4(r[it],x,b,w,T));let ct=.6-k*k-B*B-z*z-U*U;ct<0?u=0:(ct*=ct,u=ct*ct*this.dot4(r[rt],k,B,z,U));let ut=.6-G*G-V*V-H*H-j*j;ut<0?h=0:(ut*=ut,h=ut*ut*this.dot4(r[st],G,V,H,j));let ht=.6-W*W-q*q-X*X-Y*Y;ht<0?d=0:(ht*=ht,d=ht*ht*this.dot4(r[ot],W,q,X,Y));let dt=.6-$*$-J*J-Z*Z-Q*Q;return dt<0?p=0:(dt*=dt,p=dt*dt*this.dot4(r[at],$,J,Z,Q)),27*(c+u+h+d+p)}}var zZ;!function(t){t.ADD=\\\\\\\"add\\\\\\\",t.SET=\\\\\\\"set\\\\\\\",t.MULT=\\\\\\\"mult\\\\\\\",t.SUBSTRACT=\\\\\\\"substract\\\\\\\",t.DIVIDE=\\\\\\\"divide\\\\\\\"}(zZ||(zZ={}));const UZ=[zZ.ADD,zZ.SET,zZ.MULT,zZ.SUBSTRACT,zZ.DIVIDE];const GZ=new class extends aa{constructor(){super(...arguments),this.amplitude=oa.FLOAT(1),this.tamplitudeAttrib=oa.BOOLEAN(0),this.amplitudeAttrib=oa.STRING(\\\\\\\"amp\\\\\\\",{visibleIf:{tamplitudeAttrib:!0}}),this.freq=oa.VECTOR3([1,1,1]),this.offset=oa.VECTOR3([0,0,0]),this.octaves=oa.INTEGER(3,{range:[1,8],rangeLocked:[!0,!1]}),this.ampAttenuation=oa.FLOAT(.5,{range:[0,1]}),this.freqIncrease=oa.FLOAT(2,{range:[0,10]}),this.seed=oa.INTEGER(0,{range:[0,100],separatorAfter:!0}),this.useNormals=oa.BOOLEAN(0),this.attribName=oa.STRING(\\\\\\\"position\\\\\\\"),this.useRestAttributes=oa.BOOLEAN(0),this.restP=oa.STRING(\\\\\\\"restP\\\\\\\",{visibleIf:{useRestAttributes:!0}}),this.restN=oa.STRING(\\\\\\\"restN\\\\\\\",{visibleIf:{useRestAttributes:!0}}),this.operation=oa.INTEGER(UZ.indexOf(zZ.ADD),{menu:{entries:UZ.map((t=>({name:t,value:UZ.indexOf(t)})))}}),this.computeNormals=oa.BOOLEAN(1)}};class VZ extends gG{constructor(){super(...arguments),this.paramsConfig=GZ,this._simplex_by_seed=new Map,this._rest_pos=new p.a,this._rest_value2=new d.a,this._noise_value_v=new p.a}static type(){return\\\\\\\"noise\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to add noise to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Qi.FROM_NODE])}setOperation(t){this.p.operation.set(UZ.indexOf(t))}async cook(t){const e=t[0],n=e.points(),i=this.pv.attribName;if(!e.hasAttrib(i))return this.states.error.set(`attribute ${i} not found`),void this.cookController.endCook();if(e.attribType(i)!=Dr.NUMERIC)return this.states.error.set(`attribute ${i} is not a numeric attribute`),void this.cookController.endCook();const r=this._get_simplex(),s=this.pv.useNormals&&e.hasAttrib(\\\\\\\"normal\\\\\\\"),o=e.attribSize(this.pv.attribName),a=UZ[this.pv.operation],l=this.pv.useRestAttributes,c=this.pv.amplitude,u=this.pv.tamplitudeAttrib;let h,f,g=new p.a;for(let t=0;t{r[3*t.index()+0]=e}));else{let t;for(let e=0;e{r[3*t.index()+1]=e}));else{let t;for(let e=0;e{r[3*t.index()+2]=e}));else{let t;for(let e=0;e{this._update_object(t,e)})):this._update_object(t,e);return n}_update_object(t,e){e.tname&&(t.name=e.name),e.trenderOrder&&(t.renderOrder=e.renderOrder),e.tfrustumCulled&&(t.frustumCulled=e.frustumCulled),e.tmatrixAutoUpdate&&(t.matrixAutoUpdate=e.matrixAutoUpdate),e.tvisible&&(t.visible=e.visible),e.tcastShadow&&(t.castShadow=e.castShadow),e.treceiveShadow&&(t.receiveShadow=e.receiveShadow)}}JZ.DEFAULT_PARAMS={applyToChildren:!0,tname:!1,name:\\\\\\\"\\\\\\\",trenderOrder:!1,renderOrder:0,tfrustumCulled:!1,frustumCulled:!0,tmatrixAutoUpdate:!1,matrixAutoUpdate:!1,tvisible:!1,visible:!0,tcastShadow:!1,castShadow:!0,treceiveShadow:!1,receiveShadow:!0},JZ.INPUT_CLONED_STATE=Qi.FROM_NODE;const ZZ=JZ.DEFAULT_PARAMS;const QZ=new class extends aa{constructor(){super(...arguments),this.applyToChildren=oa.BOOLEAN(ZZ.applyToChildren,{separatorAfter:!0}),this.tname=oa.BOOLEAN(ZZ.tname),this.name=oa.STRING(ZZ.name,{visibleIf:{tname:!0},separatorAfter:!0}),this.trenderOrder=oa.BOOLEAN(ZZ.trenderOrder),this.renderOrder=oa.INTEGER(ZZ.renderOrder,{visibleIf:{trenderOrder:!0},range:[0,10],rangeLocked:[!1,!1],separatorAfter:!0}),this.tfrustumCulled=oa.BOOLEAN(ZZ.tfrustumCulled),this.frustumCulled=oa.BOOLEAN(ZZ.frustumCulled,{visibleIf:{tfrustumCulled:!0},separatorAfter:!0}),this.tmatrixAutoUpdate=oa.BOOLEAN(ZZ.tmatrixAutoUpdate),this.matrixAutoUpdate=oa.BOOLEAN(ZZ.matrixAutoUpdate,{visibleIf:{tmatrixAutoUpdate:!0},separatorAfter:!0}),this.tvisible=oa.BOOLEAN(ZZ.tvisible),this.visible=oa.BOOLEAN(ZZ.visible,{visibleIf:{tvisible:!0},separatorAfter:!0}),this.tcastShadow=oa.BOOLEAN(ZZ.tcastShadow),this.castShadow=oa.BOOLEAN(ZZ.castShadow,{visibleIf:{tcastShadow:!0},separatorAfter:!0}),this.treceiveShadow=oa.BOOLEAN(ZZ.treceiveShadow),this.receiveShadow=oa.BOOLEAN(ZZ.receiveShadow,{visibleIf:{treceiveShadow:!0}})}};class KZ extends gG{constructor(){super(...arguments),this.paramsConfig=QZ}static type(){return\\\\\\\"objectProperties\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to change properties of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(JZ.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new JZ(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const tQ=new class extends aa{};class eQ extends gG{constructor(){super(...arguments),this.paramsConfig=tQ,this._input_configs_by_operation_container=new WeakMap}static type(){return Il}initializeNode(){this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}set_output_operation_container(t){this._output_operation_container=t}output_operation_container(){return this._output_operation_container}add_input_config(t,e){let n=this._input_configs_by_operation_container.get(t);n||(n=new Map,this._input_configs_by_operation_container.set(t,n)),n.set(e.operation_input_index,e.node_input_index)}add_operation_container_with_path_param_resolve_required(t){this._operation_containers_requiring_resolve||(this._operation_containers_requiring_resolve=[]),this._operation_containers_requiring_resolve.push(t)}resolve_operation_containers_path_params(){if(this._operation_containers_requiring_resolve)for(let t of this._operation_containers_requiring_resolve)t.resolve_path_params(this)}async cook(t){if(this._output_operation_container){this._output_operation_container.setDirty();const e=await this._output_operation_container.compute(t,this._input_configs_by_operation_container);e&&this.setCoreGroup(e)}}}class nQ extends cf{constructor(t){super(),this._uv_name=t}set_texture_allocations_controller(t){this._texture_allocations_controller=t}handle_globals_node(t,e,n){if(!this._texture_allocations_controller)return;const i=t.io.outputs.namedOutputConnectionPointsByName(e),r=t.glVarName(e);if(this._texture_allocations_controller.variable(e)&&i){const s=i.type(),o=`${s} ${r} = ${this.read_attribute(t,s,e,n)}`;n.addBodyLines(t,[o])}else this.globals_geometry_handler=this.globals_geometry_handler||new Sf,this.globals_geometry_handler.handle_globals_node(t,e,n)}read_attribute(t,e,n,i){if(!this._texture_allocations_controller)return;const r=this._texture_allocations_controller.variable(n);if(!r)return Sf.read_attribute(t,e,n,i);{this.add_particles_sim_uv_attribute(t,i);const e=r.component(),n=r.allocation();if(n){const r=n.textureName(),s=new Af(t,Do.SAMPLER_2D,r);i.addDefinitions(t,[s]);return`texture2D( ${r}, ${this._uv_name} ).${e}`}}}add_particles_sim_uv_attribute(t,e){const n=new wf(t,Do.VEC2,nQ.UV_ATTRIB),i=new Ef(t,Do.VEC2,nQ.UV_VARYING);e.addDefinitions(t,[n,i],xf.VERTEX),e.addDefinitions(t,[i],xf.FRAGMENT),e.addBodyLines(t,[`${nQ.UV_VARYING} = ${nQ.UV_ATTRIB}`],xf.VERTEX)}}nQ.UV_ATTRIB=\\\\\\\"particles_sim_uv_attrib\\\\\\\",nQ.UV_VARYING=\\\\\\\"particles_sim_uv_varying\\\\\\\",nQ.PARTICLE_SIM_UV=\\\\\\\"particleUV\\\\\\\";class iQ{constructor(t){this.node=t,this._particles_group_objects=[],this._all_shader_names=[],this._all_uniform_names=[],this.globals_handler=new nQ(nQ.UV_VARYING)}setShadersByName(t){this._shaders_by_name=t,this._all_shader_names=[],this._all_uniform_names=[],this._shaders_by_name.forEach(((t,e)=>{this._all_shader_names.push(e),this._all_uniform_names.push(`texture_${e}`)})),this.reset_render_material()}assign_render_material(){if(this._render_material){for(let t of this._particles_group_objects){const e=t;e.geometry&&(e.material=this._render_material,fs.applyCustomMaterials(e,this._render_material),e.matrixAutoUpdate=!1,e.updateMatrix())}this._render_material.needsUpdate=!0,this.update_render_material_uniforms()}}update_render_material_uniforms(){var t;if(!this._render_material)return;let e,n;for(let i=0;i=e&&(null==this._last_simulated_frame&&(this._last_simulated_frame=e-1),null==this._last_simulated_time&&(this._last_simulated_time=this.node.scene().time()),t>this._last_simulated_frame&&this._compute_simulation(t-this._last_simulated_frame))}_compute_simulation(t=1){if(!this._gpu_compute||null==this._last_simulated_time)return;this.update_simulation_material_uniforms();for(let e=0;et)return void this.node.states.error.set(`max particles is set to (${this.node.pv.texturesSize.x}x${this.node.pv.texturesSize.y}=) ${t}`);this._used_textures_size.copy(this.node.pv.texturesSize)}this._forceTimeDependent(),this._init_particles_uvs(),this.node.renderController.reset_render_material();const n=await ai.renderersController.waitForRenderer();if(n?this._renderer=n:this.node.states.error.set(\\\\\\\"no renderer found\\\\\\\"),!this._renderer)return;const i=new sQ(this._used_textures_size.x,this._used_textures_size.y,this._renderer);if(i.setDataType(this._data_type()),this._gpu_compute=i,this._gpu_compute){this._last_simulated_frame=void 0,this.variables_by_name.forEach(((t,e)=>{t.renderTargets[0].dispose(),t.renderTargets[1].dispose(),this.variables_by_name.delete(e)})),this._all_variables=[],null===(t=this._shaders_by_name)||void 0===t||t.forEach(((t,e)=>{if(this._gpu_compute){const n=this._gpu_compute.addVariable(this._textureNameForShaderName(e),t,this._created_textures_by_name.get(e));this.variables_by_name.set(e,n),this._all_variables.push(n)}})),null===(e=this.variables_by_name)||void 0===e||e.forEach(((t,e)=>{this._gpu_compute&&this._gpu_compute.setVariableDependencies(t,this._all_variables)})),this._create_texture_render_targets(),this._fill_textures(),this.create_simulation_material_uniforms();var r=this._gpu_compute.init();null!==r&&(console.error(r),this.node.states.error.set(r))}else this.node.states.error.set(\\\\\\\"failed to create the GPUComputationRenderer\\\\\\\")}_forceTimeDependent(){this._graph_node||(this._graph_node=new Ai(this.node.scene(),\\\\\\\"gpu_compute\\\\\\\"),this._graph_node.addGraphInput(this.node.scene().timeController.graphNode),this._graph_node.addPostDirtyHook(\\\\\\\"on_time_change\\\\\\\",this._on_graph_node_dirty.bind(this)))}_on_graph_node_dirty(){this.node.is_on_frame_start()?this.node.setDirty():this.compute_similation_if_required()}materials(){const t=[];return this.variables_by_name.forEach(((e,n)=>{t.push(e.material)})),t}create_simulation_material_uniforms(){const t=this.node.assemblerController,e=null==t?void 0:t.assembler;if(!e&&!this._persisted_texture_allocations_controller)return;const n=[];this.variables_by_name.forEach(((t,e)=>{n.push(t.material)}));const i=this._readonlyAllocations();for(let t of n)t.uniforms[RR.TIME]={value:this.node.scene().time()},t.uniforms[RR.DELTA_TIME]={value:this.node.scene().time()},i&&this._assignReadonlyTextures(t,i);if(e)for(let t of n)for(let n of e.param_configs())t.uniforms[n.uniform_name]=n.uniform;else{const t=this.node.persisted_config.loaded_data();if(t){const e=this.node.persisted_config.uniforms();if(e){const r=t.param_uniform_pairs;for(let t of r){const r=t[0],s=t[1],o=this.node.params.get(r),a=e[s];for(let t of n)t.uniforms[s]=a,i&&this._assignReadonlyTextures(t,i);o&&a&&o.options.setOption(\\\\\\\"callback\\\\\\\",(()=>{for(let t of n)$f.callback(o,t.uniforms[s])}))}}}}}_assignReadonlyTextures(t,e){for(let n of e){const e=n.shaderName(),i=this._created_textures_by_name.get(e);if(i){const n=this._textureNameForShaderName(e);t.uniforms[n]={value:i}}}}update_simulation_material_uniforms(){for(let t of this._all_variables)t.material.uniforms[RR.TIME].value=this.node.scene().time(),t.material.uniforms[RR.DELTA_TIME].value=this._delta_time}_init_particles_uvs(){var t=new Float32Array(2*this._points.length);let e=0;for(var n=0,i=0;i=t.length));r++);const s=nQ.UV_ATTRIB;if(this._particles_core_group)for(let e of this._particles_core_group.coreGeometries()){const n=e.geometry(),i=e.markedAsInstance()?cX:C.a;n.setAttribute(s,new i(t,2))}}createdTexturesByName(){return this._created_textures_by_name}_fill_textures(){const t=this._textureAllocationsController();t&&this._created_textures_by_name.forEach(((e,n)=>{const i=t.allocationForShaderName(n);if(!i)return void console.warn(`no allocation found for shader ${n}`);const r=i.variables();if(!r)return void console.warn(\\\\\\\"allocation has no variables\\\\\\\");const s=e.image.data;for(let t of r){const e=t.position();let n=t.name();const i=this._points[0];if(i){if(i.hasAttrib(n)){const t=i.attribSize(n);let r=e;for(let e of this._points){if(1==t){const t=e.attribValue(n);s[r]=t}else e.attribValue(n).toArray(s,r);r+=4}}}}}))}reset_gpu_compute(){this._gpu_compute=void 0,this._simulation_restart_required=!0}set_restart_not_required(){this._simulation_restart_required=!1}reset_gpu_compute_and_set_dirty(){this.reset_gpu_compute(),this.node.setDirty()}reset_particle_groups(){this._particles_core_group=void 0}initialized(){return null!=this._particles_core_group&&null!=this._gpu_compute}_create_texture_render_targets(){this._created_textures_by_name.forEach(((t,e)=>{t.dispose()})),this._created_textures_by_name.clear(),this.variables_by_name.forEach(((t,e)=>{this._gpu_compute&&this._created_textures_by_name.set(e,this._gpu_compute.createTexture())}));const t=this._readonlyAllocations();if(t&&this._gpu_compute)for(let e of t)this._created_textures_by_name.set(e.shaderName(),this._gpu_compute.createTexture())}_textureAllocationsController(){var t;return(null===(t=this.node.assemblerController)||void 0===t?void 0:t.assembler.textureAllocationsController())||this._persisted_texture_allocations_controller}_readonlyAllocations(){var t;return null===(t=this._textureAllocationsController())||void 0===t?void 0:t.readonlyAllocations()}restart_simulation_if_required(){this._simulation_restart_required&&this._restart_simulation()}_restart_simulation(){this._last_simulated_time=void 0,this._create_texture_render_targets();this._get_points()&&(this._fill_textures(),this.variables_by_name.forEach(((t,e)=>{const n=this._created_textures_by_name.get(e);this._gpu_compute&&n&&(this._gpu_compute.renderTexture(n,t.renderTargets[0]),this._gpu_compute.renderTexture(n,t.renderTargets[1]))})))}_get_points(){if(!this._particles_core_group)return;let t=this._particles_core_group.coreGeometries();const e=t[0];if(e){const n=e.markedAsInstance(),i=[];for(let e of t)e.markedAsInstance()==n&&i.push(e);const r=[];for(let t of i)for(let e of t.points())r.push(e);return r}return[]}}class cQ{constructor(t,e){if(this._name=t,this._size=e,this._position=-1,this._readonly=!1,!t)throw\\\\\\\"TextureVariable requires a name\\\\\\\"}merge(t){var e;t.readonly()||this.setReadonly(!1),null===(e=t.graphNodeIds())||void 0===e||e.forEach(((t,e)=>{this.addGraphNodeId(e)}))}setReadonly(t){this._readonly=t}readonly(){return this._readonly}setAllocation(t){this._allocation=t}allocation(){return this._allocation}graphNodeIds(){return this._graph_node_ids}addGraphNodeId(t){this._graph_node_ids=this._graph_node_ids||new Map,this._graph_node_ids.set(t,!0)}name(){return this._name}size(){return this._size}setPosition(t){this._position=t}position(){return this._position}component(){return\\\\\\\"xyzw\\\\\\\".split(\\\\\\\"\\\\\\\").splice(this._position,this._size).join(\\\\\\\"\\\\\\\")}static fromJSON(t){return new cQ(t.name,t.size)}toJSON(t){const e=[];return this._graph_node_ids&&this._graph_node_ids.forEach(((n,i)=>{const r=t.graph.nodeFromId(i);if(r){const t=r.path();t&&e.push(t)}})),{name:this.name(),size:this.size(),nodes:e}}}class uQ{constructor(){this._size=0}addVariable(t){this._variables=this._variables||[],this._variables.push(t),t.setPosition(this._size),t.setAllocation(this),this._size+=t.size()}hasSpaceForVariable(t){return this._size+t.size()<=4}shaderName(){var t;return((null===(t=this.variables())||void 0===t?void 0:t.map((t=>t.name())))||[\\\\\\\"no_variables_allocated\\\\\\\"]).join(\\\\\\\"_SEPARATOR_\\\\\\\")}textureName(){return`texture_${this.shaderName()}`}variables(){return this._variables}variablesForInputNode(t){var e;return null===(e=this._variables)||void 0===e?void 0:e.filter((e=>{var n;return(null===(n=e.graphNodeIds())||void 0===n?void 0:n.has(t.graphNodeId()))||!1}))}inputNamesForNode(t){const e=this.variablesForInputNode(t);if(e)return t.type()==ir.ATTRIBUTE?[gf.INPUT_NAME]:e.map((t=>t.name()))}variable(t){if(this._variables)for(let e of this._variables)if(e.name()==t)return e}static fromJSON(t){const e=new uQ;for(let n of t){const t=cQ.fromJSON(n);e.addVariable(t)}return e}toJSON(t){return this._variables?this._variables.map((e=>e.toJSON(t))):[]}}const hQ=[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"color\\\\\\\",\\\\\\\"uv\\\\\\\"];class dQ{constructor(){this._writableAllocations=[],this._readonlyAllocations=[]}static _sortNodes(t){const e=t.filter((t=>t.type()==UI.type())),n=t.filter((t=>t.type()!=UI.type())),i=n.map((t=>t.name())).sort(),r=new Map;for(let t of n)r.set(t.name(),t);for(let t of i){const n=r.get(t);n&&e.push(n)}return e}allocateConnectionsFromRootNodes(t,e){const n=[];t=dQ._sortNodes(t),e=dQ._sortNodes(e);for(let e of t){const t=e.graphNodeId();switch(e.type()){case UI.type():for(let i of e.io.inputs.namedInputConnectionPoints()){if(e.io.inputs.named_input(i.name())){const e=new cQ(i.name(),Go[i.type()]);e.addGraphNodeId(t),n.push(e)}}break;case gf.type():{const i=e,r=i.connected_input_node(),s=i.connected_input_connection_point();if(r&&s){const e=new cQ(i.attribute_name,Go[s.type()]);e.addGraphNodeId(t),n.push(e)}break}}}for(let t of e){const e=t.graphNodeId();switch(t.type()){case HP.type():{const i=t;for(let t of i.io.outputs.used_output_names()){if(hQ.includes(t)){const r=i.io.outputs.namedOutputConnectionPointsByName(t);if(r){const i=r.type(),s=new cQ(t,Go[i]);s.addGraphNodeId(e),n.push(s)}}}break}case gf.type():{const i=t,r=i.output_connection_point();if(r){const t=new cQ(i.attribute_name,Go[r.type()]);i.isExporting()||t.setReadonly(!0),t.addGraphNodeId(e),n.push(t)}break}}}this._allocateVariables(n)}_allocateVariables(t){const e=f.sortBy(t,(t=>-t.size())),n=this._ensureVariablesAreUnique(e);for(let t of n)t.readonly()?this._allocateVariable(t,this._readonlyAllocations):this._allocateVariable(t,this._writableAllocations)}_ensureVariablesAreUnique(t){const e=new Map;for(let n of t)u.pushOnArrayAtEntry(e,n.name(),n);const n=[];return e.forEach(((t,e)=>{const i=t[0];n.push(i);for(let e=1;et.shaderName()));return f.uniq(t)}createShaderConfigs(){return[]}allocationForShaderName(t){const e=this._writableAllocations.filter((e=>e.shaderName()==t))[0];return e||this._readonlyAllocations.filter((e=>e.shaderName()==t))[0]}inputNamesForShaderName(t,e){const n=this.allocationForShaderName(e);if(n)return n.inputNamesForNode(t)}variable(t){for(let e of this._writableAllocations){const n=e.variable(t);if(n)return n}for(let e of this._readonlyAllocations){const n=e.variable(t);if(n)return n}}variables(){const t=this._writableAllocations.map((t=>t.variables()||[])).flat(),e=this._writableAllocations.map((t=>t.variables()||[])).flat();return t.concat(e)}hasVariable(t){return this.variables().map((t=>t.name())).includes(t)}static fromJSON(t){const e=new dQ;for(let n of t.writable){const t=n[Object.keys(n)[0]],i=uQ.fromJSON(t);e._addWritableAllocation(i)}for(let n of t.readonly){const t=n[Object.keys(n)[0]],i=uQ.fromJSON(t);e._addReadonlyAllocation(i)}return e}toJSON(t){return{writable:this._writableAllocations.map((e=>({[e.shaderName()]:e.toJSON(t)}))),readonly:this._readonlyAllocations.map((e=>({[e.shaderName()]:e.toJSON(t)})))}}print(t){console.warn(JSON.stringify(this.toJSON(t),[\\\\\\\"\\\\\\\"],2))}}class pQ extends Xf{constructor(t){super(t),this.node=t}toJSON(){const t=this.node.assemblerController;if(!t)return;const e={};this.node.shaders_by_name().forEach(((t,n)=>{e[n]=t}));const n=t.assembler.textureAllocationsController().toJSON(this.node.scene()),i=[],r=new F,s=t.assembler.param_configs();for(let t of