diff --git a/Audio-Transcription/background.js b/Audio-Transcription/background.js index 0869ed7..25ea75c 100644 --- a/Audio-Transcription/background.js +++ b/Audio-Transcription/background.js @@ -170,7 +170,8 @@ async function stopCapture() { if (optionTabId) { res = await sendMessageToTab(currentTabId, { - type: "STOP" + type: "STOP", + data: { currentTabId: currentTabId }, }); await removeChromeTab(optionTabId); } @@ -188,3 +189,15 @@ chrome.runtime.onMessage.addListener((message) => { stopCapture(); } }); + + +/** + * Listens for if the tab is reloaded. + * @param {Object} message - The message received from the runtime. + */ +chrome.tabs.onUpdated.addListener(async (tabId, changeInfo, tab) => { + if (changeInfo.status === 'complete') { + await executeScriptInTab(tabId, "content.js"); + await delayExecution(500); + } +}); diff --git a/Audio-Transcription/manifest.json b/Audio-Transcription/manifest.json index cd9aaf7..858f813 100644 --- a/Audio-Transcription/manifest.json +++ b/Audio-Transcription/manifest.json @@ -4,7 +4,9 @@ "name": "Audio Transcription", "version": "1.0.0", "description": "This extension captures the audio on the current tab, sends it to a server for transcription and shows the transcription in Real-time.", - + "content_security_policy": { + "extension_pages": "script-src 'self' 'wasm-unsafe-eval'; object-src 'self';" + }, "options_page": "options.html", "background": { "service_worker": "background.js" diff --git a/Audio-Transcription/options.html b/Audio-Transcription/options.html index e0223c9..51ab923 100644 --- a/Audio-Transcription/options.html +++ b/Audio-Transcription/options.html @@ -4,15 +4,14 @@ Audio Transcription Options - - + -

Audio Transcription

-
Stop Capture
+ + \ No newline at end of file diff --git a/Audio-Transcription/options.js b/Audio-Transcription/options.js index 63e60a0..6a8d985 100644 --- a/Audio-Transcription/options.js +++ b/Audio-Transcription/options.js @@ -38,12 +38,43 @@ function sendMessageToTab(tabId, data) { */ async function startRecord(option) { const stream = await captureTabAudio(); + var doVad = true; if (stream) { // call when the stream inactive stream.oninactive = () => { window.close(); }; + // create onnx model + // initialize onnx model + const session = await ort.InferenceSession.create('./silero_vad.onnx'); + var h = new Array(128); + for (let i = 0; i < h.length; i++) { + h[i] = 0; + } + var c = new Array(128); + for (let i = 0; i < h.length; i++) { + c[i] = 0; + } + + const sr = new BigInt64Array(1) + sr[0] = BigInt(16000); + const srate = new ort.Tensor('int64', sr, [1]); + let speech_prob = undefined; + const vad_infer = async (feed_dict) => { + // feed inputs and run + try{ + const results = await session.run(feed_dict); + + // update states + h = results.hn.data + c = results.cn.data + speech_prob = results.output.data + } catch(e) { + console.log(e) + } + } + const socket = new WebSocket("ws://localhost:9090/"); socket.onopen = function(e) { socket.send("handshake"); @@ -51,7 +82,8 @@ async function startRecord(option) { socket.onmessage = async (event) => { // console.log(event.data); - await sendMessageToTab(option.currentTabId, { + res = await sendMessageToTab(option.currentTabId, { + type: "transcript", data: event.data, }); }; @@ -67,7 +99,22 @@ async function startRecord(option) { const inputData = event.inputBuffer.getChannelData(0); audioDataCache.push(inputData); - socket.send(inputData); + + // voice activity detection inference + const audioBuffer = new ort.Tensor('float32', inputData, [1, inputData.length]); + const hh = new ort.Tensor('float32', h, [2, 1, 64]); + const hc = new ort.Tensor('float32', c, [2, 1, 64]); + const feeds = { input: audioBuffer, sr: srate, h: hh, c: hc}; + + // feed inputs and run + if (doVad) { + vad_infer(feeds) + if (speech_prob > 0.4) { + socket.send(inputData); + } + else + console.log("no speech found: " + speech_prob) + } }; // Prevent page mute diff --git a/Audio-Transcription/ort-wasm-simd-threaded.wasm b/Audio-Transcription/ort-wasm-simd-threaded.wasm new file mode 100644 index 0000000..ce834a2 Binary files /dev/null and b/Audio-Transcription/ort-wasm-simd-threaded.wasm differ diff --git a/Audio-Transcription/ort.min.js b/Audio-Transcription/ort.min.js new file mode 100644 index 0000000..73c0c78 --- /dev/null +++ b/Audio-Transcription/ort.min.js @@ -0,0 +1,6 @@ +/*! +* ONNX Runtime Web v1.15.0 +* Copyright (c) Microsoft Corporation. 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}\n `;return new o.GlslLibRoutine(a)}getOutputPackedNDCoords(e,t){const n=[t[0],t[1]],r=Math.ceil(e[e.length-1]/2),i=r*Math.ceil(e[e.length-2]/2);let a=i,s="",u="b, r, c";for(let t=2;t=0;--t)i[t]=i[t+1]*e[t+1];const a=["r","c","d"],s=i.map(((e,t)=>`int ${a[t]} = index / ${e}; ${t===i.length-1?`int ${a[t+1]} = index - ${a[t]} * ${e}`:`index -= ${a[t]} * ${e}`};`)).join("");return n=`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(TexCoords.xy *\n vec2(${t[0]}, ${t[1]}));\n int index = resTexRC.y * ${t[0]} + resTexRC.x;\n ${s}\n return ivec3(r, c, d);\n }\n `,new o.GlslLibRoutine(n)}getOutputUnpacked4DCoords(e,t){let n="";const r=e.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=e[r-1];for(let t=r-3;t>=0;--t)i[t]=i[t+1]*e[t+1];const a=["r","c","d","d2"],s=i.map(((e,t)=>`int ${a[t]} = index / ${e}; ${t===i.length-1?`int ${a[t+1]} = index - ${a[t]} * ${e}`:`index -= ${a[t]} * ${e}`};`)).join("");return n=`\n ivec4 getOutputCoords() {\n ivec2 resTexRC = ivec2(TexCoords.xy *\n vec2(${t[0]}, ${t[1]}));\n int index = resTexRC.y * ${t[0]} + resTexRC.x;\n ${s}\n return ivec4(r, c, d, d2);\n }\n `,new o.GlslLibRoutine(n)}getOutputUnpacked5DCoords(e,t){let n="";const r=e.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=e[r-1];for(let t=r-3;t>=0;--t)i[t]=i[t+1]*e[t+1];const a=["r","c","d","d2","d3"],s=i.map(((e,t)=>`int ${a[t]} = index / ${e}; ${t===i.length-1?`int ${a[t+1]} = index - ${a[t]} * ${e}`:`index -= ${a[t]} * ${e}`};`)).join("");return n=`\n ivec5 getOutputCoords() {\n ivec2 resTexRC = ivec2(TexCoords.xy *\n vec2(${t[0]}, ${t[1]}));\n int index = resTexRC.y * ${t[0]} + resTexRC.x;\n ${s}\n return ivec5(r, c, d, d2, d3);\n }\n `,new o.GlslLibRoutine(n)}getOutputUnpacked6DCoords(e,t){let n="";const r=e.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=e[r-1];for(let t=r-3;t>=0;--t)i[t]=i[t+1]*e[t+1];const a=["r","c","d","d2","d3","d4"],s=i.map(((e,t)=>`int ${a[t]} = index / ${e}; ${t===i.length-1?`int ${a[t+1]} = index - ${a[t]} * ${e}`:`index -= ${a[t]} * ${e}`};`)).join("");return n=`\n ivec6 getOutputCoords() {\n ivec2 resTexRC = ivec2(TexCoords.xy *\n vec2(${t[0]}, ${t[1]}));\n int index = resTexRC.y * ${t[0]} + resTexRC.x;\n ${s}\n return ivec6(r, c, d, d2, d3, d4);\n }\n `,new o.GlslLibRoutine(n)}getCommonUtilFuncs(){const e={};let t="uvFromFlat";e[t]=new o.GlslLibRoutine("\n vec2 uvFromFlat(int texNumR, int texNumC, int index) {\n int texC = index / texNumR;\n int texR = index - texC * texNumR;\n // TODO: swap texR, texC order in following function so row is corresponding to u and column is corresponding to\n // v.\n return (vec2(texR, texC) + halfCR) / vec2(texNumR, texNumC);\n }\n "),t="packedUVfrom1D",e[t]=new o.GlslLibRoutine("\n vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n int texelIndex = index / 2;\n int texR = texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n }\n "),t="packedUVfrom2D",e[t]=new o.GlslLibRoutine("\n vec2 packedUVfrom2D(int texNumR, int texNumC, int texelsInLogicalRow, int row, int col) {\n int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n }\n "),t="packedUVfrom3D",e[t]=new o.GlslLibRoutine("\n vec2 packedUVfrom3D(int texNumR, int texNumC,\n int texelsInBatch, int texelsInLogicalRow, int b,\n int row, int col) {\n int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n }\n "),t="sampleTexture";const n=(0,i.getGlsl)(this.context.glContext.version);return e[t]=new o.GlslLibRoutine(`\n float sampleTexture(sampler2D textureSampler, vec2 uv) {\n return ${n.texture2D}(textureSampler, uv).r;\n }`),e}getInputsSamplingSnippets(){const e={},t=this.context.outputTextureLayout;return this.context.programInfo.inputNames.forEach(((n,r)=>{const o=this.context.inputTextureLayouts[r],i=(0,s.generateShaderFuncNameFromInputSamplerName)(n);o.isPacked?e[i]=this.getPackedSamplerFromInput(i,n,o):e[i]=this.getUnpackedSamplerFromInput(i,n,o);const a=(0,s.generateShaderFuncNameFromInputSamplerNameAtOutCoords)(n);o.unpackedShape.length<=t.unpackedShape.length&&(o.isPacked?e[a]=this.getPackedSamplerAtOutputCoords(a,o,t,n):e[a]=this.getUnpackedSamplerAtOutputCoords(a,o,t,n))})),e}getPackedSamplerAtOutputCoords(e,t,n,i){const a=t.unpackedShape,u=n.unpackedShape,l=i,c=(0,s.generateShaderFuncNameFromInputSamplerName)(l),p=a.length,d=u.length,f=r.BroadcastUtil.getBroadcastDims(a,u),h=(0,s.getCoordsDataType)(d),g=d-p;let m;const b=(0,s.getGlChannels)();m=0===p?"":d<2&&f.length>=1?"coords = 0;":f.map((e=>`coords.${b[e+g]} = 0;`)).join("\n");let y="";y=d<2&&p>0?"coords":a.map(((e,t)=>`coords.${b[t+g]}`)).join(", ");let w="return outputValue;";const _=1===r.ShapeUtil.size(a),v=1===r.ShapeUtil.size(u);if(1!==p||_||v){if(_&&!v)w=1===d?"\n return vec4(outputValue.x, outputValue.x, 0., 0.);\n ":"\n return vec4(outputValue.x);\n ";else if(f.length){const e=p-2,t=p-1;f.indexOf(e)>-1&&f.indexOf(t)>-1?w="return vec4(outputValue.x);":f.indexOf(e)>-1?w="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":f.indexOf(t)>-1&&(w="return vec4(outputValue.xx, outputValue.zz);")}}else w="\n return vec4(outputValue.xy, outputValue.xy);\n ";const x=`\n vec4 ${e}() {\n ${h} coords = getOutputCoords();\n \n int lastDim = coords.${b[d-1]};\n coords.${b[d-1]} = coords.${b[d-2]};\n coords.${b[d-2]} = lastDim;\n \n ${m}\n vec4 outputValue = ${c}(${y});\n ${w}\n }\n `;return new o.GlslLibRoutine(x,["coordinates.getOutputCoords"])}getUnpackedSamplerAtOutputCoords(e,t,n,i){const a=[n.width,n.height],u=[t.width,t.height],l=t.unpackedShape.length,c=n.unpackedShape.length,p=t.unpackedShape,d=n.unpackedShape,f=(0,s.generateShaderFuncNameFromInputSamplerName)(i);if(l===c&&r.ArrayUtil.arraysEqual(u,a)){const t=`\n float ${e}() {\n return sampleTexture(${i}, TexCoords);\n }\n `;return new o.GlslLibRoutine(t,["coordinates.sampleTexture"])}const h=(0,s.getCoordsDataType)(c),g=r.BroadcastUtil.getBroadcastDims(p,d),m=c-l;let b;const y=(0,s.getGlChannels)();b=0===l?"":c<2&&g.length>=1?"coords = 0;":g.map((e=>`coords.${y[e+m]} = 0;`)).join("\n");let w="";w=c<2&&l>0?"coords":t.unpackedShape.map(((e,t)=>`coords.${y[t+m]}`)).join(", ");const _=`\n float ${e}() {\n ${h} coords = getOutputCoords();\n ${b}\n return ${f}(${w});\n }\n `;return new o.GlslLibRoutine(_,["coordinates.getOutputCoords"])}getPackedSamplerFromInput(e,t,n){switch(n.unpackedShape.length){case 0:return this.getPackedSamplerScalar(e,t);case 1:return this.getPackedSampler1D(e,t,n);case 2:return this.getPackedSampler2D(e,t,n);case 3:return this.getPackedSampler3D(e,t,n);default:return this.getPackedSamplerND(e,t,n)}}getUnpackedSamplerFromInput(e,t,n){const r=n.unpackedShape;switch(r.length){case 0:return this.getUnpackedSamplerScalar(e,t,n);case 1:return this.getUnpackedSampler1D(e,t,n);case 2:return this.getUnpackedSampler2D(e,t,n);case 3:return this.getUnpackedSampler3D(e,t,n);case 4:return this.getUnpackedSampler4D(e,t,n);case 5:return this.getUnpackedSampler5D(e,t,n);case 6:return this.getUnpackedSampler6D(e,t,n);default:throw new Error(`Unsupported dimension ${r.length}-D`)}}getPackedSamplerScalar(e,t){const n=`\n vec4 ${e}() {\n return ${(0,i.getGlsl)(this.context.glContext.version).texture2D}(${t}, halfCR);\n }\n `;return new o.GlslLibRoutine(n)}getPackedSampler1D(e,t,n){const r=[n.width,n.height],a=[r[1],r[0]],s=(0,i.getGlsl)(this.context.glContext.version),u=`vec4 ${e}(int index) {\n vec2 uv = packedUVfrom1D(\n ${a[0]}, ${a[1]}, index);\n return ${s.texture2D}(${t}, uv);\n }`;return new o.GlslLibRoutine(u,["coordinates.packedUVfrom1D"])}getPackedSampler2D(e,t,n){const a=n.unpackedShape,s=[n.width,n.height],u=(0,i.getGlsl)(this.context.glContext.version),l=s[0],c=s[1];if(null!=s&&r.ArrayUtil.arraysEqual(a,s)){const n=`vec4 ${e}(int row, int col) {\n vec2 uv = (vec2(col, row) + halfCR) / vec2(${c}.0, ${l}.0);\n return ${u.texture2D}(${t}, uv);\n }`;return new o.GlslLibRoutine(n)}const p=s,d=Math.ceil(a[1]/2),f=`vec4 ${e}(int row, int col) {\n vec2 uv = packedUVfrom2D(${p[1]}, ${p[0]}, ${d}, row, col);\n return ${u.texture2D}(${t}, uv);\n }`;return new o.GlslLibRoutine(f,["coordinates.packedUVfrom2D"])}getPackedSampler3D(e,t,n){const r=n.unpackedShape,a=[n.width,n.height],u=[a[0],a[1]],l=(0,i.getGlsl)(this.context.glContext.version);if(1===r[0]){const i=r.slice(1),a=[1,2],u=(0,s.squeezeInputShape)(r,i),l=["b","row","col"],c=JSON.parse(JSON.stringify(n));c.unpackedShape=u;const p=this.getPackedSamplerFromInput(e,t,c),d=`${p.routineBody}\n vec4 ${e}(int b, int row, int col) {\n return ${e}(${(0,s.getSqueezedParams)(l,a)});\n } `;return new o.GlslLibRoutine(d,p.dependencies)}const c=u[0],p=u[1],d=Math.ceil(r[2]/2),f=`vec4 ${e}(int b, int row, int col) {\n vec2 uv = packedUVfrom3D(\n ${p}, ${c}, ${d*Math.ceil(r[1]/2)}, ${d}, b, row, col);\n return ${l.texture2D}(${t}, uv);}`;return new o.GlslLibRoutine(f,["coordinates.packedUVfrom3D"])}getPackedSamplerND(e,t,n){const r=n.unpackedShape,a=r.length,s=[n.width,n.height],u=(0,i.getGlsl)(this.context.glContext.version),l=[s[0],s[1]],c=l[1],p=l[0],d=Math.ceil(r[a-1]/2);let f=d*Math.ceil(r[a-2]/2),h="int b, int row, int col",g=`b * ${f} + (row / 2) * ${d} + (col / 2)`;for(let e=2;e{const r=this.context.inputTextureLayouts[n],i=(r.unpackedShape.length>0?r.unpackedShape:r.shape).length;let a=`_${t}`;e[a]=new o.GlslLibRoutine(this.getValueFromSingle(t,i,r.width,r.height,!1),[`shapeUtils.indicesToOffset${a}`,"coordinates.offsetToCoords","fragcolor.getColorAsFloat"]),a+="_T",e[a]=new 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${2*n}D`);if(0!==t.kernelShape.length&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape");if("float32"!==e[0].type||"float32"!==e[1].type)throw new Error("Conv input(X,W) should be float tensor");if(3===e.length&&"float32"!==e[2].type)throw new Error("Conv input(bias) should be float tensor")}},6742:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.parseDepthToSpaceAttributes=t.depthToSpace=void 0;const r=n(5707);t.depthToSpace=(e,t,n)=>{o(t);const i=n.blocksize,a=i*i,s="DCR"===n.mode?[0,3,4,1,5,2]:[0,1,4,2,5,3],u="DCR"===n.mode?[t[0].dims[0],i,i,t[0].dims[1]/a,t[0].dims[2],t[0].dims[3]]:[t[0].dims[0],t[0].dims[1]/a,i,i,t[0].dims[2],t[0].dims[3]],l=e.reshapeUnpacked(t[0],u),c={perm:s,cacheKey:`${s}`},[p]=(0,r.transpose)(e,[l],c),d=[t[0].dims[0],t[0].dims[1]/a,t[0].dims[2]*i,t[0].dims[3]*i];return[e.reshapeUnpacked(p,d)]},t.parseDepthToSpaceAttributes=e=>{const t=e.attributes.getInt("blocksize");if(t<1)throw new Error(`blocksize must be >= 1, but got : ${t} for DepthToSpace`);const n=e.attributes.getString("mode","DCR");if("DCR"!==n&&"CRD"!==n)throw new Error(`unrecognized mode: ${n} for DepthToSpace`);return{mode:n,blocksize:t}};const o=e=>{if(1!==e.length)throw new Error(`DepthToSpace expect 1 inputs, but got ${e.length}`);if("string"===e[0].type||4!==e[0].dims.length)throw new TypeError("DepthToSpace input should be a 4-D numeric tensor")}},3281:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.createDotProductProgramInfoLoader=void 0;const r=n(7273),o=n(6757),i=n(5639),a=n(2150),s=n(1625);t.createDotProductProgramInfoLoader=(e,t,n,u)=>{const l=((e,t)=>({name:"ConvDotProduct",inputNames:e?["Im2Col","K","B"]:["Im2Col","K"],inputTypes:e?[i.TextureType.unpacked,i.TextureType.packedLastDimension,i.TextureType.unpacked]:[i.TextureType.unpacked,i.TextureType.packedLastDimension],cacheKey:t.activationCacheKey}))(t.length>2,u);return Object.assign(Object.assign({},l),{get:()=>((e,t,n,u,l)=>{const c=n[0].dims,p=n[1].dims,d=[p[0],Math.ceil(c[1]*p[2]*p[3]/4)],f=(0,s.calculateIm2ColDims)(c,p,u),[h,g]=e.calculateTextureWidthAndHeight(d,i.TextureType.packedLastDimension),m=r.ShapeUtil.computeStrides(f),[b,y]=e.calculateTextureWidthAndHeight(f,i.TextureType.packedLastDimension),w=u.length,_=n.length<3?"0.0":"_B(b)",v=Math.ceil(c[1]*p[2]*p[3]/4),{activationFunction:x,applyActivation:T}=(0,a.getActivationSnippet)(l),S=(0,o.getGlsl)(e.session.backend.glContext.version),O=`\n${x}\nfloat process(int indices[${w}]) {\n int b[1];\n b[0] = indices[1];\n int im2col[4];\n im2col[0] = indices[0];\n im2col[1] = indices[2];\n im2col[2] = indices[3];\n int im2colOffset = im2col[0] * ${m[0]} + im2col[1] * ${m[1]} + im2col[2] * ${m[2]};\n int kernelOffset = indices[1] * ${d[1]};\n float value = ${_};\n for (int i = 0; i < ${v}; ++i) {\n vec2 im2colCoords = offsetToCoords(im2colOffset, ${b}, ${y});\n vec2 kernelCoords = offsetToCoords(kernelOffset, ${h}, ${g});\n value += dot(${S.texture2D}(Im2Col, im2colCoords), ${S.texture2D}(K, kernelCoords));\n ++im2colOffset;\n ++kernelOffset;\n }\n ${T}\n return value;\n}`;return Object.assign(Object.assign({},t),{output:{dims:u,type:n[0].type,textureType:i.TextureType.unpacked},shaderSource:O})})(e,l,t,n,u)})}},4125:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.parseFlattenAttributes=t.flatten=void 0;const r=n(7273);t.flatten=(e,t,n)=>{o(t,n);const i=r.ShapeUtil.flattenShape(t[0].dims,n);return[e.reshapeUnpacked(t[0],i)]},t.parseFlattenAttributes=e=>e.attributes.getInt("axis",1);const o=(e,t)=>{if(!e||1!==e.length)throw new Error("Flatten requires 1 input.");const n=e[0].dims.length;if(0===n)throw new Error("scalar tensor is not supported.");if(t<-n||t>n)throw new Error("Invalid axis");if("string"===e[0].type)throw new Error("string tensor is not supported.")