{"id":18379,"artifact_id":17406,"version":1,"data":{"version":1,"artifact":{"chain":"tezos","title":"Nodevember 2021-15","artist":"tz1djshvCnhi5p5wAebiBb2XQLcvVKBqyH7F","tokenId":"540993","description":"An interactive 3D artwork, created by @neoyume - neoyume.com","contractAddress":"KT1RJ6PbjHpwc3M5rw5s2Nbmefwbuwbdxton"},"snapshot":{"net":[{"data":{"url":"https://ipfs.arkivo.art/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9?creator=tz1djshvCnhi5p5wAebiBb2XQLcvVKBqyH7F&viewer=&objkt=540993","host":"ipfs.arkivo.art","path":"/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9","type":"http","query":"?creator=tz1djshvCnhi5p5wAebiBb2XQLcvVKBqyH7F&viewer=&objkt=540993","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":1723912788102},{"data":{"url":"https://ipfs.arkivo.art/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9?creator=tz1djshvCnhi5p5wAebiBb2XQLcvVKBqyH7F&viewer=&objkt=540993","body":"","status":301,"headers":{"date":"Sat, 17 Aug 2024 16:39:48 GMT","server":"nginx/1.27.0","location":"/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9/?creator=tz1djshvCnhi5p5wAebiBb2XQLcvVKBqyH7F&viewer=&objkt=540993","connection":"keep-alive","x-ipfs-path":"/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9","content-type":"text/html; charset=utf-8","x-ipfs-roots":"QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9","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":1723912788148},{"data":{"url":"https://ipfs.arkivo.art/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9/?creator=tz1djshvCnhi5p5wAebiBb2XQLcvVKBqyH7F&viewer=&objkt=540993","host":"ipfs.arkivo.art","path":"/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9/","type":"http","query":"?creator=tz1djshvCnhi5p5wAebiBb2XQLcvVKBqyH7F&viewer=&objkt=540993","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":1723912788148},{"data":{"url":"https://ipfs.arkivo.art/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9/?creator=tz1djshvCnhi5p5wAebiBb2XQLcvVKBqyH7F&viewer=&objkt=540993","body":"","status":200,"headers":{"date":"Sat, 17 Aug 2024 16:39:48 GMT","etag":"\"QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9\"","server":"nginx/1.27.0","connection":"keep-alive","x-ipfs-path":"/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9/","content-type":"text/html","x-ipfs-roots":"QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9","accept-ranges":"bytes","cache-control":"public, max-age=29030400, immutable","content-length":"2705","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":1723912788159},{"data":{"url":"https://ipfs.arkivo.art/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9/style.css","host":"ipfs.arkivo.art","path":"/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9/style.css","type":"http","query":"","method":"GET","headers":{"referer":"https://ipfs.arkivo.art/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9/?creator=tz1djshvCnhi5p5wAebiBb2XQLcvVKBqyH7F&viewer=&objkt=540993","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":1723912788184},{"data":{"url":"https://ipfs.arkivo.art/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9/viewer.js","host":"ipfs.arkivo.art","path":"/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9/viewer.js","type":"http","query":"","method":"GET","headers":{"referer":"https://ipfs.arkivo.art/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9/?creator=tz1djshvCnhi5p5wAebiBb2XQLcvVKBqyH7F&viewer=&objkt=540993","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":1723912788185},{"data":{"url":"https://ipfs.arkivo.art/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9/viewer.js","body":"","status":200,"headers":{"date":"Sat, 17 Aug 2024 16:39:48 GMT","etag":"\"QmQSx7jXH1MrpU7mJeihUwX5P6M4ri8KK7zmFDozLo9c55\"","server":"nginx/1.27.0","connection":"keep-alive","x-ipfs-path":"/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9/viewer.js","content-type":"text/javascript; charset=utf-8","x-ipfs-roots":"QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9,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":1723912788195},{"data":{"url":"https://ipfs.arkivo.art/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9/style.css","body":"","status":200,"headers":{"date":"Sat, 17 Aug 2024 16:39:48 GMT","etag":"\"QmPbsQLLXNhFAwKHqFy7nJfyFfmhwqU4gypXMEAr9LWc7Y\"","server":"nginx/1.27.0","connection":"keep-alive","x-ipfs-path":"/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9/style.css","content-type":"text/css; charset=utf-8","x-ipfs-roots":"QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9,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":1723912788199},{"data":{"url":"https://ipfs.arkivo.art/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9/nodevember15_N.polygonjs","host":"ipfs.arkivo.art","path":"/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9/nodevember15_N.polygonjs","type":"http","query":"","method":"GET","headers":{"referer":"https://ipfs.arkivo.art/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9/?creator=tz1djshvCnhi5p5wAebiBb2XQLcvVKBqyH7F&viewer=&objkt=540993","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":1723912788214},{"data":{"url":"https://ipfs.arkivo.art/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9/poster.jpg","host":"ipfs.arkivo.art","path":"/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9/poster.jpg","type":"http","query":"","method":"GET","headers":{"referer":"https://ipfs.arkivo.art/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9/?creator=tz1djshvCnhi5p5wAebiBb2XQLcvVKBqyH7F&viewer=&objkt=540993","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":1723912788215},{"data":{"url":"https://ipfs.arkivo.art/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9/nodevember15_N.polygonjs","body":"","status":200,"headers":{"date":"Sat, 17 Aug 2024 16:39:48 GMT","etag":"\"QmZCuTmRR1vdp37vNdrsNEqNE5NDz5Ejc5oCwzNpJBfCG5\"","server":"nginx/1.27.0","connection":"keep-alive","x-ipfs-path":"/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9/nodevember15_N.polygonjs","content-type":"application/zip","x-ipfs-roots":"QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9,QmZCuTmRR1vdp37vNdrsNEqNE5NDz5Ejc5oCwzNpJBfCG5","accept-ranges":"bytes","cache-control":"public, max-age=29030400, immutable","content-length":"764851","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":1723912788226},{"data":{"url":"https://ipfs.arkivo.art/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9/poster.jpg","body":"","status":200,"headers":{"date":"Sat, 17 Aug 2024 16:39:48 GMT","etag":"\"QmWjMoYZfVcnUP64dFTumYQaFUBW992XWcBd1FM5QoKAYi\"","server":"nginx/1.27.0","connection":"keep-alive","x-ipfs-path":"/ipfs/QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9/poster.jpg","content-type":"image/jpeg","x-ipfs-roots":"QmP8pGovPe983vidM24T2QYzpWRV8AWWiiDu2Wcq9WmCa9,QmWjMoYZfVcnUP64dFTumYQaFUBW992XWcBd1FM5QoKAYi","accept-ranges":"bytes","cache-control":"public, max-age=29030400, immutable","content-length":"613239","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":1723912788230},{"data":{"url":"blob:https://ipfs.arkivo.art/c8a515cd-9e9c-40d8-9826-ac830a25e970","host":"","path":"https://ipfs.arkivo.art/c8a515cd-9e9c-40d8-9826-ac830a25e970","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":1723912788429},{"data":{"url":"blob:https://ipfs.arkivo.art/c8a515cd-9e9c-40d8-9826-ac830a25e970","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 s=e[i]={i:i,l:!1,exports:{}};return t[i].call(s.exports,s,s.exports,n),s.l=!0,s.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 s in t)n.d(i,s,function(e){return t[e]}.bind(null,s));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@1.1.195/dist/\\\\\\\",n(n.s=216)}([function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return r}));var i=n(3),s=n(8);class r{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,s=t.elements;return this.x=s[0]*e+s[3]*n+s[6]*i,this.y=s[1]*e+s[4]*n+s[7]*i,this.z=s[2]*e+s[5]*n+s[8]*i,this}applyNormalMatrix(t){return this.applyMatrix3(t).normalize()}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,s=t.elements,r=1/(s[3]*e+s[7]*n+s[11]*i+s[15]);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}applyQuaternion(t){const e=this.x,n=this.y,i=this.z,s=t.x,r=t.y,o=t.z,a=t.w,l=a*e+r*i-o*n,c=a*n+o*e-s*i,h=a*i+s*n-r*e,u=-s*e-r*n-o*i;return this.x=l*a+u*-s+c*-o-h*-r,this.y=c*a+u*-r+h*-s-l*-o,this.z=h*a+u*-o+l*-r-c*-s,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,s=t.elements;return this.x=s[0]*e+s[4]*n+s[8]*i,this.y=s[1]*e+s[5]*n+s[9]*i,this.z=s[2]*e+s[6]*n+s[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,s=t.z,r=e.x,o=e.y,a=e.z;return this.x=i*a-s*o,this.y=s*r-n*a,this.z=n*o-i*r,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}}r.prototype.isVector3=!0;const o=new r,a=new s.a},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"hb\\\\\\\",(function(){return i})),n.d(e,\\\\\\\"Tc\\\\\\\",(function(){return s})),n.d(e,\\\\\\\"u\\\\\\\",(function(){return r})),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 h})),n.d(e,\\\\\\\"gd\\\\\\\",(function(){return u})),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 M})),n.d(e,\\\\\\\"ib\\\\\\\",(function(){return E})),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 P})),n.d(e,\\\\\\\"A\\\\\\\",(function(){return R})),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 B})),n.d(e,\\\\\\\"tb\\\\\\\",(function(){return z})),n.d(e,\\\\\\\"g\\\\\\\",(function(){return k})),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 st})),n.d(e,\\\\\\\"E\\\\\\\",(function(){return rt})),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 ht})),n.d(e,\\\\\\\"ob\\\\\\\",(function(){return ut})),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 Mt})),n.d(e,\\\\\\\"bd\\\\\\\",(function(){return Et})),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 Pt})),n.d(e,\\\\\\\"f\\\\\\\",(function(){return Rt})),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 Bt})),n.d(e,\\\\\\\"hc\\\\\\\",(function(){return zt})),n.d(e,\\\\\\\"x\\\\\\\",(function(){return kt})),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 se})),n.d(e,\\\\\\\"Rb\\\\\\\",(function(){return re})),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 he})),n.d(e,\\\\\\\"Xb\\\\\\\",(function(){return ue})),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 Me})),n.d(e,\\\\\\\"Lc\\\\\\\",(function(){return Ee})),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 Pe})),n.d(e,\\\\\\\"cb\\\\\\\",(function(){return Re})),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 Be})),n.d(e,\\\\\\\"Q\\\\\\\",(function(){return ze})),n.d(e,\\\\\\\"id\\\\\\\",(function(){return ke})),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 $e})),n.d(e,\\\\\\\"gc\\\\\\\",(function(){return Je})),n.d(e,\\\\\\\"bb\\\\\\\",(function(){return Ze})),n.d(e,\\\\\\\"lc\\\\\\\",(function(){return Qe})),n.d(e,\\\\\\\"kc\\\\\\\",(function(){return Ke})),n.d(e,\\\\\\\"fc\\\\\\\",(function(){return tn})),n.d(e,\\\\\\\"j\\\\\\\",(function(){return en})),n.d(e,\\\\\\\"Hb\\\\\\\",(function(){return nn})),n.d(e,\\\\\\\"Uc\\\\\\\",(function(){return sn})),n.d(e,\\\\\\\"zb\\\\\\\",(function(){return rn})),n.d(e,\\\\\\\"R\\\\\\\",(function(){return on})),n.d(e,\\\\\\\"h\\\\\\\",(function(){return an})),n.d(e,\\\\\\\"Qc\\\\\\\",(function(){return ln})),n.d(e,\\\\\\\"I\\\\\\\",(function(){return cn}));const i={LEFT:0,MIDDLE:1,RIGHT:2,ROTATE:0,DOLLY:1,PAN:2},s={ROTATE:0,PAN:1,DOLLY_PAN:2,DOLLY_ROTATE:3},r=0,o=1,a=2,l=0,c=1,h=2,u=3,d=0,p=1,_=2,m=1,f=0,g=1,v=2,y=3,x=4,b=5,w=100,T=101,A=102,M=103,E=104,S=200,C=201,N=202,L=203,O=204,P=205,R=206,I=207,F=208,D=209,B=210,z=0,k=1,U=2,G=3,V=4,H=5,j=6,W=7,q=0,X=1,Y=2,$=0,J=1,Z=2,Q=3,K=4,tt=5,et=300,nt=301,it=302,st=303,rt=304,ot=306,at=307,lt=1e3,ct=1001,ht=1002,ut=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,Mt=1013,Et=1014,St=1015,Ct=1016,Nt=1017,Lt=1018,Ot=1019,Pt=1020,Rt=1021,It=1022,Ft=1023,Dt=1024,Bt=1025,zt=Ft,kt=1026,Ut=1027,Gt=1028,Vt=1029,Ht=1030,jt=1031,Wt=1032,qt=1033,Xt=33776,Yt=33777,$t=33778,Jt=33779,Zt=35840,Qt=35841,Kt=35842,te=35843,ee=36196,ne=37492,ie=37496,se=37808,re=37809,oe=37810,ae=37811,le=37812,ce=37813,he=37814,ue=37815,de=37816,pe=37817,_e=37818,me=37819,fe=37820,ge=37821,ve=36492,ye=37840,xe=37841,be=37842,we=37843,Te=37844,Ae=37845,Me=37846,Ee=37847,Se=37848,Ce=37849,Ne=37850,Le=37851,Oe=37852,Pe=37853,Re=2200,Ie=2201,Fe=2202,De=2300,Be=2301,ze=2302,ke=2400,Ue=2401,Ge=2402,Ve=2500,He=2501,je=0,We=1,qe=2,Xe=3e3,Ye=3001,$e=3007,Je=3002,Ze=3003,Qe=3004,Ke=3005,tn=3006,en=3200,nn=3201,sn=0,rn=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),s=this.x-t.x,r=this.y-t.y;return this.x=s*n-r*i+t.x,this.y=s*i+r*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 s})),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 h})),n.d(e,\\\\\\\"e\\\\\\\",(function(){return u})),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,s=180/Math.PI,r=[];for(let t=0;t<256;t++)r[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(r[255&t]+r[t>>8&255]+r[t>>16&255]+r[t>>24&255]+\\\\\\\"-\\\\\\\"+r[255&e]+r[e>>8&255]+\\\\\\\"-\\\\\\\"+r[e>>16&15|64]+r[e>>24&255]+\\\\\\\"-\\\\\\\"+r[63&n|128]+r[n>>8&255]+\\\\\\\"-\\\\\\\"+r[n>>16&255]+r[n>>24&255]+r[255&i]+r[i>>8&255]+r[i>>16&255]+r[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 h(t,e,n){return(1-n)*t+n*e}function u(t){return t*i}function d(t){return t*s}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 u})),n.d(e,\\\\\\\"a\\\\\\\",(function(){return h}));var i=n(9),s=n(0),r=n(2),o=n(6),a=n(1);const l=new s.a,c=new r.a;class h{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,s=this.itemSize;i0&&console.error(\\\\\\\"THREE.Matrix4: the constructor no longer reads arguments. use .set() instead.\\\\\\\")}set(t,e,n,i,s,r,o,a,l,c,h,u,d,p,_,m){const f=this.elements;return f[0]=t,f[4]=e,f[8]=n,f[12]=i,f[1]=s,f[5]=r,f[9]=o,f[13]=a,f[2]=l,f[6]=c,f[10]=h,f[14]=u,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 s).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/r.setFromMatrixColumn(t,0).length(),s=1/r.setFromMatrixColumn(t,1).length(),o=1/r.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]*s,e[5]=n[5]*s,e[6]=n[6]*s,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,s=t.z,r=Math.cos(n),o=Math.sin(n),a=Math.cos(i),l=Math.sin(i),c=Math.cos(s),h=Math.sin(s);if(\\\\\\\"XYZ\\\\\\\"===t.order){const t=r*c,n=r*h,i=o*c,s=o*h;e[0]=a*c,e[4]=-a*h,e[8]=l,e[1]=n+i*l,e[5]=t-s*l,e[9]=-o*a,e[2]=s-t*l,e[6]=i+n*l,e[10]=r*a}else if(\\\\\\\"YXZ\\\\\\\"===t.order){const t=a*c,n=a*h,i=l*c,s=l*h;e[0]=t+s*o,e[4]=i*o-n,e[8]=r*l,e[1]=r*h,e[5]=r*c,e[9]=-o,e[2]=n*o-i,e[6]=s+t*o,e[10]=r*a}else if(\\\\\\\"ZXY\\\\\\\"===t.order){const t=a*c,n=a*h,i=l*c,s=l*h;e[0]=t-s*o,e[4]=-r*h,e[8]=i+n*o,e[1]=n+i*o,e[5]=r*c,e[9]=s-t*o,e[2]=-r*l,e[6]=o,e[10]=r*a}else if(\\\\\\\"ZYX\\\\\\\"===t.order){const t=r*c,n=r*h,i=o*c,s=o*h;e[0]=a*c,e[4]=i*l-n,e[8]=t*l+s,e[1]=a*h,e[5]=s*l+t,e[9]=n*l-i,e[2]=-l,e[6]=o*a,e[10]=r*a}else if(\\\\\\\"YZX\\\\\\\"===t.order){const t=r*a,n=r*l,i=o*a,s=o*l;e[0]=a*c,e[4]=s-t*h,e[8]=i*h+n,e[1]=h,e[5]=r*c,e[9]=-o*c,e[2]=-l*c,e[6]=n*h+i,e[10]=t-s*h}else if(\\\\\\\"XZY\\\\\\\"===t.order){const t=r*a,n=r*l,i=o*a,s=o*l;e[0]=a*c,e[4]=-h,e[8]=l*c,e[1]=t*h+s,e[5]=r*c,e[9]=n*h-i,e[2]=i*h-n,e[6]=o*c,e[10]=s*h+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 u.subVectors(t,e),0===u.lengthSq()&&(u.z=1),u.normalize(),c.crossVectors(n,u),0===c.lengthSq()&&(1===Math.abs(n.z)?u.x+=1e-4:u.z+=1e-4,u.normalize(),c.crossVectors(n,u)),c.normalize(),h.crossVectors(u,c),i[0]=c.x,i[4]=h.x,i[8]=u.x,i[1]=c.y,i[5]=h.y,i[9]=u.y,i[2]=c.z,i[6]=h.z,i[10]=u.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,s=this.elements,r=n[0],o=n[4],a=n[8],l=n[12],c=n[1],h=n[5],u=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],M=i[1],E=i[5],S=i[9],C=i[13],N=i[2],L=i[6],O=i[10],P=i[14],R=i[3],I=i[7],F=i[11],D=i[15];return s[0]=r*b+o*M+a*N+l*R,s[4]=r*w+o*E+a*L+l*I,s[8]=r*T+o*S+a*O+l*F,s[12]=r*A+o*C+a*P+l*D,s[1]=c*b+h*M+u*N+d*R,s[5]=c*w+h*E+u*L+d*I,s[9]=c*T+h*S+u*O+d*F,s[13]=c*A+h*C+u*P+d*D,s[2]=p*b+_*M+m*N+f*R,s[6]=p*w+_*E+m*L+f*I,s[10]=p*T+_*S+m*O+f*F,s[14]=p*A+_*C+m*P+f*D,s[3]=g*b+v*M+y*N+x*R,s[7]=g*w+v*E+y*L+x*I,s[11]=g*T+v*S+y*O+x*F,s[15]=g*A+v*C+y*P+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],s=t[12],r=t[1],o=t[5],a=t[9],l=t[13],c=t[2],h=t[6],u=t[10],d=t[14];return t[3]*(+s*a*h-i*l*h-s*o*u+n*l*u+i*o*d-n*a*d)+t[7]*(+e*a*d-e*l*u+s*r*u-i*r*d+i*l*c-s*a*c)+t[11]*(+e*l*h-e*o*d-s*r*h+n*r*d+s*o*c-n*l*c)+t[15]*(-i*o*c-e*a*h+e*o*u+i*r*h-n*r*u+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],s=t[3],r=t[4],o=t[5],a=t[6],l=t[7],c=t[8],h=t[9],u=t[10],d=t[11],p=t[12],_=t[13],m=t[14],f=t[15],g=h*m*l-_*u*l+_*a*d-o*m*d-h*a*f+o*u*f,v=p*u*l-c*m*l-p*a*d+r*m*d+c*a*f-r*u*f,y=c*_*l-p*h*l+p*o*d-r*_*d-c*o*f+r*h*f,x=p*h*a-c*_*a-p*o*u+r*_*u+c*o*m-r*h*m,b=e*g+n*v+i*y+s*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]=(_*u*s-h*m*s-_*i*d+n*m*d+h*i*f-n*u*f)*w,t[2]=(o*m*s-_*a*s+_*i*l-n*m*l-o*i*f+n*a*f)*w,t[3]=(h*a*s-o*u*s-h*i*l+n*u*l+o*i*d-n*a*d)*w,t[4]=v*w,t[5]=(c*m*s-p*u*s+p*i*d-e*m*d-c*i*f+e*u*f)*w,t[6]=(p*a*s-r*m*s-p*i*l+e*m*l+r*i*f-e*a*f)*w,t[7]=(r*u*s-c*a*s+c*i*l-e*u*l-r*i*d+e*a*d)*w,t[8]=y*w,t[9]=(p*h*s-c*_*s-p*n*d+e*_*d+c*n*f-e*h*f)*w,t[10]=(r*_*s-p*o*s+p*n*l-e*_*l-r*n*f+e*o*f)*w,t[11]=(c*o*s-r*h*s-c*n*l+e*h*l+r*n*d-e*o*d)*w,t[12]=x*w,t[13]=(c*_*i-p*h*i+p*n*u-e*_*u-c*n*m+e*h*m)*w,t[14]=(p*o*i-r*_*i-p*n*a+e*_*a+r*n*m-e*o*m)*w,t[15]=(r*h*i-c*o*i+c*n*a-e*h*a-r*n*u+e*o*u)*w,this}scale(t){const e=this.elements,n=t.x,i=t.y,s=t.z;return e[0]*=n,e[4]*=i,e[8]*=s,e[1]*=n,e[5]*=i,e[9]*=s,e[2]*=n,e[6]*=i,e[10]*=s,e[3]*=n,e[7]*=i,e[11]*=s,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),s=1-n,r=t.x,o=t.y,a=t.z,l=s*r,c=s*o;return this.set(l*r+n,l*o-i*a,l*a+i*o,0,l*o+i*a,c*o+n,c*a-i*r,0,l*a-i*o,c*a+i*r,s*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,s,r){return this.set(1,n,s,0,t,1,r,0,e,i,1,0,0,0,0,1),this}compose(t,e,n){const i=this.elements,s=e._x,r=e._y,o=e._z,a=e._w,l=s+s,c=r+r,h=o+o,u=s*l,d=s*c,p=s*h,_=r*c,m=r*h,f=o*h,g=a*l,v=a*c,y=a*h,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-(u+f))*b,i[6]=(m+g)*b,i[7]=0,i[8]=(p+v)*w,i[9]=(m-g)*w,i[10]=(1-(u+_))*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 s=r.set(i[0],i[1],i[2]).length();const a=r.set(i[4],i[5],i[6]).length(),l=r.set(i[8],i[9],i[10]).length();this.determinant()<0&&(s=-s),t.x=i[12],t.y=i[13],t.z=i[14],o.copy(this);const c=1/s,h=1/a,u=1/l;return o.elements[0]*=c,o.elements[1]*=c,o.elements[2]*=c,o.elements[4]*=h,o.elements[5]*=h,o.elements[6]*=h,o.elements[8]*=u,o.elements[9]*=u,o.elements[10]*=u,e.setFromRotationMatrix(o),n.x=s,n.y=a,n.z=l,this}makePerspective(t,e,n,i,s,r){void 0===r&&console.warn(\\\\\\\"THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.\\\\\\\");const o=this.elements,a=2*s/(e-t),l=2*s/(n-i),c=(e+t)/(e-t),h=(n+i)/(n-i),u=-(r+s)/(r-s),d=-2*r*s/(r-s);return o[0]=a,o[4]=0,o[8]=c,o[12]=0,o[1]=0,o[5]=l,o[9]=h,o[13]=0,o[2]=0,o[6]=0,o[10]=u,o[14]=d,o[3]=0,o[7]=0,o[11]=-1,o[15]=0,this}makeOrthographic(t,e,n,i,s,r){const o=this.elements,a=1/(e-t),l=1/(n-i),c=1/(r-s),h=(e+t)*a,u=(n+i)*l,d=(r+s)*c;return o[0]=2*a,o[4]=0,o[8]=0,o[12]=-h,o[1]=0,o[5]=2*l,o[9]=0,o[13]=-u,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}}s.prototype.isMatrix4=!0;const r=new i.a,o=new s,a=new i.a(0,0,0),l=new i.a(1,1,1),c=new i.a,h=new i.a,u=new i.a},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return h}));var i=n(3);const s={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},r={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 h{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,s=2*n-i;this.r=a(s,i,t+1/3),this.g=a(s,i,t),this.b=a(s,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],s=n[2];switch(i){case\\\\\\\"rgb\\\\\\\":case\\\\\\\"rgba\\\\\\\":if(t=/^\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(s))return this.r=Math.min(255,parseInt(t[1],10))/255,this.g=Math.min(255,parseInt(t[2],10))/255,this.b=Math.min(255,parseInt(t[3],10))/255,e(t[4]),this;if(t=/^\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(s))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*)?$/.exec(s)){const n=parseFloat(t[1])/360,i=parseInt(t[2],10)/100,s=parseInt(t[3],10)/100;return e(t[4]),this.setHSL(n,i,s)}}}else if(n=/^\\\\#([A-Fa-f\\\\d]+)$/.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=s[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,s=Math.max(e,n,i),r=Math.min(e,n,i);let o,a;const l=(r+s)/2;if(r===s)o=0,a=0;else{const t=s-r;switch(a=l<=.5?t/(s+r):t/(2-s-r),s){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 u.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 s=!1;for(const e in this.morphAttributes){const n=this.morphAttributes[e],r=[];for(let e=0,i=n.length;e0&&(i[e]=r,s=!0)}s&&(t.data.morphAttributes=i,t.data.morphTargetsRelative=this.morphTargetsRelative);const r=this.groups;r.length>0&&(t.data.groups=JSON.parse(JSON.stringify(r)));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 s=t.morphAttributes;for(const t in s){const n=[],i=s[t];for(let t=0,s=i.length;t=0?1:-1,i=1-e*e;if(i>Number.EPSILON){const s=Math.sqrt(i),r=Math.atan2(s,e*n);t=Math.sin(t*r)/s,o=Math.sin(o*r)/s}const s=o*n;if(a=a*t+u*s,l=l*t+d*s,c=c*t+p*s,h=h*t+_*s,t===1-o){const t=1/Math.sqrt(a*a+l*l+c*c+h*h);a*=t,l*=t,c*=t,h*=t}}t[e]=a,t[e+1]=l,t[e+2]=c,t[e+3]=h}static multiplyQuaternionsFlat(t,e,n,i,s,r){const o=n[i],a=n[i+1],l=n[i+2],c=n[i+3],h=s[r],u=s[r+1],d=s[r+2],p=s[r+3];return t[e]=o*p+c*h+a*d-l*u,t[e+1]=a*p+c*u+l*h-o*d,t[e+2]=l*p+c*d+o*u-a*h,t[e+3]=c*p-o*h-a*u-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,s=t._z,r=t._order,o=Math.cos,a=Math.sin,l=o(n/2),c=o(i/2),h=o(s/2),u=a(n/2),d=a(i/2),p=a(s/2);switch(r){case\\\\\\\"XYZ\\\\\\\":this._x=u*c*h+l*d*p,this._y=l*d*h-u*c*p,this._z=l*c*p+u*d*h,this._w=l*c*h-u*d*p;break;case\\\\\\\"YXZ\\\\\\\":this._x=u*c*h+l*d*p,this._y=l*d*h-u*c*p,this._z=l*c*p-u*d*h,this._w=l*c*h+u*d*p;break;case\\\\\\\"ZXY\\\\\\\":this._x=u*c*h-l*d*p,this._y=l*d*h+u*c*p,this._z=l*c*p+u*d*h,this._w=l*c*h-u*d*p;break;case\\\\\\\"ZYX\\\\\\\":this._x=u*c*h-l*d*p,this._y=l*d*h+u*c*p,this._z=l*c*p-u*d*h,this._w=l*c*h+u*d*p;break;case\\\\\\\"YZX\\\\\\\":this._x=u*c*h+l*d*p,this._y=l*d*h+u*c*p,this._z=l*c*p-u*d*h,this._w=l*c*h-u*d*p;break;case\\\\\\\"XZY\\\\\\\":this._x=u*c*h-l*d*p,this._y=l*d*h-u*c*p,this._z=l*c*p+u*d*h,this._w=l*c*h+u*d*p;break;default:console.warn(\\\\\\\"THREE.Quaternion: .setFromEuler() encountered an unknown order: \\\\\\\"+r)}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],s=e[8],r=e[1],o=e[5],a=e[9],l=e[2],c=e[6],h=e[10],u=n+o+h;if(u>0){const t=.5/Math.sqrt(u+1);this._w=.25/t,this._x=(c-a)*t,this._y=(s-l)*t,this._z=(r-i)*t}else if(n>o&&n>h){const t=2*Math.sqrt(1+n-o-h);this._w=(c-a)/t,this._x=.25*t,this._y=(i+r)/t,this._z=(s+l)/t}else if(o>h){const t=2*Math.sqrt(1+o-n-h);this._w=(s-l)/t,this._x=(i+r)/t,this._y=.25*t,this._z=(a+c)/t}else{const t=2*Math.sqrt(1+h-n-o);this._w=(r-i)/t,this._x=(s+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,s=t._z,r=t._w,o=e._x,a=e._y,l=e._z,c=e._w;return this._x=n*c+r*o+i*l-s*a,this._y=i*c+r*a+s*o-n*l,this._z=s*c+r*l+n*a-i*o,this._w=r*c-n*o-i*a-s*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,s=this._z,r=this._w;let o=r*t._w+n*t._x+i*t._y+s*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=r,this._x=n,this._y=i,this._z=s,this;const a=1-o*o;if(a<=Number.EPSILON){const t=1-e;return this._w=t*r+e*this._w,this._x=t*n+e*this._x,this._y=t*i+e*this._y,this._z=t*s+e*this._z,this.normalize(),this._onChangeCallback(),this}const l=Math.sqrt(a),c=Math.atan2(l,o),h=Math.sin((1-e)*c)/l,u=Math.sin(e*c)/l;return this._w=r*h+this._w*u,this._x=n*h+this._x*u,this._y=i*h+this._y*u,this._z=s*h+this._z*u,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(),s=2*Math.PI*Math.random();return this.set(e*Math.cos(i),n*Math.sin(s),n*Math.cos(s),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(){}}s.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,s=this.w,r=t.elements;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.w=r[3]*e+r[7]*n+r[11]*i+r[15]*s,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,s;const r=.01,o=.1,a=t.elements,l=a[0],c=a[4],h=a[8],u=a[1],d=a[5],p=a[9],_=a[2],m=a[6],f=a[10];if(Math.abs(c-u)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),s.length>0&&(n.textures=s),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 r(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,s,r,o,a,l){const c=this.elements;return c[0]=t,c[1]=i,c[2]=o,c[3]=e,c[4]=s,c[5]=a,c[6]=n,c[7]=r,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,s=this.elements,r=n[0],o=n[3],a=n[6],l=n[1],c=n[4],h=n[7],u=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 s[0]=r*_+o*g+a*x,s[3]=r*m+o*v+a*b,s[6]=r*f+o*y+a*w,s[1]=l*_+c*g+h*x,s[4]=l*m+c*v+h*b,s[7]=l*f+c*y+h*w,s[2]=u*_+d*g+p*x,s[5]=u*m+d*v+p*b,s[8]=u*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],s=t[3],r=t[4],o=t[5],a=t[6],l=t[7],c=t[8];return e*r*c-e*o*l-n*s*c+n*o*a+i*s*l-i*r*a}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],s=t[3],r=t[4],o=t[5],a=t[6],l=t[7],c=t[8],h=c*r-o*l,u=o*a-c*s,d=l*s-r*a,p=e*h+n*u+i*d;if(0===p)return this.set(0,0,0,0,0,0,0,0,0);const _=1/p;return t[0]=h*_,t[1]=(i*l-c*n)*_,t[2]=(o*n-i*r)*_,t[3]=u*_,t[4]=(c*e-i*a)*_,t[5]=(i*s-o*e)*_,t[6]=d*_,t[7]=(n*a-l*e)*_,t[8]=(r*e-n*s)*_,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,s,r,o){const a=Math.cos(s),l=Math.sin(s);return this.set(n*a,n*l,-n*(a*r+l*o)+r+t,-i*l,i*a,-i*(-l*r+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,s=i[0],r=i[3],o=i[6],a=i[1],l=i[4],c=i[7];return i[0]=e*s+n*a,i[3]=e*r+n*l,i[6]=e*o+n*c,i[1]=-n*s+e*a,i[4]=-n*r+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),s=n(1),r=n(3);let o=0;class a extends i.a{constructor(){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:o++}),this.uuid=r.h(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Material\\\\\\\",this.fog=!0,this.blending=s.xb,this.side=s.H,this.vertexColors=!1,this.opacity=1,this.format=s.Ib,this.transparent=!1,this.blendSrc=s.Nc,this.blendDst=s.Db,this.blendEquation=s.b,this.blendSrcAlpha=null,this.blendDstAlpha=null,this.blendEquationAlpha=null,this.depthFunc=s.T,this.depthTest=!0,this.depthWrite=!0,this.stencilWriteMask=255,this.stencilFunc=s.h,this.stencilRef=0,this.stencilFuncMask=255,this.stencilFail=s.R,this.stencilZFail=s.R,this.stencilZPass=s.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===s.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!==s.xb&&(n.blending=this.blending),this.side!==s.H&&(n.side=this.side),this.vertexColors&&(n.vertexColors=!0),this.opacity<1&&(n.opacity=this.opacity),this.format!==s.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),s=i(t.images);e.length>0&&(n.textures=e),s.length>0&&(n.images=s)}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 s}));var i=n(28);class s{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,s){n.load(t,i,e,s)}))}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),s=n(2),r=n(18),o=n(39),a=n(5),l=n(10),c=n(40),h=n(1),u=n(29),d=n(7);const p=new a.a,_=new o.a,m=new r.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,M=new s.a,E=new s.a,S=new s.a,C=new i.a,N=new i.a;class L extends l.a{constructor(t=new d.a,e=new u.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,s=this.matrixWorld;if(void 0===i)return;if(null===n.boundingSphere&&n.computeBoundingSphere(),m.copy(n.boundingSphere),m.applyMatrix4(s),!1===t.ray.intersectsSphere(m))return;if(p.copy(s).invert(),_.copy(t.ray).applyMatrix4(p),null!==n.boundingBox&&!1===_.intersectsBox(n.boundingBox))return;let r;if(n.isBufferGeometry){const s=n.index,o=n.attributes.position,a=n.morphAttributes.position,l=n.morphTargetsRelative,c=n.attributes.uv,h=n.attributes.uv2,u=n.groups,d=n.drawRange;if(null!==s)if(Array.isArray(i))for(let n=0,p=u.length;nn.far?null:{distance:c,point:N.clone(),object:t}}(t,e,n,r,f,g,v,C);if(O){u&&(M.fromBufferAttribute(u,p),E.fromBufferAttribute(u,_),S.fromBufferAttribute(u,m),O.uv=c.a.getUV(C,f,g,v,M,E,S,new s.a)),d&&(M.fromBufferAttribute(d,p),E.fromBufferAttribute(d,_),S.fromBufferAttribute(d,m),O.uv2=c.a.getUV(C,f,g,v,M,E,S,new s.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;es&&(s=l),c>r&&(r=c),h>o&&(o=h)}return this.min.set(e,n,i),this.max.set(s,r,o),this}setFromBufferAttribute(t){let e=1/0,n=1/0,i=1/0,s=-1/0,r=-1/0,o=-1/0;for(let a=0,l=t.count;as&&(s=l),c>r&&(r=c),h>o&&(o=h)}return this.min.set(e,n,i),this.max.set(s,r,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,_),h.subVectors(t.c,_),u.subVectors(c,l),d.subVectors(h,c),p.subVectors(l,h);let e=[0,-u.z,u.y,0,-d.z,d.y,0,-p.z,p.y,u.z,0,-u.x,d.z,0,-d.x,p.z,0,-p.x,-u.y,u.x,0,-d.y,d.x,0,-p.y,p.x,0];return!!v(e,l,c,h,m)&&(e=[1,0,0,0,1,0,0,0,1],!!v(e,l,c,h,m)&&(f.crossVectors(u,d),e=[f.x,f.y,f.z],v(e,l,c,h,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()||(r[0].set(this.min.x,this.min.y,this.min.z).applyMatrix4(t),r[1].set(this.min.x,this.min.y,this.max.z).applyMatrix4(t),r[2].set(this.min.x,this.max.y,this.min.z).applyMatrix4(t),r[3].set(this.min.x,this.max.y,this.max.z).applyMatrix4(t),r[4].set(this.max.x,this.min.y,this.min.z).applyMatrix4(t),r[5].set(this.max.x,this.min.y,this.max.z).applyMatrix4(t),r[6].set(this.max.x,this.max.y,this.min.z).applyMatrix4(t),r[7].set(this.max.x,this.max.y,this.max.z).applyMatrix4(t),this.setFromPoints(r)),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.isBox3=!0;const r=[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 s,l=new i.a,c=new i.a,h=new i.a,u=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,s){for(let r=0,o=t.length-3;r<=o;r+=3){g.fromArray(t,r);const o=s.x*Math.abs(g.x)+s.y*Math.abs(g.y)+s.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),s=n(0);const r=new i.a,o=new s.a,a=new s.a,l=new s.a;class c{constructor(t=new s.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):r.setFromPoints(t).getCenter(n);let i=0;for(let e=0,s=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 r}));var i=n(8),s=n(1);const r={arraySlice:function(t,e,n){return r.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,s=new t.constructor(i);for(let r=0,o=0;o!==i;++r){const i=n[r]*e;for(let n=0;n!==e;++n)s[o++]=t[i+n]}return s},flattenJSON:function(t,e,n,i){let s=1,r=t[0];for(;void 0!==r&&void 0===r[i];)r=t[s++];if(void 0===r)return;let o=r[i];if(void 0!==o)if(Array.isArray(o))do{o=r[i],void 0!==o&&(e.push(r.time),n.push.apply(n,o)),r=t[s++]}while(void 0!==r);else if(void 0!==o.toArray)do{o=r[i],void 0!==o&&(e.push(r.time),o.toArray(n,n.length)),r=t[s++]}while(void 0!==r);else do{o=r[i],void 0!==o&&(e.push(r.time),n.push(o)),r=t[s++]}while(void 0!==r)},subclip:function(t,e,n,i,s=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=s.times[p]){const t=p*h+c,e=t+h-c;_=r.arraySlice(s.values,t,e)}else{const t=s.createInterpolant(),e=c,n=h-c;t.evaluate(l),_=r.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 r})),n.d(e,\\\\\\\"b\\\\\\\",(function(){return o}));const s={Int8Array:Int8Array,Uint8Array:Uint8Array,Uint8ClampedArray:Uint8ClampedArray,Int16Array:Int16Array,Uint16Array:Uint16Array,Int32Array:Int32Array,Uint32Array:Uint32Array,Float32Array:Float32Array,Float64Array:Float64Array};function r(t,e){return new s[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),s=n(13);const r={};class o extends s.a{constructor(t){super(t)}load(t,e,n,s){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!==r[t])return void r[t].push({onLoad:e,onProgress:n,onError:s});const l=t.match(/^data:(.*?)(;base64)?,(.*)$/);let c;if(l){const n=l[1],i=!!l[2];let r=l[3];r=decodeURIComponent(r),i&&(r=atob(r));try{let i;const s=(this.responseType||\\\\\\\"\\\\\\\").toLowerCase();switch(s){case\\\\\\\"arraybuffer\\\\\\\":case\\\\\\\"blob\\\\\\\":const t=new Uint8Array(r.length);for(let e=0;e1)switch(this.wrapS){case s.wc:t.x=t.x-Math.floor(t.x);break;case s.n:t.x=t.x<0?0:1;break;case s.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 s.wc:t.y=t.y-Math.floor(t.y);break;case s.n:t.y=t.y<0?0:1;break;case s.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.\\\\\\\"),{})}u.DEFAULT_IMAGE=void 0,u.DEFAULT_MAPPING=s.Yc,u.prototype.isTexture=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return r}));var i=n(12),s=n(6);class r extends i.a{constructor(t){super(),this.type=\\\\\\\"LineBasicMaterial\\\\\\\",this.color=new s.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}}r.prototype.isLineBasicMaterial=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return a}));var i=n(3),s=n(2),r=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),s=0;e.push(0);for(let r=1;r<=t;r++)n=this.getPoint(r/t),s+=n.distanceTo(i),e.push(s),i=n;return this.cacheArcLengths=e,e}updateArcLengths(){this.needsUpdate=!0,this.getLengths()}getUtoTmapping(t,e){const n=this.getLengths();let i=0;const s=n.length;let r;r=e||t*n[s-1];let o,a=0,l=s-1;for(;a<=l;)if(i=Math.floor(a+(l-a)/2),o=n[i]-r,o<0)a=i+1;else{if(!(o>0)){l=i;break}l=i-1}if(i=l,n[i]===r)return i/(s-1);const c=n[i];return(i+(r-c)/(n[i+1]-c))/(s-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 s.a:new r.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 r.a,s=[],a=[],l=[],c=new r.a,h=new o.a;for(let e=0;e<=t;e++){const n=e/t;s[e]=this.getTangentAt(n,new r.a)}a[0]=new r.a,l[0]=new r.a;let u=Number.MAX_VALUE;const d=Math.abs(s[0].x),p=Math.abs(s[0].y),_=Math.abs(s[0].z);d<=u&&(u=d,n.set(1,0,0)),p<=u&&(u=p,n.set(0,1,0)),_<=u&&n.set(0,0,1),c.crossVectors(s[0],n).normalize(),a[0].crossVectors(s[0],c),l[0].crossVectors(s[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(s[e-1],s[e]),c.length()>Number.EPSILON){c.normalize();const t=Math.acos(i.d(s[e-1].dot(s[e]),-1,1));a[e].applyMatrix4(h.makeRotationAxis(c,t))}l[e].crossVectors(s[e],a[e])}if(!0===e){let e=Math.acos(i.d(a[0].dot(a[t]),-1,1));e/=t,s[0].dot(c.crossVectors(a[0],a[t]))>0&&(e=-e);for(let n=1;n<=t;n++)a[n].applyMatrix4(h.makeRotationAxis(s[n],e*n)),l[n].crossVectors(s[n],a[n])}return{tangents:s,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),s=n(70),r=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 r.a(this.times,this.values,this.getValueSize(),t)}InterpolantFactoryMethodSmooth(t){return new s.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 s=0,r=i-1;for(;s!==i&&n[s]e;)--r;if(++r,0!==s||r!==i){s>=r&&(r=Math.max(r,1),s=r-1);const t=this.getValueSize();this.times=l.a.arraySlice(n,s,r),this.values=l.a.arraySlice(this.values,s*t,r*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,s=n.length;0===s&&(console.error(\\\\\\\"THREE.KeyframeTrack: Track is empty.\\\\\\\",this),t=!1);let r=null;for(let e=0;e!==s;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!==r&&r>i){console.error(\\\\\\\"THREE.KeyframeTrack: Out of order keys.\\\\\\\",this,e,i,r),t=!1;break}r=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(),s=this.getInterpolation()===i.Q,r=t.length-1;let o=1;for(let i=1;i0){t[o]=t[r];for(let t=r*n,i=o*n,s=0;s!==n;++s)e[i+s]=e[t+s];++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 c}));var i=n(8),s=n(0),r=n(5),o=n(3);const a=new r.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,s=i[0],r=i[4],a=i[8],l=i[1],c=i[5],h=i[9],u=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(-h,p),this._z=Math.atan2(-r,s)):(this._x=Math.atan2(d,c),this._z=0);break;case\\\\\\\"YXZ\\\\\\\":this._x=Math.asin(-Object(o.d)(h,-1,1)),Math.abs(h)<.9999999?(this._y=Math.atan2(a,p),this._z=Math.atan2(l,c)):(this._y=Math.atan2(-u,s),this._z=0);break;case\\\\\\\"ZXY\\\\\\\":this._x=Math.asin(Object(o.d)(d,-1,1)),Math.abs(d)<.9999999?(this._y=Math.atan2(-u,p),this._z=Math.atan2(-r,c)):(this._y=0,this._z=Math.atan2(l,s));break;case\\\\\\\"ZYX\\\\\\\":this._y=Math.asin(-Object(o.d)(u,-1,1)),Math.abs(u)<.9999999?(this._x=Math.atan2(d,p),this._z=Math.atan2(l,s)):(this._x=0,this._z=Math.atan2(-r,c));break;case\\\\\\\"YZX\\\\\\\":this._z=Math.asin(Object(o.d)(l,-1,1)),Math.abs(l)<.9999999?(this._x=Math.atan2(-h,c),this._y=Math.atan2(-u,s)):(this._x=0,this._y=Math.atan2(a,p));break;case\\\\\\\"XZY\\\\\\\":this._z=Math.asin(-Object(o.d)(r,-1,1)),Math.abs(r)<.9999999?(this._x=Math.atan2(d,c),this._y=Math.atan2(a,s)):(this._x=Math.atan2(-h,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 s.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 s})),n.d(e,\\\\\\\"b\\\\\\\",(function(){return i}));class i{constructor(t,e,n){const i=this;let s,r=!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===r&&void 0!==i.onStart&&i.onStart(t,o,a),r=!0},this.itemEnd=function(t){o++,void 0!==i.onProgress&&i.onProgress(t,o,a),o===a&&(r=!1,void 0!==i.onLoad&&i.onLoad())},this.itemError=function(t){void 0!==i.onError&&i.onError(t)},this.resolveURL=function(t){return s?s(t):t},this.setURLModifier=function(t){return s=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(s).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(r).applyMatrix4(t),s=this.normal.applyMatrix3(n).normalize();return this.constant=-i.dot(s),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),s=n(0),r=n(4);const o=new s.a,a=new s.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=s)break t;{const o=e[1];t=s)break e}r=n,n=0}}for(;n>>1;t0)if(p=l*h-c,_=l*c-h,f=s*d,p>=0)if(_>=-f)if(_<=f){const t=1/d;p*=t,_*=t,m=p*(p+l*_+2*c)+_*(l*p+_+2*h)+u}else _=s,p=Math.max(0,-(l*_+c)),m=-p*p+_*(_+2*h)+u;else _=-s,p=Math.max(0,-(l*_+c)),m=-p*p+_*(_+2*h)+u;else _<=-f?(p=Math.max(0,-(-l*s+c)),_=p>0?-s:Math.min(Math.max(-s,-h),s),m=-p*p+_*(_+2*h)+u):_<=f?(p=0,_=Math.min(Math.max(-s,-h),s),m=_*(_+2*h)+u):(p=Math.max(0,-(l*s+c)),_=p>0?s:Math.min(Math.max(-s,-h),s),m=-p*p+_*(_+2*h)+u);else _=l>0?-s:s,p=Math.max(0,-(l*_+c)),m=-p*p+_*(_+2*h)+u;return n&&n.copy(this.direction).multiplyScalar(p).add(this.origin),i&&i.copy(o).multiplyScalar(_).add(r),m}intersectSphere(t,e){s.subVectors(t.center,this.origin);const n=s.dot(this.direction),i=s.dot(s)-n*n,r=t.radius*t.radius;if(i>r)return null;const o=Math.sqrt(r-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,s,r,o,a;const l=1/this.direction.x,c=1/this.direction.y,h=1/this.direction.z,u=this.origin;return l>=0?(n=(t.min.x-u.x)*l,i=(t.max.x-u.x)*l):(n=(t.max.x-u.x)*l,i=(t.min.x-u.x)*l),c>=0?(s=(t.min.y-u.y)*c,r=(t.max.y-u.y)*c):(s=(t.max.y-u.y)*c,r=(t.min.y-u.y)*c),n>r||s>i?null:((s>n||n!=n)&&(n=s),(r=0?(o=(t.min.z-u.z)*h,a=(t.max.z-u.z)*h):(o=(t.max.z-u.z)*h,a=(t.min.z-u.z)*h),n>a||o>i?null:((o>n||n!=n)&&(n=o),(a=0?n:i,e)))}intersectsBox(t){return null!==this.intersectBox(t,s)}intersectTriangle(t,e,n,i,s){l.subVectors(e,t),c.subVectors(n,t),h.crossVectors(l,c);let r,o=this.direction.dot(h);if(o>0){if(i)return null;r=1}else{if(!(o<0))return null;r=-1,o=-o}a.subVectors(this.origin,t);const u=r*this.direction.dot(c.crossVectors(a,c));if(u<0)return null;const d=r*this.direction.dot(l.cross(a));if(d<0)return null;if(u+d>o)return null;const p=-r*a.dot(h);return p<0?null:this.at(p/o,s)}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 s=new i.a,r=new i.a,o=new i.a,a=new i.a,l=new i.a,c=new i.a,h=new i.a,u=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),s.subVectors(t,e),i.cross(s);const r=i.lengthSq();return r>0?i.multiplyScalar(1/Math.sqrt(r)):i.set(0,0,0)}static getBarycoord(t,e,n,i,a){s.subVectors(i,e),r.subVectors(n,e),o.subVectors(t,e);const l=s.dot(s),c=s.dot(r),h=s.dot(o),u=r.dot(r),d=r.dot(o),p=l*u-c*c;if(0===p)return a.set(-2,-1,-1);const _=1/p,m=(u*h-c*d)*_,f=(l*d-c*h)*_;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,s,r,o,l){return this.getBarycoord(t,e,n,i,a),l.set(0,0),l.addScaledVector(s,a.x),l.addScaledVector(r,a.y),l.addScaledVector(o,a.z),l}static isFrontFacing(t,e,n,i){return s.subVectors(n,e),r.subVectors(t,e),s.cross(r).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 s.subVectors(this.c,this.b),r.subVectors(this.a,this.b),.5*s.cross(r).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,s){return _.getUV(t,this.a,this.b,this.c,e,n,i,s)}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,s=this.c;let r,o;l.subVectors(i,n),c.subVectors(s,n),u.subVectors(t,n);const a=l.dot(u),_=c.dot(u);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 r=a/(a-m),e.copy(n).addScaledVector(l,r);p.subVectors(t,s);const v=l.dot(p),y=c.dot(p);if(y>=0&&v<=y)return e.copy(s);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 h.subVectors(s,i),o=(f-m)/(f-m+(v-y)),e.copy(i).addScaledVector(h,o);const w=1/(b+x+g);return r=x*w,o=g*w,e.copy(n).addScaledVector(l,r).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),s=n(39),r=n(5),o=n(10),a=n(0),l=n(24),c=n(7),h=n(4);const u=new a.a,d=new a.a,p=new r.a,_=new s.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:u.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}else{for(let n=Math.max(0,r.start),i=Math.min(s.count,r.start+r.count)-1;nl)continue;d.applyMatrix4(this.matrixWorld);const i=t.ray.origin.distanceTo(d);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.\\\\\\\")}}}f.prototype.isLine=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return r}));var i=n(12),s=n(6);class r extends i.a{constructor(t){super(),this.type=\\\\\\\"PointsMaterial\\\\\\\",this.color=new s.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}}r.prototype.isPointsMaterial=!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[s]-n,r=this.curves[s],o=r.getLength(),a=0===o?0:1-t/o;return r.getPointAt(a,e)}s++}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 h=c.getPoint(1);return this.currentPoint.copy(h),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),s=n(47),r=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 s.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,s,r){return this.currentPath.bezierCurveTo(t,e,n,i,s,r),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[r],a=-a,o=e[s],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 s=o.a.isClockWise,a=this.subPaths;if(0===a.length)return[];if(!0===e)return n(a);let l,c,h;const u=[];if(1===a.length)return c=a[0],h=new r.a,h.curves=c.curves,u.push(h),u;let d=!s(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,s,r,o){const l=u.distanceSqToPoint(t);if(ls.far)return;r.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 s}));var i=n(26);class s extends i.a{}s.prototype.ValueTypeName=\\\\\\\"number\\\\\\\"},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return s}));var i=n(26);class s extends i.a{}s.prototype.ValueTypeName=\\\\\\\"vector\\\\\\\"},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return s}));var i=n(10);class s extends i.a{constructor(){super(),this.type=\\\\\\\"Bone\\\\\\\"}}s.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=s(t,0,a,n,!0);const c=[];if(!l||l.next===l.prev)return c;let h,p,_,f,g,v,y;if(i&&(l=function(t,e,n,i){const o=[];let a,l,c,h,p;for(a=0,l=e.length;a80*n){h=_=t[0],p=f=t[1];for(let e=n;e_&&(_=g),v>f&&(f=v);y=Math.max(_-h,f-p),y=0!==y?1/y:0}return o(l,c,n,h,p,y),c};function s(t,e,n,i,s){let r,o;if(s===function(t,e,n,i){let s=0;for(let r=e,o=n-i;r0)for(r=e;r=e;r-=i)o=M(r,t[r],t[r+1],o);return o&&y(o,o.next)&&(E(o),o=o.next),o}function r(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(E(i),i=e=i.prev,i===i.next)break;n=!0}}while(n||i!==e);return e}function o(t,e,n,i,s,u,d){if(!t)return;!d&&u&&function(t,e,n,i){let s=t;do{null===s.z&&(s.z=_(s.x,s.y,e,n,i)),s.prevZ=s.prev,s.nextZ=s.next,s=s.next}while(s!==t);s.prevZ.nextZ=null,s.prevZ=null,function(t){let e,n,i,s,r,o,a,l,c=1;do{for(n=t,t=null,r=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)?(s=n,n=n.nextZ,a--):(s=i,i=i.nextZ,l--),r?r.nextZ=s:t=s,s.prevZ=r,r=s;n=i}r.nextZ=null,c*=2}while(o>1)}(s)}(t,i,s,u);let p,m,f=t;for(;t.prev!==t.next;)if(p=t.prev,m=t.next,u?l(t,i,s,u):a(t))e.push(p.i/n),e.push(t.i/n),e.push(m.i/n),E(t),t=m.next,f=m.next;else if((t=m)===f){d?1===d?o(t=c(r(t),e,n),e,n,i,s,u,2):2===d&&h(t,e,n,i,s,u):o(r(t),e,n,i,s,u,1);break}}function a(t){const e=t.prev,n=t,i=t.next;if(v(e,n,i)>=0)return!1;let s=t.next.next;for(;s!==t.prev;){if(f(e.x,e.y,n.x,n.y,i.x,i.y,s.x,s.y)&&v(s.prev,s,s.next)>=0)return!1;s=s.next}return!0}function l(t,e,n,i){const s=t.prev,r=t,o=t.next;if(v(s,r,o)>=0)return!1;const a=s.xr.x?s.x>o.x?s.x:o.x:r.x>o.x?r.x:o.x,h=s.y>r.y?s.y>o.y?s.y:o.y:r.y>o.y?r.y:o.y,u=_(a,l,e,n,i),d=_(c,h,e,n,i);let p=t.prevZ,m=t.nextZ;for(;p&&p.z>=u&&m&&m.z<=d;){if(p!==t.prev&&p!==t.next&&f(s.x,s.y,r.x,r.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(s.x,s.y,r.x,r.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>=u;){if(p!==t.prev&&p!==t.next&&f(s.x,s.y,r.x,r.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(s.x,s.y,r.x,r.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 s=i.prev,r=i.next.next;!y(s,r)&&x(s,i,i.next,r)&&T(s,r)&&T(r,s)&&(e.push(s.i/n),e.push(i.i/n),e.push(r.i/n),E(i),E(i.next),i=t=r),i=i.next}while(i!==t);return r(i)}function h(t,e,n,i,s,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=r(l,l.next),c=r(c,c.next),o(l,e,n,i,s,a),void o(c,e,n,i,s,a)}t=t.next}l=l.next}while(l!==t)}function u(t,e){return t.x-e.x}function d(t,e){if(e=function(t,e){let n=e;const i=t.x,s=t.y;let r,o=-1/0;do{if(s<=n.y&&s>=n.next.y&&n.next.y!==n.y){const t=n.x+(s-n.y)*(n.next.x-n.x)/(n.next.y-n.y);if(t<=i&&t>o){if(o=t,t===i){if(s===n.y)return n;if(s===n.next.y)return n.next}r=n.x=n.x&&n.x>=l&&i!==n.x&&f(sr.x||n.x===r.x&&p(r,n)))&&(r=n,u=h)),n=n.next}while(n!==a);return r}(t,e)){const n=A(e,t);r(e,e.next),r(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,s){return(t=1431655765&((t=858993459&((t=252645135&((t=16711935&((t=32767*(t-n)*s)|t<<8))|t<<4))|t<<2))|t<<1))|(e=1431655765&((e=858993459&((e=252645135&((e=16711935&((e=32767*(e-i)*s)|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)*(r-a)-(s-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 s=(t.x+e.x)/2,r=(t.y+e.y)/2;do{n.y>r!=n.next.y>r&&n.next.y!==n.y&&s<(n.next.x-n.x)*(r-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 s=w(v(t,e,n)),r=w(v(t,e,i)),o=w(v(n,i,t)),a=w(v(n,i,e));return s!==r&&o!==a||(!(0!==s||!b(t,n,e))||(!(0!==r||!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),s=t.next,r=e.prev;return t.next=e,e.prev=t,n.next=s,s.prev=n,i.next=n,n.prev=i,r.next=i,i.prev=r,i}function M(t,e,n,i){const s=new S(t,e,n);return i?(s.next=i.next,s.prev=i,i.next.prev=s,i.next=s):(s.prev=s,s.next=s),s}function E(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,s=0;s2&&t[e-1].equals(t[0])&&t.pop()}function L(t,e){for(let n=0;ni;)r-=i;r0!=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 l}));var i=n(0),s=n(18),r=n(34);const o=new s.a,a=new i.a;class l{constructor(t=new r.a,e=new r.a,n=new r.a,i=new r.a,s=new r.a,o=new r.a){this.planes=[t,e,n,i,s,o]}set(t,e,n,i,s,r){const o=this.planes;return o[0].copy(t),o[1].copy(e),o[2].copy(n),o[3].copy(i),o[4].copy(s),o[5].copy(r),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],s=n[1],r=n[2],o=n[3],a=n[4],l=n[5],c=n[6],h=n[7],u=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,h-a,_-u,v-m).normalize(),e[1].setComponents(o+i,h+a,_+u,v+m).normalize(),e[2].setComponents(o+s,h+l,_+d,v+f).normalize(),e[3].setComponents(o-s,h-l,_-d,v-f).normalize(),e[4].setComponents(o-r,h-c,_-p,v-g).normalize(),e[5].setComponents(o+r,h+c,_+p,v+g).normalize(),this}intersectsObject(t){const e=t.geometry;return null===e.boundingSphere&&e.computeBoundingSphere(),o.copy(e.boundingSphere).applyMatrix4(t.matrixWorld),this.intersectsSphere(o)}intersectsSprite(t){return o.center.set(0,0,0),o.radius=.7071067811865476,o.applyMatrix4(t.matrixWorld),this.intersectsSphere(o)}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,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 r}));const i=new Float32Array(1),s=new Int32Array(i.buffer);class r{static toHalfFloat(t){t>65504&&(console.warn(\\\\\\\"THREE.DataUtils.toHalfFloat(): value exceeds 65504.\\\\\\\"),t=65504),i[0]=t;const e=s[0];let n=e>>16&32768,r=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?(r|=2048,n|=(r>>114-o)+(r>>113-o&1),n):(n|=o-112<<10|r>>1,n+=1&r,n)}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return o}));var i=n(12),s=n(1),r=n(6);class o extends i.a{constructor(t){super(),this.type=\\\\\\\"MeshLambertMaterial\\\\\\\",this.color=new r.a(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new r.a(0),this.emissiveIntensity=1,this.emissiveMap=null,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=s.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),s=n(13),r=n(20);class o extends s.a{constructor(t){super(t)}load(t,e,n,s){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(r.b)(\\\\\\\"img\\\\\\\");function c(){l.removeEventListener(\\\\\\\"load\\\\\\\",c,!1),l.removeEventListener(\\\\\\\"error\\\\\\\",h,!1),i.a.add(t,this),e&&e(this),o.manager.itemEnd(t)}function h(e){l.removeEventListener(\\\\\\\"load\\\\\\\",c,!1),l.removeEventListener(\\\\\\\"error\\\\\\\",h,!1),s&&s(e),o.manager.itemError(t),o.manager.itemEnd(t)}return l.addEventListener(\\\\\\\"load\\\\\\\",c,!1),l.addEventListener(\\\\\\\"error\\\\\\\",h,!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),s=n(26),r=n(1);class o extends s.a{}o.prototype.ValueTypeName=\\\\\\\"bool\\\\\\\",o.prototype.ValueBufferType=Array,o.prototype.DefaultInterpolation=r.O,o.prototype.InterpolantFactoryMethodLinear=void 0,o.prototype.InterpolantFactoryMethodSmooth=void 0;class a extends s.a{}a.prototype.ValueTypeName=\\\\\\\"color\\\\\\\";var l=n(50),c=n(54);class h extends s.a{}h.prototype.ValueTypeName=\\\\\\\"string\\\\\\\",h.prototype.ValueBufferType=Array,h.prototype.DefaultInterpolation=r.O,h.prototype.InterpolantFactoryMethodLinear=void 0,h.prototype.InterpolantFactoryMethodSmooth=void 0;var u=n(51),d=n(3);class p{constructor(t,e=-1,n,i=r.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,s=n.length;t!==s;++t)e.push(_(n[t]).scale(i));const s=new this(t.name,t.duration,e,t.blendMode);return s.uuid=t.uuid,s}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(s.a.toJSON(n[t]));return i}static CreateFromMorphTargetSequence(t,e,n,s){const r=e.length,o=[];for(let t=0;t1){const t=r[1];let e=i[t];e||(i[t]=e=[]),e.push(n)}}const r=[];for(const t in i)r.push(this.CreateFromMorphTargetSequence(t,i[t],e,n));return r}static parseAnimation(t,e){if(!t)return console.error(\\\\\\\"THREE.AnimationClip: No animation in JSONLoader data.\\\\\\\"),null;const n=function(t,e,n,s,r){if(0!==n.length){const o=[],a=[];i.a.flattenJSON(n,o,a,s),0!==o.length&&r.push(new t(e,o,a))}},s=[],r=t.name||\\\\\\\"default\\\\\\\",o=t.fps||30,a=t.blendMode;let h=t.length||-1;const d=t.hierarchy||[];for(let t=0;ti.length-2?i.length-1:a+1],d=i[a>i.length-3?i.length-1:a+2];return n.set(Object(s.a)(l,c.x,h.x,u.x,d.x),Object(s.a)(l,c.y,h.y,u.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 s.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)}}r.prototype.isBox2=!0;var o=n(7),a=n(21),l=n(4),c=n(13),h=n(11),u=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 s=this,r=new a.a(s.manager);r.setPath(s.path),r.setRequestHeader(s.requestHeader),r.setWithCredentials(s.withCredentials),r.load(t,(function(n){try{e(s.parse(n))}catch(e){i?i(e):console.error(e),s.manager.itemError(t)}}),n,i)}parse(t){const e=this;function n(t,e,n,i,r,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,h=(a.y-l.y)/2,u=Math.cos(i)*c+Math.sin(i)*h,d=-Math.sin(i)*c+Math.cos(i)*h;let p=e*e,_=n*n;const m=u*u,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));r===o&&(x=-x);const b=x*e*d/n,w=-x*n*u/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,M=s(1,0,(u-b)/e,(d-w)/n),E=s((u-b)/e,(d-w)/n,(-u-b)/e,(-d-w)/n)%(2*Math.PI);t.currentPath.absellipse(T,A,e,n,M,M+E,0===o,i)}function s(t,e,n,i){const s=t*n+e*i,r=Math.sqrt(t*t+e*e)*Math.sqrt(n*n+i*i);let o=Math.acos(Math.max(-1,Math.min(1,s/r)));return t*i-e*n<0&&(o=-o),o}function r(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 s=i[t].trim();if(\\\\\\\"\\\\\\\"===s)continue;const r=s.indexOf(\\\\\\\"(\\\\\\\"),o=s.length;if(r>0&&r=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,s=0;t=-e[0]*Math.PI/180,e.length>=3&&(i=e[1],s=e[2]),b.identity().translate(-i,-s),w.identity().rotate(t),T.multiplyMatrices(w,b),b.identity().translate(i,s),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 E.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&&(s.copy(l),e.currentPath.currentPoint.copy(s),c=!0);break;default:console.warn(i)}h=!1}return e}(e));break;case\\\\\\\"rect\\\\\\\":s=r(e,s),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),s=d(t.getAttribute(\\\\\\\"ry\\\\\\\")||t.getAttribute(\\\\\\\"rx\\\\\\\")||0),r=d(t.getAttribute(\\\\\\\"width\\\\\\\")),o=d(t.getAttribute(\\\\\\\"height\\\\\\\")),a=.448084975506,l=new p.a;l.moveTo(e+i,n),l.lineTo(e+r-i,n),(0!==i||0!==s)&&l.bezierCurveTo(e+r-i*a,n,e+r,n+s*a,e+r,n+s);l.lineTo(e+r,n+o-s),(0!==i||0!==s)&&l.bezierCurveTo(e+r,n+o-s*a,e+r-i*a,n+o,e+r-i,n+o);l.lineTo(e+i,n+o),(0!==i||0!==s)&&l.bezierCurveTo(e+i*a,n+o,e,n+o-s*a,e,n+o-s);l.lineTo(e,n+s),(0!==i||0!==s)&&l.bezierCurveTo(e,n+s*a,e+i*a,n,e+i,n);return l}(e);break;case\\\\\\\"polygon\\\\\\\":s=r(e,s),m=function(t){function e(t,e,n){const r=d(e),o=d(n);0===s?i.moveTo(r,o):i.lineTo(r,o),s++}const n=/(-?[\\\\d\\\\.?]+)[,|\\\\s](-?[\\\\d\\\\.?]+)/g,i=new p.a;let s=0;return t.getAttribute(\\\\\\\"points\\\\\\\").replace(n,e),i.currentPath.autoClose=!0,i}(e);break;case\\\\\\\"polyline\\\\\\\":s=r(e,s),m=function(t){function e(t,e,n){const r=d(e),o=d(n);0===s?i.moveTo(r,o):i.lineTo(r,o),s++}const n=/(-?[\\\\d\\\\.?]+)[,|\\\\s](-?[\\\\d\\\\.?]+)/g,i=new p.a;let s=0;return t.getAttribute(\\\\\\\"points\\\\\\\").replace(n,e),i.currentPath.autoClose=!1,i}(e);break;case\\\\\\\"circle\\\\\\\":s=r(e,s),m=function(t){const e=d(t.getAttribute(\\\\\\\"cx\\\\\\\")||0),n=d(t.getAttribute(\\\\\\\"cy\\\\\\\")||0),i=d(t.getAttribute(\\\\\\\"r\\\\\\\")||0),s=new u.a;s.absarc(e,n,i,0,2*Math.PI);const r=new p.a;return r.subPaths.push(s),r}(e);break;case\\\\\\\"ellipse\\\\\\\":s=r(e,s),m=function(t){const e=d(t.getAttribute(\\\\\\\"cx\\\\\\\")||0),n=d(t.getAttribute(\\\\\\\"cy\\\\\\\")||0),i=d(t.getAttribute(\\\\\\\"rx\\\\\\\")||0),s=d(t.getAttribute(\\\\\\\"ry\\\\\\\")||0),r=new u.a;r.absellipse(e,n,i,s,0,2*Math.PI);const o=new p.a;return o.subPaths.push(r),o}(e);break;case\\\\\\\"line\\\\\\\":s=r(e,s),m=function(t){const e=d(t.getAttribute(\\\\\\\"x1\\\\\\\")||0),n=d(t.getAttribute(\\\\\\\"y1\\\\\\\")||0),i=d(t.getAttribute(\\\\\\\"x2\\\\\\\")||0),s=d(t.getAttribute(\\\\\\\"y2\\\\\\\")||0),r=new p.a;return r.moveTo(e,n),r.lineTo(i,s),r.currentPath.autoClose=!1,r}(e);break;case\\\\\\\"defs\\\\\\\":c=!1;break;case\\\\\\\"use\\\\\\\":s=r(e,s);const l=e.href.baseVal.substring(1),h=e.viewportElement.getElementById(l);h?t(h,s):console.warn(\\\\\\\"SVGLoader: 'use node' references non-existent node id: \\\\\\\"+l)}if(m&&(void 0!==s.fill&&\\\\\\\"none\\\\\\\"!==s.fill&&m.color.setStyle(s.fill),function(t,e){function n(t){M.set(t.x,t.y,1).applyMatrix3(e),t.set(M.x,M.y)}const i=function(t){return 0!==t.elements[1]||0!==t.elements[3]}(e),s=t.subPaths;for(let t=0,r=s.length;t0?E.copy(y[y.length-1]):E.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,s=1,o=2,a=3,l=4,c=5,h=6,p={loc:n,t:0};function m(t,e,i,s){const r=t.x,a=e.x,l=i.x,c=s.x,h=t.y,u=e.y,d=i.y,_=s.y,m=(c-l)*(h-d)-(_-d)*(r-l),g=(_-d)*(a-r)-(c-l)*(u-h),v=m/g,y=((a-r)*(h-d)-(u-h)*(r-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:s,t,e),p.loc==n){const t=0===l?i:s;return{x:t.x,y:t.y,t:p.t}}if(p.loc==o){return{x:+(r+p.t*(a-r)).toPrecision(10),y:+(h+p.t*(u-h)).toPrecision(10),t:p.t}}}return null}for(let r=0;r<2;r++)if(f(0===r?i:s,t,e),p.loc==n){const t=0===r?i:s;return{x:t.x,y:t.y,t:p.t}}return{x:+(r+v*(a-r)).toPrecision(10),y:+(h+v*(u-h)).toPrecision(10),t:v}}function f(t,e,i){const r=i.x-e.x,u=i.y-e.y,d=t.x-e.x,_=t.y-e.y,m=r*_-d*u;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=s,void(p.t=1);if(m<-Number.EPSILON)return void(p.loc=a);if(m>Number.EPSILON)return void(p.loc=l);if(r*d<0||u*_<0)return void(p.loc=c);if(Math.sqrt(r*r+u*u){if(e.boundingBox.containsPoint(s)){(function(t,e){const n=[],s=[];for(let r=1;rt.t<=r.t+Number.EPSILON&&t.t>=r.t-Number.EPSILON))&&(n.push(r),s.push(new i.a(r.x,r.y)))}}return s})(t,e.points).forEach((t=>{r.push({identifier:e.identifier,isCW:e.isCW,point:t})}))}})),r.sort(((t,e)=>t.point.x-e.point.x)),r}let v=0,y=e,x=-999999999,b=t.subPaths.map((t=>{const n=t.getPoints();let s=-999999999,o=e,a=-999999999,l=e;for(let t=0;ts&&(s=e.y),e.ya&&(a=e.x),e.x=l&&(y=l-1),{points:n,isCW:_.a.isClockWise(n),identifier:v++,boundingBox:new r(new i.a(l,o),new i.a(a,s))}}));b=b.filter((t=>t.points.length>1));const w=b.map((e=>function(t,e,n,s,r){null!=r&&\\\\\\\"\\\\\\\"!==r||(r=\\\\\\\"nonzero\\\\\\\");const o=new i.a;t.boundingBox.getCenter(o);const a=g([new i.a(n,o.y),new i.a(s,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 h=l[0].point.x,u=[];let d=0;for(;d0&&u[u.length-1]===c[d].identifier?u.pop():u.push(c[d].identifier),d++;if(u.push(t.identifier),\\\\\\\"evenodd\\\\\\\"===r){const e=u.length%2==0,n=u[u.length-2];return{identifier:t.identifier,isHole:e,for:n}}if(\\\\\\\"nonzero\\\\\\\"===r){let n=!0,i=null,s=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 u.a(n.points))})),T.push(e)}})),T}static getStrokeStyle(t,e,n,i,s){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:s=void 0!==s?s:4}}static pointsToStroke(t,e,n,i){const s=[],r=[],a=[];if(0===f.pointsToStrokeWithBuffers(t,e,n,i,s,r,a))return null;const c=new o.a;return c.setAttribute(\\\\\\\"position\\\\\\\",new l.c(s,3)),c.setAttribute(\\\\\\\"normal\\\\\\\",new l.c(r,3)),c.setAttribute(\\\\\\\"uv\\\\\\\",new l.c(a,2)),c}static pointsToStrokeWithBuffers(t,e,n,s,r,o,a,l){const c=new i.a,h=new i.a,u=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,M=new i.a;n=void 0!==n?n:12,s=void 0!==s?s:.001,l=void 0!==l?l:0;const E=(t=function(t){let e=!1;for(let n=1,i=t.length-1;n=s&&n.push(t[e]);return n.push(t[t.length-1]),n}(t)).length;if(E<2)return 0;const S=t[0].equals(t[E-1]);let C,N,L=t[0];const O=e.strokeWidth/2,P=1/(E-1);let R,I,F,D,B=0,z=!1,k=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)/r)+1)*r:0===_&&p===r-1&&(p=r-2,_=1),this.closed||p>0?u=s[(p-1)%r]:(o.subVectors(s[0],s[1]).add(s[0]),u=o);const m=s[p%r],f=s[(p+1)%r];if(this.closed||p+2{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 s}));var i=n(41);class s extends i.a{constructor(t,e){super(t,e),this.type=\\\\\\\"LineLoop\\\\\\\"}}s.prototype.isLineLoop=!0},,function(t,e,n){!function(n){\\\\\\\"use strict\\\\\\\";var i=\\\\\\\"Compound\\\\\\\",s=\\\\\\\"MemberExpression\\\\\\\",r=\\\\\\\"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},h=function(t){var e,n=0;for(var i in t)(e=i.length)>n&&t.hasOwnProperty(i)&&(n=e);return n},u=h(l),d=h(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,h=t.charAt,y=t.charCodeAt,x=function(e){return h.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=E();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))},M=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,r=i.pop(),t=m(e,r,l),i.push(t);(t=S())||o(\\\\\\\"Expected expression after \\\\\\\"+h,a),i.push(s,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,u)).length;i>0;){if(l.hasOwnProperty(n)&&(!g(b(a))||a+n.length=s.length&&o(\\\\\\\"Unexpected token \\\\\\\"+String.fromCharCode(t),a);break}if(44===e){if(a++,++l!==s.length)if(41===t)o(\\\\\\\"Unexpected token ,\\\\\\\",a);else if(93===t)for(var c=s.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(118),s=n(125);t.exports=function(t){return null!=t&&s(t.length)&&!i(t)}},function(t,e){t.exports=function(){return[]}},function(t,e,n){var i=n(130),s=n(131),r=n(111),o=n(128),a=Object.getOwnPropertySymbols?function(t){for(var e=[];t;)i(e,r(t)),t=s(t);return e}:o;t.exports=a},function(t,e){t.exports=function(t,e){for(var n=-1,i=e.length,s=t.length;++n2&&void 0!==arguments[2]?arguments[2]:{},s=arguments.length>3&&void 0!==arguments[3]?arguments[3]:i;if(Array.isArray(n))n.forEach((function(n){return t(e,n,i,s)}));else if(\\\\\\\"function\\\\\\\"==typeof n)n(e,i,s,t);else{var r=Object.keys(n)[0];Array.isArray(n[r])?(s[r]={},t(e,n[r],i,s[r])):s[r]=n[r](e,i,s,t)}return i};e.conditional=function(t,e){return function(n,i,s,r){e(n,i,s)&&r(n,t,i,s)}};e.loop=function(t,e){return function(n,i,s,r){for(var o=[];e(n,i,s);){var a={};r(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 s=function(t){return function(e){return e.data.subarray(e.pos,e.pos+=t)}};e.readBytes=s;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(s(t)(e)).map((function(t){return String.fromCharCode(t)})).join(\\\\\\\"\\\\\\\")}};e.readUnsigned=function(t){return function(e){var n=s(2)(e);return t?(n[1]<<8)+n[0]:(n[0]<<8)+n[1]}};e.readArray=function(t,e){return function(n,i,r){for(var o=\\\\\\\"function\\\\\\\"==typeof e?e(n,i,r):e,a=s(t),l=new Array(o),c=0;c-1}},function(t,e,n){var i=n(96);t.exports=function(t,e){var n=this.__data__,s=i(n,t);return s<0?(++this.size,n.push([t,e])):n[s][1]=e,this}},function(t,e,n){var i=n(95);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(95),s=n(102),r=n(154);t.exports=function(t,e){var n=this.__data__;if(n instanceof i){var o=n.__data__;if(!s||o.length<199)return o.push([t,e]),this.size=++n.size,this;n=this.__data__=new r(o)}return n.set(t,e),this.size=n.size,this}},function(t,e,n){var i=n(118),s=n(151),r=n(88),o=n(120),a=/^\\\\[object .+?Constructor\\\\]$/,l=Function.prototype,c=Object.prototype,h=l.toString,u=c.hasOwnProperty,d=RegExp(\\\\\\\"^\\\\\\\"+h.call(u).replace(/[\\\\\\\\^$.*+?()[\\\\]{}|]/g,\\\\\\\"\\\\\\\\$&\\\\\\\").replace(/hasOwnProperty|(function).*?(?=\\\\\\\\\\\\()| for .+?(?=\\\\\\\\\\\\])/g,\\\\\\\"$1.*?\\\\\\\")+\\\\\\\"$\\\\\\\");t.exports=function(t){return!(!r(t)||s(t))&&(i(t)?d:a).test(o(t))}},function(t,e,n){var i=n(103),s=Object.prototype,r=s.hasOwnProperty,o=s.toString,a=i?i.toStringTag:void 0;t.exports=function(t){var e=r.call(t,a),n=t[a];try{t[a]=void 0;var i=!0}catch(t){}var s=o.call(t);return i&&(e?t[a]=n:delete t[a]),s}},function(t,e){var n=Object.prototype.toString;t.exports=function(t){return n.call(t)}},function(t,e,n){var i,s=n(152),r=(i=/[^.]+$/.exec(s&&s.keys&&s.keys.IE_PROTO||\\\\\\\"\\\\\\\"))?\\\\\\\"Symbol(src)_1.\\\\\\\"+i:\\\\\\\"\\\\\\\";t.exports=function(t){return!!r&&r 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(155),s=n(162),r=n(164),o=n(165),a=n(166);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,s.readBytes)(o)(t)),i+=o;break}e.push((0,s.readBytes)(r)(t)),i+=r}for(var a=new Uint8Array(i),l=0,c=0;c>=o,_-=o,h>i||h==a)break;if(h==s){r=(1<<(o=d+1))-1,i=s+2,c=-1;continue}if(-1==c){A[f++]=T[h],c=h,m=h;continue}for(l=h,h==i&&(A[f++]=m,h=c);h>s;)A[f++]=T[h],h=w[h];m=255&T[h],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(),s=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,s)),!e&&(this._createConnection(i,s),t.dirtyController.clearSuccessorsCacheWithPredecessors(),!0)}return console.warn(`attempt to connect non existing node ${i} or ${s}`),!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 s=this[e](t.graphNodeId());for(;s.length>0;){const t=[];for(let n of s)for(let i of this[e](n))t.push(i);for(let t of s)n.set(t,!0);for(let e of t)s.push(e);s=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 h{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),s=i.indexOf(n);s>=0&&i.splice(s,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 u{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 s=0;sn.push(t))),n}static intersection(t,e){const n=[];return u.intersection(this.toSet(t),this.toSet(e)).forEach((t=>n.push(t))),n}static difference(t,e){const n=[];return u.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{s[r]=t,r++})),m.isString(s[0])?s.sort():s.sort(((t,e)=>t-e));const o=new Array(t.length);r=0;for(let t of s){const e=n.get(t);if(e)for(let t of e)o[r]=t,r++}return o}static range(t,e,n=1){null==e&&(e=t,t=0);const i=Math.floor((e-t)/n),s=new Array(i);for(let e=0;e0?1:-1,c.push(C.x,C.y,C.z),h.push(a/f),h.push(1-r/g),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}}F.prototype.isShaderMaterial=!0;var D=n(6),B=n(14),z=\\\\\\\"\\\\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\\\\\\\",k=\\\\\\\"\\\\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\\\\\\\";const U={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:z,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:k,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 G=n(11);const V={common:{diffuse:{value:new D.a(16777215)},opacity:{value:1},map:{value:null},uvTransform:{value:new G.a},uv2Transform:{value:new G.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 G.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 G.a}}},H={basic:{uniforms:R([V.common,V.specularmap,V.envmap,V.aomap,V.lightmap,V.fog]),vertexShader:U.meshbasic_vert,fragmentShader:U.meshbasic_frag},lambert:{uniforms:R([V.common,V.specularmap,V.envmap,V.aomap,V.lightmap,V.emissivemap,V.fog,V.lights,{emissive:{value:new D.a(0)}}]),vertexShader:U.meshlambert_vert,fragmentShader:U.meshlambert_frag},phong:{uniforms:R([V.common,V.specularmap,V.envmap,V.aomap,V.lightmap,V.emissivemap,V.bumpmap,V.normalmap,V.displacementmap,V.fog,V.lights,{emissive:{value:new D.a(0)},specular:{value:new D.a(1118481)},shininess:{value:30}}]),vertexShader:U.meshphong_vert,fragmentShader:U.meshphong_frag},standard:{uniforms:R([V.common,V.envmap,V.aomap,V.lightmap,V.emissivemap,V.bumpmap,V.normalmap,V.displacementmap,V.roughnessmap,V.metalnessmap,V.fog,V.lights,{emissive:{value:new D.a(0)},roughness:{value:1},metalness:{value:0},envMapIntensity:{value:1}}]),vertexShader:U.meshphysical_vert,fragmentShader:U.meshphysical_frag},toon:{uniforms:R([V.common,V.aomap,V.lightmap,V.emissivemap,V.bumpmap,V.normalmap,V.displacementmap,V.gradientmap,V.fog,V.lights,{emissive:{value:new D.a(0)}}]),vertexShader:U.meshtoon_vert,fragmentShader:U.meshtoon_frag},matcap:{uniforms:R([V.common,V.bumpmap,V.normalmap,V.displacementmap,V.fog,{matcap:{value:null}}]),vertexShader:U.meshmatcap_vert,fragmentShader:U.meshmatcap_frag},points:{uniforms:R([V.points,V.fog]),vertexShader:U.points_vert,fragmentShader:U.points_frag},dashed:{uniforms:R([V.common,V.fog,{scale:{value:1},dashSize:{value:1},totalSize:{value:2}}]),vertexShader:U.linedashed_vert,fragmentShader:U.linedashed_frag},depth:{uniforms:R([V.common,V.displacementmap]),vertexShader:U.depth_vert,fragmentShader:U.depth_frag},normal:{uniforms:R([V.common,V.bumpmap,V.normalmap,V.displacementmap,{opacity:{value:1}}]),vertexShader:U.meshnormal_vert,fragmentShader:U.meshnormal_frag},sprite:{uniforms:R([V.sprite,V.fog]),vertexShader:U.sprite_vert,fragmentShader:U.sprite_frag},background:{uniforms:{uvTransform:{value:new G.a},t2D:{value:null}},vertexShader:U.background_vert,fragmentShader:U.background_frag},cube:{uniforms:R([V.envmap,{opacity:{value:1}}]),vertexShader:U.cube_vert,fragmentShader:U.cube_frag},equirect:{uniforms:{tEquirect:{value:null}},vertexShader:U.equirect_vert,fragmentShader:U.equirect_frag},distanceRGBA:{uniforms:R([V.common,V.displacementmap,{referencePosition:{value:new p.a},nearDistance:{value:1},farDistance:{value:1e3}}]),vertexShader:U.distanceRGBA_vert,fragmentShader:U.distanceRGBA_frag},shadow:{uniforms:R([V.lights,V.fog,{color:{value:new D.a(0)},opacity:{value:1}}]),vertexShader:U.shadow_vert,fragmentShader:U.shadow_frag}};function j(t,e,n,i,s){const r=new D.a(0);let o,a,l=0,c=null,h=0,u=null;function d(t,e){n.buffers.color.setClear(t.r,t.g,t.b,e,s)}return{getClearColor:function(){return r},setClearColor:function(t,e=1){r.set(t),l=e,d(r,l)},getClearAlpha:function(){return l},setClearAlpha:function(t){l=t,d(r,l)},render:function(n,s){let p=!1,_=!0===s.isScene?s.background:null;_&&_.isTexture&&(_=e.get(_));const m=t.xr,f=m.getSession&&m.getSession();f&&\\\\\\\"additive\\\\\\\"===f.environmentBlendMode&&(_=null),null===_?d(r,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 B.a(new N(1,1,1),new F({name:\\\\\\\"BackgroundCubeMaterial\\\\\\\",uniforms:P(H.cube.uniforms),vertexShader:H.cube.vertexShader,fragmentShader:H.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===_&&h===_.version&&u===t.toneMapping||(a.material.needsUpdate=!0,c=_,h=_.version,u=t.toneMapping),n.unshift(a,a.geometry,a.material,0,0,null)):_&&_.isTexture&&(void 0===o&&(o=new B.a(new L(2,2),new F({name:\\\\\\\"BackgroundMaterial\\\\\\\",uniforms:P(H.background.uniforms),vertexShader:H.background.vertexShader,fragmentShader:H.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===_&&h===_.version&&u===t.toneMapping||(o.material.needsUpdate=!0,c=_,h=_.version,u=t.toneMapping),n.unshift(o,o.geometry,o.material,0,0,null))}}}function W(t,e,n,i){const s=t.getParameter(t.MAX_VERTEX_ATTRIBS),r=i.isWebGL2?null:e.get(\\\\\\\"OES_vertex_array_object\\\\\\\"),o=i.isWebGL2||null!==r,a={},l=d(null);let c=l;function h(e){return i.isWebGL2?t.bindVertexArray(e):r.bindVertexArrayOES(e)}function u(e){return i.isWebGL2?t.deleteVertexArray(e):r.deleteVertexArrayOES(e)}function d(t){const e=[],n=[],i=[];for(let t=0;t=0){let r=l[e];if(void 0===r&&(\\\\\\\"instanceMatrix\\\\\\\"===e&&s.instanceMatrix&&(r=s.instanceMatrix),\\\\\\\"instanceColor\\\\\\\"===e&&s.instanceColor&&(r=s.instanceColor)),void 0!==r){const e=r.normalized,o=r.itemSize,l=n.get(r);if(void 0===l)continue;const c=l.buffer,h=l.type,u=l.bytesPerElement;if(r.isInterleavedBufferAttribute){const n=r.data,l=n.stride,d=r.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 r=\\\\\\\"undefined\\\\\\\"!=typeof WebGL2RenderingContext&&t instanceof WebGL2RenderingContext||\\\\\\\"undefined\\\\\\\"!=typeof WebGL2ComputeRenderingContext&&t instanceof WebGL2ComputeRenderingContext;let o=void 0!==n.precision?n.precision:\\\\\\\"highp\\\\\\\";const a=s(o);a!==o&&(console.warn(\\\\\\\"THREE.WebGLRenderer:\\\\\\\",o,\\\\\\\"not supported, using\\\\\\\",a,\\\\\\\"instead.\\\\\\\"),o=a);const l=r||e.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\"),c=!0===n.logarithmicDepthBuffer,h=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS),u=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=u>0,y=r||e.has(\\\\\\\"OES_texture_float\\\\\\\");return{isWebGL2:r,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:s,precision:o,logarithmicDepthBuffer:c,maxTextures:h,maxVertexTextures:u,maxTextureSize:d,maxCubemapSize:p,maxAttributes:_,maxVertexUniforms:m,maxVaryings:f,maxFragmentUniforms:g,vertexTextures:v,floatFragmentTextures:y,floatVertexTextures:v&&y,maxSamples:r?t.getParameter(t.MAX_SAMPLES):0}}H.physical={uniforms:R([H.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:U.meshphysical_vert,fragmentShader:U.meshphysical_frag};var Y=n(34);function $(t){const e=this;let n=null,i=0,s=!1,r=!1;const o=new Y.a,a=new G.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 h(t,n,i,s){const r=null!==t?t.length:0;let c=null;if(0!==r){if(c=l.value,!0!==s||null===c){const e=i+4*r,s=n.matrixWorldInverse;a.getNormalMatrix(s),(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),s=new F({name:\\\\\\\"CubemapFromEquirect\\\\\\\",uniforms:P(n.uniforms),vertexShader:n.vertexShader,fragmentShader:n.fragmentShader,side:w.i,blending:w.ub});s.uniforms.tEquirect.value=e;const r=new B.a(i,s),o=e.minFilter;e.minFilter===w.Y&&(e.minFilter=w.V);return new nt(1,10,this).update(t,r),e.minFilter=o,r.geometry.dispose(),r.material.dispose(),this}clear(t,e,n,i){const s=t.getRenderTarget();for(let s=0;s<6;s++)t.setRenderTarget(this,s),t.clear(e,n,i);t.setRenderTarget(s)}}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 s=e.get(n);void 0!==s&&(e.delete(n),s.dispose())}return{get:function(s){if(s&&s.isTexture&&!1===s.isRenderTargetTexture){const r=s.mapping;if(r===w.D||r===w.E){if(e.has(s)){return n(e.get(s).texture,s.mapping)}{const r=s.image;if(r&&r.height>0){const o=t.getRenderTarget(),a=new st(r.height/2);return a.fromEquirectangularTexture(t,s),e.set(s,a),t.setRenderTarget(o),s.addEventListener(\\\\\\\"dispose\\\\\\\",i),n(a.texture,s.mapping)}return null}}}return s},dispose:function(){e=new WeakMap}}}st.prototype.isWebGLCubeRenderTarget=!0;var ot=n(37);class at extends F{constructor(t){super(t),this.type=\\\\\\\"RawShaderMaterial\\\\\\\"}}at.prototype.isRawShaderMaterial=!0;var lt=n(29);const ct=Math.pow(2,8),ht=[.125,.215,.35,.446,.526,.582],ut=5+ht.length,dt=20,pt={[w.U]:0,[w.ld]:1,[w.gc]:2,[w.lc]:3,[w.kc]:4,[w.fc]:5,[w.J]:6},_t=new ot.a,{_lodPlanes:mt,_sizeLods:ft,_sigmas:gt}=Mt(),vt=new D.a;let yt=null;const xt=(1+Math.sqrt(5))/2,bt=1/xt,wt=[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,xt,bt),new p.a(0,xt,-bt),new p.a(bt,0,xt),new p.a(-bt,0,xt),new p.a(xt,bt,0),new p.a(-xt,bt,0)];class Tt{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 at({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:pt[w.U]},outputEncoding:{value:pt[w.U]}},vertexShader:Lt(),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${Ot()}\\\\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})}(dt),this._equirectShader=null,this._cubemapShader=null,this._compileMaterial(this._blurMaterial)}fromScene(t,e=0,n=.1,i=100){yt=this._renderer.getRenderTarget();const s=this._allocateTargets();return this._sceneToCubeUV(t,n,i,s),e>0&&this._blur(s,0,0,e),this._applyPMREM(s),this._cleanup(s),s}fromEquirectangular(t){return this._fromTexture(t)}fromCubemap(t){return this._fromTexture(t)}compileCubemapShader(){null===this._cubemapShader&&(this._cubemapShader=Nt(),this._compileMaterial(this._cubemapShader))}compileEquirectangularShader(){null===this._equirectShader&&(this._equirectShader=Ct(),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?ct:0,ct,ct),a.setRenderTarget(i),p&&a.render(d,s),a.render(t,s)}d.geometry.dispose(),d.material.dispose(),a.toneMapping=h,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=pt[w.U]:t.value=pt[e.encoding]}_textureToCubeUV(t,e){const n=this._renderer;t.isCubeTexture?null==this._cubemapShader&&(this._cubemapShader=Nt()):null==this._equirectShader&&(this._equirectShader=Ct());const i=t.isCubeTexture?this._cubemapShader:this._equirectShader,s=new B.a(mt[0],i),r=i.uniforms;r.envMap.value=t,t.isCubeTexture||r.texelSize.value.set(1/t.image.width,1/t.image.height),this._setEncoding(r.inputEncoding,t),this._setEncoding(r.outputEncoding,e.texture),St(e,0,0,3*ct,2*ct),n.setRenderTarget(e),n.render(s,_t)}_applyPMREM(t){const e=this._renderer,n=e.autoClear;e.autoClear=!1;for(let e=1;edt&&console.warn(`sigmaRadians, ${s}, is too large and will clip, as it requested ${_} samples when the maximum is set to 20`);const m=[];let f=0;for(let t=0;t4?i-8+4:0),3*g,2*g),a.setRenderTarget(e),a.render(c,_t)}}function At(t){return void 0!==t&&t.type===w.Zc&&(t.encoding===w.U||t.encoding===w.ld||t.encoding===w.J)}function Mt(){const t=[],e=[],n=[];let i=8;for(let s=0;s4?o=ht[s-8+4-1]:0==s&&(o=0),n.push(o);const a=1/(r-1),l=-a/2,c=1+a/2,h=[l,l,c,l,c,c,l,l,c,c,l,c],u=6,d=6,p=3,_=2,m=1,f=new Float32Array(p*d*u),g=new Float32Array(_*d*u),v=new Float32Array(m*d*u);for(let t=0;t2?0:-1,i=[e,n,0,e+2/3,n,0,e+2/3,n+1,0,e,n,0,e+2/3,n+1,0,e,n+1,0];f.set(i,p*d*t),g.set(h,_*d*t);const s=[t,t,t,t,t,t];v.set(s,m*d*t)}const y=new S.a;y.setAttribute(\\\\\\\"position\\\\\\\",new C.a(f,p)),y.setAttribute(\\\\\\\"uv\\\\\\\",new C.a(g,_)),y.setAttribute(\\\\\\\"faceIndex\\\\\\\",new C.a(v,m)),t.push(y),i>4&&i--}return{_lodPlanes:t,_sizeLods:e,_sigmas:n}}function Et(t){const e=new Q(3*ct,3*ct,t);return e.texture.mapping=w.q,e.texture.name=\\\\\\\"PMREM.cubeUv\\\\\\\",e.scissorTest=!0,e}function St(t,e,n,i,s){t.viewport.set(e,n,i,s),t.scissor.set(e,n,i,s)}function Ct(){const t=new d.a(1,1);return new at({name:\\\\\\\"EquirectangularToCubeUV\\\\\\\",uniforms:{envMap:{value:null},texelSize:{value:t},inputEncoding:{value:pt[w.U]},outputEncoding:{value:pt[w.U]}},vertexShader:Lt(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform vec2 texelSize;\\\\n\\\\n\\\\t\\\\t\\\\t${Ot()}\\\\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 Nt(){return new at({name:\\\\\\\"CubemapToCubeUV\\\\\\\",uniforms:{envMap:{value:null},inputEncoding:{value:pt[w.U]},outputEncoding:{value:pt[w.U]}},vertexShader:Lt(),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${Ot()}\\\\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 Lt(){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 Ot(){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 s=e.get(n);void 0!==s&&(e.delete(n),s.dispose())}return{get:function(s){if(s&&s.isTexture&&!1===s.isRenderTargetTexture){const r=s.mapping,o=r===w.D||r===w.E,a=r===w.o||r===w.p;if(o||a){if(e.has(s))return e.get(s).texture;{const r=s.image;if(o&&r&&r.height>0||a&&r&&function(t){let e=0;const n=6;for(let i=0;i65535?C.i:C.h)(n,1);a.version=o;const l=r.get(t);l&&e.remove(l),r.set(t,a)}return{get:function(t,e){return!0===s[e.id]||(e.addEventListener(\\\\\\\"dispose\\\\\\\",o),s[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 s=n.morphAttributes;for(const n in s){const i=s[n];for(let n=0,s=i.length;ne.maxTextureSize&&(u=Math.ceil(h/e.maxTextureSize),h=e.maxTextureSize);const p=new Float32Array(h*u*4*i),_=new zt(p,h,u,i);_.format=w.Ib,_.type=w.G;const m=4*l;for(let e=0;e0)return t;const s=e*n;let r=Jt[s];if(void 0===r&&(r=new Float32Array(s),Jt[s]=r),0!==e){i.toArray(r,0);for(let i=1,s=0;i!==e;++i)s+=n,t[i].toArray(r,s)}return r}function ne(t,e){if(t.length!==e.length)return!1;for(let n=0,i=t.length;n/gm;function on(t){return t.replace(rn,an)}function an(t,e){const n=U[e];if(void 0===n)throw new Error(\\\\\\\"Can not resolve #include <\\\\\\\"+e+\\\\\\\">\\\\\\\");return on(n)}const ln=/#pragma unroll_loop[\\\\s]+?for \\\\( int i \\\\= (\\\\d+)\\\\; i < (\\\\d+)\\\\; i \\\\+\\\\+ \\\\) \\\\{([\\\\s\\\\S]+?)(?=\\\\})\\\\}/g,cn=/#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 hn(t){return t.replace(cn,dn).replace(ln,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.\\\\\\\"),dn(t,e,n,i)}function dn(t,e,n,i){let s=\\\\\\\"\\\\\\\";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(en).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\\\\\\\")}(r),m=s.createProgram();let f,g,v=n.glslVersion?\\\\\\\"#version \\\\\\\"+n.glslVersion+\\\\\\\"\\\\n\\\\\\\":\\\\\\\"\\\\\\\";n.isRawShaderMaterial?(f=[_].filter(en).join(\\\\\\\"\\\\n\\\\\\\"),f.length>0&&(f+=\\\\\\\"\\\\n\\\\\\\"),g=[p,_].filter(en).join(\\\\\\\"\\\\n\\\\\\\"),g.length>0&&(g+=\\\\\\\"\\\\n\\\\\\\")):(f=[pn(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 \\\\\\\"+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.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(en).join(\\\\\\\"\\\\n\\\\\\\"),g=[p,pn(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 \\\\\\\"+h:\\\\\\\"\\\\\\\",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.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?U.tonemapping_pars_fragment:\\\\\\\"\\\\\\\",n.toneMapping!==w.vb?tn(\\\\\\\"toneMapping\\\\\\\",n.toneMapping):\\\\\\\"\\\\\\\",n.dithering?\\\\\\\"#define DITHERING\\\\\\\":\\\\\\\"\\\\\\\",n.format===w.ic?\\\\\\\"#define OPAQUE\\\\\\\":\\\\\\\"\\\\\\\",U.encodings_pars_fragment,n.map?Qe(\\\\\\\"mapTexelToLinear\\\\\\\",n.mapEncoding):\\\\\\\"\\\\\\\",n.matcap?Qe(\\\\\\\"matcapTexelToLinear\\\\\\\",n.matcapEncoding):\\\\\\\"\\\\\\\",n.envMap?Qe(\\\\\\\"envMapTexelToLinear\\\\\\\",n.envMapEncoding):\\\\\\\"\\\\\\\",n.emissiveMap?Qe(\\\\\\\"emissiveMapTexelToLinear\\\\\\\",n.emissiveMapEncoding):\\\\\\\"\\\\\\\",n.specularTintMap?Qe(\\\\\\\"specularTintMapTexelToLinear\\\\\\\",n.specularTintMapEncoding):\\\\\\\"\\\\\\\",n.lightMap?Qe(\\\\\\\"lightMapTexelToLinear\\\\\\\",n.lightMapEncoding):\\\\\\\"\\\\\\\",Ke(\\\\\\\"linearToOutputTexel\\\\\\\",n.outputEncoding),n.depthPacking?\\\\\\\"#define DEPTH_PACKING \\\\\\\"+n.depthPacking:\\\\\\\"\\\\\\\",\\\\\\\"\\\\n\\\\\\\"].filter(en).join(\\\\\\\"\\\\n\\\\\\\")),o=on(o),o=nn(o,n),o=sn(o,n),a=on(a),a=nn(a,n),a=sn(a,n),o=hn(o),a=hn(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=Ye(s,s.VERTEX_SHADER,y),T=Ye(s,s.FRAGMENT_SHADER,x);if(s.attachShader(m,b),s.attachShader(m,T),void 0!==n.index0AttributeName?s.bindAttribLocation(m,0,n.index0AttributeName):!0===n.morphTargets&&s.bindAttribLocation(m,0,\\\\\\\"position\\\\\\\"),s.linkProgram(m),t.debug.checkShaderErrors){const t=s.getProgramInfoLog(m).trim(),e=s.getShaderInfoLog(b).trim(),n=s.getShaderInfoLog(T).trim();let i=!0,r=!0;if(!1===s.getProgramParameter(m,s.LINK_STATUS)){i=!1;const e=Ze(s,b,\\\\\\\"vertex\\\\\\\"),n=Ze(s,T,\\\\\\\"fragment\\\\\\\");console.error(\\\\\\\"THREE.WebGLProgram: Shader Error \\\\\\\"+s.getError()+\\\\\\\" - VALIDATE_STATUS \\\\\\\"+s.getProgramParameter(m,s.VALIDATE_STATUS)+\\\\\\\"\\\\n\\\\nProgram Info Log: \\\\\\\"+t+\\\\\\\"\\\\n\\\\\\\"+e+\\\\\\\"\\\\n\\\\\\\"+n)}else\\\\\\\"\\\\\\\"!==t?console.warn(\\\\\\\"THREE.WebGLProgram: Program Info Log:\\\\\\\",t):\\\\\\\"\\\\\\\"!==e&&\\\\\\\"\\\\\\\"!==n||(r=!1);r&&(this.diagnostics={runnable:i,programLog:t,vertexShader:{log:e,prefix:f},fragmentShader:{log:n,prefix:g}})}let A,M;return s.deleteShader(b),s.deleteShader(T),this.getUniforms=function(){return void 0===A&&(A=new Xe(s,m)),A},this.getAttributes=function(){return void 0===M&&(M=function(t,e){const n={},i=t.getProgramParameter(e,t.ACTIVE_ATTRIBUTES);for(let s=0;s0,N=r.clearcoat>0;return{isWebGL2:l,shaderID:T,shaderName:r.type,vertexShader:M,fragmentShader:E,defines:r.defines,isRawShaderMaterial:!0===r.isRawShaderMaterial,glslVersion:r.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:!!r.map,mapEncoding:f(r.map),matcap:!!r.matcap,matcapEncoding:f(r.matcap),envMap:!!b,envMapMode:b&&b.mapping,envMapEncoding:f(b),envMapCubeUV:!!b&&(b.mapping===w.q||b.mapping===w.r),lightMap:!!r.lightMap,lightMapEncoding:f(r.lightMap),aoMap:!!r.aoMap,emissiveMap:!!r.emissiveMap,emissiveMapEncoding:f(r.emissiveMap),bumpMap:!!r.bumpMap,normalMap:!!r.normalMap,objectSpaceNormalMap:r.normalMapType===w.zb,tangentSpaceNormalMap:r.normalMapType===w.Uc,clearcoat:N,clearcoatMap:N&&!!r.clearcoatMap,clearcoatRoughnessMap:N&&!!r.clearcoatRoughnessMap,clearcoatNormalMap:N&&!!r.clearcoatNormalMap,displacementMap:!!r.displacementMap,roughnessMap:!!r.roughnessMap,metalnessMap:!!r.metalnessMap,specularMap:!!r.specularMap,specularIntensityMap:!!r.specularIntensityMap,specularTintMap:!!r.specularTintMap,specularTintMapEncoding:f(r.specularTintMap),alphaMap:!!r.alphaMap,alphaTest:C,gradientMap:!!r.gradientMap,sheen:r.sheen>0,transmission:r.transmission>0,transmissionMap:!!r.transmissionMap,thicknessMap:!!r.thicknessMap,combine:r.combine,vertexTangents:!!r.normalMap&&!!v.geometry&&!!v.geometry.attributes.tangent,vertexColors:r.vertexColors,vertexAlphas:!0===r.vertexColors&&!!v.geometry&&!!v.geometry.attributes.color&&4===v.geometry.attributes.color.itemSize,vertexUvs:!!(r.map||r.bumpMap||r.normalMap||r.specularMap||r.alphaMap||r.emissiveMap||r.roughnessMap||r.metalnessMap||r.clearcoatMap||r.clearcoatRoughnessMap||r.clearcoatNormalMap||r.displacementMap||r.transmissionMap||r.thicknessMap||r.specularIntensityMap||r.specularTintMap),uvsVertexOnly:!(r.map||r.bumpMap||r.normalMap||r.specularMap||r.alphaMap||r.emissiveMap||r.roughnessMap||r.metalnessMap||r.clearcoatNormalMap||r.transmission>0||r.transmissionMap||r.thicknessMap||r.specularIntensityMap||r.specularTintMap||!r.displacementMap),fog:!!y,useFog:r.fog,fogExp2:y&&y.isFogExp2,flatShading:!!r.flatShading,sizeAttenuation:r.sizeAttenuation,logarithmicDepthBuffer:c,skinning:!0===v.isSkinnedMesh&&A>0,maxBones:A,useVertexTexture:h,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:r.format,dithering:r.dithering,shadowMapEnabled:t.shadowMap.enabled&&m.length>0,shadowMapType:t.shadowMap.type,toneMapping:r.toneMapped?t.toneMapping:w.vb,physicallyCorrectLights:t.physicallyCorrectLights,premultipliedAlpha:r.premultipliedAlpha,doubleSided:r.side===w.z,flipSided:r.side===w.i,depthPacking:void 0!==r.depthPacking&&r.depthPacking,index0AttributeName:r.index0AttributeName,extensionDerivatives:r.extensions&&r.extensions.derivatives,extensionFragDepth:r.extensions&&r.extensions.fragDepth,extensionDrawBuffers:r.extensions&&r.extensions.drawBuffers,extensionShaderTextureLOD:r.extensions&&r.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:r.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?s.push(h):!0===n.transparent?r.push(h):i.push(h)},unshift:function(t,e,n,o,l,c){const h=a(t,e,n,o,l,c);n.transmission>0?s.unshift(h):!0===n.transparent?r.unshift(h):i.unshift(h)},finish:function(){for(let t=n,i=e.length;t1&&i.sort(t||gn),s.length>1&&s.sort(e||vn),r.length>1&&r.sort(e||vn)}}}function xn(t){let e=new WeakMap;return{get:function(n,i){let s;return!1===e.has(n)?(s=new yn(t),e.set(n,[s])):i>=e.get(n).length?(s=new yn(t),e.get(n).push(s)):s=e.get(n)[i],s},dispose:function(){e=new WeakMap}}}function bn(){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 wn=0;function Tn(t,e){return(e.castShadow?1:0)-(t.castShadow?1:0)}function An(t,e){const n=new bn,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}}}(),s={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++)s.probe.push(new p.a);const r=new p.a,o=new A.a,a=new A.a;return{setup:function(r,o){let a=0,l=0,c=0;for(let t=0;t<9;t++)s.probe[t].set(0,0,0);let h=0,u=0,d=0,p=0,_=0,m=0,f=0,g=0;r.sort(Tn);const v=!0!==o?Math.PI:1;for(let t=0,e=r.length;t0&&(e.isWebGL2||!0===t.has(\\\\\\\"OES_texture_float_linear\\\\\\\")?(s.rectAreaLTC1=V.LTC_FLOAT_1,s.rectAreaLTC2=V.LTC_FLOAT_2):!0===t.has(\\\\\\\"OES_texture_half_float_linear\\\\\\\")?(s.rectAreaLTC1=V.LTC_HALF_1,s.rectAreaLTC2=V.LTC_HALF_2):console.error(\\\\\\\"THREE.WebGLRenderer: Unable to use RectAreaLight. Missing WebGL extensions.\\\\\\\")),s.ambient[0]=a,s.ambient[1]=l,s.ambient[2]=c;const y=s.hash;y.directionalLength===h&&y.pointLength===u&&y.spotLength===d&&y.rectAreaLength===p&&y.hemiLength===_&&y.numDirectionalShadows===m&&y.numPointShadows===f&&y.numSpotShadows===g||(s.directional.length=h,s.spot.length=d,s.rectArea.length=p,s.point.length=u,s.hemi.length=_,s.directionalShadow.length=m,s.directionalShadowMap.length=m,s.pointShadow.length=f,s.pointShadowMap.length=f,s.spotShadow.length=g,s.spotShadowMap.length=g,s.directionalShadowMatrix.length=m,s.pointShadowMatrix.length=f,s.spotShadowMatrix.length=g,y.directionalLength=h,y.pointLength=u,y.spotLength=d,y.rectAreaLength=p,y.hemiLength=_,y.numDirectionalShadows=m,y.numPointShadows=f,y.numSpotShadows=g,s.version=wn++)},setupView:function(t,e){let n=0,i=0,l=0,c=0,h=0;const u=e.matrixWorldInverse;for(let e=0,d=t.length;e=n.get(i).length?(r=new Mn(t,e),n.get(i).push(r)):r=n.get(i)[s],r},dispose:function(){n=new WeakMap}}}class Sn 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}}Sn.prototype.isMeshDepthMaterial=!0;class Cn 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}}Cn.prototype.isMeshDistanceMaterial=!0;function Nn(t,e,n){let i=new T.a;const s=new d.a,r=new d.a,o=new _.a,a=new Sn({depthPacking:w.Hb}),l=new Cn,c={},h=n.maxTextureSize,u={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 B.a(f,p),v=this;function y(n,i){const s=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,s,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,s,m,g,null)}function x(e,n,i,s,r,o,h){let d=null;const p=!0===s.isPointLight?e.customDistanceMaterial:e.customDepthMaterial;if(d=void 0!==p?p:!0===s.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 s=n[e];void 0===s&&(s=d.clone(),n[e]=s),d=s}return d.visible=i.visible,d.wireframe=i.wireframe,h===w.gd?d.side=null!==i.shadowSide?i.shadowSide:i.side:d.side=null!==i.shadowSide?i.shadowSide:u[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===s.isPointLight&&!0===d.isMeshDistanceMaterial&&(d.referencePosition.setFromMatrixPosition(s.matrixWorld),d.nearDistance=r,d.farDistance=o),d}function b(n,s,r,o,a){if(!1===n.visible)return;if(n.layers.test(s.layers)&&(n.isMesh||n.isLine||n.isPoints)&&(n.castShadow||n.receiveShadow&&a===w.gd)&&(!n.frustumCulled||i.intersectsObject(n))){n.modelViewMatrix.multiplyMatrices(r.matrixWorldInverse,n.matrixWorld);const i=e.update(n),s=n.material;if(Array.isArray(s)){const e=i.groups;for(let l=0,c=e.length;lh||s.y>h)&&(s.x>h&&(r.x=Math.floor(h/p.x),s.x=r.x*p.x,u.mapSize.x=r.x),s.y>h&&(r.y=Math.floor(h/p.y),s.y=r.y*p.y,u.mapSize.y=r.y)),null===u.map&&!u.isPointLightShadow&&this.type===w.gd){const t={minFilter:w.V,magFilter:w.V,format:w.Ib};u.map=new Q(s.x,s.y,t),u.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",u.mapPass=new Q(s.x,s.y,t),u.camera.updateProjectionMatrix()}if(null===u.map){const t={minFilter:w.ob,magFilter:w.ob,format:w.Ib};u.map=new Q(s.x,s.y,t),u.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",u.camera.updateProjectionMatrix()}t.setRenderTarget(u.map),t.clear();const _=u.getViewportCount();for(let t=0;t<_;t++){const e=u.getViewport(t);o.set(r.x*e.x,r.y*e.y,r.x*e.z,r.y*e.w),d.viewport(o),u.updateMatrices(c,t),i=u.getFrustum(),b(n,a,u.camera,c,this.type)}u.isPointLightShadow||this.type!==w.gd||y(u,a),u.needsUpdate=!1}v.needsUpdate=!1,t.setRenderTarget(l,c,u)}}function Ln(t,e,n){const i=n.isWebGL2;const s=new function(){let e=!1;const n=new _.a;let i=null;const s=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,r,o,a){!0===a&&(e*=o,i*=o,r*=o),n.set(e,i,r,o),!1===s.equals(n)&&(t.clearColor(e,i,r,o),s.copy(n))},reset:function(){e=!1,i=null,s.set(-1,0,0,0)}}},r=new function(){let e=!1,n=null,i=null,s=null;return{setTest:function(e){e?k(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){s!==e&&(t.clearDepth(e),s=e)},reset:function(){e=!1,n=null,i=null,s=null}}},o=new function(){let e=!1,n=null,i=null,s=null,r=null,o=null,a=null,l=null,c=null;return{setTest:function(n){e||(n?k(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&&s===n&&r===o||(t.stencilFunc(e,n,o),i=e,s=n,r=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,s=null,r=null,o=null,a=null,l=null,c=null}}};let a={},l=null,c={},h=null,u=!1,d=null,p=null,m=null,f=null,g=null,v=null,y=null,x=!1,b=null,T=null,A=null,M=null,E=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,P={};const R=t.getParameter(t.SCISSOR_BOX),I=t.getParameter(t.VIEWPORT),F=(new _.a).fromArray(R),D=(new _.a).fromArray(I);function B(e,n,i){const s=new Uint8Array(4),r=t.createTexture();t.bindTexture(e,r),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)&&(s=i/Math.max(t.width,t.height)),s<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?On.g:Math.floor,r=i(s*t.width),o=i(s*t.height);void 0===p&&(p=m(r,o));const a=n?m(r,o):p;a.width=r,a.height=o;return a.getContext(\\\\\\\"2d\\\\\\\").drawImage(t,0,0,r,o),console.warn(\\\\\\\"THREE.WebGLRenderer: Texture has been resized from (\\\\\\\"+t.width+\\\\\\\"x\\\\\\\"+t.height+\\\\\\\") to (\\\\\\\"+r+\\\\\\\"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 On.i(t.width)&&On.i(t.height)}function v(t,e){return t.generateMipmaps&&e&&t.minFilter!==w.ob&&t.minFilter!==w.V}function y(e,n,s,r,o=1){t.generateMipmap(e);i.get(n).__maxMipLevel=Math.log2(Math.max(s,r,o))}function x(n,i,s,r){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&&(s===t.FLOAT&&(o=t.R32F),s===t.HALF_FLOAT&&(o=t.R16F),s===t.UNSIGNED_BYTE&&(o=t.R8)),i===t.RGB&&(s===t.FLOAT&&(o=t.RGB32F),s===t.HALF_FLOAT&&(o=t.RGB16F),s===t.UNSIGNED_BYTE&&(o=t.RGB8)),i===t.RGBA&&(s===t.FLOAT&&(o=t.RGBA32F),s===t.HALF_FLOAT&&(o=t.RGBA16F),s===t.UNSIGNED_BYTE&&(o=r===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,s=i.get(e),r=i.get(n);if(!e)return;void 0!==r.__webglTexture&&(t.deleteTexture(r.__webglTexture),o.memory.textures--);e.depthTexture&&e.depthTexture.dispose();if(e.isWebGLCubeRenderTarget)for(let e=0;e<6;e++)t.deleteFramebuffer(s.__webglFramebuffer[e]),s.__webglDepthbuffer&&t.deleteRenderbuffer(s.__webglDepthbuffer[e]);else t.deleteFramebuffer(s.__webglFramebuffer),s.__webglDepthbuffer&&t.deleteRenderbuffer(s.__webglDepthbuffer),s.__webglMultisampledFramebuffer&&t.deleteFramebuffer(s.__webglMultisampledFramebuffer),s.__webglColorRenderbuffer&&t.deleteRenderbuffer(s.__webglColorRenderbuffer),s.__webglDepthRenderbuffer&&t.deleteRenderbuffer(s.__webglDepthRenderbuffer);if(e.isWebGLMultipleRenderTargets)for(let e=0,s=n.length;e0&&r.__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 P(r,e,s);console.warn(\\\\\\\"THREE.WebGLRenderer: Texture marked for update but image is incomplete\\\\\\\")}}n.activeTexture(t.TEXTURE0+s),n.bindTexture(t.TEXTURE_2D,r.__webglTexture)}function S(e,s){const o=i.get(e);e.version>0&&o.__version!==e.version?function(e,i,s){if(6!==i.image.length)return;O(e,i),n.activeTexture(t.TEXTURE0+s),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,h=[];for(let t=0;t<6;t++)h[t]=o||l?l?i.image[t].image:i.image[t]:f(i.image[t],!1,!0,c);const u=h[0],d=g(u)||a,p=r.convert(i.format),_=r.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=h[e].mipmaps;for(let s=0;s1||i.get(r).__currentAnisotropy)&&(t.texParameterf(n,o.TEXTURE_MAX_ANISOTROPY_EXT,Math.min(r.anisotropy,s.getMaxAnisotropy())),i.get(r).__currentAnisotropy=r.anisotropy)}}function O(e,n){void 0===e.__webglInit&&(e.__webglInit=!0,n.addEventListener(\\\\\\\"dispose\\\\\\\",T),e.__webglTexture=t.createTexture(),o.memory.textures++)}function P(e,i,s){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+s),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,h),u=g(c)||a,d=r.convert(i.format);let p,_=r.convert(i.type),m=x(i.internalFormat,d,_,i.encoding);L(o,i,u);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,_=r.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,_=r.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&&u){for(let e=0,i=b.length;e0&&u){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),M+=1,t},this.resetTextureUnits=function(){M=0},this.setTexture2D=E,this.setTexture2DArray=function(e,s){const r=i.get(e);e.version>0&&r.__version!==e.version?P(r,e,s):(n.activeTexture(t.TEXTURE0+s),n.bindTexture(t.TEXTURE_2D_ARRAY,r.__webglTexture))},this.setTexture3D=function(e,s){const r=i.get(e);e.version>0&&r.__version!==e.version?P(r,e,s):(n.activeTexture(t.TEXTURE0+s),n.bindTexture(t.TEXTURE_3D,r.__webglTexture))},this.setTextureCube=S,this.setupRenderTarget=function(e){const l=e.texture,c=i.get(e),h=i.get(l);e.addEventListener(\\\\\\\"dispose\\\\\\\",A),!0!==e.isWebGLMultipleRenderTargets&&(h.__webglTexture=t.createTexture(),h.__version=l.version,o.memory.textures++);const u=!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.\\\\\\\")),u){c.__webglFramebuffer=[];for(let e=0;e<6;e++)c.__webglFramebuffer[e]=t.createFramebuffer()}else if(c.__webglFramebuffer=t.createFramebuffer(),d)if(s.drawBuffers){const n=e.texture;for(let e=0,s=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&&(s=e.getPose(t.gripSpace,n),null!==s&&(a.matrix.fromArray(s.transform.matrix),a.matrix.decompose(a.position,a.rotation,a.scale),s.linearVelocity?(a.hasLinearVelocity=!0,a.linearVelocity.copy(s.linearVelocity)):a.hasLinearVelocity=!1,s.angularVelocity?(a.hasAngularVelocity=!0,a.angularVelocity.copy(s.angularVelocity)):a.hasAngularVelocity=!1));return null!==o&&(o.visible=null!==i),null!==a&&(a.visible=null!==s),null!==l&&(l.visible=null!==r),this}}class zn extends J.a{constructor(t,e){super();const n=this,i=t.state;let s=null,r=1,o=null,a=\\\\\\\"local-floor\\\\\\\",l=null,c=null,h=null,u=null,d=null,m=!1,f=null,g=null,v=null,y=null,x=null,b=null;const w=[],T=new Map,A=new tt.a;A.layers.enable(1),A.viewport=new _.a;const E=new tt.a;E.layers.enable(2),E.viewport=new _.a;const S=[A,E],C=new In;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 P(){T.forEach((function(t,e){t.disconnect(e)})),T.clear(),N=null,L=null,i.bindXRFramebuffer(null),t.setRenderTarget(t.getRenderTarget()),h&&e.deleteFramebuffer(h),f&&e.deleteFramebuffer(f),g&&e.deleteRenderbuffer(g),v&&e.deleteRenderbuffer(v),h=null,f=null,g=null,v=null,d=null,u=null,c=null,s=null,z.stop(),n.isPresenting=!1,n.dispatchEvent({type:\\\\\\\"sessionend\\\\\\\"})}function R(t){const e=s.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 s=t.get(i).__maxMipLevel;void 0!==s&&(e.maxMipLevel.value=s)}let s,r;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?s=n.map:n.specularMap?s=n.specularMap:n.displacementMap?s=n.displacementMap:n.normalMap?s=n.normalMap:n.bumpMap?s=n.bumpMap:n.roughnessMap?s=n.roughnessMap:n.metalnessMap?s=n.metalnessMap:n.alphaMap?s=n.alphaMap:n.emissiveMap?s=n.emissiveMap:n.clearcoatMap?s=n.clearcoatMap:n.clearcoatNormalMap?s=n.clearcoatNormalMap:n.clearcoatRoughnessMap?s=n.clearcoatRoughnessMap:n.specularIntensityMap?s=n.specularIntensityMap:n.specularTintMap?s=n.specularTintMap:n.transmissionMap?s=n.transmissionMap:n.thicknessMap&&(s=n.thicknessMap),void 0!==s&&(s.isWebGLRenderTarget&&(s=s.texture),!0===s.matrixAutoUpdate&&s.updateMatrix(),e.uvTransform.value.copy(s.matrix)),n.aoMap?r=n.aoMap:n.lightMap&&(r=n.lightMap),void 0!==r&&(r.isWebGLRenderTarget&&(r=r.texture),!0===r.matrixAutoUpdate&&r.updateMatrix(),e.uv2Transform.value.copy(r.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,s,r,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 s;e.map?s=e.map:e.alphaMap&&(s=e.alphaMap);void 0!==s&&(!0===s.matrixAutoUpdate&&s.updateMatrix(),t.uvTransform.value.copy(s.matrix))}(t,i,s,r):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 Un(t={}){const e=void 0!==t.canvas?t.canvas:function(){const t=Object(It.b)(\\\\\\\"canvas\\\\\\\");return t.style.display=\\\\\\\"block\\\\\\\",t}(),n=void 0!==t.context?t.context:null,i=void 0!==t.alpha&&t.alpha,s=void 0===t.depth||t.depth,r=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\\\\\\\",h=void 0!==t.failIfMajorPerformanceCaveat&&t.failIfMajorPerformanceCaveat;let u=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,P=e.width,R=e.height,I=1,F=null,D=null;const B=new _.a(0,0,P,R),z=new _.a(0,0,P,R);let k=!1;const U=[],G=new T.a;let V=!1,H=!1,Y=null;const J=new A.a,Z=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,ht,ut,dt,pt,_t,mt,ft,gt,vt,yt,xt,bt,wt,Tt,At,Mt=n;function Et(t,n){for(let i=0;i0&&function(t,e,n){if(null===Y){const t=!0===o&&!0===nt.isWebGL2;Y=new(t?Ht:Q)(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(Y),g.clear();const s=g.toneMapping;g.toneMapping=w.vb,Xt(t,e,n),g.toneMapping=s,at.updateMultisampleRenderTarget(Y),at.updateRenderTargetMipmap(Y),g.setRenderTarget(i)}(s,e,n),i&&it.viewport(N.copy(i)),s.length>0&&Xt(s,e,n),r.length>0&&Xt(r,e,n),a.length>0&&Xt(a,e,n)}function Xt(t,e,n){const i=!0===e.isScene?e.overrideMaterial:null;for(let s=0,r=t.length;s0?f[f.length-1]:null,m.pop(),u=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,s=!1,r=!1;if(t){const n=t.texture;(n.isDataTexture3D||n.isDataTexture2DArray)&&(r=!0);const o=ot.get(t).