s)i.push([t.name(),t.uniform_name]),r.uniforms[t.uniform_name]=t.uniform;const o=this._materialToJson(r,{node:this.node,suffix:\\\\\\\"main\\\\\\\"});return{shaders_by_name:e,texture_allocations:n,param_uniform_pairs:i,uniforms_owner:o||{}}}load(t){ai.playerMode()&&(this._loaded_data=t,this.node.init_with_persisted_config())}loaded_data(){return this._loaded_data}shaders_by_name(){if(this._loaded_data){const t=new Map,e=Object.keys(this._loaded_data.shaders_by_name);for(let n of e)t.set(n,this._loaded_data.shaders_by_name[n]);return t}}texture_allocations_controller(){if(this._loaded_data)return dQ.fromJSON(this._loaded_data.texture_allocations)}uniforms(){if(this._loaded_data){const t=this._loadMaterial(this._loaded_data.uniforms_owner);return(null==t?void 0:t.uniforms)||{}}}}const _Q=new class extends aa{constructor(){super(...arguments),this.startFrame=oa.FLOAT(Ml.START_FRAME,{range:[0,1e3],rangeLocked:[!0,!1]}),this.autoTexturesSize=oa.BOOLEAN(1),this.maxTexturesSize=oa.VECTOR2([1024,1024],{visibleIf:{autoTexturesSize:1}}),this.texturesSize=oa.VECTOR2([64,64],{visibleIf:{autoTexturesSize:0}}),this.dataType=oa.INTEGER(0,{menu:{entries:oQ.map(((t,e)=>({value:e,name:t})))}}),this.reset=oa.BUTTON(null,{callback:(t,e)=>{mQ.PARAM_CALLBACK_reset(t)}}),this.material=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.MAT},dependentOnFoundNode:!1})}};class mQ extends gG{constructor(){super(...arguments),this.paramsConfig=_Q,this._assembler_controller=this._create_assembler_controller(),this.persisted_config=new pQ(this),this.globals_handler=new nQ(nQ.PARTICLE_SIM_UV),this._shaders_by_name=new Map,this.gpuController=new lQ(this),this.renderController=new iQ(this),this._reset_material_if_dirty_bound=this._reset_material_if_dirty.bind(this),this._children_controller_context=Ki.GL}static type(){return\\\\\\\"particlesSystemGpu\\\\\\\"}dispose(){super.dispose(),this.gpuController.dispose()}get assemblerController(){return this._assembler_controller}usedAssembler(){return Hn.GL_PARTICLES}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}shaders_by_name(){return this._shaders_by_name}static require_webgl2(){return!0}static PARAM_CALLBACK_reset(t){t.PARAM_CALLBACK_reset()}PARAM_CALLBACK_reset(){this.gpuController.reset_gpu_compute_and_set_dirty()}static displayedInputNames(){return[\\\\\\\"points to emit particles from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.NEVER),this.addPostDirtyHook(\\\\\\\"_reset_material_if_dirty\\\\\\\",this._reset_material_if_dirty_bound)}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}childrenAllowed(){return this.assemblerController?super.childrenAllowed():(this.scene().markAsReadOnly(this),!1)}async _reset_material_if_dirty(){this.p.material.isDirty()&&(this.renderController.reset_render_material(),this.is_on_frame_start()||await this.renderController.init_render_material())}is_on_frame_start(){return this.scene().frame()==this.pv.startFrame}async cook(t){this.gpuController.set_restart_not_required();const e=t[0];this.compileIfRequired(),this.is_on_frame_start()&&this.gpuController.reset_particle_groups(),this.gpuController.initialized()||await this.gpuController.init(e),this.renderController.initialized()||(this.renderController.init_core_group(e),await this.renderController.init_render_material()),this.gpuController.restart_simulation_if_required(),this.gpuController.compute_similation_if_required(),this.is_on_frame_start()?this.setCoreGroup(e):this.cookController.endCook()}async compileIfRequired(){var t;(null===(t=this.assemblerController)||void 0===t?void 0:t.compileRequired())&&await this.run_assembler()}async run_assembler(){const t=this.assemblerController;if(!t)return;const e=this._find_export_nodes();if(e.length>0){const n=e;t.set_assembler_globals_handler(this.globals_handler),t.assembler.set_root_nodes(n),t.assembler.compile(),t.post_compile()}const n=t.assembler.shaders_by_name();this._setShaderNames(n)}_setShaderNames(t){this._shaders_by_name=t,this.gpuController.setShadersByName(this._shaders_by_name),this.renderController.setShadersByName(this._shaders_by_name),this.gpuController.reset_gpu_compute(),this.gpuController.reset_particle_groups()}init_with_persisted_config(){const t=this.persisted_config.shaders_by_name(),e=this.persisted_config.texture_allocations_controller();t&&e&&(this._setShaderNames(t),this.gpuController.set_persisted_texture_allocation_controller(e))}_find_export_nodes(){const t=Of.findAttributeExportNodes(this),e=Of.findOutputNodes(this);if(e.length>1)return this.states.error.set(\\\\\\\"only one output node is allowed\\\\\\\"),[];const n=e[0];return n&&t.push(n),t}}class fQ extends pG{static type(){return\\\\\\\"peak\\\\\\\"}cook(t,e){const n=t[0];let i,r;for(let t of n.objects())t.traverse((t=>{let n;if(null!=(n=t.geometry)){for(r of(i=new ps(n),i.points())){const t=r.normal(),n=r.position().clone().add(t.multiplyScalar(e.amount));r.setPosition(n)}i.geometry().getAttribute(\\\\\\\"position\\\\\\\").needsUpdate=!0}}));return t[0]}}fQ.DEFAULT_PARAMS={amount:0};const gQ=fQ.DEFAULT_PARAMS;const vQ=new class extends aa{constructor(){super(...arguments),this.amount=oa.FLOAT(gQ.amount,{range:[-1,1]})}};class yQ extends gG{constructor(){super(...arguments),this.paramsConfig=vQ}static type(){return\\\\\\\"peak\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}cook(t){this._operation=this._operation||new fQ(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const xQ=new p.a(0,0,1),bQ=new p.a(0,0,1),wQ=new p.a(0,1,0);class TQ extends pG{constructor(){super(...arguments),this._core_transform=new Mz,this._size=new p.a,this._center=new p.a,this._segmentsCount=new d.a(1,1)}static type(){return\\\\\\\"plane\\\\\\\"}cook(t,e){const n=t[0];return n?this._cook_with_input(n,e):this._cook_without_input(e)}_cook_without_input(t){const e=this._create_plane(t.size,t);this._core_transform.rotate_geometry(e,xQ,t.direction);const n=this._core_transform.translation_matrix(t.center);return e.applyMatrix4(n),this.createCoreGroupFromGeometry(e)}_cook_with_input(t,e){const n=t.boundingBox();n.getSize(this._size),n.getCenter(this._center);const i=new d.a(this._size.x,this._size.z),r=this._create_plane(i,e);this._core_transform.rotate_geometry(r,bQ,wQ);const s=this._core_transform.translation_matrix(this._center);return r.applyMatrix4(s),this.createCoreGroupFromGeometry(r)}_create_plane(t,e){return t=t.clone(),e.useSegmentsCount?(this._segmentsCount.x=Math.floor(e.segments.x),this._segmentsCount.y=Math.floor(e.segments.y)):e.stepSize>0&&(this._segmentsCount.x=Math.floor(t.x/e.stepSize),this._segmentsCount.y=Math.floor(t.y/e.stepSize),t.x=this._segmentsCount.x*e.stepSize,t.y=this._segmentsCount.y*e.stepSize),new L(t.x,t.y,this._segmentsCount.x,this._segmentsCount.y)}}TQ.DEFAULT_PARAMS={size:new d.a(1,1),useSegmentsCount:!1,stepSize:1,segments:new d.a(1,1),direction:new p.a(0,1,0),center:new p.a(0,0,0)},TQ.INPUT_CLONED_STATE=Qi.NEVER;const AQ=TQ.DEFAULT_PARAMS;const EQ=new class extends aa{constructor(){super(...arguments),this.size=oa.VECTOR2(AQ.size),this.useSegmentsCount=oa.BOOLEAN(AQ.useSegmentsCount),this.stepSize=oa.FLOAT(AQ.stepSize,{range:[.001,1],rangeLocked:[!1,!1],visibleIf:{useSegmentsCount:0}}),this.segments=oa.VECTOR2(AQ.segments,{visibleIf:{useSegmentsCount:1}}),this.direction=oa.VECTOR3(AQ.direction),this.center=oa.VECTOR3(AQ.center)}};class MQ extends gG{constructor(){super(...arguments),this.paramsConfig=EQ}static type(){return\\\\\\\"plane\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create plane from (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(TQ.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new TQ(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const SQ=\\\\\\\"position\\\\\\\";const CQ=new class extends aa{constructor(){super(...arguments),this.updateX=oa.BOOLEAN(0),this.x=oa.FLOAT(\\\\\\\"@P.x\\\\\\\",{visibleIf:{updateX:1},expression:{forEntities:!0}}),this.updateY=oa.BOOLEAN(0),this.y=oa.FLOAT(\\\\\\\"@P.y\\\\\\\",{visibleIf:{updateY:1},expression:{forEntities:!0}}),this.updateZ=oa.BOOLEAN(0),this.z=oa.FLOAT(\\\\\\\"@P.z\\\\\\\",{visibleIf:{updateZ:1},expression:{forEntities:!0}}),this.updateNormals=oa.BOOLEAN(1)}};class NQ extends gG{constructor(){super(...arguments),this.paramsConfig=CQ,this._x_arrays_by_geometry_uuid=new Map,this._y_arrays_by_geometry_uuid=new Map,this._z_arrays_by_geometry_uuid=new Map}static type(){return\\\\\\\"point\\\\\\\"}static displayedInputNames(){return[\\\\\\\"points to move\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}async cook(t){const e=t[0];await this._eval_expressions_for_core_group(e)}async _eval_expressions_for_core_group(t){const e=t.coreObjects();for(let t=0;t{u[t.index()]=e}));else{let t;for(let e=0;e({name:t,value:e})))}}),this.center=oa.VECTOR3(kQ.center.toArray()),this.longitude=oa.FLOAT(kQ.longitude,{range:[0,360]}),this.latitude=oa.FLOAT(kQ.latitude,{range:[-180,180]}),this.depth=oa.FLOAT(kQ.depth,{range:[0,10]})}};class zQ extends gG{constructor(){super(...arguments),this.paramsConfig=BQ}static type(){return\\\\\\\"polarTransform\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(DQ.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new DQ(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class UQ{static accumulated_curve_point_indices(t){let e=[];const n=[];let i,r=null;for(let s=0;s{s[t]=[]})),e.forEach(((e,o)=>{const a=t[e];n.forEach((t=>{const e=a.attribValue(t);let n;n=i[t]>1?e.toArray():[e],n.forEach((e=>{s[t].push(e)}))})),o>0&&(r.push(o-1),r.push(o))}));const o=new S.a;return n.forEach((t=>{const e=i[t],n=s[t];o.setAttribute(t,new C.c(n,e))})),o.setIndex(r),o}static line_segment_to_geometries(t){var e;const n=[],i=new ps(t),r=i.attribNames(),s=i.points(),o=(null===(e=t.getIndex())||void 0===e?void 0:e.array)||[],a=this.accumulated_curve_point_indices(o);if(a.length>0){const e=i.attribSizes();a.forEach(((i,o)=>{t=this.create_line_segment_geometry(s,i,r,e),n.push(t)}))}return n}}class GQ{constructor(t,e,n){this.geometry=t,this.geometry1=e,this.geometry0=n}process(){const t=new ps(this.geometry0),e=new ps(this.geometry1),n=t.segments(),i=e.segments();if(0===n.length||0===i.length)return;const r=n.length{const n=l[e];p.push(t[0]),p.push(t[1]),p.push(n[0]+h),p.push(t[1]),p.push(n[1]+h),p.push(n[0]+h)}));f.intersection(s.attribNames(),o.attribNames()).forEach((t=>{const e=s.attribSize(t);let n,i=d.map((e=>e.attribValue(t)));n=1==e?i:i.map((t=>t.toArray())).flat(),this.geometry.setAttribute(t,new C.c(n,e))})),this.geometry.setIndex(p),this.geometry.computeVertexNormals()}}const VQ=new p.a(0,0,0),HQ=new p.a(1,1,1);const jQ=new class extends aa{constructor(){super(...arguments),this.radius=oa.FLOAT(1),this.segmentsRadial=oa.INTEGER(8,{range:[3,20],rangeLocked:[!0,!1]}),this.closed=oa.BOOLEAN(0)}};class WQ extends gG{constructor(){super(...arguments),this.paramsConfig=jQ,this._core_transform=new Mz,this._geometries=[]}static type(){return\\\\\\\"polywire\\\\\\\"}static displayedInputNames(){return[\\\\\\\"lines to create tubes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.NEVER)}cook(t){const e=t[0];this._geometries=[];for(let t of e.objects())t instanceof Tr.a&&this._create_tube(t);const n=ps.mergeGeometries(this._geometries);for(let t of this._geometries)t.dispose();if(n){const t=this.createObject(n,Sr.MESH);this.setObject(t)}else this.setObjects([])}_create_tube(t){var e;const n=t.geometry,i=new ps(n).points(),r=null===(e=n.getIndex())||void 0===e?void 0:e.array,s=UQ.accumulated_curve_point_indices(r);for(let t of s){const e=t.map((t=>i[t]));this._create_tube_from_points(e)}}_create_tube_from_points(t){if(t.length<=1)return;const e=t.map((t=>t.attribValue(\\\\\\\"position\\\\\\\"))),n=wY.create(this.pv.radius,this.pv.segmentsRadial),i=[];for(let t of e){const e=t,r=this._core_transform.matrix(e,VQ,HQ,1,Ez),s=n.clone();s.applyMatrix4(r),i.push(s)}for(let t=0;t0){const e=i[t],n=i[t-1],r=this._skin(n,e);this._geometries.push(r)}}_skin(t,e){const n=new S.a;return new GQ(n,t,e).process(),n}}const qQ=\\\\\\\"dist\\\\\\\";class XQ extends pG{constructor(){super(...arguments),this._matDoubleSideTmpSetter=new z$,this._raycaster=new uL,this._pointPos=new p.a,this._pointNormal=new p.a}static type(){return\\\\\\\"ray\\\\\\\"}cook(t,e){const n=t[0],i=t[1];return this._ray(n,i,e)}_ray(t,e,n){let i,r;this._matDoubleSideTmpSetter.setCoreGroupMaterialDoubleSided(e),n.addDistAttribute&&(t.hasAttrib(qQ)||t.addNumericVertexAttrib(qQ,1,-1));const s=t.points();for(let t of s)if(t.getPosition(this._pointPos),i=n.direction,n.useNormals&&(t.getNormal(this._pointNormal),i=this._pointNormal),this._raycaster.set(this._pointPos,i),r=this._raycaster.intersectObjects(e.objects(),!0)[0],r){if(n.transformPoints&&t.setPosition(r.point),n.addDistAttribute){const e=this._pointPos.distanceTo(r.point);console.log(e),t.setAttribValue(qQ,e)}n.transferFaceNormals&&r.face&&t.setNormal(r.face.normal)}return this._matDoubleSideTmpSetter.restoreMaterialSideProperty(e),t}}XQ.DEFAULT_PARAMS={useNormals:!0,direction:new p.a(0,-1,0),transformPoints:!0,transferFaceNormals:!0,addDistAttribute:!1},XQ.INPUT_CLONED_STATE=[Qi.FROM_NODE,Qi.ALWAYS];const YQ=XQ.DEFAULT_PARAMS;const $Q=new class extends aa{constructor(){super(...arguments),this.useNormals=oa.BOOLEAN(YQ.useNormals),this.direction=oa.VECTOR3(YQ.direction.toArray(),{visibleIf:{useNormals:0}}),this.transformPoints=oa.BOOLEAN(YQ.transformPoints),this.transferFaceNormals=oa.BOOLEAN(YQ.transferFaceNormals),this.addDistAttribute=oa.BOOLEAN(YQ.addDistAttribute)}};class JQ extends gG{constructor(){super(...arguments),this.paramsConfig=$Q}static type(){return\\\\\\\"ray\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to move\\\\\\\",\\\\\\\"geometry to ray onto\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState([Qi.FROM_NODE,Qi.ALWAYS])}cook(t){this._operation=this._operation||new XQ(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const ZQ={color:{value:null},tDiffuse:{value:null},textureMatrix:{value:null},opacity:{value:.5}},QQ=\\\\\\\"uniform mat4 textureMatrix;\\\\nvarying vec4 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvUv = textureMatrix * vec4( position, 1.0 );\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n}\\\\\\\",KQ=\\\\\\\"uniform vec3 color;\\\\nuniform sampler2D tDiffuse;\\\\nvarying vec4 vUv;\\\\nuniform float opacity;\\\\n\\\\nfloat blendOverlay( float base, float blend ) {\\\\n\\\\n\\\\treturn( base < 0.5 ? ( 2.0 * base * blend ) : ( 1.0 - 2.0 * ( 1.0 - base ) * ( 1.0 - blend ) ) );\\\\n\\\\n}\\\\n\\\\nvec3 blendOverlay( vec3 base, vec3 blend ) {\\\\n\\\\n\\\\treturn vec3( blendOverlay( base.r, blend.r ), blendOverlay( base.g, blend.g ), blendOverlay( base.b, blend.b ) );\\\\n\\\\n}\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 base = texture2DProj( tDiffuse, vUv );\\\\n\\\\tgl_FragColor = vec4( blendOverlay( base.rgb, color ), opacity );\\\\n\\\\n}\\\\\\\",tK={minFilter:w.V,magFilter:w.V,format:w.ic};class eK extends k.a{constructor(t,e){super(),this.geometry=t,this._options=e,this.type=\\\\\\\"Reflector\\\\\\\",this.reflectorPlane=new X.a,this.normal=new p.a,this.reflectorWorldPosition=new p.a,this.cameraWorldPosition=new p.a,this.rotationMatrix=new A.a,this.lookAtPosition=new p.a(0,0,-1),this.clipPlane=new _.a,this.view=new p.a,this.target=new p.a,this.q=new _.a,this.textureMatrix=new A.a,this.virtualCamera=new K.a,this.onBeforeRender=this._onBeforeRender.bind(this),this._onWindowResizeBound=this._onWindowResize.bind(this);const{width:n,height:i}=this._getRendererSize(this._options.renderer);this.renderTarget=new Z(n,i,tK),Object(Ln.i)(n)&&Object(Ln.i)(i)||(this.renderTarget.texture.generateMipmaps=!1),this._coreRenderBlur=new kU(new d.a(n,i)),this.material=new F({uniforms:I.clone(ZQ),fragmentShader:KQ,vertexShader:QQ}),this.material.uniforms.tDiffuse.value=this.renderTarget.texture,this.material.uniforms.color.value=this._options.color,this.material.uniforms.textureMatrix.value=this.textureMatrix,this.material.uniforms.opacity.value=this._options.opacity,this.material.transparent=this._options.opacity<1,this._addWindowResizeEvent()}_addWindowResizeEvent(){window.addEventListener(\\\\\\\"resize\\\\\\\",this._onWindowResizeBound.bind(this),!1)}_removeWindowResizeEvent(){window.removeEventListener(\\\\\\\"resize\\\\\\\",this._onWindowResizeBound.bind(this),!1)}_onWindowResize(){this.traverseAncestors((t=>{t.parent||t.uuid!=this._options.scene.uuid&&this._removeWindowResizeEvent()}));const{width:t,height:e}=this._getRendererSize(this._options.renderer);this.renderTarget.setSize(t,e),this._coreRenderBlur.setSize(t,e)}_getRendererSize(t){const e=t.domElement;return{width:e.width*this._options.pixelRatio,height:e.height*this._options.pixelRatio}}_onBeforeRender(t,e,n,i,r,s){if(!this._options.active)return;const o=n;if(this.reflectorWorldPosition.setFromMatrixPosition(this.matrixWorld),this.cameraWorldPosition.setFromMatrixPosition(o.matrixWorld),this.rotationMatrix.extractRotation(this.matrixWorld),this.normal.set(0,0,1),this.normal.applyMatrix4(this.rotationMatrix),this.view.subVectors(this.reflectorWorldPosition,this.cameraWorldPosition),!(this.view.dot(this.normal)>0)){this.view.reflect(this.normal).negate(),this.view.add(this.reflectorWorldPosition),this.rotationMatrix.extractRotation(o.matrixWorld),this.lookAtPosition.set(0,0,-1),this.lookAtPosition.applyMatrix4(this.rotationMatrix),this.lookAtPosition.add(this.cameraWorldPosition),this.target.subVectors(this.reflectorWorldPosition,this.lookAtPosition),this.target.reflect(this.normal).negate(),this.target.add(this.reflectorWorldPosition),this.virtualCamera.position.copy(this.view),this.virtualCamera.up.set(0,1,0),this.virtualCamera.up.applyMatrix4(this.rotationMatrix),this.virtualCamera.up.reflect(this.normal),this.virtualCamera.lookAt(this.target),this.virtualCamera.far=o.far,this.virtualCamera.updateMatrixWorld(),this.virtualCamera.projectionMatrix.copy(o.projectionMatrix),this.textureMatrix.set(.5,0,0,.5,0,.5,0,.5,0,0,.5,.5,0,0,0,1),this.textureMatrix.multiply(this.virtualCamera.projectionMatrix),this.textureMatrix.multiply(this.virtualCamera.matrixWorldInverse),this.textureMatrix.multiply(this.matrixWorld),this.reflectorPlane.setFromNormalAndCoplanarPoint(this.normal,this.reflectorWorldPosition),this.reflectorPlane.applyMatrix4(this.virtualCamera.matrixWorldInverse),this.clipPlane.set(this.reflectorPlane.normal.x,this.reflectorPlane.normal.y,this.reflectorPlane.normal.z,this.reflectorPlane.constant);var a=this.virtualCamera.projectionMatrix;this.q.x=(Math.sign(this.clipPlane.x)+a.elements[8])/a.elements[0],this.q.y=(Math.sign(this.clipPlane.y)+a.elements[9])/a.elements[5],this.q.z=-1,this.q.w=(1+a.elements[10])/a.elements[14],this.clipPlane.multiplyScalar(2/this.clipPlane.dot(this.q)),a.elements[2]=this.clipPlane.x,a.elements[6]=this.clipPlane.y,a.elements[10]=this.clipPlane.z+1-this._options.clipBias,a.elements[14]=this.clipPlane.w,this.renderTarget.texture.encoding=t.outputEncoding,this.visible=!1;var l=t.getRenderTarget(),c=t.xr.enabled,u=t.shadowMap.autoUpdate;if(t.xr.enabled=!1,t.shadowMap.autoUpdate=!1,t.setRenderTarget(this.renderTarget),t.state.buffers.depth.setMask(!0),!1===t.autoClear&&t.clear(),t.render(e,this.virtualCamera),this._options.tblur){const e=this._options.blur*this._options.pixelRatio,n=e*this._options.verticalBlurMult;if(this._coreRenderBlur.applyBlur(this.renderTarget,t,e,n),this._options.tblur2){const e=this._options.blur2*this._options.pixelRatio,n=e*this._options.verticalBlur2Mult;this._coreRenderBlur.applyBlur(this.renderTarget,t,e,n)}}t.xr.enabled=c,t.shadowMap.autoUpdate=u,t.setRenderTarget(l);var h=o.viewport;void 0!