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r=0;r>>3){case 1:if(d.dims&&d.dims.length||(d.dims=[]),(7&g)==2)for(var m=e.uint32()+e.pos;e.pos>>0,e.dims[i].high>>>0).toNumber())}if(e.dataType!=null&&(r.dataType=0|e.dataType),e.segment!=null){if(typeof e.segment!="object")throw TypeError(".onnx.TensorProto.segment: object expected");r.segment=s.onnx.TensorProto.Segment.fromObject(e.segment)}if(e.floatData){if(!Array.isArray(e.floatData))throw TypeError(".onnx.TensorProto.floatData: array expected");for(r.floatData=[],i=0;i>>0,e.int64Data[i].high>>>0).toNumber())}if(e.name!=null&&(r.name=String(e.name)),e.docString!=null&&(r.docString=String(e.docString)),e.rawData!=null&&(typeof e.rawData=="string"?u.base64.decode(e.rawData,r.rawData=u.newBuffer(u.base64.length(e.rawData)),0):e.rawData.length&&(r.rawData=e.rawData)),e.externalData){if(!Array.isArray(e.externalData))throw TypeError(".onnx.TensorProto.externalData: array expected");for(r.externalData=[],i=0;i>>0,e.uint64Data[i].high>>>0).toNumber(!0))}return 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d={};if(i.dimValue!=null&&i.hasOwnProperty("dimValue")&&(d.value=1,!(u.isInteger(i.dimValue)||i.dimValue&&u.isInteger(i.dimValue.low)&&u.isInteger(i.dimValue.high))))return"dimValue: integer|Long expected";if(i.dimParam!=null&&i.hasOwnProperty("dimParam")){if(d.value===1)return"value: multiple values";if(d.value=1,!u.isString(i.dimParam))return"dimParam: string expected"}return i.denotation!=null&&i.hasOwnProperty("denotation")&&!u.isString(i.denotation)?"denotation: string expected":null},e.fromObject=function(i){if(i instanceof s.onnx.TensorShapeProto.Dimension)return i;var d=new s.onnx.TensorShapeProto.Dimension;return i.dimValue!=null&&(u.Long?(d.dimValue=u.Long.fromValue(i.dimValue)).unsigned=!1:typeof i.dimValue=="string"?d.dimValue=parseInt(i.dimValue,10):typeof i.dimValue=="number"?d.dimValue=i.dimValue:typeof i.dimValue=="object"&&(d.dimValue=new 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Object.assign(Object.assign(Object.assign(Object.assign(Object.assign(Object.assign(Object.assign({},this.offsetToCoords()),this.coordsToOffset()),this.toVec()),this.valueFrom()),this.getCommonUtilFuncs()),this.getInputsSamplingSnippets()),this.getOutputSamplingSnippet())}getCustomTypes(){return{}}offsetToCoords(){return{offsetToCoords:new c.GlslLibRoutine(` + vec2 offsetToCoords(int offset, int width, int height) { + int t = offset / width; + int s = offset - t*width; + vec2 coords = (vec2(s,t) + vec2(0.5,0.5)) / vec2(width, height); + return coords; + } + `)}}coordsToOffset(){return{coordsToOffset:new c.GlslLibRoutine(` + int coordsToOffset(vec2 coords, int width, int height) { + float s = coords.s * float(width); + float t = coords.t * float(height); + int offset = int(t) * width + int(s); + return offset; + } + `)}}getOutputSamplingSnippet(){const s=this.context.outputTextureLayout;return s.isPacked?this.getPackedOutputSamplingSnippet(s):this.getUnpackedOutputSamplingSnippet(s)}getPackedOutputSamplingSnippet(s){const t=s.unpackedShape,e=[s.width,s.height],r={},i="getOutputCoords";switch(t.length){case 0:r[i]=this.getOutputScalarCoords();break;case 1:r[i]=this.getOutputPacked1DCoords(t,e);break;case 2:r[i]=this.getOutputPacked2DCoords(t,e);break;case 3:r[i]=this.getOutputPacked3DCoords(t,e);break;default:r[i]=this.getOutputPackedNDCoords(t,e)}const d=` + void setOutput(vec4 val) { + ${(0,f.getGlsl)(this.context.glContext.version).output} = val; + } + `;return r.floatTextureSetRGBA=new c.GlslLibRoutine(d),r}getUnpackedOutputSamplingSnippet(s){const t=s.unpackedShape,e=[s.width,s.height],r={},i="getOutputCoords";switch(t.length){case 0:r[i]=this.getOutputScalarCoords();break;case 1:r[i]=this.getOutputUnpacked1DCoords(t,e);break;case 2:r[i]=this.getOutputUnpacked2DCoords(t,e);break;case 3:r[i]=this.getOutputUnpacked3DCoords(t,e);break;case 4:r[i]=this.getOutputUnpacked4DCoords(t,e);break;case 5:r[i]=this.getOutputUnpacked5DCoords(t,e);break;case 6:r[i]=this.getOutputUnpacked6DCoords(t,e);break;default:throw new Error(`Unsupported output dimensionality: ${t.length}`)}const d=` + void setOutput(float val) { + ${(0,f.getGlsl)(this.context.glContext.version).output} = vec4(val, 0, 0, 0); + } + `;return r.floatTextureSetR=new c.GlslLibRoutine(d),r}getOutputScalarCoords(){return new c.GlslLibRoutine(` + int getOutputCoords() { + return 0; + } + `)}getOutputPacked1DCoords(s,t){const e=t;let r="";return e[0]===1?(r=` + int getOutputCoords() { + return 2 * int(TexCoords.y * ${e[1]}.0); + } + `,new c.GlslLibRoutine(r)):e[1]===1?(r=` + int getOutputCoords() { + return 2 * int(TexCoords.x * ${e[0]}.0); + } + `,new c.GlslLibRoutine(r)):(r=` + int getOutputCoords() { + ivec2 resTexRC = ivec2(TexCoords.xy * + vec2(${e[0]}, ${e[1]})); + return 2 * (resTexRC.y * ${e[0]} + resTexRC.x); + } + `,new c.GlslLibRoutine(r))}getOutputPacked2DCoords(s,t){let e="";if(l.ArrayUtil.arraysEqual(s,t))return e=` + ivec2 getOutputCoords() { + return 2 * ivec2(TexCoords.xy * vec2(${t[0]}, ${t[1]})); + } + `,new c.GlslLibRoutine(e);const r=t,i=Math.ceil(s[1]/2);return e=` + ivec2 getOutputCoords() { + ivec2 resTexRC = ivec2(TexCoords.xy * + vec2(${r[0]}, ${r[1]})); + + int index = resTexRC.y * ${r[0]} + resTexRC.x; + + // reverse r and c order for packed texture + int r = imod(index, ${i}) * 2; + int c = 2 * (index / ${i}); + + return ivec2(r, c); + } + `,new c.GlslLibRoutine(e)}getOutputPacked3DCoords(s,t){const e=[t[0],t[1]],r=Math.ceil(s[2]/2),i=r*Math.ceil(s[1]/2),d=` + ivec3 getOutputCoords() { + ivec2 resTexRC = ivec2(TexCoords.xy * + vec2(${e[0]}, ${e[1]})); + int index = resTexRC.y * ${e[0]} + resTexRC.x; + + int b = index / ${i}; + index -= b * ${i}; + + // reverse r and c order for packed texture + int r = imod(index, ${r}) * 2; + int c = 2 * (index / ${r}); + + return ivec3(b, r, c); + } + `;return new c.GlslLibRoutine(d)}getOutputPackedNDCoords(s,t){const e=[t[0],t[1]],r=Math.ceil(s[s.length-1]/2),i=r*Math.ceil(s[s.length-2]/2);let d=i,g="",m="b, r, c";for(let _=2;_=0;--m)i[m]=i[m+1]*s[m+1];const d=["r","c","d"],g=i.map((m,b)=>`int ${d[b]} = index / ${m}; ${b===i.length-1?`int ${d[b+1]} = index - ${d[b]} * ${m}`:`index -= ${d[b]} * ${m}`};`).join("");return e=` + ivec3 getOutputCoords() { + ivec2 resTexRC = ivec2(TexCoords.xy * + vec2(${t[0]}, ${t[1]})); + int index = resTexRC.y * ${t[0]} + resTexRC.x; + ${g} + return ivec3(r, c, d); + } + `,new c.GlslLibRoutine(e)}getOutputUnpacked4DCoords(s,t){let e="";const r=s.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=s[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*s[m+1];const d=["r","c","d","d2"],g=i.map((m,b)=>`int ${d[b]} = index / ${m}; ${b===i.length-1?`int ${d[b+1]} = index - ${d[b]} * ${m}`:`index -= ${d[b]} * ${m}`};`).join("");return e=` + ivec4 getOutputCoords() { + ivec2 resTexRC = ivec2(TexCoords.xy * + vec2(${t[0]}, ${t[1]})); + int index = resTexRC.y * ${t[0]} + resTexRC.x; + ${g} + return ivec4(r, c, d, d2); + } + `,new c.GlslLibRoutine(e)}getOutputUnpacked5DCoords(s,t){let e="";const r=s.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=s[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*s[m+1];const d=["r","c","d","d2","d3"],g=i.map((m,b)=>`int ${d[b]} = index / ${m}; ${b===i.length-1?`int ${d[b+1]} = index - ${d[b]} * ${m}`:`index -= ${d[b]} * ${m}`};`).join("");return e=` + ivec5 getOutputCoords() { + ivec2 resTexRC = ivec2(TexCoords.xy * + vec2(${t[0]}, ${t[1]})); + int index = resTexRC.y * ${t[0]} + resTexRC.x; + ${g} + return ivec5(r, c, d, d2, d3); + } + `,new c.GlslLibRoutine(e)}getOutputUnpacked6DCoords(s,t){let e="";const r=s.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=s[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*s[m+1];const d=["r","c","d","d2","d3","d4"],g=i.map((m,b)=>`int ${d[b]} = index / ${m}; ${b===i.length-1?`int ${d[b+1]} = index - ${d[b]} * ${m}`:`index -= ${d[b]} * ${m}`};`).join("");return e=` + ivec6 getOutputCoords() { + ivec2 resTexRC = ivec2(TexCoords.xy * + vec2(${t[0]}, ${t[1]})); + int index = resTexRC.y * ${t[0]} + resTexRC.x; + ${g} + return ivec6(r, c, d, d2, d3, d4); + } + `,new c.GlslLibRoutine(e)}getCommonUtilFuncs(){const s={};let t="uvFromFlat";s[t]=new c.GlslLibRoutine(` + vec2 uvFromFlat(int texNumR, int texNumC, int index) { + int texC = index / texNumR; + int texR = index - texC * texNumR; + // TODO: swap texR, texC order in following function so row is corresponding to u and column is corresponding to + // v. + return (vec2(texR, texC) + halfCR) / vec2(texNumR, texNumC); + } + `),t="packedUVfrom1D",s[t]=new c.GlslLibRoutine(` + vec2 packedUVfrom1D(int texNumR, int texNumC, int index) { + int texelIndex = index / 2; + int texR = texelIndex / texNumC; + int texC = texelIndex - texR * texNumC; + return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); + } + `),t="packedUVfrom2D",s[t]=new c.GlslLibRoutine(` + vec2 packedUVfrom2D(int texNumR, int texNumC, int texelsInLogicalRow, int row, int col) { + int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2); + int texR = texelIndex / texNumC; + int texC = texelIndex - texR * texNumC; + return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); + } + `),t="packedUVfrom3D",s[t]=new c.GlslLibRoutine(` + vec2 packedUVfrom3D(int texNumR, int texNumC, + int texelsInBatch, int texelsInLogicalRow, int b, + int row, int col) { + int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2); + int texR = index / texNumC; + int texC = index - texR * texNumC; + return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); + } + `),t="sampleTexture";const e=(0,f.getGlsl)(this.context.glContext.version);return s[t]=new c.GlslLibRoutine(` + float sampleTexture(sampler2D textureSampler, vec2 uv) { + return ${e.texture2D}(textureSampler, uv).r; + }`),s}getInputsSamplingSnippets(){const s={},t=this.context.outputTextureLayout;return this.context.programInfo.inputNames.forEach((e,r)=>{const i=this.context.inputTextureLayouts[r],d=(0,h.generateShaderFuncNameFromInputSamplerName)(e);i.isPacked?s[d]=this.getPackedSamplerFromInput(d,e,i):s[d]=this.getUnpackedSamplerFromInput(d,e,i);const g=(0,h.generateShaderFuncNameFromInputSamplerNameAtOutCoords)(e);i.unpackedShape.length<=t.unpackedShape.length&&(i.isPacked?s[g]=this.getPackedSamplerAtOutputCoords(g,i,t,e):s[g]=this.getUnpackedSamplerAtOutputCoords(g,i,t,e))}),s}getPackedSamplerAtOutputCoords(s,t,e,r){const i=t.unpackedShape,d=e.unpackedShape,g=r,m=(0,h.generateShaderFuncNameFromInputSamplerName)(g),b=i.length,_=d.length,v=l.BroadcastUtil.getBroadcastDims(i,d),w=(0,h.getCoordsDataType)(_),S=_-b;let A;const O=(0,h.getGlChannels)();A=b===0?"":_<2&&v.length>=1?"coords = 0;":v.map(N=>`coords.${O[N+S]} = 0;`).join(` +`);let x="";x=_<2&&b>0?"coords":i.map((N,H)=>`coords.${O[H+S]}`).join(", ");let I="return outputValue;";const $=l.ShapeUtil.size(i)===1,B=l.ShapeUtil.size(d)===1;if(b!==1||$||B){if($&&!B)I=_===1?` + return vec4(outputValue.x, outputValue.x, 0., 0.); + `:` + return vec4(outputValue.x); + `;else if(v.length){const N=b-2,H=b-1;v.indexOf(N)>-1&&v.indexOf(H)>-1?I="return vec4(outputValue.x);":v.indexOf(N)>-1?I="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":v.indexOf(H)>-1&&(I="return vec4(outputValue.xx, outputValue.zz);")}}else I=` + return vec4(outputValue.xy, outputValue.xy); + `;const L=` + vec4 ${s}() { + ${w} coords = getOutputCoords(); + + int lastDim = coords.${O[_-1]}; + coords.${O[_-1]} = coords.${O[_-2]}; + coords.${O[_-2]} = lastDim; + + ${A} + vec4 outputValue = ${m}(${x}); + ${I} + } + `;return new c.GlslLibRoutine(L,["coordinates.getOutputCoords"])}getUnpackedSamplerAtOutputCoords(s,t,e,r){const i=[e.width,e.height],d=[t.width,t.height],g=t.unpackedShape.length,m=e.unpackedShape.length,b=t.unpackedShape,_=e.unpackedShape,v=(0,h.generateShaderFuncNameFromInputSamplerName)(r);if(g===m&&l.ArrayUtil.arraysEqual(d,i)){const B=` + float ${s}() { + return sampleTexture(${r}, TexCoords); + } + `;return new c.GlslLibRoutine(B,["coordinates.sampleTexture"])}const w=(0,h.getCoordsDataType)(m),S=l.BroadcastUtil.getBroadcastDims(b,_),A=m-g;let O;const x=(0,h.getGlChannels)();O=g===0?"":m<2&&S.length>=1?"coords = 0;":S.map(B=>`coords.${x[B+A]} = 0;`).join(` +`);let I="";I=m<2&&g>0?"coords":t.unpackedShape.map((B,L)=>`coords.${x[L+A]}`).join(", ");const $=` + float ${s}() { + ${w} coords = getOutputCoords(); + ${O} + return ${v}(${I}); + } + `;return new c.GlslLibRoutine($,["coordinates.getOutputCoords"])}getPackedSamplerFromInput(s,t,e){switch(e.unpackedShape.length){case 0:return this.getPackedSamplerScalar(s,t);case 1:return this.getPackedSampler1D(s,t,e);case 2:return this.getPackedSampler2D(s,t,e);case 3:return this.getPackedSampler3D(s,t,e);default:return this.getPackedSamplerND(s,t,e)}}getUnpackedSamplerFromInput(s,t,e){const r=e.unpackedShape;switch(r.length){case 0:return this.getUnpackedSamplerScalar(s,t,e);case 1:return this.getUnpackedSampler1D(s,t,e);case 2:return this.getUnpackedSampler2D(s,t,e);case 3:return this.getUnpackedSampler3D(s,t,e);case 4:return this.getUnpackedSampler4D(s,t,e);case 5:return this.getUnpackedSampler5D(s,t,e);case 6:return this.getUnpackedSampler6D(s,t,e);default:throw new Error(`Unsupported dimension ${r.length}-D`)}}getPackedSamplerScalar(s,t){const e=` + vec4 ${s}() { + return ${(0,f.getGlsl)(this.context.glContext.version).texture2D}(${t}, halfCR); + } + `;return new c.GlslLibRoutine(e)}getPackedSampler1D(s,t,e){const r=[e.width,e.height],i=[r[1],r[0]],d=(0,f.getGlsl)(this.context.glContext.version),g=`vec4 ${s}(int index) { + vec2 uv = packedUVfrom1D( + ${i[0]}, ${i[1]}, index); + return ${d.texture2D}(${t}, uv); + }`;return new c.GlslLibRoutine(g,["coordinates.packedUVfrom1D"])}getPackedSampler2D(s,t,e){const r=e.unpackedShape,i=[e.width,e.height],d=(0,f.getGlsl)(this.context.glContext.version),g=i[0],m=i[1];if(i!=null&&l.ArrayUtil.arraysEqual(r,i)){const w=`vec4 ${s}(int row, int col) { + vec2 uv = (vec2(col, row) + halfCR) / vec2(${m}.0, ${g}.0); + return ${d.texture2D}(${t}, uv); + }`;return new c.GlslLibRoutine(w)}const b=i,_=Math.ceil(r[1]/2),v=`vec4 ${s}(int row, int col) { + vec2 uv = packedUVfrom2D(${b[1]}, ${b[0]}, ${_}, row, col); + return ${d.texture2D}(${t}, uv); + }`;return new c.GlslLibRoutine(v,["coordinates.packedUVfrom2D"])}getPackedSampler3D(s,t,e){const r=e.unpackedShape,i=[e.width,e.height],d=[i[0],i[1]],g=(0,f.getGlsl)(this.context.glContext.version);if(r[0]===1){const w=r.slice(1),S=[1,2],A=(0,h.squeezeInputShape)(r,w),O=["b","row","col"],x=JSON.parse(JSON.stringify(e));x.unpackedShape=A;const I=this.getPackedSamplerFromInput(s,t,x),$=`${I.routineBody} + vec4 ${s}(int b, int row, int col) { + return ${s}(${(0,h.getSqueezedParams)(O,S)}); + } `;return new c.GlslLibRoutine($,I.dependencies)}const m=d[0],b=d[1],_=Math.ceil(r[2]/2),v=`vec4 ${s}(int b, int row, int col) { + vec2 uv = packedUVfrom3D( + ${b}, ${m}, ${_*Math.ceil(r[1]/2)}, ${_}, b, row, col); + return ${g.texture2D}(${t}, uv);}`;return new c.GlslLibRoutine(v,["coordinates.packedUVfrom3D"])}getPackedSamplerND(s,t,e){const r=e.unpackedShape,i=r.length,d=[e.width,e.height],g=(0,f.getGlsl)(this.context.glContext.version),m=[d[0],d[1]],b=m[1],_=m[0],v=Math.ceil(r[i-1]/2);let w=v*Math.ceil(r[i-2]/2),S="int b, int row, int col",A=`b * ${w} + (row / 2) * ${v} + (col / 2)`;for(let x=2;x{const r=this.context.inputTextureLayouts[e],i=(r.unpackedShape.length>0?r.unpackedShape:r.shape).length;let d=`_${t}`;s[d]=new c.GlslLibRoutine(this.getValueFromSingle(t,i,r.width,r.height,!1),[`shapeUtils.indicesToOffset${d}`,"coordinates.offsetToCoords","fragcolor.getColorAsFloat"]),d+="_T",s[d]=new 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0,(o=n.FunctionType||(n.FunctionType={}))[o.ValueBased=0]="ValueBased",o[o.Positional=1]="Positional",n.GlslContext=class{constructor(l,c,f,a){this.glContext=l,this.programInfo=c,this.inputTextureLayouts=f,this.outputTextureLayout=a}},n.GlslLib=class{constructor(l){this.context=l}},n.GlslLibRoutine=class{constructor(l,c){this.routineBody=l,this.dependencies=c}},n.GlslLibRoutineNode=class{constructor(l,c,f){this.name=l,this.dependencies=f||[],c&&(this.routineBody=c)}addDependency(l){l&&this.dependencies.push(l)}},n.TopologicalSortGlslRoutines=class{static returnOrderedNodes(l){if(!l||l.length===0)return[];if(l.length===1)return l;const c=new Set,f=new Set,a=new Array;return this.createOrderedNodes(l,c,f,a),a}static createOrderedNodes(l,c,f,a){for(let h=0;h0)for(let p=0;p{Object.defineProperty(n,"__esModule",{value:!0}),n.EncodingGlslLib=void 0;const l=o(8520);class c extends l.GlslLib{constructor(a){super(a)}getFunctions(){return Object.assign(Object.assign({},this.encodeFloat32()),this.decodeFloat32())}getCustomTypes(){return{}}encodeFloat32(){return{encode:new l.GlslLibRoutine(`highp vec4 encode(highp float f) { + return vec4(f, 0.0, 0.0, 0.0); + } + `)}}decodeFloat32(){return{decode:new l.GlslLibRoutine(`highp float decode(highp vec4 rgba) { + return rgba.r; + } + `)}}encodeUint8(){const a=c.isLittleEndian()?"rgba.rgba=rgba.abgr;":"";return{encode:new l.GlslLibRoutine(` + highp vec4 encode(highp float f) { + highp float F = abs(f); + highp float Sign = step(0.0,-f); + highp float Exponent = floor(log2(F)); + highp float Mantissa = (exp2(- Exponent) * F); + Exponent = floor(log2(F) + 127.0) + floor(log2(Mantissa)); + highp vec4 rgba; + rgba[0] = 128.0 * Sign + floor(Exponent*exp2(-1.0)); + rgba[1] = 128.0 * mod(Exponent,2.0) + mod(floor(Mantissa*128.0),128.0); + rgba[2] = floor(mod(floor(Mantissa*exp2(23.0 -8.0)),exp2(8.0))); + rgba[3] = floor(exp2(23.0)*mod(Mantissa,exp2(-15.0))); + ${a} + rgba = rgba / 255.0; // values need to be normalized to [0,1] + return rgba; + } + `)}}decodeUint8(){const a=c.isLittleEndian()?"rgba.rgba=rgba.abgr;":"";return{decode:new l.GlslLibRoutine(` + highp float decode(highp vec4 rgba) { + rgba = rgba * 255.0; // values need to be de-normalized from [0,1] to [0,255] + ${a} + highp float Sign = 1.0 - step(128.0,rgba[0])*2.0; + highp float Exponent = 2.0 * mod(rgba[0],128.0) + step(128.0,rgba[1]) - 127.0; + highp float Mantissa = mod(rgba[1],128.0)*65536.0 + rgba[2]*256.0 +rgba[3] + float(0x800000); + highp float Result = Sign * exp2(Exponent) * (Mantissa * exp2(-23.0 )); + return Result; + } + `)}}static isLittleEndian(){const a=new ArrayBuffer(4),h=new Uint32Array(a),p=new Uint8Array(a);if(h[0]=3735928559,p[0]===239)return!0;if(p[0]===222)return!1;throw new Error("unknown endianness")}}n.EncodingGlslLib=c},9894:(y,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.FragColorGlslLib=void 0;const l=o(8520),c=o(5060);class f extends 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j=(0,l.getGlsl)(i.session.backend.glContext.version),Z=` + ${A} + float getValue(${x.map(X=>"int "+X)}) { + ${N} + } + + void main() { + ${S} coords = getOutputCoords(); + int lastDim = coords.