__webglFramebuffer;t.isWebGLCubeRenderTarget?(i=o[e],s=!0):i=t.isWebGLMultisampleRenderTarget?ot.get(t).__webglMultisampledFramebuffer:o,N.copy(t.viewport),L.copy(t.scissor),O=t.scissorTest}else N.copy(B).multiplyScalar(I).floor(),L.copy(z).multiplyScalar(I).floor(),O=k;if(it.bindFramebuffer(Mt.FRAMEBUFFER,i)&&nt.drawBuffers){let e=!1;if(t)if(t.isWebGLMultipleRenderTargets){const n=t.texture;if(U.length!==n.length||U[0]!==Mt.COLOR_ATTACHMENT0){for(let t=0,e=n.length;t=0&&e<=t.width-i&&n>=0&&n<=t.height-s&&Mt.readPixels(e,n,i,s,Tt.convert(a),Tt.convert(l),r):console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: readPixels from renderTarget failed. Framebuffer not complete.\\\\\\\")}finally{const t=null!==b?ot.get(b).__webglFramebuffer:null;it.bindFramebuffer(Mt.FRAMEBUFFER,t)}}},this.copyFramebufferToTexture=function(t,e,n=0){const i=Math.pow(2,-n),s=Math.floor(e.image.width*i),r=Math.floor(e.image.height*i);let o=Tt.convert(e.format);nt.isWebGL2&&(o===Mt.RGB&&(o=Mt.RGB8),o===Mt.RGBA&&(o=Mt.RGBA8)),at.setTexture2D(e,0),Mt.copyTexImage2D(Mt.TEXTURE_2D,n,o,t.x,t.y,s,r,0),it.unbindTexture()},this.copyTextureToTexture=function(t,e,n,i=0){const s=e.image.width,r=e.image.height,o=Tt.convert(n.format),a=Tt.convert(n.type);at.setTexture2D(n,0),Mt.pixelStorei(Mt.UNPACK_FLIP_Y_WEBGL,n.flipY),Mt.pixelStorei(Mt.UNPACK_PREMULTIPLY_ALPHA_WEBGL,n.premultiplyAlpha),Mt.pixelStorei(Mt.UNPACK_ALIGNMENT,n.unpackAlignment),e.isDataTexture?Mt.texSubImage2D(Mt.TEXTURE_2D,i,t.x,t.y,s,r,o,a,e.image.data):e.isCompressedTexture?Mt.compressedTexSubImage2D(Mt.TEXTURE_2D,i,t.x,t.y,e.mipmaps[0].width,e.mipmaps[0].height,o,e.mipmaps[0].data):Mt.texSubImage2D(Mt.TEXTURE_2D,i,t.x,t.y,o,a,e.image),0===i&&n.generateMipmaps&&Mt.generateMipmap(Mt.TEXTURE_2D),it.unbindTexture()},this.copyTextureToTexture3D=function(t,e,n,i,s=0){if(g.isWebGL1Renderer)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: can only be used with WebGL2.\\\\\\\");const r=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 h;if(i.isDataTexture3D)at.setTexture3D(i,0),h=Mt.TEXTURE_3D;else{if(!i.isDataTexture2DArray)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: only supports THREE.DataTexture3D and THREE.DataTexture2DArray.\\\\\\\");at.setTexture2DArray(i,0),h=Mt.TEXTURE_2D_ARRAY}Mt.pixelStorei(Mt.UNPACK_FLIP_Y_WEBGL,i.flipY),Mt.pixelStorei(Mt.UNPACK_PREMULTIPLY_ALPHA_WEBGL,i.premultiplyAlpha),Mt.pixelStorei(Mt.UNPACK_ALIGNMENT,i.unpackAlignment);const u=Mt.getParameter(Mt.UNPACK_ROW_LENGTH),d=Mt.getParameter(Mt.UNPACK_IMAGE_HEIGHT),p=Mt.getParameter(Mt.UNPACK_SKIP_PIXELS),_=Mt.getParameter(Mt.UNPACK_SKIP_ROWS),m=Mt.getParameter(Mt.UNPACK_SKIP_IMAGES),f=n.isCompressedTexture?n.mipmaps[0]:n.image;Mt.pixelStorei(Mt.UNPACK_ROW_LENGTH,f.width),Mt.pixelStorei(Mt.UNPACK_IMAGE_HEIGHT,f.height),Mt.pixelStorei(Mt.UNPACK_SKIP_PIXELS,t.min.x),Mt.pixelStorei(Mt.UNPACK_SKIP_ROWS,t.min.y),Mt.pixelStorei(Mt.UNPACK_SKIP_IMAGES,t.min.z),n.isDataTexture||n.isDataTexture3D?Mt.texSubImage3D(h,s,e.x,e.y,e.z,r,o,a,l,c,f.data):n.isCompressedTexture?(console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: untested support for compressed srcTexture.\\\\\\\"),Mt.compressedTexSubImage3D(h,s,e.x,e.y,e.z,r,o,a,l,f.data)):Mt.texSubImage3D(h,s,e.x,e.y,e.z,r,o,a,l,c,f),Mt.pixelStorei(Mt.UNPACK_ROW_LENGTH,u),Mt.pixelStorei(Mt.UNPACK_IMAGE_HEIGHT,d),Mt.pixelStorei(Mt.UNPACK_SKIP_PIXELS,p),Mt.pixelStorei(Mt.UNPACK_SKIP_ROWS,_),Mt.pixelStorei(Mt.UNPACK_SKIP_IMAGES,m),0===s&&i.generateMipmaps&&Mt.generateMipmap(h),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 Gn={};var Vn,Hn,jn;!function(t){t.WEBGL=\\\\\\\"webgl\\\\\\\",t.WEBGL2=\\\\\\\"webgl2\\\\\\\",t.EXPERIMENTAL_WEBGL=\\\\\\\"experimental-webgl\\\\\\\",t.EXPERIMENTAL_WEBGL2=\\\\\\\"experimental-webgl2\\\\\\\"}(Vn||(Vn={}));class Wn{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(Vn.WEBGL2))}createWebGLRenderer(t){const e=new Un(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?Vn.WEBGL2:Vn.WEBGL;let i=t.getContext(n,Gn);return i?this.printDebugMessage(`create gl context: ${n}.`):(n=e?Vn.EXPERIMENTAL_WEBGL2:Vn.EXPERIMENTAL_WEBGL,this.printDebugMessage(`create gl context: ${n}.`),i=t.getContext(n,Gn)),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 Ht(t,e,n):new Q(t,e,n)}}class qn{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 Xn{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(),s=t.type().toLowerCase();let r=this._node_register.get(i);r||(r=new Map,this._node_register.set(i,r));if(r.get(s))console.error(`node ${i}/${s} already registered`);else{if(r.set(s,t),e){let t=this._node_register_categories.get(i);t||(t=new Map,this._node_register_categories.set(i,t)),t.set(s,e)}if(n){let t=this._node_register_options.get(i);t||(t=new Map,this._node_register_options.set(i,t)),t.set(s,n)}this.poly.pluginsRegister.registerNode(t)}}deregister(t,e){var n,i,s;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===(s=this._node_register_options.get(t))||void 0===s||s.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 s=n.type().toLowerCase(),r=null===(i=this._node_register_options.get(t))||void 0===i?void 0:i.get(s);if(r){const n=r.only,i=r.except,s=`${t}/${e}`;return n?n.includes(s):!i||!i.includes(s)}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 Yn{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 $n 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\\\\\\\"}(Hn||(Hn={}));class Jn 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\\\\\\\"}(jn||(jn={}));class Zn 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 Qn{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 Kn{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 ti=n(86);class ei{constructor(){this._blobUrlsByStoredUrl=new Map,this._blobsByStoredUrl=new Map,this._blobDataByNodeId=new Map,this._globalBlobsByStoredUrl=new Map}registerBlobUrl(t){console.log(\\\\\\\"registerBlobUrl\\\\\\\",t,li.playerMode()),li.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(li.playerMode())return{};try{if(this._blobUrlsByStoredUrl.get(t.storedUrl))return{};const e=li.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(li.playerMode())return{};try{if(this._blobUrlsByStoredUrl.get(t.storedUrl))return{};const e=li.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 ${t.fullUrl}`}}}forEachBlob(t){this._blobDataByNodeId.forEach(((e,n)=>{if(this._scene){if(this._scene.graph.nodeFromId(n)){const{storedUrl:n}=e,i=this._blobsByStoredUrl.get(n);i&&t(i,n)}}}));let e=[];const n=new Map;this._globalBlobsByStoredUrl.forEach(((t,i)=>{e.push(i),n.set(i,t)})),e=e.sort(),e.forEach((e=>{const n=this._globalBlobsByStoredUrl.get(e);n&&t(n,e)}))}createBlobUrl(t){return Object(ti.a)(t)}}class ni{setMap(t){this._map=t}remapedUrl(t){if(!this._map)return;const e=t.split(\\\\\\\"?\\\\\\\"),n=e[0],i=e[1],s=this._map[n];return s?i?`${s}?${i}`:s:void 0}}var ii=n(91),si=n(83);class ri{markAsLoaded(t,e){this._sceneJsonImporterContructor=e,t()}load(t){if(!this._sceneJsonImporterContructor)return;const e=[];t.forEach(((t,n)=>{e.push(n)}));for(let n of e){const e=t.get(n);e&&(this._loadElement(n,e,this._sceneJsonImporterContructor),t.delete(n))}}async _loadElement(t,e,n){const{sceneData:i,assetsManifest:s,unzippedData:r}=e,o=Object.keys(s);for(let t of o){const e=r[`assets/${s[t]}`];if(!e)return void console.error(t,e);const n=new Blob([e]),i={storedUrl:t,blobUrl:li.blobs.createBlobUrl(n)};li.blobs.registerBlobUrl(i)}li.setPlayerMode(!0),li.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]=li;const c={method:this._loadScene.bind(this),element:t,sceneData:i,sceneJsonImporterContructor:n};window[l.loadSceneArgs]=c;this._loadPolyConfig(l,r)||this._loadScene(t,i,n)}_loadPolyConfig(t,e){const n=e[si.a.POLY_CONFIG];if(!n)return!1;const i=this._createJsBlob(n,\\\\\\\"polyConfig\\\\\\\");let s=document.getElementById(t.scriptElementId);const r=[];return r.push(`import {configurePolygonjs, configureScene} from '${i}';`),r.push(`configurePolygonjs(window.${t.Poly});`),r.push(`window.${t.loadSceneArgs}.method(window.${t.loadSceneArgs}.element, window.${t.loadSceneArgs}.sceneData, window.${t.loadSceneArgs}.sceneJsonImporterContructor, configureScene);`),r.push(`delete window.${t.loadSceneArgs};`),s||(s=document.createElement(\\\\\\\"script\\\\\\\"),s.setAttribute(\\\\\\\"type\\\\\\\",\\\\\\\"module\\\\\\\"),s.text=r.join(\\\\\\\"\\\\n\\\\\\\"),document.body.append(s)),!0}async _loadScene(t,e,n,i){this._fadeOutPoster(t);const s=new n(e),r=await s.scene();i&&i(r);const o=r.mainCameraNode();if(!o)return void console.warn(\\\\\\\"no master camera found\\\\\\\");const a=o.createViewer(t);r.play(),t.scene=r,t.viewer=a}_fadeOutPoster(t){const e=t.firstElementChild;e&&(e.style.pointerEvents=\\\\\\\"none\\\\\\\",ii.a.fadeOut(e).then((()=>{var t;null===(t=e.parentElement)||void 0===t||t.removeChild(e)})))}_createJsBlob(t,e){const n=new Blob([t]),i=new File([n],`${e}.js`,{type:\\\\\\\"application/javascript\\\\\\\"});return Object(ti.a)(i)}}class oi{setPerformanceManager(t){this._performanceManager=t}performanceManager(){return this._performanceManager||window.performance}}class ai{constructor(){this.renderersController=new Wn,this.nodesRegister=new Xn(this),this.operationsRegister=new Yn(this),this.expressionsRegister=new $n,this.modulesRegister=new Jn,this.assemblersRegister=new Zn,this.pluginsRegister=new Qn(this),this.camerasRegister=new Kn(this),this.blobs=new ei,this.assetUrls=new ni,this.selfContainedScenesLoader=new ri,this.performance=new oi,this.scenesByUuid={},this._player_mode=!0,this._logger=null}static _instance_(){if(window.__POLYGONJS_POLY_INSTANCE__)return window.__POLYGONJS_POLY_INSTANCE__;{const t=new ai;return window.__POLYGONJS_POLY_INSTANCE__=t,window.__POLYGONJS_POLY_INSTANCE__}}setPlayerMode(t){this._player_mode=t}playerMode(){return this._player_mode}registerNode(t,e,n){this.nodesRegister.register(t,e,n)}registerOperation(t){this.operationsRegister.register(t)}registerCamera(t){this.camerasRegister.register(t)}registerPlugin(t){this.pluginsRegister.register(t)}registeredNodes(t,e){return this.nodesRegister.registeredNodes(t,e)}registeredOperation(t,e){return this.operationsRegister.registeredOperation(t,e)}registeredCameraTypes(){return this.camerasRegister.registeredTypes()}inWorkerThread(){return!1}desktopController(){}get libs(){return this._libs_controller=this._libs_controller||new qn}setEnv(t){this._env=t}env(){return this._env}setLogger(t){this._logger=t}get logger(){return this._logger}log(t,...e){var n;null===(n=this.logger)||void 0===n||n.log(t,...e)}warn(t,...e){var n;null===(n=this.logger)||void 0===n||n.warn(t,...e)}error(t,...e){var n;null===(n=this.logger)||void 0===n||n.error(t,...e)}}const li=ai._instance_();class ci{constructor(){this._started=!1,this._start_time=0,this._previous_timestamp=0,this._nodes_cook_data={},this._durations_by_name={},this._durations_count_by_name={}}profile(t,e){const n=li.performance.performanceManager(),i=n.now();e();const s=n.now()-i;console.log(`${t}: ${s}`)}start(){if(!this._started){this.reset(),this._started=!0;const t=li.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 s=t[e];n.push(s),null==i[s]&&(i[s]=[]),i[s].push(e)}n.sort(((t,e)=>t-e));const s=f.uniq(n);console.log(\\\\\\\"--------------- PERF RECORDINGS -----------\\\\\\\");const r=[];for(let t of s){const n=i[t];for(let i of n){const n=e[i],s={duration:t,name:i,count:n,duration_per_iteration:t/n};r.push(s)}}return console.table(r),r}}class hi{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 di{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 pi{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 _i;!function(t){t.LOADED=\\\\\\\"sceneLoaded\\\\\\\",t.PLAY=\\\\\\\"play\\\\\\\",t.PAUSE=\\\\\\\"pause\\\\\\\",t.TICK=\\\\\\\"tick\\\\\\\"}(_i||(_i={}));const mi=[_i.LOADED,_i.PLAY,_i.PAUSE,_i.TICK];class fi extends pi{type(){return\\\\\\\"scene\\\\\\\"}acceptedEventTypes(){return mi.map((t=>`${t}`))}}class gi{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(_i.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 vi={EMPTY:\\\\\\\"\\\\\\\",UV:\\\\\\\"/COP/imageUv\\\\\\\",ENV_MAP:\\\\\\\"/COP/envMap\\\\\\\",CUBE_MAP:\\\\\\\"/COP/cubeCamera\\\\\\\"};class yi{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=bi.findNode(t,this._path)}clone(){const t=new yi(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 xi{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=bi.findParam(t,this._path)}clone(){const t=new xi(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 ${this.path()}`)}}class bi{static split_parent_child(t){const e=t.split(bi.SEPARATOR).filter((t=>t.length>0)),n=e.pop();return{parent:e.join(bi.SEPARATOR),child:n}}static findNode(t,e,n){if(!t)return null;const i=e.split(bi.SEPARATOR).filter((t=>t.length>0)),s=i[0];let r=null;if(e[0]!==bi.SEPARATOR){switch(s){case bi.PARENT:null==n||n.add_path_element(s),r=t.parent();break;case bi.CURRENT:null==n||n.add_path_element(s),r=t;break;default:r=t.node(s),r&&(null==n||n.add_node(s,r))}if(null!=r&&i.length>1){const t=i.slice(1).join(bi.SEPARATOR);r=this.findNode(r,t,n)}return r}{const i=e.substr(1);r=this.findNode(t.root(),i,n)}return r}static findParam(t,e,n){if(!t)return null;const i=e.split(bi.SEPARATOR);if(1===i.length)return t.params.get(i[0]);{const e=i.slice(0,+(i.length-2)+1||void 0).join(bi.SEPARATOR),s=this.findNode(t,e,n);if(null!=s){const t=i[i.length-1],e=s.params.get(t);return n&&e&&n.add_node(t,e),e}return null}}static relativePath(t,e){const n=this.closestCommonParent(t,e);if(n){const i=this.distanceToParent(t,n);let s=\\\\\\\"\\\\\\\";if(i>0){let t=0;const e=[];for(;t++t.length>0)),o=e.path().split(bi.SEPARATOR).filter((t=>t.length>0)),a=[];let l=0;for(let t of o)r[l]||a.push(t),l++;return`${s}${a.join(bi.SEPARATOR)}`}return e.path()}static closestCommonParent(t,e){const n=this.parents(t).reverse().concat([t]),i=this.parents(e).reverse().concat([e]),s=Math.min(n.length,i.length);let r=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=bi.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 s=!1;for(let e of n)e.matches_path(t.path())&&(s=!0,e.resolve_missing_dependencies());s&&this.references.delete(e)}))}_check_for_missing_references_for_param(t){const e=t.graphNodeId();this.references.forEach(((n,i)=>{let s=!1;for(let e of n)e.matches_path(t.path())&&(s=!0,e.resolve_missing_dependencies());s&&this.references.delete(e)}))}}class Ai{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=li.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 Mi{constructor(t,e){this._scene=t,this._name=e,this._dirty_controller=new Ai(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 Si{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 Ci{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 Ni extends Ci{constructor(){super(...arguments),this._state=!1}_on_update(){this.node.emit(Ei.FLAG_BYPASS_UPDATED),this.node.setDirty()}}class Li extends Ci{_on_update(){this.node.emit(Ei.FLAG_DISPLAY_UPDATED)}}class Oi extends Ci{constructor(){super(...arguments),this._state=!1}_on_update(){this.node.emit(Ei.FLAG_OPTIMIZE_UPDATED)}}class Pi{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 Li(this.node)}hasDisplay(){return!0}}}function Ii(t){return class extends t{constructor(){super(...arguments),this.bypass=new Ni(this.node)}hasBypass(){return!0}}}function Fi(t){return class extends t{constructor(){super(...arguments),this.optimize=new Oi(this.node)}hasOptimize(){return!0}}}class Di extends(Ri(Pi)){}class Bi extends(Ii(Pi)){}class zi extends(Ii(Ri(Pi))){}class ki extends(Fi(Ii(Pi))){}class Ui extends(Fi(Ii(Ri(Pi)))){}class Gi{constructor(t){this.node=t}}class Vi extends Gi{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 Hi extends Gi{set(t){this._message!=t&&(t&&li.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 ji{constructor(t){this.node=t,this.timeDependent=new Vi(this.node),this.error=new Hi(this.node)}}class Wi{constructor(t){this.node=t,this._graph_node=new Mi(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 qi{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(Wi.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=bi.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==bi.CURRENT||t==bi.CURRENT_WITH_SLASH)return this.node;if(t==bi.PARENT||t==bi.PARENT_WITH_SLASH)return this.node.parent();const e=bi.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 bi.findNode(this.node,t)}return console.error(\\\\\\\"unexpected path given:\\\\\\\",t),null}}const Xi=/[, ]/,Yi=/\\\\d+$/,$i=/^0+/,Ji=/,| /,Zi=/^-?\\\\d+\\\\.?\\\\d*$/;var Qi,Ki,ts,es,ns,is,ss,rs;!function(t){t.TRUE=\\\\\\\"true\\\\\\\",t.FALSE=\\\\\\\"false\\\\\\\"}(Qi||(Qi={}));class os{static isBoolean(t){return t==Qi.TRUE||t==Qi.FALSE}static toBoolean(t){return t==Qi.TRUE}static isNumber(t){return Zi.test(t)}static tailDigits(t){const e=t.match(Yi);return e?parseInt(e[0]):0}static increment(t){const e=t.match(Yi);if(e){let n=e[0],i=\\\\\\\"\\\\\\\";const s=n.match($i);s&&(i=s[0]);const r=parseInt(n);0==r&&i.length>0&&\\\\\\\"0\\\\\\\"==i[i.length-1]&&(i=i.slice(0,-1));return`${t.substring(0,t.length-e[0].length)}${i}${r+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)os.matchMask(t,i)&&(n=!0);return n}static attribNames(t){const e=t.split(Xi),n=new Set;for(let t of e)t=t.trim(),t.length>0&&n.add(t);const i=new Array(n.size);let s=0;return n.forEach((t=>{i[s]=t,s++})),i}static indices(t){const e=t.split(Ji);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 as{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\\\\\\\"}(Ki||(Ki={}));class ls{static unreachable(t){throw new Error(\\\\\\\"Didn't expect to get here\\\\\\\")}}class cs{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==Ki.FROM_NODE)return!0;return!!this._cloned_state&&this._cloned_state==Ki.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 Ki.ALWAYS:return!0;case Ki.NEVER:return!1;case Ki.FROM_NODE:return!this._overridden}return ls.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 yi)return n.set_path(e);if(n instanceof xi)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 hs(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 s=this.node.parent();s&&s.childrenAllowed()&&(null===(e=s.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 s=this.node.parent();s&&s.childrenAllowed()&&(null===(e=s.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 ps{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\\\\\\\"}(ts||(ts={})),function(t){t.ANIM=\\\\\\\"animationsNetwork\\\\\\\",t.COP=\\\\\\\"copNetwork\\\\\\\",t.EVENT=\\\\\\\"eventsNetwork\\\\\\\",t.MAT=\\\\\\\"materialsNetwork\\\\\\\",t.POST=\\\\\\\"postProcessNetwork\\\\\\\",t.ROP=\\\\\\\"renderersNetwork\\\\\\\"}(es||(es={})),function(t){t.INPUT=\\\\\\\"subnetInput\\\\\\\",t.OUTPUT=\\\\\\\"subnetOutput\\\\\\\"}(ns||(ns={})),function(t){t.PERSPECTIVE=\\\\\\\"perspectiveCamera\\\\\\\",t.ORTHOGRAPHIC=\\\\\\\"orthographicCamera\\\\\\\"}(is||(is={})),function(t){t.ATTRIBUTE=\\\\\\\"attribute\\\\\\\"}(ss||(ss={})),function(t){t.DEVICE_ORIENTATION=\\\\\\\"cameraDeviceOrientationControls\\\\\\\",t.MAP=\\\\\\\"cameraMapControls\\\\\\\",t.ORBIT=\\\\\\\"cameraOrbitControls\\\\\\\",t.FIRST_PERSON=\\\\\\\"firstPersonControls\\\\\\\",t.PLAYER=\\\\\\\"playerControls\\\\\\\",t.MOBILE_JOYSTICK=\\\\\\\"mobileJoystickControls\\\\\\\"}(rs||(rs={}));const _s=[rs.DEVICE_ORIENTATION,rs.MAP,rs.ORBIT,rs.FIRST_PERSON,rs.MOBILE_JOYSTICK];class ms{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 fs=n(69);class gs extends K.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}}gs.prototype.isScene=!0;var vs=n(49),ys=n(52),xs=n(42),bs=n(55),ws=n(62),Ts=n(24),As=n(35);const Ms=new p.a,Es=new p.a;class Ss extends K.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){Ms.setFromMatrixPosition(this.matrixWorld);const n=t.ray.origin.distanceTo(Ms);this.getObjectForDistance(n).raycast(t,e)}}update(t){const e=this.levels;if(e.length>1){Ms.setFromMatrixPosition(t.matrixWorld),Es.setFromMatrixPosition(this.matrixWorld);const n=Ms.distanceTo(Es)/t.zoom;let i,s;for(e[0].object.visible=!0,i=1,s=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,s){return(t-e)/(n-e)*(s-i)+i}static blend(t,e,n){return(1-n)*t+n*e}static degrees_to_radians(t){return t*sr}static radians_to_degrees(t){return t/sr}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),s=this.deg2rad(e.lat-t.lat),r=this.deg2rad(e.lng-t.lng),o=Math.sin(s/2)*Math.sin(s/2)+Math.cos(n)*Math.cos(i)*Math.sin(r/2)*Math.sin(r/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)))}}rr.Easing=ir,rr.fract=t=>t-Math.floor(t),rr._vec={x:0,y:136574},rr._triangle_mid=new p.a,rr._triangle_mid_to_corner=new p.a;class or{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 Qs(this._core_geometry,e[n+0]),new Qs(this._core_geometry,e[n+1]),new Qs(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 Ks.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=[rr.randFloat(t),rr.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(),s=this._geometry.attributes[t].itemSize,r=this.points().map((e=>e.attribValue(t)));let o,a,l=0;switch(s){case 1:a=0;for(let t of r)a+=t*i[l],l++;o=a;break;default:for(let t of r){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 s=[e.a,e.b,e.c],r=t.getAttribute(\\\\\\\"position\\\\\\\").array,o=s.map((t=>new p.a(r[3*t+0],r[3*t+1],r[3*t+2]))),a=i.itemSize,l=i.array;let c=[];switch(a){case 1:c=s.map((t=>l[t]));break;case 2:c=s.map((t=>new d.a(l[2*t+0],l[2*t+1])));break;case 3:c=s.map((t=>new p.a(l[3*t+0],l[3*t+1],l[3*t+2])))}const h=s.map(((t,e)=>n.distanceTo(o[e]))),u=f.sum([h[0]*h[1],h[0]*h[2],h[1]*h[2]]),_=[h[1]*h[2]/u,h[0]*h[2]/u,h[0]*h[1]/u];let m;switch(a){case 1:m=f.sum(s.map(((t,e)=>_[e]*c[e])));break;default:var g=s.map(((t,e)=>c[e].multiplyScalar(_[e])));m=null;for(let t of g)m?m.add(t):m=t}return m}}class ar{from_points(t){t=this._filter_points(t);const e=new S.a,n=new _r(e),i=t[0];if(null!=i){const s=i.geometry(),r=i.core_geometry(),o={};for(let e=0;et.indexedAttribValue(i)))),r={};s.forEach(((t,e)=>r[t]=e)),n.userDataAttribs()[i]=s;const o=[];for(let e of t){const t=r[e.indexedAttribValue(i)];o.push(t)}e.setAttribute(i,new C.c(o,1))}else{const n=l[i].itemSize,s=new Array(t.length*n);switch(n){case 1:for(let e=0;enew _r(t))),n=e[0].indexedAttributeNames(),i={};for(let t of n){const n={},s=[];for(let i of e){const e=i.points();for(let i of e){s.push(i);const e=i.indexedAttribValue(t);null!=n[e]?n[e]:n[e]=Object.keys(n).length}}const r=Object.keys(n);for(let e of s){const i=n[e.indexedAttribValue(t)];e.setAttribIndex(t,i)}i[t]=r}const s=hr(t),r=new _r(s);return Object.keys(i).forEach((t=>{const e=i[t];r.setIndexedAttributeValues(t,e)})),s&&delete s.userData.mergedUserData,s}}class _r{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 _r.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=qs.remapName(t),this.userDataAttribs()[t]}isAttribIndexed(t){return t=qs.remapName(t),null!=this.userDataAttrib(t)}hasAttrib(t){return\\\\\\\"ptnum\\\\\\\"===t||(t=qs.remapName(t),null!=this._geometry.attributes[t])}attribType(t){return this.isAttribIndexed(t)?Bs.STRING:Bs.NUMERIC}static attribNames(t){return Object.keys(t.attributes)}attribNames(){return _r.attribNames(this._geometry)}static attribNamesMatchingMask(t,e){const n=os.attribNames(e),i=[];for(let e of this.attribNames(t))for(let t of n)os.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=qs.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 s=!1;if(m.isNumber(n)){for(let t=0;t1)if(m.isArray(n)){for(let t=0;tnew or(this,t)))}}var mr;_r._mesh_builder=new class extends ar{_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=[],s=i.length;let r,o,a;for(let t=0;t{};class gr{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,s,r,o,a)=>{n(e,i,s,r,o,a,t)})}t.onBeforeRender=fr}static applyCustomMaterials(t,e){const n=e;if(n.customMaterials)for(let e of Object.keys(n.customMaterials)){const i=e,s=n.customMaterials[i];s&&(t[i]=s,s.needsUpdate=!0)}}static assign_custom_uniforms(t,e,n){const i=t;if(i.customMaterials)for(let t of Object.keys(i.customMaterials)){const s=t,r=i.customMaterials[s];r&&(r.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 s=t,r=i.customMaterials[s];r&&(r.uniforms[e]=r.uniforms[e]||n)}}}const vr=\\\\\\\"name\\\\\\\";class yr extends Xs{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 _r(t):null}points(){var t;return(null===(t=this.coreGeometry())||void 0===t?void 0:t.points())||[]}pointsFromGroup(t){if(t){const e=os.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(\\\\\\\"=\\\\\\\"),s=i[0];if(\\\\\\\"@\\\\\\\"==s[0]){const t=s.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,s=t.userData;s.attributes=s.attributes||{},s.attributes[e]=i}addAttribute(t,e){yr.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=qs.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 ${t} not found`)}deleteAttribute(t){delete this._object.userData.attributes[t]}static attribValue(t,e,n=0,i){if(\\\\\\\"ptnum\\\\\\\"===e)return n;if(t.userData&&t.userData.attributes){const n=t.userData.attributes[e];if(null==n){if(e==vr)return t.name}else if(m.isArray(n)&&i)return i.fromArray(n),i;return n}return e==vr?t.name:void 0}static stringAttribValue(t,e,n=0){const i=this.attribValue(t,e,n);if(null!=i)return m.isString(i)?i:`${i}`}attribValue(t,e){return yr.attribValue(this._object,t,this._index,e)}stringAttribValue(t){return yr.stringAttribValue(this._object,t,this._index)}name(){return this.attribValue(vr)}humanType(){return Hs.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)?Bs.STRING:Bs.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:qs.attribSizeFromValue(e)}clone(){return yr.clone(this._object)}static clone(t){const e=t.clone();var n=new Map,i=new Map;return yr.parallelTraverse(t,e,(function(t,e){n.set(e,t),i.set(t,e)})),e.traverse((function(e){const s=n.get(e),r=e;if(r.geometry){const t=s.geometry;r.geometry=_r.clone(t);const e=r.geometry;e.userData&&(e.userData=b.cloneDeep(t.userData))}if(r.material){r.material=s.material,gr.applyCustomMaterials(e,r.material);const t=r.material;null==t.color&&(t.color=new D.a(1,1,1))}t.userData&&(e.userData=b.cloneDeep(s.userData));const o=s;o.animations&&(e.animations=o.animations.map((t=>t.clone())));const a=e;if(a.isSkinnedMesh){var l=a,c=s,h=c.skeleton.bones;l.skeleton=c.skeleton.clone(),l.bindMatrix.copy(c.bindMatrix);const t=h.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 _r(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 br{constructor(t){this.node=t,this._callbacks=[],this._callbacks_tmp=[];const e=xr[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 ${t} required`),this.notifyRequesters(),null)}notifyRequesters(t){let e;for(this._callbacks_tmp=this._callbacks.slice(),this._callbacks.splice(0,this._callbacks.length),t||(t=this.node.containerController.container());e=this._callbacks_tmp.pop();)e(t);this.node.scene().cookController.removeNode(this.node)}}const wr=li.performance.performanceManager();class Tr{constructor(t){this.cookController=t,this._inputs_start=0,this._params_start=0,this._cook_start=0,this._cooksCount=0,this._data={inputsTime:0,paramsTime:0,cookTime:0}}cooksCount(){return this._cooksCount}data2(){return this._data}active(){return this.cookController.performanceRecordStarted()}recordInputsStart(){this.active()&&(this._inputs_start=wr.now())}recordInputsEnd(){this.active()&&(this._data.inputsTime=wr.now()-this._inputs_start)}recordParamsStart(){this.active()&&(this._params_start=wr.now())}recordParamsEnd(){this.active()&&(this._data.paramsTime=wr.now()-this._params_start)}recordCookStart(){this.active()&&(this._cook_start=wr.now())}recordCookEnd(){this.active()&&(this._data.cookTime=wr.now()-this._cook_start,this._cooksCount+=1)}}class Ar{constructor(t){this.node=t,this._cooking=!1,this._performanceController=new Tr(this),this._inputs_evaluation_required=!0,this._core_performance=this.node.scene().performance}performanceRecordStarted(){return this._core_performance.started()}disallowInputsEvaluation(){this._inputs_evaluation_required=!1}isCooking(){return!0===this._cooking}_start_cook_if_no_errors(t){if(this.node.states.error.active())this.endCook();else try{this._performanceController.recordCookStart(),this.node.cook(t)}catch(t){this.node.states.error.set(`node internal error: '${t}'.`),li.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()?li.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()):(li.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 s;for(let r=0;rt.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 Er,Sr;!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\\\\\\\"}(Er||(Er={})),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\\\\\\\"}(Sr||(Sr={}));const Cr=\\\\\\\"dependentOnFoundNode\\\\\\\",Nr=\\\\\\\"visibleIf\\\\\\\";var Lr,Or;!function(t){t.TYPESCRIPT=\\\\\\\"typescript\\\\\\\"}(Lr||(Lr={})),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\\\\\\\"}(Or||(Or={}));class Pr{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!(Cr 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 Mi(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(Sr.VISIBLE_UPDATED)}}}class Rr{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 Ir{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 Fr{constructor(t){this.param=t}active(){const t=this.param.scene().timeController.graphNode.graphNodeId();return this.param.graphPredecessorIds().includes(t)}}class Dr{constructor(t){this.param=t}set(t){this._message!=t&&(this._message=t,this._message&&li.warn(this.param.path(),this._message),this.param.emitController.emit(Sr.ERROR_UPDATED))}message(){return this._message}clear(){this.set(void 0)}active(){return null!=this._message}}class Br{constructor(t){this.param=t,this.timeDependent=new Fr(this.param),this.error=new Dr(this.param)}}class zr extends Mi{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 s=m.isNumber(e),r=new zr(t,e,i);if(n)r.set_resolved_graph_node(n);else if(!s){const t=e;r.set_unresolved_path(t)}return r}}const kr=[];class Ur extends Mi{constructor(t,e){super(t,\\\\\\\"BaseParam\\\\\\\"),this._options=new Pr(this),this._emit_controller=new Rr(this),this._is_computing=!1,this._node=e,this.initialize_param()}get options(){return this._options=this._options||new Pr(this)}get emitController(){return this._emit_controller=this._emit_controller||new Rr(this)}get expressionController(){return this._expression_controller}get serializer(){return this._serializer=this._serializer||new Ir(this)}get states(){return this._states=this._states||new Br(this)}dispose(){var t,e;const n=this.graphPredecessors();for(let t of n)t instanceof zr&&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 Er.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(Sr.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 ${this.path()}`),this.isDirty()){if(this._is_computing)return new Promise(((t,e)=>{this._compute_resolves=this._compute_resolves||[],this._compute_resolves.push(t)}));if(this._is_computing=!0,await this.processComputation(),this._is_computing=!1,this._compute_resolves){let t;for(;t=this._compute_resolves.pop();)t()}}}async processComputation(){}setInitValue(t){this._default_value=this._clone_raw_input(this._prefilter_invalid_raw_input(t))}_setupNodeDependencies(t){var e,n;if(t?(this.options.allowCallback(),this.parent_param||(this.options.makesNodeDirtyWhenDirty()?null===(n=t.params.params_node)||void 0===n||n.addGraphInput(this,!1):this.dirtyController.addPostDirtyHook(\\\\\\\"run callback\\\\\\\",(async()=>{await this.compute(),this.options.executeCallback()})))):this._node&&(null===(e=this._node.params.params_node)||void 0===e||e.removeGraphInput(this)),this.components)for(let e of this.components)e._setupNodeDependencies(t)}get node(){return this._node}parent(){return this.node}set_parent_param(t){t.addGraphInput(this,!1),this._parent_param=t}get parent_param(){return this._parent_param}has_parent_param(){return null!=this._parent_param}path(){var t;return(null===(t=this.node)||void 0===t?void 0:t.path())+\\\\\\\"/\\\\\\\"+this.name()}pathRelativeTo(t){const e=bi.relativePath(t,this.node);return e.length>0?`${e}${bi.SEPARATOR}${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 kr}isMultiple(){return this.componentNames().length>0}initComponents(){}hasExpression(){return null!=this.expressionController&&this.expressionController.active()}toJSON(){return this.serializer.toJSON()}}var Gr=n(94),Vr=n.n(Gr);Vr.a.addUnaryOp(\\\\\\\"@\\\\\\\");Vr.a.addBinaryOp(\\\\\\\"**\\\\\\\",10);class Hr{constructor(){}parse_expression(t){try{this.reset(),this.node=Vr()(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=Hr.string_value_elements(t),n=[];for(let t=0;t{e.push(Wr.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(Wr.assign_item_size_line(t)),e.push(Wr.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=Wr.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 Wr.var_attribute_size(t)}var_array(t){return Wr.var_array(t)}}const qr={math_random:\\\\\\\"random\\\\\\\"},Xr=Object.keys(ir),Yr={};[\\\\\\\"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=>{Yr[t]=`Math.${t}`})),[\\\\\\\"cbrt\\\\\\\",\\\\\\\"hypot\\\\\\\",\\\\\\\"log10\\\\\\\",\\\\\\\"trunc\\\\\\\"].forEach((t=>{Yr[t]=`Math.${t}`})),Object.keys(qr).forEach((t=>{const e=qr[t];Yr[t]=`Math.${e}`})),[\\\\\\\"fit\\\\\\\",\\\\\\\"fit01\\\\\\\",\\\\\\\"fract\\\\\\\",\\\\\\\"deg2rad\\\\\\\",\\\\\\\"rad2deg\\\\\\\",\\\\\\\"rand\\\\\\\",\\\\\\\"clamp\\\\\\\"].forEach((t=>{Yr[t]=`Core.Math.${t}`})),Xr.forEach((t=>{Yr[t]=`Core.Math.Easing.${t}`})),[\\\\\\\"precision\\\\\\\"].forEach((t=>{Yr[t]=`Core.String.${t}`}));const $r={if:class{static if(t){return`(${t[0]}) ? (${t[1]}) : (${t[2]})`}}.if},Jr={};[\\\\\\\"E\\\\\\\",\\\\\\\"LN2\\\\\\\",\\\\\\\"LN10\\\\\\\",\\\\\\\"LOG10E\\\\\\\",\\\\\\\"LOG2E\\\\\\\",\\\\\\\"PI\\\\\\\",\\\\\\\"SQRT1_2\\\\\\\",\\\\\\\"SQRT2\\\\\\\"].forEach((t=>{Jr[t]=`Math.${t}`}));const Zr={x:0,y:1,z:2,w:3,r:0,g:1,b:2};class Qr extends jr{constructor(t){super(t),this.param=t,this._attribute_requirements_controller=new Wr,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 = ${this.function_main_string}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tresolve(value)\\\\n\\\\t\\\\t\\\\t\\\\t} catch(e) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t_set_error_from_error(e)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treject()\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t})\\\\n\\\\t\\\\t\\\\t`}eval_allowed(){return null!=this.function}eval_function(){if(this.function){this.clear_error();const t={Math:rr,String:os};return this.function(t,this.param,this.methods,this._set_error_from_error_bound)}}traverse_CallExpression(t){const e=t.arguments.map((t=>this.traverse_node(t))),n=t.callee.name;if(n){const i=$r[n];if(i)return i(e);const s=`${e.join(\\\\\\\", \\\\\\\")}`,r=Yr[n];if(r)return`${r}(${s})`;const o=li.expressionsRegister;if(o.getMethod(n)){const i=t.arguments[0],r=`return ${e[0]}`;let o,a=[];try{o=new Function(r),a=o()}catch{}return this._create_method_and_dependencies(n,a,i),`(await methods[${this.method_index}].processArguments([${s}]))`}{const t=`method not found (${n}), available methods are: ${o.availableMethods().join(\\\\\\\", \\\\\\\")}`;li.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)}.${this.traverse_node(t.property)}`}traverse_UnaryExpression(t){if(\\\\\\\"@\\\\\\\"===t.operator){let e,n,i=t.argument;switch(i.type){case\\\\\\\"Identifier\\\\\\\":e=i.name;break;case\\\\\\\"MemberExpression\\\\\\\":{const t=i,s=t.object,r=t.property;e=s.name,n=r.name;break}}if(e){if(e=qs.remapName(e),\\\\\\\"ptnum\\\\\\\"==e)return\\\\\\\"((entity != null) ? entity.index() : 0)\\\\\\\";{const t=this._attribute_requirements_controller.var_attribute_size(e),i=this._attribute_requirements_controller.var_array(e);if(this._attribute_requirements_controller.add(e),n){return`${i}[entity.index()*${t}+${Zr[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=Jr[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=li.expressionsRegister,s=i.getMethod(t);if(!s){const e=`method not found (${t}), available methods are: ${i.availableMethods().join(\\\\\\\", \\\\\\\")}`;return this.set_error(e),void li.warn(e)}const r=new s(this.param);if(this.method_index+=1,this.methods[this.method_index]=r,r.require_dependency()){const t=r.findDependency(e);t?(n&&t.set_jsep_node(n),this.method_dependencies.push(t)):n&&m.isString(e)&&this.param.scene().missingExpressionReferencesController.register(this.param,n,e)}}}class Kr extends jr{constructor(t){super(t),this.param=t}parse_tree(t){if(null==t.error_message&&t.node)try{return this.traverse_node(t.node)}catch(t){this.set_error(\\\\\\\"could not traverse tree\\\\\\\")}else this.set_error(\\\\\\\"cannot parse tree\\\\\\\")}traverse_CallExpression(t){const e=`${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 to{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 eo{constructor(t){this.param=t,this.parse_completed=!1,this.parse_started=!1,this.parsed_tree=new Hr,this.function_generator=new Qr(this.param),this.dependencies_controller=new to(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 Hr,this.reset(),this.param.type()==Er.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 Kr(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 no{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 eo(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: ${null===(i=this._manager)||void 0===i?void 0:i.error_message()}`),this.reset_entities()}compute_expression_for_points(t,e){return this.compute_expression_for_entities(t,e)}compute_expression_for_objects(t,e){return this.compute_expression_for_entities(t,e)}entities(){return this._entities}entity_callback(){return this._entity_callback}set_entities(t,e){this._entities=t,this._entity_callback=e}reset_entities(){this._entities=void 0,this._entity_callback=void 0}}class io extends Ur{isNumeric(){return!0}isDefault(){return this._raw_input==this._default_value}_prefilter_invalid_raw_input(t){return m.isArray(t)?t[0]:t}processRawInput(){this.states.error.clear();const t=this.convert(this._raw_input);null!=t?(this._expression_controller&&(this._expression_controller.set_expression(void 0,!1),this.emitController.emit(Sr.EXPRESSION_UPDATED)),t!=this._value&&(this._update_value(t),this.setSuccessorsDirty(this))):m.isString(this._raw_input)?(this._expression_controller=this._expression_controller||new no(this),this._raw_input!=this._expression_controller.expression()&&(this._expression_controller.set_expression(this._raw_input),this.emitController.emit(Sr.EXPRESSION_UPDATED))):this.states.error.set(`param input is invalid (${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(Sr.VALUE_UPDATED),this.removeDirtyState()}}class so extends io{static type(){return Er.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)&&os.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(os.isNumber(t)){const e=parseFloat(t);if(m.isNumber(e))return e}return null}convert(t){const e=so.convert(t);return e?this.options.ensureInRange(e):e}}class ro extends Ur{constructor(){super(...arguments),this._components_contructor=so}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\\\\\\\"}(oo||(oo={}));oo.NONE,oo.GAMMA_TO_LINEAR,oo.LINEAR_TO_GAMMA,oo.SRGB_TO_LINEAR,oo.LINEAR_TO_SRGB;class ao{static set_hsv(t,e,n,i){t=Object(On.f)(t,1),e=Object(On.d)(e,0,1),n=Object(On.d)(n,0,1),i.setHSL(t,e*n/((t=(2-e)*n)<1?t:2-t),.5*t)}}const lo=[\\\\\\\"r\\\\\\\",\\\\\\\"g\\\\\\\",\\\\\\\"b\\\\\\\"];class co extends io{static type(){return Er.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)&&os.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(os.isNumber(t)){const e=parseInt(t);if(m.isNumber(e))return e}return null}convert(t){const e=co.convert(t);return e?this.options.ensureInRange(e):e}}class ho{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(bi.SEPARATOR);const n=e[0];return n&&(bi.NON_LETTER_PREFIXES.includes(n)||(e=`${bi.SEPARATOR}${e}`)),e}}class uo extends Ur{constructor(){super(...arguments),this.decomposed_path=new ho}}var po;!function(t){t.NODE=\\\\\\\"NODE\\\\\\\",t.PARAM=\\\\\\\"PARAM\\\\\\\"}(po||(po={}));class _o 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 Er.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(Sr.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,s=this.options.paramSelectionOptions()?po.PARAM:po.NODE;this.scene().referencesController.reset_reference_from_param(this),this.decomposed_path.reset(),i&&(s==po.PARAM?n=bi.findParam(this.node,t,this.decomposed_path):e=bi.findNode(this.node,t,this.decomposed_path));const r=s==po.PARAM?this._found_param:this._found_node,o=s==po.PARAM?n:e;if(this.scene().referencesController.set_named_nodes_from_param(this),e&&this.scene().referencesController.set_reference_from_param(this,e),(null==r?void 0:r.graphNodeId())!==(null==o?void 0:o.graphNodeId())){const t=this.options.dependentOnFoundNode();this._found_node&&t&&this.removeGraphInput(this._found_node),s==po.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 mo,fo=n(33),go=n(70);class vo{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 vo;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 vo(t.position,t.value)}}!function(t){t.LINEAR=\\\\\\\"linear\\\\\\\"}(mo||(mo={}));class yo{constructor(t=mo.LINEAR,e=[]){this._interpolation=t,this._points=e,this._uuid=Object(On.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(vo.from_json(n));return new yo(t.interpolation,e)}toJSON(){return{interpolation:this._interpolation,points:this._points.map((t=>t.toJSON()))}}clone(){const t=new yo;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(!vo.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(vo.from_json(n)),e+=1}}}const xo=1024;class bo extends Ur{constructor(){super(...arguments),this._texture_data=new Uint8Array(3072),this._ramp_texture=new fo.a(this._texture_data,xo,1,w.ic)}static type(){return Er.RAMP}defaultValueSerialized(){return this._default_value instanceof yo?this._default_value.toJSON():this._default_value}_clone_raw_input(t){return t instanceof yo?t.clone():yo.from_json(t).toJSON()}rawInputSerialized(){return this._raw_input instanceof yo?this._raw_input.toJSON():yo.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 yo?e instanceof yo?t.is_equal(e):t.is_equal_json(e):e instanceof yo?e.is_equal_json(t):yo.are_json_equal(t,e)}static are_values_equal(t,e){return t.is_equal(e)}isDefault(){return this._default_value instanceof yo?this.value.is_equal(this._default_value):this.value.is_equal_json(this._default_value)}processRawInput(){this._raw_input instanceof yo?this._value?this._value.copy(this._raw_input):this._value=this._raw_input:this._value?this._value.from_json(this._raw_input):this._value=yo.from_json(this._raw_input),this._reset_ramp_interpolant(),this._update_rampTexture(),this.options.executeCallback(),this.emitController.emit(Sr.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/xo,n=this.value_at_position(e),this._texture_data[t]=255*n}static create_interpolant(t,e){const n=new Float32Array(1);return new go.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 s=0;for(let t of e)n[s]=t.position,i[s]=t.value,s++;return bo.create_interpolant(n,i)}value_at_position(t){return this.interpolant().evaluate(t)[0]}}bo.DEFAULT_VALUE=new yo(mo.LINEAR,[new vo(0,0),new vo(1,1)]),bo.DEFAULT_VALUE_JSON=bo.DEFAULT_VALUE.toJSON();class wo extends Ur{static type(){return Er.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 no(this),this._raw_input!=this._expression_controller.expression()&&(this._expression_controller.set_expression(this._raw_input),this.setDirty(),this.emitController.emit(Sr.EXPRESSION_UPDATED))):this._raw_input!=this._value&&(this._value=this._raw_input,this.removeDirtyState(),this.setSuccessorsDirty(this),this.emitController.emit(Sr.VALUE_UPDATED),this.options.executeCallback(),this._expression_controller&&(this._expression_controller.set_expression(void 0,!1),this.emitController.emit(Sr.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: ${this.expressionController.error_message()}`);else{const e=this.convert(t);null!=e?(this._value=e,this.emitController.emit(Sr.VALUE_UPDATED),this.options.executeCallback()):this.states.error.set(`expression returns an invalid type (${t})`),this.removeDirtyState()}}}_value_elements(t){return Hr.string_value_elements(t)}}const To=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"];const Ao=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"];const Mo=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];const Eo={[Er.BOOLEAN]:class extends io{static type(){return Er.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(os.isBoolean(t))return os.toBoolean(t);if(os.isNumber(t)){return parseFloat(t)>=1}}return null}},[Er.BUTTON]:class extends Ur{static type(){return Er.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()}},[Er.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 Er.COLOR}componentNames(){return lo}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!=oo.NONE){switch(t){case oo.GAMMA_TO_LINEAR:return void this._value.convertGammaToLinear();case oo.LINEAR_TO_GAMMA:return void this._value.convertLinearToGamma();case oo.SRGB_TO_LINEAR:return void this._value.convertSRGBToLinear();case oo.LINEAR_TO_SRGB:return void this._value.convertLinearToSRGB()}ls.unreachable(t)}this._value_serialized_dirty=!0}},[Er.FLOAT]:so,[Er.FOLDER]:class extends Ur{static type(){return Er.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}},[Er.INTEGER]:co,[Er.OPERATOR_PATH]:_o,[Er.PARAM_PATH]:class extends uo{static type(){return Er.PARAM_PATH}initialize_param(){this._value=new xi}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(Sr.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=bi.findParam(this.node,t,this.decomposed_path));const i=this._value.param(),s=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==s?void 0:s.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()}},[Er.NODE_PATH]:class extends uo{static type(){return Er.NODE_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}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(Sr.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=bi.findNode(this.node,t,this.decomposed_path));const i=this._value.node(),s=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==s?void 0:s.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()}},[Er.RAMP]:bo,[Er.STRING]:wo,[Er.VECTOR2]:class extends ro{constructor(){super(...arguments),this._value=new d.a,this._copied_value=[0,0]}static type(){return Er.VECTOR2}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 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}},[Er.VECTOR3]:class extends ro{constructor(){super(...arguments),this._value=new p.a,this._copied_value=[0,0,0]}static type(){return Er.VECTOR3}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 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}},[Er.VECTOR4]:class extends ro{constructor(){super(...arguments),this._value=new _.a,this._copied_value=[0,0,0,0]}static type(){return Er.VECTOR4}componentNames(){return Mo}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 So{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 Er.STRING:return this._handle_string_param(t);case Er.OPERATOR_PATH:return this._handle_operator_path_param(t);case Er.NODE_PATH:return this._handle_node_path_param(t);case Er.PARAM_PATH:return this._handle_param_path_param(t);case Er.FLOAT:case Er.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 Co{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 So}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 Mi(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 s;this.node.params_init_value_overrides&&(s=this.node.params_init_value_overrides[n],e=!0),this.addParam(i.type,n,i.init_value,i.options,s)}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):li.warn(`param ${t} not found with type ${n}`)}set_float(t,e){this.set_with_type(t,e,Er.FLOAT)}set_vector3(t,e){this.set_with_type(t,e,Er.VECTOR3)}has_param(t){return null!=this._params_by_name[t]}has(t){return this.has_param(t)}get(t){return this.param(t)}param_with_type(t,e){const n=this.param(t);if(n&&n.type()==e)return n}get_float(t){return this.param_with_type(t,Er.FLOAT)}get_operator_path(t){return this.param_with_type(t,Er.OPERATOR_PATH)}value(t){var e;return null===(e=this.param(t))||void 0===e?void 0:e.value}value_with_type(t,e){var n;return null===(n=this.param_with_type(t,e))||void 0===n?void 0:n.value}boolean(t){return this.value_with_type(t,Er.BOOLEAN)}float(t){return this.value_with_type(t,Er.FLOAT)}integer(t){return this.value_with_type(t,Er.INTEGER)}string(t){return this.value_with_type(t,Er.STRING)}vector2(t){return this.value_with_type(t,Er.VECTOR2)}vector3(t){return this.value_with_type(t,Er.VECTOR3)}color(t){return this.value_with_type(t,Er.COLOR)}param(t){const e=this._params_by_name[t];return null!=e?e:(li.warn(`tried to access param '${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={},s){const r=i.spare||!1;!1!==this._param_create_mode||r||li.warn(`node ${this.node.path()} (${this.node.type()}) param '${e}' cannot be created outside of create_params`),null==this.node.scene()&&li.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&&(r?a.type()!=t&&this._deleteParam(a.name()):li.warn(`a param named ${e} already exists`,this.node));const l=new o(this.node.scene(),this.node);if(l.options.set(i),l.setName(e),l.setInitValue(n),l.initComponents(),null==s)l.set(n);else if(l.options.isExpressionForEntities()&&l.set(n),null!=s.raw_input)l.set(s.raw_input);else if(null!=s.simple_data)l.set(s.simple_data);else if(null!=s.complex_data){const t=s.complex_data.raw_input;t?l.set(t):l.set(n);const e=s.complex_data.overriden_options;if(null!=e){const t=Object.keys(e);for(let n of t)l.options.setOption(n,e[n])}}if(l._setupNodeDependencies(this.node),this._params_by_name[l.name()]=l,l.isMultiple()&&l.components)for(let t of l.components)this._params_by_name[t.name()]=t;return this._params_added_since_last_params_eval=!0,l}}_updateCaches(){this._params_list=Object.values(this._params_by_name),this._param_names=Object.keys(this._params_by_name),this._non_spare_params=Object.values(this._params_by_name).filter((t=>!t.options.isSpare())),this._spare_params=Object.values(this._params_by_name).filter((t=>t.options.isSpare())),this._non_spare_param_names=Object.values(this._params_by_name).filter((t=>!t.options.isSpare())).map((t=>t.name())),this._spare_param_names=Object.values(this._params_by_name).filter((t=>t.options.isSpare())).map((t=>t.name()))}async _evalParam(t){t.isDirty()&&(await t.compute(),t.states.error.active()&&this.node.states.error.set(`param '${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 li.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)?li.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 No{constructor(){}}class Lo{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=Lo._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)}}Lo._next_id=0;class Oo{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==Ki.FROM_NODE)return!0;return!!this._cloned_state&&this._cloned_state==Ki.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 Ki.ALWAYS:return!0;case Ki.NEVER:return!1;case Ki.FROM_NODE:return!this._overridden}return ls.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.setMinCount(0),this.setMaxCount(t.length),this._initGraphNodeInputs(),this.node.emit(Ei.NAMED_INPUTS_UPDATED)}hasNamedInputs(){return this._has_named_inputs}namedInputConnectionPoints(){return this._named_input_connection_points||[]}_initGraphNodeInputs(){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 r=this._graph_node_inputs[i];if(null==r){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&&s==l||(null!=o&&this._depends_on_inputs&&r.removeGraphInput(o),null!=e?r.addGraphInput(e)?(this._depends_on_inputs||r.removeGraphInput(e),a&&a.disconnect({setInput:!1}),this._inputs[i]=e,new Lo(e,this.node,s,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(),r.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 Oo(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 s=[];for(let e of i){const n=null===(t=this.namedOutputConnectionPoints()[e])||void 0===t?void 0:t.name();n&&s.push(n)}return s}return[]}}class Io{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 Fo{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 Do{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 Bo;!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\\\\\\\"}(Bo||(Bo={}));const zo=[Bo.BOOL,Bo.INT,Bo.FLOAT,Bo.VEC2,Bo.VEC3,Bo.VEC4],ko={[Bo.BOOL]:Er.BOOLEAN,[Bo.INT]:Er.INTEGER,[Bo.FLOAT]:Er.FLOAT,[Bo.VEC2]:Er.VECTOR2,[Bo.VEC3]:Er.VECTOR3,[Bo.VEC4]:Er.VECTOR4,[Bo.SAMPLER_2D]:Er.RAMP,[Bo.SSS_MODEL]:Er.STRING},Uo={[Er.BOOLEAN]:Bo.BOOL,[Er.COLOR]:Bo.VEC3,[Er.INTEGER]:Bo.INT,[Er.FLOAT]:Bo.FLOAT,[Er.FOLDER]:void 0,[Er.VECTOR2]:Bo.VEC2,[Er.VECTOR3]:Bo.VEC3,[Er.VECTOR4]:Bo.VEC4,[Er.BUTTON]:void 0,[Er.OPERATOR_PATH]:void 0,[Er.PARAM_PATH]:void 0,[Er.NODE_PATH]:void 0,[Er.RAMP]:void 0,[Er.STRING]:void 0},Go={[Bo.BOOL]:!1,[Bo.INT]:0,[Bo.FLOAT]:0,[Bo.VEC2]:[0,0],[Bo.VEC3]:[0,0,0],[Bo.VEC4]:[0,0,0,0],[Bo.SAMPLER_2D]:bo.DEFAULT_VALUE_JSON,[Bo.SSS_MODEL]:\\\\\\\"SSSModel()\\\\\\\"},Vo={[Bo.BOOL]:1,[Bo.INT]:1,[Bo.FLOAT]:1,[Bo.VEC2]:2,[Bo.VEC3]:3,[Bo.VEC4]:4,[Bo.SAMPLER_2D]:1,[Bo.SSS_MODEL]:1};class Ho extends Do{constructor(t,e,n){super(t,e),this._name=t,this._type=e,this._init_value=n,this._init_value=this._init_value||Go[this._type]}type(){return this._type}are_types_matched(t,e){return t==e}get param_type(){return ko[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 jo;!function(t){t.BOOL=\\\\\\\"bool\\\\\\\",t.INT=\\\\\\\"int\\\\\\\",t.FLOAT=\\\\\\\"float\\\\\\\",t.VEC2=\\\\\\\"vec2\\\\\\\",t.VEC3=\\\\\\\"vec3\\\\\\\",t.VEC4=\\\\\\\"vec4\\\\\\\"}(jo||(jo={}));const Wo=[jo.BOOL,jo.INT,jo.FLOAT,jo.VEC2,jo.VEC3,jo.VEC4],qo={[jo.BOOL]:Er.BOOLEAN,[jo.INT]:Er.INTEGER,[jo.FLOAT]:Er.FLOAT,[jo.VEC2]:Er.VECTOR2,[jo.VEC3]:Er.VECTOR3,[jo.VEC4]:Er.VECTOR4},Xo={[Er.BOOLEAN]:jo.BOOL,[Er.COLOR]:jo.VEC3,[Er.INTEGER]:jo.INT,[Er.FLOAT]:jo.FLOAT,[Er.FOLDER]:void 0,[Er.VECTOR2]:jo.VEC2,[Er.VECTOR3]:jo.VEC3,[Er.VECTOR4]:jo.VEC4,[Er.BUTTON]:void 0,[Er.OPERATOR_PATH]:void 0,[Er.PARAM_PATH]:void 0,[Er.NODE_PATH]:void 0,[Er.RAMP]:void 0,[Er.STRING]:void 0},Yo={[jo.BOOL]:!1,[jo.INT]:0,[jo.FLOAT]:0,[jo.VEC2]:[0,0],[jo.VEC3]:[0,0,0],[jo.VEC4]:[0,0,0,0]};jo.BOOL,jo.INT,jo.FLOAT,jo.VEC2,jo.VEC3,jo.VEC4;class $o extends Do{constructor(t,e){super(t,e),this._name=t,this._type=e,this._init_value=Yo[this._type]}type(){return this._type}are_types_matched(t,e){return t==e}get param_type(){return qo[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 Jo;!function(t){t.BASE=\\\\\\\"base\\\\\\\",t.DRAG=\\\\\\\"drag\\\\\\\",t.KEYBOARD=\\\\\\\"keyboard\\\\\\\",t.MOUSE=\\\\\\\"mouse\\\\\\\",t.POINTER=\\\\\\\"pointer\\\\\\\"}(Jo||(Jo={}));class Zo extends Do{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 Er.FLOAT}are_types_matched(t,e){return e==Jo.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 Qo={[ts.ANIM]:void 0,[ts.COP]:void 0,[ts.EVENT]:Jo.BASE,[ts.GL]:Bo.FLOAT,[ts.JS]:jo.FLOAT,[ts.MANAGER]:void 0,[ts.MAT]:void 0,[ts.OBJ]:void 0,[ts.POST]:void 0,[ts.ROP]:void 0,[ts.SOP]:void 0};function Ko(t,e,n){switch(t){case ts.EVENT:return new Zo(e,n);case ts.GL:return new Ho(e,n);case ts.JS:return new $o(e,n);default:return}}class ta{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 ts.EVENT:return;case ts.GL:return Uo;case ts.JS:return Xo;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=Ko(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 s=t.init_value;const r=this._default_value_serialized_by_param_name.get(n);let o=this.node.paramDefaultValue(n);if(s=null!=o?o:null!=r?r:t.init_value,m.isArray(t.init_value))if(m.isNumber(s)){const e=new Array(t.init_value.length);e.fill(s),s=e}else m.isArray(s)&&s.length==t.init_value.length&&null!=r&&(s=t.init_value);null!=s&&(e.toAdd=e.toAdd||[],e.toAdd.push({name:n,type:i,init_value:b.clone(s),raw_input:b.clone(s),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 ea{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 ta(this.node,this._context)}default_connection_type(){return Qo[this._context]}create_connection_point(t,e){return Ko(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(ns.INPUT),n=this.node.nodesByType(ns.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 na{constructor(t){this.node=t,this._connections=new Io(this.node)}get connections(){return this._connections}get inputs(){return this._inputs=this._inputs||new Po(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 ea(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 Fo(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 ia{constructor(){}}class sa extends Mi{constructor(t,e=\\\\\\\"BaseNode\\\\\\\",n){super(t,e),this.params_init_value_overrides=n,this.containerController=new br(this),this.pv=new No,this.p=new ia,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 qi(this)}static displayedInputNames(){return[]}get childrenControllerContext(){return this._children_controller_context}_create_children_controller(){if(this._children_controller_context)return new ds(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 Si(this)}get states(){return this._states=this._states||new ji(this)}get lifecycle(){return this._lifecycle=this._lifecycle||new ps(this)}get serializer(){return this._serializer=this._serializer||new Mr(this)}get cookController(){return this._cook_controller=this._cook_controller||new Ar(this)}get io(){return this._io=this._io||new na(this)}get nameController(){return this._name_controller=this._name_controller||new Wi(this)}setName(t){this.nameController.setName(t)}_set_core_name(t){this._name=t}get params(){return this._params_controller=this._params_controller||new Co(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 s;return null===(s=this._params_controller)||void 0===s?void 0:s.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 sa{static context(){return ts.MANAGER}}class oa{constructor(t,e,n){this.type=t,this.init_value=e,this.options=n}}class aa{static BUTTON(t,e){return new oa(Er.BUTTON,t,e)}static BOOLEAN(t,e){return new oa(Er.BOOLEAN,t,e)}static COLOR(t,e){return t instanceof D.a&&(t=t.toArray()),new oa(Er.COLOR,t,e)}static FLOAT(t,e){return new oa(Er.FLOAT,t,e)}static FOLDER(t=null,e){return new oa(Er.FOLDER,t,e)}static INTEGER(t,e){return new oa(Er.INTEGER,t,e)}static RAMP(t=bo.DEFAULT_VALUE,e){return new oa(Er.RAMP,t,e)}static STRING(t=\\\\\\\"\\\\\\\",e){return new oa(Er.STRING,t,e)}static VECTOR2(t,e){return t instanceof d.a&&(t=t.toArray()),new oa(Er.VECTOR2,t,e)}static VECTOR3(t,e){return t instanceof p.a&&(t=t.toArray()),new oa(Er.VECTOR3,t,e)}static VECTOR4(t,e){return t instanceof _.a&&(t=t.toArray()),new oa(Er.VECTOR4,t,e)}static OPERATOR_PATH(t,e){return new oa(Er.OPERATOR_PATH,t,e)}static NODE_PATH(t,e){return new oa(Er.NODE_PATH,t,e)}static PARAM_PATH(t,e){return new oa(Er.PARAM_PATH,t,e)}}class la{}class ca{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),s=`${n=this._removeTrailingOrHeadingSlash(n)}/${e}`;if(os.matchMask(s,t))return i;const r=this.findObjectByMaskInObject(t,i,s);if(r)return r}}objectsByMask(t){return this.objectsByMaskInObject(t,this.scene.threejsScene(),[],\\\\\\\"\\\\\\\")}objectsByMaskInObject(t,e,n=[],i=\\\\\\\"\\\\\\\"){for(let s of e.children){const e=this._removeTrailingOrHeadingSlash(s.name),r=`${i=this._removeTrailingOrHeadingSlash(i)}/${e}`;os.matchMask(r,t)&&n.push(s),this.objectsByMaskInObject(t,s,n,r)}return n}_removeTrailingOrHeadingSlash(t){return\\\\\\\"/\\\\\\\"==t[0]&&(t=t.substr(1)),\\\\\\\"/\\\\\\\"==t[t.length-1]&&(t=t.substr(0,t.length-1)),t}}const ha={computeOnDirty:!1,callback:t=>{da.update(t)}};function ua(t){return class extends t{constructor(){super(...arguments),this.autoUpdate=aa.BOOLEAN(1,ha)}}}ua(la);class da{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 pa;!function(t){t.NONE=\\\\\\\"none\\\\\\\",t.COLOR=\\\\\\\"color\\\\\\\",t.TEXTURE=\\\\\\\"texture\\\\\\\"}(pa||(pa={}));const _a=[pa.NONE,pa.COLOR,pa.TEXTURE],ma={computeOnDirty:!1,callback:t=>{ga.update(t)}};function fa(t){return class extends t{constructor(){super(...arguments),this.backgroundMode=aa.INTEGER(_a.indexOf(pa.NONE),{menu:{entries:_a.map(((t,e)=>({name:t,value:e})))},...ma}),this.bgColor=aa.COLOR([0,0,0],{visibleIf:{backgroundMode:_a.indexOf(pa.COLOR)},...ma}),this.bgTexture=aa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{backgroundMode:_a.indexOf(pa.TEXTURE)},nodeSelection:{context:ts.COP},dependentOnFoundNode:!1,...ma})}}}fa(la);class ga{constructor(t){this.node=t}update(){const t=this.node.object,e=this.node.pv;if(e.backgroundMode==_a.indexOf(pa.NONE))t.background=null;else if(e.backgroundMode==_a.indexOf(pa.COLOR))t.background=e.bgColor;else{const n=e.bgTexture.nodeWithContext(ts.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 va={computeOnDirty:!1,callback:t=>{xa.update(t)}};function ya(t){return class extends t{constructor(){super(...arguments),this.useEnvironment=aa.BOOLEAN(0,va),this.environment=aa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{useEnvironment:1},nodeSelection:{context:ts.COP},dependentOnFoundNode:!1,...va})}}}ya(la);class xa{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;if(e.useEnvironment){const n=e.environment.nodeWithContext(ts.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 ba{constructor(t,e=1,n=1e3){this.name=\\\\\\\"\\\\\\\",this.color=new D.a(t),this.near=e,this.far=n}clone(){return new ba(this.color,this.near,this.far)}toJSON(){return{type:\\\\\\\"Fog\\\\\\\",color:this.color.getHex(),near:this.near,far:this.far}}}ba.prototype.isFog=!0;class wa{constructor(t,e=25e-5){this.name=\\\\\\\"\\\\\\\",this.color=new D.a(t),this.density=e}clone(){return new wa(this.color,this.density)}toJSON(){return{type:\\\\\\\"FogExp2\\\\\\\",color:this.color.getHex(),density:this.density}}}wa.prototype.isFogExp2=!0;const Ta={computeOnDirty:!1,callback:t=>{Sa.update(t)}};var Aa;!function(t){t.LINEAR=\\\\\\\"linear\\\\\\\",t.EXPONENTIAL=\\\\\\\"exponential\\\\\\\"}(Aa||(Aa={}));const Ma=[Aa.LINEAR,Aa.EXPONENTIAL];function Ea(t){return class extends t{constructor(){super(...arguments),this.useFog=aa.BOOLEAN(0,Ta),this.fogType=aa.INTEGER(Ma.indexOf(Aa.EXPONENTIAL),{visibleIf:{useFog:1},menu:{entries:Ma.map(((t,e)=>({name:t,value:e})))},...Ta}),this.fogColor=aa.COLOR([1,1,1],{visibleIf:{useFog:1},...Ta}),this.fogNear=aa.FLOAT(1,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useFog:1,fogType:Ma.indexOf(Aa.LINEAR)},...Ta}),this.fogFar=aa.FLOAT(100,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useFog:1,fogType:Ma.indexOf(Aa.LINEAR)},...Ta}),this.fogDensity=aa.FLOAT(25e-5,{visibleIf:{useFog:1,fogType:Ma.indexOf(Aa.EXPONENTIAL)},...Ta})}}}Ea(la);class Sa{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;if(e.useFog)if(e.fogType==Ma.indexOf(Aa.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 ba(16777215,t.fogNear,t.fogFar)}fogExp2(t){return this._fogExp2=this._fogExp2||new wa(16777215,t.fogDensity)}static async update(t){t.sceneFogController.update()}}const Ca={computeOnDirty:!1,callback:t=>{La.update(t)}};function Na(t){return class extends t{constructor(){super(...arguments),this.useOverrideMaterial=aa.BOOLEAN(0,Ca),this.overrideMaterial=aa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{useOverrideMaterial:1},nodeSelection:{context:ts.MAT},dependentOnFoundNode:!1,...Ca})}}}Na(la);class La{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;if(e.useOverrideMaterial){const n=e.overrideMaterial.nodeWithContext(ts.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 Oa extends(Na(ya(Ea(fa(ua(la)))))){}const Pa=new Oa;class Ra extends ra{constructor(){super(...arguments),this.paramsConfig=Pa,this._object=this._createScene(),this._queued_nodes_by_id=new Map,this.sceneAutoUpdateController=new da(this),this.sceneBackgroundController=new ga(this),this.sceneEnvController=new xa(this),this.sceneFogController=new Sa(this),this.sceneMaterialOverrideController=new La(this),this._children_controller_context=ts.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 gs;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 s=`_____${n.renderOrder}__${n.path()}`;e.push(s),t.set(s,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 Ia{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();os.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 Fa{constructor(t){this.scene=t}toJSON(t=!1){const e={},n={};for(let i of this.scene.nodesController.allNodes()){const s=new Mr(i);e[i.graphNodeId()]=s.toJSON(t);const r=i.params.all;for(let t of r)n[t.graphNodeId()]=t.toJSON()}return{nodes_by_graph_node_id:e,params_by_graph_node_id:n}}}var Da;!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\\\\\\\"}(Da||(Da={}));const Ba=[Da.auxclick,Da.click,Da.contextmenu,Da.dblclick,Da.mousedown,Da.mouseenter,Da.mouseleave,Da.mousemove,Da.mouseover,Da.mouseout,Da.mouseup,Da.pointerlockchange,Da.pointerlockerror,Da.select,Da.wheel];class za extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"mouse\\\\\\\"}acceptedEventTypes(){return Ba.map((t=>`${t}`))}}class ka extends sa{constructor(){super(...arguments),this._cook_without_inputs_bound=this._cook_without_inputs.bind(this)}static context(){return ts.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,h.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 Ua;!function(t){t.CANVAS=\\\\\\\"canvas\\\\\\\",t.DOCUMENT=\\\\\\\"document\\\\\\\"}(Ua||(Ua={}));const Ga=[Ua.CANVAS,Ua.DOCUMENT];class Va{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 s=e=>{this.processEvent(e,t)};for(let n of t.activeEventDatas()){this._eventOwner(n,e).addEventListener(n.type,s),i.set(n.type,{listener:s,data:n})}}_eventOwner(t,e){return\\\\\\\"resize\\\\\\\"==t.type?window:t.emitter==Ua.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 Ha={visibleIf:{active:1},callback:t=>{Wa.PARAM_CALLBACK_updateRegister(t)}};class ja extends ka{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:Ga[this.pv.element]})}}}activeEventDatas(){return this._activeEventDatas}}class Wa extends ja{acceptedEventTypes(){return[]}}const qa=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:t=>{Xa.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(Ga.indexOf(Ua.CANVAS),{menu:{entries:Ga.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.auxclick=aa.BOOLEAN(0,Ha),this.click=aa.BOOLEAN(0,Ha),this.contextmenu=aa.BOOLEAN(0,Ha),this.dblclick=aa.BOOLEAN(0,Ha),this.mousedown=aa.BOOLEAN(1,Ha),this.mouseenter=aa.BOOLEAN(0,Ha),this.mouseleave=aa.BOOLEAN(0,Ha),this.mousemove=aa.BOOLEAN(1,Ha),this.mouseover=aa.BOOLEAN(0,Ha),this.mouseout=aa.BOOLEAN(0,Ha),this.mouseup=aa.BOOLEAN(1,Ha),this.pointerlockchange=aa.BOOLEAN(0,Ha),this.pointerlockerror=aa.BOOLEAN(0,Ha),this.select=aa.BOOLEAN(0,Ha),this.wheel=aa.BOOLEAN(0,Ha),this.ctrlKey=aa.BOOLEAN(0,{...Ha,separatorBefore:!0}),this.altKey=aa.BOOLEAN(0,Ha),this.shiftKey=aa.BOOLEAN(0,Ha),this.metaKey=aa.BOOLEAN(0,Ha)}};class Xa extends ja{constructor(){super(...arguments),this.paramsConfig=qa}static type(){return\\\\\\\"mouse\\\\\\\"}acceptedEventTypes(){return Ba.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(Ba.map((t=>new Zo(t,Jo.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 Ya;!function(t){t.pointerdown=\\\\\\\"pointerdown\\\\\\\",t.pointermove=\\\\\\\"pointermove\\\\\\\",t.pointerup=\\\\\\\"pointerup\\\\\\\"}(Ya||(Ya={}));const $a=[Ya.pointerdown,Ya.pointermove,Ya.pointerup];class Ja extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"pointer\\\\\\\"}acceptedEventTypes(){return $a.map((t=>`${t}`))}}const Za=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:t=>{Qa.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(Ga.indexOf(Ua.CANVAS),{menu:{entries:Ga.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.pointerdown=aa.BOOLEAN(1,Ha),this.pointermove=aa.BOOLEAN(0,Ha),this.pointerup=aa.BOOLEAN(0,Ha),this.ctrlKey=aa.BOOLEAN(0,{...Ha,separatorBefore:!0}),this.altKey=aa.BOOLEAN(0,Ha),this.shiftKey=aa.BOOLEAN(0,Ha),this.metaKey=aa.BOOLEAN(0,Ha)}};class Qa extends ja{constructor(){super(...arguments),this.paramsConfig=Za}static type(){return\\\\\\\"pointer\\\\\\\"}acceptedEventTypes(){return $a.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints($a.map((t=>new Zo(t,Jo.POINTER)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.pointerdown,this.p.pointermove,this.p.pointerup];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;e.ctrlKey==this.pv.ctrlKey&&e.shiftKey==this.pv.shiftKey&&e.altKey==this.pv.altKey&&e.metaKey==this.pv.metaKey&&this.dispatchEventToOutput(t.event.type,t)}}var Ka,tl;!function(t){t.SET_FRAME=\\\\\\\"setFrame\\\\\\\"}(Ka||(Ka={})),function(t){t.TIME_REACHED=\\\\\\\"timeReached\\\\\\\"}(tl||(tl={}));const el=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:(t,e)=>{nl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(0,{hidden:!0}),this.sceneLoaded=aa.BOOLEAN(1,Ha),this.play=aa.BOOLEAN(1,Ha),this.pause=aa.BOOLEAN(1,Ha),this.tick=aa.BOOLEAN(1,{separatorAfter:!0,...Ha}),this.treachedTime=aa.BOOLEAN(0,{callback:t=>{nl.PARAM_CALLBACK_update_time_dependency(t)}}),this.reachedTime=aa.INTEGER(10,{visibleIf:{treachedTime:1},range:[0,100],separatorAfter:!0}),this.setFrameValue=aa.INTEGER(1,{range:[0,100]}),this.setFrame=aa.BUTTON(null,{callback:t=>{nl.PARAM_CALLBACK_setFrame(t)}})}};class nl extends ja{constructor(){super(...arguments),this.paramsConfig=el}static type(){return\\\\\\\"scene\\\\\\\"}acceptedEventTypes(){return mi.map((t=>`${t}`))}dispose(){var t;null===(t=this.graph_node)||void 0===t||t.dispose(),super.dispose()}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(Ka.SET_FRAME,Jo.BASE,this._onSetFrame.bind(this)),new Zo(_i.PLAY,Jo.BASE,this._play.bind(this)),new Zo(_i.PAUSE,Jo.BASE,this._pause.bind(this))]);const t=mi.map((t=>new Zo(t,Jo.BASE)));t.push(new Zo(tl.TIME_REACHED,Jo.BASE)),this.io.outputs.setNamedOutputConnectionPoints(t),this.params.onParamsCreated(\\\\\\\"update_time_dependency\\\\\\\",(()=>{this.update_time_dependency()}))}_onSetFrame(t){this.scene().setFrame(this.pv.setFrameValue)}_play(t){this.scene().play()}_pause(t){this.scene().pause()}_onFrameUpdate(){this.scene().time()>=this.pv.reachedTime&&this.dispatchEventToOutput(tl.TIME_REACHED,{})}update_time_dependency(){this.pv.treachedTime?(this.graph_node=this.graph_node||new Mi(this.scene(),\\\\\\\"scene_node_time_graph_node\\\\\\\"),this.graph_node.addGraphInput(this.scene().timeController.graphNode),this.graph_node.addPostDirtyHook(\\\\\\\"time_update\\\\\\\",this._onFrameUpdate.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 il;!function(t){t.keydown=\\\\\\\"keydown\\\\\\\",t.keypress=\\\\\\\"keypress\\\\\\\",t.keyup=\\\\\\\"keyup\\\\\\\"}(il||(il={}));const sl=[il.keydown,il.keypress,il.keyup];class rl extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"keyboard\\\\\\\"}acceptedEventTypes(){return sl.map((t=>`${t}`))}}const ol=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:(t,e)=>{al.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(Ga.indexOf(Ua.CANVAS),{menu:{entries:Ga.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.keydown=aa.BOOLEAN(1,Ha),this.keypress=aa.BOOLEAN(0,Ha),this.keyup=aa.BOOLEAN(0,Ha),this.keyCodes=aa.STRING(\\\\\\\"Digit1 KeyE ArrowDown\\\\\\\",Ha),this.ctrlKey=aa.BOOLEAN(0,Ha),this.altKey=aa.BOOLEAN(0,Ha),this.shiftKey=aa.BOOLEAN(0,Ha),this.metaKey=aa.BOOLEAN(0,Ha)}};class al extends ja{constructor(){super(...arguments),this.paramsConfig=ol}static type(){return\\\\\\\"keyboard\\\\\\\"}acceptedEventTypes(){return sl.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(sl.map((t=>new Zo(t,Jo.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 ll;!function(t){t.resize=\\\\\\\"resize\\\\\\\"}(ll||(ll={}));const cl=[ll.resize];class hl extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"window\\\\\\\"}acceptedEventTypes(){return cl.map((t=>`${t}`))}}const ul=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:t=>{dl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(0,{hidden:!0}),this.resize=aa.BOOLEAN(1,Ha)}};class dl extends ja{constructor(){super(...arguments),this.paramsConfig=ul}static type(){return\\\\\\\"window\\\\\\\"}acceptedEventTypes(){return cl.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(cl.map((t=>new Zo(t,Jo.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 pl;!function(t){t.dragover=\\\\\\\"dragover\\\\\\\"}(pl||(pl={}));const _l=[pl.dragover];class ml extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"drag\\\\\\\"}acceptedEventTypes(){return _l.map((t=>`${t}`))}}var fl;!function(t){t.touchstart=\\\\\\\"touchstart\\\\\\\",t.touchmove=\\\\\\\"touchmove\\\\\\\",t.touchend=\\\\\\\"touchend\\\\\\\"}(fl||(fl={}));const gl=[fl.touchstart,fl.touchmove,fl.touchend];class vl extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"touch\\\\\\\"}acceptedEventTypes(){return gl.map((t=>`${t}`))}}const yl=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:t=>{xl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(Ga.indexOf(Ua.CANVAS),{menu:{entries:Ga.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.dragover=aa.BOOLEAN(1,Ha),this.ctrlKey=aa.BOOLEAN(0,{...Ha,separatorBefore:!0}),this.altKey=aa.BOOLEAN(0,Ha),this.shiftKey=aa.BOOLEAN(0,Ha),this.metaKey=aa.BOOLEAN(0,Ha)}};class xl extends ja{constructor(){super(...arguments),this.paramsConfig=yl}static type(){return\\\\\\\"drag\\\\\\\"}acceptedEventTypes(){return _l.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(_l.map((t=>new Zo(t,Jo.DRAG)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.dragover];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;e.ctrlKey==this.pv.ctrlKey&&e.shiftKey==this.pv.shiftKey&&e.altKey==this.pv.altKey&&e.metaKey==this.pv.metaKey&&this.dispatchEventToOutput(t.event.type,t)}}const bl=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:t=>{wl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(Ga.indexOf(Ua.CANVAS),{menu:{entries:Ga.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.touchstart=aa.BOOLEAN(1,Ha),this.touchmove=aa.BOOLEAN(0,Ha),this.touchend=aa.BOOLEAN(0,Ha)}};class wl extends ja{constructor(){super(...arguments),this.paramsConfig=bl}static type(){return\\\\\\\"touch\\\\\\\"}acceptedEventTypes(){return gl.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(gl.map((t=>new Zo(t,Jo.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 Tl{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 al.type():return this.keyboardEventsController;case Xa.type():return this.mouseEventsController;case xl.type():return this.dragEventsController;case Qa.type():return this.pointerEventsController;case nl.type():return this.sceneEventsController;case wl.type():return this.touchEventsController;case dl.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(za)}get dragEventsController(){return this._drag_events_controller=this._drag_events_controller||this._create_controller(ml)}get pointerEventsController(){return this._pointer_events_controller=this._pointer_events_controller||this._create_controller(Ja)}get sceneEventsController(){return this._scene_events_controller=this._scene_events_controller||this._create_controller(fi)}get windowEventsController(){return this._window_events_controller=this._window_events_controller||this._create_controller(hl)}get touchEventsController(){return this._touch_events_controller=this._touch_events_controller||this._create_controller(vl)}_create_controller(t){const e=new t(this);return this._controllers.includes(e)||this._controllers.push(e),e}}class Al{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),h.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)h.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){h.popFromArrayAtEntry(this._referencing_params_by_referenced_node_id,e.graphNodeId(),t);const n=t.decomposed_path.named_nodes();for(let e of n)h.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([Er.OPERATOR_PATH,Er.NODE_PATH]),n=[];for(let i of t.params.all)e.has(i.type())&&n.push(i);const i=new Map,s=[];for(let t of n)this._check_param(t,i,s);for(let t of s)i.set(t.node.graphNodeId(),t.node);const r=[];return i.forEach((t=>{r.push(t)})),r}_check_param(t,e,n){if(t instanceof _o){const i=t.found_node(),s=t.found_param();return i&&e.set(i.graphNodeId(),i),void(s&&n.push(s))}}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 Ml;!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\\\\\\\"}(Ml||(Ml={}));class El{constructor(t){this.scene=t,this._frame=0,this._time=0,this._realtimeState=!0,this._maxFrame=600,this._maxFrameLocked=!1,this._playing=!1,this._delta=0,this._graph_node=new Mi(t,\\\\\\\"time controller\\\\\\\")}get PLAY_EVENT_CONTEXT(){return this._PLAY_EVENT_CONTEXT=this._PLAY_EVENT_CONTEXT||{event:new Event(_i.PLAY)}}get PAUSE_EVENT_CONTEXT(){return this._PAUSE_EVENT_CONTEXT=this._PAUSE_EVENT_CONTEXT||{event:new Event(_i.PAUSE)}}get TICK_EVENT_CONTEXT(){return this._TICK_EVENT_CONTEXT=this._TICK_EVENT_CONTEXT||{event:new Event(_i.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,Ml.MAX_FRAME_UPDATED)}setMaxFrameLocked(t){this._maxFrameLocked=t,this.scene.dispatchController.dispatch(this._graph_node,Ml.MAX_FRAME_UPDATED)}setRealtimeState(t){this._realtimeState=t,this.scene.dispatchController.dispatch(this._graph_node,Ml.REALTIME_STATUS_UPDATED)}setTime(t,e=!0){if(t!=this._time){if(this._time=t,this._onBeforeTickCallbacks)for(let t of this._onBeforeTickCallbacks)t(this._delta);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,Ml.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(this._delta)}}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(El.START_FRAME,!0)}incrementTimeIfPlaying(t){this._playing&&(this.scene.root().areChildrenCooking()||this.incrementTime(t))}incrementTime(t){if(this._realtimeState){this._delta=t;const e=this._time+this._delta;this.setTime(e)}else this.setFrame(this.frame()+1)}_ensureFrameWithinBounds(t){if(this._playing){if(this._maxFrameLocked&&t>this._maxFrame)return El.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,li.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 Mi(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 Pl{constructor(){this._cameras_controller=new r(this),this._cooker=new o(this),this.cookController=new a,this._graph=new l,this._missing_expression_references_controller=new Ti(this),this._expressions_controller=new ui,this._nodes_controller=new Ia(this),this._objects_controller=new ca(this),this._references_controller=new Al(this),this._time_controller=new El(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=os.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 hi(this)}get eventsDispatcher(){return this._events_dispatcher=this._events_dispatcher||new Tl(this)}get graph(){return this._graph}get lifecycleController(){return this._lifecycle_controller=this._lifecycle_controller||new di(this)}get loadingController(){return this._loading_controller=this._loading_controller||new gi(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 ci}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 Fa(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 Rl{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 s of t)i=e[s],i&&(n[s]=e)}return n}static params_data_value(t,e){let n;if(e){n={};const i=Object.keys(e);let s;for(let r of i)if(s=e[r],null!=s){const e=s.options,i=s.overriden_options;if(e||i){const o=s;e&&e.spare==t?null!=o.raw_input&&(n[r]={complex_data:o}):i&&(n[r]={complex_data:o})}else{const t=s;(i||null!=t)&&(n[r]={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,s;if(!e)return;if(!this._node.childrenAllowed()||!this._node.childrenController)return;const{optimized_names:r}=Dl.child_names_by_optimized_state(e);this._nodes=[],this._optimized_root_node_names=new Set;for(let t of r)Dl.is_optimized_root_node(e,t)&&this._optimized_root_node_names.add(t);for(let r of this._optimized_root_node_names){const o=e[r],a=this._node.createNode(Il);if(a){a.setName(r),this._nodes.push(a),(null===(n=o.flags)||void 0===n?void 0:n.display)&&(null===(s=null===(i=a.flags)||void 0===i?void 0:i.display)||void 0===s||s.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,s;if(!(null===(n=null===(e=t.flags)||void 0===e?void 0:e.optimize)||void 0===n?void 0:n.active()))return!1;const r=t.io.connections.outputConnections().map((t=>t.node_dest));let o=0;for(let t of r)(null===(s=null===(i=t.flags)||void 0===i?void 0:i.optimize)||void 0===s?void 0:s.active())||o++;return this.is_optimized_root_node_generic({outputs_count:r.length,non_optimized_count:o})}static node_outputs(t,e){const n=Object.keys(t),i=new Set;for(let s of n)if(s!=e){const n=t[s].inputs;if(n)for(let t of n)if(m.isString(t)){t==e&&i.add(s)}}return i}_create_operation_container(t,e,n,i){const s=Rl.non_spare_params_data_value(n.params),r=Dl.operation_type(n),o=this._node.create_operation_container(r,i,s);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 Bl{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:s}=Dl.child_names_by_optimized_state(e),r=[];for(let n of s){const i=e[n],s=i.type.toLowerCase(),o=Rl.non_spare_params_data_value(i.params);try{const t=this._node.createNode(s,o);t&&(t.setName(n),r.push(t))}catch(e){console.error(`error importing node: cannot create with type ${s}`,e);const i=os.camelCase(s);try{const t=this._node.createNode(i,o);t&&(t.setName(n),r.push(t))}catch(e){const a=`${s}Network`;try{const t=this._node.createNode(a,o);t&&(t.setName(n),r.push(t))}catch(e){const n=`failed to create node with type '${s}', '${i}' or '${a}'`;t.report.addWarning(n),li.warn(n,e)}}}}if(i.length>0){const i=new Dl(this._node);if(i.process_data(t,e),this._node.childrenController.context==ts.SOP){const t=Object.keys(e);let s;for(let i of t){(null===(n=e[i].flags)||void 0===n?void 0:n.display)&&(s=i)}if(s){const t=r.map((t=>t.name())),e=i.nodes();for(let n of e)t.push(n.name());if(!t.includes(s)){const t=`node '${`${this._node.path()}/${s}`}' with display flag has been optimized and does not exist in player mode`;console.error(t)}}}}const o=new Map;for(let n of r){if(e[n.name()]){const i=Wl.dispatch_node(n);o.set(n.name(),i),i.process_data(t,e[n.name()])}else li.warn(`possible import error for node ${n.name()}`)}for(let t of r){const n=o.get(t.name());n&&n.process_inputs_data(e[t.name()])}}}const zl=[\\\\\\\"overriden_options\\\\\\\",\\\\\\\"type\\\\\\\"];class kl{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;if(null!=e){const t=this._node.io.inputs.minCount();this._node.io.inputs.setCount(t,e)}this.setInputs(t.inputs)}process_ui_data(t,e){if(!e)return;if(li.playerMode())return;const n=this._node.uiData,i=e.pos;if(i){const t=(new d.a).fromArray(i);n.setPosition(t)}const s=e.comment;s&&n.setComment(s),this._node.childrenAllowed()&&this.process_nodes_ui_data(t,e.nodes)}create_nodes(t,e){if(!e)return;new Bl(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,s,r,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===(s=null===(i=this._node.flags)||void 0===i?void 0:i.display)||void 0===s||s.set(l));const c=a.optimize;null!=c&&(null===(o=null===(r=this._node.flags)||void 0===r?void 0:r.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,s=n.toAdd&&n.toAdd.length>0;if(i||s){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 zl)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 Rl{add_main(t){}}const Gl=/\\\\\\\\n+/g;class Vl extends Rl{add_main(t){let e=t.raw_input;void 0!==e&&(e=e.replace(Gl,\\\\\\\"\\\\n\\\\\\\"),this._param.set(e))}}class Hl extends Rl{add_main(t){const e=t.raw_input;e&&this._param.set(e)}}class jl extends kl{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 kl(t)}static dispatch_param(t){return t instanceof ro?new Ul(t):t instanceof wo?new Vl(t):t instanceof bo?new Hl(t):new Rl(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 Pl;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 s=e.realtimeState;null!=s&&t.timeController.setRealtimeState(s),t.setFrame(e.frame||El.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),s=[];for(let t of i){const i=`${n}/root/${t}.json?t=${e.nodes[t]}`;s.push(i)}const r=[`${n}/root.json?t=${e.root}`,`${n}/properties.json?t=${e.properties}`];if(t.editorMode){const t=Date.now();r.push(`${n}/ui.json?t=${t}`)}for(let t of s)r.push(t);let o=0;const a=r.length,l=r.map((async e=>{const n=await fetch(e);return t.onProgress&&(o++,t.onProgress({count:o,total:a})),n})),c=await Promise.all(l),h=[];for(let t of c)h.push(await t.json());const u={root:h[0],properties:h[1]};let d=2;t.editorMode&&(u.ui=h[2],d+=1);const p={},_=Object.keys(e.nodes);for(let t=0;t<_.length;t++){const e=_[t],n=h[t+d];p[e]=n}return this.assemble(u,_,p)}static async assemble(t,e,n){const i={root:t.root,properties:t.properties,ui:t.ui};for(let t=0;t{r.matrixAutoUpdate=!0},c.onComplete=()=>{r.matrixAutoUpdate=t,r.matrixAutoUpdate||r.updateMatrix()}}if(o instanceof ah.a&&this._target_value instanceof ah.a){const t={value:0},e=o,n=(new ah.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 s=[];return m.isNumber(n)?(i=t,s.push(e)):(i=n,this._target_value instanceof d.a&&s.push(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"),this._target_value instanceof p.a&&s.push(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"),this._target_value instanceof _.a&&s.push(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"),this._target_value,ah.a),{target_property:n,to_target:i,property_names:s}}{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):li.warn(`${this._property_name} not found on node ${t.path()}`)}_populateVarsForParam(t,e,n){switch(this._printDebug([\\\\\\\"_populateVarsForParam\\\\\\\",t]),t.type()){case Er.INTEGER:return this._populateVarsForParamInteger(t,e,n);case Er.FLOAT:return this._populateVarsForParamFloat(t,e,n);case Er.VECTOR2:return this._populateVarsForParamVector2(t,e,n);case Er.VECTOR3:return this._populateVarsForParamVector3(t,e,n);case Er.VECTOR4:return this._populateVarsForParamVector4(t,e,n)}this._printDebug(`param type cannot be animated (yet): '${t.type()}' '${t.path()}'`)}_populateVarsForParamInteger(t,e,n){if(!m.isNumber(this._target_value))return void li.warn(\\\\\\\"value is not a numbber\\\\\\\",this._target_value);const i=this._commonVars(e),s={num:t.value};i.onUpdate=()=>{t.set(s.num)};const r=e.operation();i.num=this.withOp(t.value,this._target_value,r),this._startTimeline(e,n,i,s)}_populateVarsForParamFloat(t,e,n){if(!m.isNumber(this._target_value))return void li.warn(\\\\\\\"value is not a numbber\\\\\\\",this._target_value);const i=this._commonVars(e),s={num:t.value};i.onUpdate=()=>{t.set(s.num)};const r=e.operation();i.num=this.withOp(t.value,this._target_value,r),this._startTimeline(e,n,i,s)}_populateVarsForParamVector2(t,e,n){if(!(this._target_value instanceof d.a))return;const i=this._commonVars(e),s=t.value.clone(),r=[0,0];i.onUpdate=()=>{s.toArray(r),t.set(r)};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,s)}_populateVarsForParamVector3(t,e,n){if(!(this._target_value instanceof p.a))return;const i=this._commonVars(e),s=t.value.clone(),r=[0,0,0];i.onUpdate=()=>{s.toArray(r),t.set(r)};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,s)}_populateVarsForParamVector4(t,e,n){if(!(this._target_value instanceof _.a))return;const i=this._commonVars(e),s=t.value.clone(),r=[0,0,0,0];i.onUpdate=()=>{s.toArray(r),t.set(r)};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,s)}withOp(t,e,n){switch(n){case N_.SET:return e;case N_.ADD:return t+e;case N_.SUBSTRACT:return t-e}ls.unreachable(n)}_commonVars(t){const e={duration:t.duration()},n=t.easing()||oh.NONE;n&&(e.ease=n);const i=t.delay();null!=i&&(e.delay=i);const s=t.repeatParams();return s&&(e.repeat=s.count,e.repeatDelay=s.delay,e.yoyo=s.yoyo),e}_startTimeline(t,e,n,i){const s=t.position(),r=s?s.toParameter():void 0;e.to(i,n,r)}}function dh(t){if(void 0===t)throw new ReferenceError(\\\\\\\"this hasn't been initialised - super() hasn't been called\\\\\\\");return t}function ph(t,e){t.prototype=Object.create(e.prototype),t.prototype.constructor=t,t.__proto__=e}var _h,mh,fh,gh,vh,yh,xh,bh,wh,Th,Ah,Mh,Eh,Sh={autoSleep:120,force3D:\\\\\\\"auto\\\\\\\",nullTargetWarn:1,units:{lineHeight:\\\\\\\"\\\\\\\"}},Ch={duration:.5,overwrite:!1,delay:0},Nh=1e8,Lh=1e-8,Oh=2*Math.PI,Ph=Oh/4,Rh=0,Ih=Math.sqrt,Fh=Math.cos,Dh=Math.sin,Bh=function(t){return\\\\\\\"string\\\\\\\"==typeof t},zh=function(t){return\\\\\\\"function\\\\\\\"==typeof t},kh=function(t){return\\\\\\\"number\\\\\\\"==typeof t},Uh=function(t){return void 0===t},Gh=function(t){return\\\\\\\"object\\\\\\\"==typeof t},Vh=function(t){return!1!==t},Hh=function(){return\\\\\\\"undefined\\\\\\\"!=typeof window},jh=function(t){return zh(t)||Bh(t)},Wh=\\\\\\\"function\\\\\\\"==typeof ArrayBuffer&&ArrayBuffer.isView||function(){},qh=Array.isArray,Xh=/(?:-?\\\\.?\\\\d|\\\\.)+/gi,Yh=/[-+=.]*\\\\d+[.e\\\\-+]*\\\\d*[e\\\\-+]*\\\\d*/g,$h=/[-+=.]*\\\\d+[.e-]*\\\\d*[a-z%]*/g,Jh=/[-+=.]*\\\\d+\\\\.?\\\\d*(?:e-|e\\\\+)?\\\\d*/gi,Zh=/[+-]=-?[.\\\\d]+/,Qh=/[#\\\\-+.]*\\\\b[a-z\\\\d-=+%.]+/gi,Kh=/[\\\\d.+\\\\-=]+(?:e[-+]\\\\d*)*/i,tu={},eu={},nu=function(t){return(eu=Cu(t,tu))&&pp},iu=function(t,e){return console.warn(\\\\\\\"Invalid property\\\\\\\",t,\\\\\\\"set to\\\\\\\",e,\\\\\\\"Missing plugin? gsap.registerPlugin()\\\\\\\")},su=function(t,e){return!e&&console.warn(t)},ru=function(t,e){return t&&(tu[t]=e)&&eu&&(eu[t]=e)||tu},ou=function(){return 0},au={},lu=[],cu={},hu={},uu={},du=30,pu=[],_u=\\\\\\\"\\\\\\\",mu=function(t){var e,n,i=t[0];if(Gh(i)||zh(i)||(t=[t]),!(e=(i._gsap||{}).harness)){for(n=pu.length;n--&&!pu[n].targetTest(i););e=pu[n]}for(n=t.length;n--;)t[n]&&(t[n]._gsap||(t[n]._gsap=new zd(t[n],e)))||t.splice(n,1);return t},fu=function(t){return t._gsap||mu(id(t))[0]._gsap},gu=function(t,e,n){return(n=t[e])&&zh(n)?t[e]():Uh(n)&&t.getAttribute&&t.getAttribute(e)||n},vu=function(t,e){return(t=t.split(\\\\\\\",\\\\\\\")).forEach(e)||t},yu=function(t){return Math.round(1e5*t)/1e5||0},xu=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},Fu=function(t){for(var e=t.parent;e&&e.parent;)e._dirty=1,e.totalDuration(),e=e.parent;return t},Du=function t(e){return!e||e._ts&&t(e.parent)},Bu=function(t){return t._repeat?zu(t._tTime,t=t.duration()+t._rDelay)*t:0},zu=function(t,e){var n=Math.floor(t/=e);return t&&n===t?n-1:n},ku=function(t,e){return(t-e._start)*e._ts+(e._ts>=0?0:e._dirty?e.totalDuration():e._tDur)},Uu=function(t){return t._end=yu(t._start+(t._tDur/Math.abs(t._ts||t._rts||Lh)||0))},Gu=function(t,e){var n=t._dp;return n&&n.smoothChildTiming&&t._ts&&(t._start=yu(n._time-(t._ts>0?e/t._ts:((t._dirty?t.totalDuration():t._tDur)-e)/-t._ts)),Uu(t),n._dirty||Iu(n,t)),t},Vu=function(t,e){var n;if((e._time||e._initted&&!e._dur)&&(n=ku(t.rawTime(),e),(!e._dur||Qu(0,e.totalDuration(),n)-e._tTime>Lh)&&e.render(n,!0)),Iu(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}},Hu=function(t,e,n,i){return e.parent&&Ru(e),e._start=yu(n+e._delay),e._end=yu(e._start+(e.totalDuration()/Math.abs(e.timeScale())||0)),function(t,e,n,i,s){void 0===n&&(n=\\\\\\\"_first\\\\\\\"),void 0===i&&(i=\\\\\\\"_last\\\\\\\");var r,o=t[i];if(s)for(r=e[s];o&&o[s]>r;)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||Vu(t,e),t},ju=function(t,e){return(tu.ScrollTrigger||iu(\\\\\\\"scrollTrigger\\\\\\\",e))&&tu.ScrollTrigger.create(e,t)},Wu=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)&&xh!==Md.frame?(lu.push(t),t._lazy=[e,i],1):void 0:1},qu=function t(e){var n=e.parent;return n&&n._ts&&n._initted&&!n._lock&&(n.rawTime()<0||t(n))},Xu=function(t,e,n,i){var s=t._repeat,r=yu(e)||0,o=t._tTime/t._tDur;return o&&!i&&(t._time*=r/t._dur),t._dur=r,t._tDur=s?s<0?1e10:yu(r*(s+1)+t._rDelay*s):r,o&&!i?Gu(t,t._tTime=t._tDur*o):t.parent&&Uu(t),n||Iu(t.parent,t),t},Yu=function(t){return t instanceof Ud?Iu(t):Xu(t,t._dur)},$u={_start:0,endTime:ou},Ju=function t(e,n){var i,s,r=e.labels,o=e._recent||$u,a=e.duration()>=Nh?o.endTime(!1):e._dur;return Bh(n)&&(isNaN(n)||n in r)?\\\\\\\"<\\\\\\\"===(i=n.charAt(0))||\\\\\\\">\\\\\\\"===i?(\\\\\\\"<\\\\\\\"===i?o._start:o.endTime(o._repeat>=0))+(parseFloat(n.substr(1))||0):(i=n.indexOf(\\\\\\\"=\\\\\\\"))<0?(n in r||(r[n]=a),r[n]):(s=+(n.charAt(i-1)+n.substr(i+1)),i>1?t(e,n.substr(0,i-1))+s:a+s):null==n?a:+n},Zu=function(t,e){return t||0===t?e(t):e},Qu=function(t,e,n){return ne?e:n},Ku=function(t){if(\\\\\\\"string\\\\\\\"!=typeof t)return\\\\\\\"\\\\\\\";var e=Kh.exec(t);return e?t.substr(e.index+e[0].length):\\\\\\\"\\\\\\\"},td=[].slice,ed=function(t,e){return t&&Gh(t)&&\\\\\\\"length\\\\\\\"in t&&(!e&&!t.length||t.length-1 in t&&Gh(t[0]))&&!t.nodeType&&t!==fh},nd=function(t,e,n){return void 0===n&&(n=[]),t.forEach((function(t){var i;return Bh(t)&&!e||ed(t,1)?(i=n).push.apply(i,id(t)):n.push(t)}))||n},id=function(t,e){return!Bh(t)||e||!gh&&Ed()?qh(t)?nd(t,e):ed(t)?td.call(t,0):t?[t]:[]:td.call(vh.querySelectorAll(t),0)},sd=function(t){return t.sort((function(){return.5-Math.random()}))},rd=function(t){if(zh(t))return t;var e=Gh(t)?t:{each:t},n=Rd(e.ease),i=e.from||0,s=parseFloat(e.base)||0,r={},o=i>0&&i<1,a=isNaN(i)||o,l=e.axis,c=i,h=i;return Bh(i)?c=h={center:.5,edges:.5,end:1}[i]||0:!o&&a&&(c=i[0],h=i[1]),function(t,o,u){var d,p,_,m,f,g,v,y,x,b=(u||e).length,w=r[b];if(!w){if(!(x=\\\\\\\"auto\\\\\\\"===e.grid?0:(e.grid||[1,Nh])[1])){for(v=-Nh;v<(v=u[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?s-b:s,w.u=Ku(e.amount||e.each)||0,n=n&&b<0?Od(n):n}return b=(w[t]-w.min)/w.max||0,yu(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+(kh(n)?0:Ku(n))}},ad=function(t,e){var n,i,s=qh(t);return!s&&Gh(t)&&(n=s=t.radius||Nh,t.values?(t=id(t.values),(i=!kh(t[0]))&&(n*=n)):t=od(t.increment)),Zu(e,s?zh(t)?function(e){return i=t(e),Math.abs(i-e)<=n?i:e}:function(e){for(var s,r,o=parseFloat(i?e.x:e),a=parseFloat(i?e.y:0),l=Nh,c=0,h=t.length;h--;)(s=i?(s=t[h].x-o)*s+(r=t[h].y-a)*r:Math.abs(t[h]-o))(s=Math.abs(s))&&(r=i,a=s);return r},pd=function(t,e,n){var i,s,r=t.vars,o=r[e];if(o)return i=r[e+\\\\\\\"Params\\\\\\\"],s=r.callbackScope||t,n&&lu.length&&wu(),i?o.apply(s,i):o.call(s)},_d=function(t){return Ru(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=zh(t),i=e&&!n&&t.init?function(){this._props=[]}:t,s={init:ou,render:sp,add:Hd,kill:op,modifier:rp,rawVars:0},r={targetTest:0,get:0,getSetter:tp,aliases:{},register:0};if(Ed(),t!==i){if(hu[e])return;Eu(i,Eu(Lu(t,s),r)),Cu(i.prototype,Cu(s,Lu(t,r))),hu[i.prop=e]=i,t.targetTest&&(pu.push(i),au[e]=1),e=(\\\\\\\"css\\\\\\\"===e?\\\\\\\"CSS\\\\\\\":e.charAt(0).toUpperCase()+e.substr(1))+\\\\\\\"Plugin\\\\\\\"}ru(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,s,r,o,a,l,c,h,u,d,p=t?kh(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),s=t.charAt(2),r=t.charAt(3),t=\\\\\\\"#\\\\\\\"+i+i+s+s+r+r+(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(Xh),e){if(~t.indexOf(\\\\\\\"=\\\\\\\"))return p=t.match(Yh),n&&p.length<4&&(p[3]=1),p}else o=+p[0]%360/360,a=+p[1]/100,i=2*(l=+p[2]/100)-(s=l<=.5?l*(a+1):l+a-l*a),p.length>3&&(p[3]*=1),p[0]=vd(o+1/3,i,s),p[1]=vd(o,i,s),p[2]=vd(o-1/3,i,s);else p=t.match(Xh)||gd.transparent;p=p.map(Number)}return e&&!d&&(i=p[0]/fd,s=p[1]/fd,r=p[2]/fd,l=((c=Math.max(i,s,r))+(h=Math.min(i,s,r)))/2,c===h?o=a=0:(u=c-h,a=l>.5?u/(2-c-h):u/(c+h),o=c===i?(s-r)/u+(sa&&(c+=y-l),((m=(g=(h+=y)-c)-d)>0||x)&&(v=++i.frame,s=g-1e3*i.time,i.time=g/=1e3,d+=m+(m>=u?4:u-m),f=1),x||(t=e(n)),f)for(r=0;r=e&&r--},_listeners:p}}(),Ed=function(){return!wh&&Md.wake()},Sd={},Cd=/^[\\\\d.\\\\-M][\\\\d.\\\\-,\\\\s]/,Nd=/[\\\\\\\"']/g,Ld=function(t){for(var e,n,i,s={},r=t.substr(1,t.length-3).split(\\\\\\\":\\\\\\\"),o=r[0],a=1,l=r.length;a1&&o.config?o.config.apply(null,~t.indexOf(\\\\\\\"{\\\\\\\")?[Ld(r[1])]:(e=t,n=e.indexOf(\\\\\\\"(\\\\\\\")+1,i=e.indexOf(\\\\\\\")\\\\\\\"),s=e.indexOf(\\\\\\\"(\\\\\\\",n),e.substring(n,~s&&s=1?n:1,r=(i||(e?.3:.45))/(n<1?n:1),o=r/Oh*(Math.asin(1/s)||0),a=function(t){return 1===t?1:s*Math.pow(2,-10*t)*Dh((t-o)*r)+1},l=\\\\\\\"out\\\\\\\"===e?a:\\\\\\\"in\\\\\\\"===e?function(t){return 1-a(1-t)}:Fd(a);return r=Oh/r,l.config=function(n,i){return t(e,n,i)},l},Bd=function t(e,n){void 0===n&&(n=1.70158);var i=function(t){return t?--t*t*((n+1)*t+n)+1:0},s=\\\\\\\"out\\\\\\\"===e?i:\\\\\\\"in\\\\\\\"===e?function(t){return 1-i(1-t)}:Fd(i);return s.config=function(n){return t(e,n)},s};vu(\\\\\\\"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()),Th=7.5625,Mh=1/(Ah=2.75),Id(\\\\\\\"Bounce\\\\\\\",(function(t){return 1-Eh(1-t)}),Eh=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,Xu(this,this._repeat<0?t:(t-this._repeat*this._rDelay)/(this._repeat+1))):this._tDur},e.totalTime=function(t,e){if(Ed(),!arguments.length)return this._tTime;var n=this._dp;if(n&&n.smoothChildTiming&&this._ts){for(Gu(this,t),!n._dp||n.parent||Vu(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)&&Hu(this._dp,this,this._start-this._delay)}return(this._tTime!==t||!this._dur&&!e||this._initted&&Math.abs(this._zTime)===Lh||!t&&!this._initted&&(this.add||this._ptLookup))&&(this._ts||(this._pTime=t),Tu(this,t,e)),this},e.time=function(t,e){return arguments.length?this.totalTime(Math.min(this.totalDuration(),t+Bu(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)+Bu(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?zu(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?ku(this.parent._time,this):this._tTime;return this._rts=+t||0,this._ts=this._ps||-1e-8===t?0:this._rts,Fu(this.totalTime(Qu(-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):(Ed(),this._ts=this._rts,this.totalTime(this.parent&&!this.parent.smoothChildTiming?this.rawTime():this._tTime||this._pTime,1===this.progress()&&(this._tTime-=Lh)&&Math.abs(this._zTime)!