==h&&t.state.viewport(h),this.visible=!0}}}class nK extends pG{static type(){return\\\\\\\"reflector\\\\\\\"}async cook(t,e){const n=t[0],i=[],r=await ai.renderersController.firstRenderer();if(!r)return this.createCoreGroupFromObjects(i);const s=n.objectsWithGeo();for(let t of s){const n=new eK(t.geometry,{clipBias:e.clipBias,renderer:r,scene:this.scene().threejsScene(),pixelRatio:e.pixelRatio,color:e.color,opacity:e.opacity,active:e.active,tblur:e.tblur,blur:e.blur,verticalBlurMult:e.verticalBlurMult,tblur2:e.tblur2,blur2:e.blur2,verticalBlur2Mult:e.verticalBlur2Mult});n.position.copy(t.position),n.rotation.copy(t.rotation),n.scale.copy(t.scale),n.updateMatrix(),i.push(n)}return this.createCoreGroupFromObjects(i)}}nK.DEFAULT_PARAMS={active:!0,clipBias:.003,color:new D.a(1,1,1),opacity:1,pixelRatio:1,tblur:!1,blur:1,verticalBlurMult:1,tblur2:!1,blur2:1,verticalBlur2Mult:1},nK.INPUT_CLONED_STATE=Qi.NEVER;const iK=nK.DEFAULT_PARAMS;const rK=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(iK.active),this.clipBias=oa.FLOAT(iK.clipBias),this.color=oa.COLOR(iK.color.toArray()),this.opacity=oa.FLOAT(iK.opacity),this.pixelRatio=oa.INTEGER(iK.pixelRatio,{range:[1,4],rangeLocked:[!0,!1]}),this.tblur=oa.BOOLEAN(iK.tblur),this.blur=oa.FLOAT(iK.blur,{visibleIf:{tblur:1}}),this.verticalBlurMult=oa.FLOAT(iK.verticalBlurMult,{visibleIf:{tblur:1}}),this.tblur2=oa.BOOLEAN(iK.tblur2,{visibleIf:{tblur:1}}),this.blur2=oa.FLOAT(iK.blur2,{visibleIf:{tblur:1,tblur2:1}}),this.verticalBlur2Mult=oa.FLOAT(iK.verticalBlur2Mult,{visibleIf:{tblur:1,tblur2:1}})}};class sK extends gG{constructor(){super(...arguments),this.paramsConfig=rK}static type(){return\\\\\\\"reflector\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create a reflector from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(nK.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new nK(this._scene,this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var oK=n(85);var aK;!function(t){t.POINTS_COUNT=\\\\\\\"pointsCount\\\\\\\",t.SEGMENT_LENGTH=\\\\\\\"segmentLength\\\\\\\"}(aK||(aK={}));const lK=[aK.POINTS_COUNT,aK.SEGMENT_LENGTH];var cK;!function(t){t.CENTRIPETAL=\\\\\\\"centripetal\\\\\\\",t.CHORDAL=\\\\\\\"chordal\\\\\\\",t.CATMULLROM=\\\\\\\"catmullrom\\\\\\\"}(cK||(cK={}));const uK=[cK.CENTRIPETAL,cK.CHORDAL,cK.CATMULLROM];const hK=new class extends aa{constructor(){super(...arguments),this.method=oa.INTEGER(lK.indexOf(aK.POINTS_COUNT),{menu:{entries:lK.map(((t,e)=>({name:t,value:e})))}}),this.curveType=oa.INTEGER(uK.indexOf(cK.CATMULLROM),{range:[0,2],rangeLocked:[!0,!0],menu:{entries:uK.map(((t,e)=>({name:t,value:e})))}}),this.tension=oa.FLOAT(.01,{range:[0,1],rangeLocked:[!0,!0]}),this.pointsCount=oa.INTEGER(100,{visibleIf:{method:lK.indexOf(aK.POINTS_COUNT)},range:[1,1e3],rangeLocked:[!0,!1]}),this.segmentLength=oa.FLOAT(1,{visibleIf:{method:lK.indexOf(aK.SEGMENT_LENGTH)}})}};class dK extends gG{constructor(){super(...arguments),this.paramsConfig=hK}static type(){return\\\\\\\"resample\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}cook(t){const e=t[0],n=[];if(this.pv.pointsCount>=2){const t=e.coreObjects();for(let e=0;ei[t])),n=this._create_curve_from_points(e);n&&o.push(n)}const a=cs(o);return this.createObject(a,Sr.LINE_SEGMENTS)}_create_curve_from_points(t){if(t.length<=1)return;const e=t.map((t=>t.attribValue(\\\\\\\"position\\\\\\\"))),n=uK[this.pv.curveType],i=this.pv.tension,r=new oK.a(e,!1,n,i),s=this._get_points_from_curve(r);let o=[];const a=[];for(let t=0;t0&&(a.push(t-1),a.push(t))}const l=new S.a;return l.setAttribute(\\\\\\\"position\\\\\\\",new C.c(o.flat(),3)),l.setIndex(a),l}_get_points_from_curve(t){const e=lK[this.pv.method];switch(e){case aK.POINTS_COUNT:return t.getSpacedPoints(Math.max(2,this.pv.pointsCount));case aK.SEGMENT_LENGTH:var n=t.getLength(),i=0!==this.pv.segmentLength?1+n/this.pv.segmentLength:2;return i=Math.max(2,i),t.getSpacedPoints(i)}ar.unreachable(e)}}class pK extends pG{static type(){return\\\\\\\"restAttributes\\\\\\\"}cook(t,e){const n=t[0].objectsWithGeo();return e.tposition&&this._create_rest_attribute(n,e.position,e.restP),e.tnormal&&this._create_rest_attribute(n,e.normal,e.restN),this.createCoreGroupFromObjects(n)}_create_rest_attribute(t,e,n){for(let i of t){const t=i.geometry;if(t){const i=t.getAttribute(e);i&&t.setAttribute(n,i.clone())}}}}pK.DEFAULT_PARAMS={tposition:!0,position:\\\\\\\"position\\\\\\\",restP:\\\\\\\"restP\\\\\\\",tnormal:!0,normal:\\\\\\\"normal\\\\\\\",restN:\\\\\\\"restN\\\\\\\"};const _K=pK.DEFAULT_PARAMS;const mK=new class extends aa{constructor(){super(...arguments),this.tposition=oa.BOOLEAN(_K.tposition),this.position=oa.STRING(_K.position,{visibleIf:{tposition:!0}}),this.restP=oa.STRING(_K.restP,{visibleIf:{tposition:!0}}),this.tnormal=oa.BOOLEAN(_K.tnormal),this.normal=oa.STRING(_K.normal,{visibleIf:{tnormal:!0}}),this.restN=oa.STRING(_K.restN,{visibleIf:{tnormal:!0}})}};class fK extends gG{constructor(){super(...arguments),this.paramsConfig=mK}static type(){return\\\\\\\"restAttributes\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Qi.FROM_NODE])}cook(t){this._operation=this._operation||new pK(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const gK=new p.a;function vK(t,e,n,i,r,s){const o=2*Math.PI*r/4,a=Math.max(s-2*r,0),l=Math.PI/4;gK.copy(e),gK[i]=0,gK.normalize();const c=.5*o/(o+a),u=1-gK.angleTo(t)/l;if(1===Math.sign(gK[n]))return u*c;return a/(o+a)+c+c*(1-u)}class yK extends N{constructor(t=1,e=1,n=1,i=2,r=.1){if(i=2*i+1,r=Math.min(t/2,e/2,n/2,r),super(1,1,1,i,i,i),1===i)return;const s=this.toNonIndexed();this.index=null,this.attributes.position=s.attributes.position,this.attributes.normal=s.attributes.normal,this.attributes.uv=s.attributes.uv;const o=new p.a,a=new p.a,l=new p.a(t,e,n).divideScalar(2).subScalar(r),c=this.attributes.position.array,u=this.attributes.normal.array,h=this.attributes.uv.array,d=c.length/6,_=new p.a,m=.5/i;for(let i=0,s=0;io.get(t.index())||-1));let l=0;for(let t of a)s+=o.get(t.index()),r[l]=s,l++;const c=[];let u=[];e.transferAttributes&&(u=n.attribNamesMatchingMask(e.attributesToTransfer));const h=new Map,d=new Map;for(let t of u)h.set(t,[]),d.set(t,n.attribSize(t));const p=new Iq,_=2454*e.seed%Number.MAX_SAFE_INTEGER;await p.startWithCount(e.pointsCount,(t=>{const e=rs.randFloat(_+t)*s;for(let t=0;te/(t-1)));e.addIdnAttribute&&g.setAttribute(\\\\\\\"idn\\\\\\\",new C.a(new Float32Array(i),1))}const v=this.createObject(g,Sr.POINTS);return this.createCoreGroupFromObjects([v])}}AK.DEFAULT_PARAMS={pointsCount:100,seed:0,transferAttributes:!0,attributesToTransfer:\\\\\\\"normal\\\\\\\",addIdAttribute:!0,addIdnAttribute:!0},AK.INPUT_CLONED_STATE=Qi.FROM_NODE;const EK=AK.DEFAULT_PARAMS;const MK=new class extends aa{constructor(){super(...arguments),this.pointsCount=oa.INTEGER(EK.pointsCount,{range:[0,100],rangeLocked:[!0,!1]}),this.seed=oa.INTEGER(EK.seed,{range:[0,100],rangeLocked:[!1,!1]}),this.transferAttributes=oa.BOOLEAN(EK.transferAttributes),this.attributesToTransfer=oa.STRING(EK.attributesToTransfer,{visibleIf:{transferAttributes:1}}),this.addIdAttribute=oa.BOOLEAN(EK.addIdAttribute),this.addIdnAttribute=oa.BOOLEAN(EK.addIdnAttribute)}};class SK extends gG{constructor(){super(...arguments),this.paramsConfig=MK}static type(){return\\\\\\\"scatter\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to scatter points onto\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.NEVER)}async cook(t){this._operation=this._operation||new AK(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var CK;!function(t){t.MATRIX=\\\\\\\"matrix\\\\\\\",t.AXIS=\\\\\\\"axis\\\\\\\"}(CK||(CK={}));const NK=[CK.MATRIX,CK.AXIS];var LK;!function(t){t.BBOX_CENTER=\\\\\\\"bbox center\\\\\\\",t.BBOX_CENTER_OFFSET=\\\\\\\"bbox center offset\\\\\\\",t.CUSTOM=\\\\\\\"custom\\\\\\\"}(LK||(LK={}));const OK=[LK.BBOX_CENTER,LK.BBOX_CENTER_OFFSET,LK.CUSTOM];class RK extends pG{constructor(){super(...arguments),this._m4=new A.a,this._axisNormalized=new p.a,this._center=new p.a,this._pointPos=new p.a,this._axisPlane=new X.a,this._pointOnPlane=new p.a,this._delta=new p.a,this._deltaNormalized=new p.a,this._offset=new p.a}static type(){return\\\\\\\"shear\\\\\\\"}cook(t,e){const n=t[0].objects();return this._applyShear(n,e),t[0]}_applyShear(t,e){const n=NK[e.mode];switch(n){case CK.MATRIX:return this._applyMatrixShear(t,e);case CK.AXIS:return this._applyAxisShear(t,e)}ar.unreachable(n)}_applyMatrixShear(t,e){this._m4.makeShear(e.xy,e.xz,e.yx,e.yz,e.zx,e.zy);for(let e of t){const t=e.geometry;t&&t.applyMatrix4(this._m4)}}_applyAxisShear(t,e){this._axisNormalized.copy(e.axis),this._axisNormalized.normalize();for(let n of t){const t=n.geometry;if(t){this._getAxisModeCenter(t,e),this._axisPlane.setFromNormalAndCoplanarPoint(e.planeAxis,this._center);const n=new ps(t).points();for(let t of n){t.getPosition(this._pointPos),this._axisPlane.projectPoint(this._pointPos,this._pointOnPlane),this._delta.copy(this._pointOnPlane).sub(this._pointPos);const n=this._delta.length();this._deltaNormalized.copy(this._delta).normalize(),this._offset.copy(this._axisNormalized).multiplyScalar(e.axisAmount*n),this._delta.dot(e.planeAxis)>0&&this._offset.multiplyScalar(-1),this._pointPos.add(this._offset),t.setPosition(this._pointPos)}}}}_getAxisModeCenter(t,e){const n=OK[e.centerMode];switch(n){case LK.BBOX_CENTER:return this._getAxisModeCenterBbox(t,e);case LK.BBOX_CENTER_OFFSET:return this._getAxisModeCenterBboxOffset(t,e);case LK.CUSTOM:return this._getAxisModeCenterCustom(e)}ar.unreachable(n)}_getAxisModeCenterBbox(t,e){t.computeBoundingBox();const n=t.boundingBox;n?n.getCenter(this._center):this._center.set(0,0,0)}_getAxisModeCenterBboxOffset(t,e){this._getAxisModeCenterBbox(t,e),this._center.add(e.centerOffset)}_getAxisModeCenterCustom(t){return this._center.copy(t.center)}}var PK;RK.DEFAULT_PARAMS={mode:NK.indexOf(CK.AXIS),xy:0,xz:0,yx:0,yz:0,zx:0,zy:0,centerMode:OK.indexOf(LK.BBOX_CENTER),centerOffset:new p.a(0,0,0),center:new p.a(0,0,0),planeAxis:new p.a(0,0,1),axis:new p.a(0,1,0),axisAmount:0},RK.INPUT_CLONED_STATE=Qi.FROM_NODE,function(t){t.SHEAR=\\\\\\\"shear\\\\\\\",t.TRANSFORM=\\\\\\\"transform\\\\\\\",t.UV_LAYOUT=\\\\\\\"uvLayout\\\\\\\",t.UV_TRANSFORM=\\\\\\\"uvTransform\\\\\\\",t.UV_UNWRAP=\\\\\\\"uvUnwrap\\\\\\\"}(PK||(PK={}));const IK=RK.DEFAULT_PARAMS;const FK=new class extends aa{constructor(){super(...arguments),this.mode=oa.INTEGER(IK.mode,{menu:{entries:NK.map(((t,e)=>({name:t,value:e})))}}),this.xy=oa.FLOAT(IK.xy,{visibleIf:{mode:NK.indexOf(CK.MATRIX)}}),this.xz=oa.FLOAT(IK.xz,{visibleIf:{mode:NK.indexOf(CK.MATRIX)}}),this.yx=oa.FLOAT(IK.yx,{visibleIf:{mode:NK.indexOf(CK.MATRIX)}}),this.yz=oa.FLOAT(IK.yz,{visibleIf:{mode:NK.indexOf(CK.MATRIX)}}),this.zx=oa.FLOAT(IK.zx,{visibleIf:{mode:NK.indexOf(CK.MATRIX)}}),this.zy=oa.FLOAT(IK.zy,{visibleIf:{mode:NK.indexOf(CK.MATRIX)}}),this.centerMode=oa.INTEGER(IK.centerMode,{visibleIf:{mode:NK.indexOf(CK.AXIS)},menu:{entries:OK.map(((t,e)=>({name:t,value:e})))}}),this.centerOffset=oa.VECTOR3(IK.centerOffset.toArray(),{visibleIf:{mode:NK.indexOf(CK.AXIS),centerMode:OK.indexOf(LK.BBOX_CENTER_OFFSET)}}),this.center=oa.VECTOR3(IK.center.toArray(),{visibleIf:{mode:NK.indexOf(CK.AXIS),centerMode:OK.indexOf(LK.CUSTOM)}}),this.planeAxis=oa.VECTOR3(IK.planeAxis.toArray(),{visibleIf:{mode:NK.indexOf(CK.AXIS)}}),this.axis=oa.VECTOR3(IK.axis.toArray(),{visibleIf:{mode:NK.indexOf(CK.AXIS)}}),this.axisAmount=oa.FLOAT(IK.axisAmount,{range:[-1,1],visibleIf:{mode:NK.indexOf(CK.AXIS)}})}};class DK extends gG{constructor(){super(...arguments),this.paramsConfig=FK}static type(){return PK.SHEAR}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(RK.INPUT_CLONED_STATE)}setMode(t){this.p.mode.set(NK.indexOf(t))}cook(t){this._operation=this._operation||new RK(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const kK=new class extends aa{};class BK extends gG{constructor(){super(...arguments),this.paramsConfig=kK}static type(){return\\\\\\\"skin\\\\\\\"}static displayedInputNames(){return[\\\\\\\"lines to create polygons from\\\\\\\",\\\\\\\"if used, lines from both inputs will be used\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2)}cook(t){switch(f.compact(this.io.inputs.inputs()).length){case 1:return this.process_one_input(t);case 2:return this.process_two_inputs(t);default:return this.states.error.set(\\\\\\\"inputs count not valid\\\\\\\")}}process_one_input(t){const e=t[0],n=this._get_line_segments(e),i=[];if(n){const t=n[0];if(t){const e=UQ.line_segment_to_geometries(t.geometry);e.forEach(((t,n)=>{if(n>0){const r=e[n-1],s=this._skin(r,t);i.push(s)}}))}}this.setGeometries(i)}process_two_inputs(t){const e=t[0],n=t[1],i=this._get_line_segments(e),r=this._get_line_segments(n),s=f.sortBy([i,r],(t=>-t.length)),o=s[0],a=s[1],l=[];o.forEach(((t,e)=>{const n=a[e];if(null!=t&&null!=n){const e=t.geometry,i=n.geometry,r=this._skin(e,i);l.push(r)}})),this.setGeometries(l)}_get_line_segments(t){return t.objects().filter((t=>t.isLineSegments))}_skin(t,e){const n=new S.a;return new GQ(n,t,e).process(),n}}var zK;!function(t){t.X=\\\\\\\"x\\\\\\\",t.Y=\\\\\\\"y\\\\\\\",t.Z=\\\\\\\"z\\\\\\\"}(zK||(zK={}));const UK=[zK.X,zK.Y,zK.Z];class GK extends pG{constructor(){super(...arguments),this._pointPos=new p.a,this._positions=[],this._indicesByPos=new Map,this._indexDest=new Map,this._debugActive=!1}static type(){return\\\\\\\"sort\\\\\\\"}cook(t,e){const n=t[0],i=n.objectsWithGeo();for(let t of i)this._sortObject(t,e);return n}_debug(t){this._debugActive}_sortObject(t,e){const n=new vs(t,0).points(),i=t.geometry.getIndex();if(!i)return void console.warn(\\\\\\\"geometry cannot be sorted since it has no index\\\\\\\");const r=i.array;this._positions=new Array(n.length),this._indicesByPos.clear(),this._indexDest.clear();const s=UK[e.axis];let o=0,a=0;for(let t of n){switch(t.getPosition(this._pointPos),s){case zK.X:o=this._pointPos.x;break;case zK.Y:o=this._pointPos.y;break;case zK.Z:o=this._pointPos.z}this._positions[a]=o,u.pushOnArrayAtEntry(this._indicesByPos,o,t.index()),a++}let l=this._positions.sort(((t,e)=>t-e));e.invert&&l.reverse();const c=new Array(n.length);a=0;const h=f.uniq(l);for(let t of h){const e=this._indicesByPos.get(t);if(e)for(let t of e)c[a]=t,this._indexDest.set(t,a),a++}const d=new Array(r.length);for(let t=0;t ${e}`),null!=e)for(let n=0;n({name:t,value:e})))}}),this.invert=oa.BOOLEAN(VK.invert)}};class jK extends gG{constructor(){super(...arguments),this.paramsConfig=HK}static type(){return\\\\\\\"sort\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to sort\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Qi.FROM_NODE])}cook(t){this._operation=this._operation||new GK(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const WK=new class extends aa{constructor(){super(...arguments),this.startFrame=oa.INTEGER(Ml.START_FRAME)}};class qK extends xG{constructor(){super(...arguments),this.paramsConfig=WK,this._last_simulated_frame=null,this.childrenDisplayController=new wG(this,{dependsOnDisplayNode:!1}),this.displayNodeController=new Lm(this,{onDisplayNodeRemove:()=>{},onDisplayNodeSet:()=>{},onDisplayNodeUpdate:()=>{}},{dependsOnDisplayNode:!1})}static type(){return\\\\\\\"solver\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4),this.io.inputs.initInputsClonedState(Qi.NEVER),this.addGraphInput(this.scene().timeController.graphNode)}previousFrameCoreGroup(){return this._previousFrameCoreGroup}async cook(t){this.pv.startFrame==this.scene().frame()&&this._reset(),this.computeSolverIfRequired()}_reset(){this._previousFrameCoreGroup=void 0,this._last_simulated_frame=null}computeSolverIfRequired(){const t=this.scene().frame(),e=this.pv.startFrame;t>=e&&(null==this._last_simulated_frame&&(this._last_simulated_frame=e-1),t>this._last_simulated_frame&&this._computeSolverMultipleTimes(t-this._last_simulated_frame))}_computeSolverMultipleTimes(t=1){for(let e=0;e({name:t,value:JK[t]})))}}),this.radius=oa.FLOAT(KK.radius,{visibleIf:{type:JK.default}}),this.resolution=oa.VECTOR2(KK.resolution,{visibleIf:{type:JK.default}}),this.open=oa.BOOLEAN(KK.open,{visibleIf:{type:JK.default}}),this.phiStart=oa.FLOAT(KK.phiStart,{range:[0,2*Math.PI],visibleIf:{type:JK.default,open:!0}}),this.phiLength=oa.FLOAT(\\\\\\\"$PI*2\\\\\\\",{range:[0,2*Math.PI],visibleIf:{type:JK.default,open:!0}}),this.thetaStart=oa.FLOAT(KK.thetaStart,{range:[0,Math.PI],visibleIf:{type:JK.default,open:!0}}),this.thetaLength=oa.FLOAT(\\\\\\\"$PI\\\\\\\",{range:[0,Math.PI],visibleIf:{type:JK.default,open:!0}}),this.detail=oa.INTEGER(KK.detail,{range:[0,5],rangeLocked:[!0,!1],visibleIf:{type:JK.isocahedron}}),this.center=oa.VECTOR3(KK.center)}};class e0 extends gG{constructor(){super(...arguments),this.paramsConfig=t0}static type(){return\\\\\\\"sphere\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(QK.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new QK(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const n0=new class extends aa{constructor(){super(...arguments),this.attribType=oa.INTEGER(kr.indexOf(Dr.NUMERIC),{menu:{entries:Br}}),this.attribName=oa.STRING(\\\\\\\"\\\\\\\")}};class i0 extends gG{constructor(){super(...arguments),this.paramsConfig=n0,this._new_objects=[]}static type(){return\\\\\\\"split\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to split in multiple objects\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1)}async cook(t){const e=t[0];this._new_objects=[],\\\\\\\"\\\\\\\"!=this.pv.attribName&&this._split_core_group(e),this.setObjects(this._new_objects)}async _split_core_group(t){const e=t.coreObjects();for(let t of e)this._split_core_object(t)}_split_core_object(t){let e=t.coreGeometry(),n=this.pv.attribName,i=new Map;if(e){const r=t.object(),s=e.pointsFromGeometry(),o=s[0];if(o){if(o.attribSize(n)!=zr.FLOAT&&!o.isAttribIndexed(n))return void this.states.error.set(`attrib '${n}' must be a float or a string`);let t;if(o.isAttribIndexed(n))for(let e of s)t=e.indexedAttribValue(n),u.pushOnArrayAtEntry(i,t,e);else for(let e of s)t=e.attribValue(n),u.pushOnArrayAtEntry(i,t,e)}const a=Nr(r.constructor);i.forEach(((t,e)=>{const i=ps.geometryFromPoints(t,a);if(i){const t=this.createObject(i,a);vs.addAttribute(t,n,e),this._new_objects.push(t)}}))}}}const r0=new A.a,s0=new Q.a,o0=new p.a;class a0 extends $.a{constructor(){super(),this.uuid=Ln.h(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Geometry\\\\\\\",this.vertices=[],this.colors=[],this.faces=[],this.faceVertexUvs=[[]],this.morphTargets=[],this.morphNormals=[],this.skinWeights=[],this.skinIndices=[],this.lineDistances=[],this.boundingBox=null,this.boundingSphere=null,this.elementsNeedUpdate=!1,this.verticesNeedUpdate=!1,this.uvsNeedUpdate=!1,this.normalsNeedUpdate=!1,this.colorsNeedUpdate=!1,this.lineDistancesNeedUpdate=!1,this.groupsNeedUpdate=!1}applyMatrix4(t){const e=(new U.a).getNormalMatrix(t);for(let e=0,n=this.vertices.length;e0)for(let t=0;t0&&(this.normalsNeedUpdate=!0)}computeFlatVertexNormals(){this.computeFaceNormals();for(let t=0,e=this.faces.length;t0&&(this.normalsNeedUpdate=!0)}computeMorphNormals(){for(let t=0,e=this.faces.length;t=0;t--){const e=s[t];this.faces.splice(e,1);for(let t=0,n=this.faceVertexUvs.length;t0,a=e.vertexNormals.length>0,l=1!==e.color.r||1!==e.color.g||1!==e.color.b,p=e.vertexColors.length>0;let _=0;if(_=c(_,0,0),_=c(_,1,i),_=c(_,2,r),_=c(_,3,s),_=c(_,4,o),_=c(_,5,a),_=c(_,6,l),_=c(_,7,p),n.push(_),n.push(e.a,e.b,e.c),n.push(e.materialIndex),s){const e=this.faceVertexUvs[0][t];n.push(d(e[0]),d(e[1]),d(e[2]))}if(o&&n.push(u(e.normal)),a){const t=e.vertexNormals;n.push(u(t[0]),u(t[1]),u(t[2]))}if(l&&n.push(h(e.color)),p){const t=e.vertexColors;n.push(h(t[0]),h(t[1]),h(t[2]))}}function c(t,e,n){return n?t|1<0&&(t.data.colors=s),a.length>0&&(t.data.uvs=[a]),t.data.faces=n,t}clone(){return(new a0).copy(this)}copy(t){this.vertices=[],this.colors=[],this.faces=[],this.faceVertexUvs=[[]],this.morphTargets=[],this.morphNormals=[],this.skinWeights=[],this.skinIndices=[],this.lineDistances=[],this.boundingBox=null,this.boundingSphere=null,this.name=t.name;const e=t.vertices;for(let t=0,n=e.length;t0){const n=new Float32Array(3*t.normals.length);e.setAttribute(\\\\\\\"normal\\\\\\\",new C.a(n,3).copyVector3sArray(t.normals))}if(t.colors.length>0){const n=new Float32Array(3*t.colors.length);e.setAttribute(\\\\\\\"color\\\\\\\",new C.a(n,3).copyColorsArray(t.colors))}if(t.uvs.length>0){const n=new Float32Array(2*t.uvs.length);e.setAttribute(\\\\\\\"uv\\\\\\\",new C.a(n,2).copyVector2sArray(t.uvs))}if(t.uvs2.length>0){const n=new Float32Array(2*t.uvs2.length);e.setAttribute(\\\\\\\"uv2\\\\\\\",new C.a(n,2).copyVector2sArray(t.uvs2))}e.groups=t.groups;for(const n in t.morphTargets){const i=[],r=t.morphTargets[n];for(let t=0,e=r.length;t0){const n=new C.c(4*t.skinIndices.length,4);e.setAttribute(\\\\\\\"skinIndex\\\\\\\",n.copyVector4sArray(t.skinIndices))}if(t.skinWeights.length>0){const n=new C.c(4*t.skinWeights.length,4);e.setAttribute(\\\\\\\"skinWeight\\\\\\\",n.copyVector4sArray(t.skinWeights))}return null!