${x[v-1]}; + coords.${x[v-1]} = coords.${x[v-2]}; + coords.${x[v-2]} = lastDim; + + vec4 result = vec4(getValue(${w}), 0., 0., 0.); + + ${w[v-1]} = ${w[v-1]} + 1; + if (${w[v-1]} < ${_[v-1]}) { + result.g = getValue(${w}); + } + + ${w[v-2]} = ${w[v-2]} + 1; + if (${w[v-2]} < ${_[v-2]}) { + result.a = getValue(${w}); + } + + ${w[v-1]} = ${w[v-1]} - 1; + if (${w[v-2]} < ${_[v-2]} && + ${w[v-1]} < ${_[v-1]}) { + result.b = getValue(${w}); + } + ${j.output} = result; + } + `;return Object.assign(Object.assign({},d),{output:{dims:_,type:g[0].type,textureType:c.TextureType.packed},shaderSource:Z,hasMain:!0})})(p,t,u,s.axis)})};const h=(p,u,s)=>{const t=p.indexOf(u);return p.map((e,r)=>r===t?`${e} - 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+ for (int wHeight = 0; wHeight < ${v[2]}; wHeight++) { + int xHeight = xRCCorner.x + wHeight * ${m.dilations[0]}; + + if (xHeight < 0 || xHeight >= ${_[2]}) { + continue; + } + + for (int wWidth = 0; wWidth < ${v[3]}; wWidth++) { + int xWidth = xRCCorner.y + wWidth * ${m.dilations[1]}; + if (xWidth < 0 || xWidth >= ${_[3]}) { + continue; + } + + float xVal = getX(batch, input_channel, xWidth, xHeight); + float wVal = getW(output_channel, wInChannel, wWidth, wHeight); + value += xVal*wVal; + } + } + } + ${b} + ${x} + ${A.output} = vec4(value, .0, .0, .0); + } +`;return Object.assign(Object.assign({},g),{output:{dims:S,type:d[0].type,textureType:f.TextureType.unpacked},shaderSource:I,hasMain:!0})})(p,u,t,s)})}},1386:(y,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.conv2DPacked=n.conv2DPackedPointwise=void 0;const l=o(8138),c=o(8555),f=o(708);n.conv2DPackedPointwise=(a,h,p)=>{const 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coords.x; + int output_channel = coords.y; + + ivec2 loc = coords.zw + pads; + + int group_id = output_channel / ${L}; + int wOutChannel = output_channel - group_id * ${L}; + + float value = ${I}; + for (int inChannelOffset = 0; inChannelOffset < ${N}; inChannelOffset++) { + int input_channel = group_id * ${N} + inChannelOffset; + for (int wWOff = 0; wWOff < ${B[2]}; wWOff++) { + for (int wHOff = 0; wHOff < ${B[3]}; wHOff++) { + ivec2 wOff = ivec2(wWOff * ${x.dilations[0]}, wHOff * ${x.dilations[1]}); + ivec2 wLoc = loc - wOff; + ivec2 wLocIn = wLoc / strides; + if ( + wLocIn * strides == wLoc && + wLocIn.x >= 0 && wLocIn.x < ${$[2]} && + wLocIn.y >= 0 && wLocIn.y < ${$[3]} + ) { + float xVal = getX(batch, input_channel, wLocIn.y, wLocIn.x); + float wVal = getW(input_channel, wOutChannel, wHOff, wWOff); + value += xVal * wVal; + } + } + } + } + ${Z} + ${M.output} = vec4(value, .0, .0, .0); + } +`;return Object.assign(Object.assign({},O),{output:{dims:H,type:A[0].type,textureType:f.TextureType.unpacked},shaderSource:X,hasMain:!0})})(g,m,_,b)})})(r,i,d),i),t=(r,i)=>{const d=r.kernelShape.slice();if(r.kernelShape.length===0)for(let _=2;_{const $=_.length-2,B=I.length===0;for(let L=0;L<$;++L){const N=B?_[L+2]*O[L]:I[L],H=h(_[L+2],O[L],A[L],v[L],w[L],N);p(H,S,A,L,L+$),B&&I.push(O[L]*(_[L+2]-1)+x[L]+(v[L]-1)*w[L]+1-A[L]-A[L+$])}})(i[0].dims,d,r.dilations,r.autoPad,g,r.strides,r.outputPadding,m);const b=Object.assign({},r);return Object.assign(b,{kernelShape:d,pads:g,outputShape:m,cacheKey:r.cacheKey}),b};n.parseConvTransposeAttributes=r=>{const 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_=m[0].dims,v=m[1].dims,w=(0,n.calculateOutputShape)(_,v,b.dilations,b.pads,b.strides),S=g.run((0,u.createIm2ColProgramInfoLoader)(g,m[0],m[1],w,b),[m[0]]),A=m.length===3?[S,m[1],m[2]]:[S,m[1]];return g.run((0,h.createDotProductProgramInfoLoader)(g,m,w,b),A)},i=(g,m)=>{const b=g.kernelShape.slice();if(g.kernelShape.length===0)for(let w=2;w{const m=g.attributes,b=(0,p.parseInternalActivationAttributes)(m),_=m.getString("auto_pad","NOTSET"),v=m.getInts("dilations",[1,1]),w=m.getInt("group",1),S=m.getInts("kernel_shape",[]),A=m.getInts("pads",[0,0,0,0]),O=m.getInts("strides",[1,1]);return(0,l.createAttributeWithCacheKey)(Object.assign({autoPad:_,dilations:v,group:w,kernelShape:S,pads:A,strides:O},b))};const d=(g,m)=>{if(!g||g.length!==2&&g.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(g[0].dims.length!==4||g[1].dims.length!==4)throw new Error("currently only support 2-dimensional conv");if(g[0].dims[1]!==g[1].dims[1]*m.group)throw new Error("FILTER_IN_CHANNEL should be 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p=h.blocksize,u=p*p,s=h.mode==="DCR"?[0,3,4,1,5,2]:[0,1,4,2,5,3],t=h.mode==="DCR"?[a[0].dims[0],p,p,a[0].dims[1]/u,a[0].dims[2],a[0].dims[3]]:[a[0].dims[0],a[0].dims[1]/u,p,p,a[0].dims[2],a[0].dims[3]],e=f.reshapeUnpacked(a[0],t),r={perm:s,cacheKey:`${s}`},[i]=(0,l.transpose)(f,[e],r),d=[a[0].dims[0],a[0].dims[1]/u,a[0].dims[2]*p,a[0].dims[3]*p];return[f.reshapeUnpacked(i,d)]},n.parseDepthToSpaceAttributes=f=>{const a=f.attributes.getInt("blocksize");if(a<1)throw new Error(`blocksize must be >= 1, but got : ${a} for DepthToSpace`);const h=f.attributes.getString("mode","DCR");if(h!=="DCR"&&h!=="CRD")throw new Error(`unrecognized mode: ${h} for DepthToSpace`);return{mode:h,blocksize:a}};const c=f=>{if(f.length!==1)throw new Error(`DepthToSpace expect 1 inputs, but got ${f.length}`);if(f[0].type==="string"||f[0].dims.length!==4)throw new TypeError("DepthToSpace input should be a 4-D numeric tensor")}},9828:(y,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createDotProductProgramInfoLoader=void 0;const l=o(2517),c=o(5060),f=o(2039),a=o(2823),h=o(3248);n.createDotProductProgramInfoLoader=(p,u,s,t)=>{const e=((r,i)=>({name:"ConvDotProduct",inputNames:r?["Im2Col","K","B"]:["Im2Col","K"],inputTypes:r?[f.TextureType.unpacked,f.TextureType.packedLastDimension,f.TextureType.unpacked]:[f.TextureType.unpacked,f.TextureType.packedLastDimension],cacheKey:i.activationCacheKey}))(u.length>2,t);return Object.assign(Object.assign({},e),{get:()=>((r,i,d,g,m)=>{const b=d[0].dims,_=d[1].dims,v=[_[0],Math.ceil(b[1]*_[2]*_[3]/4)],w=(0,h.calculateIm2ColDims)(b,_,g),[S,A]=r.calculateTextureWidthAndHeight(v,f.TextureType.packedLastDimension),O=l.ShapeUtil.computeStrides(w),[x,I]=r.calculateTextureWidthAndHeight(w,f.TextureType.packedLastDimension),$=g.length,B=d.length<3?"0.0":"_B(b)",L=Math.ceil(b[1]*_[2]*_[3]/4),{activationFunction:N,applyActivation:H}=(0,a.getActivationSnippet)(m),M=(0,c.getGlsl)(r.session.backend.glContext.version),j=` +${N} +float process(int indices[${$}]) { + int b[1]; + b[0] = indices[1]; + int im2col[4]; + im2col[0] = indices[0]; + im2col[1] = indices[2]; + im2col[2] = indices[3]; + int im2colOffset = im2col[0] * ${O[0]} + im2col[1] * ${O[1]} + im2col[2] * ${O[2]}; + int kernelOffset = indices[1] * ${v[1]}; + float value = ${B}; + for (int i = 0; i < ${L}; ++i) { + vec2 im2colCoords = offsetToCoords(im2colOffset, ${x}, ${I}); + vec2 kernelCoords = offsetToCoords(kernelOffset, ${S}, ${A}); + value += dot(${M.texture2D}(Im2Col, im2colCoords), ${M.texture2D}(K, kernelCoords)); + ++im2colOffset; + ++kernelOffset; + } + ${H} + return value; +}`;return Object.assign(Object.assign({},i),{output:{dims:g,type:d[0].type,textureType:f.TextureType.unpacked},shaderSource:j})})(p,e,u,s,t)})}},7992:(y,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseFlattenAttributes=n.flatten=void 0;const l=o(2517);n.flatten=(f,a,h)=>{c(a,h);const p=l.ShapeUtil.flattenShape(a[0].dims,h);return[f.reshapeUnpacked(a[0],p)]},n.parseFlattenAttributes=f=>f.attributes.getInt("axis",1);const c=(f,a)=>{if(!f||f.length!==1)throw new Error("Flatten requires 1 input.");const h=f[0].dims.length;if(h===0)throw new Error("scalar tensor is not supported.");if(a<-h||a>h)throw new Error("Invalid axis");if(f[0].type==="string")throw new Error("string tensor is not supported.")}},2823:(y,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseInternalActivationAttributes=n.getActivationSnippet=void 0;const l=o(2517),c=o(4909);n.getActivationSnippet=function(f){let a;switch(f.activation){case"Relu":a=(0,c.glslRelu)();break;case"Sigmoid":a=(0,c.glslSigmoid)();break;case"Clip":a=(0,c.glslClip)(f.clipMin,f.clipMax);break;default:return{activationFunction:"",applyActivation:""}}const h=a.name;return{activationFunction:a.body,applyActivation:`value = ${h}_(value);`}},n.parseInternalActivationAttributes=f=>{const a=f.getString("activation","");if(a==="Clip"){const[h,p]=f.getFloats("activation_params",[l.MIN_CLIP,l.MAX_CLIP]);return{activation:a,clipMax:p,clipMin:h,activationCacheKey:`${a}:${h},${p}`}}return{activation:a,activationCacheKey:a}}},1253:(y,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseGatherAttributes=n.gather=void 0;const l=o(246),c=o(782),f=o(2517),a=o(2039);n.gather=(s,t,e)=>(u(t,e.axis),[s.run(p(s,t,e),t)]),n.parseGatherAttributes=s=>(0,l.createAttributeWithCacheKey)({axis:s.attributes.getInt("axis",0)});const h={name:"Gather",inputNames:["A","B"],inputTypes:[a.TextureType.unpacked,a.TextureType.unpacked]},p=(s,t,e)=>{const r=Object.assign(Object.assign({},h),{cacheHint:e.cacheKey});return Object.assign(Object.assign({},r),{get:()=>((i,d,g,m)=>{const b=g[0].dims.slice(),_=g[1].dims.slice(),v=new Array(b.length+_.length-1);m=f.ShapeUtil.normalizeAxis(m,b.length);const w=[];for(let A=0;A{if(!s||s.length!==2)throw new Error("Gather requires 2 inputs.");const e=s[0].dims.length;if(e<1)throw new Error("Invalid input shape.");if(t<-e||t>e-1)throw new Error("Invalid axis.");if(c.NUMBER_TYPES.indexOf(s[0].type)===-1)throw new Error("Invaid input type.");if(s[1].type!=="int32"&&s[1].type!=="int16")throw new Error("Invaid input type.")}},4776:(y,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseGemmAttributesV11=n.parseGemmAttributesV7=n.gemm=void 0;const l=o(246),c=o(2517),f=o(2039);n.gemm=(s,t,e)=>(u(t,e),[s.run(h(t,e),t)]);const a=(s,t)=>{const e=s.attributes.getInt("transA",0)!==0,r=s.attributes.getInt("transB",0)!==0,i=s.attributes.getFloat("alpha",1),d=s.attributes.getFloat("beta",1);return(0,l.createAttributeWithCacheKey)({transA:e,transB:r,alpha:i,beta:d,isOptionalC:t})};n.parseGemmAttributesV7=s=>a(s,!1),n.parseGemmAttributesV11=s=>a(s,!0);const h=(s,t)=>{const e={name:"Gemm",inputNames:s.length===3?["A","B","C"]:["A","B"],inputTypes:s.length===3?[f.TextureType.unpacked,f.TextureType.unpacked,f.TextureType.unpacked]:[f.TextureType.unpacked,f.TextureType.unpacked],key:t.cacheKey};return Object.assign(Object.assign({},e),{get:()=>p(e,s,t)})},p=(s,t,e)=>{const r=t[0].dims.slice(),i=t[1].dims.slice(),[d,g]=c.GemmUtil.getShapeOfGemmResult(r,e.transA,i,e.transB,t.length===3?t[2].dims:void 0),m=[d,g];if(!m)throw new Error("Can't use gemm on the given tensors");let b=r[r.length-1],_="";e.transA&&(b=r[0]),e.transA&&e.transB?_="value += _A_T(a) * _B_T(b);":e.transA&&!e.transB?_="value += _A_T(a) * 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L=0;L<=1;L++)I+=` + blockIndex = rc.x + ${L}; + pos = rc.y + ${B}; + + if(blockIndex < ${S[1]} && pos < ${S[0]}) { + offsetY = int(blockIndex / (${m[w-1]})) * ${b.strides[0]} - + ${b.pads[0]}; + d0 = offsetY + ${b.dilations[0]} * (imod(pos, ${A}) / ${v[2]}); + + if(d0 < ${_[2]} && d0 >= 0) { + offsetX = imod(blockIndex, ${m[w-1]}) * ${b.strides[1]} - + ${b.pads[1]}; + d1 = offsetX + ${b.dilations[1]} * imod(imod(pos, ${A}), ${v[2]}); + + if(d1 < ${_[3]} && d1 >= 0) { + + ch = int(float(pos)/ ${A}.); + innerDims = vec2(d0, d1); + result[${2*B+L}] = getChannel( + getA(0, ch, int(innerDims.x), + int(innerDims.y)), innerDims); + } + } + } + + `;const $=` + ${O} + + void main() { + ivec2 rc = getOutputCoords(); + vec4 result = vec4(0.0); + int blockIndex, pos, offsetY, d0, offsetX, d1, ch; + vec2 innerDims; + ${I} + ${x.output} = result; + } + `;return Object.assign(Object.assign({},i),{output:{dims:S,type:d.type,textureType:c.TextureType.packed},shaderSource:$,hasMain:!0})})(a,t,h,p,u,s)})}},3248:(y,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.calculateIm2ColDims=n.createIm2ColProgramInfoLoader=void 0;const l=o(2039);n.createIm2ColProgramInfoLoader=(c,f,a,h,p)=>{const u=(s=p.cacheKey,{name:"Im2Col",inputNames:["X"],inputTypes:[l.TextureType.unpacked],cacheHint:s});var s;return Object.assign(Object.assign({},u),{get:()=>((t,e,r,i,d,g)=>{const m=r.dims,b=i.dims,_=d.length,v=(0,n.calculateIm2ColDims)(m,b,d,4),w=` + const int XC = ${m[1]}; + const int XH = ${m[2]}; + const int XW = ${m[3]}; + const int KH = ${g.kernelShape[0]}; + const int KW = ${g.kernelShape[1]}; + const int dilationH = ${g.dilations[0]}; + const int dilationW = ${g.dilations[1]}; + const int strideH = ${g.strides[0]}; + const int strideW = ${g.strides[1]}; + const int padH = ${g.pads[0]}; + const int padW = ${g.pads[1]}; + const int KHKW = KH*KW; + const int XCKHKW = XC * KHKW; + const int outputChannels = 4; + vec4 process(int indices[${_}]) { + int b = indices[0]; // batch size + int oh = indices[1] * strideH - padH; //output height + int ow = indices[2] * strideW - padW; //output width + int p = indices[3] * outputChannels; //patch + vec4 value = vec4(0.0); + for(int i=0; i < outputChannels; ++i) { + if(p < XCKHKW) { + int patchC = p / KHKW; + int patchH = (p - patchC*KHKW) / KW; + int patchW = (p - patchC*KHKW) - patchH * KW; + int xh2 = oh + patchH * dilationH; + int xw2 = ow + patchW * dilationW; + int x[${m.length}]; + x[0] = b; + x[1] = patchC; + x[2] = xh2; + x[3] = xw2; + if(xh2 >= 0 && + xh2 < XH && + xw2 >= 0 && + xw2 < XW) { + value[i] = _X(x); + } + } + ++p; + } + return value; + } + `;return Object.assign(Object.assign({},e),{output:{dims:v,type:r.type,textureType:l.TextureType.packedLastDimension},shaderSource:w})})(0,u,f,a,h,p)})},n.calculateIm2ColDims=(c,f,a,h=4)=>[a[0],a[2],a[3],Math.ceil(c[1]*f[2]*f[3]/h)]},6572:(y,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseImageScalerAttributes=n.imageScaler=void 0;const l=o(246),c=o(2039);n.imageScaler=(u,s,t)=>(p(s),[u.run(a(u,s,t),s)]),n.parseImageScalerAttributes=u=>{const s=u.attributes.getFloat("scale"),t=u.attributes.getFloats("bias");return(0,l.createAttributeWithCacheKey)({scale:s,bias:t})};const f={name:"ImageScaler",inputNames:["X"],inputTypes:[c.TextureType.unpacked]},a=(u,s,t)=>{const e=Object.assign(Object.assign({},f),{cacheHint:t.cacheKey});return Object.assign(Object.assign({},e),{get:()=>((r,i,d,g)=>{const m=d[0].dims.slice(),b=m.length,_=` + ${h(g.bias.length)} + float process(int indices[${b}]) { + return _X(indices) * scale + getBias(bias, indices[1]); + }`;return Object.assign(Object.assign({},i),{output:{dims:m,type:d[0].type,textureType:c.TextureType.unpacked},variables:[{name:"bias",type:"float",arrayLength:g.bias.length,data:g.bias},{name:"scale",type:"float",data:g.scale}],shaderSource:_})})(0,e,s,t)})},h=u=>{const s=[`float getBias(float bias[${u}], int channel) {`];for(let t=0;t{if(!u||u.length!==1)throw new Error("ImageScaler requires 1 input.");if(u[0].dims.length!==4)throw new Error("Invalid input shape.");if(u[0].type!=="float32"&&u[0].type!=="float64")throw new Error("Invalid input type.")}},3346:(y,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseInstanceNormalizationAttributes=n.instanceNormalization=void 0;const l=o(5060),c=o(2039);n.instanceNormalization=(s,t,e)=>{u(t);const r=s.run(a(t[0]),t);return[s.run(p(s,t[0],e,r.dims),[t[0],r,t[1],t[2]])]},n.parseInstanceNormalizationAttributes=s=>s.attributes.getFloat("epsilon",1e-5);const f={name:"InstanceNormalization_MeanAndVariance",inputNames:["X"],inputTypes:[c.TextureType.unpacked]},a=s=>Object.assign(Object.assign({},f),{get:()=>((t,e)=>{const r=e.dims.slice(),i=r[1],d=r[2]*r[3],g=[r[0],i],m=` + vec4 process(int[2] indices) { + vec4 v = vec4(0.0); + int a[4]; + a[0] = indices[0]; + a[1] = indices[1]; + float temp = 0.0; + for(int a2=0; a2<${r[2]}; a2++) { + a[2] = a2; + for(int a3=0; a3<${r[3]}; a3++) { + a[3] = a3; + float x = _X(a); + temp += x; + } + } + float mean = temp / float(${d}); + temp = 0.0; + for(int a2=0; a2<${r[2]}; a2++) { + a[2] = a2; + for(int a3=0; a3<${r[3]}; a3++) { + a[3] = a3; + float x = _X(a); + temp += (x - mean) * (x - mean); + } + } + v.r = mean; + v.g = temp / float(${d}); + + return v; + }`;return Object.assign(Object.assign({},t),{output:{dims:g,type:e.type,textureType:c.TextureType.packedLastDimension},shaderSource:m})})(f,s)}),h={name:"InstanceNormalization_ComputeOutput",inputNames:["X","MeanAndVariance","Scale","B"],inputTypes:[c.TextureType.unpacked,c.TextureType.packedLastDimension,c.TextureType.unpacked,c.TextureType.unpacked]},p=(s,t,e,r)=>{const i=Object.assign(Object.assign({},h),{cacheHint:`${e}`});return Object.assign(Object.assign({},i),{get:()=>((d,g,m,b,_)=>{const v=(0,l.getGlsl)(d.session.backend.glContext.version),[w,S]=d.calculateTextureWidthAndHeight(_,c.TextureType.packedLastDimension),[A,O]=[w/4,S],x=` + vec4 get_MeanAndVariance(int[2] mv) { + int offset = indicesToOffset_MeanAndVariance(mv); + vec2 coords = offsetToCoords(offset, ${A}, ${O}); + return ${v.texture2D}(MeanAndVariance, coords); + } + + float process(int[4] indices) { + int mv[2]; + mv[0] = indices[0]; + mv[1] = indices[1]; + vec4 mean_and_variance = get_MeanAndVariance(mv); + float mean = mean_and_variance.r; + float variance = mean_and_variance.g; + + int sb[1]; + sb[0] = indices[1]; + float scale = _Scale(sb); + float b = _B(sb); + + return scale * (_X(indices) - mean) / sqrt(variance + epsilon) + b; + }`;return Object.assign(Object.assign({},g),{output:{dims:m.dims,type:m.type,textureType:c.TextureType.unpacked},variables:[{name:"epsilon",type:"float",data:b}],shaderSource:x})})(s,i,t,e,r)})},u=s=>{if(!s||s.length!==3)throw new Error("InstanceNormalization requires 3 inputs.");const t=s[0],e=s[1],r=s[2];if(t.dims.length<3||e.dims.length!==1||r.dims.length!==1)throw new Error("Invalid input shape.");if(e.dims[0]!==t.dims[1]||r.dims[0]!==t.dims[1])throw new Error("Input shapes are mismatched.");if(t.type!=="float32"&&t.type!=="float64"||e.type!=="float32"&&e.type!=="float64"||r.type!=="float32"&&r.type!=="float64")throw new Error("Invalid input type.");if(s[0].dims.length!==4)throw new Error("Only support 4-D input 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x=w[w.length-1],I=Math.ceil(x/2),$=w.length,B=S.length,L=(0,c.getGlsl)(d.session.backend.glContext.version),N=(0,a.getCoordsDataType)(A.length),H=A.length,M=(0,a.getGlChannels)(),{activationFunction:j,applyActivation:Z}=(0,h.getActivationSnippet)(b),X=_?