==Lh))),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)&&Hu(e,this,t-this._delay),this}return this._start},e.endTime=function(t){return this._start+(Vh(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?ku(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,Yu(this)):-2===this._repeat?1/0:this._repeat},e.repeatDelay=function(t){return arguments.length?(this._rDelay=t,Yu(this)):this._rDelay},e.yoyo=function(t){return arguments.length?(this._yoyo=t,this):this._yoyo},e.seek=function(t,e){return this.totalTime(Ju(this,t),Vh(e))},e.restart=function(t,e){return this.play().totalTime(t?-this._delay:0,Vh(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=zh(t)?t:Mu,s=function(){var t=e.then;e.then=null,zh(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?s():e._prom=s}))},e.kill=function(){_d(this)},t}();Eu(kd.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=Vh(e.sortChildren),i.parent&&Vu(i.parent,dh(i)),e.scrollTrigger&&ju(dh(i),e.scrollTrigger),i}ph(e,t);var n=e.prototype;return n.to=function(t,e,n){return new $d(t,bu(arguments,0,this),Ju(this,kh(e)?arguments[3]:n)),this},n.from=function(t,e,n){return new $d(t,bu(arguments,1,this),Ju(this,kh(e)?arguments[3]:n)),this},n.fromTo=function(t,e,n,i){return new $d(t,bu(arguments,2,this),Ju(this,kh(e)?arguments[4]:i)),this},n.set=function(t,e,n){return e.duration=0,e.parent=this,Ou(e).repeatDelay||(e.repeat=0),e.immediateRender=!!e.immediateRender,new $d(t,e,Ju(this,n),1),this},n.call=function(t,e,n){return Hu(this,$d.delayedCall(0,t,e),Ju(this,n))},n.staggerTo=function(t,e,n,i,s,r,o){return n.duration=e,n.stagger=n.stagger||i,n.onComplete=r,n.onCompleteParams=o,n.parent=this,new $d(t,n,Ju(this,s)),this},n.staggerFrom=function(t,e,n,i,s,r,o){return n.runBackwards=1,Ou(n).immediateRender=Vh(n.immediateRender),this.staggerTo(t,e,n,i,s,r,o)},n.staggerFromTo=function(t,e,n,i,s,r,o,a){return i.startAt=n,Ou(i).immediateRender=Vh(i.immediateRender),this.staggerTo(t,e,i,s,r,o,a)},n.render=function(t,e,n){var i,s,r,o,a,l,c,h,u,d,p,_,m=this._time,f=this._dirty?this.totalDuration():this._tDur,g=this._dur,v=this!==mh&&t>f-Lh&&t>=0?f:tg&&(i=g)),d=zu(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(s=this._first;s;){if(r=s._next,(s._act||i>=s._start)&&s._ts&&c!==s){if(s.parent!==this)return this.render(t,e,n);if(s.render(s._ts>0?(i-s._start)*s._ts:(s._dirty?s.totalDuration():s._tDur)+(i-s._start)*s._ts,e,n),i!==this._time||!this._ts&&!l){c=0,r&&(v+=this._zTime=-1e-8);break}}s=r}else{s=this._last;for(var w=t<0?t:i;s;){if(r=s._prev,(s._act||w<=s._end)&&s._ts&&c!==s){if(s.parent!==this)return this.render(t,e,n);if(s.render(s._ts>0?(w-s._start)*s._ts:(s._dirty?s.totalDuration():s._tDur)+(w-s._start)*s._ts,e,n),i!==this._time||!this._ts&&!l){c=0,r&&(v+=this._zTime=w?-1e-8:Lh);break}}s=r}}if(c&&!e&&(this.pause(),c.render(i>=m?0:-1e-8)._zTime=i>=m?1:-1,this._ts))return this._start=u,Uu(this),this.render(t,e,n);this._onUpdate&&!e&&pd(this,\\\\\\\"onUpdate\\\\\\\",!0),(v===f&&f>=this.totalDuration()||!v&&m)&&(u!==this._start&&Math.abs(h)===Math.abs(this._ts)||this._lock||((t||!g)&&(v===f&&this._ts>0||!v&&this._ts<0)&&Ru(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(kh(e)||(e=Ju(this,e)),!(t instanceof kd)){if(qh(t))return t.forEach((function(t){return n.add(t,e)})),this;if(Bh(t))return this.addLabel(t,e);if(!zh(t))return this;t=$d.delayedCall(0,t)}return this!==t?Hu(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=-Nh);for(var s=[],r=this._first;r;)r._start>=i&&(r instanceof $d?e&&s.push(r):(n&&s.push(r),t&&s.push.apply(s,r.getChildren(!0,e,n)))),r=r._next;return s},n.getById=function(t){for(var e=this.getChildren(1,1,1),n=e.length;n--;)if(e[n].vars.id===t)return e[n]},n.remove=function(t){return Bh(t)?this.removeLabel(t):zh(t)?this.killTweensOf(t):(Pu(this,t),t===this._recent&&(this._recent=this._last),Iu(this))},n.totalTime=function(e,n){return arguments.length?(this._forcing=1,!this._dp&&this._ts&&(this._start=yu(Md.time-(this._ts>0?e/this._ts:(this.totalDuration()-e)/-this._ts))),t.prototype.totalTime.call(this,e,n),this._forcing=0,this):this._tTime},n.addLabel=function(t,e){return this.labels[t]=Ju(this,e),this},n.removeLabel=function(t){return delete this.labels[t],this},n.addPause=function(t,e,n){var i=$d.delayedCall(0,e||ou,n);return i.data=\\\\\\\"isPause\\\\\\\",this._hasPause=1,Hu(this,i,Ju(this,t))},n.removePause=function(t){var e=this._first;for(t=Ju(this,t);e;)e._start===t&&\\\\\\\"isPause\\\\\\\"===e.data&&Ru(e),e=e._next},n.killTweensOf=function(t,e,n){for(var i=this.getTweensOf(t,n),s=i.length;s--;)Gd!==i[s]&&i[s].kill(t,e);return this},n.getTweensOf=function(t,e){for(var n,i=[],s=id(t),r=this._first,o=kh(e);r;)r instanceof $d?xu(r._targets,s)&&(o?(!Gd||r._initted&&r._ts)&&r.globalTime(0)<=e&&r.globalTime(r.totalDuration())>e:!e||r.isActive())&&i.push(r):(n=r.getTweensOf(s,e)).length&&i.push.apply(i,n),r=r._next;return i},n.tweenTo=function(t,e){e=e||{};var n=this,i=Ju(n,t),s=e,r=s.startAt,o=s.onStart,a=s.onStartParams,l=s.immediateRender,c=$d.to(n,Eu({ease:e.ease||\\\\\\\"none\\\\\\\",lazy:!1,immediateRender:!1,time:i,overwrite:\\\\\\\"auto\\\\\\\",duration:e.duration||Math.abs((i-(r&&\\\\\\\"time\\\\\\\"in r?r.time:n._time))/n.timeScale())||Lh,onStart:function(){n.pause();var t=e.duration||Math.abs((i-n._time)/n.timeScale());c._dur!==t&&Xu(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,Eu({startAt:{time:Ju(this,t)}},n))},n.recent=function(){return this._recent},n.nextLabel=function(t){return void 0===t&&(t=this._time),dd(this,Ju(this,t))},n.previousLabel=function(t){return void 0===t&&(t=this._time),dd(this,Ju(this,t),1)},n.currentLabel=function(t){return arguments.length?this.seek(t,!0):this.previousLabel(this._time+Lh)},n.shiftChildren=function(t,e,n){void 0===n&&(n=0);for(var i,s=this._first,r=this.labels;s;)s._start>=n&&(s._start+=t,s._end+=t),s=s._next;if(e)for(i in r)r[i]>=n&&(r[i]+=t);return Iu(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={}),Iu(this)},n.totalDuration=function(t){var e,n,i,s=0,r=this,o=r._last,a=Nh;if(arguments.length)return r.timeScale((r._repeat<0?r.duration():r.totalDuration())/(r.reversed()?-t:t));if(r._dirty){for(i=r.parent;o;)e=o._prev,o._dirty&&o.totalDuration(),(n=o._start)>a&&r._sort&&o._ts&&!r._lock?(r._lock=1,Hu(r,o,n-o._delay,1)._lock=0):a=n,n<0&&o._ts&&(s-=n,(!i&&!r._dp||i&&i.smoothChildTiming)&&(r._start+=n/r._ts,r._time-=n,r._tTime-=n),r.shiftChildren(-n,!1,-Infinity),a=0),o._end>s&&o._ts&&(s=o._end),o=e;Xu(r,r===mh&&r._time>s?r._time:s,1,1),r._dirty=0}return r._tDur},e.updateRoot=function(t){if(mh._ts&&(Tu(mh,ku(t,mh)),xh=Md.frame),Md.frame>=du){du+=Sh.autoSleep||120;var e=mh._first;if((!e||!e._ts)&&Sh.autoSleep&&Md._listeners.length<2){for(;e&&!e._ts;)e=e._next;e||Md.sleep()}}},e}(kd);Eu(Ud.prototype,{_lock:0,_hasPause:0,_forcing:0});var Gd,Vd=function(t,e,n,i,s,r,o){var a,l,c,h,u,d,p,_,m=new cp(this._pt,t,e,0,1,ip,null,s),f=0,g=0;for(m.b=n,m.e=i,n+=\\\\\\\"\\\\\\\",(p=~(i+=\\\\\\\"\\\\\\\").indexOf(\\\\\\\"random(\\\\\\\"))&&(i=hd(i)),r&&(r(_=[n,i],t,e),n=_[0],i=_[1]),l=n.match(Jh)||[];a=Jh.exec(i);)h=a[0],u=i.substring(f,a.index),c?c=(c+1)%5:\\\\\\\"rgba(\\\\\\\"===u.substr(-5)&&(c=1),h!==l[g++]&&(d=parseFloat(l[g-1])||0,m._pt={_next:m._pt,p:u||1===g?u:\\\\\\\",\\\\\\\",s:d,c:\\\\\\\"=\\\\\\\"===h.charAt(1)?parseFloat(h.substr(2))*(\\\\\\\"-\\\\\\\"===h.charAt(0)?-1:1):parseFloat(h)-d,m:c&&c<4?Math.round:0},f=Jh.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),r=Eu({overwrite:!1,data:\\\\\\\"isFromStart\\\\\\\",lazy:y&&Vh(x),immediateRender:y,stagger:0,parent:O},i),_&&(r[h.prop]=_),Ru(e._startAt=$d.set(L,r)),y){if(!n)return}else t(e._startAt,Lh);for(e._pt=0,x=C&&Vh(x)||x&&!C,s=0;s\\\\\\\")}));else{if(c=M.length,d=v?rd(v):ou,Gh(v))for(h in v)~Xd.indexOf(h)&&(p||(p={}),p[h]=v[h]);for(a=0;ap-Lh&&t>=0?p:t_&&(i=_)),(l=this._yoyo&&1&r)&&(u=this._yEase,i=_-i),a=zu(this._tTime,o),i===d&&!n&&this._initted)return this;r!==a&&(h&&this._yEase&&Pd(h,l),!this.vars.repeatRefresh||l||this._lock||(this._lock=n=1,this.render(yu(o*r),!0).invalidate()._lock=0))}if(!this._initted){if(Wu(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=(u||this._ease)(i/_),this._from&&(this.ratio=c=1-c),i&&!d&&!e&&pd(this,\\\\\\\"onStart\\\\\\\"),s=this._pt;s;)s.r(c,s.d),s=s._next;h&&h.render(t<0?t:!i&&l?-1e-8:h._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&&r!==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)&&Ru(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 s,r,o,a=t.ratio,l=e<0||!e&&(!t._start&&qu(t)||(t._ts<0||t._dp._ts<0)&&\\\\\\\"isFromStart\\\\\\\"!==t.data&&\\\\\\\"isStart\\\\\\\"!==t.data)?0:1,c=t._rDelay,h=0;if(c&&t._repeat&&(h=Qu(0,t._tDur,e),r=zu(h,c),o=zu(t._tTime,c),t._yoyo&&1&r&&(l=1-l),r!==o&&(a=1-l,t.vars.repeatRefresh&&t._initted&&t.invalidate())),l!==a||i||t._zTime===Lh||!e&&t._zTime){if(!t._initted&&Wu(t,e,i,n))return;for(o=t._zTime,t._zTime=e||(n?Lh:0),n||(n=e&&!o),t.ratio=l,t._from&&(l=1-l),t._time=0,t._tTime=h,s=t._pt;s;)s.r(l,s.d),s=s._next;t._startAt&&e<0&&t._startAt.render(e,!0,!0),t._onUpdate&&!n&&pd(t,\\\\\\\"onUpdate\\\\\\\"),h&&t._repeat&&!n&&t.parent&&pd(t,\\\\\\\"onRepeat\\\\\\\"),(e>=t._tDur||e<0)&&t.ratio===l&&(l&&Ru(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()&&Xu(this,this._dur*this.timeline._tDur/n,0,1),this}var i,s,r,o,a,l,c,h=this._targets,u=t?id(t):h,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}(h,u))return\\\\\\\"all\\\\\\\"===e&&(this._pt=0),_d(this);for(i=this._op=this._op||[],\\\\\\\"all\\\\\\\"!==e&&(Bh(e)&&(a={},vu(e,(function(t){return a[t]=1})),e=a),e=function(t,e){var n,i,s,r,o=t[0]?fu(t[0]).harness:0,a=o&&o.aliases;if(!a)return e;for(i in n=Cu({},e),a)if(i in n)for(s=(r=a[i].split(\\\\\\\",\\\\\\\")).length;s--;)n[r[s]]=n[i];return n}(h,e)),c=h.length;c--;)if(~u.indexOf(h[c]))for(a in s=d[c],\\\\\\\"all\\\\\\\"===e?(i[c]=e,o=s,r={}):(r=i[c]=i[c]||{},o=e),o)(l=s&&s[a])&&(\\\\\\\"kill\\\\\\\"in l.d&&!0!==l.d.kill(a)||Pu(this,l,\\\\\\\"_pt\\\\\\\"),delete s[a]),\\\\\\\"all\\\\\\\"!==r&&(r[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,bu(arguments,1))},e.delayedCall=function(t,n,i,s){return new e(n,0,{immediateRender:!1,lazy:!1,overwrite:!1,delay:t,onComplete:n,onReverseComplete:n,onCompleteParams:i,onReverseCompleteParams:i,callbackScope:s})},e.fromTo=function(t,n,i){return new e(t,bu(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 mh.killTweensOf(t,e,n)},e}(kd);Eu($d.prototype,{_targets:[],_lazy:0,_startAt:0,_op:0,_onInit:0}),vu(\\\\\\\"staggerTo,staggerFrom,staggerFromTo\\\\\\\",(function(t){$d[t]=function(){var e=new Ud,n=td.call(arguments,0);return n.splice(\\\\\\\"staggerFromTo\\\\\\\"===t?5:4,0,0),e[t].apply(e,n)}}));var Jd=function(t,e,n){return t[e]=n},Zd=function(t,e,n){return t[e](n)},Qd=function(t,e,n,i){return t[e](i.fp,n)},Kd=function(t,e,n){return t.setAttribute(e,n)},tp=function(t,e){return zh(t[e])?Zd:Uh(t[e])&&t.setAttribute?Kd:Jd},ep=function(t,e){return e.set(e.t,e.p,Math.round(1e4*(e.s+e.c*t))/1e4,e)},np=function(t,e){return e.set(e.t,e.p,!!(e.s+e.c*t),e)},ip=function(t,e){var n=e._pt,i=\\\\\\\"\\\\\\\";if(!t&&e.b)i=e.b;else if(1===t&&e.e)i=e.e;else{for(;n;)i=n.p+(n.m?n.m(n.s+n.c*t):Math.round(1e4*(n.s+n.c*t))/1e4)+i,n=n._next;i+=e.c}e.set(e.t,e.p,i,e)},sp=function(t,e){for(var n=e._pt;n;)n.r(t,n.d),n=n._next},rp=function(t,e,n,i){for(var s,r=this._pt;r;)s=r._next,r.p===i&&r.modifier(t,e,n),r=s},op=function(t){for(var e,n,i=this._pt;i;)n=i._next,i.p===t&&!i.op||i.op===t?Pu(this,i,\\\\\\\"_pt\\\\\\\"):i.dep||(e=1),i=n;return!e},ap=function(t,e,n,i){i.mSet(t,e,i.m.call(i.tween,n,i.mt),i)},lp=function(t){for(var e,n,i,s,r=t._pt;r;){for(e=r._next,n=i;n&&n.pr>r.pr;)n=n._next;(r._prev=n?n._prev:s)?r._prev._next=r:i=r,(r._next=n)?n._prev=r:s=r,r=e}t._pt=i},cp=function(){function t(t,e,n,i,s,r,o,a,l){this.t=e,this.s=i,this.c=s,this.p=n,this.r=r||ep,this.d=o||this,this.set=a||Jd,this.pr=l||0,this._next=t,t&&(t._prev=this)}return t.prototype.modifier=function(t,e,n){this.mSet=this.mSet||this.set,this.set=ap,this.m=t,this.mt=n,this.tween=e},t}();vu(_u+\\\\\\\"parent,duration,ease,delay,overwrite,runBackwards,startAt,yoyo,immediateRender,repeat,repeatDelay,data,paused,reversed,lazy,callbackScope,stringFilter,id,yoyoEase,stagger,inherit,repeatRefresh,keyframes,autoRevert,scrollTrigger\\\\\\\",(function(t){return au[t]=1})),tu.TweenMax=tu.TweenLite=$d,tu.TimelineLite=tu.TimelineMax=Ud,mh=new Ud({sortChildren:!1,defaults:Ch,autoRemoveChildren:!0,id:\\\\\\\"root\\\\\\\",smoothChildTiming:!0}),Sh.stringFilter=Ad;var hp={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)})),s=i.length;return function(t){for(var e=s;e--;)i[e](t)}}t=t[0]||{};var r=hu[e],o=fu(t),a=o.harness&&(o.harness.aliases||{})[e]||e,l=r?function(e){var i=new r;bh._pt=0,i.init(t,n?e+n:e,bh,0,[t]),i.render(1,i),bh._pt&&sp(1,bh)}:o.set(t,a);return r?l:function(e){return l(t,a,n?e+n:e,o,1)}},isTweening:function(t){return mh.getTweensOf(t,!0).length>0},defaults:function(t){return t&&t.ease&&(t.ease=Rd(t.ease,Ch.ease)),Nu(Ch,t||{})},config:function(t){return Nu(Sh,t||{})},registerEffect:function(t){var e=t.name,n=t.effect,i=t.plugins,s=t.defaults,r=t.extendTimeline;(i||\\\\\\\"\\\\\\\").split(\\\\\\\",\\\\\\\").forEach((function(t){return t&&!hu[t]&&!tu[t]&&su(e+\\\\\\\" effect requires \\\\\\\"+t+\\\\\\\" plugin.\\\\\\\")})),uu[e]=function(t,e,i){return n(id(t),Eu(e||{},s),i)},r&&(Ud.prototype[e]=function(t,n,i){return this.add(uu[e](t,Gh(n)?n:(i=n)&&{},this),i)})},registerEase:function(t,e){Sd[t]=Rd(e)},parseEase:function(t,e){return arguments.length?Rd(t,e):Sd},getById:function(t){return mh.getById(t)},exportRoot:function(t,e){void 0===t&&(t={});var n,i,s=new Ud(t);for(s.smoothChildTiming=Vh(t.smoothChildTiming),mh.remove(s),s._dp=0,s._time=s._tTime=mh._time,n=mh._first;n;)i=n._next,!e&&!n._dur&&n instanceof $d&&n.vars.onComplete===n._targets[0]||Hu(s,n,n._start-n._delay),n=i;return Hu(mh,s,0),s},utils:{wrap:function t(e,n,i){var s=n-e;return qh(e)?cd(e,t(0,e.length),n):Zu(i,(function(t){return(s+(t-e)%s)%s+e}))},wrapYoyo:function t(e,n,i){var s=n-e,r=2*s;return qh(e)?cd(e,t(0,e.length-1),n):Zu(i,(function(t){return e+((t=(r+(t-e)%r)%r||0)>s?r-t:t)}))},distribute:rd,random:ld,snap:ad,normalize:function(t,e,n){return ud(t,e,0,1,n)},getUnit:Ku,clamp:function(t,e,n){return Zu(n,(function(n){return Qu(t,e,n)}))},splitColor:yd,toArray:id,mapRange:ud,pipe:function(){for(var t=arguments.length,e=new Array(t),n=0;n=0?Wp[s]:\\\\\\\"\\\\\\\")+t},Xp=function(){\\\\\\\"undefined\\\\\\\"!=typeof window&&window.document&&(_p=window,mp=_p.document,fp=mp.documentElement,vp=Hp(\\\\\\\"div\\\\\\\")||{style:{}},Hp(\\\\\\\"div\\\\\\\"),Gp=qp(Gp),Vp=Gp+\\\\\\\"Origin\\\\\\\",vp.style.cssText=\\\\\\\"border-width:0;line-height:0;position:absolute;padding:0\\\\\\\",xp=!!qp(\\\\\\\"perspective\\\\\\\"),gp=1)},Yp=function t(e){var n,i=Hp(\\\\\\\"svg\\\\\\\",this.ownerSVGElement&&this.ownerSVGElement.getAttribute(\\\\\\\"xmlns\\\\\\\")||\\\\\\\"http://www.w3.org/2000/svg\\\\\\\"),s=this.parentNode,r=this.nextSibling,o=this.style.cssText;if(fp.appendChild(i),i.appendChild(this),this.style.display=\\\\\\\"block\\\\\\\",e)try{n=this.getBBox(),this._gsapBBox=this.getBBox,this.getBBox=t}catch(t){}else this._gsapBBox&&(n=this._gsapBBox());return s&&(r?s.insertBefore(this,r):s.appendChild(this)),fp.removeChild(i),this.style.cssText=o,n},$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(Mp,\\\\\\\"-$1\\\\\\\").toLowerCase())):n.removeAttribute(e)}},Kp=function(t,e,n,i,s,r){var o=new cp(t._pt,e,n,0,1,r?Ip:Rp);return t._pt=o,o.b=i,o.e=s,t._props.push(n),o},t_={deg:1,rad:1,turn:1},e_=function t(e,n,i,s){var r,o,a,l,c=parseFloat(i)||0,h=(i+\\\\\\\"\\\\\\\").trim().substr((c+\\\\\\\"\\\\\\\").length)||\\\\\\\"px\\\\\\\",u=vp.style,d=Ep.test(n),p=\\\\\\\"svg\\\\\\\"===e.tagName.toLowerCase(),_=(p?\\\\\\\"client\\\\\\\":\\\\\\\"offset\\\\\\\")+(d?\\\\\\\"Width\\\\\\\":\\\\\\\"Height\\\\\\\"),m=100,f=\\\\\\\"px\\\\\\\"===s,g=\\\\\\\"%\\\\\\\"===s;return s===h||!c||t_[s]||t_[h]?c:(\\\\\\\"px\\\\\\\"!==h&&!f&&(c=t(e,n,i,\\\\\\\"px\\\\\\\")),l=e.getCTM&&Zp(e),!g&&\\\\\\\"%\\\\\\\"!==h||!bp[n]&&!~n.indexOf(\\\\\\\"adius\\\\\\\")?(u[d?\\\\\\\"width\\\\\\\":\\\\\\\"height\\\\\\\"]=m+(f?h:s),o=~n.indexOf(\\\\\\\"adius\\\\\\\")||\\\\\\\"em\\\\\\\"===s&&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===Md.time?yu(c/a.width*m):((g||\\\\\\\"%\\\\\\\"===h)&&(u.position=jp(e,\\\\\\\"position\\\\\\\")),o===e&&(u.position=\\\\\\\"static\\\\\\\"),o.appendChild(vp),r=vp[_],o.removeChild(vp),u.position=\\\\\\\"absolute\\\\\\\",d&&g&&((a=fu(o)).time=Md.time,a.width=o[_]),yu(f?r*c/m:r&&c?m/r*c:0))):(r=l?e.getBBox()[d?\\\\\\\"width\\\\\\\":\\\\\\\"height\\\\\\\"]:e[_],yu(g?c/r*m:c/100*r)))},n_=function(t,e,n,i){var s;return gp||Xp(),e in Cp&&\\\\\\\"transform\\\\\\\"!==e&&~(e=Cp[e]).indexOf(\\\\\\\",\\\\\\\")&&(e=e.split(\\\\\\\",\\\\\\\")[0]),bp[e]&&\\\\\\\"transform\\\\\\\"!==e?(s=p_(t,i),s=\\\\\\\"transformOrigin\\\\\\\"!==e?s[e]:__(jp(t,Vp))+\\\\\\\" \\\\\\\"+s.zOrigin+\\\\\\\"px\\\\\\\"):(!(s=t.style[e])||\\\\\\\"auto\\\\\\\"===s||i||~(s+\\\\\\\"\\\\\\\").indexOf(\\\\\\\"calc(\\\\\\\"))&&(s=o_[e]&&o_[e](t,e,n)||jp(t,e)||gu(t,e)||(\\\\\\\"opacity\\\\\\\"===e?1:0)),n&&!~(s+\\\\\\\"\\\\\\\").trim().indexOf(\\\\\\\" \\\\\\\")?e_(t,e,s,n)+n:s},i_=function(t,e,n,i){if(!n||\\\\\\\"none\\\\\\\"===n){var s=qp(e,t,1),r=s&&jp(t,s,1);r&&r!==n?(e=s,n=r):\\\\\\\"borderColor\\\\\\\"===e&&(n=jp(t,\\\\\\\"borderTopColor\\\\\\\"))}var o,a,l,c,h,u,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($h)||[],(i.match($h)||[]).length){for(;a=$h.exec(i);)d=a[0],_=i.substring(y,a.index),h?h=(h+1)%5:\\\\\\\"rgba(\\\\\\\"!==_.substr(-5)&&\\\\\\\"hsla(\\\\\\\"!==_.substr(-5)||(h=1),d!==(u=l[x++]||\\\\\\\"\\\\\\\")&&(c=parseFloat(u)||0,f=u.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=$h.lastIndex-m.length,m||(m=m||Sh.units[e]||f,y===i.length&&(i+=m,v.e+=m)),f!==m&&(c=e_(t,e,u,m)||0),v._pt={_next:v._pt,p:_||1===x?_:\\\\\\\",\\\\\\\",s:c,c:g?g*p:p-c,m:h&&h<4||\\\\\\\"zIndex\\\\\\\"===e?Math.round:0});v.c=y-1;)n=a[s],bp[n]&&(i=1,n=\\\\\\\"transformOrigin\\\\\\\"===n?Vp:Gp),Qp(r,n);i&&(Qp(r,Gp),l&&(l.svg&&r.removeAttribute(\\\\\\\"transform\\\\\\\"),p_(r,1),l.uncache=1))}},o_={clearProps:function(t,e,n,i,s){if(\\\\\\\"isFromStart\\\\\\\"!==s.data){var r=t._pt=new cp(t._pt,e,n,0,0,r_);return r.u=i,r.pr=-10,r.tween=s,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},h_=function(t){var e=jp(t,Gp);return c_(e)?a_:e.substr(7).match(Yh).map(yu)},u_=function(t,e){var n,i,s,r,o=t._gsap||fu(t),a=t.style,l=h_(t);return o.svg&&t.getAttribute(\\\\\\\"transform\\\\\\\")?\\\\\\\"1,0,0,1,0,0\\\\\\\"===(l=[(s=t.transform.baseVal.consolidate().matrix).a,s.b,s.c,s.d,s.e,s.f]).join(\\\\\\\",\\\\\\\")?a_:l:(l!==a_||t.offsetParent||t===fp||o.svg||(s=a.display,a.display=\\\\\\\"block\\\\\\\",(n=t.parentNode)&&t.offsetParent||(r=1,i=t.nextSibling,fp.appendChild(t)),l=h_(t),s?a.display=s:Qp(t,\\\\\\\"display\\\\\\\"),r&&(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,s,r){var o,a,l,c=t._gsap,h=s||u_(t,!0),u=c.xOrigin||0,d=c.yOrigin||0,p=c.xOffset||0,_=c.yOffset||0,m=h[0],f=h[1],g=h[2],v=h[3],y=h[4],x=h[5],b=e.split(\\\\\\\" \\\\\\\"),w=parseFloat(b[0])||0,T=parseFloat(b[1])||0;n?h!==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-u,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\\\\\\\",r&&(Kp(r,c,\\\\\\\"xOrigin\\\\\\\",u,w),Kp(r,c,\\\\\\\"yOrigin\\\\\\\",d,T),Kp(r,c,\\\\\\\"xOffset\\\\\\\",p,c.xOffset),Kp(r,c,\\\\\\\"yOffset\\\\\\\",_,c.yOffset)),t.setAttribute(\\\\\\\"data-svg-origin\\\\\\\",w+\\\\\\\" \\\\\\\"+T)},p_=function(t,e){var n=t._gsap||new zd(t);if(\\\\\\\"x\\\\\\\"in n&&!e&&!n.uncache)return n;var i,s,r,o,a,l,c,h,u,d,p,_,m,f,g,v,y,x,b,w,T,A,M,E,S,C,N,L,O,P,R,I,F=t.style,D=n.scaleX<0,B=\\\\\\\"px\\\\\\\",z=\\\\\\\"deg\\\\\\\",k=jp(t,Vp)||\\\\\\\"0\\\\\\\";return i=s=r=l=c=h=u=d=p=0,o=a=1,n.svg=!(!t.getCTM||!Zp(t)),f=u_(t,n.svg),n.svg&&(E=!n.uncache&&!e&&t.getAttribute(\\\\\\\"data-svg-origin\\\\\\\"),d_(t,E||k,!!E||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],s=M=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,(u=w||T?Ap(w,T)*wp+l:0)&&(a*=Math.abs(Math.cos(u*Tp))),n.svg&&(i-=_-(_*x+m*w),s-=m-(_*b+m*T))):(I=f[6],P=f[7],N=f[8],L=f[9],O=f[10],R=f[11],i=f[12],s=f[13],r=f[14],c=(g=Ap(I,O))*wp,g&&(E=A*(v=Math.cos(-g))+N*(y=Math.sin(-g)),S=M*v+L*y,C=I*v+O*y,N=A*-y+N*v,L=M*-y+L*v,O=I*-y+O*v,R=P*-y+R*v,A=E,M=S,I=C),h=(g=Ap(-w,O))*wp,g&&(v=Math.cos(-g),R=T*(y=Math.sin(-g))+R*v,x=E=x*v-N*y,b=S=b*v-L*y,w=C=w*v-O*y),l=(g=Ap(b,x))*wp,g&&(E=x*(v=Math.cos(g))+b*(y=Math.sin(g)),S=A*v+M*y,b=b*v-x*y,M=M*v-A*y,x=E,A=S),c&&Math.abs(c)+Math.abs(l)>359.9&&(c=l=0,h=180-h),o=yu(Math.sqrt(x*x+b*b+w*w)),a=yu(Math.sqrt(M*M+I*I)),g=Ap(A,M),u=Math.abs(g)>2e-4?g*wp:0,p=R?1/(R<0?-R:R):0),n.svg&&(E=t.getAttribute(\\\\\\\"transform\\\\\\\"),n.forceCSS=t.setAttribute(\\\\\\\"transform\\\\\\\",\\\\\\\"\\\\\\\")||!c_(jp(t,Gp)),E&&t.setAttribute(\\\\\\\"transform\\\\\\\",E))),Math.abs(u)>90&&Math.abs(u)<270&&(D?(o*=-1,u+=l<=0?180:-180,l+=l<=0?180:-180):(a*=-1,u+=u<=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)+B,n.y=s-((n.yPercent=s&&(n.yPercent||(Math.round(t.offsetHeight/2)===Math.round(-s)?-50:0)))?t.offsetHeight*n.yPercent/100:0)+B,n.z=r+B,n.scaleX=yu(o),n.scaleY=yu(a),n.rotation=yu(l)+z,n.rotationX=yu(c)+z,n.rotationY=yu(h)+z,n.skewX=u+z,n.skewY=d+z,n.transformPerspective=p+B,(n.zOrigin=parseFloat(k.split(\\\\\\\" \\\\\\\")[2])||0)&&(F[Vp]=__(k)),n.xOffset=n.yOffset=0,n.force3D=Sh.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=Ku(e);return yu(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,s=n.yPercent,r=n.x,o=n.y,a=n.z,l=n.rotation,c=n.rotationY,h=n.rotationX,u=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&&(h!==g_||c!==g_)){var b,w=parseFloat(c)*Tp,T=Math.sin(w),A=Math.cos(w);w=parseFloat(h)*Tp,b=Math.cos(w),r=m_(g,r,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||s)&&(y+=\\\\\\\"translate(\\\\\\\"+i+\\\\\\\"%, \\\\\\\"+s+\\\\\\\"%) \\\\\\\"),(x||r!==v_||o!==v_||a!==v_)&&(y+=a!==v_||x?\\\\\\\"translate3d(\\\\\\\"+r+\\\\\\\", \\\\\\\"+o+\\\\\\\", \\\\\\\"+a+\\\\\\\") \\\\\\\":\\\\\\\"translate(\\\\\\\"+r+\\\\\\\", \\\\\\\"+o+y_),l!==g_&&(y+=\\\\\\\"rotate(\\\\\\\"+l+y_),c!==g_&&(y+=\\\\\\\"rotateY(\\\\\\\"+c+y_),h!==g_&&(y+=\\\\\\\"rotateX(\\\\\\\"+h+y_),u===g_&&d===g_||(y+=\\\\\\\"skew(\\\\\\\"+u+\\\\\\\", \\\\\\\"+d+y_),1===p&&1===_||(y+=\\\\\\\"scale(\\\\\\\"+p+\\\\\\\", \\\\\\\"+_+y_),g.style[Gp]=y||\\\\\\\"translate(0, 0)\\\\\\\"},b_=function(t,e){var n,i,s,r,o,a=e||this,l=a.xPercent,c=a.yPercent,h=a.x,u=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(h),A=parseFloat(u);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,s=Math.sin(d-p)*-f,r=Math.cos(d-p)*f,p&&(_*=Tp,o=Math.tan(p-_),s*=o=Math.sqrt(1+o*o),r*=o,_&&(o=Math.tan(_),n*=o=Math.sqrt(1+o*o),i*=o)),n=yu(n),i=yu(i),s=yu(s),r=yu(r)):(n=m,r=f,i=s=0),(T&&!~(h+\\\\\\\"\\\\\\\").indexOf(\\\\\\\"px\\\\\\\")||A&&!~(u+\\\\\\\"\\\\\\\").indexOf(\\\\\\\"px\\\\\\\"))&&(T=e_(g,\\\\\\\"x\\\\\\\",h,\\\\\\\"px\\\\\\\"),A=e_(g,\\\\\\\"y\\\\\\\",u,\\\\\\\"px\\\\\\\")),(v||y||x||b)&&(T=yu(T+v-(v*n+y*s)+x),A=yu(A+y-(v*i+y*r)+b)),(l||c)&&(o=g.getBBox(),T=yu(T+l/100*o.width),A=yu(A+c/100*o.height)),o=\\\\\\\"matrix(\\\\\\\"+n+\\\\\\\",\\\\\\\"+i+\\\\\\\",\\\\\\\"+s+\\\\\\\",\\\\\\\"+r+\\\\\\\",\\\\\\\"+T+\\\\\\\",\\\\\\\"+A+\\\\\\\")\\\\\\\",g.setAttribute(\\\\\\\"transform\\\\\\\",o),w&&(g.style[Gp]=o)},w_=function(t,e,n,i,s,r){var o,a,l=360,c=Bh(s),h=parseFloat(s)*(c&&~s.indexOf(\\\\\\\"rad\\\\\\\")?wp:1),u=r?h*r:h-i,d=i+u+\\\\\\\"deg\\\\\\\";return c&&(\\\\\\\"short\\\\\\\"===(o=s.split(\\\\\\\"_\\\\\\\")[1])&&(u%=l)!==u%180&&(u+=u<0?l:-360),\\\\\\\"cw\\\\\\\"===o&&u<0?u=(u+36e9)%l-~~(u/l)*l:\\\\\\\"ccw\\\\\\\"===o&&u>0&&(u=(u-36e9)%l-~~(u/l)*l)),t._pt=a=new cp(t._pt,e,n,i,u,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,s,r,o,a,l,c,h=T_({},n._gsap),u=n.style;for(s in h.svg?(r=n.getAttribute(\\\\\\\"transform\\\\\\\"),n.setAttribute(\\\\\\\"transform\\\\\\\",\\\\\\\"\\\\\\\"),u[Gp]=e,i=p_(n,1),Qp(n,Gp),n.setAttribute(\\\\\\\"transform\\\\\\\",r)):(r=getComputedStyle(n)[Gp],u[Gp]=e,i=p_(n,1),u[Gp]=r),bp)(r=h[s])!==(o=i[s])&&\\\\\\\"perspective,force3D,transformOrigin,svgOrigin\\\\\\\".indexOf(s)<0&&(a=Ku(r)!==(c=Ku(o))?e_(n,s,r,c):parseFloat(r),l=parseFloat(o),t._pt=new cp(t._pt,i,s,a,l-a,Np),t._pt.u=c||0,t._props.push(s));T_(i,h)};vu(\\\\\\\"padding,margin,Width,Radius\\\\\\\",(function(t,e){var n=\\\\\\\"Top\\\\\\\",i=\\\\\\\"Right\\\\\\\",s=\\\\\\\"Bottom\\\\\\\",r=\\\\\\\"Left\\\\\\\",o=(e<3?[n,i,s,r]:[n+r,n+i,s+i,s+r]).map((function(n){return e<2?t+n:\\\\\\\"border\\\\\\\"+n+t}));o_[e>1?\\\\\\\"border\\\\\\\"+t:t]=function(t,e,n,i,s){var r,a;if(arguments.length<4)return r=o.map((function(e){return n_(t,e,n)})),5===(a=r.join(\\\\\\\" \\\\\\\")).split(r[0]).length?r[0]:a;r=(i+\\\\\\\"\\\\\\\").split(\\\\\\\" \\\\\\\"),a={},o.forEach((function(t,e){return a[t]=r[e]=r[e]||r[(e-1)/2|0]})),t.init(e,a,s)}}));var M_,E_,S_,C_={name:\\\\\\\"css\\\\\\\",register:Xp,targetTest:function(t){return t.style&&t.nodeType},init:function(t,e,n,i,s){var r,o,a,l,c,h,u,d,p,_,m,f,g,v,y,x,b,w,T,A=this._props,M=t.style,E=n.vars.startAt;for(u in gp||Xp(),e)if(\\\\\\\"autoRound\\\\\\\"!==u&&(o=e[u],!hu[u]||!jd(u,e,n,i,t,s)))if(c=typeof o,h=o_[u],\\\\\\\"function\\\\\\\"===c&&(c=typeof(o=o.call(n,i,t,s))),\\\\\\\"string\\\\\\\"===c&&~o.indexOf(\\\\\\\"random(\\\\\\\")&&(o=hd(o)),h)h(this,t,u,o,n)&&(y=1);else if(\\\\\\\"--\\\\\\\"===u.substr(0,2))r=(getComputedStyle(t).getPropertyValue(u)+\\\\\\\"\\\\\\\").trim(),o+=\\\\\\\"\\\\\\\",wd.lastIndex=0,wd.test(r)||(d=Ku(r),p=Ku(o)),p?d!==p&&(r=e_(t,u,r,p)+p):d&&(o+=d),this.add(M,\\\\\\\"setProperty\\\\\\\",r,o,i,s,0,0,u);else if(\\\\\\\"undefined\\\\\\\"!==c){if(E&&u in E?(r=\\\\\\\"function\\\\\\\"==typeof E[u]?E[u].call(n,i,t,s):E[u],u in Sh.units&&!Ku(r)&&(r+=Sh.units[u]),\\\\\\\"=\\\\\\\"===(r+\\\\\\\"\\\\\\\").charAt(1)&&(r=n_(t,u))):r=n_(t,u),l=parseFloat(r),(_=\\\\\\\"string\\\\\\\"===c&&\\\\\\\"=\\\\\\\"===o.charAt(1)?+(o.charAt(0)+\\\\\\\"1\\\\\\\"):0)&&(o=o.substr(2)),a=parseFloat(o),u in Cp&&(\\\\\\\"autoAlpha\\\\\\\"===u&&(1===l&&\\\\\\\"hidden\\\\\\\"===n_(t,\\\\\\\"visibility\\\\\\\")&&a&&(l=0),Kp(this,M,\\\\\\\"visibility\\\\\\\",l?\\\\\\\"inherit\\\\\\\":\\\\\\\"hidden\\\\\\\",a?\\\\\\\"inherit\\\\\\\":\\\\\\\"hidden\\\\\\\",!a)),\\\\\\\"scale\\\\\\\"!==u&&\\\\\\\"transform\\\\\\\"!==u&&~(u=Cp[u]).indexOf(\\\\\\\",\\\\\\\")&&(u=u.split(\\\\\\\",\\\\\\\")[0])),m=u 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,M,Gp,0,1,g.renderTransform,g,0,-1)).dep=1),\\\\\\\"scale\\\\\\\"===u)this._pt=new cp(this._pt,g,\\\\\\\"scaleY\\\\\\\",g.scaleY,_?_*a:a-g.scaleY),A.push(\\\\\\\"scaleY\\\\\\\",u),u+=\\\\\\\"X\\\\\\\";else{if(\\\\\\\"transformOrigin\\\\\\\"===u){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]=s_[w]||w,b[1]=s_[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,M,u,__(r),__(o)));continue}if(\\\\\\\"svgOrigin\\\\\\\"===u){d_(t,o,1,v,0,this);continue}if(u in l_){w_(this,g,u,l,o,_);continue}if(\\\\\\\"smoothOrigin\\\\\\\"===u){Kp(this,g,\\\\\\\"smooth\\\\\\\",g.smooth,o);continue}if(\\\\\\\"force3D\\\\\\\"===u){g[u]=o;continue}if(\\\\\\\"transform\\\\\\\"===u){A_(this,o,t);continue}}else u in M||(u=qp(u)||u);if(m||(a||0===a)&&(l||0===l)&&!Sp.test(o)&&u in M)a||(a=0),(d=(r+\\\\\\\"\\\\\\\").substr((l+\\\\\\\"\\\\\\\").length))!==(p=Ku(o)||(u in Sh.units?Sh.units[u]:d))&&(l=e_(t,u,r,p)),this._pt=new cp(this._pt,m?g:M,u,l,_?_*a:a-l,m||\\\\\\\"px\\\\\\\"!==p&&\\\\\\\"zIndex\\\\\\\"!==u||!1===e.autoRound?Np:Pp),this._pt.u=p||0,d!==p&&(this._pt.b=r,this._pt.r=Op);else if(u in M)i_.call(this,t,u,r,o);else{if(!(u in t)){iu(u,o);continue}this.add(t,u,t[u],o,i,s)}A.push(u)}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?zp:Bp:(yp=n||{})&&(\\\\\\\"scale\\\\\\\"===e?kp:Up):t.style&&!Uh(t.style[e])?Fp:~e.indexOf(\\\\\\\"-\\\\\\\")?Dp:tp(t,e)},core:{_removeProperty:Qp,_getMatrix:u_}};pp.utils.checkPrefix=qp,S_=vu((M_=\\\\\\\"x,y,z,scale,scaleX,scaleY,xPercent,yPercent\\\\\\\")+\\\\\\\",\\\\\\\"+(E_=\\\\\\\"rotation,rotationX,rotationY,skewX,skewY\\\\\\\")+\\\\\\\",transform,transformOrigin,svgOrigin,force3D,smoothOrigin,transformPerspective\\\\\\\",(function(t){bp[t]=1})),vu(E_,(function(t){Sh.units[t]=\\\\\\\"deg\\\\\\\",l_[t]=1})),Cp[S_[13]]=M_+\\\\\\\",\\\\\\\"+E_,vu(\\\\\\\"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]]})),vu(\\\\\\\"x,y,z,top,right,bottom,left,width,height,fontSize,padding,margin,perspective\\\\\\\",(function(t){Sh.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 P_{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 P_;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 uh}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 s=(null===(e=n.position())||void 0===e?void 0:e.toParameter())||void 0;t.add(i,s)}this._property&&this._target&&(this._property.setDebug(this._debug),this._property.addToTimeline(this,t,this._target))}}const R_=new class extends la{constructor(){super(...arguments),this.count=aa.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1]})}};class I_ extends ih{constructor(){super(...arguments),this.paramsConfig=R_}static type(){return\\\\\\\"copy\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}async cook(t){const e=new P_;for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.delay])}))}))}cook(t){const e=t[0]||new P_;e.setDelay(this.pv.delay),this.setTimelineBuilder(e)}}const B_=new class extends la{constructor(){super(...arguments),this.duration=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]})}};class z_ extends ih{constructor(){super(...arguments),this.paramsConfig=B_}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 P_;e.setDuration(this.pv.duration),this.setTimelineBuilder(e)}}const k_=new class extends la{constructor(){super(...arguments),this.name=aa.INTEGER(lh.indexOf(oh.POWER4),{menu:{entries:lh.map(((t,e)=>({name:t,value:e})))}}),this.inOut=aa.INTEGER(hh.indexOf(ch.OUT),{menu:{entries:hh.map(((t,e)=>({name:t,value:e})))}})}};class U_ extends ih{constructor(){super(...arguments),this.paramsConfig=k_}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=lh[this.pv.name];if(t==oh.NONE)return t;return`${t}.${hh[this.pv.inOut]}`}cook(t){const e=t[0]||new P_,n=this.easing_full_name();e.setEasing(n),this.setTimelineBuilder(e)}}var G_;!function(t){t.RELATIVE=\\\\\\\"relative\\\\\\\",t.ABSOLUTE=\\\\\\\"absolute\\\\\\\"}(G_||(G_={}));const V_=[G_.RELATIVE,G_.ABSOLUTE];var H_;!function(t){t.START=\\\\\\\"start\\\\\\\",t.END=\\\\\\\"end\\\\\\\"}(H_||(H_={}));const j_=[H_.START,H_.END];class W_{constructor(){this._mode=G_.RELATIVE,this._relativeTo=H_.END,this._offset=0}clone(){const t=new W_;return t.setMode(this._mode),t.setRelativeTo(this._relativeTo),t.setOffset(this._offset),t}setMode(t){this._mode=t}mode(){return this._mode}setRelativeTo(t){this._relativeTo=t}relativeTo(){return this._relativeTo}setOffset(t){this._offset=t}offset(){return this._offset}toParameter(){switch(this._mode){case G_.RELATIVE:return this._relative_position_param();case G_.ABSOLUTE:return this._absolutePositionParam()}ls.unreachable(this._mode)}_relative_position_param(){switch(this._relativeTo){case H_.END:return this._offsetString();case H_.START:return`<${this._offset}`}ls.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 la{constructor(){super(...arguments),this.mode=aa.INTEGER(0,{menu:{entries:X_.map(((t,e)=>({name:t,value:e})))}}),this.offset=aa.FLOAT(0,{range:[-1,1]}),this.overridePositions=aa.BOOLEAN(0),this.inputsCount=aa.INTEGER(4,{range:[1,32],rangeLocked:[!0,!1],callback:t=>{$_.PARAM_CALLBACK_setInputsCount(t)}})}};class $_ extends ih{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 P_;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)}ls.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 la{constructor(){super(...arguments),this.play=aa.BUTTON(null,{callback:t=>{Z_.PARAM_CALLBACK_play(t)}}),this.pause=aa.BUTTON(null,{callback:t=>{Z_.PARAM_CALLBACK_pause(t)}}),this.debug=aa.BOOLEAN(0)}};class Z_ extends ih{constructor(){super(...arguments),this.paramsConfig=J_}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}cook(t){const e=t[0]||new P_;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 la{constructor(){super(...arguments),this.operation=aa.INTEGER(0,{menu:{entries:O_.map(((t,e)=>({value:e,name:t})))}})}};class K_ extends ih{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 P_;e.setOperation(O_[this.pv.operation]),this.setTimelineBuilder(e)}}const tm=new class extends la{constructor(){super(...arguments),this.mode=aa.INTEGER(0,{menu:{entries:V_.map(((t,e)=>({name:t,value:e})))}}),this.relativeTo=aa.INTEGER(0,{menu:{entries:j_.map(((t,e)=>({name:t,value:e})))}}),this.offset=aa.FLOAT(0)}};class em extends ih{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 P_,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 la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"position\\\\\\\")}};class im extends ih{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 P_;e.setPropertyName(this.pv.name),this.setTimelineBuilder(e)}}var sm;!function(t){t.CUSTOM=\\\\\\\"custom\\\\\\\",t.FROM_SCENE_GRAPH=\\\\\\\"from scene graph\\\\\\\",t.FROM_NODE=\\\\\\\"from node\\\\\\\"}(sm||(sm={}));const rm=[sm.CUSTOM,sm.FROM_SCENE_GRAPH,sm.FROM_NODE],om=rm.indexOf(sm.CUSTOM),am=rm.indexOf(sm.FROM_SCENE_GRAPH),lm=rm.indexOf(sm.FROM_NODE);const cm=new class extends la{constructor(){super(...arguments),this.mode=aa.INTEGER(om,{menu:{entries:rm.map(((t,e)=>({name:t,value:e})))}}),this.nodePath=aa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{mode:lm}}),this.objectMask=aa.STRING(\\\\\\\"*geo1\\\\\\\",{visibleIf:{mode:am}}),this.printResolve=aa.BUTTON(null,{visibleIf:{mode:am},callback:t=>{hm.PARAM_CALLBACK_print_resolve(t)}}),this.overridePropertyName=aa.BOOLEAN(0,{visibleIf:[{mode:am},{mode:lm}]}),this.propertyName=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:[{overridePropertyName:!0,mode:am},{overridePropertyName:!0,mode:lm}]}),this.size=aa.INTEGER(3,{range:[1,4],rangeLocked:[!0,!0],visibleIf:{mode:om}}),this.value1=aa.FLOAT(0,{visibleIf:{mode:om,size:1}}),this.value2=aa.VECTOR2([0,0],{visibleIf:{mode:om,size:2}}),this.value3=aa.VECTOR3([0,0,0],{visibleIf:{mode:om,size:3}}),this.value4=aa.VECTOR4([0,0,0,0],{visibleIf:{mode:om,size:4}})}};class hm extends ih{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 P_;await this._prepare_timeline_builder(e),this.setTimelineBuilder(e)}setMode(t){this.p.mode.set(rm.indexOf(t))}async _prepare_timeline_builder(t){const e=rm[this.pv.mode];switch(e){case sm.CUSTOM:return this._prepare_timebuilder_custom(t);case sm.FROM_SCENE_GRAPH:return this._prepare_timebuilder_from_scene_graph(t);case sm.FROM_NODE:return await this._prepare_timebuilder_from_node(t)}ls.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 ah.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 s=i.value;s&&(m.isNumber(s)||m.isVector(s))&&t.setPropertyValue(s)}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 um=new class extends la{constructor(){super(...arguments),this.unlimited=aa.BOOLEAN(0),this.count=aa.INTEGER(1,{range:[0,10],visibleIf:{unlimited:0}}),this.delay=aa.FLOAT(0),this.yoyo=aa.BOOLEAN(0)}};class dm extends ih{constructor(){super(...arguments),this.paramsConfig=um}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 P_;e.setRepeatParams(this._repeat_params()),this.setTimelineBuilder(e)}}const pm=new class extends la{constructor(){super(...arguments),this.input=aa.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class _m extends ih{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 la{constructor(){super(...arguments),this.type=aa.INTEGER(ym,{menu:{entries:vm.map(((t,e)=>({name:t,value:e})))}}),this.nodePath=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{type:xm}}),this.objectMask=aa.STRING(\\\\\\\"/geo*\\\\\\\",{visibleIf:{type:ym}}),this.updateMatrix=aa.BOOLEAN(0,{visibleIf:{type:ym}}),this.printResolve=aa.BUTTON(null,{callback:(t,e)=>{wm.PARAM_CALLBACK_print_resolve(t)}})}};class wm extends ih{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}ls.unreachable(t)}))}))}))}cook(t){const e=t[0]||new P_,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})}ls.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)))}ls.unreachable(e)}static PARAM_CALLBACK_print_resolve(t){t.print_resolve()}print_resolve(){const t=vm[this.pv.type],e=new P_,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 sa{static context(){return ts.ANIM}cook(){this.cookController.endCook()}}class Am extends Tm{}class Mm extends Am{constructor(){super(...arguments),this._children_controller_context=ts.ANIM}static type(){return es.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Em extends Am{constructor(){super(...arguments),this._children_controller_context=ts.COP}static type(){return es.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=ts.EVENT}static type(){return es.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=ts.MAT}static type(){return es.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 Mi(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=Pm(),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=Pm();t=(e-this.oldTime)/1e3,this.oldTime=e,this.elapsedTime+=t}return t}}function Pm(){return(\\\\\\\"undefined\\\\\\\"==typeof performance?Date:performance).now()}var Rm={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 ot.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 Bm{constructor(t){this._mesh=new B.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 zm 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 Bm(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 km 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(),s=t.state;let r,o;s.buffers.color.setMask(!1),s.buffers.depth.setMask(!1),s.buffers.color.setLocked(!0),s.buffers.depth.setLocked(!0),this.inverse?(r=0,o=1):(r=1,o=0),s.buffers.stencil.setTest(!0),s.buffers.stencil.setOp(i.REPLACE,i.REPLACE,i.REPLACE),s.buffers.stencil.setFunc(i.ALWAYS,r,4294967295),s.buffers.stencil.setClear(o),s.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),s.buffers.color.setLocked(!1),s.buffers.depth.setLocked(!1),s.buffers.stencil.setLocked(!1),s.buffers.stencil.setFunc(i.EQUAL,1,4294967295),s.buffers.stencil.setOp(i.KEEP,i.KEEP,i.KEEP),s.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 Q(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===Rm&&console.error(\\\\\\\"THREE.EffectComposer relies on CopyShader\\\\\\\"),void 0===zm&&console.error(\\\\\\\"THREE.EffectComposer relies on ShaderPass\\\\\\\"),this.copyPass=new zm(Rm),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 la{constructor(){super(...arguments),this.prependRenderPass=aa.BOOLEAN(1),this.useRenderTarget=aa.BOOLEAN(1),this.tmagFilter=aa.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.magFilter=aa.INTEGER(qm,{visibleIf:{useRenderTarget:1,tmagFilter:1},menu:{entries:Ym}}),this.tminFilter=aa.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.minFilter=aa.INTEGER(Xm,{visibleIf:{useRenderTarget:1,tminFilter:1},menu:{entries:$m}}),this.stencilBuffer=aa.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.sampling=aa.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=li.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=ts.POST}static type(){return es.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=ts.ROP}static type(){return es.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}var tf=n(44);const ef=\\\\\\\"input texture\\\\\\\",nf=[ef,ef,ef,ef];for(var sf=new Uint16Array(32),rf=0;rf<32;rf++)sf[rf]=28898;const of=new fo.a(sf,32,1,w.gb,w.M);class af extends sa{constructor(t){super(t,\\\\\\\"BaseCopNode\\\\\\\"),this.flags=new ki(this)}static context(){return ts.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 hf{static any(t){return m.isString(t)?t:m.isBoolean(t)?`${t}`:m.isNumber(t)?`${os.ensureFloat(t)}`:m.isArray(t)?this.numeric_array(t):t instanceof d.a||t instanceof p.a||t instanceof _.a||t instanceof D.a?this.numeric_array(t.toArray()):`ThreeToGl error: unknown value type '${t}'`}static numeric_array(t){const e=new Array(t.length);for(let n=0;n`${os.ensureFloat(t)}`)).join(\\\\\\\", \\\\\\\")})`}static vector3(t){if(m.isString(t))return t;return`vec3(${t.toArray().map((t=>`${os.ensureFloat(t)}`)).join(\\\\\\\", \\\\\\\")})`}static vector2(t){if(m.isString(t))return t;return`vec2(${t.toArray().map((t=>`${os.ensureFloat(t)}`)).join(\\\\\\\", \\\\\\\")})`}static vector3_float(t,e){return m.isNumber(e)&&(e=os.ensureFloat(e)),`vec4(${this.vector3(t)}, ${e})`}static float4(t,e,n,i){return m.isNumber(t)&&(t=os.ensureFloat(t)),m.isNumber(e)&&(e=os.ensureFloat(e)),m.isNumber(n)&&(n=os.ensureFloat(n)),m.isNumber(i)&&(i=os.ensureFloat(i)),`vec4(${t}, ${e}, ${n}, ${i})`}static float3(t,e,n){return m.isNumber(t)&&(t=os.ensureFloat(t)),m.isNumber(e)&&(e=os.ensureFloat(e)),m.isNumber(n)&&(n=os.ensureFloat(n)),`vec3(${t}, ${e}, ${n})`}static float2(t,e){return m.isNumber(t)&&(t=os.ensureFloat(t)),m.isNumber(e)&&(e=os.ensureFloat(e)),`vec2(${t}, ${e})`}static float(t){if(m.isNumber(t))return os.ensureFloat(t);{const e=parseFloat(t);return m.isNaN(e)?t:os.ensureFloat(e)}}static integer(t){if(m.isNumber(t))return os.ensureInteger(t);{const e=parseInt(t);return m.isNaN(e)?t:os.ensureInteger(e)}}static bool(t){return m.isBoolean(t)?`${t}`:t}}const uf=/\\\\/+/g;class df extends sa{static context(){return ts.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()==ts.GL?null===(t=e)||void 0===t?void 0:t.material_node:e}glVarName(t){return`v_POLY_${this.path(this.material_node).replace(uf,\\\\\\\"_\\\\\\\")}_${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 hf.any(null===(e=this.params.get(t))||void 0===e?void 0:e.value);{const t=this.io.inputs.namedInputConnectionPoints()[n];return hf.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 la{};class _f extends df{constructor(){super(...arguments),this.paramsConfig=pf}}const mf=[Bo.FLOAT,Bo.VEC2,Bo.VEC3,Bo.VEC4];const ff=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"\\\\\\\"),this.type=aa.INTEGER(0,{menu:{entries:mf.map(((t,e)=>({name:t,value:e})))}}),this.texportWhenConnected=aa.BOOLEAN(0,{hidden:!0}),this.exportWhenConnected=aa.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 ss.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(),s=t.get(i);s?s.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 ${s.data_type} from node '${s.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,Bo.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 Mf 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} ${this.name()}`}}!function(t){t.VERTEX=\\\\\\\"vertex\\\\\\\",t.FRAGMENT=\\\\\\\"fragment\\\\\\\",t.LEAVES_FROM_NODES_SHADER=\\\\\\\"leaves_from_nodes_shader\\\\\\\"}(xf||(xf={}));const Ef={position:\\\\\\\"vec3( position )\\\\\\\"};class Sf extends cf{handle_globals_node(t,e,n){var i,s;const r=t.io.outputs.namedOutputConnectionPointsByName(e);if(!r)return;const o=t.glVarName(e),a=r.type(),l=new Mf(t,a,o);n.addDefinitions(t,[l]);const c=null===(s=null===(i=t.material_node)||void 0===i?void 0:i.assemblerController)||void 0===s?void 0:s.assembler;if(!c)return;const h=c.shader_config(n.current_shader_name);if(!h)return;const u=h.dependencies(),d=[],p=`${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 u)n.addDefinitions(t,[l],e),n.addBodyLines(t,d,e);0==u.length&&n.addBodyLines(t,d)}static variable_config_default(t){return Ef[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 s,r;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 Mf(t,e,a),c=new Map;c.set(xf.FRAGMENT,[]);const u=new Map;u.set(xf.FRAGMENT,[]),h.pushOnArrayAtEntry(c,o,l);const d=`${a} = ${e}(${n})`,p=null===(r=null===(s=t.material_node)||void 0===s?void 0:s.assemblerController)||void 0===r?void 0:r.assembler.shader_config(o);if(p){const e=p.dependencies();for(let t of e)h.pushOnArrayAtEntry(c,t,l),h.pushOnArrayAtEntry(u,t,d);c.forEach(((e,n)=>{i.addDefinitions(t,e,n)})),u.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=[Bo.FLOAT,Bo.VEC2,Bo.VEC3,Bo.VEC4];const Nf=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"\\\\\\\"),this.type=aa.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 Mf(this,e,n),s=`${n} = ${hf.any(this.variableForInput(Lf.INPUT_NAME))}`;t.addDefinitions(this,[i],xf.VERTEX),t.addBodyLines(this,[s],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 Pf{static overlay(t,e){return new Promise(((n,i)=>{let s=document.createElement(\\\\\\\"canvas\\\\\\\");s.width=Math.max(t.width,e.width),s.height=Math.max(t.height,e.height);let r=s.getContext(\\\\\\\"2d\\\\\\\");r.drawImage(t,0,0,t.width,t.height),r.drawImage(e,0,0,e.width,e.height);const o=s.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 s=document.createElement(\\\\\\\"canvas\\\\\\\");s.width=t,s.height=e;let r=s.getContext(\\\\\\\"2d\\\\\\\");r.beginPath(),r.rect(0,0,t,e),r.fillStyle=\\\\\\\"white\\\\\\\",r.fill();const o=s.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 s=Math.min(t.width,t.height),r=t.width/t.height;i.width=s,i.height=s;let o=i.getContext(\\\\\\\"2d\\\\\\\");const a=r>1,l=a?(t.width-s)/2:(t.height-s)/2;a?o.drawImage(t,l,0,s,s,0,0,s,s):o.drawImage(t,0,l,s,s,0,0,s,s);const c=i.toDataURL(\\\\\\\"image/png\\\\\\\"),h=new Image;h.onload=()=>{e(h)},h.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 Rf;!function(t){t.Uint8Array=\\\\\\\"Uint8Array\\\\\\\",t.Uint8ClampedArray=\\\\\\\"Uint8ClampedArray\\\\\\\",t.Float32Array=\\\\\\\"Float32Array\\\\\\\"}(Rf||(Rf={}));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=Pf.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,s=this._data_texture.image.data,r=4*n;for(let t=0;t0:s.vertexColors=t.vertexColors),void 0!==t.uniforms)for(const e in t.uniforms){const i=t.uniforms[e];switch(s.uniforms[e]={},i.type){case\\\\\\\"t\\\\\\\":s.uniforms[e].value=n(i.value);break;case\\\\\\\"c\\\\\\\":s.uniforms[e].value=(new D.a).setHex(i.value);break;case\\\\\\\"v2\\\\\\\":s.uniforms[e].value=(new d.a).fromArray(i.value);break;case\\\\\\\"v3\\\\\\\":s.uniforms[e].value=(new p.a).fromArray(i.value);break;case\\\\\\\"v4\\\\\\\":s.uniforms[e].value=(new _.a).fromArray(i.value);break;case\\\\\\\"m3\\\\\\\":s.uniforms[e].value=(new G.a).fromArray(i.value);break;case\\\\\\\"m4\\\\\\\":s.uniforms[e].value=(new A.a).fromArray(i.value);break;default:s.uniforms[e].value=i.value}}if(void 0!==t.defines&&(s.defines=t.defines),void 0!==t.vertexShader&&(s.vertexShader=t.vertexShader),void 0!==t.fragmentShader&&(s.fragmentShader=t.fragmentShader),void 0!==t.extensions)for(const e in t.extensions)s.extensions[e]=t.extensions[e];if(void 0!==t.shading&&(s.flatShading=1===t.shading),void 0!==t.size&&(s.size=t.size),void 0!==t.sizeAttenuation&&(s.sizeAttenuation=t.sizeAttenuation),void 0!==t.map&&(s.map=n(t.map)),void 0!==t.matcap&&(s.matcap=n(t.matcap)),void 0!==t.alphaMap&&(s.alphaMap=n(t.alphaMap)),void 0!==t.bumpMap&&(s.bumpMap=n(t.bumpMap)),void 0!==t.bumpScale&&(s.bumpScale=t.bumpScale),void 0!==t.normalMap&&(s.normalMap=n(t.normalMap)),void 0!==t.normalMapType&&(s.normalMapType=t.normalMapType),void 0!==t.normalScale){let e=t.normalScale;!1===Array.isArray(e)&&(e=[e,e]),s.normalScale=(new d.a).fromArray(e)}return void 0!==t.displacementMap&&(s.displacementMap=n(t.displacementMap)),void 0!==t.displacementScale&&(s.displacementScale=t.displacementScale),void 0!==t.displacementBias&&(s.displacementBias=t.displacementBias),void 0!==t.roughnessMap&&(s.roughnessMap=n(t.roughnessMap)),void 0!==t.metalnessMap&&(s.metalnessMap=n(t.metalnessMap)),void 0!==t.emissiveMap&&(s.emissiveMap=n(t.emissiveMap)),void 0!==t.emissiveIntensity&&(s.emissiveIntensity=t.emissiveIntensity),void 0!==t.specularMap&&(s.specularMap=n(t.specularMap)),void 0!==t.specularIntensityMap&&(s.specularIntensityMap=n(t.specularIntensityMap)),void 0!==t.specularTintMap&&(s.specularTintMap=n(t.specularTintMap)),void 0!==t.envMap&&(s.envMap=n(t.envMap)),void 0!==t.envMapIntensity&&(s.envMapIntensity=t.envMapIntensity),void 0!==t.reflectivity&&(s.reflectivity=t.reflectivity),void 0!==t.refractionRatio&&(s.refractionRatio=t.refractionRatio),void 0!==t.lightMap&&(s.lightMap=n(t.lightMap)),void 0!==t.lightMapIntensity&&(s.lightMapIntensity=t.lightMapIntensity),void 0!==t.aoMap&&(s.aoMap=n(t.aoMap)),void 0!==t.aoMapIntensity&&(s.aoMapIntensity=t.aoMapIntensity),void 0!==t.gradientMap&&(s.gradientMap=n(t.gradientMap)),void 0!==t.clearcoatMap&&(s.clearcoatMap=n(t.clearcoatMap)),void 0!==t.clearcoatRoughnessMap&&(s.clearcoatRoughnessMap=n(t.clearcoatRoughnessMap)),void 0!==t.clearcoatNormalMap&&(s.clearcoatNormalMap=n(t.clearcoatNormalMap)),void 0!==t.clearcoatNormalScale&&(s.clearcoatNormalScale=(new d.a).fromArray(t.clearcoatNormalScale)),void 0!==t.transmissionMap&&(s.transmissionMap=n(t.transmissionMap)),void 0!==t.thicknessMap&&(s.thicknessMap=n(t.thicknessMap)),s}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 G.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(ts.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 la{constructor(){super(...arguments),this.resolution=aa.VECTOR2(Qf),this.useCameraRenderer=aa.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 B.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 gs,this._texture_camera=new tf.a,this._children_controller_context=ts.GL,this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this)}static type(){return\\\\\\\"builder\\\\\\\"}usedAssembler(){return jn.GL_TEXTURE}_create_assembler_controller(){const t=li.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(),s=t.assembler.uniforms();i&&s&&(this._fragment_shader=i,this._uniforms=s),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(),s=`${t.graphNodeId()}`;e?(t.states.timeDependent.forceTimeDependent(),n&&i.uniformsController.addTimeDependentUniformOwner(s,n)):(t.states.timeDependent.unforceTimeDependent(),i.uniformsController.removeTimeDependentUniformOwner(s))}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(Rf.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,s=w.V,r=w.ob;var o=new Q(t,e,{wrapS:n,wrapT:i,minFilter:s,magFilter:r,format:w.Ib,type:Zf.isiOS()?w.M:w.G,stencilBuffer:!1,depthBuffer:!1});return li.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}],sg=[{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}],rg=[{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\\\\\\\"),hg=og(\\\\\\\"PARAM_CALLBACK_update_wrap\\\\\\\"),ug=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=aa.BOOLEAN(0,{...lg}),this.encoding=aa.INTEGER(ag.ENCODING,{visibleIf:{tencoding:1},menu:{entries:eg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...lg}),this.tmapping=aa.BOOLEAN(0,{...cg}),this.mapping=aa.INTEGER(ag.MAPPING,{visibleIf:{tmapping:1},menu:{entries:ig.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...cg}),this.twrap=aa.BOOLEAN(0,{...hg}),this.wrapS=aa.INTEGER(ag.WRAPPING,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...hg}),this.wrapT=aa.INTEGER(ag.WRAPPING,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},separatorAfter:!0,...hg}),this.tminFilter=aa.BOOLEAN(0,{...ug}),this.minFilter=aa.INTEGER(Xm,{visibleIf:{tminFilter:1},menu:{entries:$m},...ug}),this.tmagFilter=aa.BOOLEAN(0,{...ug}),this.magFilter=aa.INTEGER(qm,{visibleIf:{tmagFilter:1},menu:{entries:Ym},...ug}),this.tanisotropy=aa.BOOLEAN(0,{...dg}),this.useRendererMaxAnisotropy=aa.BOOLEAN(0,{visibleIf:{tanisotropy:1},...dg}),this.anisotropy=aa.INTEGER(2,{visibleIf:{tanisotropy:1,useRendererMaxAnisotropy:0},range:[0,32],rangeLocked:[!0,!1],...dg}),this.tflipY=aa.BOOLEAN(0,{...pg}),this.flipY=aa.BOOLEAN(0,{visibleIf:{tflipY:1},...pg}),this.ttransform=aa.BOOLEAN(0,{..._g}),this.offset=aa.VECTOR2([0,0],{visibleIf:{ttransform:1},...fg}),this.repeat=aa.VECTOR2([1,1],{visibleIf:{ttransform:1},...mg}),this.rotation=aa.FLOAT(0,{range:[-1,1],visibleIf:{ttransform:1},...gg}),this.center=aa.VECTOR2([0,0],{visibleIf:{ttransform:1},...vg}),this.tadvanced=aa.BOOLEAN(0,{...yg}),this.tformat=aa.BOOLEAN(0,{visibleIf:{tadvanced:1},...yg}),this.format=aa.INTEGER(ag.FORMAT,{visibleIf:{tadvanced:1,tformat:1},menu:{entries:rg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...yg}),this.ttype=aa.BOOLEAN(0,{visibleIf:{tadvanced:1},...yg}),this.type=aa.INTEGER(ag.TYPE,{visibleIf:{tadvanced:1,ttype:1},menu:{entries:sg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...yg})}}}class bg extends(xg(la)){}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: ${i}`)}if(e.twrap){function n(n){for(let i of ng){const s=Object.keys(i)[0];i[s]==e[n]&&t.push(`${n}: ${s}`)}}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: ${e.flipY}`),t}}class Tg extends Z.a{constructor(t,e,n,i,s,r,o,a,l){super(t,e,n,i,s,r,o,a,l),this.needsUpdate=!0}}Tg.prototype.isCanvasTexture=!0;class Ag extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.canvasId=aa.STRING(\\\\\\\"canvas-id\\\\\\\"),this.update=aa.BUTTON(null,{cook:!1,callback:t=>{Eg.PARAM_CALLBACK_update(t)}})}}}(la))){}const Mg=new Ag;class Eg extends af{constructor(){super(...arguments),this.paramsConfig=Mg,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"canvas\\\\\\\"}async cook(){const t=this.pv.canvasId,e=document.getElementById(t);if(!e)return this.states.error.set(`element with id '${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 la{constructor(){super(...arguments),this.resolution=aa.VECTOR2([256,256],{callback:t=>{Cg.PARAM_CALLBACK_reset(t)}}),this.color=aa.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(),s=255*i[0],r=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=Rg}static type(){return Lg.CUBE_CAMERA}async cook(){const t=this.pv.cubeCamera.nodeWithContext(ts.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;Pg[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 Bg=new class extends la{constructor(){super(...arguments),this.useCameraRenderer=aa.BOOLEAN(1),this.mode=aa.INTEGER(0,{menu:{entries:Dg.map(((t,e)=>({name:t,value:e})))}})}};class zg extends af{constructor(){super(...arguments),this.paramsConfig=Bg}static type(){return\\\\\\\"envMap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.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 Tt(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(Rf.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 kg extends Z.a{constructor(t,e,n,i,s,r,o,a,l){super(t,e,n,i,s,r,o,a,l),this.format=void 0!==o?o:w.ic,this.minFilter=void 0!==r?r:w.V,this.magFilter=void 0!==s?s: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)}}kg.prototype.isVideoTexture=!0;var Ug=n(80);const Gg=\\\\\\\"https://raw.githubusercontent.com/polygonjs/polygonjs-assets/master/\\\\\\\";var Vg=n(28);const Hg=new Vg.b;Hg.setURLModifier((t=>{const e=li.assetUrls.remapedUrl(t);if(e)return e;const n=li.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 li.blobs.fetchBlobForNode({storedUrl:e,fullUrl:t,node:this._node});return li.blobs.blobUrl(e)||t}static async _loadMultipleBlobGlobal(t){const e=[];for(let n of t.files){const i=n.storedUrl,s=n.fullUrl,r=t.node;e.push(li.blobs.fetchBlobGlobal({storedUrl:i,fullUrl:s,node:r}))}const n=await Promise.all(e);for(let e of n)e.error&&t.node.states.error.set(t.error)}}jg.loadingManager=Hg;const Wg=[\\\\\\\"mp4\\\\\\\",\\\\\\\"ogv\\\\\\\",\\\\\\\"ogg\\\\\\\"];var qg;!function(t){t.JPG=\\\\\\\"jpg\\\\\\\",t.JPEG=\\\\\\\"jpeg\\\\\\\",t.PNG=\\\\\\\"png\\\\\\\",t.EXR=\\\\\\\"exr\\\\\\\",t.BASIS=\\\\\\\"basis\\\\\\\",t.HDR=\\\\\\\"hdr\\\\\\\"}(qg||(qg={}));const Xg=[qg.JPEG,qg.JPG,qg.PNG,qg.EXR,qg.BASIS,qg.HDR];function Yg(t){const e=t.split(\\\\\\\"?\\\\\\\")[0].split(\\\\\\\".\\\\\\\");return e[e.length-1]}class $g extends jg{constructor(t,e,n,i,s){super(t,i,n),this._param=e,this._node=n,this._scene=i,this._forceVideo=!1,this._forceImage=!1,this._forceVideo=(null==s?void 0:s.forceVideo)||this._forceVideo,this._forceImage=(null==s?void 0:s.forceImage)||this._forceImage}static onTextureLoaded(t){this._onTextureLoadedCallback=t}async load_texture_from_url_or_op(t){let e=null,n=null;if(\\\\\\\"op:\\\\\\\"==this._url.substring(0,3)){const t=this._url.substring(3);if(n=bi.findNode(this._node,t),n)if(n instanceof lf){e=(await n.compute()).texture()}else this._node.states.error.set(\\\\\\\"found node is not a texture node\\\\\\\");else this._node.states.error.set(`no node found in path '${t}'`)}else e=await this._loadUrl(t),e||this._node.states.error.set(`could not load texture ${this._url}`);return n&&this._param.graphPredecessors()[0]!=n&&(this._param.graphDisconnectPredecessors(),this._param.addGraphInput(n)),e}async _loadUrl(t){return new Promise((async(e,n)=>{const i=this.extension(),s=await this._urlToLoad();if(this._forceVideo||Wg.includes(i)){e(await this._loadVideo(s))}else if(this._forceImage||Xg.includes(i))try{e(await this._loadImage(s,t))}catch(t){n()}}))}_loadImage(t,e){return new Promise((async(n,i)=>{const s=this.extension();this.loader_for_ext(s,e).then((async e=>{e?($g.incrementInProgressLoadsCount(),await $g.waitForMaxConcurrentLoadsQueueFreed(),e.load(t,(e=>{$g.decrementInProgressLoadsCount();const i=$g._onTextureLoadedCallback;i&&i(t,e),n(e)}),void 0,(t=>{$g.decrementInProgressLoadsCount(),li.warn(\\\\\\\"error\\\\\\\",t),i()}))):i()}))}))}_loadVideo(t){return new Promise((async(e,n)=>{$g.incrementInProgressLoadsCount(),await $g.waitForMaxConcurrentLoadsQueueFreed();const i=document.createElement(\\\\\\\"video\\\\\\\");i.setAttribute(\\\\\\\"crossOrigin\\\\\\\",\\\\\\\"anonymous\\\\\\\"),i.setAttribute(\\\\\\\"autoplay\\\\\\\",\\\\\\\"true\\\\\\\"),i.setAttribute(\\\\\\\"loop\\\\\\\",\\\\\\\"true\\\\\\\"),i.onloadedmetadata=function(){i.pause();const n=new kg(i);$g.decrementInProgressLoadsCount();const s=$g._onTextureLoadedCallback;s&&s(t,n),e(n)};const s=document.createElement(\\\\\\\"source\\\\\\\"),r=jg.extension(t);let o=$g.VIDEO_SOURCE_TYPE_BY_EXT[r];o=o||$g._default_video_source_type(t),s.setAttribute(\\\\\\\"type\\\\\\\",o),s.setAttribute(\\\\\\\"src\\\\\\\",t),i.appendChild(s);let a=t;a=\\\\\\\"mp4\\\\\\\"==r?$g.replaceExtension(t,\\\\\\\"ogv\\\\\\\"):$g.replaceExtension(t,\\\\\\\"mp4\\\\\\\");const l=document.createElement(\\\\\\\"source\\\\\\\"),c=jg.extension(a);o=$g.VIDEO_SOURCE_TYPE_BY_EXT[c],o=o||$g._default_video_source_type(t),l.setAttribute(\\\\\\\"type\\\\\\\",o),l.setAttribute(\\\\\\\"src\\\\\\\",t),i.appendChild(l)}))}static module_names(t){switch(t){case qg.EXR:return[Hn.EXRLoader];case qg.HDR:return[Hn.RGBELoader];case qg.BASIS:return[Hn.BasisTextureLoader]}}async loader_for_ext(t,e){switch(t.toLowerCase()){case qg.EXR:return await this._exr_loader(e);case qg.HDR:return await this._hdr_loader(e);case qg.BASIS:return await $g._basis_loader(this._node)}return new Ug.a(this.loadingManager)}async _exr_loader(t){const e=await li.modulesRegister.module(Hn.EXRLoader);if(e){const n=new e(this.loadingManager);return t.tdataType&&n.setDataType(t.dataType),n}}async _hdr_loader(t){const e=await li.modulesRegister.module(Hn.RGBELoader);if(e){const n=new e(this.loadingManager);return t.tdataType&&n.setDataType(t.dataType),n}}static async _basis_loader(t){const e=await li.modulesRegister.module(Hn.BasisTextureLoader);if(e){const n=new e(this.loadingManager),i=li.libs.root(),s=li.libs.BASISPath();if(i||s){const e=`${i||\\\\\\\"\\\\\\\"}${s||\\\\\\\"\\\\\\\"}/`;if(t){const n=[\\\\\\\"basis_transcoder.js\\\\\\\",\\\\\\\"basis_transcoder.wasm\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((t=>({storedUrl:`${s}/${t}`,fullUrl:`${e}${t}`}))),node:t,error:\\\\\\\"failed to load basis libraries. Make sure to install them to load .basis files\\\\\\\"})}n.setTranscoderPath(e)}else n.setTranscoderPath(void 0);const r=await li.renderersController.waitForRenderer();return r?n.detectSupport(r):li.warn(\\\\\\\"texture loader found no renderer for basis texture loader\\\\\\\"),n}}static _default_video_source_type(t){return`video/${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 incrementInProgressLoadsCount(){this.in_progress_loads_count++}static decrementInProgressLoadsCount(){this.in_progress_loads_count--;const t=this._queue.pop();if(t){const e=this.CONCURRENT_LOADS_DELAY;setTimeout((()=>{t()}),e)}}static async waitForMaxConcurrentLoadsQueueFreed(){return this.in_progress_loads_count<=this.MAX_CONCURRENT_LOADS_COUNT?void 0:new Promise((t=>{this._queue.push(t)}))}}$g.PARAM_DEFAULT=`${Gg}/textures/uv.jpg`,$g.PARAM_ENV_DEFAULT=`${Gg}/textures/piz_compressed.exr`,$g.VIDEO_SOURCE_TYPE_BY_EXT={ogg:'video/ogg; codecs=\\\\\\\"theora, vorbis\\\\\\\"',ogv:'video/ogg; codecs=\\\\\\\"theora, vorbis\\\\\\\"',mp4:'video/mp4; codecs=\\\\\\\"avc1.42E01E, mp4a.40.2\\\\\\\"'},$g.MAX_CONCURRENT_LOADS_COUNT=$g._init_max_concurrent_loads_count(),$g.CONCURRENT_LOADS_DELAY=$g._init_concurrent_loads_delay(),$g.in_progress_loads_count=0,$g._queue=[];var Jg=n(114);class Zg extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.url=aa.STRING($g.PARAM_DEFAULT,{fileBrowse:{type:[Or.TEXTURE_IMAGE]}}),this.reload=aa.BUTTON(null,{callback:(t,e)=>{Kg.PARAM_CALLBACK_reload(t)}}),this.play=aa.BOOLEAN(1,{cook:!1,callback:t=>{Kg.PARAM_CALLBACK_gifUpdatePlay(t)}}),this.gifFrame=aa.INTEGER(0,{cook:!1,range:[0,100],rangeLocked:[!0,!1],callback:t=>{Kg.PARAM_CALLBACK_gifUpdateFrameIndex(t)}})}}}(la))){}const Qg=new Zg;class Kg extends af{constructor(){super(...arguments),this.paramsConfig=Qg,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 $g.module_names(t)}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ki.NEVER),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{let t=[this.p.url];t=t.concat(this.textureParamsController.paramLabelsParams()),this.params.label.init(t,(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\"),n=e[e.length-1],i=this.textureParamsController.paramLabels();return[n].concat(i)}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){if(\\\\\\\"gif\\\\\\\"!=Yg(this.pv.url).toLowerCase())this.states.error.set(\\\\\\\"url is not an image\\\\\\\");else{$g.incrementInProgressLoadsCount(),await $g.waitForMaxConcurrentLoadsQueueFreed();const t=await fetch(this.pv.url),e=await t.arrayBuffer(),n=await Object(Jg.parseGIF)(e),i=!0;this._parsedFrames=await Object(Jg.decompressFrames)(n,i);const s=this._parsedFrames[0];if(this._frameDelay=s.delay,this._frameIndex=this.pv.gifFrame-1,this._createCanvas(),$g.decrementInProgressLoadsCount(),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 ${this._frameIndex}, using last frame`),t=this._parsedFrames[this._parsedFrames.length-1]),t){const e=t.dims;this._frameImageData&&e.width==this._frameImageData.width&&e.height==this._frameImageData.height||(this._tmpCanvasElement.width=e.width,this._tmpCanvasElement.height=e.height,this._frameImageData=this._tmpCanvasContext.createImageData(e.width,e.height)),this._frameImageData.data.set(t.patch),this._tmpCanvasContext.putImageData(this._frameImageData,0,0),this._gifCanvasContext.drawImage(this._tmpCanvasElement,e.left,e.top);const n=this.containerController.container().texture();if(!n)return;n.needsUpdate=!0}}_drawNextFrame(){this._frameIndex++,this._frameIndex>=this._parsedFrames.length&&(this._frameIndex=0),this._drawOnCanvas(),this.pv.play&&setTimeout((()=>{this._drawNextFrame()}),this._frameDelay)}gifUpdateFrameIndex(){this._frameIndex=this.pv.gifFrame,this._drawOnCanvas()}static PARAM_CALLBACK_reload(t){t.paramCallbackReload()}paramCallbackReload(){this.p.url.setDirty()}static PARAM_CALLBACK_gifUpdatePlay(t){t.gifUpdatePlay()}gifUpdatePlay(){this.pv.play&&this._drawNextFrame()}static PARAM_CALLBACK_gifUpdateFrameIndex(t){t.gifUpdateFrameIndex()}}function tv(t){return class extends t{constructor(){super(...arguments),this.checkFileType=aa.BOOLEAN(!0)}}}class ev extends(tv(xg(function(t){return class extends t{constructor(){super(...arguments),this.url=aa.STRING($g.PARAM_DEFAULT,{fileBrowse:{type:[Or.TEXTURE_IMAGE]}}),this.reload=aa.BUTTON(null,{callback:(t,e)=>{iv.PARAM_CALLBACK_reload(t,e)}})}}}(la)))){}const nv=new ev;class iv extends af{constructor(){super(...arguments),this.paramsConfig=nv,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 $g.module_names(t)}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ki.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(this.pv.checkFileType&&(e=this.pv.url,!Xg.includes(Yg(e).toLowerCase())))this.states.error.set(\\\\\\\"url is not an image\\\\\\\");else{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()}var e}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 $g(t,n,this,this.scene(),{forceImage:!this.pv.checkFileType});try{e=await i.load_texture_from_url_or_op({tdataType:this.pv.ttype&&this.pv.tadvanced,dataType:this.pv.type}),e&&(e.matrixAutoUpdate=!1)}catch(t){}return e||this.states.error.set(`could not load texture '${t}'`),e}}var sv=n(32);const rv=.005;class ov{constructor(t,e=1024){this.renderer=t,this.res=e,this.objectTargets=[],this.lights=[],this.scene=new gs,this.buffer1Active=!1,this._params={lightRadius:1,iterations:1,iterationBlend:rv,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 ah.a,this._s=new p.a;const n=Zf.isAndroid()||Zf.isiOS()?w.M:w.G;this.progressiveLightMap1=new Q(this.res,this.res,{type:n}),this.progressiveLightMap2=new Q(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 s=i.fragmentShader.indexOf(\\\\\\\"void main() {\\\\\\\");i.fragmentShader=\\\\\\\"#define USE_UV\\\\n\\\\\\\"+i.fragmentShader.slice(0,s)+\\\\\\\"\\\\tuniform sampler2D previousShadowMap;\\\\n\\\\tuniform float pixelOffset;\\\\n\\\\\\\"+i.fragmentShader.slice(s-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 r={previousShadowMap:{value:e.texture},pixelOffset:{value:.5/t}};i.uniforms.previousShadowMap=r.previousShadowMap,i.uniforms.pixelOffset=r.pixelOffset,n.uniforms.previousShadowMap=r.previousShadowMap,n.uniforms.pixelOffset=r.pixelOffset,n.userData.shader=i},n}_createUVMat(){const t=new Gf.a;return t.uniforms={previousShadowMap:{value:null},iterationBlend:{value:rv}},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:rv}};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 av=new class extends la{constructor(){super(...arguments),this.update=aa.BUTTON(null,{callback:t=>{lv.PARAM_CALLBACK_updateManual(t)}}),this.useCameraRenderer=aa.BOOLEAN(1),this.lightMapRes=aa.INTEGER(1024,{range:[1,2048],rangeLocked:[!0,!1]}),this.iterations=aa.INTEGER(512,{range:[1,2048],rangeLocked:[!0,!1]}),this.iterationBlend=aa.FLOAT(rv,{range:[0,1],rangeLocked:[!0,!0]}),this.blur=aa.BOOLEAN(1),this.blurAmount=aa.FLOAT(1,{visibleIf:{blur:1},range:[0,1],rangeLocked:[!0,!1]}),this.lightRadius=aa.FLOAT(1,{range:[0,10]}),this.objectsMask=aa.STRING(\\\\\\\"\\\\\\\"),this.lightsMask=aa.STRING(\\\\\\\"*\\\\\\\"),this.printResolveObjectsList=aa.BUTTON(null,{callback:t=>{lv.PARAM_CALLBACK_printResolveObjectsList(t)}})}};class lv extends af{constructor(){super(...arguments),this.paramsConfig=av,this._includedObjects=[],this._includedLights=[]}static type(){return\\\\\\\"lightMap\\\\\\\"}async cook(){this._updateManual()}async _createLightMapController(){const t=await li.renderersController.firstRenderer();if(!t)return void console.warn(\\\\\\\"no renderer found\\\\\\\");return new ov(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(Rf.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 sv.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 sv.a||!n.has(e)&&e instanceof B.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 cv=new la;class hv extends af{constructor(){super(...arguments),this.paramsConfig=cv}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.NEVER)}async cook(t){const e=t[0];this.setTexture(e)}}const uv=[is.ORTHOGRAPHIC,is.PERSPECTIVE];class dv extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.camera=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ,types:uv}}),this.resolution=aa.VECTOR2([1024,1024]),this.useCameraRenderer=aa.BOOLEAN(1),this.render=aa.BUTTON(null,{callback:t=>{_v.PARAM_CALLBACK_render(t)}})}}}(la))){}const pv=new dv;class _v extends af{constructor(){super(...arguments),this.paramsConfig=pv,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"render\\\\\\\"}async cook(){this._texture_scene=this.scene().threejsScene(),this._camera_node=this.pv.camera.nodeWithContext(ts.OBJ),this._camera_node&&uv.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(Rf.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,s=w.V,r=w.ob;var o=new Q(t,e,{wrapS:n,wrapT:i,minFilter:s,magFilter:r,format:w.Ib,generateMipmaps:!0,type:Zf.isiOS()?w.M:w.G,stencilBuffer:!1,depthBuffer:!1});return await this.textureParamsController.update(o.texture),li.warn(\\\\\\\"created render target\\\\\\\",this.path(),t,e),o}static PARAM_CALLBACK_render(t){t.renderOnTarget()}}const mv=new class extends la{constructor(){super(...arguments),this.input=aa.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class fv extends af{constructor(){super(...arguments),this.paramsConfig=mv}static type(){return\\\\\\\"switch\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4),this.io.inputs.initInputsClonedState(Ki.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 ${t}`);this.cookController.endCook()}}class gv extends(xg(la)){}const vv=new gv;class yv extends af{constructor(){super(...arguments),this.paramsConfig=vv,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"textureProperties\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Ki.FROM_NODE])}async cook(t){const e=t[0];this.textureParamsController.update(e),this.setTexture(e)}}class xv extends(tv(xg(function(t){return class extends t{constructor(){super(...arguments),this.url=aa.STRING($g.PARAM_DEFAULT,{fileBrowse:{type:[Or.TEXTURE_VIDEO]}}),this.reload=aa.BUTTON(null,{callback:(t,e)=>{wv.PARAM_CALLBACK_reload(t,e)}}),this.play=aa.BOOLEAN(1,{cook:!1,callback:t=>{wv.PARAM_CALLBACK_video_update_play(t)}}),this.muted=aa.BOOLEAN(1,{cook:!1,callback:t=>{wv.PARAM_CALLBACK_video_update_muted(t)}}),this.loop=aa.BOOLEAN(1,{cook:!1,callback:t=>{wv.PARAM_CALLBACK_video_update_loop(t)}}),this.videoTime=aa.FLOAT(0,{cook:!1}),this.setVideoTime=aa.BUTTON(null,{cook:!1,callback:t=>{wv.PARAM_CALLBACK_video_update_time(t)}})}}}(la)))){}const bv=new xv;class wv extends af{constructor(){super(...arguments),this.paramsConfig=bv,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 $g.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(Ki.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(this.pv.checkFileType&&(e=this.pv.url,!Wg.includes(Yg(e).toLowerCase())))this.states.error.set(\\\\\\\"url 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()}var e}dispose(){var t;super.dispose(),this._video&&(null===(t=this._video.parentElement)||void 0===t||t.removeChild(this._video))}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 $g(t,n,this,this.scene(),{forceVideo:!this.pv.checkFileType});try{e=await this._texture_loader.load_texture_from_url_or_op({tdataType:this.pv.ttype&&this.pv.tadvanced,dataType:this.pv.type}),e&&(e.matrixAutoUpdate=!1)}catch(t){}return e||this.states.error.set(`could not load texture '${t}'`),e}}class Tv extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.res=aa.VECTOR2([1024,1024])}}}(la))){}const Av=new Tv;class Mv extends af{constructor(){super(...arguments),this.paramsConfig=Av,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(Ki.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 kg(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 Ev extends sa{static context(){return ts.COP}cook(){this.cookController.endCook()}}class Sv extends Ev{}class Cv extends Sv{constructor(){super(...arguments),this._children_controller_context=ts.ANIM}static type(){return es.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Nv extends Sv{constructor(){super(...arguments),this._children_controller_context=ts.EVENT}static type(){return es.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Lv extends Sv{constructor(){super(...arguments),this._children_controller_context=ts.COP}static type(){return es.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Ov extends Sv{constructor(){super(...arguments),this._children_controller_context=ts.MAT}static type(){return es.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Pv extends Ev{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=ts.POST}static type(){return es.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Rv extends Sv{constructor(){super(...arguments),this._children_controller_context=ts.ROP}static type(){return es.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}var Iv,Fv;!function(t){t.START=\\\\\\\"start\\\\\\\",t.STOP=\\\\\\\"stop\\\\\\\",t.UPDATE=\\\\\\\"update\\\\\\\"}(Iv||(Iv={})),function(t){t.START=\\\\\\\"start\\\\\\\",t.COMPLETE=\\\\\\\"completed\\\\\\\"}(Fv||(Fv={}));const Dv=new class extends la{constructor(){super(...arguments),this.animation=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.ANIM},dependentOnFoundNode:!1}),this.play=aa.BUTTON(null,{callback:t=>{Bv.PARAM_CALLBACK_play(t)}}),this.pause=aa.BUTTON(null,{callback:t=>{Bv.PARAM_CALLBACK_pause(t)}})}};class Bv extends ka{constructor(){super(...arguments),this.paramsConfig=Dv}static type(){return\\\\\\\"animation\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(Iv.START,Jo.BASE,this._play.bind(this)),new Zo(Iv.STOP,Jo.BASE,this._pause.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(Fv.START,Jo.BASE),new Zo(Fv.COMPLETE,Jo.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(ts.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(Fv.START,t)}trigger_animation_completed(t){this.dispatchEventToOutput(Fv.COMPLETE,t)}}const zv=\\\\\\\"event\\\\\\\";const kv=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(1),this.inputsCount=aa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1]})}};class Uv extends ka{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((()=>zv)),this.io.connection_points.set_expected_output_types_function((()=>[Jo.BASE]))}_expected_input_types(){const t=new Array(this.pv.inputsCount);return t.fill(Jo.BASE),t}_input_name(t){return`trigger${t}`}async processEvent(t){this.p.active.isDirty()&&await this.p.active.compute(),this.pv.active&&this.dispatchEventToOutput(zv,t)}}const Gv=new class extends la{constructor(){super(...arguments),this.blocking=aa.BOOLEAN(1)}};class Vv extends ka{constructor(){super(...arguments),this.paramsConfig=Gv}static type(){return\\\\\\\"block\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(\\\\\\\"in\\\\\\\",Jo.BASE,this._process_incoming_event.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(Vv.OUTPUT,Jo.BASE)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.blocking],(()=>this.pv.blocking?\\\\\\\"blocking (X)\\\\\\\":\\\\\\\"pass-through (--\\\\x3e)\\\\\\\"))}))}))}trigger_output(t){this.dispatchEventToOutput(Vv.OUTPUT,t)}_process_incoming_event(t){this.pv.blocking||this.trigger_output(t)}}var Hv;Vv.OUTPUT=\\\\\\\"output\\\\\\\",function(t){t.OUT=\\\\\\\"out\\\\\\\"}(Hv||(Hv={}));const jv=new class extends la{constructor(){super(...arguments),this.dispatch=aa.BUTTON(null,{callback:t=>{Wv.PARAM_CALLBACK_execute(t)}})}};class Wv extends ka{constructor(){super(...arguments),this.paramsConfig=jv}static type(){return\\\\\\\"button\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Zo(Hv.OUT,Jo.BASE)])}processEvent(t){}process_event_execute(t){this.dispatchEventToOutput(Hv.OUT,t)}static PARAM_CALLBACK_execute(t){t.process_event_execute({})}}class qv extends ka{constructor(){super(...arguments),this._controls_by_viewer=new Map}async apply_controls(t,e){var n;null===(n=e.controlsController)||void 0===n||n.dispose_controls();const i=e.canvas();if(!i)return;const s=await this.createControlsInstance(t,i),r=this._controls_by_viewer.get(e);r&&r.dispose(),this._controls_by_viewer.set(e,s);const o=li.performance.performanceManager().now();return s.name=`${this.path()}:${t.name}:${o}:${this.controls_id()}`,await this.params.evalAll(),this.setupControls(s),s}controls_id(){return JSON.stringify(this.params.all.map((t=>t.valueSerialized())))}}var Xv=n(27);const Yv=new p.a(0,0,1),$v=new Xv.a,Jv=new ah.a,Zv=new ah.a(-Math.sqrt(.5),0,0,Math.sqrt(.5)),Qv={type:\\\\\\\"change\\\\\\\"};class Kv extends J.a{constructor(t){super(),!1===window.isSecureContext&&console.error(\\\\\\\"THREE.DeviceOrientationControls: DeviceOrientationEvent is only available in secure contexts (https)\\\\\\\");const e=this,n=new ah.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},s=function(){e.screenOrientation=window.orientation||0};this.connect=function(){s(),void 0!==window.DeviceOrientationEvent&&\\\\\\\"function\\\\\\\"==typeof window.DeviceOrientationEvent.requestPermission?window.DeviceOrientationEvent.requestPermission().then((function(t){\\\\\\\"granted\\\\\\\"==t&&(window.addEventListener(\\\\\\\"orientationchange\\\\\\\",s),window.addEventListener(\\\\\\\"deviceorientation\\\\\\\",i))})).catch((function(t){console.error(\\\\\\\"THREE.DeviceOrientationControls: Unable to use DeviceOrientation API:\\\\\\\",t)})):(window.addEventListener(\\\\\\\"orientationchange\\\\\\\",s),window.addEventListener(\\\\\\\"deviceorientation\\\\\\\",i)),e.enabled=!0},this.disconnect=function(){window.removeEventListener(\\\\\\\"orientationchange\\\\\\\",s),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?On.e(t.alpha)+e.alphaOffset:0,s=t.beta?On.e(t.beta):0,r=t.gamma?On.e(t.gamma):0,o=e.screenOrientation?On.e(e.screenOrientation):0;!function(t,e,n,i,s){$v.set(n,e,-i,\\\\\\\"YXZ\\\\\\\"),t.setFromEuler($v),t.multiply(Zv),t.multiply(Jv.setFromAxisAngle(Yv,-s))}(e.object.quaternion,i,s,r,o),8*(1-n.dot(e.object.quaternion))>1e-6&&(n.copy(e.object.quaternion),e.dispatchEvent(Qv))}},this.dispose=function(){e.disconnect()},this.connect()}}const ty=new class extends la{constructor(){super(...arguments),this.enabled=aa.BOOLEAN(1)}};class ey extends qv{constructor(){super(...arguments),this.paramsConfig=ty,this._controls_by_element_id=new Map}static type(){return rs.DEVICE_ORIENTATION}endEventName(){return\\\\\\\"end\\\\\\\"}async createControlsInstance(t,e){const n=new Kv(t);return this._controls_by_element_id.set(e.id,n),n}setupControls(t){t.enabled=this.pv.enabled}updateRequired(){return!0}disposeControlsForHtmlElementId(t){const e=this._controls_by_element_id.get(t);e&&(e.dispose(),this._controls_by_element_id.delete(t))}}class ny{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(On.d(e/this.radius,-1,1))),this}clone(){return(new this.constructor).copy(this)}}const iy={type:\\\\\\\"change\\\\\\\"},sy={type:\\\\\\\"start\\\\\\\"},ry={type:\\\\\\\"end\\\\\\\"};class oy extends J.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(iy),n.update(),s=i.NONE},this.update=function(){const e=new p.a,u=(new ah.a).setFromUnitVectors(t.up,new p.a(0,1,0)),d=u.clone().invert(),_=new p.a,m=new ah.a,f=2*Math.PI;let g=!1;return function(){const t=n.object.position;if(e.copy(t).sub(n.target),e.applyQuaternion(u),o.setFromVector3(e),n.autoRotate&&s===i.NONE&&E(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,!!(h||_.distanceToSquared(n.object.position)>r||8*(1-m.dot(n.object.quaternion))>r)&&(n.dispatchEvent(iy),_.copy(n.object.position),m.copy(n.object.quaternion),h=!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 s=i.NONE;const r=1e-6,o=new ny,a=new ny;let l=1;const c=new p.a;let h=!1;const u=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 M(){return Math.pow(.95,n.zoomSpeed)}function E(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 s=n.domElement;if(n.object.isPerspectiveCamera){const r=n.object.position;t.copy(r).sub(n.target);let o=t.length();o*=Math.tan(n.object.fov/2*Math.PI/180),C(2*e*o/s.clientHeight,n.object.matrix),N(2*i*o/s.clientHeight,n.object.matrix)}else n.object.isOrthographicCamera?(C(e*(n.object.right-n.object.left)/n.object.zoom/s.clientWidth,n.object.matrix),N(i*(n.object.top-n.object.bottom)/n.object.zoom/s.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(),h=!0):(console.warn(\\\\\\\"WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled.\\\\\\\"),n.enableZoom=!1)}function P(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(),h=!0):(console.warn(\\\\\\\"WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled.\\\\\\\"),n.enableZoom=!1)}function R(t){u.set(t.clientX,t.clientY)}function I(t){f.set(t.clientX,t.clientY)}function F(){if(1===T.length)u.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);u.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 B(){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 z(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(_,u).multiplyScalar(n.rotateSpeed);const e=n.domElement;E(2*Math.PI*m.x/e.clientHeight),S(2*Math.PI*m.y/e.clientHeight),u.copy(_)}function k(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,s=t.pageY-e.y,r=Math.sqrt(i*i+s*s);x.set(0,r),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($(t),T.length){case 1:switch(n.touches.ONE){case w.Tc.ROTATE:if(!1===n.enableRotate)return;F(),s=i.TOUCH_ROTATE;break;case w.Tc.PAN:if(!1===n.enablePan)return;D(),s=i.TOUCH_PAN;break;default:s=i.NONE}break;case 2:switch(n.touches.TWO){case w.Tc.DOLLY_PAN:if(!1===n.enableZoom&&!1===n.enablePan)return;n.enableZoom&&B(),n.enablePan&&D(),s=i.TOUCH_DOLLY_PAN;break;case w.Tc.DOLLY_ROTATE:if(!1===n.enableZoom&&!1===n.enableRotate)return;n.enableZoom&&B(),n.enableRotate&&F(),s=i.TOUCH_DOLLY_ROTATE;break;default:s=i.NONE}break;default:s=i.NONE}s!==i.NONE&&n.dispatchEvent(sy)}(t):function(t){let e;switch(t.button){case 0:e=n.mouseButtons.LEFT;break;case 1:e=n.mouseButtons.MIDDLE;break;case 2:e=n.mouseButtons.RIGHT;break;default:e=-1}switch(e){case w.hb.DOLLY:if(!1===n.enableZoom)return;!function(t){y.set(t.clientX,t.clientY)}(t),s=i.DOLLY;break;case w.hb.ROTATE:if(t.ctrlKey||t.metaKey||t.shiftKey){if(!1===n.enablePan)return;I(t),s=i.PAN}else{if(!1===n.enableRotate)return;R(t),s=i.ROTATE}break;case w.hb.PAN:if(t.ctrlKey||t.metaKey||t.shiftKey){if(!1===n.enableRotate)return;R(t),s=i.ROTATE}else{if(!1===n.enablePan)return;I(t),s=i.PAN}break;default:s=i.NONE}s!==i.NONE&&n.dispatchEvent(sy)}(t))}function V(t){!1!==n.enabled&&(\\\\\\\"touch\\\\\\\"===t.pointerType?function(t){switch($(t),s){case i.TOUCH_ROTATE:if(!1===n.enableRotate)return;z(t),n.update();break;case i.TOUCH_PAN:if(!1===n.enablePan)return;k(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&&k(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&&z(t)}(t),n.update();break;default:s=i.NONE}}(t):function(t){if(!1===n.enabled)return;switch(s){case i.ROTATE:if(!1===n.enableRotate)return;!function(t){_.set(t.clientX,t.clientY),m.subVectors(_,u).multiplyScalar(n.rotateSpeed);var e=n.domElement;E(2*Math.PI*m.x/e.clientHeight),S(2*Math.PI*m.y/e.clientHeight),u.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(M()):b.y<0&&P(M()),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(ry),s=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||s!==i.NONE&&s!==i.ROTATE||(t.preventDefault(),n.dispatchEvent(sy),function(t){t.deltaY<0?P(M()):t.deltaY>0&&O(M()),n.update()}(t),n.dispatchEvent(ry))}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:E(n.keyRotateSpeedHorizontal),e=!0;break;case n.keys.RIGHT:E(-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{py.PARAM_CALLBACK_update_target(t)}}),this.enableKeys=aa.BOOLEAN(0),this.keysMode=aa.INTEGER(uy.indexOf(hy.PAN),{visibleIf:{enableKeys:1},menu:{entries:uy.map(((t,e)=>({name:t,value:e})))}}),this.keysPanSpeed=aa.FLOAT(7,{range:[0,10],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:uy.indexOf(hy.PAN)}}),this.keysRotateSpeedVertical=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:uy.indexOf(hy.ROTATE)}}),this.keysRotateSpeedHorizontal=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:uy.indexOf(hy.ROTATE)}})}};class py extends qv{constructor(){super(...arguments),this.paramsConfig=dy,this._controls_by_element_id=new Map,this._target_array=[0,0,0]}static type(){return rs.ORBIT}endEventName(){return\\\\\\\"end\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Zo(ly,Jo.BASE),new Zo(cy,Jo.BASE),new Zo(\\\\\\\"end\\\\\\\",Jo.BASE)])}async createControlsInstance(t,e){const n=new oy(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(ly,{})})),t.addEventListener(\\\\\\\"change\\\\\\\",(()=>{this.dispatchEventToOutput(cy,{})})),t.addEventListener(\\\\\\\"end\\\\\\\",(()=>{this.dispatchEventToOutput(\\\\\\\"end\\\\\\\",{})}))}setupControls(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=uy[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)}updateRequired(){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())}))}disposeControlsForHtmlElementId(t){this._controls_by_element_id.get(t)&&this._controls_by_element_id.delete(t)}}class _y extends py{static type(){return rs.MAP}async create_controls_instance(t,e){const n=new ay(t,e);return this._bind_listeners_to_controls_instance(n),n}}const my=new class extends la{constructor(){super(...arguments),this.delay=aa.INTEGER(1e3,{range:[0,1e3],rangeLocked:[!0,!1]})}};class fy extends ka{constructor(){super(...arguments),this.paramsConfig=my}static type(){return\\\\\\\"delay\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(\\\\\\\"in\\\\\\\",Jo.BASE,this._process_input.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(\\\\\\\"out\\\\\\\",Jo.BASE)])}_process_input(t){setTimeout((()=>{this.dispatchEventToOutput(\\\\\\\"out\\\\\\\",t)}),this.pv.delay)}}const gy={type:\\\\\\\"change\\\\\\\"},vy={type:\\\\\\\"lock\\\\\\\"},yy={type:\\\\\\\"unlock\\\\\\\"},xy=Math.PI/2,by=new p.a,wy=new ny;class Ty extends J.a{constructor(t,e,n){super(),this.camera=t,this.domElement=e,this.player=n,this.isLocked=!1,this.minPolarAngle=0,this.maxPolarAngle=Math.PI,this.rotateSpeed=1,this.euler=new Xv.a(0,0,0,\\\\\\\"YXZ\\\\\\\"),this.boundMethods={onMouseMove:this.onMouseMove.bind(this),onPointerlockChange:this.onPointerlockChange.bind(this),onPointerlockError:this.onPointerlockError.bind(this)},this._azimuthalAngle=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.rotateSpeed,this.euler.x-=.002*n*this.rotateSpeed,this.euler.x=Math.max(xy-this.maxPolarAngle,Math.min(xy-this.minPolarAngle,this.euler.x)),this.camera.quaternion.setFromEuler(this.euler),this._computeAzimuthalAngle(),this.dispatchEvent(gy)}}_computeAzimuthalAngle(){this.camera.updateMatrixWorld(),by.set(0,0,1),this.camera.localToWorld(by),by.sub(this.camera.position),wy.setFromVector3(by),this._azimuthalAngle=wy.theta}onPointerlockChange(){this.domElement.ownerDocument.pointerLockElement===this.domElement?(this.dispatchEvent(vy),this.isLocked=!0):(this.dispatchEvent(yy),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}lock(){this.domElement.requestPointerLock()}unlock(){this.domElement.ownerDocument.exitPointerLock()}update(t){this.player&&(this.player.setAzimuthalAngle(this._azimuthalAngle),this.player.update(t))}}var Ay=n(16);const My=new p.a,Ey=new p.a;class Sy{constructor(t=new p.a,e=new p.a){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){My.subVectors(t,this.start),Ey.subVectors(this.end,this.start);const n=Ey.dot(Ey);let i=Ey.dot(My)/n;return e&&(i=On.d(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)}}const Cy=new p.a;function Ny(t,e,n,i,s,r){const o=2*Math.PI*s/4,a=Math.max(r-2*s,0),l=Math.PI/4;Cy.copy(e),Cy[i]=0,Cy.normalize();const c=.5*o/(o+a),h=1-Cy.angleTo(t)/l;if(1===Math.sign(Cy[n]))return h*c;return a/(o+a)+c+c*(1-h)}class Ly extends N{constructor(t=1,e=1,n=1,i=2,s=.1){if(i=2*i+1,s=Math.min(t/2,e/2,n/2,s),super(1,1,1,i,i,i),1===i)return;const r=this.toNonIndexed();this.index=null,this.attributes.position=r.attributes.position,this.attributes.normal=r.attributes.normal,this.attributes.uv=r.attributes.uv;const o=new p.a,a=new p.a,l=new p.a(t,e,n).divideScalar(2).subScalar(s),c=this.attributes.position.array,h=this.attributes.normal.array,u=this.attributes.uv.array,d=c.length/6,_=new p.a,m=.5/i;for(let i=0,r=0;ithis.object.position.y<-25,this.object=t.object,this.object.matrixAutoUpdate=!0,this.collider=t.collider,t.meshName&&(this._mesh=new B.a,this._mesh.geometry=Oy({radius:this.capsuleInfo.radius,height:1}),this._mesh.name=t.meshName,this._mesh.receiveShadow=!0,this._mesh.castShadow=!0)}setCollider(t){this.collider=t}setCapsule(t){this.capsuleInfo.radius=t.radius,this.capsuleInfo.segment.end.y=-t.height,this._mesh&&(this._mesh.geometry=Oy(t))}setUsePlayerMesh(t){t?(this._mesh=this._mesh||this._createMesh(),this.object.add(this._mesh)):this._mesh&&this.object.remove(this._mesh)}_createMesh(){const t=new B.a;return t.geometry=Oy({radius:this.capsuleInfo.radius,height:1}),t.name=this._meshName||\\\\\\\"defaultPlayerMeshName\\\\\\\",t.receiveShadow=!0,t.castShadow=!0,t}setMaterial(t){this._mesh&&(this._mesh.material=t)}reset(){this.stop(),this.object.position.copy(this.startPosition),ky.copy(this.startRotation).multiplyScalar(On.a),this.object.rotation.setFromVector3(ky)}stop(){this._pressed.forward=!1,this._pressed.backward=!1,this._pressed.left=!1,this._pressed.right=!1,this._running=!1}setResetRequiredCallback(t){this._resetRequiredCallback=t}setAzimuthalAngle(t){this._azimuthalAngle=t}update(t){const e=Math.min(t,.1);for(let t=0;tt.intersectsBox(Dy),intersectsTriangle:t=>{const e=Iy,n=Fy,s=t.closestPointToSegment(zy,e,n);if(sMath.abs(t*this._velocity.y*.25);const o=Math.max(0,r.length()-1e-5);r.normalize().multiplyScalar(o),this.object.position.add(r),this._onGround?this._velocity.set(0,0,0):(r.normalize(),this._velocity.addScaledVector(r,-r.dot(this._velocity))),this._resetRequiredCallback()&&this.reset()}setForward(t){this._pressed.forward=t}setBackward(t){this._pressed.backward=t}setLeft(t){this._pressed.left=t}setRight(t){this._pressed.right=t}jump(){this._onGround&&this.jumpAllowed&&(this._velocity.y=this.jumpStrength)}setRun(t){t?this._onGround&&this.runAllowed&&(this._running=!0):this._running=!1}}function Gy(t){t.preventDefault()}class Vy{constructor(t){this.player=t,this._bounds={keydown:this._onKeyDown.bind(this),keyup:this._onKeyUp.bind(this)}}_onKeyDown(t){if(!t.ctrlKey)switch(t.code){case\\\\\\\"ArrowUp\\\\\\\":case\\\\\\\"KeyW\\\\\\\":this.player.setForward(!0),Gy(t);break;case\\\\\\\"ArrowDown\\\\\\\":case\\\\\\\"KeyS\\\\\\\":this.player.setBackward(!0),Gy(t);break;case\\\\\\\"ArrowRight\\\\\\\":case\\\\\\\"KeyD\\\\\\\":this.player.setRight(!0),Gy(t);break;case\\\\\\\"ArrowLeft\\\\\\\":case\\\\\\\"KeyA\\\\\\\":this.player.setLeft(!0),Gy(t);break;case\\\\\\\"Space\\\\\\\":this.player.jump(),Gy(t);break;case\\\\\\\"ShiftLeft\\\\\\\":case\\\\\\\"ShiftRight\\\\\\\":this.player.setRun(!0),Gy(t)}}_onKeyUp(t){switch(t.code){case\\\\\\\"ArrowUp\\\\\\\":case\\\\\\\"KeyW\\\\\\\":this.player.setForward(!1);break;case\\\\\\\"ArrowDown\\\\\\\":case\\\\\\\"KeyS\\\\\\\":this.player.setBackward(!1);break;case\\\\\\\"ArrowRight\\\\\\\":case\\\\\\\"KeyD\\\\\\\":this.player.setRight(!1);break;case\\\\\\\"ArrowLeft\\\\\\\":case\\\\\\\"KeyA\\\\\\\":this.player.setLeft(!1);break;case\\\\\\\"ShiftLeft\\\\\\\":case\\\\\\\"ShiftRight\\\\\\\":this.player.setRun(!1),Gy(t)}}addEvents(){document.addEventListener(\\\\\\\"keydown\\\\\\\",this._bounds.keydown),document.addEventListener(\\\\\\\"keyup\\\\\\\",this._bounds.keyup)}removeEvents(){document.removeEventListener(\\\\\\\"keydown\\\\\\\",this._bounds.keydown),document.removeEventListener(\\\\\\\"keyup\\\\\\\",this._bounds.keyup)}}const Hy=\\\\\\\"lock\\\\\\\",jy=\\\\\\\"change\\\\\\\",Wy=\\\\\\\"unlock\\\\\\\";function qy(){return{cook:!1,callback:t=>{Yy.PARAM_CALLBACK_updatePlayerParams(t)}}}const Xy=new class extends la{constructor(){super(...arguments),this.main=aa.FOLDER(),this.colliderObject=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.SOP}}),this.lock=aa.BUTTON(null,{callback:t=>{Yy.PARAM_CALLBACK_lockControls(t)}}),this.unlock=aa.BUTTON(null,{callback:t=>{Yy.PARAM_CALLBACK_unlockControls(t)}}),this.capsuleRadius=aa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!1],...qy()}),this.capsuleHeight=aa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1],...qy()}),this.physics=aa.FOLDER(),this.physicsSteps=aa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1],...qy()}),this.gravity=aa.VECTOR3([0,-30,0],{...qy()}),this.translateSpeed=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1],...qy()}),this.rotateSpeed=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]}),this.jumpAllowed=aa.BOOLEAN(!0,{...qy()}),this.jumpStrength=aa.FLOAT(10,{range:[0,100],rangeLocked:[!0,!1],...qy()}),this.runAllowed=aa.BOOLEAN(!0,{...qy()}),this.runSpeedMult=aa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],...qy()}),this.updateCollider=aa.BUTTON(null,{callback:t=>{Yy.PARAM_CALLBACK_updateCollider(t)}}),this.init=aa.FOLDER(),this.startPosition=aa.VECTOR3([0,2,0],{...qy()}),this.startRotation=aa.VECTOR3([0,0,0],{...qy()}),this.reset=aa.BUTTON(null,{callback:t=>{Yy.PARAM_CALLBACK_resetPlayer(t)}}),this.minPolarAngle=aa.FLOAT(0,{range:[0,Math.PI],rangeLocked:[!0,!0]}),this.maxPolarAngle=aa.FLOAT(\\\\\\\"$PI\\\\\\\",{range:[0,Math.PI],rangeLocked:[!0,!0]})}};class Yy extends qv{constructor(){super(...arguments),this.paramsConfig=Xy,this._controls_by_element_id=new Map}static type(){return rs.FIRST_PERSON}endEventName(){return\\\\\\\"unlock\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(Hy,Jo.BASE,this.lockControls.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(Hy,Jo.BASE),new Zo(jy,Jo.BASE),new Zo(Wy,Jo.BASE)])}async createControlsInstance(t,e){await this._initPlayer(t);const n=new Ty(t,e,this._player);return this._controls_by_element_id.set(e.id,n),this._bind_listeners_to_controls_instance(n),n}async _initPlayer(t){this._player=this._player||await this._createPlayer(t),this._player&&(this._updatePlayerParams(),this._player.reset())}async _updatePlayerParams(){this._player&&(this._player.startPosition.copy(this.pv.startPosition),this._player.startRotation.copy(this.pv.startRotation),this._player.physicsSteps=this.pv.physicsSteps,this._player.jumpAllowed=this.pv.jumpAllowed,this._player.jumpStrength=this.pv.jumpStrength,this._player.runAllowed=this.pv.runAllowed,this._player.runSpeedMult=this.pv.runSpeedMult,this._player.gravity.copy(this.pv.gravity),this._player.speed=this.pv.translateSpeed,this._player.setCapsule({radius:this.pv.capsuleRadius,height:this.pv.capsuleHeight}))}async _createPlayer(t){const e=t,n=await this._getCollider();if(!n)return void this.states.error.set(\\\\\\\"invalid collider\\\\\\\");return new Uy({object:e,collider:n})}_resetPlayer(){var t;null===(t=this._player)||void 0===t||t.reset()}async _getCollider(){const t=this.pv.colliderObject.nodeWithContext(ts.SOP);if(!t)return void this.states.error.set(\\\\\\\"collider node not found\\\\\\\");const e=(await t.compute()).coreContent();if(!e)return void this.states.error.set(\\\\\\\"invalid collider node\\\\\\\");return e.objects()[0]}async _updateCollider(){var t;const e=await this._getCollider();e?null===(t=this._player)||void 0===t||t.setCollider(e):this.states.error.set(\\\\\\\"invalid collider\\\\\\\")}_bind_listeners_to_controls_instance(t){t.addEventListener(Hy,(()=>{this.dispatchEventToOutput(Hy,{})})),t.addEventListener(jy,(()=>{this.dispatchEventToOutput(jy,{})})),t.addEventListener(Wy,(()=>{this.dispatchEventToOutput(Wy,{})}))}updateRequired(){return!0}setupControls(t){t.minPolarAngle=this.pv.minPolarAngle,t.maxPolarAngle=this.pv.maxPolarAngle,t.rotateSpeed=this.pv.rotateSpeed}disposeControlsForHtmlElementId(t){const e=this._controls_by_element_id.get(t);e&&(e.dispose(),this._controls_by_element_id.delete(t))}unlockControls(){var t,e;const n=this._firstControls();n&&(n.unlock(),null===(t=this._corePlayerKeyEvents)||void 0===t||t.removeEvents(),null===(e=this._player)||void 0===e||e.stop())}lockControls(){const t=this._firstControls();t&&(this._player&&(this._corePlayerKeyEvents=this._corePlayerKeyEvents||new Vy(this._player),this._corePlayerKeyEvents.addEvents()),t.lock())}_firstControls(){let t;return this._controls_by_element_id.forEach(((e,n)=>{t=t||e})),t}static PARAM_CALLBACK_lockControls(t){t.lockControls()}static PARAM_CALLBACK_unlockControls(t){t.unlockControls()}static PARAM_CALLBACK_updateCollider(t){t._updateCollider()}static PARAM_CALLBACK_updatePlayerParams(t){t._updatePlayerParams()}static PARAM_CALLBACK_resetPlayer(t){t._resetPlayer()}}var $y,Jy;!function(t){t.TRIGGER=\\\\\\\"trigger\\\\\\\",t.RESET=\\\\\\\"reset\\\\\\\"}($y||($y={})),function(t){t.OUT=\\\\\\\"out\\\\\\\",t.LAST=\\\\\\\"last\\\\\\\"}(Jy||(Jy={}));const Zy=new class extends la{constructor(){super(...arguments),this.maxCount=aa.INTEGER(5,{range:[0,10],rangeLocked:[!0,!1]}),this.reset=aa.BUTTON(null,{callback:t=>{Qy.PARAM_CALLBACK_reset(t)}})}};class Qy extends ka{constructor(){super(...arguments),this.paramsConfig=Zy,this._process_count=0,this._last_dispatched=!1}static type(){return\\\\\\\"limit\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo($y.TRIGGER,Jo.BASE,this.process_event_trigger.bind(this)),new Zo($y.RESET,Jo.BASE,this.process_event_reset.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(Jy.OUT,Jo.BASE),new Zo(Jy.LAST,Jo.BASE)])}processEvent(t){}process_event_trigger(t){this._process_count(t.preventDefault(),!1);class nx{static disableContextMenu(){document.addEventListener(\\\\\\\"contextmenu\\\\\\\",ex)}static reEstablishContextMenu(){document.removeEventListener(\\\\\\\"contextmenu\\\\\\\",ex)}}const ix=100,sx=301,rx=302,ox=303,ax=304,lx=306,cx=307,hx=1e3,ux=1001,dx=1002,px=1003,_x=1004,mx=1005,fx=1006,gx=1007,vx=1008,yx=1009,xx=1012,bx=1014,wx=1015,Tx=1016,Ax=1020,Mx=1022,Ex=1023,Sx=1026,Cx=1027,Nx=2300,Lx=2301,Ox=2302,Px=2400,Rx=2401,Ix=2402,Fx=2500,Dx=3e3,Bx=3001,zx=3007,kx=3002,Ux=7680,Gx=35044,Vx=35048,Hx=\\\\\\\"300 es\\\\\\\";class jx{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]+Yx[t>>16&255]+Yx[t>>24&255]+\\\\\\\"-\\\\\\\"+Yx[255&e]+Yx[e>>8&255]+\\\\\\\"-\\\\\\\"+Yx[e>>16&15|64]+Yx[e>>24&255]+\\\\\\\"-\\\\\\\"+Yx[63&n|128]+Yx[n>>8&255]+\\\\\\\"-\\\\\\\"+Yx[n>>16&255]+Yx[n>>24&255]+Yx[255&i]+Yx[i>>8&255]+Yx[i>>16&255]+Yx[i>>24&255]).toUpperCase()}function Zx(t,e,n){return Math.max(e,Math.min(n,t))}function Qx(t,e){return(t%e+e)%e}function Kx(t,e,n){return(1-n)*t+n*e}function tb(t){return 0==(t&t-1)&&0!==t}function eb(t){return Math.pow(2,Math.ceil(Math.log(t)/Math.LN2))}function nb(t){return Math.pow(2,Math.floor(Math.log(t)/Math.LN2))}var ib=Object.freeze({__proto__:null,DEG2RAD:qx,RAD2DEG:Xx,generateUUID:Jx,clamp:Zx,euclideanModulo:Qx,mapLinear:function(t,e,n,i,s){return i+(t-e)*(s-i)/(n-e)},inverseLerp:function(t,e,n){return t!