}},2150:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.parseInternalActivationAttributes=t.getActivationSnippet=void 0;const r=n(7273),o=n(9087);t.getActivationSnippet=function(e){let t;switch(e.activation){case"Relu":t=(0,o.glslRelu)();break;case"Sigmoid":t=(0,o.glslSigmoid)();break;case"Clip":t=(0,o.glslClip)(e.clipMin,e.clipMax);break;default:return{activationFunction:"",applyActivation:""}}const n=t.name;return{activationFunction:t.body,applyActivation:`value = ${n}_(value);`}},t.parseInternalActivationAttributes=e=>{const t=e.getString("activation","");if("Clip"===t){const[n,o]=e.getFloats("activation_params",[r.MIN_CLIP,r.MAX_CLIP]);return{activation:t,clipMax:o,clipMin:n,activationCacheKey:`${t}:${n},${o}`}}return{activation:t,activationCacheKey:t}}},6149:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.parseGatherAttributes=t.gather=void 0;const r=n(4910),o=n(6145),i=n(7273),a=n(5639);t.gather=(e,t,n)=>(l(t,n.axis),[e.run(u(e,t,n),t)]),t.parseGatherAttributes=e=>(0,r.createAttributeWithCacheKey)({axis:e.attributes.getInt("axis",0)});const s={name:"Gather",inputNames:["A","B"],inputTypes:[a.TextureType.unpacked,a.TextureType.unpacked]},u=(e,t,n)=>{const r=Object.assign(Object.assign({},s),{cacheHint:n.cacheKey});return Object.assign(Object.assign({},r),{get:()=>((e,t,n,r)=>{const o=n[0].dims.slice(),s=n[1].dims.slice(),u=new Array(o.length+s.length-1);r=i.ShapeUtil.normalizeAxis(r,o.length);const l=[];for(let e=0;e{if(!e||2!==e.length)throw new Error("Gather requires 2 inputs.");const n=e[0].dims.length;if(n<1)throw new Error("Invalid input shape.");if(t<-n||t>n-1)throw new Error("Invalid axis.");if(-1===o.NUMBER_TYPES.indexOf(e[0].type))throw new Error("Invaid input type.");if("int32"!==e[1].type&&"int16"!==e[1].type)throw new Error("Invaid input type.")}},5378:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.parseGemmAttributesV11=t.parseGemmAttributesV7=t.gemm=void 0;const r=n(4910),o=n(7273),i=n(5639);t.gemm=(e,t,n)=>(l(t,n),[e.run(s(t,n),t)]);const a=(e,t)=>{const n=0!==e.attributes.getInt("transA",0),o=0!==e.attributes.getInt("transB",0),i=e.attributes.getFloat("alpha",1),a=e.attributes.getFloat("beta",1);return(0,r.createAttributeWithCacheKey)({transA:n,transB:o,alpha:i,beta:a,isOptionalC:t})};t.parseGemmAttributesV7=e=>a(e,!1),t.parseGemmAttributesV11=e=>a(e,!0);const s=(e,t)=>{const n={name:"Gemm",inputNames:3===e.length?["A","B","C"]:["A","B"],inputTypes:3===e.length?[i.TextureType.unpacked,i.TextureType.unpacked,i.TextureType.unpacked]:[i.TextureType.unpacked,i.TextureType.unpacked],key:t.cacheKey};return Object.assign(Object.assign({},n),{get:()=>u(n,e,t)})},u=(e,t,n)=>{const r=t[0].dims.slice(),a=t[1].dims.slice(),[s,u]=o.GemmUtil.getShapeOfGemmResult(r,n.transA,a,n.transB,3===t.length?t[2].dims:void 0),l=[s,u];if(!l)throw new Error("Can't use gemm on the given tensors");let c=r[r.length-1],p="";n.transA&&(c=r[0]),n.transA&&n.transB?p="value += _A_T(a) * _B_T(b);":n.transA&&!n.transB?p="value += _A_T(a) * _B(b);":!n.transA&&n.transB?p="value += _A(a) * _B_T(b);":n.transA||n.transB||(p="value += _A(a) * _B(b);");const d=l.length,f=`\n float process(int indices[${d}]) {\n int a[${d}];\n int b[${d}];\n ${3===t.length?`int c[${t[2].dims.length}];`:""}\n\n copyVec(indices, a);\n copyVec(indices, b);\n ${3===t.length?"bcastIndices_C(indices, c);":""}\n\n float value = 0.0;\n for (int k=0; k<${c}; ++k) {\n a[${d-1}] = k;\n b[${d-2}] = k;\n ${p}\n }\n\n value = value * alpha;\n ${3===t.length?"value += beta * _C(c);":""}\n return value;\n }`;return Object.assign(Object.assign({},e),{output:{dims:l,type:t[0].type,textureType:i.TextureType.unpacked},variables:[{name:"alpha",type:"float",data:n.alpha},{name:"beta",type:"float",data:n.beta}],shaderSource:f})},l=(e,t)=>{if(!e)throw new Error("Input is missing");if(t.isOptionalC&&(e.length<2||e.length>3))throw new Error("Invaid input shape.");if(!t.isOptionalC&&3!==e.length)throw new Error("Gemm requires 3 inputs");if(3===e.length&&1!==e[2].dims.length&&2!==e[2].dims.length)throw new Error("Invalid input shape of C");if("float32"!==e[0].type&&"float64"!==e[0].type||"float32"!==e[1].type&&"float64"!==e[1].type||3===e.length&&"float32"!==e[2].type&&"float64"!==e[2].type)throw new Error("Invalid input type.");if(e[0].type!==e[1].type||3===e.length&&e[0].type!==e[2].type)throw new Error("Input types are mismatched")}},5950:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.createPackedIm2ColProgramInfoLoader=void 0;const r=n(6757),o=n(5639),i=n(5614);t.createPackedIm2ColProgramInfoLoader=(e,t,n,a,s)=>{const u=(l=s.cacheKey,{name:"Im2Col (packed)",inputNames:["A"],inputTypes:[o.TextureType.packed],cacheHint:l});var l;return Object.assign(Object.assign({},u),{get:()=>((e,t,n,a,s,u)=>{const l=n.dims,c=a.dims,p=s.length,d=[c[1]*c[2]*c[3],s[2]*s[3]],f=c[2]*c[3],h=(0,i.unpackFromChannel)(),g=(0,r.getGlsl)(e.session.backend.glContext.version);let m="";for(let e=0;e<=1;e++)for(let t=0;t<=1;t++)m+=`\n blockIndex = rc.x + ${t};\n pos = rc.y + ${e};\n\n if(blockIndex < ${d[1]} && pos < ${d[0]}) {\n offsetY = int(blockIndex / (${s[p-1]})) * ${u.strides[0]} -\n ${u.pads[0]};\n d0 = offsetY + ${u.dilations[0]} * (imod(pos, ${f}) / ${c[2]});\n\n if(d0 < ${l[2]} && d0 >= 0) {\n offsetX = imod(blockIndex, ${s[p-1]}) * ${u.strides[1]} -\n ${u.pads[1]};\n d1 = offsetX + ${u.dilations[1]} * imod(imod(pos, ${f}), ${c[2]});\n\n if(d1 < ${l[3]} && d1 >= 0) {\n\n ch = int(float(pos)/ ${f}.);\n innerDims = vec2(d0, d1);\n result[${2*e+t}] = getChannel(\n getA(0, ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n\n `;const b=`\n ${h}\n\n void main() {\n ivec2 rc = getOutputCoords();\n vec4 result = vec4(0.0);\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n ${m}\n ${g.output} = result;\n }\n `;return Object.assign(Object.assign({},t),{output:{dims:d,type:n.type,textureType:o.TextureType.packed},shaderSource:b,hasMain:!0})})(e,u,t,n,a,s)})}},1625:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.calculateIm2ColDims=t.createIm2ColProgramInfoLoader=void 0;const r=n(5639);t.createIm2ColProgramInfoLoader=(e,n,o,i,a)=>{const s=(u=a.cacheKey,{name:"Im2Col",inputNames:["X"],inputTypes:[r.TextureType.unpacked],cacheHint:u});var u;return Object.assign(Object.assign({},s),{get:()=>((e,n,o,i,a,s)=>{const u=o.dims,l=i.dims,c=a.length,p=(0,t.calculateIm2ColDims)(u,l,a,4),d=`\n const int XC = ${u[1]};\n const int XH = ${u[2]};\n const int XW = ${u[3]};\n const int KH = ${s.kernelShape[0]};\n const int KW = ${s.kernelShape[1]};\n const int dilationH = ${s.dilations[0]};\n const int dilationW = ${s.dilations[1]};\n const int strideH = ${s.strides[0]};\n const int strideW = ${s.strides[1]};\n const int padH = ${s.pads[0]};\n const int padW = ${s.pads[1]};\n const int KHKW = KH*KW;\n const int XCKHKW = XC * KHKW;\n const int outputChannels = 4;\n vec4 process(int indices[${c}]) {\n int b = indices[0]; // batch size\n int oh = indices[1] * strideH - padH; //output height\n int ow = indices[2] * strideW - padW; //output width\n int p = indices[3] * outputChannels; //patch\n vec4 value = vec4(0.0);\n for(int i=0; i < outputChannels; ++i) {\n if(p < XCKHKW) {\n int patchC = p / KHKW;\n int patchH = (p - patchC*KHKW) / KW;\n int patchW = (p - patchC*KHKW) - patchH * KW;\n int xh2 = oh + patchH * dilationH;\n int xw2 = ow + patchW * dilationW;\n int x[${u.length}];\n x[0] = b;\n x[1] = patchC;\n x[2] = xh2;\n x[3] = xw2;\n if(xh2 >= 0 &&\n xh2 < XH &&\n xw2 >= 0 &&\n xw2 < XW) {\n value[i] = _X(x);\n }\n }\n ++p;\n }\n return value;\n }\n `;return Object.assign(Object.assign({},n),{output:{dims:p,type:o.type,textureType:r.TextureType.packedLastDimension},shaderSource:d})})(0,s,n,o,i,a)})},t.calculateIm2ColDims=(e,t,n,r=4)=>[n[0],n[2],n[3],Math.ceil(e[1]*t[2]*t[3]/r)]},6981:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.parseImageScalerAttributes=t.imageScaler=void 0;const r=n(4910),o=n(5639);t.imageScaler=(e,t,n)=>(u(t),[e.run(a(e,t,n),t)]),t.parseImageScalerAttributes=e=>{const t=e.attributes.getFloat("scale"),n=e.attributes.getFloats("bias");return(0,r.createAttributeWithCacheKey)({scale:t,bias:n})};const i={name:"ImageScaler",inputNames:["X"],inputTypes:[o.TextureType.unpacked]},a=(e,t,n)=>{const r=Object.assign(Object.assign({},i),{cacheHint:n.cacheKey});return Object.assign(Object.assign({},r),{get:()=>((e,t,n,r)=>{const i=n[0].dims.slice(),a=i.length,u=`\n ${s(r.bias.length)}\n float process(int indices[${a}]) {\n return _X(indices) * scale + getBias(bias, indices[1]);\n }`;return Object.assign(Object.assign({},t),{output:{dims:i,type:n[0].type,textureType:o.TextureType.unpacked},variables:[{name:"bias",type:"float",arrayLength:r.bias.length,data:r.bias},{name:"scale",type:"float",data:r.scale}],shaderSource:u})})(0,r,t,n)})},s=e=>{const t=[`float getBias(float bias[${e}], int channel) {`];for(let n=0;n{if(!e||1!==e.length)throw new Error("ImageScaler requires 1 input.");if(4!==e[0].dims.length)throw new Error("Invalid input shape.");if("float32"!==e[0].type&&"float64"!==e[0].type)throw new Error("Invalid input type.")}},7413:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.parseInstanceNormalizationAttributes=t.instanceNormalization=void 0;const r=n(6757),o=n(5639);t.instanceNormalization=(e,t,n)=>{l(t);const r=e.run(a(t[0]),t);return[e.run(u(e,t[0],n,r.dims),[t[0],r,t[1],t[2]])]},t.parseInstanceNormalizationAttributes=e=>e.attributes.getFloat("epsilon",1e-5);const i={name:"InstanceNormalization_MeanAndVariance",inputNames:["X"],inputTypes:[o.TextureType.unpacked]},a=e=>Object.assign(Object.assign({},i),{get:()=>((e,t)=>{const n=t.dims.slice(),r=n[1],i=n[2]*n[3],a=[n[0],r],s=`\n vec4 process(int[2] indices) {\n vec4 v = vec4(0.0);\n int a[4];\n a[0] = indices[0];\n a[1] = indices[1];\n float temp = 0.0;\n for(int a2=0; a2<${n[2]}; a2++) {\n a[2] = a2;\n for(int a3=0; a3<${n[3]}; a3++) {\n a[3] = a3;\n float x = _X(a);\n temp += x;\n }\n }\n float mean = temp / float(${i});\n temp = 0.0;\n for(int a2=0; a2<${n[2]}; a2++) {\n a[2] = a2;\n for(int a3=0; a3<${n[3]}; a3++) {\n a[3] = a3;\n float x = _X(a);\n temp += (x - mean) * (x - mean);\n }\n }\n v.r = mean;\n v.g = temp / float(${i});\n\n return v;\n }`;return Object.assign(Object.assign({},e),{output:{dims:a,type:t.type,textureType:o.TextureType.packedLastDimension},shaderSource:s})})(i,e)}),s={name:"InstanceNormalization_ComputeOutput",inputNames:["X","MeanAndVariance","Scale","B"],inputTypes:[o.TextureType.unpacked,o.TextureType.packedLastDimension,o.TextureType.unpacked,o.TextureType.unpacked]},u=(e,t,n,i)=>{const a=Object.assign(Object.assign({},s),{cacheHint:`${n}`});return Object.assign(Object.assign({},a),{get:()=>((e,t,n,i,a)=>{const s=(0,r.getGlsl)(e.session.backend.glContext.version),[u,l]=e.calculateTextureWidthAndHeight(a,o.TextureType.packedLastDimension),[c,p]=[u/4,l],d=`\n vec4 get_MeanAndVariance(int[2] mv) {\n int offset = indicesToOffset_MeanAndVariance(mv);\n vec2 coords = offsetToCoords(offset, ${c}, ${p});\n return ${s.texture2D}(MeanAndVariance, coords);\n }\n\n float process(int[4] indices) {\n int mv[2];\n mv[0] = indices[0];\n mv[1] = indices[1];\n vec4 mean_and_variance = get_MeanAndVariance(mv);\n float mean = mean_and_variance.r;\n float variance = mean_and_variance.g;\n\n int sb[1];\n sb[0] = indices[1];\n float scale = _Scale(sb);\n float b = _B(sb);\n\n return scale * (_X(indices) - mean) / sqrt(variance + epsilon) + b;\n }`;return Object.assign(Object.assign({},t),{output:{dims:n.dims,type:n.type,textureType:o.TextureType.unpacked},variables:[{name:"epsilon",type:"float",data:i}],shaderSource:d})})(e,a,t,n,i)})},l=e=>{if(!e||3!==e.length)throw new Error("InstanceNormalization requires 3 inputs.");const t=e[0],n=e[1],r=e[2];if(t.dims.length<3||1!==n.dims.length||1!==r.dims.length)throw new Error("Invalid input shape.");if(n.dims[0]!==t.dims[1]||r.dims[0]!==t.dims[1])throw new Error("Input shapes are mismatched.");if("float32"!==t.type&&"float64"!==t.type||"float32"!==n.type&&"float64"!==n.type||"float32"!==r.type&&"float64"!==r.type)throw new Error("Invalid input type.");if(4!==e[0].dims.length)throw new Error("Only support 4-D input shape.")}},7006:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.createLrnProgramInfoLoader=t.parseLrnAttributes=t.lrn=void 0;const r=n(4910),o=n(5639);t.lrn=(e,t,n)=>(s(t),[e.run(a(t,n),t)]),t.parseLrnAttributes=e=>{const t=e.attributes.getFloat("alpha",1e-4),n=e.attributes.getFloat("beta",.75),o=e.attributes.getFloat("bias",1),i=e.attributes.getInt("size");return(0,r.createAttributeWithCacheKey)({alpha:t,beta:n,bias:o,size:i})};const i={name:"LRN",inputNames:["X"],inputTypes:[o.TextureType.unpacked]};function a(e,t){return Object.assign(Object.assign({},i),{cacheHint:t.cacheKey,get:()=>function(e,t){const n=e[0].dims[1],r=e[0].dims.length,a=-Math.floor((t.size-1)/2),s=Math.ceil((t.size-1)/2),u=`float(${t.alpha}) / float(${t.size})`,l=`\n float process(int indices[${r}]) {\n int c = indices[1];\n float x = _X(indices);\n float square_sum = 0.0;\n\n for (int i = ${a}; i <= ${s}; i++) {\n int idx = c + i;\n if (c >= 0 && c < ${n}) {\n indices[1] = idx;\n float j = _X(indices);\n square_sum += j * j;\n }\n }\n return x / pow(float(${t.bias}) + ${u} * square_sum, float(${t.beta}));\n }`;return Object.assign(Object.assign({},i),{cacheHint:t.cacheKey,output:{dims:e[0].dims,type:e[0].type,textureType:o.TextureType.unpacked},shaderSource:l})}(e,t)})}t.createLrnProgramInfoLoader=a;const s=e=>{if(!e||1!==e.length)throw new Error("LRN requires 1 input.");if(4!==e[0].dims.length)throw new Error('currently only support LRN for input with "NCHW" format');if("float32"!==e[0].type)throw new Error("input should be float type")}},5632:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.createPackedMatmulProgramInfoLoader=void 0;const r=n(7273),o=n(6757),i=n(5639),a=n(432),s=n(2150),u=n(8276);t.createPackedMatmulProgramInfoLoader=(e,t,n)=>{const l=(c=t.length>2,p=n.activationCacheKey,{name:"MatMul 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r=n(7273),o=n(5639),i=n(432),a=n(2150),s=n(5632);t.matMul=(e,t,n)=>(c(t),e.session.pack?[e.run((0,s.createPackedMatmulProgramInfoLoader)(e,t,n),t)]:[e.run(l(t,n),t)]),t.parseMatMulAttributes=e=>(0,a.parseInternalActivationAttributes)(e.attributes);const u=(e,t)=>({name:"MatMul",inputNames:e?["A","B","Bias"]:["A","B"],inputTypes:e?[o.TextureType.unpacked,o.TextureType.unpacked,o.TextureType.unpacked]:[o.TextureType.unpacked,o.TextureType.unpacked],cacheHint:t});function l(e,t){const n=u(e.length>2,t.activationCacheKey);return Object.assign(Object.assign({},n),{get:()=>function(e,t,n){const s=t[0].dims,u=t[1].dims,l=r.BroadcastUtil.calcShape(s,u,!0);if(!l)throw new Error("Can't use matmul on the given tensors");const c=(0,i.getCoordsDataType)(l.length),d=(0,i.getGlChannels)(),{activationFunction:f,applyActivation:h}=(0,a.getActivationSnippet)(n),g=t.length>2,m=g?"value += getBiasForMatmul();":"",b=g?`${p(c,d,t[2].dims,l,!1)}`:"",y=l.length,w=s.length,_=u.length,v=`\n ${f}\n ${b}\n float process(int indices[${y}]) {\n int a[${w}];\n int b[${_}];\n bcastMatmulIndices_A(indices, a);\n bcastMatmulIndices_B(indices, b);\n\n float value;\n for (int k=0; k<${s[s.length-1]}; ++k) {\n a[${w-1}] = k;\n b[${_-2}] = k;\n value += _A(a) * _B(b);\n }\n ${m}\n ${h}\n return value;\n }`;return Object.assign(Object.assign({},e),{output:{dims:l,type:t[0].type,textureType:o.TextureType.unpacked},shaderSource:v})}(n,e,t)})}t.createMatmulProgramInfoLoader=l;const c=e=>{if(!e||2!==e.length)throw new Error("MatMul requires 2 inputs.");if(e[0].dims[e[0].dims.length-1]!==e[1].dims[e[1].dims.length-2])throw new Error("shared dimension does not match.");if("float32"!==e[0].type&&"float64"!==e[0].type||"float32"!==e[1].type&&"float64"!==e[1].type)throw new Error("inputs should be float type");if(e[0].type!==e[1].type)throw new Error("inputs types should match")};function p(e,t,n,o,i){let a="";const s=n.length,u=o.length,l=u-s;a=u<2&&s>0?"coords":n.map(((e,n)=>`coords.${t[n+l]}`)).join(", ");const c=r.BroadcastUtil.getBroadcastDims(n,o).map((e=>`coords.${t[e+l]} = 0;`)).join("\n");let p="vec4(outputValue.xx, outputValue.yy)";return 1===r.ShapeUtil.size(n)&&(p="vec4(outputValue.x)"),i?`\nvec4 getBiasForMatmul() {\n ${e} coords = getOutputCoords();\n ${c}\n vec4 outputValue = getBias(${a});\n return ${p};\n}`:`\nfloat getBiasForMatmul() {\n ${e} coords = getOutputCoords();\n ${c}\n return getBias(coords.x);\n}`}t.getBiasForMatmul=p},9:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.createPackProgramInfoLoader=void 0;const r=n(6757),o=n(5639),i=n(432),a=n(5614),s={name:"pack",inputNames:["A"],inputTypes:[o.TextureType.unpackedReversed]};t.createPackProgramInfoLoader=(e,t)=>Object.assign(Object.assign({},s),{get:()=>((e,t)=>{const n=(0,r.getGlsl)(e.session.backend.glContext.version),u=t.dims,l=u.length,c=t.dims.length,p=(0,i.getCoordsDataType)(c),d=(0,a.getChannels)("rc",c),f=(h=c,g=d,m=u[u.length-2],b=u[u.length-1],0===h||1===h?"":`\n int r = ${g[h-2]};\n int c = ${g[h-1]};\n int rp1 = ${g[h-2]} + 1;\n int cp1 = ${g[h-1]} + 1;\n bool rEdge = rp1 >= ${b};\n bool cEdge = cp1 >= ${m};\n `);var h,g,m,b;let y;y=0===l?[1,1]:1===l?[u[0],1]:[u[c-1],u[c-2]];const w=function(e,t,n){if(0===e)return"false";if(1===e)return`rc > ${t[0]}`;let r="";for(let o=e-2;o= ${t[o-e+2]}`,o= ${e[0]} ? 0. : getA(rc + 1),\n 0, 0`;let r="";if(n>2)for(let e=0;e{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.unpackFromChannel=t.getChannels=t.getVecChannels=void 0;const r=n(432);function o(e,t){return(0,r.getGlChannels)(t).map((t=>`${e}.${t}`))}t.getVecChannels=o,t.getChannels=function(e,t){return 1===t?[e]:o(e,t)},t.unpackFromChannel=function(){return"\n float getChannel(vec4 frag, int dim) {\n int modCoord = imod(dim, 2);\n return modCoord == 0 ? frag.r : frag.g;\n }\n\n float getChannel(vec4 frag, vec2 innerDims) {\n vec2 modCoord = mod(innerDims, 2.);\n return modCoord.x == 0. ?