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r=o.onnx.TypeProto.verify(e.type);if(r)return"type."+r}return e.docString!=null&&e.hasOwnProperty("docString")&&!c.isString(e.docString)?"docString: string expected":null},t.fromObject=function(e){if(e instanceof o.onnx.ValueInfoProto)return e;var r=new o.onnx.ValueInfoProto;if(e.name!=null&&(r.name=String(e.name)),e.type!=null){if(typeof e.type!="object")throw TypeError(".onnx.ValueInfoProto.type: object expected");r.type=o.onnx.TypeProto.fromObject(e.type)}return e.docString!=null&&(r.docString=String(e.docString)),r},t.toObject=function(e,r){r||(r={});var i={};return r.defaults&&(i.name="",i.type=null,i.docString=""),e.name!=null&&e.hasOwnProperty("name")&&(i.name=e.name),e.type!=null&&e.hasOwnProperty("type")&&(i.type=o.onnx.TypeProto.toObject(e.type,r)),e.docString!=null&&e.hasOwnProperty("docString")&&(i.docString=e.docString),i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t}(),p.NodeProto=function(){function t(e){if(this.input=[],this.output=[],this.attribute=[],e)for(var r=Object.keys(e),i=0;i>>3){case 1:d.input&&d.input.length||(d.input=[]),d.input.push(e.string());break;case 2:d.output&&d.output.length||(d.output=[]),d.output.push(e.string());break;case 3:d.name=e.string();break;case 4:d.opType=e.string();break;case 7:d.domain=e.string();break;case 5:d.attribute&&d.attribute.length||(d.attribute=[]),d.attribute.push(o.onnx.AttributeProto.decode(e,e.uint32()));break;case 6:d.docString=e.string();break;default:e.skipType(7&g)}}return d},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.input!=null&&e.hasOwnProperty("input")){if(!Array.isArray(e.input))return"input: array expected";for(var r=0;r>>3){case 1:d.irVersion=e.int64();break;case 8:d.opsetImport&&d.opsetImport.length||(d.opsetImport=[]),d.opsetImport.push(o.onnx.OperatorSetIdProto.decode(e,e.uint32()));break;case 2:d.producerName=e.string();break;case 3:d.producerVersion=e.string();break;case 4:d.domain=e.string();break;case 5:d.modelVersion=e.int64();break;case 6:d.docString=e.string();break;case 7:d.graph=o.onnx.GraphProto.decode(e,e.uint32());break;case 14:d.metadataProps&&d.metadataProps.length||(d.metadataProps=[]),d.metadataProps.push(o.onnx.StringStringEntryProto.decode(e,e.uint32()));break;default:e.skipType(7&g)}}return d},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.irVersion!=null&&e.hasOwnProperty("irVersion")&&!(c.isInteger(e.irVersion)||e.irVersion&&c.isInteger(e.irVersion.low)&&c.isInteger(e.irVersion.high)))return"irVersion: integer|Long expected";if(e.opsetImport!=null&&e.hasOwnProperty("opsetImport")){if(!Array.isArray(e.opsetImport))return"opsetImport: array expected";for(var r=0;r>>0,e.irVersion.high>>>0).toNumber())),e.opsetImport){if(!Array.isArray(e.opsetImport))throw TypeError(".onnx.ModelProto.opsetImport: array expected");r.opsetImport=[];for(var i=0;i>>0,e.modelVersion.high>>>0).toNumber())),e.docString!=null&&(r.docString=String(e.docString)),e.graph!=null){if(typeof e.graph!="object")throw TypeError(".onnx.ModelProto.graph: object expected");r.graph=o.onnx.GraphProto.fromObject(e.graph)}if(e.metadataProps){if(!Array.isArray(e.metadataProps))throw TypeError(".onnx.ModelProto.metadataProps: array expected");for(r.metadataProps=[],i=0;i>>0,e.irVersion.high>>>0).toNumber():e.irVersion),e.producerName!=null&&e.hasOwnProperty("producerName")&&(i.producerName=e.producerName),e.producerVersion!=null&&e.hasOwnProperty("producerVersion")&&(i.producerVersion=e.producerVersion),e.domain!=null&&e.hasOwnProperty("domain")&&(i.domain=e.domain),e.modelVersion!=null&&e.hasOwnProperty("modelVersion")&&(typeof e.modelVersion=="number"?i.modelVersion=r.longs===String?String(e.modelVersion):e.modelVersion:i.modelVersion=r.longs===String?c.Long.prototype.toString.call(e.modelVersion):r.longs===Number?new c.LongBits(e.modelVersion.low>>>0,e.modelVersion.high>>>0).toNumber():e.modelVersion),e.docString!=null&&e.hasOwnProperty("docString")&&(i.docString=e.docString),e.graph!=null&&e.hasOwnProperty("graph")&&(i.graph=o.onnx.GraphProto.toObject(e.graph,r)),e.opsetImport&&e.opsetImport.length){i.opsetImport=[];for(var g=0;g>>3){case 1:d.key=e.string();break;case 2:d.value=e.string();break;default:e.skipType(7&g)}}return d},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){return typeof e!="object"||e===null?"object expected":e.key!=null&&e.hasOwnProperty("key")&&!c.isString(e.key)?"key: string expected":e.value!=null&&e.hasOwnProperty("value")&&!c.isString(e.value)?"value: string expected":null},t.fromObject=function(e){if(e instanceof o.onnx.StringStringEntryProto)return e;var r=new o.onnx.StringStringEntryProto;return e.key!=null&&(r.key=String(e.key)),e.value!=null&&(r.value=String(e.value)),r},t.toObject=function(e,r){r||(r={});var i={};return r.defaults&&(i.key="",i.value=""),e.key!=null&&e.hasOwnProperty("key")&&(i.key=e.key),e.value!=null&&e.hasOwnProperty("value")&&(i.value=e.value),i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t}(),p.TensorAnnotation=function(){function t(e){if(this.quantParameterTensorNames=[],e)for(var r=Object.keys(e),i=0;i>>3){case 1:d.tensorName=e.string();break;case 2:d.quantParameterTensorNames&&d.quantParameterTensorNames.length||(d.quantParameterTensorNames=[]),d.quantParameterTensorNames.push(o.onnx.StringStringEntryProto.decode(e,e.uint32()));break;default:e.skipType(7&g)}}return d},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.tensorName!=null&&e.hasOwnProperty("tensorName")&&!c.isString(e.tensorName))return"tensorName: string expected";if(e.quantParameterTensorNames!=null&&e.hasOwnProperty("quantParameterTensorNames")){if(!Array.isArray(e.quantParameterTensorNames))return"quantParameterTensorNames: array expected";for(var r=0;r>>3){case 1:d.node&&d.node.length||(d.node=[]),d.node.push(o.onnx.NodeProto.decode(e,e.uint32()));break;case 2:d.name=e.string();break;case 5:d.initializer&&d.initializer.length||(d.initializer=[]),d.initializer.push(o.onnx.TensorProto.decode(e,e.uint32()));break;case 10:d.docString=e.string();break;case 11:d.input&&d.input.length||(d.input=[]),d.input.push(o.onnx.ValueInfoProto.decode(e,e.uint32()));break;case 12:d.output&&d.output.length||(d.output=[]),d.output.push(o.onnx.ValueInfoProto.decode(e,e.uint32()));break;case 13:d.valueInfo&&d.valueInfo.length||(d.valueInfo=[]),d.valueInfo.push(o.onnx.ValueInfoProto.decode(e,e.uint32()));break;case 14:d.quantizationAnnotation&&d.quantizationAnnotation.length||(d.quantizationAnnotation=[]),d.quantizationAnnotation.push(o.onnx.TensorAnnotation.decode(e,e.uint32()));break;default:e.skipType(7&g)}}return d},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.node!=null&&e.hasOwnProperty("node")){if(!Array.isArray(e.node))return"node: array expected";for(var 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=o.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"?c.base64.decode(e.rawData,r.rawData=c.newBuffer(c.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 r},t.toObject=function(e,r){r||(r={});var i={};if((r.arrays||r.defaults)&&(i.dims=[],i.floatData=[],i.int32Data=[],i.stringData=[],i.int64Data=[],i.doubleData=[],i.uint64Data=[],i.externalData=[]),r.defaults&&(i.dataType=0,i.segment=null,i.name="",r.bytes===String?i.rawData="":(i.rawData=[],r.bytes!==Array&&(i.rawData=c.newBuffer(i.rawData))),i.docString="",i.dataLocation=r.enums===String?"DEFAULT":0),e.dims&&e.dims.length){i.dims=[];for(var d=0;d>>0,e.dims[d].high>>>0).toNumber():e.dims[d]}if(e.dataType!=null&&e.hasOwnProperty("dataType")&&(i.dataType=e.dataType),e.segment!=null&&e.hasOwnProperty("segment")&&(i.segment=o.onnx.TensorProto.Segment.toObject(e.segment,r)),e.floatData&&e.floatData.length)for(i.floatData=[],d=0;d>>0,e.int64Data[d].high>>>0).toNumber():e.int64Data[d];if(e.name!=null&&e.hasOwnProperty("name")&&(i.name=e.name),e.rawData!=null&&e.hasOwnProperty("rawData")&&(i.rawData=r.bytes===String?c.base64.encode(e.rawData,0,e.rawData.length):r.bytes===Array?Array.prototype.slice.call(e.rawData):e.rawData),e.doubleData&&e.doubleData.length)for(i.doubleData=[],d=0;d>>0,e.uint64Data[d].high>>>0).toNumber(!0):e.uint64Data[d];if(e.docString!=null&&e.hasOwnProperty("docString")&&(i.docString=e.docString),e.externalData&&e.externalData.length)for(i.externalData=[],d=0;d>>3){case 1:g.begin=r.int64();break;case 2:g.end=r.int64();break;default:r.skipType(7&m)}}return g},e.decodeDelimited=function(r){return r instanceof h||(r=new h(r)),this.decode(r,r.uint32())},e.verify=function(r){return typeof r!="object"||r===null?"object expected":r.begin!=null&&r.hasOwnProperty("begin")&&!(c.isInteger(r.begin)||r.begin&&c.isInteger(r.begin.low)&&c.isInteger(r.begin.high))?"begin: integer|Long expected":r.end!