==e?(n-t)/(e-t):0},lerp:Kx,damp:function(t,e,n,i){return Kx(t,e,1-Math.exp(-n*i))},pingpong:function(t,e=1){return e-Math.abs(Qx(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&&(Wx=t%2147483647),Wx=16807*Wx%2147483647,(Wx-1)/2147483646},degToRad:function(t){return t*qx},radToDeg:function(t){return t*Xx},isPowerOfTwo:tb,ceilPowerOfTwo:eb,floorPowerOfTwo:nb,setQuaternionFromProperEuler:function(t,e,n,i,s){const r=Math.cos,o=Math.sin,a=r(n/2),l=o(n/2),c=r((e+i)/2),h=o((e+i)/2),u=r((e-i)/2),d=o((e-i)/2),p=r((i-e)/2),_=o((i-e)/2);switch(s){case\\\\\\\"XYX\\\\\\\":t.set(a*h,l*u,l*d,a*c);break;case\\\\\\\"YZY\\\\\\\":t.set(l*d,a*h,l*u,a*c);break;case\\\\\\\"ZXZ\\\\\\\":t.set(l*u,l*d,a*h,a*c);break;case\\\\\\\"XZX\\\\\\\":t.set(a*h,l*_,l*p,a*c);break;case\\\\\\\"YXY\\\\\\\":t.set(l*p,a*h,l*_,a*c);break;case\\\\\\\"ZYZ\\\\\\\":t.set(l*_,l*p,a*h,a*c);break;default:console.warn(\\\\\\\"THREE.MathUtils: .setQuaternionFromProperEuler() encountered an unknown order: \\\\\\\"+s)}}});class sb{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),s=this.x-t.x,r=this.y-t.y;return this.x=s*n-r*i+t.x,this.y=s*i+r*n+t.y,this}random(){return this.x=Math.random(),this.y=Math.random(),this}*[Symbol.iterator](){yield this.x,yield this.y}}sb.prototype.isVector2=!0;class rb{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,s,r,o,a,l){const c=this.elements;return c[0]=t,c[1]=i,c[2]=o,c[3]=e,c[4]=s,c[5]=a,c[6]=n,c[7]=r,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,s=this.elements,r=n[0],o=n[3],a=n[6],l=n[1],c=n[4],h=n[7],u=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 s[0]=r*_+o*g+a*x,s[3]=r*m+o*v+a*b,s[6]=r*f+o*y+a*w,s[1]=l*_+c*g+h*x,s[4]=l*m+c*v+h*b,s[7]=l*f+c*y+h*w,s[2]=u*_+d*g+p*x,s[5]=u*m+d*v+p*b,s[8]=u*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],s=t[3],r=t[4],o=t[5],a=t[6],l=t[7],c=t[8];return e*r*c-e*o*l-n*s*c+n*o*a+i*s*l-i*r*a}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],s=t[3],r=t[4],o=t[5],a=t[6],l=t[7],c=t[8],h=c*r-o*l,u=o*a-c*s,d=l*s-r*a,p=e*h+n*u+i*d;if(0===p)return this.set(0,0,0,0,0,0,0,0,0);const _=1/p;return t[0]=h*_,t[1]=(i*l-c*n)*_,t[2]=(o*n-i*r)*_,t[3]=u*_,t[4]=(c*e-i*a)*_,t[5]=(i*s-o*e)*_,t[6]=d*_,t[7]=(n*a-l*e)*_,t[8]=(r*e-n*s)*_,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,s,r,o){const a=Math.cos(s),l=Math.sin(s);return this.set(n*a,n*l,-n*(a*r+l*o)+r+t,-i*l,i*a,-i*(-l*r+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,s=i[0],r=i[3],o=i[6],a=i[1],l=i[4],c=i[7];return i[0]=e*s+n*a,i[3]=e*r+n*l,i[6]=e*o+n*c,i[1]=-n*s+e*a,i[4]=-n*r+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 ob(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}rb.prototype.isMatrix3=!0;Int8Array,Uint8Array,Uint8ClampedArray,Int16Array,Uint16Array,Int32Array,Uint32Array,Float32Array,Float64Array;function ab(t){return document.createElementNS(\\\\\\\"http://www.w3.org/1999/xhtml\\\\\\\",t)}let lb;class cb{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===lb&&(lb=ab(\\\\\\\"canvas\\\\\\\")),lb.width=t.width,lb.height=t.height;const n=lb.getContext(\\\\\\\"2d\\\\\\\");t instanceof ImageData?n.putImageData(t,0,0):n.drawImage(t,0,0,t.width,t.height),e=lb}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 hb=0;class ub extends jx{constructor(t=ub.DEFAULT_IMAGE,e=ub.DEFAULT_MAPPING,n=1001,i=1001,s=1006,r=1008,o=1023,a=1009,l=1,c=3e3){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:hb++}),this.uuid=Jx(),this.name=\\\\\\\"\\\\\\\",this.image=t,this.mipmaps=[],this.mapping=e,this.wrapS=n,this.wrapT=i,this.magFilter=s,this.minFilter=r,this.anisotropy=l,this.format=o,this.internalFormat=null,this.type=a,this.offset=new sb(0,0),this.repeat=new sb(1,1),this.center=new sb(0,0),this.rotation=0,this.matrixAutoUpdate=!0,this.matrix=new rb,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=Jx()),!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 hx:t.x=t.x-Math.floor(t.x);break;case ux:t.x=t.x<0?0:1;break;case dx: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 hx:t.y=t.y-Math.floor(t.y);break;case ux:t.y=t.y<0?0:1;break;case dx: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 db(t){return\\\\\\\"undefined\\\\\\\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\\\\\\\"undefined\\\\\\\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\\\\\\\"undefined\\\\\\\"!=typeof ImageBitmap&&t instanceof ImageBitmap?cb.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.\\\\\\\"),{})}ub.DEFAULT_IMAGE=void 0,ub.DEFAULT_MAPPING=300,ub.prototype.isTexture=!0;class pb{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,s=this.w,r=t.elements;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.w=r[3]*e+r[7]*n+r[11]*i+r[15]*s,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,s;const r=.01,o=.1,a=t.elements,l=a[0],c=a[4],h=a[8],u=a[1],d=a[5],p=a[9],_=a[2],m=a[6],f=a[10];if(Math.abs(c-u)a&&t>g?tg?a=0?1:-1,i=1-e*e;if(i>Number.EPSILON){const s=Math.sqrt(i),r=Math.atan2(s,e*n);t=Math.sin(t*r)/s,o=Math.sin(o*r)/s}const s=o*n;if(a=a*t+u*s,l=l*t+d*s,c=c*t+p*s,h=h*t+_*s,t===1-o){const t=1/Math.sqrt(a*a+l*l+c*c+h*h);a*=t,l*=t,c*=t,h*=t}}t[e]=a,t[e+1]=l,t[e+2]=c,t[e+3]=h}static multiplyQuaternionsFlat(t,e,n,i,s,r){const o=n[i],a=n[i+1],l=n[i+2],c=n[i+3],h=s[r],u=s[r+1],d=s[r+2],p=s[r+3];return t[e]=o*p+c*h+a*d-l*u,t[e+1]=a*p+c*u+l*h-o*d,t[e+2]=l*p+c*d+o*u-a*h,t[e+3]=c*p-o*h-a*u-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,s=t._z,r=t._order,o=Math.cos,a=Math.sin,l=o(n/2),c=o(i/2),h=o(s/2),u=a(n/2),d=a(i/2),p=a(s/2);switch(r){case\\\\\\\"XYZ\\\\\\\":this._x=u*c*h+l*d*p,this._y=l*d*h-u*c*p,this._z=l*c*p+u*d*h,this._w=l*c*h-u*d*p;break;case\\\\\\\"YXZ\\\\\\\":this._x=u*c*h+l*d*p,this._y=l*d*h-u*c*p,this._z=l*c*p-u*d*h,this._w=l*c*h+u*d*p;break;case\\\\\\\"ZXY\\\\\\\":this._x=u*c*h-l*d*p,this._y=l*d*h+u*c*p,this._z=l*c*p+u*d*h,this._w=l*c*h-u*d*p;break;case\\\\\\\"ZYX\\\\\\\":this._x=u*c*h-l*d*p,this._y=l*d*h+u*c*p,this._z=l*c*p-u*d*h,this._w=l*c*h+u*d*p;break;case\\\\\\\"YZX\\\\\\\":this._x=u*c*h+l*d*p,this._y=l*d*h+u*c*p,this._z=l*c*p-u*d*h,this._w=l*c*h-u*d*p;break;case\\\\\\\"XZY\\\\\\\":this._x=u*c*h-l*d*p,this._y=l*d*h-u*c*p,this._z=l*c*p+u*d*h,this._w=l*c*h+u*d*p;break;default:console.warn(\\\\\\\"THREE.Quaternion: .setFromEuler() encountered an unknown order: \\\\\\\"+r)}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],s=e[8],r=e[1],o=e[5],a=e[9],l=e[2],c=e[6],h=e[10],u=n+o+h;if(u>0){const t=.5/Math.sqrt(u+1);this._w=.25/t,this._x=(c-a)*t,this._y=(s-l)*t,this._z=(r-i)*t}else if(n>o&&n>h){const t=2*Math.sqrt(1+n-o-h);this._w=(c-a)/t,this._x=.25*t,this._y=(i+r)/t,this._z=(s+l)/t}else if(o>h){const t=2*Math.sqrt(1+o-n-h);this._w=(s-l)/t,this._x=(i+r)/t,this._y=.25*t,this._z=(a+c)/t}else{const t=2*Math.sqrt(1+h-n-o);this._w=(r-i)/t,this._x=(s+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(Zx(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,s=t._z,r=t._w,o=e._x,a=e._y,l=e._z,c=e._w;return this._x=n*c+r*o+i*l-s*a,this._y=i*c+r*a+s*o-n*l,this._z=s*c+r*l+n*a-i*o,this._w=r*c-n*o-i*a-s*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,s=this._z,r=this._w;let o=r*t._w+n*t._x+i*t._y+s*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=r,this._x=n,this._y=i,this._z=s,this;const a=1-o*o;if(a<=Number.EPSILON){const t=1-e;return this._w=t*r+e*this._w,this._x=t*n+e*this._x,this._y=t*i+e*this._y,this._z=t*s+e*this._z,this.normalize(),this._onChangeCallback(),this}const l=Math.sqrt(a),c=Math.atan2(l,o),h=Math.sin((1-e)*c)/l,u=Math.sin(e*c)/l;return this._w=r*h+this._w*u,this._x=n*h+this._x*u,this._y=i*h+this._y*u,this._z=s*h+this._z*u,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(),s=2*Math.PI*Math.random();return this.set(e*Math.cos(i),n*Math.sin(s),n*Math.cos(s),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(){}}fb.prototype.isQuaternion=!0;class gb{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(yb.setFromEuler(t))}applyAxisAngle(t,e){return this.applyQuaternion(yb.setFromAxisAngle(t,e))}applyMatrix3(t){const e=this.x,n=this.y,i=this.z,s=t.elements;return this.x=s[0]*e+s[3]*n+s[6]*i,this.y=s[1]*e+s[4]*n+s[7]*i,this.z=s[2]*e+s[5]*n+s[8]*i,this}applyNormalMatrix(t){return this.applyMatrix3(t).normalize()}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,s=t.elements,r=1/(s[3]*e+s[7]*n+s[11]*i+s[15]);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}applyQuaternion(t){const e=this.x,n=this.y,i=this.z,s=t.x,r=t.y,o=t.z,a=t.w,l=a*e+r*i-o*n,c=a*n+o*e-s*i,h=a*i+s*n-r*e,u=-s*e-r*n-o*i;return this.x=l*a+u*-s+c*-o-h*-r,this.y=c*a+u*-r+h*-s-l*-o,this.z=h*a+u*-o+l*-r-c*-s,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,s=t.elements;return this.x=s[0]*e+s[4]*n+s[8]*i,this.y=s[1]*e+s[5]*n+s[9]*i,this.z=s[2]*e+s[6]*n+s[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,s=t.z,r=e.x,o=e.y,a=e.z;return this.x=i*a-s*o,this.y=s*r-n*a,this.z=n*o-i*r,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 vb.copy(this).projectOnVector(t),this.sub(vb)}reflect(t){return this.sub(vb.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(Zx(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}}gb.prototype.isVector3=!0;const vb=new gb,yb=new fb;class xb{constructor(t=new gb(1/0,1/0,1/0),e=new gb(-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,s=-1/0,r=-1/0,o=-1/0;for(let a=0,l=t.length;as&&(s=l),c>r&&(r=c),h>o&&(o=h)}return this.min.set(e,n,i),this.max.set(s,r,o),this}setFromBufferAttribute(t){let e=1/0,n=1/0,i=1/0,s=-1/0,r=-1/0,o=-1/0;for(let a=0,l=t.count;as&&(s=l),c>r&&(r=c),h>o&&(o=h)}return this.min.set(e,n,i),this.max.set(s,r,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,wb),wb.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(Lb),Ob.subVectors(this.max,Lb),Ab.subVectors(t.a,Lb),Mb.subVectors(t.b,Lb),Eb.subVectors(t.c,Lb),Sb.subVectors(Mb,Ab),Cb.subVectors(Eb,Mb),Nb.subVectors(Ab,Eb);let e=[0,-Sb.z,Sb.y,0,-Cb.z,Cb.y,0,-Nb.z,Nb.y,Sb.z,0,-Sb.x,Cb.z,0,-Cb.x,Nb.z,0,-Nb.x,-Sb.y,Sb.x,0,-Cb.y,Cb.x,0,-Nb.y,Nb.x,0];return!!Ib(e,Ab,Mb,Eb,Ob)&&(e=[1,0,0,0,1,0,0,0,1],!!Ib(e,Ab,Mb,Eb,Ob)&&(Pb.crossVectors(Sb,Cb),e=[Pb.x,Pb.y,Pb.z],Ib(e,Ab,Mb,Eb,Ob)))}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return wb.copy(t).clamp(this.min,this.max).sub(t).length()}getBoundingSphere(t){return this.getCenter(t.center),t.radius=.5*this.getSize(wb).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()||(bb[0].set(this.min.x,this.min.y,this.min.z).applyMatrix4(t),bb[1].set(this.min.x,this.min.y,this.max.z).applyMatrix4(t),bb[2].set(this.min.x,this.max.y,this.min.z).applyMatrix4(t),bb[3].set(this.min.x,this.max.y,this.max.z).applyMatrix4(t),bb[4].set(this.max.x,this.min.y,this.min.z).applyMatrix4(t),bb[5].set(this.max.x,this.min.y,this.max.z).applyMatrix4(t),bb[6].set(this.max.x,this.max.y,this.min.z).applyMatrix4(t),bb[7].set(this.max.x,this.max.y,this.max.z).applyMatrix4(t),this.setFromPoints(bb)),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)}}xb.prototype.isBox3=!0;const bb=[new gb,new gb,new gb,new gb,new gb,new gb,new gb,new gb],wb=new gb,Tb=new xb,Ab=new gb,Mb=new gb,Eb=new gb,Sb=new gb,Cb=new gb,Nb=new gb,Lb=new gb,Ob=new gb,Pb=new gb,Rb=new gb;function Ib(t,e,n,i,s){for(let r=0,o=t.length-3;r<=o;r+=3){Rb.fromArray(t,r);const o=s.x*Math.abs(Rb.x)+s.y*Math.abs(Rb.y)+s.z*Math.abs(Rb.z),a=e.dot(Rb),l=n.dot(Rb),c=i.dot(Rb);if(Math.max(-Math.max(a,l,c),Math.min(a,l,c))>o)return!1}return!0}const Fb=new xb,Db=new gb,Bb=new gb,zb=new gb;class kb{constructor(t=new gb,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):Fb.setFromPoints(t).getCenter(n);let i=0;for(let e=0,s=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){zb.subVectors(t,this.center);const e=zb.lengthSq();if(e>this.radius*this.radius){const t=Math.sqrt(e),n=.5*(t-this.radius);this.center.add(zb.multiplyScalar(n/t)),this.radius+=n}return this}union(t){return Bb.subVectors(t.center,this.center).normalize().multiplyScalar(t.radius),this.expandByPoint(Db.copy(t.center).add(Bb)),this.expandByPoint(Db.copy(t.center).sub(Bb)),this}equals(t){return t.center.equals(this.center)&&t.radius===this.radius}clone(){return(new this.constructor).copy(this)}}const Ub=new gb,Gb=new gb,Vb=new gb,Hb=new gb,jb=new gb,Wb=new gb,qb=new gb;class Xb{constructor(t=new gb,e=new gb(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,Ub)),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=Ub.subVectors(t,this.origin).dot(this.direction);return e<0?this.origin.distanceToSquared(t):(Ub.copy(this.direction).multiplyScalar(e).add(this.origin),Ub.distanceToSquared(t))}distanceSqToSegment(t,e,n,i){Gb.copy(t).add(e).multiplyScalar(.5),Vb.copy(e).sub(t).normalize(),Hb.copy(this.origin).sub(Gb);const s=.5*t.distanceTo(e),r=-this.direction.dot(Vb),o=Hb.dot(this.direction),a=-Hb.dot(Vb),l=Hb.lengthSq(),c=Math.abs(1-r*r);let h,u,d,p;if(c>0)if(h=r*a-o,u=r*o-a,p=s*c,h>=0)if(u>=-p)if(u<=p){const t=1/c;h*=t,u*=t,d=h*(h+r*u+2*o)+u*(r*h+u+2*a)+l}else u=s,h=Math.max(0,-(r*u+o)),d=-h*h+u*(u+2*a)+l;else u=-s,h=Math.max(0,-(r*u+o)),d=-h*h+u*(u+2*a)+l;else u<=-p?(h=Math.max(0,-(-r*s+o)),u=h>0?-s:Math.min(Math.max(-s,-a),s),d=-h*h+u*(u+2*a)+l):u<=p?(h=0,u=Math.min(Math.max(-s,-a),s),d=u*(u+2*a)+l):(h=Math.max(0,-(r*s+o)),u=h>0?s:Math.min(Math.max(-s,-a),s),d=-h*h+u*(u+2*a)+l);else u=r>0?-s:s,h=Math.max(0,-(r*u+o)),d=-h*h+u*(u+2*a)+l;return n&&n.copy(this.direction).multiplyScalar(h).add(this.origin),i&&i.copy(Vb).multiplyScalar(u).add(Gb),d}intersectSphere(t,e){Ub.subVectors(t.center,this.origin);const n=Ub.dot(this.direction),i=Ub.dot(Ub)-n*n,s=t.radius*t.radius;if(i>s)return null;const r=Math.sqrt(s-i),o=n-r,a=n+r;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,s,r,o,a;const l=1/this.direction.x,c=1/this.direction.y,h=1/this.direction.z,u=this.origin;return l>=0?(n=(t.min.x-u.x)*l,i=(t.max.x-u.x)*l):(n=(t.max.x-u.x)*l,i=(t.min.x-u.x)*l),c>=0?(s=(t.min.y-u.y)*c,r=(t.max.y-u.y)*c):(s=(t.max.y-u.y)*c,r=(t.min.y-u.y)*c),n>r||s>i?null:((s>n||n!=n)&&(n=s),(r=0?(o=(t.min.z-u.z)*h,a=(t.max.z-u.z)*h):(o=(t.max.z-u.z)*h,a=(t.min.z-u.z)*h),n>a||o>i?null:((o>n||n!=n)&&(n=o),(a=0?n:i,e)))}intersectsBox(t){return null!==this.intersectBox(t,Ub)}intersectTriangle(t,e,n,i,s){jb.subVectors(e,t),Wb.subVectors(n,t),qb.crossVectors(jb,Wb);let r,o=this.direction.dot(qb);if(o>0){if(i)return null;r=1}else{if(!(o<0))return null;r=-1,o=-o}Hb.subVectors(this.origin,t);const a=r*this.direction.dot(Wb.crossVectors(Hb,Wb));if(a<0)return null;const l=r*this.direction.dot(jb.cross(Hb));if(l<0)return null;if(a+l>o)return null;const c=-r*Hb.dot(qb);return c<0?null:this.at(c/o,s)}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 Yb{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,s,r,o,a,l,c,h,u,d,p,_,m){const f=this.elements;return f[0]=t,f[4]=e,f[8]=n,f[12]=i,f[1]=s,f[5]=r,f[9]=o,f[13]=a,f[2]=l,f[6]=c,f[10]=h,f[14]=u,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 Yb).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/$b.setFromMatrixColumn(t,0).length(),s=1/$b.setFromMatrixColumn(t,1).length(),r=1/$b.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]*s,e[5]=n[5]*s,e[6]=n[6]*s,e[7]=0,e[8]=n[8]*r,e[9]=n[9]*r,e[10]=n[10]*r,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,s=t.z,r=Math.cos(n),o=Math.sin(n),a=Math.cos(i),l=Math.sin(i),c=Math.cos(s),h=Math.sin(s);if(\\\\\\\"XYZ\\\\\\\"===t.order){const t=r*c,n=r*h,i=o*c,s=o*h;e[0]=a*c,e[4]=-a*h,e[8]=l,e[1]=n+i*l,e[5]=t-s*l,e[9]=-o*a,e[2]=s-t*l,e[6]=i+n*l,e[10]=r*a}else if(\\\\\\\"YXZ\\\\\\\"===t.order){const t=a*c,n=a*h,i=l*c,s=l*h;e[0]=t+s*o,e[4]=i*o-n,e[8]=r*l,e[1]=r*h,e[5]=r*c,e[9]=-o,e[2]=n*o-i,e[6]=s+t*o,e[10]=r*a}else if(\\\\\\\"ZXY\\\\\\\"===t.order){const t=a*c,n=a*h,i=l*c,s=l*h;e[0]=t-s*o,e[4]=-r*h,e[8]=i+n*o,e[1]=n+i*o,e[5]=r*c,e[9]=s-t*o,e[2]=-r*l,e[6]=o,e[10]=r*a}else if(\\\\\\\"ZYX\\\\\\\"===t.order){const t=r*c,n=r*h,i=o*c,s=o*h;e[0]=a*c,e[4]=i*l-n,e[8]=t*l+s,e[1]=a*h,e[5]=s*l+t,e[9]=n*l-i,e[2]=-l,e[6]=o*a,e[10]=r*a}else if(\\\\\\\"YZX\\\\\\\"===t.order){const t=r*a,n=r*l,i=o*a,s=o*l;e[0]=a*c,e[4]=s-t*h,e[8]=i*h+n,e[1]=h,e[5]=r*c,e[9]=-o*c,e[2]=-l*c,e[6]=n*h+i,e[10]=t-s*h}else if(\\\\\\\"XZY\\\\\\\"===t.order){const t=r*a,n=r*l,i=o*a,s=o*l;e[0]=a*c,e[4]=-h,e[8]=l*c,e[1]=t*h+s,e[5]=r*c,e[9]=n*h-i,e[2]=i*h-n,e[6]=o*c,e[10]=s*h+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(Zb,t,Qb)}lookAt(t,e,n){const i=this.elements;return ew.subVectors(t,e),0===ew.lengthSq()&&(ew.z=1),ew.normalize(),Kb.crossVectors(n,ew),0===Kb.lengthSq()&&(1===Math.abs(n.z)?ew.x+=1e-4:ew.z+=1e-4,ew.normalize(),Kb.crossVectors(n,ew)),Kb.normalize(),tw.crossVectors(ew,Kb),i[0]=Kb.x,i[4]=tw.x,i[8]=ew.x,i[1]=Kb.y,i[5]=tw.y,i[9]=ew.y,i[2]=Kb.z,i[6]=tw.z,i[10]=ew.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,s=this.elements,r=n[0],o=n[4],a=n[8],l=n[12],c=n[1],h=n[5],u=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],M=i[1],E=i[5],S=i[9],C=i[13],N=i[2],L=i[6],O=i[10],P=i[14],R=i[3],I=i[7],F=i[11],D=i[15];return s[0]=r*b+o*M+a*N+l*R,s[4]=r*w+o*E+a*L+l*I,s[8]=r*T+o*S+a*O+l*F,s[12]=r*A+o*C+a*P+l*D,s[1]=c*b+h*M+u*N+d*R,s[5]=c*w+h*E+u*L+d*I,s[9]=c*T+h*S+u*O+d*F,s[13]=c*A+h*C+u*P+d*D,s[2]=p*b+_*M+m*N+f*R,s[6]=p*w+_*E+m*L+f*I,s[10]=p*T+_*S+m*O+f*F,s[14]=p*A+_*C+m*P+f*D,s[3]=g*b+v*M+y*N+x*R,s[7]=g*w+v*E+y*L+x*I,s[11]=g*T+v*S+y*O+x*F,s[15]=g*A+v*C+y*P+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],s=t[12],r=t[1],o=t[5],a=t[9],l=t[13],c=t[2],h=t[6],u=t[10],d=t[14];return t[3]*(+s*a*h-i*l*h-s*o*u+n*l*u+i*o*d-n*a*d)+t[7]*(+e*a*d-e*l*u+s*r*u-i*r*d+i*l*c-s*a*c)+t[11]*(+e*l*h-e*o*d-s*r*h+n*r*d+s*o*c-n*l*c)+t[15]*(-i*o*c-e*a*h+e*o*u+i*r*h-n*r*u+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],s=t[3],r=t[4],o=t[5],a=t[6],l=t[7],c=t[8],h=t[9],u=t[10],d=t[11],p=t[12],_=t[13],m=t[14],f=t[15],g=h*m*l-_*u*l+_*a*d-o*m*d-h*a*f+o*u*f,v=p*u*l-c*m*l-p*a*d+r*m*d+c*a*f-r*u*f,y=c*_*l-p*h*l+p*o*d-r*_*d-c*o*f+r*h*f,x=p*h*a-c*_*a-p*o*u+r*_*u+c*o*m-r*h*m,b=e*g+n*v+i*y+s*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]=(_*u*s-h*m*s-_*i*d+n*m*d+h*i*f-n*u*f)*w,t[2]=(o*m*s-_*a*s+_*i*l-n*m*l-o*i*f+n*a*f)*w,t[3]=(h*a*s-o*u*s-h*i*l+n*u*l+o*i*d-n*a*d)*w,t[4]=v*w,t[5]=(c*m*s-p*u*s+p*i*d-e*m*d-c*i*f+e*u*f)*w,t[6]=(p*a*s-r*m*s-p*i*l+e*m*l+r*i*f-e*a*f)*w,t[7]=(r*u*s-c*a*s+c*i*l-e*u*l-r*i*d+e*a*d)*w,t[8]=y*w,t[9]=(p*h*s-c*_*s-p*n*d+e*_*d+c*n*f-e*h*f)*w,t[10]=(r*_*s-p*o*s+p*n*l-e*_*l-r*n*f+e*o*f)*w,t[11]=(c*o*s-r*h*s-c*n*l+e*h*l+r*n*d-e*o*d)*w,t[12]=x*w,t[13]=(c*_*i-p*h*i+p*n*u-e*_*u-c*n*m+e*h*m)*w,t[14]=(p*o*i-r*_*i-p*n*a+e*_*a+r*n*m-e*o*m)*w,t[15]=(r*h*i-c*o*i+c*n*a-e*h*a-r*n*u+e*o*u)*w,this}scale(t){const e=this.elements,n=t.x,i=t.y,s=t.z;return e[0]*=n,e[4]*=i,e[8]*=s,e[1]*=n,e[5]*=i,e[9]*=s,e[2]*=n,e[6]*=i,e[10]*=s,e[3]*=n,e[7]*=i,e[11]*=s,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),s=1-n,r=t.x,o=t.y,a=t.z,l=s*r,c=s*o;return this.set(l*r+n,l*o-i*a,l*a+i*o,0,l*o+i*a,c*o+n,c*a-i*r,0,l*a-i*o,c*a+i*r,s*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,s,r){return this.set(1,n,s,0,t,1,r,0,e,i,1,0,0,0,0,1),this}compose(t,e,n){const i=this.elements,s=e._x,r=e._y,o=e._z,a=e._w,l=s+s,c=r+r,h=o+o,u=s*l,d=s*c,p=s*h,_=r*c,m=r*h,f=o*h,g=a*l,v=a*c,y=a*h,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-(u+f))*b,i[6]=(m+g)*b,i[7]=0,i[8]=(p+v)*w,i[9]=(m-g)*w,i[10]=(1-(u+_))*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 s=$b.set(i[0],i[1],i[2]).length();const r=$b.set(i[4],i[5],i[6]).length(),o=$b.set(i[8],i[9],i[10]).length();this.determinant()<0&&(s=-s),t.x=i[12],t.y=i[13],t.z=i[14],Jb.copy(this);const a=1/s,l=1/r,c=1/o;return Jb.elements[0]*=a,Jb.elements[1]*=a,Jb.elements[2]*=a,Jb.elements[4]*=l,Jb.elements[5]*=l,Jb.elements[6]*=l,Jb.elements[8]*=c,Jb.elements[9]*=c,Jb.elements[10]*=c,e.setFromRotationMatrix(Jb),n.x=s,n.y=r,n.z=o,this}makePerspective(t,e,n,i,s,r){void 0===r&&console.warn(\\\\\\\"THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.\\\\\\\");const o=this.elements,a=2*s/(e-t),l=2*s/(n-i),c=(e+t)/(e-t),h=(n+i)/(n-i),u=-(r+s)/(r-s),d=-2*r*s/(r-s);return o[0]=a,o[4]=0,o[8]=c,o[12]=0,o[1]=0,o[5]=l,o[9]=h,o[13]=0,o[2]=0,o[6]=0,o[10]=u,o[14]=d,o[3]=0,o[7]=0,o[11]=-1,o[15]=0,this}makeOrthographic(t,e,n,i,s,r){const o=this.elements,a=1/(e-t),l=1/(n-i),c=1/(r-s),h=(e+t)*a,u=(n+i)*l,d=(r+s)*c;return o[0]=2*a,o[4]=0,o[8]=0,o[12]=-h,o[1]=0,o[5]=2*l,o[9]=0,o[13]=-u,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}}Yb.prototype.isMatrix4=!0;const $b=new gb,Jb=new Yb,Zb=new gb(0,0,0),Qb=new gb(1,1,1),Kb=new gb,tw=new gb,ew=new gb,nw=new Yb,iw=new fb;class sw{constructor(t=0,e=0,n=0,i=sw.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,s=i[0],r=i[4],o=i[8],a=i[1],l=i[5],c=i[9],h=i[2],u=i[6],d=i[10];switch(e){case\\\\\\\"XYZ\\\\\\\":this._y=Math.asin(Zx(o,-1,1)),Math.abs(o)<.9999999?(this._x=Math.atan2(-c,d),this._z=Math.atan2(-r,s)):(this._x=Math.atan2(u,l),this._z=0);break;case\\\\\\\"YXZ\\\\\\\":this._x=Math.asin(-Zx(c,-1,1)),Math.abs(c)<.9999999?(this._y=Math.atan2(o,d),this._z=Math.atan2(a,l)):(this._y=Math.atan2(-h,s),this._z=0);break;case\\\\\\\"ZXY\\\\\\\":this._x=Math.asin(Zx(u,-1,1)),Math.abs(u)<.9999999?(this._y=Math.atan2(-h,d),this._z=Math.atan2(-r,l)):(this._y=0,this._z=Math.atan2(a,s));break;case\\\\\\\"ZYX\\\\\\\":this._y=Math.asin(-Zx(h,-1,1)),Math.abs(h)<.9999999?(this._x=Math.atan2(u,d),this._z=Math.atan2(a,s)):(this._x=0,this._z=Math.atan2(-r,l));break;case\\\\\\\"YZX\\\\\\\":this._z=Math.asin(Zx(a,-1,1)),Math.abs(a)<.9999999?(this._x=Math.atan2(-c,l),this._y=Math.atan2(-h,s)):(this._x=0,this._y=Math.atan2(o,d));break;case\\\\\\\"XZY\\\\\\\":this._z=Math.asin(-Zx(r,-1,1)),Math.abs(r)<.9999999?(this._x=Math.atan2(u,l),this._y=Math.atan2(o,s)):(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 nw.makeRotationFromQuaternion(t),this.setFromRotationMatrix(nw,e,n)}setFromVector3(t,e=this._order){return this.set(t.x,t.y,t.z,e)}reorder(t){return iw.setFromEuler(this),this.setFromQuaternion(iw,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 gb(this._x,this._y,this._z)}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}sw.prototype.isEuler=!0,sw.DefaultOrder=\\\\\\\"XYZ\\\\\\\",sw.RotationOrders=[\\\\\\\"XYZ\\\\\\\",\\\\\\\"YZX\\\\\\\",\\\\\\\"ZXY\\\\\\\",\\\\\\\"XZY\\\\\\\",\\\\\\\"YXZ\\\\\\\",\\\\\\\"ZYX\\\\\\\"];class rw{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),s.length>0&&(n.textures=s),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 r(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(s)):i.set(0,0,0)}static getBarycoord(t,e,n,i,s){xw.subVectors(i,e),bw.subVectors(n,e),ww.subVectors(t,e);const r=xw.dot(xw),o=xw.dot(bw),a=xw.dot(ww),l=bw.dot(bw),c=bw.dot(ww),h=r*l-o*o;if(0===h)return s.set(-2,-1,-1);const u=1/h,d=(l*a-o*c)*u,p=(r*c-o*a)*u;return s.set(1-d-p,p,d)}static containsPoint(t,e,n,i){return this.getBarycoord(t,e,n,i,Tw),Tw.x>=0&&Tw.y>=0&&Tw.x+Tw.y<=1}static getUV(t,e,n,i,s,r,o,a){return this.getBarycoord(t,e,n,i,Tw),a.set(0,0),a.addScaledVector(s,Tw.x),a.addScaledVector(r,Tw.y),a.addScaledVector(o,Tw.z),a}static isFrontFacing(t,e,n,i){return xw.subVectors(n,e),bw.subVectors(t,e),xw.cross(bw).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 xw.subVectors(this.c,this.b),bw.subVectors(this.a,this.b),.5*xw.cross(bw).length()}getMidpoint(t){return t.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}getNormal(t){return Lw.getNormal(this.a,this.b,this.c,t)}getPlane(t){return t.setFromCoplanarPoints(this.a,this.b,this.c)}getBarycoord(t,e){return Lw.getBarycoord(t,this.a,this.b,this.c,e)}getUV(t,e,n,i,s){return Lw.getUV(t,this.a,this.b,this.c,e,n,i,s)}containsPoint(t){return Lw.containsPoint(t,this.a,this.b,this.c)}isFrontFacing(t){return Lw.isFrontFacing(this.a,this.b,this.c,t)}intersectsBox(t){return t.intersectsTriangle(this)}closestPointToPoint(t,e){const n=this.a,i=this.b,s=this.c;let r,o;Aw.subVectors(i,n),Mw.subVectors(s,n),Sw.subVectors(t,n);const a=Aw.dot(Sw),l=Mw.dot(Sw);if(a<=0&&l<=0)return e.copy(n);Cw.subVectors(t,i);const c=Aw.dot(Cw),h=Mw.dot(Cw);if(c>=0&&h<=c)return e.copy(i);const u=a*h-c*l;if(u<=0&&a>=0&&c<=0)return r=a/(a-c),e.copy(n).addScaledVector(Aw,r);Nw.subVectors(t,s);const d=Aw.dot(Nw),p=Mw.dot(Nw);if(p>=0&&d<=p)return e.copy(s);const _=d*l-a*p;if(_<=0&&l>=0&&p<=0)return o=l/(l-p),e.copy(n).addScaledVector(Mw,o);const m=c*p-d*h;if(m<=0&&h-c>=0&&d-p>=0)return Ew.subVectors(s,i),o=(h-c)/(h-c+(d-p)),e.copy(i).addScaledVector(Ew,o);const f=1/(m+_+u);return r=_*f,o=u*f,e.copy(n).addScaledVector(Aw,r).addScaledVector(Mw,o)}equals(t){return t.a.equals(this.a)&&t.b.equals(this.b)&&t.c.equals(this.c)}}let Ow=0;class Pw extends jx{constructor(){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:Ow++}),this.uuid=Jx(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Material\\\\\\\",this.fog=!0,this.blending=1,this.side=0,this.vertexColors=!1,this.opacity=1,this.format=Ex,this.transparent=!1,this.blendSrc=204,this.blendDst=205,this.blendEquation=ix,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=Ux,this.stencilZFail=Ux,this.stencilZPass=Ux,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!==Ex&&(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),s=i(t.images);e.length>0&&(n.textures=e),s.length>0&&(n.images=s)}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++}}Pw.prototype.isMaterial=!0;const Rw={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},Iw={h:0,s:0,l:0},Fw={h:0,s:0,l:0};function Dw(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 Bw(t){return t<.04045?.0773993808*t:Math.pow(.9478672986*t+.0521327014,2.4)}function zw(t){return t<.0031308?12.92*t:1.055*Math.pow(t,.41666)-.055}class kw{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=Qx(t,1),e=Zx(e,0,1),n=Zx(n,0,1),0===e)this.r=this.g=this.b=n;else{const i=n<=.5?n*(1+e):n+e-n*e,s=2*n-i;this.r=Dw(s,i,t+1/3),this.g=Dw(s,i,t),this.b=Dw(s,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],s=n[2];switch(i){case\\\\\\\"rgb\\\\\\\":case\\\\\\\"rgba\\\\\\\":if(t=/^\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(s))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*)?$/.exec(s))return this.r=Math.min(100,parseInt(t[1],10))/100,this.g=Math.min(100,parseInt(t[2],10))/100,this.b=Math.min(100,parseInt(t[3],10))/100,e(t[4]),this;break;case\\\\\\\"hsl\\\\\\\":case\\\\\\\"hsla\\\\\\\":if(t=/^\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(s)){const n=parseFloat(t[1])/360,i=parseInt(t[2],10)/100,s=parseInt(t[3],10)/100;return e(t[4]),this.setHSL(n,i,s)}}}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=Rw[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=Bw(t.r),this.g=Bw(t.g),this.b=Bw(t.b),this}copyLinearToSRGB(t){return this.r=zw(t.r),this.g=zw(t.g),this.b=zw(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,s=Math.max(e,n,i),r=Math.min(e,n,i);let o,a;const l=(r+s)/2;if(r===s)o=0,a=0;else{const t=s-r;switch(a=l<=.5?t/(s+r):t/(2-s-r),s){case e:o=(n-i)/t+(n65535?Ww:jw)(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 rb).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 Yw.makeRotationFromQuaternion(t),this.applyMatrix4(Yw),this}rotateX(t){return Yw.makeRotationX(t),this.applyMatrix4(Yw),this}rotateY(t){return Yw.makeRotationY(t),this.applyMatrix4(Yw),this}rotateZ(t){return Yw.makeRotationZ(t),this.applyMatrix4(Yw),this}translate(t,e,n){return Yw.makeTranslation(t,e,n),this.applyMatrix4(Yw),this}scale(t,e,n){return Yw.makeScale(t,e,n),this.applyMatrix4(Yw),this}lookAt(t){return $w.lookAt(t),$w.updateMatrix(),this.applyMatrix4($w.matrix),this}center(){return this.computeBoundingBox(),this.boundingBox.getCenter(Jw).negate(),this.translate(Jw.x,Jw.y,Jw.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 s=!1;for(const e in this.morphAttributes){const n=this.morphAttributes[e],r=[];for(let e=0,i=n.length;e0&&(i[e]=r,s=!0)}s&&(t.data.morphAttributes=i,t.data.morphTargetsRelative=this.morphTargetsRelative);const r=this.groups;r.length>0&&(t.data.groups=JSON.parse(JSON.stringify(r)));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 s=t.morphAttributes;for(const t in s){const n=[],i=s[t];for(let t=0,s=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,s=this.matrixWorld;if(void 0===i)return;if(null===n.boundingSphere&&n.computeBoundingSphere(),iT.copy(n.boundingSphere),iT.applyMatrix4(s),!1===t.ray.intersectsSphere(iT))return;if(eT.copy(s).invert(),nT.copy(t.ray).applyMatrix4(eT),null!==n.boundingBox&&!1===nT.intersectsBox(n.boundingBox))return;let r;if(n.isBufferGeometry){const s=n.index,o=n.attributes.position,a=n.morphAttributes.position,l=n.morphTargetsRelative,c=n.attributes.uv,h=n.attributes.uv2,u=n.groups,d=n.drawRange;if(null!==s)if(Array.isArray(i))for(let n=0,p=u.length;nn.far?null:{distance:c,point:gT.clone(),object:t}}(t,e,n,i,sT,rT,oT,fT);if(p){a&&(pT.fromBufferAttribute(a,c),_T.fromBufferAttribute(a,h),mT.fromBufferAttribute(a,u),p.uv=Lw.getUV(fT,sT,rT,oT,pT,_T,mT,new sb)),l&&(pT.fromBufferAttribute(l,c),_T.fromBufferAttribute(l,h),mT.fromBufferAttribute(l,u),p.uv2=Lw.getUV(fT,sT,rT,oT,pT,_T,mT,new sb));const t={a:c,b:h,c:u,normal:new gb,materialIndex:0};Lw.getNormal(sT,rT,oT,t.normal),p.face=t}return p}vT.prototype.isMesh=!0;class xT extends tT{constructor(t=1,e=1,n=1,i=1,s=1,r=1){super(),this.type=\\\\\\\"BoxGeometry\\\\\\\",this.parameters={width:t,height:e,depth:n,widthSegments:i,heightSegments:s,depthSegments:r};const o=this;i=Math.floor(i),s=Math.floor(s),r=Math.floor(r);const a=[],l=[],c=[],h=[];let u=0,d=0;function p(t,e,n,i,s,r,p,_,m,f,g){const v=r/m,y=p/f,x=r/2,b=p/2,w=_/2,T=m+1,A=f+1;let M=0,E=0;const S=new gb;for(let r=0;r0?1:-1,c.push(S.x,S.y,S.z),h.push(a/m),h.push(1-r/f),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}}AT.prototype.isShaderMaterial=!0;class MT extends yw{constructor(){super(),this.type=\\\\\\\"Camera\\\\\\\",this.matrixWorldInverse=new Yb,this.projectionMatrix=new Yb,this.projectionMatrixInverse=new Yb}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)}}MT.prototype.isCamera=!0;class ET extends MT{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*Xx*Math.atan(e),this.updateProjectionMatrix()}getFocalLength(){const t=Math.tan(.5*qx*this.fov);return.5*this.getFilmHeight()/t}getEffectiveFOV(){return 2*Xx*Math.atan(Math.tan(.5*qx*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,s,r){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=s,this.view.height=r,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=this.near;let e=t*Math.tan(.5*qx*this.fov)/this.zoom,n=2*e,i=this.aspect*n,s=-.5*i;const r=this.view;if(null!==this.view&&this.view.enabled){const t=r.fullWidth,o=r.fullHeight;s+=r.offsetX*i/t,e-=r.offsetY*n/o,i*=r.width/t,n*=r.height/o}const o=this.filmOffset;0!==o&&(s+=t*o/this.getFilmWidth()),this.projectionMatrix.makePerspective(s,s+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}}ET.prototype.isPerspectiveCamera=!0;const ST=90;class CT extends yw{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 ET(ST,1,t,e);i.layers=this.layers,i.up.set(0,-1,0),i.lookAt(new gb(1,0,0)),this.add(i);const s=new ET(ST,1,t,e);s.layers=this.layers,s.up.set(0,-1,0),s.lookAt(new gb(-1,0,0)),this.add(s);const r=new ET(ST,1,t,e);r.layers=this.layers,r.up.set(0,0,1),r.lookAt(new gb(0,1,0)),this.add(r);const o=new ET(ST,1,t,e);o.layers=this.layers,o.up.set(0,0,-1),o.lookAt(new gb(0,-1,0)),this.add(o);const a=new ET(ST,1,t,e);a.layers=this.layers,a.up.set(0,-1,0),a.lookAt(new gb(0,0,1)),this.add(a);const l=new ET(ST,1,t,e);l.layers=this.layers,l.up.set(0,-1,0),l.lookAt(new gb(0,0,-1)),this.add(l)}update(t,e){null===this.parent&&this.updateMatrixWorld();const n=this.renderTarget,[i,s,r,o,a,l]=this.children,c=t.xr.enabled,h=t.getRenderTarget();t.xr.enabled=!1;const u=n.texture.generateMipmaps;n.texture.generateMipmaps=!1,t.setRenderTarget(n,0),t.render(e,i),t.setRenderTarget(n,1),t.render(e,s),t.setRenderTarget(n,2),t.render(e,r),t.setRenderTarget(n,3),t.render(e,o),t.setRenderTarget(n,4),t.render(e,a),n.texture.generateMipmaps=u,t.setRenderTarget(n,5),t.render(e,l),t.setRenderTarget(h),t.xr.enabled=c}}class NT extends ub{constructor(t,e,n,i,s,r,o,a,l,c){super(t=void 0!==t?t:[],e=void 0!==e?e:sx,n,i,s,r,o,a,l,c),this.flipY=!1}get images(){return this.image}set images(t){this.image=t}}NT.prototype.isCubeTexture=!0;class LT extends _b{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 NT(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:fx,this.texture._needsFlipEnvMap=!1}fromEquirectangularTexture(t,e){this.texture.type=e.type,this.texture.format=Ex,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 xT(5,5,5),s=new AT({name:\\\\\\\"CubemapFromEquirect\\\\\\\",uniforms:bT(n.uniforms),vertexShader:n.vertexShader,fragmentShader:n.fragmentShader,side:1,blending:0});s.uniforms.tEquirect.value=e;const r=new vT(i,s),o=e.minFilter;e.minFilter===vx&&(e.minFilter=fx);return new CT(1,10,this).update(t,r),e.minFilter=o,r.geometry.dispose(),r.material.dispose(),this}clear(t,e,n,i){const s=t.getRenderTarget();for(let s=0;s<6;s++)t.setRenderTarget(this,s),t.clear(e,n,i);t.setRenderTarget(s)}}LT.prototype.isWebGLCubeRenderTarget=!0;const OT=new gb,PT=new gb,RT=new rb;class IT{constructor(t=new gb(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=OT.subVectors(n,e).cross(PT.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(OT),i=this.normal.dot(n);if(0===i)return 0===this.distanceToPoint(t.start)?e.copy(t.start):null;const s=-(t.start.dot(this.normal)+this.constant)/i;return s<0||s>1?null:e.copy(n).multiplyScalar(s).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||RT.getNormalMatrix(t),i=this.coplanarPoint(OT).applyMatrix4(t),s=this.normal.applyMatrix3(n).normalize();return this.constant=-i.dot(s),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)}}IT.prototype.isPlane=!0;const FT=new kb,DT=new gb;class BT{constructor(t=new IT,e=new IT,n=new IT,i=new IT,s=new IT,r=new IT){this.planes=[t,e,n,i,s,r]}set(t,e,n,i,s,r){const o=this.planes;return o[0].copy(t),o[1].copy(e),o[2].copy(n),o[3].copy(i),o[4].copy(s),o[5].copy(r),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],s=n[1],r=n[2],o=n[3],a=n[4],l=n[5],c=n[6],h=n[7],u=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,h-a,_-u,v-m).normalize(),e[1].setComponents(o+i,h+a,_+u,v+m).normalize(),e[2].setComponents(o+s,h+l,_+d,v+f).normalize(),e[3].setComponents(o-s,h-l,_-d,v-f).normalize(),e[4].setComponents(o-r,h-c,_-p,v-g).normalize(),e[5].setComponents(o+r,h+c,_+p,v+g).normalize(),this}intersectsObject(t){const e=t.geometry;return null===e.boundingSphere&&e.computeBoundingSphere(),FT.copy(e.boundingSphere).applyMatrix4(t.matrixWorld),this.intersectsSphere(FT)}intersectsSprite(t){return FT.center.set(0,0,0),FT.radius=.7071067811865476,FT.applyMatrix4(t.matrixWorld),this.intersectsSphere(FT)}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,DT.y=i.normal.y>0?t.max.y:t.min.y,DT.z=i.normal.z>0?t.max.z:t.min.z,i.distanceToPoint(DT)<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 zT(){let t=null,e=!1,n=null,i=null;function s(e,r){n(e,r),i=t.requestAnimationFrame(s)}return{start:function(){!0!==e&&null!==n&&(i=t.requestAnimationFrame(s),e=!0)},stop:function(){t.cancelAnimationFrame(i),e=!1},setAnimationLoop:function(t){n=t},setContext:function(e){t=e}}}function kT(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,s){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}\\\\\\\"},VT={common:{diffuse:{value:new kw(16777215)},opacity:{value:1},map:{value:null},uvTransform:{value:new rb},uv2Transform:{value:new rb},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 sb(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 kw(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 kw(16777215)},opacity:{value:1},size:{value:1},scale:{value:1},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new rb}},sprite:{diffuse:{value:new kw(16777215)},opacity:{value:1},center:{value:new sb(.5,.5)},rotation:{value:0},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new rb}}},HT={basic:{uniforms:wT([VT.common,VT.specularmap,VT.envmap,VT.aomap,VT.lightmap,VT.fog]),vertexShader:GT.meshbasic_vert,fragmentShader:GT.meshbasic_frag},lambert:{uniforms:wT([VT.common,VT.specularmap,VT.envmap,VT.aomap,VT.lightmap,VT.emissivemap,VT.fog,VT.lights,{emissive:{value:new kw(0)}}]),vertexShader:GT.meshlambert_vert,fragmentShader:GT.meshlambert_frag},phong:{uniforms:wT([VT.common,VT.specularmap,VT.envmap,VT.aomap,VT.lightmap,VT.emissivemap,VT.bumpmap,VT.normalmap,VT.displacementmap,VT.fog,VT.lights,{emissive:{value:new kw(0)},specular:{value:new kw(1118481)},shininess:{value:30}}]),vertexShader:GT.meshphong_vert,fragmentShader:GT.meshphong_frag},standard:{uniforms:wT([VT.common,VT.envmap,VT.aomap,VT.lightmap,VT.emissivemap,VT.bumpmap,VT.normalmap,VT.displacementmap,VT.roughnessmap,VT.metalnessmap,VT.fog,VT.lights,{emissive:{value:new kw(0)},roughness:{value:1},metalness:{value:0},envMapIntensity:{value:1}}]),vertexShader:GT.meshphysical_vert,fragmentShader:GT.meshphysical_frag},toon:{uniforms:wT([VT.common,VT.aomap,VT.lightmap,VT.emissivemap,VT.bumpmap,VT.normalmap,VT.displacementmap,VT.gradientmap,VT.fog,VT.lights,{emissive:{value:new kw(0)}}]),vertexShader:GT.meshtoon_vert,fragmentShader:GT.meshtoon_frag},matcap:{uniforms:wT([VT.common,VT.bumpmap,VT.normalmap,VT.displacementmap,VT.fog,{matcap:{value:null}}]),vertexShader:GT.meshmatcap_vert,fragmentShader:GT.meshmatcap_frag},points:{uniforms:wT([VT.points,VT.fog]),vertexShader:GT.points_vert,fragmentShader:GT.points_frag},dashed:{uniforms:wT([VT.common,VT.fog,{scale:{value:1},dashSize:{value:1},totalSize:{value:2}}]),vertexShader:GT.linedashed_vert,fragmentShader:GT.linedashed_frag},depth:{uniforms:wT([VT.common,VT.displacementmap]),vertexShader:GT.depth_vert,fragmentShader:GT.depth_frag},normal:{uniforms:wT([VT.common,VT.bumpmap,VT.normalmap,VT.displacementmap,{opacity:{value:1}}]),vertexShader:GT.meshnormal_vert,fragmentShader:GT.meshnormal_frag},sprite:{uniforms:wT([VT.sprite,VT.fog]),vertexShader:GT.sprite_vert,fragmentShader:GT.sprite_frag},background:{uniforms:{uvTransform:{value:new rb},t2D:{value:null}},vertexShader:GT.background_vert,fragmentShader:GT.background_frag},cube:{uniforms:wT([VT.envmap,{opacity:{value:1}}]),vertexShader:GT.cube_vert,fragmentShader:GT.cube_frag},equirect:{uniforms:{tEquirect:{value:null}},vertexShader:GT.equirect_vert,fragmentShader:GT.equirect_frag},distanceRGBA:{uniforms:wT([VT.common,VT.displacementmap,{referencePosition:{value:new gb},nearDistance:{value:1},farDistance:{value:1e3}}]),vertexShader:GT.distanceRGBA_vert,fragmentShader:GT.distanceRGBA_frag},shadow:{uniforms:wT([VT.lights,VT.fog,{color:{value:new kw(0)},opacity:{value:1}}]),vertexShader:GT.shadow_vert,fragmentShader:GT.shadow_frag}};function jT(t,e,n,i,s){const r=new kw(0);let o,a,l=0,c=null,h=0,u=null;function d(t,e){n.buffers.color.setClear(t.r,t.g,t.b,e,s)}return{getClearColor:function(){return r},setClearColor:function(t,e=1){r.set(t),l=e,d(r,l)},getClearAlpha:function(){return l},setClearAlpha:function(t){l=t,d(r,l)},render:function(n,s){let p=!1,_=!0===s.isScene?s.background:null;_&&_.isTexture&&(_=e.get(_));const m=t.xr,f=m.getSession&&m.getSession();f&&\\\\\\\"additive\\\\\\\"===f.environmentBlendMode&&(_=null),null===_?d(r,l):_&&_.isColor&&(d(_,1),p=!0),(t.autoClear||p)&&t.clear(t.autoClearColor,t.autoClearDepth,t.autoClearStencil),_&&(_.isCubeTexture||_.mapping===lx)?(void 0===a&&(a=new vT(new xT(1,1,1),new AT({name:\\\\\\\"BackgroundCubeMaterial\\\\\\\",uniforms:bT(HT.cube.uniforms),vertexShader:HT.cube.vertexShader,fragmentShader:HT.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===_&&h===_.version&&u===t.toneMapping||(a.material.needsUpdate=!0,c=_,h=_.version,u=t.toneMapping),n.unshift(a,a.geometry,a.material,0,0,null)):_&&_.isTexture&&(void 0===o&&(o=new vT(new UT(2,2),new AT({name:\\\\\\\"BackgroundMaterial\\\\\\\",uniforms:bT(HT.background.uniforms),vertexShader:HT.background.vertexShader,fragmentShader:HT.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===_&&h===_.version&&u===t.toneMapping||(o.material.needsUpdate=!0,c=_,h=_.version,u=t.toneMapping),n.unshift(o,o.geometry,o.material,0,0,null))}}}function WT(t,e,n,i){const s=t.getParameter(34921),r=i.isWebGL2?null:e.get(\\\\\\\"OES_vertex_array_object\\\\\\\"),o=i.isWebGL2||null!==r,a={},l=d(null);let c=l;function h(e){return i.isWebGL2?t.bindVertexArray(e):r.bindVertexArrayOES(e)}function u(e){return i.isWebGL2?t.deleteVertexArray(e):r.deleteVertexArrayOES(e)}function d(t){const e=[],n=[],i=[];for(let t=0;t=0){let r=l[e];if(void 0===r&&(\\\\\\\"instanceMatrix\\\\\\\"===e&&s.instanceMatrix&&(r=s.instanceMatrix),\\\\\\\"instanceColor\\\\\\\"===e&&s.instanceColor&&(r=s.instanceColor)),void 0!==r){const e=r.normalized,o=r.itemSize,l=n.get(r);if(void 0===l)continue;const c=l.buffer,h=l.type,u=l.bytesPerElement;if(r.isInterleavedBufferAttribute){const n=r.data,l=n.stride,d=r.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 r=\\\\\\\"undefined\\\\\\\"!=typeof WebGL2RenderingContext&&t instanceof WebGL2RenderingContext||\\\\\\\"undefined\\\\\\\"!=typeof WebGL2ComputeRenderingContext&&t instanceof WebGL2ComputeRenderingContext;let o=void 0!==n.precision?n.precision:\\\\\\\"highp\\\\\\\";const a=s(o);a!==o&&(console.warn(\\\\\\\"THREE.WebGLRenderer:\\\\\\\",o,\\\\\\\"not supported, using\\\\\\\",a,\\\\\\\"instead.\\\\\\\"),o=a);const l=r||e.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\"),c=!0===n.logarithmicDepthBuffer,h=t.getParameter(34930),u=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=u>0,y=r||e.has(\\\\\\\"OES_texture_float\\\\\\\");return{isWebGL2:r,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:s,precision:o,logarithmicDepthBuffer:c,maxTextures:h,maxVertexTextures:u,maxTextureSize:d,maxCubemapSize:p,maxAttributes:_,maxVertexUniforms:m,maxVaryings:f,maxFragmentUniforms:g,vertexTextures:v,floatFragmentTextures:y,floatVertexTextures:v&&y,maxSamples:r?t.getParameter(36183):0}}function YT(t){const e=this;let n=null,i=0,s=!1,r=!1;const o=new IT,a=new rb,l={value:null,needsUpdate:!1};function c(){l.value!==n&&(l.value=n,l.needsUpdate=i>0),e.numPlanes=i,e.numIntersection=0}function h(t,n,i,s){const r=null!==t?t.length:0;let c=null;if(0!==r){if(c=l.value,!0!==s||null===c){const e=i+4*r,s=n.matrixWorldInverse;a.getNormalMatrix(s),(null===c||c.length0){const o=t.getRenderTarget(),a=new LT(r.height/2);return a.fromEquirectangularTexture(t,s),e.set(s,a),t.setRenderTarget(o),s.addEventListener(\\\\\\\"dispose\\\\\\\",i),n(a.texture,s.mapping)}return null}}}return s},dispose:function(){e=new WeakMap}}}HT.physical={uniforms:wT([HT.standard.uniforms,{clearcoat:{value:0},clearcoatMap:{value:null},clearcoatRoughness:{value:0},clearcoatRoughnessMap:{value:null},clearcoatNormalScale:{value:new sb(1,1)},clearcoatNormalMap:{value:null},sheen:{value:0},sheenTint:{value:new kw(0)},sheenRoughness:{value:0},transmission:{value:0},transmissionMap:{value:null},transmissionSamplerSize:{value:new sb},transmissionSamplerMap:{value:null},thickness:{value:0},thicknessMap:{value:null},attenuationDistance:{value:0},attenuationTint:{value:new kw(0)},specularIntensity:{value:0},specularIntensityMap:{value:null},specularTint:{value:new kw(1,1,1)},specularTintMap:{value:null}}]),vertexShader:GT.meshphysical_vert,fragmentShader:GT.meshphysical_frag};class JT extends MT{constructor(t=-1,e=1,n=1,i=-1,s=.1,r=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=s,this.far=r,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,s,r){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=s,this.view.height=r,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 s=n-t,r=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;s+=t*this.view.offsetX,r=s+t*this.view.width,o-=e*this.view.offsetY,a=o-e*this.view.height}this.projectionMatrix.makeOrthographic(s,r,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}}JT.prototype.isOrthographicCamera=!0;class ZT extends AT{constructor(t){super(t),this.type=\\\\\\\"RawShaderMaterial\\\\\\\"}}ZT.prototype.isRawShaderMaterial=!0;const QT=Math.pow(2,8),KT=[.125,.215,.35,.446,.526,.582],tA=5+KT.length,eA=20,nA={[Dx]:0,[Bx]:1,[kx]:2,3004:3,3005:4,3006:5,[zx]:6},iA=new JT,{_lodPlanes:sA,_sizeLods:rA,_sigmas:oA}=_A(),aA=new kw;let lA=null;const cA=(1+Math.sqrt(5))/2,hA=1/cA,uA=[new gb(1,1,1),new gb(-1,1,1),new gb(1,1,-1),new gb(-1,1,-1),new gb(0,cA,hA),new gb(0,cA,-hA),new gb(hA,0,cA),new gb(-hA,0,cA),new gb(cA,hA,0),new gb(-cA,hA,0)];class dA{constructor(t){this._renderer=t,this._pingPongRenderTarget=null,this._blurMaterial=function(t){const e=new Float32Array(t),n=new gb(0,1,0);return new ZT({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:nA[3e3]},outputEncoding:{value:nA[3e3]}},vertexShader:yA(),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${xA()}\\\\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})}(eA),this._equirectShader=null,this._cubemapShader=null,this._compileMaterial(this._blurMaterial)}fromScene(t,e=0,n=.1,i=100){lA=this._renderer.getRenderTarget();const s=this._allocateTargets();return this._sceneToCubeUV(t,n,i,s),e>0&&this._blur(s,0,0,e),this._applyPMREM(s),this._cleanup(s),s}fromEquirectangular(t){return this._fromTexture(t)}fromCubemap(t){return this._fromTexture(t)}compileCubemapShader(){null===this._cubemapShader&&(this._cubemapShader=vA(),this._compileMaterial(this._cubemapShader))}compileEquirectangularShader(){null===this._equirectShader&&(this._equirectShader=gA(),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?QT:0,QT,QT),a.setRenderTarget(i),p&&a.render(d,s),a.render(t,s)}d.geometry.dispose(),d.material.dispose(),a.toneMapping=h,a.outputEncoding=c,a.autoClear=l,t.background=_}_setEncoding(t,e){!0===this._renderer.capabilities.isWebGL2&&e.format===Ex&&e.type===yx&&e.encoding===Bx?t.value=nA[3e3]:t.value=nA[e.encoding]}_textureToCubeUV(t,e){const n=this._renderer;t.isCubeTexture?null==this._cubemapShader&&(this._cubemapShader=vA()):null==this._equirectShader&&(this._equirectShader=gA());const i=t.isCubeTexture?this._cubemapShader:this._equirectShader,s=new vT(sA[0],i),r=i.uniforms;r.envMap.value=t,t.isCubeTexture||r.texelSize.value.set(1/t.image.width,1/t.image.height),this._setEncoding(r.inputEncoding,t),this._setEncoding(r.outputEncoding,e.texture),fA(e,0,0,3*QT,2*QT),n.setRenderTarget(e),n.render(s,iA)}_applyPMREM(t){const e=this._renderer,n=e.autoClear;e.autoClear=!1;for(let e=1;eeA&&console.warn(`sigmaRadians, ${s}, is too large and will clip, as it requested ${_} samples when the maximum is set to 20`);const m=[];let f=0;for(let t=0;t4?i-8+4:0),3*g,2*g),a.setRenderTarget(e),a.render(c,iA)}}function pA(t){return void 0!==t&&t.type===yx&&(t.encoding===Dx||t.encoding===Bx||t.encoding===zx)}function _A(){const t=[],e=[],n=[];let i=8;for(let s=0;s4?o=KT[s-8+4-1]:0==s&&(o=0),n.push(o);const a=1/(r-1),l=-a/2,c=1+a/2,h=[l,l,c,l,c,c,l,l,c,c,l,c],u=6,d=6,p=3,_=2,m=1,f=new Float32Array(p*d*u),g=new Float32Array(_*d*u),v=new Float32Array(m*d*u);for(let t=0;t2?0:-1,i=[e,n,0,e+2/3,n,0,e+2/3,n+1,0,e,n,0,e+2/3,n+1,0,e,n+1,0];f.set(i,p*d*t),g.set(h,_*d*t);const s=[t,t,t,t,t,t];v.set(s,m*d*t)}const y=new tT;y.setAttribute(\\\\\\\"position\\\\\\\",new Hw(f,p)),y.setAttribute(\\\\\\\"uv\\\\\\\",new Hw(g,_)),y.setAttribute(\\\\\\\"faceIndex\\\\\\\",new Hw(v,m)),t.push(y),i>4&&i--}return{_lodPlanes:t,_sizeLods:e,_sigmas:n}}function mA(t){const e=new _b(3*QT,3*QT,t);return e.texture.mapping=lx,e.texture.name=\\\\\\\"PMREM.cubeUv\\\\\\\",e.scissorTest=!0,e}function fA(t,e,n,i,s){t.viewport.set(e,n,i,s),t.scissor.set(e,n,i,s)}function gA(){const t=new sb(1,1);return new ZT({name:\\\\\\\"EquirectangularToCubeUV\\\\\\\",uniforms:{envMap:{value:null},texelSize:{value:t},inputEncoding:{value:nA[3e3]},outputEncoding:{value:nA[3e3]}},vertexShader:yA(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform vec2 texelSize;\\\\n\\\\n\\\\t\\\\t\\\\t${xA()}\\\\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 vA(){return new ZT({name:\\\\\\\"CubemapToCubeUV\\\\\\\",uniforms:{envMap:{value:null},inputEncoding:{value:nA[3e3]},outputEncoding:{value:nA[3e3]}},vertexShader:yA(),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${xA()}\\\\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 yA(){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 xA(){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 bA(t){let e=new WeakMap,n=null;function i(t){const n=t.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",i);const s=e.get(n);void 0!==s&&(e.delete(n),s.dispose())}return{get:function(s){if(s&&s.isTexture&&!1===s.isRenderTargetTexture){const r=s.mapping,o=r===ox||r===ax,a=r===sx||r===rx;if(o||a){if(e.has(s))return e.get(s).texture;{const r=s.image;if(o&&r&&r.height>0||a&&r&&function(t){let e=0;const n=6;for(let i=0;i65535?Ww:jw)(n,1);a.version=o;const l=r.get(t);l&&e.remove(l),r.set(t,a)}return{get:function(t,e){return!0===s[e.id]||(e.addEventListener(\\\\\\\"dispose\\\\\\\",o),s[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&&(u=Math.ceil(h/e.maxTextureSize),h=e.maxTextureSize);const d=new Float32Array(h*u*4*i),p=new EA(d,h,u,i);p.format=Ex,p.type=wx;const _=4*l;for(let e=0;e0)return t;const s=e*n;let r=BA[s];if(void 0===r&&(r=new Float32Array(s),BA[s]=r),0!==e){i.toArray(r,0);for(let i=1,s=0;i!==e;++i)s+=n,t[i].toArray(r,s)}return r}function HA(t,e){if(t.length!==e.length)return!1;for(let n=0,i=t.length;n/gm;function qM(t){return t.replace(WM,XM)}function XM(t,e){const n=GT[e];if(void 0===n)throw new Error(\\\\\\\"Can not resolve #include <\\\\\\\"+e+\\\\\\\">\\\\\\\");return qM(n)}const YM=/#pragma unroll_loop[\\\\s]+?for \\\\( int i \\\\= (\\\\d+)\\\\; i < (\\\\d+)\\\\; i \\\\+\\\\+ \\\\) \\\\{([\\\\s\\\\S]+?)(?=\\\\})\\\\}/g,$M=/#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 JM(t){return t.replace($M,QM).replace(YM,ZM)}function ZM(t,e,n,i){return console.warn(\\\\\\\"WebGLProgram: #pragma unroll_loop shader syntax is deprecated. Please use #pragma unroll_loop_start syntax instead.\\\\\\\"),QM(t,e,n,i)}function QM(t,e,n,i){let s=\\\\\\\"\\\\\\\";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(VM).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\\\\\\\")}(r),m=s.createProgram();let f,g,v=n.glslVersion?\\\\\\\"#version \\\\\\\"+n.glslVersion+\\\\\\\"\\\\n\\\\\\\":\\\\\\\"\\\\\\\";n.isRawShaderMaterial?(f=[_].filter(VM).join(\\\\\\\"\\\\n\\\\\\\"),f.length>0&&(f+=\\\\\\\"\\\\n\\\\\\\"),g=[p,_].filter(VM).join(\\\\\\\"\\\\n\\\\\\\"),g.length>0&&(g+=\\\\\\\"\\\\n\\\\\\\")):(f=[KM(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 \\\\\\\"+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.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(VM).join(\\\\\\\"\\\\n\\\\\\\"),g=[p,KM(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 \\\\\\\"+h:\\\\\\\"\\\\\\\",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.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?GT.tonemapping_pars_fragment:\\\\\\\"\\\\\\\",0!==n.toneMapping?GM(\\\\\\\"toneMapping\\\\\\\",n.toneMapping):\\\\\\\"\\\\\\\",n.dithering?\\\\\\\"#define DITHERING\\\\\\\":\\\\\\\"\\\\\\\",n.format===Mx?\\\\\\\"#define OPAQUE\\\\\\\":\\\\\\\"\\\\\\\",GT.encodings_pars_fragment,n.map?kM(\\\\\\\"mapTexelToLinear\\\\\\\",n.mapEncoding):\\\\\\\"\\\\\\\",n.matcap?kM(\\\\\\\"matcapTexelToLinear\\\\\\\",n.matcapEncoding):\\\\\\\"\\\\\\\",n.envMap?kM(\\\\\\\"envMapTexelToLinear\\\\\\\",n.envMapEncoding):\\\\\\\"\\\\\\\",n.emissiveMap?kM(\\\\\\\"emissiveMapTexelToLinear\\\\\\\",n.emissiveMapEncoding):\\\\\\\"\\\\\\\",n.specularTintMap?kM(\\\\\\\"specularTintMapTexelToLinear\\\\\\\",n.specularTintMapEncoding):\\\\\\\"\\\\\\\",n.lightMap?kM(\\\\\\\"lightMapTexelToLinear\\\\\\\",n.lightMapEncoding):\\\\\\\"\\\\\\\",UM(\\\\\\\"linearToOutputTexel\\\\\\\",n.outputEncoding),n.depthPacking?\\\\\\\"#define DEPTH_PACKING \\\\\\\"+n.depthPacking:\\\\\\\"\\\\\\\",\\\\\\\"\\\\n\\\\\\\"].filter(VM).join(\\\\\\\"\\\\n\\\\\\\")),o=qM(o),o=HM(o,n),o=jM(o,n),a=qM(a),a=HM(a,n),a=jM(a,n),o=JM(o),a=JM(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===Hx?\\\\\\\"\\\\\\\":\\\\\\\"out highp vec4 pc_fragColor;\\\\\\\",n.glslVersion===Hx?\\\\\\\"\\\\\\\":\\\\\\\"#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=FM(s,35633,v+f+o),b=FM(s,35632,y);if(s.attachShader(m,x),s.attachShader(m,b),void 0!==n.index0AttributeName?s.bindAttribLocation(m,0,n.index0AttributeName):!0===n.morphTargets&&s.bindAttribLocation(m,0,\\\\\\\"position\\\\\\\"),s.linkProgram(m),t.debug.checkShaderErrors){const t=s.getProgramInfoLog(m).trim(),e=s.getShaderInfoLog(x).trim(),n=s.getShaderInfoLog(b).trim();let i=!0,r=!0;if(!1===s.getProgramParameter(m,35714)){i=!1;const e=zM(s,x,\\\\\\\"vertex\\\\\\\"),n=zM(s,b,\\\\\\\"fragment\\\\\\\");console.error(\\\\\\\"THREE.WebGLProgram: Shader Error \\\\\\\"+s.getError()+\\\\\\\" - VALIDATE_STATUS \\\\\\\"+s.getProgramParameter(m,35715)+\\\\\\\"\\\\n\\\\nProgram Info Log: \\\\\\\"+t+\\\\\\\"\\\\n\\\\\\\"+e+\\\\\\\"\\\\n\\\\\\\"+n)}else\\\\\\\"\\\\\\\"!==t?console.warn(\\\\\\\"THREE.WebGLProgram: Program Info Log:\\\\\\\",t):\\\\\\\"\\\\\\\"!==e&&\\\\\\\"\\\\\\\"!==n||(r=!1);r&&(this.diagnostics={runnable:i,programLog:t,vertexShader:{log:e,prefix:f},fragmentShader:{log:n,prefix:g}})}let w,T;return s.deleteShader(x),s.deleteShader(b),this.getUniforms=function(){return void 0===w&&(w=new IM(s,m)),w},this.getAttributes=function(){return void 0===T&&(T=function(t,e){const n={},i=t.getProgramParameter(e,35721);for(let s=0;s0,C=r.clearcoat>0;return{isWebGL2:l,shaderID:w,shaderName:r.type,vertexShader:A,fragmentShader:M,defines:r.defines,isRawShaderMaterial:!0===r.isRawShaderMaterial,glslVersion:r.glslVersion,precision:p,instancing:!0===v.isInstancedMesh,instancingColor:!0===v.isInstancedMesh&&null!==v.instanceColor,supportsVertexTextures:d,outputEncoding:null!==E?f(E.texture):t.outputEncoding,map:!!r.map,mapEncoding:f(r.map),matcap:!!r.matcap,matcapEncoding:f(r.matcap),envMap:!!b,envMapMode:b&&b.mapping,envMapEncoding:f(b),envMapCubeUV:!!b&&(b.mapping===lx||b.mapping===cx),lightMap:!!r.lightMap,lightMapEncoding:f(r.lightMap),aoMap:!!r.aoMap,emissiveMap:!!r.emissiveMap,emissiveMapEncoding:f(r.emissiveMap),bumpMap:!!r.bumpMap,normalMap:!!r.normalMap,objectSpaceNormalMap:1===r.normalMapType,tangentSpaceNormalMap:0===r.normalMapType,clearcoat:C,clearcoatMap:C&&!!r.clearcoatMap,clearcoatRoughnessMap:C&&!!r.clearcoatRoughnessMap,clearcoatNormalMap:C&&!!r.clearcoatNormalMap,displacementMap:!!r.displacementMap,roughnessMap:!!r.roughnessMap,metalnessMap:!!r.metalnessMap,specularMap:!!r.specularMap,specularIntensityMap:!!r.specularIntensityMap,specularTintMap:!!r.specularTintMap,specularTintMapEncoding:f(r.specularTintMap),alphaMap:!!r.alphaMap,alphaTest:S,gradientMap:!!r.gradientMap,sheen:r.sheen>0,transmission:r.transmission>0,transmissionMap:!!r.transmissionMap,thicknessMap:!!r.thicknessMap,combine:r.combine,vertexTangents:!!r.normalMap&&!!v.geometry&&!!v.geometry.attributes.tangent,vertexColors:r.vertexColors,vertexAlphas:!0===r.vertexColors&&!!v.geometry&&!!v.geometry.attributes.color&&4===v.geometry.attributes.color.itemSize,vertexUvs:!!(r.map||r.bumpMap||r.normalMap||r.specularMap||r.alphaMap||r.emissiveMap||r.roughnessMap||r.metalnessMap||r.clearcoatMap||r.clearcoatRoughnessMap||r.clearcoatNormalMap||r.displacementMap||r.transmissionMap||r.thicknessMap||r.specularIntensityMap||r.specularTintMap),uvsVertexOnly:!(r.map||r.bumpMap||r.normalMap||r.specularMap||r.alphaMap||r.emissiveMap||r.roughnessMap||r.metalnessMap||r.clearcoatNormalMap||r.transmission>0||r.transmissionMap||r.thicknessMap||r.specularIntensityMap||r.specularTintMap||!r.displacementMap),fog:!!y,useFog:r.fog,fogExp2:y&&y.isFogExp2,flatShading:!!r.flatShading,sizeAttenuation:r.sizeAttenuation,logarithmicDepthBuffer:c,skinning:!0===v.isSkinnedMesh&&T>0,maxBones:T,useVertexTexture:h,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:r.format,dithering:r.dithering,shadowMapEnabled:t.shadowMap.enabled&&m.length>0,shadowMapType:t.shadowMap.type,toneMapping:r.toneMapped?t.toneMapping:0,physicallyCorrectLights:t.physicallyCorrectLights,premultipliedAlpha:r.premultipliedAlpha,doubleSided:2===r.side,flipSided:1===r.side,depthPacking:void 0!==r.depthPacking&&r.depthPacking,index0AttributeName:r.index0AttributeName,extensionDerivatives:r.extensions&&r.extensions.derivatives,extensionFragDepth:r.extensions&&r.extensions.fragDepth,extensionDrawBuffers:r.extensions&&r.extensions.drawBuffers,extensionShaderTextureLOD:r.extensions&&r.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:r.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?s.push(h):!0===n.transparent?r.push(h):i.push(h)},unshift:function(t,e,n,o,l,c){const h=a(t,e,n,o,l,c);n.transmission>0?s.unshift(h):!0===n.transparent?r.unshift(h):i.unshift(h)},finish:function(){for(let t=n,i=e.length;t1&&i.sort(t||iE),s.length>1&&s.sort(e||sE),r.length>1&&r.sort(e||sE)}}}function oE(t){let e=new WeakMap;return{get:function(n,i){let s;return!1===e.has(n)?(s=new rE(t),e.set(n,[s])):i>=e.get(n).length?(s=new rE(t),e.get(n).push(s)):s=e.get(n)[i],s},dispose:function(){e=new WeakMap}}}function aE(){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 gb,color:new kw};break;case\\\\\\\"SpotLight\\\\\\\":n={position:new gb,direction:new gb,color:new kw,distance:0,coneCos:0,penumbraCos:0,decay:0};break;case\\\\\\\"PointLight\\\\\\\":n={position:new gb,color:new kw,distance:0,decay:0};break;case\\\\\\\"HemisphereLight\\\\\\\":n={direction:new gb,skyColor:new kw,groundColor:new kw};break;case\\\\\\\"RectAreaLight\\\\\\\":n={color:new kw,position:new gb,halfWidth:new gb,halfHeight:new gb}}return t[e.id]=n,n}}}let lE=0;function cE(t,e){return(e.castShadow?1:0)-(t.castShadow?1:0)}function hE(t,e){const n=new aE,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 sb};break;case\\\\\\\"PointLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new sb,shadowCameraNear:1,shadowCameraFar:1e3}}return t[e.id]=n,n}}}(),s={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++)s.probe.push(new gb);const r=new gb,o=new Yb,a=new Yb;return{setup:function(r,o){let a=0,l=0,c=0;for(let t=0;t<9;t++)s.probe[t].set(0,0,0);let h=0,u=0,d=0,p=0,_=0,m=0,f=0,g=0;r.sort(cE);const v=!0!==o?Math.PI:1;for(let t=0,e=r.length;t0&&(e.isWebGL2||!0===t.has(\\\\\\\"OES_texture_float_linear\\\\\\\")?(s.rectAreaLTC1=VT.LTC_FLOAT_1,s.rectAreaLTC2=VT.LTC_FLOAT_2):!0===t.has(\\\\\\\"OES_texture_half_float_linear\\\\\\\")?(s.rectAreaLTC1=VT.LTC_HALF_1,s.rectAreaLTC2=VT.LTC_HALF_2):console.error(\\\\\\\"THREE.WebGLRenderer: Unable to use RectAreaLight. Missing WebGL extensions.\\\\\\\")),s.ambient[0]=a,s.ambient[1]=l,s.ambient[2]=c;const y=s.hash;y.directionalLength===h&&y.pointLength===u&&y.spotLength===d&&y.rectAreaLength===p&&y.hemiLength===_&&y.numDirectionalShadows===m&&y.numPointShadows===f&&y.numSpotShadows===g||(s.directional.length=h,s.spot.length=d,s.rectArea.length=p,s.point.length=u,s.hemi.length=_,s.directionalShadow.length=m,s.directionalShadowMap.length=m,s.pointShadow.length=f,s.pointShadowMap.length=f,s.spotShadow.length=g,s.spotShadowMap.length=g,s.directionalShadowMatrix.length=m,s.pointShadowMatrix.length=f,s.spotShadowMatrix.length=g,y.directionalLength=h,y.pointLength=u,y.spotLength=d,y.rectAreaLength=p,y.hemiLength=_,y.numDirectionalShadows=m,y.numPointShadows=f,y.numSpotShadows=g,s.version=lE++)},setupView:function(t,e){let n=0,i=0,l=0,c=0,h=0;const u=e.matrixWorldInverse;for(let e=0,d=t.length;e=n.get(i).length?(r=new uE(t,e),n.get(i).push(r)):r=n.get(i)[s],r},dispose:function(){n=new WeakMap}}}class pE extends Pw{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}}pE.prototype.isMeshDepthMaterial=!0;class _E extends Pw{constructor(t){super(),this.type=\\\\\\\"MeshDistanceMaterial\\\\\\\",this.referencePosition=new gb,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}}_E.prototype.isMeshDistanceMaterial=!0;function mE(t,e,n){let i=new BT;const s=new sb,r=new sb,o=new pb,a=new pE({depthPacking:3201}),l=new _E,c={},h=n.maxTextureSize,u={0:1,1:0,2:2},d=new AT({uniforms:{shadow_pass:{value:null},resolution:{value:new sb},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 tT;_.setAttribute(\\\\\\\"position\\\\\\\",new Hw(new Float32Array([-1,-1,.5,3,-1,.5,-1,3,.5]),3));const m=new vT(_,d),f=this;function g(n,i){const s=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,s,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,s,p,m,null)}function v(e,n,i,s,r,o,h){let d=null;const p=!0===s.isPointLight?e.customDistanceMaterial:e.customDepthMaterial;if(d=void 0!==p?p:!0===s.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 s=n[e];void 0===s&&(s=d.clone(),n[e]=s),d=s}return d.visible=i.visible,d.wireframe=i.wireframe,d.side=3===h?null!==i.shadowSide?i.shadowSide:i.side:null!==i.shadowSide?i.shadowSide:u[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===s.isPointLight&&!0===d.isMeshDistanceMaterial&&(d.referencePosition.setFromMatrixPosition(s.matrixWorld),d.nearDistance=r,d.farDistance=o),d}function y(n,s,r,o,a){if(!1===n.visible)return;if(n.layers.test(s.layers)&&(n.isMesh||n.isLine||n.isPoints)&&(n.castShadow||n.receiveShadow&&3===a)&&(!n.frustumCulled||i.intersectsObject(n))){n.modelViewMatrix.multiplyMatrices(r.matrixWorldInverse,n.matrixWorld);const i=e.update(n),s=n.material;if(Array.isArray(s)){const e=i.groups;for(let l=0,c=e.length;lh||s.y>h)&&(s.x>h&&(r.x=Math.floor(h/p.x),s.x=r.x*p.x,u.mapSize.x=r.x),s.y>h&&(r.y=Math.floor(h/p.y),s.y=r.y*p.y,u.mapSize.y=r.y)),null===u.map&&!u.isPointLightShadow&&3===this.type){const t={minFilter:fx,magFilter:fx,format:Ex};u.map=new _b(s.x,s.y,t),u.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",u.mapPass=new _b(s.x,s.y,t),u.camera.updateProjectionMatrix()}if(null===u.map){const t={minFilter:px,magFilter:px,format:Ex};u.map=new _b(s.x,s.y,t),u.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",u.camera.updateProjectionMatrix()}t.setRenderTarget(u.map),t.clear();const _=u.getViewportCount();for(let t=0;t<_;t++){const e=u.getViewport(t);o.set(r.x*e.x,r.y*e.y,r.x*e.z,r.y*e.w),d.viewport(o),u.updateMatrices(c,t),i=u.getFrustum(),y(n,a,u.camera,c,this.type)}u.isPointLightShadow||3!==this.type||g(u,a),u.needsUpdate=!1}f.needsUpdate=!1,t.setRenderTarget(l,c,u)}}function fE(t,e,n){const i=n.isWebGL2;const s=new function(){let e=!1;const n=new pb;let i=null;const s=new pb(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,r,o,a){!0===a&&(e*=o,i*=o,r*=o),n.set(e,i,r,o),!1===s.equals(n)&&(t.clearColor(e,i,r,o),s.copy(n))},reset:function(){e=!1,i=null,s.set(-1,0,0,0)}}},r=new function(){let e=!1,n=null,i=null,s=null;return{setTest:function(t){t?B(2929):z(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){s!==e&&(t.clearDepth(e),s=e)},reset:function(){e=!1,n=null,i=null,s=null}}},o=new function(){let e=!1,n=null,i=null,s=null,r=null,o=null,a=null,l=null,c=null;return{setTest:function(t){e||(t?B(2960):z(2960))},setMask:function(i){n===i||e||(t.stencilMask(i),n=i)},setFunc:function(e,n,o){i===e&&s===n&&r===o||(t.stencilFunc(e,n,o),i=e,s=n,r=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,s=null,r=null,o=null,a=null,l=null,c=null}}};let a={},l=null,c={},h=null,u=!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 M=t.getParameter(35661);let E=!1,S=0;const C=t.getParameter(7938);-1!==C.indexOf(\\\\\\\"WebGL\\\\\\\")?(S=parseFloat(/^WebGL (\\\\d)/.exec(C)[1]),E=S>=1):-1!==C.indexOf(\\\\\\\"OpenGL ES\\\\\\\")&&(S=parseFloat(/^OpenGL ES (\\\\d)/.exec(C)[1]),E=S>=2);let N=null,L={};const O=t.getParameter(3088),P=t.getParameter(2978),R=(new pb).fromArray(O),I=(new pb).fromArray(P);function F(e,n,i){const s=new Uint8Array(4),r=t.createTexture();t.bindTexture(e,r),t.texParameteri(e,10241,9728),t.texParameteri(e,10240,9728);for(let e=0;ei||t.height>i)&&(s=i/Math.max(t.width,t.height)),s<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?nb:Math.floor,r=i(s*t.width),o=i(s*t.height);void 0===p&&(p=m(r,o));const a=n?m(r,o):p;a.width=r,a.height=o;return a.getContext(\\\\\\\"2d\\\\\\\").drawImage(t,0,0,r,o),console.warn(\\\\\\\"THREE.WebGLRenderer: Texture has been resized from (\\\\\\\"+t.width+\\\\\\\"x\\\\\\\"+t.height+\\\\\\\") to (\\\\\\\"+r+\\\\\\\"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 tb(t.width)&&tb(t.height)}function v(t,e){return t.generateMipmaps&&e&&t.minFilter!==px&&t.minFilter!==fx}function y(e,n,s,r,o=1){t.generateMipmap(e);i.get(n).__maxMipLevel=Math.log2(Math.max(s,r,o))}function x(n,i,s,r){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===s&&(o=33326),5131===s&&(o=33325),5121===s&&(o=33321)),6407===i&&(5126===s&&(o=34837),5131===s&&(o=34843),5121===s&&(o=32849)),6408===i&&(5126===s&&(o=34836),5131===s&&(o=34842),5121===s&&(o=r===Bx?35907:32856)),33325!==o&&33326!==o&&34842!==o&&34836!==o||e.get(\\\\\\\"EXT_color_buffer_float\\\\\\\"),o}function b(t){return t===px||t===_x||t===mx?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,s=i.get(e),r=i.get(n);if(!e)return;void 0!==r.__webglTexture&&(t.deleteTexture(r.__webglTexture),o.memory.textures--);e.depthTexture&&e.depthTexture.dispose();if(e.isWebGLCubeRenderTarget)for(let e=0;e<6;e++)t.deleteFramebuffer(s.__webglFramebuffer[e]),s.__webglDepthbuffer&&t.deleteRenderbuffer(s.__webglDepthbuffer[e]);else t.deleteFramebuffer(s.__webglFramebuffer),s.__webglDepthbuffer&&t.deleteRenderbuffer(s.__webglDepthbuffer),s.__webglMultisampledFramebuffer&&t.deleteFramebuffer(s.__webglMultisampledFramebuffer),s.__webglColorRenderbuffer&&t.deleteRenderbuffer(s.__webglColorRenderbuffer),s.__webglDepthRenderbuffer&&t.deleteRenderbuffer(s.__webglDepthRenderbuffer);if(e.isWebGLMultipleRenderTargets)for(let e=0,s=n.length;e0&&s.__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(s,t,e);console.warn(\\\\\\\"THREE.WebGLRenderer: Texture marked for update but image is incomplete\\\\\\\")}}n.activeTexture(33984+e),n.bindTexture(3553,s.__webglTexture)}function E(e,s){const o=i.get(e);e.version>0&&o.__version!==e.version?function(e,i,s){if(6!==i.image.length)return;L(e,i),n.activeTexture(33984+s),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,h=[];for(let t=0;t<6;t++)h[t]=o||l?l?i.image[t].image:i.image[t]:f(i.image[t],!1,!0,c);const u=h[0],d=g(u)||a,p=r.convert(i.format),_=r.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=h[t].mipmaps;for(let e=0;e1||i.get(r).__currentAnisotropy)&&(t.texParameterf(n,o.TEXTURE_MAX_ANISOTROPY_EXT,Math.min(r.anisotropy,s.getMaxAnisotropy())),i.get(r).__currentAnisotropy=r.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,s){let o=3553;i.isDataTexture2DArray&&(o=35866),i.isDataTexture3D&&(o=32879),L(e,i),n.activeTexture(33984+s),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!==ux||t.wrapT!==ux||t.minFilter!==px&&t.minFilter!==fx)}(i)&&!1===g(i.image),c=f(i.image,l,!1,h),u=g(c)||a,d=r.convert(i.format);let p,_=r.convert(i.type),m=x(i.internalFormat,d,_,i.encoding);N(o,i,u);const b=i.mipmaps;if(i.isDepthTexture)m=6402,a?m=i.type===wx?36012:i.type===bx?33190:i.type===Ax?35056:33189:i.type===wx&&console.error(\\\\\\\"WebGLRenderer: Floating point depth texture requires WebGL2.\\\\\\\"),i.format===Sx&&6402===m&&i.type!==xx&&i.type!==bx&&(console.warn(\\\\\\\"THREE.WebGLRenderer: Use UnsignedShortType or UnsignedIntType for DepthFormat DepthTexture.\\\\\\\"),i.type=xx,_=r.convert(i.type)),i.format===Cx&&6402===m&&(m=34041,i.type!==Ax&&(console.warn(\\\\\\\"THREE.WebGLRenderer: Use UnsignedInt248Type for DepthStencilFormat DepthTexture.\\\\\\\"),i.type=Ax,_=r.convert(i.type))),n.texImage2D(3553,0,m,c.width,c.height,0,d,_,null);else if(i.isDataTexture)if(b.length>0&&u){for(let t=0,e=b.length;t0&&u){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=M,this.setTexture2DArray=function(t,e){const s=i.get(t);t.version>0&&s.__version!==t.version?O(s,t,e):(n.activeTexture(33984+e),n.bindTexture(35866,s.__webglTexture))},this.setTexture3D=function(t,e){const s=i.get(t);t.version>0&&s.__version!==t.version?O(s,t,e):(n.activeTexture(33984+e),n.bindTexture(32879,s.__webglTexture))},this.setTextureCube=E,this.setupRenderTarget=function(e){const l=e.texture,c=i.get(e),h=i.get(l);e.addEventListener(\\\\\\\"dispose\\\\\\\",T),!0!==e.isWebGLMultipleRenderTargets&&(h.__webglTexture=t.createTexture(),h.__version=l.version,o.memory.textures++);const u=!0===e.isWebGLCubeRenderTarget,d=!0===e.isWebGLMultipleRenderTargets,p=!0===e.isWebGLMultisampleRenderTarget,_=l.isDataTexture3D||l.isDataTexture2DArray,m=g(e)||a;if(!a||l.format!==Mx||l.type!==wx&&l.type!==Tx||(l.format=Ex,console.warn(\\\\\\\"THREE.WebGLRenderer: Rendering to textures with RGB format is not supported. Using RGBA format instead.\\\\\\\")),u){c.__webglFramebuffer=[];for(let e=0;e<6;e++)c.__webglFramebuffer[e]=t.createFramebuffer()}else if(c.__webglFramebuffer=t.createFramebuffer(),d)if(s.drawBuffers){const n=e.texture;for(let e=0,s=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&&(s=e.getPose(t.gripSpace,n),null!==s&&(a.matrix.fromArray(s.transform.matrix),a.matrix.decompose(a.position,a.rotation,a.scale),s.linearVelocity?(a.hasLinearVelocity=!0,a.linearVelocity.copy(s.linearVelocity)):a.hasLinearVelocity=!1,s.angularVelocity?(a.hasAngularVelocity=!0,a.angularVelocity.copy(s.angularVelocity)):a.hasAngularVelocity=!1));return null!==o&&(o.visible=null!==i),null!==a&&(a.visible=null!==s),null!==l&&(l.visible=null!==r),this}}class TE extends jx{constructor(t,e){super();const n=this,i=t.state;let s=null,r=1,o=null,a=\\\\\\\"local-floor\\\\\\\",l=null,c=null,h=null,u=null,d=null,p=!1,_=null,m=null,f=null,g=null,v=null,y=null;const x=[],b=new Map,w=new ET;w.layers.enable(1),w.viewport=new pb;const T=new ET;T.layers.enable(2),T.viewport=new pb;const A=[w,T],M=new yE;M.layers.enable(1),M.layers.enable(2);let E=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(),E=null,S=null,i.bindXRFramebuffer(null),t.setRenderTarget(t.getRenderTarget()),h&&e.deleteFramebuffer(h),_&&e.deleteFramebuffer(_),m&&e.deleteRenderbuffer(m),f&&e.deleteRenderbuffer(f),h=null,_=null,m=null,f=null,d=null,u=null,c=null,s=null,F.stop(),n.isPresenting=!1,n.dispatchEvent({type:\\\\\\\"sessionend\\\\\\\"})}function L(t){const e=s.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 s=t.get(i).__maxMipLevel;void 0!==s&&(e.maxMipLevel.value=s)}let s,r;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?s=n.map:n.specularMap?s=n.specularMap:n.displacementMap?s=n.displacementMap:n.normalMap?s=n.normalMap:n.bumpMap?s=n.bumpMap:n.roughnessMap?s=n.roughnessMap:n.metalnessMap?s=n.metalnessMap:n.alphaMap?s=n.alphaMap:n.emissiveMap?s=n.emissiveMap:n.clearcoatMap?s=n.clearcoatMap:n.clearcoatNormalMap?s=n.clearcoatNormalMap:n.clearcoatRoughnessMap?s=n.clearcoatRoughnessMap:n.specularIntensityMap?s=n.specularIntensityMap:n.specularTintMap?s=n.specularTintMap:n.transmissionMap?s=n.transmissionMap:n.thicknessMap&&(s=n.thicknessMap),void 0!==s&&(s.isWebGLRenderTarget&&(s=s.texture),!0===s.matrixAutoUpdate&&s.updateMatrix(),e.uvTransform.value.copy(s.matrix)),n.aoMap?r=n.aoMap:n.lightMap&&(r=n.lightMap),void 0!==r&&(r.isWebGLRenderTarget&&(r=r.texture),!0===r.matrixAutoUpdate&&r.updateMatrix(),e.uv2Transform.value.copy(r.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,s,r,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 s;e.map?s=e.map:e.alphaMap&&(s=e.alphaMap);void 0!==s&&(!0===s.matrixAutoUpdate&&s.updateMatrix(),t.uvTransform.value.copy(s.matrix))}(t,i,s,r):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 ME(t={}){const e=void 0!==t.canvas?t.canvas:function(){const t=ab(\\\\\\\"canvas\\\\\\\");return t.style.display=\\\\\\\"block\\\\\\\",t}(),n=void 0!==t.context?t.context:null,i=void 0!==t.alpha&&t.alpha,s=void 0===t.depth||t.depth,r=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\\\\\\\",h=void 0!==t.failIfMajorPerformanceCaveat&&t.failIfMajorPerformanceCaveat;let u=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=Dx,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 pb,T=new pb;let A=null,M=e.width,E=e.height,S=1,C=null,N=null;const L=new pb(0,0,M,E),O=new pb(0,0,M,E);let P=!1;const R=[],I=new BT;let F=!1,D=!1,B=null;const z=new Yb,k=new gb,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,st,rt,ot,at,lt,ct,ht,ut=n;function dt(t,n){for(let i=0;i0&&function(t,e,n){if(null===B){const t=!0===o&&!0===H.isWebGL2;B=new(t?mb:_b)(1024,1024,{generateMipmaps:!0,type:null!==ct.convert(Tx)?Tx:yx,minFilter:vx,magFilter:px,wrapS:ux,wrapT:ux})}const i=m.getRenderTarget();m.setRenderTarget(B),m.clear();const s=m.toneMapping;m.toneMapping=0,At(t,e,n),m.toneMapping=s,X.updateMultisampleRenderTarget(B),X.updateRenderTargetMipmap(B),m.setRenderTarget(i)}(s,e,n),i&&j.viewport(w.copy(i)),s.length>0&&At(s,e,n),r.length>0&&At(r,e,n),a.length>0&&At(a,e,n)}function At(t,e,n){const i=!0===e.isScene?e.overrideMaterial:null;for(let s=0,r=t.length;s0?_[_.length-1]:null,p.pop(),u=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,s=!1,r=!1;if(t){const n=t.texture;(n.isDataTexture3D||n.isDataTexture2DArray)&&(r=!0);const o=q.get(t).__webglFramebuffer;t.isWebGLCubeRenderTarget?(i=o[e],s=!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=P;if(j.bindFramebuffer(36160,i)&&H.drawBuffers){let e=!1;if(t)if(t.isWebGLMultipleRenderTargets){const n=t.texture;if(R.length!==n.length||36064!==R[0]){for(let t=0,e=n.length;t=0&&e<=t.width-i&&n>=0&&n<=t.height-s&&ut.readPixels(e,n,i,s,ct.convert(a),ct.convert(l),r):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),s=Math.floor(e.image.width*i),r=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),ut.copyTexImage2D(3553,n,o,t.x,t.y,s,r,0),j.unbindTexture()},this.copyTextureToTexture=function(t,e,n,i=0){const s=e.image.width,r=e.image.height,o=ct.convert(n.format),a=ct.convert(n.type);X.setTexture2D(n,0),ut.pixelStorei(37440,n.flipY),ut.pixelStorei(37441,n.premultiplyAlpha),ut.pixelStorei(3317,n.unpackAlignment),e.isDataTexture?ut.texSubImage2D(3553,i,t.x,t.y,s,r,o,a,e.image.data):e.isCompressedTexture?ut.compressedTexSubImage2D(3553,i,t.x,t.y,e.mipmaps[0].width,e.mipmaps[0].height,o,e.mipmaps[0].data):ut.texSubImage2D(3553,i,t.x,t.y,o,a,e.image),0===i&&n.generateMipmaps&&ut.generateMipmap(3553),j.unbindTexture()},this.copyTextureToTexture3D=function(t,e,n,i,s=0){if(m.isWebGL1Renderer)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: can only be used with WebGL2.\\\\\\\");const r=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 h;if(i.isDataTexture3D)X.setTexture3D(i,0),h=32879;else{if(!i.isDataTexture2DArray)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: only supports THREE.DataTexture3D and THREE.DataTexture2DArray.\\\\\\\");X.setTexture2DArray(i,0),h=35866}ut.pixelStorei(37440,i.flipY),ut.pixelStorei(37441,i.premultiplyAlpha),ut.pixelStorei(3317,i.unpackAlignment);const u=ut.getParameter(3314),d=ut.getParameter(32878),p=ut.getParameter(3316),_=ut.getParameter(3315),f=ut.getParameter(32877),g=n.isCompressedTexture?n.mipmaps[0]:n.image;ut.pixelStorei(3314,g.width),ut.pixelStorei(32878,g.height),ut.pixelStorei(3316,t.min.x),ut.pixelStorei(3315,t.min.y),ut.pixelStorei(32877,t.min.z),n.isDataTexture||n.isDataTexture3D?ut.texSubImage3D(h,s,e.x,e.y,e.z,r,o,a,l,c,g.data):n.isCompressedTexture?(console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: untested support for compressed srcTexture.\\\\\\\"),ut.compressedTexSubImage3D(h,s,e.x,e.y,e.z,r,o,a,l,g.data)):ut.texSubImage3D(h,s,e.x,e.y,e.z,r,o,a,l,c,g),ut.pixelStorei(3314,u),ut.pixelStorei(32878,d),ut.pixelStorei(3316,p),ut.pixelStorei(3315,_),ut.pixelStorei(32877,f),0===s&&i.generateMipmaps&&ut.generateMipmap(h),j.unbindTexture()},this.initTexture=function(t){X.setTexture2D(t,0),j.unbindTexture()},this.resetState=function(){g=0,v=0,y=null,j.reset(),ht.reset()},\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}(class extends ME{}).prototype.isWebGL1Renderer=!0;class EE{constructor(t,e=25e-5){this.name=\\\\\\\"\\\\\\\",this.color=new kw(t),this.density=e}clone(){return new EE(this.color,this.density)}toJSON(){return{type:\\\\\\\"FogExp2\\\\\\\",color:this.color.getHex(),density:this.density}}}EE.prototype.isFogExp2=!0;class SE{constructor(t,e=1,n=1e3){this.name=\\\\\\\"\\\\\\\",this.color=new kw(t),this.near=e,this.far=n}clone(){return new SE(this.color,this.near,this.far)}toJSON(){return{type:\\\\\\\"Fog\\\\\\\",color:this.color.getHex(),near:this.near,far:this.far}}}SE.prototype.isFog=!0;class CE extends yw{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}}CE.prototype.isScene=!0;class NE{constructor(t,e){this.array=t,this.stride=e,this.count=void 0!==t?t.length/e:0,this.usage=Gx,this.updateRange={offset:0,count:-1},this.version=0,this.uuid=Jx()}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,s=this.stride;it.far||e.push({distance:a,point:IE.clone(),uv:Lw.getUV(IE,UE,GE,VE,HE,jE,WE,new sb),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 XE(t,e,n,i,s,r){BE.subVectors(t,n).addScalar(.5).multiply(i),void 0!==s?(zE.x=r*BE.x-s*BE.y,zE.y=s*BE.x+r*BE.y):zE.copy(BE),t.copy(e),t.x+=zE.x,t.y+=zE.y,t.applyMatrix4(kE)}qE.prototype.isSprite=!0;const YE=new gb,$E=new pb,JE=new pb,ZE=new gb,QE=new Yb;class KE extends vT{constructor(t,e){super(t,e),this.type=\\\\\\\"SkinnedMesh\\\\\\\",this.bindMode=\\\\\\\"attached\\\\\\\",this.bindMatrix=new Yb,this.bindMatrixInverse=new Yb}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 pb,e=this.geometry.attributes.skinWeight;for(let n=0,i=e.count;na)continue;u.applyMatrix4(this.matrixWorld);const d=t.ray.origin.distanceTo(u);dt.far||e.push({distance:d,point:h.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}else{for(let n=Math.max(0,r.start),i=Math.min(s.count,r.start+r.count)-1;na)continue;u.applyMatrix4(this.matrixWorld);const i=t.ray.origin.distanceTo(u);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.\\\\\\\")}}}_S.prototype.isLine=!0;const mS=new gb,fS=new gb;class gS extends _S{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 MS(t,e,n,i,s,r,o){const a=bS.distanceSqToPoint(t);if(as.far)return;r.push({distance:l,distanceToRay:Math.sqrt(a),point:n,index:e,face:null,object:o})}}AS.prototype.isPoints=!0;(class extends ub{constructor(t,e,n,i,s,r,o,a,l){super(t,e,n,i,s,r,o,a,l),this.format=void 0!==o?o:Mx,this.minFilter=void 0!==r?r:fx,this.magFilter=void 0!==s?s:fx,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 ES extends ub{constructor(t,e,n,i,s,r,o,a,l,c,h,u){super(null,r,o,a,l,c,i,s,h,u),this.image={width:e,height:n},this.mipmaps=t,this.flipY=!1,this.generateMipmaps=!1}}ES.prototype.isCompressedTexture=!0;(class extends ub{constructor(t,e,n,i,s,r,o,a,l){super(t,e,n,i,s,r,o,a,l),this.needsUpdate=!0}}).prototype.isCanvasTexture=!0;(class extends ub{constructor(t,e,n,i,s,r,o,a,l,c){if((c=void 0!==c?c:Sx)!==Sx&&c!==Cx)throw new Error(\\\\\\\"DepthTexture format must be either THREE.DepthFormat or THREE.DepthStencilFormat\\\\\\\");void 0===n&&c===Sx&&(n=xx),void 0===n&&c===Cx&&(n=Ax),super(null,i,s,r,o,a,c,n,l),this.image={width:t,height:e},this.magFilter=void 0!==o?o:px,this.minFilter=void 0!==a?a:px,this.flipY=!1,this.generateMipmaps=!1}}).prototype.isDepthTexture=!0;new gb,new gb,new gb,new Lw;class SS{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),s=0;e.push(0);for(let r=1;r<=t;r++)n=this.getPoint(r/t),s+=n.distanceTo(i),e.push(s),i=n;return this.cacheArcLengths=e,e}updateArcLengths(){this.needsUpdate=!0,this.getLengths()}getUtoTmapping(t,e){const n=this.getLengths();let i=0;const s=n.length;let r;r=e||t*n[s-1];let o,a=0,l=s-1;for(;a<=l;)if(i=Math.floor(a+(l-a)/2),o=n[i]-r,o<0)a=i+1;else{if(!(o>0)){l=i;break}l=i-1}if(i=l,n[i]===r)return i/(s-1);const c=n[i];return(i+(r-c)/(n[i+1]-c))/(s-1)}getTangent(t,e){const n=1e-4;let i=t-n,s=t+n;i<0&&(i=0),s>1&&(s=1);const r=this.getPoint(i),o=this.getPoint(s),a=e||(r.isVector2?new sb:new gb);return a.copy(o).sub(r).normalize(),a}getTangentAt(t,e){const n=this.getUtoTmapping(t);return this.getTangent(n,e)}computeFrenetFrames(t,e){const n=new gb,i=[],s=[],r=[],o=new gb,a=new Yb;for(let e=0;e<=t;e++){const n=e/t;i[e]=this.getTangentAt(n,new gb)}s[0]=new gb,r[0]=new gb;let l=Number.MAX_VALUE;const c=Math.abs(i[0].x),h=Math.abs(i[0].y),u=Math.abs(i[0].z);c<=l&&(l=c,n.set(1,0,0)),h<=l&&(l=h,n.set(0,1,0)),u<=l&&n.set(0,0,1),o.crossVectors(i[0],n).normalize(),s[0].crossVectors(i[0],o),r[0].crossVectors(i[0],s[0]);for(let e=1;e<=t;e++){if(s[e]=s[e-1].clone(),r[e]=r[e-1].clone(),o.crossVectors(i[e-1],i[e]),o.length()>Number.EPSILON){o.normalize();const t=Math.acos(Zx(i[e-1].dot(i[e]),-1,1));s[e].applyMatrix4(a.makeRotationAxis(o,t))}r[e].crossVectors(i[e],s[e])}if(!0===e){let e=Math.acos(Zx(s[0].dot(s[t]),-1,1));e/=t,i[0].dot(o.crossVectors(s[0],s[t]))>0&&(e=-e);for(let n=1;n<=t;n++)s[n].applyMatrix4(a.makeRotationAxis(i[n],e*n)),r[n].crossVectors(i[n],s[n])}return{tangents:i,normals:s,binormals:r}}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 CS extends SS{constructor(t=0,e=0,n=1,i=1,s=0,r=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=s,this.aEndAngle=r,this.aClockwise=o,this.aRotation=a}getPoint(t,e){const n=e||new sb,i=2*Math.PI;let s=this.aEndAngle-this.aStartAngle;const r=Math.abs(s)i;)s-=i;s0?0:(Math.floor(Math.abs(l)/s)+1)*s:0===c&&l===s-1&&(l=s-2,c=1),this.closed||l>0?o=i[(l-1)%s]:(OS.subVectors(i[0],i[1]).add(i[0]),o=OS);const h=i[l%s],u=i[(l+1)%s];if(this.closed||l+2i.length-2?i.length-1:r+1],h=i[r>i.length-3?i.length-1:r+2];return n.set(DS(o,a.x,l.x,c.x,h.x),DS(o,a.y,l.y,c.y,h.y)),n}copy(t){super.copy(t),this.points=[];for(let e=0,n=t.points.length;e=n){const t=i[s]-n,r=this.curves[s],o=r.getLength(),a=0===o?0:1-t/o;return r.getPointAt(a,e)}s++}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 YS extends XS{constructor(t){super(t),this.uuid=Jx(),this.type=\\\\\\\"Shape\\\\\\\",this.holes=[]}getPointsHoles(t){const e=[];for(let n=0,i=this.holes.length;n80*n){a=c=t[0],l=h=t[1];for(let e=n;ec&&(c=u),d>h&&(h=d);p=Math.max(c-a,h-l),p=0!==p?1/p:0}return QS(r,o,n,a,l,p),o};function JS(t,e,n,i,s){let r,o;if(s===function(t,e,n,i){let s=0;for(let r=e,o=n-i;r0)for(r=e;r=e;r-=i)o=gC(r,t[r],t[r+1],o);return o&&uC(o,o.next)&&(vC(o),o=o.next),o}function ZS(t,e){if(!t)return t;e||(e=t);let n,i=t;do{if(n=!1,i.steiner||!uC(i,i.next)&&0!==hC(i.prev,i,i.next))i=i.next;else{if(vC(i),i=e=i.prev,i===i.next)break;n=!0}}while(n||i!==e);return e}function QS(t,e,n,i,s,r,o){if(!t)return;!o&&r&&function(t,e,n,i){let s=t;do{null===s.z&&(s.z=oC(s.x,s.y,e,n,i)),s.prevZ=s.prev,s.nextZ=s.next,s=s.next}while(s!==t);s.prevZ.nextZ=null,s.prevZ=null,function(t){let e,n,i,s,r,o,a,l,c=1;do{for(n=t,t=null,r=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)?(s=n,n=n.nextZ,a--):(s=i,i=i.nextZ,l--),r?r.nextZ=s:t=s,s.prevZ=r,r=s;n=i}r.nextZ=null,c*=2}while(o>1)}(s)}(t,i,s,r);let a,l,c=t;for(;t.prev!==t.next;)if(a=t.prev,l=t.next,r?tC(t,i,s,r):KS(t))e.push(a.i/n),e.push(t.i/n),e.push(l.i/n),vC(t),t=l.next,c=l.next;else if((t=l)===c){o?1===o?QS(t=eC(ZS(t),e,n),e,n,i,s,r,2):2===o&&nC(t,e,n,i,s,r):QS(ZS(t),e,n,i,s,r,1);break}}function KS(t){const e=t.prev,n=t,i=t.next;if(hC(e,n,i)>=0)return!1;let s=t.next.next;for(;s!==t.prev;){if(lC(e.x,e.y,n.x,n.y,i.x,i.y,s.x,s.y)&&hC(s.prev,s,s.next)>=0)return!1;s=s.next}return!0}function tC(t,e,n,i){const s=t.prev,r=t,o=t.next;if(hC(s,r,o)>=0)return!1;const a=s.xr.x?s.x>o.x?s.x:o.x:r.x>o.x?r.x:o.x,h=s.y>r.y?s.y>o.y?s.y:o.y:r.y>o.y?r.y:o.y,u=oC(a,l,e,n,i),d=oC(c,h,e,n,i);let p=t.prevZ,_=t.nextZ;for(;p&&p.z>=u&&_&&_.z<=d;){if(p!==t.prev&&p!==t.next&&lC(s.x,s.y,r.x,r.y,o.x,o.y,p.x,p.y)&&hC(p.prev,p,p.next)>=0)return!1;if(p=p.prevZ,_!==t.prev&&_!==t.next&&lC(s.x,s.y,r.x,r.y,o.x,o.y,_.x,_.y)&&hC(_.prev,_,_.next)>=0)return!1;_=_.nextZ}for(;p&&p.z>=u;){if(p!==t.prev&&p!==t.next&&lC(s.x,s.y,r.x,r.y,o.x,o.y,p.x,p.y)&&hC(p.prev,p,p.next)>=0)return!1;p=p.prevZ}for(;_&&_.z<=d;){if(_!==t.prev&&_!==t.next&&lC(s.x,s.y,r.x,r.y,o.x,o.y,_.x,_.y)&&hC(_.prev,_,_.next)>=0)return!1;_=_.nextZ}return!0}function eC(t,e,n){let i=t;do{const s=i.prev,r=i.next.next;!uC(s,r)&&dC(s,i,i.next,r)&&mC(s,r)&&mC(r,s)&&(e.push(s.i/n),e.push(i.i/n),e.push(r.i/n),vC(i),vC(i.next),i=t=r),i=i.next}while(i!==t);return ZS(i)}function nC(t,e,n,i,s,r){let o=t;do{let t=o.next.next;for(;t!==o.prev;){if(o.i!==t.i&&cC(o,t)){let a=fC(o,t);return o=ZS(o,o.next),a=ZS(a,a.next),QS(o,e,n,i,s,r),void QS(a,e,n,i,s,r)}t=t.next}o=o.next}while(o!==t)}function iC(t,e){return t.x-e.x}function sC(t,e){if(e=function(t,e){let n=e;const i=t.x,s=t.y;let r,o=-1/0;do{if(s<=n.y&&s>=n.next.y&&n.next.y!==n.y){const t=n.x+(s-n.y)*(n.next.x-n.x)/(n.next.y-n.y);if(t<=i&&t>o){if(o=t,t===i){if(s===n.y)return n;if(s===n.next.y)return n.next}r=n.x=n.x&&n.x>=l&&i!==n.x&&lC(sr.x||n.x===r.x&&rC(r,n)))&&(r=n,u=h)),n=n.next}while(n!==a);return r}(t,e)){const n=fC(e,t);ZS(e,e.next),ZS(n,n.next)}}function rC(t,e){return hC(t.prev,t,e.prev)<0&&hC(e.next,t,t.next)<0}function oC(t,e,n,i,s){return(t=1431655765&((t=858993459&((t=252645135&((t=16711935&((t=32767*(t-n)*s)|t<<8))|t<<4))|t<<2))|t<<1))|(e=1431655765&((e=858993459&((e=252645135&((e=16711935&((e=32767*(e-i)*s)|e<<8))|e<<4))|e<<2))|e<<1))<<1}function aC(t){let e=t,n=t;do{(e.x=0&&(t-o)*(i-a)-(n-o)*(e-a)>=0&&(n-o)*(r-a)-(s-o)*(i-a)>=0}function cC(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&&dC(n,n.next,t,e))return!0;n=n.next}while(n!==t);return!1}(t,e)&&(mC(t,e)&&mC(e,t)&&function(t,e){let n=t,i=!1;const s=(t.x+e.x)/2,r=(t.y+e.y)/2;do{n.y>r!=n.next.y>r&&n.next.y!==n.y&&s<(n.next.x-n.x)*(r-n.y)/(n.next.y-n.y)+n.x&&(i=!i),n=n.next}while(n!==t);return i}(t,e)&&(hC(t.prev,t,e.prev)||hC(t,e.prev,e))||uC(t,e)&&hC(t.prev,t,t.next)>0&&hC(e.prev,e,e.next)>0)}function hC(t,e,n){return(e.y-t.y)*(n.x-e.x)-(e.x-t.x)*(n.y-e.y)}function uC(t,e){return t.x===e.x&&t.y===e.y}function dC(t,e,n,i){const s=_C(hC(t,e,n)),r=_C(hC(t,e,i)),o=_C(hC(n,i,t)),a=_C(hC(n,i,e));return s!==r&&o!==a||(!(0!==s||!pC(t,n,e))||(!(0!==r||!pC(t,i,e))||(!(0!==o||!pC(n,t,i))||!(0!==a||!pC(n,e,i)))))}function pC(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 _C(t){return t>0?1:t<0?-1:0}function mC(t,e){return hC(t.prev,t,t.next)<0?hC(t,e,t.next)>=0&&hC(t,t.prev,e)>=0:hC(t,e,t.prev)<0||hC(t,t.next,e)<0}function fC(t,e){const n=new yC(t.i,t.x,t.y),i=new yC(e.i,e.x,e.y),s=t.next,r=e.prev;return t.next=e,e.prev=t,n.next=s,s.prev=n,i.next=n,n.prev=i,r.next=i,i.prev=r,i}function gC(t,e,n,i){const s=new yC(t,e,n);return i?(s.next=i.next,s.prev=i,i.next.prev=s,i.next=s):(s.prev=s,s.next=s),s}function vC(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 yC(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 xC{static area(t){const e=t.length;let n=0;for(let i=e-1,s=0;s2&&t[e-1].equals(t[0])&&t.pop()}function wC(t,e){for(let n=0;nNumber.EPSILON){const u=Math.sqrt(h),d=Math.sqrt(l*l+c*c),p=e.x-a/u,_=e.y+o/u,m=((n.x-c/d-p)*c-(n.y+l/d-_)*l)/(o*c-a*l);i=p+o*m-t.x,s=_+a*m-t.y;const f=i*i+s*s;if(f<=2)return new sb(i,s);r=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,s=o,r=Math.sqrt(h)):(i=o,s=a,r=Math.sqrt(h/2))}return new sb(i/r,s/r)}const O=[];for(let t=0,e=E.length,n=e-1,i=t+1;t=0;t--){const e=t/p,n=h*Math.cos(e*Math.PI/2),i=u*Math.sin(e*Math.PI/2)+d;for(let t=0,e=E.length;t=0;){const i=n;let s=n-1;s<0&&(s=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}}CC.prototype.isMeshPhysicalMaterial=!0;class NC extends Pw{constructor(t){super(),this.type=\\\\\\\"MeshPhongMaterial\\\\\\\",this.color=new kw(16777215),this.specular=new kw(1118481),this.shininess=30,this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new kw(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new sb(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}}NC.prototype.isMeshPhongMaterial=!0;class LC extends Pw{constructor(t){super(),this.defines={TOON:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshToonMaterial\\\\\\\",this.color=new kw(16777215),this.map=null,this.gradientMap=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new kw(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new sb(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}}LC.prototype.isMeshToonMaterial=!0;class OC extends Pw{constructor(t){super(),this.type=\\\\\\\"MeshNormalMaterial\\\\\\\",this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new sb(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}}OC.prototype.isMeshNormalMaterial=!0;class PC extends Pw{constructor(t){super(),this.type=\\\\\\\"MeshLambertMaterial\\\\\\\",this.color=new kw(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new kw(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}}PC.prototype.isMeshLambertMaterial=!0;class RC extends Pw{constructor(t){super(),this.defines={MATCAP:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshMatcapMaterial\\\\\\\",this.color=new kw(16777215),this.matcap=null,this.map=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new sb(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}}RC.prototype.isMeshMatcapMaterial=!0;class IC extends lS{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}}IC.prototype.isLineDashedMaterial=!0;const FC={arraySlice:function(t,e,n){return FC.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,s=new t.constructor(i);for(let r=0,o=0;o!==i;++r){const i=n[r]*e;for(let n=0;n!==e;++n)s[o++]=t[i+n]}return s},flattenJSON:function(t,e,n,i){let s=1,r=t[0];for(;void 0!==r&&void 0===r[i];)r=t[s++];if(void 0===r)return;let o=r[i];if(void 0!==o)if(Array.isArray(o))do{o=r[i],void 0!==o&&(e.push(r.time),n.push.apply(n,o)),r=t[s++]}while(void 0!==r);else if(void 0!==o.toArray)do{o=r[i],void 0!==o&&(e.push(r.time),o.toArray(n,n.length)),r=t[s++]}while(void 0!==r);else do{o=r[i],void 0!==o&&(e.push(r.time),n.push(o)),r=t[s++]}while(void 0!==r)},subclip:function(t,e,n,i,s=30){const r=t.clone();r.name=e;const o=[];for(let t=0;t=i)){l.push(e.times[t]);for(let n=0;nr.tracks[t].times[0]&&(a=r.tracks[t].times[0]);for(let t=0;t=i.times[u]){const t=u*l+a,e=t+l-a;d=FC.arraySlice(i.values,t,e)}else{const t=i.createInterpolant(),e=a,n=l-a;t.evaluate(r),d=FC.arraySlice(t.resultBuffer,e,n)}if(\\\\\\\"quaternion\\\\\\\"===s){(new fb).fromArray(d).normalize().conjugate().toArray(d)}const p=o.times.length;for(let t=0;t=s)break t;{const o=e[1];t=s)break e}r=n,n=0}}for(;n>>1;te;)--r;if(++r,0!==s||r!==i){s>=r&&(r=Math.max(r,1),s=r-1);const t=this.getValueSize();this.times=FC.arraySlice(n,s,r),this.values=FC.arraySlice(this.values,s*t,r*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,s=n.length;0===s&&(console.error(\\\\\\\"THREE.KeyframeTrack: Track is empty.\\\\\\\",this),t=!1);let r=null;for(let e=0;e!==s;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!==r&&r>i){console.error(\\\\\\\"THREE.KeyframeTrack: Out of order keys.\\\\\\\",this,e,i,r),t=!1;break}r=i}if(void 0!==i&&FC.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=FC.arraySlice(this.times),e=FC.arraySlice(this.values),n=this.getValueSize(),i=this.getInterpolation()===Ox,s=t.length-1;let r=1;for(let o=1;o0){t[r]=t[s];for(let t=s*n,i=r*n,o=0;o!==n;++o)e[i+o]=e[t+o];++r}return r!==t.length?(this.times=FC.arraySlice(t,0,r),this.values=FC.arraySlice(e,0,r*n)):(this.times=t,this.values=e),this}clone(){const t=FC.arraySlice(this.times,0),e=FC.arraySlice(this.values,0),n=new(0,this.constructor)(this.name,t,e);return n.createInterpolant=this.createInterpolant,n}}UC.prototype.TimeBufferType=Float32Array,UC.prototype.ValueBufferType=Float32Array,UC.prototype.DefaultInterpolation=Lx;class GC extends UC{}GC.prototype.ValueTypeName=\\\\\\\"bool\\\\\\\",GC.prototype.ValueBufferType=Array,GC.prototype.DefaultInterpolation=Nx,GC.prototype.InterpolantFactoryMethodLinear=void 0,GC.prototype.InterpolantFactoryMethodSmooth=void 0;class VC extends UC{}VC.prototype.ValueTypeName=\\\\\\\"color\\\\\\\";class HC extends UC{}HC.prototype.ValueTypeName=\\\\\\\"number\\\\\\\";class jC extends DC{constructor(t,e,n,i){super(t,e,n,i)}interpolate_(t,e,n,i){const s=this.resultBuffer,r=this.sampleValues,o=this.valueSize,a=(n-e)/(i-e);let l=t*o;for(let t=l+o;l!==t;l+=4)fb.slerpFlat(s,0,r,l-o,r,l,a);return s}}class WC extends UC{InterpolantFactoryMethodLinear(t){return new jC(this.times,this.values,this.getValueSize(),t)}}WC.prototype.ValueTypeName=\\\\\\\"quaternion\\\\\\\",WC.prototype.DefaultInterpolation=Lx,WC.prototype.InterpolantFactoryMethodSmooth=void 0;class qC extends UC{}qC.prototype.ValueTypeName=\\\\\\\"string\\\\\\\",qC.prototype.ValueBufferType=Array,qC.prototype.DefaultInterpolation=Nx,qC.prototype.InterpolantFactoryMethodLinear=void 0,qC.prototype.InterpolantFactoryMethodSmooth=void 0;class XC extends UC{}XC.prototype.ValueTypeName=\\\\\\\"vector\\\\\\\";class YC{constructor(t,e=-1,n,i=2500){this.name=t,this.tracks=n,this.duration=e,this.blendMode=i,this.uuid=Jx(),this.duration<0&&this.resetDuration()}static parse(t){const e=[],n=t.tracks,i=1/(t.fps||1);for(let t=0,s=n.length;t!==s;++t)e.push($C(n[t]).scale(i));const s=new this(t.name,t.duration,e,t.blendMode);return s.uuid=t.uuid,s}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(UC.toJSON(n[t]));return i}static CreateFromMorphTargetSequence(t,e,n,i){const s=e.length,r=[];for(let t=0;t1){const t=r[1];let e=i[t];e||(i[t]=e=[]),e.push(n)}}const r=[];for(const t in i)r.push(this.CreateFromMorphTargetSequence(t,i[t],e,n));return r}static parseAnimation(t,e){if(!t)return console.error(\\\\\\\"THREE.AnimationClip: No animation in JSONLoader data.\\\\\\\"),null;const n=function(t,e,n,i,s){if(0!==n.length){const r=[],o=[];FC.flattenJSON(n,r,o,i),0!==r.length&&s.push(new t(e,r,o))}},i=[],s=t.name||\\\\\\\"default\\\\\\\",r=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,s=e+e;t!==s;++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,s=i;t!==s;++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!==s;++i)t[e+i]=t[n+i]}_slerp(t,e,n,i){fb.slerpFlat(t,e,t,e,t,n,i)}_slerpAdditive(t,e,n,i,s){const r=this._workIndex*s;fb.multiplyQuaternionsFlat(t,r,t,e,t,n),fb.slerpFlat(t,e,t,e,t,r,i)}_lerp(t,e,n,i,s){const r=1-i;for(let o=0;o!==s;++o){const s=e+o;t[s]=t[s]*r+t[n+o]*i}}_lerpAdditive(t,e,n,i,s){for(let r=0;r!==s;++r){const s=e+r;t[s]=t[s]+t[n+r]*i}}}const ON=\\\\\\\"\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/\\\\\\\",PN=new RegExp(\\\\\\\"[\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",\\\\\\\"g\\\\\\\"),RN=\\\\\\\"[^\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",IN=\\\\\\\"[^\\\\\\\"+ON.replace(\\\\\\\"\\\\\\\\.\\\\\\\",\\\\\\\"\\\\\\\")+\\\\\\\"]\\\\\\\",FN=/((?:WC+[\\\\/:])*)/.source.replace(\\\\\\\"WC\\\\\\\",RN),DN=/(WCOD+)?/.source.replace(\\\\\\\"WCOD\\\\\\\",IN),BN=/(?:\\\\.(WC+)(?:\\\\[(.+)\\\\])?)?/.source.replace(\\\\\\\"WC\\\\\\\",RN),zN=/\\\\.(WC+)(?:\\\\[(.+)\\\\])?/.source.replace(\\\\\\\"WC\\\\\\\",RN),kN=new RegExp(\\\\\\\"^\\\\\\\"+FN+DN+BN+zN+\\\\\\\"$\\\\\\\"),UN=[\\\\\\\"material\\\\\\\",\\\\\\\"materials\\\\\\\",\\\\\\\"bones\\\\\\\"];class GN{constructor(t,e,n){this.path=e,this.parsedPath=n||GN.parseTrackName(e),this.node=GN.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 GN.Composite(t,e,n):new GN(t,e,n)}static sanitizeNodeName(t){return t.replace(/\\\\s/g,\\\\\\\"_\\\\\\\").replace(PN,\\\\\\\"\\\\\\\")}static parseTrackName(t){const e=kN.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!==UN.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(r),e[n].accumulateAdditive(o);break;case Fx:default:for(let n=0,s=t.length;n!==s;++n)t[n].evaluate(r),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,s=this._loopCount;const r=2202===n;if(0===t)return-1===s?i:r&&1==(1&s)?e-i:i;if(2200===n){-1===s&&(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===s&&(t>=0?(s=0,this._setEndings(!0,0===this.repetitions,r)):this._setEndings(0===this.repetitions,!0,r)),i>=e||i<0){const n=Math.floor(i/e);i-=e*n,s+=Math.abs(n);const o=this.repetitions-s;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,r)}else this._setEndings(!1,!1,r);this._loopCount=s,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"loop\\\\\\\",action:this,loopDelta:n})}}else this.time=i;if(r&&1==(1&s))return e-i}return i}_setEndings(t,e,n){const i=this._interpolantSettings;n?(i.endingStart=Rx,i.endingEnd=Rx):(i.endingStart=t?this.zeroSlopeAtStart?Rx:Px:Ix,i.endingEnd=e?this.zeroSlopeAtEnd?Rx:Px:Ix)}_scheduleFading(t,e,n){const i=this._mixer,s=i.time;let r=this._weightInterpolant;null===r&&(r=i._lendControlInterpolant(),this._weightInterpolant=r);const o=r.parameterPositions,a=r.sampleValues;return o[0]=s,a[0]=e,o[1]=s+t,a[1]=n,this}}(class extends jx{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,s=i.length,r=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!==s;++t){const s=i[t],l=s.name;let h=c[l];if(void 0!==h)r[t]=h;else{if(h=r[t],void 0!==h){null===h._cacheIndex&&(++h.referenceCount,this._addInactiveBinding(h,a,l));continue}const i=e&&e._propertyBindings[t].binding.parsedPath;h=new LN(GN.create(n,l,i),s.ValueTypeName,s.getValueSize()),++h.referenceCount,this._addInactiveBinding(h,a,l),r[t]=h}o[t].resultBuffer=h.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,s=Math.sign(t),r=this._accuIndex^=1;for(let o=0;o!==n;++o){e[o]._update(i,t,s,r)}const o=this._bindings,a=this._nActiveBindings;for(let t=0;t!==a;++t)o[t].apply(r);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 jN.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)}}WN.prototype.isBox2=!0;const qN=new gb,XN=new gb;class YN{constructor(t=new gb,e=new gb){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){qN.subVectors(t,this.start),XN.subVectors(this.end,this.start);const n=XN.dot(XN);let i=XN.dot(qN)/n;return e&&(i=Zx(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 yw{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 $N=new gb,JN=new Yb,ZN=new Yb;function QN(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 s=Math.abs(n/(Math.abs(n)+Math.abs(i))),r=sL.copy(t.start).lerp(t.end,s);if(e.containsPoint(r))return{normal:oL.normal.clone(),point:r.clone(),depth:Math.abs(Math.min(n,i))};const o=t.radius*t.radius,a=aL.