\n (modCoord.y == 0. ? frag.r : frag.g) :\n (modCoord.y == 0. ? frag.b : frag.a);\n }\n "}},5565:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.parsePadAttributesV11=t.padV11=t.parsePadAttributesV2=t.padV2=void 0;const r=n(4910),o=n(7273),i=n(6757),a=n(5639),s={name:"Pad",inputNames:["A"],inputTypes:[a.TextureType.unpacked]};t.padV2=(e,t,n)=>(c(t),[e.run(Object.assign(Object.assign({},s),{cacheHint:n.cacheKey,get:()=>l(e,t[0],n)}),t)]),t.parsePadAttributesV2=e=>{const t=e.attributes.getString("mode","constant"),n=e.attributes.getFloat("value",0),o=e.attributes.getInts("pads");return(0,r.createAttributeWithCacheKey)({mode:t,value:n,pads:o})},t.padV11=(e,n,r)=>{p(n);const o=u(e,n,r);return(0,t.padV2)(e,[n[0]],o)},t.parsePadAttributesV11=e=>e.attributes.getString("mode","constant");const u=(e,t,n)=>{if(!e.session.isInitializer(t[1].dataId)||t.length>=3&&!e.session.isInitializer(t[2].dataId))throw new Error("dynamic pad attributes are not allowed");const o=Array.from(t[1].integerData),i=t.length>=3?t[2].floatData[0]:0;return(0,r.createAttributeWithCacheKey)({mode:n,pads:o,value:i})},l=(e,t,n)=>{const r=o.ShapeUtil.padShape(t.dims.slice(),n.pads),i=r.length,s=`\n ${d(e,t,n)}\n float process(int[${i}] indices) {\n return padA(indices);\n }`;return{name:"Pad",inputNames:["A"],inputTypes:[a.TextureType.unpacked],output:{dims:r,type:t.type,textureType:a.TextureType.unpacked},shaderSource:s}},c=e=>{if(!e||1!==e.length)throw new Error("Pad requires 1 input");if("float32"!==e[0].type&&"float64"!==e[0].type)throw new Error("Invalid input type.")},p=e=>{if(!e||2!==e.length&&3!==e.length)throw new Error("Pad requires 2 or 3 inputs");if("int32"!==e[1].type)throw new Error("Invalid input type.");if(e.length>=3&&"string"===e[2].type)throw new Error("Invalid input type.")},d=(e,t,n)=>{const r=(0,i.getGlsl)(e.session.backend.glContext.version),[s,u]=e.calculateTextureWidthAndHeight(t.dims,a.TextureType.unpacked),l=o.ShapeUtil.computeStrides(t.dims);switch(n.mode){case"constant":return f(r,t.dims,l,s,u,n.pads,n.value);case"reflect":return h(r,t.dims,l,s,u,n.pads);case"edge":return g(r,t.dims,l,s,u,n.pads);default:throw new Error("Invalid mode")}},f=(e,t,n,r,o,i,a)=>{const s=t.length;let u="";for(let e=s-1;e>=0;--e)u+=`\n k = m[${e}] - ${i[e]};\n if (k < 0) return constant;\n if (k >= ${t[e]}) return constant;\n offset += k * ${n[e]};\n `;return`\n float padA(int m[${s}]) {\n const float constant = float(${a});\n int offset = 0;\n int k = 0;\n ${u}\n vec2 coords = offsetToCoords(offset, ${r}, ${o});\n float value = getColorAsFloat(${e.texture2D}(A, coords));\n return value;\n }\n `},h=(e,t,n,r,o,i)=>{const a=t.length;let s="";for(let e=a-1;e>=0;--e)s+=`\n k = m[${e}] - ${i[e]};\n if (k < 0) { k = -k; }\n {\n const int _2n_1 = ${2*(t[e]-1)};\n k = int( mod( float(k), float(_2n_1) ) ) ;\n if(k >= ${t[e]}) { k = _2n_1 - k; }\n }\n offset += k * ${n[e]};\n `;return`\n float padA(int m[${a}]) {\n int offset = 0;\n int k = 0;\n ${s}\n vec2 coords = offsetToCoords(offset, ${r}, ${o});\n float value = getColorAsFloat(${e.texture2D}(A, coords));\n return value;\n }\n `},g=(e,t,n,r,o,i)=>{const a=t.length;let s="";for(let e=a-1;e>=0;--e)s+=`\n k = m[${e}] - ${i[e]};\n if (k < 0) k = 0;\n if (k >= ${t[e]}) k = ${t[e]-1};\n offset += k * ${n[e]};\n `;return`\n float padA(int m[${a}]) {\n int offset = 0;\n int k = 0;\n ${s}\n vec2 coords = offsetToCoords(offset, ${r}, ${o});\n float value = getColorAsFloat(${e.texture2D}(A, coords));\n return value;\n }\n `}},2834:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.globalMaxPool=t.parseMaxPoolAttributes=t.maxPool=t.parseGlobalAveragePoolAttributes=t.globalAveragePool=t.parseAveragePoolAttributes=t.averagePool=void 0;const r=n(4910),o=n(7273),i=n(5639);t.averagePool=(e,t,n)=>{p(t);const r={name:"AveragePool",inputNames:["X"],inputTypes:[i.TextureType.unpacked],cacheHint:n.cacheKey};return[e.run(Object.assign(Object.assign({},r),{get:()=>a(t,r,!1,n)}),t)]},t.parseAveragePoolAttributes=e=>{const t=e.attributes.getString("auto_pad","NOTSET"),n=e.attributes.getInt("ceil_mode",0),o=0!==e.attributes.getInt("count_include_pad",0),i=e.attributes.getInts("kernel_shape"),a=e.attributes.getInts("strides",[]),s=e.attributes.getInts("pads",[]);if(0!==n)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");return(0,r.createAttributeWithCacheKey)({autoPad:t,ceilMode:n,countIncludePad:o,kernelShape:i,strides:a,pads:s})};const a=(e,t,n,r)=>{const[a,s]=u(e,r,n),l=o.ShapeUtil.size(a.kernelShape);let c="";a.countIncludePad?c+=`value /= float(${l});`:c+=`value /= float(${l} - pad);`;const p=`\n ${d(e[0].dims,a,"value += _X(x);",c,"0.0")}\n `;return Object.assign(Object.assign({},t),{output:{dims:s,type:e[0].type,textureType:i.TextureType.unpacked},shaderSource:p})};t.globalAveragePool=(e,t,n)=>{p(t);const r={name:"GlobalAveragePool",inputNames:["X"],inputTypes:[i.TextureType.unpacked],cacheHint:`${n.countIncludePad}`};return[e.run(Object.assign(Object.assign({},r),{get:()=>a(t,r,!0,n)}),t)]},t.parseGlobalAveragePoolAttributes=e=>{const t=0!==e.attributes.getInt("count_include_pad",0);return(0,r.createAttributeWithCacheKey)({autoPad:"",ceilMode:0,countIncludePad:t,kernelShape:[],strides:[],pads:[]})},t.maxPool=(e,t,n)=>{p(t);const r={name:"MaxPool",inputNames:["X"],inputTypes:[i.TextureType.unpacked],cacheHint:n.cacheKey};return[e.run(Object.assign(Object.assign({},r),{get:()=>s(t,r,!1,n)}),t)]},t.parseMaxPoolAttributes=e=>{const t=e.attributes.getString("auto_pad","NOTSET"),n=e.attributes.getInt("ceil_mode",0),o=e.attributes.getInts("kernel_shape"),i=e.attributes.getInts("strides",[]),a=e.attributes.getInts("pads",[]),s=e.attributes.getInt("storage_order",0),u=e.attributes.getInts("dilations",[]);if(0!==s)throw new Error("column major storage order is not yet supported for MaxPool");if(0!==n)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");return(0,r.createAttributeWithCacheKey)({autoPad:t,ceilMode:n,countIncludePad:!1,kernelShape:o,strides:i,pads:a,storageOrder:s,dilations:u})};const s=(e,t,n,r)=>{const[o,a]=u(e,r,n),s=`\n ${d(e[0].dims,o,"\n value = max(_X(x), value);\n ","","-1e5")}\n `;return Object.assign(Object.assign({},t),{output:{dims:a,type:e[0].type,textureType:i.TextureType.unpacked},shaderSource:s})},u=(e,t,n)=>{const r=e[0].dims.slice(),i=Object.hasOwnProperty.call(t,"dilations"),a=t.kernelShape.slice(),s=t.strides.slice(),u=i?t.dilations.slice():[],l=t.pads.slice();o.PoolConvUtil.adjustPoolAttributes(n,r,a,s,u,l);const c=o.PoolConvUtil.computePoolOutputShape(n,r,s,u,a,l,t.autoPad),p=Object.assign({},t);return i?Object.assign(p,{kernelShape:a,strides:s,pads:l,dilations:u,cacheKey:t.cacheKey}):Object.assign(p,{kernelShape:a,strides:s,pads:l,cacheKey:t.cacheKey}),[p,c]},l={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[],cacheKey:""},c={name:"GlobalMaxPool",inputNames:["X"],inputTypes:[i.TextureType.unpacked]};t.globalMaxPool=(e,t)=>(p(t),[e.run(Object.assign(Object.assign({},c),{get:()=>s(t,c,!0,l)}),t)]);const p=e=>{if(!e||1!==e.length)throw new Error("Pool ops requires 1 input.");if("float32"!==e[0].type&&"float64"!==e[0].type)throw new Error("Invalid input type.")},d=(e,t,n,r,i)=>{const a=e.length;if(t.kernelShape.length<=2){const o=t.kernelShape[t.kernelShape.length-1],s=t.strides[t.strides.length-1],u=t.pads[t.pads.length/2-1],l=t.pads[t.pads.length-1],c=e[a-1];let p="",d="",f="";if(p=u+l!==0?`\n for (int i = 0; i < ${o}; i++) {\n x[${a} - 1] = indices[${a} - 1] * ${s} - ${u} + i;\n if (x[${a} - 1] < 0 || x[${a} - 1] >= ${c}) {\n pad++;\n continue;\n }\n ${n}\n }`:`\n for (int i = 0; i < ${o}; i++) {\n x[${a} - 1] = indices[${a} - 1] * ${s} - ${u} + i;\n ${n}\n }`,2===t.kernelShape.length){const n=t.kernelShape[t.kernelShape.length-2],r=t.strides[t.strides.length-2],i=t.pads[t.pads.length/2-2],s=t.pads[t.pads.length-2],u=e[a-2];d=i+s!==0?`\n for (int j = 0; j < ${n}; j++) {\n x[${a} - 2] = indices[${a} - 2] * ${r} - ${i} + j;\n if (x[${a} - 2] < 0 || x[${a} - 2] >= ${u}) {\n pad+= ${o};\n continue;\n }\n `:`\n for (int j = 0; j < ${n}; j++) {\n x[${a} - 2] = indices[${a} - 2] * ${r} - ${i} + j;\n `,f="\n }\n "}return`\n float process(int indices[${a}]) {\n int x[${a}];\n copyVec(indices, x);\n\n float value = ${i};\n int pad = 0;\n ${d}\n ${p}\n ${f}\n ${r}\n return value;\n }\n `}{const s=o.ShapeUtil.size(t.kernelShape),u=o.ShapeUtil.computeStrides(t.kernelShape),l=u.length,c=t.pads.length,p=h(l),d=f(e,"inputDims"),g=f(t.pads,"pads"),m=f(u,"kernelStrides"),b=f(t.strides,"strides");let y="";return y=t.pads.reduce(((e,t)=>e+t))?`\n if (x[j] >= inputDims[j] || x[j] < 0) {\n pad++;\n isPad = true;\n break;\n }\n }\n if (!isPad) {\n ${n}\n }`:`\n }\n ${n}\n `,`\n ${p}\n float process(int indices[${a}]) {\n int x[${a}];\n copyVec(indices, x);\n int offset[${l}];\n int pads[${c}];\n int inputDims[${a}];\n int kernelStrides[${l}];\n int strides[${l}];\n ${g}\n ${d}\n ${b}\n ${m}\n\n float value = ${i};\n int pad = 0;\n bool isPad = false;\n for (int i = 0; i < ${s}; i++) {\n offsetToIndices(i, kernelStrides, offset);\n isPad = false;\n for (int j = ${a} - ${l}; j < ${a}; j++) {\n x[j] = indices[j] * strides[j - ${a} + ${l}]\n + offset[j - ${a} + ${l}] - pads[j - 2];\n ${y}\n }\n ${r}\n\n return value;\n }\n `}},f=(e,t)=>{let n="";for(let r=0;r`\n void offsetToIndices(int offset, int[${e}] strides, out int[${e}] indices) {\n if (${e} == 0) {\n return;\n }\n for (int i = 0; i < ${e} - 1; ++i) {\n indices[i] = offset / strides[i];\n offset -= indices[i] * strides[i];\n }\n indices[${e} - 1] = offset;\n }`},1010:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.reduceLogSumSquare=t.reduceLogSum=t.reduceProd=t.reduceMin=t.reduceMax=t.reduceMean=t.reduceSum=t.parseReduceAttributes=void 0;const r=n(4910),o=n(6145),i=n(7273),a=n(5639),s=(e,t,n,r,o)=>{l(t);const i={name:r,inputNames:["A"],inputTypes:[a.TextureType.unpacked]};return[e.run(Object.assign(Object.assign({},i),{cacheHint:n.cacheKey,get:()=>u(e,t,n,r,o,i)}),t)]};t.parseReduceAttributes=e=>{const t=e.attributes.getInts("axes",[]),n=1===e.attributes.getInt("keepdims",1);return(0,r.createAttributeWithCacheKey)({axes:t,keepDims:n})};const u=(e,t,n,r,o,s)=>{const u=[],l=t[0].dims.length||1,c=[],p=i.ShapeUtil.normalizeAxes(n.axes,t[0].dims.length),d=o(t,p);let f=d[1];for(let e=0;e=0||0===p.length?(n.keepDims&&u.push(1),f=`\n for(int j${e} = 0; j${e} < ${t[0].dims[e]}; j${e}++) {\n inputIdx[${e}] = j${e};\n ${f}\n }`):(c.push(`inputIdx[${e}] = outputIdx[${u.length}];`),u.push(t[0].dims[e]));const h=`\n float process(int outputIdx[${u.length||1}]) {\n float value; // final result\n int inputIdx[${l}]; // addressing input data\n ${c.join("\n")}\n ${d[0]} // init ops for reduce max/min\n ${f}\n ${d[2]} // final computation for reduce mean\n return value;\n }`;return Object.assign(Object.assign({},s),{output:{dims:u,type:t[0].type,textureType:a.TextureType.unpacked},shaderSource:h})},l=e=>{if(!e||1!==e.length)throw new Error("Reduce op requires 1 input.");if(-1===o.NUMBER_TYPES.indexOf(e[0].type))throw new Error("Invalid input type.")};t.reduceSum=(e,t,n)=>s(e,t,n,"ReduceSum",(()=>["value = 0.0;","value += _A(inputIdx);",""])),t.reduceMean=(e,t,n)=>s(e,t,n,"ReduceMean",((e,t)=>{let n=1;for(let r=0;r=0||0===t.length)&&(n*=e[0].dims[r]);return["value = 0.0;","value += _A(inputIdx);",`value /= ${n}.;`]})),t.reduceMax=(e,t,n)=>s(e,t,n,"ReduceMax",((e,t)=>{const n=[];for(let r=0;r=0||0===t.length)&&n.push(`inputIdx[${r}] = 0;`);return[`${n.join("\n")}\nvalue = _A(inputIdx);`,"value = max(value, _A(inputIdx));",""]})),t.reduceMin=(e,t,n)=>s(e,t,n,"ReduceMin",((e,t)=>{const n=[];for(let r=0;r=0||0===t.length)&&n.push(`inputIdx[${r}] = 0;`);return[`${n.join("\n")}\nvalue = _A(inputIdx);`,"value = min(value, _A(inputIdx));",""]})),t.reduceProd=(e,t,n)=>s(e,t,n,"ReduceProd",(()=>["value = 1.0;","value *= _A(inputIdx);",""])),t.reduceLogSum=(e,t,n)=>s(e,t,n,"ReduceLogSum",(()=>["value = 0.0;","value += _A(inputIdx);","value = log(value);"])),t.reduceLogSumSquare=(e,t,n)=>s(e,t,n,"ReduceLogSumSquare",(()=>["float t; value = 0.0;","t = _A(inputIdx); value += t * t;",""]))},7379:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.isReshapeCheap=t.processDims3D=t.createPackedReshape3DProgramInfoLoader=void 0;const r=n(7273),o=n(6757),i=n(5639),a=n(5614);t.createPackedReshape3DProgramInfoLoader=(e,t,n)=>{const s=(e=>({name:"Reshape (packed)",inputTypes:[i.TextureType.packed],inputNames:["A"],cacheHint:`${e}`}))(n);return Object.assign(Object.assign({},s),{get:()=>((e,t,n,s)=>{const u=t.dims,l=s;let c="";for(let e=0;e<4;e++){let t="";switch(e){case 0:t="outputCoords = rc;";break;case 1:t="outputCoords = ivec3(rc.x, rc.y+1, rc.z);";break;case 2:t="outputCoords = ivec3(rc.x, rc.y, rc.z+1);";break;case 3:t="outputCoords = ivec3(rc.x, rc.y+1, rc.z+1);";break;default:throw new Error}c+=`\n ${t}\n ${e>0?"if(outputCoords.y < rows && outputCoords.z < cols){":""}\n int flattenedIndex = getFlattenedIndex(outputCoords);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flattenedIndex);\n vec2 innerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result[${e}] = getChannel(getA(inputRC.x, inputRC.y, inputRC.z), innerDims);\n\n ${e>0?"}":""}\n `}const p=(0,o.getGlsl)(e.session.backend.glContext.version),d=`\n ${function(e){const t=r.ShapeUtil.computeStrides(e),n=["b","r","c"],o="index";return`\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n ${t.map(((e,r)=>`int ${n[r]} = ${o} / ${e}; ${r===t.length-1?`int ${n[r+1]} = ${o} - ${n[r]} * ${e}`:`index -= ${n[r]} * ${e}`};`)).join("")}\n return ivec3(b, r, c);\n }\n `}(u)}\n ${function(e){const t=r.ShapeUtil.computeStrides(e);return`\n int getFlattenedIndex(ivec3 coords) {\n // reverse y, z order\n return coords.x * ${t[0]} + coords.z * ${t[1]} + coords.y;\n }\n`}(l)}\n ${(0,a.unpackFromChannel)()}\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.0);\n\n ivec3 outputCoords;\n int rows = ${l[2]};\n int cols = ${l[1]};\n\n ${c}\n ${p.output} = result;\n }\n `;return Object.assign(Object.assign({},n),{output:{dims:l,type:t.type,textureType:i.TextureType.packed},shaderSource:d,hasMain:!0})})(e,t,s,n)})},t.processDims3D=function(e){if(0===e.length)return[1,1,1];let t=1;for(let n=0;n1?e[e.length-2]:1,e[e.length-1]]},t.isReshapeCheap=function(e,t){let n=!1;return n=0===e.length||0===t.length||(e.length<2||t.length<2?e[e.length-1]===t[t.length-1]:e[e.length-1]===t[t.length-1]&&e[e.length-2]===t[t.length-2]),n}},8126:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.reshape=void 0;const r=n(7273);t.reshape=(e,t)=>{const n=r.ShapeUtil.calculateReshapedDims(t[0].dims,t[1].integerData);return e.session.pack?[e.reshapePacked(t[0],n)]:[e.reshapeUnpacked(t[0],n)]}},2801:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.parseResizeAttributesV11=t.parseResizeAttributesV10=t.resize=void 0;const r=n(6757),o=n(5639),i=n(432),a=n(5614),s=n(3980),u={name:"Resize",inputNames:["A"],inputTypes:[o.TextureType.packed]};t.resize=(e,t,n)=>((0,s.validateInputs)(t,n),[e.run(Object.assign(Object.assign({},u),{cacheHint:n.cacheKey,get:()=>l(e,t,n)}),t)]),t.parseResizeAttributesV10=e=>(0,s.parseUpsampleAttributes)(e,10),t.parseResizeAttributesV11=e=>(0,s.parseUpsampleAttributes)(e,11);const l=(e,t,n)=>{const s=(0,r.getGlsl)(e.session.backend.glContext.version),[l,p]=c(t,n);if(l.every((e=>1===e))&&"tf_crop_and_resize"!==n.coordinateTransformMode)return Object.assign(Object.assign({},u),{output:{dims:p,type:t[0].type,textureType:o.TextureType.packed},hasMain:!0,shaderSource:`void main() {\n vec4 v = ${s.texture2D}(X, TexCoords);\n ${s.output} = v;\n }`});const d=p.length;if(d<2)throw new Error(`output dimension should be at least 2, but got ${d}`);const f=p[d-2],h=p[d-1],g=t[0].dims;if(d!==g.length)throw new Error(`output dimension should match input ${g.length}, but got ${d}`);const m=g[d-2],b=g[d-1],y=l[d-2],w=l[d-1];let _="";if("linear"!==n.mode)throw new Error(`resize (packed) does not support mode: '${n.mode}'`);switch(n.coordinateTransformMode){case"asymmetric":_="\n vec4 getSourceFracIndex(ivec4 coords) {\n return vec4(coords) / scaleWHWH;\n }\n ";break;case"half_pixel":_="\n vec4 getSourceFracIndex(ivec4 coords) {\n return (vec4(coords) + 0.5) / scaleWHWH - 0.5;\n }\n ";break;case"pytorch_half_pixel":_=`\n vec4 getSourceFracIndex(ivec4 coords) {\n vec4 fcoords = vec4(coords);\n return vec4(\n ${h}.0 > 1.0 ? (fcoords.x + 0.5) / scaleWHWH.x - 0.5 : 0.0,\n ${f}.0 > 1.0 ? (fcoords.y + 0.5) / scaleWHWH.y - 0.5 : 0.0,\n ${h}.0 > 1.0 ? (fcoords.z + 0.5) / scaleWHWH.z - 0.5 : 0.0,\n ${f}.0 > 1.0 ? (fcoords.w + 0.5) / scaleWHWH.w - 0.5 : 0.0\n );\n }\n `;break;case"align_corners":_=`\n vec4 getSourceFracIndex(ivec4 coords) {\n vec4 resized = vec4(${h}.0 - 1.0, ${f}.0 - 1.0, ${h}.0 - 1.0,\n ${f}.0 - 1.0);\n vec4 original = vec4(${b}.0 - 1.0, ${m}.0 - 1.0, ${b}.0 - 1.0,\n ${m}.0 - 1.0);\n vec4 new_scale = original / resized;\n return vec4(coords) * new_scale;\n }\n `;break;default:throw new Error(`resize (packed) does not support coordinateTransformMode: '${n.coordinateTransformMode}'`)}const v=(0,i.getCoordsDataType)(d),x=`\n const vec2 inputWH = vec2(${m}.0, ${b}.0);\n const vec4 scaleWHWH = vec4(float(${y}), float(${w}), float(${y}), float(${w}));\n ${(0,a.unpackFromChannel)()}\n ${_}\n float getAValue(int x10, int r, int c, int d) {\n return getChannel(getA(x10, r, c, d), vec2(c, d));\n }\n void main() {\n ${v} rc = getOutputCoords();\n\n int batch = rc[0];\n int depth = rc[1];\n\n // retrieve the 4 coordinates that is used in the 4 packed output values.\n ivec4 coords = ivec4(rc.wz, rc.w + 1, rc.z + 1);\n\n // calculate the source index in fraction\n vec4 sourceFrac = getSourceFracIndex(coords);\n\n // get the lower and upper bound of the 4 values that will be packed into one texel.