`${(0,p.getBiasForMatmul)(N,M,m[2].dims,A,!0)}`:"",Q=O?`${function(xe,oe,we,ye){let ke=[],Ne=[];const Te=we[0].dims,$e=we[1].dims,Ce=Te.length,Ee=$e.length,Oe=ye.length,Be=Oe-Ce,Ve=Oe-Ee;ke=Te.map((Ie,je)=>`coords.${oe[je+Be]}`),ke[Ce-1]="i*2",ke.join(", "),Ne=$e.map((Ie,je)=>`coords.${oe[je+Ve]}`),Ne[Ee-2]="i*2",Ne.join(", ");const Ge=l.BroadcastUtil.getBroadcastDims(Te,ye),Xe=l.BroadcastUtil.getBroadcastDims($e,ye),Ze=Ge.map(Ie=>`coords.${oe[Ie+Be]} = 0;`).join(` +`),qe=Xe.map(Ie=>`coords.${oe[Ie+Ve]} = 0;`).join(` +`),Ue=`int lastDim = coords.${oe[Oe-1]}; + coords.${oe[Oe-1]} = coords.${oe[Oe-2]}; + coords.${oe[Oe-2]} = lastDim;`;return` +vec4 getAAtOutCoordsMatmul(int i) { + ${xe} coords = getOutputCoords(); + ${Ue} + ${Ze} + vec4 outputValue = getA(${ke}); + return outputValue; +} + +vec4 getBAtOutCoordsMatmul(int i) { + ${xe} coords = getOutputCoords(); + ${Ue} + ${qe} + vec4 outputValue = getB(${Ne}); + return outputValue; +}`}(N,M,m,A)}`:"",ee=O?"getAAtOutCoordsMatmul(i)":`getA(${function(xe,oe){let we="";for(let ye=0;ye{Object.defineProperty(n,"__esModule",{value:!0}),n.getBiasForMatmul=n.createMatmulProgramInfoLoader=n.parseMatMulAttributes=n.matMul=void 0;const l=o(2517),c=o(2039),f=o(9390),a=o(2823),h=o(708);function p(t,e){const r=(i=t.length>2,d=e.activationCacheKey,{name:"MatMul",inputNames:i?["A","B","Bias"]:["A","B"],inputTypes:i?[c.TextureType.unpacked,c.TextureType.unpacked,c.TextureType.unpacked]:[c.TextureType.unpacked,c.TextureType.unpacked],cacheHint:d});var i,d;return Object.assign(Object.assign({},r),{get:()=>function(g,m,b){const _=m[0].dims,v=m[1].dims,w=l.BroadcastUtil.calcShape(_,v,!0);if(!w)throw new Error("Can't use matmul on the given tensors");const S=(0,f.getCoordsDataType)(w.length),A=(0,f.getGlChannels)(),{activationFunction:O,applyActivation:x}=(0,a.getActivationSnippet)(b),I=m.length>2,$=I?"value += getBiasForMatmul();":"",B=I?`${s(S,A,m[2].dims,w,!1)}`:"",L=w.length,N=_.length,H=v.length,M=` + ${O} + ${B} + float process(int indices[${L}]) { + int a[${N}]; + int b[${H}]; + bcastMatmulIndices_A(indices, a); + bcastMatmulIndices_B(indices, b); + + float value; + for (int k=0; k<${_[_.length-1]}; ++k) { + a[${N-1}] = k; + b[${H-2}] = k; + value += _A(a) * _B(b); + } + ${$} + ${x} + return value; + }`;return Object.assign(Object.assign({},g),{output:{dims:w,type:m[0].type,textureType:c.TextureType.unpacked},shaderSource:M})}(r,t,e)})}n.matMul=(t,e,r)=>(u(e),t.session.pack?[t.run((0,h.createPackedMatmulProgramInfoLoader)(t,e,r),e)]:[t.run(p(e,r),e)]),n.parseMatMulAttributes=t=>(0,a.parseInternalActivationAttributes)(t.attributes),n.createMatmulProgramInfoLoader=p;const u=t=>{if(!t||t.length!==2)throw new Error("MatMul requires 2 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+}`}n.getBiasForMatmul=s},2403:(y,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createPackProgramInfoLoader=void 0;const l=o(5060),c=o(2039),f=o(9390),a=o(2827),h={name:"pack",inputNames:["A"],inputTypes:[c.TextureType.unpackedReversed]};n.createPackProgramInfoLoader=(p,u)=>Object.assign(Object.assign({},h),{get:()=>((s,t)=>{const e=(0,l.getGlsl)(s.session.backend.glContext.version),r=t.dims,i=r.length,d=t.dims.length,g=(0,f.getCoordsDataType)(d),m=(0,a.getChannels)("rc",d),b=(_=d,v=m,w=r[r.length-2],S=r[r.length-1],_===0||_===1?"":` + int r = ${v[_-2]}; + int c = ${v[_-1]}; + int rp1 = ${v[_-2]} + 1; + int cp1 = ${v[_-1]} + 1; + bool rEdge = rp1 >= ${S}; + bool cEdge = cp1 >= ${w}; + `);var _,v,w,S;let A;A=i===0?[1,1]:i===1?[r[0],1]:[r[d-1],r[d-2]];const O=function($,B,L){if($===0)return"false";if($===1)return`rc > ${B[0]}`;let N="";for(let H=$-2;H<$;H++)N+=`${L[H]} >= ${B[H-$+2]}`,H<$-1&&(N+="||");return N}(d,A,m),x=function($,B){const L=$.length;if(L===0)return"getA(), 0, 0, 0";if(L===1)return`getA(rc), + rc + 1 >= ${$[0]} ? 0. : getA(rc + 1), + 0, 0`;let N="";if(L>2)for(let H=0;H{Object.defineProperty(n,"__esModule",{value:!0}),n.unpackFromChannel=n.getChannels=n.getVecChannels=void 0;const l=o(9390);function c(f,a){return(0,l.getGlChannels)(a).map(h=>`${f}.${h}`)}n.getVecChannels=c,n.getChannels=function(f,a){return a===1?[f]:c(f,a)},n.unpackFromChannel=function(){return` + float getChannel(vec4 frag, int dim) { + int modCoord = imod(dim, 2); + return modCoord == 0 ? frag.r : frag.g; + } + + float getChannel(vec4 frag, vec2 innerDims) { + vec2 modCoord = mod(innerDims, 2.); + return modCoord.x == 0. ? + (modCoord.y == 0. ? frag.r : frag.g) : + (modCoord.y == 0. ? frag.b : frag.a); + } + `}},2870:(y,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parsePadAttributesV11=n.padV11=n.parsePadAttributesV2=n.padV2=void 0;const l=o(246),c=o(2517),f=o(5060),a=o(2039),h={name:"Pad",inputNames:["A"],inputTypes:[a.TextureType.unpacked]};n.padV2=(g,m,b)=>(s(m),[g.run(Object.assign(Object.assign({},h),{cacheHint:b.cacheKey,get:()=>u(g,m[0],b)}),m)]),n.parsePadAttributesV2=g=>{const m=g.attributes.getString("mode","constant"),b=g.attributes.getFloat("value",0),_=g.attributes.getInts("pads");return(0,l.createAttributeWithCacheKey)({mode:m,value:b,pads:_})},n.padV11=(g,m,b)=>{t(m);const _=p(g,m,b);return(0,n.padV2)(g,[m[0]],_)},n.parsePadAttributesV11=g=>g.attributes.getString("mode","constant");const p=(g,m,b)=>{if(!g.session.isInitializer(m[1].dataId)||m.length>=3&&!g.session.isInitializer(m[2].dataId))throw new Error("dynamic pad attributes are not allowed");const _=Array.from(m[1].integerData),v=m.length>=3?m[2].floatData[0]:0;return(0,l.createAttributeWithCacheKey)({mode:b,pads:_,value:v})},u=(g,m,b)=>{const _=c.ShapeUtil.padShape(m.dims.slice(),b.pads),v=_.length,w=` + ${e(g,m,b)} + float process(int[${v}] indices) { + return padA(indices); + }`;return{name:"Pad",inputNames:["A"],inputTypes:[a.TextureType.unpacked],output:{dims:_,type:m.type,textureType:a.TextureType.unpacked},shaderSource:w}},s=g=>{if(!g||g.length!==1)throw new Error("Pad requires 1 input");if(g[0].type!=="float32"&&g[0].type!=="float64")throw new Error("Invalid input type.")},t=g=>{if(!g||g.length!==2&&g.length!==3)throw new Error("Pad requires 2 or 3 inputs");if(g[1].type!=="int32")throw new Error("Invalid input type.");if(g.length>=3&&g[2].type==="string")throw new Error("Invalid input type.")},e=(g,m,b)=>{const _=(0,f.getGlsl)(g.session.backend.glContext.version),[v,w]=g.calculateTextureWidthAndHeight(m.dims,a.TextureType.unpacked),S=c.ShapeUtil.computeStrides(m.dims);switch(b.mode){case"constant":return r(_,m.dims,S,v,w,b.pads,b.value);case"reflect":return i(_,m.dims,S,v,w,b.pads);case"edge":return d(_,m.dims,S,v,w,b.pads);default:throw new Error("Invalid mode")}},r=(g,m,b,_,v,w,S)=>{const A=m.length;let O="";for(let x=A-1;x>=0;--x)O+=` + k = m[${x}] - ${w[x]}; + if (k < 0) return constant; + if (k >= ${m[x]}) return constant; + offset += k * ${b[x]}; + `;return` + float padA(int m[${A}]) { + const float constant = float(${S}); + int offset = 0; + int k = 0; + ${O} + vec2 coords = offsetToCoords(offset, ${_}, ${v}); + float value = getColorAsFloat(${g.texture2D}(A, coords)); + return value; + } + `},i=(g,m,b,_,v,w)=>{const S=m.length;let A="";for(let O=S-1;O>=0;--O)A+=` + k = m[${O}] - ${w[O]}; + if (k < 0) { k = -k; } + { + const int _2n_1 = ${2*(m[O]-1)}; + k = int( mod( float(k), float(_2n_1) ) ) ; + if(k >= ${m[O]}) { k = _2n_1 - k; } + } + offset += k * ${b[O]}; + `;return` + float padA(int m[${S}]) { + int offset = 0; + int k = 0; + ${A} + vec2 coords = offsetToCoords(offset, ${_}, ${v}); + float value = getColorAsFloat(${g.texture2D}(A, coords)); + return value; + } + `},d=(g,m,b,_,v,w)=>{const S=m.length;let A="";for(let O=S-1;O>=0;--O)A+=` + k = m[${O}] - ${w[O]}; + if (k < 0) k = 0; + if (k >= ${m[O]}) k = ${m[O]-1}; + offset += k * ${b[O]}; + `;return` + float padA(int m[${S}]) { + int offset = 0; + int k = 0; + ${A} + vec2 coords = offsetToCoords(offset, ${_}, ${v}); + float value = getColorAsFloat(${g.texture2D}(A, coords)); + return value; + } + `}},2143:(y,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.globalMaxPool=n.parseMaxPoolAttributes=n.maxPool=n.parseGlobalAveragePoolAttributes=n.globalAveragePool=n.parseAveragePoolAttributes=n.averagePool=void 0;const l=o(246),c=o(2517),f=o(2039);n.averagePool=(d,g,m)=>{t(g);const b={name:"AveragePool",inputNames:["X"],inputTypes:[f.TextureType.unpacked],cacheHint:m.cacheKey};return[d.run(Object.assign(Object.assign({},b),{get:()=>a(g,b,!1,m)}),g)]},n.parseAveragePoolAttributes=d=>{const 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b={name:"GlobalAveragePool",inputNames:["X"],inputTypes:[f.TextureType.unpacked],cacheHint:`${m.countIncludePad}`};return[d.run(Object.assign(Object.assign({},b),{get:()=>a(g,b,!0,m)}),g)]},n.parseGlobalAveragePoolAttributes=d=>{const g=d.attributes.getInt("count_include_pad",0)!==0;return(0,l.createAttributeWithCacheKey)({autoPad:"",ceilMode:0,countIncludePad:g,kernelShape:[],strides:[],pads:[]})},n.maxPool=(d,g,m)=>{t(g);const b={name:"MaxPool",inputNames:["X"],inputTypes:[f.TextureType.unpacked],cacheHint:m.cacheKey};return[d.run(Object.assign(Object.assign({},b),{get:()=>h(g,b,!1,m)}),g)]},n.parseMaxPoolAttributes=d=>{const g=d.attributes.getString("auto_pad","NOTSET"),m=d.attributes.getInt("ceil_mode",0),b=d.attributes.getInts("kernel_shape"),_=d.attributes.getInts("strides",[]),v=d.attributes.getInts("pads",[]),w=d.attributes.getInt("storage_order",0),S=d.attributes.getInts("dilations",[]);if(w!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(m!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");return(0,l.createAttributeWithCacheKey)({autoPad:g,ceilMode:m,countIncludePad:!1,kernelShape:b,strides:_,pads:v,storageOrder:w,dilations:S})};const h=(d,g,m,b)=>{const[_,v]=p(d,b,m),w=` + ${e(d[0].dims,_,` + value = max(_X(x), value); + `,"","-1e5")} + `;return Object.assign(Object.assign({},g),{output:{dims:v,type:d[0].type,textureType:f.TextureType.unpacked},shaderSource:w})},p=(d,g,m)=>{const b=d[0].dims.slice(),_=Object.hasOwnProperty.call(g,"dilations"),v=g.kernelShape.slice(),w=g.strides.slice(),S=_?g.dilations.slice():[],A=g.pads.slice();c.PoolConvUtil.adjustPoolAttributes(m,b,v,w,S,A);const O=c.PoolConvUtil.computePoolOutputShape(m,b,w,S,v,A,g.autoPad),x=Object.assign({},g);return _?Object.assign(x,{kernelShape:v,strides:w,pads:A,dilations:S,cacheKey:g.cacheKey}):Object.assign(x,{kernelShape:v,strides:w,pads:A,cacheKey:g.cacheKey}),[x,O]},u={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[],cacheKey:""},s={name:"GlobalMaxPool",inputNames:["X"],inputTypes:[f.TextureType.unpacked]};n.globalMaxPool=(d,g)=>(t(g),[d.run(Object.assign(Object.assign({},s),{get:()=>h(g,s,!0,u)}),g)]);const t=d=>{if(!d||d.length!==1)throw new Error("Pool ops requires 1 input.");if(d[0].type!=="float32"&&d[0].type!=="float64")throw new Error("Invalid input type.")},e=(d,g,m,b,_)=>{const v=d.length;if(g.kernelShape.length<=2){const w=g.kernelShape[g.kernelShape.length-1],S=g.strides[g.strides.length-1],A=g.pads[g.pads.length/2-1],O=g.pads[g.pads.length-1],x=d[v-1];let I="",$="",B="";if(I=A+O!==0?` + for (int i = 0; i < ${w}; i++) { + x[${v} - 1] = indices[${v} - 1] * ${S} - ${A} + i; + if (x[${v} - 1] < 0 || x[${v} - 1] >= ${x}) { + pad++; + continue; + } + ${m} + }`:` + for (int i = 0; i < ${w}; i++) { + x[${v} - 1] = indices[${v} - 1] * ${S} - ${A} + i; + ${m} + }`,g.kernelShape.length===2){const L=g.kernelShape[g.kernelShape.length-2],N=g.strides[g.strides.length-2],H=g.pads[g.pads.length/2-2],M=g.pads[g.pads.length-2],j=d[v-2];$=H+M!==0?` + for (int j = 0; j < ${L}; j++) { + x[${v} - 2] = indices[${v} - 2] * ${N} - ${H} + j; + if (x[${v} - 2] < 0 || x[${v} - 2] >= ${j}) { + pad+= ${w}; + continue; + } + `:` + for (int j = 0; j < ${L}; j++) { + x[${v} - 2] = indices[${v} - 2] * ${N} - ${H} + j; + `,B=` + } + `}return` + float process(int indices[${v}]) { + int x[${v}]; + copyVec(indices, x); + + float value = ${_}; + int pad = 0; + ${$} + ${I} + ${B} + ${b} + return value; + } + `}{const w=c.ShapeUtil.size(g.kernelShape),S=c.ShapeUtil.computeStrides(g.kernelShape),A=S.length,O=g.pads.length,x=i(A),I=r(d,"inputDims"),$=r(g.pads,"pads"),B=r(S,"kernelStrides"),L=r(g.strides,"strides");let N="";return N=g.pads.reduce((H,M)=>H+M)?` + if (x[j] >= inputDims[j] || x[j] < 0) { + pad++; + isPad = true; + break; + } + } + if (!isPad) { + ${m} + }`:` + } + ${m} + `,` + ${x} + float process(int indices[${v}]) { + int x[${v}]; + copyVec(indices, x); + int offset[${A}]; + int pads[${O}]; + int inputDims[${v}]; + int kernelStrides[${A}]; + int strides[${A}]; + ${$} + ${I} + ${L} + ${B} + + float value = ${_}; + int pad = 0; + bool isPad = false; + for (int i = 0; i < ${w}; i++) { + offsetToIndices(i, kernelStrides, offset); + isPad = false; + for (int j = ${v} - ${A}; j < ${v}; j++) { + x[j] = indices[j] * strides[j - ${v} + ${A}] + + offset[j - ${v} + ${A}] - pads[j - 2]; + ${N} + } + ${b} + + return value; + } + `}},r=(d,g)=>{let m="";for(let b=0;b` + void offsetToIndices(int offset, int[${d}] strides, out int[${d}] indices) { + if (${d} == 0) { + return; + } + for (int i = 0; i < ${d} - 1; ++i) { + indices[i] = offset / strides[i]; + offset -= indices[i] * strides[i]; + } + indices[${d} - 1] = offset; + }`},4939:(y,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.reduceLogSumSquare=n.reduceLogSum=n.reduceProd=n.reduceMin=n.reduceMax=n.reduceMean=n.reduceSum=n.parseReduceAttributes=void 0;const l=o(246),c=o(782),f=o(2517),a=o(2039),h=(s,t,e,r,i)=>{u(t);const d={name:r,inputNames:["A"],inputTypes:[a.TextureType.unpacked]};return[s.run(Object.assign(Object.assign({},d),{cacheHint:e.cacheKey,get:()=>p(s,t,e,r,i,d)}),t)]};n.parseReduceAttributes=s=>{const t=s.attributes.getInts("axes",[]),e=s.attributes.getInt("keepdims",1)===1;return(0,l.createAttributeWithCacheKey)({axes:t,keepDims:e})};const p=(s,t,e,r,i,d)=>{const g=[],m=t[0].dims.length||1,b=[],_=f.ShapeUtil.normalizeAxes(e.axes,t[0].dims.length),v=i(t,_);let w=v[1];for(let A=0;A=0||_.length===0?(e.keepDims&&g.push(1),w=` + for(int j${A} = 0; j${A} < ${t[0].dims[A]}; j${A}++) { + inputIdx[${A}] = j${A}; + ${w} + }`):(b.push(`inputIdx[${A}] = outputIdx[${g.length}];`),g.push(t[0].dims[A]));const S=` + float process(int outputIdx[${g.length||1}]) { + float value; // final result + int inputIdx[${m}]; // addressing input data + ${b.join(` +`)} + ${v[0]} // init ops for reduce max/min + ${w} + ${v[2]} // final computation for reduce mean + return value; + }`;return Object.assign(Object.assign({},d),{output:{dims:g,type:t[0].type,textureType:a.TextureType.unpacked},shaderSource:S})},u=s=>{if(!s||s.length!==1)throw new Error("Reduce op requires 1 input.");if(c.NUMBER_TYPES.indexOf(s[0].type)===-1)throw new Error("Invalid input type.")};n.reduceSum=(s,t,e)=>h(s,t,e,"ReduceSum",()=>["value = 0.0;","value += _A(inputIdx);",""]),n.reduceMean=(s,t,e)=>h(s,t,e,"ReduceMean",(r,i)=>{let d=1;for(let g=0;g=0||i.length===0)&&(d*=r[0].dims[g]);return["value = 0.0;","value += _A(inputIdx);",`value /= ${d}.;`]}),n.reduceMax=(s,t,e)=>h(s,t,e,"ReduceMax",(r,i)=>{const d=[];for(let g=0;g=0||i.length===0)&&d.push(`inputIdx[${g}] = 0;`);return[`${d.join(` +`)} +value = _A(inputIdx);`,"value = max(value, 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${O}`};`).join("")} + return ivec3(b, r, c); + } + `}(d)} + ${function(v){const w=l.ShapeUtil.computeStrides(v);return` + int getFlattenedIndex(ivec3 coords) { + // reverse y, z order + return coords.x * ${w[0]} + coords.z * ${w[1]} + coords.y; + } +`}(g)} + ${(0,a.unpackFromChannel)()} + + void main() { + ivec3 rc = getOutputCoords(); + + vec4 result = vec4(0.0); + + ivec3 outputCoords; + int rows = ${g[2]}; + int cols = ${g[1]}; + + ${m} + ${b.output} = result; + } + `;return Object.assign(Object.assign({},r),{output:{dims:g,type:e.type,textureType:f.TextureType.packed},shaderSource:_,hasMain:!0})})(h,p,s,u)})},n.processDims3D=function(h){if(h.length===0)return[1,1,1];let p=1;for(let u=0;u1?h[h.length-2]:1,h[h.length-1]]},n.isReshapeCheap=function(h,p){let u=!1;return u=h.length===0||p.length===0||(h.length<2||p.length<2?h[h.length-1]===p[p.length-1]:h[h.length-1]===p[p.length-1]&&h[h.length-2]===p[p.length-2]),u}},718:(y,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.reshape=void 0;const l=o(2517);n.reshape=(c,f)=>{const a=l.ShapeUtil.calculateReshapedDims(f[0].dims,f[1].integerData);return c.session.pack?