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),s=a,n=e[r])}}return s<1e100&&{distance:s,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 dL(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)}setJumpParams(t){}setGravity(t){}setPlayerMass(t){}}const _L={rotateSpeed:1,rotationRange:{min:.25*-Math.PI,max:.25*Math.PI}},mL={type:\\\\\\\"change\\\\\\\"},fL=new p.a,gL=new ny;class vL extends pL{constructor(t,e,n){super(),this._camera=t,this.domElement=e,this.player=n,this.translationData={direction:new p.a},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 Xv.a,this._rotationSpeed=_L.rotateSpeed,this._rotationRange={min:_L.rotationRange.min,max:_L.rotationRange.max},this._azimuthalAngle=0,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._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),s=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=`${i}px`,t.style.position=\\\\\\\"absolute\\\\\\\",t.style.bottom=`${s}px`,t.style.left=`${s}px`,t}_addEvents(){var t;nx.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;nx.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._translateDomElement.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}_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=rr.clamp(this._startCameraRotation.x+-n,this._rotationRange.min,this._rotationRange.max),this._computeAzimuthalAngle(),this.dispatchEvent(mL)}_computeAzimuthalAngle(){this._camera.updateMatrixWorld(),fL.set(0,0,1),this._camera.localToWorld(fL),fL.sub(this._camera.position),gL.setFromVector3(fL),this._azimuthalAngle=gL.theta}_onTranslateStart(t){this._startCameraPosition.copy(this._camera.position);if(!this._getTouch(t,this._translateDomElement))return;this._translateDomElementRect=this._translateDomElement.getBoundingClientRect();const e=this._translateDomElementRect.left+.5*this._translateDomElementRect.width,n=this._translateDomElementRect.top+.5*this._translateDomElementRect.height;this._translationStartPosition.set(e,n)}_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._translationDelta.x/this._translateDomElementRect.width*.5,this.translationData.direction.z=-this._translationDelta.y/this._translateDomElementRect.height*.5,this._updatePlayerTranslate(),this.dispatchEvent(mL))}_onTranslateEnd(){this.translationData.direction.x=0,this.translationData.direction.z=0,this._updatePlayerTranslate()}_updatePlayerTranslate(){if(!this.player)return;const t=this.translationData.direction;this.player.setForward(!1),this.player.setBackward(!1),this.player.setLeft(!1),this.player.setRight(!1);const e=Math.abs(t.x),n=Math.abs(t.z),i=n-e;function s(e){t.z>0&&e.setForward(!0),t.z<0&&e.setBackward(!0)}function r(e){t.x>0&&e.setRight(!0),t.x<0&&e.setLeft(!0)}i>0?(s(this.player),i<.5*n&&r(this.player)):(r(this.player),i<.5*e&&s(this.player))}update(t){this.player&&(this.player.setAzimuthalAngle(this._azimuthalAngle),this.player.update(t))}_getTouch(t,e){for(let n=0;n{AL.PARAM_CALLBACK_updatePlayerParams(t)}}}const TL=new class extends la{constructor(){super(...arguments),this.main=aa.FOLDER(),this.colliderObject=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.SOP}}),this.capsuleRadius=aa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!1],...wL()}),this.capsuleHeight=aa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1],...wL()}),this.physics=aa.FOLDER(),this.physicsSteps=aa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1],...wL()}),this.gravity=aa.VECTOR3([0,-30,0],{...wL()}),this.translateSpeed=aa.FLOAT(1),this.rotateSpeed=aa.FLOAT(_L.rotateSpeed),this.updateCollider=aa.BUTTON(null,{callback:t=>{AL.PARAM_CALLBACK_updateCollider(t)}}),this.init=aa.FOLDER(),this.startPosition=aa.VECTOR3([0,2,0],{...wL()}),this.startRotation=aa.VECTOR3([0,0,0],{...wL()}),this.reset=aa.BUTTON(null,{callback:t=>{AL.PARAM_CALLBACK_resetPlayer(t)}}),this.minPolarAngle=aa.FLOAT(\\\\\\\"-$PI*0.5\\\\\\\",{range:[-Math.PI,Math.PI],rangeLocked:[!0,!0]}),this.maxPolarAngle=aa.FLOAT(\\\\\\\"$PI*0.5\\\\\\\",{range:[-Math.PI,Math.PI],rangeLocked:[!0,!0]})}};class AL extends qv{constructor(){super(...arguments),this.paramsConfig=TL,this._controls_by_element_id=new Map}static type(){return rs.MOBILE_JOYSTICK}endEventName(){return\\\\\\\"end\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Zo(yL,Jo.BASE),new Zo(xL,Jo.BASE),new Zo(bL,Jo.BASE)])}async createControlsInstance(t,e){await this._initPlayer(t);const n=new vL(t,e,this._player);return this._controls_by_element_id.set(e.id,n),this._bind_listeners_to_controls_instance(n),n}async _initPlayer(t){this._player=this._player||await this._createPlayer(t),this._player&&(this._updatePlayerParams(),this._player.reset())}async _updatePlayerParams(){this._player&&(this._player.startPosition.copy(this.pv.startPosition),this._player.physicsSteps=this.pv.physicsSteps,this._player.jumpAllowed=!1,this._player.runAllowed=!1,this._player.gravity.copy(this.pv.gravity),this._player.speed=this.pv.translateSpeed,this._player.setCapsule({radius:this.pv.capsuleRadius,height:this.pv.capsuleHeight}))}async _createPlayer(t){const e=t,n=await this._getCollider();if(!n)return void this.states.error.set(\\\\\\\"invalid collider\\\\\\\");return new Uy({object:e,collider:n})}_resetPlayer(){var t;null===(t=this._player)||void 0===t||t.reset()}async _getCollider(){const t=this.pv.colliderObject.nodeWithContext(ts.SOP);if(!t)return void this.states.error.set(\\\\\\\"collider node not found\\\\\\\");const e=(await t.compute()).coreContent();if(!e)return void this.states.error.set(\\\\\\\"invalid collider node\\\\\\\");return e.objects()[0]}async _updateCollider(){var t;const e=await this._getCollider();e?null===(t=this._player)||void 0===t||t.setCollider(e):this.states.error.set(\\\\\\\"invalid collider\\\\\\\")}_bind_listeners_to_controls_instance(t){t.addEventListener(yL,(()=>{this.dispatchEventToOutput(yL,{})})),t.addEventListener(xL,(()=>{this.dispatchEventToOutput(xL,{})})),t.addEventListener(bL,(()=>{this.dispatchEventToOutput(bL,{})}))}updateRequired(){return!0}setupControls(t){t.setRotationSpeed(this.pv.rotateSpeed),t.setRotationRange({min:this.pv.minPolarAngle,max:this.pv.maxPolarAngle})}disposeControlsForHtmlElementId(t){this._controls_by_element_id.get(t)&&this._controls_by_element_id.delete(t)}static PARAM_CALLBACK_updateCollider(t){t._updateCollider()}static PARAM_CALLBACK_updatePlayerParams(t){t._updatePlayerParams()}static PARAM_CALLBACK_resetPlayer(t){t._resetPlayer()}}var ML;!function(t){t.ALL_TOGETHER=\\\\\\\"all together\\\\\\\",t.BATCH=\\\\\\\"batch\\\\\\\"}(ML||(ML={}));const EL=[ML.ALL_TOGETHER,ML.BATCH];const SL=new class extends la{constructor(){super(...arguments),this.mask=aa.STRING(\\\\\\\"/geo*\\\\\\\",{callback:t=>{CL.PARAM_CALLBACK_update_resolved_nodes(t)}}),this.force=aa.BOOLEAN(0),this.cookMode=aa.INTEGER(EL.indexOf(ML.ALL_TOGETHER),{menu:{entries:EL.map(((t,e)=>({name:t,value:e})))}}),this.batchSize=aa.INTEGER(1,{visibleIf:{cookMode:EL.indexOf(ML.BATCH)},separatorAfter:!0}),this.updateResolve=aa.BUTTON(null,{callback:(t,e)=>{CL.PARAM_CALLBACK_update_resolve(t)}}),this.printResolve=aa.BUTTON(null,{callback:(t,e)=>{CL.PARAM_CALLBACK_print_resolve(t)}})}};class CL extends ka{constructor(){super(...arguments),this.paramsConfig=SL,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 Zo(CL.INPUT_TRIGGER,Jo.BASE,this.process_event_trigger.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(CL.OUTPUT_FIRST_NODE,Jo.BASE),new Zo(CL.OUTPUT_EACH_NODE,Jo.BASE),new Zo(CL.OUTPUT_ALL_NODES,Jo.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=EL[this.pv.cookMode];switch(t){case ML.ALL_TOGETHER:return this._cook_nodes_all_together();case ML.BATCH:return this._cook_nodes_batch()}ls.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(CL.OUTPUT_FIRST_NODE,e)),this._cook_state_by_node_id.get(t.graphNodeId())||this.dispatchEventToOutput(CL.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(CL.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 NL,LL;CL.INPUT_TRIGGER=\\\\\\\"trigger\\\\\\\",CL.OUTPUT_FIRST_NODE=\\\\\\\"first\\\\\\\",CL.OUTPUT_EACH_NODE=\\\\\\\"each\\\\\\\",CL.OUTPUT_ALL_NODES=\\\\\\\"all\\\\\\\",function(t){t.TRIGGER=\\\\\\\"trigger\\\\\\\"}(NL||(NL={})),function(t){t.OUT=\\\\\\\"out\\\\\\\"}(LL||(LL={}));const OL=new class extends la{};class PL extends ka{constructor(){super(...arguments),this.paramsConfig=OL}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(NL.TRIGGER,Jo.BASE,this.process_event_trigger.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(LL.OUT,Jo.BASE)])}processEvent(t){}process_event_trigger(t){this.dispatchEventToOutput(LL.OUT,t)}}const RL=\\\\\\\"init\\\\\\\",IL=\\\\\\\"dispose\\\\\\\",FL=\\\\\\\"reset\\\\\\\";function DL(){return{callback:t=>{GL.PARAM_CALLBACK_updatePlayerParams(t)}}}const BL=new p.a,zL=new p.a,kL=new ny;const UL=new class extends la{constructor(){super(...arguments),this.main=aa.FOLDER(),this.playerObject=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ}}),this.colliderObject=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.SOP}}),this.camera=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{types:[is.PERSPECTIVE,is.ORTHOGRAPHIC],context:ts.OBJ}}),this.initPlayer=aa.BUTTON(null,{callback:t=>{GL.PARAM_CALLBACK_initPlayer(t)}}),this.capsuleRadius=aa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!1],...DL()}),this.capsuleHeight=aa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1],...DL()}),this.physics=aa.FOLDER(),this.physicsSteps=aa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1],...DL()}),this.gravity=aa.VECTOR3([0,-30,0],{...DL()}),this.speed=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1],...DL()}),this.jumpAllowed=aa.BOOLEAN(!0,{...DL()}),this.jumpStrength=aa.FLOAT(10,{range:[0,100],rangeLocked:[!0,!1],...DL()}),this.runAllowed=aa.BOOLEAN(!0,{...DL()}),this.runSpeedMult=aa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],...DL()}),this.updateCollider=aa.BUTTON(null,{callback:t=>{GL.PARAM_CALLBACK_updateCollider(t)}}),this.mesh=aa.FOLDER(),this.useMesh=aa.BOOLEAN(!0,{callback:t=>{GL.PARAM_CALLBACK_updatePlayerMesh(t)}}),this.material=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.MAT},callback:t=>{GL.PARAM_CALLBACK_updatePlayerMaterial(t)}}),this.init=aa.FOLDER(),this.startPosition=aa.VECTOR3([0,5,0],{...DL()}),this.reset=aa.BUTTON(null,{callback:t=>{GL.PARAM_CALLBACK_resetPlayer(t)}})}};class GL extends ka{constructor(){super(...arguments),this.paramsConfig=UL}static type(){return rs.PLAYER}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(RL,Jo.BASE,this._initPlayer.bind(this)),new Zo(IL,Jo.BASE,this._disposePlayer.bind(this)),new Zo(FL,Jo.BASE,this._resetPlayer.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(RL,Jo.BASE),new Zo(IL,Jo.BASE),new Zo(FL,Jo.BASE)])}async _initPlayer(){if(this._player=this._player||await this._createPlayer(),!this._player)return void this.states.error.set(\\\\\\\"could not create player\\\\\\\");this._updatePlayerMesh(),this._updatePlayerMaterial(),this._updatePlayerParams(),this._corePlayerKeyEvents=new Vy(this._player),this._corePlayerKeyEvents.addEvents(),this._player.reset();const t=this._player;this.scene().registerOnBeforeTick(this._callbackName(),(e=>{t.setAzimuthalAngle(this._getAzimuthalAngle()),t.update(e)})),this.dispatchEventToOutput(RL,{})}_callbackName(){return`event/PlayerControls-${this.graphNodeId()}`}_disposePlayer(){var t;this._player&&(null===(t=this._corePlayerKeyEvents)||void 0===t||t.removeEvents(),this.scene().unRegisterOnBeforeTick(this._callbackName())),this.dispatchEventToOutput(IL,{})}_resetPlayer(){this._player&&this._player.reset(),this.dispatchEventToOutput(FL,{})}async _updatePlayerParams(){this._player&&(this._player.startPosition.copy(this.pv.startPosition),this._player.physicsSteps=this.pv.physicsSteps,this._player.jumpAllowed=this.pv.jumpAllowed,this._player.jumpStrength=this.pv.jumpStrength,this._player.runAllowed=this.pv.runAllowed,this._player.runSpeedMult=this.pv.runSpeedMult,this._player.gravity.copy(this.pv.gravity),this._player.speed=this.pv.speed,this._player.setCapsule({radius:this.pv.capsuleRadius,height:this.pv.capsuleHeight}))}_updatePlayerMesh(){this._player&&this._player.setUsePlayerMesh(this.pv.useMesh)}async _updatePlayerMaterial(){if(!this._player)return;const t=this.pv.material.nodeWithContext(ts.MAT);if(!t)return void this.states.error.set(\\\\\\\"material node not found\\\\\\\");const e=(await t.compute()).material();this._player.setMaterial(e)}async _createPlayer(){const t=this.pv.playerObject.nodeWithContext(ts.OBJ);if(!t)return void this.states.error.set(\\\\\\\"player node not found\\\\\\\");const e=this.pv.camera.nodeWithContext(ts.OBJ);if(!e)return void this.states.error.set(\\\\\\\"invalid camera node\\\\\\\");this._cameraObject=e.object;const n=t.object,i=await this._getCollider();if(!i)return void this.states.error.set(\\\\\\\"invalid collider\\\\\\\");return new Uy({object:n,collider:i,meshName:this.path()})}async _getCollider(){const t=this.pv.colliderObject.nodeWithContext(ts.SOP);if(!t)return void this.states.error.set(\\\\\\\"collider node not found\\\\\\\");const e=(await t.compute()).coreContent();if(!e)return void this.states.error.set(\\\\\\\"invalid collider node\\\\\\\");return e.objects()[0]}async _updateCollider(){var t;const e=await this._getCollider();e?null===(t=this._player)||void 0===t||t.setCollider(e):this.states.error.set(\\\\\\\"invalid collider\\\\\\\")}_getAzimuthalAngle(){if(!this._cameraObject||!this._player)return 0;const t=this._cameraObject.position,e=this._player.object.position;return BL.copy(t),zL.copy(e),BL.sub(zL),kL.setFromVector3(BL),kL.theta}static PARAM_CALLBACK_initPlayer(t){t._initPlayer()}static PARAM_CALLBACK_updatePlayerParams(t){t._updatePlayerParams()}static PARAM_CALLBACK_updatePlayerMaterial(t){t._updatePlayerMaterial()}static PARAM_CALLBACK_updatePlayerMesh(t){t._updatePlayerMesh()}static PARAM_CALLBACK_updateCollider(t){t._updateCollider()}static PARAM_CALLBACK_resetPlayer(t){t._resetPlayer()}}var VL,HL=n(39),jL=n(36);class WL{constructor(t,e,n=0,i=1/0){this.ray=new HL.a(t,e),this.near=n,this.far=i,this.camera=null,this.layers=new jL.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 XL(t,this,n,e),n.sort(qL),n}intersectObjects(t,e=!0,n=[]){for(let i=0,s=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=JL[this._node.pv.intersectWith];switch(e){case $L.GEOMETRY:return this._intersect_with_geometry(t);case $L.PLANE:return this._intersect_with_plane(t)}ls.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=zs[this._node.pv.geoAttributeType],n=QL.resolve_geometry_attribute(t,this._node.pv.geoAttributeName,e);if(null!=n){switch(e){case Bs.NUMERIC:return void this._node.p.geoAttributeValue1.set(n);case Bs.STRING:return void(m.isString(n)&&this._node.p.geoAttributeValues.set(n))}ls.unreachable(e)}}static resolve_geometry_attribute(t,e,n){switch(Ls(t.object.constructor)){case Cs.MESH:return this.resolve_geometry_attribute_for_mesh(t,e,n);case Cs.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 s=i.getAttribute(e);if(s){switch(n){case Bs.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(s,t.face.a),this._uvB.fromBufferAttribute(s,t.face.b),this._uvC.fromBufferAttribute(s,t.face.c),t.uv=Ks.a.getUV(t.point,this._vA,this._vB,this._vC,this._uvA,this._uvB,this._uvC,this._hitUV),this._hitUV.x):void 0}case Bs.STRING:{const t=new _r(i).points()[0];return t?t.stringAttribValue(e):void 0}}ls.unreachable(n)}}}static resolve_geometry_attribute_for_point(t,e,n){const i=t.object.geometry;if(i&&null!=t.index){switch(n){case Bs.NUMERIC:{const n=i.getAttribute(e);return n?n.array[t.index]:void 0}case Bs.STRING:{const n=new _r(i).points()[t.index];return n?n.stringAttribValue(e):void 0}}ls.unreachable(n)}}_set_position_param(t){if(t.toArray(this._hit_position_array),this._node.pv.tpositionTarget){if(li.playerMode())this._found_position_target_param=this._found_position_target_param||this._node.pv.positionTarget.paramWithType(Er.VECTOR3);else{const t=this._node.pv.positionTarget;this._found_position_target_param=t.paramWithType(Er.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(ts.OBJ);t&&(n=t)}n&&!this._node.pv.overrideRay&&n.prepareRaycaster(this._mouse,this._raycaster)}update_target(){const t=lO[this._node.pv.targetType];switch(t){case aO.NODE:return this._update_target_from_node();case aO.SCENE_GRAPH:return this._update_target_from_scene_graph()}ls.unreachable(t)}_update_target_from_node(){const t=this._node.p.targetNode.value.nodeWithContext(ts.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)}}QL._vA=new p.a,QL._vB=new p.a,QL._vC=new p.a,QL._uvA=new d.a,QL._uvB=new d.a,QL._uvC=new d.a,QL._hitUV=new d.a;class KL{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,s=i.renderController;if(s){if(this._render_target=this._render_target||new Q(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,r=s.resolved_scene||i.scene().threejsScene(),o=s.renderer(n);this._modify_scene_and_renderer(r,o),o.setRenderTarget(this._render_target),o.clear(),o.render(r,e.object),o.setRenderTarget(null),this._restore_scene_and_renderer(r,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()==ts.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 tO=1e3/60;var eO;!function(t){t.CPU=\\\\\\\"cpu\\\\\\\",t.GPU=\\\\\\\"gpu\\\\\\\"}(eO||(eO={}));const nO=[eO.CPU,eO.GPU];function iO(t={}){return t.mode=nO.indexOf(eO.CPU),{visibleIf:t}}function sO(t={}){return t.mode=nO.indexOf(eO.CPU),t.intersectWith=JL.indexOf($L.GEOMETRY),{visibleIf:t}}function rO(t={}){return t.mode=nO.indexOf(eO.CPU),t.intersectWith=JL.indexOf($L.PLANE),{visibleIf:t}}function oO(t={}){return t.mode=nO.indexOf(eO.GPU),{visibleIf:t}}var aO;!function(t){t.SCENE_GRAPH=\\\\\\\"scene graph\\\\\\\",t.NODE=\\\\\\\"node\\\\\\\"}(aO||(aO={}));const lO=[aO.SCENE_GRAPH,aO.NODE];const cO=new class extends la{constructor(){super(...arguments),this.mode=aa.INTEGER(nO.indexOf(eO.CPU),{menu:{entries:nO.map(((t,e)=>({name:t,value:e})))}}),this.mouse=aa.VECTOR2([0,0],{cook:!1}),this.overrideCamera=aa.BOOLEAN(0),this.overrideRay=aa.BOOLEAN(0,{visibleIf:{mode:nO.indexOf(eO.CPU),overrideCamera:1}}),this.camera=aa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{nodeSelection:{context:ts.OBJ},dependentOnFoundNode:!1,visibleIf:{overrideCamera:1,overrideRay:0}}),this.rayOrigin=aa.VECTOR3([0,0,0],{visibleIf:{overrideCamera:1,overrideRay:1}}),this.rayDirection=aa.VECTOR3([0,0,1],{visibleIf:{overrideCamera:1,overrideRay:1}}),this.material=aa.OPERATOR_PATH(\\\\\\\"/MAT/mesh_basic_builder1\\\\\\\",{nodeSelection:{context:ts.MAT},dependentOnFoundNode:!1,callback:(t,e)=>{KL.PARAM_CALLBACK_update_material(t)},...oO()}),this.pixelValue=aa.VECTOR4([0,0,0,0],{cook:!1,...oO()}),this.hitThreshold=aa.FLOAT(.5,{cook:!1,...oO()}),this.intersectWith=aa.INTEGER(JL.indexOf($L.GEOMETRY),{menu:{entries:JL.map(((t,e)=>({name:t,value:e})))},...iO()}),this.pointsThreshold=aa.FLOAT(1,{range:[0,100],rangeLocked:[!0,!1],...iO()}),this.planeDirection=aa.VECTOR3([0,1,0],{...rO()}),this.planeOffset=aa.FLOAT(0,{...rO()}),this.targetType=aa.INTEGER(0,{menu:{entries:lO.map(((t,e)=>({name:t,value:e})))},...sO()}),this.targetNode=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ},dependentOnFoundNode:!1,callback:(t,e)=>{QL.PARAM_CALLBACK_update_target(t)},...sO({targetType:lO.indexOf(aO.NODE)})}),this.objectMask=aa.STRING(\\\\\\\"*geo1*\\\\\\\",{callback:(t,e)=>{QL.PARAM_CALLBACK_update_target(t)},...sO({targetType:lO.indexOf(aO.SCENE_GRAPH)})}),this.printFoundObjectsFromMask=aa.BUTTON(null,{callback:(t,e)=>{QL.PARAM_CALLBACK_print_resolve(t)},...sO({targetType:lO.indexOf(aO.SCENE_GRAPH)})}),this.traverseChildren=aa.BOOLEAN(!0,{callback:(t,e)=>{QL.PARAM_CALLBACK_update_target(t)},...sO(),separatorAfter:!0}),this.tpositionTarget=aa.BOOLEAN(0,{cook:!1,...iO()}),this.position=aa.VECTOR3([0,0,0],{cook:!1,...iO({tpositionTarget:0})}),this.positionTarget=aa.PARAM_PATH(\\\\\\\"\\\\\\\",{cook:!1,...iO({tpositionTarget:1}),paramSelection:Er.VECTOR3,computeOnDirty:!0}),this.tvelocity=aa.BOOLEAN(0,{cook:!1}),this.tvelocityTarget=aa.BOOLEAN(0,{cook:!1,...iO({tvelocity:1})}),this.velocity=aa.VECTOR3([0,0,0],{cook:!1,...iO({tvelocity:1,tvelocityTarget:0})}),this.velocityTarget=aa.PARAM_PATH(\\\\\\\"\\\\\\\",{cook:!1,...iO({tvelocity:1,tvelocityTarget:1}),paramSelection:Er.VECTOR3,computeOnDirty:!0}),this.geoAttribute=aa.BOOLEAN(0,sO()),this.geoAttributeName=aa.STRING(\\\\\\\"id\\\\\\\",{cook:!1,...sO({geoAttribute:1})}),this.geoAttributeType=aa.INTEGER(zs.indexOf(Bs.NUMERIC),{menu:{entries:ks},...sO({geoAttribute:1})}),this.geoAttributeValue1=aa.FLOAT(0,{cook:!1,...sO({geoAttribute:1,geoAttributeType:zs.indexOf(Bs.NUMERIC)})}),this.geoAttributeValues=aa.STRING(\\\\\\\"\\\\\\\",{...sO({geoAttribute:1,geoAttributeType:zs.indexOf(Bs.STRING)})})}};class hO extends ka{constructor(){super(...arguments),this.paramsConfig=cO,this.cpuController=new QL(this),this.gpuController=new KL(this),this._last_event_processed_at=-1}static type(){return\\\\\\\"raycast\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(hO.INPUT_TRIGGER,Jo.BASE,this._process_trigger_event_throttled.bind(this)),new Zo(hO.INPUT_MOUSE,Jo.MOUSE,this._process_mouse_event.bind(this)),new Zo(hO.INPUT_UPDATE_OBJECTS,Jo.BASE,this._process_trigger_update_objects.bind(this)),new Zo(hO.INPUT_TRIGGER_VEL_RESET,Jo.BASE,this._process_trigger_vel_reset.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(hO.OUTPUT_HIT,Jo.BASE),new Zo(hO.OUTPUT_MISS,Jo.BASE)])}trigger_hit(t){this.dispatchEventToOutput(hO.OUTPUT_HIT,t)}trigger_miss(t){this.dispatchEventToOutput(hO.OUTPUT_MISS,t)}_process_mouse_event(t){this.pv.mode==nO.indexOf(eO.CPU)?this.cpuController.updateMouse(t):this.gpuController.updateMouse(t)}_process_trigger_event_throttled(t){const e=this._last_event_processed_at,n=li.performance.performanceManager().now();this._last_event_processed_at=n;const i=n-e;i{this._process_trigger_event(t)}),tO-i):this._process_trigger_event(t)}_process_trigger_event(t){this.pv.mode==nO.indexOf(eO.CPU)?this.cpuController.processEvent(t):this.gpuController.processEvent(t)}_process_trigger_update_objects(t){this.pv.mode==nO.indexOf(eO.CPU)&&this.cpuController.update_target()}_process_trigger_vel_reset(t){this.pv.mode==nO.indexOf(eO.CPU)&&this.cpuController.velocity_controller.reset()}}var uO;hO.INPUT_TRIGGER=\\\\\\\"trigger\\\\\\\",hO.INPUT_MOUSE=\\\\\\\"mouse\\\\\\\",hO.INPUT_UPDATE_OBJECTS=\\\\\\\"updateObjects\\\\\\\",hO.INPUT_TRIGGER_VEL_RESET=\\\\\\\"triggerVelReset\\\\\\\",hO.OUTPUT_HIT=\\\\\\\"hit\\\\\\\",hO.OUTPUT_MISS=\\\\\\\"miss\\\\\\\",function(t){t.SET=\\\\\\\"set\\\\\\\",t.TOGGLE=\\\\\\\"toggle\\\\\\\"}(uO||(uO={}));const dO=[uO.SET,uO.TOGGLE];const pO=new class extends la{constructor(){super(...arguments),this.mask=aa.STRING(\\\\\\\"/geo*\\\\\\\",{separatorAfter:!0}),this.tdisplay=aa.BOOLEAN(0),this.displayMode=aa.INTEGER(dO.indexOf(uO.SET),{visibleIf:{tdisplay:1},menu:{entries:dO.map(((t,e)=>({name:t,value:e})))}}),this.display=aa.BOOLEAN(0,{visibleIf:{tdisplay:1,displayMode:dO.indexOf(uO.SET)},separatorAfter:!0}),this.tbypass=aa.BOOLEAN(0),this.bypassMode=aa.INTEGER(dO.indexOf(uO.SET),{visibleIf:{tbypass:1},menu:{entries:dO.map(((t,e)=>({name:t,value:e})))}}),this.bypass=aa.BOOLEAN(0,{visibleIf:{tbypass:1,displayMode:dO.indexOf(uO.SET)}}),this.execute=aa.BUTTON(null,{callback:t=>{_O.PARAM_CALLBACK_execute(t)}})}};class _O extends ka{constructor(){super(...arguments),this.paramsConfig=pO}static type(){return\\\\\\\"setFlag\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(\\\\\\\"trigger\\\\\\\",Jo.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=dO[this.pv.displayMode];switch(i){case uO.SET:return void n.set(this.pv.display);case uO.TOGGLE:return void n.set(!n.active())}ls.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=dO[this.pv.bypassMode];switch(i){case uO.SET:return void n.set(this.pv.bypass);case uO.TOGGLE:return void n.set(!n.active())}ls.unreachable(i)}static PARAM_CALLBACK_execute(t){t.processEvent({})}}var mO;!function(t){t.BOOLEAN=\\\\\\\"boolean\\\\\\\",t.BUTTON=\\\\\\\"button\\\\\\\",t.NUMBER=\\\\\\\"number\\\\\\\",t.VECTOR2=\\\\\\\"vector2\\\\\\\",t.VECTOR3=\\\\\\\"vector3\\\\\\\",t.VECTOR4=\\\\\\\"vector4\\\\\\\",t.STRING=\\\\\\\"string\\\\\\\"}(mO||(mO={}));const fO=[mO.BOOLEAN,mO.BUTTON,mO.NUMBER,mO.VECTOR2,mO.VECTOR3,mO.VECTOR4,mO.STRING],gO=fO.indexOf(mO.BOOLEAN),vO=fO.indexOf(mO.NUMBER),yO=fO.indexOf(mO.VECTOR2),xO=fO.indexOf(mO.VECTOR3),bO=fO.indexOf(mO.VECTOR4),wO=fO.indexOf(mO.STRING),TO=\\\\\\\"output\\\\\\\";const AO=new class extends la{constructor(){super(...arguments),this.param=aa.PARAM_PATH(\\\\\\\"\\\\\\\",{paramSelection:!0,computeOnDirty:!0}),this.type=aa.INTEGER(vO,{menu:{entries:fO.map(((t,e)=>({name:t,value:e})))}}),this.toggle=aa.BOOLEAN(0,{visibleIf:{type:gO}}),this.boolean=aa.BOOLEAN(0,{visibleIf:{type:gO,toggle:0}}),this.number=aa.FLOAT(0,{visibleIf:{type:vO}}),this.vector2=aa.VECTOR2([0,0],{visibleIf:{type:yO}}),this.vector3=aa.VECTOR3([0,0,0],{visibleIf:{type:xO}}),this.vector4=aa.VECTOR4([0,0,0,0],{visibleIf:{type:bO}}),this.increment=aa.BOOLEAN(0,{visibleIf:[{type:vO},{type:yO},{type:xO},{type:bO}]}),this.string=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{type:wO}}),this.execute=aa.BUTTON(null,{callback:t=>{MO.PARAM_CALLBACK_execute(t)}})}};class MO extends ka{constructor(){super(...arguments),this.paramsConfig=AO,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 Zo(\\\\\\\"trigger\\\\\\\",Jo.BASE)]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(TO,Jo.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(TO,t)}async _new_param_value(t){const e=fO[this.pv.type];switch(e){case mO.BOOLEAN:return await this._compute_params_if_dirty([this.p.toggle]),this.pv.toggle?t.value?0:1:this.pv.boolean?1:0;case mO.BUTTON:return t.options.executeCallback();case mO.NUMBER:return await this._compute_params_if_dirty([this.p.increment,this.p.number]),this.pv.increment?t.type()==Er.FLOAT?t.value+this.pv.number:t.value:this.pv.number;case mO.VECTOR2:return await this._compute_params_if_dirty([this.p.increment,this.p.vector2]),this.pv.increment?t.type()==Er.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 mO.VECTOR3:return await this._compute_params_if_dirty([this.p.increment,this.p.vector3]),this.pv.increment?t.type()==Er.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 mO.VECTOR4:return await this._compute_params_if_dirty([this.p.increment,this.p.vector4]),this.pv.increment?t.type()==Er.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 mO.STRING:return await this._compute_params_if_dirty([this.p.string]),this.pv.string}ls.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 EO=new class extends la{constructor(){super(...arguments),this.outputsCount=aa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1]})}};class SO extends ka{constructor(){super(...arguments),this.paramsConfig=EO}static type(){return\\\\\\\"sequence\\\\\\\"}initializeNode(){this.io.connection_points.set_input_name_function((()=>\\\\\\\"trigger\\\\\\\")),this.io.connection_points.set_expected_input_types_function((()=>[Jo.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(Jo.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 RO extends sa{static context(){return ts.EVENT}cook(){this.cookController.endCook()}}class IO extends RO{}class FO extends IO{constructor(){super(...arguments),this._children_controller_context=ts.ANIM}static type(){return es.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class DO extends IO{constructor(){super(...arguments),this._children_controller_context=ts.COP}static type(){return es.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class BO extends IO{constructor(){super(...arguments),this._children_controller_context=ts.EVENT}static type(){return es.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class zO extends IO{constructor(){super(...arguments),this._children_controller_context=ts.MAT}static type(){return es.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class kO extends RO{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=ts.POST}static type(){return es.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class UO extends IO{constructor(){super(...arguments),this._children_controller_context=ts.ROP}static type(){return es.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}const GO=\\\\\\\"int\\\\\\\";const VO=new class extends la{constructor(){super(...arguments),this.float=aa.FLOAT(0)}};class HO extends df{constructor(){super(...arguments),this.paramsConfig=VO}static type(){return\\\\\\\"floatToInt\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(GO,Bo.INT)])}setLines(t){const e=this.variableForInputParam(this.p.float),n=`int ${this.glVarName(GO)} = int(${hf.float(e)})`;t.addBodyLines(this,[n])}}const jO=\\\\\\\"float\\\\\\\";const WO=new class extends la{constructor(){super(...arguments),this.int=aa.INTEGER(0)}};class qO extends df{constructor(){super(...arguments),this.paramsConfig=WO}static type(){return\\\\\\\"intToFloat\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(jO,Bo.FLOAT)])}setLines(t){const e=this.variableForInputParam(this.p.int),n=`float ${this.glVarName(jO)} = float(${hf.integer(e)})`;t.addBodyLines(this,[n])}}const XO=\\\\\\\"bool\\\\\\\";const YO=new class extends la{constructor(){super(...arguments),this.int=aa.INTEGER(0)}};class $O extends df{constructor(){super(...arguments),this.paramsConfig=YO}static type(){return\\\\\\\"intToBool\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(XO,Bo.BOOL)])}setLines(t){const e=this.variableForInputParam(this.p.int),n=`bool ${this.glVarName(XO)} = bool(${hf.integer(e)})`;t.addBodyLines(this,[n])}}const JO=new class extends la{constructor(){super(...arguments),this.bool=aa.BOOLEAN(0)}};class ZO extends df{constructor(){super(...arguments),this.paramsConfig=JO}static type(){return\\\\\\\"boolToInt\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(GO,Bo.INT)])}setLines(t){const e=this.variableForInputParam(this.p.bool),n=`int ${this.glVarName(GO)} = int(${hf.bool(e)})`;t.addBodyLines(this,[n])}}const QO=new class extends la{constructor(){super(...arguments),this.x=aa.FLOAT(0),this.y=aa.FLOAT(0)}};class KO extends df{constructor(){super(...arguments),this.paramsConfig=QO}static type(){return\\\\\\\"floatToVec2\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(KO.OUTPUT_NAME,Bo.VEC2)])}setLines(t){const e=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=`vec2 ${this.glVarName(KO.OUTPUT_NAME)} = ${hf.float2(e,n)}`;t.addBodyLines(this,[i])}}KO.OUTPUT_NAME=\\\\\\\"vec2\\\\\\\";const tP=new class extends la{constructor(){super(...arguments),this.x=aa.FLOAT(0),this.y=aa.FLOAT(0),this.z=aa.FLOAT(0)}};class eP extends df{constructor(){super(...arguments),this.paramsConfig=tP}static type(){return\\\\\\\"floatToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(eP.OUTPUT_NAME,Bo.VEC3)])}setLines(t){const e=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=this.variableForInputParam(this.p.z),s=`vec3 ${this.glVarName(eP.OUTPUT_NAME)} = ${hf.float3(e,n,i)}`;t.addBodyLines(this,[s])}}eP.OUTPUT_NAME=\\\\\\\"vec3\\\\\\\";const nP=new class extends la{constructor(){super(...arguments),this.x=aa.FLOAT(0),this.y=aa.FLOAT(0),this.z=aa.FLOAT(0),this.w=aa.FLOAT(0)}};class iP extends df{constructor(){super(...arguments),this.paramsConfig=nP}static type(){return\\\\\\\"floatToVec4\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(iP.OUTPUT_NAME,Bo.VEC4)])}setLines(t){const e=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=this.variableForInputParam(this.p.z),s=this.variableForInputParam(this.p.w),r=`vec4 ${this.glVarName(iP.OUTPUT_NAME)} = ${hf.float4(e,n,i,s)}`;t.addBodyLines(this,[r])}}iP.OUTPUT_NAME=\\\\\\\"vec4\\\\\\\";const sP=new class extends la{};class rP extends df{constructor(){super(...arguments),this.paramsConfig=sP}}function oP(t,e){const n=e.components,i=e.param_type;return class extends rP{static type(){return t}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(n.map((t=>new Ho(t,Bo.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 aP=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"],lP=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"],cP=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];class hP extends(oP(\\\\\\\"vec2ToFloat\\\\\\\",{components:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"],param_type:Er.VECTOR2})){}class uP extends(oP(\\\\\\\"vec3ToFloat\\\\\\\",{components:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"],param_type:Er.VECTOR3})){}class dP extends(oP(\\\\\\\"vec4ToFloat\\\\\\\",{components:cP,param_type:Er.VECTOR4})){}class pP extends rP{static type(){return\\\\\\\"vec4ToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(pP.OUTPUT_NAME_VEC3,Bo.VEC3),new Ho(pP.OUTPUT_NAME_W,Bo.FLOAT)])}createParams(){this.addParam(Er.VECTOR4,pP.INPUT_NAME_VEC4,cP.map((t=>0)))}setLines(t){const e=[],n=pP.INPUT_NAME_VEC4,i=pP.OUTPUT_NAME_VEC3,s=pP.OUTPUT_NAME_W,r=this.variableForInput(n),o=this.io.outputs.used_output_names();if(o.indexOf(i)>=0){const t=this.glVarName(i);e.push(`vec3 ${t} = ${r}.xyz`)}if(o.indexOf(s)>=0){const t=this.glVarName(s);e.push(`float ${t} = ${r}.w`)}t.addBodyLines(this,e)}}pP.INPUT_NAME_VEC4=\\\\\\\"vec4\\\\\\\",pP.OUTPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",pP.OUTPUT_NAME_W=\\\\\\\"w\\\\\\\";class _P extends rP{static type(){return\\\\\\\"vec3ToVec2\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(_P.OUTPUT_NAME_VEC2,Bo.VEC2),new Ho(_P.OUTPUT_NAME_Z,Bo.FLOAT)])}createParams(){this.addParam(Er.VECTOR3,_P.INPUT_NAME_VEC3,lP.map((t=>0)))}setLines(t){const e=[],n=_P.INPUT_NAME_VEC3,i=_P.OUTPUT_NAME_VEC2,s=_P.OUTPUT_NAME_Z,r=this.variableForInput(n),o=this.io.outputs.used_output_names();if(o.indexOf(i)>=0){const t=this.glVarName(i);e.push(`vec2 ${t} = ${r}.xy`)}if(o.indexOf(s)>=0){const t=this.glVarName(s);e.push(`float ${t} = ${r}.z`)}t.addBodyLines(this,e)}}_P.INPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",_P.OUTPUT_NAME_VEC2=\\\\\\\"vec2\\\\\\\",_P.OUTPUT_NAME_Z=\\\\\\\"z\\\\\\\";class mP extends rP{static type(){return\\\\\\\"vec2ToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(mP.OUTPUT_NAME_VEC3,Bo.VEC3)])}createParams(){this.addParam(Er.VECTOR2,mP.INPUT_NAME_VEC2,aP.map((t=>0))),this.addParam(Er.FLOAT,mP.INPUT_NAME_Z,0)}setLines(t){const e=[],n=mP.INPUT_NAME_VEC2,i=mP.INPUT_NAME_Z,s=mP.OUTPUT_NAME_VEC3,r=this.variableForInput(n),o=this.variableForInput(i),a=this.glVarName(s);e.push(`vec3 ${a} = vec3(${r}.xy, ${o})`),t.addBodyLines(this,e)}}mP.INPUT_NAME_VEC2=\\\\\\\"vec3\\\\\\\",mP.INPUT_NAME_Z=\\\\\\\"z\\\\\\\",mP.OUTPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\";class fP extends rP{static type(){return\\\\\\\"vec3ToVec4\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(fP.OUTPUT_NAME_VEC4,Bo.VEC4)])}createParams(){this.addParam(Er.VECTOR3,fP.INPUT_NAME_VEC3,lP.map((t=>0))),this.addParam(Er.FLOAT,fP.INPUT_NAME_W,0)}setLines(t){const e=[],n=fP.INPUT_NAME_VEC3,i=fP.INPUT_NAME_W,s=fP.OUTPUT_NAME_VEC4,r=this.variableForInput(n),o=this.variableForInput(i),a=this.glVarName(s);e.push(`vec4 ${a} = vec4(${r}.xyz, ${o})`),t.addBodyLines(this,e)}}fP.INPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",fP.INPUT_NAME_W=\\\\\\\"w\\\\\\\",fP.OUTPUT_NAME_VEC4=\\\\\\\"vec4\\\\\\\";const gP=new class extends la{};class vP extends df{constructor(){super(...arguments),this.paramsConfig=gP}gl_method_name(){return\\\\\\\"\\\\\\\"}gl_function_definitions(){return[]}initializeNode(){super.initializeNode(),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this))}_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Bo.FLOAT;if(this.io.connections.firstInputConnection()){const e=this.io.connections.inputConnections();if(e){let n=Math.max(f.compact(e).length+1,2);return f.range(n).map((e=>t))}return[]}return f.range(2).map((e=>t))}_expected_output_types(){return[this._expected_input_types()[0]]}_gl_input_name(t){return\\\\\\\"in\\\\\\\"}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.io.inputs.namedInputConnectionPoints().map(((t,e)=>{const n=t.name();return hf.any(this.variableForInput(n))})).join(\\\\\\\", \\\\\\\"),i=`${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 yP extends vP{_gl_input_name(t){return\\\\\\\"in\\\\\\\"}_expected_input_types(){return[this.io.connection_points.first_input_connection_type()||Bo.FLOAT]}}class xP extends vP{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Bo.FLOAT;return[t,t]}}class bP extends vP{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Bo.FLOAT;return[t,t,t]}}class wP extends vP{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Bo.FLOAT;return[t,t,t,t]}}class TP extends vP{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Bo.FLOAT;return[t,t,t,t,t]}}function AP(t,e={}){const n=e.method||t,i=e.out||\\\\\\\"val\\\\\\\",s=e.in||\\\\\\\"in\\\\\\\";return class extends yP{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 s}_gl_output_name(t){return i}gl_method_name(){return n}}}class MP extends(AP(\\\\\\\"abs\\\\\\\")){}class EP extends(AP(\\\\\\\"acos\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class SP extends(AP(\\\\\\\"asin\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class CP extends(AP(\\\\\\\"atan\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class NP extends(AP(\\\\\\\"ceil\\\\\\\")){}class LP extends(AP(\\\\\\\"cos\\\\\\\",{in:\\\\\\\"radians\\\\\\\"})){}class OP extends(AP(\\\\\\\"degrees\\\\\\\",{in:\\\\\\\"radians\\\\\\\",out:\\\\\\\"degrees\\\\\\\"})){}class PP extends(AP(\\\\\\\"exp\\\\\\\")){}class RP extends(AP(\\\\\\\"exp2\\\\\\\")){}class IP extends(AP(\\\\\\\"floor\\\\\\\")){}class FP extends(AP(\\\\\\\"fract\\\\\\\")){}class DP extends(AP(\\\\\\\"inverseSqrt\\\\\\\",{method:\\\\\\\"inversesqrt\\\\\\\"})){}class BP extends(AP(\\\\\\\"log\\\\\\\")){}class zP extends(AP(\\\\\\\"log2\\\\\\\")){}class kP extends(AP(\\\\\\\"normalize\\\\\\\",{out:\\\\\\\"normalized\\\\\\\"})){}class UP extends(AP(\\\\\\\"radians\\\\\\\",{in:\\\\\\\"degrees\\\\\\\",out:\\\\\\\"radians\\\\\\\"})){}class GP extends(AP(\\\\\\\"sign\\\\\\\")){}class VP extends(AP(\\\\\\\"sin\\\\\\\",{in:\\\\\\\"radians\\\\\\\"})){}class HP extends(AP(\\\\\\\"sqrt\\\\\\\")){}class jP extends(AP(\\\\\\\"tan\\\\\\\")){}function WP(t,e={}){const n=e.method||t,i=e.out||\\\\\\\"val\\\\\\\",s=e.in||[\\\\\\\"in0\\\\\\\",\\\\\\\"in1\\\\\\\"],r=e.default_in_type,o=e.allowed_in_types,a=e.out_type,l=e.functions||[];return class extends xP{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 s[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():r}const e=t||r||Bo.FLOAT;return[e,e]}}}class qP extends(WP(\\\\\\\"distance\\\\\\\",{in:[\\\\\\\"p0\\\\\\\",\\\\\\\"p1\\\\\\\"],default_in_type:Bo.VEC3,allowed_in_types:[Bo.VEC2,Bo.VEC3,Bo.VEC4],out_type:Bo.FLOAT})){}class XP extends(WP(\\\\\\\"dot\\\\\\\",{in:[\\\\\\\"vec0\\\\\\\",\\\\\\\"vec1\\\\\\\"],default_in_type:Bo.VEC3,allowed_in_types:[Bo.VEC2,Bo.VEC3,Bo.VEC4],out_type:Bo.FLOAT})){}class YP extends(WP(\\\\\\\"max\\\\\\\")){}class $P extends(WP(\\\\\\\"min\\\\\\\")){}class JP extends(WP(\\\\\\\"mod\\\\\\\")){paramDefaultValue(t){return{in1:1}[t]}_expected_input_types(){const t=Bo.FLOAT;return[t,t]}}class ZP extends(WP(\\\\\\\"pow\\\\\\\",{in:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"]})){}class QP extends(WP(\\\\\\\"reflect\\\\\\\",{in:[\\\\\\\"I\\\\\\\",\\\\\\\"N\\\\\\\"],default_in_type:Bo.VEC3})){}class KP extends(WP(\\\\\\\"step\\\\\\\",{in:[\\\\\\\"edge\\\\\\\",\\\\\\\"x\\\\\\\"]})){}function tR(t,e={}){const n=e.method||t,i=e.out||\\\\\\\"val\\\\\\\",s=e.in||[\\\\\\\"in0\\\\\\\",\\\\\\\"in1\\\\\\\",\\\\\\\"in2\\\\\\\"],r=e.default||{},o=e.out_type||Bo.FLOAT,a=e.functions||[];return class extends bP{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 s[t]}_gl_output_name(t){return i}gl_method_name(){return n}_expected_output_types(){return[o]}paramDefaultValue(t){return r[t]}gl_function_definitions(){return a.map((t=>new Tf(this,t)))}}}class eR extends(tR(\\\\\\\"clamp\\\\\\\",{in:[\\\\\\\"value\\\\\\\",\\\\\\\"min\\\\\\\",\\\\\\\"max\\\\\\\"],default:{max:1}})){_expected_output_types(){return[this._expected_input_types()[0]]}}class nR extends(tR(\\\\\\\"faceForward\\\\\\\",{in:[\\\\\\\"N\\\\\\\",\\\\\\\"I\\\\\\\",\\\\\\\"Nref\\\\\\\"]})){}class iR extends(tR(\\\\\\\"smoothstep\\\\\\\",{in:[\\\\\\\"edge0\\\\\\\",\\\\\\\"edge1\\\\\\\",\\\\\\\"x\\\\\\\"],default:{edge1:1}})){_expected_output_types(){return[this._expected_input_types()[0]]}}function sR(t,e){const n=e.in_prefix||t,i=e.out||\\\\\\\"val\\\\\\\",s=e.operation,r=e.allowed_in_types;return class extends xP{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 hf.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 s}_expected_input_types(){let t=this.io.connection_points.first_input_connection_type();if(t&&r&&!r.includes(t)){const e=this.io.inputs.namedInputConnectionPoints()[0];e&&(t=e.type())}const e=t||Bo.FLOAT,n=this.io.connections.existingInputConnections(),i=n?Math.max(n.length+1,2):2,s=[];for(let t=0;t{const n=t.name();return hf.any(this.variableForInput(n))})).join(` ${this.boolean_operation()} `),n=`bool ${this.glVarName(this.io.connection_points.output_name(0))} = ${e}`;t.addBodyLines(this,[n])}}function hR(t,e){return class extends cR{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}boolean_operation(){return e.op}_gl_output_name(e){return t}_gl_input_name(e=0){return`${t}${e}`}}}class uR extends(hR(\\\\\\\"and\\\\\\\",{op:\\\\\\\"&&\\\\\\\"})){}class dR extends(hR(\\\\\\\"or\\\\\\\",{op:\\\\\\\"||\\\\\\\"})){}var pR;!function(t){t.TIME=\\\\\\\"time\\\\\\\",t.DELTA_TIME=\\\\\\\"delta_time\\\\\\\"}(pR||(pR={}));var _R,mR;!function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.VELOCITY=\\\\\\\"velocity\\\\\\\",t.MASS=\\\\\\\"mass\\\\\\\",t.FORCE=\\\\\\\"force\\\\\\\"}(_R||(_R={})),function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.VELOCITY=\\\\\\\"velocity\\\\\\\"}(mR||(mR={}));const fR=[_R.POSITION,_R.VELOCITY,_R.MASS,_R.FORCE],gR=[mR.POSITION,mR.VELOCITY],vR={[_R.POSITION]:[0,0,0],[_R.VELOCITY]:[0,0,0],[_R.MASS]:1,[_R.FORCE]:[0,-9.8,0]};const yR=new class extends la{};class xR extends df{constructor(){super(...arguments),this.paramsConfig=yR}static type(){return\\\\\\\"acceleration\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(mR.POSITION,Bo.VEC3),new Ho(mR.VELOCITY,Bo.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()||Bo.VEC3;return[t,t,Bo.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 gR[t]}paramDefaultValue(t){return vR[t]}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=new Af(this,Bo.FLOAT,pR.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 s=hf.any(this.variableForInput(_R.POSITION)),r=hf.any(this.variableForInput(_R.VELOCITY)),o=hf.float(this.variableForInput(_R.MASS)),a=hf.any(this.variableForInput(_R.FORCE)),l=this.glVarName(mR.POSITION),c=this.glVarName(mR.VELOCITY),h=`${e} ${c} = compute_velocity_from_acceleration(${[r,a,o,pR.DELTA_TIME].join(\\\\\\\", \\\\\\\")})`,u=`${e} ${l} = compute_position_from_velocity(${[s,c,pR.DELTA_TIME].join(\\\\\\\", \\\\\\\")})`;t.addBodyLines(this,[h,u])}}var bR,wR=\\\\\\\"\\\\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\\\\\\\"}(bR||(bR={}));const TR=[bR.DIR,bR.UP],AR={[bR.DIR]:[0,0,1],[bR.UP]:[0,1,0]};class MR extends xP{static type(){return\\\\\\\"align\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>TR[t])),this.io.connection_points.set_expected_input_types_function((()=>[Bo.VEC3,Bo.VEC3])),this.io.connection_points.set_expected_output_types_function((()=>[Bo.VEC4]))}paramDefaultValue(t){return AR[t]}gl_method_name(){return\\\\\\\"align\\\\\\\"}gl_function_definitions(){return[new Tf(this,wR)]}}var ER;!function(t){t.LINEAR=\\\\\\\"Linear\\\\\\\",t.GAMMA=\\\\\\\"Gamma\\\\\\\",t.SRGB=\\\\\\\"sRGB\\\\\\\",t.RGBE=\\\\\\\"RGBE\\\\\\\",t.RGBM=\\\\\\\"RGBM\\\\\\\",t.RGBD=\\\\\\\"RGBD\\\\\\\",t.LogLuv=\\\\\\\"LogLuv\\\\\\\"}(ER||(ER={}));const SR=[ER.LINEAR,ER.GAMMA,ER.SRGB,ER.RGBE,ER.RGBM,ER.RGBD,ER.LogLuv];const CR=new class extends la{constructor(){super(...arguments),this.color=aa.VECTOR4([1,1,1,1]),this.from=aa.INTEGER(SR.indexOf(ER.LINEAR),{menu:{entries:SR.map(((t,e)=>({name:t,value:e})))}}),this.to=aa.INTEGER(SR.indexOf(ER.GAMMA),{menu:{entries:SR.map(((t,e)=>({name:t,value:e})))}}),this.gammaFactor=aa.FLOAT(2.2)}};class NR extends df{constructor(){super(...arguments),this.paramsConfig=CR}static type(){return\\\\\\\"colorCorrect\\\\\\\"}initializeNode(){this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"to\\\\\\\",\\\\\\\"from\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Ho(NR.OUTPUT_NAME,Bo.VEC4)])}setLines(t){const e=SR[this.pv.from],n=SR[this.pv.to],i=this.glVarName(NR.OUTPUT_NAME),s=hf.any(this.variableForInput(NR.INPUT_NAME)),r=[];if(e!=n){const t=`${e}To${n}`,o=[];if(o.push(s),e==ER.GAMMA||n==ER.GAMMA){const t=hf.any(this.variableForInputParam(this.p.gammaFactor));o.push(t)}r.push(`vec4 ${i} = ${t}(${o.join(\\\\\\\", \\\\\\\")})`)}else r.push(`vec4 ${i} = ${s}`);t.addBodyLines(this,r)}}var LR,OR;NR.INPUT_NAME=\\\\\\\"color\\\\\\\",NR.INPUT_GAMMA_FACTOR=\\\\\\\"gammaFactor\\\\\\\",NR.OUTPUT_NAME=\\\\\\\"out\\\\\\\",function(t){t.EQUAL=\\\\\\\"Equal\\\\\\\",t.LESS_THAN=\\\\\\\"Less Than\\\\\\\",t.GREATER_THAN=\\\\\\\"Greater Than\\\\\\\",t.LESS_THAN_OR_EQUAL=\\\\\\\"Less Than Or Equal\\\\\\\",t.GREATER_THAN_OR_EQUAL=\\\\\\\"Greater Than Or Equal\\\\\\\",t.NOT_EQUAL=\\\\\\\"Not Equal\\\\\\\"}(LR||(LR={})),function(t){t.EQUAL=\\\\\\\"==\\\\\\\",t.LESS_THAN=\\\\\\\"<\\\\\\\",t.GREATER_THAN=\\\\\\\">\\\\\\\",t.LESS_THAN_OR_EQUAL=\\\\\\\"<=\\\\\\\",t.GREATER_THAN_OR_EQUAL=\\\\\\\">=\\\\\\\",t.NOT_EQUAL=\\\\\\\"!=\\\\\\\"}(OR||(OR={}));const PR=[LR.EQUAL,LR.LESS_THAN,LR.GREATER_THAN,LR.LESS_THAN_OR_EQUAL,LR.GREATER_THAN_OR_EQUAL,LR.NOT_EQUAL],RR=[OR.EQUAL,OR.LESS_THAN,OR.GREATER_THAN,OR.LESS_THAN_OR_EQUAL,OR.GREATER_THAN_OR_EQUAL,OR.NOT_EQUAL],IR=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];const FR=new class extends la{constructor(){super(...arguments),this.test=aa.INTEGER(0,{menu:{entries:PR.map(((t,e)=>({name:`${RR[e].padEnd(2,\\\\\\\" \\\\\\\")} (${t})`,value:e})))}})}};class DR extends df{constructor(){super(...arguments),this.paramsConfig=FR}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((()=>[Bo.BOOL]))}set_test_name(t){this.p.test.set(PR.indexOf(t))}_gl_input_name(t){return[\\\\\\\"value0\\\\\\\",\\\\\\\"value1\\\\\\\"][t]}_expected_input_type(){const t=this.io.connection_points.first_input_connection_type()||Bo.FLOAT;return[t,t]}setLines(t){const e=[],n=this.glVarName(\\\\\\\"val\\\\\\\"),i=RR[this.pv.test],s=hf.any(this.variableForInput(this._gl_input_name(0))),r=hf.any(this.variableForInput(this._gl_input_name(1))),o=this.io.inputs.namedInputConnectionPoints()[0];let a=1;if(o&&(a=Vo[o.type()]||1),a>1){let t=[];for(let n=0;n({name:t,value:e})))}}),this.bool=aa.BOOLEAN(0,zR(Bo.BOOL)),this.int=aa.INTEGER(0,zR(Bo.INT)),this.float=aa.FLOAT(0,zR(Bo.FLOAT)),this.vec2=aa.VECTOR2([0,0],zR(Bo.VEC2)),this.vec3=aa.VECTOR3([0,0,0],zR(Bo.VEC3)),this.vec4=aa.VECTOR4([0,0,0,0],zR(Bo.VEC4))}};class UR extends df{constructor(){super(...arguments),this.paramsConfig=kR,this._allow_inputs_created_from_params=!1}static type(){return\\\\\\\"constant\\\\\\\"}initializeNode(){this.io.connection_points.set_output_name_function((t=>UR.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=hf.any(e.value);e.name()==this.p.int.name()&&m.isNumber(e.value)&&(i=hf.integer(e.value));const s=`${n} ${this._current_var_name} = ${i}`;t.addBodyLines(this,[s])}else console.warn(`no param found for constant node for type '${this.pv.type}'`)}get _current_connection_type(){null==this.pv.type&&console.warn(\\\\\\\"constant gl node type if not valid\\\\\\\");const t=zo[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([[Bo.BOOL,this.p.bool],[Bo.INT,this.p.int],[Bo.FLOAT,this.p.float],[Bo.VEC2,this.p.vec2],[Bo.VEC3,this.p.vec3],[Bo.VEC4,this.p.vec4]]);const t=zo[this.pv.type];return this._params_by_type.get(t)}get _current_var_name(){return this.glVarName(UR.OUTPUT_NAME)}set_gl_type(t){this.p.type.set(zo.indexOf(t))}}UR.OUTPUT_NAME=\\\\\\\"val\\\\\\\";const GR=\\\\\\\"cross\\\\\\\";const VR=new class extends la{constructor(){super(...arguments),this.x=aa.VECTOR3([0,0,1]),this.y=aa.VECTOR3([0,1,0])}};class HR extends df{constructor(){super(...arguments),this.paramsConfig=VR}static type(){return\\\\\\\"cross\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(GR,Bo.VEC3)])}setLines(t){const e=hf.float(this.variableForInputParam(this.p.x)),n=hf.float(this.variableForInputParam(this.p.y)),i=`vec3 ${this.glVarName(GR)} = cross(${e}, ${n})`;t.addBodyLines(this,[i])}}class jR extends(tR(\\\\\\\"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 WR=\\\\\\\"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 qR=new class extends la{constructor(){super(...arguments),this.position=aa.VECTOR2([0,0]),this.center=aa.VECTOR2([0,0]),this.radius=aa.FLOAT(1),this.feather=aa.FLOAT(.1)}};class XR extends df{constructor(){super(...arguments),this.paramsConfig=qR}static type(){return\\\\\\\"disk\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"float\\\\\\\",Bo.FLOAT)])}setLines(t){const e=hf.vector2(this.variableForInputParam(this.p.position)),n=hf.vector2(this.variableForInputParam(this.p.center)),i=hf.float(this.variableForInputParam(this.p.radius)),s=hf.float(this.variableForInputParam(this.p.feather)),r=`float ${this.glVarName(\\\\\\\"float\\\\\\\")} = disk2d(${e}, ${n}, ${i}, ${s})`;t.addBodyLines(this,[r]),t.addDefinitions(this,[new Tf(this,WR)])}}var YR=\\\\\\\"\\\\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 $R=[\\\\\\\"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\\\\\\\"],JR={\\\\\\\"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\\\\\\\":YR,\\\\\\\"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\\\\\\\"},ZR={\\\\\\\"bounce-in\\\\\\\":[YR],\\\\\\\"bounce-in-out\\\\\\\":[YR]},QR={\\\\\\\"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\\\\\\\"},KR=$R.indexOf(\\\\\\\"sine-in-out\\\\\\\");const tI=new class extends la{constructor(){super(...arguments),this.type=aa.INTEGER(KR,{menu:{entries:$R.map(((t,e)=>({name:t,value:e})))}}),this.input=aa.FLOAT(0)}};class eI extends df{constructor(){super(...arguments),this.paramsConfig=tI}static type(){return\\\\\\\"easing\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"type\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"out\\\\\\\",Bo.FLOAT)])}setLines(t){const e=$R[this.pv.type],n=QR[e];let i=[new Tf(this,JR[e])];const s=(ZR[e]||[]).map((t=>new Tf(this,t)));s&&(i=s.concat(i));const r=hf.float(this.variableForInputParam(this.p.input)),o=`float ${this.glVarName(\\\\\\\"out\\\\\\\")} = ${n}(${r})`;t.addDefinitions(this,i),t.addBodyLines(this,[o])}}var nI=\\\\\\\"//\\\\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 iI={srcMin:0,srcMax:1,destMin:0,destMax:1};class sI extends TP{static type(){return\\\\\\\"fit\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"srcMin\\\\\\\",\\\\\\\"srcMax\\\\\\\",\\\\\\\"destMin\\\\\\\",\\\\\\\"destMax\\\\\\\"][t]}paramDefaultValue(t){return iI[t]}gl_method_name(){return\\\\\\\"fit\\\\\\\"}gl_function_definitions(){return[new Tf(this,nI)]}}const rI={srcMin:0,srcMax:1};class oI extends bP{static type(){return\\\\\\\"fitTo01\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"srcMin\\\\\\\",\\\\\\\"srcMax\\\\\\\"][t]}paramDefaultValue(t){return rI[t]}gl_method_name(){return\\\\\\\"fitTo01\\\\\\\"}gl_function_definitions(){return[new Tf(this,nI)]}}const aI={destMin:0,destMax:1};class lI extends bP{static type(){return\\\\\\\"fitFrom01\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"destMin\\\\\\\",\\\\\\\"destMax\\\\\\\"][t]}paramDefaultValue(t){return aI[t]}gl_method_name(){return\\\\\\\"fitFrom01\\\\\\\"}gl_function_definitions(){return[new Tf(this,nI)]}}const cI={center:.5,variance:.5};class hI extends bP{static type(){return\\\\\\\"fitFrom01ToVariance\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"center\\\\\\\",\\\\\\\"variance\\\\\\\"][t]}paramDefaultValue(t){return cI[t]}gl_method_name(){return\\\\\\\"fitFrom01ToVariance\\\\\\\"}gl_function_definitions(){return[new Tf(this,nI)]}}const uI=\\\\\\\"color\\\\\\\";const dI=new class extends la{constructor(){super(...arguments),this.mvPosition=aa.VECTOR4([0,0,0,0]),this.baseColor=aa.COLOR([0,0,0]),this.fogColor=aa.COLOR([1,1,1]),this.near=aa.FLOAT(0),this.far=aa.FLOAT(0)}};class pI extends df{constructor(){super(...arguments),this.paramsConfig=dI}static type(){return\\\\\\\"fog\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(uI,Bo.VEC3)])}setLines(t){if(t.current_shader_name==xf.FRAGMENT){const e=this.glVarName(this.name()),n=new Mf(this,Bo.VEC4,e),i=`${e} = modelViewMatrix * vec4(position, 1.0)`;t.addDefinitions(this,[n],xf.VERTEX),t.addBodyLines(this,[i],xf.VERTEX);const s=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}\\\\\\\"),r=hf.vector4(this.variableForInputParam(this.p.mvPosition)),o=hf.vector3(this.variableForInputParam(this.p.baseColor)),a=hf.vector3(this.variableForInputParam(this.p.fogColor)),l=hf.vector3(this.variableForInputParam(this.p.near)),c=hf.vector3(this.variableForInputParam(this.p.far)),h=`vec3 ${this.glVarName(uI)} = compute_fog(${[r,o,a,l,c].join(\\\\\\\", \\\\\\\")})`;t.addDefinitions(this,[n,s]),t.addBodyLines(this,[h])}}}const _I=new class extends la{};class mI extends df{constructor(){super(...arguments),this.paramsConfig=_I}static type(){return ns.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${t}`}_expected_input_types(){const t=this.parent();return(null==t?void 0:t.child_expected_output_connection_point_types())||[]}setLines(t){const e=this.parent();if(!e)return;const n=[],i=this.io.connections.inputConnections();if(i)for(let t of i)if(t){const i=t.dest_connection_point(),s=hf.any(this.variableForInput(i.name())),r=`\\\\t${e.glVarName(i.name())} = ${s}`;n.push(r)}t.addBodyLines(this,n),e.set_lines_block_end(t,this)}}class fI extends df{constructor(){super(...arguments),this._children_controller_context=ts.GL}initializeNode(){var t;null===(t=this.childrenController)||void 0===t||t.set_output_node_find_method((()=>this.nodesByType(mI.type())[0])),this.io.connection_points.set_input_name_function(this._expected_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._expected_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_expected_inputs_count(){const t=this.io.connections.inputConnections();return t?t.length+1:1}_expected_input_types(){const t=[],e=Bo.FLOAT,n=this.io.connections.inputConnections(),i=this._expected_inputs_count();for(let s=0;s=0){const e=t.dest_connection_point(),i=hf.any(this.variableForInput(e.name())),s=`${e.type()} ${this.glVarName(e.name())} = ${i}`;n.push(s)}const r=this.pv.start,o=this.pv.max,a=this.pv.step,l=hf.float(r),c=hf.float(o),h=hf.float(a),u=this.glVarName(\\\\\\\"i\\\\\\\"),d=`for(float ${u} = ${l}; ${u} < ${c}; ${u}+= ${h}){`;n.push(d);const p=`\\\\tfloat ${e.glVarName(yI.START_INDEX)} = ${u}`;if(n.push(p),s)for(let t of s)if(t&&t.input_index>=0){const i=t.dest_connection_point(),s=this.glVarName(i.name()),r=`\\\\t${i.type()} ${e.glVarName(i.name())} = ${s}`;n.push(r)}t.addBodyLines(e,n)}setLines(t){}}const TI=new class extends la{};class AI extends df{constructor(){super(...arguments),this.paramsConfig=TI}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 MI=new class extends la{constructor(){super(...arguments),this.hsluv=aa.VECTOR3([1,1,1])}};class EI extends df{constructor(){super(...arguments),this.paramsConfig=MI}static type(){return\\\\\\\"hsluvToRgb\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"rgb\\\\\\\",Bo.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=hf.vector3(this.variableForInputParam(this.p.hsluv)),s=this.glVarName(\\\\\\\"rgb\\\\\\\");n.push(`vec3 ${s} = hsluvToRgb(${i}.x * 360.0, ${i}.y * 100.0, ${i}.z * 100.0)`),t.addDefinitions(this,e),t.addBodyLines(this,n)}}const SI=new class extends la{constructor(){super(...arguments),this.hsv=aa.VECTOR3([1,1,1])}};class CI extends df{constructor(){super(...arguments),this.paramsConfig=SI}static type(){return\\\\\\\"hsvToRgb\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"rgb\\\\\\\",Bo.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=hf.vector3(this.variableForInputParam(this.p.hsv)),s=this.glVarName(\\\\\\\"rgb\\\\\\\");n.push(`vec3 ${s} = hsv2rgb(${i})`),t.addDefinitions(this,e),t.addBodyLines(this,n)}}const NI=\\\\\\\"condition\\\\\\\";const LI=new class extends la{};class OI extends vI{constructor(){super(...arguments),this.paramsConfig=LI}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=[Bo.BOOL],e=Bo.FLOAT,n=this.io.connections.inputConnections(),i=this._expected_inputs_count();for(let s=1;s[\\\\\\\"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()||Bo.FLOAT;return[t,t,Bo.FLOAT]}_expected_output_types(){return[this._expected_input_types()[0]]}}const YI=\\\\\\\"mvMult\\\\\\\";const $I=new class extends la{constructor(){super(...arguments),this.vector=aa.VECTOR3([0,0,0])}};class JI extends df{constructor(){super(...arguments),this.paramsConfig=$I}static type(){return\\\\\\\"modelViewMatrixMult\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(YI,Bo.VEC4)])}setLines(t){if(t.current_shader_name==xf.VERTEX){const e=hf.vector3(this.variableForInputParam(this.p.vector)),n=`vec4 ${this.glVarName(YI)} = modelViewMatrix * vec4(${e}, 1.0)`;t.addBodyLines(this,[n],xf.VERTEX)}}}const ZI={mult:1};var QI;!function(t){t.VALUE=\\\\\\\"value\\\\\\\",t.PRE_ADD=\\\\\\\"preAdd\\\\\\\",t.MULT=\\\\\\\"mult\\\\\\\",t.POST_ADD=\\\\\\\"postAdd\\\\\\\"}(QI||(QI={}));class KI extends wP{static type(){return\\\\\\\"multAdd\\\\\\\"}_gl_input_name(t){return[QI.VALUE,QI.PRE_ADD,QI.MULT,QI.POST_ADD][t]}paramDefaultValue(t){return ZI[t]}setLines(t){const e=hf.any(this.variableForInput(QI.VALUE)),n=hf.any(this.variableForInput(QI.PRE_ADD)),i=hf.any(this.variableForInput(QI.MULT)),s=hf.any(this.variableForInput(QI.POST_ADD)),r=this._expected_output_types()[0],o=this.io.outputs.namedOutputConnectionPoints()[0].name(),a=`${r} ${this.glVarName(o)} = (${i}*(${e} + ${n})) + ${s}`;t.addBodyLines(this,[a])}}class tF extends yP{static type(){return\\\\\\\"negate\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"in\\\\\\\"][t]))}_gl_input_name(t){return[\\\\\\\"in\\\\\\\"][t]}setLines(t){const e=hf.any(this.variableForInput(this._gl_input_name(0))),n=`${this.io.inputs.namedInputConnectionPoints()[0].type()} ${this.glVarName(this.io.connection_points.output_name(0))} = -1.0 * ${e}`;t.addBodyLines(this,[n])}}var eF;!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\\\\\\\"}(eF||(eF={}));const nF=[eF.CLASSIC_PERLIN_2D,eF.CLASSIC_PERLIN_3D,eF.CLASSIC_PERLIN_4D,eF.NOISE_2D,eF.NOISE_3D,eF.NOISE_4D],iF={[eF.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',[eF.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',[eF.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',[eF.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\\\\\\\",[eF.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\\\\\\\",[eF.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\\\\\\\"},sF={[eF.CLASSIC_PERLIN_2D]:Bo.VEC2,[eF.CLASSIC_PERLIN_3D]:Bo.VEC3,[eF.CLASSIC_PERLIN_4D]:Bo.VEC4,[eF.NOISE_2D]:Bo.VEC2,[eF.NOISE_3D]:Bo.VEC3,[eF.NOISE_4D]:Bo.VEC4},rF={[eF.CLASSIC_PERLIN_2D]:Bo.FLOAT,[eF.CLASSIC_PERLIN_3D]:Bo.FLOAT,[eF.CLASSIC_PERLIN_4D]:Bo.FLOAT,[eF.NOISE_2D]:Bo.FLOAT,[eF.NOISE_3D]:Bo.FLOAT,[eF.NOISE_4D]:Bo.FLOAT},oF={[eF.CLASSIC_PERLIN_2D]:\\\\\\\"cnoise\\\\\\\",[eF.CLASSIC_PERLIN_3D]:\\\\\\\"cnoise\\\\\\\",[eF.CLASSIC_PERLIN_4D]:\\\\\\\"cnoise\\\\\\\",[eF.NOISE_2D]:\\\\\\\"snoise\\\\\\\",[eF.NOISE_3D]:\\\\\\\"snoise\\\\\\\",[eF.NOISE_4D]:\\\\\\\"snoise\\\\\\\"};var aF;!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\\\\\\\"}(aF||(aF={}));const lF=[aF.NoChange,aF.Float,aF.Vec2,aF.Vec3,aF.Vec4],cF={[aF.NoChange]:\\\\\\\"Same as noise\\\\\\\",[aF.Float]:\\\\\\\"Float\\\\\\\",[aF.Vec2]:\\\\\\\"Vec2\\\\\\\",[aF.Vec3]:\\\\\\\"Vec3\\\\\\\",[aF.Vec4]:\\\\\\\"Vec4\\\\\\\"},hF={[aF.NoChange]:Bo.FLOAT,[aF.Float]:Bo.FLOAT,[aF.Vec2]:Bo.VEC2,[aF.Vec3]:Bo.VEC3,[aF.Vec4]:Bo.VEC4},uF=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"],dF=\\\\\\\"noise\\\\\\\",pF=nF.indexOf(eF.NOISE_3D),_F=aF.NoChange,mF={amp:1,freq:1};var fF;!function(t){t.AMP=\\\\\\\"amp\\\\\\\",t.POSITION=\\\\\\\"position\\\\\\\",t.FREQ=\\\\\\\"freq\\\\\\\",t.OFFSET=\\\\\\\"offset\\\\\\\"}(fF||(fF={}));const gF=new class extends la{constructor(){super(...arguments),this.type=aa.INTEGER(pF,{menu:{entries:nF.map(((t,e)=>({name:`${t} (output: ${rF[t]})`,value:e})))}}),this.outputType=aa.INTEGER(_F,{menu:{entries:lF.map((t=>{const e=lF[t];return{name:cF[e],value:e}}))}}),this.octaves=aa.INTEGER(3,{range:[1,10],rangeLocked:[!0,!1]}),this.ampAttenuation=aa.FLOAT(.5,{range:[0,1]}),this.freqIncrease=aa.FLOAT(2,{range:[0,10],separatorAfter:!0})}};class vF extends df{constructor(){super(...arguments),this.paramsConfig=gF}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 Ho(dF,Bo.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((()=>dF))}_gl_input_name(t){return[fF.AMP,fF.POSITION,fF.FREQ,fF.OFFSET][t]}paramDefaultValue(t){return mF[t]}_expected_input_types(){const t=nF[this.pv.type],e=this._expected_output_types()[0],n=sF[t];return[e,n,n,n]}_expected_output_types(){const t=nF[this.pv.type],e=lF[this.pv.outputType];return e==aF.NoChange?[sF[t]]:[hF[e]]}setLines(t){const e=[],n=[],i=nF[this.pv.type],s=iF[i],r=rF[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,s)),e.push(new Tf(this,this.fbm_function()));const o=this._expected_output_types()[0];if(o==r){const t=this.single_noise_line();n.push(t)}else{const t=Vo[o],e=[],s=this.glVarName(\\\\\\\"noise\\\\\\\");for(let r=0;rhf.float(1e3*r))).join(\\\\\\\", \\\\\\\")})`,c=this.single_noise_line(t,t,l);n.push(c)}const r=`vec${t} ${s} = vec${t}(${e.join(\\\\\\\", \\\\\\\")})`;n.push(r)}t.addDefinitions(this,e),t.addBodyLines(this,n)}fbm_method_name(){const t=nF[this.pv.type];return`fbm_${oF[t]}_${this.name()}`}fbm_function(){const t=nF[this.pv.type],e=oF[t],n=sF[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 < ${hf.integer(this.pv.octaves)}; i++) {\\\\n\\\\t\\\\tvalue += amplitude * ${e}(st);\\\\n\\\\t\\\\tst *= ${hf.float(this.pv.freqIncrease)};\\\\n\\\\t\\\\tamplitude *= ${hf.float(this.pv.ampAttenuation)};\\\\n\\\\t}\\\\n\\\\treturn value;\\\\n}\\\\n`}single_noise_line(t,e,n){const i=this.fbm_method_name(),s=hf.any(this.variableForInput(fF.AMP)),r=hf.any(this.variableForInput(fF.POSITION)),o=hf.any(this.variableForInput(fF.FREQ));let a=hf.any(this.variableForInput(fF.OFFSET));n&&(a=`(${a}+${n})`);const l=[`(${r}*${o})+${a}`].join(\\\\\\\", \\\\\\\"),c=this.glVarName(dF),h=`${s}*${i}(${l})`;if(e)return`float ${c}${t} = (${h}).${e}`;return`${this.io.outputs.namedOutputConnectionPoints()[0].type()} ${c} = ${h}`}}class yF extends yP{static type(){return\\\\\\\"null\\\\\\\"}setLines(t){const e=hf.any(this.variableForInput(this._gl_input_name(0))),n=this.io.outputs.namedOutputConnectionPoints()[0],i=`${n.type()} ${this.glVarName(n.name())} = ${e}`;t.addBodyLines(this,[i])}}const xF=new class extends la{};class bF extends df{constructor(){super(...arguments),this.paramsConfig=xF}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 wF{constructor(){this._param_configs=[]}reset(){this._param_configs=[]}push(t){this._param_configs.push(t)}list(){return this._param_configs}}const TF=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"\\\\\\\"),this.type=aa.INTEGER(zo.indexOf(Bo.FLOAT),{menu:{entries:zo.map(((t,e)=>({name:t,value:e})))}}),this.asColor=aa.BOOLEAN(0,{visibleIf:{type:zo.indexOf(Bo.VEC3)}})}};class AF extends df{constructor(){super(...arguments),this.paramsConfig=TF,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((()=>[zo[this.pv.type]])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}setLines(t){const e=[],n=zo[this.pv.type],i=this.uniform_name();e.push(new Af(this,n,i)),t.addDefinitions(this,e)}paramsGenerating(){return!0}setParamConfigs(){const t=zo[this.pv.type],e=Go[t];let n=ko[t];if(this._param_configs_controller=this._param_configs_controller||new wF,this._param_configs_controller.reset(),n==Er.VECTOR3&&this.p.asColor.value&&m.isArray(e)&&3==e.length){const t=new $f(Er.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=zo.indexOf(t);this.p.type.set(e)}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}class MF extends vP{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()||Bo.VEC3;return[t,t,Bo.FLOAT]}_expected_output_types(){return[this._expected_input_types()[0]]}}const EF=\\\\\\\"SSSModel\\\\\\\";const SF=new class extends la{constructor(){super(...arguments),this.color=aa.COLOR([1,1,1]),this.thickness=aa.FLOAT(.1),this.power=aa.FLOAT(2),this.scale=aa.FLOAT(16),this.distortion=aa.FLOAT(.1),this.ambient=aa.FLOAT(.4),this.attenuation=aa.FLOAT(.8)}};class CF extends df{constructor(){super(...arguments),this.paramsConfig=SF}static type(){return\\\\\\\"SSSModel\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(EF,Bo.SSS_MODEL)])}setLines(t){const e=[],n=this.glVarName(EF);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()} = ${hf.vector3(this.variableForInputParam(e))};`}}class NF extends yP{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((()=>[Bo.VEC4,Bo.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[Bo.VEC4]))}gl_method_name(){return\\\\\\\"quatMult\\\\\\\"}gl_function_definitions(){return[new Tf(this,wR)]}}var LF;!function(t){t.AXIS=\\\\\\\"axis\\\\\\\",t.ANGLE=\\\\\\\"angle\\\\\\\"}(LF||(LF={}));const OF=[LF.AXIS,LF.ANGLE],PF={[LF.AXIS]:[0,0,1],[LF.ANGLE]:0};class RF extends xP{static type(){return\\\\\\\"quatFromAxisAngle\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>OF[t])),this.io.connection_points.set_expected_input_types_function((()=>[Bo.VEC3,Bo.FLOAT])),this.io.connection_points.set_expected_output_types_function((()=>[Bo.VEC4]))}paramDefaultValue(t){return PF[t]}gl_method_name(){return\\\\\\\"quatFromAxisAngle\\\\\\\"}gl_function_definitions(){return[new Tf(this,wR)]}}class IF extends yP{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((()=>[Bo.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[Bo.FLOAT]))}gl_method_name(){return\\\\\\\"quatToAngle\\\\\\\"}gl_function_definitions(){return[new Tf(this,wR)]}}class FF extends yP{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((()=>[Bo.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[Bo.VEC3]))}gl_method_name(){return\\\\\\\"quatToAxis\\\\\\\"}gl_function_definitions(){return[new Tf(this,wR)]}}const DF=\\\\\\\"val\\\\\\\";const BF=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"ramp\\\\\\\"),this.input=aa.FLOAT(0)}};class zF extends df{constructor(){super(...arguments),this.paramsConfig=BF}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 Ho(DF,Bo.FLOAT)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}setLines(t){const e=Bo.FLOAT,n=this._uniform_name(),i=this.glVarName(DF),s=new Af(this,Bo.SAMPLER_2D,n);t.addDefinitions(this,[s]);const r=this.variableForInputParam(this.p.input),o=`${e} ${i} = texture2D(${this._uniform_name()}, vec2(${r}, 0.0)).x`;t.addBodyLines(this,[o])}paramsGenerating(){return!0}setParamConfigs(){this._param_configs_controller=this._param_configs_controller||new wF,this._param_configs_controller.reset();const t=new $f(Er.RAMP,this.pv.name,bo.DEFAULT_VALUE,this._uniform_name());this._param_configs_controller.push(t)}_uniform_name(){return\\\\\\\"ramp_texture_\\\\\\\"+this.glVarName(DF)}}const kF=\\\\\\\"rand\\\\\\\";const UF=new class extends la{constructor(){super(...arguments),this.seed=aa.VECTOR2([1,1])}};class GF extends df{constructor(){super(...arguments),this.paramsConfig=UF}static type(){return\\\\\\\"random\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(kF,Bo.FLOAT)])}setLines(t){const e=this.io.inputs.namedInputConnectionPoints()[0].name(),n=hf.vector2(this.variableForInput(e)),i=`float ${this.glVarName(kF)} = rand(${n})`;t.addBodyLines(this,[i])}}const VF=new class extends la{constructor(){super(...arguments),this.rgb=aa.VECTOR3([1,1,1])}};class HF extends df{constructor(){super(...arguments),this.paramsConfig=VF}static type(){return\\\\\\\"rgbToHsv\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"hsv\\\\\\\",Bo.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=hf.vector3(this.variableForInputParam(this.p.rgb)),s=this.glVarName(\\\\\\\"hsv\\\\\\\");n.push(`vec3 ${s} = rgb2hsv(${i})`),t.addDefinitions(this,e),t.addBodyLines(this,n)}}var jF;!function(t){t[t.AXIS=0]=\\\\\\\"AXIS\\\\\\\",t[t.QUAT=1]=\\\\\\\"QUAT\\\\\\\"}(jF||(jF={}));const WF=[jF.AXIS,jF.QUAT],qF={[jF.AXIS]:\\\\\\\"from axis + angle\\\\\\\",[jF.QUAT]:\\\\\\\"from quaternion\\\\\\\"},XF={[jF.AXIS]:[\\\\\\\"vector\\\\\\\",\\\\\\\"axis\\\\\\\",\\\\\\\"angle\\\\\\\"],[jF.QUAT]:[\\\\\\\"vector\\\\\\\",\\\\\\\"quat\\\\\\\"]},YF={[jF.AXIS]:\\\\\\\"rotateWithAxisAngle\\\\\\\",[jF.QUAT]:\\\\\\\"rotateWithQuat\\\\\\\"},$F={[jF.AXIS]:[Bo.VEC3,Bo.VEC3,Bo.FLOAT],[jF.QUAT]:[Bo.VEC3,Bo.VEC4]},JF={vector:[0,0,1],axis:[0,1,0]};const ZF=new class extends la{constructor(){super(...arguments),this.signature=aa.INTEGER(jF.AXIS,{menu:{entries:WF.map(((t,e)=>({name:qF[t],value:e})))}})}};class QF extends df{constructor(){super(...arguments),this.paramsConfig=ZF}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=WF.indexOf(t);this.p.signature.set(e)}_gl_input_name(t){const e=WF[this.pv.signature];return XF[e][t]}paramDefaultValue(t){return JF[t]}gl_method_name(){const t=WF[this.pv.signature];return YF[t]}_expected_input_types(){const t=WF[this.pv.signature];return $F[t]}_expected_output_types(){return[Bo.VEC3]}gl_function_definitions(){return[new Tf(this,wR)]}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.io.inputs.namedInputConnectionPoints().map(((t,e)=>{const n=t.name();return hf.any(this.variableForInput(n))})).join(\\\\\\\", \\\\\\\"),i=`${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 KF=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];class tD extends yP{static type(){return\\\\\\\"round\\\\\\\"}setLines(t){const e=this.io.inputs.namedInputConnectionPoints()[0],n=hf.vector2(this.variableForInput(e.name())),i=this.io.outputs.namedOutputConnectionPoints()[0],s=this.glVarName(i.name()),r=[];if(1==Vo[i.type()])r.push(`${i.type()} ${s} = ${this._simple_line(n)}`);else{const t=KF.map((t=>this._simple_line(`${n}.${t}`)));r.push(`${i.type()} ${s} = ${i.type()}(${t.join(\\\\\\\",\\\\\\\")})`)}t.addBodyLines(this,r)}_simple_line(t){return`sign(${t})*floor(abs(${t})+0.5)`}}const eD=new class extends la{constructor(){super(...arguments),this.position=aa.VECTOR3([0,0,0]),this.center=aa.VECTOR3([0,0,0]),this.radius=aa.FLOAT(1),this.feather=aa.FLOAT(.1)}};class nD extends df{constructor(){super(...arguments),this.paramsConfig=eD}static type(){return\\\\\\\"sphere\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"float\\\\\\\",Bo.FLOAT)])}setLines(t){const e=hf.vector2(this.variableForInputParam(this.p.position)),n=hf.vector2(this.variableForInputParam(this.p.center)),i=hf.float(this.variableForInputParam(this.p.radius)),s=hf.float(this.variableForInputParam(this.p.feather)),r=`float ${this.glVarName(\\\\\\\"float\\\\\\\")} = disk3d(${e}, ${n}, ${i}, ${s})`;t.addBodyLines(this,[r]),t.addDefinitions(this,[new Tf(this,WR)])}}const iD=new class extends la{};class sD extends df{constructor(){super(...arguments),this.paramsConfig=iD}static type(){return ns.INPUT}initializeNode(){this.io.connection_points.set_output_name_function(this._expected_output_names.bind(this)),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}parent(){return super.parent()}_expected_output_names(t){const e=this.parent();return(null==e?void 0:e.child_expected_input_connection_point_name(t))||`out${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 rD=new class extends la{};class oD extends df{constructor(){super(...arguments),this.paramsConfig=rD}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?oD.INPUT_INDEX:\\\\\\\"in\\\\\\\"+(t-1)}_expected_input_types(){const t=this.io.connection_points.input_connection_type(1)||Bo.FLOAT,e=this.io.connections.inputConnections(),n=e?rr.clamp(e.length,2,16):2,i=[Bo.INT];for(let e=0;e{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.paramName])}))}))}setLines(t){const e=hf.vector2(this.variableForInputParam(this.p.uv)),n=this.glVarName(lD.OUTPUT_NAME),i=this._uniform_name(),s=new Af(this,Bo.SAMPLER_2D,i),r=`vec4 ${n} = texture2D(${i}, ${e})`;t.addDefinitions(this,[s]),t.addBodyLines(this,[r])}paramsGenerating(){return!0}setParamConfigs(){this._param_configs_controller=this._param_configs_controller||new wF,this._param_configs_controller.reset();const t=new $f(Er.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 cD;lD.OUTPUT_NAME=\\\\\\\"rgba\\\\\\\",function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.DIR_VEC=\\\\\\\"direction vector\\\\\\\"}(cD||(cD={}));const hD=[cD.POSITION,cD.DIR_VEC];const uD=new class extends la{constructor(){super(...arguments),this.vec=aa.VECTOR3([0,0,0]),this.interpretation=aa.INTEGER(0,{menu:{entries:hD.map(((t,e)=>({name:t,value:e})))}})}};class dD extends df{constructor(){super(...arguments),this.paramsConfig=uD}static type(){return\\\\\\\"toWorldSpace\\\\\\\"}initializeNode(){this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"interpretation\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"out\\\\\\\",Bo.VEC3)])}setLines(t){const e=[],n=hf.vector3(this.variableForInputParam(this.p.vec)),i=this.glVarName(\\\\\\\"out\\\\\\\");switch(hD[this.pv.interpretation]){case cD.POSITION:e.push(`vec3 ${i} = (modelMatrix * vec4( ${n}, 1.0 )).xyz`);break;case cD.DIR_VEC:e.push(`vec3 ${i} = normalize( mat3( modelMatrix[0].xyz, modelMatrix[1].xyz, modelMatrix[2].xyz ) * ${n} )`)}t.addBodyLines(this,e)}}var pD;!function(t){t.CONDITION=\\\\\\\"condition\\\\\\\",t.IF_TRUE=\\\\\\\"ifTrue\\\\\\\",t.IF_FALSE=\\\\\\\"ifFalse\\\\\\\"}(pD||(pD={}));const _D=[pD.CONDITION,pD.IF_TRUE,pD.IF_FALSE];class mD extends _f{static type(){return\\\\\\\"twoWaySwitch\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}_gl_input_name(t){return _D[t]}_gl_output_name(){return\\\\\\\"val\\\\\\\"}_expected_input_types(){const t=this.io.connections.inputConnection(1)||this.io.connections.inputConnection(2),e=t?t.src_connection_point().type():Bo.FLOAT;return[Bo.BOOL,e,e]}_expected_output_types(){return[this._expected_input_types()[1]]}setLines(t){const e=[],n=this.glVarName(\\\\\\\"val\\\\\\\"),i=hf.bool(this.variableForInput(pD.CONDITION)),s=hf.any(this.variableForInput(pD.IF_TRUE)),r=hf.any(this.variableForInput(pD.IF_FALSE)),o=this._expected_output_types()[0];e.push(`${o} ${n}`),e.push(`if(${i}){`),e.push(`${n} = ${s}`),e.push(\\\\\\\"} else {\\\\\\\"),e.push(`${n} = ${r}`),e.push(\\\\\\\"}\\\\\\\"),t.addBodyLines(this,e)}}const fD=[Bo.FLOAT,Bo.VEC2,Bo.VEC3,Bo.VEC4];const gD=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"\\\\\\\"),this.type=aa.INTEGER(0,{menu:{entries:fD.map(((t,e)=>({name:t,value:e})))}})}};class vD extends df{constructor(){super(...arguments),this.paramsConfig=gD,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"varyingRead\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_output_name_function((()=>this.output_name)),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[fD[this.pv.type]])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}get output_name(){return vD.OUTPUT_NAME}setLines(t){if(t.current_shader_name==xf.FRAGMENT){const e=this.pv.name,n=new Mf(this,this.gl_type(),e),i=this.glVarName(vD.OUTPUT_NAME),s=`${this.gl_type()} ${i} = ${e}`;t.addDefinitions(this,[n]),t.addBodyLines(this,[s])}}get attribute_name(){return this.pv.name.trim()}gl_type(){return this.io.outputs.namedOutputConnectionPoints()[0].type()}set_gl_type(t){this.p.type.set(fD.indexOf(t))}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}vD.OUTPUT_NAME=\\\\\\\"fragment\\\\\\\";const yD={start:[0,0,1],end:[1,0,0],up:[0,1,0]};class xD extends(tR(\\\\\\\"vectorAlign\\\\\\\",{in:[\\\\\\\"start\\\\\\\",\\\\\\\"end\\\\\\\",\\\\\\\"up\\\\\\\"],method:\\\\\\\"vectorAlignWithUp\\\\\\\",functions:[wR]})){_expected_input_types(){const t=Bo.VEC3;return[t,t,t]}_expected_output_types(){return[Bo.VEC4]}paramDefaultValue(t){return yD[t]}}const bD={start:[0,0,1],end:[1,0,0]};class wD extends(WP(\\\\\\\"vectorAngle\\\\\\\",{in:[\\\\\\\"start\\\\\\\",\\\\\\\"end\\\\\\\"],method:\\\\\\\"vectorAngle\\\\\\\",functions:[wR]})){_expected_input_types(){const t=Bo.VEC3;return[t,t]}_expected_output_types(){return[Bo.FLOAT]}paramDefaultValue(t){return bD[t]}}const TD={only:[`${OI.context()}/${OI.type()}`,`${vI.context()}/${vI.type()}`,`${wI.context()}/${wI.type()}`]};class AD extends sa{static context(){return ts.JS}initializeBaseNode(){this.uiData.setLayoutHorizontal(),this.io.connection_points.initializeNode()}cook(){console.warn(\\\\\\\"js nodes should never cook\\\\\\\")}_set_function_node_to_recompile(){var t;null===(t=this.function_node)||void 0===t||t.assembler_controller.set_compilation_required_and_dirty(this)}get function_node(){var t;const e=this.parent();if(e)return e.type()==this.type()?null===(t=e)||void 0===t?void 0:t.function_node:e}js_var_name(t){return`v_POLY_${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 la{};const MD=[jo.FLOAT,jo.VEC2,jo.VEC3,jo.VEC4];const ED=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"\\\\\\\"),this.type=aa.INTEGER(0,{menu:{entries:MD.map(((t,e)=>({name:t,value:e})))}})}};class SD extends AD{constructor(){super(...arguments),this.paramsConfig=ED,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((()=>[MD[this.pv.type]]))}get input_name(){return SD.INPUT_NAME}get output_name(){return SD.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(MD.indexOf(t))}connected_input_node(){return this.io.inputs.named_input(SD.INPUT_NAME)}connected_input_connection_point(){return this.io.inputs.named_input_connection_point(SD.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())}}SD.INPUT_NAME=\\\\\\\"export\\\\\\\",SD.OUTPUT_NAME=\\\\\\\"val\\\\\\\";const CD=new class extends la{};class ND extends AD{constructor(){super(...arguments),this.paramsConfig=CD}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 LD=new class extends la{};class OD extends AD{constructor(){super(...arguments),this.paramsConfig=LD}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 PD{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(),s=t.get(i);s?s.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 ${s.data_type} from node '${s.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 RD;!function(t){t.ATTRIBUTE=\\\\\\\"attribute\\\\\\\",t.FUNCTION=\\\\\\\"function\\\\\\\",t.UNIFORM=\\\\\\\"uniform\\\\\\\"}(RD||(RD={}));class ID{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 PD}}class FD extends ID{constructor(t,e,n){super(RD.UNIFORM,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`uniform ${this.data_type} ${this.name()}`}}class DD 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 Er.BOOLEAN:case Er.BUTTON:return{value:0};case Er.COLOR:return{value:new D.a(0,0,0)};case Er.FLOAT:case Er.FOLDER:case Er.INTEGER:case Er.OPERATOR_PATH:case Er.NODE_PATH:case Er.PARAM_PATH:return{value:0};case Er.RAMP:case Er.STRING:return{value:null};case Er.VECTOR2:return{value:new d.a(0,0)};case Er.VECTOR3:return{value:new p.a(0,0,0)};case Er.VECTOR4:return{value:new _.a(0,0,0,0)}}ls.unreachable(t)}}const BD=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"\\\\\\\"),this.type=aa.INTEGER(Wo.indexOf(jo.FLOAT),{menu:{entries:Wo.map(((t,e)=>({name:t,value:e})))}}),this.asColor=aa.BOOLEAN(0,{visibleIf:{type:Wo.indexOf(jo.VEC3)}})}};class zD extends AD{constructor(){super(...arguments),this.paramsConfig=BD,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((()=>[Wo[this.pv.type]]))}setLines(t){const e=[],n=Wo[this.pv.type],i=this.uniform_name();e.push(new FD(this,n,i)),t.addDefinitions(this,e)}setParamConfigs(){const t=Wo[this.pv.type],e=Yo[t];let n=qo[t];if(this._param_configs_controller=this._param_configs_controller||new wF,this._param_configs_controller.reset(),n==Er.VECTOR3&&this.p.asColor.value&&m.isArray(e)&&3==e.length){const t=new DD(Er.COLOR,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}else{const t=new DD(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=Wo.indexOf(t);this.p.type.set(e)}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}class kD extends sa{constructor(){super(...arguments),this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this)}static context(){return ts.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 UD{constructor(t){this.node=t}add_params(){}update(){}get material(){return this.node.material}}const GD={NoBlending:w.ub,NormalBlending:w.xb,AdditiveBlending:w.e,SubtractiveBlending:w.Sc,MultiplyBlending:w.mb},VD=Object.keys(GD);function HD(t){return class extends t{constructor(){super(...arguments),this.doubleSided=aa.BOOLEAN(0),this.front=aa.BOOLEAN(1,{visibleIf:{doubleSided:!1}}),this.overrideShadowSide=aa.BOOLEAN(0),this.shadowDoubleSided=aa.BOOLEAN(0,{visibleIf:{overrideShadowSide:!0}}),this.shadowFront=aa.BOOLEAN(1,{visibleIf:{overrideShadowSide:!0,shadowDoubleSided:!1}}),this.colorWrite=aa.BOOLEAN(1,{separatorBefore:!0,cook:!1,callback:(t,e)=>{jD.update(t)}}),this.depthWrite=aa.BOOLEAN(1,{cook:!1,callback:(t,e)=>{jD.update(t)}}),this.depthTest=aa.BOOLEAN(1,{cook:!1,callback:(t,e)=>{jD.update(t)}}),this.premultipliedAlpha=aa.BOOLEAN(!1,{separatorAfter:!0}),this.blending=aa.INTEGER(w.xb,{menu:{entries:VD.map((t=>({name:t,value:GD[t]})))}}),this.dithering=aa.BOOLEAN(0),this.polygonOffset=aa.BOOLEAN(!1,{separatorBefore:!0}),this.polygonOffsetFactor=aa.INTEGER(0,{range:[0,1e3],visibleIf:{polygonOffset:1}}),this.polygonOffsetUnits=aa.INTEGER(0,{range:[0,1e3],visibleIf:{polygonOffset:1}})}}}HD(la);class jD extends UD{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 s=t.customMaterials;if(s){const t=Object.keys(s);for(let n of t){const t=s[n];t&&this._updateSides(t,e)}}}static async update(t){t.controllers.advancedCommon.update()}}class WD extends(HD(la)){constructor(){super(...arguments),this.color=aa.COLOR([1,1,1]),this.lineWidth=aa.FLOAT(1,{range:[1,10],rangeLocked:[!0,!1]})}}const qD=new WD;class XD extends kD{constructor(){super(...arguments),this.paramsConfig=qD,this.controllers={advancedCommon:new jD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"lineBasic\\\\\\\"}createMaterial(){return new Ts.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 YD(t){return class extends t{constructor(){super(...arguments),this.transparent=aa.BOOLEAN(0),this.opacity=aa.FLOAT(1),this.alphaTest=aa.FLOAT(0)}}}YD(la);class $D extends UD{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 JD 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=[],s=t.assembler.param_configs();for(let t of s)i.push([t.name(),t.uniform_name]);const r=this._materialToJson(this.node.material,{node:this.node,suffix:\\\\\\\"main\\\\\\\"});r||console.warn(\\\\\\\"failed to save material from node\\\\\\\",this.node.path());return{material:r||{},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),s=this._material.uniforms[n],r=Object.keys(this._material.customMaterials);let o;for(let t of r){const e=this._material.customMaterials[t],i=null==e?void 0:e.uniforms[n];i&&(o=o||[],o.push(i))}i&&(s||o)&&i.options.setOption(\\\\\\\"callback\\\\\\\",(()=>{if(s&&$f.callback(i,s),o)for(let t of o)$f.callback(i,t)}))}}}material(){if(li.playerMode())return this._material}}function ZD(t){return class extends t{constructor(){super(...arguments),this.setBuilderNode=aa.BOOLEAN(0,{callback:t=>{QD.PARAM_CALLBACK_setCompileRequired(t)}}),this.builderNode=aa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setBuilderNode:!0},callback:t=>{QD.PARAM_CALLBACK_setCompileRequired(t)}})}}}ZD(la);class QD extends kD{constructor(){super(...arguments),this._children_controller_context=ts.GL,this.persisted_config=new JD(this)}createMaterial(){var t;let e;return this.persisted_config&&(e=this.persisted_config.material()),e||(e=null===(t=this.assemblerController)||void 0===t?void 0:t.assembler.createMaterial()),e}get assemblerController(){return this._assembler_controller=this._assembler_controller||this._create_assembler_controller()}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}childrenAllowed(){return this.assemblerController?super.childrenAllowed():(this.scene().markAsReadOnly(this),!1)}compileIfRequired(){var t;(null===(t=this.assemblerController)||void 0===t?void 0:t.compileRequired())&&this._compile()}_compile(){const t=this.assemblerController;this.material&&t&&(t.assembler.setGlParentNode(this),this._setAssemblerGlParentNode(t),t.assembler.compileMaterial(this.material),t.post_compile())}_setAssemblerGlParentNode(t){if(!this.pv.setBuilderNode)return;const e=this.pv.builderNode.nodeWithContext(ts.MAT);if(!e)return;const n=e;n.assemblerController?n.type()==this.type()?t.assembler.setGlParentNode(n):this.states.error.set(`resolved node '${e.path()}' does not have the same type '${e.type()}' as current node '${this.type()}'`):this.states.error.set(`resolved node '${e.path()}' is not a builder node`)}static PARAM_CALLBACK_setCompileRequired(t){t.PARAM_CALLBACK_setCompileRequired()}PARAM_CALLBACK_setCompileRequired(){var t;null===(t=this.assemblerController)||void 0===t||t.setCompilationRequired(!0)}}function KD(t){return class extends t{constructor(){super(...arguments),this.useFog=aa.BOOLEAN(0)}}}KD(la);class tB extends UD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.fog=n.useFog}}function eB(t){return class extends t{constructor(){super(...arguments),this.default=aa.FOLDER(null)}}}function nB(t){return class extends t{constructor(){super(...arguments),this.advanced=aa.FOLDER(null)}}}class iB extends(KD(HD(ZD(nB(YD(eB(la))))))){constructor(){super(...arguments),this.linewidth=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]})}}const sB=new iB;class rB extends QD{constructor(){super(...arguments),this.paramsConfig=sB,this.controllers={advancedCommon:new jD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"lineBasicBuilder\\\\\\\"}usedAssembler(){return jn.GL_LINE}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();$D.update(this),tB.update(this),this.compileIfRequired(),this.material.linewidth=this.pv.linewidth,this.setMaterial(this.material)}}function oB(t){return class extends t{constructor(){super(...arguments),this.color=aa.COLOR([1,1,1],{conversion:oo.SRGB_TO_LINEAR}),this.useVertexColors=aa.BOOLEAN(0,{separatorAfter:!0}),this.transparent=aa.BOOLEAN(0),this.opacity=aa.FLOAT(1),this.alphaTest=aa.FLOAT(0)}}}O.a;oB(la);class aB extends UD{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 lB(t){return class extends t{constructor(){super(...arguments),this.useFog=aa.BOOLEAN(0)}}}lB(la);class cB extends UD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.fog=n.useFog}}function hB(t){return{cook:!1,callback:(e,n)=>{t.update(e)}}}function uB(t,e,n){return{visibleIf:{[e]:1},nodeSelection:{context:ts.COP,types:null==n?void 0:n.types},cook:!1,callback:(e,n)=>{t.update(e)}}}class dB extends UD{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 s=t,r=e;await this._update_texture_on_uniforms(s,r,n,i)}if(this._update_options.directParams){const s=t,r=e;await this._update_texture_on_material(s,r,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 s=null!=e[n]&&null!=e[n].value;let r=!1;if(s){e[n].value.uuid!=i.uuid&&(r=!0)}if(!s||r){e[n]&&(e[n].value=i),this._apply_texture_on_material(t,t,n,i),t.needsUpdate=!0;const s=t.customMaterials;if(s){const t=Object.keys(s);for(let e of t){const t=s[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 li.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 s=null!=e[n];let r=!1;if(s){e[n].uuid!=i.uuid&&(r=!0)}s&&!r||(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,s,r,o){i.isDirty()&&await i.compute();if(i.value){s.isDirty()&&await s.compute();const i=s.value.nodeWithContext(ts.COP);if(i){const s=(await i.compute()).texture();if(s)return void r(t,e,n,s)}}o(t,e,n)}}function pB(t){return class extends t{constructor(){super(...arguments),this.useMap=aa.BOOLEAN(0,hB(_B)),this.map=aa.NODE_PATH(vi.EMPTY,uB(_B,\\\\\\\"useMap\\\\\\\"))}}}O.a;pB(la);class _B extends dB{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 mB(t){return class extends t{constructor(){super(...arguments),this.useAlphaMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(fB)}),this.alphaMap=aa.NODE_PATH(vi.EMPTY,uB(fB,\\\\\\\"useAlphaMap\\\\\\\"))}}}O.a;mB(la);class fB extends dB{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 gB(t){return class extends t{constructor(){super(...arguments),this.useAOMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(vB)}),this.aoMap=aa.NODE_PATH(vi.EMPTY,uB(vB,\\\\\\\"useAOMap\\\\\\\")),this.aoMapIntensity=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{useAOMap:1}})}}}O.a;gB(la);class vB extends dB{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 yB;!function(t){t.MULT=\\\\\\\"mult\\\\\\\",t.ADD=\\\\\\\"add\\\\\\\",t.MIX=\\\\\\\"mix\\\\\\\"}(yB||(yB={}));const xB=[yB.MULT,yB.ADD,yB.MIX],bB={[yB.MULT]:w.nb,[yB.ADD]:w.c,[yB.MIX]:w.lb};function wB(t){return class extends t{constructor(){super(...arguments),this.useEnvMap=aa.BOOLEAN(0,hB(TB)),this.envMap=aa.NODE_PATH(vi.EMPTY,uB(TB,\\\\\\\"useEnvMap\\\\\\\",{types:[Lg.CUBE_CAMERA]})),this.combine=aa.INTEGER(0,{visibleIf:{useEnvMap:1},menu:{entries:xB.map(((t,e)=>({name:t,value:e})))}}),this.reflectivity=aa.FLOAT(1,{visibleIf:{useEnvMap:1}}),this.refractionRatio=aa.FLOAT(.98,{range:[-1,1],rangeLocked:[!1,!1],visibleIf:{useEnvMap:1}})}}}wB(la);class TB extends dB{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=bB[xB[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 AB(t){return class extends t{constructor(){super(...arguments),this.useLightMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(MB)}),this.lightMap=aa.NODE_PATH(vi.EMPTY,uB(MB,\\\\\\\"useLightMap\\\\\\\")),this.lightMapIntensity=aa.FLOAT(1,{visibleIf:{useLightMap:1}})}}}O.a;AB(la);class MB extends dB{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 EB;!function(t){t.ROUND=\\\\\\\"round\\\\\\\",t.BUTT=\\\\\\\"butt\\\\\\\",t.SQUARE=\\\\\\\"square\\\\\\\"}(EB||(EB={}));const SB=[EB.ROUND,EB.BUTT,EB.SQUARE];var CB;!function(t){t.ROUND=\\\\\\\"round\\\\\\\",t.BEVEL=\\\\\\\"bevel\\\\\\\",t.MITER=\\\\\\\"miter\\\\\\\"}(CB||(CB={}));const NB=[CB.ROUND,CB.BEVEL,CB.MITER];function LB(t){return class extends t{constructor(){super(...arguments),this.wireframe=aa.BOOLEAN(0,{separatorBefore:!0}),this.wireframeLinecap=aa.INTEGER(0,{menu:{entries:SB.map(((t,e)=>({name:t,value:e})))},visibleIf:{wireframe:1}}),this.wireframeLinejoin=aa.INTEGER(0,{menu:{entries:NB.map(((t,e)=>({name:t,value:e})))},visibleIf:{wireframe:1}})}}}O.a;LB(la);class OB extends UD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.wireframe=n.wireframe,e.wireframeLinecap=SB[n.wireframeLinecap],e.wireframeLinejoin=NB[n.wireframeLinejoin],e.needsUpdate=!0}}function PB(t){return class extends t{constructor(){super(...arguments),this.textures=aa.FOLDER(null)}}}const RB={directParams:!0};class IB extends(lB(LB(HD(nB(AB(wB(gB(mB(pB(PB(oB(eB(la))))))))))))){}const FB=new IB;class DB extends kD{constructor(){super(...arguments),this.paramsConfig=FB,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,RB),aoMap:new vB(this,RB),envMap:new TB(this,RB),lightMap:new MB(this,RB),map:new _B(this,RB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshBasic\\\\\\\"}createMaterial(){return new lt.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();aB.update(this),cB.update(this),OB.update(this),this.setMaterial(this.material)}}function BB(t){return class extends t{constructor(){super(...arguments),this.wireframe=aa.BOOLEAN(0)}}}BB(la);class zB extends UD{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 kB={uniforms:!0};class UB extends(KD(BB(HD(ZD(nB(wB(gB(mB(pB(PB(YD(eB(la))))))))))))){}const GB=new UB;class VB extends QD{constructor(){super(...arguments),this.paramsConfig=GB,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,kB),aoMap:new vB(this,kB),envMap:new TB(this,kB),map:new _B(this,kB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshBasicBuilder\\\\\\\"}usedAssembler(){return jn.GL_MESH_BASIC}_create_assembler_controller(){return li.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),tB.update(this),zB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function HB(t){return class extends t{constructor(){super(...arguments),this.emissive=aa.COLOR([0,0,0],{separatorBefore:!0}),this.useEmissiveMap=aa.BOOLEAN(0,hB(jB)),this.emissiveMap=aa.NODE_PATH(vi.EMPTY,uB(jB,\\\\\\\"useEmissiveMap\\\\\\\")),this.emissiveIntensity=aa.FLOAT(1)}}}O.a;HB(la);class jB extends dB{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 WB={directParams:!0};class qB extends(lB(LB(HD(nB(AB(wB(HB(gB(mB(pB(PB(oB(eB(la)))))))))))))){}const XB=new qB;class YB extends kD{constructor(){super(...arguments),this.paramsConfig=XB,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,WB),aoMap:new vB(this,WB),emissiveMap:new jB(this,WB),envMap:new TB(this,WB),lightMap:new MB(this,WB),map:new _B(this,WB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshLambert\\\\\\\"}createMaterial(){return new ws.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();aB.update(this),cB.update(this),OB.update(this),this.setMaterial(this.material)}}function $B(t){return class extends t{constructor(){super(...arguments),this.shadowPCSS=aa.BOOLEAN(0,{callback:t=>{JB.PARAM_CALLBACK_setRecompileRequired(t)},separatorBefore:!0}),this.shadowPCSSSamplesCount=aa.INTEGER(16,{visibleIf:{shadowPCSS:1},range:[0,128],rangeLocked:[!0,!1]}),this.shadowPCSSFilterSize=aa.FLOAT(1,{visibleIf:{shadowPCSS:1},range:[0,10],rangeLocked:[!0,!1]})}}}$B(la);class JB extends UD{constructor(t){super(t),this.node=t}initializeNode(){}static filterFragmentShader(t,e){const n=`\\\\n#define NUM_SAMPLES ${hf.integer(t.pv.shadowPCSSSamplesCount)}\\\\n#define PCSS_FILTER_SIZE ${hf.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=z;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=>JB.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 ZB={uniforms:!0};class QB extends($B(KD(BB(HD(ZD(nB(AB(wB(HB(gB(mB(pB(PB(YD(eB(la)))))))))))))))){}const KB=new QB;class tz extends QD{constructor(){super(...arguments),this.paramsConfig=KB,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,ZB),aoMap:new vB(this,ZB),emissiveMap:new jB(this,ZB),envMap:new TB(this,ZB),lightMap:new MB(this,ZB),map:new _B(this,ZB),PCSS:new JB(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshLambertBuilder\\\\\\\"}usedAssembler(){return jn.GL_MESH_LAMBERT}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();$D.update(this),tB.update(this),zB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function ez(t){return class extends t{constructor(){super(...arguments),this.useBumpMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(nz)}),this.bumpMap=aa.NODE_PATH(\\\\\\\"\\\\\\\",uB(nz,\\\\\\\"useBumpMap\\\\\\\")),this.bumpScale=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...uB(nz,\\\\\\\"useBumpMap\\\\\\\")}),this.bumpBias=aa.FLOAT(0,{range:[0,1],rangeLocked:[!1,!1],...uB(nz,\\\\\\\"useBumpMap\\\\\\\")})}}}O.a;ez(la);class nz extends dB{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 iz;!function(t){t.TANGENT=\\\\\\\"tangent\\\\\\\",t.OBJECT=\\\\\\\"object\\\\\\\"}(iz||(iz={}));const sz=[iz.TANGENT,iz.OBJECT],rz={[iz.TANGENT]:w.Uc,[iz.OBJECT]:w.zb};function oz(t){return class extends t{constructor(){super(...arguments),this.useNormalMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(az)}),this.normalMap=aa.NODE_PATH(vi.EMPTY,uB(az,\\\\\\\"useNormalMap\\\\\\\")),this.normalMapType=aa.INTEGER(0,{visibleIf:{useNormalMap:1},menu:{entries:sz.map(((t,e)=>({name:t,value:e})))}}),this.normalScale=aa.VECTOR2([1,1],{visibleIf:{useNormalMap:1}})}}}O.a;oz(la);class az extends dB{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=rz[sz[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 lz(t){return class extends t{constructor(){super(...arguments),this.useDisplacementMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(cz)}),this.displacementMap=aa.NODE_PATH(\\\\\\\"\\\\\\\",uB(cz,\\\\\\\"useDisplacementMap\\\\\\\")),this.displacementScale=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...uB(cz,\\\\\\\"useDisplacementMap\\\\\\\")}),this.displacementBias=aa.FLOAT(0,{range:[0,1],rangeLocked:[!1,!1],...uB(cz,\\\\\\\"useDisplacementMap\\\\\\\")})}}}O.a;lz(la);class cz extends dB{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 hz(t){return class extends t{constructor(){super(...arguments),this.useMatcapMap=aa.BOOLEAN(0,hB(uz)),this.matcapMap=aa.NODE_PATH(vi.EMPTY,uB(uz,\\\\\\\"useMatcapMap\\\\\\\"))}}}O.a;hz(la);class uz extends dB{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 dz={directParams:!0};class pz extends(lB(HD(nB(oz(lz(ez(mB(pB(hz(PB(oB(eB(la))))))))))))){}const _z=new pz;class mz extends kD{constructor(){super(...arguments),this.paramsConfig=_z,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,dz),bumpMap:new nz(this,dz),displacementMap:new cz(this,dz),map:new _B(this,dz),matcap:new uz(this,dz),normalMap:new az(this,dz)},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();aB.update(this),cB.update(this),this.setMaterial(this.material)}}const fz={directParams:!0};class gz extends(lB(HD(oz(lz(ez(PB(eB(la)))))))){}const vz=new gz;class yz extends kD{constructor(){super(...arguments),this.paramsConfig=vz,this.controllers={advancedCommon:new jD(this),bumpMap:new nz(this,fz),displacementMap:new cz(this,fz),normalMap:new az(this,fz)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshNormal\\\\\\\"}createMaterial(){return new Hf({vertexColors:!1,side:w.H,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();cB.update(this),this.setMaterial(this.material)}}function xz(t){return class extends t{constructor(){super(...arguments),this.useSpecularMap=aa.BOOLEAN(0,hB(bz)),this.specularMap=aa.NODE_PATH(vi.EMPTY,uB(bz,\\\\\\\"useSpecularMap\\\\\\\"))}}}O.a;xz(la);class bz extends dB{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 wz={directParams:!0};class Tz extends(lB(LB(HD(nB(xz(oz(AB(wB(HB(lz(ez(gB(mB(pB(PB(oB(eB(la)))))))))))))))))){constructor(){super(...arguments),this.flatShading=aa.BOOLEAN(0)}}const Az=new Tz;class Mz extends kD{constructor(){super(...arguments),this.paramsConfig=Az,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,wz),aoMap:new vB(this,wz),bumpMap:new nz(this,wz),displacementMap:new cz(this,wz),emissiveMap:new jB(this,wz),envMap:new TB(this,wz),lightMap:new MB(this,wz),map:new _B(this,wz),normalMap:new az(this,wz),specularMap:new bz(this,wz)},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();aB.update(this),cB.update(this),OB.update(this),this.material.flatShading!