\n ivec4 x00 = ivec4(max(sourceFrac.xy, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.xy)));\n ivec4 x01 = ivec4(max(sourceFrac.xw, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.xw)));\n ivec4 x10 = ivec4(max(sourceFrac.zy, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.zy)));\n ivec4 x11 = ivec4(max(sourceFrac.zw, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.zw)));\n\n bool hasNextRow = rc.w < ${f-1};\n bool hasNextCol = rc.z < ${h-1};\n\n // pack x00, x01, x10, x11's top-left corner into one vec4 structure\n vec4 topLeft = vec4(\n getAValue(batch, depth, x00.x, x00.y),\n hasNextCol ? getAValue(batch, depth, x01.x, x01.y) : 0.0,\n hasNextRow ? getAValue(batch, depth, x10.x, x10.y) : 0.0,\n (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.x, x11.y) : 0.0);\n\n // pack x00, x01, x10, x11's top-right corner into one vec4 structure\n vec4 topRight = vec4(\n getAValue(batch, depth, x00.x, x00.w),\n hasNextCol ? getAValue(batch, depth, x01.x, x01.w) : 0.0,\n hasNextRow ? getAValue(batch, depth, x10.x, x10.w) : 0.0,\n (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.x, x11.w) : 0.0);\n\n // pack x00, x01, x10, x11's bottom-left corner into one vec4 structure\n vec4 bottomLeft = vec4(\n getAValue(batch, depth, x00.z, x00.y),\n hasNextCol ? getAValue(batch, depth, x01.z, x01.y) : 0.0,\n hasNextRow ? getAValue(batch, depth, x10.z, x10.y) : 0.0,\n (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.z, x11.y) : 0.0);\n\n // pack x00, x01, x10, x11's bottom-right corner into one vec4 structure\n vec4 bottomRight = vec4(\n getAValue(batch, depth, x00.z, x00.w),\n hasNextCol ? getAValue(batch, depth, x01.z, x01.w) : 0.0,\n hasNextRow ? getAValue(batch, depth, x10.z, x10.w) : 0.0,\n (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.z, x11.w) : 0.0);\n\n // calculate the interpolation fraction on u and v direction\n vec4 frac = vec4(sourceFrac) - floor(sourceFrac);\n vec4 clampFrac = clamp(frac, vec4(0.0), vec4(1.0));\n\n vec4 top = mix(topLeft, topRight, clampFrac.ywyw);\n vec4 bottom = mix(bottomLeft, bottomRight, clampFrac.ywyw);\n vec4 newValue = mix(top, bottom, clampFrac.xxzz);\n\n ${s.output} = vec4(newValue);\n }\n `;return Object.assign(Object.assign({},u),{output:{dims:p,type:t[0].type,textureType:o.TextureType.packed},hasMain:!0,shaderSource:x})},c=(e,t)=>{const n=e[0].dims;let r,o=t.scales;if(0===o.length){const i=e[t.scalesInputIdx];if(i&&0!==i.size){if(e[t.sizesInputIdx])throw new Error("Only one of scales or sizes must be provided as input.");o=p(i,t.mode,t.isResize)}else{const i=e[t.sizesInputIdx];if(!i||0===i.size)throw new Error("Either scales or sizes MUST be provided as input.");r=Array.from(i.integerData),o=d(r,n,t.mode,t.isResize)}}else if(e[t.sizesInputIdx])throw new Error("Only one of scales or sizes must be provided as input.");const i=r||n.map(((e,t)=>Math.floor(e*o[t])));return[o,i]},p=(e,t,n)=>{const r=Array.from(e.floatData);return(0,s.scalesValidation)(r,t,n),r},d=(e,t,n,r)=>{const o=t.length,i=new Array(o);for(let n=0,r=o;n{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.shape=void 0;const r=n(9240);t.shape=(e,t)=>(o(t),[new r.Tensor([t[0].dims.length],"int32",void 0,void 0,new Int32Array(t[0].dims))]);const o=e=>{if(!e||1!==e.length)throw new Error("Shape requires 1 input.")}},2444:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.sliceV10=t.parseSliceAttributes=t.slice=void 0;const r=n(4910),o=n(6145),i=n(7273),a=n(5639),s={name:"Slice",inputNames:["A"],inputTypes:[a.TextureType.unpacked]};t.slice=(e,t,n)=>(l(t),[e.run(Object.assign(Object.assign({},s),{cacheHint:n.cacheKey,get:()=>u(e,t[0],n)}),t)]),t.parseSliceAttributes=e=>{const t=e.attributes.getInts("starts"),n=e.attributes.getInts("ends"),o=e.attributes.getInts("axes",[]);return(0,r.createAttributeWithCacheKey)({starts:t,ends:n,axes:o})};const u=(e,t,n)=>{const r=0===n.axes.length?t.dims.slice(0).map(((e,t)=>t)):n.axes,o=i.ShapeUtil.normalizeAxes(r,t.dims.length),u=n.starts.map(((e,n)=>e>t.dims[o[n]]-1?t.dims[o[n]]:i.ShapeUtil.normalizeAxis(e,t.dims[o[n]]))),l=n.ends.map(((e,n)=>e>t.dims[o[n]]-1?t.dims[o[n]]:i.ShapeUtil.normalizeAxis(e,t.dims[o[n]]))),c=t.dims.slice(),p=[];for(let e=0;e0&&p.push(`outputIdx[${o[e]}] += ${u[e]};`);const d=`\n float process(int outputIdx[${c.length}]) {\n ${p.join("\n ")}\n return _A(outputIdx);\n }`;return Object.assign(Object.assign({},s),{output:{dims:c,type:t.type,textureType:a.TextureType.unpacked},shaderSource:d})},l=e=>{if(!e||1!==e.length)throw new Error("Slice requires 1 input.");if(-1===o.NUMBER_TYPES.indexOf(e[0].type))throw new Error("Invalid input type.")};t.sliceV10=(e,t)=>{p(t);const n=c(e,t);return[e.run(Object.assign(Object.assign({},s),{cacheHint:n.cacheKey,get:()=>u(e,t[0],n)}),[t[0]])]};const c=(e,t)=>{if(!e.session.isInitializer(t[1].dataId)||!e.session.isInitializer(t[2].dataId)||t.length>=4&&!e.session.isInitializer(t[3].dataId)||t.length>=5&&!e.session.isInitializer(t[4].dataId))throw new Error("dynamic slice attributes are not allowed");if(t.length>=5&&t[4].integerData.some((e=>1!==e)))throw new Error("currently non-1 steps is not supported for Slice");const n=Array.from(t[1].integerData),r=Array.from(t[2].integerData),o=t.length>=4?Array.from(t[3].integerData):[];return{starts:n,ends:r,axes:o,cacheKey:`${o};${n};${r}`}},p=e=>{if(!e||e.length<3||e.length>5)throw new Error("Invalid input number.");if("int32"!==e[1].type||1!==e[1].dims.length)throw new Error("Invalid input type.");if("int32"!==e[2].type||1!==e[2].dims.length)throw new Error("Invalid input type.");if(e.length>=4&&("int32"!==e[3].type||1!==e[3].dims.length))throw new Error("Invalid input 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e.experimental.fbs.SparseTensor).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos+r)+4*t),this.bb):null}sparseInitializersLength(){let e=this.bb.__offset(this.bb_pos,18);return e?this.bb.__vector_len(this.bb_pos+e):0}static startGraph(e){e.startObject(8)}static addInitializers(e,t){e.addFieldOffset(0,t,0)}static createInitializersVector(e,t){e.startVector(4,t.length,4);for(let n=t.length-1;n>=0;n--)e.addOffset(t[n]);return e.endVector()}static startInitializersVector(e,t){e.startVector(4,t,4)}static addNodeArgs(e,t){e.addFieldOffset(1,t,0)}static createNodeArgsVector(e,t){e.startVector(4,t.length,4);for(let n=t.length-1;n>=0;n--)e.addOffset(t[n]);return e.endVector()}static startNodeArgsVector(e,t){e.startVector(4,t,4)}static addNodes(e,t){e.addFieldOffset(2,t,0)}static createNodesVector(e,t){e.startVector(4,t.length,4);for(let n=t.length-1;n>=0;n--)e.addOffset(t[n]);return e.endVector()}static startNodesVector(e,t){e.startVector(4,t,4)}static addMaxNodeIndex(e,t){e.addFieldInt32(3,t,0)}static addNodeEdges(e,t){e.addFieldOffset(4,t,0)}static createNodeEdgesVector(e,t){e.startVector(4,t.length,4);for(let n=t.length-1;n>=0;n--)e.addOffset(t[n]);return e.endVector()}static startNodeEdgesVector(e,t){e.startVector(4,t,4)}static addInputs(e,t){e.addFieldOffset(5,t,0)}static createInputsVector(e,t){e.startVector(4,t.length,4);for(let n=t.length-1;n>=0;n--)e.addOffset(t[n]);return e.endVector()}static startInputsVector(e,t){e.startVector(4,t,4)}static addOutputs(e,t){e.addFieldOffset(6,t,0)}static createOutputsVector(e,t){e.startVector(4,t.length,4);for(let n=t.length-1;n>=0;n--)e.addOffset(t[n]);return e.endVector()}static startOutputsVector(e,t){e.startVector(4,t,4)}static addSparseInitializers(e,t){e.addFieldOffset(7,t,0)}static createSparseInitializersVector(e,t){e.startVector(4,t.length,4);for(let n=t.length-1;n>=0;n--)e.addOffset(t[n]);return e.endVector()}static startSparseInitializersVector(e,t){e.startVector(4,t,4)}static endGraph(e){return e.endObject()}static createGraph(e,t,r,o,i,a,s,u,l){return n.startGraph(e),n.addInitializers(e,t),n.addNodeArgs(e,r),n.addNodes(e,o),n.addMaxNodeIndex(e,i),n.addNodeEdges(e,a),n.addInputs(e,s),n.addOutputs(e,u),n.addSparseInitializers(e,l),n.endGraph(e)}}t.Graph=n}(t.fbs||(t.fbs={}))}(e.experimental||(e.experimental={}))}(t.onnxruntime||(t.onnxruntime={})),function(e){!function(t){!function(t){class n{constructor(){this.bb=null,this.bb_pos=0}__init(e,t){return this.bb_pos=e,this.bb=t,this}static getRootAsModel(e,t){return(t||new n).__init(e.readInt32(e.position())+e.position(),e)}static getSizePrefixedRootAsModel(e,t){return e.setPosition(e.position()+r.flatbuffers.SIZE_PREFIX_LENGTH),(t||new n).__init(e.readInt32(e.position())+e.position(),e)}irVersion(){let e=this.bb.__offset(this.bb_pos,4);return e?this.bb.readInt64(this.bb_pos+e):this.bb.createLong(0,0)}opsetImport(t,n){let r=this.bb.__offset(this.bb_pos,6);return r?(n||new e.experimental.fbs.OperatorSetId).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos+r)+4*t),this.bb):null}opsetImportLength(){let e=this.bb.__offset(this.bb_pos,6);return e?this.bb.__vector_len(this.bb_pos+e):0}producerName(e){let t=this.bb.__offset(this.bb_pos,8);return t?this.bb.__string(this.bb_pos+t,e):null}producerVersion(e){let t=this.bb.__offset(this.bb_pos,10);return t?this.bb.__string(this.bb_pos+t,e):null}domain(e){let t=this.bb.__offset(this.bb_pos,12);return t?this.bb.__string(this.bb_pos+t,e):null}modelVersion(){let e=this.bb.__offset(this.bb_pos,14);return e?this.bb.readInt64(this.bb_pos+e):this.bb.createLong(0,0)}docString(e){let t=this.bb.__offset(this.bb_pos,16);return t?this.bb.__string(this.bb_pos+t,e):null}graph(t){let n=this.bb.__offset(this.bb_pos,18);return n?(t||new e.experimental.fbs.Graph).__init(this.bb.__indirect(this.bb_pos+n),this.bb):null}graphDocString(e){let t=this.bb.__offset(this.bb_pos,20);return t?this.bb.__string(this.bb_pos+t,e):null}static startModel(e){e.startObject(9)}static addIrVersion(e,t){e.addFieldInt64(0,t,e.createLong(0,0))}static addOpsetImport(e,t){e.addFieldOffset(1,t,0)}static createOpsetImportVector(e,t){e.startVector(4,t.length,4);for(let n=t.length-1;n>=0;n--)e.addOffset(t[n]);return e.endVector()}static startOpsetImportVector(e,t){e.startVector(4,t,4)}static addProducerName(e,t){e.addFieldOffset(2,t,0)}static addProducerVersion(e,t){e.addFieldOffset(3,t,0)}static addDomain(e,t){e.addFieldOffset(4,t,0)}static addModelVersion(e,t){e.addFieldInt64(5,t,e.createLong(0,0))}static addDocString(e,t){e.addFieldOffset(6,t,0)}static addGraph(e,t){e.addFieldOffset(7,t,0)}static addGraphDocString(e,t){e.addFieldOffset(8,t,0)}static endModel(e){return e.endObject()}static createModel(e,t,r,o,i,a,s,u,l,c){return n.startModel(e),n.addIrVersion(e,t),n.addOpsetImport(e,r),n.addProducerName(e,o),n.addProducerVersion(e,i),n.addDomain(e,a),n.addModelVersion(e,s),n.addDocString(e,u),n.addGraph(e,l),n.addGraphDocString(e,c),n.endModel(e)}}t.Model=n}(t.fbs||(t.fbs={}))}(e.experimental||(e.experimental={}))}(t.onnxruntime||(t.onnxruntime={})),function(e){!function(e){!function(e){class t{constructor(){this.bb=null,this.bb_pos=0}__init(e,t){return this.bb_pos=e,this.bb=t,this}static getRootAsKernelCreateInfos(e,n){return(n||new t).__init(e.readInt32(e.position())+e.position(),e)}static getSizePrefixedRootAsKernelCreateInfos(e,n){return e.setPosition(e.position()+r.flatbuffers.SIZE_PREFIX_LENGTH),(n||new t).__init(e.readInt32(e.position())+e.position(),e)}nodeIndices(e){let t=this.bb.__offset(this.bb_pos,4);return t?this.bb.readUint32(this.bb.__vector(this.bb_pos+t)+4*e):0}nodeIndicesLength(){let e=this.bb.__offset(this.bb_pos,4);return e?this.bb.__vector_len(this.bb_pos+e):0}nodeIndicesArray(){let e=this.bb.__offset(this.bb_pos,4);return e?new Uint32Array(this.bb.bytes().buffer,this.bb.bytes().byteOffset+this.bb.__vector(this.bb_pos+e),this.bb.__vector_len(this.bb_pos+e)):null}kernelDefHashes(e){let t=this.bb.__offset(this.bb_pos,6);return t?this.bb.readUint64(this.bb.__vector(this.bb_pos+t)+8*e):this.bb.createLong(0,0)}kernelDefHashesLength(){let e=this.bb.__offset(this.bb_pos,6);return e?this.bb.__vector_len(this.bb_pos+e):0}static startKernelCreateInfos(e){e.startObject(2)}static addNodeIndices(e,t){e.addFieldOffset(0,t,0)}static createNodeIndicesVector(e,t){e.startVector(4,t.length,4);for(let n=t.length-1;n>=0;n--)e.addInt32(t[n]);return e.endVector()}static startNodeIndicesVector(e,t){e.startVector(4,t,4)}static addKernelDefHashes(e,t){e.addFieldOffset(1,t,0)}static createKernelDefHashesVector(e,t){e.startVector(8,t.length,8);for(let n=t.length-1;n>=0;n--)e.addInt64(t[n]);return e.endVector()}static startKernelDefHashesVector(e,t){e.startVector(8,t,8)}static endKernelCreateInfos(e){return e.endObject()}static createKernelCreateInfos(e,n,r){return t.startKernelCreateInfos(e),t.addNodeIndices(e,n),t.addKernelDefHashes(e,r),t.endKernelCreateInfos(e)}}e.KernelCreateInfos=t}(e.fbs||(e.fbs={}))}(e.experimental||(e.experimental={}))}(t.onnxruntime||(t.onnxruntime={})),function(e){!function(t){!function(t){class n{constructor(){this.bb=null,this.bb_pos=0}__init(e,t){return this.bb_pos=e,this.bb=t,this}static getRootAsSubGraphSessionState(e,t){return(t||new n).__init(e.readInt32(e.position())+e.position(),e)}static getSizePrefixedRootAsSubGraphSessionState(e,t){return e.setPosition(e.position()+r.flatbuffers.SIZE_PREFIX_LENGTH),(t||new n).__init(e.readInt32(e.position())+e.position(),e)}graphId(e){let t=this.bb.__offset(this.bb_pos,4);return t?this.bb.__string(this.bb_pos+t,e):null}sessionState(t){let n=this.bb.__offset(this.bb_pos,6);return n?(t||new e.experimental.fbs.SessionState).__init(this.bb.__indirect(this.bb_pos+n),this.bb):null}static startSubGraphSessionState(e){e.startObject(2)}static addGraphId(e,t){e.addFieldOffset(0,t,0)}static addSessionState(e,t){e.addFieldOffset(1,t,0)}static endSubGraphSessionState(e){let t=e.endObject();return e.requiredField(t,4),t}static createSubGraphSessionState(e,t,r){return n.startSubGraphSessionState(e),n.addGraphId(e,t),n.addSessionState(e,r),n.endSubGraphSessionState(e)}}t.SubGraphSessionState=n}(t.fbs||(t.fbs={}))}(e.experimental||(e.experimental={}))}(t.onnxruntime||(t.onnxruntime={})),function(e){!function(t){!function(t){class n{constructor(){this.bb=null,this.bb_pos=0}__init(e,t){return this.bb_pos=e,this.bb=t,this}static getRootAsSessionState(e,t){return(t||new n).__init(e.readInt32(e.position())+e.position(),e)}static getSizePrefixedRootAsSessionState(e,t){return e.setPosition(e.position()+r.flatbuffers.SIZE_PREFIX_LENGTH),(t||new n).__init(e.readInt32(e.position())+e.position(),e)}kernels(t){let n=this.bb.__offset(this.bb_pos,4);return n?(t||new e.experimental.fbs.KernelCreateInfos).__init(this.bb.__indirect(this.bb_pos+n),this.bb):null}subGraphSessionStates(t,n){let r=this.bb.__offset(this.bb_pos,6);return r?(n||new e.experimental.fbs.SubGraphSessionState).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos+r)+4*t),this.bb):null}subGraphSessionStatesLength(){let e=this.bb.__offset(this.bb_pos,6);return e?this.bb.__vector_len(this.bb_pos+e):0}static startSessionState(e){e.startObject(2)}static addKernels(e,t){e.addFieldOffset(0,t,0)}static addSubGraphSessionStates(e,t){e.addFieldOffset(1,t,0)}static createSubGraphSessionStatesVector(e,t){e.startVector(4,t.length,4);for(let n=t.length-1;n>=0;n--)e.addOffset(t[n]);return e.endVector()}static startSubGraphSessionStatesVector(e,t){e.startVector(4,t,4)}static endSessionState(e){return e.endObject()}static createSessionState(e,t,r){return n.startSessionState(e),n.addKernels(e,t),n.addSubGraphSessionStates(e,r),n.endSessionState(e)}}t.SessionState=n}(t.fbs||(t.fbs={}))}(e.experimental||(e.experimental={}))}(t.onnxruntime||(t.onnxruntime={})),function(e){!function(t){!function(t){class n{constructor(){this.bb=null,this.bb_pos=0}__init(e,t){return this.bb_pos=e,this.bb=t,this}static getRootAsInferenceSession(e,t){return(t||new n).__init(e.readInt32(e.position())+e.position(),e)}static getSizePrefixedRootAsInferenceSession(e,t){return e.setPosition(e.position()+r.flatbuffers.SIZE_PREFIX_LENGTH),(t||new n).__init(e.readInt32(e.position())+e.position(),e)}static bufferHasIdentifier(e){return e.__has_identifier("ORTM")}ortVersion(e){let t=this.bb.__offset(this.bb_pos,4);return t?this.bb.__string(this.bb_pos+t,e):null}model(t){let n=this.bb.__offset(this.bb_pos,6);return n?(t||new e.experimental.fbs.Model).__init(this.bb.__indirect(this.bb_pos+n),this.bb):null}sessionState(t){let n=this.bb.__offset(this.bb_pos,8);return n?(t||new e.experimental.fbs.SessionState).__init(this.bb.