==t.boundingSphere&&(e.boundingSphere=t.boundingSphere.clone()),null!==t.boundingBox&&(e.boundingBox=t.boundingBox.clone()),e}computeTangents(){console.error(\\\\\\\"THREE.Geometry: .computeTangents() has been removed.\\\\\\\")}computeLineDistances(){console.error(\\\\\\\"THREE.Geometry: .computeLineDistances() has been removed. Use THREE.Line.computeLineDistances() instead.\\\\\\\")}applyMatrix(t){return console.warn(\\\\\\\"THREE.Geometry: .applyMatrix() has been renamed to .applyMatrix4().\\\\\\\"),this.applyMatrix4(t)}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}static createBufferGeometryFromObject(t){let e=new S.a;const n=t.geometry;if(t.isPoints||t.isLine){const t=new C.c(3*n.vertices.length,3),i=new C.c(3*n.colors.length,3);if(e.setAttribute(\\\\\\\"position\\\\\\\",t.copyVector3sArray(n.vertices)),e.setAttribute(\\\\\\\"color\\\\\\\",i.copyColorsArray(n.colors)),n.lineDistances&&n.lineDistances.length===n.vertices.length){const t=new C.c(n.lineDistances.length,1);e.setAttribute(\\\\\\\"lineDistance\\\\\\\",t.copyArray(n.lineDistances))}null!==n.boundingSphere&&(e.boundingSphere=n.boundingSphere.clone()),null!==n.boundingBox&&(e.boundingBox=n.boundingBox.clone())}else t.isMesh&&(e=n.toBufferGeometry());return e}}a0.prototype.isGeometry=!0;class l0{constructor(){this.vertices=[],this.normals=[],this.colors=[],this.uvs=[],this.uvs2=[],this.groups=[],this.morphTargets={},this.skinWeights=[],this.skinIndices=[],this.boundingBox=null,this.boundingSphere=null,this.verticesNeedUpdate=!1,this.normalsNeedUpdate=!1,this.colorsNeedUpdate=!1,this.uvsNeedUpdate=!1,this.groupsNeedUpdate=!1}computeGroups(t){const e=[];let n,i,r;const s=t.faces;for(i=0;i0,s=i[1]&&i[1].length>0,o=t.morphTargets,a=o.length;let l;if(a>0){l=[];for(let t=0;t0){h=[];for(let t=0;t0&&0===e.length&&console.error(\\\\\\\"THREE.DirectGeometry: Faceless geometries are not supported.\\\\\\\");for(let t=0;t0;)this.smooth(t);return t.computeFaceNormals(),t.computeVertexNormals(),e?t.toBufferGeometry():t},function(){var t=[\\\\\\\"a\\\\\\\",\\\\\\\"b\\\\\\\",\\\\\\\"c\\\\\\\"];function e(t,e,n){return n[Math.min(t,e)+\\\\\\\"_\\\\\\\"+Math.max(t,e)]}function n(t,e,n,i,r,s){var o,a=Math.min(t,e),l=Math.max(t,e),c=a+\\\\\\\"_\\\\\\\"+l;c in i?o=i[c]:(o={a:n[a],b:n[l],newEdge:null,faces:[]},i[c]=o);o.faces.push(r),s[t].edges.push(o),s[e].edges.push(o)}function i(t,e,n,i,r){t.push(new c0(e,n,i,void 0,void 0,r))}function r(t,e){return Math.abs(e-t)/2+Math.min(t,e)}function s(t,e,n,i){t.push([e.clone(),n.clone(),i.clone()])}u0.prototype.smooth=function(o){var a,l,c,u,h,_,m,f,g,v,y,x,b,w=new p.a,T=[];a=o.vertices,l=o.faces;var A,E,M,S,C,N,L,O,R,P,I,F,D,k,B=void 0!==(c=o.faceVertexUvs)[0]&&c[0].length>0;if(B)for(var z=0;z3&&(O=3/(8*_)),R=1-_*O,P=O,_<=2&&2==_&&(R=3/4,P=1/8),k=D.clone().multiplyScalar(R),w.set(0,0,0),z=0;z<_;z++)A=(I=F[z]).a!==D?I.a:I.b,w.add(A);w.multiplyScalar(P),k.add(w),b.push(k)}u=b.concat(x);var U,G,V,H,j,W,q,X=b.length;h=[];var Y=new d.a,$=new d.a,J=new d.a;for(m=0,f=l.length;m{v0.PARAM_CALLBACK_reset(t)}})}};class v0 extends gG{constructor(){super(...arguments),this.paramsConfig=g0}static type(){return er.INPUT}initializeNode(){this.io.inputs.setCount(0),this.lifecycle.add_on_add_hook((()=>{this.set_parent_input_dependency()}))}async cook(){const t=this.pv.input,e=this.parent();if(e){if(e.io.inputs.has_input(t)){const n=await e.containerController.requestInputContainer(t);if(n){const t=n.coreContent();if(t)return void this.setCoreGroup(t)}}else this.states.error.set(`parent has no input ${t}`);this.cookController.endCook()}else this.states.error.set(\\\\\\\"subnet input has no parent\\\\\\\")}static PARAM_CALLBACK_reset(t){t.set_parent_input_dependency()}set_parent_input_dependency(){this._current_parent_input_graph_node&&this.removeGraphInput(this._current_parent_input_graph_node);const t=this.parent();t&&(this._current_parent_input_graph_node=t.io.inputs.input_graph_node(this.pv.input),this.addGraphInput(this._current_parent_input_graph_node))}}var y0=n(82);class x0 extends jg{constructor(t,e,n){super(t,e,n)}load(t){return new Promise((async(e,n)=>{const i=new y0.a(this.loadingManager),r=await this._urlToLoad();i.load(r,(i=>{try{const n=this._onLoaded(i,t);e(n)}catch(t){n([])}}))}))}parse(t,e){const n=new y0.a(this.loadingManager).parse(t);return this._onLoaded(n,e)}_onLoaded(t,e){const n=t.paths,i=new In.a;for(let t=0;t{const i=await n.load(e);for(let t of i.children)this._ensure_geometry_has_index(t);t(this.createCoreGroupFromObjects(i.children))}))}_ensure_geometry_has_index(t){const e=t.geometry;e&&this.createIndexIfNone(e)}}w0.DEFAULT_PARAMS={url:b0,drawFillShapes:!0,fillShapesWireframe:!1,drawStrokes:!0,strokesWireframe:!1};const T0=w0.DEFAULT_PARAMS;const A0=new class extends aa{constructor(){super(...arguments),this.url=oa.STRING(T0.url,{fileBrowse:{type:[Ls.SVG]}}),this.reload=oa.BUTTON(null,{callback:(t,e)=>{E0.PARAM_CALLBACK_reload(t)}}),this.drawFillShapes=oa.BOOLEAN(T0.drawFillShapes),this.fillShapesWireframe=oa.BOOLEAN(T0.fillShapesWireframe),this.drawStrokes=oa.BOOLEAN(T0.drawStrokes),this.strokesWireframe=oa.BOOLEAN(T0.strokesWireframe)}};class E0 extends gG{constructor(){super(...arguments),this.paramsConfig=A0}static type(){return\\\\\\\"svg\\\\\\\"}async requiredModules(){return[Vn.SVGLoader]}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const t=this.pv.url;if(t){const e=t.split(\\\\\\\"/\\\\\\\");return e[e.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){this._operation=this._operation||new w0(this.scene(),this.states,this);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}static PARAM_CALLBACK_reload(t){t.param_callback_reload()}param_callback_reload(){this.p.url.setDirty()}}const M0=\\\\\\\"geometry to switch to\\\\\\\";const S0=new class extends aa{constructor(){super(...arguments),this.input=oa.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class C0 extends gG{constructor(){super(...arguments),this.paramsConfig=S0}static type(){return\\\\\\\"switch\\\\\\\"}static displayedInputNames(){return[M0,M0,M0,M0]}initializeNode(){this.io.inputs.setCount(0,4),this.io.inputs.initInputsClonedState(Qi.NEVER),this.cookController.disallowInputsEvaluation()}async cook(){const t=this.pv.input;if(this.io.inputs.has_input(t)){const e=await this.containerController.requestInputContainer(t);if(e){const t=e.coreContent();if(t)return void this.setCoreGroup(t)}}else this.states.error.set(`no input ${t}`);this.cookController.endCook()}}class N0 extends nZ{constructor(t,e,n){super([1,1,1,-1,-1,1,-1,1,-1,1,-1,-1],[2,1,0,0,3,2,1,3,0,2,3,1],t,e,n),this.type=\\\\\\\"TetrahedronBufferGeometry\\\\\\\",this.parameters={radius:t,detail:e}}}const L0=new class extends aa{constructor(){super(...arguments),this.radius=oa.FLOAT(1),this.detail=oa.INTEGER(0,{range:[0,10],rangeLocked:[!0,!1]}),this.pointsOnly=oa.BOOLEAN(0),this.center=oa.VECTOR3([0,0,0])}};class O0 extends gG{constructor(){super(...arguments),this.paramsConfig=L0}static type(){return\\\\\\\"tetrahedron\\\\\\\"}cook(){const t=this.pv.pointsOnly,e=new N0(this.pv.radius,this.pv.detail,t);if(e.translate(this.pv.center.x,this.pv.center.y,this.pv.center.z),t){const t=this.createObject(e,Sr.POINTS);this.setObject(t)}else e.computeVertexNormals(),this.setGeometry(e)}}class R0 extends YX{constructor(t,e={}){const n=e.font;if(!n||!n.isFont)return new S.a;const i=n.generateShapes(t,e.size);e.depth=void 0!==e.height?e.height:50,void 0===e.bevelThickness&&(e.bevelThickness=10),void 0===e.bevelSize&&(e.bevelSize=8),void 0===e.bevelEnabled&&(e.bevelEnabled=!1),super(i,e),this.type=\\\\\\\"TextGeometry\\\\\\\"}}var P0=n(48);class I0 extends kf.a{constructor(t){super(t)}load(t,e,n,i){const r=this,s=new Df.a(this.manager);s.setPath(this.path),s.setRequestHeader(this.requestHeader),s.setWithCredentials(r.withCredentials),s.load(t,(function(t){let n;try{n=JSON.parse(t)}catch(e){console.warn(\\\\\\\"THREE.FontLoader: typeface.js support is being deprecated. Use typeface.json instead.\\\\\\\"),n=JSON.parse(t.substring(65,t.length-2))}const i=r.parse(n);e&&e(i)}),n,i)}parse(t){return new F0(t)}}class F0{constructor(t){this.type=\\\\\\\"Font\\\\\\\",this.data=t}generateShapes(t,e=100){const n=[],i=function(t,e,n){const i=Array.from(t),r=e/n.resolution,s=(n.boundingBox.yMax-n.boundingBox.yMin+n.underlineThickness)*r,o=[];let a=0,l=0;for(let t=0;t{const i=await this._loadTTFLoader();i&&i.load(t,(t=>{const n=this._font_loader.parse(t);e(n)}),void 0,(()=>{n()}))}))}_loadJSON(t){return new Promise(((e,n)=>{this._font_loader.load(t,(t=>{e(t)}),void 0,(()=>{n()}))}))}async _loadTTFLoader(){const t=await ai.modulesRegister.module(Vn.TTFLoader);if(t)return new t(this.loadingManager)}static async loadSVGLoader(){const t=await ai.modulesRegister.module(Vn.SVGLoader);if(t)return t}}var B0;!function(t){t.MESH=\\\\\\\"mesh\\\\\\\",t.FLAT=\\\\\\\"flat\\\\\\\",t.LINE=\\\\\\\"line\\\\\\\",t.STROKE=\\\\\\\"stroke\\\\\\\"}(B0||(B0={}));const z0=[B0.MESH,B0.FLAT,B0.LINE,B0.STROKE],U0=\\\\\\\"failed to generate geometry. Try to remove some characters\\\\\\\";const G0=new class extends aa{constructor(){super(...arguments),this.font=oa.STRING(\\\\\\\"https://raw.githubusercontent.com/polygonjs/polygonjs-assets/master/fonts/droid_sans_regular.typeface.json\\\\\\\",{fileBrowse:{type:[Ls.FONT]}}),this.text=oa.STRING(\\\\\\\"polygonjs\\\\\\\",{multiline:!0}),this.type=oa.INTEGER(0,{menu:{entries:z0.map(((t,e)=>({name:t,value:e})))}}),this.size=oa.FLOAT(1,{range:[0,1],rangeLocked:[!0,!1]}),this.extrude=oa.FLOAT(.1,{visibleIf:{type:z0.indexOf(B0.MESH)}}),this.segments=oa.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1],visibleIf:{type:z0.indexOf(B0.MESH)}}),this.strokeWidth=oa.FLOAT(.02,{visibleIf:{type:z0.indexOf(B0.STROKE)}})}};class V0 extends gG{constructor(){super(...arguments),this.paramsConfig=G0,this._loaded_fonts={}}static type(){return\\\\\\\"text\\\\\\\"}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.text],(()=>this.p.text.rawInput()))}))}))}async cook(){try{this._loaded_fonts[this.pv.font]=this._loaded_fonts[this.pv.font]||await this._loadFont()}catch(t){return void this.states.error.set(`count not load font (${this.pv.font})`)}const t=this._loaded_fonts[this.pv.font];if(t)switch(z0[this.pv.type]){case B0.MESH:return this._create_geometry_from_type_mesh(t);case B0.FLAT:return this._create_geometry_from_type_flat(t);case B0.LINE:return this._create_geometry_from_type_line(t);case B0.STROKE:return this._create_geometry_from_type_stroke(t);default:console.warn(\\\\\\\"type is not valid\\\\\\\")}}_create_geometry_from_type_mesh(t){const e=this.displayed_text(),n={font:t,size:this.pv.size,height:this.pv.extrude,curveSegments:this.pv.segments};try{const t=new R0(e,n);if(!t.index){const e=t.getAttribute(\\\\\\\"position\\\\\\\").array;t.setIndex(f.range(e.length/3))}this.setGeometry(t)}catch(t){this.states.error.set(U0)}}_create_geometry_from_type_flat(t){const e=this._get_shapes(t);if(e){var n=new KX(e);this.setGeometry(n)}}_create_geometry_from_type_line(t){const e=this.shapes_from_font(t);if(e){const t=[],n=[];let i=0;for(let r=0;r0&&e0)for(let e=0;e{const n=t.material;n&&(m.isArray(n)||r||(r=n[e.textureName]))}));if(r)for(let t of n.objects())t.traverse((t=>{const n=t.material;if(n&&!m.isArray(n)){n[e.textureName]=r;const t=n.uniforms;if(t){const n=t[e.textureName];n&&(n.value=r)}n.needsUpdate=!0}}));return n}}H0.DEFAULT_PARAMS={textureName:\\\\\\\"map\\\\\\\"},H0.INPUT_CLONED_STATE=[Qi.FROM_NODE,Qi.NEVER];const j0=H0.DEFAULT_PARAMS;const W0=new class extends aa{constructor(){super(...arguments),this.textureName=oa.STRING(j0.textureName)}};class q0 extends gG{constructor(){super(...arguments),this.paramsConfig=W0}static type(){return\\\\\\\"TextureCopy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to copy textures to\\\\\\\",\\\\\\\"objects to copy textures from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState(H0.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new H0(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class X0 extends pG{static type(){return\\\\\\\"textureProperties\\\\\\\"}async cook(t,e){const n=t[0],i=[];for(let t of n.objects())e.applyToChildren?t.traverse((t=>{i.push(t)})):i.push(t);const r=i.map((t=>this._update_object(t,e)));return await Promise.all(r),n}async _update_object(t,e){const n=t.material;n&&await this._update_material(n,e)}async _update_material(t,e){let n=t.map;n&&await this._update_texture(n,e)}async _update_texture(t,e){this._updateEncoding(t,e),this._updateMapping(t,e),this._updateWrap(t,e),await this._updateAnisotropy(t,e),this._updateFilter(t,e)}_updateEncoding(t,e){e.tencoding&&(t.encoding=e.encoding,t.needsUpdate=!0)}_updateMapping(t,e){e.tmapping&&(t.mapping=e.mapping)}_updateWrap(t,e){e.twrap&&(t.wrapS=e.wrapS,t.wrapT=e.wrapT)}async _updateAnisotropy(t,e){if(e.tanisotropy)if(e.useRendererMaxAnisotropy){const e=await ai.renderersController.firstRenderer();e&&(t.anisotropy=e.capabilities.getMaxAnisotropy())}else t.anisotropy=e.anisotropy}_updateFilter(t,e){e.tminFilter&&(t.minFilter=e.minFilter),e.tmagFilter&&(t.magFilter=e.magFilter)}}X0.DEFAULT_PARAMS={applyToChildren:!1,tencoding:!1,encoding:w.U,tmapping:!1,mapping:w.Yc,twrap:!1,wrapS:w.wc,wrapT:w.wc,tanisotropy:!1,useRendererMaxAnisotropy:!1,anisotropy:2,tminFilter:!1,minFilter:Xm,tmagFilter:!1,magFilter:qm},X0.INPUT_CLONED_STATE=Qi.FROM_NODE;const Y0=X0.DEFAULT_PARAMS;const $0=new class extends aa{constructor(){super(...arguments),this.applyToChildren=oa.BOOLEAN(Y0.applyToChildren,{separatorAfter:!0}),this.tencoding=oa.BOOLEAN(Y0.tencoding),this.encoding=oa.INTEGER(Y0.encoding,{visibleIf:{tencoding:1},menu:{entries:eg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.tmapping=oa.BOOLEAN(Y0.tmapping),this.mapping=oa.INTEGER(Y0.mapping,{visibleIf:{tmapping:1},menu:{entries:ig.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.twrap=oa.BOOLEAN(Y0.twrap),this.wrapS=oa.INTEGER(Y0.wrapS,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.wrapT=oa.INTEGER(Y0.wrapT,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},separatorAfter:!0}),this.tanisotropy=oa.BOOLEAN(Y0.tanisotropy),this.useRendererMaxAnisotropy=oa.BOOLEAN(Y0.useRendererMaxAnisotropy,{visibleIf:{tanisotropy:1}}),this.anisotropy=oa.INTEGER(Y0.anisotropy,{visibleIf:{tanisotropy:1,useRendererMaxAnisotropy:0},range:[0,32],rangeLocked:[!0,!1]}),this.tminFilter=oa.BOOLEAN(0),this.minFilter=oa.INTEGER(Y0.minFilter,{visibleIf:{tminFilter:1},menu:{entries:$m}}),this.tmagFilter=oa.BOOLEAN(0),this.magFilter=oa.INTEGER(Y0.magFilter,{visibleIf:{tmagFilter:1},menu:{entries:Ym}})}};class J0 extends gG{constructor(){super(...arguments),this.paramsConfig=$0}static type(){return\\\\\\\"textureProperties\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects with textures to change properties of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(X0.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new X0(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const Z0=new p.a(0,0,1);class Q0 extends pG{constructor(){super(...arguments),this._core_transform=new Mz}static type(){return\\\\\\\"torus\\\\\\\"}cook(t,e){const n=e.radius,i=e.radiusTube,r=e.segmentsRadial,s=e.segmentsTube,o=new eY(n,i,r,s);return o.translate(e.center.x,e.center.y,e.center.z),this._core_transform.rotate_geometry(o,Z0,e.direction),this.createCoreGroupFromGeometry(o)}}Q0.DEFAULT_PARAMS={radius:1,radiusTube:1,segmentsRadial:20,segmentsTube:12,direction:new p.a(0,1,0),center:new p.a(0,0,0)},Q0.INPUT_CLONED_STATE=Qi.FROM_NODE;const K0=Q0.DEFAULT_PARAMS;const t1=new class extends aa{constructor(){super(...arguments),this.radius=oa.FLOAT(K0.radius,{range:[0,1]}),this.radiusTube=oa.FLOAT(K0.radiusTube,{range:[0,1]}),this.segmentsRadial=oa.INTEGER(K0.segmentsRadial,{range:[1,50],rangeLocked:[!0,!1]}),this.segmentsTube=oa.INTEGER(K0.segmentsTube,{range:[1,50],rangeLocked:[!0,!1]}),this.direction=oa.VECTOR3(K0.direction),this.center=oa.VECTOR3(K0.center)}};class e1 extends gG{constructor(){super(...arguments),this.paramsConfig=t1}static type(){return\\\\\\\"torus\\\\\\\"}cook(t){this._operation=this._operation||new Q0(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class n1 extends pG{static type(){return\\\\\\\"torusKnot\\\\\\\"}cook(t,e){const n=e.radius,i=e.radiusTube,r=e.segmentsRadial,s=e.segmentsTube,o=e.p,a=e.q,l=new nY(n,i,r,s,o,a);return l.translate(e.center.x,e.center.y,e.center.z),this.createCoreGroupFromGeometry(l)}}n1.DEFAULT_PARAMS={radius:1,radiusTube:1,segmentsRadial:64,segmentsTube:8,p:2,q:3,center:new p.a(0,0,0)},n1.INPUT_CLONED_STATE=Qi.FROM_NODE;const i1=n1.DEFAULT_PARAMS;const r1=new class extends aa{constructor(){super(...arguments),this.radius=oa.FLOAT(i1.radius),this.radiusTube=oa.FLOAT(i1.radiusTube),this.segmentsRadial=oa.INTEGER(i1.segmentsRadial,{range:[1,128]}),this.segmentsTube=oa.INTEGER(i1.segmentsTube,{range:[1,32]}),this.p=oa.INTEGER(i1.p,{range:[1,10]}),this.q=oa.INTEGER(i1.q,{range:[1,10]}),this.center=oa.VECTOR3(i1.center)}};class s1 extends gG{constructor(){super(...arguments),this.paramsConfig=r1}static type(){return\\\\\\\"torusKnot\\\\\\\"}initializeNode(){}cook(t){this._operation=this._operation||new n1(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var o1;!function(t){t.SET_PARAMS=\\\\\\\"set params\\\\\\\",t.UPDATE_MATRIX=\\\\\\\"update matrix\\\\\\\"}(o1||(o1={}));const a1=[o1.SET_PARAMS,o1.UPDATE_MATRIX];class l1 extends pG{constructor(){super(...arguments),this._core_transform=new Mz,this._point_pos=new p.a,this._object_scale=new p.a,this._r=new p.a,this._object_position=new p.a}static type(){return\\\\\\\"transform\\\\\\\"}cook(t,e){const n=t[0].objects();return this._apply_transform(n,e),t[0]}_apply_transform(t,e){const n=wz[e.applyOn];switch(n){case yz.GEOMETRIES:return this._update_geometries(t,e);case yz.OBJECTS:return this._update_objects(t,e)}ar.unreachable(n)}_update_geometries(t,e){const n=this._matrix(e);if(\\\\\\\"\\\\\\\"===e.group.trim())for(let i of t){const t=i.geometry;t&&(t.translate(-e.pivot.x,-e.pivot.y,-e.pivot.z),t.applyMatrix4(n),t.translate(e.pivot.x,e.pivot.y,e.pivot.z))}else{const i=dG._fromObjects(t).pointsFromGroup(e.group);for(let t of i){const i=t.getPosition(this._point_pos).sub(e.pivot);i.applyMatrix4(n),t.setPosition(i.add(e.pivot))}}}_update_objects(t,e){const n=a1[e.objectMode];switch(n){case o1.SET_PARAMS:return this._update_objects_params(t,e);case o1.UPDATE_MATRIX:return this._update_objects_matrix(t,e)}ar.unreachable(n)}_update_objects_params(t,e){for(let n of t){n.position.copy(e.t);const t=Az[e.rotationOrder];this._r.copy(e.r).multiplyScalar(Ln.a),n.rotation.set(this._r.x,this._r.y,this._r.z,t),this._object_scale.copy(e.s).multiplyScalar(e.scale),n.scale.copy(this._object_scale),n.updateMatrix()}}_update_objects_matrix(t,e){const n=this._matrix(e);for(let e of t)this._object_position.copy(e.position),e.position.multiplyScalar(0),e.updateMatrix(),e.applyMatrix4(n),e.position.add(this._object_position),e.updateMatrix()}_matrix(t){return this._core_transform.matrix(t.t,t.r,t.s,t.scale,Az[t.rotationOrder])}}l1.DEFAULT_PARAMS={applyOn:wz.indexOf(yz.GEOMETRIES),objectMode:a1.indexOf(o1.SET_PARAMS),group:\\\\\\\"\\\\\\\",rotationOrder:Az.indexOf(Tz.XYZ),t:new p.a(0,0,0),r:new p.a(0,0,0),s:new p.a(1,1,1),scale:1,pivot:new p.a(0,0,0)},l1.INPUT_CLONED_STATE=Qi.FROM_NODE;const c1=l1.DEFAULT_PARAMS;const u1=new class extends aa{constructor(){super(...arguments),this.applyOn=oa.INTEGER(c1.applyOn,{menu:{entries:wz.map(((t,e)=>({name:t,value:e})))}}),this.objectMode=oa.INTEGER(c1.objectMode,{visibleIf:{applyOn:wz.indexOf(yz.OBJECTS)},menu:{entries:a1.map(((t,e)=>({name:t,value:e})))}}),this.group=oa.STRING(c1.group,{visibleIf:{applyOn:wz.indexOf(yz.GEOMETRIES)}}),this.rotationOrder=oa.INTEGER(c1.rotationOrder,{menu:{entries:Az.map(((t,e)=>({name:t,value:e})))}}),this.t=oa.VECTOR3(c1.t),this.r=oa.VECTOR3(c1.r),this.s=oa.VECTOR3(c1.s),this.scale=oa.FLOAT(c1.scale,{range:[0,10]}),this.pivot=oa.VECTOR3(c1.pivot,{visibleIf:{applyOn:wz.indexOf(yz.GEOMETRIES)}})}};class h1 extends gG{constructor(){super(...arguments),this.paramsConfig=u1}static type(){return PK.TRANSFORM}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(l1.