=this.pv.flatShading&&(this.material.flatShading=this.pv.flatShading,this.material.needsUpdate=!0),this.setMaterial(this.material)}}const Ez={uniforms:!0};class Sz extends($B(KD(BB(HD(ZD(nB(xz(oz(AB(wB(HB(lz(ez(gB(mB(pB(PB(YD(eB(la)))))))))))))))))))){}const Cz=new Sz;class Nz extends QD{constructor(){super(...arguments),this.paramsConfig=Cz,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,Ez),aoMap:new vB(this,Ez),bumpMap:new nz(this,Ez),displacementMap:new cz(this,Ez),emissiveMap:new jB(this,Ez),envMap:new TB(this,Ez),lightMap:new MB(this,Ez),map:new _B(this,Ez),normalMap:new az(this,Ez),specularMap:new bz(this,Ez),PCSS:new JB(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhongBuilder\\\\\\\"}usedAssembler(){return jn.GL_MESH_PHONG}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();$D.update(this),tB.update(this),zB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function Lz(t){return class extends t{constructor(){super(...arguments),this.useEnvMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(Oz)}),this.envMap=aa.NODE_PATH(vi.EMPTY,uB(Oz,\\\\\\\"useEnvMap\\\\\\\")),this.envMapIntensity=aa.FLOAT(1,{visibleIf:{useEnvMap:1}}),this.refractionRatio=aa.FLOAT(.98,{range:[-1,1],rangeLocked:[!1,!1],visibleIf:{useEnvMap:1}})}}}Lz(la);class Oz extends dB{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 Pz(t){return class extends t{constructor(){super(...arguments),this.useMetalnessMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(Rz)}),this.metalnessMap=aa.NODE_PATH(vi.EMPTY,uB(Rz,\\\\\\\"useMetalnessMap\\\\\\\")),this.metalness=aa.FLOAT(1),this.useRoughnessMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(Rz)}),this.roughnessMap=aa.NODE_PATH(vi.EMPTY,uB(Rz,\\\\\\\"useRoughnessMap\\\\\\\")),this.roughness=aa.FLOAT(.5)}}}O.a;Pz(la);class Rz extends dB{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 Iz(t){return class extends t{constructor(){super(...arguments),this.clearcoat=aa.FLOAT(0,{separatorBefore:!0}),this.useClearCoatMap=aa.BOOLEAN(0,hB(Dz)),this.clearcoatMap=aa.NODE_PATH(vi.EMPTY,uB(Dz,\\\\\\\"useClearCoatMap\\\\\\\")),this.useClearCoatNormalMap=aa.BOOLEAN(0,hB(Dz)),this.clearcoatNormalMap=aa.NODE_PATH(vi.EMPTY,uB(Dz,\\\\\\\"useClearCoatNormalMap\\\\\\\")),this.clearcoatNormalScale=aa.VECTOR2([1,1],{visibleIf:{useClearCoatNormalMap:1}}),this.clearcoatRoughness=aa.FLOAT(0),this.useClearCoatRoughnessMap=aa.BOOLEAN(0,hB(Dz)),this.clearcoatRoughnessMap=aa.NODE_PATH(vi.EMPTY,uB(Dz,\\\\\\\"useClearCoatRoughnessMap\\\\\\\")),this.useSheen=aa.BOOLEAN(0),this.sheen=aa.FLOAT(0,{range:[0,1],rangeLocked:[!0,!1],visibleIf:{useSheen:1}}),this.sheenRoughness=aa.FLOAT(1,{range:[0,1],rangeLocked:[!0,!1],visibleIf:{useSheen:1}}),this.sheenColor=aa.COLOR([1,1,1],{visibleIf:{useSheen:1}}),this.transmission=aa.FLOAT(0,{range:[0,1]}),this.useTransmissionMap=aa.BOOLEAN(0),this.transmissionMap=aa.NODE_PATH(vi.EMPTY,{visibleIf:{useTransmissionMap:1}}),this.ior=aa.FLOAT(1.5,{range:[1,2.3333],rangeLocked:[!0,!0]}),this.thickness=aa.FLOAT(.01,{range:[0,10],rangeLocked:[!0,!1]}),this.useThicknessMap=aa.BOOLEAN(0),this.thicknessMap=aa.NODE_PATH(vi.EMPTY,{visibleIf:{useThicknessMap:1}}),this.attenuationDistance=aa.FLOAT(0,{range:[0,10],rangeLocked:[!0,!1]}),this.attenuationColor=aa.COLOR([1,1,1])}}}Iz(la);const Fz=new Uf.a;class Dz extends dB{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;Fz.ior=t.ior;const e=Fz.reflectivity;if(this._update_options.uniforms){const n=this.node.material;n.uniforms.clearcoat.value=t.clearcoat,n.uniforms.clearcoatNormalScale.value.copy(t.clearcoatNormalScale),n.uniforms.clearcoatRoughness.value=t.clearcoatRoughness,n.uniforms.reflectivity.value=e,n.uniforms.transmission.value=t.transmission,n.uniforms.thickness.value=t.thickness,n.uniforms.attenuationDistance.value=t.attenuationDistance,n.uniforms.attenuationTint.value=t.attenuationColor,t.useSheen?(this._sheenColorClone.copy(t.sheenColor),n.uniforms.sheen.value=t.sheen,n.uniforms.sheenRoughness.value=t.sheenRoughness,n.uniforms.sheenTint.value=this._sheenColorClone):n.uniforms.sheen.value=0,n.uniforms.ior.value=t.ior,n.specularTint=n.uniforms.specularTint.value,n.ior=n.uniforms.ior.value}if(this._update_options.directParams){const n=this.node.material;n.clearcoat=t.clearcoat,null!=n.clearcoatNormalScale&&n.clearcoatNormalScale.copy(t.clearcoatNormalScale),n.clearcoatRoughness=t.clearcoatRoughness,n.reflectivity=e,t.useSheen?(this._sheenColorClone.copy(t.sheenColor),n.sheen=t.sheen,n.sheenRoughness=t.sheenRoughness,n.sheenTint=this._sheenColorClone):n.sheen=0,n.transmission=t.transmission,n.thickness=t.thickness,n.attenuationDistance=t.attenuationDistance,n.attenuationTint=t.attenuationColor}}static async update(t){t.controllers.physical.update()}}const Bz={directParams:!0};class zz extends(lB(LB(HD(nB(Iz(Pz(oz(AB(Lz(HB(lz(ez(gB(mB(pB(PB(oB(eB(la))))))))))))))))))){}const kz=new zz;class Uz extends kD{constructor(){super(...arguments),this.paramsConfig=kz,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,Bz),aoMap:new vB(this,Bz),bumpMap:new nz(this,Bz),displacementMap:new cz(this,Bz),emissiveMap:new jB(this,Bz),envMap:new Oz(this,Bz),lightMap:new MB(this,Bz),map:new _B(this,Bz),metalnessRoughnessMap:new Rz(this,Bz),normalMap:new az(this,Bz),physical:new Dz(this,Bz)},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();aB.update(this),cB.update(this),OB.update(this),this.setMaterial(this.material)}}const Gz={uniforms:!0};class Vz extends(function(t){return class extends($B(KD(BB(HD(ZD(t)))))){}}(nB(Iz(Pz(oz(AB(Lz(HB(lz(ez(gB(mB(pB(PB(YD(eB(la))))))))))))))))){}const Hz=new Vz;class jz extends QD{constructor(){super(...arguments),this.paramsConfig=Hz,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,Gz),aoMap:new vB(this,Gz),bumpMap:new nz(this,Gz),displacementMap:new cz(this,Gz),emissiveMap:new jB(this,Gz),envMap:new Oz(this,{uniforms:!0,directParams:!0}),lightMap:new MB(this,Gz),map:new _B(this,Gz),metalnessRoughnessMap:new Rz(this,{uniforms:!0,directParams:!0}),normalMap:new az(this,Gz),physical:new Dz(this,{uniforms:!0,directParams:!0}),PCSS:new JB(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhysicalBuilder\\\\\\\"}usedAssembler(){return jn.GL_MESH_PHYSICAL}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}createMaterial(){const t=super.createMaterial();return t.isMeshStandardMaterial=!0,t.isMeshPhysicalMaterial=!0,t}async cook(){for(let t of this.controllerNames)this.controllers[t].update();$D.update(this),tB.update(this),zB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}const Wz={directParams:!0};class qz extends(lB(LB(HD(nB(Pz(oz(AB(Lz(HB(lz(ez(gB(mB(pB(PB(oB(eB(la)))))))))))))))))){}const Xz=new qz;class Yz extends kD{constructor(){super(...arguments),this.paramsConfig=Xz,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,Wz),aoMap:new vB(this,Wz),bumpMap:new nz(this,Wz),displacementMap:new cz(this,Wz),emissiveMap:new jB(this,Wz),envMap:new Oz(this,Wz),lightMap:new MB(this,Wz),map:new _B(this,Wz),metalnessRoughnessMap:new Rz(this,Wz),normalMap:new az(this,Wz)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshStandard\\\\\\\"}createMaterial(){return new bs.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();aB.update(this),cB.update(this),OB.update(this),this.setMaterial(this.material)}}const $z={uniforms:!0};class Jz extends($B(KD(BB(HD(ZD(nB(Pz(oz(AB(Lz(HB(lz(ez(gB(mB(pB(PB(YD(eB(la)))))))))))))))))))){}const Zz=new Jz;class Qz extends QD{constructor(){super(...arguments),this.paramsConfig=Zz,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,$z),aoMap:new vB(this,$z),bumpMap:new nz(this,$z),displacementMap:new cz(this,$z),emissiveMap:new jB(this,$z),envMap:new Oz(this,$z),lightMap:new MB(this,$z),map:new _B(this,$z),metalnessRoughnessMap:new Rz(this,$z),normalMap:new az(this,$z),PCSS:new JB(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshStandardBuilder\\\\\\\"}usedAssembler(){return jn.GL_MESH_STANDARD}_create_assembler_controller(){return li.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),tB.update(this),zB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}const Kz=U.meshphong_frag.slice(0,U.meshphong_frag.indexOf(\\\\\\\"void main() {\\\\\\\")),tk=U.meshphong_frag.slice(U.meshphong_frag.indexOf(\\\\\\\"void main() {\\\\\\\")),ek={uniforms:I.merge([H.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\\\\\\\",U.meshphong_vert].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"#define USE_UV\\\\\\\",\\\\\\\"#define SUBSURFACE\\\\\\\",Kz,\\\\\\\"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;\\\\\\\",\\\\\\\"}\\\\\\\",tk.replace(\\\\\\\"#include \\\\\\\",(nk=U.lights_fragment_begin,ik=\\\\\\\"RE_Direct( directLight, geometry, material, reflectedLight );\\\\\\\",sk=[\\\\\\\"RE_Direct( directLight, geometry, material, reflectedLight );\\\\\\\",\\\\\\\"#if defined( SUBSURFACE ) && defined( USE_UV )\\\\\\\",\\\\\\\" RE_Direct_Scattering(directLight, vUv, geometry, reflectedLight);\\\\\\\",\\\\\\\"#endif\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),nk.split(ik).join(sk)))].join(\\\\\\\"\\\\n\\\\\\\")};var nk,ik,sk;function rk(t){return{cook:!1,callback:(e,n)=>{hk.PARAM_CALLBACK_update_uniformColor(e,n,t)}}}function ok(t){return{cook:!1,callback:(e,n)=>{hk.PARAM_CALLBACK_update_uniformN(e,n,t)}}}const ak={uniforms:!0};class lk extends(lB(BB(HD(nB(mB(pB(PB(function(t){return class extends t{constructor(){var t;super(...arguments),this.diffuse=aa.COLOR([1,1,1],{...rk(\\\\\\\"diffuse\\\\\\\")}),this.shininess=aa.FLOAT(1,{range:[0,1e3]}),this.thicknessMap=aa.NODE_PATH(vi.EMPTY,{nodeSelection:{context:ts.COP},...(t=\\\\\\\"thicknessMap\\\\\\\",{cook:!1,callback:(e,n)=>{hk.PARAM_CALLBACK_update_uniformTexture(e,n,t)}})}),this.thicknessColor=aa.COLOR([.5,.3,0],{...rk(\\\\\\\"thicknessColor\\\\\\\")}),this.thicknessDistortion=aa.FLOAT(.1,{...ok(\\\\\\\"thicknessDistortion\\\\\\\")}),this.thicknessAmbient=aa.FLOAT(.4,{...ok(\\\\\\\"thicknessAmbient\\\\\\\")}),this.thicknessAttenuation=aa.FLOAT(.8,{...ok(\\\\\\\"thicknessAttenuation\\\\\\\")}),this.thicknessPower=aa.FLOAT(2,{range:[0,10],...ok(\\\\\\\"thicknessPower\\\\\\\")}),this.thicknessScale=aa.FLOAT(16,{range:[0,100],...ok(\\\\\\\"thicknessScale\\\\\\\")})}}}(eB(la)))))))))){}const ck=new lk;class hk extends kD{constructor(){super(...arguments),this.paramsConfig=ck,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,ak),map:new _B(this,ak)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshSubsurfaceScattering\\\\\\\"}createMaterial(){const t=I.clone(ek.uniforms),e=new F({uniforms:t,vertexShader:ek.vertexShader,fragmentShader:ek.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();cB.update(this),zB.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(ts.COP);n||(this.material.uniforms[e].value=null);const i=n,s=await i.compute();this.material.uniforms[e].value=s.texture()}}function uk(t){return class extends t{constructor(){super(...arguments),this.useGradientMap=aa.BOOLEAN(0,hB(dk)),this.gradientMap=aa.NODE_PATH(vi.EMPTY,uB(dk,\\\\\\\"useGradientMap\\\\\\\"))}}}O.a;uk(la);class dk extends dB{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 pk={directParams:!0};class _k extends(lB(LB(HD(nB(oz(AB(uk(HB(lz(ez(gB(mB(pB(PB(oB(eB(la))))))))))))))))){}const mk=new _k;class fk extends kD{constructor(){super(...arguments),this.paramsConfig=mk,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,pk),aoMap:new vB(this,pk),bumpMap:new nz(this,pk),displacementMap:new cz(this,pk),emissiveMap:new jB(this,pk),gradientMap:new dk(this,pk),lightMap:new MB(this,pk),map:new _B(this,pk),normalMap:new az(this,pk)},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();aB.update(this),cB.update(this),OB.update(this),this.setMaterial(this.material)}}const gk={directParams:!0};class vk extends(KD(HD(nB(mB(pB(PB(oB(function(t){return class extends t{constructor(){super(...arguments),this.size=aa.FLOAT(1),this.sizeAttenuation=aa.BOOLEAN(1)}}}(eB(la)))))))))){}const yk=new vk;class xk extends kD{constructor(){super(...arguments),this.paramsConfig=yk,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,gk),map:new _B(this,gk)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"points\\\\\\\"}createMaterial(){return new xs.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();aB.update(this),tB.update(this),this.material.size=this.pv.size,this.material.sizeAttenuation=this.pv.sizeAttenuation,this.setMaterial(this.material)}}class bk extends(KD(HD(ZD(nB(YD(eB(la))))))){}const wk=new bk;class Tk extends QD{constructor(){super(...arguments),this.paramsConfig=wk,this.controllers={advancedCommon:new jD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"pointsBuilder\\\\\\\"}usedAssembler(){return jn.GL_POINTS}_create_assembler_controller(){return li.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),tB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}class Ak extends(HD(oB(la))){}const Mk=new Ak;class Ek extends kD{constructor(){super(...arguments),this.paramsConfig=Mk,this.controllers={advancedCommon:new jD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"shadow\\\\\\\"}createMaterial(){return new zf({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();aB.update(this),this.setMaterial(this.material)}}class Sk extends B.a{constructor(){const t=Sk.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)}}Sk.prototype.isSky=!0,Sk.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 Ck=new class extends la{constructor(){super(...arguments),this.turbidity=aa.FLOAT(2,{range:[0,20]}),this.rayleigh=aa.FLOAT(1,{range:[0,4]}),this.mieCoefficient=aa.FLOAT(.005),this.mieDirectional=aa.FLOAT(.8),this.inclination=aa.FLOAT(.5),this.azimuth=aa.FLOAT(.25),this.up=aa.VECTOR3([0,1,0])}};class Nk extends kD{constructor(){super(...arguments),this.paramsConfig=Ck}static type(){return\\\\\\\"sky\\\\\\\"}createMaterial(){const t=(new Sk).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 Lk=\\\\\\\"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}\\\\\\\",Ok=\\\\\\\"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 Pk={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)}};function Rk(t){return class extends t{constructor(){super(...arguments),this.color=aa.COLOR([1,1,1]),this.stepSize=aa.FLOAT(.01),this.density=aa.FLOAT(1),this.shadowDensity=aa.FLOAT(1),this.lightDir=aa.VECTOR3([-1,-1,-1])}}}Rk(la);class Ik{constructor(t){this.node=t}static render_hook(t,e,n,i,s,r,o){if(o){this._object_bbox.setFromObject(o);const t=s;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)}}Ik._object_bbox=new Ay.a;class Fk extends(Rk(la)){}const Dk=new Fk;class Bk extends kD{constructor(){super(...arguments),this.paramsConfig=Dk,this._volume_controller=new Ik(this)}static type(){return\\\\\\\"volume\\\\\\\"}createMaterial(){const t=new F({vertexShader:Lk,fragmentShader:Ok,side:w.H,transparent:!0,depthTest:!0,uniforms:I.clone(Pk)});return gr.add_user_data_render_hook(t,Ik.render_hook.bind(Ik)),t}initializeNode(){}async cook(){this._volume_controller.update_uniforms_from_params(),this.setMaterial(this.material)}}class zk extends(ZD(Rk(la))){}const kk=new zk;class Uk extends QD{constructor(){super(...arguments),this.paramsConfig=kk,this._volume_controller=new Ik(this)}static type(){return\\\\\\\"volumeBuilder\\\\\\\"}usedAssembler(){return jn.GL_VOLUME}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){}async cook(){this._volume_controller.update_uniforms_from_params(),this.compileIfRequired(),this.setMaterial(this.material)}}class Gk extends sa{static context(){return ts.MAT}cook(){this.cookController.endCook()}}class Vk extends Gk{}class Hk extends Vk{constructor(){super(...arguments),this._children_controller_context=ts.ANIM}static type(){return es.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class jk extends Vk{constructor(){super(...arguments),this._children_controller_context=ts.COP}static type(){return es.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Wk extends Vk{constructor(){super(...arguments),this._children_controller_context=ts.EVENT}static type(){return es.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class qk extends Vk{constructor(){super(...arguments),this._children_controller_context=ts.MAT}static type(){return es.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Xk extends Gk{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=ts.POST}static type(){return es.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Yk extends Vk{constructor(){super(...arguments),this._children_controller_context=ts.ROP}static type(){return es.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}var $k=n(87);const Jk=\\\\\\\"parent object\\\\\\\",Zk=[Jk,Jk,Jk,Jk];var Qk;!function(t){t[t.MANAGER=0]=\\\\\\\"MANAGER\\\\\\\",t[t.CAMERA=2]=\\\\\\\"CAMERA\\\\\\\",t[t.LIGHT=3]=\\\\\\\"LIGHT\\\\\\\"}(Qk||(Qk={}));class Kk extends sa{constructor(){super(...arguments),this.renderOrder=Qk.MANAGER,this._children_group=this._create_children_group(),this._attachableToHierarchy=!0,this._used_in_scene=!0}static context(){return ts.OBJ}static displayedInputNames(){return Zk}_create_children_group(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}attachableToHierarchy(){return this._attachableToHierarchy}usedInScene(){return this._used_in_scene}addObjectToParent(t){this.attachableToHierarchy()&&t.add(this.object)}removeObjectFromParent(){if(this.attachableToHierarchy()){const t=this.object.parent;t&&t.remove(this.object)}}initializeBaseNode(){this._object=this._create_object_with_attributes(),this.nameController.add_post_set_fullPath_hook(this.set_object_name.bind(this)),this.set_object_name()}get children_group(){return this._children_group}get object(){return this._object}_create_object_with_attributes(){const t=this.createObject();return t.node=this,t.add(this._children_group),t}set_object_name(){this._object&&(this._object.name=this.path(),this._children_group.name=`${this.path()}:parented_outputs`)}createObject(){const t=new K.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 tU extends Kk{constructor(){super(...arguments),this.flags=new Di(this),this.renderOrder=Qk.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 eU=new class extends la{constructor(){super(...arguments),this.color=aa.COLOR([1,1,1],{conversion:oo.SRGB_TO_LINEAR}),this.intensity=aa.FLOAT(1)}};class nU extends tU{constructor(){super(...arguments),this.paramsConfig=eU}static type(){return\\\\\\\"ambientLight\\\\\\\"}createLight(){const t=new $k.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 iU extends sv.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}}iU.prototype.isRectAreaLight=!0;var sU,rU=n(61);class oU{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);V.LTC_FLOAT_1=new fo.a(n,64,64,w.Ib,w.G,w.Yc,w.n,w.n,w.V,w.ob,1),V.LTC_FLOAT_2=new fo.a(i,64,64,w.Ib,w.G,w.Yc,w.n,w.n,w.V,w.ob,1);const s=new Uint16Array(t.length);t.forEach((function(t,e){s[e]=rU.a.toHalfFloat(t)}));const r=new Uint16Array(e.length);e.forEach((function(t,e){r[e]=rU.a.toHalfFloat(t)})),V.LTC_HALF_1=new fo.a(s,64,64,w.Ib,w.M,w.Yc,w.n,w.n,w.V,w.ob,1),V.LTC_HALF_2=new fo.a(r,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\\\\\\\"}(sU||(sU={}));const aU=[sU.GEOMETRIES,sU.OBJECTS];var lU;!function(t){t.XYZ=\\\\\\\"XYZ\\\\\\\",t.XZY=\\\\\\\"XZY\\\\\\\",t.YXZ=\\\\\\\"YXZ\\\\\\\",t.YZX=\\\\\\\"YZX\\\\\\\",t.ZYX=\\\\\\\"ZYX\\\\\\\",t.ZXY=\\\\\\\"ZXY\\\\\\\"}(lU||(lU={}));const cU=[lU.XYZ,lU.XZY,lU.YXZ,lU.YZX,lU.ZXY,lU.ZYX],hU=lU.XYZ;class uU{constructor(){this._translation_matrix=new A.a,this._translation_matrix_q=new ah.a,this._translation_matrix_s=new p.a(1,1,1),this._matrix=(new A.a).identity(),this._matrix_q=new ah.a,this._matrix_euler=new Xv.a,this._matrix_s=new p.a,this._rotate_geometry_m=new A.a,this._rotate_geometry_q=new ah.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,s){return this._matrix_euler.set(Object(On.e)(e.x),Object(On.e)(e.y),Object(On.e)(e.z),s),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 dU(t,e){const n=(null==e?void 0:e.matrixAutoUpdate)||!1;return class extends t{constructor(){super(...arguments),this.transform=aa.FOLDER(),this.keepPosWhenParenting=aa.BOOLEAN(0),this.rotationOrder=aa.INTEGER(cU.indexOf(lU.XYZ),{menu:{entries:cU.map(((t,e)=>({name:t,value:e})))}}),this.t=aa.VECTOR3([0,0,0]),this.r=aa.VECTOR3([0,0,0]),this.s=aa.VECTOR3([1,1,1]),this.scale=aa.FLOAT(1),this.matrixAutoUpdate=aa.BOOLEAN(n?1:0),this.updateTransformFromObject=aa.BUTTON(null,{callback:t=>{_U.PARAM_CALLBACK_update_transform_from_object(t)}})}}}uU.set_params_from_matrix_position=new p.a,uU.set_params_from_matrix_quaternion=new ah.a,uU.set_params_from_matrix_scale=new p.a,uU.set_params_from_matrix_euler=new Xv.a,uU.set_params_from_matrix_rotation=new p.a,uU.set_params_from_matrix_t=[0,0,0],uU.set_params_from_matrix_r=[0,0,0],uU.set_params_from_matrix_s=[0,0,0],uU.set_params_from_object_position_array=[0,0,0],uU.set_params_from_object_rotation_deg=new p.a,uU.set_params_from_object_rotation_array=[0,0,0];dU(la);const pU=\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\";class _U{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 uU,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(pU)||this.node.dirtyController.addPostDirtyHook(pU,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,cU[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={}){uU.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),uU.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(),uU.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 mU{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&&_U.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(){mU.on_input_updated(this.node)}}dU(la);class fU extends tU{constructor(){super(...arguments),this.flags=new Di(this),this.hierarchyController=new mU(this),this.transformController=new _U(this)}initializeBaseNode(){super.initializeBaseNode(),this.hierarchyController.initializeNode(),this.transformController.initializeNode()}cook(){this.transformController.update(),this.updateLightParams(),this.updateShadowParams(),this.cookController.endCook()}}class gU{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 vU=n(41);class yU extends vU.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 Ts.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 B.a(i,new lt.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()}}xU=la;var xU;class bU{constructor(t,e){this.node=t,this._name=e,this._object=this.createObject(),this._material=new lt.a({wireframe:!0,fog:!1})}build(){this._object.matrixAutoUpdate=!1,this._object.name=this._name,this.buildHelper()}get object(){return this._object}}class wU extends bU{createObject(){return new yU(this.node.light)}buildHelper(){}update(){this._object.updateMatrixWorld()}}class TU extends(function(t){return class extends t{constructor(){super(...arguments),this.light=aa.FOLDER(),this.color=aa.COLOR([1,1,1],{conversion:oo.SRGB_TO_LINEAR}),this.intensity=aa.FLOAT(1,{range:[0,10]}),this.width=aa.FLOAT(1,{range:[0,10]}),this.height=aa.FLOAT(1,{range:[0,10]}),this.showHelper=aa.BOOLEAN(0)}}}(dU(la))){}const AU=new TU;class MU extends fU{constructor(){super(...arguments),this.paramsConfig=AU,this._helperController=new gU(this,wU,\\\\\\\"RectAreaLightObjNodeHelper\\\\\\\")}static type(){return\\\\\\\"areaLight\\\\\\\"}initializeNode(){this._helperController.initializeNode()}createLight(){const t=new iU(16777215,1,1,1);return t.matrixAutoUpdate=!1,oU.initialized||(oU.init(),oU.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 EU=n(72);const SU=new p.a,CU=new tf.a;class NU extends As.a{constructor(t){const e=new S.a,n=new Ts.a({color:16777215,vertexColors:!0,toneMapped:!1}),i=[],s=[],r={},o=new D.a(16755200),a=new D.a(16711680),l=new D.a(43775),c=new D.a(16777215),h=new D.a(3355443);function u(t,e,n){d(t,n),d(e,n)}function d(t,e){i.push(0,0,0),s.push(e.r,e.g,e.b),void 0===r[t]&&(r[t]=[]),r[t].push(i.length/3-1)}u(\\\\\\\"n1\\\\\\\",\\\\\\\"n2\\\\\\\",o),u(\\\\\\\"n2\\\\\\\",\\\\\\\"n4\\\\\\\",o),u(\\\\\\\"n4\\\\\\\",\\\\\\\"n3\\\\\\\",o),u(\\\\\\\"n3\\\\\\\",\\\\\\\"n1\\\\\\\",o),u(\\\\\\\"f1\\\\\\\",\\\\\\\"f2\\\\\\\",o),u(\\\\\\\"f2\\\\\\\",\\\\\\\"f4\\\\\\\",o),u(\\\\\\\"f4\\\\\\\",\\\\\\\"f3\\\\\\\",o),u(\\\\\\\"f3\\\\\\\",\\\\\\\"f1\\\\\\\",o),u(\\\\\\\"n1\\\\\\\",\\\\\\\"f1\\\\\\\",o),u(\\\\\\\"n2\\\\\\\",\\\\\\\"f2\\\\\\\",o),u(\\\\\\\"n3\\\\\\\",\\\\\\\"f3\\\\\\\",o),u(\\\\\\\"n4\\\\\\\",\\\\\\\"f4\\\\\\\",o),u(\\\\\\\"p\\\\\\\",\\\\\\\"n1\\\\\\\",a),u(\\\\\\\"p\\\\\\\",\\\\\\\"n2\\\\\\\",a),u(\\\\\\\"p\\\\\\\",\\\\\\\"n3\\\\\\\",a),u(\\\\\\\"p\\\\\\\",\\\\\\\"n4\\\\\\\",a),u(\\\\\\\"u1\\\\\\\",\\\\\\\"u2\\\\\\\",l),u(\\\\\\\"u2\\\\\\\",\\\\\\\"u3\\\\\\\",l),u(\\\\\\\"u3\\\\\\\",\\\\\\\"u1\\\\\\\",l),u(\\\\\\\"c\\\\\\\",\\\\\\\"t\\\\\\\",c),u(\\\\\\\"p\\\\\\\",\\\\\\\"c\\\\\\\",h),u(\\\\\\\"cn1\\\\\\\",\\\\\\\"cn2\\\\\\\",h),u(\\\\\\\"cn3\\\\\\\",\\\\\\\"cn4\\\\\\\",h),u(\\\\\\\"cf1\\\\\\\",\\\\\\\"cf2\\\\\\\",h),u(\\\\\\\"cf3\\\\\\\",\\\\\\\"cf4\\\\\\\",h),e.setAttribute(\\\\\\\"position\\\\\\\",new C.c(i,3)),e.setAttribute(\\\\\\\"color\\\\\\\",new C.c(s,3)),super(e,n),this.type=\\\\\\\"CameraHelper\\\\\\\",this.camera=t,this.camera.updateProjectionMatrix&&this.camera.updateProjectionMatrix(),this.matrixAutoUpdate=!1,this.pointMap=r,this.update()}update(){const t=this.geometry,e=this.pointMap;CU.projectionMatrixInverse.copy(this.camera.projectionMatrixInverse),LU(\\\\\\\"c\\\\\\\",e,t,CU,0,0,-1),LU(\\\\\\\"t\\\\\\\",e,t,CU,0,0,1),LU(\\\\\\\"n1\\\\\\\",e,t,CU,-1,-1,-1),LU(\\\\\\\"n2\\\\\\\",e,t,CU,1,-1,-1),LU(\\\\\\\"n3\\\\\\\",e,t,CU,-1,1,-1),LU(\\\\\\\"n4\\\\\\\",e,t,CU,1,1,-1),LU(\\\\\\\"f1\\\\\\\",e,t,CU,-1,-1,1),LU(\\\\\\\"f2\\\\\\\",e,t,CU,1,-1,1),LU(\\\\\\\"f3\\\\\\\",e,t,CU,-1,1,1),LU(\\\\\\\"f4\\\\\\\",e,t,CU,1,1,1),LU(\\\\\\\"u1\\\\\\\",e,t,CU,.7,1.1,-1),LU(\\\\\\\"u2\\\\\\\",e,t,CU,-.7,1.1,-1),LU(\\\\\\\"u3\\\\\\\",e,t,CU,0,2,-1),LU(\\\\\\\"cf1\\\\\\\",e,t,CU,-1,0,1),LU(\\\\\\\"cf2\\\\\\\",e,t,CU,1,0,1),LU(\\\\\\\"cf3\\\\\\\",e,t,CU,0,-1,1),LU(\\\\\\\"cf4\\\\\\\",e,t,CU,0,1,1),LU(\\\\\\\"cn1\\\\\\\",e,t,CU,-1,0,-1),LU(\\\\\\\"cn2\\\\\\\",e,t,CU,1,0,-1),LU(\\\\\\\"cn3\\\\\\\",e,t,CU,0,-1,-1),LU(\\\\\\\"cn4\\\\\\\",e,t,CU,0,1,-1),t.getAttribute(\\\\\\\"position\\\\\\\").needsUpdate=!0}}function LU(t,e,n,i,s,r,o){SU.set(s,r,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&&(r[t+0]+=1),n<.2&&(r[t+2]+=1),i<.2&&(r[t+4]+=1))}}()}(),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(s,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new C.c(s.slice(),3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(r,2)),0===i?this.computeVertexNormals():this.normalizeNormals()}static fromJSON(t){return new zU(t.vertices,t.indices,t.radius,t.details)}}class kU extends zU{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 kU(t.radius,t.detail)}}class UU extends bU{constructor(){super(...arguments),this._geometry=new kU(1),this._quat=new ah.a,this._default_position=new p.a(0,1,0),this._color1=new D.a,this._color2=new D.a}createObject(){return new B.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,s,l),(t!==n-1||a0&&y(!0),e>0&&y(!1)),this.setIndex(c),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(h,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new C.c(u,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(_,2))}static fromJSON(t){return new QU(t.radiusTop,t.radiusBottom,t.height,t.radialSegments,t.heightSegments,t.openEnded,t.thetaStart,t.thetaLength)}}class KU extends QU{constructor(t=1,e=1,n=8,i=1,s=!1,r=0,o=2*Math.PI){super(0,t,e,n,i,s,r,o),this.type=\\\\\\\"ConeGeometry\\\\\\\",this.parameters={radius:t,height:e,radialSegments:n,heightSegments:i,openEnded:s,thetaStart:r,thetaLength:o}}static fromJSON(t){return new KU(t.radius,t.height,t.radialSegments,t.heightSegments,t.openEnded,t.thetaStart,t.thetaLength)}}class tG{constructor(t){this.node=t}update(){const t=this.node.pv;if(t.tvolumetric){const e=this.object(),n=this.node.light;ZU.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 KU(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 B.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 eG extends(dU(la)){constructor(){super(...arguments),this.light=aa.FOLDER(),this.color=aa.COLOR([1,1,1],{conversion:oo.SRGB_TO_LINEAR}),this.intensity=aa.FLOAT(1),this.angle=aa.FLOAT(45,{range:[0,180]}),this.penumbra=aa.FLOAT(.1),this.decay=aa.FLOAT(.1,{range:[0,1]}),this.distance=aa.FLOAT(100,{range:[0,100]}),this.showHelper=aa.BOOLEAN(0),this.helperSize=aa.FLOAT(1,{visibleIf:{showHelper:1}}),this.shadow=aa.FOLDER(),this.castShadow=aa.BOOLEAN(1),this.shadowAutoUpdate=aa.BOOLEAN(1,{visibleIf:{castShadow:1}}),this.shadowUpdateOnNextRender=aa.BOOLEAN(0,{visibleIf:{castShadow:1,shadowAutoUpdate:0}}),this.shadowRes=aa.VECTOR2([256,256],{visibleIf:{castShadow:1}}),this.shadowBias=aa.FLOAT(.001,{visibleIf:{castShadow:1},range:[-.01,.01],rangeLocked:[!1,!1]}),this.shadowNear=aa.FLOAT(.1,{visibleIf:{castShadow:1},range:[0,100],rangeLocked:[!0,!1]}),this.shadowFar=aa.FLOAT(100,{visibleIf:{castShadow:1},range:[0,100],rangeLocked:[!0,!1]}),this.shadowRadius=aa.FLOAT(0,{visibleIf:{castShadow:1},range:[0,10],rangeLocked:[!0,!1]}),this.volumetric=aa.FOLDER(),this.tvolumetric=aa.BOOLEAN(0),this.volAttenuation=aa.FLOAT(5,{range:[0,10],rangeLocked:[!0,!1]}),this.volAnglePower=aa.FLOAT(10,{range:[0,20],rangeLocked:[!0,!1]})}}const nG=new eG;class iG extends fU{constructor(){super(...arguments),this.paramsConfig=nG,this._helperController=new gU(this,ZU,\\\\\\\"SpotLightHelper\\\\\\\"),this._volumetricController=new tG(this)}static type(){return PU.SPOT}initializeNode(){this._helperController.initializeNode()}createLight(){const t=new JU.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 sG;const rG=function(){return void 0===sG&&(sG=new(window.AudioContext||window.webkitAudioContext)),sG},oG=new p.a,aG=new ah.a,lG=new p.a,cG=new p.a;class hG extends K.a{constructor(){super(),this.type=\\\\\\\"AudioListener\\\\\\\",this.context=rG(),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(oG,aG,lG),cG.set(0,0,-1).applyQuaternion(aG),e.positionX){const t=this.context.currentTime+this.timeDelta;e.positionX.linearRampToValueAtTime(oG.x,t),e.positionY.linearRampToValueAtTime(oG.y,t),e.positionZ.linearRampToValueAtTime(oG.z,t),e.forwardX.linearRampToValueAtTime(cG.x,t),e.forwardY.linearRampToValueAtTime(cG.y,t),e.forwardZ.linearRampToValueAtTime(cG.z,t),e.upX.linearRampToValueAtTime(n.x,t),e.upY.linearRampToValueAtTime(n.y,t),e.upZ.linearRampToValueAtTime(n.z,t)}else e.setPosition(oG.x,oG.y,oG.z),e.setOrientation(cG.x,cG.y,cG.z,n.x,n.y,n.z)}}class uG extends(dU(la)){}const dG=new uG;class pG extends Kk{constructor(){super(...arguments),this.paramsConfig=dG,this.hierarchyController=new mU(this),this.transformController=new _U(this),this.flags=new Di(this)}static type(){return Ng.AUDIO_LISTENER}createObject(){const t=new hG;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode()}cook(){this.transformController.update(),this.cookController.endCook()}}class _G extends As.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 Ts.a({vertexColors:!0,toneMapped:!1})),this.type=\\\\\\\"AxesHelper\\\\\\\"}setColors(t,e,n){const i=new D.a,s=this.geometry.attributes.color.array;return i.set(t),i.toArray(s,0),i.toArray(s,3),i.set(e),i.toArray(s,6),i.toArray(s,9),i.set(n),i.toArray(s,12),i.toArray(s,15),this.geometry.attributes.color.needsUpdate=!0,this}dispose(){this.geometry.dispose(),this.material.dispose()}}var mG;!function(t){t.TOGETHER=\\\\\\\"translate + rotate together\\\\\\\",t.SEPARATELY=\\\\\\\"translate + rotate separately\\\\\\\"}(mG||(mG={}));const fG=[mG.TOGETHER,mG.SEPARATELY];const gG=new class extends la{constructor(){super(...arguments),this.object0=aa.OPERATOR_PATH(\\\\\\\"/geo1\\\\\\\",{nodeSelection:{context:ts.OBJ}}),this.object1=aa.OPERATOR_PATH(\\\\\\\"/geo2\\\\\\\",{nodeSelection:{context:ts.OBJ}}),this.mode=aa.INTEGER(fG.indexOf(mG.TOGETHER),{menu:{entries:fG.map(((t,e)=>({name:t,value:e})))}}),this.blend=aa.FLOAT(0,{visibleIf:{mode:fG.indexOf(mG.TOGETHER)},range:[0,1],rangeLocked:[!1,!1]}),this.blendT=aa.FLOAT(0,{visibleIf:{mode:fG.indexOf(mG.SEPARATELY)},range:[0,1],rangeLocked:[!1,!1]}),this.blendR=aa.FLOAT(0,{visibleIf:{mode:fG.indexOf(mG.SEPARATELY)},range:[0,1],rangeLocked:[!1,!1]})}};class vG extends Kk{constructor(){super(...arguments),this.paramsConfig=gG,this.hierarchyController=new mU(this),this.flags=new Di(this),this._helper=new _G(1),this._t0=new p.a,this._q0=new ah.a,this._s0=new p.a,this._t1=new p.a,this._q1=new ah.a,this._s1=new p.a}static type(){return\\\\\\\"blend\\\\\\\"}createObject(){const t=new Fn.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(ts.OBJ),e=this.p.object1.found_node_with_context(ts.OBJ);t&&e&&this._blend(t.object,e.object),this.cookController.endCook()}_blend(t,e){const n=fG[this.pv.mode];switch(n){case mG.TOGETHER:return this._blend_together(t,e);case mG.SEPARATELY:return this._blend_separately(t,e)}ls.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 yG={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 xG={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}\\\\\\\"},bG=1/256e3;class wG{constructor(t){this._renderTargetBlur=this._createRenderTarget(t),this._camera=this._createCamera(),this._blurPlane=this._createBlurPlane(),this._horizontalBlurMaterial=new F(yG),this._horizontalBlurMaterial.depthTest=!1,this._verticalBlurMaterial=new F(xG),this._verticalBlurMaterial.depthTest=!1}setSize(t,e){this._renderTargetBlur.setSize(t,e)}_createRenderTarget(t){const e=new Q(t.x,t.y);return e.texture.generateMipmaps=!1,e}_createCamera(){const t=new ot.a(-.5,.5,.5,-.5,0,1);return t.position.z=.5,t}_createBlurPlane(){const t=new L(1,1);return new B.a(t)}applyBlur(t,e,n,i){const s=Math.max(this._renderTargetBlur.width,this._renderTargetBlur.height);this._horizontalBlurMaterial.uniforms.tDiffuse.value=t.texture,this._horizontalBlurMaterial.uniforms.h.value=n*s*bG,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*s*bG,this._blurPlane.material=this._verticalBlurMaterial,e.setRenderTarget(t),e.render(this._blurPlane,this._camera)}}var TG;!function(t){t.ON_RENDER=\\\\\\\"On Every Render\\\\\\\",t.MANUAL=\\\\\\\"Manual\\\\\\\"}(TG||(TG={}));const AG=[TG.ON_RENDER,TG.MANUAL];class MG extends(dU(la)){constructor(){super(...arguments),this.shadow=aa.FOLDER(),this.dist=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]}),this.planeSize=aa.VECTOR2([1,1]),this.shadowRes=aa.VECTOR2([256,256]),this.blur=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]}),this.tblur2=aa.BOOLEAN(1),this.blur2=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1],visibleIf:{tblur2:1}}),this.darkness=aa.FLOAT(1),this.opacity=aa.FLOAT(1),this.showHelper=aa.BOOLEAN(0),this.updateMode=aa.INTEGER(AG.indexOf(TG.ON_RENDER),{callback:t=>{CG.PARAM_CALLBACK_update_updateMode(t)},menu:{entries:AG.map(((t,e)=>({name:t,value:e})))}}),this.update=aa.BUTTON(null,{callback:t=>{CG.PARAM_CALLBACK_updateManual(t)},visibleIf:{updateMode:AG.indexOf(TG.MANUAL)}}),this.scene=aa.FOLDER(),this.include=aa.STRING(\\\\\\\"\\\\\\\"),this.exclude=aa.STRING(\\\\\\\"\\\\\\\"),this.updateObjectsList=aa.BUTTON(null,{callback:t=>{CG.PARAM_CALLBACK_updateObjectsList(t)}}),this.printResolveObjectsList=aa.BUTTON(null,{callback:t=>{CG.PARAM_CALLBACK_printResolveObjectsList(t)}})}}const EG=new MG,SG=new d.a(256,256);class CG extends Kk{constructor(){super(...arguments),this.paramsConfig=EG,this.hierarchyController=new mU(this),this.flags=new Di(this),this._renderTarget=this._createRenderTarget(SG),this._coreRenderBlur=this._createCoreRenderBlur(SG),this._includedObjects=[],this._includedAncestors=[],this._excludedObjects=[],this.transformController=new _U(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 Q(t.x,t.y);return e.texture.generateMipmaps=!1,e}_createCoreRenderBlur(t){return new wG(t)}createObject(){const t=new Fn.a;this._shadowGroup=new Fn.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 lt.a({map:this._renderTarget.texture,opacity:1,transparent:!0,depthWrite:!1}),this._plane=new B.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 ot.a(-.5,.5,.5,-.5,0,1),this._shadowCamera.rotation.x=Math.PI/2,t.add(this._shadowCamera),this._helper=new NU(this._shadowCamera),this._helper.visible=!1,this._shadowCamera.add(this._helper)}_createMaterials(){this._depthMaterial=new Sn,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,s=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=s,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=AG[this.pv.updateMode];switch(t){case TG.ON_RENDER:return this._addRenderHook();case TG.MANUAL:return this._removeRenderHook()}ls.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,s,r){t&&e?this._renderShadow(t,e):console.log(\\\\\\\"no renderer or scene\\\\\\\")}_updateManual(){const t=li.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 NG=\\\\\\\"display\\\\\\\";class LG{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 Fn.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(NG),i=this.node.params.boolean(NG),s=this.node.usedInScene(),r=(null===(e=null===(t=this.node.flags)||void 0===t?void 0:t.display)||void 0===e?void 0:e.active())||!1;return s&&r&&(!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 OG extends(dU(la)){constructor(){super(...arguments),this.display=aa.BOOLEAN(1),this.renderOrder=aa.INTEGER(0,{range:[0,10],rangeLocked:[!0,!1]})}}const PG=new OG;class RG extends Kk{constructor(){super(...arguments),this.paramsConfig=PG,this.hierarchyController=new mU(this),this.transformController=new _U(this),this.flags=new Di(this),this.childrenDisplayController=new LG(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=ts.SOP,this._onChildAddBound=this._onChildAdd.bind(this)}static type(){return Ng.GEO}createObject(){const t=new Fn.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 IG extends(dU(la)){}const FG=new IG;class DG extends Kk{constructor(){super(...arguments),this.paramsConfig=FG,this.hierarchyController=new mU(this),this.transformController=new _U(this),this.flags=new Di(this),this._helper=new _G(1)}static type(){return\\\\\\\"null\\\\\\\"}createObject(){const t=new Fn.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 BG=new class extends la{constructor(){super(...arguments),this.center=aa.VECTOR3([0,0,0]),this.longitude=aa.FLOAT(0,{range:[0,360]}),this.latitude=aa.FLOAT(0,{range:[-180,180]}),this.depth=aa.FLOAT(1,{range:[0,10]})}},zG=\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",kG=new p.a(0,1,0),UG=new p.a(-1,0,0);class GG extends Kk{constructor(){super(...arguments),this.paramsConfig=BG,this.hierarchyController=new mU(this),this.flags=new Di(this),this._helper=new _G(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 ah.a,s:new p.a}}static type(){return\\\\\\\"polarTransform\\\\\\\"}createObject(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.dirtyController.hasHook(zG)||this.dirtyController.addPostDirtyHook(zG,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(kG,Object(On.e)(this.pv.longitude)),this._latitudeMatrix.makeRotationAxis(UG,Object(On.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 VG{constructor(t){this._scene=t,this._data={}}data(t){this._scene.nodesController.reset_node_context_signatures();const e=JG.dispatch_node(this._scene.root()),n=e.data(),i=e.ui_data();return this._data={properties:{frame:this._scene.frame()||El.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=os.escapeLineBreaks(t)}}class HG{constructor(t){this._node=t}data(t={}){var e,n,i,s,r,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===(s=this._node.flags.display)||void 0===s?void 0:s.active())),this._node.flags.hasOptimize()&&(null===(r=this._node.flags.optimize)||void 0===r?void 0:r.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=JG.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=VG.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 s=this._node.io.connections.inputConnection(n);if(this._node.io.inputs.hasNamedInputs()){const r=s.output_index,o=null===(i=e.io.outputs.namedOutputConnectionPoints()[r])||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=JG.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=JG.dispatch_node(n);e[n.name()]=i.data(t)}return e}add_custom(){}}class jG{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 WG extends jG{add_main(){if(!this._require_data_complex())return this._param.rawInputSerialized();this._complex_data.raw_input=this._param.rawInputSerialized()}}class qG extends jG{add_main(){let t=this._param.rawInput();if(t=VG.sanitize_string(t),!this._require_data_complex())return t;this._complex_data.raw_input=t}}class XG extends jG{add_main(){let t=this._param.rawInput();if(t=VG.sanitize_string(t),!this._require_data_complex())return t;this._complex_data.raw_input=t}}class YG extends jG{add_main(){if(!this._require_data_complex())return this._param.rawInputSerialized();this._complex_data.raw_input=this._param.rawInputSerialized()}}class $G extends HG{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 JG{static dispatch_node(t){return t.polyNodeController?new $G(t):new HG(t)}static dispatch_param(t){return t instanceof io?new WG(t):t instanceof _o?new qG(t):t instanceof wo?new XG(t):t instanceof bo?new YG(t):new jG(t)}}class ZG{constructor(){this._objects=[],this._objects_with_geo=[],this.touch()}timestamp(){return this._timestamp}touch(){const t=li.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 ZG;if(this._objects){const e=[];for(let t of this._objects)e.push(yr.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 yr(t,e))):[]}objectsData(){return this._objects?this._objects.map((t=>this._objectData(t))):[]}_objectData(t){let e=0;return t.geometry&&(e=_r.pointsCount(t.geometry)),{type:Ls(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 _r(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 _r(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+=_r.pointsCount(n))}));return t}return 0}pointsFromGroup(t){if(t){const e=os.indices(t),n=this.points();return f.compact(e.map((t=>n[t])))}return this.points()}static _fromObjects(t){const e=new ZG;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=>os.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 Ay.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 Hs.ATTRIB_CLASS.VERTEX:if(this.hasAttrib(t)&&this._objects)for(let n of this._objects)n.traverse((n=>{const i=ZG.geometryFromObject(n);if(i){new _r(i).renameAttrib(t,e)}}));break;case Hs.ATTRIB_CLASS.OBJECT:if(this.hasAttrib(t)&&this._objects)for(let n of this._objects)n.traverse((n=>{new yr(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=os.attribNames(t),n=[];for(let t of this.attribNames())for(let i of e)if(os.matchMask(t,i))n.push(t);else{t==qs.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=qs.default_value(e));for(let i of this.coreGeometries())i.addNumericAttrib(t,e,n)}static clone(t){const e=new Fn.a;return t.children.forEach((t=>{const n=yr.clone(t);e.add(n)})),e}}class QG extends Fl{static context(){return ts.SOP}cook(t,e){}createCoreGroupFromObjects(t){const e=new ZG;return e.setObjects(t),e}createCoreGroupFromGeometry(t,e=Cs.MESH){const n=QG.createObject(t,e);return this.createCoreGroupFromObjects([n])}createObject(t,e,n){return QG.createObject(t,e,n)}static createObject(t,e,n){this.createIndexIfNone(t);const i=new(0,Ns[e])(t,n=n||Hs.MATERIALS[e].clone());return i.castShadow=!0,i.receiveShadow=!0,i.frustumCulled=!1,i.matrixAutoUpdate=!1,i}createIndexIfNone(t){QG.createIndexIfNone(t)}static createIndexIfNone(t){dr.createIndexIfNone(t)}}var KG;!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\\\\\\\"}(KG||(KG={}));const tV=\\\\\\\"input geometry\\\\\\\",eV=[tV,tV,tV,tV];class nV extends sa{constructor(){super(...arguments),this.flags=new Ui(this)}static context(){return ts.SOP}static displayedInputNames(){return eV}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,KG.FROM_SET_CORE_GROUP)}setObject(t){this._setContainerObjects([t],KG.FROM_SET_OBJECT)}setObjects(t){this._setContainerObjects(t,KG.FROM_SET_OBJECTS)}setGeometry(t,e=Cs.MESH){const n=this.createObject(t,e);this._setContainerObjects([n],KG.FROM_SET_GEOMETRY)}setGeometries(t,e=Cs.MESH){const n=[];let i;for(let s of t)i=this.createObject(s,e),n.push(i);this._setContainerObjects(n,KG.FROM_SET_GEOMETRIES)}_setContainerObjects(t,e){const n=this.containerController.container().coreContent()||new ZG;n.setObjects(t),n.touch(),this._setContainer(n)}static createObject(t,e,n){return QG.createObject(t,e,n)}createObject(t,e,n){return nV.createObject(t,e,n)}static createIndexIfNone(t){QG.createIndexIfNone(t)}_createIndexIfNone(t){nV.createIndexIfNone(t)}}const iV=new class extends la{};class sV extends nV{constructor(){super(...arguments),this.paramsConfig=iV}static type(){return ns.OUTPUT}initializeNode(){this.io.inputs.setCount(1),this.io.outputs.setHasNoOutput(),this.io.inputs.initInputsClonedState(Ki.NEVER)}cook(t){this.setCoreGroup(t[0])}}class rV extends nV{constructor(){super(...arguments),this.childrenDisplayController=new aV(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=ts.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 oV={dependsOnDisplayNode:!0};class aV{constructor(t,e=oV){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(sV.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 Mi(this.node.scene(),\\\\\\\"subnetChildrenDisplayController\\\\\\\");return t.addPostDirtyHook(\\\\\\\"subnetChildrenDisplayController\\\\\\\",(()=>{this.node.setDirty()})),t}}function lV(t,e){const n=new class extends la{constructor(){super(...arguments),this.template=aa.OPERATOR_PATH(\\\\\\\"../template\\\\\\\"),this.debug=aa.BUTTON(null,{callback:t=>{i.PARAM_CALLBACK_debug(t)}})}};class i extends rV{constructor(){super(...arguments),this.paramsConfig=n,this.polyNodeController=new uV(this,e)}static type(){return t}static PARAM_CALLBACK_debug(t){t._debug()}_debug(){this.polyNodeController.debug(this.p.template)}}return i}const cV=lV(\\\\\\\"poly\\\\\\\",{nodeContext:ts.SOP,inputs:[0,4]});class hV extends cV{}class uV{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 kl(this.node);i.create_nodes(n,t);const s=this._definition.ui;s&&i.process_nodes_ui_data(n,s),e&&this.node.scene().loadingController.markAsLoaded()}debug(t){const e=t.found_node();if(e){const t=JG.dispatch_node(e),n=t.data({showPolyNodesData:!0}),i=t.ui_data({showPolyNodesData:!0}),s={nodeContext:e.context(),inputs:[0,0],params:[],nodes:n.nodes,ui:i.nodes};console.log(JSON.stringify(s))}}static createNodeClass(t,e,n){switch(e){case ts.SOP:return lV(t,n);case ts.OBJ:return dV(t,n)}}}function dV(t,e){const n=new class extends la{constructor(){super(...arguments),this.display=aa.BOOLEAN(1),this.template=aa.OPERATOR_PATH(\\\\\\\"../template\\\\\\\"),this.debug=aa.BUTTON(null,{callback:t=>{i.PARAM_CALLBACK_debug(t)}})}};class i extends Kk{constructor(){super(...arguments),this.paramsConfig=n,this.hierarchyController=new mU(this),this.flags=new Di(this),this.childrenDisplayController=new LG(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=ts.SOP,this.polyNodeController=new uV(this,e)}static type(){return t}createObject(){const t=new Fn.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 pV=dV(\\\\\\\"poly\\\\\\\",{nodeContext:ts.OBJ});class _V extends pV{}class mV extends K.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 MV;!function(t){t.LINEAR=\\\\\\\"linear\\\\\\\",t.INVERSE=\\\\\\\"inverse\\\\\\\",t.EXPONENTIAL=\\\\\\\"exponential\\\\\\\"}(MV||(MV={}));const EV=[MV.LINEAR,MV.INVERSE,MV.EXPONENTIAL];class SV extends(dU(la)){constructor(){super(...arguments),this.audio=aa.FOLDER(),this.listener=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ,types:[Ng.AUDIO_LISTENER]}}),this.url=aa.STRING(\\\\\\\"\\\\\\\",{fileBrowse:{type:[Or.AUDIO]}}),this.volume=aa.FLOAT(1),this.loop=aa.BOOLEAN(1,{separatorBefore:!0}),this.loopStart=aa.FLOAT(0,{visibleIf:{loop:1}}),this.loopEnd=aa.FLOAT(0,{visibleIf:{loop:1},separatorAfter:!0}),this.refDistance=aa.FLOAT(10,{range:[0,10],rangeLocked:[!0,!1]}),this.rolloffFactor=aa.FLOAT(10,{range:[0,10],rangeLocked:[!0,!1]}),this.maxDistance=aa.FLOAT(100,{range:[.001,100],rangeLocked:[!0,!1]}),this.distanceModel=aa.INTEGER(EV.indexOf(MV.LINEAR),{menu:{entries:EV.map(((t,e)=>({name:t,value:e})))}}),this.coneInnerAngle=aa.FLOAT(180,{range:[0,360],rangeLocked:[!0,!0]}),this.coneOuterAngle=aa.FLOAT(230,{range:[0,360],rangeLocked:[!0,!0]}),this.coneOuterGain=aa.FLOAT(.1,{range:[0,1],rangeLocked:[!0,!0]}),this.autoplay=aa.BOOLEAN(1),this.showHelper=aa.BOOLEAN(0),this.play=aa.BUTTON(null,{callback:t=>{NV.PARAM_CALLBACK_play(t)}}),this.pause=aa.BUTTON(null,{callback:t=>{NV.PARAM_CALLBACK_pause(t)}})}}const CV=new SV;class NV extends Kk{constructor(){super(...arguments),this.paramsConfig=CV,this.hierarchyController=new mU(this),this.transformController=new _U(this),this.flags=new Di(this)}static type(){return Ng.POSITIONAL_AUDIO}createObject(){const t=new Fn.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(EV[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 bV(t);return e.matrixAutoUpdate=!1,e}async _createPositionalAudio(){const t=this.pv.listener.nodeWithContext(ts.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 xV(e),this._positionalAudio.matrixAutoUpdate=!1;const n=new AV(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 LV;!function(t){t.ON_RENDER=\\\\\\\"On Every Render\\\\\\\",t.MANUAL=\\\\\\\"Manual\\\\\\\"}(LV||(LV={}));const OV=[LV.ON_RENDER,LV.MANUAL];const PV=new class extends la{constructor(){super(...arguments),this.object=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ},dependentOnFoundNode:!1,computeOnDirty:!0,callback:t=>{RV.PARAM_CALLBACK_update_resolved_object(t)}}),this.pointIndex=aa.INTEGER(0,{range:[0,100]}),this.updateMode=aa.INTEGER(OV.indexOf(LV.ON_RENDER),{callback:t=>{RV.PARAM_CALLBACK_update_updateMode(t)},menu:{entries:OV.map(((t,e)=>({name:t,value:e})))}}),this.update=aa.BUTTON(null,{callback:t=>{RV.PARAM_CALLBACK_update(t)},visibleIf:{updateMode:OV.indexOf(LV.MANUAL)}})}};class RV extends Kk{constructor(){super(...arguments),this.paramsConfig=PV,this.hierarchyController=new mU(this),this.flags=new Di(this),this._helper=new _G(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 B.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=OV[this.pv.updateMode];switch(t){case LV.ON_RENDER:return this._add_render_hook();case LV.MANUAL:return this._remove_render_hook()}ls.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,s,r){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()==ts.OBJ&&t.type()==RG.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 IV extends(Na(Ea(ya(fa(ua(la)))))){}const FV=new IV;class DV extends Kk{constructor(){super(...arguments),this.paramsConfig=FV,this.hierarchyController=new mU(this),this.SceneAutoUpdateController=new da(this),this.sceneBackgroundController=new ga(this),this.SceneEnvController=new xa(this),this.sceneFogController=new Sa(this),this.sceneMaterialOverrideController=new La(this)}static type(){return\\\\\\\"scene\\\\\\\"}createObject(){const t=new gs;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 BV{constructor(t,e,n){this._camera_node_id=t,this._controls_node=e,this._controls=n,this._updateRequired=this._controls_node.updateRequired()}updateRequired(){return this._updateRequired}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 zV=\\\\\\\"controls\\\\\\\";class kV{constructor(t){this.node=t,this._applied_controls_by_element_id=new Map,this._controls_node=null}controls_param(){return this.node.params.has(zV)?this.node.params.get(zV):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(_s.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 s=!1,r=this._applied_controls_by_element_id.get(e.id);if(r&&r.get(i)&&(s=!0),!s){r=new Map,this._applied_controls_by_element_id.set(e.id,r),r.set(i,n);const s=await n.apply_controls(this.node.object,t);if(!s)return;const o=new BV(this.node.graphNodeId(),n,s);return this.set_controls_events(s),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.disposeControlsForHtmlElementId(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=OH[this.node.pv.updateFromControlsMode];switch(e){case LH.ON_END:return this._set_controls_events_to_update_on_end(t);case LH.ALWAYS:return this._set_controls_events_to_update_always(t);case LH.NEVER:return this._reset(t)}ls.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 UV(t){return class extends t{constructor(){super(...arguments),this.layer=aa.INTEGER(0,{range:[0,31],rangeLocked:[!0,!0]})}}}class GV{constructor(t){this.node=t}update(){const t=this.node.object;t.layers.set(0),t.layers.enable(this.node.params.integer(\\\\\\\"layer\\\\\\\"))}}const VV={callback:t=>{UH.PARAM_CALLBACK_reset_effects_composer(t)}};function HV(t){return class extends t{constructor(){super(...arguments),this.doPostProcess=aa.BOOLEAN(0),this.postProcessNode=aa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{doPostProcess:1},nodeSelection:{types:[es.POST]},...VV})}}}class jV{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,s=this.node.p.postProcessNode.value.node();if(s){if(s.type()==es.POST){const r=s,o=this.node.renderController.canvas_resolution(t);return r.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 WV extends sa{constructor(){super(...arguments),this.flags=new Pi(this)}static context(){return ts.ROP}initializeBaseNode(){this.dirtyController.addPostDirtyHook(\\\\\\\"cook_immediately\\\\\\\",(()=>{this.cookController.cookMainWithoutInputs()}))}cook(){this.cookController.endCook()}}var qV,XV,YV,$V;!function(t){t.CSS2D=\\\\\\\"CSS2DRenderer\\\\\\\",t.CSS3D=\\\\\\\"CSS3DRenderer\\\\\\\",t.WEBGL=\\\\\\\"WebGLRenderer\\\\\\\"}(qV||(qV={})),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\\\\\\\"}(XV||(XV={})),($V=YV||(YV={}))[$V.Linear=w.U]=\\\\\\\"Linear\\\\\\\",$V[$V.sRGB=w.ld]=\\\\\\\"sRGB\\\\\\\",$V[$V.Gamma=w.J]=\\\\\\\"Gamma\\\\\\\",$V[$V.RGBE=w.gc]=\\\\\\\"RGBE\\\\\\\",$V[$V.LogLuv=w.bb]=\\\\\\\"LogLuv\\\\\\\",$V[$V.RGBM7=w.lc]=\\\\\\\"RGBM7\\\\\\\",$V[$V.RGBM16=w.kc]=\\\\\\\"RGBM16\\\\\\\",$V[$V.RGBD=w.fc]=\\\\\\\"RGBD\\\\\\\";const JV=[XV.Linear,XV.sRGB,XV.Gamma,XV.RGBE,XV.LogLuv,XV.RGBM7,XV.RGBM16,XV.RGBD],ZV=[YV.Linear,YV.sRGB,YV.Gamma,YV.RGBE,YV.LogLuv,YV.RGBM7,YV.RGBM16,YV.RGBD],QV=YV.sRGB;var KV,tH,eH;!function(t){t.No=\\\\\\\"No\\\\\\\",t.Linear=\\\\\\\"Linear\\\\\\\",t.Reinhard=\\\\\\\"Reinhard\\\\\\\",t.Cineon=\\\\\\\"Cineon\\\\\\\",t.ACESFilmic=\\\\\\\"ACESFilmic\\\\\\\"}(KV||(KV={})),(eH=tH||(tH={}))[eH.No=w.vb]=\\\\\\\"No\\\\\\\",eH[eH.Linear=w.ab]=\\\\\\\"Linear\\\\\\\",eH[eH.Reinhard=w.vc]=\\\\\\\"Reinhard\\\\\\\",eH[eH.Cineon=w.m]=\\\\\\\"Cineon\\\\\\\",eH[eH.ACESFilmic=w.a]=\\\\\\\"ACESFilmic\\\\\\\";const nH=[KV.No,KV.Linear,KV.Reinhard,KV.Cineon,KV.ACESFilmic],iH=[tH.No,tH.Linear,tH.Reinhard,tH.Cineon,tH.ACESFilmic],sH=tH.ACESFilmic,rH=nH.map(((t,e)=>({name:t,value:iH[e]})));var oH;!function(t){t.HIGH=\\\\\\\"highp\\\\\\\",t.MEDIUM=\\\\\\\"mediump\\\\\\\",t.LOW=\\\\\\\"lowp\\\\\\\"}(oH||(oH={}));const aH=[oH.HIGH,oH.MEDIUM,oH.LOW];var lH;!function(t){t.HIGH=\\\\\\\"high-performance\\\\\\\",t.LOW=\\\\\\\"low-power\\\\\\\",t.DEFAULT=\\\\\\\"default\\\\\\\"}(lH||(lH={}));const cH=[lH.HIGH,lH.LOW,lH.DEFAULT];var hH,uH,dH;!function(t){t.Basic=\\\\\\\"Basic\\\\\\\",t.PCF=\\\\\\\"PCF\\\\\\\",t.PCFSoft=\\\\\\\"PCFSoft\\\\\\\",t.VSM=\\\\\\\"VSM\\\\\\\"}(hH||(hH={})),(dH=uH||(uH={}))[dH.Basic=w.k]=\\\\\\\"Basic\\\\\\\",dH[dH.PCF=w.Fb]=\\\\\\\"PCF\\\\\\\",dH[dH.PCFSoft=w.Gb]=\\\\\\\"PCFSoft\\\\\\\",dH[dH.VSM=w.gd]=\\\\\\\"VSM\\\\\\\";const pH=[hH.Basic,hH.PCF,hH.PCFSoft,hH.VSM],_H=[uH.Basic,uH.PCF,uH.PCFSoft,uH.VSM],mH=(w.k,w.Fb,w.Gb,w.gd,uH.PCFSoft),fH={alpha:!1,precision:oH.HIGH,premultipliedAlpha:!0,antialias:!1,stencil:!0,preserveDrawingBuffer:!1,powerPreference:lH.DEFAULT,depth:!0,logarithmicDepthBuffer:!1};const gH=new class extends la{constructor(){super(...arguments),this.tprecision=aa.BOOLEAN(0),this.precision=aa.INTEGER(aH.indexOf(oH.HIGH),{visibleIf:{tprecision:1},menu:{entries:aH.map(((t,e)=>({value:e,name:t})))}}),this.tpowerPreference=aa.BOOLEAN(0),this.powerPreference=aa.INTEGER(cH.indexOf(lH.DEFAULT),{visibleIf:{tpowerPreference:1},menu:{entries:cH.map(((t,e)=>({value:e,name:t})))}}),this.alpha=aa.BOOLEAN(1),this.premultipliedAlpha=aa.BOOLEAN(1),this.antialias=aa.BOOLEAN(1),this.stencil=aa.BOOLEAN(1),this.depth=aa.BOOLEAN(1),this.logarithmicDepthBuffer=aa.BOOLEAN(0),this.toneMapping=aa.INTEGER(sH,{menu:{entries:rH}}),this.toneMappingExposure=aa.FLOAT(1,{range:[0,2]}),this.outputEncoding=aa.INTEGER(QV,{menu:{entries:JV.map(((t,e)=>({name:t,value:ZV[e]})))}}),this.physicallyCorrectLights=aa.BOOLEAN(1),this.sortObjects=aa.BOOLEAN(1),this.tpixelRatio=aa.BOOLEAN(0),this.pixelRatio=aa.INTEGER(2,{visibleIf:{tpixelRatio:!0},range:[1,4],rangeLocked:[!0,!1]}),this.tshadowMap=aa.BOOLEAN(1),this.shadowMapAutoUpdate=aa.BOOLEAN(1,{visibleIf:{tshadowMap:1}}),this.shadowMapNeedsUpdate=aa.BOOLEAN(0,{visibleIf:{tshadowMap:1}}),this.shadowMapType=aa.INTEGER(mH,{visibleIf:{tshadowMap:1},menu:{entries:pH.map(((t,e)=>({name:t,value:_H[e]})))}})}};class vH extends WV{constructor(){super(...arguments),this.paramsConfig=gH,this._renderers_by_canvas_id={}}static type(){return qV.WEBGL}createRenderer(t,e){const n={},i=Object.keys(fH);let s;for(s of i)n[s]=fH[s];if(this.pv.tprecision){const t=aH[this.pv.precision];n.precision=t}if(this.pv.tpowerPreference){const t=cH[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 r=li.renderersController.createWebGLRenderer(n);return li.renderersController.printDebug()&&(li.renderersController.printDebugMessage(`create renderer from node '${this.path()}'`),li.renderersController.printDebugMessage({params:n})),this._update_renderer(r),this._renderers_by_canvas_id[t.id]=r,r}cook(){const t=Object.keys(this._renderers_by_canvas_id);for(let e of t){const t=this._renderers_by_canvas_id[e];this._update_renderer(t)}this._traverse_scene_and_update_materials(),this.cookController.endCook()}_update_renderer(t){t.physicallyCorrectLights=this.pv.physicallyCorrectLights,t.outputEncoding=this.pv.outputEncoding,t.toneMapping=this.pv.toneMapping,t.toneMappingExposure=this.pv.toneMappingExposure,t.shadowMap.enabled=this.pv.tshadowMap,t.shadowMap.autoUpdate=this.pv.shadowMapAutoUpdate,t.shadowMap.needsUpdate=this.pv.shadowMapNeedsUpdate,t.shadowMap.type=this.pv.shadowMapType,t.sortObjects=this.pv.sortObjects;const e=this.pv.tpixelRatio?this.pv.pixelRatio:xH.defaultPixelRatio();li.renderersController.printDebug()&&(li.renderersController.printDebugMessage(`set renderer pixelRatio from '${this.path()}'`),li.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 yH(t){return class extends t{constructor(){super(...arguments),this.render=aa.FOLDER(),this.setScene=aa.BOOLEAN(0),this.scene=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setScene:1},nodeSelection:{context:ts.OBJ,types:[DV.type()]}}),this.setRenderer=aa.BOOLEAN(0),this.renderer=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setRenderer:1},nodeSelection:{context:ts.ROP,types:[vH.type()]}}),this.setCSSRenderer=aa.BOOLEAN(0),this.CSSRenderer=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setCSSRenderer:1},nodeSelection:{context:ts.ROP,types:[qV.CSS2D,qV.CSS3D]}})}}}class xH{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(ts.OBJ,DV.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(ts.ROP,qV.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(ts.ROP,[qV.CSS2D,qV.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=li.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=xH._createDefaultRenderer(t,n)),li.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=li.renderersController.createWebGLRenderer(n),s=this.defaultPixelRatio();return i.setPixelRatio(s),i.shadowMap.enabled=!0,i.shadowMap.type=mH,i.physicallyCorrectLights=!0,i.toneMapping=sH,i.toneMappingExposure=1,i.outputEncoding=QV,li.renderersController.printDebug()&&(li.renderersController.printDebugMessage(\\\\\\\"create default renderer\\\\\\\"),li.renderersController.printDebugMessage({params:n,pixelRatio:s})),i}delete_renderer(t){const e=this.renderer(t);e&&li.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 bH{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(t){this._config&&this._controls&&this._config.updateRequired()&&this._controls.update(t)}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 Mi(this.viewer.cameraNode().scene(),\\\\\\\"viewer-controls\\\\\\\");return t.addPostDirtyHook(\\\\\\\"this.viewer.controls_controller\\\\\\\",(async()=>{await this.create_controls()})),t}}class wH{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 TH{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 AH=\\\\\\\"hovered\\\\\\\";class MH{constructor(t,e,n){this._container=t,this._scene=e,this._camera_node=n,this._active=!1,this._id=MH._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 wH(this)}get controlsController(){return this._controls_controller}get eventsController(){return this._events_controller=this._events_controller||new Va(this)}get webglController(){return this._webgl_controller=this._webgl_controller||new TH(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(AH)}setContainerClassHovered(){this.domElement().classList.add(AH)}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 ${t} already registered`):(i.push(e),n.push(t))}_unregisterCallback(t,e,n){if(!e||!n)return;const i=e.indexOf(t);e.splice(i,1),n.splice(i,1)}}MH._next_viewer_id=0;class EH extends MH{constructor(t,e,n,i){super(t,e,n),this._scene=e,this._camera_node=n,this._properties=i,this._do_render=!0,this._clock=new Om,this._delta=0,this._animate_method=this.animate.bind(this),this._onResizeBound=this.onResize.bind(this),this._do_render=null==this._properties||this._properties.autoRender,this._canvas=document.createElement(\\\\\\\"canvas\\\\\\\"),this._canvas.id=`canvas_id_${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 bH(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._delta=this._clock.getDelta(),this._request_animation_frame_id=requestAnimationFrame(this._animate_method),this._onBeforeTickCallbacks)for(let t of this._onBeforeTickCallbacks)t(this._delta);if(this._scene.timeController.incrementTimeIfPlaying(this._delta),this._onAfterTickCallbacks)for(let t of this._onAfterTickCallbacks)t(this._delta);this.render(this._delta),null===(t=this._controls_controller)||void 0===t||t.update(this._delta)}}_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(t){if(this.camerasController.cameraNode()&&this._canvas){if(this._onBeforeRenderCallbacks)for(let e of this._onBeforeRenderCallbacks)e(t);const e=this.camerasController.size,n=this.camerasController.aspect;if(this._camera_node.renderController.render(this._canvas,e,n),this._onAfterRenderCallbacks)for(let e of this._onAfterRenderCallbacks)e(t)}else console.warn(\\\\\\\"no camera to render with\\\\\\\")}renderer(){if(this._canvas)return this._camera_node.renderController.renderer(this._canvas)}}const SH={type:\\\\\\\"change\\\\\\\"},CH=1,NH=100;var LH;!function(t){t.ON_END=\\\\\\\"on move end\\\\\\\",t.ALWAYS=\\\\\\\"always\\\\\\\",t.NEVER=\\\\\\\"never\\\\\\\"}(LH||(LH={}));const OH=[LH.ON_END,LH.ALWAYS,LH.NEVER];function PH(t){return class extends t{constructor(){super(...arguments),this.setMainCamera=aa.BUTTON(null,{callback:(t,e)=>{kH.PARAM_CALLBACK_setMasterCamera(t)}})}}}function RH(t){return class extends t{constructor(){super(...arguments),this.camera=aa.FOLDER(),this.controls=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.EVENT}}),this.updateFromControlsMode=aa.INTEGER(OH.indexOf(LH.ON_END),{menu:{entries:OH.map(((t,e)=>({name:t,value:e})))}}),this.near=aa.FLOAT(CH,{range:[0,100],cook:!1,computeOnDirty:!0,callback:(t,e)=>{UH.PARAM_CALLBACK_update_near_far_from_param(t,e)}}),this.far=aa.FLOAT(NH,{range:[0,100],cook:!1,computeOnDirty:!0,callback:(t,e)=>{UH.PARAM_CALLBACK_update_near_far_from_param(t,e)}}),this.display=aa.BOOLEAN(1),this.showHelper=aa.BOOLEAN(0)}}}var IH;!function(t){t.DEFAULT=\\\\\\\"default\\\\\\\",t.COVER=\\\\\\\"cover\\\\\\\",t.CONTAIN=\\\\\\\"contain\\\\\\\"}(IH||(IH={}));const FH=[IH.DEFAULT,IH.COVER,IH.CONTAIN];function DH(t){return class extends t{constructor(){super(...arguments),this.fovAdjustMode=aa.INTEGER(FH.indexOf(IH.DEFAULT),{menu:{entries:FH.map(((t,e)=>({name:t,value:e})))}}),this.expectedAspectRatio=aa.FLOAT(\\\\\\\"16/9\\\\\\\",{visibleIf:[{fovAdjustMode:FH.indexOf(IH.COVER)},{fovAdjustMode:FH.indexOf(IH.CONTAIN)}],range:[0,2],rangeLocked:[!0,!1]})}}}PH(la);HV(yH(dU(UV(RH(PH(la))))));class BH extends Kk{constructor(){super(...arguments),this.renderOrder=Qk.CAMERA,this._aspect=-1}get object(){return this._object}async cook(){this.updateCamera(),this._object.dispatchEvent(SH),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(){uU.set_params_from_object(this._object,this)}static PARAM_CALLBACK_update_from_param(t,e){t.object[e.name()]=t.pv[e.name()]}}class zH extends BH{constructor(){super(...arguments),this.flags=new Di(this),this.hierarchyController=new mU(this),this.transformController=new _U(this),this.childrenDisplayController=new LG(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=ts.SOP}get controls_controller(){return this._controls_controller=this._controls_controller||new kV(this)}get layers_controller(){return this._layers_controller=this._layers_controller||new GV(this)}get renderController(){return this._render_controller=this._render_controller||new xH(this)}get postProcessController(){return this._post_process_controller=this._post_process_controller||new jV(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(SH),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 EH(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 NU(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 kH extends BH{}class UH extends zH{PARAM_CALLBACK_update_effects_composer(t){}}const GH=-.5,VH=.5,HH=.5,jH=-.5;class WH extends(HV(yH(UV(PH(DH(function(t){return class extends t{constructor(){super(...arguments),this.size=aa.FLOAT(1)}}}(RH(dU(la,{matrixAutoUpdate:!0}))))))))){}const qH=new WH;class XH extends zH{constructor(){super(...arguments),this.paramsConfig=qH}static type(){return is.ORTHOGRAPHIC}createObject(){return new ot.a(2*GH,2*VH,2*HH,2*jH,CH,NH)}updateCamera(){this._updateForAspectRatio()}_updateForAspectRatio(){this._aspect&&(this._adjustFOVFromMode(),this._object.updateProjectionMatrix())}_adjustFOVFromMode(){const t=FH[this.pv.fovAdjustMode];switch(t){case IH.DEFAULT:return this._adjustFOVFromModeDefault();case IH.COVER:return this._adjustFOVFromModeCover();case IH.CONTAIN:return this._adjustFOVFromModeContain()}ls.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=GH*e*1,this._object.right=VH*e*1,this._object.top=HH*t*1,this._object.bottom=jH*t*1}}const YH=50;class $H extends(HV(yH(UV(PH(DH(function(t){return class extends t{constructor(){super(...arguments),this.fov=aa.FLOAT(YH,{range:[0,100]})}}}(RH(dU(la,{matrixAutoUpdate:!0}))))))))){}const JH=new $H;class ZH extends zH{constructor(){super(...arguments),this.paramsConfig=JH}static type(){return is.PERSPECTIVE}createObject(){return new tt.a(YH,1,CH,NH)}updateCamera(){this._object.fov!=this.pv.fov&&(this._object.fov=this.pv.fov,this._object.updateProjectionMatrix()),this._updateForAspectRatio()}_updateForAspectRatio(){this._aspect&&(this._object.aspect=this._aspect,this._adjustFOVFromMode(),this._object.updateProjectionMatrix())}_adjustFOVFromMode(){const t=FH[this.pv.fovAdjustMode];switch(t){case IH.DEFAULT:return this._adjustFOVFromModeDefault();case IH.COVER:return this._adjustFOVFromModeCover();case IH.CONTAIN:return this._adjustFOVFromModeContain()}ls.unreachable(t)}_adjustFOVFromModeDefault(){this._object.fov=this.pv.fov}_adjustFOVFromModeCover(){if(this._object.aspect>this.pv.expectedAspectRatio){const t=Math.tan(Object(On.e)(this.pv.fov/2))/(this._object.aspect/this.pv.expectedAspectRatio);this._object.fov=2*Object(On.k)(Math.atan(t))}else this._object.fov=this.pv.fov}_adjustFOVFromModeContain(){if(this._object.aspect>this.pv.expectedAspectRatio)this._object.fov=this.pv.fov;else{const t=Math.tan(Object(On.e)(this.pv.fov/2))/(this._object.aspect/this.pv.expectedAspectRatio);this._object.fov=2*Object(On.k)(Math.atan(t))}}}class QH extends(function(t){return class extends t{constructor(){super(...arguments),this.main=aa.FOLDER(),this.resolution=aa.INTEGER(256),this.excludedObjects=aa.STRING(\\\\\\\"*`$OS`\\\\\\\"),this.printResolve=aa.BUTTON(null,{callback:t=>{tj.PARAM_CALLBACK_printResolve(t)}}),this.near=aa.FLOAT(1),this.far=aa.FLOAT(100),this.render=aa.BUTTON(null,{callback:t=>{tj.PARAM_CALLBACK_render(t)}}),this.renderTarget=aa.FOLDER(),this.tencoding=aa.BOOLEAN(0),this.encoding=aa.INTEGER(w.ld,{visibleIf:{tencoding:1},menu:{entries:eg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.tminFilter=aa.BOOLEAN(0),this.minFilter=aa.INTEGER(Xm,{visibleIf:{tminFilter:1},menu:{entries:$m}}),this.tmagFilter=aa.BOOLEAN(0),this.magFilter=aa.INTEGER(qm,{visibleIf:{tmagFilter:1},menu:{entries:Ym}})}}}(dU(la))){}const KH=new QH;class tj extends Kk{constructor(){super(...arguments),this.paramsConfig=KH,this.hierarchyController=new mU(this),this.transformController=new _U(this),this.flags=new Di(this),this._excludedObjects=[],this._previousVisibleStateByUuid=new Map,this._helper=new _G(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 Fn.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,s=this._cubeCamera.renderTarget.width;n==this.pv.near&&i==this.pv.far&&s==this.pv.resolution||(t=!0),t&&this.object.remove(this._cubeCamera)}return t}_createCubeCamera(){const t=new st(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 nt(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=li.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 ej extends Kk{constructor(){super(...arguments),this._attachableToHierarchy=!1}createObject(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}cook(){this.cookController.endCook()}}class nj extends ej{}class ij extends nj{constructor(){super(...arguments),this._children_controller_context=ts.ANIM}static type(){return es.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class sj extends ij{constructor(){super(...arguments),this.renderOrder=Qk.MANAGER}}class rj extends nj{constructor(){super(...arguments),this._children_controller_context=ts.COP}static type(){return es.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class oj extends nj{constructor(){super(...arguments),this.renderOrder=Qk.MANAGER,this._children_controller_context=ts.EVENT}static type(){return es.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class aj extends nj{constructor(){super(...arguments),this.renderOrder=Qk.MANAGER,this._children_controller_context=ts.MAT}static type(){return es.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class lj extends ej{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=ts.POST}static type(){return es.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class cj extends nj{constructor(){super(...arguments),this.renderOrder=Qk.MANAGER,this._children_controller_context=ts.ROP}static type(){return es.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}const hj=[\\\\\\\"input pass\\\\\\\"];const uj={cook:!1,callback:function(t,e){dj.PARAM_CALLBACK_updatePasses(t)},computeOnDirty:!0};class dj extends sa{constructor(){super(...arguments),this.flags=new zi(this),this._passes_by_requester_id=new Map,this._update_pass_bound=this.updatePass.bind(this)}static context(){return ts.POST}static displayedInputNames(){return hj}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 pj={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 _j={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 mj 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===Rm&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on CopyShader\\\\\\\");const n=Rm;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===pj&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on LuminosityShader\\\\\\\"),this.materialLuminance=new F({uniforms:I.clone(pj.uniforms),vertexShader:pj.vertexShader,fragmentShader:pj.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===_j&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on ToneMapShader\\\\\\\"),this.materialToneMap=new F({uniforms:I.clone(_j.uniforms),vertexShader:_j.vertexShader,fragmentShader:_j.fragmentShader,blending:w.ub}),this.fsQuad=new Bm(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 Q(this.resolution,this.resolution,t),this.luminanceRT.texture.name=\\\\\\\"AdaptiveToneMappingPass.l\\\\\\\",this.luminanceRT.texture.generateMipmaps=!1,this.previousLuminanceRT=new Q(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 Q(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 lt.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 fj=new class extends la{constructor(){super(...arguments),this.adaptive=aa.BOOLEAN(1,{...uj}),this.averageLuminance=aa.FLOAT(.7,{...uj}),this.midGrey=aa.FLOAT(.04,{...uj}),this.maxLuminance=aa.FLOAT(16,{range:[0,20],...uj}),this.adaptiveRange=aa.FLOAT(2,{range:[0,10],...uj})}};class gj extends dj{constructor(){super(...arguments),this.paramsConfig=fj}static type(){return\\\\\\\"adaptiveToneMapping\\\\\\\"}_createPass(t){const e=new mj(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 vj={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 yj extends Im{constructor(t=.96){super(),void 0===vj&&console.error(\\\\\\\"THREE.AfterimagePass relies on AfterimageShader\\\\\\\"),this.shader=vj,this.uniforms=I.clone(this.shader.uniforms),this.uniforms.damp.value=t,this.textureComp=new Q(window.innerWidth,window.innerHeight,{minFilter:w.V,magFilter:w.ob,format:w.Ib}),this.textureOld=new Q(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 Bm(this.shaderMaterial);const e=new lt.a;this.copyFsQuad=new Bm(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 xj=new class extends la{constructor(){super(...arguments),this.damp=aa.FLOAT(.96,{range:[0,1],rangeLocked:[!0,!0],...uj})}};class bj extends dj{constructor(){super(...arguments),this.paramsConfig=xj}static type(){return\\\\\\\"afterImage\\\\\\\"}_createPass(t){const e=new yj;return this.updatePass(e),e}updatePass(t){t.uniforms.damp.value=this.pv.damp}}const wj={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 Tj=new class extends la{constructor(){super(...arguments),this.opacity=aa.FLOAT(.95,{range:[-5,5],rangeLocked:[!0,!0],...uj})}};class Aj extends dj{constructor(){super(...arguments),this.paramsConfig=Tj}static type(){return\\\\\\\"bleach\\\\\\\"}_createPass(t){const e=new zm(wj);return this.updatePass(e),e}updatePass(t){t.uniforms.opacity.value=this.pv.opacity}}const Mj={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 Ej=new class extends la{constructor(){super(...arguments),this.brightness=aa.FLOAT(0,{range:[-1,1],rangeLocked:[!1,!1],...uj}),this.contrast=aa.FLOAT(0,{range:[-1,1],rangeLocked:[!1,!1],...uj}),this.transparent=aa.BOOLEAN(1,uj)}};class Sj extends dj{constructor(){super(...arguments),this.paramsConfig=Ej}static type(){return\\\\\\\"brightnessContrast\\\\\\\"}_createPass(t){const e=new zm(Mj);return 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 Cj 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 Nj=new class extends la{};class Lj extends dj{constructor(){super(...arguments),this.paramsConfig=Nj}static type(){return\\\\\\\"clear\\\\\\\"}_createPass(t){const e=new Cj;return this.updatePass(e),e}updatePass(t){}}const Oj=new class extends la{};class Pj extends dj{constructor(){super(...arguments),this.paramsConfig=Oj}static type(){return\\\\\\\"clearMask\\\\\\\"}_createPass(t){const e=new Um;return this.updatePass(e),e}updatePass(t){}}const Rj={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 Ij=new class extends la{constructor(){super(...arguments),this.pow=aa.VECTOR3([2,2,2],{...uj}),this.mult=aa.COLOR([1,1,1],{...uj}),this.add=aa.COLOR([0,0,0],{...uj})}};class Fj extends dj{constructor(){super(...arguments),this.paramsConfig=Ij}static type(){return\\\\\\\"colorCorrection\\\\\\\"}_createPass(t){const e=new zm(Rj);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 Dj=new class extends la{constructor(){super(...arguments),this.opacity=aa.FLOAT(1,{range:[0,1],rangeLocked:[!0,!0],...uj}),this.transparent=aa.BOOLEAN(1,uj)}};class Bj extends dj{constructor(){super(...arguments),this.paramsConfig=Dj}static type(){return\\\\\\\"copy\\\\\\\"}_createPass(t){const e=new zm(Rm);return this.updatePass(e),e}updatePass(t){t.uniforms.opacity.value=this.pv.opacity,t.material.transparent=this.pv.transparent}}const zj={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}\\\\\\\"},kj={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 Uj{constructor(t){this._scene=t}scene(){return this._scene}with_overriden_material(t,e,n,i){const s={};let r;this._scene.traverse((i=>{const o=i;if(o.material){const i=o.geometry;if(i){const a=o.customDepthDOFMaterial;if(a){if(r=a,r.uniforms)for(let t of Object.keys(n))r.uniforms[t].value=n[t].value}else r=_r.markedAsInstance(i)?e:t;r&&(s[o.uuid]=o.material,o.material=r)}}})),i(),this._scene.traverse((t=>{const e=t;if(e.material){e.geometry&&(e.material=s[e.uuid])}}));for(let t of Object.keys(s))delete s[t]}}class Gj{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 gs,this.clear_color=new D.a(1,1,1),this._prev_clear_color=new D.a,this._core_scene=new Uj(this._scene);const s=3,r=4;this._processing_camera=new ot.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 Q(this._resolution.x,this._resolution.y,o),this._rtTextureColor=new Q(this._resolution.x,this._resolution.y,o);var a=zj;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:s,SAMPLES:r}}),this._quad=new B.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=kj;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=Hj.smoothstep(e.near,e.far,t.pv.focalDepth),i=Hj.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 Vj=new class extends la{constructor(){super(...arguments),this.focalDepth=aa.FLOAT(10,{range:[0,50],rangeLocked:[!0,!1],step:.001,...uj}),this.fStep=aa.FLOAT(10,{range:[.1,22],rangeLocked:[!0,!0],...uj}),this.maxBlur=aa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],...uj}),this.vignetting=aa.BOOLEAN(0,{...uj}),this.depthBlur=aa.BOOLEAN(0,{...uj}),this.threshold=aa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0],step:.001,...uj}),this.gain=aa.FLOAT(1,{range:[0,100],rangeLocked:[!0,!0],step:.001,...uj}),this.bias=aa.FLOAT(1,{range:[0,3],rangeLocked:[!0,!0],step:.001,...uj}),this.fringe=aa.FLOAT(.7,{range:[0,5],rangeLocked:[!0,!1],step:.001,...uj}),this.noise=aa.BOOLEAN(0,{...uj}),this.dithering=aa.FLOAT(0,{range:[0,.001],rangeLocked:[!0,!0],step:1e-4,...uj}),this.pentagon=aa.BOOLEAN(0,{...uj}),this.rings=aa.INTEGER(3,{range:[1,8],rangeLocked:[!0,!0],...uj}),this.samples=aa.INTEGER(4,{range:[1,13],rangeLocked:[!0,!0],...uj}),this.clearColor=aa.COLOR([1,1,1],{...uj})}};class Hj extends dj{constructor(){super(...arguments),this.paramsConfig=Vj}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 Gj(this,t.scene,e.object,t.resolution);this.updatePass(n);const i=new Mi(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 jj={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 Wj=new class extends la{constructor(){super(...arguments),this.center=aa.VECTOR2([.5,.5],{...uj}),this.angle=aa.FLOAT(\\\\\\\"$PI*0.5\\\\\\\",{range:[0,10],rangeLocked:[!1,!1],...uj}),this.scale=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...uj})}};class qj extends dj{constructor(){super(...arguments),this.paramsConfig=Wj}static type(){return\\\\\\\"dotScreen\\\\\\\"}_createPass(t){const e=new zm(jj);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 Xj={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 Yj extends Im{constructor(t,e,n,i){super(),void 0===Xj&&console.error(\\\\\\\"THREE.FilmPass relies on FilmShader\\\\\\\");const s=Xj;this.uniforms=I.clone(s.uniforms),this.material=new F({uniforms:this.uniforms,vertexShader:s.vertexShader,fragmentShader:s.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 Bm(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 $j=new class extends la{constructor(){super(...arguments),this.noiseIntensity=aa.FLOAT(.5,{range:[0,1],rangeLocked:[!1,!1],...uj}),this.scanlinesIntensity=aa.FLOAT(.05,{range:[0,1],rangeLocked:[!0,!1],...uj}),this.scanlinesCount=aa.FLOAT(4096,{range:[0,4096],rangeLocked:[!0,!1],...uj}),this.grayscale=aa.BOOLEAN(1,{...uj})}};class Jj extends dj{constructor(){super(...arguments),this.paramsConfig=$j}static type(){return\\\\\\\"film\\\\\\\"}_createPass(t){const e=new Yj(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 Zj={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 Qj=new class extends la{constructor(){super(...arguments),this.transparent=aa.BOOLEAN(1,uj)}};class Kj extends dj{constructor(){super(...arguments),this.paramsConfig=Qj}static type(){return\\\\\\\"FXAA\\\\\\\"}_createPass(t){const e=new zm(Zj);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 tW={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 eW=new class extends la{};class nW extends dj{constructor(){super(...arguments),this.paramsConfig=eW}static type(){return\\\\\\\"gammaCorrection\\\\\\\"}_createPass(t){const e=new zm(tW);return this.updatePass(e),e}updatePass(t){}}const iW=new class extends la{constructor(){super(...arguments),this.amount=aa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],step:.01,...uj}),this.transparent=aa.BOOLEAN(1,uj)}};class sW extends dj{constructor(){super(...arguments),this.paramsConfig=iW}static type(){return\\\\\\\"horizontalBlur\\\\\\\"}_createPass(t){const e=new zm(yG);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 rW=new class extends la{constructor(){super(...arguments),this.map=aa.OPERATOR_PATH(vi.UV,{nodeSelection:{context:ts.COP},...uj}),this.darkness=aa.FLOAT(0,{range:[0,2],rangeLocked:[!0,!1],...uj}),this.offset=aa.FLOAT(0,{range:[0,2],rangeLocked:[!0,!1],...uj})}};class oW extends dj{constructor(){super(...arguments),this.paramsConfig=rW}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 zm(oW._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()==ts.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 aW={tDiffuse:{value:null},texture1:{value:null},texture2:{value:null},h:{value:1/512}},lW=\\\\\\\"varying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvUv = uv;\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n}\\\\\\\",cW=\\\\\\\"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 hW extends Im{constructor(t,e){super(),this._composer1=t,this._composer2=e,this.uniforms=I.clone(aW),this.material=new F({uniforms:this.uniforms,vertexShader:lW,fragmentShader:cW,transparent:!0}),this.fsQuad=new Bm(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 uW=new class extends la{};class dW extends dj{constructor(){super(...arguments),this.paramsConfig=uW}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=li.renderersController.renderTarget(e.domElement.offsetWidth,e.domElement.offsetHeight,n),s=li.renderersController.renderTarget(e.domElement.offsetWidth,e.domElement.offsetHeight,n),r=new Gm(e,i),o=new Gm(e,s);r.renderToScreen=!1,o.renderToScreen=!1;const a={...t},l={...t};a.composer=r,l.composer=o,this._addPassFromInput(0,a),this._addPassFromInput(1,l);const c=new hW(r,o);this.updatePass(c),t.composer.addPass(c)}updatePass(t){}}const pW=new class extends la{constructor(){super(...arguments),this.overrideScene=aa.BOOLEAN(0,uj),this.scene=aa.OPERATOR_PATH(\\\\\\\"/scene1\\\\\\\",{visibleIf:{overrideScene:1},nodeSelection:{context:ts.OBJ,types:[DV.type()]},...uj}),this.overrideCamera=aa.BOOLEAN(0,uj),this.camera=aa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{overrideCamera:1},nodeSelection:{context:ts.OBJ},...uj}),this.inverse=aa.BOOLEAN(0,uj)}};class _W extends dj{constructor(){super(...arguments),this.paramsConfig=pW}static type(){return\\\\\\\"mask\\\\\\\"}_createPass(t){const e=new km(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 mW=new class extends la{};class fW extends dj{constructor(){super(...arguments),this.paramsConfig=mW}static type(){return\\\\\\\"null\\\\\\\"}}class gW 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 s={minFilter:w.V,magFilter:w.V,format:w.Ib},r=Math.round(this.resolution.x/this.downSampleRatio),o=Math.round(this.resolution.y/this.downSampleRatio);this.maskBufferMaterial=new lt.a({color:16777215}),this.maskBufferMaterial.side=w.z,this.renderTargetMaskBuffer=new Q(this.resolution.x,this.resolution.y,s),this.renderTargetMaskBuffer.texture.name=\\\\\\\"OutlinePass.mask\\\\\\\",this.renderTargetMaskBuffer.texture.generateMipmaps=!1,this.depthMaterial=new Sn,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 Q(this.resolution.x,this.resolution.y,s),this.renderTargetDepthBuffer.texture.name=\\\\\\\"OutlinePass.depth\\\\\\\",this.renderTargetDepthBuffer.texture.generateMipmaps=!1,this.renderTargetMaskDownSampleBuffer=new Q(r,o,s),this.renderTargetMaskDownSampleBuffer.texture.name=\\\\\\\"OutlinePass.depthDownSample\\\\\\\",this.renderTargetMaskDownSampleBuffer.texture.generateMipmaps=!1,this.renderTargetBlurBuffer1=new Q(r,o,s),this.renderTargetBlurBuffer1.texture.name=\\\\\\\"OutlinePass.blur1\\\\\\\",this.renderTargetBlurBuffer1.texture.generateMipmaps=!1,this.renderTargetBlurBuffer2=new Q(Math.round(r/2),Math.round(o/2),s),this.renderTargetBlurBuffer2.texture.name=\\\\\\\"OutlinePass.blur2\\\\\\\",this.renderTargetBlurBuffer2.texture.generateMipmaps=!1,this.edgeDetectionMaterial=this.getEdgeDetectionMaterial(),this.renderTargetEdgeBuffer1=new Q(r,o,s),this.renderTargetEdgeBuffer1.texture.name=\\\\\\\"OutlinePass.edge1\\\\\\\",this.renderTargetEdgeBuffer1.texture.generateMipmaps=!1,this.renderTargetEdgeBuffer2=new Q(Math.round(r/2),Math.round(o/2),s),this.renderTargetEdgeBuffer2.texture.name=\\\\\\\"OutlinePass.edge2\\\\\\\",this.renderTargetEdgeBuffer2.texture.generateMipmaps=!1;this.separableBlurMaterial1=this.getSeperableBlurMaterial(4),this.separableBlurMaterial1.uniforms.texSize.value.set(r,o),this.separableBlurMaterial1.uniforms.kernelRadius.value=1,this.separableBlurMaterial2=this.getSeperableBlurMaterial(4),this.separableBlurMaterial2.uniforms.texSize.value.set(Math.round(r/2),Math.round(o/2)),this.separableBlurMaterial2.uniforms.kernelRadius.value=4,this.overlayMaterial=this.getOverlayMaterial(),void 0===Rm&&console.error(\\\\\\\"THREE.OutlinePass relies on CopyShader\\\\\\\");const a=Rm;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 Bm(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,s&&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=gW.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=gW.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=gW.BlurDirectionX,t.setRenderTarget(this.renderTargetBlurBuffer2),t.clear(),this.fsQuad.render(t),this.separableBlurMaterial2.uniforms.colorTexture.value=this.renderTargetBlurBuffer2.texture,this.separableBlurMaterial2.uniforms.direction.value=gW.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,s&&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})}}gW.BlurDirectionX=new d.a(1,0),gW.BlurDirectionY=new d.a(0,1);const vW=new class extends la{constructor(){super(...arguments),this.objectsMask=aa.STRING(\\\\\\\"*outlined*\\\\\\\",{...uj}),this.refreshObjects=aa.BUTTON(null,{...uj}),this.printObjects=aa.BUTTON(null,{cook:!1,callback:t=>{yW.PARAM_CALLBACK_printResolve(t)}}),this.edgeStrength=aa.FLOAT(3,{range:[0,10],rangeLocked:[!0,!1],...uj}),this.edgeThickness=aa.FLOAT(1,{range:[0,4],rangeLocked:[!0,!1],...uj}),this.edgeGlow=aa.FLOAT(0,{range:[0,1],rangeLocked:[!0,!1],...uj}),this.pulsePeriod=aa.FLOAT(0,{range:[0,5],rangeLocked:[!0,!1],...uj}),this.visibleEdgeColor=aa.COLOR([1,1,1],{...uj}),this.hiddenEdgeColor=aa.COLOR([.2,.1,.4],{...uj})}};class yW extends dj{constructor(){super(...arguments),this.paramsConfig=vW,this._resolvedObjects=[],this._map=new Map}static type(){return\\\\\\\"outline\\\\\\\"}_createPass(t){const e=new gW(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 xW={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 bW=new class extends la{constructor(){super(...arguments),this.pixelSize=aa.INTEGER(16,{range:[1,50],rangeLocked:[!0,!1],...uj})}};class wW extends dj{constructor(){super(...arguments),this.paramsConfig=bW}static type(){return\\\\\\\"pixel\\\\\\\"}_createPass(t){const e=new zm(xW);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 TW=new class extends la{constructor(){super(...arguments),this.overrideScene=aa.BOOLEAN(0,uj),this.scene=aa.OPERATOR_PATH(\\\\\\\"/scene1\\\\\\\",{visibleIf:{overrideScene:1},nodeSelection:{context:ts.OBJ,types:[DV.type()]},...uj}),this.overrideCamera=aa.BOOLEAN(0,uj),this.camera=aa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{overrideCamera:1},nodeSelection:{context:ts.OBJ},...uj})}};class AW extends dj{constructor(){super(...arguments),this.paramsConfig=TW}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(ts.OBJ);if(e&&(e.type()==is.PERSPECTIVE||e.type()==is.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(ts.OBJ);if(e&&e.type()==DV.type()){const n=e.object;t.scene=n}}else t.scene=t.context.scene}}const MW={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 EW=new class extends la{constructor(){super(...arguments),this.amount=aa.FLOAT(.005,{range:[0,1],rangeLocked:[!0,!1],...uj}),this.angle=aa.FLOAT(0,{range:[0,10],rangeLocked:[!0,!1],...uj})}};class SW extends dj{constructor(){super(...arguments),this.paramsConfig=EW}static type(){return\\\\\\\"RGBShift\\\\\\\"}_createPass(t){const e=new zm(MW);return this.updatePass(e),e}updatePass(t){t.uniforms.amount.value=this.pv.amount,t.uniforms.angle.value=this.pv.angle}}const CW={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 NW=new class extends la{constructor(){super(...arguments),this.amount=aa.FLOAT(.5,{range:[0,2],rangeLocked:[!1,!1],...uj})}};class LW extends dj{constructor(){super(...arguments),this.paramsConfig=NW}static type(){return\\\\\\\"sepia\\\\\\\"}_createPass(t){const e=new zm(CW);return this.updatePass(e),e}updatePass(t){t.uniforms.amount.value=this.pv.amount}}const OW=new class extends la{};class PW extends dj{constructor(){super(...arguments),this.paramsConfig=OW}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 RW=I.clone(yG.uniforms);RW.delta={value:new d.a};const IW={uniforms:RW,vertexShader:yG.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 FW=new class extends la{constructor(){super(...arguments),this.delta=aa.VECTOR2([2,2],{...uj})}};class DW extends dj{constructor(){super(...arguments),this.paramsConfig=FW}static type(){return\\\\\\\"triangleBlur\\\\\\\"}_createPass(t){const e=new zm(IW);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 BW={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 zW 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 s={minFilter:w.V,magFilter:w.V,format:w.Ib};this.renderTargetsHorizontal=[],this.renderTargetsVertical=[],this.nMips=5;let r=Math.round(this.resolution.x/2),o=Math.round(this.resolution.y/2);this.renderTargetBright=new Q(r,o,s),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}\\\\\\\"})}}zW.BlurDirectionX=new d.a(1,0),zW.BlurDirectionY=new d.a(0,1);const kW=new class extends la{constructor(){super(...arguments),this.strength=aa.FLOAT(1.5,{range:[0,3],rangeLocked:[!0,!1],...uj}),this.radius=aa.FLOAT(1,{...uj}),this.threshold=aa.FLOAT(0,{...uj})}};class UW extends dj{constructor(){super(...arguments),this.paramsConfig=kW}static type(){return\\\\\\\"unrealBloom\\\\\\\"}_createPass(t){return new zW(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 GW=new class extends la{constructor(){super(...arguments),this.amount=aa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],step:.01,...uj}),this.transparent=aa.BOOLEAN(1,uj)}};class VW extends dj{constructor(){super(...arguments),this.paramsConfig=GW}static type(){return\\\\\\\"verticalBlur\\\\\\\"}_createPass(t){const e=new zm(xG);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 HW={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 jW=new class extends la{constructor(){super(...arguments),this.offset=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...uj}),this.darkness=aa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1],...uj})}};class WW extends dj{constructor(){super(...arguments),this.paramsConfig=jW}static type(){return\\\\\\\"vignette\\\\\\\"}_createPass(t){const e=new zm(HW);return this.updatePass(e),e}updatePass(t){t.uniforms.offset.value=this.pv.offset,t.uniforms.darkness.value=this.pv.darkness}}class qW extends sa{static context(){return ts.POST}cook(){this.cookController.endCook()}}class XW extends qW{}class YW extends XW{constructor(){super(...arguments),this._children_controller_context=ts.ANIM}static type(){return es.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class $W extends XW{constructor(){super(...arguments),this._children_controller_context=ts.COP}static type(){return es.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class JW extends XW{constructor(){super(...arguments),this._children_controller_context=ts.EVENT}static type(){return es.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class ZW extends XW{constructor(){super(...arguments),this._children_controller_context=ts.MAT}static type(){return es.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class QW extends qW{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=ts.POST}static type(){return es.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class KW extends XW{constructor(){super(...arguments),this._children_controller_context=ts.ROP}static type(){return es.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class tq extends sa{static context(){return ts.ROP}cook(){this.cookController.endCook()}}class eq extends tq{}class nq extends eq{constructor(){super(...arguments),this._children_controller_context=ts.COP}static type(){return es.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class iq extends K.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 iq&&t.element instanceof Element&&null!==t.element.parentNode&&t.element.parentNode.removeChild(t.element)}))}get element(){return this._element}copy(t,e){return K.a.prototype.copy.call(this,t,e),this._element=t.element.cloneNode(!0),this.matrixAutoUpdate=t.matrixAutoUpdate,this}}class sq{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 iq){this.vector.setFromMatrixPosition(t.matrixWorld),this.vector.applyMatrix4(this.viewProjectionMatrix);var i=t.element,s=\\\\\\\"translate(-50%,-50%) translate(\\\\\\\"+(this.vector.x*this._widthHalf+this._widthHalf)+\\\\\\\"px,\\\\\\\"+(-this.vector.y*this._heightHalf+this._heightHalf)+\\\\\\\"px)\\\\\\\";if(i.style.webkitTransform=s,i.style.transform=s,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=rr.fit(t,this._fog_near,this._fog_far,0,1),s=rr.clamp(1-n,0,1);i.style.opacity=`${s}`,0==s&&(i.style.display=\\\\\\\"none\\\\\\\")}this.cache_distanceToCameraSquared.set(t,e)}i.parentNode!==this.domElement&&this.domElement.appendChild(i)}for(var r=0,o=t.children.length;r{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 aq extends eq{constructor(){super(...arguments),this._children_controller_context=ts.ANIM}static type(){return es.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class lq extends eq{constructor(){super(...arguments),this._children_controller_context=ts.EVENT}static type(){return es.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class cq extends eq{constructor(){super(...arguments),this._children_controller_context=ts.MAT}static type(){return es.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class hq extends tq{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=ts.POST}static type(){return es.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class uq extends eq{constructor(){super(...arguments),this._children_controller_context=ts.ROP}static type(){return es.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class dq extends QG{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 s=[];const r=[];let o;for(let t=0;t0&&(r.push(t-1),r.push(t));if(i.length>2&&n.connectToLastPoint){i[0].position().toArray(s,s.length);const t=r[r.length-1];r.push(t),r.push(0)}const a=new S.a;a.setAttribute(\\\\\\\"position\\\\\\\",new C.c(s,3)),a.setIndex(r);const l=this.createObject(a,Cs.LINE_SEGMENTS);e.push(l)}}dq.DEFAULT_PARAMS={createPoint:!0,pointsCount:1,position:new p.a(0,0,0),connectInputPoints:!1,connectToLastPoint:!1};const pq=dq.DEFAULT_PARAMS;const _q=new class extends la{constructor(){super(...arguments),this.createPoint=aa.BOOLEAN(pq.createPoint),this.pointsCount=aa.INTEGER(pq.pointsCount,{range:[1,100],rangeLocked:[!0,!1],visibleIf:{createPoint:!0}}),this.position=aa.VECTOR3(pq.position,{visibleIf:{createPoint:!0}}),this.connectInputPoints=aa.BOOLEAN(pq.connectInputPoints),this.connectToLastPoint=aa.BOOLEAN(pq.connectToLastPoint)}};class mq extends nV{constructor(){super(...arguments),this.paramsConfig=_q}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 dq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const fq=new class extends la{};class gq extends nV{constructor(){super(...arguments),this.paramsConfig=fq}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([Ki.FROM_NODE,Ki.NEVER])}cook(t){const e=t[0],n=t[1].objects()[0],i=e.objects()[0],s=n.animations;s?(i.animations=s.map((t=>t.clone())),this.setCoreGroup(e)):this.states.error.set(\\\\\\\"no animation found\\\\\\\")}}class vq{constructor(t,e,n=null,i=e.blendMode){this._mixer=t,this._clip=e,this._localRoot=n,this.blendMode=i;const s=e.tracks,r=s.length,o=new Array(r),a={endingStart:w.id,endingEnd:w.id};for(let t=0;t!