[c.reshapePacked(f[0],a)]:[c.reshapeUnpacked(f[0],a)]}},2268:(y,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseResizeAttributesV11=n.parseResizeAttributesV10=n.resize=void 0;const l=o(5060),c=o(2039),f=o(9390),a=o(2827),h=o(9793),p={name:"Resize",inputNames:["A"],inputTypes:[c.TextureType.packed]};n.resize=(r,i,d)=>((0,h.validateInputs)(i,d),[r.run(Object.assign(Object.assign({},p),{cacheHint:d.cacheKey,get:()=>u(r,i,d)}),i)]),n.parseResizeAttributesV10=r=>(0,h.parseUpsampleAttributes)(r,10),n.parseResizeAttributesV11=r=>(0,h.parseUpsampleAttributes)(r,11);const u=(r,i,d)=>{const 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`;break;case"pytorch_half_pixel":$=` + vec4 getSourceFracIndex(ivec4 coords) { + vec4 fcoords = vec4(coords); + return vec4( + ${w}.0 > 1.0 ? (fcoords.x + 0.5) / scaleWHWH.x - 0.5 : 0.0, + ${v}.0 > 1.0 ? (fcoords.y + 0.5) / scaleWHWH.y - 0.5 : 0.0, + ${w}.0 > 1.0 ? (fcoords.z + 0.5) / scaleWHWH.z - 0.5 : 0.0, + ${v}.0 > 1.0 ? (fcoords.w + 0.5) / scaleWHWH.w - 0.5 : 0.0 + ); + } + `;break;case"align_corners":$=` + vec4 getSourceFracIndex(ivec4 coords) { + vec4 resized = vec4(${w}.0 - 1.0, ${v}.0 - 1.0, ${w}.0 - 1.0, + ${v}.0 - 1.0); + vec4 original = vec4(${O}.0 - 1.0, ${A}.0 - 1.0, ${O}.0 - 1.0, + ${A}.0 - 1.0); + vec4 new_scale = original / resized; + return vec4(coords) * new_scale; + } + `;break;default:throw new Error(`resize (packed) does not support coordinateTransformMode: '${d.coordinateTransformMode}'`)}const B=(0,f.getCoordsDataType)(_),L=` + const vec2 inputWH = vec2(${A}.0, ${O}.0); + const vec4 scaleWHWH = vec4(float(${x}), float(${I}), float(${x}), float(${I})); + ${(0,a.unpackFromChannel)()} + ${$} + float getAValue(int x10, int r, int c, int d) { + return getChannel(getA(x10, r, c, d), vec2(c, d)); + } + void main() { + ${B} rc = getOutputCoords(); + + int batch = rc[0]; + int depth = rc[1]; + + // retrieve the 4 coordinates that is used in the 4 packed output values. + ivec4 coords = ivec4(rc.wz, rc.w + 1, rc.z + 1); + + // calculate the source index in fraction + vec4 sourceFrac = getSourceFracIndex(coords); + + // get the lower and upper bound of the 4 values that will be packed into one texel. + ivec4 x00 = ivec4(max(sourceFrac.xy, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.xy))); + ivec4 x01 = ivec4(max(sourceFrac.xw, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.xw))); + ivec4 x10 = ivec4(max(sourceFrac.zy, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.zy))); + ivec4 x11 = ivec4(max(sourceFrac.zw, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.zw))); + + bool hasNextRow = rc.w < ${v-1}; + bool hasNextCol = rc.z < ${w-1}; + + // pack x00, x01, x10, x11's top-left corner into one vec4 structure + vec4 topLeft = vec4( + getAValue(batch, depth, x00.x, x00.y), + hasNextCol ? getAValue(batch, depth, x01.x, x01.y) : 0.0, + hasNextRow ? getAValue(batch, depth, x10.x, x10.y) : 0.0, + (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.x, x11.y) : 0.0); + + // pack x00, x01, x10, x11's top-right corner into one vec4 structure + vec4 topRight = vec4( + getAValue(batch, depth, x00.x, x00.w), + hasNextCol ? getAValue(batch, depth, x01.x, x01.w) : 0.0, + hasNextRow ? getAValue(batch, depth, x10.x, x10.w) : 0.0, + (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.x, x11.w) : 0.0); + + // pack x00, x01, x10, x11's bottom-left corner into one vec4 structure + vec4 bottomLeft = vec4( + getAValue(batch, depth, x00.z, x00.y), + hasNextCol ? getAValue(batch, depth, x01.z, x01.y) : 0.0, + hasNextRow ? getAValue(batch, depth, x10.z, x10.y) : 0.0, + (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.z, x11.y) : 0.0); + + // pack x00, x01, x10, x11's bottom-right corner into one vec4 structure + vec4 bottomRight = vec4( + getAValue(batch, depth, x00.z, x00.w), + hasNextCol ? getAValue(batch, depth, x01.z, x01.w) : 0.0, + hasNextRow ? getAValue(batch, depth, x10.z, x10.w) : 0.0, + (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.z, x11.w) : 0.0); + + // calculate the interpolation fraction on u and v direction + vec4 frac = vec4(sourceFrac) - floor(sourceFrac); + vec4 clampFrac = clamp(frac, vec4(0.0), vec4(1.0)); + + vec4 top = mix(topLeft, topRight, clampFrac.ywyw); + vec4 bottom = mix(bottomLeft, bottomRight, clampFrac.ywyw); + vec4 newValue = mix(top, bottom, clampFrac.xxzz); + + ${g.output} = vec4(newValue); + } + `;return Object.assign(Object.assign({},p),{output:{dims:b,type:i[0].type,textureType:c.TextureType.packed},hasMain:!0,shaderSource:L})},s=(r,i)=>{const d=r[0].dims;let g,m=i.scales;if(m.length===0){const _=r[i.scalesInputIdx];if(_&&_.size!==0){if(r[i.sizesInputIdx])throw new Error("Only one of scales or sizes must be provided as input.");m=t(_,i.mode,i.isResize)}else{const v=r[i.sizesInputIdx];if(!v||v.size===0)throw new Error("Either scales or sizes MUST be provided as input.");g=Array.from(v.integerData),m=e(g,d,i.mode,i.isResize)}}else if(r[i.sizesInputIdx])throw new Error("Only one of scales 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i=1; i<${_}; ++i) + { + float current = getColorAsFloat(${O.texture2D}(A, offsetToCoords(logical_row_start_offset + i, + ${w}, ${S}))); + if(current > max) + max = current; + } + + return max; + }`;return Object.assign(Object.assign({},p),{output:{dims:v,type:m.type,textureType:a.TextureType.unpacked},shaderSource:x})},r=(g,m,b,_,v,w)=>{const[S,A]=g.calculateTextureWidthAndHeight(m.dims,a.TextureType.unpacked),O=w.length;if(b<1||_<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(w.length!==1)throw new Error("Dimensionality of the output should be 1");if(w[0]!==b)throw new Error("Shape of the output should be equal to logical row count");if(v.length!==1)throw new Error("Dimensionality of the intermediate results should be 1");if(v[0]!==b)throw new Error("Shape of the intermediate results should be equal to logical row count");const x=` + float process(int[${O}] indices) { + int logical_row_start_offset = indices[0] * ${_}; + + float 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int a[${m}]; + perm(a, indices); + return _A(a); + }`;return Object.assign(Object.assign({},a),{output:{dims:g,type:r.type,textureType:f.TextureType.unpacked},shaderSource:b})},p=(e,r)=>(r&&r.length!==e.length&&(r=[...e.keys()].reverse()),r),u=(e,r)=>(r=p(e,r),c.ShapeUtil.sortBasedOnPerm(e,r)),s=(e,r,i)=>{const d=[];d.push(`void ${e}(out int a[${i}], int src[${i}]) {`);for(let g=0;g{if(!e||e.length!==1)throw new Error("Transpose requires 1 input.");if(e[0].type!=="float32"&&e[0].type!=="float64")throw new Error("input should be float tensor")}},8710:(y,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.encodeAsUint8=void 0;const l=o(5060),c=o(2039);n.encodeAsUint8=(f,a)=>{const h=a.shape,p=(0,l.getGlsl)(f.session.backend.glContext.version),u=` + const float FLOAT_MAX = 1.70141184e38; + const float FLOAT_MIN = 1.17549435e-38; + + bool isNaN(float val) { + return (val < 1.0 || 0.0 < val || val == 0.0) ? false : true; + } + + highp vec4 encodeAsUint8(highp float v) { + if (isNaN(v)) { + return vec4(255, 255, 255, 255); + } + + highp float av = abs(v); + + if(av < FLOAT_MIN) { + return vec4(0.0, 0.0, 0.0, 0.0); + } else if(v > FLOAT_MAX) { + return vec4(0.0, 0.0, 128.0, 127.0) / 255.0; + } else if(v < -FLOAT_MAX) { + return vec4(0.0, 0.0, 128.0, 255.0) / 255.0; + } + + highp vec4 c = vec4(0,0,0,0); + + highp float e = floor(log2(av)); + highp float m = exp2(fract(log2(av))) - 1.0; + + c[2] = floor(128.0 * m); + m -= c[2] / 128.0; + c[1] = floor(32768.0 * m); + m -= c[1] / 32768.0; + c[0] = floor(8388608.0 * m); + + highp float ebias = e + 127.0; + c[3] = floor(ebias / 2.0); + ebias -= c[3] * 2.0; + c[2] += floor(ebias) * 128.0; + + c[3] += 128.0 * step(0.0, -v); + + return c / 255.0; + } + + void main() { + float value = ${p.texture2D}(X,TexCoords).r; + ${p.output} = encodeAsUint8(value); + 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getColorAsFloat(${t.texture2D}(X, coords)); + return value; + } + `,S=s.mode==="nearest"?` + ${w} + float process(int indices[${m}]) { + int input_index = 0; + int output_index = coordsToOffset(TexCoords, ${d}, ${g}); + + ${v} + + int d, m; + for (int dim = 0; dim < ${m}; ++dim) { + d = output_index / output_pitches[dim]; + m = output_index - d * output_pitches[dim]; + output_index = m; + + if (scales[dim] != 1 && d > 0) { + int d2 = d / scales[dim]; + m = d - d2 * scales[dim]; + d = d2; + } + input_index += input_pitches[dim] * d; + } + + return getInputFloat(input_index); + }`:m===4?` + ${w} + float process(int indices[4]) { + int input_index = 0; + int output_index = coordsToOffset(TexCoords, ${d}, ${g}); + + ${v} + + int m; + int index_of_dim0, index_of_dim1, index_of_dim2, index_of_dim3; + index_of_dim0 = output_index / output_pitches[0]; + m = output_index - index_of_dim0 * output_pitches[0]; + index_of_dim1 = m / output_pitches[1]; + m = m - index_of_dim1 * output_pitches[1]; + index_of_dim2 = m / output_pitches[2]; + m = m - index_of_dim2 * output_pitches[2]; + index_of_dim3 = m; + + int index_of_input_dim2, index_of_input_dim3, x_offset, y_offset; + index_of_input_dim2 = index_of_dim2 / scales[2]; + y_offset = index_of_dim2 - index_of_input_dim2 * scales[2]; + index_of_input_dim3 = index_of_dim3 / scales[3]; + x_offset = index_of_dim3 - index_of_input_dim3 * scales[3]; + + input_index = index_of_dim0 * input_pitches[0] + + index_of_dim1 * input_pitches[1] + + index_of_input_dim2 * input_pitches[2] + + index_of_input_dim3; + + float x00 = getInputFloat(input_index); + float x10, x01, x11; + + bool end_of_dim2 = false; + if (index_of_input_dim2 == (${u[0].dims[2]} - 1)) { + // It's the end in dimension 2 + x01 = x00; + end_of_dim2 = true; + } else { + x01 = getInputFloat(input_index + input_pitches[2]); + } + + if (index_of_input_dim3 == (input_pitches[2] - 1)) { + // It's the end in dimension 3 + x10 = x00; + x11 = x01; + } + else { + x10 = getInputFloat(input_index + 1); + x11 = end_of_dim2 ? x10 : getInputFloat(input_index + input_pitches[2] + 1); + } + + float y0 = x00 + float(y_offset) * (x01 - x00) / float(scales[2]); + float y1 = x10 + float(y_offset) * (x11 - x10) / float(scales[2]); + return y0 + float(x_offset) * (y1 - y0) / float(scales[3]); + }`:` + ${w} + float process(int indices[2]) { + int input_index = 0; + int output_index = coordsToOffset(TexCoords, ${d}, ${g}); + + ${v} + + int m; + int index_of_dim0, index_of_dim1; + index_of_dim0 = output_index / output_pitches[0]; + m = output_index - index_of_dim0 * output_pitches[0]; + index_of_dim1 = m; + + int index_of_input_dim0, index_of_input_dim1, x_offset, y_offset; + index_of_input_dim0 = index_of_dim0 / scales[0]; + y_offset = index_of_dim0 - index_of_input_dim0 * scales[0]; + index_of_input_dim1 = index_of_dim1 / scales[1]; + x_offset = index_of_dim1 - index_of_input_dim1 * scales[1]; + + input_index = index_of_input_dim0 * input_pitches[0] + index_of_input_dim1; + + float x00 = getInputFloat(input_index); + float x10, x01, x11; + + bool end_of_dim0 = false; + if (index_of_input_dim0 == (${u[0].dims[0]} - 1)) { + // It's the end in dimension 0 + x01 = x00; + end_of_dim0 = true; + } else { + x01 = getInputFloat(input_index + input_pitches[0]); + } + + if (index_of_input_dim1 == (input_pitches[0] - 1)) { + // It's the end in dimension 1 + x10 = x00; + x11 = x01; + } + else { + x10 = getInputFloat(input_index + 1); + x11 = end_of_dim0 ? x10 : getInputFloat(input_index + input_pitches[0] + 1); + } + + float y0 = x00 + float(y_offset) * (x01 - x00) / float(scales[0]); + float y1 = x10 + float(y_offset) * (x11 - x10) / float(scales[0]); + return y0 + float(x_offset) * (y1 - y0) / float(scales[1]); + }`;return 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r=this.bb.__offset(this.bb_pos,14);return r?this.bb.__string(this.bb.__vector(this.bb_pos+r)+4*t,e):null}inputsLength(){let t=this.bb.__offset(this.bb_pos,14);return t?this.bb.__vector_len(this.bb_pos+t):0}outputs(t,e){let r=this.bb.__offset(this.bb_pos,16);return r?this.bb.__string(this.bb.__vector(this.bb_pos+r)+4*t,e):null}outputsLength(){let t=this.bb.__offset(this.bb_pos,16);return t?this.bb.__vector_len(this.bb_pos+t):0}sparseInitializers(t,e){let r=this.bb.__offset(this.bb_pos,18);return r?(e||new a.experimental.fbs.SparseTensor).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos+r)+4*t),this.bb):null}sparseInitializersLength(){let t=this.bb.__offset(this.bb_pos,18);return t?this.bb.__vector_len(this.bb_pos+t):0}static startGraph(t){t.startObject(8)}static addInitializers(t,e){t.addFieldOffset(0,e,0)}static createInitializersVector(t,e){t.startVector(4,e.length,4);for(let r=e.length-1;r>=0;r--)t.addOffset(e[r]);return t.endVector()}static startInitializersVector(t,e){t.startVector(4,e,4)}static addNodeArgs(t,e){t.addFieldOffset(1,e,0)}static createNodeArgsVector(t,e){t.startVector(4,e.length,4);for(let r=e.length-1;r>=0;r--)t.addOffset(e[r]);return t.endVector()}static startNodeArgsVector(t,e){t.startVector(4,e,4)}static addNodes(t,e){t.addFieldOffset(2,e,0)}static createNodesVector(t,e){t.startVector(4,e.length,4);for(let r=e.length-1;r>=0;r--)t.addOffset(e[r]);return t.endVector()}static startNodesVector(t,e){t.startVector(4,e,4)}static addMaxNodeIndex(t,e){t.addFieldInt32(3,e,0)}static addNodeEdges(t,e){t.addFieldOffset(4,e,0)}static createNodeEdgesVector(t,e){t.startVector(4,e.length,4);for(let r=e.length-1;r>=0;r--)t.addOffset(e[r]);return t.endVector()}static startNodeEdgesVector(t,e){t.startVector(4,e,4)}static addInputs(t,e){t.addFieldOffset(5,e,0)}static createInputsVector(t,e){t.startVector(4,e.length,4);for(let r=e.length-1;r>=0;r--)t.addOffset(e[r]);return t.endVector()}static startInputsVector(t,e){t.startVector(4,e,4)}static addOutputs(t,e){t.addFieldOffset(6,e,0)}static createOutputsVector(t,e){t.startVector(4,e.length,4);for(let r=e.length-1;r>=0;r--)t.addOffset(e[r]);return t.endVector()}static startOutputsVector(t,e){t.startVector(4,e,4)}static addSparseInitializers(t,e){t.addFieldOffset(7,e,0)}static createSparseInitializersVector(t,e){t.startVector(4,e.length,4);for(let r=e.length-1;r>=0;r--)t.addOffset(e[r]);return t.endVector()}static startSparseInitializersVector(t,e){t.startVector(4,e,4)}static endGraph(t){return t.endObject()}static createGraph(t,e,r,i,d,g,m,b,_){return u.startGraph(t),u.addInitializers(t,e),u.addNodeArgs(t,r),u.addNodes(t,i),u.addMaxNodeIndex(t,d),u.addNodeEdges(t,g),u.addInputs(t,m),u.addOutputs(t,b),u.addSparseInitializers(t,_),u.endGraph(t)}}p.Graph=u})(h.fbs||(h.fbs={}))})(a.experimental||(a.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(a){(function(h){(function(p){class u{constructor(){this.bb=null,this.bb_pos=0}__init(t,e){return this.bb_pos=t,this.bb=e,this}static getRootAsModel(t,e){return(e||new u).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsModel(t,e){return t.setPosition(t.position()+l.flatbuffers.SIZE_PREFIX_LENGTH),(e||new u).__init(t.readInt32(t.position())+t.position(),t)}irVersion(){let t=this.bb.__offset(this.bb_pos,4);return t?this.bb.readInt64(this.bb_pos+t):this.bb.createLong(0,0)}opsetImport(t,e){let r=this.bb.__offset(this.bb_pos,6);return r?(e||new a.experimental.fbs.OperatorSetId).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos+r)+4*t),this.bb):null}opsetImportLength(){let t=this.bb.__offset(this.bb_pos,6);return t?this.bb.__vector_len(this.bb_pos+t):0}producerName(t){let e=this.bb.__offset(this.bb_pos,8);return e?this.bb.__string(this.bb_pos+e,t):null}producerVersion(t){let e=this.bb.__offset(this.bb_pos,10);return e?this.bb.__string(this.bb_pos+e,t):null}domain(t){let e=this.bb.__offset(this.bb_pos,12);return e?this.bb.__string(this.bb_pos+e,t):null}modelVersion(){let t=this.bb.__offset(this.bb_pos,14);return t?this.bb.readInt64(this.bb_pos+t):this.bb.createLong(0,0)}docString(t){let e=this.bb.__offset(this.bb_pos,16);return e?this.bb.__string(this.bb_pos+e,t):null}graph(t){let e=this.bb.__offset(this.bb_pos,18);return e?(t||new a.experimental.fbs.Graph).__init(this.bb.__indirect(this.bb_pos+e),this.bb):null}graphDocString(t){let e=this.bb.__offset(this.bb_pos,20);return e?this.bb.__string(this.bb_pos+e,t):null}static startModel(t){t.startObject(9)}static addIrVersion(t,e){t.addFieldInt64(0,e,t.createLong(0,0))}static addOpsetImport(t,e){t.addFieldOffset(1,e,0)}static createOpsetImportVector(t,e){t.startVector(4,e.length,4);for(let r=e.length-1;r>=0;r--)t.addOffset(e[r]);return t.endVector()}static startOpsetImportVector(t,e){t.startVector(4,e,4)}static addProducerName(t,e){t.addFieldOffset(2,e,0)}static addProducerVersion(t,e){t.addFieldOffset(3,e,0)}static addDomain(t,e){t.addFieldOffset(4,e,0)}static addModelVersion(t,e){t.addFieldInt64(5,e,t.createLong(0,0))}static addDocString(t,e){t.addFieldOffset(6,e,0)}static addGraph(t,e){t.addFieldOffset(7,e,0)}static addGraphDocString(t,e){t.addFieldOffset(8,e,0)}static endModel(t){return t.endObject()}static createModel(t,e,r,i,d,g,m,b,_,v){return u.startModel(t),u.addIrVersion(t,e),u.addOpsetImport(t,r),u.addProducerName(t,i),u.addProducerVersion(t,d),u.addDomain(t,g),u.addModelVersion(t,m),u.addDocString(t,b),u.addGraph(t,_),u.addGraphDocString(t,v),u.endModel(t)}}p.Model=u})(h.fbs||(h.fbs={}))})(a.experimental||(a.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(a){(function(h){(function(p){class u{constructor(){this.bb=null,this.bb_pos=0}__init(t,e){return this.bb_pos=t,this.bb=e,this}static getRootAsKernelCreateInfos(t,e){return(e||new u).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsKernelCreateInfos(t,e){return t.setPosition(t.position()+l.flatbuffers.SIZE_PREFIX_LENGTH),(e||new u).__init(t.readInt32(t.position())+t.position(),t)}nodeIndices(t){let e=this.bb.__offset(this.bb_pos,4);return e?this.bb.readUint32(this.bb.__vector(this.bb_pos+e)+4*t):0}nodeIndicesLength(){let t=this.bb.__offset(this.bb_pos,4);return t?this.bb.__vector_len(this.bb_pos+t):0}nodeIndicesArray(){let t=this.bb.__offset(this.bb_pos,4);return t?new Uint32Array(this.bb.bytes().buffer,this.bb.bytes().byteOffset+this.bb.__vector(this.bb_pos+t),this.bb.__vector_len(this.bb_pos+t)):null}kernelDefHashes(t){let e=this.bb.__offset(this.bb_pos,6);return e?this.bb.readUint64(this.bb.__vector(this.bb_pos+e)+8*t):this.bb.createLong(0,0)}kernelDefHashesLength(){let t=this.bb.__offset(this.bb_pos,6);return t?this.bb.__vector_len(this.bb_pos+t):0}static startKernelCreateInfos(t){t.startObject(2)}static addNodeIndices(t,e){t.addFieldOffset(0,e,0)}static createNodeIndicesVector(t,e){t.startVector(4,e.length,4);for(let r=e.length-1;r>=0;r--)t.addInt32(e[r]);return t.endVector()}static startNodeIndicesVector(t,e){t.startVector(4,e,4)}static addKernelDefHashes(t,e){t.addFieldOffset(1,e,0)}static createKernelDefHashesVector(t,e){t.startVector(8,e.length,8);for(let r=e.length-1;r>=0;r--)t.addInt64(e[r]);return t.endVector()}static startKernelDefHashesVector(t,e){t.startVector(8,e,8)}static endKernelCreateInfos(t){return t.endObject()}static createKernelCreateInfos(t,e,r){return u.startKernelCreateInfos(t),u.addNodeIndices(t,e),u.addKernelDefHashes(t,r),u.endKernelCreateInfos(t)}}p.KernelCreateInfos=u})(h.fbs||(h.fbs={}))})(a.experimental||(a.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(a){(function(h){(function(p){class u{constructor(){this.bb=null,this.bb_pos=0}__init(t,e){return this.bb_pos=t,this.bb=e,this}static getRootAsSubGraphSessionState(t,e){return(e||new u).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsSubGraphSessionState(t,e){return t.setPosition(t.position()+l.flatbuffers.SIZE_PREFIX_LENGTH),(e||new u).__init(t.readInt32(t.position())+t.position(),t)}graphId(t){let e=this.bb.__offset(this.bb_pos,4);return e?this.bb.__string(this.bb_pos+e,t):null}sessionState(t){let e=this.bb.__offset(this.bb_pos,6);return e?(t||new a.experimental.fbs.SessionState).__init(this.bb.__indirect(this.bb_pos+e),this.bb):null}static startSubGraphSessionState(t){t.startObject(2)}static addGraphId(t,e){t.addFieldOffset(0,e,0)}static addSessionState(t,e){t.addFieldOffset(1,e,0)}static endSubGraphSessionState(t){let e=t.endObject();return t.requiredField(e,4),e}static createSubGraphSessionState(t,e,r){return u.startSubGraphSessionState(t),u.addGraphId(t,e),u.addSessionState(t,r),u.endSubGraphSessionState(t)}}p.SubGraphSessionState=u})(h.fbs||(h.fbs={}))})(a.experimental||(a.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(a){(function(h){(function(p){class u{constructor(){this.bb=null,this.bb_pos=0}__init(t,e){return this.bb_pos=t,this.bb=e,this}static getRootAsSessionState(t,e){return(e||new u).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsSessionState(t,e){return t.setPosition(t.position()+l.flatbuffers.SIZE_PREFIX_LENGTH),(e||new u).