__indirect(this.bb_pos+n),this.bb):null}static startInferenceSession(e){e.startObject(3)}static addOrtVersion(e,t){e.addFieldOffset(0,t,0)}static addModel(e,t){e.addFieldOffset(1,t,0)}static addSessionState(e,t){e.addFieldOffset(2,t,0)}static endInferenceSession(e){return e.endObject()}static finishInferenceSessionBuffer(e,t){e.finish(t,"ORTM")}static finishSizePrefixedInferenceSessionBuffer(e,t){e.finish(t,"ORTM",!0)}static createInferenceSession(e,t,r,o){return n.startInferenceSession(e),n.addOrtVersion(e,t),n.addModel(e,r),n.addSessionState(e,o),n.endInferenceSession(e)}}t.InferenceSession=n}(t.fbs||(t.fbs={}))}(e.experimental||(e.experimental={}))}(t.onnxruntime||(t.onnxruntime={}))},1723:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.OnnxjsSessionHandler=void 0;const r=n(8453),o=n(9240);t.OnnxjsSessionHandler=class{constructor(e){this.session=e,this.inputNames=this.session.inputNames,this.outputNames=this.session.outputNames}async dispose(){}async run(e,t,n){const i=new Map;for(const t in e)if(Object.hasOwnProperty.call(e,t)){const n=e[t];i.set(t,new o.Tensor(n.dims,n.type,void 0,void 0,n.data))}const a=await this.session.run(i),s={};return a.forEach(((e,t)=>{s[t]=new r.Tensor(e.type,e.data,e.dims)})),s}startProfiling(){this.session.startProfiling()}endProfiling(){this.session.endProfiling()}}},6027:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.Session=void 0;const r=n(7067),o=n(1296),i=n(1975),a=n(6496),s=n(1315),u=n(1745);t.Session=class{constructor(e={}){this._initialized=!1,this.backendHint=e.backendHint,this.profiler=s.Profiler.create(e.profiler),this.context={profiler:this.profiler,graphInputTypes:[],graphInputDims:[]}}get inputNames(){return this._model.graph.getInputNames()}get outputNames(){return this._model.graph.getOutputNames()}startProfiling(){this.profiler.start()}endProfiling(){this.profiler.stop()}async loadModel(e,t,n){await this.profiler.event("session","Session.loadModel",(async()=>{const a=await(0,i.resolveBackend)(this.backendHint);if(this.sessionHandler=a.createSessionHandler(this.context),this._model=new u.Model,"string"==typeof e){const t=e.endsWith(".ort");if("undefined"==typeof fetch){const n=await(0,o.promisify)(r.readFile)(e);this.initialize(n,t)}else{const n=await fetch(e),r=await n.arrayBuffer();this.initialize(new Uint8Array(r),t)}}else if(ArrayBuffer.isView(e))this.initialize(e);else{const r=new Uint8Array(e,t||0,n||e.byteLength);this.initialize(r)}}))}initialize(e,t){if(this._initialized)throw new Error("already initialized");this.profiler.event("session","Session.initialize",(()=>{const n=this.sessionHandler.transformGraph?this.sessionHandler:void 0;this._model.load(e,n,t),this.sessionHandler.onGraphInitialized&&this.sessionHandler.onGraphInitialized(this._model.graph),this.initializeOps(this._model.graph),this._executionPlan=new a.ExecutionPlan(this._model.graph,this._ops,this.profiler)})),this._initialized=!0}async run(e){if(!this._initialized)throw new Error("session not initialized yet");return this.profiler.event("session","Session.run",(async()=>{const t=this.normalizeAndValidateInputs(e),n=await this._executionPlan.execute(this.sessionHandler,t);return this.createOutput(n)}))}normalizeAndValidateInputs(e){const t=this._model.graph.getInputNames();if(Array.isArray(e)){if(e.length!==t.length)throw new Error(`incorrect input array length: expected ${t.length} but got ${e.length}`)}else{if(e.size!==t.length)throw new Error(`incorrect input map size: expected ${t.length} but got ${e.size}`);const n=new Array(e.size);let r=0;for(let o=0;o"string"==typeof e))))throw new TypeError("cache should be a string array");l&&(this.cache=new Array(s))}else{if(void 0!==i){const e=d(t);if(!(i instanceof e))throw new TypeError(`cache should be type ${e.name}`)}if(l){const e=new ArrayBuffer(s*function(e){switch(e){case"bool":case"int8":case"uint8":return 1;case"int16":case"uint16":return 2;case"int32":case"uint32":case"float32":return 4;case"float64":return 8;default:throw new Error(`cannot calculate sizeof() on type ${e}`)}}(t));this.cache=function(e,t){return new(d(t))(e)}(e,t)}}}static fromProto(e){if(!e)throw new Error("cannot construct Value from an empty tensor");const t=u.ProtoUtil.tensorDataTypeFromProto(e.dataType),n=u.ProtoUtil.tensorDimsFromProto(e.dims),r=new c(n,t);if("string"===t)e.stringData.forEach(((e,t)=>{r.data[t]=(0,u.decodeUtf8String)(e)}));else if(e.rawData&&"number"==typeof e.rawData.byteLength&&e.rawData.byteLength>0){const t=r.data,n=new DataView(e.rawData.buffer,e.rawData.byteOffset,e.rawData.byteLength),o=p(e.dataType),i=e.rawData.byteLength/o;if(e.rawData.byteLength%o!=0)throw new Error("invalid buffer length");if(t.length!==i)throw new Error("buffer length mismatch");for(let r=0;r0){const t=r.data,n=new DataView(e.rawDataArray().buffer,e.rawDataArray().byteOffset,e.rawDataLength()),o=p(e.dataType()),i=e.rawDataLength()/o;if(e.rawDataLength()%o!=0)throw new Error("invalid buffer length");if(t.length!==i)throw new Error("buffer length mismatch");for(let r=0;r1&&u>1)return;a[i-s]=Math.max(n,u)}return a}static index(e,t){const n=new Array(t.length);return l.fillIndex(e,t,n),n}static fillIndex(e,t,n){const r=e.length-t.length;for(let o=0;o=0;e--)r[e]=c%i[e],c=Math.floor(c/i[e]);f||(l.fillIndex(r,e.dims,o),p=e.get(o)),h||(l.fillIndex(r,t.dims,s),d=t.get(s)),u.set(r,n(p,d))}}return u}}static isValidBroadcast(e,t){const n=e.length,r=t.length;if(n>r)return!1;for(let o=1;o<=n;o++)if(1!==e[n-o]&&e[n-o]!==t[r-o])return!1;return!0}static getBroadcastDims(e,t){const n=e.length,r=[];for(let o=0;o1&&1===a&&r.unshift(i)}return r}}t.BroadcastUtil=l,t.arrayCopyHelper=function(e,t,n,r,o){if(r<0||r>=t.length)throw new Error("sourceIndex out of bounds");if(n<0||n>=e.length)throw new Error("targetIndex out of bounds");if(r+o>t.length)throw new Error("source indices to be copied are outside bounds");if(n+o>e.length)throw new Error("target array is too small to hold result");for(let i=0;ii.default.isLong(e)?e.toNumber():e))}static tensorValueTypeFromProto(e){return{tensorType:c.tensorDataTypeFromProto(e.elemType),shape:{dims:c.tensorDimsFromProto(e.shape.dim.map((e=>e.dimValue)))}}}static tensorDimsFromORTFormat(e){const t=[];for(let n=0;ne.length)throw new Error(`invalid dimension of ${t} for sizeFromDimension as Tensor has ${e.length} dimensions.`);return d.getSizeFromDimensionRange(e,t,e.length)}static sizeToDimension(e,t){if(t<0||t>e.length)throw new Error(`invalid dimension of ${t} for sizeToDimension as Tensor has ${e.length} dimensions.`);return d.getSizeFromDimensionRange(e,0,t)}static getSizeFromDimensionRange(e,t,n){let r=1;for(let o=t;o=0;--r)n[r]=n[r+1]*e[r+1];return n}static transpose(e){return e.slice().reverse()}static indicesToOffset(e,t,n){void 0===n&&(n=e.length);let r=0;for(let o=0;o=t)throw new Error("unsupported axis for this operation.");return e<0?e+t:e}static normalizeAxes(e,t){return e.map((e=>this.normalizeAxis(e,t)))}static incrementIndex(e,t,n){if(0===t.length||0===e.length)throw new Error("Index incrementing unsupported for scalar Tensor");if(void 0===n)n=t.length;else if(n<=0||n>t.length)throw new Error("Incorrect axis to increment on");for(let r=n-1;r>=0&&(e[r]++,!(e[r]=e.length)throw new Error("the dimension with value zero exceeds the dimension size of the input tensor");r[a]=e[a]}else r[a]=t[a];i*=r[a]}}const a=d.size(e);if(-1!==o){if(a%i!=0)throw new Error(`the input tensor cannot be reshaped to the requested shape. Input shape: [${e}] Output shape: [${t}]`);r[o]=a/i}else if(i!==a)throw new Error("reshapedDims and originalDims don't have matching sizes");return r}static sortBasedOnPerm(e,t){return t?t.map((t=>e[t])):e.slice().reverse()}static padShape(e,t){const n=e.length;return e.map(((e,r)=>e+t[r]+t[r+n]))}static areEqual(e,t){return e.length===t.length&&e.every(((e,n)=>e===t[n]))}static validateDimsAndCalcSize(e){if(e.length>6)throw new TypeError("Only rank 0 to 6 is supported for tensor shape.");let t=1;for(const n of e){if(!Number.isInteger(n))throw new TypeError(`Invalid shape: ${n} is not an integer`);if(n<0||n>2147483647)throw new TypeError(`Invalid shape: length ${n} is not allowed`);t*=n}return t}static flattenShape(e,t){t<0&&(t+=e.length);const n=e.reduce(((e,t)=>e*t),1),r=e.slice(t).reduce(((e,t)=>e*t),1);return[n/r,r]}static squeezeShape(e,t){const n=new Array;t=d.normalizeAxes(t,e.length);for(let r=0;r=0;if(o&&1!==e[r])throw new Error("squeeze an axis of size different than 1");(0===t.length&&e[r]>1||t.length>0&&!o)&&n.push(e[r])}return n}static unsqueezeShape(e,t){const n=new Array(e.length+t.length);n.fill(0);for(let e=0;e=n.length)throw new Error("'axes' has an out of range axis");if(0!==n[r])throw new Error("'axes' has a duplicate axis");n[r]=1}let r=0;for(let t=0;t=t.length)throw new Error("sourceIndex out of bounds");if(n<0||n>=e.length)throw new Error("targetIndex out of bounds");if(r+o>t.length)throw new Error("source indices to be copied are outside bounds");if(n+o>e.length)throw new Error("target array is too small to hold result");for(let i=0;i=t.length)throw new Error("sourceIndex out of bounds");if(n<0||n>=e.length)throw new Error("targetIndex out of bounds");if(r+o>t.length)throw new Error("source indices to be copied are outside bounds");if(n+o>e.length)throw new Error("target array is too small to hold result");for(let a=0;a=t.length)throw new Error("sourceIndex out of bounds");if(n<0||n>=e.length)throw new Error("targetIndex out of bounds");if(r+o>t.length)throw new Error("source indices to be copied are outside bounds");if(n+o>e.length)throw new Error("target array is too small to hold result");for(let a=0;a=t.length)throw new Error("sourceIndex out of bounds");if(n<0||n>=e.length)throw new Error("targetIndex out of bounds");if(r+o>t.length)throw new Error("source indices to be copied are outside bounds");if(n+o>e.length)throw new Error("target array is too small to hold result");for(let i=0;it.push(n)));const a=h.calcReduceShape(i,t,!0),u=d.size(a),c=new s.Tensor(a,e.type),p=d.computeStrides(a),f=d.computeStrides(i),g=new Array(i.length);for(let n=0;n=t.length)return i(e[o]);const u=t[r],l=u>=n.length?1:d.size(n.slice(u+1));for(let c=0;c0!==e))}}t.ReduceUtil=h;class g{static adjustPoolAttributes(e,t,n,r,o,i){if(!e&&n.length!==t.length-2)throw new Error("length of specified kernel shapes should be 2 less than length of input dimensions");if(e)for(let e=0;e=n.length?n.push(t[e+2]):n[e]=t[e+2];for(let e=0;e=n[e]||i[e+n.length]>=n[e])throw new Error("pads should be smaller than kernel")}}static adjustPadsBasedOnAutoPad(e,t,n,r,o,i){if(i){if(o.length!==2*(e.length-2))throw new Error("length of pads should be twice the length of data dimensions");if(t.length!==e.length-2)throw new Error("length of strides should be the length of data dimensions");if(r.length!==e.length-2)throw new Error("length of kernel shapes should be the length of data dimensions");for(let a=0;a{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.WebGpuBackend=void 0;const r=n(8453),o=n(4955),i=n(7771),a=n(8510),s=n(8305);t.WebGpuBackend=class{constructor(){this.currentKernelId=null,this.commandEncoder=null,this.computePassEncoder=null,this.pendingDispatchNumber=0,this.profilingEnabled=!1}get currentKernelCustomData(){if(null===this.currentKernelId)throw new Error("currentKernelCustomData(): currentKernelId is null. 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Error: ${e}`),1}finally{for(const e of this.temporaryData)this.gpuDataManager.release(e.id);this.temporaryData=[],this.currentKernelId=null}}}},7675:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.init=void 0;const r=n(7917),o=n(3838),i=n(4955),a=n(6952);class s{constructor(e,t,n,r){this.module=e,this.dataType=t,this.data=n,this.dims=r}getFloat32Array(){return new Float32Array(this.module.HEAP8.buffer,this.data,a.ShapeUtil.size(this.dims))}reshape(e){if(a.ShapeUtil.size(e)!==a.ShapeUtil.size(this.dims))throw new Error("Invalid new shape");return new s(this.module,this.dataType,this.data,e)}}class u{get customData(){return this.backend.currentKernelCustomData}constructor(e,t,n){this.module=e,this.backend=t;const r=e.HEAPU32;let o=n>>2;this.opKernelContext=r[o++];const i=r[o++],a=[];for(let t=0;t"number"==typeof e?this.inputs[e]:e)))&&void 0!==o?o:this.inputs,l=null!==(i=null==t?void 0:t.outputs)&&void 0!==i?i:[];return this.backend.run(e,u,l,((e,t,n)=>new s(this.module,t,this.output(e,n),n)),((e,t)=>{const n=(0,r.getTensorElementSize)(e);if(!n)throw new Error(`Unsupported data type: ${e}`);const o=n*a.ShapeUtil.size(t);return new s(this.module,e,this.backend.gpuDataManager.create(o).id,t)}))}output(e,t){const n=this.module.stackSave();try{const n=this.module.stackAlloc(4*(1+t.length));let r=n>>2;this.module.HEAPU32[r++]=t.length;for(let e=0;e{const t=e.jsepInit;if(t&&navigator.gpu){const n=new o.WebGpuBackend;await n.initialize(),t({backend:n},(e=>n.alloc(e)),(e=>n.free(e)),((t,r,o,a=!1)=>{if(a)(0,i.LOG_DEBUG)("verbose",(()=>`[WebGPU] jsepCopyGpuToGpu: src=${t}, dst=${r}, size=${o}`)),n.memcpy(t,r);else{(0,i.LOG_DEBUG)("verbose",(()=>`[WebGPU] jsepCopyCpuToGpu: dataOffset=${t}, gpuDataId=${r}, size=${o}`));const a=e.HEAPU8.subarray(t,t+o);n.upload(r,a)}}),(async(t,r,o)=>{(0,i.LOG_DEBUG)("verbose",(()=>`[WebGPU] jsepCopyGpuToCpu: gpuDataId=${t}, dataOffset=${r}, size=${o}`)),await 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t;if(0===i)return e;const a=Math.max(e.length,t.length),s=new Array(a);if(r){if(o<2||i<2)return;const r=n.calcMatMulShape([e[o-2],e[o-1]],[t[i-2],t[i-1]]);if(void 0===r)return;[s[a-2],s[a-1]]=r}for(let n=r?3:1;n<=a;n++){const r=o-n<0?1:e[o-n],u=i-n<0?1:t[i-n];if(r!==u&&r>1&&u>1)return;s[a-n]=Math.max(r,u)}return s}static isValidBroadcast(e,t){const n=e.length,r=t.length;if(n>r)return!1;for(let o=1;o<=n;o++)if(1!==e[n-o]&&e[n-o]!==t[r-o])return!1;return!0}}t.BroadcastUtil=r;class o{static size(e){return o.getSizeFromDimensionRange(e,0,e.length)}static sizeFromDimension(e,t){if(t<0||t>e.length)throw new Error(`invalid dimension of ${t} for sizeFromDimension as Tensor has ${e.length} dimensions.`);return o.getSizeFromDimensionRange(e,t,e.length)}static sizeToDimension(e,t){if(t<0||t>e.length)throw new Error(`invalid dimension of ${t} for sizeToDimension as Tensor has ${e.length} dimensions.`);return o.getSizeFromDimensionRange(e,0,t)}static getSizeFromDimensionRange(e,t,n){let r=1;for(let 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Error("gpu data for uploading does not exist");if(u.originalSize!==a)throw new Error(`inconsistent data size. gpu data size=${u.originalSize}, data size=${a}`);const l=this.backend.device.createBuffer({mappedAtCreation:!0,size:s,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC}),c=l.getMappedRange();new Uint8Array(c).set(new Uint8Array(n,o,a)),l.unmap();const p=this.backend.getCommandEncoder();this.backend.endComputePass(),p.copyBufferToBuffer(l,0,u.gpuData.buffer,0,s),(0,r.LOG_DEBUG)("verbose",(()=>`[WebGPU] GpuDataManager.upload(id=${e})`)),this.buffersForUploadingPending.push(l)}memcpy(e,t){const n=this.storageCache.get(e);if(!n)throw new Error("source gpu data for memcpy does not exist");const r=this.storageCache.get(t);if(!r)throw new Error("destination gpu data for memcpy does not exist");if(n.originalSize!==r.originalSize)throw new Error("inconsistent source and destination gpu data size");const o=i(n.originalSize);this.backend.getCommandEncoder().copyBufferToBuffer(n.gpuData.buffer,0,r.gpuData.buffer,0,o)}create(e,t=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST){const n=i(e),s=this.backend.device.createBuffer({size:n,usage:t}),u={id:a++,type:o.GpuDataType.default,buffer:s};return this.storageCache.set(u.id,{gpuData:u,originalSize:e}),(0,r.LOG_DEBUG)("verbose",(()=>`[WebGPU] GpuDataManager.create(size=${e}) => id=${u.id}`)),u}get(e){var t;return null===(t=this.storageCache.get(e))||void 0===t?void 0:t.gpuData}release(e){const t=this.storageCache.get(e);if(!t)throw new Error("releasing data does not exist");return(0,r.LOG_DEBUG)("verbose",(()=>`[WebGPU] GpuDataManager.release(id=${e}), gpuDataId=${t.gpuData.id}`)),this.storageCache.delete(e),this.buffersPending.push(t.gpuData.buffer),this.downloadCache.get(e)&&this.downloadCache.delete(e),t.originalSize}async download(e){const t=this.downloadCache.get(e);if(t)return t.data;const n=this.storageCache.get(e);if(!n)throw new Error("data does not exist");const r=this.backend.getCommandEncoder();this.backend.endComputePass();const o=this.backend.device.createBuffer({size:n.originalSize,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ});r.copyBufferToBuffer(n.gpuData.buffer,0,o,0,n.originalSize),this.backend.flush();const i=new Promise((e=>{o.mapAsync(GPUMapMode.READ).then((()=>{const t=o.getMappedRange().slice(0);o.destroy(),e(t)}))}));return this.downloadCache.set(e,{data:i}),i}refreshPendingBuffers(){for(const e of this.buffersForUploadingPending)e.destroy();for(const e of this.buffersPending)e.destroy()}}t.createGpuDataManager=(...e)=>new s(...e)},8510:function(e,t,n){"use strict";var r=this&&this.__createBinding||(Object.create?function(e,t,n,r){void 0===r&&(r=n);var o=Object.getOwnPropertyDescriptor(t,n);o&&!("get"in o?!t.__esModule:o.writable||o.configurable)||(o={enumerable:!0,get:function(){return 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Map([["Abs",[d.abs]],["Acos",[d.acos]],["Acosh",[d.acosh]],["Add",[a.add]],["Asin",[d.asin]],["Asinh",[d.asinh]],["Atan",[d.atan]],["Atanh",[d.atanh]],["AveragePool",[c.averagePool,c.parseAveragePoolAttributes]],["Ceil",[d.ceil]],["ClipV10",[d.clipV10]],["Clip",[d.clip]],["Conv",[s.conv,s.parseConvAttributes]],["Cos",[d.cos]],["Cosh",[d.cosh]],["Div",[a.div]],["Elu",[d.elu,d.parseAlphaAttributes]],["Erf",[d.erf]],["Exp",[d.exp]],["Floor",[d.floor]],["Gemm",[u.gemm,u.parseGemmAttributes]],["GlobalAveragePool",[c.globalAveragePool,c.parseGlobalAveragePoolAttributes]],["GlobalMaxPool",[c.globalMaxPool,c.parseGlobalMaxPoolAttributes]],["LeakyRelu",[d.leakyRelu,d.parseAlphaAttributes]],["MatMul",[l.matMul]],["MaxPool",[c.maxPool,c.parseMaxPoolAttributes]],["Mul",[a.mul]],["Neg",[d.neg]],["Pow",[a.pow]],["Reciprocal",[d.reciprocal]],["Relu",[d.relu]],["Sigmoid",[d.sigmoid]],["Sin",[d.sin]],["Sinh",[d.sinh]],["Sqrt",[d.sqrt]],["Sub",[a.sub]],["Tan",[d.tan]],["Tanh",[d.tanh]],["ThresholdedRelu",[d.thresholdedRelu,d.parseAlphaAttributes]],["Transpose",[p.transpose,p.parseTransposeAttributes]]])},1427:(e,t)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.biasActivationSnippet=t.activationFnSnippet=t.typeSnippet=void 0,t.typeSnippet=e=>{switch(e){case 1:return"f32";case 2:return"vec2";case 3:return"vec3";case 4:return"vec4";default:throw new Error(`${e}-component is not supported.`)}},t.activationFnSnippet=(e,t=!1,n=!1,r=3)=>"",t.biasActivationSnippet=(e,t)=>`\n ${e?"value = value + getBiasByOutputCoords(coords);":""}\n ${t?"value = activation(value, coords);":""}\n `},9456:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.createConv2DMatMulProgramInfo=void 0;const r=n(4955),o=n(6952),i=n(1163),a=n(1427),s=n(4085),u=n(158);t.createConv2DMatMulProgramInfo=(e,t,n,l,c,p,d,f,h)=>{const g="NHWC"===n.format,m=g?e[0].dims[3]:e[0].dims[1],b=l[0],y=g?l[2]:l[3],w=g?l[1]:l[2],_=g?l[3]:l[1],v=((m%4==0||m%3==0)&&g||y%4==0&&!g)&&_%4==0,x=g?_:y*w,T=g?y*w:_,S=v?