=null&&r.hasOwnProperty("end")&&!(c.isInteger(r.end)||r.end&&c.isInteger(r.end.low)&&c.isInteger(r.end.high))?"end: integer|Long expected":null},e.fromObject=function(r){if(r instanceof o.onnx.TensorProto.Segment)return r;var i=new o.onnx.TensorProto.Segment;return r.begin!=null&&(c.Long?(i.begin=c.Long.fromValue(r.begin)).unsigned=!1:typeof r.begin=="string"?i.begin=parseInt(r.begin,10):typeof r.begin=="number"?i.begin=r.begin:typeof r.begin=="object"&&(i.begin=new c.LongBits(r.begin.low>>>0,r.begin.high>>>0).toNumber())),r.end!=null&&(c.Long?(i.end=c.Long.fromValue(r.end)).unsigned=!1:typeof r.end=="string"?i.end=parseInt(r.end,10):typeof r.end=="number"?i.end=r.end:typeof r.end=="object"&&(i.end=new c.LongBits(r.end.low>>>0,r.end.high>>>0).toNumber())),i},e.toObject=function(r,i){i||(i={});var d={};if(i.defaults){if(c.Long){var g=new c.Long(0,0,!1);d.begin=i.longs===String?g.toString():i.longs===Number?g.toNumber():g}else d.begin=i.longs===String?"0":0;c.Long?(g=new c.Long(0,0,!1),d.end=i.longs===String?g.toString():i.longs===Number?g.toNumber():g):d.end=i.longs===String?"0":0}return r.begin!=null&&r.hasOwnProperty("begin")&&(typeof r.begin=="number"?d.begin=i.longs===String?String(r.begin):r.begin:d.begin=i.longs===String?c.Long.prototype.toString.call(r.begin):i.longs===Number?new c.LongBits(r.begin.low>>>0,r.begin.high>>>0).toNumber():r.begin),r.end!=null&&r.hasOwnProperty("end")&&(typeof r.end=="number"?d.end=i.longs===String?String(r.end):r.end:d.end=i.longs===String?c.Long.prototype.toString.call(r.end):i.longs===Number?new c.LongBits(r.end.low>>>0,r.end.high>>>0).toNumber():r.end),d},e.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},e}(),t.DataLocation=function(){var e={},r=Object.create(e);return r[e[0]="DEFAULT"]=0,r[e[1]="EXTERNAL"]=1,r}(),t}(),p.TensorShapeProto=function(){function t(e){if(this.dim=[],e)for(var r=Object.keys(e),i=0;i>>3==1?(d.dim&&d.dim.length||(d.dim=[]),d.dim.push(o.onnx.TensorShapeProto.Dimension.decode(e,e.uint32()))):e.skipType(7&g)}return d},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.dim!=null&&e.hasOwnProperty("dim")){if(!Array.isArray(e.dim))return"dim: array expected";for(var r=0;r>>3){case 1:m.dimValue=i.int64();break;case 2:m.dimParam=i.string();break;case 3:m.denotation=i.string();break;default:i.skipType(7&b)}}return m},e.decodeDelimited=function(i){return i instanceof h||(i=new h(i)),this.decode(i,i.uint32())},e.verify=function(i){if(typeof i!="object"||i===null)return"object expected";var d={};if(i.dimValue!=null&&i.hasOwnProperty("dimValue")&&(d.value=1,!(c.isInteger(i.dimValue)||i.dimValue&&c.isInteger(i.dimValue.low)&&c.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,!c.isString(i.dimParam))return"dimParam: string expected"}return i.denotation!=null&&i.hasOwnProperty("denotation")&&!c.isString(i.denotation)?"denotation: string expected":null},e.fromObject=function(i){if(i instanceof o.onnx.TensorShapeProto.Dimension)return i;var d=new o.onnx.TensorShapeProto.Dimension;return i.dimValue!=null&&(c.Long?(d.dimValue=c.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 c.LongBits(i.dimValue.low>>>0,i.dimValue.high>>>0).toNumber())),i.dimParam!=null&&(d.dimParam=String(i.dimParam)),i.denotation!=null&&(d.denotation=String(i.denotation)),d},e.toObject=function(i,d){d||(d={});var g={};return d.defaults&&(g.denotation=""),i.dimValue!=null&&i.hasOwnProperty("dimValue")&&(typeof i.dimValue=="number"?g.dimValue=d.longs===String?String(i.dimValue):i.dimValue:g.dimValue=d.longs===String?c.Long.prototype.toString.call(i.dimValue):d.longs===Number?new c.LongBits(i.dimValue.low>>>0,i.dimValue.high>>>0).toNumber():i.dimValue,d.oneofs&&(g.value="dimValue")),i.dimParam!=null&&i.hasOwnProperty("dimParam")&&(g.dimParam=i.dimParam,d.oneofs&&(g.value="dimParam")),i.denotation!=null&&i.hasOwnProperty("denotation")&&(g.denotation=i.denotation),g},e.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},e}(),t}(),p.TypeProto=function(){function t(r){if(r)for(var i=Object.keys(r),d=0;d>>3){case 1:g.tensorType=o.onnx.TypeProto.Tensor.decode(r,r.uint32());break;case 6:g.denotation=r.string();break;default:r.skipType(7&m)}}return g},t.decodeDelimited=function(r){return r instanceof h||(r=new h(r)),this.decode(r,r.uint32())},t.verify=function(r){if(typeof r!="object"||r===null)return"object expected";if(r.tensorType!=null&&r.hasOwnProperty("tensorType")){var i=o.onnx.TypeProto.Tensor.verify(r.tensorType);if(i)return"tensorType."+i}return r.denotation!=null&&r.hasOwnProperty("denotation")&&!c.isString(r.denotation)?"denotation: string expected":null},t.fromObject=function(r){if(r instanceof o.onnx.TypeProto)return r;var i=new o.onnx.TypeProto;if(r.tensorType!=null){if(typeof r.tensorType!="object")throw TypeError(".onnx.TypeProto.tensorType: object expected");i.tensorType=o.onnx.TypeProto.Tensor.fromObject(r.tensorType)}return r.denotation!=null&&(i.denotation=String(r.denotation)),i},t.toObject=function(r,i){i||(i={});var d={};return i.defaults&&(d.denotation=""),r.tensorType!=null&&r.hasOwnProperty("tensorType")&&(d.tensorType=o.onnx.TypeProto.Tensor.toObject(r.tensorType,i),i.oneofs&&(d.value="tensorType")),r.denotation!=null&&r.hasOwnProperty("denotation")&&(d.denotation=r.denotation),d},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t.Tensor=function(){function r(i){if(i)for(var d=Object.keys(i),g=0;g>>3){case 1:m.elemType=i.int32();break;case 2:m.shape=o.onnx.TensorShapeProto.decode(i,i.uint32());break;default:i.skipType(7&b)}}return m},r.decodeDelimited=function(i){return i instanceof h||(i=new h(i)),this.decode(i,i.uint32())},r.verify=function(i){if(typeof i!="object"||i===null)return"object expected";if(i.elemType!=null&&i.hasOwnProperty("elemType")&&!c.isInteger(i.elemType))return"elemType: integer expected";if(i.shape!=null&&i.hasOwnProperty("shape")){var d=o.onnx.TensorShapeProto.verify(i.shape);if(d)return"shape."+d}return null},r.fromObject=function(i){if(i 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e="";if(u.ArrayUtil.arraysEqual(o,t))return e=` ivec2 getOutputCoords() { return 2 * ivec2(TexCoords.xy * vec2(${t[0]}, ${t[1]})); } `,new l.GlslLibRoutine(e);const r=t,i=Math.ceil(o[1]/2);return e=` ivec2 getOutputCoords() { ivec2 resTexRC = ivec2(TexCoords.xy * vec2(${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 / ${i}); return ivec2(r, c); } `,new l.GlslLibRoutine(e)}getOutputPacked3DCoords(o,t){const e=[t[0],t[1]],r=Math.ceil(o[2]/2),i=r*Math.ceil(o[1]/2),d=` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(TexCoords.xy * vec2(${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 l.GlslLibRoutine(d)}getOutputPackedNDCoords(o,t){const 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Object.assign(Object.assign({},O),{output:{dims:G,type:A[0].type,textureType:p.TextureType.unpacked},shaderSource:X,hasMain:!0})})(g,m,y,b)})})(r,i,d),i),t=(r,i)=>{const d=r.kernelShape.slice();if(r.kernelShape.length===0)for(let y=2;y{const N=y.length-2,R=I.length===0;for(let L=0;L{const i=r.attributes,d=(0,s.parseInternalActivationAttributes)(i),g=i.getString("auto_pad","NOTSET"),m=i.getInts("dilations",[1,1]),b=i.getInt("group",1),y=i.getInts("kernel_shape",[]),w=i.getInts("output_padding",[0,0]),v=i.getInts("output_shape",[]),S=i.getInts("pads",[0,0,0,0]),A=i.getInts("strides",[1,1]);return(0,u.createAttributeWithCacheKey)(Object.assign({autoPad:g,dilations:m,group:b,kernelShape:y,outputPadding:w,outputShape:v,pads:S,strides:A},d))};const e=(r,i)=>{if(!r||r.length!==2&&r.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(r[0].dims.length!==4||r[1].dims.length!==4)throw new Error("currently only support 2-dimensional conv");if(r[0].dims[1]!==r[1].dims[0])throw new 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tensor")}},8138:(_,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseConvAttributes=n.conv=n.calculateOutputShape=void 0;const u=a(246),l=a(2517),p=a(4770),s=a(1386),h=a(9828),f=a(2823),c=a(3248),o=a(5623);n.calculateOutputShape=(g,m,b,y,w)=>{const v=g[0],S=g.slice(2),A=S.length,O=m[0],x=m.slice(2).map((N,R)=>N+(N-1)*(b[R]-1)),I=S.map((N,R)=>N+y[R]+y[R+A]).map((N,R)=>Math.floor((N-x[R]+w[R])/w[R]));return[v,O].concat(...I)},n.conv=(g,m,b)=>(d(m,b),t(g,m,b));const t=(g,m,b)=>{const y=i(b,m),w=g.session.pack,v=y.kernelShape[0]===1&&y.kernelShape[1]===1;return y.group>1?[g.run((0,p.createUnpackedGroupedConvProgramInfoLoader)(g,m,y),m)]:v&&w?[e(g,m,y)]:w&&m[0].dims.length===4&&m[0].dims[0]===1&&!v?[(0,s.conv2DPacked)(g,m,y)]:[r(g,m,y)]},e=(g,m,b)=>{const y=m[0].dims,w=m[1].dims,v=(0,n.calculateOutputShape)(y,w,b.dilations,b.pads,b.strides),S=g.reshapeUnpacked(m[0],[y[1],y[2]*y[3]]),A=g.reshapeUnpacked(m[1],[w[0],w[1]]),O=m.length>2?