__init(t.readInt32(t.position())+t.position(),t)}kernels(t){let e=this.bb.__offset(this.bb_pos,4);return e?(t||new a.experimental.fbs.KernelCreateInfos).__init(this.bb.__indirect(this.bb_pos+e),this.bb):null}subGraphSessionStates(t,e){let r=this.bb.__offset(this.bb_pos,6);return r?(e||new a.experimental.fbs.SubGraphSessionState).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos+r)+4*t),this.bb):null}subGraphSessionStatesLength(){let t=this.bb.__offset(this.bb_pos,6);return t?this.bb.__vector_len(this.bb_pos+t):0}static startSessionState(t){t.startObject(2)}static addKernels(t,e){t.addFieldOffset(0,e,0)}static addSubGraphSessionStates(t,e){t.addFieldOffset(1,e,0)}static createSubGraphSessionStatesVector(t,e){t.startVector(4,e.length,4);for(let r=e.length-1;r>=0;r--)t.addOffset(e[r]);return t.endVector()}static startSubGraphSessionStatesVector(t,e){t.startVector(4,e,4)}static endSessionState(t){return t.endObject()}static createSessionState(t,e,r){return u.startSessionState(t),u.addKernels(t,e),u.addSubGraphSessionStates(t,r),u.endSessionState(t)}}p.SessionState=u})(h.fbs||(h.fbs={}))})(a.experimental||(a.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(a){(function(h){(function(p){class u{constructor(){this.bb=null,this.bb_pos=0}__init(t,e){return this.bb_pos=t,this.bb=e,this}static getRootAsInferenceSession(t,e){return(e||new u).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsInferenceSession(t,e){return t.setPosition(t.position()+l.flatbuffers.SIZE_PREFIX_LENGTH),(e||new u).__init(t.readInt32(t.position())+t.position(),t)}static bufferHasIdentifier(t){return t.__has_identifier("ORTM")}ortVersion(t){let e=this.bb.__offset(this.bb_pos,4);return e?this.bb.__string(this.bb_pos+e,t):null}model(t){let e=this.bb.__offset(this.bb_pos,6);return e?(t||new a.experimental.fbs.Model).__init(this.bb.__indirect(this.bb_pos+e),this.bb):null}sessionState(t){let e=this.bb.__offset(this.bb_pos,8);return e?(t||new a.experimental.fbs.SessionState).__init(this.bb.__indirect(this.bb_pos+e),this.bb):null}static startInferenceSession(t){t.startObject(3)}static addOrtVersion(t,e){t.addFieldOffset(0,e,0)}static addModel(t,e){t.addFieldOffset(1,e,0)}static addSessionState(t,e){t.addFieldOffset(2,e,0)}static endInferenceSession(t){return t.endObject()}static finishInferenceSessionBuffer(t,e){t.finish(e,"ORTM")}static finishSizePrefixedInferenceSessionBuffer(t,e){t.finish(e,"ORTM",!0)}static createInferenceSession(t,e,r,i){return u.startInferenceSession(t),u.addOrtVersion(t,e),u.addModel(t,r),u.addSessionState(t,i),u.endInferenceSession(t)}}p.InferenceSession=u})(h.fbs||(h.fbs={}))})(a.experimental||(a.experimental={}))}(n.onnxruntime||(n.onnxruntime={}))},7448:(y,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.OnnxjsSessionHandler=void 0;const l=o(1670),c=o(9162);n.OnnxjsSessionHandler=class{constructor(f){this.session=f,this.inputNames=this.session.inputNames,this.outputNames=this.session.outputNames}async dispose(){}async run(f,a,h){const p=new Map;for(const t in f)if(Object.hasOwnProperty.call(f,t)){const e=f[t];p.set(t,new c.Tensor(e.dims,e.type,void 0,void 0,e.data))}const u=await this.session.run(p),s={};return u.forEach((t,e)=>{s[e]=new l.Tensor(t.type,t.data,t.dims)}),s}startProfiling(){this.session.startProfiling()}endProfiling(){this.session.endProfiling()}}},6919:(y,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.Session=void 0;const l=o(7067),c=o(1296),f=o(7091),a=o(1036),h=o(6231),p=o(2644);n.Session=class{constructor(u={}){this._initialized=!1,this.backendHint=u.backendHint,this.profiler=h.Profiler.create(u.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(u,s,t){await this.profiler.event("session","Session.loadModel",async()=>{const e=await(0,f.resolveBackend)(this.backendHint);if(this.sessionHandler=e.createSessionHandler(this.context),this._model=new p.Model,typeof u=="string"){const r=u.endsWith(".ort");if(typeof fetch>"u"){const i=await(0,c.promisify)(l.readFile)(u);this.initialize(i,r)}else{const i=await fetch(u),d=await i.arrayBuffer();this.initialize(new Uint8Array(d),r)}}else if(ArrayBuffer.isView(u))this.initialize(u);else{const r=new Uint8Array(u,s||0,t||u.byteLength);this.initialize(r)}})}initialize(u,s){if(this._initialized)throw new Error("already initialized");this.profiler.event("session","Session.initialize",()=>{const t=this.sessionHandler.transformGraph?this.sessionHandler:void 0;this._model.load(u,t,s),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(u){if(!this._initialized)throw new Error("session not initialized yet");return this.profiler.event("session","Session.run",async()=>{const s=this.normalizeAndValidateInputs(u),t=await this._executionPlan.execute(this.sessionHandler,s);return this.createOutput(t)})}normalizeAndValidateInputs(u){const s=this._model.graph.getInputNames();if(Array.isArray(u)){if(u.length!==s.length)throw new Error(`incorrect input array length: expected ${s.length} but got ${u.length}`)}else{if(u.size!==s.length)throw new Error(`incorrect input map size: expected ${s.length} but got ${u.size}`);const t=new Array(u.size);let e=0;for(let r=0;rtypeof O=="string")))throw new TypeError("cache should be a string array");A&&(this.cache=new Array(S))}else{if(v!==void 0){const O=e(m);if(!(v instanceof O))throw new TypeError(`cache should be type ${O.name}`)}if(A){const O=new ArrayBuffer(S*function(x){switch(x){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 ${x}`)}}(m));this.cache=function(x,I){return new(e(I))(x)}(O,m)}}}static fromProto(g){if(!g)throw new Error("cannot construct Value from an empty tensor");const m=p.ProtoUtil.tensorDataTypeFromProto(g.dataType),b=p.ProtoUtil.tensorDimsFromProto(g.dims),_=new s(b,m);if(m==="string")g.stringData.forEach((v,w)=>{_.data[w]=(0,p.decodeUtf8String)(v)});else if(g.rawData&&typeof g.rawData.byteLength=="number"&&g.rawData.byteLength>0){const v=_.data,w=new DataView(g.rawData.buffer,g.rawData.byteOffset,g.rawData.byteLength),S=t(g.dataType),A=g.rawData.byteLength/S;if(g.rawData.byteLength%S!=0)throw new Error("invalid buffer length");if(v.length!==A)throw new Error("buffer length mismatch");for(let O=0;O0){const v=_.data,w=new DataView(g.rawDataArray().buffer,g.rawDataArray().byteOffset,g.rawDataLength()),S=t(g.dataType()),A=g.rawDataLength()/S;if(g.rawDataLength()%S!=0)throw new Error("invalid buffer length");if(v.length!==A)throw new Error("buffer length mismatch");for(let O=0;O1&&I>1)return;A[S-O]=Math.max(x,I)}return A}static index(m,b){const _=new Array(b.length);return u.fillIndex(m,b,_),_}static fillIndex(m,b,_){const v=m.length-b.length;for(let w=0;w=0;Z--)x[Z]=B%S[Z],B=Math.floor(B/S[Z]);H||(u.fillIndex(x,m.dims,I),L=m.get(I)),M||(u.fillIndex(x,b.dims,$),N=b.get($)),O.set(x,_(L,N))}}return O}}static isValidBroadcast(m,b){const _=m.length,v=b.length;if(_>v)return!1;for(let w=1;w<=_;w++)if(m[_-w]!==1&&m[_-w]!==b[v-w])return!1;return!0}static getBroadcastDims(m,b){const _=m.length,v=[];for(let w=0;w<_;w++){const S=_-1-w,A=m[S]||1;(b[b.length-1-w]||1)>1&&A===1&&v.unshift(S)}return v}}n.BroadcastUtil=u,n.arrayCopyHelper=function(g,m,b,_,v){if(_<0||_>=m.length)throw new Error("sourceIndex out of bounds");if(b<0||b>=g.length)throw new Error("targetIndex out of bounds");if(_+v>m.length)throw new Error("source indices to be copied are outside bounds");if(b+v>g.length)throw new Error("target array is too small to hold result");for(let w=0;wf.default.isLong(b)?b.toNumber():b)}static tensorValueTypeFromProto(m){return{tensorType:s.tensorDataTypeFromProto(m.elemType),shape:{dims:s.tensorDimsFromProto(m.shape.dim.map(b=>b.dimValue))}}}static tensorDimsFromORTFormat(m){const b=[];for(let _=0;_m.length)throw new Error(`invalid dimension of ${b} for sizeFromDimension as Tensor has ${m.length} dimensions.`);return e.getSizeFromDimensionRange(m,b,m.length)}static sizeToDimension(m,b){if(b<0||b>m.length)throw new Error(`invalid dimension of ${b} for sizeToDimension as Tensor has ${m.length} dimensions.`);return e.getSizeFromDimensionRange(m,0,b)}static getSizeFromDimensionRange(m,b,_){let v=1;for(let w=b;w<_;w++){if(m[w]<=0)throw new Error("cannot get valid size from specified dimension range. 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file",403:"Forbidden access to file",404:"Could not locate file",408:"Request timeout error occurred while trying to load file",500:"Internal server error error occurred while trying to load file",502:"Bad gateway error occurred while trying to load file",503:"Service unavailable error occurred while trying to load file",504:"Gateway timeout error occurred while trying to load file"};function handleError(y,n,o){if(!o)return null;const l=ERROR_MAPPING[y]??`Error (${y}) occurred while trying to load file`;throw Error(`${l}: "${n}".`)}class FileCache{constructor(n){this.path=n}async match(n){let o=fs.join(this.path,n),l=new FileResponse(o);if(l.exists)return l}async put(n,o){const l=Buffer.from(await o.arrayBuffer());let c=fs.join(this.path,n);try{await fs.promises.mkdir(fs.dirname(c),{recursive:!0}),await fs.promises.writeFile(c,l)}catch(f){console.warn("An error occurred while writing the file to cache:",f)}}}async function tryCache(y,...n){for(let o of n)try{let l=await y.match(o);if(l)return l}catch{continue}}async function getModelFile(y,n,o=!0,l={}){if(!env.allowLocalModels&&l.local_files_only)throw Error("Invalid configuration detected: local models are disabled (`env.allowLocalModels=false`) but you have requested to only use local models (`local_files_only=true`).");dispatchCallback(l.progress_callback,{status:"initiate",name:y,file:n});let c;if(!c&&env.useBrowserCache){if(typeof caches>"u")throw Error("Browser cache is not available in this environment.");c=await caches.open("transformers-cache")}!c&&env.useFSCache&&(c=new FileCache(l.cache_dir??env.cacheDir));const f=l.revision??"main";let a=pathJoin(y,n),h=pathJoin(env.localModelPath,a),p=pathJoin(env.remoteHost,env.remotePathTemplate.replaceAll("{model}",y).replaceAll("{revision}",f),n),u=f==="main"?a:pathJoin(y,f,n),s,t=c instanceof FileCache?u:p,e,r;if(c&&(r=await tryCache(c,h,t)),r===void 0){let d=isValidHttpUrl(a);if(env.allowLocalModels)if(d){if(l.local_files_only)throw new Error(`\`local_files_only=true\`, but attempted to load a remote file from: ${a}.`)}else try{r=await getFile(h),s=h}catch(g){console.warn(`Unable to load from local path "${h}": "${g}"`)}if(r===void 0||r.status===404){if(l.local_files_only||!env.allowRemoteModels){if(o)throw Error(`\`local_files_only=true\` or \`env.allowRemoteModels=false\` and file was not found locally at "${h}".`);return null}if(r=await getFile(p),r.status!==200)return handleError(r.status,p,o);s=t}c&&r instanceof Response&&r.status===200&&(e=r.clone())}dispatchCallback(l.progress_callback,{status:"download",name:y,file:n});const i=await readResponse(r,d=>{dispatchCallback(l.progress_callback,{status:"progress",...d,name:y,file:n})});return e&&s&&await c.match(s)===void 0&&await c.put(s,e).catch(d=>{console.warn(`Unable to add response to browser cache: ${d}.`)}),dispatchCallback(l.progress_callback,{status:"done",name:y,file:n}),i}async function getModelJSON(y,n,o=!0,l={}){let c=await getModelFile(y,n,o,l);if(c===null)return{};let a=new TextDecoder("utf-8").decode(c);return JSON.parse(a)}async function readResponse(y,n){const o=y.headers.get("Content-Length");o===null&&console.warn("Unable to determine content-length from response headers. Will expand buffer when needed.");let l=parseInt(o??"0"),c=new Uint8Array(l),f=0;const a=y.body.getReader();async function h(){const{done:p,value:u}=await a.read();if(p)return;let s=f+u.length;if(s>l){l=s;let e=new Uint8Array(l);e.set(c),c=e}c.set(u,f),f=s;const t=f/l*100;return n({progress:t,loaded:f,total:l}),h()}return await h(),c}function pathJoin(...y){return y=y.map((n,o)=>(o&&(n=n.replace(new RegExp("^/"),"")),o!==y.length-1&&(n=n.replace(new RegExp("/$"),"")),n)),y.join("/")}function interpolate_data(y,[n,o,l],[c,f],a="bilinear",h=!1){const p=f/l,u=c/o,s=new y.constructor(c*f*n),t=o*l,e=c*f;for(let r=0;r=0;--h)c[h]=p,l[h]=n[o[h]],p*=l[h];const f=o.map((h,p)=>c[o.indexOf(p)]),a=new y.constructor(y.length);for(let h=0;h=0;--u)p+=s%n[u]*f[u],s=Math.floor(s/n[u]);a[p]=y[h]}return[a,l]}function softmax(y){const n=max(y)[0],o=y.map(f=>Math.exp(f-n)),l=o.reduce((f,a)=>f+a,0);return o.map(f=>f/l)}function log_softmax(y){return softmax(y).map(l=>Math.log(l))}function getTopItems(y,n=0){return y=Array.from(y).map((o,l)=>[l,o]).sort((o,l)=>l[1]-o[1]),n>0&&(y=y.slice(0,n)),y}function min(y){if(y.length===0)throw Error("Array must not be empty");let n=y[0],o=0;for(let l=1;ln&&(n=y[l],o=l);return[n,o]}function rfftfreq(y,n=1){if(!Number.isInteger(y))throw new TypeError(`n should be an integer, but ${y} given.`);const o=1/(y*n),l=Math.floor(y/2)+1,c=new Array(l);for(let f=0;fl;l<<=1)++o;this._width=o%2===0?o-1:o,this._bitrev=new Int32Array(1<>>c&3)<>>1);for(let c=0;c>>1]=n[c];return l}toComplexArray(n,o){const l=o||this.createComplexArray();for(let c=0;c>>1],l[c+1]=0;return l}completeSpectrum(n){const o=this._csize,l=o>>>1;for(let c=2;c>=2;a>=2;a>>=2){h=c/a<<1;let t=h>>>2;for(p=0;p>>1,a>>>1)}else for(p=0,u=0;p>>1,a>>>1,l)}for(a>>=2;a>=2;a>>=2){h=c/a<<1;const t=h>>>1,e=t>>>1,r=e>>>1;for(p=0;p{if(typeof l=="string"){let c=Number(l);if(Number.isInteger(c))return o._getitem(c)}return o[l]},set:(o,l,c)=>o[l]=c})}*[Symbol.iterator](){const[n,...o]=this.dims;if(o.length>0){const l=o.reduce((c,f)=>c*f);for(let c=0;c=o||n<-o)throw new Error(`Index ${n} is out of bounds for dimension 0 with size ${o}`);if(n<0&&(n+=o),l.length>0){const c=l.reduce((f,a)=>f*a);return this._subarray(n,c,l)}else return new Tensor(this.type,[this.data[n]],l)}indexOf(n){for(let o=0;o=this.dims[p])throw new Error(`IndexError: index ${u} is out of bounds for dimension ${p} with size ${this.dims[p]}`);u<0&&(u+=this.dims[p]),l.push([u,u+1])}else if(Array.isArray(u)&&u.length===2){if(u[0]>u[1])throw new Error(`Invalid slice: ${u}`);let s=[Math.max(u[0],0),Math.min(u[1],this.dims[p])];l.push(s),o.push(s[1]-s[0])}else throw new Error(`Invalid slice: ${u}`)}let c=l.map(([p,u])=>u-p),f=c.reduce((p,u)=>p*u),a=new this.data.constructor(f);const h=new Array(this.dims.length);for(let p=c.length-1,u=1;p>=0;--p)h[p]=u,u*=this.dims[p];for(let p=0;p=0;--s){const e=c[s];u+=(t%e+l[s][0])*h[s],t=Math.floor(t/e)}a[p]=this.data[u]}return new Tensor(this.type,a,o)}transpose(...n){return transpose(this,n)}sum(n=null,o=!1){return this.norm(1,n,o)}norm(n="fro",o=null,l=!1){if(n==="fro")n=2;else if(typeof n=="string")throw Error(`Unsupported norm: ${n}`);if(o===null){let a=this.data.reduce((h,p)=>h+p**n,0)**(1/n);return new Tensor(this.type,[a],[1])}o<0&&(o+=this.dims.length);const c=this.dims.slice();c[o]=1;const f=new this.data.constructor(this.data.length/this.dims[o]);for(let a=0;a=0;--p){const t=this.dims[p];if(p!==o){const e=u%t;h+=e*s,s*=c[p]}u=Math.floor(u/t)}f[h]+=this.data[a]**n}if(n!==1)for(let a=0;a=0;--a){const u=this.dims[a];if(a!==o){const s=h%u;f+=s*p,p*=this.dims[a]}h=Math.floor(h/u)}this.data[c]/=l.data[f]}return this}normalize(n=2,o=1){return this.clone().normalize_(n,o)}stride(){const n=new Array(this.dims.length);for(let o=this.dims.length-1,l=1;o>=0;--o)n[o]=l,l*=this.dims[o];return n}squeeze(n=null){return new Tensor(this.type,this.data,calc_squeeze_dims(this.dims,n))}squeeze_(n=null){return this.dims=calc_squeeze_dims(this.dims,n),this}unsqueeze(n=null){return new Tensor(this.type,this.data,calc_unsqueeze_dims(this.dims,n))}unsqueeze_(n=null){return this.dims=calc_unsqueeze_dims(this.dims,n),this}}function reshape(y,n){const o=y.length,l=n.reduce((f,a)=>f*a);if(o!==l)throw Error(`cannot reshape array of size ${o} into shape (${n})`);let c=y;for(let f=n.length-1;f>=0;f--)c=c.reduce((a,h)=>{let p=a[a.length-1];return p.lengtho!==1):typeof n=="number"?y[n]===1&&y.splice(n,1):Array.isArray(n)&&(y=y.filter((o,l)=>o!==1||!n.includes(l))),y}function calc_unsqueeze_dims(y,n){return y=y.slice(),n<0&&(n=(n%y.length+y.length)%y.length),y.splice(n,0,1),y}async function loadTokenizer(y,n){return await Promise.all([getModelJSON(y,"tokenizer.json",!0,n),getModelJSON(y,"tokenizer_config.json",!0,n)])}function createPattern(y){return y.Regex?new RegExp(y.Regex,"gu"):y.String?y.String:(console.warn("Unknown pattern type:",y),null)}function clean_up_tokenization(y){return y.replace(/ \./g,".").replace(/ \?/g,"?").replace(/ \!/g,"!").replace(/ ,/g,",").replace(/ \' /g,"'").replace(/ n\'t/g,"n't").replace(/ \'m/g,"'m").replace(/ \'s/g,"'s").replace(/ \'ve/g,"'ve").replace(/ \'re/g,"'re")}function fuse(y,n){let o=[],l=0;for(;lthis.tokens_to_ids.get(l)??this.unk_token_id);return this.fuse_unk&&(o=fuse(o,this.unk_token_id)),o}convert_ids_to_tokens(n){return n.map(o=>this.vocab[o]??this.unk_token)}}class WordPieceTokenizer extends TokenizerModel{constructor(n){super(n),this.tokens_to_ids=n.vocab,this.unk_token_id=this.tokens_to_ids.get(n.unk_token),this.unk_token=n.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[o,l]of this.tokens_to_ids)this.vocab[l]=o}encode(n){let o=[];for(let l of n){let c=[...l],f=!1,a=0,h=[];for(;a0&&(s=this.config.continuing_subword_prefix+s),this.tokens_to_ids.has(s)){u=s;break}--p}if(u===null){f=!0;break}h.push(u),a=p}f?o.push(this.unk_token):o.push(...h)}return o}}class Unigram extends TokenizerModel{constructor(n,o){super(n),this.vocab=new Array(n.vocab.size),this.scores=new Array(n.vocab.size);let l=0;n.vocab.forEach((c,f)=>{this.vocab[l]=f,this.scores[l]=c,++l}),this.unk_token_id=n.unk_id,this.unk_token=this.vocab[n.unk_id],this.tokens_to_ids=new Map(this.vocab.map((c,f)=>[c,f])),this.bosToken=" ",this.bosTokenId=this.tokens_to_ids.get(this.bosToken),this.eosToken=o.eos_token,this.eosTokenId=this.tokens_to_ids.get(this.eosToken),this.unkToken=this.vocab[this.unk_token_id],this.minScore=min(this.scores)[0],this.unkScore=this.minScore-10,this.scores[this.unk_token_id]=this.unkScore,this.trie=new CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(n){const o=n.sentence,l=o.length;let c=0;for(;c{const y=[...Array.from({length:"~".charCodeAt(0)-"!".charCodeAt(0)+1},(c,f)=>f+"!".charCodeAt(0)),...Array.from({length:"¬".charCodeAt(0)-"¡".charCodeAt(0)+1},(c,f)=>f+"¡".charCodeAt(0)),...Array.from({length:"ÿ".charCodeAt(0)-"®".charCodeAt(0)+1},(c,f)=>f+"®".charCodeAt(0))];let n=y.slice(),o=0;for(let c=0;c<256;++c)y.includes(c)||(y.push(c),n.push(256+o),o+=1);let l=n.map(c=>String.fromCharCode(c));return Object.fromEntries(y.map((c,f)=>[c,l[f]]))})(),UNICODE_TO_BYTES=reverseDictionary(BYTES_TO_UNICODE);class BPE extends TokenizerModel{constructor(n){super(n),this.tokens_to_ids=n.vocab,this.unk_token_id=this.tokens_to_ids.get(n.unk_token),this.unk_token=n.