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.applyOn],(()=>wz[this.pv.applyOn]))}))}))}setApplyOn(t){this.p.applyOn.set(wz.indexOf(t))}setObjectMode(t){this.p.objectMode.set(a1.indexOf(t))}cook(t){this._operation=this._operation||new l1(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const d1=new class extends aa{constructor(){super(...arguments),this.useSecondInput=oa.BOOLEAN(1),this.reference=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.SOP},visibleIf:{useSecondInput:0}})}};class p1 extends gG{constructor(){super(...arguments),this.paramsConfig=d1}static type(){return\\\\\\\"transformCopy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to transform\\\\\\\",\\\\\\\"objects to copy transform from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState([Qi.FROM_NODE,Qi.NEVER])}cook(t){this.pv.useSecondInput&&t[1]?this._copy_from_src_objects(t[0].objects(),t[1].objects()):this._copy_from_found_node(t[0].objects())}_copy_from_src_objects(t,e){let n,i;for(let r=0;r({name:t,value:e})))}};function f1(t){const e=[];for(let n=t+1;n<=6;n++)e.push({count:n});return{visibleIf:e}}const g1=new class extends aa{constructor(){super(...arguments),this.applyOn=oa.INTEGER(wz.indexOf(yz.GEOMETRIES),{menu:{entries:wz.map(((t,e)=>({name:t,value:e})))}}),this.count=oa.INTEGER(2,{range:[0,6],rangeLocked:[!0,!0]}),this.rotationOrder0=oa.INTEGER(_1,{separatorBefore:!0,...m1,...f1(0)}),this.r0=oa.VECTOR3([0,0,0],{...f1(0)}),this.rotationOrder1=oa.INTEGER(_1,{separatorBefore:!0,...m1,...f1(1)}),this.r1=oa.VECTOR3([0,0,0],{...f1(1)}),this.rotationOrder2=oa.INTEGER(_1,{separatorBefore:!0,...m1,...f1(2)}),this.r2=oa.VECTOR3([0,0,0],{...f1(2)}),this.rotationOrder3=oa.INTEGER(_1,{separatorBefore:!0,...m1,...f1(3)}),this.r3=oa.VECTOR3([0,0,0],{...f1(3)}),this.rotationOrder4=oa.INTEGER(_1,{separatorBefore:!0,...m1,...f1(4)}),this.r4=oa.VECTOR3([0,0,0],{...f1(4)}),this.rotationOrder5=oa.INTEGER(_1,{separatorBefore:!0,...m1,...f1(5)}),this.r5=oa.VECTOR3([0,0,0],{...f1(5)})}};class v1 extends gG{constructor(){super(...arguments),this.paramsConfig=g1,this._core_transform=new Mz,this._t=new p.a(0,0,0),this._s=new p.a(1,1,1),this._scale=1}static type(){return\\\\\\\"transformMulti\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to transform\\\\\\\",\\\\\\\"objects to copy initial transform from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState([Qi.FROM_NODE,Qi.NEVER]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.applyOn],(()=>wz[this.pv.applyOn]))}))})),this.params.onParamsCreated(\\\\\\\"cache param pairs\\\\\\\",(()=>{this._rot_and_index_pairs=[[this.p.r0,this.p.rotationOrder0],[this.p.r1,this.p.rotationOrder1],[this.p.r2,this.p.rotationOrder2],[this.p.r3,this.p.rotationOrder3],[this.p.r4,this.p.rotationOrder4],[this.p.r5,this.p.rotationOrder5]]}))}cook(t){const e=t[0].objectsWithGeo(),n=t[1]?t[1].objectsWithGeo()[0]:void 0;this._apply_transforms(e,n),this.setObjects(e)}_apply_transforms(t,e){const n=wz[this.pv.applyOn];switch(n){case yz.GEOMETRIES:return this._apply_matrix_to_geometries(t,e);case yz.OBJECTS:return this._apply_matrix_to_objects(t,e)}ar.unreachable(n)}_apply_matrix_to_geometries(t,e){if(!this._rot_and_index_pairs)return;if(e){const n=e.geometry;if(n){const e=[Hr.POSITION,Hr.NORMAL,Hr.TANGENT];for(let i of e){const e=n.attributes[i];for(let n of t){const t=n.geometry.attributes[i];e&&t&&Wr.copy(e,t)}}}}let n;for(let e=0;e({name:t,value:e})))}})}};class w1 extends gG{constructor(){super(...arguments),this.paramsConfig=b1,this._bbox_center=new p.a,this._translate_matrix=new A.a}static type(){return\\\\\\\"transformReset\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to reset transform\\\\\\\",\\\\\\\"optional reference for center\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}setMode(t){this.p.mode.set(x1.indexOf(t))}cook(t){const e=x1[this.pv.mode];this._select_mode(e,t)}_select_mode(t,e){switch(t){case y1.RESET_OBJECT:return this._reset_objects(e);case y1.CENTER_GEO:return this._center_geos(e,!1);case y1.PROMOTE_GEO_TO_OBJECT:return this._center_geos(e,!0)}ar.unreachable(t)}_reset_objects(t){const e=t[0],n=e.objects();for(let t of n)t.matrix.identity(),Mz.decompose_matrix(t);this.setCoreGroup(e)}_center_geos(t,e){const n=t[0],i=n.objectsWithGeo();let r=i;const s=t[1];s&&(r=s.objectsWithGeo());for(let t=0;te.h-t.h));const i=[{x:0,y:0,w:Math.max(Math.ceil(Math.sqrt(e/.95)),n),h:1/0}];let r=0,s=0;for(const e of t)for(let t=i.length-1;t>=0;t--){const n=i[t];if(!(e.w>n.w||e.h>n.h)){if(e.x=n.x,e.y=n.y,s=Math.max(s,e.y+e.h),r=Math.max(r,e.x+e.w),e.w===n.w&&e.h===n.h){const e=i.pop();t=this.minAllowedArgumentsCount()&&t<=this.maxAllowedArgumentsCount()}processArguments(t){throw\\\\\\\"Expression.Method._Base.process_arguments virtual method call. Please override\\\\\\\"}async getReferencedNodeContainer(t){const e=this.getReferencedNode(t);if(e){let t;if(t=e.isDirty()?await e.compute():e.containerController.container(),t){if(t.coreContent())return t}throw`referenced node invalid: ${e.path()}`}throw`invalid input (${t})`}getReferencedParam(t,e){const n=this.node();return n?xi.findParam(n,t,e):null}findReferencedGraphNode(t,e){if(!m.isNumber(t)){const n=t;return this.getReferencedNode(n,e)}{const e=t,n=this.node();if(n){return n.io.inputs.input_graph_node(e)}}return null}getReferencedNode(t,e){let n=null;const i=this.node();if(m.isString(t)){if(i){const r=t;n=xi.findNode(i,r,e)}}else if(i){const e=t;n=i.io.inputs.input(e)}return n||null}findDependency(t){return null}createDependencyFromIndexOrPath(t){const e=new co,n=this.findReferencedGraphNode(t,e);return n?this.createDependency(n,t,e):(ai.warn(\\\\\\\"node not found for path\\\\\\\",t),null)}createDependency(t,e,n){return ks.create(this.param,e,t,n)}}class J1 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"arguments list\\\\\\\"],[\\\\\\\"number\\\\\\\",\\\\\\\"index\\\\\\\"]]}processArguments(t){return new Promise(((e,n)=>{if(2==t.length){const n=t[0],i=t[1];e(n.split(\\\\\\\" \\\\\\\")[i])}else e(0)}))}}class Z1 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"arguments list\\\\\\\"]]}processArguments(t){return new Promise(((e,n)=>{if(1==t.length){e(t[0].split(\\\\\\\" \\\\\\\").length)}else e(0)}))}}const Q1=[\\\\\\\"min\\\\\\\",\\\\\\\"max\\\\\\\",\\\\\\\"size\\\\\\\",\\\\\\\"center\\\\\\\"],K1=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"];class t2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"vector name, min, max, size or center\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"component_name, x,y or z\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){let e=0;return new Promise((async(n,i)=>{if(t.length>=1){const r=t[0],s=t[1],o=t[2];let a=null;try{a=await this.getReferencedNodeContainer(r)}catch(t){i(t)}a&&(e=this._get_value_from_container(a,s,o),n(e))}else n(0)}))}_get_value_from_container(t,e,n){const i=t.boundingBox();if(!e)return i;if(Q1.indexOf(e)>=0){let t=new p.a;switch(e){case\\\\\\\"size\\\\\\\":i.getSize(t);break;case\\\\\\\"center\\\\\\\":i.getCenter(t);break;default:t=i[e]}return n?K1.indexOf(n)>=0?t[n]:-1:t}return-1}}class e2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"component_name, x,y or z\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(t.length>=1){const i=t[0],r=t[1];let s=null;try{s=await this.getReferencedNodeContainer(i)}catch(t){n(t)}if(s){const t=s.boundingBox(),n=t.min.clone().add(t.max).multiplyScalar(.5);if(r){const t=n[r];e(null!=t?t:0)}else e(n)}}else e(0)}))}}class n2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to param\\\\\\\"]]}findDependency(t){const e=new co,n=this.getReferencedParam(t,e);return n?this.createDependency(n,t,e):null}async processArguments(t){return new Promise((async(e,n)=>{let i=0;if(1==t.length){const r=t[0],s=this.getReferencedParam(r);if(s){s.isDirty()&&await s.compute();const t=s.value;null!=t&&(i=t,e(i))}else n(0)}}))}}class i2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to copy\\\\\\\"],[\\\\\\\"integer\\\\\\\",\\\\\\\"default value\\\\\\\"]]}static optionalArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"attribute name (optional)\\\\\\\"]]}findDependency(t){const e=this.findReferencedGraphNode(t);if(e&&\\\\\\\"copy\\\\\\\"==e.type()){const n=e.stamp_node;return this.createDependency(n,t)}return null}processArguments(t){return new Promise(((e,n)=>{if(2==t.length||3==t.length){const n=t[0],i=t[1],r=t[2],s=this.node(),o=s?xi.findNode(s,n):null;let a;o&&o.type()==e$.type()&&(a=o.stamp_value(r)),null==a&&(a=i),e(a)}else e(0)}))}}class r2 extends $1{constructor(){super(...arguments),this._require_dependency=!0,this._resolution=new d.a}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"component_name: x or y\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}async processArguments(t){if(1==t.length||2==t.length){const e=t[0],n=t[1],i=await this.getReferencedNodeContainer(e);if(i){const t=i.resolution();if(!n)return this._resolution.set(t[0],t[1]),this._resolution;if([0,\\\\\\\"0\\\\\\\",\\\\\\\"x\\\\\\\"].includes(n))return t[0];if([1,\\\\\\\"1\\\\\\\",\\\\\\\"y\\\\\\\"].includes(n))return t[1]}}return-1}}class s2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[]}async processArguments(t){return new Promise((async(t,e)=>{t(Zf.isMobile())}))}}class o2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[]}async processArguments(t){return new Promise((async(t,e)=>{t(Zf.isTouchDevice())}))}}class a2 extends $1{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"javascript expression\\\\\\\"]]}async processArguments(t){let e=0;if(1==t.length){const n=t[0];if(this._function=this._function||this._create_function(n),this._function)try{e=this._function(this.param.scene(),this.param.node,this.param)}catch(t){console.warn(\\\\\\\"expression error\\\\\\\"),console.warn(t)}}return e}_create_function(t){return new Function(\\\\\\\"scene\\\\\\\",\\\\\\\"node\\\\\\\",\\\\\\\"param\\\\\\\",`return ${t}`)}}class l2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"attribute name\\\\\\\"],[\\\\\\\"index\\\\\\\",\\\\\\\"object index\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(3==t.length){const i=t[0],r=t[1],s=t[2];let o=null;try{o=await this.getReferencedNodeContainer(i)}catch(t){n(t)}if(o){e(this._get_value_from_container(o,r,s))}}else console.warn(`${t.length} given when expected 3`),e(0)}))}_get_value_from_container(t,e,n){const i=t.coreContent();if(i){const t=i.coreObjects()[n];return t?t.attribValue(e):0}return null}}class c2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(1==t.length){const i=t[0];let r;try{r=await this.getReferencedNodeContainer(i)}catch(t){return void n(t)}if(r){e(r.objectsCount())}}else e(0)}))}}class u2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(t){const e=this.findReferencedGraphNode(t);if(e){const n=e;if(n.nameController){const e=n.nameController.graph_node;return this.createDependency(e,t)}}return null}processArguments(t){return new Promise(((e,n)=>{if(1==t.length){const n=t[0],i=this.getReferencedNode(n);if(i){const t=i.name();e(sr.tailDigits(t))}else e(0)}else e(0)}))}}class h2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(t){const e=this.findReferencedGraphNode(t);if(e){const n=e;if(n.nameController){const e=n.nameController.graph_node;return this.createDependency(e,t)}}return null}processArguments(t){return new Promise(((e,n)=>{if(1==t.length){const n=t[0],i=this.getReferencedNode(n);if(i){e(i.name())}else e(0)}else e(0)}))}}class d2 extends $1{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"number\\\\\\\"]]}processArguments(t){return new Promise((e=>{const n=t[0]||2;e(`${t[1]||0}`.padStart(n,\\\\\\\"0\\\\\\\"))}))}}class p2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"attribute name\\\\\\\"],[\\\\\\\"index\\\\\\\",\\\\\\\"point index\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(3==t.length){const i=t[0],r=t[1],s=t[2];let o=null;try{o=await this.getReferencedNodeContainer(i)}catch(t){n(t)}if(o){e(this._get_value_from_container(o,r,s))}}else console.warn(`${t.length} given when expected 3`),e(0)}))}_get_value_from_container(t,e,n){const i=t.coreContent();if(i){const t=i.points()[n];return t?t.attribValue(e):0}return null}}class _2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(1==t.length){const i=t[0];let r;try{r=await this.getReferencedNodeContainer(i)}catch(t){return void n(t)}if(r){e(r.pointsCount())}}else e(0)}))}}class m2 extends $1{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"string to count characters of\\\\\\\"]]}async processArguments(t){let e=0;if(1==t.length){e=t[0].length}return e}}class f2 extends $1{static requiredArguments(){return[]}async processArguments(t){let e=\\\\\\\"\\\\\\\";for(let n of t)null==n&&(n=\\\\\\\"\\\\\\\"),e+=`${n}`;return e}}class g2 extends $1{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"string to get index from\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"char to find index of\\\\\\\"]]}async processArguments(t){let e=-1;if(2==t.length){const n=t[0],i=t[1];e=n.indexOf(i)}return e}}class v2 extends $1{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"string to get range from\\\\\\\"],[\\\\\\\"integer\\\\\\\",\\\\\\\"range start\\\\\\\"],[\\\\\\\"integer\\\\\\\",\\\\\\\"range size\\\\\\\"]]}async processArguments(t){let e=\\\\\\\"\\\\\\\";const n=t[0],i=t[1]||0;let r=t[2]||1;return n&&(e=n.substr(i,r)),e}}class y2 extends $1{constructor(){super(...arguments),this._require_dependency=!0,this._windowSize=new d.a}static requiredArguments(){return[[]]}findDependency(t){return this.param.addGraphInput(this.param.scene().windowController.graphNode()),null}processArguments(t){return new Promise((t=>{this._windowSize.set(window.innerWidth,window.innerHeight),t(this._windowSize)}))}}class x2{constructor(t,e){this._controller=t,this._node=e,this._deleted_params_data=new Map,this._created_spare_param_names=[],this._raw_input_serialized_by_param_name=new Map,this._init_value_serialized_by_param_name=new Map}get assembler(){return this._controller.assembler}createSpareParameters(){var t;const e={},n=this.assembler.param_configs(),i=n.map((t=>t.name())),r=b.clone(i);if(0==this._validateNames(r))return;b.clone(this._created_spare_param_names).concat(r).forEach((t=>{const n=this._node.params.get(t);if(n){this._raw_input_serialized_by_param_name.set(n.name(),n.rawInputSerialized()),this._init_value_serialized_by_param_name.set(n.name(),n.defaultValueSerialized());const t=hG.dispatch_param(n);if(t.required()){const e=t.data();this._deleted_params_data.set(n.name(),e)}}e.namesToDelete=e.namesToDelete||[],e.namesToDelete.push(t)}));for(let t of n)if(r.indexOf(t.name())>=0){const n=b.clone(t.param_options),i={spare:!0,computeOnDirty:!0,cook:!1},r=b.merge(n,i);let s=this._init_value_serialized_by_param_name.get(t.name());null==s&&(s=t.default_value);let o=this._raw_input_serialized_by_param_name.get(t.name());null==o&&(o=t.default_value),e.toAdd=e.toAdd||[],e.toAdd.push({name:t.name(),type:t.type(),init_value:s,raw_input:o,options:r})}this._node.params.updateParams(e),this._created_spare_param_names=(null===(t=e.toAdd)||void 0===t?void 0:t.map((t=>t.name)))||[];for(let t of n){const e=this._node.params.get(t.name());e&&(t.execute_callback(this._node,e),e.type()==Es.OPERATOR_PATH&&setTimeout((async()=>{e.isDirty()&&await e.compute(),e.options.executeCallback()}),200))}}_validateNames(t){const e=b.clone(this._node.params.non_spare_names),n=f.intersection(t,e);if(n.length>0){const t=`${this._node.path()} attempts to create spare params called '${n.join(\\\\\\\", \\\\\\\")}' with same name as params`;return this._node.states.error.set(t),!1}return!0}}class b2{constructor(t,e){this.node=t,this._globals_handler=new Sf,this._compile_required=!0,this._assembler=new e(this.node),this._spare_params_controller=new x2(this,this.node)}set_assembler_globals_handler(t){(this._globals_handler?this._globals_handler.id():null)!=(t?t.id():null)&&(this._globals_handler=t,this.set_compilation_required_and_dirty(),this._assembler.reset_configs())}get assembler(){return this._assembler}get globals_handler(){return this._globals_handler}add_output_inputs(t){this._assembler.add_output_inputs(t)}add_globals_outputs(t){this._assembler.add_globals_outputs(t)}allow_attribute_exports(){return this._assembler.allow_attribute_exports()}setCompilationRequired(t=!0){this._compile_required=t}set_compilation_required_and_dirty(t){this.setCompilationRequired(),this.node.setDirty(t)}compileRequired(){return this._compile_required}post_compile(){this.createSpareParameters(),this.setCompilationRequired(!1)}createSpareParameters(){this._spare_params_controller.createSpareParameters()}addFilterFragmentShaderCallback(t,e){this.assembler._addFilterFragmentShaderCallback(t,e),this.setCompilationRequired()}removeFilterFragmentShaderCallback(t){this.assembler._removeFilterFragmentShaderCallback(t),this.setCompilationRequired()}}var w2;!function(t){t.FUNCTION_DECLARATION=\\\\\\\"function_declaration\\\\\\\",t.DEFINE=\\\\\\\"define\\\\\\\",t.BODY=\\\\\\\"body\\\\\\\"}(w2||(w2={}));class T2{constructor(t,e,n){this._name=t,this._input_names=e,this._dependencies=n}name(){return this._name}input_names(){return this._input_names}dependencies(){return this._dependencies}}class A2{constructor(t,e={}){this._name=t,this._options=e}name(){return this._name}default_from_attribute(){return this._options.default_from_attribute||!1}default(){return this._options.default}if_condition(){return this._options.if}prefix(){return this._options.prefix||\\\\\\\"\\\\\\\"}suffix(){return this._options.suffix||\\\\\\\"\\\\\\\"}postLines(){return this._options.postLines}}class E2{constructor(t){this._shader_name=t,this._definitions_by_node_id=new Map,this._body_lines_by_node_id=new Map}get shader_name(){return this._shader_name}addDefinitions(t,e){for(let n of e)u.pushOnArrayAtEntry(this._definitions_by_node_id,t.graphNodeId(),n)}definitions(t){return this._definitions_by_node_id.get(t.graphNodeId())}addBodyLines(t,e){for(let n of e)u.pushOnArrayAtEntry(this._body_lines_by_node_id,t.graphNodeId(),n)}body_lines(t){return this._body_lines_by_node_id.get(t.graphNodeId())}}class M2{constructor(t,e,n){this._shader_names=t,this._current_shader_name=e,this._assembler=n,this._lines_controller_by_shader_name=new Map;for(let t of this._shader_names)this._lines_controller_by_shader_name.set(t,new E2(t))}assembler(){return this._assembler}shaderNames(){return this._shader_names}set_current_shader_name(t){this._current_shader_name=t}get current_shader_name(){return this._current_shader_name}addDefinitions(t,e,n){if(0==e.length)return;n=n||this._current_shader_name;const i=this._lines_controller_by_shader_name.get(n);i&&i.addDefinitions(t,e)}definitions(t,e){const n=this._lines_controller_by_shader_name.get(t);if(n)return n.definitions(e)}addBodyLines(t,e,n){if(0==e.length)return;n=n||this._current_shader_name;const i=this._lines_controller_by_shader_name.get(n);i&&i.addBodyLines(t,e)}body_lines(t,e){const n=this._lines_controller_by_shader_name.get(t);if(n)return n.body_lines(e)}}const S2={[w2.FUNCTION_DECLARATION]:\\\\\\\"\\\\\\\",[w2.DEFINE]:\\\\\\\";\\\\\\\",[w2.BODY]:\\\\\\\";\\\\\\\"},C2={[w2.FUNCTION_DECLARATION]:\\\\\\\"\\\\\\\",[w2.DEFINE]:\\\\\\\"\\\\\\\",[w2.BODY]:\\\\\\\"\\\\t\\\\\\\"};class N2{static node_comment(t,e){let n=`// ${t.path()}`,i=C2[e];if(e==w2.BODY){let e=this.node_distance_to_material(t);t.type()==er.OUTPUT&&(e+=1),i=i.repeat(e)}return e==w2.BODY&&(n=`${i}${n}`),n}static line_wrap(t,e,n){let i=!0;0!=e.indexOf(\\\\\\\"#if\\\\\\\")&&0!=e.indexOf(\\\\\\\"#endif\\\\\\\")||(i=!1);let r=C2[n];if(n==w2.BODY&&(r=r.repeat(this.node_distance_to_material(t))),e=`${r}${e}`,i){const t=e[e.length-1],i=S2[n];t!=i&&\\\\\\\"{\\\\\\\"!=t&&\\\\\\\"}\\\\\\\"!=t&&(e+=i)}return e}static post_line_separator(t){return t==w2.BODY?\\\\\\\"\\\\t\\\\\\\":\\\\\\\"\\\\\\\"}static node_distance_to_material(t){const e=t.parent();if(!e)return 0;if(e.context()!