[A,S,m[2]]:[A,S],x=g.run((0,o.createMatmulProgramInfoLoader)(O,b),O);return g.reshapeUnpacked(x,v)},r=(g,m,b)=>{const y=m[0].dims,w=m[1].dims,v=(0,n.calculateOutputShape)(y,w,b.dilations,b.pads,b.strides),S=g.run((0,c.createIm2ColProgramInfoLoader)(g,m[0],m[1],v,b),[m[0]]),A=m.length===3?[S,m[1],m[2]]:[S,m[1]];return g.run((0,h.createDotProductProgramInfoLoader)(g,m,v,b),A)},i=(g,m)=>{const b=g.kernelShape.slice();if(g.kernelShape.length===0)for(let v=2;v{const m=g.attributes,b=(0,f.parseInternalActivationAttributes)(m),y=m.getString("auto_pad","NOTSET"),w=m.getInts("dilations",[1,1]),v=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,u.createAttributeWithCacheKey)(Object.assign({autoPad:y,dilations:w,group:v,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 equal to DATA_CHANNEL");if(g.length===3&&(g[2].dims.length!==1||g[1].dims[0]!==g[2].dims[0]))throw new Error("invalid bias");const b=g[0].dims.length-2;if(m.dilations.length!==b)throw new Error(`dilations should be ${b}D`);if(m.strides.length!==b)throw new Error(`strides should be ${b}D`);if(m.pads.length!==2*b)throw new Error(`pads should be ${2*b}D`);if(m.kernelShape.length!==0&&m.kernelShape.length!==g[1].dims.length-2)throw new Error("invalid kernel shape");if(g[0].type!=="float32"||g[1].type!=="float32")throw new Error("Conv input(X,W) should be float tensor");if(g.length===3&&g[2].type!=="float32")throw new Error("Conv input(bias) should be float tensor")}},5193:(_,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseDepthToSpaceAttributes=n.depthToSpace=void 0;const u=a(3738);n.depthToSpace=(p,s,h)=>{l(s);const f=h.blocksize,c=f*f,o=h.mode==="DCR"?[0,3,4,1,5,2]:[0,1,4,2,5,3],t=h.mode==="DCR"?[s[0].dims[0],f,f,s[0].dims[1]/c,s[0].dims[2],s[0].dims[3]]:[s[0].dims[0],s[0].dims[1]/c,f,f,s[0].dims[2],s[0].dims[3]],e=p.reshapeUnpacked(s[0],t),r={perm:o,cacheKey:`${o}`},[i]=(0,u.transpose)(p,[e],r),d=[s[0].dims[0],s[0].dims[1]/c,s[0].dims[2]*f,s[0].dims[3]*f];return[p.reshapeUnpacked(i,d)]},n.parseDepthToSpaceAttributes=p=>{const s=p.attributes.getInt("blocksize");if(s<1)throw new Error(`blocksize must be >= 1, but got : ${s} for DepthToSpace`);const h=p.attributes.getString("mode","DCR");if(h!=="DCR"&&h!=="CRD")throw new Error(`unrecognized mode: ${h} for DepthToSpace`);return{mode:h,blocksize:s}};const l=p=>{if(p.length!==1)throw new Error(`DepthToSpace expect 1 inputs, but got ${p.length}`);if(p[0].type==="string"||p[0].dims.length!==4)throw new TypeError("DepthToSpace input should be a 4-D numeric tensor")}},9828:(_,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createDotProductProgramInfoLoader=void 0;const u=a(2517),l=a(5060),p=a(2039),s=a(2823),h=a(3248);n.createDotProductProgramInfoLoader=(f,c,o,t)=>{const e=((r,i)=>({name:"ConvDotProduct",inputNames:r?["Im2Col","K","B"]:["Im2Col","K"],inputTypes:r?[p.TextureType.unpacked,p.TextureType.packedLastDimension,p.TextureType.unpacked]:[p.TextureType.unpacked,p.TextureType.packedLastDimension],cacheKey:i.activationCacheKey}))(c.length>2,t);return Object.assign(Object.assign({},e),{get:()=>((r,i,d,g,m)=>{const b=d[0].dims,y=d[1].dims,w=[y[0],Math.ceil(b[1]*y[2]*y[3]/4)],v=(0,h.calculateIm2ColDims)(b,y,g),[S,A]=r.calculateTextureWidthAndHeight(w,p.TextureType.packedLastDimension),O=u.ShapeUtil.computeStrides(v),[x,I]=r.calculateTextureWidthAndHeight(v,p.TextureType.packedLastDimension),N=g.length,R=d.length<3?"0.0":"_B(b)",L=Math.ceil(b[1]*y[2]*y[3]/4),{activationFunction:$,applyActivation:G}=(0,s.getActivationSnippet)(m),D=(0,l.getGlsl)(r.session.backend.glContext.version),j=` ${$} float process(int indices[${N}]) { 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] * ${w[1]}; float value = ${R}; for (int i = 0; i < ${L}; ++i) { vec2 im2colCoords = offsetToCoords(im2colOffset, ${x}, ${I}); vec2 kernelCoords = offsetToCoords(kernelOffset, ${S}, ${A}); value += dot(${D.texture2D}(Im2Col, im2colCoords), ${D.texture2D}(K, kernelCoords)); ++im2colOffset; ++kernelOffset; } ${G} return value; }`;return Object.assign(Object.assign({},i),{output:{dims:g,type:d[0].type,textureType:p.TextureType.unpacked},shaderSource:j})})(f,e,c,o,t)})}},7992:(_,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseFlattenAttributes=n.flatten=void 0;const u=a(2517);n.flatten=(p,s,h)=>{l(s,h);const f=u.ShapeUtil.flattenShape(s[0].dims,h);return[p.reshapeUnpacked(s[0],f)]},n.parseFlattenAttributes=p=>p.attributes.getInt("axis",1);const l=(p,s)=>{if(!p||p.length!==1)throw new Error("Flatten requires 1 input.");const h=p[0].dims.length;if(h===0)throw new Error("scalar tensor is not supported.");if(s<-h||s>h)throw new Error("Invalid axis");if(p[0].type==="string")throw new Error("string tensor is not supported.")}},2823:(_,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseInternalActivationAttributes=n.getActivationSnippet=void 0;const u=a(2517),l=a(4909);n.getActivationSnippet=function(p){let s;switch(p.activation){case"Relu":s=(0,l.glslRelu)();break;case"Sigmoid":s=(0,l.glslSigmoid)();break;case"Clip":s=(0,l.glslClip)(p.clipMin,p.clipMax);break;default:return{activationFunction:"",applyActivation:""}}const h=s.name;return{activationFunction:s.body,applyActivation:`value = ${h}_(value);`}},n.parseInternalActivationAttributes=p=>{const s=p.getString("activation","");if(s==="Clip"){const[h,f]=p.getFloats("activation_params",[u.MIN_CLIP,u.MAX_CLIP]);return{activation:s,clipMax:f,clipMin:h,activationCacheKey:`${s}:${h},${f}`}}return{activation:s,activationCacheKey:s}}},1253:(_,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseGatherAttributes=n.gather=void 0;const u=a(246),l=a(782),p=a(2517),s=a(2039);n.gather=(o,t,e)=>(c(t,e.axis),[o.run(f(o,t,e),t)]),n.parseGatherAttributes=o=>(0,u.createAttributeWithCacheKey)({axis:o.attributes.getInt("axis",0)});const h={name:"Gather",inputNames:["A","B"],inputTypes:[s.TextureType.unpacked,s.TextureType.unpacked]},f=(o,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(),y=g[1].dims.slice(),w=new Array(b.length+y.length-1);m=p.ShapeUtil.normalizeAxis(m,b.length);const v=[];for(let A=0;A{if(!o||o.length!==2)throw new Error("Gather requires 2 inputs.");const e=o[0].dims.length;if(e<1)throw new Error("Invalid input shape.");if(t<-e||t>e-1)throw new Error("Invalid axis.");if(l.NUMBER_TYPES.indexOf(o[0].type)===-1)throw new Error("Invaid input type.");if(o[1].type!=="int32"&&o[1].type!=="int16")throw new Error("Invaid input type.")}},4776:(_,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseGemmAttributesV11=n.parseGemmAttributesV7=n.gemm=void 0;const u=a(246),l=a(2517),p=a(2039);n.gemm=(o,t,e)=>(c(t,e),[o.run(h(t,e),t)]);const s=(o,t)=>{const e=o.attributes.getInt("transA",0)!==0,r=o.attributes.getInt("transB",0)!==0,i=o.attributes.getFloat("alpha",1),d=o.attributes.getFloat("beta",1);return(0,u.createAttributeWithCacheKey)({transA:e,transB:r,alpha:i,beta:d,isOptionalC:t})};n.parseGemmAttributesV7=o=>s(o,!1),n.parseGemmAttributesV11=o=>s(o,!0);const h=(o,t)=>{const e={name:"Gemm",inputNames:o.length===3?["A","B","C"]:["A","B"],inputTypes:o.length===3?[p.TextureType.unpacked,p.TextureType.unpacked,p.TextureType.unpacked]:[p.TextureType.unpacked,p.TextureType.unpacked],key:t.cacheKey};return Object.assign(Object.assign({},e),{get:()=>f(e,o,t)})},f=(o,t,e)=>{const r=t[0].dims.slice(),i=t[1].dims.slice(),[d,g]=l.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],y="";e.transA&&(b=r[0]),e.transA&&e.transB?y="value += _A_T(a) * _B_T(b);":e.transA&&!e.transB?y="value += _A_T(a) * _B(b);":!e.transA&&e.transB?y="value += _A(a) * _B_T(b);":e.transA||e.transB||(y="value += _A(a) * _B(b);");const w=m.length,v=` float process(int indices[${w}]) { int a[${w}]; int b[${w}]; ${t.length===3?`int c[${t[2].dims.length}];`:""} copyVec(indices, a); copyVec(indices, b); ${t.length===3?"bcastIndices_C(indices, c);":""} float value = 0.0; for (int k=0; k<${b}; ++k) { a[${w-1}] = k; b[${w-2}] = k; ${y} } value = value * alpha; ${t.length===3?"value += beta * _C(c);":""} return value; }`;return Object.assign(Object.assign({},o),{output:{dims:m,type:t[0].type,textureType:p.TextureType.unpacked},variables:[{name:"alpha",type:"float",data:e.alpha},{name:"beta",type:"float",data:e.beta}],shaderSource:v})},c=(o,t)=>{if(!o)throw new Error("Input is missing");if(t.isOptionalC&&(o.length<2||o.length>3))throw new Error("Invaid input shape.");if(!t.isOptionalC&&o.length!==3)throw new Error("Gemm requires 3 inputs");if(o.length===3&&o[2].dims.length!==1&&o[2].dims.length!==2)throw new Error("Invalid input shape of C");if(o[0].type!=="float32"&&o[0].type!=="float64"||o[1].type!=="float32"&&o[1].type!=="float64"||o.length===3&&o[2].type!=="float32"&&o[2].type!=="float64")throw new Error("Invalid input type.");if(o[0].type!==o[1].type||o.length===3&&o[0].type!