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[o,l]of this.tokens_to_ids)this.vocab[l]=o;this.bpe_ranks=Object.fromEntries(n.merges.map((o,l)=>[o,l])),this.merges=n.merges.map(o=>o.split(/\s+/)),this.end_of_word_suffix=n.end_of_word_suffix,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.cache=Object.create(null),this.fuse_unk??(this.fuse_unk=this.config.fuse_unk)}get_pairs(n){let o=new Set,l=n[0];for(let c=1;c{let r=this.bpe_ranks[t]??1/0,i=this.bpe_ranks[e]??1/0;return r<=i?t:e});if(!(f in this.bpe_ranks))break;let[a,h]=f.split(/\s+/g),p=[],u=0,s=-1;for(;u`<0x${a.toString(16).toUpperCase().padStart(2,"0")}>`)):o.push(this.unk_token)}return o}}class Normalizer extends Callable{constructor(n){super(),this.config=n}static fromConfig(n){if(n===null)return null;switch(n.type){case"BertNormalizer":return new BertNormalizer(n);case"Precompiled":return new Precompiled(n);case"Sequence":return new NormalizerSequence(n);case"Replace":return new Replace(n);case"NFC":return new NFC(n);case"NFKD":return new NFKD(n);case"StripAccents":return new StripAccents(n);case"Lowercase":return new Lowercase(n);case"Prepend":return new Prepend(n);default:throw new Error(`Unknown Normalizer type: ${n.type}`)}}normalize(n){throw Error("normalize should be implemented in subclass.")}_call(n){return this.normalize(n)}}class Replace extends Normalizer{normalize(n){let o=createPattern(this.config.pattern);return o===null||(n=n.replaceAll(o,this.config.content)),n}}class NFC extends Normalizer{normalize(n){return n=n.normalize("NFC"),n}}class NFKD extends Normalizer{normalize(n){return n=n.normalize("NFKD"),n}}class StripAccents extends Normalizer{normalize(n){return n=n.replace(/[\u0300-\u036f]/g,""),n}}class Lowercase extends Normalizer{normalize(n){return n=n.toLowerCase(),n}}class Prepend extends Normalizer{normalize(n){return n=this.config.prepend+n,n}}class NormalizerSequence extends Normalizer{constructor(n){super(n),this.normalizers=n.normalizers.map(o=>Normalizer.fromConfig(o))}normalize(n){return this.normalizers.reduce((o,l)=>l.normalize(o),n)}}class BertNormalizer extends Normalizer{_tokenize_chinese_chars(n){let o=[];for(let l=0;l=19968&&n<=40959||n>=13312&&n<=19903||n>=131072&&n<=173791||n>=173824&&n<=177983||n>=177984&&n<=178207||n>=178208&&n<=183983||n>=63744&&n<=64255||n>=194560&&n<=195103}stripAccents(n){return n.normalize("NFD").replace(/[\u0300-\u036f]/g,"")}normalize(n){return this.config.handle_chinese_chars&&(n=this._tokenize_chinese_chars(n)),this.config.lowercase?(n=n.toLowerCase(),this.config.strip_accents!==!1&&(n=this.stripAccents(n))):this.config.strip_accents&&(n=this.stripAccents(n)),n}}class PreTokenizer extends Callable{static fromConfig(n){if(n===null)return null;switch(n.type){case"BertPreTokenizer":return new BertPreTokenizer(n);case"Sequence":return new PreTokenizerSequence(n);case"WhitespaceSplit":return new WhitespaceSplit(n);case"Metaspace":return new MetaspacePreTokenizer(n);case"ByteLevel":return new ByteLevelPreTokenizer(n);case"Split":return new SplitPreTokenizer(n);default:throw new Error(`Unknown PreTokenizer type: ${n.type}`)}}pre_tokenize_text(n){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(n){let o=[];return Array.isArray(n)?o=n.map(l=>this.pre_tokenize_text(l)):o=this.pre_tokenize_text(n),o.flat()}_call(n){return this.pre_tokenize(n)}}class BertPreTokenizer extends PreTokenizer{constructor(n){super();const o="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E";this.pattern=new RegExp(`[^\\s${o}]+|[${o}]`,"gu")}pre_tokenize_text(n){return n.trim().match(this.pattern)||[]}}class ByteLevelPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=/'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+/gu,this.byte_encoder=BYTES_TO_UNICODE,this.text_encoder=new TextEncoder}pre_tokenize_text(n){return(this.use_regex?n.match(this.pattern)||[]:[n]).map(l=>(this.add_prefix_space&&!l.startsWith(" ")&&(l=" "+l),l=Array.from(this.text_encoder.encode(l),c=>this.byte_encoder[c]).join(""),l))}}class SplitPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n}pre_tokenize_text(n){let o=createPattern(this.config.pattern);if(o===null)return[];switch(this.config.behavior.toLowerCase()){case"isolated":case"removed":return n.match(o)||[];default:return console.warn(`Unknown split behavior: "${this.config.behavior}"`),[]}}}class PostProcessor extends Callable{constructor(n){super(),this.config=n}static fromConfig(n){switch(n.type){case"TemplateProcessing":return new TemplateProcessing(n);case"ByteLevel":return new ByteLevelPostProcessor(n);case"RobertaProcessing":return new RobertaProcessing(n);default:throw new Error(`Unknown PostProcessor type: ${n.type}`)}}post_process(n,...o){throw Error("post_process should be implemented in subclass.")}_call(n,...o){return this.post_process(n,...o)}}class RobertaProcessing extends PostProcessor{constructor(n){super(n),this.cls=n.cls[0],this.sep=n.sep[0]}post_process(n,o=null){return n=[this.cls,...n,this.sep],o!==null&&(n=[...n,this.sep,...o,this.sep]),n}}class TemplateProcessing extends PostProcessor{constructor(n){super(n),this.single=n.single,this.pair=n.pair}post_process(n,o=null){let l=o===null?this.single:this.pair,c=[];for(let f of l)"SpecialToken"in f?c.push(f.SpecialToken.id):"Sequence"in f&&(f.Sequence.id==="A"?c.push(...n):f.Sequence.id==="B"&&c.push(...o));return c}}class ByteLevelPostProcessor extends PostProcessor{post_process(n){return n}}class Decoder extends Callable{constructor(n){super(),this.config=n,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=n.trim_offsets}static fromConfig(n){switch(n.type){case"WordPiece":return new WordPieceDecoder(n);case"Metaspace":return new MetaspaceDecoder(n);case"ByteLevel":return new ByteLevelDecoder(n);case"Replace":return new ReplaceDecoder(n);case"ByteFallback":return new ByteFallback(n);case"Fuse":return new FuseDecoder(n);case"Strip":return new StripDecoder(n);case"Sequence":return new DecoderSequence(n);default:throw new Error(`Unknown Decoder type: ${n.type}`)}}_call(n){return this.decode(n)}decode(n){return this.decode_chain(n).join("")}decode_chain(n){throw Error("`decode_chain` should be implemented in subclass.")}}class ReplaceDecoder extends Decoder{constructor(n){super(n)}decode_chain(n){let o=createPattern(this.config.pattern);return o===null?n:n.map(l=>l.replaceAll(o,this.config.content))}}class ByteFallback extends Decoder{constructor(n){super(n),this.text_decoder=new TextDecoder}decode_chain(n){let o=[],l=[];for(let c of n){let f=null;if(c.length===6&&c.startsWith("<0x")&&c.endsWith(">")){let a=parseInt(c.slice(3,5),16);isNaN(a)||(f=a)}if(f!==null)l.push(f);else{if(l.length>0){let a=this.text_decoder.decode(Uint8Array.from(l));o.push(a),l=[]}o.push(c)}}if(l.length>0){let c=this.text_decoder.decode(Uint8Array.from(l));o.push(c),l=[]}return o}}class FuseDecoder extends Decoder{constructor(n){super(n)}decode_chain(n){return[n.join("")]}}class StripDecoder extends Decoder{constructor(n){super(n),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(n){return n.map(o=>{let l=0;for(let f=0;f(l!==0&&(o.startsWith(this.config.prefix)?o=o.replace(this.config.prefix,""):o=" "+o),this.cleanup&&(o=clean_up_tokenization(o)),o))}}class ByteLevelDecoder extends Decoder{constructor(n){super(n),this.byte_decoder=UNICODE_TO_BYTES,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(n){let o=n.join(""),l=new Uint8Array([...o].map(f=>this.byte_decoder[f]));return this.text_decoder.decode(l)}decode_chain(n){let o=[],l=[];for(let c of n)this.added_tokens.includes(c)?(l.length>0&&(o.push(this.convert_tokens_to_string(l)),l=[]),o.push(c)):l.push(c);return l.length>0&&o.push(this.convert_tokens_to_string(l)),o}}class DecoderSequence extends Decoder{constructor(n){super(n),this.decoders=n.decoders.map(o=>Decoder.fromConfig(o))}decode_chain(n){return this.decoders.reduce((o,l)=>l.decode_chain(o),n)}}class MetaspacePreTokenizer extends PreTokenizer{constructor(n){super(),this.addPrefixSpace=n.add_prefix_space,this.replacement=n.replacement,this.strRep=n.str_rep||this.replacement}pre_tokenize(n){typeof n=="string"&&(n=n.trimStart().split(/\s+/));const o=[];for(let l of n){let c=l.replaceAll(" ",this.strRep);this.addPrefixSpace&&!c.startsWith(this.replacement)&&(c=this.strRep+c),o.push(c)}return o}}class MetaspaceDecoder extends Decoder{constructor(n){super(n),this.addPrefixSpace=n.add_prefix_space,this.replacement=n.replacement}decode_chain(n){let o=[];for(let l=0;lPreTokenizer.fromConfig(o))}pre_tokenize_text(n){return typeof n=="string"&&(n=[n]),this.tokenizers.reduce((o,l)=>l.pre_tokenize(o),n)}}class WhitespaceSplit extends PreTokenizer{constructor(n){super()}pre_tokenize_text(n){return whitespace_split(n)}}class PreTrainedTokenizer extends Callable{constructor(n,o){super(),this.normalizer=Normalizer.fromConfig(n.normalizer),this.pre_tokenizer=PreTokenizer.fromConfig(n.pre_tokenizer),n.model.vocab&&(Array.isArray(n.model.vocab)||(n.model.vocab=Object.entries(n.model.vocab)),n.model.vocab=new Map(n.model.vocab)),this.model=TokenizerModel.fromConfig(n.model,o),this.post_processor=PostProcessor.fromConfig(n.post_processor),this.decoder=Decoder.fromConfig(n.decoder),this.decoder.end_of_word_suffix=this.model.end_of_word_suffix,this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(let l of n.added_tokens){let c=l.id,f=l.content;this.added_tokens.push(f),this.model.tokens_to_ids.set(f,c),this.model.vocab[c]=f,l.special&&(this.special_tokens.push(f),this.all_special_ids.push(c))}this.decoder.added_tokens=this.added_tokens,this.added_tokens_regex=new RegExp("("+this.added_tokens.map(escapeRegExp).join("|")+")"),this.mask_token=this.getToken(o,"mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken(o,"pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken(o,"sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.model_max_length=o.model_max_length,this.remove_space=o.remove_space,this.clean_up_tokenization_spaces=o.clean_up_tokenization_spaces??!0,this.padding_side="right"}getToken(n,...o){for(let l of o){let c=n[l];if(c)if(typeof c=="object"){if(c.__type==="AddedToken")return c.content;throw Error(`Unknown token: ${c}`)}else return c}return null}static async from_pretrained(n,{progress_callback:o=null,config:l=null,cache_dir:c=null,local_files_only:f=!1,revision:a="main"}={}){let h=await loadTokenizer(n,{progress_callback:o,config:l,cache_dir:c,local_files_only:f,revision:a});return new this(...h)}prepare_model_inputs(n){return n}_call(n,{text_pair:o=null,padding:l=!1,truncation:c=null,max_length:f=null,return_tensor:a=!0}={}){let h;if(Array.isArray(n)){if(n.length===0)throw Error("text array must be non-empty");if(o!==null){if(Array.isArray(o)){if(n.length!==o.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");h=n.map((t,e)=>this.encode(t,o[e]))}else h=n.map(t=>this.encode(t))}else{if(n===null)throw Error("text may not be null");if(Array.isArray(o))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");h=[this.encode(n,o)]}let p=Math.max(...h.map(t=>t.length));f===null&&(f=p),f=Math.min(f,this.model_max_length);let u=[];if(l||c)for(let t=0;tf)c&&(h[t]=h[t].slice(0,f)),u.push(new Array(h[t].length).fill(1));else if(l){let e=f-h[t].length;this.padding_side==="right"?(u.push(new Array(h[t].length).fill(1).concat(new Array(e).fill(0))),h[t].push(...new Array(e).fill(this.pad_token_id))):(u.push(new Array(e).fill(0).concat(new Array(h[t].length).fill(1))),h[t].unshift(...new Array(e).fill(this.pad_token_id)))}else u.push(new Array(h[t].length).fill(1));else u=h.map(t=>new Array(t.length).fill(1));if(a){if(!(l&&c)&&h.some(e=>e.length!==h[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=true' and 'truncation=true' to have batched tensors with the same length.");let t=[h.length,h[0].length];h=new Tensor("int64",BigInt64Array.from(h.flat().map(BigInt)),t),u=new Tensor("int64",BigInt64Array.from(u.flat().map(BigInt)),t)}else Array.isArray(n)||(h=h[0],u=u[0]);let s={input_ids:h,attention_mask:u};return s=this.prepare_model_inputs(s),s}_encode_text(n){return n===null?null:n.split(this.added_tokens_regex).filter(c=>c).map(c=>{if(this.added_tokens.includes(c))return c;{this.remove_space===!0&&(c=c.trim().split(/\s+/).join(" ")),this.normalizer!==null&&(c=this.normalizer(c));let f=this.pre_tokenizer!==null?this.pre_tokenizer(c):[c];return this.model(f)}}).flat()}encode(n,o=null){let l=this._encode_text(n),c=this._encode_text(o),f=this.post_processor(l,c);return this.model.convert_tokens_to_ids(f)}batch_decode(n,o={}){return n.map(l=>this.decode(l,o))}decode(n,o={}){if(!Array.isArray(n)||n.length===0||!isIntegralNumber(n[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(n,o)}decode_single(n,{skip_special_tokens:o=!1,clean_up_tokenization_spaces:l=null}){let c=this.model.convert_ids_to_tokens(n);o&&(c=c.filter(a=>!this.special_tokens.includes(a)));let f=this.decoder(c);return this.decoder.end_of_word_suffix&&(f=f.replaceAll(this.decoder.end_of_word_suffix," "),o&&(f=f.trim())),(l??this.clean_up_tokenization_spaces)&&(f=clean_up_tokenization(f)),f}}function add_token_types(y){if(y.input_ids instanceof Tensor)y.token_type_ids=new Tensor("int64",new BigInt64Array(y.input_ids.data.length),y.input_ids.dims);else if(Array.isArray(y.input_ids))Array.isArray(y.input_ids[0])?y.token_type_ids=y.input_ids.map(n=>new Array(n.length).fill(0)):y.token_type_ids=new Array(y.input_ids.length).fill(0);else throw new Error("Input ids must be a Tensor or an Array");return y}class BertTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class AlbertTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class MobileBertTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class SqueezeBertTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class DistilBertTokenizer extends PreTrainedTokenizer{}class T5Tokenizer extends PreTrainedTokenizer{}class GPT2Tokenizer extends PreTrainedTokenizer{}class BartTokenizer extends PreTrainedTokenizer{}class RobertaTokenizer extends PreTrainedTokenizer{}class BloomTokenizer extends PreTrainedTokenizer{}class LlamaTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class NllbTokenizer extends PreTrainedTokenizer{constructor(n,o){super(n,o),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(l=>this.languageRegex.test(l))}_build_translation_inputs(n,o,l){if(!this.language_codes.includes(l.tgt_lang))throw new Error(`Target language code "${l.tgt_lang}" is not valid. Must be one of: {${this.language_codes.join(", ")}}`);if(l.src_lang!==void 0){if(!this.language_codes.includes(l.src_lang))throw new Error(`Source language code "${l.src_lang}" is not valid. Must be one of: {${this.language_codes.join(", ")}}`);for(let c of this.post_processor.config.single)if("SpecialToken"in c&&this.languageRegex.test(c.SpecialToken.id)){c.SpecialToken.id=l.src_lang;break}}return l.forced_bos_token_id=this.model.convert_tokens_to_ids([l.tgt_lang])[0],this._call(n,o)}}const WHISPER_LANGUAGES=[["en","english"],["zh","chinese"],["de","german"],["es","spanish"],["ru","russian"],["ko","korean"],["fr","french"],["ja","japanese"],["pt","portuguese"],["tr","turkish"],["pl","polish"],["ca","catalan"],["nl","dutch"],["ar","arabic"],["sv","swedish"],["it","italian"],["id","indonesian"],["hi","hindi"],["fi","finnish"],["vi","vietnamese"],["he","hebrew"],["uk","ukrainian"],["el","greek"],["ms","malay"],["cs","czech"],["ro","romanian"],["da","danish"],["hu","hungarian"],["ta","tamil"],["no","norwegian"],["th","thai"],["ur","urdu"],["hr","croatian"],["bg","bulgarian"],["lt","lithuanian"],["la","latin"],["mi","maori"],["ml","malayalam"],["cy","welsh"],["sk","slovak"],["te","telugu"],["fa","persian"],["lv","latvian"],["bn","bengali"],["sr","serbian"],["az","azerbaijani"],["sl","slovenian"],["kn","kannada"],["et","estonian"],["mk","macedonian"],["br","breton"],["eu","basque"],["is","icelandic"],["hy","armenian"],["ne","nepali"],["mn","mongolian"],["bs","bosnian"],["kk","kazakh"],["sq","albanian"],["sw","swahili"],["gl","galician"],["mr","marathi"],["pa","punjabi"],["si","sinhala"],["km","khmer"],["sn","shona"],["yo","yoruba"],["so","somali"],["af","afrikaans"],["oc","occitan"],["ka","georgian"],["be","belarusian"],["tg","tajik"],["sd","sindhi"],["gu","gujarati"],["am","amharic"],["yi","yiddish"],["lo","lao"],["uz","uzbek"],["fo","faroese"],["ht","haitian creole"],["ps","pashto"],["tk","turkmen"],["nn","nynorsk"],["mt","maltese"],["sa","sanskrit"],["lb","luxembourgish"],["my","myanmar"],["bo","tibetan"],["tl","tagalog"],["mg","malagasy"],["as","assamese"],["tt","tatar"],["haw","hawaiian"],["ln","lingala"],["ha","hausa"],["ba","bashkir"],["jw","javanese"],["su","sundanese"]],WHISPER_LANGUAGE_MAPPING=new Map(WHISPER_LANGUAGES),WHISPER_TO_LANGUAGE_CODE_MAPPING=new Map([...WHISPER_LANGUAGES.map(([y,n])=>[n,y]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);class WhisperTokenizer extends PreTrainedTokenizer{_decode_asr(n,{return_timestamps:o=!1,return_language:l=!1,time_precision:c=null,force_full_sequences:f=!0}={}){if(c===null)throw Error("Must specify time_precision");let a=null;function h(){return{language:a,timestamp:[null,null],text:""}}const p=[];let u=h(),s=0;const t=this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1;let e=[],r=!1,i=null;const d=new Set(this.all_special_ids);for(let b of n){const _=b.tokens;let v=null,w=t;if("stride"in b){const[A,O,x]=b.stride;if(s-=O,i=A-x,O&&(w=O/c+t),x)for(let I=_.length-1;I>=0;--I){const $=_[I];if($>=t){if(v!==null&&($-t)*c=t){const O=(A-t)*c+s,x=Math.round(O*100)/100;if(v!==null&&A>=v)r=!0;else if(r||e.length>0&&A0?e.push(S):e.every(A=>A.length===0)&&(u=h(),e=[],S=[])}if(e.length>0){if(f&&o)throw new Error("There was an error while processing timestamps, we haven't found a timestamp as last token.");const b=this.findLongestCommonSequence(e),_=this.decode(b);u.text=_,p.push(u)}let g=Object.create(null);const m=p.map(b=>b.text).join("");if(o||l){for(let b=0;bI===A[$]).length,x=O/g+m;O>1&&x>h&&(h=x,p=[b,_,w,S])}const[s,t,e,r]=p,i=Math.floor((t+s)/2),d=Math.floor((r+e)/2);c.push(...o.slice(0,i)),o=a.slice(d),l=o.length}return c.push(...o),c}get_decoder_prompt_ids({language:n=null,task:o=null,no_timestamps:l=!0}={}){let c=[];if(n){n=n.toLowerCase();let f=WHISPER_TO_LANGUAGE_CODE_MAPPING.get(n);if(f===void 0)if(WHISPER_LANGUAGE_MAPPING.has(n))f=n;else{const p=n.length===2?WHISPER_LANGUAGE_MAPPING.keys():WHISPER_LANGUAGE_MAPPING.values();throw new Error(`Language "${n}" is not supported. Must be one of: ${JSON.stringify(p)}`)}let a=this.model.tokens_to_ids.get(`<|${f}|>`);if(a===void 0)throw new Error(`Unable to find language "${f}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);c.push(a)}else c.push(null);if(o){if(o=o.toLowerCase(),o!