=t.context())return 1;{let n=1;return t.type()!=er.INPUT&&t.type()!=er.OUTPUT||(n=0),n+this.node_distance_to_material(e)}}}class L2{constructor(t,e,n){this._node_traverser=t,this._root_nodes_for_shader_method=e,this._assembler=n,this._param_configs_controller=new GI,this._param_configs_set_allowed=!0,this._lines=new Map}shaderNames(){return this._node_traverser.shaderNames()}buildFromNodes(t,e){this._node_traverser.traverse(t);const n=new Map;for(let t of this.shaderNames()){const e=this._node_traverser.nodes_for_shader_name(t);n.set(t,e)}const i=this._node_traverser.sorted_nodes();for(let t of this.shaderNames()){const e=this._root_nodes_for_shader_method(t);for(let i of e)u.pushOnArrayAtEntry(n,t,i)}const r=new Map;for(let t of i)r.set(t.graphNodeId(),!0);for(let e of t)r.get(e.graphNodeId())||(i.push(e),r.set(e.graphNodeId(),!0));for(let t of i)t.reset_code();for(let t of e)t.reset_code();this._shaders_collection_controller=new M2(this.shaderNames(),this.shaderNames()[0],this._assembler),this.reset();for(let t of this.shaderNames()){let e=n.get(t)||[];if(e=f.uniq(e),this._shaders_collection_controller.set_current_shader_name(t),e)for(let t of e)t.setLines(this._shaders_collection_controller)}if(this._param_configs_set_allowed){for(let t of e)t.setParamConfigs();this.setParamConfigs(e)}this.set_code_lines(i)}shaders_collection_controller(){return this._shaders_collection_controller}disallow_new_param_configs(){this._param_configs_set_allowed=!1}allow_new_param_configs(){this._param_configs_set_allowed=!0}reset(){for(let t of this.shaderNames()){const e=new Map;this._lines.set(t,e)}}param_configs(){return this._param_configs_controller.list()||[]}lines(t,e){var n;return(null===(n=this._lines.get(t))||void 0===n?void 0:n.get(e))||[]}all_lines(){return this._lines}setParamConfigs(t){this._param_configs_controller.reset();for(let e of t){const t=e.param_configs();if(t)for(let e of t)this._param_configs_controller.push(e)}}set_code_lines(t){for(let e of this.shaderNames())this.add_code_lines(t,e)}add_code_lines(t,e){this.addDefinitions(t,e,yf.FUNCTION,w2.FUNCTION_DECLARATION),this.addDefinitions(t,e,yf.UNIFORM,w2.DEFINE),this.addDefinitions(t,e,yf.VARYING,w2.DEFINE),this.addDefinitions(t,e,yf.ATTRIBUTE,w2.DEFINE),this.add_code_line_for_nodes_and_line_type(t,e,w2.BODY)}addDefinitions(t,e,n,i){if(!this._shaders_collection_controller)return;const r=[];for(let i of t){let t=this._shaders_collection_controller.definitions(e,i);if(t){t=t.filter((t=>t.definition_type==n));for(let e of t)r.push(e)}}if(r.length>0){const t=new vf(r),n=t.uniq();if(t.errored)throw`code builder error: ${t.error_message}`;const s=new Map,o=new Map;for(let t of n){const e=t.node.graphNodeId();o.has(e)||o.set(e,!0),u.pushOnArrayAtEntry(s,e,t)}const a=this._lines.get(e);o.forEach(((t,e)=>{const n=s.get(e);if(n){const t=n[0];if(t){const e=N2.node_comment(t.node,i);u.pushOnArrayAtEntry(a,i,e);for(let e of n){const n=N2.line_wrap(t.node,e.line,i);u.pushOnArrayAtEntry(a,i,n)}const r=N2.post_line_separator(i);u.pushOnArrayAtEntry(a,i,r)}}}))}}add_code_line_for_nodes_and_line_type(t,e,n){var i=(t=t.filter((t=>{if(this._shaders_collection_controller){const n=this._shaders_collection_controller.body_lines(e,t);return n&&n.length>0}}))).length;for(let r=0;r0){const s=this._lines.get(e),o=N2.node_comment(t,n);if(u.pushOnArrayAtEntry(s,n,o),f.uniq(r).forEach((e=>{e=N2.line_wrap(t,e,n),u.pushOnArrayAtEntry(s,n,e)})),n!=w2.BODY||!i){const t=N2.post_line_separator(n);u.pushOnArrayAtEntry(s,n,t)}}}}class O2{constructor(t,e,n){this._parent_node=t,this._shader_names=e,this._input_names_for_shader_name_method=n,this._leaves_graph_id=new Map,this._graph_ids_by_shader_name=new Map,this._outputs_by_graph_id=new Map,this._depth_by_graph_id=new Map,this._graph_id_by_depth=new Map,this._graph=this._parent_node.scene().graph}reset(){this._leaves_graph_id.clear(),this._graph_ids_by_shader_name.clear(),this._outputs_by_graph_id.clear(),this._depth_by_graph_id.clear(),this._graph_id_by_depth.clear(),this._shader_names.forEach((t=>{this._graph_ids_by_shader_name.set(t,new Map)}))}shaderNames(){return this._shader_names}input_names_for_shader_name(t,e){return this._input_names_for_shader_name_method(t,e)}traverse(t){this.reset();for(let t of this.shaderNames())this._leaves_graph_id.set(t,new Map);for(let e of this.shaderNames()){this._shader_name=e;for(let e of t)this.find_leaves_from_root_node(e),this.set_nodes_depth()}this._depth_by_graph_id.forEach(((t,e)=>{null!=t&&u.pushOnArrayAtEntry(this._graph_id_by_depth,t,e)}))}leaves_from_nodes(t){var e;this._shader_name=xf.LEAVES_FROM_NODES_SHADER,this._graph_ids_by_shader_name.set(this._shader_name,new Map),this._leaves_graph_id.set(this._shader_name,new Map);for(let e of t)this.find_leaves(e);const n=[];return null===(e=this._leaves_graph_id.get(this._shader_name))||void 0===e||e.forEach(((t,e)=>{n.push(e)})),this._graph.nodesFromIds(n)}nodes_for_shader_name(t){const e=[];this._graph_id_by_depth.forEach(((t,n)=>{e.push(n)})),e.sort(((t,e)=>t-e));const n=[],i=new Map;return e.forEach((e=>{const r=this._graph_id_by_depth.get(e);r&&r.forEach((e=>{var r;if(null===(r=this._graph_ids_by_shader_name.get(t))||void 0===r?void 0:r.get(e)){const r=this._graph.nodeFromId(e);this.add_nodes_with_children(r,i,n,t)}}))})),n}sorted_nodes(){const t=[];this._graph_id_by_depth.forEach(((e,n)=>{t.push(n)})),t.sort(((t,e)=>t-e));const e=[],n=new Map;return t.forEach((t=>{const i=this._graph_id_by_depth.get(t);if(i)for(let t of i){const i=this._graph.nodeFromId(t);i&&this.add_nodes_with_children(i,n,e)}})),e}add_nodes_with_children(t,e,n,i){if(e.get(t.graphNodeId())||(n.push(t),e.set(t.graphNodeId(),!0)),t.type()==er.INPUT){const r=t.parent();if(r){const s=this.sorted_nodes_for_shader_name_for_parent(r,i);for(let r of s)r.graphNodeId()!=t.graphNodeId()&&this.add_nodes_with_children(r,e,n,i)}}}sorted_nodes_for_shader_name_for_parent(t,e){const n=[];this._graph_id_by_depth.forEach(((t,e)=>{n.push(e)})),n.sort(((t,e)=>t-e));const i=[];n.forEach((n=>{const r=this._graph_id_by_depth.get(n);r&&r.forEach((n=>{var r;if(!e||(null===(r=this._graph_ids_by_shader_name.get(e))||void 0===r?void 0:r.get(n))){const e=this._graph.nodeFromId(n);e.parent()==t&&i.push(e)}}))}));const r=i[0];return t.context()==r.context()&&i.push(t),i}find_leaves_from_root_node(t){var e;null===(e=this._graph_ids_by_shader_name.get(this._shader_name))||void 0===e||e.set(t.graphNodeId(),!0);const n=this.input_names_for_shader_name(t,this._shader_name);if(n)for(let e of n){const n=t.io.inputs.named_input(e);n&&(u.pushOnArrayAtEntry(this._outputs_by_graph_id,n.graphNodeId(),t.graphNodeId()),this.find_leaves(n))}this._outputs_by_graph_id.forEach(((t,e)=>{this._outputs_by_graph_id.set(e,f.uniq(t))}))}find_leaves(t){var e;null===(e=this._graph_ids_by_shader_name.get(this._shader_name))||void 0===e||e.set(t.graphNodeId(),!0);const n=this._find_inputs_or_children(t),i=f.compact(n),r=f.uniq(i.map((t=>t.graphNodeId()))).map((t=>this._graph.nodeFromId(t)));if(r.length>0)for(let e of r)u.pushOnArrayAtEntry(this._outputs_by_graph_id,e.graphNodeId(),t.graphNodeId()),this.find_leaves(e);else this._leaves_graph_id.get(this._shader_name).set(t.graphNodeId(),!0)}_find_inputs_or_children(t){var e,n;if(t.type()==er.INPUT)return(null===(e=t.parent())||void 0===e?void 0:e.io.inputs.inputs())||[];if(t.childrenAllowed()){return[null===(n=t.childrenController)||void 0===n?void 0:n.output_node()]}return t.io.inputs.inputs()}set_nodes_depth(){this._leaves_graph_id.forEach(((t,e)=>{t.forEach(((t,e)=>{this.set_node_depth(e)}))}))}set_node_depth(t,e=0){const n=this._depth_by_graph_id.get(t);null!=n?this._depth_by_graph_id.set(t,Math.max(n,e)):this._depth_by_graph_id.set(t,e);const i=this._outputs_by_graph_id.get(t);i&&i.forEach((t=>{this.set_node_depth(t,e+1)}))}}const R2=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.FRAGMENT,\\\\\\\"#include \\\\\\\"]]),P2=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( diffuse, opacity );\\\\\\\"]]),I2=new Map([[xf.VERTEX,[\\\\\\\"#include \\\\\\\",\\\\\\\"#include \\\\\\\"]],[xf.FRAGMENT,[]]]);class F2 extends class{}{constructor(t){super(),this._gl_parent_node=t,this._shaders_by_name=new Map,this._lines=new Map,this._root_nodes=[],this._leaf_nodes=[],this._uniforms_time_dependent=!1,this._uniforms_resolution_dependent=!1}setGlParentNode(t){this._overriden_gl_parent_node=t}currentGlParentNode(){return this._overriden_gl_parent_node||this._gl_parent_node}compile(){}_template_shader_for_shader_name(t){var e,n;switch(t){case xf.VERTEX:return null===(e=this.templateShader())||void 0===e?void 0:e.vertexShader;case xf.FRAGMENT:return null===(n=this.templateShader())||void 0===n?void 0:n.fragmentShader}}get globals_handler(){var t;return null===(t=this.currentGlParentNode().assemblerController)||void 0===t?void 0:t.globals_handler}compileAllowed(){var t;return null!=(null===(t=this.currentGlParentNode().assemblerController)||void 0===t?void 0:t.globals_handler)}shaders_by_name(){return this._shaders_by_name}_build_lines(){for(let t of this.shaderNames()){const e=this._template_shader_for_shader_name(t);e&&this._replace_template(e,t)}}set_root_nodes(t){this._root_nodes=t}templateShader(){}addUniforms(t){for(let e of this.param_configs())t[e.uniform_name]=e.uniform;this.uniformsTimeDependent()&&(t.time={value:this.currentGlParentNode().scene().time()}),this.uniforms_resolution_dependent()&&(t.resolution={value:new d.a(1e3,1e3)})}root_nodes_by_shader_name(t){const e=[];for(let t of this._root_nodes)switch(t.type()){case UI.type():case HI.type():e.push(t);break;case gf.type():case Lf.type():e.push(t)}return e}leaf_nodes_by_shader_name(t){const e=[];for(let t of this._leaf_nodes)switch(t.type()){case HP.type():e.push(t);break;case gf.type():}return e}set_node_lines_globals(t,e){}set_node_lines_output(t,e){}set_node_lines_attribute(t,e){}codeBuilder(){return this._code_builder=this._code_builder||this._create_code_builder()}_resetCodeBuilder(){this._code_builder=void 0}_create_code_builder(){const t=new O2(this.currentGlParentNode(),this.shaderNames(),((t,e)=>this.input_names_for_shader_name(t,e)));return new L2(t,(t=>this.root_nodes_by_shader_name(t)),this)}build_code_from_nodes(t){const e=Of.findParamGeneratingNodes(this.currentGlParentNode());this.codeBuilder().buildFromNodes(t,e)}allow_new_param_configs(){this.codeBuilder().allow_new_param_configs()}disallow_new_param_configs(){this.codeBuilder().disallow_new_param_configs()}builder_param_configs(){return this.codeBuilder().param_configs()}builder_lines(t,e){return this.codeBuilder().lines(t,e)}all_builder_lines(){return this.codeBuilder().all_lines()}param_configs(){return(this._param_config_owner||this.codeBuilder()).param_configs()}set_param_configs_owner(t){this._param_config_owner=t,this._param_config_owner?this.codeBuilder().disallow_new_param_configs():this.codeBuilder().allow_new_param_configs()}static output_input_connection_points(){return[new Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"normal\\\\\\\",Do.VEC3),new Vo(\\\\\\\"color\\\\\\\",Do.VEC3),new Vo(\\\\\\\"alpha\\\\\\\",Do.FLOAT),new Vo(\\\\\\\"uv\\\\\\\",Do.VEC2)]}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints(F2.output_input_connection_points())}static create_globals_node_output_connections(){return[new Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"normal\\\\\\\",Do.VEC3),new Vo(\\\\\\\"color\\\\\\\",Do.VEC3),new Vo(\\\\\\\"uv\\\\\\\",Do.VEC2),new Vo(\\\\\\\"mvPosition\\\\\\\",Do.VEC4),new Vo(\\\\\\\"worldPosition\\\\\\\",Do.VEC4),new Vo(\\\\\\\"worldNormal\\\\\\\",Do.VEC3),new Vo(\\\\\\\"gl_Position\\\\\\\",Do.VEC4),new Vo(\\\\\\\"gl_FragCoord\\\\\\\",Do.VEC4),new Vo(\\\\\\\"cameraPosition\\\\\\\",Do.VEC3),new Vo(\\\\\\\"resolution\\\\\\\",Do.VEC2),new Vo(\\\\\\\"time\\\\\\\",Do.FLOAT)]}create_globals_node_output_connections(){return F2.create_globals_node_output_connections()}add_globals_outputs(t){t.io.outputs.setNamedOutputConnectionPoints(this.create_globals_node_output_connections())}allow_attribute_exports(){return!1}reset_configs(){this._reset_shader_configs(),this._reset_variable_configs(),this._resetUniformsTimeDependency(),this._reset_uniforms_resolution_dependency()}shaderConfigs(){return this._shader_configs=this._shader_configs||this.create_shader_configs()}set_shader_configs(t){this._shader_configs=t}shaderNames(){var t;return(null===(t=this.shaderConfigs())||void 0===t?void 0:t.map((t=>t.name())))||[]}_reset_shader_configs(){this._shader_configs=void 0}create_shader_configs(){return[new T2(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",Lf.INPUT_NAME],[]),new T2(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[xf.VERTEX])]}shader_config(t){var e;return null===(e=this.shaderConfigs())||void 0===e?void 0:e.filter((e=>e.name()==t))[0]}variable_configs(){return this._variable_configs=this._variable_configs||this.create_variable_configs()}set_variable_configs(t){this._variable_configs=t}variable_config(t){return this.variable_configs().filter((e=>e.name()==t))[0]}static create_variable_configs(){return[new A2(\\\\\\\"position\\\\\\\",{default_from_attribute:!0,prefix:\\\\\\\"vec3 transformed = \\\\\\\"}),new A2(\\\\\\\"normal\\\\\\\",{default_from_attribute:!0,prefix:\\\\\\\"vec3 objectNormal = \\\\\\\",postLines:[\\\\\\\"#ifdef USE_TANGENT\\\\\\\",\\\\\\\"\\\\tvec3 objectTangent = vec3( tangent.xyz );\\\\\\\",\\\\\\\"#endif\\\\\\\"]}),new A2(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new A2(\\\\\\\"alpha\\\\\\\",{prefix:\\\\\\\"diffuseColor.a = \\\\\\\"}),new A2(\\\\\\\"uv\\\\\\\",{prefix:\\\\\\\"vUv = \\\\\\\",if:Sf.IF_RULE.uv})]}create_variable_configs(){return F2.create_variable_configs()}_reset_variable_configs(){this._variable_configs=void 0,this.variable_configs()}input_names_for_shader_name(t,e){var n;return(null===(n=this.shader_config(e))||void 0===n?void 0:n.input_names())||[]}_resetUniformsTimeDependency(){this._uniforms_time_dependent=!1}setUniformsTimeDependent(){this._uniforms_time_dependent=!0}uniformsTimeDependent(){return this._uniforms_time_dependent}_reset_uniforms_resolution_dependency(){this._uniforms_resolution_dependent=!1}set_uniforms_resolution_dependent(){this._uniforms_resolution_dependent=!0}uniforms_resolution_dependent(){return this._uniforms_resolution_dependent}insert_define_after(t){return R2.get(t)}insert_body_after(t){return P2.get(t)}lines_to_remove(t){return I2.get(t)}_replace_template(t,e){const n=this.builder_lines(e,w2.FUNCTION_DECLARATION),i=this.builder_lines(e,w2.DEFINE),r=this.builder_lines(e,w2.BODY);let s=t.split(\\\\\\\"\\\\n\\\\\\\");const o=[],a=this.insert_define_after(e),l=this.insert_body_after(e),c=this.lines_to_remove(e);let u=!1,h=!1;for(let t of s){1==u&&(n&&this._insert_lines(o,n),i&&this._insert_lines(o,i),u=!1),1==h&&(r&&this._insert_lines(o,r),h=!1);let e=!1;if(c)for(let n of c)t.indexOf(n)>=0&&(e=!0);e?(o.push(\\\\\\\"// removed:\\\\\\\"),o.push(`//${t}`)):o.push(t),a&&t.indexOf(a)>=0&&(u=!0),l&&t.indexOf(l)>=0&&(h=!0)}this._lines.set(e,o)}_insert_lines(t,e){if(e.length>0){for(let e=0;e<3;e++)t.push(\\\\\\\"\\\\\\\");for(let n of e)t.push(n);for(let e=0;e<3;e++)t.push(\\\\\\\"\\\\\\\")}}_addFilterFragmentShaderCallback(t,e){}_removeFilterFragmentShaderCallback(t){}getCustomMaterials(){return new Map}static expandShader(t){return function t(e){return e.replace(/^[ \\\\t]*#include +<([\\\\w\\\\d./]+)>/gm,(function(e,n){var i=z[n];if(void 0===i)throw new Error(\\\\\\\"Can not resolve #include <\\\\\\\"+n+\\\\\\\">\\\\\\\");return t(i)}))}(t)}}var D2,k2;!function(t){t.DISTANCE=\\\\\\\"customDistanceMaterial\\\\\\\",t.DEPTH=\\\\\\\"customDepthMaterial\\\\\\\",t.DEPTH_DOF=\\\\\\\"customDepthDOFMaterial\\\\\\\"}(D2||(D2={})),function(t){t.TIME=\\\\\\\"time\\\\\\\",t.RESOLUTION=\\\\\\\"resolution\\\\\\\",t.MV_POSITION=\\\\\\\"mvPosition\\\\\\\",t.GL_POSITION=\\\\\\\"gl_Position\\\\\\\",t.GL_FRAGCOORD=\\\\\\\"gl_FragCoord\\\\\\\",t.GL_POINTCOORD=\\\\\\\"gl_PointCoord\\\\\\\"}(k2||(k2={}));const B2=[k2.GL_FRAGCOORD,k2.GL_POINTCOORD];class z2 extends F2{constructor(){super(...arguments),this._assemblers_by_custom_name=new Map,this._filterFragmentShaderCallbacks=new Map}createMaterial(){return new F}custom_assembler_class_by_custom_name(){}_addCustomMaterials(t){const e=this.custom_assembler_class_by_custom_name();e&&e.forEach(((e,n)=>{this._add_custom_material(t,n,e)}))}_add_custom_material(t,e,n){let i=this._assemblers_by_custom_name.get(e);i||(i=new n(this.currentGlParentNode()),this._assemblers_by_custom_name.set(e,i)),t.customMaterials=t.customMaterials||{};const r=i.createMaterial();r.name=e,t.customMaterials[e]=r}compileCustomMaterials(t){const e=this.custom_assembler_class_by_custom_name();e&&e.forEach(((e,n)=>{if(this._code_builder){let i=this._assemblers_by_custom_name.get(n);i||(i=new e(this.currentGlParentNode()),this._assemblers_by_custom_name.set(n,i)),i.set_root_nodes(this._root_nodes),i.set_param_configs_owner(this._code_builder),i.set_shader_configs(this.shaderConfigs()),i.set_variable_configs(this.variable_configs());const r=t.customMaterials[n];r&&(i.setFilterFragmentShaderMethodOwner(this),i.compileMaterial(r),i.setFilterFragmentShaderMethodOwner(void 0))}}))}_resetFilterFragmentShaderCallbacks(){this._filterFragmentShaderCallbacks.clear()}_addFilterFragmentShaderCallback(t,e){this._filterFragmentShaderCallbacks.set(t,e)}_removeFilterFragmentShaderCallback(t){this._filterFragmentShaderCallbacks.delete(t)}setFilterFragmentShaderMethodOwner(t){this._filterFragmentShaderMethodOwner=t}filterFragmentShader(t){return this._filterFragmentShaderCallbacks.forEach(((e,n)=>{t=e(t)})),t}processFilterFragmentShader(t){return this._filterFragmentShaderMethodOwner?this._filterFragmentShaderMethodOwner.filterFragmentShader(t):this.filterFragmentShader(t)}compileMaterial(t){if(!this.compileAllowed())return;const e=Of.findOutputNodes(this.currentGlParentNode());e.length>1&&this.currentGlParentNode().states.error.set(\\\\\\\"only one output node allowed\\\\\\\");const n=Of.findVaryingNodes(this.currentGlParentNode()),i=e.concat(n);this.set_root_nodes(i),this._update_shaders();const r=this._shaders_by_name.get(xf.VERTEX),s=this._shaders_by_name.get(xf.FRAGMENT);r&&s&&(t.vertexShader=r,t.fragmentShader=this.processFilterFragmentShader(s),this.addUniforms(t.uniforms),t.needsUpdate=!0);const o=this.currentGlParentNode().scene();this.uniformsTimeDependent()?o.uniformsController.addTimeDependentUniformOwner(t.uuid,t.uniforms):o.uniformsController.removeTimeDependentUniformOwner(t.uuid),this.uniforms_resolution_dependent()?o.uniformsController.addResolutionDependentUniformOwner(t.uuid,t.uniforms):o.uniformsController.removeResolutionDependentUniformOwner(t.uuid),t.customMaterials&&this.compileCustomMaterials(t)}_update_shaders(){this._shaders_by_name=new Map,this._lines=new Map;for(let t of this.shaderNames()){const e=this._template_shader_for_shader_name(t);e&&this._lines.set(t,e.split(\\\\\\\"\\\\n\\\\\\\"))}this._root_nodes.length>0&&(this.build_code_from_nodes(this._root_nodes),this._build_lines());for(let t of this.shaderNames()){const e=this._lines.get(t);e&&this._shaders_by_name.set(t,e.join(\\\\\\\"\\\\n\\\\\\\"))}}shadow_assembler_class_by_custom_name(){return{}}add_output_body_line(t,e,n){var i;const r=t.io.inputs.named_input(n),s=t.variableForInput(n),o=this.variable_config(n);let a=null;if(r)a=uf.vector3(s);else if(o.default_from_attribute()){const r=t.io.inputs.namedInputConnectionPointsByName(n);if(r){const s=r.type(),o=null===(i=this.globals_handler)||void 0===i?void 0:i.read_attribute(t,s,n,e);o&&(a=o)}}else{const t=o.default();t&&(a=t)}if(a){const n=o.prefix(),i=o.suffix(),r=o.if_condition();r&&e.addBodyLines(t,[`#if ${r}`]),e.addBodyLines(t,[`${n}${a}${i}`]);const s=o.postLines();s&&e.addBodyLines(t,s),r&&e.addBodyLines(t,[\\\\\\\"#endif\\\\\\\"])}}set_node_lines_output(t,e){var n;const i=e.current_shader_name,r=null===(n=this.shader_config(i))||void 0===n?void 0:n.input_names();if(r)for(let n of r)t.io.inputs.has_named_input(n)&&this.add_output_body_line(t,e,n)}set_node_lines_attribute(t,e){var n;const