==r;++t){const e=s[t].createInterpolant(null);o[t]=e,e.settings=a}this._interpolantSettings=a,this._interpolants=o,this._propertyBindings=new Array(r),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,s=i/n,r=n/i;t.warp(1,s,e),this.warp(r,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,s=i.time,r=this.timeScale;let o=this._timeScaleInterpolant;null===o&&(o=i._lendControlInterpolant(),this._timeScaleInterpolant=o);const a=o.parameterPositions,l=o.sampleValues;return a[0]=s,a[1]=s+n,l[0]=t/r,l[1]=e/r,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 s=this._startTime;if(null!==s){const i=(t-s)*n;if(i<0||0===n)return;this._startTime=null,e=n*i}e*=this._updateTimeScale(t);const r=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(r),e[n].accumulateAdditive(o);break;case w.wb:default:for(let n=0,s=t.length;n!==s;++n)t[n].evaluate(r),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,s=this._loopCount;const r=n===w.db;if(0===t)return-1===s?i:r&&1==(1&s)?e-i:i;if(n===w.cb){-1===s&&(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===s&&(t>=0?(s=0,this._setEndings(!0,0===this.repetitions,r)):this._setEndings(0===this.repetitions,!0,r)),i>=e||i<0){const n=Math.floor(i/e);i-=e*n,s+=Math.abs(n);const o=this.repetitions-s;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,r)}else this._setEndings(!1,!1,r);this._loopCount=s,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"loop\\\\\\\",action:this,loopDelta:n})}}else this.time=i;if(r&&1==(1&s))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,s=i.time;let r=this._weightInterpolant;null===r&&(r=i._lendControlInterpolant(),this._weightInterpolant=r);const o=r.parameterPositions,a=r.sampleValues;return o[0]=s,a[0]=e,o[1]=s+t,a[1]=n,this}}var yq=n(71),xq=n(66);class bq{constructor(t,e,n){let i,s,r;switch(this.binding=t,this.valueSize=n,e){case\\\\\\\"quaternion\\\\\\\":i=this._slerp,s=this._slerpAdditive,r=this._setAdditiveIdentityQuaternion,this.buffer=new Float64Array(6*n),this._workIndex=5;break;case\\\\\\\"string\\\\\\\":case\\\\\\\"bool\\\\\\\":i=this._select,s=this._select,r=this._setAdditiveIdentityOther,this.buffer=new Array(5*n);break;default:i=this._lerp,s=this._lerpAdditive,r=this._setAdditiveIdentityNumeric,this.buffer=new Float64Array(5*n)}this._mixBufferRegion=i,this._mixBufferRegionAdditive=s,this._setIdentity=r,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,s=t*i+i;let r=this.cumulativeWeight;if(0===r){for(let t=0;t!==i;++t)n[s+t]=n[t];r=e}else{r+=e;const t=e/r;this._mixBufferRegion(n,s,0,t,i)}this.cumulativeWeight=r}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,s=this.cumulativeWeight,r=this.cumulativeWeightAdditive,o=this.binding;if(this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,s<1){const t=e*this._origIndex;this._mixBufferRegion(n,i,t,1-s,e)}r>0&&this._mixBufferRegionAdditive(n,i,this._addIndex*e,1,e);for(let t=e,s=e+e;t!==s;++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,s=i;t!==s;++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!==s;++i)t[e+i]=t[n+i]}_slerp(t,e,n,i){ah.a.slerpFlat(t,e,t,e,t,n,i)}_slerpAdditive(t,e,n,i,s){const r=this._workIndex*s;ah.a.multiplyQuaternionsFlat(t,r,t,e,t,n),ah.a.slerpFlat(t,e,t,e,t,r,i)}_lerp(t,e,n,i,s){const r=1-i;for(let o=0;o!==s;++o){const s=e+o;t[s]=t[s]*r+t[n+o]*i}}_lerpAdditive(t,e,n,i,s){for(let r=0;r!==s;++r){const s=e+r;t[s]=t[s]+t[n+r]*i}}}var wq=n(64);class Tq extends J.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,s=i.length,r=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!==s;++t){const s=i[t],l=s.name;let h=c[l];if(void 0!==h)r[t]=h;else{if(h=r[t],void 0!==h){null===h._cacheIndex&&(++h.referenceCount,this._addInactiveBinding(h,a,l));continue}const i=e&&e._propertyBindings[t].binding.parsedPath;h=new bq(xq.a.create(n,l,i),s.ValueTypeName,s.getValueSize()),++h.referenceCount,this._addInactiveBinding(h,a,l),r[t]=h}o[t].resultBuffer=h.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,s=Math.sign(t),r=this._accuIndex^=1;for(let o=0;o!==n;++o){e[o]._update(i,t,s,r)}const o=this._bindings,a=this._nActiveBindings;for(let t=0;t!==a;++t)o[t].apply(r);return this}setTime(t){this.time=0;for(let t=0;t{Mq.PARAM_CALLBACK_reset(t,e)}})}};class Mq extends nV{constructor(){super(...arguments),this.paramsConfig=Aq}static type(){return\\\\\\\"animationMixer\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to be animated\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.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(ts.ANIM)){return new Tq(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 Eq extends QG{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,s=n.mult,r=n.postAdd;for(let n=0;n({name:t,value:e})))}})}};class Bq extends nV{constructor(){super(...arguments),this.paramsConfig=Dq}static type(){return\\\\\\\"attribCast\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Iq.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.type],(()=>Oq[this.pv.type]))}))}))}cook(t){this._operation=this._operation||new Iq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class zq extends QG{static type(){return\\\\\\\"attribCopy\\\\\\\"}cook(t,e){const n=t[0],i=t[1]||n,s=i.attribNamesMatchingMask(e.name);for(let t of s)this.copy_vertex_attribute_between_core_groups(n,i,t,e);return n}copy_vertex_attribute_between_core_groups(t,e,n,i){var s;const r=e.objectsWithGeo(),o=t.objectsWithGeo();if(o.length>r.length)null===(s=this.states)||void 0===s||s.error.set(\\\\\\\"second input does not have enough objects to copy attributes from\\\\\\\");else for(let t=0;ta&&(null===(s=this.states)||void 0===s||s.error.set(\\\\\\\"not enough points in second input\\\\\\\"));const c=i.tnewName?i.newName:n;let h=t.getAttribute(c);if(h)this._fill_dest_array(h,o,i),h.needsUpdate=!0;else{const e=o.array.slice(0,l*r);t.setAttribute(c,new C.c(e,r))}}else null===(r=this.states)||void 0===r||r.error.set(`attribute '${n}' does not exist on second input`)}_fill_dest_array(t,e,n){const i=t.array,s=e.array,r=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 zq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class Vq extends QG{static type(){return\\\\\\\"attribCreate\\\\\\\"}cook(t,e){var n;const i=t[0];return e.name&&\\\\\\\"\\\\\\\"!=e.name.trim()?this._add_attribute(Fs[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=zs[n.type];switch(t){case Is.VERTEX:return void await this.add_point_attribute(i,e,n);case Is.OBJECT:return void await this.add_object_attribute(i,e,n)}ls.unreachable(t)}async add_point_attribute(t,e,n){const i=e.coreObjects();switch(t){case Bs.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(Fs[this.pv.class],t[0]):this.states.error.set(\\\\\\\"attribute name is not valid\\\\\\\");else{this._operation=this._operation||new Vq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}async _add_attribute(t,e){const n=zs[this.pv.type];switch(t){case Is.VERTEX:return await this.add_point_attribute(n,e),this.setCoreGroup(e);case Is.OBJECT:return await this.add_object_attribute(n,e),this.setCoreGroup(e)}ls.unreachable(t)}async add_point_attribute(t,e){const n=e.coreObjects();switch(t){case Bs.NUMERIC:for(let t=0;t{s[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 r;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=r.value;for(let e of n)s[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()][r]=e}));else for(let e of t){n[e.index()][r]=s.value}}for(let e=0;e{i[t.index()]=e}));const s=qs.arrayToIndexedArrays(i),r=t.coreGeometry();r&&r.setIndexedAttribute(this.pv.name,s.values,s.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,s=e[i];return s?s.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 Is.VERTEX:this.delete_vertex_attribute(e,t);case Is.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 _r(n.geometry).deleteAttribute(e)}}))}delete_object_attribute(t,e){for(let n of t.objects()){let t=0;n.traverse((n=>{new yr(n,t).deleteAttribute(e),t++}))}}}class $q{set_attrib(t){const e=t.geometry,n=t.targetAttribSize;if(n<1||n>4)return;const i=t.add,s=t.mult,r=this._data_from_texture(t.texture);if(!r)return;const{data:o,resx:a,resy:l}=r,c=o.length/(a*l),h=e.getAttribute(t.uvAttribName).array,u=h.length/2,d=new Array(u*n);let p,_,m,f,g,v,y,x,b;const w=rr.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 Jq(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var tX;!function(t){t.MIN_MAX_TO_01=\\\\\\\"min/max to 0/1\\\\\\\",t.VECTOR_TO_LENGTH_1=\\\\\\\"vectors to length 1\\\\\\\"}(tX||(tX={}));const eX=[tX.MIN_MAX_TO_01,tX.VECTOR_TO_LENGTH_1];class nX extends QG{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(),s=os.attribNames(e.name);for(let t of i){const n=t.geometry;for(let t of s){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(eX[n.mode]){case tX.MIN_MAX_TO_01:return this._normalize_from_min_max_to_01(t,e);case tX.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,s=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=aa.STRING(iX.name),this.changeName=aa.BOOLEAN(iX.changeName),this.newName=aa.STRING(iX.newName,{visibleIf:{changeName:1}})}};class rX extends nV{constructor(){super(...arguments),this.paramsConfig=sX}static type(){return\\\\\\\"attribNormalize\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(nX.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(eX.indexOf(t))}cook(t){this._operation=this._operation||new nX(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var oX;!function(t){t[t.MIN=0]=\\\\\\\"MIN\\\\\\\",t[t.MAX=1]=\\\\\\\"MAX\\\\\\\",t[t.FIRST_FOUND=2]=\\\\\\\"FIRST_FOUND\\\\\\\"}(oX||(oX={}));class aX extends QG{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=os.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 Is.VERTEX:return this.find_values_from_points(t,e);case Is.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 s=0;s{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name,this.p.classFrom,this.p.classTo],(()=>{if(\\\\\\\"\\\\\\\"!=this.pv.name){const t=Ds.filter((t=>t.value==this.pv.classFrom))[0].name,e=Ds.filter((t=>t.value==this.pv.classTo))[0].name;return`${this.pv.name} (${t} -> ${e})`}return\\\\\\\"\\\\\\\"}))}))}))}cook(t){this._operation=this._operation||new aX(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const dX=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(),this.ramp=aa.RAMP(),this.changeName=aa.BOOLEAN(0),this.newName=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{changeName:1}})}};class pX extends nV{constructor(){super(...arguments),this.paramsConfig=dX}static type(){return\\\\\\\"attribRemap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}cook(t){const e=t[0];this._remap_attribute(e),this.setCoreGroup(e)}_remap_attribute(t){const e=t.points();if(0===e.length)return;if(\\\\\\\"\\\\\\\"===this.pv.name)return;const n=e[0].attribSize(this.pv.name),i=e.map((t=>t.attribValue(this.pv.name)));let s=new Array(e.length);this._get_remaped_values(n,i,s);let r=this.pv.name;this.pv.changeName&&(r=this.pv.newName,t.hasAttrib(r)||t.addNumericVertexAttrib(r,n,0));let o=0;for(let t of s){e[o].setAttribValue(r,t),o++}}_get_remaped_values(t,e,n){switch(t){case Us.FLOAT:return this._get_normalized_float(e,n);case Us.VECTOR2:return this._get_normalized_vector2(e,n);case Us.VECTOR3:return this._get_normalized_vector3(e,n);case Us.VECTOR4:return this._get_normalized_vector4(e,n)}ls.unreachable(t)}_get_normalized_float(t,e){const n=t,i=this.p.ramp;for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.oldName,this.p.newName],(()=>\\\\\\\"\\\\\\\"!=this.pv.oldName&&\\\\\\\"\\\\\\\"!=this.pv.newName?`${this.pv.oldName} -> ${this.pv.newName}`:\\\\\\\"\\\\\\\"))}))}))}cook(t){const e=t[0];e.renameAttrib(this.pv.oldName,this.pv.newName,this.pv.class),this.setCoreGroup(e)}}var fX=n(18);class gX{constructor(t,e=0){this._bbox=t,this._level=e,this._leaves_by_octant={},this._points_by_octant_id={},this._leaves=[],this._bounding_boxes_by_octant={},this._bounding_boxes_by_octant_prepared=!1,this._center=this._bbox.max.clone().add(this._bbox.min).multiplyScalar(.5)}level(){return this._level}traverse(t){t(this);Object.values(this._leaves_by_octant).forEach((e=>{e.traverse(t)}))}intersects_sphere(t){return!!this._bbox&&this._bbox.intersectsSphere(t)}points_in_sphere(t,e){if(0==this._leaves.length){Object.values(this._points_by_octant_id).flat().filter((e=>t.containsPoint(e.position()))).forEach((t=>{e.push(t)}))}else{this._leaves.filter((e=>e.intersects_sphere(t))).forEach((n=>n.points_in_sphere(t,e)))}}bounding_box(){return this._bbox}set_points(t){this._points_by_octant_id={};for(let e of t)this.add_point(e);const e=Object.keys(this._points_by_octant_id);e.length>1&&e.forEach((t=>{this.create_leaf(t)}))}create_leaf(t){const e=this._leaf_bbox(t),n=new gX(e,this._level+1);this._leaves_by_octant[t]=n,this._leaves.push(n),n.set_points(this._points_by_octant_id[t])}add_point(t){const e=this._octant_id(t.position());null==this._points_by_octant_id[e]&&(this._points_by_octant_id[e]=[]),this._points_by_octant_id[e].push(t)}_octant_id(t){return`${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 Ay.a(n.clone().sub(e),n.clone().add(e));this._bounding_boxes_by_octant[t]=i}}}class vX{constructor(t){this._root=new gX(t)}set_points(t){this._root.set_points(t)}traverse(t){this._root.traverse(t)}find_points(t,e,n){const i=new fX.a(t,e);let s=[];return this._root.intersects_sphere(i)&&this._root.points_in_sphere(i,s),null==n||s.length>n&&(s=f.sortBy(s,(e=>e.position().distanceTo(t))),s=s.slice(0,n)),s}}class yX{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=li.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=li.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 xX=new class extends la{constructor(){super(...arguments),this.srcGroup=aa.STRING(),this.destGroup=aa.STRING(),this.name=aa.STRING(),this.maxSamplesCount=aa.INTEGER(1,{range:[1,10],rangeLocked:[!0,!1]}),this.distanceThreshold=aa.FLOAT(1),this.blendWidth=aa.FLOAT(0)}};class bX extends nV{constructor(){super(...arguments),this.paramsConfig=xX}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([Ki.FROM_NODE,Ki.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 vX(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 yX;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,s){switch(e.length){case 0:return t.attribValue(n);case 1:return this._interpolate_with_1_point(t,e[0],n,i,s);default:return this._interpolate_with_multiple_points(t,e,n,i,s)}}static _interpolate_with_1_point(t,e,n,i,s){const r=t.position(),o=e.position(),a=r.distanceTo(o),l=e.attribValue(n);return m.isNumber(l)?this._weighted_value_from_distance(t,l,n,a,i,s):(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,s,r){if(i<=s)return e;{const o=t.attribValue(n);if(m.isNumber(o)){const t=this._weight_from_distance(i,s,r);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,s){const r=e.map((e=>this._interpolate_with_1_point(t,e,n,i,s)));return f.max(r)||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 wX=new class extends la{constructor(){super(...arguments),this.stepSize=aa.FLOAT(.1)}};class TX extends nV{constructor(){super(...arguments),this.paramsConfig=wX}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(),s=i.min,r=i.max,o=[];for(let t=s.x;t<=r.x;t+=n)for(let e=s.y;e<=r.y;e+=n)for(let i=s.z;i<=r.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,Cs.POINTS)}}const AX=new class extends la{constructor(){super(...arguments),this.attribName=aa.STRING(\\\\\\\"position\\\\\\\"),this.blend=aa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0]})}};class MX extends nV{constructor(){super(...arguments),this.paramsConfig=AX}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([Ki.FROM_NODE,Ki.NEVER])}cook(t){const e=t[0],n=t[1],i=e.objects(),s=n.objects();let r,o;for(let t=0;tt.flip())),t}}EX.fromPolygons=function(t){let e=new EX;return e.polygons=t,e};class SX{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 SX(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(CX.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,s=e.z,r=t.x,o=t.y,a=t.z;return this.x=i*a-s*o,this.y=s*r-n*a,this.z=n*o-i*r,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z}}let CX=new SX,NX=new SX;class LX{constructor(t,e,n,i){this.pos=(new SX).copy(t),this.normal=(new SX).copy(e),this.uv=(new SX).copy(n),this.uv.z=0,i&&(this.color=(new SX).copy(i))}clone(){return new LX(this.pos,this.normal,this.uv,this.color)}flip(){this.normal.negate()}interpolate(t,e){return new LX(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 OX{constructor(t,e){this.normal=t,this.w=e}clone(){return new OX(this.normal.clone(),this.w)}flip(){this.normal.negate(),this.w=-this.w}splitPolygon(t,e,n,i,s){let r=0,o=[];for(let e=0;eOX.EPSILON?1:0;r|=i,o.push(i)}switch(r){case 0:(this.normal.dot(t.plane.normal)>0?e:n).push(t);break;case 1:i.push(t);break;case 2:s.push(t);break;case 3:let r=[],a=[];for(let e=0;e=3&&i.push(new PX(r,t.shared)),a.length>=3&&s.push(new PX(a,t.shared))}}}OX.EPSILON=1e-5,OX.fromPoints=function(t,e,n){let i=CX.copy(e).sub(t).cross(NX.copy(n).sub(t)).normalize();return new OX(i.clone(),i.dot(t))};class PX{constructor(t,e){this.vertices=t,this.shared=e,this.plane=OX.fromPoints(t[0].pos,t[1].pos,t[2].pos)}clone(){return new PX(this.vertices.map((t=>t.clone())),this.shared)}flip(){this.vertices.reverse().map((t=>t.flip())),this.plane.flip()}}class RX{constructor(t){this.plane=null,this.front=null,this.back=null,this.polygons=[],t&&this.build(t)}clone(){let t=new RX;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 PX(t.vertices.map((t=>new LX(t.pos,t.normal,t.uv))),t.shared))))},EX.fromGeometry=function(t,e){let n=[];if(t.isGeometry){let i=t.faces,s=t.vertices,r=[\\\\\\\"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}}),BX=t=>({top:0,array:new Float32Array(t),write:function(t){this.array[this.top++]=t.x,this.array[this.top++]=t.y}});var zX;EX.toMesh=function(t,e,n){let i,s,r=t.polygons;{let t=0;r.forEach((e=>t+=e.vertices.length-2)),i=new S.a;let e,n=DX(3*t*3),o=DX(3*t*3),a=BX(2*t*3),l=[];if(r.forEach((i=>{let s=i.vertices,r=s.length;void 0!==i.shared&&(l[i.shared]||(l[i.shared]=[])),r&&void 0!==s[0].color&&(e||(e=DX(3*t*3)));for(let t=3;t<=r;t++)void 0!==i.shared&&l[i.shared].push(n.top/3,n.top/3+1,n.top/3+2),n.write(s[0].pos),n.write(s[t-2].pos),n.write(s[t-1].pos),o.write(s[0].normal),o.write(s[t-2].normal),o.write(s[t-1].normal),a.write(s[0].uv),a.write(s[t-2].uv),a.write(s[t-1].uv),e&&(e.write(s[0].color)||e.write(s[t-2].color)||e.write(s[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=aa.BOOLEAN(GX.useBothMaterials)}};class HX extends nV{constructor(){super(...arguments),this.paramsConfig=VX}static type(){return\\\\\\\"boolean\\\\\\\"}initializeNode(){super.initializeNode(),this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState(UX.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.operation],(()=>kX[this.pv.operation]))}))}))}setOperation(t){this.p.operation.set(kX.indexOf(t))}async cook(t){this._operation=this._operation||new UX(this.scene(),this.states,this);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class jX extends QG{constructor(){super(...arguments),this._core_transform=new uU}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(),s=i.max.clone().sub(i.min),r=i.max.clone().add(i.min).multiplyScalar(.5),o=new N(s.x,s.y,s.z,n,n,n),a=this._core_transform.translation_matrix(r);return o.applyMatrix4(a),o}}jX.DEFAULT_PARAMS={size:1,divisions:1,center:new p.a(0,0,0)},jX.INPUT_CLONED_STATE=Ki.NEVER;const WX=jX.DEFAULT_PARAMS;const qX=new class extends la{constructor(){super(...arguments),this.size=aa.FLOAT(WX.size),this.divisions=aa.INTEGER(WX.divisions,{range:[1,10],rangeLocked:[!0,!1]}),this.center=aa.VECTOR3(WX.center)}};class XX extends nV{constructor(){super(...arguments),this.paramsConfig=qX}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(jX.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new jX(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class YX{constructor(){}}function $X(t,e,n){return n.min.x=e[t],n.min.y=e[t+1],n.min.z=e[t+2],n.max.x=e[t+3],n.max.y=e[t+4],n.max.z=e[t+5],n}function JX(t){let e=-1,n=-1/0;for(let i=0;i<3;i++){const s=t[i+3]-t[i];s>n&&(n=s,e=i)}return e}function ZX(t,e){e.set(t)}function QX(t,e,n){let i,s;for(let r=0;r<3;r++){const o=r+3;i=t[r],s=e[r],n[r]=is?i:s}}function KX(t){const e=t[3]-t[0],n=t[4]-t[1],i=t[5]-t[2];return 2*(e*n+n*i+i*e)}const tY=Math.pow(2,-24);function eY(t,e,n,i,s=null){let r=1/0,o=1/0,a=1/0,l=-1/0,c=-1/0,h=-1/0,u=1/0,d=1/0,p=1/0,_=-1/0,m=-1/0,f=-1/0;const g=null!==s;for(let i=6*e,s=6*(e+n);il&&(l=v),g&&e_&&(_=e);const y=t[i+2],x=t[i+3],b=y-x,w=y+x;bc&&(c=w),g&&ym&&(m=y);const T=t[i+4],A=t[i+5],M=T-A,E=T+A;Mh&&(h=E),g&&Tf&&(f=T)}i[0]=r,i[1]=o,i[2]=a,i[3]=l,i[4]=c,i[5]=h,g&&(s[0]=u,s[1]=d,s[2]=p,s[3]=_,s[4]=m,s[5]=f)}const nY=32,iY=new Array(nY).fill().map((()=>({count:0,bounds:new Float32Array(6),rightCacheBounds:new Float32Array(6),candidate:0}))),sY=new Float32Array(6);function rY(t,e){function n(e,i,d,p=null,_=0){if(!u&&_>=a&&(u=!0,l&&(console.warn(`MeshBVH: Max depth of ${a} reached when generating BVH. Consider increasing maxDepth.`),console.warn(t))),d<=c||_>=a)return e.offset=i,e.count=d,e;const m=function(t,e,n,i,s,r){let o=-1,a=0;if(0===r)o=JX(e),-1!==o&&(a=(e[o]+e[o+3])/2);else if(1===r)o=JX(t),-1!==o&&(a=function(t,e,n,i){let s=0;for(let r=e,o=e+n;r=nY&&(s=31);const r=iY[s];r.count++;const o=r.bounds;for(let t=0;t<3;t++){const i=n[e+2*t],s=n[e+2*t+1],r=i-s,a=i+s;ro[t+3]&&(o[t+3]=a)}}const d=iY[31];ZX(d.bounds,d.rightCacheBounds);for(let t=30;t>=0;t--){const e=iY[t],n=iY[t+1];QX(e.bounds,n.rightCacheBounds,e.rightCacheBounds)}let p=0;for(let e=0;e<31;e++){const n=iY[e],i=n.count,c=n.bounds,h=iY[e+1].rightCacheBounds;0!==i&&(0===p?ZX(c,sY):QX(c,sY,sY)),p+=i;let u=0,d=0;0!==p&&(u=KX(sY)/r);const _=s-p;0!==_&&(d=KX(h)/r);const m=1+1.25*(u*p+d*_);m=a;)o--;if(!(r65535?new Uint32Array(new i(4*n)):new Uint16Array(new i(2*n)),t.setIndex(new Hw(s,1));for(let t=0;td&&(d=s),a>d&&(d=a);const p=(d-l)/2,_=2*t;o[r+_+0]=l+p,o[r+_+1]=p+(Math.abs(l)+p)*tY,le[t+3]&&(e[t+3]=d)}}return o}(t,i),o=t.index.array,a=e.maxDepth,l=e.verbose,c=e.maxLeafTris,h=e.strategy;let u=!1;const d=[],p=function(t){if(!t.groups||!t.groups.length)return[{offset:0,count:t.index.count/3}];const e=[],n=new Set;for(const e of t.groups)n.add(e.start),n.add(e.start+e.count);const i=Array.from(n.values()).sort(((t,e)=>t-e));for(let t=0;ta&&(a=e);const n=t[i+2];nl&&(l=n);const h=t[i+4];hc&&(c=h)}i[0]=s,i[1]=r,i[2]=o,i[3]=a,i[4]=l,i[5]=c}(r,t.offset,t.count,s),n(e,t.offset,t.count,s),d.push(e)}else for(let t of p){const e=new YX;e.boundingData=new Float32Array(6),eY(r,t.offset,t.count,e.boundingData,s),n(e,t.offset,t.count,s),d.push(e)}return d}const oY=65535;class aY{constructor(){this.min=1/0,this.max=-1/0}setFromPointsField(t,e){let n=1/0,i=-1/0;for(let s=0,r=t.length;si?r:i}this.min=n,this.max=i}setFromPoints(t,e){let n=1/0,i=-1/0;for(let s=0,r=e.length;si?o:i}this.min=n,this.max=i}isSeparated(t){return this.min>t.max||t.min>this.max}}aY.prototype.setFromBox=function(){const t=new gb;return function(e,n){const i=n.min,s=n.max;let r=1/0,o=-1/0;for(let n=0;n<=1;n++)for(let a=0;a<=1;a++)for(let l=0;l<=1;l++){t.x=i.x*n+s.x*(1-n),t.y=i.y*a+s.y*(1-a),t.z=i.z*l+s.z*(1-l);const c=e.dot(t);r=Math.min(c,r),o=Math.max(c,o)}this.min=r,this.max=o}}();!function(){const t=new aY}();const lY=function(){const t=new gb,e=new gb,n=new gb;return function(i,s,r){const o=i.start,a=t,l=s.start,c=e;n.subVectors(o,l),t.subVectors(i.end,s.start),e.subVectors(s.end,s.start);const h=n.dot(c),u=c.dot(a),d=c.dot(c),p=n.dot(a),_=a.dot(a)*d-u*u;let m,f;m=0!==_?(h*u-p*d)/_:0,f=(h+m*u)/d,r.x=m,r.y=f}}(),cY=function(){const t=new sb,e=new gb,n=new gb;return function(i,s,r,o){lY(i,s,t);let a=t.x,l=t.y;if(a>=0&&a<=1&&l>=0&&l<=1)return i.at(a,r),void s.at(l,o);if(a>=0&&a<=1)return l<0?s.at(0,o):s.at(1,o),void i.closestPointToPoint(o,!0,r);if(l>=0&&l<=1)return a<0?i.at(0,r):i.at(1,r),void s.closestPointToPoint(r,!0,o);{let t,c;t=a<0?i.start:i.end,c=l<0?s.start:s.end;const h=e,u=n;return i.closestPointToPoint(c,!0,e),s.closestPointToPoint(t,!0,n),h.distanceToSquared(c)<=u.distanceToSquared(t)?(r.copy(h),void o.copy(c)):(r.copy(t),void o.copy(u))}}}(),hY=function(){const t=new gb,e=new gb,n=new IT,i=new YN;return function(s,r){const{radius:o,center:a}=s,{a:l,b:c,c:h}=r;i.start=l,i.end=c;if(i.closestPointToPoint(a,!0,t).distanceTo(a)<=o)return!0;i.start=l,i.end=h;if(i.closestPointToPoint(a,!0,t).distanceTo(a)<=o)return!0;i.start=c,i.end=h;if(i.closestPointToPoint(a,!0,t).distanceTo(a)<=o)return!0;const u=r.getPlane(n);if(Math.abs(u.distanceToPoint(a))<=o){const t=u.projectPoint(a,e);if(r.containsPoint(t))return!0}return!1}}();class uY extends Lw{constructor(...t){super(...t),this.isSeparatingAxisTriangle=!0,this.satAxes=new Array(4).fill().map((()=>new gb)),this.satBounds=new Array(4).fill().map((()=>new aY)),this.points=[this.a,this.b,this.c],this.sphere=new kb,this.plane=new IT,this.needsUpdate=!1}intersectsSphere(t){return hY(t,this)}update(){const t=this.a,e=this.b,n=this.c,i=this.points,s=this.satAxes,r=this.satBounds,o=s[0],a=r[0];this.getNormal(o),a.setFromPoints(o,i);const l=s[1],c=r[1];l.subVectors(t,e),c.setFromPoints(l,i);const h=s[2],u=r[2];h.subVectors(e,n),u.setFromPoints(h,i);const d=s[3],p=r[3];d.subVectors(n,t),p.setFromPoints(d,i),this.sphere.setFromPoints(this.points),this.plane.setFromNormalAndCoplanarPoint(o,t),this.needsUpdate=!1}}uY.prototype.closestPointToSegment=function(){const t=new gb,e=new gb,n=new YN;return function(i,s=null,r=null){const{start:o,end:a}=i,l=this.points;let c,h=1/0;for(let o=0;o<3;o++){const a=(o+1)%3;n.start.copy(l[o]),n.end.copy(l[a]),cY(n,i,t,e),c=t.distanceToSquared(e),c1-1e-10)console.warn(\\\\\\\"SeparatingAxisTriangle.intersectsTriangle: Triangles are coplanar which does not support an output edge. Setting edge to 0, 0, 0.\\\\\\\"),p.start.set(0,0,0),p.end.set(0,0,0);else{const n=this.points;let i=!1;for(let t=0;t<3;t++){const s=n[t],r=n[(t+1)%3];if(c.start.copy(s),c.end.copy(r),e.intersectLine(c,i?h.start:h.end)){if(i)break;i=!0}}const s=d.points;let r=!1;for(let e=0;e<3;e++){const n=s[e],i=s[(e+1)%3];if(c.start.copy(n),c.end.copy(i),t.intersectLine(c,r?u.start:u.end)){if(r)break;r=!0}}if(h.delta(o),u.delta(a),o.dot(a)<0){let t=u.start;u.start=u.end,u.end=t}l.subVectors(h.start,u.start),l.dot(o)>0?p.start.copy(h.start):p.start.copy(u.start),l.subVectors(h.end,u.end),l.dot(o)<0?p.end.copy(h.end):p.end.copy(u.end)}}return!0}}(),uY.prototype.distanceToPoint=function(){const t=new gb;return function(e){return this.closestPointToPoint(e,t),e.distanceTo(t)}}(),uY.prototype.distanceToTriangle=function(){const t=new gb,e=new gb,n=[\\\\\\\"a\\\\\\\",\\\\\\\"b\\\\\\\",\\\\\\\"c\\\\\\\"],i=new YN,s=new YN;return function(r,o=null,a=null){const l=o||a?i:null;if(this.intersectsTriangle(r,l))return(o||a)&&(o&&l.getCenter(o),a&&l.getCenter(a)),0;let c=1/0;for(let e=0;e<3;e++){let i;const s=n[e],l=r[s];this.closestPointToPoint(l,t),i=l.distanceToSquared(t),inew gb)),this.satAxes=new Array(3).fill().map((()=>new gb)),this.satBounds=new Array(3).fill().map((()=>new aY)),this.alignedSatBounds=new Array(3).fill().map((()=>new aY)),this.needsUpdate=!1}set(t,e,n){super.set(t,e),this.matrix=n,this.needsUpdate=!0}copy(t){super.copy(t),this.matrix.copy(t.matrix),this.needsUpdate=!0}}dY.prototype.update=function(){const t=this.matrix,e=this.min,n=this.max,i=this.points;for(let s=0;s<=1;s++)for(let r=0;r<=1;r++)for(let o=0;o<=1;o++){const a=i[1*s|2*r|4*o];a.x=s?n.x:e.x,a.y=r?n.y:e.y,a.z=o?n.z:e.z,a.applyMatrix4(t)}const s=this.satBounds,r=this.satAxes,o=i[0];for(let t=0;t<3;t++){const e=r[t],n=s[t],a=i[1<new YN)),n=new Array(12).fill().map((()=>new YN)),i=new gb,s=new gb;return function(r,o=0,a=null,l=null){if(this.needsUpdate&&this.update(),this.intersectsBox(r))return(a||l)&&(r.getCenter(s),this.closestPointToPoint(s,i),r.closestPointToPoint(i,s),a&&a.copy(i),l&&l.copy(s)),0;const c=o*o,h=r.min,u=r.max,d=this.points;let p=1/0;for(let t=0;t<8;t++){const e=d[t];s.copy(e).clamp(h,u);const n=e.distanceToSquared(s);if(nn.far?null:t)}function wY(t,e,n,i){const s=t.a,r=t.b,o=t.c;let a=e,l=e+1,c=e+2;n&&(a=n.getX(e),l=n.getX(e+1),c=n.getX(e+2)),s.x=i.getX(a),s.y=i.getY(a),s.z=i.getZ(a),r.x=i.getX(l),r.y=i.getY(l),r.z=i.getZ(l),o.x=i.getX(c),o.y=i.getY(c),o.z=i.getZ(c)}function TY(t,e,n,i,s,r,o){const a=n.index,l=n.attributes.position;for(let n=t,c=e+t;n=0;let c,h;l?(c=CY(t),h=NY(t,a)):(c=NY(t,a),h=CY(t));const u=BY(c,r,i,OY)?IY(c,e,n,i):null;if(u){const t=u.point[o];if(l?t<=r[h+s]:t>=r[h+s+3])return u}const d=BY(h,r,i,OY)?IY(h,e,n,i):null;return u&&d?u.distance<=d.distance?u:d:u||d||null}}const FY=function(){let t,e;const n=[],i=new AY((()=>new xb));return function(...r){t=i.getPrimitive(),e=i.getPrimitive(),n.push(t,e);const o=s(...r);i.releasePrimitive(t),i.releasePrimitive(e),n.pop(),n.pop();const a=n.length;return a>0&&(e=n[a-1],t=n[a-2]),o};function s(n,i,r,o,a=null,l=0,c=0){function h(t){let e=2*t,n=GY,i=VY;for(;!MY(e,n);)e=2*(t=CY(t));return EY(t,i)}function u(t){let e=2*t,n=GY,i=VY;for(;!MY(e,n);)e=2*(t=NY(t,i));return EY(t,i)+SY(e,n)}let d=2*n,p=UY,_=GY,m=VY;if(MY(d,_)){const e=EY(n,m),i=SY(d,_);return $X(n,p,t),o(e,i,!1,c,l+n,t)}{const d=CY(n),f=NY(n,m);let g,v,y,x,b=d,w=f;if(a&&(y=t,x=e,$X(b,p,y),$X(w,p,x),g=a(y),v=a(x),vs.intersectsBox(t),intersectsTriangle:t=>{t.a.applyMatrix4(c),t.b.applyMatrix4(c),t.c.applyMatrix4(c),t.needsUpdate=!0;for(let n=3*g,i=3*(v+g);nnew uY));class s${static serialize(t,e={}){if(e.isBufferGeometry)return console.warn(\\\\\\\"MeshBVH.serialize: The arguments for the function have changed. See documentation for new signature.\\\\\\\"),s$.serialize(arguments[0],{cloneBuffers:void 0===arguments[2]||arguments[2]});e={cloneBuffers:!0,...e};const n=t.geometry,i=t._roots,s=n.getIndex();let r;return r=e.cloneBuffers?{roots:i.map((t=>t.slice())),index:s.array.slice()}:{roots:i,index:s.array},r}static deserialize(t,e,n={}){if(\\\\\\\"boolean\\\\\\\"==typeof n)return console.warn(\\\\\\\"MeshBVH.deserialize: The arguments for the function have changed. See documentation for new signature.\\\\\\\"),s$.deserialize(arguments[0],arguments[1],{setIndex:void 0===arguments[2]||arguments[2]});n={setIndex:!0,...n};const{index:i,roots:s}=t,r=new s$(e,{...n,[WY]:!0});if(r._roots=s,n.setIndex){const n=e.getIndex();if(null===n){const n=new Hw(t.index,1,!1);e.setIndex(n)}else n.array!==i&&(n.array.set(i),n.needsUpdate=!0)}return r}constructor(t,e={}){if(!t.isBufferGeometry)throw new Error(\\\\\\\"MeshBVH: Only BufferGeometries are supported.\\\\\\\");if(t.index&&t.index.isInterleavedBufferAttribute)throw new Error(\\\\\\\"MeshBVH: InterleavedBufferAttribute is not supported for the index attribute.\\\\\\\");if((e=Object.assign({strategy:0,maxDepth:40,maxLeafTris:10,verbose:!0,useSharedArrayBuffer:!1,setBoundingBox:!0,[WY]:!1},e)).useSharedArrayBuffer&&\\\\\\\"undefined\\\\\\\"==typeof SharedArrayBuffer)throw new Error(\\\\\\\"MeshBVH: SharedArrayBuffer is not available.\\\\\\\");this._roots=null,e[WY]||(this._roots=function(t,e){const n=rY(t,e);let i,s,r;const o=[],a=e.useSharedArrayBuffer?SharedArrayBuffer:ArrayBuffer;for(let t=0;tMath.pow(2,32))throw new Error(\\\\\\\"MeshBVH: Cannot store child pointer greater than 32 bits.\\\\\\\");return s[n+6]=a/4,a=c(a,r),s[n+7]=o,a}}}(t,e),!t.boundingBox&&e.setBoundingBox&&(t.boundingBox=this.getBoundingBox(new xb))),this.geometry=t}refit(t=null){t&&Array.isArray(t)&&(t=new Set(t));const e=this.geometry,n=e.index.array,i=e.attributes.position,s=i.array,r=i.offset||0;let o,a,l,c,h=3;i.isInterleavedBufferAttribute&&(h=i.data.stride);let u=0;const d=this._roots;for(let t=0,e=d.length;tp&&(p=a),l_&&(_=l),cm&&(m=c)}return(c[e+0]!==i||c[e+1]!==o||c[e+2]!==d||c[e+3]!==p||c[e+4]!==_||c[e+5]!==m)&&(c[e+0]=i,c[e+1]=o,c[e+2]=d,c[e+3]=p,c[e+4]=_,c[e+5]=m,!0)}{const n=e+8,s=a[e+6],r=n+i,l=s+i;let h=o,u=!1,d=!1;t?h||(u=t.has(r),d=t.has(l),h=!u&&!d):(u=!0,d=!0);const _=h||d;let m=!1;(h||u)&&(m=p(n,i,h));let f=!1;_&&(f=p(s,i,h));const g=m||f;if(g)for(let t=0;t<3;t++){const i=n+t,r=s+t,o=c[i],a=c[i+3],l=c[r],h=c[r+3];c[e+t]=oh?a:h}return g}}}traverse(t,e=0){const n=this._roots[e],i=new Uint32Array(n),s=new Uint16Array(n);!function e(r,o=0){const a=2*r,l=s[a+15]===oY;if(l){const e=i[r+6],c=s[a+14];t(o,l,new Float32Array(n,4*r,6),e,c)}else{const s=r+8,a=i[r+6],c=i[r+7];t(o,l,new Float32Array(n,4*r,6),c)||(e(s,o+1),e(a,o+1))}}(0)}raycast(t,e=0){const n=this._roots,i=this.geometry,s=[],r=e.isMaterial,o=Array.isArray(e),a=i.groups,l=r?e.side:e;for(let r=0,c=n.length;r{const r=3*n;return t(e,r,r+1,r+2,i,s)}}t={boundsTraverseOrder:n,intersectsBounds:t,intersectsTriangle:e,intersectsRange:null},console.warn(\\\\\\\"MeshBVH: Shapecast function signature has changed and now takes an object of callbacks as a second argument. See docs for new signature.\\\\\\\")}const s=i$.getPrimitive();let{boundsTraverseOrder:r,intersectsBounds:o,intersectsRange:a,intersectsTriangle:l}=t;if(a&&l){const t=a;a=(e,n,r,o,a)=>!!t(e,n,r,o,a)||TY(e,n,i,l,r,o,s)}else a||(a=l?(t,e,n,r)=>TY(t,e,i,l,n,r,s):(t,e,n)=>n);let c=!1,h=0;for(const t of this._roots){if(HY(t),c=FY(0,i,o,a,r,h),jY(),c)break;h+=t.byteLength}return i$.releasePrimitive(s),c}bvhcast(t,e,n){let{intersectsRanges:i,intersectsTriangles:s}=n;const r=t.geometry,o=r.index,a=r.attributes.position;YY.copy(e).invert();const l=i$.getPrimitive(),c=i$.getPrimitive();if(s){function h(t,n,i,r,h,u,d,p){for(let _=i,m=i+r;_XY.intersectsBox(t),intersectsRange:(e,n,s,r,o,a)=>(qY.copy(a),qY.applyMatrix4(YY),t.shapecast({intersectsBounds:t=>qY.intersectsBox(t),intersectsRange:(t,s,a,l,c)=>i(e,n,t,s,r,o,l,c)}))});return i$.releasePrimitive(l),i$.releasePrimitive(c),u}intersectsBox(t,e){return $Y.set(t.min,t.max,e),$Y.needsUpdate=!0,this.shapecast({intersectsBounds:t=>$Y.intersectsBox(t),intersectsTriangle:t=>$Y.intersectsTriangle(t)})}intersectsSphere(t){return this.shapecast({intersectsBounds:e=>t.intersectsBox(e),intersectsTriangle:e=>e.intersectsSphere(t)})}closestPointToGeometry(t,e,n={},i={},s=0,r=1/0){t.boundingBox||t.computeBoundingBox(),$Y.set(t.boundingBox.min,t.boundingBox.max,e),$Y.needsUpdate=!0;const o=this.geometry,a=o.attributes.position,l=o.index,c=t.attributes.position,h=t.index,u=i$.getPrimitive(),d=i$.getPrimitive();let p=QY,_=KY,m=null,f=null;i&&(m=t$,f=e$);let g=1/0,v=null,y=null;return YY.copy(e).invert(),JY.matrix.copy(YY),this.shapecast({boundsTraverseOrder:t=>$Y.distanceToBox(t,Math.min(g,r)),intersectsBounds:(t,e,n)=>n{if(t.boundsTree)return t.boundsTree.shapecast({boundsTraverseOrder:t=>JY.distanceToBox(t,Math.min(g,r)),intersectsBounds:(t,e,n)=>n{for(let o=3*t,x=3*(t+r);o(ZY.copy(t).clamp(e.min,e.max),ZY.distanceToSquared(t)),intersectsBounds:(t,e,n)=>n{e.closestPointToPoint(t,ZY);const i=t.distanceToSquared(ZY);return i{$X(0,new Float32Array(e),n$),t.union(n$)})),t}}const r$=s$.prototype.raycast;s$.prototype.raycast=function(...t){if(t[0].isMesh){console.warn('MeshBVH: The function signature and results frame for \\\\\\\"raycast\\\\\\\" has changed. See docs for new signature.');const[e,n,i,s]=t;return r$.call(this,i,e.material).forEach((t=>{(t=bY(t,e,n))&&s.push(t)})),s}return r$.apply(this,t)};const o$=s$.prototype.raycastFirst;s$.prototype.raycastFirst=function(...t){if(t[0].isMesh){console.warn('MeshBVH: The function signature and results frame for \\\\\\\"raycastFirst\\\\\\\" has changed. See docs for new signature.');const[e,n,i]=t;return bY(o$.call(this,i,e.material),e,n)}return o$.apply(this,t)};const a$=s$.prototype.closestPointToPoint;s$.prototype.closestPointToPoint=function(...t){if(t[0].isMesh){console.warn('MeshBVH: The function signature and results frame for \\\\\\\"closestPointToPoint\\\\\\\" has changed. See docs for new signature.'),t.unshift();const e=t[1],n={};return t[1]=n,a$.apply(this,t),e&&e.copy(n.point),n.distance}return a$.apply(this,t)};const l$=s$.prototype.closestPointToGeometry;s$.prototype.closestPointToGeometry=function(...t){const e=t[2],n=t[3];if(e&&e.isVector3||n&&n.isVector3){console.warn('MeshBVH: The function signature and results frame for \\\\\\\"closestPointToGeometry\\\\\\\" has changed. See docs for new signature.');const i={},s={},r=t[1];return t[2]=i,t[3]=s,l$.apply(this,t),e&&e.copy(i.point),n&&n.copy(s.point).applyMatrix4(r),i.distance}return l$.apply(this,t)};const c$=s$.prototype.refit;s$.prototype.refit=function(...t){const e=t[0],n=t[1];if(n&&(n instanceof Set||Array.isArray(n))){console.warn('MeshBVH: The function signature for \\\\\\\"refit\\\\\\\" has changed. See docs for new signature.');const t=new Set;n.forEach((e=>t.add(e))),e&&e.forEach((e=>t.add(e))),c$.call(this,t)}else c$.apply(this,t)},[\\\\\\\"intersectsGeometry\\\\\\\",\\\\\\\"shapecast\\\\\\\",\\\\\\\"intersectsBox\\\\\\\",\\\\\\\"intersectsSphere\\\\\\\"].forEach((t=>{const e=s$.prototype[t];s$.prototype[t]=function(...n){return(null===n[0]||n[0].isMesh)&&(n.shift(),console.warn(`MeshBVH: The function signature for \\\\\\\"${t}\\\\\\\" has changed and no longer takes Mesh. See docs for new signature.`)),e.apply(this,n)}}));const h$=new Xb,u$=new Yb,d$=vT.prototype.raycast;function p$(t,e){if(this.geometry.boundsTree){if(void 0===this.material)return;u$.copy(this.matrixWorld).invert(),h$.copy(t.ray).applyMatrix4(u$);const n=this.geometry.boundsTree;if(!0===t.firstHitOnly){const i=bY(n.raycastFirst(h$,this.material),this,t);i&&e.push(i)}else{const i=n.raycast(h$,this.material);for(let n=0,s=i.length;n{if(t===i||e)return r++,!0;s&&r++}),n);let o=0;const a=new Float32Array(24*r);let l,c;e.traverse(((t,e,n)=>{const r=t===i||e;if(r||s){$X(0,n,_$);const{min:t,max:e}=_$;for(let n=-1;n<=1;n+=2){const i=n<0?t.x:e.x;for(let n=-1;n<=1;n+=2){const s=n<0?t.y:e.y;for(let n=-1;n<=1;n+=2){const r=n<0?t.z:e.z;a[o+0]=i,a[o+1]=s,a[o+2]=r,o+=3}}}return r}}),n),c=this.displayEdges?new Uint8Array([0,4,1,5,2,6,3,7,0,2,1,3,4,6,5,7,0,1,2,3,4,5,6,7]):new Uint8Array([0,1,2,2,1,3,4,6,5,6,7,5,1,4,5,0,4,1,2,3,6,3,7,6,0,2,4,2,6,4,1,5,3,3,5,7]),l=a.length>65535?new Uint32Array(c.length*r):new Uint16Array(c.length*r);const h=c.length;for(let t=0;te;)this._roots.pop();for(let t=0;t=this._roots.length){const e=new m$(this.mesh,this.edgeMaterial,this.depth,t);this.add(e),this._roots.push(e)}const e=this._roots[t];e.depth=this.depth,e.mesh=this.mesh,e.displayParents=this.displayParents,e.displayEdges=this.displayEdges,e.material=this.displayEdges?this.edgeMaterial:this.meshMaterial,e.update()}}updateMatrixWorld(...t){this.position.copy(this.mesh.position),this.rotation.copy(this.mesh.rotation),this.scale.copy(this.mesh.scale),super.updateMatrixWorld(...t)}copy(t){this.depth=t.depth,this.mesh=t.mesh}clone(){return new f$(this.mesh,this.depth)}dispose(){this.edgeMaterial.dispose(),this.meshMaterial.dispose();const t=this.children;for(let e=0,n=t.length;e{const e=t;if(e.isMesh){const t=e.geometry;for(const e in t.attributes)\\\\\\\"position\\\\\\\"!==e&&t.deleteAttribute(e);n.push(e)}}))}const i=this._makeCompact(n);if(i){i.matrixAutoUpdate=!1,i.raycast=p$;const t=new s$(i.geometry,{verbose:!1});return i.geometry.boundsTree=t,this.createCoreGroupFromObjects([i])}return this.createCoreGroupFromObjects([])}_makeCompact(t){var e,n;const i=[];let s;for(let e of t){s=s||e.material;const t=e.geometry;t.applyMatrix4(e.matrix),i.push(t)}try{const t=_r.mergeGeometries(i);if(t){return this.createObject(t,Cs.MESH,s)}null===(e=this.states)||void 0===e||e.error.set(\\\\\\\"merge failed, check that input geometries have the same attributes\\\\\\\")}catch(t){null===(n=this.states)||void 0===n||n.error.set(t.message)}}}g$.DEFAULT_PARAMS={},g$.INPUT_CLONED_STATE=Ki.ALWAYS;const v$=new class extends la{};class y$ extends nV{constructor(){super(...arguments),this.paramsConfig=v$}static type(){return\\\\\\\"BVH\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create BVH from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(g$.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new g$(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class x$ extends QG{static type(){return\\\\\\\"BVHVisualizer\\\\\\\"}cook(t,e){const n=t[0].objects()[0],i=new f$(n,e.depth);return i.opacity=1,i.update(),this.createCoreGroupFromObjects([i])}}x$.DEFAULT_PARAMS={depth:0},x$.INPUT_CLONED_STATE=Ki.NEVER;const b$=x$.DEFAULT_PARAMS;const w$=new class extends la{constructor(){super(...arguments),this.depth=aa.INTEGER(b$.depth,{range:[0,20],rangeLocked:[!0,!1]})}};class T$ extends nV{constructor(){super(...arguments),this.paramsConfig=w$}static type(){return\\\\\\\"BVHVisualizer\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry with bvh\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(x$.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new x$(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class A$ 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}}A$.prototype.isInstancedBufferAttribute=!0;const M$=new A.a,E$=new A.a,S$=[],C$=new B.a;class N$ extends B.a{constructor(t,e,n){super(t,e),this.instanceMatrix=new A$(new Float32Array(16*n),16),this.instanceColor=null,this.count=n,this.frustumCulled=!1}copy(t){return super.copy(t),this.instanceMatrix.copy(t.instanceMatrix),null!==t.instanceColor&&(this.instanceColor=t.instanceColor.clone()),this.count=t.count,this}getColorAt(t,e){e.fromArray(this.instanceColor.array,3*t)}getMatrixAt(t,e){e.fromArray(this.instanceMatrix.array,16*t)}raycast(t,e){const n=this.matrixWorld,i=this.count;if(C$.geometry=this.geometry,C$.material=this.material,void 0!==C$.material)for(let s=0;st.far||e.push({distance:a,point:O$.clone(),uv:Ks.a.getUV(O$,B$,z$,k$,U$,G$,V$,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 j$(t,e,n,i,s,r){I$.subVectors(t,n).addScalar(.5).multiply(i),void 0!==s?(F$.x=r*I$.x-s*I$.y,F$.y=s*I$.x+r*I$.y):F$.copy(I$),t.copy(e),t.x+=F$.x,t.y+=F$.y,t.applyMatrix4(D$)}H$.prototype.isSprite=!0;var W$=n(92),q$=n(81),X$=n(46);class Y${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,s=t.z,r=this.coefficients;return e.copy(r[0]).multiplyScalar(.282095),e.addScaledVector(r[1],.488603*i),e.addScaledVector(r[2],.488603*s),e.addScaledVector(r[3],.488603*n),e.addScaledVector(r[4],n*i*1.092548),e.addScaledVector(r[5],i*s*1.092548),e.addScaledVector(r[6],.315392*(3*s*s-1)),e.addScaledVector(r[7],n*s*1.092548),e.addScaledVector(r[8],.546274*(n*n-i*i)),e}getIrradianceAt(t,e){const n=t.x,i=t.y,s=t.z,r=this.coefficients;return e.copy(r[0]).multiplyScalar(.886227),e.addScaledVector(r[1],1.023328*i),e.addScaledVector(r[2],1.023328*s),e.addScaledVector(r[3],1.023328*n),e.addScaledVector(r[4],.858086*n*i),e.addScaledVector(r[5],.858086*i*s),e.addScaledVector(r[6],.743125*s*s-.247708),e.addScaledVector(r[7],.858086*n*s),e.addScaledVector(r[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,s=t.z;e[0]=.282095,e[1]=.488603*i,e[2]=.488603*s,e[3]=.488603*n,e[4]=1.092548*n*i,e[5]=1.092548*i*s,e[6]=.315392*(3*s*s-1),e[7]=1.092548*n*s,e[8]=.546274*(n*n-i*i)}}Y$.prototype.isSphericalHarmonics3=!0;class $$ extends sv.a{constructor(t=new Y$,e=1){super(void 0,e),this.sh=t}copy(t){return super.copy(t),this.sh.copy(t.sh),this}fromJSON(t){return this.intensity=t.intensity,this.sh.fromArray(t.sh),this}toJSON(t){const e=super.toJSON(t);return e.object.sh=this.sh.toArray(),e}}$$.prototype.isLightProbe=!0;var J$=n(63),Z$=n(43);class Q$ 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}}Q$.prototype.isInstancedBufferGeometry=!0;class K$ extends Bf.a{constructor(t){super(t)}load(t,e,n,i){const s=this,r=new Df.a(s.manager);r.setPath(s.path),r.setRequestHeader(s.requestHeader),r.setWithCredentials(s.withCredentials),r.load(t,(function(n){try{e(s.parse(JSON.parse(n)))}catch(e){i?i(e):console.error(e),s.manager.itemError(t)}}),n,i)}parse(t){const e={},n={};function i(t,i){if(void 0!==e[i])return e[i];const s=t.interleavedBuffers[i],r=function(t,e){if(void 0!==n[e])return n[e];const i=t.arrayBuffers[e],s=new Uint32Array(i).buffer;return n[e]=s,s}(t,s.buffer),o=Object(It.c)(s.type,r),a=new lr.a(o,s.stride);return a.uuid=s.uuid,e[i]=a,a}const s=t.isInstancedBufferGeometry?new Q$:new S.a,r=t.data.index;if(void 0!==r){const t=Object(It.c)(r.type,r.array);s.setIndex(new C.a(t,1))}const o=t.data.attributes;for(const e in o){const n=o[e];let r;if(n.isInterleavedBufferAttribute){const e=i(t.data,n.data);r=new cr.a(e,n.itemSize,n.offset,n.normalized)}else{const t=Object(It.c)(n.type,n.array);r=new(n.isInstancedBufferAttribute?A$:C.a)(t,n.itemSize,n.normalized)}void 0!==n.name&&(r.name=n.name),void 0!==n.usage&&r.setUsage(n.usage),void 0!==n.updateRange&&(r.updateRange.offset=n.updateRange.offset,r.updateRange.count=n.updateRange.count),s.setAttribute(e,r)}const a=t.data.morphAttributes;if(a)for(const e in a){const n=a[e],r=[];for(let e=0,s=n.length;eNumber.EPSILON){const u=Math.sqrt(h),p=Math.sqrt(l*l+c*c),_=e.x-a/u,m=e.y+o/u,f=((n.x-c/p-_)*c-(n.y+l/p-m)*l)/(o*c-a*l);i=_+o*f-t.x,s=m+a*f-t.y;const g=i*i+s*s;if(g<=2)return new d.a(i,s);r=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,s=o,r=Math.sqrt(h)):(i=o,s=a,r=Math.sqrt(h/2))}return new d.a(i/r,s/r)}const R=[];for(let t=0,e=C.length,n=e-1,i=t+1;t=0;t--){const e=t/m,n=h*Math.cos(e*Math.PI/2),i=u*Math.sin(e*Math.PI/2)+_;for(let t=0,e=C.length;t=0;){const i=n;let s=n-1;s<0&&(s=t.length-1);for(let t=0,n=a+2*m;t0){const n=new Vg.b(e);s=new J$.a(n),s.setCrossOrigin(this.crossOrigin);for(let e=0,n=t.length;e0){i=new J$.a(this.manager),i.setCrossOrigin(this.crossOrigin);for(let e=0,i=t.length;e{SJ.PARAM_CALLBACK_reset(t,e)}})}};class SJ extends nV{constructor(){super(...arguments),this.paramsConfig=EJ}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 wJ,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 CJ=[is.ORTHOGRAPHIC,is.PERSPECTIVE],NJ={direction:new p.a(0,1,0)},LJ=[new d.a(-1,-1),new d.a(-1,1),new d.a(1,1),new d.a(1,-1)],OJ=new p.a(0,0,1);const PJ=new class extends la{constructor(){super(...arguments),this.camera=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ,types:CJ}}),this.direction=aa.VECTOR3(NJ.direction),this.offset=aa.FLOAT(0,{range:[-10,10],rangeLocked:[!1,!1]}),this.useSegmentsCount=aa.BOOLEAN(!0),this.stepSize=aa.FLOAT(1,{range:[.001,1],rangeLocked:[!1,!1],visibleIf:{useSegmentsCount:0}}),this.segments=aa.VECTOR2([10,10],{visibleIf:{useSegmentsCount:1}}),this.sizeMult=aa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1]}),this.updateOnWindowResize=aa.BOOLEAN(1),this.update=aa.BUTTON(null,{callback:t=>{RJ.PARAM_CALLBACK_update(t)}})}};class RJ extends nV{constructor(){super(...arguments),this.paramsConfig=PJ,this._plane=new Y.a,this._raycaster=new WL,this._planeCorners=[new p.a,new p.a,new p.a,new p.a],this._planeCenter=new p.a,this._core_transform=new uU,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(ts.OBJ);if(!t)return this.states.error.set(\\\\\\\"no camera found\\\\\\\"),void this.cookController.endCook();if(!CJ.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 LJ){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]),s=this._planeCorners[0].distanceTo(this._planeCorners[1]),r=this._planeCorners[2].distanceTo(this._planeCorners[3]),o=Math.max(n,i)*this.pv.sizeMult,a=Math.max(s,r)*this.pv.sizeMult;this.planeSize.set(o,a);const l=this._createPlane(this.planeSize);this._core_transform.rotate_geometry(l,OJ,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 IJ extends QG{constructor(){super(...arguments),this._geo_center=new p.a}static type(){return\\\\\\\"center\\\\\\\"}cook(t,e){var n;const i=t[0].objectsWithGeo(),s=new Array(3*i.length);s.fill(0);for(let t=0;t0&&(r.push(t-1),r.push(t));r.push(e-1),r.push(0);const a=new S.a;return a.setAttribute(\\\\\\\"position\\\\\\\",new C.c(s,3)),a.setIndex(r),a}}const zJ=new p.a(0,0,1);class kJ extends QG{constructor(){super(...arguments),this._core_transform=new uU}static type(){return\\\\\\\"circle\\\\\\\"}cook(t,e){return e.open?this._create_circle(e):this._create_disk(e)}_create_circle(t){const e=BJ.create(t.radius,t.segments,t.arcAngle);return this._core_transform.rotate_geometry(e,zJ,t.direction),this.createCoreGroupFromGeometry(e,Cs.LINE_SEGMENTS)}_create_disk(t){const e=new tJ(t.radius,t.segments);return this._core_transform.rotate_geometry(e,zJ,t.direction),this.createCoreGroupFromGeometry(e)}}kJ.DEFAULT_PARAMS={radius:1,segments:12,open:!0,arcAngle:360,direction:new p.a(0,1,0)};const UJ=kJ.DEFAULT_PARAMS;const GJ=new class extends la{constructor(){super(...arguments),this.radius=aa.FLOAT(UJ.radius),this.segments=aa.INTEGER(UJ.segments,{range:[1,50],rangeLocked:[!0,!1]}),this.open=aa.BOOLEAN(UJ.open),this.arcAngle=aa.FLOAT(UJ.arcAngle,{range:[0,360],rangeLocked:[!1,!1],visibleIf:{open:1}}),this.direction=aa.VECTOR3(UJ.direction)}};class VJ extends nV{constructor(){super(...arguments),this.paramsConfig=GJ}static type(){return\\\\\\\"circle\\\\\\\"}initializeNode(){}cook(){this._operation=this._operation||new kJ(this._scene,this.states);const t=this._operation.cook([],this.pv);this.setCoreGroup(t)}}var HJ;!function(t){t.SEGMENTS_COUNT=\\\\\\\"segments count\\\\\\\",t.SEGMENTS_LENGTH=\\\\\\\"segments length\\\\\\\"}(HJ||(HJ={}));const jJ=[HJ.SEGMENTS_COUNT,HJ.SEGMENTS_LENGTH];var WJ;!function(t){t.ABC=\\\\\\\"abc\\\\\\\",t.ACB=\\\\\\\"acb\\\\\\\",t.AB=\\\\\\\"ab\\\\\\\",t.BC=\\\\\\\"bc\\\\\\\",t.AC=\\\\\\\"ac\\\\\\\"}(WJ||(WJ={}));const qJ=[WJ.ABC,WJ.ACB,WJ.AB,WJ.AC,WJ.BC];class XJ{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 s=0;for(s=0;s0&&(n[2*(s-1)]=s-1,n[2*(s-1)+1]=s);this.params.full&&(n.push(s-1),n.push(0));const r=new S.a;if(r.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(e),3)),r.setIndex(n),this.params.addIdAttribute||this.params.addIdnAttribute){const e=new Array(t);for(let t=0;te/(t-1)));this.params.addIdnAttribute&&r.setAttribute(\\\\\\\"idn\\\\\\\",new C.a(new Float32Array(n),1))}this._created_geometries.arc=r}_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 HJ.SEGMENTS_COUNT:return this.params.segmentsCount+1;case HJ.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)}}ls.unreachable(t)}_set_x_from_joinMode(){const t=this.params.joinMode;switch(this.x.copy(this.a).sub(this.center).normalize(),t){case WJ.ABC:case WJ.ACB:case WJ.AB:case WJ.AC:return this.x.copy(this.an);case WJ.BC:return this.x.copy(this.bn)}ls.unreachable(t)}_set_angle_from_joinMode(){const t=this.params.joinMode;switch(t){case WJ.ABC:return void(this.angle=this.angle_ab+this.angle_bc);case WJ.ACB:return this.angle=this.angle_ac+this.angle_bc,void(this.angle*=-1);case WJ.AB:return void(this.angle=this.angle_ab);case WJ.AC:return this.angle=this.angle_ac,void(this.angle*=-1);case WJ.BC:return void(this.angle=this.angle_bc)}ls.unreachable(t)}}const YJ=new class extends la{constructor(){super(...arguments),this.arc=aa.BOOLEAN(1),this.pointsCountMode=aa.INTEGER(jJ.indexOf(HJ.SEGMENTS_COUNT),{visibleIf:{arc:1},menu:{entries:jJ.map(((t,e)=>({value:e,name:t})))}}),this.segmentsLength=aa.FLOAT(.1,{visibleIf:{arc:1,pointsCountMode:jJ.indexOf(HJ.SEGMENTS_LENGTH)},range:[0,1],rangeLocked:[!0,!1]}),this.segmentsCount=aa.INTEGER(100,{visibleIf:{arc:1,pointsCountMode:jJ.indexOf(HJ.SEGMENTS_COUNT)},range:[1,100],rangeLocked:[!0,!1]}),this.full=aa.BOOLEAN(1,{visibleIf:{arc:1}}),this.joinMode=aa.INTEGER(qJ.indexOf(WJ.ABC),{visibleIf:{arc:1,full:0},menu:{entries:qJ.map(((t,e)=>({value:e,name:t})))}}),this.addIdAttribute=aa.BOOLEAN(1),this.addIdnAttribute=aa.BOOLEAN(1),this.center=aa.BOOLEAN(0)}};class $J extends nV{constructor(){super(...arguments),this.paramsConfig=YJ,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([Ki.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 XJ({arc:this.pv.arc,center:this.pv.center,pointsCountMode:jJ[this.pv.pointsCountMode],segmentsLength:this.pv.segmentsLength,segmentsCount:this.pv.segmentsCount,full:this.pv.full,joinMode:qJ[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,Cs.LINE_SEGMENTS)),i.center&&n.push(this.createObject(i.center,Cs.POINTS)),this.setObjects(n)}}class JJ extends QG{static type(){return\\\\\\\"color\\\\\\\"}cook(t,e){}}JJ.DEFAULT_PARAMS={fromAttribute:!1,attribName:\\\\\\\"\\\\\\\",color:new D.a(1,1,1),asHsv:!1};const ZJ=new D.a(1,1,1),QJ=\\\\\\\"color\\\\\\\",KJ=JJ.DEFAULT_PARAMS;const tZ=new class extends la{constructor(){super(...arguments),this.fromAttribute=aa.BOOLEAN(KJ.fromAttribute),this.attribName=aa.STRING(KJ.attribName,{visibleIf:{fromAttribute:1}}),this.color=aa.COLOR(KJ.color,{visibleIf:{fromAttribute:0},expression:{forEntities:!0}}),this.asHsv=aa.BOOLEAN(KJ.asHsv,{visibleIf:{fromAttribute:0}})}};class eZ extends nV{constructor(){super(...arguments),this.paramsConfig=tZ,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(Ki.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(QJ).needsUpdate=!0}this.setCoreGroup(e)}_set_fromAttribute(t){const e=t.coreGeometry();if(!e)return;this._create_init_color(e,ZJ);const n=e.points(),i=e.attribSize(this.pv.attribName),s=e.geometry(),r=s.getAttribute(this.pv.attribName).array,o=s.getAttribute(QJ).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]=r;return a}_init_array_if_required(t,e,n){const i=t.uuid,s=e[i];return s?s.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(rZ.SCALE);const i=this._is_pscale_present?t.attribValue(\\\\\\\"pscale\\\\\\\"):rZ.PSCALE;this._point_scale.multiplyScalar(i);const s=this._matrices.scale;s.makeScale(this._point_scale.x,this._point_scale.y,this._point_scale.z);const r=this._matrices.translate;if(r.makeTranslation(n.x,n.y,n.z),e.multiply(r),this._do_rotate_matrices){const n=this._matrices.rotate,i=rZ.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(rZ.UP),this._point_up.normalize(),n.lookAt(i,this._point_normal,this._point_up),e.multiply(n)}e.multiply(s)}static create_instance_buffer_geo(t,e,n){const i=e.points(),s=new Q$;s.copy(t),s.instanceCount=1/0;const r=i.length,o=new Float32Array(3*r),a=new Float32Array(3*r),l=new Float32Array(3*r),c=new Float32Array(4*r),h=e.hasAttrib(lZ),u=new p.a(0,0,0),d=new ah.a,_=new p.a(1,1,1),f=new uZ(e).matrices();i.forEach(((t,e)=>{const n=3*e,i=4*e;f[e].decompose(u,d,_),u.toArray(o,n),d.toArray(c,i),_.toArray(l,n);(h?t.attribValue(lZ,this._point_color):oZ).toArray(a,n)}));const g=e.hasAttrib(\\\\\\\"uv\\\\\\\");if(g){const t=new Float32Array(2*r);i.forEach(((e,n)=>{const i=2*n;(g?e.attribValue(\\\\\\\"uv\\\\\\\",this._point_uv):aZ).toArray(t,i)})),s.setAttribute(cZ.UV,new A$(t,2))}s.setAttribute(cZ.POSITION,new A$(o,3)),s.setAttribute(cZ.SCALE,new A$(l,3)),s.setAttribute(cZ.ORIENTATION,new A$(c,4)),s.setAttribute(cZ.COLOR,new A$(a,3));e.attribNamesMatchingMask(n).forEach((t=>{const n=e.attribSize(t),o=new Float32Array(r*n);i.forEach(((e,i)=>{const s=e.attribValue(t);m.isNumber(s)?o[i]=s:s.toArray(o,i*n)})),s.setAttribute(t,new A$(o,n))}));return new _r(s).markAsInstance(),s}}uZ._point_color=new p.a,uZ._point_uv=new d.a;class dZ extends sh{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\\\\\\\"}(hZ||(hZ={}));const pZ=[hZ.OBJECT,hZ.GEOMETRY],_Z=[{name:\\\\\\\"object\\\\\\\",value:hZ.OBJECT},{name:\\\\\\\"geometry\\\\\\\",value:hZ.GEOMETRY}];const mZ=new class extends la{constructor(){super(...arguments),this.count=aa.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1]}),this.transformOnly=aa.BOOLEAN(0),this.transformMode=aa.INTEGER(0,{menu:{entries:_Z}}),this.copyAttributes=aa.BOOLEAN(0),this.attributesToCopy=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{copyAttributes:!0}}),this.useCopyExpr=aa.BOOLEAN(0)}};class fZ extends nV{constructor(){super(...arguments),this.paramsConfig=mZ,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([Ki.ALWAYS,Ki.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 uZ(e).matrices();const s=new p.a,r=new ah.a,o=new p.a;i[0].decompose(s,r,o),this._attribute_names_to_copy=os.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=yr.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 s=e.attribValue(n);new yr(t,i).addAttribute(n,s)}))}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 dZ(this.scene());return this.dirtyController.setForbiddenTriggerNodes([t]),t}dispose(){super.dispose(),this._stamp_node&&this._stamp_node.dispose()}}const gZ=\\\\\\\"id\\\\\\\",vZ=\\\\\\\"class\\\\\\\",yZ=\\\\\\\"html\\\\\\\";class xZ extends QG{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(gZ):e.className,s=e.useClassAttrib?t.attribValue(vZ):e.className,r=e.useHtmlAttrib?t.attribValue(yZ):e.html,o=xZ.create_css_object({id:n,className:s,html:r}),a=o.element;if(e.copyAttributes){const n=os.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 xZ.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 iq(e);return n.matrixAutoUpdate=!1,n}}xZ.DEFAULT_PARAMS={useIdAttrib:!1,id:\\\\\\\"my_css_object\\\\\\\",useClassAttrib:!1,className:\\\\\\\"CSS2DObject\\\\\\\",useHtmlAttrib:!1,html:\\\\\\\"
default html
\\\\\\\",copyAttributes:!1,attributesToCopy:\\\\\\\"\\\\\\\"},xZ.INPUT_CLONED_STATE=Ki.FROM_NODE;const bZ=xZ.DEFAULT_PARAMS;const wZ=new class extends la{constructor(){super(...arguments),this.useIdAttrib=aa.BOOLEAN(bZ.useIdAttrib),this.id=aa.STRING(bZ.id,{visibleIf:{useIdAttrib:0}}),this.useClassAttrib=aa.BOOLEAN(bZ.useClassAttrib),this.className=aa.STRING(bZ.className,{visibleIf:{useClassAttrib:0}}),this.useHtmlAttrib=aa.BOOLEAN(bZ.useHtmlAttrib),this.html=aa.STRING(bZ.html,{visibleIf:{useHtmlAttrib:0},multiline:!0}),this.copyAttributes=aa.BOOLEAN(bZ.copyAttributes),this.attributesToCopy=aa.STRING(bZ.attributesToCopy,{visibleIf:{copyAttributes:!0}})}};class TZ extends nV{constructor(){super(...arguments),this.paramsConfig=wZ}static type(){return\\\\\\\"CSS2DObject\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1)}cook(t){this._operation=this._operation||new xZ(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class AZ{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)?Bs.STRING:Bs.NUMERIC;return new this(m.isArray(t)?t.length:1,e)}}class MZ{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 _r(t);if(null!=this._json){const n=this._json.length;e.initPositionAttribute(n),this._find_attributes();const i=os.attribNames(this._options.convertToNumeric||\\\\\\\"\\\\\\\");for(let n of Object.keys(this._attribute_datas_by_name)){const s=qs.remapName(n);let r=this._attribute_values_for_name(n).flat();const o=this._attribute_datas_by_name[n],a=o.size();if(o.type()===Bs.STRING)if(this._options.doConvert&&os.matchesOneMask(n,i)){const e=r.map((t=>m.isString(t)?parseFloat(t)||0:t));t.setAttribute(s,new C.c(e,a))}else{const t=qs.arrayToIndexedArrays(r);e.setIndexedAttribute(s,t.values,t.indices)}else{const e=r;t.setAttribute(s,new C.c(e,a))}}}return t}_find_attributes(){let t;const e=os.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 s=[n,t].join(\\\\\\\":\\\\\\\"),r=i[n];os.matchesOneMask(s,e)||(this._attribute_datas_by_name[s]=AZ.from_value(r))}else os.matchesOneMask(n,e)||(this._attribute_datas_by_name[n]=AZ.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 EZ=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 SZ=new class extends la{constructor(){super(...arguments),this.data=aa.STRING(EZ)}};class CZ extends nV{constructor(){super(...arguments),this.paramsConfig=SZ}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 MZ;e.setJSON(t);const n=e.createObject();this.setGeometry(n,Cs.POINTS)}catch(t){this.states.error.set(\\\\\\\"could not build geometry from json\\\\\\\")}else this.cookController.endCook()}}class NZ extends jg{constructor(t,e,n={},i){super(t,e,i),this._node=i,this._parser=new MZ(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 s=this._parser.createObject();t(s)})).catch((t=>{li.error(\\\\\\\"error\\\\\\\",t),n(t)}))}}const LZ=\\\\\\\"position\\\\\\\";class OZ{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(OZ.SEPARATOR)),this.attribute_names=this.attribute_names.map((t=>qs.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(OZ.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=aa.STRING(IZ,{fileBrowse:{type:[Or.JSON]}}),this.jsonDataKeysPrefix=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:RZ.indexOf(PZ.JSON)}}),this.skipEntries=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:RZ.indexOf(PZ.JSON)}}),this.convert=aa.BOOLEAN(0,{visibleIf:{dataType:RZ.indexOf(PZ.JSON)}}),this.convertToNumeric=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:RZ.indexOf(PZ.JSON),convert:1}}),this.readAttribNamesFromFile=aa.BOOLEAN(1,{visibleIf:{dataType:RZ.indexOf(PZ.CSV)}}),this.attribNames=aa.STRING(\\\\\\\"height scale\\\\\\\",{visibleIf:{dataType:RZ.indexOf(PZ.CSV),readAttribNamesFromFile:0}}),this.reload=aa.BUTTON(null,{callback:(t,e)=>{DZ.PARAM_CALLBACK_reload(t,e)}})}};class DZ extends nV{constructor(){super(...arguments),this.paramsConfig=FZ}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(RZ[this.pv.dataType]){case PZ.JSON:return this._load_json();case PZ.CSV:return this._load_csv()}}_url(){const t=this.scene().assets.root();return t?`${t}${this.pv.url}`:this.pv.url}_load_json(){new NZ(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,Cs.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 OZ(t),n=await e.load(this._url());n?this.setGeometry(n,Cs.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 BZ extends S.a{constructor(t,e,n,i){super();const s=[],r=[],o=[],a=new p.a,l=new A.a;l.makeRotationFromEuler(n),l.setPosition(e);const c=new A.a;function h(e,n,i){n.applyMatrix4(t.matrixWorld),n.applyMatrix4(c),i.transformDirection(t.matrixWorld),e.push(new zZ(n.clone(),i.clone()))}function u(t,e){const n=[],s=.5*Math.abs(i.dot(e));for(let i=0;i0,u=t[i+1].position.dot(e)-s>0,p=t[i+2].position.dot(e)-s>0;switch(c=(h?1:0)+(u?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(h&&(r=t[i+1],o=t[i+2],a=d(t[i],r,e,s),l=d(t[i],o,e,s)),u){r=t[i],o=t[i+2],a=d(t[i+1],r,e,s),l=d(t[i+1],o,e,s),n.push(a),n.push(o.clone()),n.push(r.clone()),n.push(o.clone()),n.push(a.clone()),n.push(l);break}p&&(r=t[i],o=t[i+1],a=d(t[i+2],r,e,s),l=d(t[i+2],o,e,s)),n.push(r.clone()),n.push(o.clone()),n.push(a),n.push(l),n.push(a.clone()),n.push(o.clone());break;case 2:h||(r=t[i].clone(),o=d(r,t[i+1],e,s),a=d(r,t[i+2],e,s),n.push(r),n.push(o),n.push(a)),u||(r=t[i+1].clone(),o=d(r,t[i+2],e,s),a=d(r,t[i],e,s),n.push(r),n.push(o),n.push(a)),p||(r=t[i+2].clone(),o=d(r,t[i],e,s),a=d(r,t[i+1],e,s),n.push(r),n.push(o),n.push(a))}}return n}function d(t,e,n,i){const s=t.position.dot(n)-i,r=s/(s-(e.position.dot(n)-i));return new zZ(new p.a(t.position.x+r*(e.position.x-t.position.x),t.position.y+r*(e.position.y-t.position.y),t.position.z+r*(e.position.z-t.position.z)),new p.a(t.normal.x+r*(e.normal.x-t.normal.x),t.normal.y+r*(e.normal.y-t.normal.y),t.normal.z+r*(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 WZ{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,s)=>{n==e&&(t[i]=s,i++)})),t}}}var qZ;!function(t){t.EQUAL=\\\\\\\"==\\\\\\\",t.LESS_THAN=\\\\\\\"<\\\\\\\",t.EQUAL_OR_LESS_THAN=\\\\\\\"<=\\\\\\\",t.EQUAL_OR_GREATER_THAN=\\\\\\\">=\\\\\\\",t.GREATER_THAN=\\\\\\\">\\\\\\\",t.DIFFERENT=\\\\\\\"!=\\\\\\\"}(qZ||(qZ={}));const XZ=[qZ.EQUAL,qZ.LESS_THAN,qZ.EQUAL_OR_LESS_THAN,qZ.EQUAL_OR_GREATER_THAN,qZ.GREATER_THAN,qZ.DIFFERENT],YZ={[qZ.EQUAL]:(t,e)=>t==e,[qZ.LESS_THAN]:(t,e)=>tt<=e,[qZ.EQUAL_OR_GREATER_THAN]:(t,e)=>t>=e,[qZ.GREATER_THAN]:(t,e)=>t>e,[qZ.DIFFERENT]:(t,e)=>t!=e},$Z=XZ.map(((t,e)=>({name:t,value:e})));class JZ{constructor(t){this.node=t}evalForEntities(t){const e=zs[this.node.pv.attribType];switch(e){case Bs.NUMERIC:return void this._eval_for_numeric(t);case Bs.STRING:return void this._eval_for_string(t)}ls.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=Gs[this.node.pv.attribSize-1];switch(e){case Us.FLOAT:return this._eval_for_points_numeric_float(t);case Us.VECTOR2:return this._eval_for_points_numeric_vector2(t);case Us.VECTOR3:return this._eval_for_points_numeric_vector3(t);case Us.VECTOR4:return this._eval_for_points_numeric_vector4(t)}ls.unreachable(e)}_eval_for_points_numeric_float(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue1;let i;const s=XZ[this.node.pv.attribComparisonOperator],r=YZ[s];for(let s of t)i=s.attribValue(e),r(i,n)&&this.node.entitySelectionHelper.select(s)}_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 s of t){const t=s.attribValue(e,i);n.equals(t)&&this.node.entitySelectionHelper.select(s)}}_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 s of t){const t=s.attribValue(e,i);n.equals(t)&&this.node.entitySelectionHelper.select(s)}}_eval_for_points_numeric_vector4(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue4;let i=new _.a;for(let s of t){const t=s.attribValue(e,i);n.equals(t)&&this.node.entitySelectionHelper.select(s)}}}class ZZ{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 QZ{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 Ay.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 KZ{constructor(t){this.node=t}eval_for_objects(t){const e=Os[this.node.pv.objectType];for(let n of t){Ls(n.object().constructor)==e&&this.node.entitySelectionHelper.select(n)}}}class tQ{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 eQ=new p.a(0,1,0),nQ=new p.a(0,-1,0);class iQ{constructor(t){this.node=t,this._matDoubleSideTmpSetter=new tQ,this._point_position=new p.a,this._raycaster=new WL,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 s=n.geometry;s.computeBoundingBox();const r=s.boundingBox;for(let e of t)e.getPosition(this._point_position),r.containsPoint(this._point_position)?this._isPositionInObject(this._point_position,i,eQ)&&this._isPositionInObject(this._point_position,i,nQ)&&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 s=this._raycaster.intersectObject(e,!1,this._intersections);if(!s)return!1;if(0==s.length)return!1;const r=null===(i=s[0].face)||void 0===i?void 0:i.normal;if(!r)return!1;return this._raycaster.ray.direction.dot(r)>=0}}const sQ=new class extends la{constructor(){super(...arguments),this.class=aa.INTEGER(Fs.indexOf(Is.VERTEX),{menu:{entries:Ds}}),this.invert=aa.BOOLEAN(0),this.byObjectType=aa.BOOLEAN(0,{visibleIf:{class:Fs.indexOf(Is.OBJECT)}}),this.objectType=aa.INTEGER(Os.indexOf(Cs.MESH),{menu:{entries:Ps},visibleIf:{class:Fs.indexOf(Is.OBJECT),byObjectType:!0},separatorAfter:!0}),this.byExpression=aa.BOOLEAN(0),this.expression=aa.BOOLEAN(\\\\\\\"@ptnum==0\\\\\\\",{visibleIf:{byExpression:!0},expression:{forEntities:!0},separatorAfter:!0}),this.byAttrib=aa.BOOLEAN(0),this.attribType=aa.INTEGER(zs.indexOf(Bs.NUMERIC),{menu:{entries:ks},visibleIf:{byAttrib:1}}),this.attribName=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{byAttrib:1}}),this.attribSize=aa.INTEGER(1,{range:Vs,rangeLocked:[!0,!0],visibleIf:{byAttrib:1,attribType:zs.indexOf(Bs.NUMERIC)}}),this.attribComparisonOperator=aa.INTEGER(XZ.indexOf(qZ.EQUAL),{menu:{entries:$Z},visibleIf:{byAttrib:!0,attribType:zs.indexOf(Bs.NUMERIC),attribSize:Us.FLOAT}}),this.attribValue1=aa.FLOAT(0,{visibleIf:{byAttrib:1,attribType:zs.indexOf(Bs.NUMERIC),attribSize:1}}),this.attribValue2=aa.VECTOR2([0,0],{visibleIf:{byAttrib:1,attribType:zs.indexOf(Bs.NUMERIC),attribSize:2}}),this.attribValue3=aa.VECTOR3([0,0,0],{visibleIf:{byAttrib:1,attribType:zs.indexOf(Bs.NUMERIC),attribSize:3}}),this.attribValue4=aa.VECTOR4([0,0,0,0],{visibleIf:{byAttrib:1,attribType:zs.indexOf(Bs.NUMERIC),attribSize:4}}),this.attribString=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{byAttrib:1,attribType:zs.indexOf(Bs.STRING)},separatorAfter:!0}),this.byBbox=aa.BOOLEAN(0,{visibleIf:{class:Fs.indexOf(Is.VERTEX)}}),this.bboxSize=aa.VECTOR3([1,1,1],{visibleIf:{class:Fs.indexOf(Is.VERTEX),byBbox:!0}}),this.bboxCenter=aa.VECTOR3([0,0,0],{visibleIf:{class:Fs.indexOf(Is.VERTEX),byBbox:!0},separatorAfter:!0}),this.byBoundingObject=aa.BOOLEAN(0,{visibleIf:{class:Fs.indexOf(Is.VERTEX)}}),this.keepPoints=aa.BOOLEAN(0,{visibleIf:{class:Fs.indexOf(Is.OBJECT)}})}};class rQ extends nV{constructor(){super(...arguments),this.paramsConfig=sQ,this._marked_for_deletion_per_object_index=new Map,this.entitySelectionHelper=new WZ(this),this.byExpressionHelper=new ZZ(this),this.byAttributeHelper=new JZ(this),this.byObjectTypeHelper=new KZ(this),this.byBboxHelper=new QZ(this),this.byBoundingObjectHelper=new iQ(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(Ki.FROM_NODE)}async cook(t){const e=t[0],n=t[1];switch(this.pv.class){case Is.VERTEX:await this._eval_for_points(e,n);break;case Is.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,s=[];for(let t=0;t0){const e=_r.geometryFromPoints(a,Ls(t.constructor));e&&(t.geometry=e,s.push(t))}}}this.setObjects(s)}_point_object(t){const e=t.points(),n=_r.geometryFromPoints(e,Cs.POINTS);if(n)return this.createObject(n,Cs.POINTS)}}const oQ=new class extends la{constructor(){super(...arguments),this.start=aa.INTEGER(0,{range:[0,100],rangeLocked:[!0,!1]}),this.useCount=aa.BOOLEAN(0),this.count=aa.INTEGER(0,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useCount:1}})}};class aQ extends nV{constructor(){super(...arguments),this.paramsConfig=oQ}static type(){return\\\\\\\"drawRange\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ki.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 lQ{constructor(){this.pluginCallbacks=[],this.register((function(t){return new DQ(t)})),this.register((function(t){return new BQ(t)})),this.register((function(t){return new zQ(t)})),this.register((function(t){return new kQ(t)})),this.register((function(t){return new UQ(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 FQ,s=[];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,s=i.options,r=i.extensionsUsed,o=new Blob(t,{type:\\\\\\\"application/octet-stream\\\\\\\"}),a=Object.keys(r);if(a.length>0&&(n.extensionsUsed=a),n.buffers&&n.buffers.length>0&&(n.buffers[0].byteLength=o.size),!0===s.binary){const t=new window.FileReader;t.readAsArrayBuffer(o),t.onloadend=function(){const i=RQ(t.result),s=new DataView(new ArrayBuffer(8));s.setUint32(0,i.byteLength,!0),s.setUint32(4,5130562,!0);const r=RQ(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,r.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+r.byteLength+s.byteLength+i.byteLength;l.setUint32(8,c,!0);const h=new Blob([a,o,r,s,i],{type:\\\\\\\"application/octet-stream\\\\\\\"}),u=new window.FileReader;u.readAsArrayBuffer(h),u.onloadend=function(){e(u.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 s=JSON.parse(JSON.stringify(t.userData));if(n.includeCustomExtensions&&s.gltfExtensions){void 0===e.extensions&&(e.extensions={});for(const t in s.gltfExtensions)e.extensions[t]=s.gltfExtensions[t],i[t]=!0;delete s.gltfExtensions}Object.keys(s).length>0&&(e.extras=s)}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 gb;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 gb;for(let t=0,e=n.count;t4?i=t.array[s*t.itemSize+n]:0===n?i=t.getX(s):1===n?i=t.getY(s):2===n?i=t.getZ(s):3===n&&(i=t.getW(s)),e===fQ?l.setFloat32(c,i,!0):e===gQ?l.setUint32(c,i,!0):e===mQ?l.setUint16(c,i,!0):e===_Q&&l.setUint8(c,i),c+=o}const h={buffer:this.processBuffer(l.buffer),byteOffset:this.byteOffset,byteLength:a};void 0!==s&&(h.target=s),s===vQ&&(h.byteStride=t.itemSize*o),this.byteOffset+=a,r.bufferViews.push(h);return{id:r.bufferViews.length-1,byteLength:0}}processBufferViewImage(t){const e=this,n=e.json;return n.bufferViews||(n.bufferViews=[]),new Promise((function(i){const s=new window.FileReader;s.readAsArrayBuffer(t),s.onloadend=function(){const t=RQ(s.result),r={buffer:e.processBuffer(t),byteOffset:e.byteOffset,byteLength:t.byteLength};e.byteOffset+=t.byteLength,i(n.bufferViews.push(r)-1)}}))}processAccessor(t,e,n,i){const s=this.options,r=this.json;let o;if(t.array.constructor===Float32Array)o=fQ;else if(t.array.constructor===Uint32Array)o=gQ;else if(t.array.constructor===Uint16Array)o=mQ;else{if(t.array.constructor!==Uint8Array)throw new Error(\\\\\\\"THREE.GLTFExporter: Unsupported bufferAttribute component type.\\\\\\\");o=_Q}if(void 0===n&&(n=0),void 0===i&&(i=t.count),s.truncateDrawRange&&void 0!==e&&null===e.index){const s=n+i,r=e.drawRange.count===1/0?t.count:e.drawRange.start+e.drawRange.count;n=Math.max(n,e.drawRange.start),(i=Math.min(s,r)-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 s=e;s4?n=t.array[s*t.itemSize+e]:0===e?n=t.getX(s):1===e?n=t.getY(s):2===e?n=t.getZ(s):3===e&&(n=t.getW(s)),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?yQ:vQ);const c=this.processBufferView(t,o,n,i,l),h={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&&(h.normalized=!0),r.accessors||(r.accessors=[]),r.accessors.push(h)-1}processImage(t,e,n){const i=this,s=i.cache,r=i.json,o=i.options,a=i.pending;s.images.has(t)||s.images.set(t,{});const l=s.images.get(t),c=e===Ex?\\\\\\\"image/png\\\\\\\":\\\\\\\"image/jpeg\\\\\\\",h=c+\\\\\\\":flipY/\\\\\\\"+n.toString();if(void 0!==l[h])return l[h];r.images||(r.images=[]);const u={mimeType:c};if(o.embedImages){const s=IQ=IQ||document.createElement(\\\\\\\"canvas\\\\\\\");s.width=Math.min(t.width,o.maxTextureSize),s.height=Math.min(t.height,o.maxTextureSize);const r=s.getContext(\\\\\\\"2d\\\\\\\");if(!0===n&&(r.translate(0,s.height),r.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)r.drawImage(t,0,0,s.width,s.height);else{e!==Ex&&e!==Mx&&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===Ex)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 r=n.materials.push(i)-1;return e.materials.set(t,r),r}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=[],s={};if(void 0!==t.morphTargetDictionary)for(const e in t.morphTargetDictionary)s[t.morphTargetDictionary[e]]=e;for(let o=0;o0&&(a.extras={},a.extras.targetNames=i)}const _=Array.isArray(t.material);if(_&&0===r.groups.length)return null;const m=_?t.material:[t.material],f=_?r.groups:[{materialIndex:0,start:void 0,count:void 0}];for(let t=0,n=f.length;t0&&(n.targets=h),null!==r.index){let i=this.getUID(r.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(r.index,r,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(s,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:ib.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 s=(t=lQ.Utils.mergeMorphTargetTracks(t.clone(),e)).tracks,r=[],o=[];for(let t=0;t0){const e=[];for(let i=0,s=t.children.length;i0&&(s.children=e)}this._invokeAll((function(e){e.writeNode&&e.writeNode(t,s)}));const r=e.nodes.push(s)-1;return i.set(t,r),r}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 s=[];for(let e=0,i=t.children.length;e0&&(i.nodes=s),this.serializeUserData(t,i)}processObjects(t){const e=new CE;e.name=\\\\\\\"AuxScene\\\\\\\";for(let n=0;n0&&this.processObjects(n);for(let t=0;t0&&(r.range=t.distance)):t.isSpotLight&&(r.type=\\\\\\\"spot\\\\\\\",t.distance>0&&(r.range=t.distance),r.spot={},r.spot.innerConeAngle=(t.penumbra-1)*t.angle*-1,r.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.\\\\\\\"),s[this.name]||(i.extensions=i.extensions||{},i.extensions[this.name]={lights:[]},s[this.name]=!0);const o=i.extensions[this.name].lights;o.push(r),e.extensions=e.extensions||{},e.extensions[this.name]={light:o.length-1}}}class BQ{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 zQ{constructor(t){this.writer=t,this.name=\\\\\\\"KHR_materials_pbrSpecularGlossiness\\\\\\\"}writeMaterial(t,e){if(!t.isGLTFSpecularGlossinessMaterial)return;const n=this.writer,i=n.extensionsUsed,s={};e.pbrMetallicRoughness.baseColorFactor&&(s.diffuseFactor=e.pbrMetallicRoughness.baseColorFactor);const r=[1,1,1];if(t.specular.toArray(r,0),s.specularFactor=r,s.glossinessFactor=t.glossiness,e.pbrMetallicRoughness.baseColorTexture&&(s.diffuseTexture=e.pbrMetallicRoughness.baseColorTexture),t.specularMap){const e={index:n.processTexture(t.specularMap)};n.applyTextureTransform(e,t.specularMap),s.specularGlossinessTexture=e}e.extensions=e.extensions||{},e.extensions[this.name]=s,i[this.name]=!0}}class kQ{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,s={};if(s.transmissionFactor=t.transmission,t.transmissionMap){const e={index:n.processTexture(t.transmissionMap)};n.applyTextureTransform(e,t.transmissionMap),s.transmissionTexture=e}e.extensions=e.extensions||{},e.extensions[this.name]=s,i[this.name]=!0}}class UQ{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,s={};if(s.thicknessFactor=t.thickness,t.thicknessMap){const e={index:n.processTexture(t.thicknessMap)};n.applyTextureTransform(e,t.thicknessMap),s.thicknessTexture=e}s.attenuationDistance=t.attenuationDistance,s.attenuationColor=t.attenuationTint.toArray(),e.extensions=e.extensions||{},e.extensions[this.name]=s,i[this.name]=!0}}function GQ(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)}lQ.Utils={insertKeyframe:function(t,e){const n=.001,i=t.getValueSize(),s=new t.TimeBufferType(t.times.length+1),r=new t.ValueBufferType(t.values.length+i),o=t.createInterpolant(new t.ValueBufferType(i));let a;if(0===t.times.length){s[0]=e;for(let t=0;tt.times[t.times.length-1]){if(Math.abs(t.times[t.times.length-1]-e)e){s.set(t.times.slice(0,l+1),0),s[l+1]=e,s.set(t.times.slice(l+1),l+2),r.set(t.values.slice(0,(l+1)*i),0),r.set(o.evaluate(e),(l+1)*i),r.set(t.values.slice((l+1)*i),(l+2)*i),a=l+1;break}}return t.times=s,t.values=r,a},mergeMorphTargetTracks:function(t,e){const n=[],i={},s=t.tracks;for(let t=0;t{HQ.PARAM_CALLBACK_export(t)}})}};class HQ extends nV{constructor(){super(...arguments),this.paramsConfig=VQ}static type(){return\\\\\\\"exporter\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.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 gs;for(let t of n)i.add(t);(new lQ).parse(i,(t=>{if(t instanceof ArrayBuffer)i=\\\\\\\"scene.glb\\\\\\\",GQ(new Blob([t],{type:\\\\\\\"application/octet-stream\\\\\\\"}),i);else{!function(t,e){GQ(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 jQ=new class extends la{constructor(){super(...arguments),this.makeFacesUnique=aa.BOOLEAN(0),this.addFaceCenterAttribute=aa.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.addFaceId=aa.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.transform=aa.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.scale=aa.FLOAT(1,{visibleIf:{makeFacesUnique:1,transform:1}})}};class WQ extends nV{constructor(){super(...arguments),this.paramsConfig=jQ}static type(){return\\\\\\\"face\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.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),s=3*i.length;for(let e of Object.keys(t.attributes)){const n=t.attributes[e],r=n.itemSize,o=new Float32Array(s*r);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(),h=t.coreGeometry();if(c.isMesh&&h){r=h.faces(),h.hasAttrib(e)||h.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==qQ.JSON&&this._options.format==qQ.AUTO)$Q.increment_in_progress_loads_count(),await $Q.wait_for_max_concurrent_loads_queue_freed(),fetch(n).then((async e=>{const n=await e.json();new wJ(this.loadingManager).parse(n,(e=>{$Q.decrement_in_progress_loads_count(),t(this.on_load_success(e.children[0]))}))})).catch((t=>{$Q.decrement_in_progress_loads_count(),e(t)}));else{const s=await this._loaderForFormat();if(s)$Q.increment_in_progress_loads_count(),await $Q.wait_for_max_concurrent_loads_queue_freed(),s.load(n,(e=>{this.on_load_success(e).then((e=>{$Q.decrement_in_progress_loads_count(),t(e)}))}),void 0,(t=>{li.warn(\\\\\\\"error loading\\\\\\\",n,t),$Q.decrement_in_progress_loads_count(),e(t)}));else{e(`format not supported (${i})`)}}}))}async on_load_success(t){const e=this.extension();if(e==qQ.JSON)return[t];const n=t;if(n.isObject3D)switch(e){case YQ.PDB:return this.on_load_succes_pdb(t);case YQ.OBJ:default:return[n]}const i=t;if(i.isBufferGeometry)switch(e){case YQ.DRC:return this.on_load_succes_drc(i);default:return[new B.a(i)]}const s=t;if(null!=s.scene)switch(e){case YQ.GLTF:case YQ.GLB:return this.on_load_succes_gltf(s);default:return[n]}const r=t;if(r.geometryAtoms||r.geometryBonds)switch(e){case YQ.PDB:return this.on_load_succes_pdb(r);default:return[]}return[]}on_load_succes_drc(t){return[new B.a(t,$Q._default_mat_mesh)]}on_load_succes_gltf(t){const e=t.scene;return e.animations=t.animations,[e]}on_load_succes_pdb(t){return[new vs.a(t.geometryAtoms,$Q._default_mat_point),new As.a(t.geometryBonds,$Q._default_mat_line)]}static moduleNamesFromFormat(t,e){switch(t){case qQ.AUTO:return this.moduleNamesFromExt(e);case qQ.DRC:return[Hn.DRACOLoader];case qQ.FBX:return[Hn.FBXLoader];case qQ.JSON:return[];case qQ.GLTF:return[Hn.GLTFLoader];case qQ.GLTF_WITH_DRACO:return[Hn.GLTFLoader,Hn.DRACOLoader];case qQ.OBJ:return[Hn.OBJLoader];case qQ.PDB:return[Hn.PDBLoader];case qQ.PLY:return[Hn.PLYLoader];case qQ.STL:return[Hn.STLLoader]}ls.unreachable(t)}static moduleNamesFromExt(t){switch(t){case YQ.DRC:return[Hn.DRACOLoader];case YQ.FBX:return[Hn.FBXLoader];case YQ.GLTF:return[Hn.GLTFLoader];case YQ.GLB:return[Hn.GLTFLoader,Hn.DRACOLoader];case YQ.OBJ:return[Hn.OBJLoader];case YQ.PDB:return[Hn.PDBLoader];case YQ.PLY:return[Hn.PLYLoader];case YQ.STL:return[Hn.STLLoader]}}async _loaderForFormat(){const t=this._options.format;switch(t){case qQ.AUTO:return this._loaderForExt();case qQ.DRC:return this.loader_for_drc(this._node);case qQ.FBX:return this.loader_for_fbx();case qQ.JSON:return;case qQ.GLTF:return this.loader_for_gltf();case qQ.GLTF_WITH_DRACO:return this.loader_for_glb(this._node);case qQ.OBJ:return this.loader_for_obj();case qQ.PDB:return this.loader_for_pdb();case qQ.PLY:return this.loader_for_ply();case qQ.STL:return this.loader_for_stl()}ls.unreachable(t)}async _loaderForExt(){switch(this.extension().toLowerCase()){case YQ.DRC:return this.loader_for_drc(this._node);case YQ.FBX:return this.loader_for_fbx();case YQ.GLTF:return this.loader_for_gltf();case YQ.GLB:return this.loader_for_glb(this._node);case YQ.OBJ:return this.loader_for_obj();case YQ.PDB:return this.loader_for_pdb();case YQ.PLY:return this.loader_for_ply();case YQ.STL:return this.loader_for_stl()}}loader_for_fbx(){const t=li.modulesRegister.module(Hn.FBXLoader);if(t)return new t(this.loadingManager)}loader_for_gltf(){const t=li.modulesRegister.module(Hn.GLTFLoader);if(t)return new t(this.loadingManager)}static async loader_for_drc(t){const e=li.modulesRegister.module(Hn.DRACOLoader);if(e){const n=new e(this.loadingManager),i=li.libs.root(),s=li.libs.DRACOPath();if(i||s){const e=`${i||\\\\\\\"\\\\\\\"}${s||\\\\\\\"\\\\\\\"}/`;if(t){const n=[\\\\\\\"draco_decoder.js\\\\\\\",\\\\\\\"draco_decoder.wasm\\\\\\\",\\\\\\\"draco_wasm_wrapper.js\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((t=>({storedUrl:`${s}/${t}`,fullUrl:`${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 $Q.loader_for_drc(t)}static async loader_for_glb(t){const e=li.modulesRegister.module(Hn.GLTFLoader),n=li.modulesRegister.module(Hn.DRACOLoader);if(e&&n){this.gltf_loader=this.gltf_loader||new e(this.loadingManager),this.draco_loader=this.draco_loader||new n(this.loadingManager);const i=li.libs.root(),s=li.libs.DRACOGLTFPath();if(i||s){const e=`${i||\\\\\\\"\\\\\\\"}${s||\\\\\\\"\\\\\\\"}/`;if(t){const n=[\\\\\\\"draco_decoder.js\\\\\\\",\\\\\\\"draco_decoder.wasm\\\\\\\",\\\\\\\"draco_wasm_wrapper.js\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((t=>({storedUrl:`${s}/${t}`,fullUrl:`${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 $Q.loader_for_glb(t)}loader_for_obj(){const t=li.modulesRegister.module(Hn.OBJLoader);if(t)return new t(this.loadingManager)}loader_for_pdb(){const t=li.modulesRegister.module(Hn.PDBLoader);if(t)return new t(this.loadingManager)}loader_for_ply(){const t=li.modulesRegister.module(Hn.PLYLoader);if(t)return new t(this.loadingManager)}loader_for_stl(){const t=li.modulesRegister.module(Hn.STLLoader);if(t)return new t(this.loadingManager)}static setMaxConcurrentLoadsCount(t){this._maxConcurrentLoadsCountMethod=t}static _init_max_concurrent_loads_count(){return this._maxConcurrentLoadsCountMethod?this._maxConcurrentLoadsCountMethod():Zf.isChrome()?4:1}static _init_concurrent_loads_delay(){return Zf.isChrome()?1:10}static increment_in_progress_loads_count(){this.in_progress_loads_count++}static decrement_in_progress_loads_count(){this.in_progress_loads_count--;const t=this._queue.pop();if(t){const e=this.CONCURRENT_LOADS_DELAY;setTimeout((()=>{t()}),e)}}static async wait_for_max_concurrent_loads_queue_freed(){return this.in_progress_loads_count<=this.MAX_CONCURRENT_LOADS_COUNT?void 0:new Promise((t=>{this._queue.push(t)}))}}$Q._default_mat_mesh=new ws.a,$Q._default_mat_point=new xs.a,$Q._default_mat_line=new Ts.a,$Q.MAX_CONCURRENT_LOADS_COUNT=$Q._init_max_concurrent_loads_count(),$Q.CONCURRENT_LOADS_DELAY=$Q._init_concurrent_loads_delay(),$Q.in_progress_loads_count=0,$Q._queue=[];const JQ=`${Gg}/models/wolf.obj`;class ZQ extends QG{static type(){return\\\\\\\"file\\\\\\\"}cook(t,e){const n=new $Q({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)}}ZQ.DEFAULT_PARAMS={url:JQ,format:qQ.AUTO};const QQ=ZQ.DEFAULT_PARAMS;const KQ=new class extends la{constructor(){super(...arguments),this.url=aa.STRING(QQ.url,{fileBrowse:{type:[Or.GEOMETRY]}}),this.format=aa.STRING(QQ.format,{menuString:{entries:XQ.map((t=>({name:t,value:t})))}}),this.reload=aa.BUTTON(null,{callback:t=>{tK.PARAM_CALLBACK_reload(t)}})}};class tK extends nV{constructor(){super(...arguments),this.paramsConfig=KQ}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 $Q.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 ZQ(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 eK=new class extends la{constructor(){super(...arguments),this.dist=aa.FLOAT(.1,{range:[0,1],rangeLocked:[!0,!1]})}};class nK extends nV{constructor(){super(...arguments),this.paramsConfig=eK}static type(){return\\\\\\\"fuse\\\\\\\"}static displayedInputNames(){return[\\\\\\\"points to fuse together\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.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,s={};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(\\\\\\\"-\\\\\\\");s[n]=s[n]||[],s[n].push(t)}const r=[];if(Object.keys(s).forEach((t=>{r.push(s[t][0])})),e.geometry.dispose(),r.length>0){const t=_r.geometryFromPoints(r,Ls(e.constructor));return t&&(e.geometry=t),e}}}class iK{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,s=Math.floor(this._param_size.x/n),r=Math.floor(this._param_size.y/i);let o=[],a=[];for(let t=0;t=1&&(0==r||r==s-1?0==r?a.push([r+1+(t-1)*s,r+(t-1)*s,r+t*s]):a.push([r+t*s,r+(t-1)*s,r-1+t*s]):(a.push([r+t*s,r+(t-1)*s,r-1+t*s]),a.push([r+t*s,r+1+(t-1)*s,r+(t-1)*s])));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 sK=new p.a(0,1,0);const rK=new class extends la{constructor(){super(...arguments),this.size=aa.VECTOR2([1,1]),this.hexagonRadius=aa.FLOAT(.1,{range:[.001,1],rangeLocked:[!1,!1]}),this.direction=aa.VECTOR3([0,1,0]),this.pointsOnly=aa.BOOLEAN(0)}};class oK extends nV{constructor(){super(...arguments),this.paramsConfig=rK,this._core_transform=new uU}static type(){return\\\\\\\"hexagons\\\\\\\"}initializeNode(){}cook(){if(this.pv.hexagonRadius>0){const t=new iK(this.pv.size,this.pv.hexagonRadius,this.pv.pointsOnly).process();this._core_transform.rotate_geometry(t,sK,this.pv.direction),this.pv.pointsOnly?this.setGeometry(t,Cs.POINTS):this.setGeometry(t)}else this.setObjects([])}}var aK;!function(t){t.ADD_PARENT=\\\\\\\"add_parent\\\\\\\",t.REMOVE_PARENT=\\\\\\\"remove_parent\\\\\\\",t.ADD_CHILD=\\\\\\\"add_child\\\\\\\"}(aK||(aK={}));const lK=[aK.ADD_PARENT,aK.REMOVE_PARENT,aK.ADD_CHILD];class cK extends QG{static type(){return\\\\\\\"hierarchy\\\\\\\"}cook(t,e){const n=t[0],i=lK[e.mode];switch(i){case aK.ADD_PARENT:{const t=this._add_parent_to_core_group(n,e);return this.createCoreGroupFromObjects(t)}case aK.REMOVE_PARENT:{const t=this._remove_parent_from_core_group(n,e);return this.createCoreGroupFromObjects(t)}case aK.ADD_CHILD:{const i=this._add_child_to_core_group(n,t[1],e);return this.createCoreGroupFromObjects(i)}}ls.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 Fn.a;if(n.matrixAutoUpdate=!1,n.add(...t),e.levels>0)for(let t=0;t({name:t,value:e})))}}),this.levels=aa.INTEGER(uK.levels,{range:[0,5],visibleIf:[{mode:lK.indexOf(aK.ADD_PARENT)},{mode:lK.indexOf(aK.REMOVE_PARENT)}]}),this.objectMask=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{mode:lK.indexOf(aK.ADD_CHILD)}}),this.debugObjectMask=aa.BOOLEAN(0,{visibleIf:{mode:lK.indexOf(aK.ADD_CHILD)}})}};class pK extends nV{constructor(){super(...arguments),this.paramsConfig=dK}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(cK.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=lK[this.pv.mode];return hK.includes(t)?`${t} ${this.pv.levels}`:`${t} (with mask: ${this.pv.objectMask})`}))}))}))}cook(t){this._operation=this._operation||new cK(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const _K=new class extends la{constructor(){super(...arguments),this.texture=aa.OPERATOR_PATH(vi.UV,{nodeSelection:{context:ts.COP}}),this.mult=aa.FLOAT(1)}};class mK extends nV{constructor(){super(...arguments),this.paramsConfig=_K}static type(){return\\\\\\\"heightMap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}async cook(t){const e=t[0],n=this.p.texture.found_node();if(n){if(n.context()==ts.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:s,resx:r,resy:o}=i,a=s.length/(r*o),l=null===(n=t.coreGeometry())||void 0===n?void 0:n.geometry();if(!l)return;const c=l.getAttribute(\\\\\\\"position\\\\\\\").array,h=l.getAttribute(\\\\\\\"uv\\\\\\\"),u=l.getAttribute(\\\\\\\"normal\\\\\\\");if(null==h)return void this.states.error.set(\\\\\\\"uvs are required\\\\\\\");if(null==u)return void this.states.error.set(\\\\\\\"normals are required\\\\\\\");const d=h.array,p=u.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((r-1)*f),y=Math.floor((o-1)*(1-g)),x=y*r+v,b=s[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:Pf.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 fK(t){return Math.atan2(-t.y,Math.sqrt(t.x*t.x+t.z*t.z))}class gK extends S.a{constructor(t,e,n,i,s){super(),this.type=\\\\\\\"PolyhedronBufferGeometry\\\\\\\",this.parameters={vertices:t,indices:e,radius:n,detail:i},n=n||1,i=i||0;const r=[],o=[],a=new Map;function l(t,e,n,i){const s=i+1,r=[];for(let i=0;i<=s;i++){r[i]=[];const o=t.clone().lerp(n,i/s),a=e.clone().lerp(n,i/s),l=s-i;for(let t=0;t<=l;t++)r[i][t]=0===t&&i===s?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 FK=new class extends la{constructor(){super(...arguments),this.length=aa.FLOAT(1,{range:[0,10]}),this.pointsCount=aa.INTEGER(1,{range:[2,100],rangeLocked:[!0,!1]}),this.origin=aa.VECTOR3([0,0,0]),this.direction=aa.VECTOR3([0,1,0])}};class DK extends nV{constructor(){super(...arguments),this.paramsConfig=FK}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 s=0;s0&&(n[2*(s-1)]=s-1,n[2*(s-1)+1]=s)}const s=new S.a;s.setAttribute(\\\\\\\"position\\\\\\\",new C.c(e,3)),s.setIndex(n),this.setGeometry(s,Cs.LINE_SEGMENTS)}}const BK=new class extends la{constructor(){super(...arguments),this.distance0=aa.FLOAT(1),this.distance1=aa.FLOAT(2),this.autoUpdate=aa.BOOLEAN(1),this.update=aa.BUTTON(null,{callback:t=>{zK.PARAM_CALLBACK_update(t)}}),this.camera=aa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{autoUpdate:0},dependentOnFoundNode:!1})}};class zK extends nV{constructor(){super(...arguments),this.paramsConfig=BK,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(Ki.FROM_NODE)}_create_LOD(){const t=new Ss;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: '${r.states.error.message()}')`)}else null===(s=this.states)||void 0===s||s.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&&!yr.isInGroup(n.group,t))return;const i=n.cloneMat?gr.clone(e):e;if(e instanceof F&&i instanceof F)for(let t in e.uniforms)i.uniforms[t]=e.uniforms[t];const s=t;this._old_mat_by_old_new_id.set(i.uuid,s.material),s.material=i,gr.apply_render_hook(t,i),gr.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})}}}}kK.DEFAULT_PARAMS={group:\\\\\\\"\\\\\\\",assignMat:!0,material:new yi(\\\\\\\"\\\\\\\"),applyToChildren:!0,cloneMat:!1,shareUniforms:!0,swapCurrentTex:!1,texSrc0:\\\\\\\"emissiveMap\\\\\\\",texDest0:\\\\\\\"map\\\\\\\"},kK.INPUT_CLONED_STATE=Ki.FROM_NODE;const UK=kK.DEFAULT_PARAMS;const GK=new class extends la{constructor(){super(...arguments),this.group=aa.STRING(UK.group),this.assignMat=aa.BOOLEAN(UK.assignMat),this.material=aa.NODE_PATH(UK.material.path(),{nodeSelection:{context:ts.MAT},dependentOnFoundNode:!1,visibleIf:{assignMat:1}}),this.applyToChildren=aa.BOOLEAN(UK.applyToChildren,{visibleIf:{assignMat:1}}),this.cloneMat=aa.BOOLEAN(UK.cloneMat,{visibleIf:{assignMat:1}}),this.shareUniforms=aa.BOOLEAN(UK.shareUniforms,{visibleIf:{assignMat:1,cloneMat:1}}),this.swapCurrentTex=aa.BOOLEAN(UK.swapCurrentTex),this.texSrc0=aa.STRING(UK.texSrc0,{visibleIf:{swapCurrentTex:1}}),this.texDest0=aa.STRING(UK.texDest0,{visibleIf:{swapCurrentTex:1}})}};class VK extends nV{constructor(){super(...arguments),this.paramsConfig=GK}static type(){return\\\\\\\"material\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to assign material to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(kK.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 kK(this._scene,this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class HK extends QG{static type(){return\\\\\\\"merge\\\\\\\"}cook(t,e){let n=[];for(let i of t)if(i){const t=i.objects();if(e.compact)for(let e of t)e.traverse((t=>{n.push(t)}));else for(let t of i.objects())n.push(t)}e.compact&&(n=this._makeCompact(n));for(let t of n)t.traverse((t=>{t.matrixAutoUpdate=!1}));return this.createCoreGroupFromObjects(n)}_makeCompact(t){const e=new Map,n=new Map,i=[];for(let s of t)s.traverse((t=>{if(t instanceof Fn.a)return;const s=t;if(s.geometry){const t=Ls(s.constructor);if(i.includes(t)||i.push(t),t){e.get(t)||e.set(t,s.material),h.pushOnArrayAtEntry(n,t,s)}}}));const s=[];return i.forEach((t=>{var i,r;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 r=_r.mergeGeometries(n);if(r){const n=e.get(t),i=this.createObject(r,t,n);s.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===(r=this.states)||void 0===r||r.error.set(t.message)}}})),s}}HK.DEFAULT_PARAMS={compact:!1},HK.INPUT_CLONED_STATE=Ki.FROM_NODE;const jK=\\\\\\\"geometry to merge\\\\\\\",WK=HK.DEFAULT_PARAMS;const qK=new class extends la{constructor(){super(...arguments),this.compact=aa.BOOLEAN(WK.compact),this.inputsCount=aa.INTEGER(4,{range:[1,32],rangeLocked:[!0,!1],callback:t=>{XK.PARAM_CALLBACK_setInputsCount(t)}})}};class XK extends nV{constructor(){super(...arguments),this.paramsConfig=qK}static type(){return\\\\\\\"merge\\\\\\\"}static displayedInputNames(){return[jK,jK,jK,jK]}setCompactMode(t){this.p.compact.set(t)}initializeNode(){this.io.inputs.setCount(1,4),this.io.inputs.initInputsClonedState(HK.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 HK(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 YK extends K.a{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}}YK.prototype.isImmediateRenderObject=!0;class $K extends YK{constructor(t,e,n,i){super(e);const s=this,r=new Float32Array(36),o=new Float32Array(36),a=new Float32Array(36);function l(t,e,n){return t+(e-t)*n}function c(t,e,n,i,c,h,u,d,p,_){const m=(n-u)/(d-u),f=s.normal_cache;r[e+0]=i+m*s.delta,r[e+1]=c,r[e+2]=h,o[e+0]=l(f[t+0],f[t+3],m),o[e+1]=l(f[t+1],f[t+4],m),o[e+2]=l(f[t+2],f[t+5],m),a[e+0]=l(s.palette[3*p+0],s.palette[3*_+0],m),a[e+1]=l(s.palette[3*p+1],s.palette[3*_+1],m),a[e+2]=l(s.palette[3*p+2],s.palette[3*_+2],m)}function h(t,e,n,i,c,h,u,d,p,_){const m=(n-u)/(d-u),f=s.normal_cache;r[e+0]=i,r[e+1]=c+m*s.delta,r[e+2]=h;const g=t+3*s.yd;o[e+0]=l(f[t+0],f[g+0],m),o[e+1]=l(f[t+1],f[g+1],m),o[e+2]=l(f[t+2],f[g+2],m),a[e+0]=l(s.palette[3*p+0],s.palette[3*_+0],m),a[e+1]=l(s.palette[3*p+1],s.palette[3*_+1],m),a[e+2]=l(s.palette[3*p+2],s.palette[3*_+2],m)}function u(t,e,n,i,c,h,u,d,p,_){const m=(n-u)/(d-u),f=s.normal_cache;r[e+0]=i,r[e+1]=c,r[e+2]=h+m*s.delta;const g=t+3*s.zd;o[e+0]=l(f[t+0],f[g+0],m),o[e+1]=l(f[t+1],f[g+1],m),o[e+2]=l(f[t+2],f[g+2],m),a[e+0]=l(s.palette[3*p+0],s.palette[3*_+0],m),a[e+1]=l(s.palette[3*p+1],s.palette[3*_+1],m),a[e+2]=l(s.palette[3*p+2],s.palette[3*_+2],m)}function d(t){const e=3*t;0===s.normal_cache[e]&&(s.normal_cache[e+0]=s.field[t-1]-s.field[t+1],s.normal_cache[e+1]=s.field[t-s.yd]-s.field[t+s.yd],s.normal_cache[e+2]=s.field[t-s.zd]-s.field[t+s.zd])}function p(t,e,n,i,l,p){const m=i+1,f=i+s.yd,g=i+s.zd,v=m+s.yd,y=m+s.zd,x=i+s.yd+s.zd,b=m+s.yd+s.zd;let w=0;const T=s.field[i],A=s.field[m],M=s.field[f],E=s.field[v],S=s.field[g],C=s.field[y],N=s.field[x],L=s.field[b];T=s.maxCount-3&&(s.hasPositions=!0,s.hasNormals=!0,s.enableUvs&&(s.hasUvs=!0),s.enableColors&&(s.hasColors=!0),a(s))}function m(t,e,n){const i=new Float32Array(t.length+n);return i.set(t,0),i.set(e.slice(0,n),t.length),i}this.enableUvs=void 0!==n&&n,this.enableColors=void 0!==i&&i,this.init=function(t){this.resolution=t,this.isolation=80,this.size=t,this.size2=this.size*this.size,this.size3=this.size2*this.size,this.halfsize=this.size/2,this.delta=2/this.size,this.yd=this.size,this.zd=this.size2,this.field=new Float32Array(this.size3),this.normal_cache=new Float32Array(3*this.size3),this.palette=new Float32Array(3*this.size3),this.maxCount=4096,this.count=0,this.hasPositions=!1,this.hasNormals=!1,this.hasColors=!1,this.hasUvs=!1,this.positionArray=new Float32Array(3*this.maxCount),this.normalArray=new Float32Array(3*this.maxCount),this.enableUvs&&(this.uvArray=new Float32Array(2*this.maxCount)),this.enableColors&&(this.colorArray=new Float32Array(3*this.maxCount))},this.begin=function(){this.count=0,this.hasPositions=!1,this.hasNormals=!1,this.hasUvs=!1,this.hasColors=!1},this.end=function(t){if(0!==this.count){for(let t=3*this.count;tthis.size-1&&(_=this.size-1);let m=Math.floor(u-c);m<1&&(m=1);let f=Math.floor(u+c);f>this.size-1&&(f=this.size-1);let g=Math.floor(d-c);g<1&&(g=1);let v,y,x,b,w,T,A,M,E,S,C,N=Math.floor(d+c);for(N>this.size-1&&(N=this.size-1),x=p;x<_;x++)for(w=this.size2*x,M=x/this.size-n,E=M*M,y=m;y0){this.field[b+v]+=C*o;const t=Math.sqrt((v-d)*(v-d)+(y-u)*(y-u)+(x-h)*(x-h))/c,e=1-t*t*t*(t*(6*t-15)+10);this.palette[3*(b+v)+0]+=l.r*e,this.palette[3*(b+v)+1]+=l.g*e,this.palette[3*(b+v)+2]+=l.b*e}},this.addPlaneX=function(t,e){const n=this.size,i=this.yd,s=this.zd,r=this.field;let o,a,l,c,h,u,d,p=n*Math.sqrt(t/e);for(p>n&&(p=n),o=0;o0)for(a=0;an&&(_=n),a=0;a<_;a++)if(u=a/n,c=u*u,h=t/(1e-4+c)-e,h>0)for(d=a*i,o=0;on&&(_=n),l=0;l<_;l++)if(u=l/n,c=u*u,h=t/(1e-4+c)-e,h>0)for(d=s*l,a=0;a=i))for(let e=-1;e<=1;e+=2){const r=e+o;if(!