=="transcribe"&&o!=="translate")throw new Error(`Task "${o}" is not supported. Must be one of: ["transcribe", "translate"]`);let f=this.model.tokens_to_ids.get(`<|${o}|>`);if(f===void 0)throw new Error(`Unable to find task "${o}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);c.push(f)}else c.push(null);if(l){let f=this.model.tokens_to_ids.get("<|notimestamps|>");if(f===void 0)throw new Error('Unable to find "<|notimestamps|>" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.');c.push(f)}return c.map((f,a)=>[a+1,f]).filter(f=>f[1]!==null)}}class CodeGenTokenizer extends PreTrainedTokenizer{}class CLIPTokenizer extends PreTrainedTokenizer{}class MarianTokenizer extends PreTrainedTokenizer{constructor(n,o){super(n,o),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(l=>this.languageRegex.test(l)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(n){if(n===null)return null;let[o,...l]=n.trim().split(this.languageRegex);if(l.length===0)return super._encode_text(o);if(l.length===2){let[c,f]=l;return this.supported_language_codes.includes(c)||console.warn(`Unsupported language code "${c}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),[c,...super._encode_text(f)]}}}class CharTrie{constructor(){this.root=CharTrieNode.default()}extend(n){for(let o of n)this.push(o)}push(n){let o=this.root;for(let l of n){let c=o.children.get(l);c===void 0&&(c=CharTrieNode.default(),o.children.set(l,c)),o=c}o.isLeaf=!0}*commonPrefixSearch(n){let o=this.root,l="";for(let c=0;cp)&&(u=s.clone(),p=t)}if(u!==null)h.prev=u,h.backtraceScore=p;else return[]}++o}const l=[],f=this.beginNodes[n][0].prev;if(f===null)return[];let a=f.clone();for(;a.prev!==null;)l.push(a.clone()),a=a.clone().prev.clone();return l.reverse(),l}piece(n){return this.sentence.slice(n.pos,n.pos+n.length)}tokens(){return this.viterbi().map(o=>this.piece(o))}tokenIds(){return this.viterbi().map(o=>o.tokenId)}}class TokenLatticeNode{constructor(n,o,l,c,f){this.tokenId=n,this.nodeId=o,this.pos=l,this.length=c,this.score=f,this.prev=null,this.backtraceScore=0}clone(){const n=new TokenLatticeNode(this.tokenId,this.nodeId,this.pos,this.length,this.score);return n.prev=this.prev,n.backtraceScore=this.backtraceScore,n}}class AutoTokenizer{static async from_pretrained(n,{quantized:o=!0,progress_callback:l=null,config:c=null,cache_dir:f=null,local_files_only:a=!1,revision:h="main"}={}){let[p,u]=await loadTokenizer(n,{quantized:o,progress_callback:l,config:c,cache_dir:f,local_files_only:a,revision:h}),s=u.tokenizer_class.replace(/Fast$/,""),t=this.TOKENIZER_CLASS_MAPPING[s];return t||(console.warn(`Unknown tokenizer class "${s}", attempting to construct from base class.`),t=PreTrainedTokenizer),new t(p,u)}}le(AutoTokenizer,"TOKENIZER_CLASS_MAPPING",{T5Tokenizer,DistilBertTokenizer,BertTokenizer,MobileBertTokenizer,SqueezeBertTokenizer,AlbertTokenizer,GPT2Tokenizer,BartTokenizer,RobertaTokenizer,WhisperTokenizer,CodeGenTokenizer,CLIPTokenizer,MarianTokenizer,BloomTokenizer,NllbTokenizer,LlamaTokenizer});async function loadConfig(y,n){return await getModelJSON(y,"config.json",!0,n)}class PretrainedConfig{constructor(n){this.is_encoder_decoder=!1,Object.assign(this,n)}static async from_pretrained(n,{progress_callback:o=null,config:l=null,cache_dir:c=null,local_files_only:f=!1,revision:a="main"}={}){let h=l??await loadConfig(n,{progress_callback:o,config:l,cache_dir:c,local_files_only:f,revision:a});return new this(h)}}class AutoConfig{static async from_pretrained(...n){return PretrainedConfig.from_pretrained(...n)}}class LogitsProcessorList extends Callable{constructor(){super(),this.processors=[]}push(n){this.processors.push(n)}extend(n){this.processors.push(...n)}_call(n,o){for(let l of o)this.processors.forEach(c=>c(n,l))}[Symbol.iterator](){return this.processors.values()}}class LogitsProcessor extends Callable{_call(n,o){throw Error("`_call` should be implemented in a subclass")}}class ForceTokensLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.force_token_map=Object.fromEntries(n??[])}_call(n,o){let l=this.force_token_map[n.length];return exists(l)&&(o.data.fill(-1/0),o.data[l]=0),o}}class ForcedBOSTokenLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.bos_token_id=n}_call(n,o){return n.length===1&&(o.data.fill(-1/0),o.data[this.bos_token_id]=0),o}}class ForcedEOSTokenLogitsProcessor extends LogitsProcessor{constructor(n,o){super(),this.max_length=n,this.forced_eos_token_id=o}_call(n,o){}}class SuppressTokensAtBeginLogitsProcessor extends LogitsProcessor{constructor(n,o){super(),this.begin_suppress_tokens=n,this.begin_index=o}_call(n,o){if(n.length===this.begin_index)for(let l of this.begin_suppress_tokens)o.data[l]=-1/0;return o}}class WhisperTimeStampLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.eos_token_id=n.eos_token_id,this.no_timestamps_token_id=n.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=(n.forced_decoder_ids||[]).length+2,n.forced_decoder_ids.slice(-1)[0][1]===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=n.max_initial_timestamp_index}_call(n,o){if(o.data[this.no_timestamps_token_id]=-1/0,n.length===this.begin_index-1)return o.data.fill(-1/0),o.data[this.timestamp_begin]=0,o;const l=n.slice(this.begin_index),c=l.length>=1&&l[l.length-1]>=this.timestamp_begin,f=l.length<2||l[l.length-2]>=this.timestamp_begin;if(c&&(f?o.data.subarray(this.timestamp_begin).fill(-1/0):o.data.subarray(0,this.eos_token_id).fill(-1/0)),n.length===this.begin_index&&this.max_initial_timestamp_index!==null){const u=this.timestamp_begin+this.max_initial_timestamp_index;o.data.subarray(u+1).fill(-1/0)}const a=log_softmax(o.data),h=Math.log(a.subarray(this.timestamp_begin).map(Math.exp).reduce((u,s)=>u+s)),p=max(a.subarray(0,this.timestamp_begin))[0];return h>p&&o.data.subarray(0,this.timestamp_begin).fill(-1/0),o}}class NoRepeatNGramLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.no_repeat_ngram_size=n}getNgrams(n){const o=n.length,l=[];for(let f=0;f0&&(c=c.map(f=>f/this.temperature)),c}randomSelect(n){let o=n.reduce((c,f)=>c+f,0),l=Math.random()*o;for(let c=0;c1)return new BeamSearchSampler(n.temperature,n.num_beams,n.do_sample,n.top_k);if(n.do_sample)return new TopKSampler(n.temperature,n.top_k);if(n.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${n.num_return_sequences}.`);return new GreedySampler(n.temperature)}}class GreedySampler extends Sampler{sample(n,o=-1){let l=this.getLogits(n,o);return[[max(l)[1],0]]}}class TopKSampler extends Sampler{constructor(n,o){super(n),this.k=o}sample(n,o=-1){let[l,c,f]=n.dims,a=f;this.k>0&&(a=Math.min(this.k,a));let h=this.getLogits(n,o),p=getTopItems(h,a),u=softmax(p.map(r=>r[1])),s=this.randomSelect(u),t=p[s][0],e=Math.log(u[s]);return[[t,e]]}}class BeamSearchSampler extends Sampler{constructor(n,o,l,c){super(n),this.num_beams=o,this.do_sample=l,this.top_k=c}sample(n,o=-1){let l=this.getLogits(n,o);if(this.do_sample||this.top_k>0){const[c,f,a]=n.dims;let h=a;this.top_k>0&&(h=Math.min(this.top_k,h));const p=getTopItems(l,h),u=softmax(p.map(s=>s[1]));return Array.from({length:this.num_beams},()=>{const s=this.randomSelect(u);return[p[s][0],Math.log(u[s])]})}else{const c=log_softmax(l);return getTopItems(c,this.num_beams)}}}const{InferenceSession,Tensor:ONNXTensor}=ONNX;async function constructSession(y,n,o){let l=`onnx/${n}${o.quantized?"_quantized":""}.onnx`,c=await getModelFile(y,l,!0,o);try{return await InferenceSession.create(c,{executionProviders})}catch(f){if(executionProviders.length===1&&executionProviders[0]==="wasm")throw f;return console.warn(f),console.warn("Something went wrong during model construction (most likely a missing operation). Using `wasm` as a fallback. "),await InferenceSession.create(c,{executionProviders:["wasm"]})}}async function sessionRun(y,n){const o={},l=[];for(let a of y.inputNames)n[a]===void 0?l.push(a):o[a]=n[a];if(l.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${l.join(", ")}.`);const c=Object.keys(n).length,f=y.inputNames.length;if(c>f){let a=Object.keys(n).filter(h=>!y.inputNames.includes(h));console.warn(`WARNING: Too many inputs were provided (${c} > ${f}). The following inputs will be ignored: "${a.join(", ")}".`)}try{let a=await y.run(o);return a=replaceTensors(a),a}catch(a){throw console.error(`An error occurred during model execution: "${a}".`),console.error("Inputs given to model:",o),a}}function replaceTensors(y){for(let n in y)y[n]instanceof ONNXTensor&&(y[n]=new Tensor(y[n]));return y}function toI64Tensor(y){if(y instanceof Tensor)return y;if(y.length===0)throw Error("items must be non-empty");if(Array.isArray(y[0])){if(y.some(n=>n.length!==y[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.");return new Tensor("int64",BigInt64Array.from(y.flat().map(n=>BigInt(n))),[y.length,y[0].length])}else return new Tensor("int64",BigInt64Array.from(y.map(n=>BigInt(n))),[1,y.length])}function _prepare_attention_mask(y,n){let o=y.config.pad_token_id??null,l=y.config.eos_token_id??null;isIntegralNumber(l)&&(l=[l]);let c=n.indexOf(o)!==-1,f=l===null||!l.includes(o);if(c&&f){let a=BigInt64Array.from(n.data.map(h=>h!=o));return new Tensor("int64",a,n.dims)}else return new Tensor("int64",new BigInt64Array(n.data.length).fill(1n),n.dims)}function boolTensor(y){return new Tensor("bool",[y],[1])}async function loadAutoModel(y,n){let o=await AutoConfig.from_pretrained(y,n),l=o.is_encoder_decoder?"encoder_model":"model",c=await constructSession(y,l,n);return[o,c]}async function loadModel(y,n){return await Promise.all([AutoConfig.from_pretrained(y,n),constructSession(y,"model",n)])}async function seq2seqLoadModel(y,n){return await Promise.all([AutoConfig.from_pretrained(y,n),constructSession(y,"encoder_model",n),constructSession(y,"decoder_model_merged",n),getModelJSON(y,"generation_config.json",!1,n)])}async function encoderDecoderLoadModel(y,n){return await Promise.all([AutoConfig.from_pretrained(y,n),constructSession(y,"encoder_model",n),constructSession(y,"decoder_model_merged",n)])}async function decoderLoadModel(y,n){return await Promise.all([AutoConfig.from_pretrained(y,n),constructSession(y,"decoder_model_merged",n)])}async function seq2seq_forward(y,n,{encoder_input_name:o="input_ids",add_decoder_pkv:l=!0}={}){let c=n.encoder_outputs,f=n.past_key_values;if(c===null){const u={[o]:n[o]};y.session.inputNames.includes("attention_mask")&&(u.attention_mask=n.attention_mask),c=(await sessionRun(y.session,u)).last_hidden_state}let a={input_ids:n.decoder_input_ids,encoder_hidden_states:c,use_cache_branch:boolTensor(f!==null)};y.decoder_merged_session.inputNames.includes("encoder_attention_mask")&&(a.encoder_attention_mask=n.attention_mask),y.addPastKeyValues(a,f,l);const h=await sessionRun(y.decoder_merged_session,a);let p=h.logits;return f=y.getPastKeyValues(h,f),new Seq2SeqLMOutput(p,f,c)}function seq2seqStartBeams(y,n,o,l=!0){let c=[],f=0,a=y.config.decoder_start_token_id;Array.isArray(a)||(a=[a]);for(let h of n){h.dims=[1,...h.dims];let p={inputs:h,encoder_outputs:null,past_key_values:null,output_token_ids:a,done:!1,score:0,id:f++};l&&(p.attention_mask=_prepare_attention_mask(y,h)),c.push(p)}return c}async function seq2seqRunBeam(y,n,{input_name:o="input_ids"}={}){let l={[o]:n.inputs,decoder_input_ids:toI64Tensor(n.output_token_ids.slice(-1)),encoder_outputs:n.encoder_outputs,past_key_values:n.past_key_values};n.attention_mask&&(l.attention_mask=n.attention_mask);let c=await y.forward(l);return n.past_key_values=c.past_key_values,n.encoder_outputs=c.encoder_outputs,c}async function textgen_forward(y,n){let o=n.past_key_values,l={input_ids:n.input_ids,attention_mask:n.attention_mask,use_cache_branch:boolTensor(o!==null)};y.addPastKeyValues(l,o);let c=await sessionRun(y.session,l),f=c.logits;return o=y.getPastKeyValues(c,o),{logits:f,past_key_values:o}}function textgenStartBeams(y,n,o,l){let c=[],f=0;for(let a of n){a.dims=[1,...a.dims];let h;l?(h=l[f],h.dims=[1,...h.dims]):h=_prepare_attention_mask(y,a);let p={input:a,model_input_ids:a,attention_mask:h,past_key_values:null,output_token_ids:[],num_output_tokens:o,done:!1,score:0,id:f++};c.push(p)}return c}async function textgenRunBeam(y,n){let o=new BigInt64Array(n.input.data.length+n.output_token_ids.length).fill(1n),l={input_ids:n.model_input_ids,attention_mask:new Tensor("int64",o,[1,o.length]),past_key_values:n.past_key_values},c=await y.forward(l);return n.past_key_values=c.past_key_values,c}function textgenUpdatebeam(y,n){y.output_token_ids=[...y.output_token_ids,n],y.model_input_ids=new Tensor("int64",[BigInt(n)],[1,1])}class PreTrainedModel extends Callable{constructor(n,o){super(),this.config=n,this.session=o}async dispose(){let n=[];for(let o of Object.keys(this)){let l=this[o];l instanceof InferenceSession&&n.push(l.handler.dispose())}return await Promise.all(n)}static async from_pretrained(n,{quantized:o=!0,progress_callback:l=null,config:c=null,cache_dir:f=null,local_files_only:a=!1,revision:h="main"}={}){let p=await loadAutoModel(n,{quantized:o,progress_callback:l,config:c,cache_dir:f,local_files_only:a,revision:h});return new this(...p)}async _call(n){return await sessionRun(this.session,n)}async forward(n){throw Error("forward should be implemented in subclasses.")}_get_logits_processor(n,o,l=null){const c=new LogitsProcessorList;if(n.repetition_penalty!==null&&n.repetition_penalty!==1&&c.push(new RepetitionPenaltyLogitsProcessor(n.repetition_penalty)),n.no_repeat_ngram_size!==null&&n.no_repeat_ngram_size>0&&c.push(new NoRepeatNGramLogitsProcessor(n.no_repeat_ngram_size)),n.forced_bos_token_id!==null&&c.push(new ForcedBOSTokenLogitsProcessor(n.forced_bos_token_id)),n.forced_eos_token_id!==null&&c.push(new ForcedEOSTokenLogitsProcessor(n.max_length,n.forced_eos_token_id)),n.begin_suppress_tokens!==null){let f=o>1||n.forced_bos_token_id===null?o:o+1;n.forced_decoder_ids!==null&&(f+=n.forced_decoder_ids[n.forced_decoder_ids.length-1][0]),c.push(new SuppressTokensAtBeginLogitsProcessor(n.begin_suppress_tokens,f))}return n.forced_decoder_ids!==null&&c.push(new ForceTokensLogitsProcessor(n.forced_decoder_ids)),l!==null&&c.extend(l),c}_get_generation_config(n){let o=new GenerationConfig;return"generation_config"in this&&Object.assign(o,this.generation_config),n!==null&&Object.assign(o,n),o}async generate(n,o=null,l=null,{inputs_attention_mask:c=null}={}){if(!(n instanceof Tensor)&&!isTypedArray(n)&&!Array.isArray(n))throw Error(`\`inputs\` must be a Tensor, TypedArray, or Array, but is "${n.constructor.name}".`);let f;if(this.config.is_encoder_decoder)f=0;else if(f=n instanceof Tensor?n.dims[0]:n.length,f===0)throw Error("Must supply a non-empty array of input token ids.");o=this._get_generation_config(o),l=l??new LogitsProcessorList,l=this._get_logits_processor(o,f,l);let a=1;const h=a+(o.max_new_tokens??1/0),p=Number.isInteger(o.max_length)&&(o.max_new_tokens??null)===null;let u=Sampler.getSampler(o),s=this.getStartBeams(n,a,c);for(;s.some(t=>!t.done)&&a=o.max_length){e.done=!0,t.push(e);continue}let i=(await this.runBeam(e)).logits.slice(null,-1,null);l(e.output_token_ids,i);let d=u(i);for(let[g,m]of d){let b={...e};this.updateBeam(b,g),b.score+=m,g===this.config.eos_token_id&&(b.done=!0),t.push(b)}}++a,t=this.groupBeams(t).map(e=>e.sort((r,i)=>i.score-r.score).slice(0,o.num_beams)),s=t.flat(),o.callback_function&&o.callback_function(s)}return this.groupBeams(s).map(t=>o.num_return_sequences>1?t.slice(0,o.num_return_sequences).map(e=>e.output_token_ids):[t[0].output_token_ids]).flat()}groupBeams(n){const o=Object.create(null);for(const l of n)o[l.id]===void 0?o[l.id]=[l]:o[l.id].push(l);return Object.values(o)}getPastKeyValues(n,o){const l=Object.create(null);for(const c in n)if(c.startsWith("present")){let f=c.replace("present","past_key_values");o!==null&&c.includes("encoder")?l[f]=o[f]:l[f]=n[c]}return l}addPastKeyValues(n,o,l=!1){if(o===null)if(l){let c=[1,this.num_encoder_heads,0,this.encoder_dim_kv];for(let a=0;a{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this browser.");return new self.OffscreenCanvas(y,n)},loadImageFunction=self.createImageBitmap,ImageDataClass=self.ImageData;else if(fs)loadImageFunction=async y=>{let{data:n,info:o}=await y.raw().toBuffer({resolveWithObject:!0});return new RawImage(new Uint8ClampedArray(n),o.width,o.height,o.channels)};else throw new Error("Unable to load image processing library.");const RESAMPLING_MAPPING={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"};class RawImage{constructor(n,o,l,c){this._update(n,o,l,c)}static async read(n){if(n instanceof RawImage)return n;if(isString(n)||n instanceof URL)return await this.fromURL(n);throw new Error(`Unsupported input type: ${typeof n}`)}static async fromURL(n){let l=await(await getFile(n)).blob();return this.fromBlob(l)}static async fromBlob(n){if(BROWSER_ENV){let o=await loadImageFunction(n);const l=createCanvasFunction(o.width,o.height).getContext("2d");return l.drawImage(o,0,0),new this(l.getImageData(0,0,o.width,o.height).data,o.width,o.height,4)}else{let o=fs(await n.arrayBuffer());return await loadImageFunction(o)}}grayscale(){if(this.channels===1)return this;let n=new Uint8ClampedArray(this.width*this.height*3);switch(this.channels){case 3:case 4:for(let o=0,l=0;o=0?p=l:s=-l,c>=0?u=c:t=-c,h.drawImage(a,p,u,n,o,s,t,n,o),new RawImage(h.getImageData(0,0,n,o).data,n,o,4).convert(f)}else{let f=fs(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}});if(l>=0&&c>=0)f=f.extract({left:Math.floor(l),top:Math.floor(c),width:n,height:o});else if(l<=0&&c<=0){let a=Math.floor(-c),h=Math.floor(-l);f=f.extend({top:a,left:h,right:n-this.width-h,bottom:o-this.height-a})}else{let a=[0,0],h=0;c<0?(a[0]=Math.floor(-c),a[1]=o-this.height-a[0]):h=Math.floor(c);let p=[0,0],u=0;l<0?(p[0]=Math.floor(-l),p[1]=n-this.width-p[0]):u=Math.floor(l),f=f.extend({top:a[0],bottom:a[1],left:p[0],right:p[1]}).extract({left:u,top:h,width:n,height:o})}return await loadImageFunction(f)}}toCanvas(){let n=this.clone().rgba(),o=createCanvasFunction(n.width,n.height),l=new ImageDataClass(n.data,n.width,n.height);return o.getContext("2d").putImageData(l,0,0),o}_update(n,o,l,c=null){return this.data=n,this.width=o,this.height=l,c!