[8,8,1]:[x<=4?4:16,x>4&&T<=4?4:16,1],O=v?[4,4,1]:[x<=4?1:2,x>4&&T<=4?1:2,1],A=[Math.ceil(x/S[0]/O[0]),Math.ceil(T/S[1]/O[1]),Math.ceil(b/S[2]/O[1])];(0,r.LOG_DEBUG)("verbose",(()=>`[conv2d_mm_webgpu] dispatch = ${A}`));const E=v?g&&m%4!=0?3:4:O[0],I=S[1]*O[1],$=S[0]*O[0],P=Math.max(S[0]*E,S[1]),D=c%I==0,k=p%$==0,C=d%P==0,R=v?[E,4,4]:[1,1,1],M=[`@group(0) @binding(0) var x: array<${v&&4===E?"vec4":"f32"}>;`,`@group(0) @binding(1) var w: array<${v?"vec4":"f32"}>;`];let N=`\n fn setOutputAtIndex(flatIndex : i32, value : ${v?"vec4":"f32"}) {\n result[flatIndex] = ${v?"vec4":"f32"}(value);\n }\n fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${v?"vec4":"f32"}) {\n let flatIndex = getOutputIndexFromCoords(vec4(d0, d1, d2, d3));\n setOutputAtIndex(flatIndex ${v?"/ 4":""}, value);\n }`;return f&&(M.push(`@group(0) @binding(2) var bias: array<${v?"vec4":"f32"}>;`),N+=`\n fn getBiasByOutputCoords(coords : vec4) -> ${v?"vec4":"f32"} {\n return bias[coords.${g?"w":"y"}${v?"/ 4":""}];\n }`),Object.assign(Object.assign({},t),{outputs:[{dims:l,dataType:e[0].dataType,gpuDataType:i.GpuDataType.default}],dispatchGroup:()=>({x:A[0],y:A[1],z:A[2]}),getShaderSource:()=>`\n ${s.utilFunctions}\n //struct Uniforms { xShape : vec4, wShape : vec4, outShape : vec4,\n // outShapeStrides: vec3, filterDims : vec2, pad : vec2, stride : vec2,\n // dilation : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32 };\n ${M.join("")}\n @group(0) @binding(${M.length}) var result: array<${v?"vec4":"f32"}>;\n //@group(0) @binding(${M.length+1}) var uniforms: Uniforms;\n\n const xShape : vec4 = vec4(${e[0].dims.join(",")});\n const wShape : vec4 = vec4(${e[1].dims.join(",")});\n const outShape : vec4 = vec4(${l.join(",")});\n const outShapeStrides : vec3 = vec3(${o.ShapeUtil.computeStrides(l).slice(0,3).join(",")});\n const filterDims : vec2 = vec2(${n.kernelShape[0]}, ${n.kernelShape[1]});\n const pad : vec2 = vec2(${n.pads[0]}, ${n.pads[1]});\n const stride : vec2 = vec2(${n.strides[0]}, ${n.strides[1]});\n const dilation : vec2 = vec2(${n.dilations[0]}, ${n.dilations[1]});\n const dimAOuter : i32 = ${c};\n const dimBOuter : i32 = ${p};\n const dimInner : i32 = ${d};\n ${N}\n ${((e,t,n,r,o=!1,i,s=!1,u=4,l=4,c=4)=>{const p=e?"\n let coord = vec4(batch, xRow, xCol, xCh);\n ":"\n let coord = vec4(batch, xCh, xRow, xCol);\n ",d=e?"\n let coords = vec4(\n batch,\n row / outWidth,\n row % outWidth,\n col);\n ":"\n let coords = vec4(\n batch,\n row,\n col / outWidth,\n col % outWidth);\n ",f=e?"xShape[1]":"xShape[2]",h=e?"xShape[2]":"xShape[3]",g=e?"row":"col",m=e?"col":"row",b=`\n let inChannels = wShape[2];\n let outWidth = ${e?"outShape[2]":"outShape[3]"};\n let outRow = ${g} / outWidth;\n let outCol = ${g} % outWidth;\n\n let WRow = ${m} / (filterDims[1] * inChannels);\n let WCol = ${m} / inChannels % filterDims[1];\n let xRow = outRow * stride[0] + dilation[0] * WRow - pad[0];\n let xCol = outCol * stride[1] + dilation[1] * WCol - pad[1];\n let xCh = ${m} % inChannels;\n var resData = ${(0,a.typeSnippet)(u)}(0.0);\n // The bounds checking is always needed since we use it to pad zero for\n // the 'same' padding type.\n if (xRow >= 0 && xRow < ${f} && xCol >= 0 && xCol < ${h}) {\n ${p}\n let xIndex = getIndexFromCoords4D(coord, xShape);\n ${(e=>{switch(e){case 1:return"resData = x[xIndex];";case 3:return"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);";case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${e} is not supported.`)}})(u)}\n }\n return resData;`,y=e?t&&r?`\n let col = colIn * ${u};\n ${b}`:`\n let col = colIn * ${u};\n if (row < dimAOuter && col < dimInner) {\n ${b}\n }\n return ${(0,a.typeSnippet)(u)}(0.0);`:r&&n?`\n let col = colIn * ${u};\n ${b}`:`\n let col = colIn * ${u};\n if (row < dimInner && col < dimBOuter) {\n ${b}\n }\n return ${(0,a.typeSnippet)(u)}(0.0);`,w=`${(e=>{switch(e){case 1:return"return w[row * wShape[3] + colIn];";case 4:return"return w[row * wShape[3] / 4 + colIn];";default:throw new Error(`innerElementSize ${e} is not supported.`)}})(l)}`,_=(0,a.typeSnippet)(c),v=e?(0,a.typeSnippet)(u):(0,a.typeSnippet)(l),x=e?(0,a.typeSnippet)(l):(0,a.typeSnippet)(u);return`\n ${(0,a.activationFnSnippet)(i,s,4===c,4)}\n fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${v} {\n ${e?y:w}\n }\n\n fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${x} {\n ${e?w:y}\n }\n\n fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${_}) {\n let col = colIn * ${c};\n if (row < dimAOuter && col < dimBOuter)\n {\n var value = valueIn;\n let outWidth = ${e?"outShape[2]":"outShape[3]"};\n ${d}\n ${(0,a.biasActivationSnippet)(o,i)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }`})(g,D,k,C,f,void 0,!1,R[0],R[1],R[2])}\n ${v?(0,u.makeMatMulPackedVec4Source)(O,S,!g,P):(0,u.makeMatMulPackedSource)(O,S,!g,P,!1,void 0,h)}`})}},4085:(e,t)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.utilFunctions=void 0,t.utilFunctions="\nfn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 {\n return dot(coords, vec4(\n shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n}\nfn getOutputIndexFromCoords(coords : vec4) -> i32 {\n return dot(coords, vec4(\n outShapeStrides.x, outShapeStrides.y, outShapeStrides.z, 1));\n}\n"},158:(e,t)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.makeMatMulPackedSource=t.makeMatMulPackedVec4Source=void 0,t.makeMatMulPackedVec4Source=(e,t,n=!1,r=32,o=!1,i=32,a=!1)=>{const s=t[1]*e[1],u=t[0]*e[0],l=n?s:r,c=n?r:s,p=l/t[0],d=r/t[1];if((!n||4!==p||4!==e[1])&&(n||3!==p&&4!==p)||l%t[0]!=0||r%t[1]!=0||4!==e[0])throw new Error(`If transposeA ${n} is true, innerElementSize ${p} and workPerThread[1] ${e[1]} must be 4.\n Otherwise, innerElementSize ${p} must be 3 or 4.\n tileAWidth ${l} must be divisible by workgroupSize[0]${t[0]}. tileInner ${r} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return`\nvar mm_Asub : array, ${l/p}>, ${c}>;\nvar mm_Bsub : array, ${u/e[0]}>, ${r}>;\n\nconst rowPerThread = ${e[1]};\nconst colPerThread = ${e[0]};\nconst innerElementSize = ${p};\nconst tileInner = ${r};\n\n@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]})\nfn main(@builtin(local_invocation_id) localId : vec3,\n @builtin(global_invocation_id) globalId : vec3,\n @builtin(workgroup_id) workgroupId : vec3) {\n let localRow = i32(localId.y);\n let tileRow = ${a?"0":"localRow * rowPerThread"};\n let tileCol = i32(localId.x);\n\n let globalRow = ${a?"0":"i32(globalId.y) * rowPerThread"};\n let globalCol = i32(globalId.x);\n let batch = ${o?"0":"i32(globalId.z)"};\n let globalRowStart = i32(workgroupId.y) * ${s};\n\n let numTiles = ${o?`${Math.ceil(i/r)}`:"(dimInner - 1) / tileInner + 1"};\n var kStart = ${o?`i32(globalId.z) * ${i}`:"0"};\n\n var acc: array, rowPerThread>;\n\n // Loop over shared dimension.\n let tileRowB = localRow * ${d};\n for (var t = 0; t < numTiles; t = t + 1) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n let inputRow = tileRow + innerRow;\n let inputCol = tileCol;\n ${f=n,f?"\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n kStart + inputRow,\n globalRowStart / innerElementSize + inputCol);\n ":"\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n globalRow + innerRow,\n kStart / innerElementSize + inputCol);\n "}\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${d}; innerRow = innerRow + 1) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol);\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n for (var k = 0; k < tileInner / innerElementSize; k = k + 1) {\n let BCached0 = mm_Bsub[k * innerElementSize][tileCol];\n let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol];\n let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol];\n ${3===p?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"}\n\n ${((e,t)=>e?`\n let ACached0 = mm_Asub[k * innerElementSize][localRow];\n let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];\n let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];\n ${3===t?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"}\n for (var i = 0; i < rowPerThread; i = i + 1) {\n acc[i] = BCached0 * ACached0[i] + acc[i];\n acc[i] = BCached1 * ACached1[i] + acc[i];\n acc[i] = BCached2 * ACached2[i] + acc[i];\n ${3===t?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"}\n }`:`\n for (var i = 0; i < rowPerThread; i = i + 1) {\n let ACached = mm_Asub[tileRow + i][k];\n acc[i] = BCached0 * ACached.x + acc[i];\n acc[i] = BCached1 * ACached.y + acc[i];\n acc[i] = BCached2 * ACached.z + acc[i];\n ${3===t?"":"acc[i] = BCached3 * ACached.w + acc[i];"}\n }`)(n,p)}\n }\n\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n }\n}`;var f};const n=e=>e?"\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n kStart + inputRow,\n globalRowStart + inputCol);\n ":"\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n globalRowStart + inputRow,\n kStart + inputCol);\n ";t.makeMatMulPackedSource=(e,t,r=!1,o=32,i=!1,a=32,s=!1)=>{const u=e[1]*t[1],l=e[0]*t[0],c=r?u:o,p=r?o:u;if(p%t[1]!=0||c%t[0]!=0||o%t[1]!=0)throw new Error(`tileAHight ${p} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${c} must be divisible by workgroupSize[0]${t[0]}, tileInner ${o} must be divisible by workgroupSize[1]${t[1]}`);const d=p/t[1],f=c/t[0],h=o/t[1],g=s?`\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n let globalRowStart = i32(workgroupId.y) * ${u};\n let globalColStart = i32(workgroupId.x) * ${l};\n\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t = t + 1) {\n // Load one tile of A into local memory.\n for (var inputRow = localRow; inputRow < ${p}; inputRow = inputRow + ${t[1]}) {\n for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${t[0]}) {\n ${n(r)}\n }\n }\n // Load one tile of B into local memory.\n for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${t[1]}) {\n for (var inputCol = localCol; inputCol < ${l}; inputCol = inputCol + ${t[0]}) {\n mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n kStart + inputRow,\n globalColStart + inputCol);\n }\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array;\n for (var k = 0; k < tileInner; k = k + 1) {\n for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n BCached[inner] = mm_Bsub[k][localCol + inner * ${t[0]}];\n }\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n let ACached = ${r?`mm_Asub[k][localRow + innerRow * ${t[1]}];`:`mm_Asub[localRow + innerRow * ${t[1]}][k];`}\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n acc[innerRow][innerCol] = acc[innerRow][innerCol] +\n ACached * BCached[innerCol];\n }\n }\n }\n workgroupBarrier();\n }\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n let gRow = globalRowStart + localRow + innerRow * ${t[1]};\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n let gCol = globalColStart + localCol + innerCol * ${t[0]};\n mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);\n }\n }\n `:`\nlet tileRow = i32(localId.y) * rowPerThread;\nlet tileCol = i32(localId.x) * colPerThread;\n\nlet globalRow = i32(globalId.y) * rowPerThread;\nlet globalCol = i32(globalId.x) * colPerThread;\nlet globalRowStart = i32(workgroupId.y) * ${u};\n\nlet tileRowA = i32(localId.y) * ${d};\nlet tileColA = i32(localId.x) * ${f};\nlet tileRowB = i32(localId.y) * ${h};\n// Loop over shared dimension.\nfor (var t = 0; t < numTiles; t = t + 1) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < ${d}; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < ${f}; innerCol = innerCol + 1) {\n let inputRow = tileRowA + innerRow;\n let inputCol = tileColA + innerCol;\n ${n(r)}\n }\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${h}; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol + innerCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n kStart + inputRow,\n globalCol + innerCol);\n }\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array;\n for (var k = 0; k < tileInner; k = k + 1) {\n for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n BCached[inner] = mm_Bsub[k][tileCol + inner];\n }\n\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n ${(e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];")(r)}\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n }\n }\n }\n\n workgroupBarrier();\n}\n\nfor (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n acc[innerRow][innerCol]);\n }\n}\n`;return`\n var mm_Asub : array, ${p}>;\n var mm_Bsub : array, ${o}>;\n const rowPerThread = ${e[1]};\n const colPerThread = ${e[0]};\n const tileInner = ${o};\n\n@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]})\nfn main(@builtin(local_invocation_id) localId : vec3,\n @builtin(global_invocation_id) globalId : vec3,\n @builtin(workgroup_id) workgroupId : vec3) {\n let batch = ${i?"0":"i32(globalId.z)"};\n let numTiles = ${i?`${Math.ceil(a/o)}`:"(dimInner - 1) / tileInner + 1"};\n var kStart = ${i?`i32(globalId.z) * ${a}`:"0"};\n\n var acc : array, rowPerThread>;\n\n // Without this initialization strange values show up in acc.\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n acc[innerRow][innerCol] = 0.0;\n }\n }\n ${g}\n }\n`}},504:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.sub=t.pow=t.mul=t.div=t.add=void 0;const r=n(6952),o=n(1163),i=n(2075),a=(e,t,n,a,s)=>{const u={name:t,inputTypes:[o.GpuDataType.default,o.GpuDataType.default],cacheHint:s};return Object.assign(Object.assign({},u),{get:()=>((e,t,n,a,s,u=t.dataType)=>{var l,c;const p=!r.ShapeUtil.areEqual(t.dims,n.dims);let d=t.dims,f=r.ShapeUtil.size(t.dims),h=!1;if(p){const e=r.BroadcastUtil.calcShape(t.dims,n.dims,!1);if(!e)throw new Error("Can't perform binary op on the given tensors");d=e,f=r.ShapeUtil.size(d);let o=1;for(let e=0;e((e,t,n,o,a,s,u,l,c="f32",p="f32",d="f32")=>{const f=r.ShapeUtil.size(o),h=Math.ceil(f/4);let g,m;"string"==typeof u?g=m=(e,t)=>`${u}((${e}),(${t}))`:"function"==typeof u?g=m=u:(g=u.scalar,m=u.vector);let b="";const y=(0,i.createIndicesHelper)("output",o);if(s){const e=e=>{const t=r.ShapeUtil.computeStrides(e),n=[];for(let r=e.length-1;r>=0;r--){const i=0===o.length?"0u":1===o.length?"(*outputIndices)":`(*outputIndices)[${r+o.length-e.length}]`;n.push(`${t[r]}u * (${i} % ${e[r]}u)`)}return n.length>0?n.join("+"):"0u"};b=`\n ${y.o2iImpl}\n\n fn calcOffsetA(outputIndices: ptr) -> u32 {\n return ${e(t)};\n }\n\n fn calcOffsetB(outputIndices: ptr) -> u32 {\n return ${e(n)};\n }\n `}let w;if(a)w=s?`\n ${y.indicesVariableDeclaration("outputIndices")}\n ${y.o2iCall("global_idx * 4u","outputIndices")}\n let offsetA = calcOffsetA(&outputIndices);\n let offsetB = calcOffsetB(&outputIndices);\n outputData[global_idx] = ${m("aData[offsetA / 4u]","bData[offsetB / 4u]")};`:`outputData[global_idx] = ${m("aData[global_idx]","bData[global_idx]")};`;else{if(!s)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");const e=e=>{const t=`aData[indexA${e}][componentA${e}]`,n=`bData[indexB${e}][componentB${e}]`;return`\n ${y.o2iCall(`global_idx * 4u + ${e}u`,"outputIndices")}\n let offsetA${e} = calcOffsetA(&outputIndices);\n let offsetB${e} = calcOffsetB(&outputIndices);\n let indexA${e} = offsetA${e} / 4u;\n let indexB${e} = offsetB${e} / 4u;\n let componentA${e} = offsetA${e} % 4u;\n let componentB${e} = offsetB${e} % 4u;\n outputData[global_idx][${e}] = ${g(t,n)};`};w=`\n ${y.indicesVariableDeclaration("outputIndices")}\n ${e(0)}\n ${e(1)}\n ${e(2)}\n ${e(3)}`}return`\n @group(0) @binding(0) var aData : array>;\n @group(0) @binding(1) var bData : array>;\n @group(0) @binding(2) var outputData : array>;\n\n ${null!=l?l:""}\n ${b}\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes(h)}\n ${w}\n }`})(e,t.dims,n.dims,d,h,p,a,s),outputs:[{dims:d,dataType:u,gpuDataType:o.GpuDataType.default}],dispatchGroup:()=>({x:Math.ceil(f/64/(h?4:1))})})})(u,e[0],e[1],n,a)})};t.add=e=>{e.compute(a(e.inputs,"Add",((e,t)=>`${e}+${t}`)))},t.div=e=>{e.compute(a(e.inputs,"Div",((e,t)=>`${e}/${t}`)))},t.mul=e=>{e.compute(a(e.inputs,"Mul",((e,t)=>`${e}*${t}`)))},t.pow=e=>{e.compute(a(e.inputs,"Pow",{scalar:(e,t)=>`pow_f32(${e},${t})`,vector:(e,t)=>`pow_vf32(${e},${t})`},"\n fn pow_f32(a : f32, b : f32) -> f32 {\n if (b == 0.0) {\n return 1.0;\n } else if (a < 0.0 && b != floor(b)) {\n return pow(a, b); // NaN\n }\n return select(sign(a), 1.0, round(abs(b) % 2.0) != 1.0) * pow(abs(a), b);\n }\n fn pow_vf32(a : vec4, b : vec4) -> vec4 {\n // TODO: implement vectorized pow\n return vec4(pow_f32(a.x, b.x), pow_f32(a.y, b.y), pow_f32(a.z, b.z), pow_f32(a.w, b.w));\n }\n "))},t.sub=e=>{e.compute(a(e.inputs,"Sub",((e,t)=>`${e}-${t}`)))}},2075:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.createShaderHelper=t.createIndicesHelper=t.WORKGROUP_SIZE=void 0;const r=n(6952);t.WORKGROUP_SIZE=64,t.createIndicesHelper=(e,t)=>{const n=t.length<2?"u32":`array`,o=r.ShapeUtil.computeStrides(t);let i="";for(let e=0;e) {\n var current = offset;\n ${i}\n }`,s=[];if(0===t.length)s.push("0u");else if(t.length<2)s.push("(*indices)");else for(let e=t.length-1;e>=0;e--)s.push(`${o[e]}u * ((*indices)[${e}])`);return{o2iImpl:a,o2iCall:(n,r)=>t.length<2?`${r}=${n};`:`ih_o2i_${e}(${n}, &${r});`,i2oImpl:t.length<2?"":`\n fn ih_i2o_${e}(indices: ptr) -> u32 {\n return ${s.join("+")};\n }`,i2oExpression:(n,r)=>t.length<2?`(${r?"*":""}${n})`:`ih_i2o_${e}(${r?"":"&"}${n})`,indicesVariableDeclaration:(e,t)=>`var ${e}:${n}${t?`=${n}(${t.join(",")})`:""};`,iType:n}};class o{constructor(e){this.normalizedDispatchGroup=e}guardAgainstOutOfBoundsWorkgroupSizes(e){return`if (global_idx >= ${"number"==typeof e?`${e}u`:e}) { return; }`}mainStart(e=t.WORKGROUP_SIZE){const n="number"==typeof e?e:e[0],r="number"==typeof e?1:e[1],o="number"==typeof e?1:e[2],i=1===this.normalizedDispatchGroup[1]&&1===this.normalizedDispatchGroup[2];return`@compute @workgroup_size(${n}, ${r}, ${o})\n fn main(${i?"