==o[2].type)throw new Error("Input types are mismatched")}},8555:(_,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createPackedIm2ColProgramInfoLoader=void 0;const u=a(5060),l=a(2039),p=a(2827);n.createPackedIm2ColProgramInfoLoader=(s,h,f,c,o)=>{const t=(e=o.cacheKey,{name:"Im2Col (packed)",inputNames:["A"],inputTypes:[l.TextureType.packed],cacheHint:e});var e;return Object.assign(Object.assign({},t),{get:()=>((r,i,d,g,m,b)=>{const y=d.dims,w=g.dims,v=m.length,S=[w[1]*w[2]*w[3],m[2]*m[3]],A=w[2]*w[3],O=(0,p.unpackFromChannel)(),x=(0,u.getGlsl)(r.session.backend.glContext.version);let I="";for(let R=0;R<=1;R++)for(let L=0;L<=1;L++)I+=` blockIndex = rc.x + ${L}; pos = rc.y + ${R}; if(blockIndex < ${S[1]} && pos < ${S[0]}) { offsetY = int(blockIndex / (${m[v-1]})) * ${b.strides[0]} - ${b.pads[0]}; d0 = offsetY + ${b.dilations[0]} * (imod(pos, ${A}) / ${w[2]}); if(d0 < ${y[2]} && d0 >= 0) { offsetX = imod(blockIndex, ${m[v-1]}) * ${b.strides[1]} - ${b.pads[1]}; d1 = offsetX + ${b.dilations[1]} * imod(imod(pos, ${A}), ${w[2]}); if(d1 < ${y[3]} && d1 >= 0) { ch = int(float(pos)/ ${A}.); innerDims = vec2(d0, d1); result[${2*R+L}] = getChannel( getA(0, ch, int(innerDims.x), int(innerDims.y)), innerDims); } } } `;const N=` ${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:l.TextureType.packed},shaderSource:N,hasMain:!0})})(s,t,h,f,c,o)})}},3248:(_,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.calculateIm2ColDims=n.createIm2ColProgramInfoLoader=void 0;const u=a(2039);n.createIm2ColProgramInfoLoader=(l,p,s,h,f)=>{const c=(o=f.cacheKey,{name:"Im2Col",inputNames:["X"],inputTypes:[u.TextureType.unpacked],cacheHint:o});var o;return Object.assign(Object.assign({},c),{get:()=>((t,e,r,i,d,g)=>{const m=r.dims,b=i.dims,y=d.length,w=(0,n.calculateIm2ColDims)(m,b,d,4),v=` 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 = ${g.pads[1]}; const int KHKW = KH*KW; const int XCKHKW = XC * KHKW; const int outputChannels = 4; vec4 process(int indices[${y}]) { 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[${m.length}]; x[0] = b; x[1] = patchC; x[2] = xh2; x[3] = xw2; if(xh2 >= 0 && xh2 < XH && xw2 >= 0 && xw2 < XW) { value[i] = _X(x); } } ++p; } return value; } `;return Object.assign(Object.assign({},e),{output:{dims:w,type:r.type,textureType:u.TextureType.packedLastDimension},shaderSource:v})})(0,c,p,s,h,f)})},n.calculateIm2ColDims=(l,p,s,h=4)=>[s[0],s[2],s[3],Math.ceil(l[1]*p[2]*p[3]/h)]},6572:(_,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseImageScalerAttributes=n.imageScaler=void 0;const u=a(246),l=a(2039);n.imageScaler=(c,o,t)=>(f(o),[c.run(s(c,o,t),o)]),n.parseImageScalerAttributes=c=>{const o=c.attributes.getFloat("scale"),t=c.attributes.getFloats("bias");return(0,u.createAttributeWithCacheKey)({scale:o,bias:t})};const 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type.")}},3346:(_,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseInstanceNormalizationAttributes=n.instanceNormalization=void 0;const u=a(5060),l=a(2039);n.instanceNormalization=(o,t,e)=>{c(t);const r=o.run(s(t[0]),t);return[o.run(f(o,t[0],e,r.dims),[t[0],r,t[1],t[2]])]},n.parseInstanceNormalizationAttributes=o=>o.attributes.getFloat("epsilon",1e-5);const p={name:"InstanceNormalization_MeanAndVariance",inputNames:["X"],inputTypes:[l.TextureType.unpacked]},s=o=>Object.assign(Object.assign({},p),{get:()=>((t,e)=>{const r=e.dims.slice(),i=r[1],d=r[2]*r[3],g=[r[0],i],m=` vec4 process(int[2] indices) { vec4 v = vec4(0.0); int a[4]; a[0] = indices[0]; a[1] = indices[1]; float temp = 0.0; for(int a2=0; a2<${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) 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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:l.TextureType.unpacked},variables:[{name:"epsilon",type:"float",data:b}],shaderSource:x})})(o,i,t,e,r)})},c=o=>{if(!o||o.length!==3)throw new Error("InstanceNormalization requires 3 inputs.");const t=o[0],e=o[1],r=o[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(o[0].dims.length!==4)throw new Error("Only support 4-D input 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x=v[v.length-1],I=Math.ceil(x/2),N=v.length,R=S.length,L=(0,l.getGlsl)(d.session.backend.glContext.version),$=(0,s.getCoordsDataType)(A.length),G=A.length,D=(0,s.getGlChannels)(),{activationFunction:j,applyActivation:Z}=(0,h.getActivationSnippet)(b),X=y?`${(0,f.getBiasForMatmul)($,D,m[2].dims,A,!0)}`:"",J=O?`${function(ve,oe,ye,be){let ke=[],Fe=[];const xe=ye[0].dims,Ne=ye[1].dims,Ce=xe.length,Oe=Ne.length,Ae=be.length,Be=Ae-Ce,Ge=Ae-Oe;ke=xe.map((Pe,je)=>`coords.${oe[je+Be]}`),ke[Ce-1]="i*2",ke.join(", "),Fe=Ne.map((Pe,je)=>`coords.${oe[je+Ge]}`),Fe[Oe-2]="i*2",Fe.join(", ");const Ve=u.BroadcastUtil.getBroadcastDims(xe,be),Xe=u.BroadcastUtil.getBroadcastDims(Ne,be),Ze=Ve.map(Pe=>`coords.${oe[Pe+Be]} = 0;`).join(` `),qe=Xe.map(Pe=>`coords.${oe[Pe+Ge]} = 0;`).join(` `),Ue=`int lastDim = coords.${oe[Ae-1]}; coords.${oe[Ae-1]} = coords.${oe[Ae-2]}; coords.${oe[Ae-2]} = lastDim;`;return` vec4 getAAtOutCoordsMatmul(int i) { ${ve} coords = getOutputCoords(); ${Ue} ${Ze} vec4 outputValue = getA(${ke}); return outputValue; } vec4 getBAtOutCoordsMatmul(int i) { ${ve} coords = getOutputCoords(); ${Ue} ${qe} vec4 outputValue = getB(${Fe}); return outputValue; }`}($,D,m,A)}`:"",ee=O?"getAAtOutCoordsMatmul(i)":`getA(${function(ve,oe){let ye="";for(let be=0;be{Object.defineProperty(n,"__esModule",{value:!0}),n.getBiasForMatmul=n.createMatmulProgramInfoLoader=n.parseMatMulAttributes=n.matMul=void 0;const u=a(2517),l=a(2039),p=a(9390),s=a(2823),h=a(708);function f(t,e){const r=(i=t.length>2,d=e.activationCacheKey,{name:"MatMul",inputNames:i?["A","B","Bias"]:["A","B"],inputTypes:i?[l.TextureType.unpacked,l.TextureType.unpacked,l.TextureType.unpacked]:[l.TextureType.unpacked,l.TextureType.unpacked],cacheHint:d});var i,d;return Object.assign(Object.assign({},r),{get:()=>function(g,m,b){const y=m[0].dims,w=m[1].dims,v=u.BroadcastUtil.calcShape(y,w,!0);if(!v)throw new Error("Can't use matmul on the given tensors");const 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l(p,s){return(0,u.getGlChannels)(s).map(h=>`${p}.${h}`)}n.getVecChannels=l,n.getChannels=function(p,s){return s===1?[p]:l(p,s)},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:(_,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parsePadAttributesV11=n.padV11=n.parsePadAttributesV2=n.padV2=void 0;const u=a(246),l=a(2517),p=a(5060),s=a(2039),h={name:"Pad",inputNames:["A"],inputTypes:[s.TextureType.unpacked]};n.padV2=(g,m,b)=>(o(m),[g.run(Object.assign(Object.assign({},h),{cacheHint:b.cacheKey,get:()=>c(g,m[0],b)}),m)]),n.parsePadAttributesV2=g=>{const m=g.attributes.getString("mode","constant"),b=g.attributes.getFloat("value",0),y=g.attributes.getInts("pads");return(0,u.createAttributeWithCacheKey)({mode:m,value:b,pads:y})},n.padV11=(g,m,b)=>{t(m);const y=f(g,m,b);return(0,n.padV2)(g,[m[0]],y)},n.parsePadAttributesV11=g=>g.attributes.getString("mode","constant");const f=(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 y=Array.from(m[1].integerData),w=m.length>=3?m[2].floatData[0]:0;return(0,u.createAttributeWithCacheKey)({mode:b,pads:y,value:w})},c=(g,m,b)=>{const y=l.ShapeUtil.padShape(m.dims.slice(),b.pads),w=y.length,v=` ${e(g,m,b)} float process(int[${w}] indices) { return padA(indices); 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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, ${y}, ${w}); float value = getColorAsFloat(${g.texture2D}(A, coords)); return value; } `},i=(g,m,b,y,w,v)=>{const S=m.length;let A="";for(let O=S-1;O>=0;--O)A+=` k = m[${O}] - ${v[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, ${y}, ${w}); float value = getColorAsFloat(${g.texture2D}(A, coords)); return value; } `},d=(g,m,b,y,w,v)=>{const S=m.length;let A="";for(let O=S-1;O>=0;--O)A+=` k = m[${O}] - ${v[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; 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`,"","-1e5")} `;return Object.assign(Object.assign({},g),{output:{dims:w,type:d[0].type,textureType:p.TextureType.unpacked},shaderSource:v})},f=(d,g,m)=>{const b=d[0].dims.slice(),y=Object.hasOwnProperty.call(g,"dilations"),w=g.kernelShape.slice(),v=g.strides.slice(),S=y?g.dilations.slice():[],A=g.pads.slice();l.PoolConvUtil.adjustPoolAttributes(m,b,w,v,S,A);const O=l.PoolConvUtil.computePoolOutputShape(m,b,v,S,w,A,g.autoPad),x=Object.assign({},g);return y?Object.assign(x,{kernelShape:w,strides:v,pads:A,dilations:S,cacheKey:g.cacheKey}):Object.assign(x,{kernelShape:w,strides:v,pads:A,cacheKey:g.cacheKey}),[x,O]},c={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[],cacheKey:""},o={name:"GlobalMaxPool",inputNames:["X"],inputTypes:[p.TextureType.unpacked]};n.globalMaxPool=(d,g)=>(t(g),[d.run(Object.assign(Object.assign({},o),{get:()=>h(g,o,!0,c)}),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,y)=>{const w=d.length;if(g.kernelShape.length<=2){const v=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[w-1];let I="",N="",R="";if(I=A+O!