==null&&(this.channels=c),this}clone(){return new RawImage(this.data.slice(),this.width,this.height,this.channels)}convert(n){if(this.channels===n)return this;switch(n){case 1:this.grayscale();break;case 3:this.rgb();break;case 4:this.rgba();break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this}save(n,o="image/png"){if(!env.useFS)throw new Error("Unable to save the image because filesystem is disabled in this environment.");const c=this.toCanvas().toBuffer(o);fs.writeFileSync(n,c)}}async function read_audio(y,n){if(typeof AudioContext>"u")throw Error("Unable to load audio from path/URL since `AudioContext` is not available in your environment. As a result, audio data must be passed directly to the processor. If you are running in node.js, you can use an external library (e.g., https://github.com/audiojs/web-audio-api) to do this.");const o=await(await getFile(y)).arrayBuffer(),l=new AudioContext({sampleRate:n});typeof n>"u"&&console.warn(`No sampling rate provided, using default of ${l.sampleRate}Hz.`);const c=await l.decodeAudioData(o);let f;if(c.numberOfChannels===2){const a=Math.sqrt(2);let h=c.getChannelData(0),p=c.getChannelData(1);f=new Float32Array(h.length);for(let u=0;u=i?e[b]=r*Math.exp(d*(_-i)):e[b]=s+t*_,g[b]=f.map(v=>e[b]-v)}const m=e.slice(1).map((b,_)=>1/(b-e[_]));for(let b=0;bthis.preprocess(c)));return o.forEach(c=>c.pixel_values.dims=[1,...c.pixel_values.dims]),{pixel_values:cat(o.map(c=>c.pixel_values)),original_sizes:o.map(c=>c.original_size),reshaped_input_sizes:o.map(c=>c.reshaped_input_size)}}}class ViTFeatureExtractor extends ImageFeatureExtractor{}class DetrFeatureExtractor extends ImageFeatureExtractor{async _call(n){let o=await super._call(n),l=[o.pixel_values.dims[0],64,64];return o.pixel_mask=new Tensor("int64",new BigInt64Array(l.reduce((c,f)=>c*f)).fill(1n),l),o}center_to_corners_format([n,o,l,c]){return[n-l/2,o-c/2,n+l/2,o+c/2]}post_process_object_detection(n,o=.5,l=null){const c=n.logits,f=n.pred_boxes,[a,h,p]=c.dims;if(l!==null&&l.length!==a)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let u=[];for(let s=0;so){let v=i[d].data;v=this.center_to_corners_format(v),t!==null&&(v=v.map((w,S)=>w*t[(S+1)%2])),e.boxes.push(v),e.classes.push(m),e.scores.push(_)}}u.push(e)}return u}remove_low_and_no_objects(n,o,l,c){let f=[],a=[],h=[];for(let p=0;pl&&(f.push(s),a.push(r),h.push(t))}return[f,a,h]}check_segment_validity(n,o,l,c=.5,f=.8){let a=[],h=0,p=0;for(let s=0;s=c&&++p;let u=h>0&&p>0;return u&&(u=h/p>f),[u,a]}compute_segments(n,o,l,c,f,a=null,h=null){let[p,u]=h??n[0].dims,s=new Tensor("int32",new Int32Array(p*u),[p,u]),t=[];if(h!==null)for(let d=0;dr[m]&&(e[m]=d,r[m]=n[d].data[m])}let i=0;for(let d=0;dBigInt(Math.round(p)))),a);return{pixel_values:l,original_sizes:c,reshaped_input_sizes:f,input_points:h}}post_process_masks(n,o,l,{mask_threshold:c=0,binarize:f=!0,pad_size:a=null}={}){let h=[];a=a??this.pad_size;let p=[a.height,a.width];for(let u=0;ub>c),m.dims)),m.dims=[1,...m.dims],r.push(m)}let i=cat(r);h.push(i)}return h}}class WhisperFeatureExtractor extends FeatureExtractor{constructor(n){var o;super(n),(o=this.config).mel_filters??(o.mel_filters=getMelFilters(this.config.sampling_rate,this.config.n_fft,this.config.feature_size))}calcOffset(n,o){return Math.abs((n+o)%(2*o)-o)}padReflect(n,o,l){const c=new Float32Array(n.length+o+l),f=n.length-1;for(let a=0;a>1;++w){const S=(w+1-l)**2/2,A=Math.sqrt(m**2+b**2)**S,O=S*Math.atan2(b,m);let x=2*w;u[x]=A*Math.cos(O),u[x+1]=A*Math.sin(O),s[x]=u[x],s[x+1]=-u[x+1]}const _=u.subarray(c,f),v=new FFT(a>>1);v.transform(r,s);for(let w=0;w>1,$=S[I]*o[I];t[O]=$*_[O],t[x]=$*_[x]}v.transform(i,t);for(let O=0;Oc?a-c:0,u=a>1,u=new Float32Array(h*p);for(let m=0;mthis.config.n_samples&&console.warn("Attempting to extract features for audio longer than 30 seconds. If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`.");let o=n.slice(0,this.config.n_samples),l=this._extract_fbank_features(o);return{input_features:new Tensor("float32",l.data,[1,...l.dims])}}}class Processor extends Callable{constructor(n){super(),this.feature_extractor=n}async _call(n){return await this.feature_extractor(n)}}class SamProcessor extends Processor{async _call(n,o){return await this.feature_extractor(n,o)}post_process_masks(...n){return this.feature_extractor.post_process_masks(...n)}}class WhisperProcessor extends Processor{async _call(n){return await this.feature_extractor(n)}}class AutoProcessor{static async from_pretrained(n,{progress_callback:o=null,config:l=null,cache_dir:c=null,local_files_only:f=!1,revision:a="main"}={}){let h=l??await getModelJSON(n,"preprocessor_config.json",!0,{progress_callback:o,config:l,cache_dir:c,local_files_only:f,revision:a}),p=h.feature_extractor_type??h.image_processor_type,u=this.FEATURE_EXTRACTOR_CLASS_MAPPING[p];if(!u)if(h.size!==void 0)console.warn("Feature extractor type not specified, assuming ImageFeatureExtractor due to size parameter in config."),u=ImageFeatureExtractor;else throw new Error(`Unknown Feature Extractor type: ${h.feature_extractor_type}`);let s=this.PROCESSOR_CLASS_MAPPING[h.processor_class]??Processor,t=new u(h);return new s(t)}}le(AutoProcessor,"FEATURE_EXTRACTOR_CLASS_MAPPING",{WhisperFeatureExtractor,ViTFeatureExtractor,DetrFeatureExtractor,SamImageProcessor}),le(AutoProcessor,"PROCESSOR_CLASS_MAPPING",{WhisperProcessor,SamProcessor});async function prepareImages(y){return Array.isArray(y)||(y=[y]),y=await Promise.all(y.map(n=>RawImage.read(n))),y}class Pipeline extends Callable{constructor(n,o,l){super(),this.task=n,this.tokenizer=o,this.model=l}async dispose(){await this.model.dispose()}async _call(n){let o=this.tokenizer(n,{padding:!0,truncation:!0}),l=await this.model(o);return[o,l]}}class TextClassificationPipeline extends Pipeline{async _call(n,{topk:o=1}={}){let[l,c]=await super._call(n),f=this.model.config.id2label,a=[];for(let h of c.logits){let u=getTopItems(softmax(h.data),o).map(function(s){return{label:f[s[0]],score:s[1]}});o===1?a.push(...u):a.push(u)}return Array.isArray(n)||o===1?a:a[0]}}class TokenClassificationPipeline extends Pipeline{async _call(n,{ignore_labels:o=["O"]}={}){let l=Array.isArray(n);l||(n=[n]);let c=this.tokenizer,[f,a]=await super._call(n),h=a.logits,p=this.model.config.id2label,u=[];for(let s=0;s[r,i]).filter(r=>r[1]>u),t=Array.from(softmax(f.end_logits[h].data)).map((r,i)=>[r,i]).filter(r=>r[1]>u),e=product(s,t).filter(r=>r[0][1]<=r[1][1]).map(r=>[r[0][1],r[1][1],r[0][0]*r[1][0]]).sort((r,i)=>i[2]-r[2]);for(let r=0;r{let i=[...p];return i[u]=r[0],{score:r[1],token:r[0],token_str:f.model.vocab[r[0]],sequence:f.decode(i,{skip_special_tokens:!0})}}))}return Array.isArray(n)?a:a[0]}}class Text2TextGenerationPipeline extends Pipeline{constructor(){super(...arguments);le(this,"_key",null)}async _call(o,l={}){Array.isArray(o)||(o=[o]),this.model.config.prefix&&(o=o.map(u=>this.model.config.prefix+u));let c=this.model.config.task_specific_params;c&&c[this.task]&&c[this.task].prefix&&(o=o.map(u=>c[this.task].prefix+u));let f={padding:!0,truncation:!0},a;this instanceof TranslationPipeline&&"_build_translation_inputs"in this.tokenizer?a=this.tokenizer._build_translation_inputs(o,f,l).input_ids:a=this.tokenizer(o,f).input_ids;let h=await this.model.generate(a,l),p=this.tokenizer.batch_decode(h,{skip_special_tokens:!0});return this._key!==null&&(p=p.map(u=>this._key===null?u:{[this._key]:u})),p}}class SummarizationPipeline extends Text2TextGenerationPipeline{constructor(){super(...arguments);le(this,"_key","summary_text")}}class TranslationPipeline extends Text2TextGenerationPipeline{constructor(){super(...arguments);le(this,"_key","translation_text")}}class TextGenerationPipeline extends Pipeline{async _call(n,o={}){let l=typeof n=="string"||n instanceof String;l&&(n=[n]),this.tokenizer.padding_side="left";let c=this.tokenizer(n,{padding:!0,truncation:!0}),f=c.input_ids,a=c.attention_mask,h=await this.model.generate(f,o,null,{inputs_attention_mask:a});const p=n.map(t=>t.trim()),u=this.tokenizer.batch_decode(h,{skip_special_tokens:!0}),s=Array.from({length:n.length},t=>[]);for(let t=0;t[c.toLowerCase(),f])),this.entailment_id=this.label2id.entailment,this.entailment_id===void 0&&(console.warn("Could not find 'entailment' in label2id mapping. Using 2 as entailment_id."),this.entailment_id=2),this.contradiction_id=this.label2id.contradiction,this.contradiction_id===void 0&&(console.warn("Could not find 'contradiction' in label2id mapping. Using 0 as contradiction_id."),this.contradiction_id=0)}async _call(n,o,{hypothesis_template:l="This example is {}.",multi_label:c=!1}={}){let f=Array.isArray(n);f||(n=[n]),Array.isArray(o)||(o=[o]);let a=o.map(u=>l.replace("{}",u)),h=c||o.length===1,p=[];for(let u of n){let s=[];for(let r of a){let i=this.tokenizer(u,{text_pair:r}),d=await this.model(i);h?s.push([d.logits.data[this.contradiction_id],d.logits.data[this.entailment_id]]):s.push(d.logits.data[this.entailment_id])}let t;h?t=s.map(r=>softmax(r)[1]):t=softmax(s);let e=t.map((r,i)=>[r,i]).sort((r,i)=>i[0]-r[0]);p.push({sequence:u,labels:e.map(r=>o[r[1]]),scores:e.map(r=>r[0])})}return f?p:p[0]}}class FeatureExtractionPipeline extends Pipeline{async _call(n,{pooling:o="none",normalize:l=!1}={}){let[c,f]=await super._call(n),a=f.last_hidden_state??f.logits;if(o!=="none")if(o==="mean")a=mean_pooling(a,c.attention_mask);else throw Error(`Pooling method '${o}' not supported.`);return l&&(a=a.normalize(2,-1)),a}}class AutomaticSpeechRecognitionPipeline extends Pipeline{constructor(n,o,l,c){super(n,o,l),this.processor=c}async _preprocess(n,o){return isString(n)&&(n=await read_audio(n,o)),n}async _call(n,o={}){let l=o.return_timestamps??!1,c=o.chunk_length_s??0,f=o.stride_length_s??null,a=o.chunk_callback??null,h=o.force_full_sequences??!1,p=pop(o,"language",null),u=pop(o,"task",null);if(p||u||l){if(o.forced_decoder_ids)throw new Error("Cannot specify `language`/`task`/`return_timestamps` and `forced_decoder_ids` at the same time.");let i=this.tokenizer.get_decoder_prompt_ids({language:p,task:u,no_timestamps:!l});i.length>0&&(o.forced_decoder_ids=i)}let s=!Array.isArray(n);s&&(n=[n]);const t=this.processor.feature_extractor.config.sampling_rate,e=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions;let r=[];for(let i of n){i=await this._preprocess(i,t);let d=[];if(c>0){if(f===null)f=c/6;else if(c<=f)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const b=t*c,_=t*f,v=b-2*_;let w=0;for(;w=i.length;d.push({stride:[S.length,O?0:_,x?0:_],input_features:A.input_features,is_last:x}),w+=v}}else d=[{stride:[i.length,0,0],input_features:(await this.processor(i)).input_features,is_last:!0}];for(let b of d){let _=await this.model.generate(b.input_features,o);b.tokens=_[0],b.stride=b.stride.map(v=>v/t),a!==null&&a(b)}let[g,m]=this.tokenizer._decode_asr(d,{time_precision:e,return_timestamps:l,force_full_sequences:h});r.push({text:g,...m})}return s?r[0]:r}}class ImageToTextPipeline extends Pipeline{constructor(n,o,l,c){super(n,o,l),this.processor=c}async _call(n,o={}){let l=Array.isArray(n);n=await prepareImages(n);let{pixel_values:c}=await this.processor(n),f=[];for(let a of c){a.dims=[1,...a.dims];let h=await this.model.generate(a,o),p=this.tokenizer.batch_decode(h,{skip_special_tokens:!0}).map(u=>({generated_text:u.trim()}));f.push(p)}return l?f:f[0]}}class ImageClassificationPipeline extends Pipeline{constructor(n,o,l){super(n,null,o),this.processor=l}async _call(n,{topk:o=1}={}){let l=Array.isArray(n);n=await prepareImages(n);let{pixel_values:c}=await this.processor(n),f=await this.model({pixel_values:c}),a=this.model.config.id2label,h=[];for(let p of f.logits){let s=getTopItems(softmax(p.data),o).map(function(t){return{label:a[t[0]],score:t[1]}});o===1?h.push(...s):h.push(s)}return l||o===1?h:h[0]}}class ImageSegmentationPipeline extends Pipeline{constructor(n,o,l){super(n,null,o),this.processor=l,this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(n,{threshold:o=.5,mask_threshold:l=.5,overlap_mask_area_threshold:c=.8,label_ids_to_fuse:f=null,target_sizes:a=null,subtask:h=null}={}){if(Array.isArray(n)&&n.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");n=await prepareImages(n);let u=n.map(d=>[d.height,d.width]),{pixel_values:s,pixel_mask:t}=await this.processor(n),e=await this.model({pixel_values:s,pixel_mask:t}),r=null;if(h!==null)r=this.subtasks_mapping[h];else for(let[d,g]of Object.entries(this.subtasks_mapping))if(g in this.processor.feature_extractor){r=this.processor.feature_extractor[g].bind(this.processor.feature_extractor),h=d;break}let i=[];if(h==="panoptic"||h==="instance"){let d=r(e,o,l,c,f,a??u)[0],g=d.segmentation,m=this.model.config.id2label;for(let b of d.segments_info){let _=new Uint8ClampedArray(g.data.length);for(let w=0;wl.replace("{}",s)),a=this.tokenizer(f,{padding:!0,truncation:!0}),{pixel_values:h}=await this.processor(n),p=await this.model({...a,pixel_values:h}),u=[];for(let s of p.logits_per_image){let t=softmax(s.data);u.push([...t].map((e,r)=>({score:e,label:o[r]})))}return c?u:u[0]}}class ObjectDetectionPipeline extends Pipeline{constructor(n,o,l){super(n,null,o),this.processor=l}async _call(n,{threshold:o=.9,percentage:l=!1}={}){let c=Array.isArray(n);if(c&&n.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");n=await prepareImages(n);let f=l?null:n.map(t=>[t.height,t.width]),{pixel_values:a,pixel_mask:h}=await this.processor(n),p=await this.model({pixel_values:a,pixel_mask:h}),u=this.processor.feature_extractor.post_process_object_detection(p,o,f),s=this.model.config.id2label;return u.forEach(t=>t.labels=t.classes.map(e=>s[e])),c?u:u[0]}}const SUPPORTED_TASKS={"text-classification":{tokenizer:AutoTokenizer,pipeline:TextClassificationPipeline,model:AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:AutoTokenizer,pipeline:TokenClassificationPipeline,model:AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:AutoTokenizer,pipeline:QuestionAnsweringPipeline,model:AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:AutoTokenizer,pipeline:FillMaskPipeline,model:AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:AutoTokenizer,pipeline:SummarizationPipeline,model:AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:AutoTokenizer,pipeline:TranslationPipeline,model:AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:AutoTokenizer,pipeline:Text2TextGenerationPipeline,model:AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:AutoTokenizer,pipeline:TextGenerationPipeline,model:AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:AutoTokenizer,pipeline:ZeroShotClassificationPipeline,model:AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"automatic-speech-recognition":{tokenizer:AutoTokenizer,pipeline:AutomaticSpeechRecognitionPipeline,model:AutoModelForSeq2SeqLM,processor:AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"image-to-text":{tokenizer:AutoTokenizer,pipeline:ImageToTextPipeline,model:AutoModelForVision2Seq,processor:AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:ImageClassificationPipeline,model:AutoModelForImageClassification,processor:AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:ImageSegmentationPipeline,model:AutoModelForImageSegmentation,processor:AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:AutoTokenizer,pipeline:ZeroShotImageClassificationPipeline,model:AutoModel,processor:AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:ObjectDetectionPipeline,model:AutoModelForObjectDetection,processor:AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"feature-extraction":{tokenizer:AutoTokenizer,pipeline:FeatureExtractionPipeline,model:AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"}},TASK_ALIASES={"sentiment-analysis":"text-classification",ner:"token-classification",vqa:"visual-question-answering",asr:"automatic-speech-recognition",embeddings:"feature-extraction"};async function pipeline(y,n=null,{quantized:o=!0,progress_callback:l=null,config:c=null,cache_dir:f=null,local_files_only:a=!1,revision:h="main"}={}){y=TASK_ALIASES[y]??y;let p=SUPPORTED_TASKS[y.split("_",1)[0]];if(!p)throw Error(`Unsupported pipeline: ${y}. Must be one of [${Object.keys(SUPPORTED_TASKS)}]`);n||(n=p.default.model,console.log(`No model specified. Using default model: "${n}".`));let u=p.tokenizer,s=p.model,t=p.pipeline,e=p.processor,r=[],i={quantized:o,progress_callback:l,config:c,cache_dir:f,local_files_only:a,revision:h};u&&r.push(u.from_pretrained(n,i)),s&&r.push(s.from_pretrained(n,i)),e&&r.push(e.from_pretrained(n,i));let d=await Promise.all(r);return dispatchCallback(l,{status:"ready",task:y,model:n}),new t(y,...d)}function product(...y){return y.reduce((n,o)=>n.flatMap(l=>o.map(c=>[l,c])))}env.allowLocalModels=!1;class PipelineFactory{constructor(n,o,l){this.tokenizer=n,this.model=o,this.quantized=l}static async getInstance(n=null){return this.instance===null&&(this.instance=pipeline(this.task,this.model,{quantized:this.quantized,progress_callback:n})),this.instance}}le(PipelineFactory,"task",null),le(PipelineFactory,"model",null),le(PipelineFactory,"quantized",null),le(PipelineFactory,"instance",null),self.addEventListener("message",async y=>{const n=y.data;let o=await transcribe(n.audio,n.model,n.multilingual,n.quantized,n.subtask,n.language);o!==null&&self.postMessage({status:"complete",task:"automatic-speech-recognition",data:o})});class AutomaticSpeechRecognitionPipelineFactory extends PipelineFactory{}le(AutomaticSpeechRecognitionPipelineFactory,"task","automatic-speech-recognition"),le(AutomaticSpeechRecognitionPipelineFactory,"model",null),le(AutomaticSpeechRecognitionPipelineFactory,"quantized",null);const transcribe=async(y,n,o,l,c,f)=>{const a=`Xenova/whisper-${n}${o?"":".en"}`,h=AutomaticSpeechRecognitionPipelineFactory;(h.model!==a||h.quantized!==l)&&(h.model=a,h.quantized=l,h.instance!==null&&((await h.getInstance()).dispose(),h.instance=null));let p=await h.getInstance(i=>{self.postMessage(i)});const u=p.processor.feature_extractor.config.chunk_length/p.model.config.max_source_positions;let s=[{tokens:[],finalised:!1}];function t(i){let d=s[s.length-1];Object.assign(d,i),d.finalised=!0,i.is_last||s.push({tokens:[],finalised:!1})}function e(i){let d=s[s.length-1];d.tokens=[...i[0].output_token_ids];let g=p.tokenizer._decode_asr(s,{time_precision:u,return_timestamps:!0,force_full_sequences:!1});self.postMessage({status:"update",task:"automatic-speech-recognition",data:g})}return await p(y,{top_k:0,do_sample:!1,chunk_length_s:30,stride_length_s:5,language:f,task:c,return_timestamps:!0,force_full_sequences:!1,callback_function:e,chunk_callback:t}).catch(i=>(self.postMessage({status:"error",task:"automatic-speech-recognition",data:i}),null))}})();