(r<0||r>=i))for(let e=-1;e<=1;e+=2){const o=e+a;if(o<0||o>=i)continue;const u=n[s*o+i*r+l];h++,c+=t*(u-c)/h}}}e[l]=c}},this.reset=function(){for(let t=0;tu?(d=1,p=0):(d=0,p=1);const _=h-d+l,m=u-p+l,f=h-1+2*l,g=u-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-h*h-u*u;T<0?n=0:(T*=T,n=T*T*this.dot(this.grad3[x],h,u));let A=.5-_*_-m*m;A<0?i=0:(A*=A,i=A*A*this.dot(this.grad3[b],_,m));let M=.5-f*f-g*g;return M<0?s=0:(M*=M,s=M*M*this.dot(this.grad3[w],f,g)),70*(n+i+s)}noise3d(t,e,n){let i,s,r,o;const a=(t+e+n)*(1/3),l=Math.floor(t+a),c=Math.floor(e+a),h=Math.floor(n+a),u=1/6,d=(l+c+h)*u,p=t-(l-d),_=e-(c-d),m=n-(h-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),M=r[A][0]>=3?1:0,E=r[A][1]>=3?1:0,S=r[A][2]>=3?1:0,C=r[A][3]>=3?1:0,N=r[A][0]>=2?1:0,L=r[A][1]>=2?1:0,O=r[A][2]>=2?1:0,P=r[A][3]>=2?1:0,R=r[A][0]>=1?1:0,I=r[A][1]>=1?1:0,F=r[A][2]>=1?1:0,D=r[A][3]>=1?1:0,B=x-M+l,z=b-E+l,k=w-S+l,U=T-C+l,G=x-N+2*l,V=b-L+2*l,H=w-O+2*l,j=T-P+2*l,W=x-R+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,st=o[K+M+o[tt+E+o[et+S+o[nt+C]]]]%32,rt=o[K+N+o[tt+L+o[et+O+o[nt+P]]]]%32,ot=o[K+R+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(s[it],x,b,w,T));let ct=.6-B*B-z*z-k*k-U*U;ct<0?h=0:(ct*=ct,h=ct*ct*this.dot4(s[st],B,z,k,U));let ht=.6-G*G-V*V-H*H-j*j;ht<0?u=0:(ht*=ht,u=ht*ht*this.dot4(s[rt],G,V,H,j));let ut=.6-W*W-q*q-X*X-Y*Y;ut<0?d=0:(ut*=ut,d=ut*ut*this.dot4(s[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(s[at],$,J,Z,Q)),27*(c+h+u+d+p)}}var s0;!function(t){t.ADD=\\\\\\\"add\\\\\\\",t.SET=\\\\\\\"set\\\\\\\",t.MULT=\\\\\\\"mult\\\\\\\",t.SUBSTRACT=\\\\\\\"substract\\\\\\\",t.DIVIDE=\\\\\\\"divide\\\\\\\"}(s0||(s0={}));const r0=[s0.ADD,s0.SET,s0.MULT,s0.SUBSTRACT,s0.DIVIDE];const o0=new class extends la{constructor(){super(...arguments),this.amplitude=aa.FLOAT(1),this.tamplitudeAttrib=aa.BOOLEAN(0),this.amplitudeAttrib=aa.STRING(\\\\\\\"amp\\\\\\\",{visibleIf:{tamplitudeAttrib:!0}}),this.freq=aa.VECTOR3([1,1,1]),this.offset=aa.VECTOR3([0,0,0]),this.octaves=aa.INTEGER(3,{range:[1,8],rangeLocked:[!0,!1]}),this.ampAttenuation=aa.FLOAT(.5,{range:[0,1]}),this.freqIncrease=aa.FLOAT(2,{range:[0,10]}),this.seed=aa.INTEGER(0,{range:[0,100],separatorAfter:!0}),this.useNormals=aa.BOOLEAN(0),this.attribName=aa.STRING(\\\\\\\"position\\\\\\\"),this.useRestAttributes=aa.BOOLEAN(0),this.restP=aa.STRING(\\\\\\\"restP\\\\\\\",{visibleIf:{useRestAttributes:!0}}),this.restN=aa.STRING(\\\\\\\"restN\\\\\\\",{visibleIf:{useRestAttributes:!0}}),this.operation=aa.INTEGER(r0.indexOf(s0.ADD),{menu:{entries:r0.map((t=>({name:t,value:r0.indexOf(t)})))}}),this.computeNormals=aa.BOOLEAN(1)}};class a0 extends nV{constructor(){super(...arguments),this.paramsConfig=o0,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([Ki.FROM_NODE])}setOperation(t){this.p.operation.set(r0.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)!=Bs.NUMERIC)return this.states.error.set(`attribute ${i} is not a numeric attribute`),void this.cookController.endCook();const s=this._get_simplex(),r=this.pv.useNormals&&e.hasAttrib(\\\\\\\"normal\\\\\\\"),o=e.attribSize(this.pv.attribName),a=r0[this.pv.operation],l=this.pv.useRestAttributes,c=this.pv.amplitude,h=this.pv.tamplitudeAttrib;let u,f,g=new p.a;for(let t=0;t{s[3*t.index()+0]=e}));else{let t;for(let e=0;e{s[3*t.index()+1]=e}));else{let t;for(let e=0;e{s[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)}}m0.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},m0.INPUT_CLONED_STATE=Ki.FROM_NODE;const f0=m0.DEFAULT_PARAMS;const g0=new class extends la{constructor(){super(...arguments),this.applyToChildren=aa.BOOLEAN(f0.applyToChildren,{separatorAfter:!0}),this.tname=aa.BOOLEAN(f0.tname),this.name=aa.STRING(f0.name,{visibleIf:{tname:!0},separatorAfter:!0}),this.trenderOrder=aa.BOOLEAN(f0.trenderOrder),this.renderOrder=aa.INTEGER(f0.renderOrder,{visibleIf:{trenderOrder:!0},range:[0,10],rangeLocked:[!1,!1],separatorAfter:!0}),this.tfrustumCulled=aa.BOOLEAN(f0.tfrustumCulled),this.frustumCulled=aa.BOOLEAN(f0.frustumCulled,{visibleIf:{tfrustumCulled:!0},separatorAfter:!0}),this.tmatrixAutoUpdate=aa.BOOLEAN(f0.tmatrixAutoUpdate),this.matrixAutoUpdate=aa.BOOLEAN(f0.matrixAutoUpdate,{visibleIf:{tmatrixAutoUpdate:!0},separatorAfter:!0}),this.tvisible=aa.BOOLEAN(f0.tvisible),this.visible=aa.BOOLEAN(f0.visible,{visibleIf:{tvisible:!0},separatorAfter:!0}),this.tcastShadow=aa.BOOLEAN(f0.tcastShadow),this.castShadow=aa.BOOLEAN(f0.castShadow,{visibleIf:{tcastShadow:!0},separatorAfter:!0}),this.treceiveShadow=aa.BOOLEAN(f0.treceiveShadow),this.receiveShadow=aa.BOOLEAN(f0.receiveShadow,{visibleIf:{treceiveShadow:!0}})}};class v0 extends nV{constructor(){super(...arguments),this.paramsConfig=g0}static type(){return\\\\\\\"objectProperties\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to change properties of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(m0.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new m0(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const y0=new class extends la{};class x0 extends nV{constructor(){super(...arguments),this.paramsConfig=y0,this._input_configs_by_operation_container=new WeakMap}static type(){return Il}initializeNode(){this.io.inputs.initInputsClonedState(Ki.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 b0 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),s=t.glVarName(e);if(this._texture_allocations_controller.variable(e)&&i){const r=i.type(),o=`${r} ${s} = ${this.read_attribute(t,r,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 s=this._texture_allocations_controller.variable(n);if(!s)return Sf.read_attribute(t,e,n,i);{this.add_particles_sim_uv_attribute(t,i);const e=s.component(),n=s.allocation();if(n){const s=n.textureName(),r=new Af(t,Bo.SAMPLER_2D,s);i.addDefinitions(t,[r]);return`texture2D( ${s}, ${this._uv_name} ).${e}`}}}add_particles_sim_uv_attribute(t,e){const n=new wf(t,Bo.VEC2,b0.UV_ATTRIB),i=new Mf(t,Bo.VEC2,b0.UV_VARYING);e.addDefinitions(t,[n,i],xf.VERTEX),e.addDefinitions(t,[i],xf.FRAGMENT),e.addBodyLines(t,[`${b0.UV_VARYING} = ${b0.UV_ATTRIB}`],xf.VERTEX)}}b0.UV_ATTRIB=\\\\\\\"particles_sim_uv_attrib\\\\\\\",b0.UV_VARYING=\\\\\\\"particles_sim_uv_varying\\\\\\\",b0.PARTICLE_SIM_UV=\\\\\\\"particleUV\\\\\\\";class w0{constructor(t){this.node=t,this._particles_group_objects=[],this._all_shader_names=[],this._all_uniform_names=[],this.globals_handler=new b0(b0.UV_VARYING)}setShadersByName(t){this._shaders_by_name=t,this._all_shader_names=[],this._all_uniform_names=[],this._shaders_by_name.forEach(((t,e)=>{this._all_shader_names.push(e),this._all_uniform_names.push(`texture_${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,gr.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 li.renderersController.waitForRenderer();if(n?this._renderer=n:this.node.states.error.set(\\\\\\\"no renderer found\\\\\\\"),!this._renderer)return;const i=new A0(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 s=this._gpu_compute.init();null!==s&&(console.error(s),this.node.states.error.set(s))}else this.node.states.error.set(\\\\\\\"failed to create the GPUComputationRenderer\\\\\\\")}_forceTimeDependent(){this._graph_node||(this._graph_node=new Mi(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[pR.TIME]={value:this.node.scene().time()},t.uniforms[pR.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 s=t.param_uniform_pairs;for(let t of s){const s=t[0],r=t[1],o=this.node.params.get(s),a=e[r];for(let t of n)t.uniforms[r]=a,i&&this._assignReadonlyTextures(t,i);o&&a&&o.options.setOption(\\\\\\\"callback\\\\\\\",(()=>{for(let t of n)$f.callback(o,t.uniforms[r])}))}}}}}_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[pR.TIME].value=this.node.scene().time(),t.material.uniforms[pR.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));s++);const r=b0.UV_ATTRIB;if(this._particles_core_group)for(let e of this._particles_core_group.coreGeometries()){const n=e.geometry(),i=e.markedAsInstance()?A$:C.a;n.setAttribute(r,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 s=i.variables();if(!s)return void console.warn(\\\\\\\"allocation has no variables\\\\\\\");const r=e.image.data;for(let t of s){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 s=e;for(let e of this._points){if(1==t){const t=e.attribValue(n);r[s]=t}else e.attribValue(n).toArray(r,s);s+=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 s=[];for(let t of i)for(let e of t.points())s.push(e);return s}return[]}}class C0{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 C0(t.name,t.size)}toJSON(t){const e=[];return this._graph_node_ids&&this._graph_node_ids.forEach(((n,i)=>{const s=t.graph.nodeFromId(i);if(s){const t=s.path();t&&e.push(t)}})),{name:this.name(),size:this.size(),nodes:e}}}class N0{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()==ss.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 N0;for(let n of t){const t=C0.fromJSON(n);e.addVariable(t)}return e}toJSON(t){return this._variables?this._variables.map((e=>e.toJSON(t))):[]}}const L0=[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"color\\\\\\\",\\\\\\\"uv\\\\\\\"];class O0{constructor(){this._writableAllocations=[],this._readonlyAllocations=[]}static _sortNodes(t){const e=t.filter((t=>t.type()==bF.type())),n=t.filter((t=>t.type()!=bF.type())),i=n.map((t=>t.name())).sort(),s=new Map;for(let t of n)s.set(t.name(),t);for(let t of i){const n=s.get(t);n&&e.push(n)}return e}allocateConnectionsFromRootNodes(t,e){const n=[];t=O0._sortNodes(t),e=O0._sortNodes(e);for(let e of t){const t=e.graphNodeId();switch(e.type()){case bF.type():for(let i of e.io.inputs.namedInputConnectionPoints()){if(e.io.inputs.named_input(i.name())){const e=new C0(i.name(),Vo[i.type()]);e.addGraphNodeId(t),n.push(e)}}break;case gf.type():{const i=e,s=i.connected_input_node(),r=i.connected_input_connection_point();if(s&&r){const e=new C0(i.attribute_name,Vo[r.type()]);e.addGraphNodeId(t),n.push(e)}break}}}for(let t of e){const e=t.graphNodeId();switch(t.type()){case AI.type():{const i=t;for(let t of i.io.outputs.used_output_names()){if(L0.includes(t)){const s=i.io.outputs.namedOutputConnectionPointsByName(t);if(s){const i=s.type(),r=new C0(t,Vo[i]);r.addGraphNodeId(e),n.push(r)}}}break}case gf.type():{const i=t,s=i.output_connection_point();if(s){const t=new C0(i.attribute_name,Vo[s.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)h.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 O0;for(let n of t.writable){const t=n[Object.keys(n)[0]],i=N0.fromJSON(t);e._addWritableAllocation(i)}for(let n of t.readonly){const t=n[Object.keys(n)[0]],i=N0.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 P0 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=[],s=new F,r=t.assembler.param_configs();for(let t of r)i.push([t.name(),t.uniform_name]),s.uniforms[t.uniform_name]=t.uniform;const o=this._materialToJson(s,{node:this.node,suffix:\\\\\\\"main\\\\\\\"});return{shaders_by_name:e,texture_allocations:n,param_uniform_pairs:i,uniforms_owner:o||{}}}load(t){li.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 O0.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 R0=new class extends la{constructor(){super(...arguments),this.startFrame=aa.FLOAT(El.START_FRAME,{range:[0,1e3],rangeLocked:[!0,!1]}),this.autoTexturesSize=aa.BOOLEAN(1),this.maxTexturesSize=aa.VECTOR2([1024,1024],{visibleIf:{autoTexturesSize:1}}),this.texturesSize=aa.VECTOR2([64,64],{visibleIf:{autoTexturesSize:0}}),this.dataType=aa.INTEGER(0,{menu:{entries:M0.map(((t,e)=>({value:e,name:t})))}}),this.reset=aa.BUTTON(null,{callback:(t,e)=>{I0.PARAM_CALLBACK_reset(t)}}),this.material=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.MAT},dependentOnFoundNode:!1})}};class I0 extends nV{constructor(){super(...arguments),this.paramsConfig=R0,this._assembler_controller=this._create_assembler_controller(),this.persisted_config=new P0(this),this.globals_handler=new b0(b0.PARTICLE_SIM_UV),this._shaders_by_name=new Map,this.gpuController=new S0(this),this.renderController=new w0(this),this._reset_material_if_dirty_bound=this._reset_material_if_dirty.bind(this),this._children_controller_context=ts.GL}static type(){return\\\\\\\"particlesSystemGpu\\\\\\\"}dispose(){super.dispose(),this.gpuController.dispose()}get assemblerController(){return this._assembler_controller}usedAssembler(){return jn.GL_PARTICLES}_create_assembler_controller(){return li.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(Ki.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 F0 extends QG{static type(){return\\\\\\\"peak\\\\\\\"}cook(t,e){const n=t[0];let i,s;for(let t of n.objects())t.traverse((t=>{let n;if(null!=(n=t.geometry)){for(s of(i=new _r(n),i.points())){const t=s.normal(),n=s.position().clone().add(t.multiplyScalar(e.amount));s.setPosition(n)}i.geometry().getAttribute(\\\\\\\"position\\\\\\\").needsUpdate=!0}}));return t[0]}}F0.DEFAULT_PARAMS={amount:0};const D0=F0.DEFAULT_PARAMS;const B0=new class extends la{constructor(){super(...arguments),this.amount=aa.FLOAT(D0.amount,{range:[-1,1]})}};class z0 extends nV{constructor(){super(...arguments),this.paramsConfig=B0}static type(){return\\\\\\\"peak\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}cook(t){this._operation=this._operation||new F0(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const k0=new p.a(0,0,1),U0=new p.a(0,0,1),G0=new p.a(0,1,0);class V0 extends QG{constructor(){super(...arguments),this._core_transform=new uU,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,k0,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),s=this._create_plane(i,e);this._core_transform.rotate_geometry(s,U0,G0);const r=this._core_transform.translation_matrix(this._center);return s.applyMatrix4(r),this.createCoreGroupFromGeometry(s)}_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)}}V0.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)},V0.INPUT_CLONED_STATE=Ki.NEVER;const H0=V0.DEFAULT_PARAMS;const j0=new class extends la{constructor(){super(...arguments),this.size=aa.VECTOR2(H0.size),this.useSegmentsCount=aa.BOOLEAN(H0.useSegmentsCount),this.stepSize=aa.FLOAT(H0.stepSize,{range:[.001,1],rangeLocked:[!1,!1],visibleIf:{useSegmentsCount:0}}),this.segments=aa.VECTOR2(H0.segments,{visibleIf:{useSegmentsCount:1}}),this.direction=aa.VECTOR3(H0.direction),this.center=aa.VECTOR3(H0.center)}};class W0 extends nV{constructor(){super(...arguments),this.paramsConfig=j0}static type(){return\\\\\\\"plane\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create plane from (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(V0.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new V0(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class q0 extends QG{static type(){return\\\\\\\"playerCapsule\\\\\\\"}cook(t,e){return this.createCoreGroupFromGeometry(Oy(e))}}q0.DEFAULT_PARAMS={radius:.5,height:1};const X0=q0.DEFAULT_PARAMS;const Y0=new class extends la{constructor(){super(...arguments),this.radius=aa.FLOAT(X0.radius,{range:[0,1],rangeLocked:[!0,!1]}),this.height=aa.FLOAT(X0.height,{range:[0,1],rangeLocked:[!0,!1]})}};class $0 extends nV{constructor(){super(...arguments),this.paramsConfig=Y0}static type(){return\\\\\\\"playerCapsule\\\\\\\"}initializeNode(){this.io.inputs.setCount(0)}cook(t){this._operation=this._operation||new q0(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const J0=\\\\\\\"position\\\\\\\";const Z0=new class extends la{constructor(){super(...arguments),this.updateX=aa.BOOLEAN(0),this.x=aa.FLOAT(\\\\\\\"@P.x\\\\\\\",{visibleIf:{updateX:1},expression:{forEntities:!0}}),this.updateY=aa.BOOLEAN(0),this.y=aa.FLOAT(\\\\\\\"@P.y\\\\\\\",{visibleIf:{updateY:1},expression:{forEntities:!0}}),this.updateZ=aa.BOOLEAN(0),this.z=aa.FLOAT(\\\\\\\"@P.z\\\\\\\",{visibleIf:{updateZ:1},expression:{forEntities:!0}}),this.updateNormals=aa.BOOLEAN(1)}};class Q0 extends nV{constructor(){super(...arguments),this.paramsConfig=Z0,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(Ki.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{h[t.index()]=e}));else{let t;for(let e=0;e({name:t,value:e})))}}),this.center=aa.VECTOR3(o1.center.toArray()),this.longitude=aa.FLOAT(o1.longitude,{range:[0,360]}),this.latitude=aa.FLOAT(o1.latitude,{range:[-180,180]}),this.depth=aa.FLOAT(o1.depth,{range:[0,10]})}};class l1 extends nV{constructor(){super(...arguments),this.paramsConfig=a1}static type(){return\\\\\\\"polarTransform\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(r1.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new r1(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class c1{static accumulated_curve_point_indices(t){let e=[];const n=[];let i,s=null;for(let r=0;r{r[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=>{r[t].push(e)}))})),o>0&&(s.push(o-1),s.push(o))}));const o=new S.a;return n.forEach((t=>{const e=i[t],n=r[t];o.setAttribute(t,new C.c(n,e))})),o.setIndex(s),o}static line_segment_to_geometries(t){var e;const n=[],i=new _r(t),s=i.attribNames(),r=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(r,i,s,e),n.push(t)}))}return n}}class h1{constructor(t,e,n){this.geometry=t,this.geometry1=e,this.geometry0=n}process(){const t=new _r(this.geometry0),e=new _r(this.geometry1),n=t.segments(),i=e.segments();if(0===n.length||0===i.length)return;const s=n.length{const n=l[e];p.push(t[0]),p.push(t[1]),p.push(n[0]+u),p.push(t[1]),p.push(n[1]+u),p.push(n[0]+u)}));f.intersection(r.attribNames(),o.attribNames()).forEach((t=>{const e=r.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 u1=new p.a(0,0,0),d1=new p.a(1,1,1);const p1=new class extends la{constructor(){super(...arguments),this.radius=aa.FLOAT(1),this.segmentsRadial=aa.INTEGER(8,{range:[3,20],rangeLocked:[!0,!1]}),this.closed=aa.BOOLEAN(0)}};class _1 extends nV{constructor(){super(...arguments),this.paramsConfig=p1,this._core_transform=new uU,this._geometries=[]}static type(){return\\\\\\\"polywire\\\\\\\"}static displayedInputNames(){return[\\\\\\\"lines to create tubes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.NEVER)}cook(t){const e=t[0];this._geometries=[];for(let t of e.objects())t instanceof As.a&&this._create_tube(t);const n=_r.mergeGeometries(this._geometries);for(let t of this._geometries)t.dispose();if(n){const t=this.createObject(n,Cs.MESH);this.setObject(t)}else this.setObjects([])}_create_tube(t){var e;const n=t.geometry,i=new _r(n).points(),s=null===(e=n.getIndex())||void 0===e?void 0:e.array,r=c1.accumulated_curve_point_indices(s);for(let t of r){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=BJ.create(this.pv.radius,this.pv.segmentsRadial),i=[];for(let t of e){const e=t,s=this._core_transform.matrix(e,u1,d1,1,hU),r=n.clone();r.applyMatrix4(s),i.push(r)}for(let t=0;t0){const e=i[t],n=i[t-1],s=this._skin(n,e);this._geometries.push(s)}}_skin(t,e){const n=new S.a;return new h1(n,t,e).process(),n}}const m1=\\\\\\\"dist\\\\\\\";class f1 extends QG{constructor(){super(...arguments),this._matDoubleSideTmpSetter=new tQ,this._raycaster=function(){const t=new WL;return t.firstHitOnly=!0,t}(),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,s;this._matDoubleSideTmpSetter.setCoreGroupMaterialDoubleSided(e),n.addDistAttribute&&(t.hasAttrib(m1)||t.addNumericVertexAttrib(m1,1,-1));const r=t.points();for(let t of r)if(t.getPosition(this._pointPos),i=n.direction,n.useNormals&&(t.getNormal(this._pointNormal),i=this._pointNormal),this._raycaster.set(this._pointPos,i),s=this._raycaster.intersectObjects(e.objects(),!0)[0],s){if(n.transformPoints&&t.setPosition(s.point),n.addDistAttribute){const e=this._pointPos.distanceTo(s.point);console.log(e),t.setAttribValue(m1,e)}n.transferFaceNormals&&s.face&&t.setNormal(s.face.normal)}return this._matDoubleSideTmpSetter.restoreMaterialSideProperty(e),t}}f1.DEFAULT_PARAMS={useNormals:!0,direction:new p.a(0,-1,0),transformPoints:!0,transferFaceNormals:!0,addDistAttribute:!1},f1.INPUT_CLONED_STATE=[Ki.FROM_NODE,Ki.NEVER];const g1=f1.DEFAULT_PARAMS;const v1=new class extends la{constructor(){super(...arguments),this.useNormals=aa.BOOLEAN(g1.useNormals),this.direction=aa.VECTOR3(g1.direction.toArray(),{visibleIf:{useNormals:0}}),this.transformPoints=aa.BOOLEAN(g1.transformPoints),this.transferFaceNormals=aa.BOOLEAN(g1.transferFaceNormals),this.addDistAttribute=aa.BOOLEAN(g1.addDistAttribute)}};class y1 extends nV{constructor(){super(...arguments),this.paramsConfig=v1}static type(){return\\\\\\\"ray\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to move\\\\\\\",\\\\\\\"geometry to ray onto\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState(f1.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new f1(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const x1={color:{value:null},tDiffuse:{value:null},textureMatrix:{value:null},opacity:{value:.5}},b1=\\\\\\\"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}\\\\\\\",w1=\\\\\\\"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}\\\\\\\",T1={minFilter:w.V,magFilter:w.V,format:w.ic};class A1 extends B.a{constructor(t,e){super(),this.geometry=t,this._options=e,this.type=\\\\\\\"Reflector\\\\\\\",this.reflectorPlane=new Y.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 tt.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 Q(n,i,T1),Object(On.i)(n)&&Object(On.i)(i)||(this.renderTarget.texture.generateMipmaps=!1),this._coreRenderBlur=new wG(new d.a(n,i)),this.material=new F({uniforms:I.clone(x1),fragmentShader:w1,vertexShader:b1}),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,s,r){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,h=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=h,t.setRenderTarget(l);var u=o.viewport;void 0!==u&&t.state.viewport(u),this.visible=!0}}}class M1 extends QG{static type(){return\\\\\\\"reflector\\\\\\\"}async cook(t,e){const n=t[0],i=[],s=await li.renderersController.firstRenderer();if(!s)return this.createCoreGroupFromObjects(i);const r=n.objectsWithGeo();for(let t of r){const n=new A1(t.geometry,{clipBias:e.clipBias,renderer:s,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)}}M1.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},M1.INPUT_CLONED_STATE=Ki.NEVER;const E1=M1.DEFAULT_PARAMS;const S1=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(E1.active),this.clipBias=aa.FLOAT(E1.clipBias),this.color=aa.COLOR(E1.color.toArray()),this.opacity=aa.FLOAT(E1.opacity),this.pixelRatio=aa.INTEGER(E1.pixelRatio,{range:[1,4],rangeLocked:[!0,!1]}),this.tblur=aa.BOOLEAN(E1.tblur),this.blur=aa.FLOAT(E1.blur,{visibleIf:{tblur:1}}),this.verticalBlurMult=aa.FLOAT(E1.verticalBlurMult,{visibleIf:{tblur:1}}),this.tblur2=aa.BOOLEAN(E1.tblur2,{visibleIf:{tblur:1}}),this.blur2=aa.FLOAT(E1.blur2,{visibleIf:{tblur:1,tblur2:1}}),this.verticalBlur2Mult=aa.FLOAT(E1.verticalBlur2Mult,{visibleIf:{tblur:1,tblur2:1}})}};class C1 extends nV{constructor(){super(...arguments),this.paramsConfig=S1}static type(){return\\\\\\\"reflector\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create a reflector from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(M1.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new M1(this._scene,this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var N1=n(85);var L1;!function(t){t.POINTS_COUNT=\\\\\\\"pointsCount\\\\\\\",t.SEGMENT_LENGTH=\\\\\\\"segmentLength\\\\\\\"}(L1||(L1={}));const O1=[L1.POINTS_COUNT,L1.SEGMENT_LENGTH];var P1;!function(t){t.CENTRIPETAL=\\\\\\\"centripetal\\\\\\\",t.CHORDAL=\\\\\\\"chordal\\\\\\\",t.CATMULLROM=\\\\\\\"catmullrom\\\\\\\"}(P1||(P1={}));const R1=[P1.CENTRIPETAL,P1.CHORDAL,P1.CATMULLROM];const I1=new class extends la{constructor(){super(...arguments),this.method=aa.INTEGER(O1.indexOf(L1.POINTS_COUNT),{menu:{entries:O1.map(((t,e)=>({name:t,value:e})))}}),this.curveType=aa.INTEGER(R1.indexOf(P1.CATMULLROM),{range:[0,2],rangeLocked:[!0,!0],menu:{entries:R1.map(((t,e)=>({name:t,value:e})))}}),this.tension=aa.FLOAT(.01,{range:[0,1],rangeLocked:[!0,!0]}),this.pointsCount=aa.INTEGER(100,{visibleIf:{method:O1.indexOf(L1.POINTS_COUNT)},range:[1,1e3],rangeLocked:[!0,!1]}),this.segmentLength=aa.FLOAT(1,{visibleIf:{method:O1.indexOf(L1.SEGMENT_LENGTH)}})}};class F1 extends nV{constructor(){super(...arguments),this.paramsConfig=I1}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=hr(o);return this.createObject(a,Cs.LINE_SEGMENTS)}_create_curve_from_points(t){if(t.length<=1)return;const e=t.map((t=>t.attribValue(\\\\\\\"position\\\\\\\"))),n=R1[this.pv.curveType],i=this.pv.tension,s=new N1.a(e,!1,n,i),r=this._get_points_from_curve(s);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=O1[this.pv.method];switch(e){case L1.POINTS_COUNT:return t.getSpacedPoints(Math.max(2,this.pv.pointsCount));case L1.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)}ls.unreachable(e)}}class D1 extends QG{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())}}}}D1.DEFAULT_PARAMS={tposition:!0,position:\\\\\\\"position\\\\\\\",restP:\\\\\\\"restP\\\\\\\",tnormal:!0,normal:\\\\\\\"normal\\\\\\\",restN:\\\\\\\"restN\\\\\\\"};const B1=D1.DEFAULT_PARAMS;const z1=new class extends la{constructor(){super(...arguments),this.tposition=aa.BOOLEAN(B1.tposition),this.position=aa.STRING(B1.position,{visibleIf:{tposition:!0}}),this.restP=aa.STRING(B1.restP,{visibleIf:{tposition:!0}}),this.tnormal=aa.BOOLEAN(B1.tnormal),this.normal=aa.STRING(B1.normal,{visibleIf:{tnormal:!0}}),this.restN=aa.STRING(B1.restN,{visibleIf:{tnormal:!0}})}};class k1 extends nV{constructor(){super(...arguments),this.paramsConfig=z1}static type(){return\\\\\\\"restAttributes\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Ki.FROM_NODE])}cook(t){this._operation=this._operation||new D1(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class U1 extends QG{constructor(){super(...arguments),this._core_transform=new uU}static type(){return\\\\\\\"roundedBox\\\\\\\"}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.size,n=new Ly(e.x,e.y,e.z,t.divisions,t.bevel);return n.translate(t.center.x,t.center.y,t.center.z),n.computeVertexNormals(),n}_cook_with_input(t,e){const n=e.divisions,i=t.boundingBox(),s=i.max.clone().sub(i.min),r=i.max.clone().add(i.min).multiplyScalar(.5),o=new Ly(s.x,s.y,s.z,n,e.bevel),a=this._core_transform.translation_matrix(r);return o.applyMatrix4(a),o}}U1.DEFAULT_PARAMS={size:new p.a(1,1,1),divisions:2,bevel:.1,center:new p.a(0,0,0)},U1.INPUT_CLONED_STATE=Ki.NEVER;const G1=U1.DEFAULT_PARAMS;const V1=new class extends la{constructor(){super(...arguments),this.size=aa.VECTOR3(G1.size),this.divisions=aa.INTEGER(G1.divisions,{range:[1,10],rangeLocked:[!0,!1]}),this.bevel=aa.FLOAT(G1.bevel,{range:[0,1],rangeLocked:[!0,!1]}),this.center=aa.VECTOR3(G1.center)}};class H1 extends nV{constructor(){super(...arguments),this.paramsConfig=V1}static type(){return\\\\\\\"roundedBox\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create bounding box from (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(U1.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new U1(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class j1 extends QG{static type(){return\\\\\\\"scatter\\\\\\\"}async cook(t,e){const n=t[0];let i=n.faces();const s=[];let r=0;const o=new Map;for(let t of i){const e=t.area();o.set(t.index(),e)}const a=f.sortBy(i,(t=>o.get(t.index())||-1));let l=0;for(let t of a)r+=o.get(t.index()),s[l]=r,l++;const c=[];let h=[];e.transferAttributes&&(h=n.attribNamesMatchingMask(e.attributesToTransfer));const u=new Map,d=new Map;for(let t of h)u.set(t,[]),d.set(t,n.attribSize(t));const p=new yX,_=2454*e.seed%Number.MAX_SAFE_INTEGER;await p.startWithCount(e.pointsCount,(t=>{const e=rr.randFloat(_+t)*r;for(let t=0;te/(t-1)));e.addIdnAttribute&&g.setAttribute(\\\\\\\"idn\\\\\\\",new C.a(new Float32Array(i),1))}const v=this.createObject(g,Cs.POINTS);return this.createCoreGroupFromObjects([v])}}j1.DEFAULT_PARAMS={pointsCount:100,seed:0,transferAttributes:!0,attributesToTransfer:\\\\\\\"normal\\\\\\\",addIdAttribute:!0,addIdnAttribute:!0},j1.INPUT_CLONED_STATE=Ki.FROM_NODE;const W1=j1.DEFAULT_PARAMS;const q1=new class extends la{constructor(){super(...arguments),this.pointsCount=aa.INTEGER(W1.pointsCount,{range:[0,100],rangeLocked:[!0,!1]}),this.seed=aa.INTEGER(W1.seed,{range:[0,100],rangeLocked:[!1,!1]}),this.transferAttributes=aa.BOOLEAN(W1.transferAttributes),this.attributesToTransfer=aa.STRING(W1.attributesToTransfer,{visibleIf:{transferAttributes:1}}),this.addIdAttribute=aa.BOOLEAN(W1.addIdAttribute),this.addIdnAttribute=aa.BOOLEAN(W1.addIdnAttribute)}};class X1 extends nV{constructor(){super(...arguments),this.paramsConfig=q1}static type(){return\\\\\\\"scatter\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to scatter points onto\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.NEVER)}async cook(t){this._operation=this._operation||new j1(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var Y1;!function(t){t.MATRIX=\\\\\\\"matrix\\\\\\\",t.AXIS=\\\\\\\"axis\\\\\\\"}(Y1||(Y1={}));const $1=[Y1.MATRIX,Y1.AXIS];var J1;!function(t){t.BBOX_CENTER=\\\\\\\"bbox center\\\\\\\",t.BBOX_CENTER_OFFSET=\\\\\\\"bbox center offset\\\\\\\",t.CUSTOM=\\\\\\\"custom\\\\\\\"}(J1||(J1={}));const Z1=[J1.BBOX_CENTER,J1.BBOX_CENTER_OFFSET,J1.CUSTOM];class Q1 extends QG{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 Y.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=$1[e.mode];switch(n){case Y1.MATRIX:return this._applyMatrixShear(t,e);case Y1.AXIS:return this._applyAxisShear(t,e)}ls.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 _r(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=Z1[e.centerMode];switch(n){case J1.BBOX_CENTER:return this._getAxisModeCenterBbox(t,e);case J1.BBOX_CENTER_OFFSET:return this._getAxisModeCenterBboxOffset(t,e);case J1.CUSTOM:return this._getAxisModeCenterCustom(e)}ls.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 K1;Q1.DEFAULT_PARAMS={mode:$1.indexOf(Y1.AXIS),xy:0,xz:0,yx:0,yz:0,zx:0,zy:0,centerMode:Z1.indexOf(J1.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},Q1.INPUT_CLONED_STATE=Ki.FROM_NODE,function(t){t.SHEAR=\\\\\\\"shear\\\\\\\",t.TRANSFORM=\\\\\\\"transform\\\\\\\",t.UV_LAYOUT=\\\\\\\"uvLayout\\\\\\\",t.UV_TRANSFORM=\\\\\\\"uvTransform\\\\\\\",t.UV_UNWRAP=\\\\\\\"uvUnwrap\\\\\\\"}(K1||(K1={}));const t2=Q1.DEFAULT_PARAMS;const e2=new class extends la{constructor(){super(...arguments),this.mode=aa.INTEGER(t2.mode,{menu:{entries:$1.map(((t,e)=>({name:t,value:e})))}}),this.xy=aa.FLOAT(t2.xy,{visibleIf:{mode:$1.indexOf(Y1.MATRIX)}}),this.xz=aa.FLOAT(t2.xz,{visibleIf:{mode:$1.indexOf(Y1.MATRIX)}}),this.yx=aa.FLOAT(t2.yx,{visibleIf:{mode:$1.indexOf(Y1.MATRIX)}}),this.yz=aa.FLOAT(t2.yz,{visibleIf:{mode:$1.indexOf(Y1.MATRIX)}}),this.zx=aa.FLOAT(t2.zx,{visibleIf:{mode:$1.indexOf(Y1.MATRIX)}}),this.zy=aa.FLOAT(t2.zy,{visibleIf:{mode:$1.indexOf(Y1.MATRIX)}}),this.centerMode=aa.INTEGER(t2.centerMode,{visibleIf:{mode:$1.indexOf(Y1.AXIS)},menu:{entries:Z1.map(((t,e)=>({name:t,value:e})))}}),this.centerOffset=aa.VECTOR3(t2.centerOffset.toArray(),{visibleIf:{mode:$1.indexOf(Y1.AXIS),centerMode:Z1.indexOf(J1.BBOX_CENTER_OFFSET)}}),this.center=aa.VECTOR3(t2.center.toArray(),{visibleIf:{mode:$1.indexOf(Y1.AXIS),centerMode:Z1.indexOf(J1.CUSTOM)}}),this.planeAxis=aa.VECTOR3(t2.planeAxis.toArray(),{visibleIf:{mode:$1.indexOf(Y1.AXIS)}}),this.axis=aa.VECTOR3(t2.axis.toArray(),{visibleIf:{mode:$1.indexOf(Y1.AXIS)}}),this.axisAmount=aa.FLOAT(t2.axisAmount,{range:[-1,1],visibleIf:{mode:$1.indexOf(Y1.AXIS)}})}};class n2 extends nV{constructor(){super(...arguments),this.paramsConfig=e2}static type(){return K1.SHEAR}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Q1.INPUT_CLONED_STATE)}setMode(t){this.p.mode.set($1.indexOf(t))}cook(t){this._operation=this._operation||new Q1(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const i2=new class extends la{};class s2 extends nV{constructor(){super(...arguments),this.paramsConfig=i2}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=c1.line_segment_to_geometries(t.geometry);e.forEach(((t,n)=>{if(n>0){const s=e[n-1],r=this._skin(s,t);i.push(r)}}))}}this.setGeometries(i)}process_two_inputs(t){const e=t[0],n=t[1],i=this._get_line_segments(e),s=this._get_line_segments(n),r=f.sortBy([i,s],(t=>-t.length)),o=r[0],a=r[1],l=[];o.forEach(((t,e)=>{const n=a[e];if(null!=t&&null!=n){const e=t.geometry,i=n.geometry,s=this._skin(e,i);l.push(s)}})),this.setGeometries(l)}_get_line_segments(t){return t.objects().filter((t=>t.isLineSegments))}_skin(t,e){const n=new S.a;return new h1(n,t,e).process(),n}}var r2;!function(t){t.X=\\\\\\\"x\\\\\\\",t.Y=\\\\\\\"y\\\\\\\",t.Z=\\\\\\\"z\\\\\\\"}(r2||(r2={}));const o2=[r2.X,r2.Y,r2.Z];class a2 extends QG{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 yr(t,0).points(),i=t.geometry.getIndex();if(!i)return void console.warn(\\\\\\\"geometry cannot be sorted since it has no index\\\\\\\");const s=i.array;this._positions=new Array(n.length),this._indicesByPos.clear(),this._indexDest.clear();const r=o2[e.axis];let o=0,a=0;for(let t of n){switch(t.getPosition(this._pointPos),r){case r2.X:o=this._pointPos.x;break;case r2.Y:o=this._pointPos.y;break;case r2.Z:o=this._pointPos.z}this._positions[a]=o,h.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 u=f.uniq(l);for(let t of u){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(s.length);for(let t=0;t ${e}`),null!=e)for(let n=0;n({name:t,value:e})))}}),this.invert=aa.BOOLEAN(l2.invert)}};class h2 extends nV{constructor(){super(...arguments),this.paramsConfig=c2}static type(){return\\\\\\\"sort\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to sort\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Ki.FROM_NODE])}cook(t){this._operation=this._operation||new a2(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const u2=new class extends la{constructor(){super(...arguments),this.startFrame=aa.INTEGER(El.START_FRAME)}};class d2 extends rV{constructor(){super(...arguments),this.paramsConfig=u2,this._last_simulated_frame=null,this.childrenDisplayController=new aV(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(Ki.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:f2[t]})))}}),this.radius=aa.FLOAT(y2.radius,{visibleIf:{type:f2.default}}),this.resolution=aa.VECTOR2(y2.resolution,{visibleIf:{type:f2.default}}),this.open=aa.BOOLEAN(y2.open,{visibleIf:{type:f2.default}}),this.phiStart=aa.FLOAT(y2.phiStart,{range:[0,2*Math.PI],visibleIf:{type:f2.default,open:!0}}),this.phiLength=aa.FLOAT(\\\\\\\"$PI*2\\\\\\\",{range:[0,2*Math.PI],visibleIf:{type:f2.default,open:!0}}),this.thetaStart=aa.FLOAT(y2.thetaStart,{range:[0,Math.PI],visibleIf:{type:f2.default,open:!0}}),this.thetaLength=aa.FLOAT(\\\\\\\"$PI\\\\\\\",{range:[0,Math.PI],visibleIf:{type:f2.default,open:!0}}),this.detail=aa.INTEGER(y2.detail,{range:[0,5],rangeLocked:[!0,!1],visibleIf:{type:f2.isocahedron}}),this.center=aa.VECTOR3(y2.center)}};class b2 extends nV{constructor(){super(...arguments),this.paramsConfig=x2}static type(){return\\\\\\\"sphere\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(v2.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new v2(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const w2=new class extends la{constructor(){super(...arguments),this.attribType=aa.INTEGER(zs.indexOf(Bs.NUMERIC),{menu:{entries:ks}}),this.attribName=aa.STRING(\\\\\\\"\\\\\\\")}};class T2 extends nV{constructor(){super(...arguments),this.paramsConfig=w2,this._new_objects=[]}static type(){return\\\\\\\"split\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to split in multiple objects\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1)}async cook(t){const e=t[0];this._new_objects=[],\\\\\\\"\\\\\\\"!=this.pv.attribName&&this._split_core_group(e),this.setObjects(this._new_objects)}async _split_core_group(t){const e=t.coreObjects();for(let t of e)this._split_core_object(t)}_split_core_object(t){let e=t.coreGeometry(),n=this.pv.attribName,i=new Map;if(e){const s=t.object(),r=e.pointsFromGeometry(),o=r[0];if(o){if(o.attribSize(n)!=Us.FLOAT&&!o.isAttribIndexed(n))return void this.states.error.set(`attrib '${n}' must be a float or a string`);let t;if(o.isAttribIndexed(n))for(let e of r)t=e.indexedAttribValue(n),h.pushOnArrayAtEntry(i,t,e);else for(let e of r)t=e.attribValue(n),h.pushOnArrayAtEntry(i,t,e)}const a=Ls(s.constructor);i.forEach(((t,e)=>{const i=_r.geometryFromPoints(t,a);if(i){const t=this.createObject(i,a);yr.addAttribute(t,n,e),this._new_objects.push(t)}}))}}}const A2=new A.a,M2=new K.a,E2=new p.a;class S2 extends J.a{constructor(){super(),this.uuid=On.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 G.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=r[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,s),_=c(_,3,r),_=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),r){const e=this.faceVertexUvs[0][t];n.push(d(e[0]),d(e[1]),d(e[2]))}if(o&&n.push(h(e.normal)),a){const t=e.vertexNormals;n.push(h(t[0]),h(t[1]),h(t[2]))}if(l&&n.push(u(e.color)),p){const t=e.vertexColors;n.push(u(t[0]),u(t[1]),u(t[2]))}}function c(t,e,n){return n?t|1<0&&(t.data.colors=r),a.length>0&&(t.data.uvs=[a]),t.data.faces=n,t}clone(){return(new S2).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=[],s=t.morphTargets[n];for(let t=0,e=s.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}}S2.prototype.isGeometry=!0;class C2{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,s;const r=t.faces;for(i=0;i0,r=i[1]&&i[1].length>0,o=t.morphTargets,a=o.length;let l;if(a>0){l=[];for(let t=0;t0){u=[];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,s,r){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(s),r[t].edges.push(o),r[e].edges.push(o)}function i(t,e,n,i,s){t.push(new N2(e,n,i,void 0,void 0,s))}function s(t,e){return Math.abs(e-t)/2+Math.min(t,e)}function r(t,e,n,i){t.push([e.clone(),n.clone(),i.clone()])}L2.prototype.smooth=function(o){var a,l,c,h,u,_,m,f,g,v,y,x,b,w=new p.a,T=[];a=o.vertices,l=o.faces;var A,M,E,S,C,N,L,O,P,R,I,F,D,B,z=void 0!==(c=o.faceVertexUvs)[0]&&c[0].length>0;if(z)for(var k=0;k3&&(O=3/(8*_)),P=1-_*O,R=O,_<=2&&2==_&&(P=3/4,R=1/8),B=D.clone().multiplyScalar(P),w.set(0,0,0),k=0;k<_;k++)A=(I=F[k]).a!==D?I.a:I.b,w.add(A);w.multiplyScalar(R),B.add(w),b.push(B)}h=b.concat(x);var U,G,V,H,j,W,q,X=b.length;u=[];var Y=new d.a,$=new d.a,J=new d.a;for(m=0,f=l.length;m{z2.PARAM_CALLBACK_reset(t)}})}};class z2 extends nV{constructor(){super(...arguments),this.paramsConfig=B2}static type(){return ns.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.inputGraphNode(this.pv.input),this.addGraphInput(this._current_parent_input_graph_node))}}var k2=n(82);class U2 extends jg{constructor(t,e,n){super(t,e,n)}load(t){return new Promise((async(e,n)=>{const i=new k2.a(this.loadingManager),s=await this._urlToLoad();i.load(s,(i=>{try{const n=this._onLoaded(i,t);e(n)}catch(t){n([])}}))}))}parse(t,e){const n=new k2.a(this.loadingManager).parse(t);return this._onLoaded(n,e)}_onLoaded(t,e){const n=t.paths,i=new Fn.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)}}V2.DEFAULT_PARAMS={url:G2,drawFillShapes:!0,fillShapesWireframe:!1,drawStrokes:!0,strokesWireframe:!1};const H2=V2.DEFAULT_PARAMS;const j2=new class extends la{constructor(){super(...arguments),this.url=aa.STRING(H2.url,{fileBrowse:{type:[Or.SVG]}}),this.reload=aa.BUTTON(null,{callback:(t,e)=>{W2.PARAM_CALLBACK_reload(t)}}),this.drawFillShapes=aa.BOOLEAN(H2.drawFillShapes),this.fillShapesWireframe=aa.BOOLEAN(H2.fillShapesWireframe),this.drawStrokes=aa.BOOLEAN(H2.drawStrokes),this.strokesWireframe=aa.BOOLEAN(H2.strokesWireframe)}};class W2 extends nV{constructor(){super(...arguments),this.paramsConfig=j2}static type(){return\\\\\\\"svg\\\\\\\"}async requiredModules(){return[Hn.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 V2(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 q2=\\\\\\\"geometry to switch to\\\\\\\";const X2=new class extends la{constructor(){super(...arguments),this.input=aa.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class Y2 extends nV{constructor(){super(...arguments),this.paramsConfig=X2}static type(){return\\\\\\\"switch\\\\\\\"}static displayedInputNames(){return[q2,q2,q2,q2]}initializeNode(){this.io.inputs.setCount(0,4),this.io.inputs.initInputsClonedState(Ki.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 $2 extends gK{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 J2=new class extends la{constructor(){super(...arguments),this.radius=aa.FLOAT(1),this.detail=aa.INTEGER(0,{range:[0,10],rangeLocked:[!0,!1]}),this.pointsOnly=aa.BOOLEAN(0),this.center=aa.VECTOR3([0,0,0])}};class Z2 extends nV{constructor(){super(...arguments),this.paramsConfig=J2}static type(){return\\\\\\\"tetrahedron\\\\\\\"}cook(){const t=this.pv.pointsOnly,e=new $2(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,Cs.POINTS);this.setObject(t)}else e.computeVertexNormals(),this.setGeometry(e)}}class Q2 extends cJ{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 K2=n(48);class t9 extends Bf.a{constructor(t){super(t)}load(t,e,n,i){const s=this,r=new Df.a(this.manager);r.setPath(this.path),r.setRequestHeader(this.requestHeader),r.setWithCredentials(s.withCredentials),r.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=s.parse(n);e&&e(i)}),n,i)}parse(t){return new e9(t)}}class e9{constructor(t){this.type=\\\\\\\"Font\\\\\\\",this.data=t}generateShapes(t,e=100){const n=[],i=function(t,e,n){const i=Array.from(t),s=e/n.resolution,r=(n.boundingBox.yMax-n.boundingBox.yMin+n.underlineThickness)*s,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 li.modulesRegister.module(Hn.TTFLoader);if(t)return new t(this.loadingManager)}static async loadSVGLoader(){const t=await li.modulesRegister.module(Hn.SVGLoader);if(t)return t}}var s9;!function(t){t.MESH=\\\\\\\"mesh\\\\\\\",t.FLAT=\\\\\\\"flat\\\\\\\",t.LINE=\\\\\\\"line\\\\\\\",t.STROKE=\\\\\\\"stroke\\\\\\\"}(s9||(s9={}));const r9=[s9.MESH,s9.FLAT,s9.LINE,s9.STROKE],o9=\\\\\\\"failed to generate geometry. Try to remove some characters\\\\\\\";const a9=new class extends la{constructor(){super(...arguments),this.font=aa.STRING(\\\\\\\"https://raw.githubusercontent.com/polygonjs/polygonjs-assets/master/fonts/droid_sans_regular.typeface.json\\\\\\\",{fileBrowse:{type:[Or.FONT]}}),this.text=aa.STRING(\\\\\\\"polygonjs\\\\\\\",{multiline:!0}),this.type=aa.INTEGER(0,{menu:{entries:r9.map(((t,e)=>({name:t,value:e})))}}),this.size=aa.FLOAT(1,{range:[0,1],rangeLocked:[!0,!1]}),this.extrude=aa.FLOAT(.1,{visibleIf:{type:r9.indexOf(s9.MESH)}}),this.segments=aa.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1],visibleIf:{type:r9.indexOf(s9.MESH)}}),this.strokeWidth=aa.FLOAT(.02,{visibleIf:{type:r9.indexOf(s9.STROKE)}})}};class l9 extends nV{constructor(){super(...arguments),this.paramsConfig=a9,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(r9[this.pv.type]){case s9.MESH:return this._create_geometry_from_type_mesh(t);case s9.FLAT:return this._create_geometry_from_type_flat(t);case s9.LINE:return this._create_geometry_from_type_line(t);case s9.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 Q2(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(o9)}}_create_geometry_from_type_flat(t){const e=this._get_shapes(t);if(e){var n=new _J(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 s=0;s0&&e0)for(let e=0;e{const n=t.material;n&&(m.isArray(n)||s||(s=n[e.textureName]))}));if(s)for(let t of n.objects())t.traverse((t=>{const n=t.material;if(n&&!m.isArray(n)){n[e.textureName]=s;const t=n.uniforms;if(t){const n=t[e.textureName];n&&(n.value=s)}n.needsUpdate=!0}}));return n}}c9.DEFAULT_PARAMS={textureName:\\\\\\\"map\\\\\\\"},c9.INPUT_CLONED_STATE=[Ki.FROM_NODE,Ki.NEVER];const h9=c9.DEFAULT_PARAMS;const u9=new class extends la{constructor(){super(...arguments),this.textureName=aa.STRING(h9.textureName)}};class d9 extends nV{constructor(){super(...arguments),this.paramsConfig=u9}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(c9.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new c9(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class p9 extends QG{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 s=i.map((t=>this._update_object(t,e)));return await Promise.all(s),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 li.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)}}p9.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},p9.INPUT_CLONED_STATE=Ki.FROM_NODE;const _9=p9.DEFAULT_PARAMS;const m9=new class extends la{constructor(){super(...arguments),this.applyToChildren=aa.BOOLEAN(_9.applyToChildren,{separatorAfter:!0}),this.tencoding=aa.BOOLEAN(_9.tencoding),this.encoding=aa.INTEGER(_9.encoding,{visibleIf:{tencoding:1},menu:{entries:eg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.tmapping=aa.BOOLEAN(_9.tmapping),this.mapping=aa.INTEGER(_9.mapping,{visibleIf:{tmapping:1},menu:{entries:ig.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.twrap=aa.BOOLEAN(_9.twrap),this.wrapS=aa.INTEGER(_9.wrapS,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.wrapT=aa.INTEGER(_9.wrapT,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},separatorAfter:!0}),this.tanisotropy=aa.BOOLEAN(_9.tanisotropy),this.useRendererMaxAnisotropy=aa.BOOLEAN(_9.useRendererMaxAnisotropy,{visibleIf:{tanisotropy:1}}),this.anisotropy=aa.INTEGER(_9.anisotropy,{visibleIf:{tanisotropy:1,useRendererMaxAnisotropy:0},range:[0,32],rangeLocked:[!0,!1]}),this.tminFilter=aa.BOOLEAN(0),this.minFilter=aa.INTEGER(_9.minFilter,{visibleIf:{tminFilter:1},menu:{entries:$m}}),this.tmagFilter=aa.BOOLEAN(0),this.magFilter=aa.INTEGER(_9.magFilter,{visibleIf:{tmagFilter:1},menu:{entries:Ym}})}};class f9 extends nV{constructor(){super(...arguments),this.paramsConfig=m9}static type(){return\\\\\\\"textureProperties\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects with textures to change properties of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(p9.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new p9(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const g9=new p.a(0,0,1);class v9 extends QG{constructor(){super(...arguments),this._core_transform=new uU}static type(){return\\\\\\\"torus\\\\\\\"}cook(t,e){const n=e.radius,i=e.radiusTube,s=e.segmentsRadial,r=e.segmentsTube,o=new fJ(n,i,s,r);return o.translate(e.center.x,e.center.y,e.center.z),this._core_transform.rotate_geometry(o,g9,e.direction),this.createCoreGroupFromGeometry(o)}}v9.DEFAULT_PARAMS={radius:1,radiusTube:1,segmentsRadial:20,segmentsTube:12,direction:new p.a(0,1,0),center:new p.a(0,0,0)},v9.INPUT_CLONED_STATE=Ki.FROM_NODE;const y9=v9.DEFAULT_PARAMS;const x9=new class extends la{constructor(){super(...arguments),this.radius=aa.FLOAT(y9.radius,{range:[0,1]}),this.radiusTube=aa.FLOAT(y9.radiusTube,{range:[0,1]}),this.segmentsRadial=aa.INTEGER(y9.segmentsRadial,{range:[1,50],rangeLocked:[!0,!1]}),this.segmentsTube=aa.INTEGER(y9.segmentsTube,{range:[1,50],rangeLocked:[!0,!1]}),this.direction=aa.VECTOR3(y9.direction),this.center=aa.VECTOR3(y9.center)}};class b9 extends nV{constructor(){super(...arguments),this.paramsConfig=x9}static type(){return\\\\\\\"torus\\\\\\\"}cook(t){this._operation=this._operation||new v9(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class w9 extends QG{static type(){return\\\\\\\"torusKnot\\\\\\\"}cook(t,e){const n=e.radius,i=e.radiusTube,s=e.segmentsRadial,r=e.segmentsTube,o=e.p,a=e.q,l=new gJ(n,i,s,r,o,a);return l.translate(e.center.x,e.center.y,e.center.z),this.createCoreGroupFromGeometry(l)}}w9.DEFAULT_PARAMS={radius:1,radiusTube:1,segmentsRadial:64,segmentsTube:8,p:2,q:3,center:new p.a(0,0,0)},w9.INPUT_CLONED_STATE=Ki.FROM_NODE;const T9=w9.DEFAULT_PARAMS;const A9=new class extends la{constructor(){super(...arguments),this.radius=aa.FLOAT(T9.radius),this.radiusTube=aa.FLOAT(T9.radiusTube),this.segmentsRadial=aa.INTEGER(T9.segmentsRadial,{range:[1,128]}),this.segmentsTube=aa.INTEGER(T9.segmentsTube,{range:[1,32]}),this.p=aa.INTEGER(T9.p,{range:[1,10]}),this.q=aa.INTEGER(T9.q,{range:[1,10]}),this.center=aa.VECTOR3(T9.center)}};class M9 extends nV{constructor(){super(...arguments),this.paramsConfig=A9}static type(){return\\\\\\\"torusKnot\\\\\\\"}initializeNode(){}cook(t){this._operation=this._operation||new w9(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var E9;!function(t){t.SET_PARAMS=\\\\\\\"set params\\\\\\\",t.UPDATE_MATRIX=\\\\\\\"update matrix\\\\\\\"}(E9||(E9={}));const S9=[E9.SET_PARAMS,E9.UPDATE_MATRIX];class C9 extends QG{constructor(){super(...arguments),this._core_transform=new uU,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=aU[e.applyOn];switch(n){case sU.GEOMETRIES:return this._update_geometries(t,e);case sU.OBJECTS:return this._update_objects(t,e)}ls.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=ZG._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=S9[e.objectMode];switch(n){case E9.SET_PARAMS:return this._update_objects_params(t,e);case E9.UPDATE_MATRIX:return this._update_objects_matrix(t,e)}ls.unreachable(n)}_update_objects_params(t,e){for(let n of t){n.position.copy(e.t);const t=cU[e.rotationOrder];this._r.copy(e.r).multiplyScalar(On.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,cU[t.rotationOrder])}}C9.DEFAULT_PARAMS={applyOn:aU.indexOf(sU.GEOMETRIES),objectMode:S9.indexOf(E9.SET_PARAMS),group:\\\\\\\"\\\\\\\",rotationOrder:cU.indexOf(lU.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)},C9.INPUT_CLONED_STATE=Ki.FROM_NODE;const N9=C9.DEFAULT_PARAMS;const L9=new class extends la{constructor(){super(...arguments),this.applyOn=aa.INTEGER(N9.applyOn,{menu:{entries:aU.map(((t,e)=>({name:t,value:e})))}}),this.objectMode=aa.INTEGER(N9.objectMode,{visibleIf:{applyOn:aU.indexOf(sU.OBJECTS)},menu:{entries:S9.map(((t,e)=>({name:t,value:e})))}}),this.group=aa.STRING(N9.group,{visibleIf:{applyOn:aU.indexOf(sU.GEOMETRIES)}}),this.rotationOrder=aa.INTEGER(N9.rotationOrder,{menu:{entries:cU.map(((t,e)=>({name:t,value:e})))}}),this.t=aa.VECTOR3(N9.t),this.r=aa.VECTOR3(N9.r),this.s=aa.VECTOR3(N9.s),this.scale=aa.FLOAT(N9.scale,{range:[0,10]}),this.pivot=aa.VECTOR3(N9.pivot,{visibleIf:{applyOn:aU.indexOf(sU.GEOMETRIES)}})}};class O9 extends nV{constructor(){super(...arguments),this.paramsConfig=L9}static type(){return K1.TRANSFORM}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(C9.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.applyOn],(()=>aU[this.pv.applyOn]))}))}))}setApplyOn(t){this.p.applyOn.set(aU.indexOf(t))}setObjectMode(t){this.p.objectMode.set(S9.indexOf(t))}cook(t){this._operation=this._operation||new C9(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const P9=new class extends la{constructor(){super(...arguments),this.useSecondInput=aa.BOOLEAN(1),this.reference=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.SOP},visibleIf:{useSecondInput:0}})}};class R9 extends nV{constructor(){super(...arguments),this.paramsConfig=P9}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([Ki.FROM_NODE,Ki.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 s=0;s({name:t,value:e})))}};function D9(t){const e=[];for(let n=t+1;n<=6;n++)e.push({count:n});return{visibleIf:e}}const B9=new class extends la{constructor(){super(...arguments),this.applyOn=aa.INTEGER(aU.indexOf(sU.GEOMETRIES),{menu:{entries:aU.map(((t,e)=>({name:t,value:e})))}}),this.count=aa.INTEGER(2,{range:[0,6],rangeLocked:[!0,!0]}),this.rotationOrder0=aa.INTEGER(I9,{separatorBefore:!0,...F9,...D9(0)}),this.r0=aa.VECTOR3([0,0,0],{...D9(0)}),this.rotationOrder1=aa.INTEGER(I9,{separatorBefore:!0,...F9,...D9(1)}),this.r1=aa.VECTOR3([0,0,0],{...D9(1)}),this.rotationOrder2=aa.INTEGER(I9,{separatorBefore:!0,...F9,...D9(2)}),this.r2=aa.VECTOR3([0,0,0],{...D9(2)}),this.rotationOrder3=aa.INTEGER(I9,{separatorBefore:!0,...F9,...D9(3)}),this.r3=aa.VECTOR3([0,0,0],{...D9(3)}),this.rotationOrder4=aa.INTEGER(I9,{separatorBefore:!0,...F9,...D9(4)}),this.r4=aa.VECTOR3([0,0,0],{...D9(4)}),this.rotationOrder5=aa.INTEGER(I9,{separatorBefore:!0,...F9,...D9(5)}),this.r5=aa.VECTOR3([0,0,0],{...D9(5)})}};class z9 extends nV{constructor(){super(...arguments),this.paramsConfig=B9,this._core_transform=new uU,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([Ki.FROM_NODE,Ki.NEVER]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.applyOn],(()=>aU[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=aU[this.pv.applyOn];switch(n){case sU.GEOMETRIES:return this._apply_matrix_to_geometries(t,e);case sU.OBJECTS:return this._apply_matrix_to_objects(t,e)}ls.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=[js.POSITION,js.NORMAL,js.TANGENT];for(let i of e){const e=n.attributes[i];for(let n of t){const t=n.geometry.attributes[i];e&&t&&qs.copy(e,t)}}}}let n;for(let e=0;e({name:t,value:e})))}})}};class V9 extends nV{constructor(){super(...arguments),this.paramsConfig=G9,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(Ki.FROM_NODE)}setMode(t){this.p.mode.set(U9.indexOf(t))}cook(t){const e=U9[this.pv.mode];this._select_mode(e,t)}_select_mode(t,e){switch(t){case k9.RESET_OBJECT:return this._reset_objects(e);case k9.CENTER_GEO:return this._center_geos(e,!1);case k9.PROMOTE_GEO_TO_OBJECT:return this._center_geos(e,!0)}ls.unreachable(t)}_reset_objects(t){const e=t[0],n=e.objects();for(let t of n)t.matrix.identity(),uU.decompose_matrix(t);this.setCoreGroup(e)}_center_geos(t,e){const n=t[0],i=n.objectsWithGeo();let s=i;const r=t[1];r&&(s=r.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 s=0,r=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,r=Math.max(r,e.y+e.h),s=Math.max(s,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?bi.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.inputGraphNode(e)}}return null}getReferencedNode(t,e){let n=null;const i=this.node();if(m.isString(t)){if(i){const s=t;n=bi.findNode(i,s,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 ho,n=this.findReferencedGraphNode(t,e);return n?this.createDependency(n,t,e):(li.warn(\\\\\\\"node not found for path\\\\\\\",t),null)}createDependency(t,e,n){return zr.create(this.param,e,t,n)}}class f3 extends m3{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 g3 extends m3{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 v3=[\\\\\\\"min\\\\\\\",\\\\\\\"max\\\\\\\",\\\\\\\"size\\\\\\\",\\\\\\\"center\\\\\\\"],y3=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"];class x3 extends m3{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 s=t[0],r=t[1],o=t[2];let a=null;try{a=await this.getReferencedNodeContainer(s)}catch(t){i(t)}a&&(e=this._get_value_from_container(a,r,o),n(e))}else n(0)}))}_get_value_from_container(t,e,n){const i=t.boundingBox();if(!e)return i;if(v3.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?y3.indexOf(n)>=0?t[n]:-1:t}return-1}}class b3 extends m3{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],s=t[1];let r=null;try{r=await this.getReferencedNodeContainer(i)}catch(t){n(t)}if(r){const t=r.boundingBox(),n=t.min.clone().add(t.max).multiplyScalar(.5);if(s){const t=n[s];e(null!=t?t:0)}else e(n)}}else e(0)}))}}class w3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to param\\\\\\\"]]}findDependency(t){const e=new ho,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 s=t[0],r=this.getReferencedParam(s);if(r){r.isDirty()&&await r.compute();const t=r.value;null!=t&&(i=t,e(i))}else n(0)}}))}}class T3 extends m3{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],s=t[2],r=this.node(),o=r?bi.findNode(r,n):null;let a;o&&o.type()==fZ.type()&&(a=o.stamp_value(s)),null==a&&(a=i),e(a)}else e(0)}))}}class A3 extends m3{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 M3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[]}async processArguments(t){return new Promise((async(t,e)=>{t(Zf.isMobile())}))}}class E3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[]}async processArguments(t){return new Promise((async(t,e)=>{t(Zf.isTouchDevice())}))}}class S3 extends m3{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 ${t}`)}}class C3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"attribute name\\\\\\\"],[\\\\\\\"index\\\\\\\",\\\\\\\"object index\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(3==t.length){const i=t[0],s=t[1],r=t[2];let o=null;try{o=await this.getReferencedNodeContainer(i)}catch(t){n(t)}if(o){e(this._get_value_from_container(o,s,r))}}else console.warn(`${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 N3 extends m3{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 s;try{s=await this.getReferencedNodeContainer(i)}catch(t){return void n(t)}if(s){e(s.objectsCount())}}else e(0)}))}}class L3 extends m3{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(os.tailDigits(t))}else e(0)}else e(0)}))}}class O3 extends m3{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 P3 extends m3{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"number\\\\\\\"]]}processArguments(t){return new Promise((e=>{const n=t[0]||2;e(`${t[1]||0}`.padStart(n,\\\\\\\"0\\\\\\\"))}))}}class R3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"attribute name\\\\\\\"],[\\\\\\\"index\\\\\\\",\\\\\\\"point index\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(3==t.length){const i=t[0],s=t[1],r=t[2];let o=null;try{o=await this.getReferencedNodeContainer(i)}catch(t){n(t)}if(o){e(this._get_value_from_container(o,s,r))}}else console.warn(`${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 I3 extends m3{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 s;try{s=await this.getReferencedNodeContainer(i)}catch(t){return void n(t)}if(s){e(s.pointsCount())}}else e(0)}))}}class F3 extends m3{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 D3 extends m3{static requiredArguments(){return[]}async processArguments(t){let e=\\\\\\\"\\\\\\\";for(let n of t)null==n&&(n=\\\\\\\"\\\\\\\"),e+=`${n}`;return e}}class B3 extends m3{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 z3 extends m3{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 s=t[2]||1;return n&&(e=n.substr(i,s)),e}}class k3 extends m3{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 U3{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())),s=b.clone(i);if(0==this._validateNames(s))return;b.clone(this._created_spare_param_names).concat(s).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=JG.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(s.indexOf(t.name())>=0){const n=b.clone(t.param_options),i={spare:!0,computeOnDirty:!0,cook:!1},s=b.merge(n,i);let r=this._init_value_serialized_by_param_name.get(t.name());null==r&&(r=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:r,raw_input:o,options:s})}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()==Er.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 G3{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 U3(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 V3;!function(t){t.FUNCTION_DECLARATION=\\\\\\\"function_declaration\\\\\\\",t.DEFINE=\\\\\\\"define\\\\\\\",t.BODY=\\\\\\\"body\\\\\\\"}(V3||(V3={}));class H3{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 j3{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 W3{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)h.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)h.pushOnArrayAtEntry(this._body_lines_by_node_id,t.graphNodeId(),n)}body_lines(t){return this._body_lines_by_node_id.get(t.graphNodeId())}}class q3{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 W3(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 X3={[V3.FUNCTION_DECLARATION]:\\\\\\\"\\\\\\\",[V3.DEFINE]:\\\\\\\";\\\\\\\",[V3.BODY]:\\\\\\\";\\\\\\\"},Y3={[V3.FUNCTION_DECLARATION]:\\\\\\\"\\\\\\\",[V3.DEFINE]:\\\\\\\"\\\\\\\",[V3.BODY]:\\\\\\\"\\\\t\\\\\\\"};class $3{static node_comment(t,e){let n=`// ${t.path()}`,i=Y3[e];if(e==V3.BODY){let e=this.node_distance_to_material(t);t.type()==ns.OUTPUT&&(e+=1),i=i.repeat(e)}return e==V3.BODY&&(n=`${i}${n}`),n}static line_wrap(t,e,n){let i=!0;0!=e.indexOf(\\\\\\\"#if\\\\\\\")&&0!=e.indexOf(\\\\\\\"#endif\\\\\\\")||(i=!1);let s=Y3[n];if(n==V3.BODY&&(s=s.repeat(this.node_distance_to_material(t))),e=`${s}${e}`,i){const t=e[e.length-1],i=X3[n];t!=i&&\\\\\\\"{\\\\\\\"!=t&&\\\\\\\"}\\\\\\\"!=t&&(e+=i)}return e}static post_line_separator(t){return t==V3.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()!=ns.INPUT&&t.type()!=ns.OUTPUT||(n=0),n+this.node_distance_to_material(e)}}}class J3{constructor(t,e,n){this._node_traverser=t,this._root_nodes_for_shader_method=e,this._assembler=n,this._param_configs_controller=new wF,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)h.pushOnArrayAtEntry(n,t,i)}const s=new Map;for(let t of i)s.set(t.graphNodeId(),!0);for(let e of t)s.get(e.graphNodeId())||(i.push(e),s.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 q3(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,V3.FUNCTION_DECLARATION),this.addDefinitions(t,e,yf.UNIFORM,V3.DEFINE),this.addDefinitions(t,e,yf.VARYING,V3.DEFINE),this.addDefinitions(t,e,yf.ATTRIBUTE,V3.DEFINE),this.add_code_line_for_nodes_and_line_type(t,e,V3.BODY)}addDefinitions(t,e,n,i){if(!this._shaders_collection_controller)return;const s=[];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)s.push(e)}}if(s.length>0){const t=new vf(s),n=t.uniq();if(t.errored)throw`code builder error: ${t.error_message}`;const r=new Map,o=new Map;for(let t of n){const e=t.node.graphNodeId();o.has(e)||o.set(e,!0),h.pushOnArrayAtEntry(r,e,t)}const a=this._lines.get(e);o.forEach(((t,e)=>{const n=r.get(e);if(n){const t=n[0];if(t){const e=$3.node_comment(t.node,i);h.pushOnArrayAtEntry(a,i,e);for(let e of n){const n=$3.line_wrap(t.node,e.line,i);h.pushOnArrayAtEntry(a,i,n)}const s=$3.post_line_separator(i);h.pushOnArrayAtEntry(a,i,s)}}}))}}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 s=0;s0){const r=this._lines.get(e),o=$3.node_comment(t,n);if(h.pushOnArrayAtEntry(r,n,o),f.uniq(s).forEach((e=>{e=$3.line_wrap(t,e,n),h.pushOnArrayAtEntry(r,n,e)})),n!=V3.BODY||!i){const t=$3.post_line_separator(n);h.pushOnArrayAtEntry(r,n,t)}}}}class Z3{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&&h.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 s=this._graph_id_by_depth.get(e);s&&s.forEach((e=>{var s;if(null===(s=this._graph_ids_by_shader_name.get(t))||void 0===s?void 0:s.get(e)){const s=this._graph.nodeFromId(e);this.add_nodes_with_children(s,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()==ns.INPUT){const s=t.parent();if(s){const r=this.sorted_nodes_for_shader_name_for_parent(s,i);for(let s of r)s.graphNodeId()!=t.graphNodeId()&&this.add_nodes_with_children(s,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 s=this._graph_id_by_depth.get(n);s&&s.forEach((n=>{var s;if(!e||(null===(s=this._graph_ids_by_shader_name.get(e))||void 0===s?void 0:s.get(n))){const e=this._graph.nodeFromId(n);e.parent()==t&&i.push(e)}}))}));const s=i[0];return t.context()==s.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&&(h.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),s=f.uniq(i.map((t=>t.graphNodeId()))).map((t=>this._graph.nodeFromId(t)));if(s.length>0)for(let e of s)h.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()==ns.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 Q3=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.FRAGMENT,\\\\\\\"#include \\\\\\\"]]),K3=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( diffuse, opacity );\\\\\\\"]]),t4=new Map([[xf.VERTEX,[\\\\\\\"#include \\\\\\\",\\\\\\\"#include \\\\\\\"]],[xf.FRAGMENT,[]]]);class e4 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 bF.type():case AF.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 AI.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 Z3(this.currentGlParentNode(),this.shaderNames(),((t,e)=>this.input_names_for_shader_name(t,e)));return new J3(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 Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"normal\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"color\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"alpha\\\\\\\",Bo.FLOAT),new Ho(\\\\\\\"uv\\\\\\\",Bo.VEC2)]}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints(e4.output_input_connection_points())}static create_globals_node_output_connections(){return[new Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"normal\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"color\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"uv\\\\\\\",Bo.VEC2),new Ho(\\\\\\\"mvPosition\\\\\\\",Bo.VEC4),new Ho(\\\\\\\"worldPosition\\\\\\\",Bo.VEC4),new Ho(\\\\\\\"worldNormal\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"gl_Position\\\\\\\",Bo.VEC4),new Ho(\\\\\\\"gl_FragCoord\\\\\\\",Bo.VEC4),new Ho(\\\\\\\"cameraPosition\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"resolution\\\\\\\",Bo.VEC2),new Ho(\\\\\\\"time\\\\\\\",Bo.FLOAT)]}create_globals_node_output_connections(){return e4.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 H3(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",Lf.INPUT_NAME],[]),new H3(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 j3(\\\\\\\"position\\\\\\\",{default_from_attribute:!0,prefix:\\\\\\\"vec3 transformed = \\\\\\\"}),new j3(\\\\\\\"normal\\\\\\\",{default_from_attribute:!0,prefix:\\\\\\\"vec3 objectNormal = \\\\\\\",postLines:[\\\\\\\"#ifdef USE_TANGENT\\\\\\\",\\\\\\\"\\\\tvec3 objectTangent = vec3( tangent.xyz );\\\\\\\",\\\\\\\"#endif\\\\\\\"]}),new j3(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new j3(\\\\\\\"alpha\\\\\\\",{prefix:\\\\\\\"diffuseColor.a = \\\\\\\"}),new j3(\\\\\\\"uv\\\\\\\",{prefix:\\\\\\\"vUv = \\\\\\\",if:Sf.IF_RULE.uv})]}create_variable_configs(){return e4.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 Q3.get(t)}insert_body_after(t){return K3.get(t)}lines_to_remove(t){return t4.get(t)}_replace_template(t,e){const n=this.builder_lines(e,V3.FUNCTION_DECLARATION),i=this.builder_lines(e,V3.DEFINE),s=this.builder_lines(e,V3.BODY);let r=t.split(\\\\\\\"\\\\n\\\\\\\");const o=[],a=this.insert_define_after(e),l=this.insert_body_after(e),c=this.lines_to_remove(e);let h=!1,u=!1;for(let t of r){1==h&&(n&&this._insert_lines(o,n),i&&this._insert_lines(o,i),h=!1),1==u&&(s&&this._insert_lines(o,s),u=!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&&(h=!0),l&&t.indexOf(l)>=0&&(u=!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=U[n];if(void 0===i)throw new Error(\\\\\\\"Can not resolve #include <\\\\\\\"+n+\\\\\\\">\\\\\\\");return t(i)}))}(t)}}var n4,i4;!function(t){t.DISTANCE=\\\\\\\"customDistanceMaterial\\\\\\\",t.DEPTH=\\\\\\\"customDepthMaterial\\\\\\\",t.DEPTH_DOF=\\\\\\\"customDepthDOFMaterial\\\\\\\"}(n4||(n4={})),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\\\\\\\"}(i4||(i4={}));const s4=[i4.GL_FRAGCOORD,i4.GL_POINTCOORD];class r4 extends e4{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 s=i.createMaterial();s.name=e,t.customMaterials[e]=s}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 s=t.customMaterials[n];s&&(i.setFilterFragmentShaderMethodOwner(this),i.compileMaterial(s),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 s=this._shaders_by_name.get(xf.VERTEX),r=this._shaders_by_name.get(xf.FRAGMENT);s&&r&&(t.vertexShader=s,t.fragmentShader=this.processFilterFragmentShader(r),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 s=t.io.inputs.named_input(n),r=t.variableForInput(n),o=this.variable_config(n);let a=null;if(s)a=hf.vector3(r);else if(o.default_from_attribute()){const s=t.io.inputs.namedInputConnectionPointsByName(n);if(s){const r=s.type(),o=null===(i=this.globals_handler)||void 0===i?void 0:i.read_attribute(t,r,n,e);o&&(a=o)}}else{const t=o.default();t&&(a=t)}if(a){const n=o.prefix(),i=o.suffix(),s=o.if_condition();s&&e.addBodyLines(t,[`#if ${s}`]),e.addBodyLines(t,[`${n}${a}${i}`]);const r=o.postLines();r&&e.addBodyLines(t,r),s&&e.addBodyLines(t,[\\\\\\\"#endif\\\\\\\"])}}set_node_lines_output(t,e){var n;const i=e.current_shader_name,s=null===(n=this.shader_config(i))||void 0===n?void 0:n.input_names();if(s)for(let n of s)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(),s=null===(n=this.globals_handler)||void 0===n?void 0:n.read_attribute(t,i,t.attribute_name,e),r=t.glVarName(t.output_name);e.addBodyLines(t,[`${i} ${r} = ${s}`])}handle_globals_output_name(t){var e;switch(t.output_name){case i4.TIME:return void this.handleTime(t);case i4.RESOLUTION:return void this.handle_resolution(t);case i4.MV_POSITION:return void this.handle_mvPosition(t);case i4.GL_POSITION:return void this.handle_gl_Position(t);case i4.GL_FRAGCOORD:return void this.handle_gl_FragCoord(t);case i4.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,Bo.FLOAT,t.output_name);t.globals_shader_name&&h.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)h.pushOnArrayAtEntry(t.definitions_by_shader_name,i,e),h.pushOnArrayAtEntry(t.body_lines_by_shader_name,i,n);t.body_lines.push(n),this.setUniformsTimeDependent()}handle_resolution(t){t.body_lines.push(`vec2 ${t.var_name} = resolution`);const e=new Af(t.globals_node,Bo.VEC2,t.output_name);t.globals_shader_name&&h.pushOnArrayAtEntry(t.definitions_by_shader_name,t.globals_shader_name,e);for(let n of t.dependencies)h.pushOnArrayAtEntry(t.definitions_by_shader_name,n,e);this.set_uniforms_resolution_dependent()}handle_mvPosition(t){if(t.shader_name==xf.FRAGMENT){const e=t.globals_node,n=t.shaders_collection_controller,i=new Mf(e,Bo.VEC4,t.var_name),s=`${t.var_name} = modelViewMatrix * vec4(position, 1.0)`;n.addDefinitions(e,[i],xf.VERTEX),n.addBodyLines(e,[s],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 Mf(e,Bo.VEC4,t.var_name),s=`${t.var_name} = projectionMatrix * modelViewMatrix * vec4(position, 1.0)`;n.addDefinitions(e,[i],xf.VERTEX),n.addBodyLines(e,[s],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,s=this.shader_config(i);if(!s)return;const r=s.dependencies(),o=new Map,a=new Map,l=this.used_output_names_for_shader(t,i);for(let s of l){const l=t.glVarName(s),c=e.current_shader_name,h={globals_node:t,shaders_collection_controller:e,output_name:s,globals_shader_name:c,definitions_by_shader_name:o,body_lines:n,var_name:l,shader_name:i,dependencies:r,body_lines_by_shader_name:a};this.handle_globals_output_name(h)}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&&s4.includes(t)||i.push(t);return i}}const o4=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( 1.0 );\\\\\\\"]]);const a4=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( 1.0 );\\\\\\\"]]);var l4=\\\\\\\"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 c4=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),h4=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const u4=new Map([]);u4.set(n4.DISTANCE,class extends r4{templateShader(){const t=H.distanceRGBA;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}insert_body_after(t){return o4.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})}}),u4.set(n4.DEPTH,class extends r4{templateShader(){const t=H.depth;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}insert_body_after(t){return a4.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})}}),u4.set(n4.DEPTH_DOF,class extends r4{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:l4,uniforms:{mNear:{value:0},mFar:{value:10}}}}insert_define_after(t){return c4.get(t)}insert_body_after(t){return h4.get(t)}createMaterial(){const t=this.templateShader();return new F({uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}});class d4 extends r4{custom_assembler_class_by_custom_name(){return u4}}class p4 extends d4{templateShader(){const t=H.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 _4 extends d4{templateShader(){const t=H.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 m4 extends d4{templateShader(){const t=H.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 f4=\\\\\\\"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 g4 extends d4{constructor(t){super(t),this._gl_parent_node=t,this._addFilterFragmentShaderCallback(\\\\\\\"MeshStandardBuilderMatNode\\\\\\\",g4.filterFragmentShader)}isPhysical(){return!1}templateShader(){const t=this.isPhysical()?H.physical:H.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;\\\\\\\"),g4.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=e4.output_input_connection_points();e.push(new Ho(\\\\\\\"metalness\\\\\\\",Bo.FLOAT,1)),e.push(new Ho(\\\\\\\"roughness\\\\\\\",Bo.FLOAT,1)),e.push(new Ho(\\\\\\\"emissive\\\\\\\",Bo.VEC3,[1,1,1])),g4.USE_SSS&&e.push(new Ho(\\\\\\\"SSSModel\\\\\\\",Bo.SSS_MODEL,f4)),this.isPhysical()&&(e.push(new Ho(\\\\\\\"transmission\\\\\\\",Bo.FLOAT,1)),e.push(new Ho(\\\\\\\"thickness\\\\\\\",Bo.FLOAT,1))),t.io.inputs.setNamedInputConnectionPoints(e)}create_shader_configs(){const t=[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\",\\\\\\\"metalness\\\\\\\",\\\\\\\"roughness\\\\\\\",\\\\\\\"emissive\\\\\\\",\\\\\\\"SSSModel\\\\\\\"];return this.isPhysical()&&(t.push(\\\\\\\"transmission\\\\\\\"),t.push(\\\\\\\"thickness\\\\\\\")),[new H3(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\"],[]),new H3(xf.FRAGMENT,t,[xf.VERTEX])]}create_variable_configs(){const t=e4.create_variable_configs();return t.push(new j3(\\\\\\\"metalness\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_metalness = \\\\\\\"})),t.push(new j3(\\\\\\\"roughness\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_roughness = \\\\\\\"})),t.push(new j3(\\\\\\\"emissive\\\\\\\",{default:\\\\\\\"vec3(1.0, 1.0, 1.0)\\\\\\\",prefix:\\\\\\\"vec3 POLY_emissive = \\\\\\\"})),g4.USE_SSS&&t.push(new j3(\\\\\\\"SSSModel\\\\\\\",{default:f4,prefix:\\\\\\\"SSSModel POLY_SSSModel = \\\\\\\"})),this.isPhysical()&&(t.push(new j3(\\\\\\\"transmission\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_transmission = \\\\\\\"})),t.push(new j3(\\\\\\\"thickness\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_thickness = \\\\\\\"}))),t}}g4.USE_SSS=!0;class v4 extends g4{constructor(t){super(t),this._gl_parent_node=t,this._addFilterFragmentShaderCallback(\\\\\\\"MeshPhysicalBuilderMatNode\\\\\\\",v4.filterFragmentShader)}isPhysical(){return!0}static filterFragmentShader(t){return t=t.replace(\\\\\\\"#include \\\\\\\",function(t){const e=t.split(\\\\\\\"\\\\n\\\\\\\");let n=0;for(let t of e)t.includes(\\\\\\\"float transmissionFactor = transmission;\\\\\\\")&&(t=\\\\\\\"float transmissionFactor = transmission * POLY_transmission;\\\\\\\",e[n]=t),t.includes(\\\\\\\"float thicknessFactor = thickness;\\\\\\\")&&(t=\\\\\\\"float thicknessFactor = thickness * POLY_thickness;\\\\\\\",e[n]=t),n++;return e.join(\\\\\\\"\\\\n\\\\\\\")}(k))}}const y4=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),x4=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const b4=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),w4=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const T4=new Map([[xf.VERTEX,[\\\\\\\"#include \\\\\\\",\\\\\\\"gl_PointSize = size;\\\\\\\"]],[xf.FRAGMENT,[]]]),A4=new Map;A4.set(n4.DISTANCE,class extends r4{templateShader(){const t=H.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 y4.get(t)}insert_body_after(t){return x4.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})}}),A4.set(n4.DEPTH_DOF,class extends r4{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:l4,uniforms:{size:{value:1},scale:{value:1},mNear:{value:0},mFar:{value:10}}}}insert_define_after(t){return b4.get(t)}insert_body_after(t){return w4.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 M4 extends r4{custom_assembler_class_by_custom_name(){return A4}templateShader(){const t=H.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=e4.output_input_connection_points();e.push(new Ho(\\\\\\\"gl_PointSize\\\\\\\",Bo.FLOAT)),t.io.inputs.setNamedInputConnectionPoints(e)}create_globals_node_output_connections(){return e4.create_globals_node_output_connections().concat([new Ho(i4.GL_POINTCOORD,Bo.VEC2)])}create_shader_configs(){return[new H3(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",\\\\\\\"gl_PointSize\\\\\\\"],[]),new H3(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[xf.VERTEX])]}create_variable_configs(){return e4.create_variable_configs().concat([new j3(\\\\\\\"gl_PointSize\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"gl_PointSize = \\\\\\\",suffix:\\\\\\\" * size * 10.0\\\\\\\"})])}lines_to_remove(t){return T4.get(t)}}const E4=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),S4=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const C4=new Map([]);C4.set(n4.DEPTH_DOF,class extends r4{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:l4,uniforms:{scale:{value:1},mNear:{value:0},mFar:{value:10}}}}insert_define_after(t){return E4.get(t)}insert_body_after(t){return S4.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 N4=new Map([[xf.VERTEX,[\\\\\\\"#include \\\\\\\",\\\\\\\"#include \\\\\\\"]],[xf.FRAGMENT,[]]]);class L4 extends r4{templateShader(){const t=H.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\\\\\\\",C4),C4}create_shader_configs(){return[new H3(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"uv\\\\\\\"],[]),new H3(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[xf.VERTEX])]}static output_input_connection_points(){return[new Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"color\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"alpha\\\\\\\",Bo.FLOAT),new Ho(\\\\\\\"uv\\\\\\\",Bo.VEC2)]}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints(L4.output_input_connection_points())}static create_globals_node_output_connections(){return[new Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"color\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"uv\\\\\\\",Bo.VEC2),new Ho(\\\\\\\"gl_FragCoord\\\\\\\",Bo.VEC4),new Ho(\\\\\\\"resolution\\\\\\\",Bo.VEC2),new Ho(\\\\\\\"time\\\\\\\",Bo.FLOAT)]}create_globals_node_output_connections(){return L4.create_globals_node_output_connections()}create_variable_configs(){return[new j3(\\\\\\\"position\\\\\\\",{default:\\\\\\\"vec3( position )\\\\\\\",prefix:\\\\\\\"vec3 transformed = \\\\\\\",suffix:\\\\\\\";vec4 mvPosition = vec4( transformed, 1.0 ); gl_Position = projectionMatrix * modelViewMatrix * mvPosition;\\\\\\\"}),new j3(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new j3(\\\\\\\"alpha\\\\\\\",{prefix:\\\\\\\"diffuseColor.w = \\\\\\\"}),new j3(\\\\\\\"uv\\\\\\\",{prefix:\\\\\\\"vUv = \\\\\\\",if:Sf.IF_RULE.uv})]}lines_to_remove(t){return N4.get(t)}}class O4 extends e4{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 s of this._root_nodes)switch(s.type()){case bF.type():i.push(s);break;case gf.type():{const r=s.attribute_name,o=null===(e=this._texture_allocations_controller)||void 0===e?void 0:e.variable(r);if(o&&o.allocation()){(null===(n=o.allocation())||void 0===n?void 0:n.shaderName())==t&&i.push(s)}break}}return i}leaf_nodes_by_shader_name(t){var e,n;const i=[];for(let s of this._leaf_nodes)switch(s.type()){case AI.type():i.push(s);break;case gf.type():{const r=s.attribute_name,o=null===(e=this._texture_allocations_controller)||void 0===e?void 0:e.variable(r);if(o&&o.allocation()){(null===(n=o.allocation())||void 0===n?void 0:n.shaderName())==t&&i.push(s)}break}}return i}setup_shader_names_and_variables(){var t;const e=new Z3(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 O0,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 Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"velocity\\\\\\\",Bo.VEC3)])}add_globals_outputs(t){t.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"velocity\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"time\\\\\\\",Bo.FLOAT)])}allow_attribute_exports(){return!0}textureAllocationsController(){return this._texture_allocations_controller=this._texture_allocations_controller||new O0}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,s){var r;if(n){const n=t.variableForInput(e),o=hf.vector3(n);if(o){const e=this.textureAllocationsController().variable(i),n=s.current_shader_name;if(e&&(null===(r=e.allocation())||void 0===r?void 0:r.shaderName())==n){const i=`gl_FragColor.${e.component()} = ${o}`;s.addBodyLines(t,[i],n)}}}}set_node_lines_output(t,e){const n=e.current_shader_name,i=this.textureAllocationsController().inputNamesForShaderName(t,n);if(i)for(let n of i){const i=t.io.inputs.named_input(n);if(i){const s=n;this.add_export_body_line(t,n,i,s,e)}}}set_node_lines_attribute(t,e){var n,i;if(t.isImporting()){const s=t.gl_type(),r=t.attribute_name,o=null===(n=this.globals_handler)||void 0===n?void 0:n.read_attribute(t,s,r,e),a=t.glVarName(t.output_name),l=`${s} ${a} = ${o}`;e.addBodyLines(t,[l]);const c=this.textureAllocationsController().variable(r),h=e.current_shader_name;if(c&&(null===(i=c.allocation())||void 0===i?void 0:i.shaderName())==h){const n=this.textureAllocationsController().variable(r);if(n){const i=`gl_FragColor.${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,Bo.FLOAT,e);n.addDefinitions(t,[i]);const s=`float ${t.glVarName(e)} = ${e}`;n.addBodyLines(t,[s]),this.setUniformsTimeDependent()}_handle_globals_default(t,e,n){var i;const s=t.io.outputs.namedOutputConnectionPointsByName(e);if(s){const r=s.type(),o=null===(i=this.globals_handler)||void 0===i?void 0:i.read_attribute(t,r,e,n);if(o){const i=`${r} ${t.glVarName(e)} = ${o}`;n.addBodyLines(t,[i])}}}}class P4 extends e4{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 Ho(\\\\\\\"color\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"alpha\\\\\\\",Bo.FLOAT)])}add_globals_outputs(t){t.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"gl_FragCoord\\\\\\\",Bo.VEC2),new Ho(\\\\\\\"time\\\\\\\",Bo.FLOAT)])}create_shader_configs(){return[new H3(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[])]}create_variable_configs(){return[new j3(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new j3(\\\\\\\"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 s;\\\\\\\"color\\\\\\\"==i&&(s=`diffuseColor.xyz = ${hf.any(n)}`),\\\\\\\"alpha\\\\\\\"==i&&(s=`diffuseColor.a = ${hf.any(n)}`),s&&e.addBodyLines(t,[s])}}}set_node_lines_globals(t,e){const n=e.current_shader_name;if(!this.shader_config(n))return;const i=[],s=[];for(let e of t.io.outputs.used_output_names()){const r=t.glVarName(e);switch(e){case\\\\\\\"time\\\\\\\":s.push(new Af(t,Bo.FLOAT,e)),i.push(`float ${r} = ${e}`),this.setUniformsTimeDependent();break;case\\\\\\\"gl_FragCoord\\\\\\\":this.handle_gl_FragCoord(i,n,r)}}e.addDefinitions(t,s,n),e.addBodyLines(t,i)}}const R4=new Map([]);class I4 extends r4{custom_assembler_class_by_custom_name(){return R4}}const F4=new Map([[xf.VERTEX,\\\\\\\"// start builder body code\\\\\\\"],[xf.FRAGMENT,\\\\\\\"// start builder body code\\\\\\\"]]),D4=new Map([[xf.FRAGMENT,[]]]);class B4 extends I4{templateShader(){return{vertexShader:Lk,fragmentShader:Ok,uniforms:I.clone(Pk)}}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 gr.add_user_data_render_hook(e,Ik.render_hook.bind(Ik)),this._addCustomMaterials(e),e}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints([new Ho(\\\\\\\"density\\\\\\\",Bo.FLOAT,1)])}static create_globals_node_output_connections(){return[new Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"pos_normalized\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"time\\\\\\\",Bo.FLOAT)]}create_globals_node_output_connections(){return B4.create_globals_node_output_connections()}insert_body_after(t){return F4.get(t)}lines_to_remove(t){return D4.get(t)}create_shader_configs(){return[new H3(xf.VERTEX,[],[]),new H3(xf.FRAGMENT,[\\\\\\\"density\\\\\\\"],[xf.VERTEX])]}static create_variable_configs(){return[new j3(\\\\\\\"position\\\\\\\",{}),new j3(\\\\\\\"density\\\\\\\",{prefix:\\\\\\\"density *= \\\\\\\"})]}create_variable_configs(){return B4.create_variable_configs()}set_node_lines_globals(t,e){const n=[],i=e.current_shader_name,s=this.shader_config(i);if(!s)return;const r=s.dependencies(),o=new Map,a=new Map;let l,c;for(let s of t.io.outputs.used_output_names()){const u=t.glVarName(s),d=e.current_shader_name;switch(s){case\\\\\\\"time\\\\\\\":l=new Af(t,Bo.FLOAT,s),d&&h.pushOnArrayAtEntry(o,d,l),c=`float ${u} = ${s}`;for(let t of r)h.pushOnArrayAtEntry(o,t,l),h.pushOnArrayAtEntry(a,t,c);n.push(c),this.setUniformsTimeDependent();break;case\\\\\\\"position\\\\\\\":i==xf.FRAGMENT&&n.push(`vec3 ${u} = position_for_step`);break;case\\\\\\\"pos_normalized\\\\\\\":i==xf.FRAGMENT&&n.push(`vec3 ${u} = (position_for_step - u_BoundingBoxMax) / (u_BoundingBoxMax - u_BoundingBoxMin)`)}}o.forEach(((n,i)=>{e.addDefinitions(t,n,i)})),a.forEach(((n,i)=>{e.addBodyLines(t,n,i)})),e.addBodyLines(t,n)}}class z4{static async run(){this._started||(this._started=!0,class{static async run(t){(class{static run(t){t.registerNode(I_,Ql),t.registerNode(D_,ec),t.registerNode(z_,Ql),t.registerNode(U_,Ql),t.registerNode($_,Ql),t.registerNode(Z_,Zl),t.registerNode(K_,Ql),t.registerNode(em,ec),t.registerNode(im,tc),t.registerNode(hm,tc),t.registerNode(dm,Ql),t.registerNode(_m,Zl),t.registerNode(wm,tc),t.registerNode(Mm,Kl),t.registerNode(Em,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(Eg,ic),t.registerNode(Cg,ic),t.registerNode(Ig,ic),t.registerNode(zg,ic),t.registerNode(Kg,ic),t.registerNode(iv,ic),t.registerNode(lv,sc),t.registerNode(hv,sc),t.registerNode(_v,sc),t.registerNode(fv,sc),t.registerNode(yv,nc),t.registerNode(wv,ic),t.registerNode(Mv,nc),t.registerNode(Cv,rc),t.registerNode(Lv,rc),t.registerNode(Nv,rc),t.registerNode(Ov,rc),t.registerNode(Pv,rc),t.registerNode(Rv,rc)}}.run(t),class{static run(t){t.registerNode(Bv,cc),t.registerNode(Uv,lc),t.registerNode(Vv,lc),t.registerNode(Wv,lc),t.registerNode(ey,oc),t.registerNode(_y,oc),t.registerNode(py,oc),t.registerNode(fy,lc),t.registerNode(xl,ac),t.registerNode(Yy,oc),t.registerNode(al,ac),t.registerNode(Qy,lc),t.registerNode(tx,lc),t.registerNode(AL,oc),t.registerNode(Xa,ac),t.registerNode(CL,cc),t.registerNode(PL,lc),t.registerNode(GL,ac),t.registerNode(Qa,ac),t.registerNode(hO,lc),t.registerNode(nl,cc),t.registerNode(_O,cc),t.registerNode(MO,cc),t.registerNode(SO,lc),t.registerNode(LO,lc),t.registerNode(wl,ac),t.registerNode(PO,lc),t.registerNode(dl,ac),t.registerNode(FO,hc),t.registerNode(DO,hc),t.registerNode(BO,hc),t.registerNode(zO,hc),t.registerNode(kO,hc),t.registerNode(UO,hc)}}.run(t),class{static run(t){t.registerNode(MP,gc),t.registerNode(xR,vc),t.registerNode(EP,xc),t.registerNode(rR,gc),t.registerNode(MR,xc),t.registerNode(uR,fc),t.registerNode(SP,xc),t.registerNode(CP,xc),t.registerNode(gf,_c,{except:[`${ts.COP}/builder`]}),t.registerNode(ZO,dc),t.registerNode(NP,gc),t.registerNode(eR,gc),t.registerNode(NR,uc),t.registerNode(DR,fc),t.registerNode(BR,gc),t.registerNode(UR,_c),t.registerNode(LP,xc),t.registerNode(HR,pc),t.registerNode(jR,gc),t.registerNode(OP,dc),t.registerNode(XR,pc),t.registerNode(qP,pc),t.registerNode(oR,gc),t.registerNode(XP,pc),t.registerNode(eI,gc),t.registerNode(PP,gc),t.registerNode(RP,gc),t.registerNode(nR,pc),t.registerNode(sI,gc),t.registerNode(oI,gc),t.registerNode(lI,gc),t.registerNode(hI,gc),t.registerNode(HO,dc),t.registerNode(KO,dc),t.registerNode(eP,dc),t.registerNode(iP,dc),t.registerNode(IP,gc),t.registerNode(pI,uc),t.registerNode(wI,fc),t.registerNode(FP,gc),t.registerNode(AI,_c),t.registerNode(EI,uc),t.registerNode(CI,uc),t.registerNode(OI,fc),t.registerNode(RI,bc),t.registerNode($O,dc),t.registerNode(qO,dc),t.registerNode(DP,gc),t.registerNode(UI,pc),t.registerNode(GI,pc),t.registerNode(HI,uc),t.registerNode(BP,gc),t.registerNode(zP,gc),t.registerNode(YP,gc),t.registerNode(WI,gc),t.registerNode($P,gc),t.registerNode(JP,gc),t.registerNode(JI,gc),t.registerNode(XI,gc),t.registerNode(lR,gc),t.registerNode(KI,gc),t.registerNode(tF,gc),t.registerNode(yF,bc),t.registerNode(vF,pc),t.registerNode(kP,gc),t.registerNode(dR,fc),t.registerNode(bF,_c),t.registerNode(AF,_c),t.registerNode(ZP,gc),t.registerNode(NF,yc),t.registerNode(RF,yc),t.registerNode(IF,yc),t.registerNode(FF,yc),t.registerNode(zF,_c),t.registerNode(GF,_c),t.registerNode(UP,dc),t.registerNode(QP,pc),t.registerNode(MF,pc),t.registerNode(HF,uc),t.registerNode(QF,pc),t.registerNode(tD,gc),t.registerNode(GP,gc),t.registerNode(VP,xc),t.registerNode(iR,gc),t.registerNode(nD,pc),t.registerNode(HP,gc),t.registerNode(CF,mc),t.registerNode(KP,pc),t.registerNode(vI,fc),t.registerNode(sD,fc,TD),t.registerNode(mI,fc,TD),t.registerNode(aR,gc),t.registerNode(oD,fc),t.registerNode(jP,xc),t.registerNode(lD,uc),t.registerNode(dD,_c),t.registerNode(mD,fc),t.registerNode(Lf,_c),t.registerNode(vD,_c),t.registerNode(hP,dc),t.registerNode(mP,dc),t.registerNode(uP,dc),t.registerNode(_P,dc),t.registerNode(fP,dc),t.registerNode(dP,dc),t.registerNode(pP,dc),t.registerNode(xD,pc),t.registerNode(wD,pc)}}.run(t),class{static run(t){t.registerNode(SD,wc),t.registerNode(ND,wc),t.registerNode(OD,wc),t.registerNode(zD,wc)}}.run(t),class{static run(t){t.registerNode(XD,Ac),t.registerNode(rB,Ac),t.registerNode(DB,Mc),t.registerNode(VB,Tc),t.registerNode(YB,Mc),t.registerNode(tz,Tc),t.registerNode(mz,Mc),t.registerNode(yz,Mc),t.registerNode(Mz,Mc),t.registerNode(Nz,Tc),t.registerNode(Uz,Mc),t.registerNode(jz,Tc),t.registerNode(Yz,Mc),t.registerNode(Qz,Tc),t.registerNode(hk,Mc),t.registerNode(fk,Mc),t.registerNode(xk,Sc),t.registerNode(Tk,Tc),t.registerNode(Ek,Tc),t.registerNode(Nk,Mc),t.registerNode(Bk,Cc),t.registerNode(Uk,Cc),t.registerNode(Hk,Ec),t.registerNode(jk,Ec),t.registerNode(Wk,Ec),t.registerNode(qk,Ec),t.registerNode(Xk,Ec),t.registerNode(Yk,Ec)}}.run(t),class{static run(t){t.registerNode(nU,Rc),t.registerNode(MU,Rc),t.registerNode(DU,Rc),t.registerNode(HU,Rc),t.registerNode($U,Rc),t.registerNode(iG,Rc),t.registerNode(pG,Lc),t.registerNode(vG,Fc),t.registerNode(CG,Nc),t.registerNode(RG,Pc),t.registerNode(DG,Fc),t.registerNode(GG,Fc),t.registerNode(_V,Nc),t.registerNode(NV,Lc),t.registerNode(RV,Fc),t.registerNode(DV,Nc),t.registerNode(XH,Oc),t.registerNode(ZH,Oc),t.registerNode(tj,Oc),t.registerNode(sj,Ic),t.registerNode(rj,Ic),t.registerNode(oj,Ic),t.registerNode(aj,Ic),t.registerNode(lj,Ic),t.registerNode(cj,Ic)}}.run(t),class{static run(t){t.registerNode(gj,Qc),t.registerNode(bj,Qc),t.registerNode(Aj,Zc),t.registerNode(Sj,Zc),t.registerNode(Lj,Kc),t.registerNode(Pj,Kc),t.registerNode(Fj,Kc),t.registerNode(Bj,Kc),t.registerNode(qj,Qc),t.registerNode(Hj,Qc),t.registerNode(Jj,Qc),t.registerNode(Kj,Kc),t.registerNode(nW,Zc),t.registerNode(sW,Jc),t.registerNode(oW,Kc),t.registerNode(dW,Kc),t.registerNode(_W,Kc),t.registerNode(fW,Kc),t.registerNode(yW,Qc),t.registerNode(wW,Qc),t.registerNode(AW,Kc),t.registerNode(SW,Qc),t.registerNode(LW,Zc),t.registerNode(PW,Kc),t.registerNode(DW,Jc),t.registerNode(UW,Qc),t.registerNode(VW,Jc),t.registerNode(WW,Qc),t.registerNode(YW,th),t.registerNode($W,th),t.registerNode(JW,th),t.registerNode(ZW,th),t.registerNode(QW,th),t.registerNode(KW,th)}}.run(t),class{static run(t){t.registerNode(oq,Dc),t.registerNode(vH,zc),t.registerNode(aq,Bc),t.registerNode(nq,Bc),t.registerNode(lq,Bc),t.registerNode(cq,Bc),t.registerNode(hq,Bc),t.registerNode(uq,Bc)}}.run(t),class{static run(t){t.registerOperation(dq),t.registerOperation(Eq),t.registerOperation(Iq),t.registerOperation(zq),t.registerOperation(Vq),t.registerOperation(nX),t.registerOperation(Jq),t.registerOperation(aX),t.registerOperation(UX),t.registerOperation(jX),t.registerOperation(g$),t.registerOperation(x$),t.registerOperation(IJ),t.registerOperation(kJ),t.registerOperation(xZ),t.registerOperation(kZ),t.registerOperation(ZQ),t.registerOperation(cK),t.registerOperation(yK),t.registerOperation(TK),t.registerOperation(NK),t.registerOperation(HK),t.registerOperation(KK),t.registerOperation(kK),t.registerOperation(h0),t.registerOperation(m0),t.registerOperation(F0),t.registerOperation(V0),t.registerOperation(q0),t.registerOperation(K0),t.registerOperation(r1),t.registerOperation(f1),t.registerOperation(M1),t.registerOperation(D1),t.registerOperation(U1),t.registerOperation(j1),t.registerOperation(Q1),t.registerOperation(a2),t.registerOperation(v2),t.registerOperation(O2),t.registerOperation(V2),t.registerOperation(c9),t.registerOperation(p9),t.registerOperation(v9),t.registerOperation(w9),t.registerOperation(C9),t.registerOperation(X9),t.registerOperation(t3),t.registerOperation(s3),t.registerNode(mq,Hc),t.registerNode(gq,Uc),t.registerNode(Mq,Uc),t.registerNode(Nq,Gc),t.registerNode(Bq,Gc),t.registerNode(Gq,Gc),t.registerNode(qq,Gc),t.registerNode(Yq,Gc),t.registerNode(Kq,Gc),t.registerNode(rX,Gc),t.registerNode(uX,Gc),t.registerNode(pX,Gc),t.registerNode(mX,Gc),t.registerNode(bX,Gc),t.registerNode(TX,qc),t.registerNode(MX,qc),t.registerNode(HX,qc),t.registerNode(XX,Yc),t.registerNode(y$,kc),t.registerNode(T$,kc),t.registerNode(SJ,Wc),t.registerNode(RJ,Yc),t.registerNode(DJ,Yc),t.registerNode(VJ,Yc),t.registerNode($J,Yc),t.registerNode(eZ,qc),t.registerNode(sZ,Yc),t.registerNode(fZ,qc),t.registerNode(TZ,Yc),t.registerNode(CZ,Hc),t.registerNode(DZ,Hc),t.registerNode(VZ,Wc),t.registerNode(jZ,Wc),t.registerNode(rQ,qc),t.registerNode(aQ,qc),t.registerNode(HQ,kc),t.registerNode(WQ,qc),t.registerNode(tK,Hc),t.registerNode(nK,qc),t.registerNode(oK,Yc),t.registerNode(mK,qc),t.registerNode(pK,Wc),t.registerNode(wK,Yc),t.registerNode(EK,$c),t.registerNode(CK,$c),t.registerNode(PK,qc),t.registerNode(IK,qc),t.registerNode(DK,Yc),t.registerNode(zK,kc),t.registerNode(VK,$c),t.registerNode(XK,Wc),t.registerNode(n0,Yc),t.registerNode(a0,Wc),t.registerNode(c0,qc),t.registerNode(d0,Wc),t.registerNode(_0,Hc),t.registerNode(v0,qc),t.registerNode(x0,kc,{userAllowed:!1}),t.registerNode(I0,Vc),t.registerNode(z0,qc),t.registerNode(W0,Yc),t.registerNode($0,qc),t.registerNode(l1,qc),t.registerNode(Q0,qc),t.registerNode(n1,jc),t.registerNode(hV,kc),t.registerNode(_1,qc),t.registerNode(y1,qc),t.registerNode(C1,$c),t.registerNode(F1,qc),t.registerNode(k1,Gc),t.registerNode(H1,Hc),t.registerNode(X1,qc),t.registerNode(s2,qc),t.registerNode(n2,qc),t.registerNode(d2,kc),t.registerNode(_2,kc),t.registerNode(h2,qc),t.registerNode(b2,Yc),t.registerNode(T2,qc),t.registerNode(I2,qc),t.registerNode(D2,Wc),t.registerNode(z2,Wc),t.registerNode(sV,Wc),t.registerNode(W2,Hc),t.registerNode(Y2,Wc),t.registerNode(Z2,Yc),t.registerNode(l9,Yc),t.registerNode(d9,qc),t.registerNode(f9,qc),t.registerNode(b9,Yc),t.registerNode(M9,Yc),t.registerNode(O9,qc),t.registerNode(R9,qc),t.registerNode(z9,qc),t.registerNode(V9,qc),t.registerNode(W9,Yc),t.registerNode($9,qc),t.registerNode(K9,qc),t.registerNode(i3,qc),t.registerNode(o3,qc),t.registerNode(c3,Xc),t.registerNode(h3,Xc),t.registerNode(u3,Xc),t.registerNode(d3,Xc),t.registerNode(p3,Xc),t.registerNode(_3,Xc)}}.run(t)}}.run(li),class{static run(t){t.registerCamera(XH),t.registerCamera(ZH)}}.run(li),class{static run(t){t.expressionsRegister.register(f3,\\\\\\\"arg\\\\\\\"),t.expressionsRegister.register(g3,\\\\\\\"argc\\\\\\\"),t.expressionsRegister.register(x3,\\\\\\\"bbox\\\\\\\"),t.expressionsRegister.register(b3,\\\\\\\"centroid\\\\\\\"),t.expressionsRegister.register(w3,\\\\\\\"ch\\\\\\\"),t.expressionsRegister.register(T3,\\\\\\\"copy\\\\\\\"),t.expressionsRegister.register(A3,\\\\\\\"copRes\\\\\\\"),t.expressionsRegister.register(M3,\\\\\\\"isDeviceMobile\\\\\\\"),t.expressionsRegister.register(E3,\\\\\\\"isDeviceTouch\\\\\\\"),t.expressionsRegister.register(S3,\\\\\\\"js\\\\\\\"),t.expressionsRegister.register(C3,\\\\\\\"object\\\\\\\"),t.expressionsRegister.register(N3,\\\\\\\"objectsCount\\\\\\\"),t.expressionsRegister.register(L3,\\\\\\\"opdigits\\\\\\\"),t.expressionsRegister.register(O3,\\\\\\\"opname\\\\\\\"),t.expressionsRegister.register(P3,\\\\\\\"padzero\\\\\\\"),t.expressionsRegister.register(R3,\\\\\\\"point\\\\\\\"),t.expressionsRegister.register(I3,\\\\\\\"pointsCount\\\\\\\"),t.expressionsRegister.register(F3,\\\\\\\"strCharsCount\\\\\\\"),t.expressionsRegister.register(D3,\\\\\\\"strConcat\\\\\\\"),t.expressionsRegister.register(B3,\\\\\\\"strIndex\\\\\\\"),t.expressionsRegister.register(z3,\\\\\\\"strSub\\\\\\\"),t.expressionsRegister.register(k3,\\\\\\\"windowSize\\\\\\\")}}.run(li),class{static run(t){t.assemblersRegister.register(jn.GL_MESH_BASIC,G3,p4),t.assemblersRegister.register(jn.GL_MESH_LAMBERT,G3,_4),t.assemblersRegister.register(jn.GL_MESH_PHONG,G3,m4),t.assemblersRegister.register(jn.GL_MESH_STANDARD,G3,g4),t.assemblersRegister.register(jn.GL_MESH_PHYSICAL,G3,v4),t.assemblersRegister.register(jn.GL_PARTICLES,G3,O4),t.assemblersRegister.register(jn.GL_POINTS,G3,M4),t.assemblersRegister.register(jn.GL_LINE,G3,L4),t.assemblersRegister.register(jn.GL_TEXTURE,G3,P4),t.assemblersRegister.register(jn.GL_VOLUME,G3,B4)}}.run(li))}}z4._started=!1,z4.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":"2813173"}},"type":2,"external":true,"timestamp":1723912788968},{"data":{"url":"blob:https://ipfs.arkivo.art/97c620bc-4a41-44b6-9dd3-8a5a3879d3c5","host":"","path":"https://ipfs.arkivo.art/97c620bc-4a41-44b6-9dd3-8a5a3879d3c5","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":1723912788974},{"data":{"url":"blob:https://ipfs.arkivo.art/97c620bc-4a41-44b6-9dd3-8a5a3879d3c5","body":"\"// ../../Documents/PJS/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":"175"}},"type":2,"external":true,"timestamp":1723912793251}],"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":1723912788182},{"text":"[.WebGL-0xfc0015dd500]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels","level":"warning","timestamp":1723912793281},{"text":"[.WebGL-0xfc0015dd500]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels","level":"warning","timestamp":1723912793281},{"text":"[.WebGL-0xfc0015dd500]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels","level":"warning","timestamp":1723912793281},{"text":"[.WebGL-0xfc0015dd500]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels (this message will no longer repeat)","level":"warning","timestamp":1723912793281}],"screenshotDelay":10000},"timestamp":1723912787738},"created_at":"2024-08-17T16:40:08.614+00:00","updated_at":"2024-08-17T16:40:08.614+00:00"}