==0?` for (int i = 0; i < ${v}; i++) { x[${w} - 1] = indices[${w} - 1] * ${S} - ${A} + i; if (x[${w} - 1] < 0 || x[${w} - 1] >= ${x}) { pad++; continue; } ${m} }`:` for (int i = 0; i < ${v}; i++) { x[${w} - 1] = indices[${w} - 1] * ${S} - ${A} + i; ${m} }`,g.kernelShape.length===2){const L=g.kernelShape[g.kernelShape.length-2],$=g.strides[g.strides.length-2],G=g.pads[g.pads.length/2-2],D=g.pads[g.pads.length-2],j=d[w-2];N=G+D!==0?` for (int j = 0; j < ${L}; j++) { x[${w} - 2] = indices[${w} - 2] * ${$} - ${G} + j; if (x[${w} - 2] < 0 || x[${w} - 2] >= ${j}) { pad+= ${v}; continue; } `:` for (int j = 0; j < ${L}; j++) { x[${w} - 2] = indices[${w} - 2] * ${$} - ${G} + j; `,R=` } `}return` float process(int indices[${w}]) { int x[${w}]; copyVec(indices, x); float value = ${y}; int pad = 0; ${N} ${I} ${R} ${b} return value; } `}{const v=l.ShapeUtil.size(g.kernelShape),S=l.ShapeUtil.computeStrides(g.kernelShape),A=S.length,O=g.pads.length,x=i(A),I=r(d,"inputDims"),N=r(g.pads,"pads"),R=r(S,"kernelStrides"),L=r(g.strides,"strides");let $="";return $=g.pads.reduce((G,D)=>G+D)?` if (x[j] >= inputDims[j] || x[j] < 0) { pad++; isPad = true; break; } } if (!isPad) { ${m} }`:` } ${m} `,` ${x} float process(int indices[${w}]) { int x[${w}]; copyVec(indices, x); int offset[${A}]; int pads[${O}]; int inputDims[${w}]; int kernelStrides[${A}]; int strides[${A}]; ${N} ${I} ${L} ${R} float value = ${y}; int pad = 0; bool isPad = false; for (int i = 0; i < ${v}; i++) { offsetToIndices(i, kernelStrides, offset); isPad = false; for (int j = ${w} - ${A}; j < ${w}; j++) { x[j] = indices[j] * strides[j - ${w} + ${A}] + offset[j - ${w} + ${A}] - pads[j - 2]; ${$} } ${b} return value; } `}},r=(d,g)=>{let m="";for(let b=0;b` void offsetToIndices(int offset, int[${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:(_,n,a)=>{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 u=a(246),l=a(782),p=a(2517),s=a(2039),h=(o,t,e,r,i)=>{c(t);const d={name:r,inputNames:["A"],inputTypes:[s.TextureType.unpacked]};return[o.run(Object.assign(Object.assign({},d),{cacheHint:e.cacheKey,get:()=>f(o,t,e,r,i,d)}),t)]};n.parseReduceAttributes=o=>{const t=o.attributes.getInts("axes",[]),e=o.attributes.getInt("keepdims",1)===1;return(0,u.createAttributeWithCacheKey)({axes:t,keepDims:e})};const f=(o,t,e,r,i,d)=>{const g=[],m=t[0].dims.length||1,b=[],y=p.ShapeUtil.normalizeAxes(e.axes,t[0].dims.length),w=i(t,y);let v=w[1];for(let A=0;A=0||y.length===0?(e.keepDims&&g.push(1),v=` for(int j${A} = 0; j${A} < ${t[0].dims[A]}; j${A}++) { inputIdx[${A}] = j${A}; ${v} }`):(b.push(`inputIdx[${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(` `)} ${w[0]} // init ops for reduce max/min ${v} ${w[2]} // final computation for reduce mean return value; }`;return Object.assign(Object.assign({},d),{output:{dims:g,type:t[0].type,textureType:s.TextureType.unpacked},shaderSource:S})},c=o=>{if(!o||o.length!==1)throw new Error("Reduce op requires 1 input.");if(l.NUMBER_TYPES.indexOf(o[0].type)===-1)throw new Error("Invalid input type.")};n.reduceSum=(o,t,e)=>h(o,t,e,"ReduceSum",()=>["value = 0.0;","value += _A(inputIdx);",""]),n.reduceMean=(o,t,e)=>h(o,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 /= ${d}.;`]}),n.reduceMax=(o,t,e)=>h(o,t,e,"ReduceMax",(r,i)=>{const d=[];for(let g=0;g=0||i.length===0)&&d.push(`inputIdx[${g}] = 0;`);return[`${d.join(` `)} value = _A(inputIdx);`,"value = max(value, _A(inputIdx));",""]}),n.reduceMin=(o,t,e)=>h(o,t,e,"ReduceMin",(r,i)=>{const d=[];for(let g=0;g=0||i.length===0)&&d.push(`inputIdx[${g}] = 0;`);return[`${d.join(` `)} value = _A(inputIdx);`,"value = min(value, _A(inputIdx));",""]}),n.reduceProd=(o,t,e)=>h(o,t,e,"ReduceProd",()=>["value = 1.0;","value *= _A(inputIdx);",""]),n.reduceLogSum=(o,t,e)=>h(o,t,e,"ReduceLogSum",()=>["value = 0.0;","value += _A(inputIdx);","value = log(value);"]),n.reduceLogSumSquare=(o,t,e)=>h(o,t,e,"ReduceLogSumSquare",()=>["float t; value = 0.0;","t = _A(inputIdx); value += t * t;",""])},7019:(_,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.isReshapeCheap=n.processDims3D=n.createPackedReshape3DProgramInfoLoader=void 0;const u=a(2517),l=a(5060),p=a(2039),s=a(2827);n.createPackedReshape3DProgramInfoLoader=(h,f,c)=>{const o=(t=>({name:"Reshape (packed)",inputTypes:[p.TextureType.packed],inputNames:["A"],cacheHint:`${t}`}))(c);return Object.assign(Object.assign({},o),{get:()=>((t,e,r,i)=>{const d=e.dims,g=i;let m="";for(let w=0;w<4;w++){let v="";switch(w){case 0:v="outputCoords = rc;";break;case 1:v="outputCoords = ivec3(rc.x, rc.y+1, rc.z);";break;case 2:v="outputCoords = ivec3(rc.x, rc.y, rc.z+1);";break;case 3:v="outputCoords = ivec3(rc.x, rc.y+1, rc.z+1);";break;default:throw new Error}m+=` ${v} ${w>0?"if(outputCoords.y < rows && outputCoords.z < cols){":""} int flattenedIndex = getFlattenedIndex(outputCoords); ivec3 inputRC = inputCoordsFromReshapedOutCoords(flattenedIndex); vec2 innerDims = vec2(float(inputRC.y),float(inputRC.z)); result[${w}] = getChannel(getA(inputRC.x, inputRC.y, inputRC.z), innerDims); ${w>0?"}":""} `}const b=(0,l.getGlsl)(t.session.backend.glContext.version),y=` ${function(w){const v=u.ShapeUtil.computeStrides(w),S=["b","r","c"],A="index";return` ivec3 inputCoordsFromReshapedOutCoords(int index) { ${v.map((O,x)=>`int ${S[x]} = ${A} / ${O}; ${x===v.length-1?`int ${S[x+1]} = ${A} - ${S[x]} * ${O}`:`index -= ${S[x]} * ${O}`};`).join("")} return ivec3(b, r, c); } `}(d)} ${function(w){const v=u.ShapeUtil.computeStrides(w);return` int getFlattenedIndex(ivec3 coords) { // reverse y, z order return coords.x * ${v[0]} + coords.z * ${v[1]} + coords.y; } `}(g)} ${(0,s.unpackFromChannel)()} void main() { ivec3 rc = getOutputCoords(); vec4 result = vec4(0.0); ivec3 outputCoords; int rows = ${g[2]}; int cols = ${g[1]}; ${m} ${b.output} = result; } `;return Object.assign(Object.assign({},r),{output:{dims:g,type:e.type,textureType:p.TextureType.packed},shaderSource:y,hasMain:!0})})(h,f,o,c)})},n.processDims3D=function(h){if(h.length===0)return[1,1,1];let f=1;for(let c=0;c1?h[h.length-2]:1,h[h.length-1]]},n.isReshapeCheap=function(h,f){let c=!1;return c=h.length===0||f.length===0||(h.length<2||f.length<2?h[h.length-1]===f[f.length-1]:h[h.length-1]===f[f.length-1]&&h[h.length-2]===f[f.length-2]),c}},718:(_,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.reshape=void 0;const u=a(2517);n.reshape=(l,p)=>{const s=u.ShapeUtil.calculateReshapedDims(p[0].dims,p[1].integerData);return l.session.pack?[l.reshapePacked(p[0],s)]:[l.reshapeUnpacked(p[0],s)]}},2268:(_,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseResizeAttributesV11=n.parseResizeAttributesV10=n.resize=void 0;const u=a(5060),l=a(2039),p=a(9390),s=a(2827),h=a(9793),f={name:"Resize",inputNames:["A"],inputTypes:[l.TextureType.packed]};n.resize=(r,i,d)=>((0,h.validateInputs)(i,d),[r.run(Object.assign(Object.assign({},f),{cacheHint:d.cacheKey,get:()=>c(r,i,d)}),i)]),n.parseResizeAttributesV10=r=>(0,h.parseUpsampleAttributes)(r,10),n.parseResizeAttributesV11=r=>(0,h.parseUpsampleAttributes)(r,11);const c=(r,i,d)=>{const g=(0,u.getGlsl)(r.session.backend.glContext.version),[m,b]=o(i,d);if(m.every($=>$===1)&&d.coordinateTransformMode!=="tf_crop_and_resize")return Object.assign(Object.assign({},f),{output:{dims:b,type:i[0].type,textureType:l.TextureType.packed},hasMain:!0,shaderSource:`void main() { vec4 v = ${g.texture2D}(X, TexCoords); ${g.output} = v; }`});const y=b.length;if(y<2)throw new Error(`output dimension should be at least 2, but got ${y}`);const w=b[y-2],v=b[y-1],S=i[0].dims;if(y!==S.length)throw new Error(`output dimension should match input ${S.length}, but got ${y}`);const A=S[y-2],O=S[y-1],x=m[y-2],I=m[y-1];let N="";if(d.mode!