@builtin(global_invocation_id) global_id : vec3":"@builtin(local_invocation_index) local_index : u32,\n @builtin(workgroup_id) workgroup_id : vec3"}) {\n ${i?"let global_idx = global_id.x;":`let global_idx = (workgroup_id.z * ${this.normalizedDispatchGroup[0]*this.normalizedDispatchGroup[1]}u +\n workgroup_id.y * ${this.normalizedDispatchGroup[0]}u + workgroup_id.x) * ${n*r*o}u + local_index;`}\n `}}t.createShaderHelper=e=>new o(e)},9192:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.createGroupedConvProgramInfoLoader=void 0;const r=n(6952),o=n(1163),i=n(2075),a=n(9770),s=n(3997);t.createGroupedConvProgramInfoLoader=(e,t,n)=>{const u=(l=e.length>2,c=t.cacheKey,{name:"GroupedConv",inputTypes:l?[o.GpuDataType.default,o.GpuDataType.default,o.GpuDataType.default]:[o.GpuDataType.default,o.GpuDataType.default],cacheHint:c});var l,c;return Object.assign(Object.assign({},u),{get:()=>((e,t,n,u)=>{const l=e.length>2,c=l?"value += b[output_channel];":"",p=e[0].dims,d=e[1].dims,f=d[0]/n.group,h="f32",{activationFunction:g,applyActivation:m}=(0,s.getActicationSnippet)(n),b=[`@group(0) @binding(0) var x : array<${h}>;`,`@group(0) @binding(1) var w : array<${h}>;`];l&&b.push(`@group(0) @binding(2) var b : array<${h}>;`);const y="NHWC"===n.format,w=(0,a.calculateOutputShape)(p,d,n.dilations,n.pads,n.strides,y),_=r.ShapeUtil.size(w),v=(0,i.createIndicesHelper)("output",w),x=(0,i.createIndicesHelper)("x",p),T=(0,i.createIndicesHelper)("w",d);return Object.assign(Object.assign({},t),{outputs:[{dims:u?u(w):w,dataType:e[0].dataType,gpuDataType:o.GpuDataType.default}],getShaderSource:e=>`\n const strides: vec2 = vec2(${n.strides[0]}u, ${n.strides[1]}u);\n const pads: vec2 = vec2(${n.pads[0]}u, ${n.pads[1]}u);\n\n ${b.join("\n")}\n @group(0) @binding(${b.length}) var output : array<${h}>;\n\n ${g}\n ${v.o2iImpl}\n ${x.i2oImpl}\n ${T.i2oImpl}\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes(_)}\n\n ${v.indicesVariableDeclaration("outputIndices")}\n ${v.o2iCall("global_idx","outputIndices")}\n let batch: u32 = outputIndices[0];\n let output_channel: u32 = outputIndices[${y?3:1}];\n let xRCCorner: vec2 = vec2(outputIndices[${y?1:2}], outputIndices[${y?2:3}]) * strides - pads;\n let group_id: u32 = output_channel / ${f}u;\n\n var value: ${h} = ${h}(0);\n for (var wInChannel: u32 = 0u; wInChannel < ${d[1]}u; wInChannel++) {\n let input_channel = group_id * ${d[1]}u + wInChannel;\n for (var wHeight: u32 = 0u; wHeight < ${d[2]}u; wHeight++) {\n let xHeight = xRCCorner.x + wHeight * ${n.dilations[0]}u;\n\n if (xHeight < 0u || xHeight >= ${p[y?1:2]}u) {\n continue;\n }\n\n for (var wWidth: u32 = 0u; wWidth < ${d[3]}u; wWidth++) {\n let xWidth = xRCCorner.y + wWidth * ${n.dilations[1]}u;\n if (xWidth < 0u || xWidth >= ${p[y?2:3]}u) {\n continue;\n }\n\n ${x.indicesVariableDeclaration("xIndices",y?["batch","xHeight","xWidth","input_channel"]:["batch","input_channel","xHeight","xWidth"])}\n let xVal = x[${x.i2oExpression("xIndices")}];\n ${T.indicesVariableDeclaration("wIndices",["output_channel","wInChannel","wHeight","wWidth"])}\n let wVal = w[${T.i2oExpression("wIndices")}];\n value += xVal*wVal;\n }\n }\n }\n ${c}\n ${m}\n output[global_idx] = value;\n }`,dispatchGroup:()=>({x:Math.ceil(_/64)})})})(e,u,t,n)})}},9770:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.conv=t.parseConvAttributes=t.calculateOutputShape=void 0;const r=n(6952),o=n(387),i=n(9192),a=n(3822),s=n(3997),u=n(2625);t.calculateOutputShape=(e,t,n,r,o,i)=>{const a=e[0],s=e.slice(i?1:2,i?3:4),u=s.length,l=t[0],c=t.slice(2).map(((e,t)=>e+(e-1)*(n[t]-1))),p=s.map(((e,t)=>e+r[t]+r[t+u])).map(((e,t)=>Math.floor((e-c[t]+o[t])/o[t])));return p.splice(0,0,a),p.splice(i?3:1,0,l),p};const l=(0,o.createAttributeWithCacheKey)({perm:[2,3,1,0]}),c=(e,t)=>{const n=e.kernelShape.slice();for(let e=2;e{const t=(0,s.parseInternalActivationAttributes)(e),n=e.format,r=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],i=e.dilations,a=e.group,u=e.kernel_shape,l=e.pads,c=e.strides,p=e.w_is_const();return(0,o.createAttributeWithCacheKey)(Object.assign({autoPad:r,format:n,dilations:i,group:a,kernelShape:u,pads:l,strides:c,wIsConst:p},t))},t.conv=(e,n)=>{((e,t)=>{if(!e||2!==e.length&&3!==e.length)throw new Error("Conv requires 2 or 3 inputs");if(4!==e[0].dims.length&&3!==e[0].dims.length)throw new Error("currently only support conv 1D and 2D");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");if(e[0].dims["NHWC"===t.format?e[0].dims.length-1:1]!==e[1].dims[1]*t.group)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(3===e.length&&(1!==e[2].dims.length||e[1].dims[0]!==e[2].dims[0]))throw new Error("invalid bias");const n=e[0].dims.length-2;if(t.dilations.length!==n)throw new Error(`dilations should be ${n}D`);if(t.strides.length!==n)throw new Error(`strides should be ${n}D`);if(t.pads.length!==2*n)throw new Error(`pads should be ${2*n}D`);if(0!==t.kernelShape.length&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape");if(1!==e[0].dataType||1!==e[1].dataType)throw new Error("Conv input(X,W) should be float tensor");if(3===e.length&&1!==e[2].dataType)throw new Error("Conv input(bias) should be float tensor")})(e.inputs,n),3===e.inputs[0].dims.length?((e,t)=>{const n="NHWC"===t.format,r=[e.inputs[0].reshape(n?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];3===e.inputs.length&&r.push(e.inputs[2]);const o=[0,t.pads[0],0,t.pads[1]],a=[1].concat(t.strides),s=[1].concat(t.dilations),u=[1].concat(t.kernelShape),l=c(Object.assign(Object.assign({},t),{pads:o,strides:a,dilations:s,kernelShape:u}),r);e.compute((0,i.createGroupedConvProgramInfoLoader)(r,l,(e=>n?[e[0],e[2],e[3]]:[])))})(e,n):((e,n,r)=>{var o;const s=c(r,n),p=3===n.length,d="NHWC"===r.format,f=n[0].dims[d?1:2],h=n[0].dims[d?2:3],g=n[0].dims[d?3:1],m=n[1].dims[2],b=n[1].dims[3],y=(0,t.calculateOutputShape)(n[0].dims,n[1].dims,r.dilations,s.pads,r.strides,d),w=y[d?1:2],_=y[d?2:3],v=y[d?3:1];if(d&&m===f&&b===h&&"VALID"===r.autoPad||1===m&&1===b&&1===r.dilations[0]&&1===r.dilations[1]&&1===r.strides[0]&&1===r.strides[1]&&("SAME_UPPER"===r.autoPad||"SAME_LOWER"===r.autoPad||"VALID"===r.autoPad))return void e.compute((0,i.createGroupedConvProgramInfoLoader)(n,s));if(!d||1!==r.group)return void e.compute((0,i.createGroupedConvProgramInfoLoader)(n,s));const x=d?w*_:v,T=d?v:w*_,S=m*b*g,O=null!==(o=e.customData.wT)&&void 0!==o?o:e.compute(Object.assign(Object.assign({},u.transposeProgramMetadata),{cacheHint:l.cacheKey,get:()=>(0,u.createTransposeProgramInfo)(n[1],l.perm)}),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.customData.wT&&(e.customData.wT=O);const A=[n[0],O];p&&(d||1!==n[2].dims.length?A.push(n[2]):A.push(n[2].reshape([n[2].dims[0],1,1]))),e.compute((0,a.createConv2DMatMulProgramInfoLoader)(A,s,y,x,T,S,p,!0),{inputs:A})})(e,e.inputs,n)}},3822:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.createConv2DMatMulProgramInfoLoader=void 0;const r=n(1163),o=n(9456);t.createConv2DMatMulProgramInfoLoader=(e,t,n,i,a,s,u,l)=>{const c=((e,t)=>({name:"Conv2DMatMul",inputTypes:e?[r.GpuDataType.default,r.GpuDataType.default,r.GpuDataType.default]:[r.GpuDataType.default,r.GpuDataType.default],cacheHint:t}))(u,t.cacheKey);return Object.assign(Object.assign({},c),{get:()=>(0,o.createConv2DMatMulProgramInfo)(e,c,t,n,i,a,s,u,l)})}},3997:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.parseInternalActivationAttributes=t.getActicationSnippet=void 0;const r=n(6952);t.getActicationSnippet=e=>{switch(e.activation){case"Relu":return{activationFunction:"",applyActivation:"value = max(value, 0.0);"};case"Sigmoid":return{activationFunction:"",applyActivation:"value = (1.0 / (1.0 + exp(-value)));"};case"Clip":return{activationFunction:`const clip_min_=f32(${e.clipMin});const clip_max_=f32(${e.clipMax});`,applyActivation:"value = clamp(value, clip_min_, clip_max_);"};default:return{activationFunction:"",applyActivation:""}}},t.parseInternalActivationAttributes=e=>{const t=(null==e?void 0:e.activation)||"";if("Clip"===t){const[n,o]=(null==e?void 0:e.activation_params)||[r.MIN_CLIP,r.MAX_CLIP];return{activation:t,clipMax:o,clipMin:n,activationCacheKey:`${t}:${n},${o}`}}return{activation:t,activationCacheKey:t}}},4271:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.parseGemmAttributes=t.gemm=void 0;const r=n(6952),o=n(387),i=n(1163);t.gemm=(e,t)=>{(e=>{if(!e)throw new Error("Input is missing");if(e.length<2||e.length>3)throw new Error("Invaid input number.");if(3===e.length&&e[2].dims.length>2)throw new Error("Invalid input shape of C");if(1!==e[0].dataType||1!==e[1].dataType||3===e.length&&1!==e[2].dataType)throw new Error("Invalid input type.");if(e[0].dataType!==e[1].dataType||3===e.length&&e[0].dataType!==e[2].dataType)throw new Error("Input types are mismatched")})(e.inputs),e.compute(((e,t)=>{const n={name:"Gemm",inputTypes:3===e.length?[i.GpuDataType.default,i.GpuDataType.default,i.GpuDataType.default]:[i.GpuDataType.default,i.GpuDataType.default],cacheHint:t.cacheKey};return Object.assign(Object.assign({},n),{get:()=>((e,t,n)=>{const o=t[0].dims.slice(),a=t[1].dims.slice(),[s,u,l]=r.GemmUtil.getShapeOfGemmResult(o,n.transA,a,n.transB,3===t.length?t[2].dims:void 0),c=[s,u];if(!c)throw new Error("Can't use gemm on the given tensors");const p=r.ShapeUtil.size(c);let d="";n.transA&&n.transB?d="value += a[k * M + m] * b[n * K + k];":n.transA&&!n.transB?d="value += a[k * M + m] * b[k * N + n];":!n.transA&&n.transB?d="value += a[m * K + k] * b[n * K + k];":n.transA||n.transB||(d="value += a[m * K + k] * b[k * N + n];");const f="f32",h=1===n.alpha?"":"value *= alpha;",g=3===t.length?`value += beta * c[${((e,t,n)=>{if(0===n.length)return"0u";const r=1===n.length&&1!==e||2===n.length&&n[0]!==e,o=n[n.length-1]!==t;let i="0u";return r||(i+=`+ m * ${n[n.length-1]}u`),o||(i+="+n"),i})(s,u,t[2].dims)}];`:"",m=[`@group(0) @binding(0) var a : array<${f}>;`,`@group(0) @binding(1) var b : array<${f}>;`];return 3===t.length&&m.push(`@group(0) @binding(2) var c : array<${f}>;`),Object.assign(Object.assign({},e),{outputs:[{dims:c,dataType:t[0].dataType,gpuDataType:i.GpuDataType.default}],getShaderSource:e=>`\n const M: u32 = ${s}u;\n const N: u32 = ${u}u;\n const K: u32 = ${l}u;\n const alpha = ${f}(${n.alpha});\n const beta = ${f}(${n.beta});\n\n ${m.join("\n")}\n @group(0) @binding(${t.length}) var output : array<${f}>;\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes(p)}\n\n let m = global_id.x / N;\n let n = global_id.x % N;\n\n var value = ${f}(0);\n for (var k: u32 = 0u; k<${l}u; k++) {\n ${d}\n }\n\n ${h}\n ${g}\n output[global_id.x] = value;\n\n }`,dispatchGroup:()=>({x:Math.ceil(p/64)})})})(n,e,t)})})(e.inputs,t))},t.parseGemmAttributes=e=>(0,o.createAttributeWithCacheKey)(e)},1522:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.matMul=t.createMatmulProgramInfoLoader=void 0;const r=n(6952),o=n(1163),i=n(3997);t.createMatmulProgramInfoLoader=(e,t)=>{const n=(a=e.length>2,s=t.activationCacheKey,{name:"MatMul",inputTypes:a?[o.GpuDataType.default,o.GpuDataType.default,o.GpuDataType.default]:[o.GpuDataType.default,o.GpuDataType.default],cacheHint:s});var a,s;return Object.assign(Object.assign({},n),{get:()=>((e,t,n)=>{const a=t[0].dims,s=t[1].dims,u=r.BroadcastUtil.calcShape(a,s,!0);if(!u)throw new Error("Can't use matmul on the given tensors");const l=r.ShapeUtil.size(u),c="f32",{activationFunction:p,applyActivation:d}=(0,i.getActicationSnippet)(n),f=u[u.length-2],h=a[a.length-1],g=u[u.length-1];return Object.assign(Object.assign({},e),{outputs:[{dims:u,dataType:t[0].dataType,gpuDataType:o.GpuDataType.default}],getShaderSource:e=>`\n const M: u32 = ${f}u;\n const N: u32 = ${g}u;\n const K: u32 = ${h}u;\n\n @group(0) @binding(0) var a : array<${c}>;\n @group(0) @binding(1) var b : array<${c}>;\n @group(0) @binding(2) var output : array<${c}>;\n\n ${p}\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes(l)}\n\n let stack = global_idx / (M * N);\n let mn = global_idx % (M * N);\n let n = global_idx % N;\n let m = mn / N;\n\n let offsetA = stack * (M * K);\n let offsetB = stack * (K * N);\n\n var value = ${c}(0);\n for (var k: u32 = 0u; k<${h}u; k++) {\n value += a[offsetA + m * K + k] * b[offsetB + k * N + n];\n }\n ${d}\n output[global_idx] = value;\n }`,dispatchGroup:()=>({x:Math.ceil(l/64)})})})(n,e,t)})},t.matMul=e=>{(e=>{if(!e||2!==e.length)throw new Error("MatMul requires 2 inputs.");if(e[0].dims[e[0].dims.length-1]!==e[1].dims[e[1].dims.length-2])throw new Error("shared dimension does not match.");if(1!==e[0].dataType||1!==e[1].dataType)throw new Error("inputs should be float type")})(e.inputs),e.compute((0,t.createMatmulProgramInfoLoader)(e.inputs,{activation:"",activationCacheKey:""}))}},5262:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.globalMaxPool=t.parseGlobalMaxPoolAttributes=t.parseMaxPoolAttributes=t.maxPool=t.globalAveragePool=t.parseGlobalAveragePoolAttributes=t.averagePool=t.parseAveragePoolAttributes=void 0;const r=n(6952),o=n(387),i=n(1163),a=n(2075),s=e=>{if(!e||1!==e.length)throw new Error("Pool ops requires 1 input.");if(4!==e[0].dims.length)throw new Error("Pool ops supports 2-D inputs only for now.");if(1!==e[0].dataType)throw new Error("Invalid input type.")},u=(e,t,n)=>{const o="NHWC"===t.format,i=o?[e[0].dims[0],e[0].dims[3],e[0].dims[1],e[0].dims[2]]:e[0].dims.slice(),a=Object.hasOwnProperty.call(t,"dilations"),s=t.kernelShape.slice(),u=t.strides.slice(),l=a?t.dilations.slice():[],c=t.pads.slice();r.PoolConvUtil.adjustPoolAttributes(n,i,s,u,l,c);const p=r.PoolConvUtil.computePoolOutputShape(n,i,u,l,s,c,t.autoPad),d=Object.assign({},t);return a?Object.assign(d,{kernelShape:s,strides:u,pads:c,dilations:l,cacheKey:t.cacheKey}):Object.assign(d,{kernelShape:s,strides:u,pads:c,cacheKey:t.cacheKey}),[d,o?[p[0],p[2],p[3],p[1]]:p]},l=(e,t,n,o,i,s,u,l)=>{const c="NHWC"===o.format,p=t.length,d=r.ShapeUtil.size(n),f=(0,a.createIndicesHelper)("output",n),h=(0,a.createIndicesHelper)("x",t);if(o.kernelShape.length<=2){const n=o.kernelShape[o.kernelShape.length-1],r=o.strides[o.strides.length-1],a=o.pads[o.pads.length/2-1],g=p-(c?2:1);let m="",b="",y="";if(m=a+o.pads[o.pads.length-1]!==0?`\n for (var i: u32 = 0u; i < ${n}u; i++) {\n xIndices[${g}] = indices[${g}] * ${r} - ${a} + i;\n if (xIndices[${g}] < 0 || xIndices[${g}] >= ${t[g]}) {\n pad++;\n continue;\n }\n let x_val = x[${h.i2oExpression("xIndices")}];\n ${i}\n }`:`\n for (var i: u32 = 0u; i < ${n}u; i++) {\n xIndices[${g}] = indices[${g}] * ${r} - ${a} + i;\n let x_val = x[${h.i2oExpression("xIndices")}];\n ${i}\n }`,2===o.kernelShape.length){const e=o.kernelShape[o.kernelShape.length-2],r=o.strides[o.strides.length-2],i=o.pads[o.pads.length/2-2],a=o.pads[o.pads.length-2],s=p-(c?3:2),u=t[s];b=i+a!==0?`\n for (var j: u32 = 0u; j < ${e}u; j++) {\n xIndices[${s}] = indices[${s}] * ${r} - ${i} + j;\n if (xIndices[${s}] < 0 || xIndices[${s}] >= ${u}) {\n pad+= ${n};\n continue;\n }\n `:`\n for (var j: u32 = 0u; j < ${e}u; j++) {\n xIndices[${s}] = indices[${s}] * ${r} - ${i} + j;\n `,y="\n }\n "}return`\n @group(0) @binding(0) var x : array<${u}>;\n @group(0) @binding(1) var output : array<${u}>;\n\n ${f.o2iImpl}\n ${h.i2oImpl}\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes(d)}\n\n ${f.indicesVariableDeclaration("indices")}\n ${f.o2iCall("global_idx","indices")}\n ${f.indicesVariableDeclaration("xIndices")}\n ${f.o2iCall("global_idx","xIndices")}\n\n var value: ${u} = ${u}(${l});\n var pad = 0;\n ${b}\n ${m}\n ${y}\n ${s}\n\n output[global_idx] = value;\n }`}{if(c)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");const n=r.ShapeUtil.size(o.kernelShape),a=r.ShapeUtil.computeStrides(o.kernelShape),g=a.length,m=o.pads.length;let b="";return b=o.pads.reduce(((e,t)=>e+t))?`\n if (xIndices[j] >= inputDims[j]) {\n pad++;\n isPad = true;\n break;\n }\n }\n if (!isPad) {\n let x_val = x[${h.i2oExpression("xIndices")}];\n ${i}\n }`:`\n }\n let x_val = x[${h.i2oExpression("xIndices")}];\n ${i}\n `,`\n @group(0) @binding(0) var x : array<${u}>;\n @group(0) @binding(1) var output : array<${u}>;\n\n ${f.o2iImpl}\n ${h.i2oImpl}\n\n const pads = array(${o.pads.map((e=>`${e}u`)).join(",")});\n const inputDims = array(${t.map((e=>`${e}u`)).join(",")});\n const kernelStrides = array(${a.map((e=>`${e}u`)).join(",")});\n const strides = array(${o.strides.map((e=>`${e}u`)).join(",")});\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes(d)}\n\n ${f.indicesVariableDeclaration("indices")}\n ${f.o2iCall("global_idx","indices")}\n ${f.indicesVariableDeclaration("xIndices")}\n ${f.o2iCall("global_idx","xIndices")}\n\n var offsets: array;\n\n var value = ${u}(${l});\n var pad = 0;\n var isPad = false;\n\n for (var i: u32 = 0u; i < ${n}u; i++) {\n var offset = i;\n for (var j = 0u; j < ${g-1}u; j++) {\n offsets[j] = offset / kernelStrides[j];\n offset -= offsets[j] * kernelStrides[j];\n }\n offsets[${g-1}] = offset;\n\n isPad = false;\n for (var j = ${p-g}u; j < ${p}u; j++) {\n xIndices[j] = indices[j] * strides[j - ${p-g}u]\n + offsets[j - ${p-g}u] - pads[j - 2u];\n ${b}\n }\n ${s}\n\n output[global_idx] = value;\n }`}},c=e=>({format:e.format,autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],ceilMode:e.ceil_mode,kernelShape:e.kernel_shape,strides:e.strides,pads:e.pads}),p=(e,t,n,o)=>{const[a,s]=u(e,o,n),c=r.ShapeUtil.size(a.kernelShape),p="f32";let d="";return a.countIncludePad?d+=`value /= ${p}(${c});`:d+=`value /= ${p}(${c} - pad);`,Object.assign(Object.assign({},t),{outputs:[{dims:s,dataType:e[0].dataType,gpuDataType:i.GpuDataType.default}],getShaderSource:t=>l(t,e[0].dims,s,a,"value += x_val;",d,p,"0.0"),dispatchGroup:()=>({x:Math.ceil(r.ShapeUtil.size(s)/64)})})};t.parseAveragePoolAttributes=e=>{const t=0!==e.count_include_pad,n=c(e);if(0!