i=t.gl_type(),r=null===(n=this.globals_handler)||void 0===n?void 0:n.read_attribute(t,i,t.attribute_name,e),s=t.glVarName(t.output_name);e.addBodyLines(t,[`${i} ${s} = ${r}`])}handle_globals_output_name(t){var e;switch(t.output_name){case k2.TIME:return void this.handleTime(t);case k2.RESOLUTION:return void this.handle_resolution(t);case k2.MV_POSITION:return void this.handle_mvPosition(t);case k2.GL_POSITION:return void this.handle_gl_Position(t);case k2.GL_FRAGCOORD:return void this.handle_gl_FragCoord(t);case k2.GL_POINTCOORD:return void this.handle_gl_PointCoord(t);default:null===(e=this.globals_handler)||void 0===e||e.handle_globals_node(t.globals_node,t.output_name,t.shaders_collection_controller)}}handleTime(t){const e=new Af(t.globals_node,Do.FLOAT,t.output_name);t.globals_shader_name&&u.pushOnArrayAtEntry(t.definitions_by_shader_name,t.globals_shader_name,e);const n=`float ${t.var_name} = ${t.output_name}`;for(let i of t.dependencies)u.pushOnArrayAtEntry(t.definitions_by_shader_name,i,e),u.pushOnArrayAtEntry(t.body_lines_by_shader_name,i,n);t.body_lines.push(n),this.setUniformsTimeDependent()}handle_resolution(t){t.body_lines.push(`vec2 ${t.var_name} = resolution`);const e=new Af(t.globals_node,Do.VEC2,t.output_name);t.globals_shader_name&&u.pushOnArrayAtEntry(t.definitions_by_shader_name,t.globals_shader_name,e);for(let n of t.dependencies)u.pushOnArrayAtEntry(t.definitions_by_shader_name,n,e);this.set_uniforms_resolution_dependent()}handle_mvPosition(t){if(t.shader_name==xf.FRAGMENT){const e=t.globals_node,n=t.shaders_collection_controller,i=new Ef(e,Do.VEC4,t.var_name),r=`${t.var_name} = modelViewMatrix * vec4(position, 1.0)`;n.addDefinitions(e,[i],xf.VERTEX),n.addBodyLines(e,[r],xf.VERTEX),n.addDefinitions(e,[i])}}handle_gl_Position(t){if(t.shader_name==xf.FRAGMENT){const e=t.globals_node,n=t.shaders_collection_controller,i=new Ef(e,Do.VEC4,t.var_name),r=`${t.var_name} = projectionMatrix * modelViewMatrix * vec4(position, 1.0)`;n.addDefinitions(e,[i],xf.VERTEX),n.addBodyLines(e,[r],xf.VERTEX),n.addDefinitions(e,[i])}}handle_gl_FragCoord(t){t.shader_name==xf.FRAGMENT&&t.body_lines.push(`vec4 ${t.var_name} = gl_FragCoord`)}handle_gl_PointCoord(t){t.shader_name==xf.FRAGMENT?t.body_lines.push(`vec2 ${t.var_name} = gl_PointCoord`):t.body_lines.push(`vec2 ${t.var_name} = vec2(0.0, 0.0)`)}set_node_lines_globals(t,e){const n=[],i=e.current_shader_name,r=this.shader_config(i);if(!r)return;const s=r.dependencies(),o=new Map,a=new Map,l=this.used_output_names_for_shader(t,i);for(let r of l){const l=t.glVarName(r),c=e.current_shader_name,u={globals_node:t,shaders_collection_controller:e,output_name:r,globals_shader_name:c,definitions_by_shader_name:o,body_lines:n,var_name:l,shader_name:i,dependencies:s,body_lines_by_shader_name:a};this.handle_globals_output_name(u)}o.forEach(((n,i)=>{e.addDefinitions(t,n,i)})),a.forEach(((n,i)=>{e.addBodyLines(t,n,i)})),e.addBodyLines(t,n)}used_output_names_for_shader(t,e){const n=t.io.outputs.used_output_names(),i=[];for(let t of n)e==xf.VERTEX&&B2.includes(t)||i.push(t);return i}}const U2=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( 1.0 );\\\\\\\"]]);const G2=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( 1.0 );\\\\\\\"]]);var V2=\\\\\\\"uniform float mNear;\\\\nuniform float mFar;\\\\n\\\\nvarying float vViewZDepth;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tfloat color = 1.0 - smoothstep( mNear, mFar, vViewZDepth );\\\\n\\\\tgl_FragColor = vec4( vec3( color ), 1.0 );\\\\n\\\\n}\\\\n\\\\\\\";const H2=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),j2=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const W2=new Map([]);W2.set(D2.DISTANCE,class extends z2{templateShader(){const t=V.distanceRGBA;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}insert_body_after(t){return U2.get(t)}createMaterial(){const t=this.templateShader();return new F({defines:{DEPTH_PACKING:[w.Hb,w.j][0]},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}}),W2.set(D2.DEPTH,class extends z2{templateShader(){const t=V.depth;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}insert_body_after(t){return G2.get(t)}createMaterial(){const t=this.templateShader();return new F({defines:{DEPTH_PACKING:[w.Hb,w.j][0]},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}}),W2.set(D2.DEPTH_DOF,class extends z2{templateShader(){return{vertexShader:\\\\\\\"#include \\\\n\\\\nvarying float vViewZDepth;\\\\n\\\\n// INSERT DEFINES\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvViewZDepth = - mvPosition.z;\\\\n}\\\\\\\",fragmentShader:V2,uniforms:{mNear:{value:0},mFar:{value:10}}}}insert_define_after(t){return H2.get(t)}insert_body_after(t){return j2.get(t)}createMaterial(){const t=this.templateShader();return new F({uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}});class q2 extends z2{custom_assembler_class_by_custom_name(){return W2}}class X2 extends q2{templateShader(){const t=V.basic;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({lights:!1,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}}class Y2 extends q2{templateShader(){const t=V.lambert;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({lights:!0,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}}class $2 extends q2{templateShader(){const t=V.phong;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({lights:!0,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}}var J2=\\\\\\\"SSSModel(/*isActive*/false,/*color*/vec3(1.0, 1.0, 1.0), /*thickness*/0.1, /*power*/2.0, /*scale*/16.0, /*distortion*/0.1,/*ambient*/0.4,/*attenuation*/0.8 )\\\\\\\";class Z2 extends q2{constructor(t){super(t),this._gl_parent_node=t,this._addFilterFragmentShaderCallback(\\\\\\\"MeshStandardBuilderMatNode\\\\\\\",Z2.filterFragmentShader)}isPhysical(){return!1}templateShader(){const t=this.isPhysical()?V.physical:V.standard;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}static filterFragmentShader(t){return t=(t=(t=t.replace(\\\\\\\"#include \\\\\\\",\\\\\\\"float metalnessFactor = metalness * POLY_metalness;\\\\n\\\\n#ifdef USE_METALNESSMAP\\\\n\\\\n\\\\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\\\\n\\\\n\\\\t// reads channel B, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\tmetalnessFactor *= texelMetalness.b;\\\\n\\\\n#endif\\\\n\\\\\\\")).replace(\\\\\\\"#include \\\\\\\",\\\\\\\"float roughnessFactor = roughness * POLY_roughness;\\\\n\\\\n#ifdef USE_ROUGHNESSMAP\\\\n\\\\n\\\\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\\\\n\\\\n\\\\t// reads channel G, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\troughnessFactor *= texelRoughness.g;\\\\n\\\\n#endif\\\\n\\\\\\\")).replace(\\\\\\\"vec3 totalEmissiveRadiance = emissive;\\\\\\\",\\\\\\\"vec3 totalEmissiveRadiance = emissive * POLY_emissive;\\\\\\\"),Z2.USE_SSS&&(t=(t=t.replace(/void main\\\\s?\\\\(\\\\) {/,\\\\\\\"struct SSSModel {\\\\n\\\\tbool isActive;\\\\n\\\\tvec3 color;\\\\n\\\\tfloat thickness;\\\\n\\\\tfloat power;\\\\n\\\\tfloat scale;\\\\n\\\\tfloat distortion;\\\\n\\\\tfloat ambient;\\\\n\\\\tfloat attenuation;\\\\n};\\\\n\\\\nvoid RE_Direct_Scattering(\\\\n\\\\tconst in IncidentLight directLight,\\\\n\\\\tconst in GeometricContext geometry,\\\\n\\\\tconst in SSSModel sssModel,\\\\n\\\\tinout ReflectedLight reflectedLight\\\\n\\\\t){\\\\n\\\\tvec3 scatteringHalf = normalize(directLight.direction + (geometry.normal * sssModel.distortion));\\\\n\\\\tfloat scatteringDot = pow(saturate(dot(geometry.viewDir, -scatteringHalf)), sssModel.power) * sssModel.scale;\\\\n\\\\tvec3 scatteringIllu = (scatteringDot + sssModel.ambient) * (sssModel.color * (1.0-sssModel.thickness));\\\\n\\\\treflectedLight.directDiffuse += scatteringIllu * sssModel.attenuation * directLight.color;\\\\n}\\\\n\\\\nvoid main() {\\\\\\\")).replace(\\\\\\\"#include \\\\\\\",\\\\\\\"#include \\\\nif(POLY_SSSModel.isActive){\\\\n\\\\tRE_Direct_Scattering(directLight, geometry, POLY_SSSModel, reflectedLight);\\\\n}\\\\n\\\\n\\\\\\\")),t}createMaterial(){const t=this.templateShader(),e={lights:!0,extensions:{derivatives:!0},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader},n=new F(e);return this.isPhysical()&&(n.defines.PHYSICAL=!0),this._addCustomMaterials(n),n}add_output_inputs(t){const e=F2.output_input_connection_points();e.push(new Vo(\\\\\\\"metalness\\\\\\\",Do.FLOAT,1)),e.push(new Vo(\\\\\\\"roughness\\\\\\\",Do.FLOAT,1)),e.push(new Vo(\\\\\\\"emissive\\\\\\\",Do.VEC3,[1,1,1])),Z2.USE_SSS&&e.push(new Vo(\\\\\\\"SSSModel\\\\\\\",Do.SSS_MODEL,J2)),t.io.inputs.setNamedInputConnectionPoints(e)}create_shader_configs(){return[new T2(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\"],[]),new T2(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\",\\\\\\\"metalness\\\\\\\",\\\\\\\"roughness\\\\\\\",\\\\\\\"emissive\\\\\\\",\\\\\\\"SSSModel\\\\\\\"],[xf.VERTEX])]}create_variable_configs(){const t=F2.create_variable_configs();return t.push(new A2(\\\\\\\"metalness\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_metalness = \\\\\\\"})),t.push(new A2(\\\\\\\"roughness\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_roughness = \\\\\\\"})),t.push(new A2(\\\\\\\"emissive\\\\\\\",{default:\\\\\\\"vec3(1.0, 1.0, 1.0)\\\\\\\",prefix:\\\\\\\"vec3 POLY_emissive = \\\\\\\"})),Z2.USE_SSS&&t.push(new A2(\\\\\\\"SSSModel\\\\\\\",{default:J2,prefix:\\\\\\\"SSSModel POLY_SSSModel = \\\\\\\"})),t}}Z2.USE_SSS=!0;class Q2 extends Z2{isPhysical(){return!0}}const K2=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),t3=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const e3=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),n3=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const i3=new Map([[xf.VERTEX,[\\\\\\\"#include \\\\\\\",\\\\\\\"gl_PointSize = size;\\\\\\\"]],[xf.FRAGMENT,[]]]),r3=new Map;r3.set(D2.DISTANCE,class extends z2{templateShader(){const t=V.distanceRGBA,e=I.clone(t.uniforms);return e.size={value:1},e.scale={value:1},{vertexShader:\\\\\\\"uniform float size;\\\\nuniform float scale;\\\\n#define DISTANCE\\\\nvarying vec3 vWorldPosition;\\\\n#include \\\\n#include \\\\nvarying float vViewZDepth;\\\\n\\\\n// INSERT DEFINES\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#ifdef USE_SIZEATTENUATION\\\\n\\\\t\\\\tbool isPerspective = ( projectionMatrix[ 2 ][ 3 ] == - 1.0 );\\\\n\\\\t\\\\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\\\\n\\\\t#endif\\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n}\\\\n\\\\n// #define DISTANCE\\\\n// varying vec3 vWorldPosition;\\\\n// #include \\\\n// #include \\\\n// #include \\\\n// #include \\\\n// #include \\\\n// #include \\\\n// void main() {\\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#ifdef USE_DISPLACEMENTMAP\\\\n// \\\\t\\\\t#include \\\\n// \\\\t\\\\t#include \\\\n// \\\\t\\\\t#include \\\\n// \\\\t#endif\\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\tvWorldPosition = worldPosition.xyz;\\\\n// }\\\\n\\\\n\\\\n\\\\\\\",fragmentShader:t.fragmentShader,uniforms:e}}insert_define_after(t){return K2.get(t)}insert_body_after(t){return t3.get(t)}createMaterial(){const t=this.templateShader();return new F({defines:{USE_SIZEATTENUATION:1,DEPTH_PACKING:[w.Hb,w.j][0]},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}}),r3.set(D2.DEPTH_DOF,class extends z2{templateShader(){return{vertexShader:\\\\\\\"uniform float size;\\\\nuniform float scale;\\\\n#include \\\\n\\\\nvarying float vViewZDepth;\\\\n\\\\n// INSERT DEFINES\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvViewZDepth = - mvPosition.z;\\\\n\\\\t#ifdef USE_SIZEATTENUATION\\\\n\\\\t\\\\tbool isPerspective = ( projectionMatrix[ 2 ][ 3 ] == - 1.0 );\\\\n\\\\t\\\\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\\\\n\\\\t#endif\\\\n\\\\n}\\\\n\\\\n\\\\\\\",fragmentShader:V2,uniforms:{size:{value:1},scale:{value:1},mNear:{value:0},mFar:{value:10}}}}insert_define_after(t){return e3.get(t)}insert_body_after(t){return n3.get(t)}createMaterial(){const t=this.templateShader();return new F({depthTest:!0,defines:{USE_SIZEATTENUATION:1},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}});class s3 extends z2{custom_assembler_class_by_custom_name(){return r3}templateShader(){const t=V.points;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({transparent:!0,fog:!0,defines:{USE_SIZEATTENUATION:1},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}add_output_inputs(t){const e=F2.output_input_connection_points();e.push(new Vo(\\\\\\\"gl_PointSize\\\\\\\",Do.FLOAT)),t.io.inputs.setNamedInputConnectionPoints(e)}create_globals_node_output_connections(){return F2.create_globals_node_output_connections().concat([new Vo(k2.GL_POINTCOORD,Do.VEC2)])}create_shader_configs(){return[new T2(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",\\\\\\\"gl_PointSize\\\\\\\"],[]),new T2(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[xf.VERTEX])]}create_variable_configs(){return F2.create_variable_configs().concat([new A2(\\\\\\\"gl_PointSize\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"gl_PointSize = \\\\\\\",suffix:\\\\\\\" * size * 10.0\\\\\\\"})])}lines_to_remove(t){return i3.get(t)}}const o3=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),a3=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const l3=new Map([]);l3.set(D2.DEPTH_DOF,class extends z2{templateShader(){return{vertexShader:\\\\\\\"uniform float scale;\\\\nattribute float lineDistance;\\\\nvarying float vLineDistance;\\\\n#include \\\\n\\\\nvarying float vViewZDepth;\\\\n\\\\n// INSERT DEFINES\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\n\\\\tvLineDistance = scale * lineDistance;\\\\n\\\\tgl_Position = projectionMatrix * mvPosition;\\\\n\\\\n\\\\tvViewZDepth = - mvPosition.z;\\\\n\\\\n\\\\n}\\\\n\\\\n\\\\n\\\\n\\\\\\\",fragmentShader:V2,uniforms:{scale:{value:1},mNear:{value:0},mFar:{value:10}}}}insert_define_after(t){return o3.get(t)}insert_body_after(t){return a3.get(t)}createMaterial(){const t=this.templateShader();return new F({depthTest:!0,linewidth:100,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}});const c3=new Map([[xf.VERTEX,[\\\\\\\"#include \\\\\\\",\\\\\\\"#include \\\\\\\"]],[xf.FRAGMENT,[]]]);class u3 extends z2{templateShader(){const t=V.dashed;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({depthTest:!0,alphaTest:.5,linewidth:1,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}custom_assembler_class_by_custom_name(){return console.log(\\\\\\\"custom_assembler_class_by_custom_name\\\\\\\",l3),l3}create_shader_configs(){return[new T2(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"uv\\\\\\\"],[]),new T2(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[xf.VERTEX])]}static output_input_connection_points(){return[new Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"color\\\\\\\",Do.VEC3),new Vo(\\\\\\\"alpha\\\\\\\",Do.FLOAT),new Vo(\\\\\\\"uv\\\\\\\",Do.VEC2)]}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints(u3.output_input_connection_points())}static create_globals_node_output_connections(){return[new Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"color\\\\\\\",Do.VEC3),new Vo(\\\\\\\"uv\\\\\\\",Do.VEC2),new Vo(\\\\\\\"gl_FragCoord\\\\\\\",Do.VEC4),new Vo(\\\\\\\"resolution\\\\\\\",Do.VEC2),new Vo(\\\\\\\"time\\\\\\\",Do.FLOAT)]}create_globals_node_output_connections(){return u3.create_globals_node_output_connections()}create_variable_configs(){return[new A2(\\\\\\\"position\\\\\\\",{default:\\\\\\\"vec3( position )\\\\\\\",prefix:\\\\\\\"vec3 transformed = \\\\\\\",suffix:\\\\\\\";vec4 mvPosition = vec4( transformed, 1.0 ); gl_Position = projectionMatrix * modelViewMatrix * mvPosition;\\\\\\\"}),new A2(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new A2(\\\\\\\"alpha\\\\\\\",{prefix:\\\\\\\"diffuseColor.w = \\\\\\\"}),new A2(\\\\\\\"uv\\\\\\\",{prefix:\\\\\\\"vUv = \\\\\\\",if:Sf.IF_RULE.uv})]}lines_to_remove(t){return c3.get(t)}}class h3 extends F2{templateShader(){}_template_shader_for_shader_name(t){return\\\\\\\"#include \\\\n\\\\n// INSERT DEFINE\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec2 particleUV = (gl_FragCoord.xy / resolution.xy);\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n}\\\\\\\"}compile(){this.setup_shader_names_and_variables(),this.update_shaders()}root_nodes_by_shader_name(t){var e,n;const i=[];for(let r of this._root_nodes)switch(r.type()){case UI.type():i.push(r);break;case gf.type():{const s=r.attribute_name,o=null===(e=this._texture_allocations_controller)||void 0===e?void 0:e.variable(s);if(o&&o.allocation()){(null===(n=o.allocation())||void 0===n?void 0:n.shaderName())==t&&i.push(r)}break}}return i}leaf_nodes_by_shader_name(t){var e,n;const i=[];for(let r of this._leaf_nodes)switch(r.type()){case HP.type():i.push(r);break;case gf.type():{const s=r.attribute_name,o=null===(e=this._texture_allocations_controller)||void 0===e?void 0:e.variable(s);if(o&&o.allocation()){(null===(n=o.allocation())||void 0===n?void 0:n.shaderName())==t&&i.push(r)}break}}return i}setup_shader_names_and_variables(){var t;const e=new O2(this.currentGlParentNode(),this.shaderNames(),((t,e)=>this.input_names_for_shader_name(t,e)));this._leaf_nodes=e.leaves_from_nodes(this._root_nodes),this._texture_allocations_controller=new dQ,this._texture_allocations_controller.allocateConnectionsFromRootNodes(this._root_nodes,this._leaf_nodes),this.globals_handler&&(null===(t=this.globals_handler)||void 0===t||t.set_texture_allocations_controller(this._texture_allocations_controller)),this._reset_shader_configs()}update_shaders(){this._shaders_by_name.clear(),this._lines.clear();for(let t of this.shaderNames()){const e=this._template_shader_for_shader_name(t);this._lines.set(t,e.split(\\\\\\\"\\\\n\\\\\\\"))}this._root_nodes.length>0&&(this._resetCodeBuilder(),this.build_code_from_nodes(this._root_nodes),this._build_lines());for(let t of this.shaderNames()){const e=this._lines.get(t);e&&this._shaders_by_name.set(t,e.join(\\\\\\\"\\\\n\\\\\\\"))}}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints([new Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"velocity\\\\\\\",Do.VEC3)])}add_globals_outputs(t){t.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"velocity\\\\\\\",Do.VEC3),new Vo(\\\\\\\"time\\\\\\\",Do.FLOAT)])}allow_attribute_exports(){return!0}textureAllocationsController(){return this._texture_allocations_controller=this._texture_allocations_controller||new dQ}create_shader_configs(){var t;return(null===(t=this._texture_allocations_controller)||void 0===t?void 0:t.createShaderConfigs())||[]}create_variable_configs(){return[]}shaderNames(){return this.textureAllocationsController().shaderNames()||[]}input_names_for_shader_name(t,e){return this.textureAllocationsController().inputNamesForShaderName(t,e)||[]}insert_define_after(t){return\\\\\\\"// INSERT DEFINE\\\\\\\"}insert_body_after(t){return\\\\\\\"// INSERT BODY\\\\\\\"}lines_to_remove(t){return[\\\\\\\"// INSERT DEFINE\\\\\\\",\\\\\\\"// INSERT BODY\\\\\\\"]}add_export_body_line(t,e,n,i,r){var s;if(n){const n=t.variableForInput(e),o=uf.vector3(n);if(o){const e=this.textureAllocationsController().variable(i),n=r.current_shader_name;if(e&&(null===(s=e.allocation())||void 0===s?void 0:s.shaderName())==n){const i=`gl_FragColor.${e.component()} = ${o}`;r.addBodyLines(t,[i],n)}}}}set_node_lines_output(t,e){const n=e.current_shader_name,i=this.textureAllocationsController().inputNamesForShaderName(t,n);if(i)for(let n of i){const i=t.io.inputs.named_input(n);if(i){const r=n;this.add_export_body_line(t,n,i,r,e)}}}set_node_lines_attribute(t,e){var n,i;if(t.isImporting()){const r=t.gl_type(),s=t.attribute_name,o=null===(n=this.globals_handler)||void 0===n?void 0:n.read_attribute(t,r,s,e),a=t.glVarName(t.output_name),l=`${r} ${a} = ${o}`;e.addBodyLines(t,[l]);const c=this.textureAllocationsController().variable(s),u=e.current_shader_name;if(c&&(null===(i=c.allocation())||void 0===i?void 0:i.shaderName())==u){const n=this.textureAllocationsController().variable(s);if(n){const i=`gl_FragColor.