=="linear")throw new Error(`resize (packed) does not support mode: '${d.mode}'`);switch(d.coordinateTransformMode){case"asymmetric":N=` vec4 getSourceFracIndex(ivec4 coords) { return vec4(coords) / scaleWHWH; } `;break;case"half_pixel":N=` vec4 getSourceFracIndex(ivec4 coords) { return (vec4(coords) + 0.5) / scaleWHWH - 0.5; } `;break;case"pytorch_half_pixel":N=` vec4 getSourceFracIndex(ivec4 coords) { vec4 fcoords = vec4(coords); return vec4( ${v}.0 > 1.0 ? (fcoords.x + 0.5) / scaleWHWH.x - 0.5 : 0.0, ${w}.0 > 1.0 ? (fcoords.y + 0.5) / scaleWHWH.y - 0.5 : 0.0, ${v}.0 > 1.0 ? (fcoords.z + 0.5) / scaleWHWH.z - 0.5 : 0.0, ${w}.0 > 1.0 ? (fcoords.w + 0.5) / scaleWHWH.w - 0.5 : 0.0 ); } `;break;case"align_corners":N=` vec4 getSourceFracIndex(ivec4 coords) { vec4 resized = vec4(${v}.0 - 1.0, ${w}.0 - 1.0, ${v}.0 - 1.0, ${w}.0 - 1.0); vec4 original = vec4(${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 R=(0,p.getCoordsDataType)(y),L=` const vec2 inputWH = vec2(${A}.0, ${O}.0); const vec4 scaleWHWH = vec4(float(${x}), float(${I}), float(${x}), float(${I})); ${(0,s.unpackFromChannel)()} ${N} float getAValue(int x10, int r, int c, int d) { return getChannel(getA(x10, r, c, d), vec2(c, d)); } void main() { ${R} 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 < ${w-1}; bool hasNextCol = rc.z < ${v-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({},f),{output:{dims:b,type:i[0].type,textureType:l.TextureType.packed},hasMain:!0,shaderSource:L})},o=(r,i)=>{const d=r[0].dims;let g,m=i.scales;if(m.length===0){const y=r[i.scalesInputIdx];if(y&&y.size!==0){if(r[i.sizesInputIdx])throw new Error("Only one of scales or sizes must be provided as input.");m=t(y,i.mode,i.isResize)}else{const w=r[i.sizesInputIdx];if(!w||w.size===0)throw new Error("Either scales or sizes MUST be provided as input.");g=Array.from(w.integerData),m=e(g,d,i.mode,i.isResize)}}else if(r[i.sizesInputIdx])throw new Error("Only one of scales or sizes must be provided as input.");const b=g||d.map((y,w)=>Math.floor(y*m[w]));return[m,b]},t=(r,i,d)=>{const g=Array.from(r.floatData);return(0,h.scalesValidation)(g,i,d),g},e=(r,i,d,g)=>{const m=i.length,b=new Array(m);for(let y=0,w=m;y{Object.defineProperty(n,"__esModule",{value:!0}),n.shape=void 0;const u=a(9162);n.shape=(p,s)=>(l(s),[new u.Tensor([s[0].dims.length],"int32",void 0,void 0,new Int32Array(s[0].dims))]);const l=p=>{if(!p||p.length!==1)throw new Error("Shape requires 1 input.")}},2278:(_,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.sliceV10=n.parseSliceAttributes=n.slice=void 0;const u=a(246),l=a(782),p=a(2517),s=a(2039),h={name:"Slice",inputNames:["A"],inputTypes:[s.TextureType.unpacked]};n.slice=(e,r,i)=>(c(r),[e.run(Object.assign(Object.assign({},h),{cacheHint:i.cacheKey,get:()=>f(e,r[0],i)}),r)]),n.parseSliceAttributes=e=>{const r=e.attributes.getInts("starts"),i=e.attributes.getInts("ends"),d=e.attributes.getInts("axes",[]);return(0,u.createAttributeWithCacheKey)({starts:r,ends:i,axes:d})};const f=(e,r,i)=>{const d=i.axes.length===0?r.dims.slice(0).map((S,A)=>A):i.axes,g=p.ShapeUtil.normalizeAxes(d,r.dims.length),m=i.starts.map((S,A)=>S>r.dims[g[A]]-1?r.dims[g[A]]:p.ShapeUtil.normalizeAxis(S,r.dims[g[A]])),b=i.ends.map((S,A)=>S>r.dims[g[A]]-1?r.dims[g[A]]:p.ShapeUtil.normalizeAxis(S,r.dims[g[A]])),y=r.dims.slice(),w=[];for(let S=0;S0&&w.push(`outputIdx[${g[S]}] += ${m[S]};`);const v=` float process(int outputIdx[${y.length}]) { ${w.join(` `)} return _A(outputIdx); }`;return Object.assign(Object.assign({},h),{output:{dims:y,type:r.type,textureType:s.TextureType.unpacked},shaderSource:v})},c=e=>{if(!e||e.length!==1)throw new Error("Slice requires 1 input.");if(l.NUMBER_TYPES.indexOf(e[0].type)===-1)throw new Error("Invalid input type.")};n.sliceV10=(e,r)=>{t(r);const i=o(e,r);return[e.run(Object.assign(Object.assign({},h),{cacheHint:i.cacheKey,get:()=>f(e,r[0],i)}),[r[0]])]};const 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(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]); }`:` ${v} float process(int indices[2]) { int input_index = 0; int output_index = coordsToOffset(TexCoords, ${d}, ${g}); ${w} int m; int index_of_dim0, index_of_dim1; index_of_dim0 = output_index / output_pitches[0]; m = output_index - index_of_dim0 * output_pitches[0]; index_of_dim1 = m; int index_of_input_dim0, index_of_input_dim1, x_offset, y_offset; index_of_input_dim0 = index_of_dim0 / scales[0]; y_offset = index_of_dim0 - index_of_input_dim0 * scales[0]; index_of_input_dim1 = index_of_dim1 / scales[1]; x_offset = index_of_dim1 - index_of_input_dim1 * scales[1]; input_index = index_of_input_dim0 * input_pitches[0] + index_of_input_dim1; float x00 = getInputFloat(input_index); float x10, x01, x11; bool end_of_dim0 = false; if (index_of_input_dim0 == (${c[0].dims[0]} - 1)) { // It's the end in dimension 0 x01 = x00; 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Promise(t=>{this.addItemToPoll(()=>o.isFencePassed(),()=>t())})}pollItems(){const o=c(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=o;++t){const{resolveFn:e}=this.itemsToPoll[t];e()}this.itemsToPoll=this.itemsToPoll.slice(o+1)}async addItemToPoll(o,t){this.itemsToPoll.push({isDoneFn:o,resolveFn:t}),this.itemsToPoll.length>1||await(0,f.repeatedTry)(()=>(this.pollItems(),this.itemsToPoll.length===0))}}},1036:(_,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.ExecutionPlan=void 0;const u=a(6231);class l{constructor(s,h){this.op=s,this.node=h}}n.ExecutionPlan=class{constructor(p,s,h){this.graph=p,this.profiler=h,this.initialize(s)}initialize(p){this.profiler.event("session","ExecutionPlan.initialize",()=>{const s=this.graph.getNodes();if(s.length!==p.length)throw new Error("The size of nodes and OPs do not match.");this._ops=p.map((h,f)=>new l(h,s[f])),this.reset(),this._starter=[],this._ops.forEach((h,f)=>{let c=!0;for(const o of 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this.profiler.event("node",d.node.name,async()=>d.op.impl(h,m,d.op.context));if(b.length!==d.node.outputs.length)throw new Error("the size of output does not match model definition.");b.forEach((w,v)=>{const S=d.node.outputs[v];if(this._values[S])throw new Error(`output [${S}] already has value: op:${d.node.name}`);this._values[S]=w});const y=new Set;b.forEach((w,v)=>{const S=d.node.outputs[v];for(const A of o[S].to){const O=t[A];let x=!0;for(const I of O.inputs)if(!this._values[I]){x=!1;break}x&&y.add(A)}}),c.push(...y)}const r=[];for(let i=0;i{Object.defineProperty(n,"__esModule",{value:!0}),n.Graph=void 0;const u=a(1446),l=a(7778),p=a(9395),s=a(9162),h=a(2517);var f=p.onnxruntime.experimental.fbs;n.Graph={from:(e,r)=>new t(e,r)};class c{constructor(r){this._from=void 0,this._to=[],this.tensor=void 0,this.type=void 0,r&&(this.type=h.ProtoUtil.tensorValueTypeFromProto(r.type.tensorType))}get from(){return this._from}get to(){return this._to}}class o{constructor(r,i){r instanceof 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Map;this._allData=[],this._allInputIndices=[],this._allInputNames=[],this._allOutputIndices=[],this._allOutputNames=[],this._nodes=[];const d=new Map;if(!r.input)throw new Error("missing information in graph: input");const g=[];for(const m of r.input){if(i.has(m.name))throw new Error(`duplicated input name: ${m.name}`);const b=this._allData.push(new c(m))-1;i.set(m.name,b),g.push(m.name)}if(!r.initializer)throw new Error("missing information in graph: initializer");for(const m of r.initializer){let b=i.get(m.name);if(b===void 0){const y=new c;y.type={shape:{dims:h.ProtoUtil.tensorDimsFromProto(m.dims)},tensorType:h.ProtoUtil.tensorDataTypeFromProto(m.dataType)},b=this._allData.push(y)-1,i.set(m.name,b)}this._allData[b]._from=-1,this._allData[b].tensor=s.Tensor.fromProto(m)}for(let m=0;m{this._allData[g]._to.forEach(m=>{r.add(m)})});const i=Array.from(r),d=new Array(this._nodes.length).fill("white");for(;i.length>0;){const 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d=-1;this._allData[i].from!==void 0&&this._allData[i].from!==-1?(d=this._nodes[this._allData[i].from].outputs.indexOf(i+r),d!==-1&&(this._nodes[this._allData[i].from].outputs[d]=i)):(d=this._allInputIndices.indexOf(i+r),d!==-1&&(this._allInputIndices[d]=i)),this._allData[i].to.forEach(g=>{d=this._nodes[g].inputs.indexOf(i+r),d!==-1&&(this._nodes[g].inputs[d]=i)}),this._allData[i].to.length===0&&(d=this._allOutputIndices.indexOf(i+r),d!==-1&&(this._allOutputIndices[d]=i))}}else r++,this._allData.splice(i,1),i--}deleteNode(r){const i=this._nodes[r];if(i.outputs.length>1){for(let w=1;w0)throw new Error("Node deletion with more than one output connected to other nodes is not supported. ")}i.executeNode=!1;const d=i.inputs[0],g=i.outputs[0],m=this._allData[g].to,b=this._allData[d].to.indexOf(r);if(b===-1)throw new Error("The Value object doesn't have the current Node in it's 'to' property ");this._allData[d].to.splice(b,1),this._allData[g]._to=[];const y=this._allOutputIndices.indexOf(g);if(y!==-1&&(this._allOutputIndices[y]=d),m&&m.length>0)for(const w of m){const v=this._nodes[w].inputs.indexOf(g);if(v===-1)throw new Error("The Node object doesn't have the output Value in it's 'inputs' property ");this._nodes[w].inputs[v]=d,this._allData[d].to.push(w)}}removeAllDropoutNodes(){let r=0;for(const i of this._nodes){if(i.opType==="Dropout"){if(i.inputs.length!==1)throw new Error("Dropout nodes should only contain one input. 