==n.ceilMode)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");return(0,o.createAttributeWithCacheKey)(Object.assign({countIncludePad:t},n))},t.averagePool=(e,t)=>{s(e.inputs);const n={name:"AveragePool",inputTypes:[i.GpuDataType.default],cacheHint:t.cacheKey};e.compute(Object.assign(Object.assign({},n),{get:()=>p(e.inputs,n,!1,t)}))};const d={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[],cacheKey:""};t.parseGlobalAveragePoolAttributes=e=>{const t=e.format;return Object.assign(Object.assign({format:t},d),{cacheKey:t})},t.globalAveragePool=(e,t)=>{s(e.inputs);const n={name:"GlobalAveragePool",inputTypes:[i.GpuDataType.default],cacheHint:t.cacheKey};e.compute(Object.assign(Object.assign({},n),{get:()=>p(e.inputs,n,!0,t)}))};const f=(e,t,n,o)=>{const[a,s]=u(e,o,n);return Object.assign(Object.assign({},t),{outputs:[{dims:s,dataType:e[0].dataType,gpuDataType:i.GpuDataType.default}],getShaderSource:t=>l(t,e[0].dims,s,a,"\n value = max(x_val, value);\n ","","f32","-1e5"),dispatchGroup:()=>({x:Math.ceil(r.ShapeUtil.size(s)/64)})})};t.maxPool=(e,t)=>{s(e.inputs);const n={name:"MaxPool",inputTypes:[i.GpuDataType.default],cacheHint:t.cacheKey};e.compute(Object.assign(Object.assign({},n),{get:()=>f(e.inputs,n,!1,t)}))},t.parseMaxPoolAttributes=e=>{const t=e.storage_order,n=e.dilations,r=c(e);if(0!==t)throw new Error("column major storage order is not yet supported for MaxPool");if(0!==r.ceilMode)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");return(0,o.createAttributeWithCacheKey)(Object.assign({storageOrder:t,dilations:n},r))},t.parseGlobalMaxPoolAttributes=e=>{const t=e.format;return Object.assign(Object.assign({format:t},d),{cacheKey:t})},t.globalMaxPool=(e,t)=>{s(e.inputs);const n={name:"GlobalMaxPool",inputTypes:[i.GpuDataType.default],cacheHint:t.cacheKey};e.compute(Object.assign(Object.assign({},n),{get:()=>f(e.inputs,n,!0,t)}))}},2625:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.parseTransposeAttributes=t.transpose=t.createTransposeProgramInfo=t.transposeProgramMetadata=void 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${null!=r?r:""}\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes(o)}\n\n let a = inputData[global_idx];\n outputData[global_idx] = ${i};\n }`})(e,r.ShapeUtil.size(t.dims),n,o),outputs:[{dims:t.dims,dataType:t.dataType,gpuDataType:i.GpuDataType.default}],dispatchGroup:e=>({x:Math.ceil(r.ShapeUtil.size(e[0].dims)/64/4)})}))(s,e,n,o)})};t.abs=e=>{e.compute(a(e.inputs[0],"Abs","abs"))},t.acos=e=>{e.compute(a(e.inputs[0],"Acos","acos"))},t.acosh=e=>{e.compute(a(e.inputs[0],"Acosh","acosh"))},t.asin=e=>{e.compute(a(e.inputs[0],"Asin","asin"))},t.asinh=e=>{e.compute(a(e.inputs[0],"Asinh","asinh"))},t.atan=e=>{e.compute(a(e.inputs[0],"Atan","atan"))},t.atanh=e=>{e.compute(a(e.inputs[0],"Atanh","atanh"))},t.clipV10=(e,t)=>{e.compute(a(e.inputs[0],"Clip",(e=>`clamp(${e}, clip_min_, clip_max_)`),`\n const clip_min_: vec4 = vec4(f32(${t.min}));\n const clip_max_: vec4 = vec4(f32(${t.max}));\n`,t.cacheKey),{inputs:[0]})},t.clip=e=>{const n=(e=>{const t=e.length>=2?e[1].getFloat32Array()[0]:r.MIN_CLIP,n=e.length>=3?e[2].getFloat32Array()[0]:r.MAX_CLIP;return(0,o.createAttributeWithCacheKey)({min:t,max:n})})(e.inputs);(0,t.clipV10)(e,n)},t.ceil=e=>{e.compute(a(e.inputs[0],"Ceil","ceil"))},t.cos=e=>{e.compute(a(e.inputs[0],"Cos","cos"))},t.cosh=e=>{e.compute(a(e.inputs[0],"Cosh","cosh"))},t.parseAlphaAttributes=e=>(0,o.createAttributeWithCacheKey)(e),t.elu=(e,t)=>{e.compute(a(e.inputs[0],"Elu",(e=>`elu_vf32(${e})`),`\n const elu_alpha_: f32 = f32(${t.alpha});\n\n fn elu_f32(a: f32) -> f32 {\n return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0);\n }\n\n fn elu_vf32(v: vec4) -> vec4 {\n return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w));\n }`,t.cacheKey))},t.erf=e=>{e.compute(a(e.inputs[0],"Erf",(e=>`erf_vf32(${e})`),"\n const r0: f32 = 0.3275911;\n const r1: f32 = 0.254829592;\n const r2: f32 = -0.284496736;\n const r3: f32 = 1.421413741;\n const r4: f32 = -1.453152027;\n const r5: f32 = 1.061405429;\n\n fn erf_vf32(v: vec4) -> vec4 {\n let absv = abs(v);\n let x = 1.0 / (1.0 + r0 * absv);\n return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv));\n }"))},t.exp=e=>{e.compute(a(e.inputs[0],"Exp","exp"))},t.floor=e=>{e.compute(a(e.inputs[0],"Floor","floor"))},t.leakyRelu=(e,t)=>{e.compute(a(e.inputs[0],"LeakyRelu",(e=>`select(leaky_relu_alpha_ * ${e}, ${e}, ${e} >= vec4(0.0))`),`const leaky_relu_alpha_: f32 = f32(${t.alpha});`,t.cacheKey))},t.neg=e=>{e.compute(a(e.inputs[0],"Neg",(e=>`-${e}`)))},t.reciprocal=e=>{e.compute(a(e.inputs[0],"Reciprocal",(e=>`1.0/${e}`)))},t.relu=e=>{e.compute(a(e.inputs[0],"Relu",(e=>`select(vec4(0.0), ${e}, ${e} > vec4(0.0))`)))},t.sigmoid=e=>{e.compute(a(e.inputs[0],"Sigmoid",(e=>`(1.0 / (1.0 + exp(-${e})))`)))},t.sin=e=>{e.compute(a(e.inputs[0],"Sin","sin"))},t.sinh=e=>{e.compute(a(e.inputs[0],"Sinh","sinh"))},t.sqrt=e=>{e.compute(a(e.inputs[0],"Sqrt","sqrt"))},t.tan=e=>{e.compute(a(e.inputs[0],"Tan","tan"))},t.tanh=e=>{e.compute(a(e.inputs[0],"Tanh","tanh"))},t.thresholdedRelu=(e,t)=>(e.compute(a(e.inputs[0],"ThresholdedRelu",(e=>`select(vec4(0.0), ${e}, ${e} > thresholded_relu_alpha_)`),`const thresholded_relu_alpha_: vec4 = vec4(${t.alpha});`,t.cacheKey)),0)},8305:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.ProgramManager=void 0;const r=n(4955),o=n(2075);t.ProgramManager=class{constructor(e){this.backend=e,this.repo=new Map,this.attributesBound=!1}getArtifact(e){return this.repo.get(e)}setArtifact(e,t){this.repo.set(e,t)}run(e,t,n,r){const o=this.backend.device,i=this.backend.getComputePassEncoder();this.backend.profilingEnabled&&i.writeTimestamp(this.backend.profilingQuerySet,0),i.setPipeline(e.computePipeline);const a=[];for(const e of t)a.push({binding:a.length,resource:{buffer:e.buffer}});for(const e of n)a.push({binding:a.length,resource:{buffer:e.buffer}});const s=o.createBindGroup({layout:e.computePipeline.getBindGroupLayout(0),entries:a});if(i.setBindGroup(0,s),i.dispatchWorkgroups(...r),this.backend.pendingDispatchNumber++,this.backend.profilingEnabled){i.writeTimestamp(this.backend.profilingQuerySet,1);const e=this.backend.gpuDataManager.create(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),t=this.backend.gpuDataManager.create(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.backend.endComputePass(),this.backend.getCommandEncoder().resolveQuerySet(this.backend.profilingQuerySet,0,2,e.buffer,0),this.backend.getCommandEncoder().copyBufferToBuffer(e.buffer,0,t.buffer,0,16),this.backend.flush();const n=this.backend.currentKernelId,r=this.backend.kernels.get(n)[0];t.buffer.mapAsync(GPUMapMode.READ).then((()=>{const o=new BigUint64Array(t.buffer.getMappedRange()),i=o[0],a=o[1];t.buffer.unmap(),void 0===this.backend.profilingTimeBase&&(this.backend.profilingTimeBase=i);const s=Number(i-this.backend.profilingTimeBase),u=Number(a-this.backend.profilingTimeBase);if(!Number.isSafeInteger(s)||!Number.isSafeInteger(u))throw new RangeError("incorrect timestamp range");this.backend.gpuDataManager.release(e.id),this.backend.gpuDataManager.release(t.id),console.log(`[profiling] kernel "${n}|${r}" execution time: ${u-s} ns`)}))}this.backend.pendingDispatchNumber>=16&&this.backend.flush()}dispose(){}build(e,t){const n=this.backend.device,i=e.getShaderSource((0,o.createShaderHelper)(t)),a=n.createShaderModule({code:i});return(0,r.LOG_DEBUG)("verbose",(()=>`[WebGPU] shader code: ${i}`)),{programInfo:e,computePipeline:n.createComputePipeline({compute:{module:a,entryPoint:"main"},layout:"auto"})}}normalizeDispatchGroupSize(e){const t="number"==typeof e?e:e.x,n="number"==typeof e?1:e.y||1,r="number"==typeof 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t=0;t>>3){case 1:if(r.dims&&r.dims.length||(r.dims=[]),2==(7&o))for(var i=e.uint32()+e.pos;e.pos>>0,e.dims[n].high>>>0).toNumber())}if(null!=e.dataType&&(t.dataType=0|e.dataType),null!=e.segment){if("object"!=typeof e.segment)throw TypeError(".onnx.TensorProto.segment: object expected");t.segment=c.onnx.TensorProto.Segment.fromObject(e.segment)}if(e.floatData){if(!Array.isArray(e.floatData))throw TypeError(".onnx.TensorProto.floatData: array expected");for(t.floatData=[],n=0;n>>0,e.int64Data[n].high>>>0).toNumber())}if(null!=e.name&&(t.name=String(e.name)),null!=e.docString&&(t.docString=String(e.docString)),null!=e.rawData&&("string"==typeof e.rawData?l.base64.decode(e.rawData,t.rawData=l.newBuffer(l.base64.length(e.rawData)),0):e.rawData.length&&(t.rawData=e.rawData)),e.externalData){if(!Array.isArray(e.externalData))throw TypeError(".onnx.TensorProto.externalData: array expected");for(t.externalData=[],n=0;n>>0,e.uint64Data[n].high>>>0).toNumber(!0))}return t},e.toObject=function(e,t){t||(t={});var n={};if((t.arrays||t.defaults)&&(n.dims=[],n.floatData=[],n.int32Data=[],n.stringData=[],n.int64Data=[],n.doubleData=[],n.uint64Data=[],n.externalData=[]),t.defaults&&(n.dataType=0,n.segment=null,n.name="",t.bytes===String?n.rawData="":(n.rawData=[],t.bytes!==Array&&(n.rawData=l.newBuffer(n.rawData))),n.docString="",n.dataLocation=t.enums===String?"DEFAULT":0),e.dims&&e.dims.length){n.dims=[];for(var r=0;r>>0,e.dims[r].high>>>0).toNumber():e.dims[r]}if(null!=e.dataType&&e.hasOwnProperty("dataType")&&(n.dataType=e.dataType),null!=e.segment&&e.hasOwnProperty("segment")&&(n.segment=c.onnx.TensorProto.Segment.toObject(e.segment,t)),e.floatData&&e.floatData.length)for(n.floatData=[],r=0;r>>0,e.int64Data[r].high>>>0).toNumber():e.int64Data[r];if(null!=e.name&&e.hasOwnProperty("name")&&(n.name=e.name),null!=e.rawData&&e.hasOwnProperty("rawData")&&(n.rawData=t.bytes===String?l.base64.encode(e.rawData,0,e.rawData.length):t.bytes===Array?Array.prototype.slice.call(e.rawData):e.rawData),e.doubleData&&e.doubleData.length)for(n.doubleData=[],r=0;r>>0,e.uint64Data[r].high>>>0).toNumber(!0):e.uint64Data[r];if(null!=e.docString&&e.hasOwnProperty("docString")&&(n.docString=e.docString),e.externalData&&e.externalData.length)for(n.externalData=[],r=0;r>>3){case 1:r.begin=e.int64();break;case 2:r.end=e.int64();break;default:e.skipType(7&o)}}return r},e.decodeDelimited=function(e){return e instanceof s||(e=new s(e)),this.decode(e,e.uint32())},e.verify=function(e){return"object"!=typeof e||null===e?"object expected":null!=e.begin&&e.hasOwnProperty("begin")&&!(l.isInteger(e.begin)||e.begin&&l.isInteger(e.begin.low)&&l.isInteger(e.begin.high))?"begin: integer|Long expected":null!=e.end&&e.hasOwnProperty("end")&&!(l.isInteger(e.end)||e.end&&l.isInteger(e.end.low)&&l.isInteger(e.end.high))?"end: integer|Long expected":null},e.fromObject=function(e){if(e instanceof c.onnx.TensorProto.Segment)return e;var t=new c.onnx.TensorProto.Segment;return null!=e.begin&&(l.Long?(t.begin=l.Long.fromValue(e.begin)).unsigned=!1:"string"==typeof e.begin?t.begin=parseInt(e.begin,10):"number"==typeof e.begin?t.begin=e.begin:"object"==typeof e.begin&&(t.begin=new l.LongBits(e.begin.low>>>0,e.begin.high>>>0).toNumber())),null!=e.end&&(l.Long?(t.end=l.Long.fromValue(e.end)).unsigned=!1:"string"==typeof e.end?t.end=parseInt(e.end,10):"number"==typeof e.end?t.end=e.end:"object"==typeof e.end&&(t.end=new l.LongBits(e.end.low>>>0,e.end.high>>>0).toNumber())),t},e.toObject=function(e,t){t||(t={});var n={};if(t.defaults){if(l.Long){var r=new l.Long(0,0,!1);n.begin=t.longs===String?r.toString():t.longs===Number?r.toNumber():r}else n.begin=t.longs===String?"0":0;l.Long?(r=new l.Long(0,0,!1),n.end=t.longs===String?r.toString():t.longs===Number?r.toNumber():r):n.end=t.longs===String?"0":0}return null!=e.begin&&e.hasOwnProperty("begin")&&("number"==typeof e.begin?n.begin=t.longs===String?String(e.begin):e.begin:n.begin=t.longs===String?l.Long.prototype.toString.call(e.begin):t.longs===Number?new l.LongBits(e.begin.low>>>0,e.begin.high>>>0).toNumber():e.begin),null!=e.end&&e.hasOwnProperty("end")&&("number"==typeof e.end?n.end=t.longs===String?String(e.end):e.end:n.end=t.longs===String?l.Long.prototype.toString.call(e.end):t.longs===Number?new l.LongBits(e.end.low>>>0,e.end.high>>>0).toNumber():e.end),n},e.prototype.toJSON=function(){return this.constructor.toObject(this,a.util.toJSONOptions)},e}(),e.DataLocation=function(){var e={},t=Object.create(e);return t[e[0]="DEFAULT"]=0,t[e[1]="EXTERNAL"]=1,t}(),e}(),i.TensorShapeProto=function(){function e(e){if(this.dim=[],e)for(var t=Object.keys(e),n=0;n>>3==1?(r.dim&&r.dim.length||(r.dim=[]),r.dim.push(c.onnx.TensorShapeProto.Dimension.decode(e,e.uint32()))):e.skipType(7&o)}return r},e.decodeDelimited=function(e){return e instanceof s||(e=new s(e)),this.decode(e,e.uint32())},e.verify=function(e){if("object"!=typeof e||null===e)return"object expected";if(null!=e.dim&&e.hasOwnProperty("dim")){if(!Array.isArray(e.dim))return"dim: array expected";for(var t=0;t>>3){case 1:r.dimValue=e.int64();break;case 2:r.dimParam=e.string();break;case 3:r.denotation=e.string();break;default:e.skipType(7&o)}}return r},e.decodeDelimited=function(e){return e instanceof s||(e=new s(e)),this.decode(e,e.uint32())},e.verify=function(e){if("object"!=typeof e||null===e)return"object expected";var t={};if(null!=e.dimValue&&e.hasOwnProperty("dimValue")&&(t.value=1,!(l.isInteger(e.dimValue)||e.dimValue&&l.isInteger(e.dimValue.low)&&l.isInteger(e.dimValue.high))))return"dimValue: integer|Long expected";if(null!=e.dimParam&&e.hasOwnProperty("dimParam")){if(1===t.value)return"value: multiple values";if(t.value=1,!l.isString(e.dimParam))return"dimParam: string expected"}return null!=e.denotation&&e.hasOwnProperty("denotation")&&!l.isString(e.denotation)?"denotation: string expected":null},e.fromObject=function(e){if(e instanceof c.onnx.TensorShapeProto.Dimension)return e;var t=new c.onnx.TensorShapeProto.Dimension;return null!=e.dimValue&&(l.Long?(t.dimValue=l.Long.fromValue(e.dimValue)).unsigned=!1:"string"==typeof e.dimValue?t.dimValue=parseInt(e.dimValue,10):"number"==typeof e.dimValue?t.dimValue=e.dimValue:"object"==typeof e.dimValue&&(t.dimValue=new l.LongBits(e.dimValue.low>>>0,e.dimValue.high>>>0).toNumber())),null!=e.dimParam&&(t.dimParam=String(e.dimParam)),null!=e.denotation&&(t.denotation=String(e.denotation)),t},e.toObject=function(e,t){t||(t={});var n={};return t.defaults&&(n.denotation=""),null!=e.dimValue&&e.hasOwnProperty("dimValue")&&("number"==typeof e.dimValue?n.dimValue=t.longs===String?String(e.dimValue):e.dimValue:n.dimValue=t.longs===String?l.Long.prototype.toString.call(e.dimValue):t.longs===Number?new l.LongBits(e.dimValue.low>>>0,e.dimValue.high>>>0).toNumber():e.dimValue,t.oneofs&&(n.value="dimValue")),null!=e.dimParam&&e.hasOwnProperty("dimParam")&&(n.dimParam=e.dimParam,t.oneofs&&(n.value="dimParam")),null!=e.denotation&&e.hasOwnProperty("denotation")&&(n.denotation=e.denotation),n},e.prototype.toJSON=function(){return this.constructor.toObject(this,a.util.toJSONOptions)},e}(),e}(),i.TypeProto=function(){function e(e){if(e)for(var t=Object.keys(e),n=0;n>>3){case 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t.defaults&&(n.denotation=""),null!=e.tensorType&&e.hasOwnProperty("tensorType")&&(n.tensorType=c.onnx.TypeProto.Tensor.toObject(e.tensorType,t),t.oneofs&&(n.value="tensorType")),null!=e.denotation&&e.hasOwnProperty("denotation")&&(n.denotation=e.denotation),n},e.prototype.toJSON=function(){return this.constructor.toObject(this,a.util.toJSONOptions)},e.Tensor=function(){function e(e){if(e)for(var t=Object.keys(e),n=0;n>>3){case 1:r.elemType=e.int32();break;case 2:r.shape=c.onnx.TensorShapeProto.decode(e,e.uint32());break;default:e.skipType(7&o)}}return r},e.decodeDelimited=function(e){return e instanceof s||(e=new s(e)),this.decode(e,e.uint32())},e.verify=function(e){if("object"!=typeof e||null===e)return"object expected";if(null!=e.elemType&&e.hasOwnProperty("elemType")&&!l.isInteger(e.elemType))return"elemType: integer expected";if(null!=e.shape&&e.hasOwnProperty("shape")){var t=c.onnx.TensorShapeProto.verify(e.shape);if(t)return"shape."+t}return null},e.fromObject=function(e){if(e instanceof c.onnx.TypeProto.Tensor)return e;var t=new c.onnx.TypeProto.Tensor;if(null!=e.elemType&&(t.elemType=0|e.elemType),null!=e.shape){if("object"!=typeof e.shape)throw TypeError(".onnx.TypeProto.Tensor.shape: object expected");t.shape=c.onnx.TensorShapeProto.fromObject(e.shape)}return t},e.toObject=function(e,t){t||(t={});var n={};return t.defaults&&(n.elemType=0,n.shape=null),null!=e.elemType&&e.hasOwnProperty("elemType")&&(n.elemType=e.elemType),null!=e.shape&&e.hasOwnProperty("shape")&&(n.shape=c.onnx.TensorShapeProto.toObject(e.shape,t)),n},e.prototype.toJSON=function(){return this.constructor.toObject(this,a.util.toJSONOptions)},e}(),e}(),i.OperatorSetIdProto=function(){function e(e){if(e)for(var t=Object.keys(e),n=0;n>>3){case 1:r.domain=e.string();break;case 2:r.version=e.int64();break;default:e.skipType(7&o)}}return r},e.decodeDelimited=function(e){return e instanceof s||(e=new s(e)),this.decode(e,e.uint32())},e.verify=function(e){return"object"!=typeof e||null===e?"object expected":null!=e.domain&&e.hasOwnProperty("domain")&&!l.isString(e.domain)?"domain: string expected":null!=e.version&&e.hasOwnProperty("version")&&!(l.isInteger(e.version)||e.version&&l.isInteger(e.version.low)&&l.isInteger(e.version.high))?"version: integer|Long expected":null},e.fromObject=function(e){if(e instanceof c.onnx.OperatorSetIdProto)return e;var t=new c.onnx.OperatorSetIdProto;return null!=e.domain&&(t.domain=String(e.domain)),null!=e.version&&(l.Long?(t.version=l.Long.fromValue(e.version)).unsigned=!1:"string"==typeof e.version?t.version=parseInt(e.version,10):"number"==typeof e.version?t.version=e.version:"object"==typeof e.version&&(t.version=new l.LongBits(e.version.low>>>0,e.version.high>>>0).toNumber())),t},e.toObject=function(e,t){t||(t={});var n={};if(t.defaults)if(n.domain="",l.Long){var r=new l.Long(0,0,!1);n.version=t.longs===String?r.toString():t.longs===Number?r.toNumber():r}else n.version=t.longs===String?"0":0;return 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