${n.component()} = ${a}`;e.addBodyLines(t,[i])}}}if(t.isExporting()){const n=t.connected_input_node();if(n){const i=t.attribute_name;this.add_export_body_line(t,t.input_name,n,i,e)}}}set_node_lines_globals(t,e){for(let n of t.io.outputs.used_output_names())switch(n){case\\\\\\\"time\\\\\\\":this._handle_globals_time(t,n,e);break;default:this._handle_globals_default(t,n,e)}}_handle_globals_time(t,e,n){const i=new Af(t,Do.FLOAT,e);n.addDefinitions(t,[i]);const r=`float ${t.glVarName(e)} = ${e}`;n.addBodyLines(t,[r]),this.setUniformsTimeDependent()}_handle_globals_default(t,e,n){var i;const r=t.io.outputs.namedOutputConnectionPointsByName(e);if(r){const s=r.type(),o=null===(i=this.globals_handler)||void 0===i?void 0:i.read_attribute(t,s,e,n);if(o){const i=`${s} ${t.glVarName(e)} = ${o}`;n.addBodyLines(t,[i])}}}}class d3 extends F2{templateShader(){return{fragmentShader:\\\\\\\"#include \\\\n\\\\nuniform vec2 resolution;\\\\n\\\\n// INSERT DEFINE\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 diffuseColor = vec4(0.0,0.0,0.0,1.0);\\\\n\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\tgl_FragColor = vec4( diffuseColor );\\\\n}\\\\\\\",vertexShader:void 0,uniforms:void 0}}fragment_shader(){return this._shaders_by_name.get(xf.FRAGMENT)}uniforms(){return this._uniforms}update_fragment_shader(){this._lines=new Map,this._shaders_by_name=new Map;for(let t of this.shaderNames())if(t==xf.FRAGMENT){const e=this.templateShader().fragmentShader;this._lines.set(t,e.split(\\\\\\\"\\\\n\\\\\\\"))}this._root_nodes.length>0&&(this.build_code_from_nodes(this._root_nodes),this._build_lines()),this._uniforms=this._uniforms||{},this.addUniforms(this._uniforms);for(let t of this.shaderNames()){const e=this._lines.get(t);e&&this._shaders_by_name.set(t,e.join(\\\\\\\"\\\\n\\\\\\\"))}tg.handle_dependencies(this.currentGlParentNode(),this.uniformsTimeDependent(),this._uniforms)}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints([new Vo(\\\\\\\"color\\\\\\\",Do.VEC3),new Vo(\\\\\\\"alpha\\\\\\\",Do.FLOAT)])}add_globals_outputs(t){t.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"gl_FragCoord\\\\\\\",Do.VEC2),new Vo(\\\\\\\"time\\\\\\\",Do.FLOAT)])}create_shader_configs(){return[new T2(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[])]}create_variable_configs(){return[new A2(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new A2(\\\\\\\"alpha\\\\\\\",{prefix:\\\\\\\"diffuseColor.a = \\\\\\\",default:\\\\\\\"1.0\\\\\\\"})]}insert_define_after(t){return\\\\\\\"// INSERT DEFINE\\\\\\\"}insert_body_after(t){return\\\\\\\"// INSERT BODY\\\\\\\"}lines_to_remove(t){return[\\\\\\\"// INSERT DEFINE\\\\\\\",\\\\\\\"// INSERT BODY\\\\\\\"]}handle_gl_FragCoord(t,e,n){\\\\\\\"fragment\\\\\\\"==e&&t.push(`vec2 ${n} = vec2(gl_FragCoord.x / resolution.x, gl_FragCoord.y / resolution.y)`)}set_node_lines_output(t,e){const n=this.input_names_for_shader_name(t,e.current_shader_name);if(n)for(let i of n){if(t.io.inputs.named_input(i)){const n=t.variableForInput(i);let r;\\\\\\\"color\\\\\\\"==i&&(r=`diffuseColor.xyz = ${uf.any(n)}`),\\\\\\\"alpha\\\\\\\"==i&&(r=`diffuseColor.a = ${uf.any(n)}`),r&&e.addBodyLines(t,[r])}}}set_node_lines_globals(t,e){const n=e.current_shader_name;if(!this.shader_config(n))return;const i=[],r=[];for(let e of t.io.outputs.used_output_names()){const s=t.glVarName(e);switch(e){case\\\\\\\"time\\\\\\\":r.push(new Af(t,Do.FLOAT,e)),i.push(`float ${s} = ${e}`),this.setUniformsTimeDependent();break;case\\\\\\\"gl_FragCoord\\\\\\\":this.handle_gl_FragCoord(i,n,s)}}e.addDefinitions(t,r,n),e.addBodyLines(t,i)}}const p3=new Map([]);class _3 extends z2{custom_assembler_class_by_custom_name(){return p3}}const m3=new Map([[xf.VERTEX,\\\\\\\"// start builder body code\\\\\\\"],[xf.FRAGMENT,\\\\\\\"// start builder body code\\\\\\\"]]),f3=new Map([[xf.FRAGMENT,[]]]);class g3 extends _3{templateShader(){return{vertexShader:jB,fragmentShader:WB,uniforms:I.clone(qB)}}createMaterial(){const t=this.templateShader(),e=new F({vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,side:w.H,transparent:!0,depthTest:!0,uniforms:I.clone(t.uniforms)});return fs.add_user_data_render_hook(e,$B.render_hook.bind($B)),this._addCustomMaterials(e),e}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints([new Vo(\\\\\\\"density\\\\\\\",Do.FLOAT,1)])}static create_globals_node_output_connections(){return[new Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"pos_normalized\\\\\\\",Do.VEC3),new Vo(\\\\\\\"time\\\\\\\",Do.FLOAT)]}create_globals_node_output_connections(){return g3.create_globals_node_output_connections()}insert_body_after(t){return m3.get(t)}lines_to_remove(t){return f3.get(t)}create_shader_configs(){return[new T2(xf.VERTEX,[],[]),new T2(xf.FRAGMENT,[\\\\\\\"density\\\\\\\"],[xf.VERTEX])]}static create_variable_configs(){return[new A2(\\\\\\\"position\\\\\\\",{}),new A2(\\\\\\\"density\\\\\\\",{prefix:\\\\\\\"density *= \\\\\\\"})]}create_variable_configs(){return g3.create_variable_configs()}set_node_lines_globals(t,e){const n=[],i=e.current_shader_name,r=this.shader_config(i);if(!r)return;const s=r.dependencies(),o=new Map,a=new Map;let l,c;for(let r of t.io.outputs.used_output_names()){const h=t.glVarName(r),d=e.current_shader_name;switch(r){case\\\\\\\"time\\\\\\\":l=new Af(t,Do.FLOAT,r),d&&u.pushOnArrayAtEntry(o,d,l),c=`float ${h} = ${r}`;for(let t of s)u.pushOnArrayAtEntry(o,t,l),u.pushOnArrayAtEntry(a,t,c);n.push(c),this.setUniformsTimeDependent();break;case\\\\\\\"position\\\\\\\":i==xf.FRAGMENT&&n.push(`vec3 ${h} = position_for_step`);break;case\\\\\\\"pos_normalized\\\\\\\":i==xf.FRAGMENT&&n.push(`vec3 ${h} = (position_for_step - u_BoundingBoxMax) / (u_BoundingBoxMax - u_BoundingBoxMin)`)}}o.forEach(((n,i)=>{e.addDefinitions(t,n,i)})),a.forEach(((n,i)=>{e.addBodyLines(t,n,i)})),e.addBodyLines(t,n)}}class v3{static async run(){this._started||(this._started=!0,class{static async run(t){(class{static run(t){t.registerNode(I_,Ql),t.registerNode(D_,ec),t.registerNode(B_,Ql),t.registerNode(U_,Ql),t.registerNode($_,Ql),t.registerNode(Z_,Zl),t.registerNode(K_,Ql),t.registerNode(em,ec),t.registerNode(im,tc),t.registerNode(um,tc),t.registerNode(dm,Ql),t.registerNode(_m,Zl),t.registerNode(wm,tc),t.registerNode(Em,Kl),t.registerNode(Mm,Kl),t.registerNode(Sm,Kl),t.registerNode(Cm,Kl),t.registerNode(Qm,Kl),t.registerNode(Km,Kl)}}).run(t),class{static run(t){t.registerNode(tg,nc),t.registerNode(Mg,ic),t.registerNode(Cg,ic),t.registerNode(Ig,ic),t.registerNode(Bg,ic),t.registerNode(Jg,ic),t.registerNode(ev,ic),t.registerNode(ov,rc),t.registerNode(lv,rc),t.registerNode(dv,rc),t.registerNode(_v,rc),t.registerNode(gv,nc),t.registerNode(xv,ic),t.registerNode(Tv,nc),t.registerNode(Mv,sc),t.registerNode(Cv,sc),t.registerNode(Sv,sc),t.registerNode(Nv,sc),t.registerNode(Lv,sc),t.registerNode(Ov,sc)}}.run(t),class{static run(t){t.registerNode(Fv,cc),t.registerNode(Bv,lc),t.registerNode(Uv,lc),t.registerNode(Hv,lc),t.registerNode(Kv,oc),t.registerNode(dy,oc),t.registerNode(hy,oc),t.registerNode(_y,lc),t.registerNode(yl,ac),t.registerNode(DN,oc),t.registerNode(ol,ac),t.registerNode(UN,lc),t.registerNode(VN,lc),t.registerNode(QN,oc),t.registerNode(qa,ac),t.registerNode(nL,cc),t.registerNode(oL,lc),t.registerNode(Za,ac),t.registerNode(NL,lc),t.registerNode(el,cc),t.registerNode(PL,cc),t.registerNode(jL,cc),t.registerNode(qL,lc),t.registerNode($L,lc),t.registerNode(bl,ac),t.registerNode(ZL,lc),t.registerNode(hl,ac),t.registerNode(tO,uc),t.registerNode(eO,uc),t.registerNode(nO,uc),t.registerNode(iO,uc),t.registerNode(rO,uc),t.registerNode(sO,uc)}}.run(t),class{static run(t){t.registerNode(jO,gc),t.registerNode(zR,vc),t.registerNode(WO,xc),t.registerNode(AR,gc),t.registerNode(jR,xc),t.registerNode(LR,fc),t.registerNode(qO,xc),t.registerNode(XO,xc),t.registerNode(gf,_c,{except:[`${Ki.COP}/builder`]}),t.registerNode(fO,dc),t.registerNode(YO,gc),t.registerNode(xR,gc),t.registerNode(YR,hc),t.registerNode(eP,fc),t.registerNode(nP,gc),t.registerNode(sP,_c),t.registerNode($O,xc),t.registerNode(lP,pc),t.registerNode(cP,gc),t.registerNode(JO,dc),t.registerNode(dP,pc),t.registerNode(hR,pc),t.registerNode(ER,gc),t.registerNode(dR,pc),t.registerNode(xP,gc),t.registerNode(ZO,gc),t.registerNode(QO,gc),t.registerNode(bR,pc),t.registerNode(TP,gc),t.registerNode(EP,gc),t.registerNode(SP,gc),t.registerNode(NP,gc),t.registerNode(lO,dc),t.registerNode(vO,dc),t.registerNode(xO,dc),t.registerNode(wO,dc),t.registerNode(KO,gc),t.registerNode(RP,hc),t.registerNode(GP,fc),t.registerNode(tR,gc),t.registerNode(HP,_c),t.registerNode(WP,hc),t.registerNode(XP,hc),t.registerNode(JP,fc),t.registerNode(QP,bc),t.registerNode(_O,dc),t.registerNode(hO,dc),t.registerNode(eR,gc),t.registerNode(sI,pc),t.registerNode(oI,pc),t.registerNode(lI,hc),t.registerNode(nR,gc),t.registerNode(iR,gc),t.registerNode(pR,gc),t.registerNode(uI,gc),t.registerNode(_R,gc),t.registerNode(mR,gc),t.registerNode(mI,gc),t.registerNode(dI,gc),t.registerNode(SR,gc),t.registerNode(vI,gc),t.registerNode(yI,gc),t.registerNode(BI,bc),t.registerNode(kI,pc),t.registerNode(rR,gc),t.registerNode(OR,fc),t.registerNode(UI,_c),t.registerNode(HI,_c),t.registerNode(fR,gc),t.registerNode(YI,yc),t.registerNode(QI,yc),t.registerNode(KI,yc),t.registerNode(tF,yc),t.registerNode(iF,_c),t.registerNode(oF,_c),t.registerNode(sR,dc),t.registerNode(gR,pc),t.registerNode(jI,pc),t.registerNode(lF,hc),t.registerNode(gF,pc),t.registerNode(yF,gc),t.registerNode(oR,gc),t.registerNode(aR,xc),t.registerNode(wR,gc),t.registerNode(bF,pc),t.registerNode(lR,gc),t.registerNode(XI,mc),t.registerNode(vR,pc),t.registerNode(kP,fc),t.registerNode(TF,fc,VF),t.registerNode(IP,fc,VF),t.registerNode(MR,gc),t.registerNode(EF,fc),t.registerNode(cR,xc),t.registerNode(SF,hc),t.registerNode(OF,_c),t.registerNode(IF,fc),t.registerNode(Lf,_c),t.registerNode(kF,_c),t.registerNode(NO,dc),t.registerNode(IO,dc),t.registerNode(LO,dc),t.registerNode(PO,dc),t.registerNode(FO,dc),t.registerNode(OO,dc),t.registerNode(RO,dc),t.registerNode(zF,pc),t.registerNode(GF,pc)}}.run(t),class{static run(t){t.registerNode(qF,wc),t.registerNode(YF,wc),t.registerNode(JF,wc),t.registerNode(iD,wc)}}.run(t),class{static run(t){t.registerNode(dD,Ac),t.registerNode(AD,Ac),t.registerNode(ek,Ec),t.registerNode(ak,Tc),t.registerNode(pk,Ec),t.registerNode(yk,Tc),t.registerNode(Ik,Ec),t.registerNode(Uk,Ec),t.registerNode(jk,Tc),t.registerNode(tB,Ec),t.registerNode(rB,Tc),t.registerNode(lB,Ec),t.registerNode(dB,Tc),t.registerNode(AB,Ec),t.registerNode(LB,Ec),t.registerNode(IB,Sc),t.registerNode(kB,Tc),t.registerNode(UB,Tc),t.registerNode(HB,Ec),t.registerNode(QB,Cc),t.registerNode(ez,Cc),t.registerNode(rz,Mc),t.registerNode(sz,Mc),t.registerNode(oz,Mc),t.registerNode(az,Mc),t.registerNode(lz,Mc),t.registerNode(cz,Mc)}}.run(t),class{static run(t){t.registerNode(gz,Pc),t.registerNode(Uz,Pc),t.registerNode(Zz,Pc),t.registerNode(rU,Pc),t.registerNode(uU,Pc),t.registerNode(vU,Pc),t.registerNode(CU,Lc),t.registerNode(PU,Fc),t.registerNode(HU,Nc),t.registerNode(YU,Rc),t.registerNode(ZU,Fc),t.registerNode(nG,Fc),t.registerNode(NG,Nc),t.registerNode(jG,Lc),t.registerNode(YG,Fc),t.registerNode(ZG,Nc),t.registerNode(lH,Oc),t.registerNode(dH,Oc),t.registerNode(mH,Oc),t.registerNode(yH,Ic),t.registerNode(xH,Ic),t.registerNode(bH,Ic),t.registerNode(wH,Ic),t.registerNode(TH,Ic),t.registerNode(AH,Ic)}}.run(t),class{static run(t){t.registerNode(RH,Qc),t.registerNode(DH,Qc),t.registerNode(zH,Zc),t.registerNode(VH,Zc),t.registerNode(WH,Kc),t.registerNode(XH,Kc),t.registerNode(JH,Kc),t.registerNode(QH,Kc),t.registerNode(aj,Qc),t.registerNode(rj,Qc),t.registerNode(hj,Qc),t.registerNode(_j,Kc),t.registerNode(gj,Zc),t.registerNode(yj,Jc),t.registerNode(bj,Kc),t.registerNode(Sj,Kc),t.registerNode(Nj,Kc),t.registerNode(Oj,Kc),t.registerNode(Ij,Qc),t.registerNode(kj,Qc),t.registerNode(zj,Kc),t.registerNode(Vj,Qc),t.registerNode(Wj,Zc),t.registerNode(Xj,Kc),t.registerNode(Zj,Jc),t.registerNode(eW,Qc),t.registerNode(iW,Jc),t.registerNode(oW,Qc),t.registerNode(cW,tu),t.registerNode(uW,tu),t.registerNode(hW,tu),t.registerNode(dW,tu),t.registerNode(pW,tu),t.registerNode(_W,tu)}}.run(t),class{static run(t){t.registerNode(bW,Dc),t.registerNode(PV,Bc),t.registerNode(wW,kc),t.registerNode(gW,kc),t.registerNode(TW,kc),t.registerNode(AW,kc),t.registerNode(EW,kc),t.registerNode(MW,kc)}}.run(t),class{static run(t){t.registerOperation(SW),t.registerOperation(GW),t.registerOperation($W),t.registerOperation(KW),t.registerOperation(iq),t.registerOperation(gq),t.registerOperation(hq),t.registerOperation(wq),t.registerOperation(eX),t.registerOperation(sX),t.registerOperation(yY),t.registerOperation(AY),t.registerOperation(s$),t.registerOperation(A$),t.registerOperation(DJ),t.registerOperation(YJ),t.registerOperation(rZ),t.registerOperation(lZ),t.registerOperation(_Z),t.registerOperation(PZ),t.registerOperation(AZ),t.registerOperation(WZ),t.registerOperation(JZ),t.registerOperation(fQ),t.registerOperation(TQ),t.registerOperation(LQ),t.registerOperation(DQ),t.registerOperation(XQ),t.registerOperation(nK),t.registerOperation(pK),t.registerOperation(xK),t.registerOperation(AK),t.registerOperation(RK),t.registerOperation(GK),t.registerOperation(QK),t.registerOperation(h0),t.registerOperation(w0),t.registerOperation(H0),t.registerOperation(X0),t.registerOperation(Q0),t.registerOperation(n1),t.registerOperation(l1),t.registerOperation(S1),t.registerOperation(I1),t.registerOperation(B1),t.registerNode(LW,Hc),t.registerNode(RW,Uc),t.registerNode(UW,Uc),t.registerNode(jW,Gc),t.registerNode(QW,Gc),t.registerNode(nq,Gc),t.registerNode(aq,Gc),t.registerNode(cq,Gc),t.registerNode(_q,Gc),t.registerNode(xq,Gc),t.registerNode(Mq,Gc),t.registerNode(Cq,Gc),t.registerNode(Lq,Gc),t.registerNode(Dq,Gc),t.registerNode(Bq,qc),t.registerNode(Uq,qc),t.registerNode(rX,qc),t.registerNode(lX,Yc),t.registerNode(dY,Wc),t.registerNode(vY,Yc),t.registerNode(bY,Yc),t.registerNode(SY,Yc),t.registerNode(IY,Yc),t.registerNode(UY,qc),t.registerNode(HY,Yc),t.registerNode(e$,qc),t.registerNode(l$,Yc),t.registerNode(p$,Hc),t.registerNode(b$,Hc),t.registerNode(S$,Wc),t.registerNode(N$,Wc),t.registerNode(j$,qc),t.registerNode(q$,qc),t.registerNode(CJ,zc),t.registerNode(LJ,qc),t.registerNode(zJ,Hc),t.registerNode(GJ,qc),t.registerNode(WJ,Yc),t.registerNode(tZ,qc),t.registerNode(QJ,Wc),t.registerNode(aZ,Yc),t.registerNode(hZ,$c),t.registerNode(pZ,$c),t.registerNode(gZ,qc),t.registerNode(yZ,qc),t.registerNode(bZ,Yc),t.registerNode(TZ,zc),t.registerNode(SZ,$c),t.registerNode(RZ,zc),t.registerNode(kZ,Wc),t.registerNode(VZ,Wc),t.registerNode(jZ,qc),t.registerNode(XZ,Wc),t.registerNode($Z,Hc),t.registerNode(KZ,qc),t.registerNode(eQ,zc,{userAllowed:!1}),t.registerNode(mQ,Vc),t.registerNode(yQ,qc),t.registerNode(MQ,Yc),t.registerNode(zQ,qc),t.registerNode(NQ,qc),t.registerNode(PQ,jc),t.registerNode(EG,zc),t.registerNode(WQ,qc),t.registerNode(JQ,qc),t.registerNode(sK,$c),t.registerNode(dK,qc),t.registerNode(fK,Gc),t.registerNode(TK,Hc),t.registerNode(SK,qc),t.registerNode(BK,qc),t.registerNode(DK,qc),t.registerNode(qK,zc),t.registerNode(YK,zc),t.registerNode(jK,qc),t.registerNode(e0,Yc),t.registerNode(i0,qc),t.registerNode(_0,qc),t.registerNode(f0,Wc),t.registerNode(v0,Wc),t.registerNode(yG,Wc),t.registerNode(E0,Hc),t.registerNode(C0,Wc),t.registerNode(O0,Yc),t.registerNode(V0,Yc),t.registerNode(q0,qc),t.registerNode(J0,qc),t.registerNode(e1,Yc),t.registerNode(s1,Yc),t.registerNode(h1,qc),t.registerNode(p1,qc),t.registerNode(v1,qc),t.registerNode(w1,qc),t.registerNode(E1,Yc),t.registerNode(N1,qc),t.registerNode(P1,qc),t.registerNode(k1,qc),t.registerNode(U1,qc),t.registerNode(H1,Xc),t.registerNode(j1,Xc),t.registerNode(W1,Xc),t.registerNode(q1,Xc),t.registerNode(X1,Xc),t.registerNode(Y1,Xc)}}.run(t)}}.run(ai),class{static run(t){t.registerCamera(lH),t.registerCamera(dH)}}.run(ai),class{static run(t){t.expressionsRegister.register(J1,\\\\\\\"arg\\\\\\\"),t.expressionsRegister.register(Z1,\\\\\\\"argc\\\\\\\"),t.expressionsRegister.register(t2,\\\\\\\"bbox\\\\\\\"),t.expressionsRegister.register(e2,\\\\\\\"centroid\\\\\\\"),t.expressionsRegister.register(n2,\\\\\\\"ch\\\\\\\"),t.expressionsRegister.register(i2,\\\\\\\"copy\\\\\\\"),t.expressionsRegister.register(r2,\\\\\\\"copRes\\\\\\\"),t.expressionsRegister.register(s2,\\\\\\\"isDeviceMobile\\\\\\\"),t.expressionsRegister.register(o2,\\\\\\\"isDeviceTouch\\\\\\\"),t.expressionsRegister.register(a2,\\\\\\\"js\\\\\\\"),t.expressionsRegister.register(l2,\\\\\\\"object\\\\\\\"),t.expressionsRegister.register(c2,\\\\\\\"objectsCount\\\\\\\"),t.expressionsRegister.register(u2,\\\\\\\"opdigits\\\\\\\"),t.expressionsRegister.register(h2,\\\\\\\"opname\\\\\\\"),t.expressionsRegister.register(d2,\\\\\\\"padzero\\\\\\\"),t.expressionsRegister.register(p2,\\\\\\\"point\\\\\\\"),t.expressionsRegister.register(_2,\\\\\\\"pointsCount\\\\\\\"),t.expressionsRegister.register(m2,\\\\\\\"strCharsCount\\\\\\\"),t.expressionsRegister.register(f2,\\\\\\\"strConcat\\\\\\\"),t.expressionsRegister.register(g2,\\\\\\\"strIndex\\\\\\\"),t.expressionsRegister.register(v2,\\\\\\\"strSub\\\\\\\"),t.expressionsRegister.register(y2,\\\\\\\"windowSize\\\\\\\")}}.run(ai),class{static run(t){t.assemblersRegister.register(Hn.GL_MESH_BASIC,b2,X2),t.assemblersRegister.register(Hn.GL_MESH_LAMBERT,b2,Y2),t.assemblersRegister.register(Hn.GL_MESH_PHONG,b2,$2),t.assemblersRegister.register(Hn.GL_MESH_STANDARD,b2,Z2),t.assemblersRegister.register(Hn.GL_MESH_PHYSICAL,b2,Q2),t.assemblersRegister.register(Hn.GL_PARTICLES,b2,h3),t.assemblersRegister.register(Hn.GL_POINTS,b2,s3),t.assemblersRegister.register(Hn.GL_LINE,b2,u3),t.assemblersRegister.register(Hn.GL_TEXTURE,b2,d3),t.assemblersRegister.register(Hn.GL_VOLUME,b2,g3)}}.run(ai))}}v3._started=!1,v3.run()}]);void 0===POLY&&console.error(\\\\\\\"esm-webpack-plugin: nothing exported!\\\\\\\");const _POLY$PolyScene=POLY.PolyScene,_POLY$Poly=POLY.Poly,_POLY$SceneJsonImporter=POLY.SceneJsonImporter,_POLY$SceneDataManifestImporter=POLY.SceneDataManifestImporter,_POLY$mountScene=POLY.mountScene;export{_POLY$PolyScene as PolyScene,_POLY$Poly as Poly,_POLY$SceneJsonImporter as SceneJsonImporter,_POLY$SceneDataManifestImporter as SceneDataManifestImporter,_POLY$mountScene as mountScene};\\n//# sourceMappingURL=all.js.map\"","status":200,"headers":{"content-type":"application/javascript","content-length":"2809283"}},"type":2,"external":true,"timestamp":1723910782427},{"data":{"url":"blob:https://ipfs.arkivo.art/5b0cfe7c-0af3-4c0d-a3bc-49030169b6c7","host":"","path":"https://ipfs.arkivo.art/5b0cfe7c-0af3-4c0d-a3bc-49030169b6c7","type":"http","query":"","method":"GET","headers":{"origin":"https://ipfs.arkivo.art","referer":"","user-agent":"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) HeadlessChrome/119.0.6045.9 Safari/537.36"},"fragment":"","postData":null,"protocol":"blob:"},"type":1,"external":false,"timestamp":1723910782429},{"data":{"url":"blob:https://ipfs.arkivo.art/5b0cfe7c-0af3-4c0d-a3bc-49030169b6c7","body":"\"// src/polygonjs/PolyConfig.js\\nfunction configurePolygonjs(poly) {\\n}\\nfunction configureScene(scene) {\\n}\\nexport {\\n configurePolygonjs,\\n configureScene\\n};\\n\"","status":200,"headers":{"content-type":"application/javascript","content-length":"155"}},"type":2,"external":true,"timestamp":1723910788271}],"browser":{"name":"chromium","version":"119.0.6045.9"},"viewport":{"width":2000,"height":2000},"screenshot":"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","consoleMessages":[{"text":"Unrecognized Content-Security-Policy directive 'prefetch-src'.","level":"error","timestamp":1723910781784},{"text":"brightness Bm","level":"log","timestamp":1723910782680},{"text":"[.WebGL-0x2a9401bc1500]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels","level":"warning","timestamp":1723910788320},{"text":"[.WebGL-0x2a9401bc1500]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels","level":"warning","timestamp":1723910788320},{"text":"[.WebGL-0x2a9401bc1500]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels","level":"warning","timestamp":1723910788320},{"text":"[.WebGL-0x2a9401bc1500]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels (this message will no longer repeat)","level":"warning","timestamp":1723910788320}],"screenshotDelay":10000},"timestamp":1723910781369},"created_at":"2024-08-17T16:06:47.251+00:00","updated_at":"2024-08-17T16:06:47.251+00:00"}