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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 s.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,y){return c.startGraph(t),c.addInitializers(t,e),c.addNodeArgs(t,r),c.addNodes(t,i),c.addMaxNodeIndex(t,d),c.addNodeEdges(t,g),c.addInputs(t,m),c.addOutputs(t,b),c.addSparseInitializers(t,y),c.endGraph(t)}}f.Graph=c})(h.fbs||(h.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(h){(function(f){class c{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 c).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsModel(t,e){return t.setPosition(t.position()+u.flatbuffers.SIZE_PREFIX_LENGTH),(e||new c).__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 s.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 s.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,y,w){return c.startModel(t),c.addIrVersion(t,e),c.addOpsetImport(t,r),c.addProducerName(t,i),c.addProducerVersion(t,d),c.addDomain(t,g),c.addModelVersion(t,m),c.addDocString(t,b),c.addGraph(t,y),c.addGraphDocString(t,w),c.endModel(t)}}f.Model=c})(h.fbs||(h.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(h){(function(f){class c{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 c).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsKernelCreateInfos(t,e){return t.setPosition(t.position()+u.flatbuffers.SIZE_PREFIX_LENGTH),(e||new c).__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 c.startKernelCreateInfos(t),c.addNodeIndices(t,e),c.addKernelDefHashes(t,r),c.endKernelCreateInfos(t)}}f.KernelCreateInfos=c})(h.fbs||(h.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(h){(function(f){class c{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 c).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsSubGraphSessionState(t,e){return t.setPosition(t.position()+u.flatbuffers.SIZE_PREFIX_LENGTH),(e||new c).__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 s.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 c.startSubGraphSessionState(t),c.addGraphId(t,e),c.addSessionState(t,r),c.endSubGraphSessionState(t)}}f.SubGraphSessionState=c})(h.fbs||(h.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(h){(function(f){class c{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 c).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsSessionState(t,e){return t.setPosition(t.position()+u.flatbuffers.SIZE_PREFIX_LENGTH),(e||new c).__init(t.readInt32(t.position())+t.position(),t)}kernels(t){let e=this.bb.__offset(this.bb_pos,4);return e?(t||new s.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 s.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 c.startSessionState(t),c.addKernels(t,e),c.addSubGraphSessionStates(t,r),c.endSessionState(t)}}f.SessionState=c})(h.fbs||(h.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(h){(function(f){class c{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 c).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsInferenceSession(t,e){return t.setPosition(t.position()+u.flatbuffers.SIZE_PREFIX_LENGTH),(e||new c).__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 s.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 s.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 c.startInferenceSession(t),c.addOrtVersion(t,e),c.addModel(t,r),c.addSessionState(t,i),c.endInferenceSession(t)}}f.InferenceSession=c})(h.fbs||(h.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={}))},7448:(_,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.OnnxjsSessionHandler=void 0;const u=a(1670),l=a(9162);n.OnnxjsSessionHandler=class{constructor(p){this.session=p,this.inputNames=this.session.inputNames,this.outputNames=this.session.outputNames}async dispose(){}async run(p,s,h){const f=new Map;for(const t in p)if(Object.hasOwnProperty.call(p,t)){const e=p[t];f.set(t,new l.Tensor(e.dims,e.type,void 0,void 0,e.data))}const c=await this.session.run(f),o={};return c.forEach((t,e)=>{o[e]=new u.Tensor(t.type,t.data,t.dims)}),o}startProfiling(){this.session.startProfiling()}endProfiling(){this.session.endProfiling()}}},6919:(_,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.Session=void 0;const u=a(7067),l=a(1296),p=a(7091),s=a(1036),h=a(6231),f=a(2644);n.Session=class{constructor(c={}){this._initialized=!1,this.backendHint=c.backendHint,this.profiler=h.Profiler.create(c.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(c,o,t){await this.profiler.event("session","Session.loadModel",async()=>{const e=await(0,p.resolveBackend)(this.backendHint);if(this.sessionHandler=e.createSessionHandler(this.context),this._model=new f.Model,typeof c=="string"){const r=c.endsWith(".ort");if(typeof fetch>"u"){const i=await(0,l.promisify)(u.readFile)(c);this.initialize(i,r)}else{const i=await fetch(c),d=await i.arrayBuffer();this.initialize(new Uint8Array(d),r)}}else if(ArrayBuffer.isView(c))this.initialize(c);else{const r=new Uint8Array(c,o||0,t||c.byteLength);this.initialize(r)}})}initialize(c,o){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(c,t,o),this.sessionHandler.onGraphInitialized&&this.sessionHandler.onGraphInitialized(this._model.graph),this.initializeOps(this._model.graph),this._executionPlan=new s.ExecutionPlan(this._model.graph,this._ops,this.profiler)}),this._initialized=!0}async run(c){if(!this._initialized)throw new Error("session not initialized yet");return this.profiler.event("session","Session.run",async()=>{const o=this.normalizeAndValidateInputs(c),t=await this._executionPlan.execute(this.sessionHandler,o);return this.createOutput(t)})}normalizeAndValidateInputs(c){const o=this._model.graph.getInputNames();if(Array.isArray(c)){if(c.length!==o.length)throw new Error(`incorrect input array length: expected ${o.length} but got ${c.length}`)}else{if(c.size!==o.length)throw new Error(`incorrect input map size: expected ${o.length} but got ${c.size}`);const t=new Array(c.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(w!==void 0){const O=e(m);if(!(w 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 ${x}`)}}(m));this.cache=function(x,I){return new(e(I))(x)}(O,m)}}}static fromProto(g){if(!g)throw new Error("cannot construct Value from an empty tensor");const m=f.ProtoUtil.tensorDataTypeFromProto(g.dataType),b=f.ProtoUtil.tensorDimsFromProto(g.dims),y=new o(b,m);if(m==="string")g.stringData.forEach((w,v)=>{y.data[v]=(0,f.decodeUtf8String)(w)});else if(g.rawData&&typeof g.rawData.byteLength=="number"&&g.rawData.byteLength>0){const w=y.data,v=new DataView(g.rawData.buffer,g.rawData.byteOffset,g.rawData.byteLength),S=t(g.dataType),A=g.rawData.byteLength/S;if(g.rawData.byteLength%S!=0)throw new Error("invalid buffer length");if(w.length!==A)throw new Error("buffer length mismatch");for(let O=0;O0){const w=y.data,v=new DataView(g.rawDataArray().buffer,g.rawDataArray().byteOffset,g.rawDataLength()),S=t(g.dataType()),A=g.rawDataLength()/S;if(g.rawDataLength()%S!=0)throw new Error("invalid buffer length");if(w.length!==A)throw new Error("buffer length mismatch");for(let O=0;O1&&I>1)return;A[S-O]=Math.max(x,I)}return A}static index(m,b){const y=new Array(b.length);return c.fillIndex(m,b,y),y}static fillIndex(m,b,y){const w=m.length-b.length;for(let v=0;v=0;Z--)x[Z]=R%S[Z],R=Math.floor(R/S[Z]);G||(c.fillIndex(x,m.dims,I),L=m.get(I)),D||(c.fillIndex(x,b.dims,N),$=b.get(N)),O.set(x,y(L,$))}}return O}}static isValidBroadcast(m,b){const y=m.length,w=b.length;if(y>w)return!1;for(let v=1;v<=y;v++)if(m[y-v]!==1&&m[y-v]!==b[w-v])return!1;return!0}static getBroadcastDims(m,b){const y=m.length,w=[];for(let v=0;v1&&A===1&&w.unshift(S)}return w}}n.BroadcastUtil=c,n.arrayCopyHelper=function(g,m,b,y,w){if(y<0||y>=m.length)throw new Error("sourceIndex out of bounds");if(b<0||b>=g.length)throw new Error("targetIndex out of bounds");if(y+w>m.length)throw new Error("source indices to be copied are outside bounds");if(b+w>g.length)throw new Error("target array is too small to hold result");for(let v=0;vp.default.isLong(b)?b.toNumber():b)}static tensorValueTypeFromProto(m){return{tensorType:o.tensorDataTypeFromProto(m.elemType),shape:{dims:o.tensorDimsFromProto(m.shape.dim.map(b=>b.dimValue))}}}static tensorDimsFromORTFormat(m){const b=[];for(let y=0;ym.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 ${m.length} dimensions.`);return e.getSizeFromDimensionRange(m,0,b)}static getSizeFromDimensionRange(m,b,y){let w=1;for(let v=b;v=0;--w)y[w]=y[w+1]*m[w+1];return y}static transpose(m){return m.slice().reverse()}static indicesToOffset(m,b,y){y===void 0&&(y=m.length);let w=0;for(let v=0;v=b)throw new Error("unsupported axis for this operation.");return m<0?m+b:m}static normalizeAxes(m,b){return m.map(y=>this.normalizeAxis(y,b))}static incrementIndex(m,b,y){if(b.length===0||m.length===0)throw new Error("Index incrementing unsupported for scalar Tensor");if(y===void 0)y=b.length;else if(y<=0||y>b.length)throw new Error("Incorrect axis to increment on");for(let w=y-1;w>=0&&(m[w]++,!(m[w]=m.length)throw new Error("the dimension with value zero exceeds the dimension size of the input tensor");w[O]=m[O]}else w[O]=b[O];S*=w[O]}}const A=e.size(m);if(v!==-1){if(A%S!=0)throw new Error(`the input tensor cannot be reshaped to the requested shape. 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Will expand buffer when needed.");let u=parseInt(a??"0"),l=new Uint8Array(u),p=0;const s=_.body.getReader();async function h(){const{done:f,value:c}=await s.read();if(f)return;let o=p+c.length;if(o>u){u=o;let e=new Uint8Array(u);e.set(l),l=e}l.set(c,p),p=o;const t=p/u*100;return n({progress:t,loaded:p,total:u}),h()}return await h(),l}function pathJoin(..._){return _=_.map((n,a)=>(a&&(n=n.replace(new RegExp("^/"),"")),a!==_.length-1&&(n=n.replace(new RegExp("/$"),"")),n)),_.join("/")}function interpolate_data(_,[n,a,u],[l,p],s="bilinear",h=!1){const f=p/u,c=l/a,o=new _.constructor(l*p*n),t=a*u,e=l*p;for(let r=0;r=0;--h)l[h]=f,u[h]=n[a[h]],f*=u[h];const p=a.map((h,f)=>l[a.indexOf(f)]),s=new _.constructor(_.length);for(let h=0;h<_.length;++h){let f=0;for(let c=n.length-1,o=h;c>=0;--c)f+=o%n[c]*p[c],o=Math.floor(o/n[c]);s[f]=_[h]}return[s,u]}function softmax(_){const n=max(_)[0],a=_.map(p=>Math.exp(p-n)),u=a.reduce((p,s)=>p+s,0);return a.map(p=>p/u)}function log_softmax(_){return softmax(_).map(u=>Math.log(u))}function getTopItems(_,n=0){return _=Array.from(_).map((a,u)=>[u,a]).sort((a,u)=>u[1]-a[1]),n>0&&(_=_.slice(0,n)),_}function min(_){if(_.length===0)throw Error("Array must not be empty");let n=_[0],a=0;for(let u=1;u<_.length;++u)_[u]n&&(n=_[u],a=u);return[n,a]}function rfftfreq(_,n=1){if(!Number.isInteger(_))throw new TypeError(`n should be an integer, but ${_} given.`);const a=1/(_*n),u=Math.floor(_/2)+1,l=new Array(u);for(let p=0;pu;u<<=1)++a;this._width=a%2===0?a-1:a,this._bitrev=new Int32Array(1<>>l&3)<>>1);for(let l=0;l>>1]=n[l];return u}toComplexArray(n,a){const u=a||this.createComplexArray();for(let l=0;l>>1],u[l+1]=0;return u}completeSpectrum(n){const a=this._csize,u=a>>>1;for(let l=2;l>=2;s>=2;s>>=2){h=l/s<<1;let t=h>>>2;for(f=0;f>>1,s>>>1)}else for(f=0,c=0;f>>1,s>>>1,u)}for(s>>=2;s>=2;s>>=2){h=l/s<<1;const t=h>>>1,e=t>>>1,r=e>>>1;for(f=0;f=_.length&&(f=2*(_.length-1)-f),u[s++]=_[f]}u.sort(),a[p]=u[l]}return a}function round(_,n){const a=Math.pow(10,n);return Math.round(_*a)/a}const ONNXTensor$1=ONNX.Tensor;class Tensor extends ONNXTensor$1{constructor(...n){return n[0]instanceof ONNX.Tensor?super(n[0].type,n[0].data,n[0].dims):super(...n),new Proxy(this,{get:(a,u)=>{if(typeof u=="string"){let l=Number(u);if(Number.isInteger(l))return a._getitem(l)}return a[u]},set:(a,u,l)=>a[u]=l})}*[Symbol.iterator](){const[n,...a]=this.dims;if(a.length>0){const u=a.reduce((l,p)=>l*p);for(let l=0;l0){const l=u.reduce((p,s)=>p*s);return this._subarray(n,l,u)}else return new Tensor(this.type,[this.data[n]],u)}indexOf(n){for(let a=0;ac[1])throw new Error(`Invalid slice: ${c}`);let o=[Math.max(c[0],0),Math.min(c[1],this.dims[f])];u.push(o),a.push(o[1]-o[0])}else throw new Error(`Invalid slice: ${c}`)}let l=u.map(([f,c])=>c-f),p=l.reduce((f,c)=>f*c),s=new this.data.constructor(p);const h=this.stride();for(let f=0;f=0;--o){const e=l[o];c+=(t%e+u[o][0])*h[o],t=Math.floor(t/e)}s[f]=this.data[c]}return new Tensor(this.type,s,a)}transpose(...n){return transpose(this,n)}sum(n=null,a=!1){return this.norm(1,n,a)}norm(n="fro",a=null,u=!1){if(n==="fro")n=2;else if(typeof n=="string")throw Error(`Unsupported norm: ${n}`);if(a===null){let s=this.data.reduce((h,f)=>h+f**n,0)**(1/n);return new Tensor(this.type,[s],[])}a=safeIndex(a,this.dims.length);const l=this.dims.slice();l[a]=1;const p=new this.data.constructor(this.data.length/this.dims[a]);for(let s=0;s=0;--f){const t=this.dims[f];if(f!==a){const e=c%t;h+=e*o,o*=l[f]}c=Math.floor(c/t)}p[h]+=this.data[s]**n}if(n!==1)for(let s=0;s=0;--s){const c=this.dims[s];if(s!==a){const o=h%c;p+=o*f,f*=this.dims[s]}h=Math.floor(h/c)}this.data[l]/=u.data[p]}return this}normalize(n=2,a=1){return this.clone().normalize_(n,a)}stride(){return dimsToStride(this.dims)}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}flatten_(n=0,a=-1){a=(a+this.dims.length)%this.dims.length;let u=this.dims.slice(0,n),l=this.dims.slice(n,a+1),p=this.dims.slice(a+1);return this.dims=[...u,l.reduce((s,h)=>s*h,1),...p],this}flatten(n=0,a=-1){return this.clone().flatten_(n,a)}view(...n){let a=-1;for(let u=0;us!==a?l*p:l,1);n[a]=this.data.length/u}return new Tensor(this.type,this.data,n)}neg_(){for(let n=0;np*s);if(a!==u)throw Error(`cannot reshape array of size ${a} into shape (${n})`);let l=_;for(let p=n.length-1;p>=0;p--)l=l.reduce((s,h)=>{let f=s[s.length-1];return f.lengtha!==1):typeof n=="number"?_[n]===1&&_.splice(n,1):Array.isArray(n)&&(_=_.filter((a,u)=>a!==1||!n.includes(u))),_}function calc_unsqueeze_dims(_,n){return n=safeIndex(n,_.length+1),_=_.slice(),_.splice(n,0,1),_}function safeIndex(_,n,a=null){if(_<-n||_>=n)throw new Error(`IndexError: index ${_} is out of bounds for dimension${a===null?"":" "+a} with size ${n}`);return _<0&&(_=(_%n+n)%n),_}function cat(_,n=0){n=safeIndex(n,_[0].dims.length);const a=_[0].dims.slice();a[n]=_.reduce((s,h)=>s+h.dims[n],0);const u=a.reduce((s,h)=>s*h,1),l=new _[0].data.constructor(u),p=_[0].type;if(n===0){let s=0;for(let h of _)l.set(h.data,s),s+=h.data.length}else{let s=0;for(let h=0;h<_.length;++h){let f=_[h];for(let c=0;c=0;--t){const i=f.dims[t];let d=e%i;t===n&&(d+=s),o+=d*r,r*=a[t],e=Math.floor(e/i)}l[o]=f.data[c]}s+=f.dims[n]}}return new Tensor(p,l,a)}function stack(_,n=0){return cat(_.map(a=>a.unsqueeze(n)),n)}function std_mean(_,n=null,a=1,u=!1){if(n===null){const c=_.data.reduce((r,i)=>r+i,0)/_.data.length,o=Math.sqrt(_.data.reduce((r,i)=>r+(i-c)**2,0)/(_.data.length-a)),t=new Tensor(_.type,[c],[]);return[new Tensor(_.type,[o],[]),t]}n=safeIndex(n,_.dims.length);const l=mean(_,n,u),p=_.dims.slice();p[n]=1;const s=new _.data.constructor(_.data.length/_.dims[n]);for(let f=0;f<_.data.length;++f){let c=0;for(let o=_.dims.length-1,t=f,e=1;o>=0;--o){const r=_.dims[o];if(o!==n){const i=t%r;c+=i*e,e*=p[o]}t=Math.floor(t/r)}s[c]+=(_.data[f]-l.data[c])**2}for(let f=0;fs+h,0);return new Tensor(_.type,[p/_.data.length],[])}n=safeIndex(n,_.dims.length);const u=_.dims.slice();u[n]=1;const l=new _.data.constructor(_.data.length/_.dims[n]);for(let p=0;p<_.data.length;++p){let s=0;for(let h=_.dims.length-1,f=p,c=1;h>=0;--h){const o=_.dims[h];if(h!==n){const t=f%o;s+=t*c,c*=u[h]}f=Math.floor(f/o)}l[s]+=_.data[p]}if(_.dims[n]!==1)for(let p=0;p0||h>0;)switch(f.push(s-1),c.push(h-1),p[s][h].item()){case 0:--s,--h;break;case 1:--s;break;case 2:--h;break;default:throw new Error(`Internal error in dynamic time warping. Unexpected trace[${s}, ${h}]. Please file a bug report.`)}return f.reverse(),c.reverse(),[f,c]}function dimsToStride(_){const n=new Array(_.length);for(let a=_.length-1,u=1;a>=0;--a)n[a]=u,u*=_[a];return n}async function loadTokenizer(_,n){return await Promise.all([getModelJSON(_,"tokenizer.json",!0,n),getModelJSON(_,"tokenizer_config.json",!0,n)])}function createPattern(_,n=!0){return _.Regex?new RegExp(n?_.Regex:`(${_.Regex})`,"gu"):_.String?_.String:(console.warn("Unknown pattern type:",_),null)}function clean_up_tokenization(_){return _.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(_,n){let a=[],u=0;for(;u<_.length;){if(a.push(_[u]),_[u]!==n){++u;continue}for(;u<_.length&&_[u]===n;)++u}return a}function whitespace_split(_){return _.match(/\S+/g)||[]}const PUNCTUATION_REGEX="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E";class TokenizerModel extends Callable{constructor(n){super(),this.config=n,this.vocab=[],this.tokens_to_ids=new Map,this.unk_token_id=void 0,this.unk_token=void 0,this.end_of_word_suffix=void 0,this.fuse_unk=!1}static fromConfig(n,...a){switch(n.type){case"WordPiece":return new WordPieceTokenizer(n);case"Unigram":return new Unigram(n,...a);case"BPE":return new BPE(n,...a);default:throw new Error(`Unknown TokenizerModel type: ${n.type}`)}}_call(n){return this.encode(n)}encode(n){throw Error("encode should be implemented in subclass.")}convert_tokens_to_ids(n){let a=n.map(u=>this.tokens_to_ids.get(u)??this.unk_token_id);return this.fuse_unk&&(a=fuse(a,this.unk_token_id)),a}convert_ids_to_tokens(n){return n.map(a=>this.vocab[a]??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[a,u]of this.tokens_to_ids)this.vocab[u]=a}encode(n){let a=[];for(let u of n){let l=[...u],p=!1,s=0,h=[];for(;s0&&(o=this.config.continuing_subword_prefix+o),this.tokens_to_ids.has(o)){c=o;break}--f}if(c===null){p=!0;break}h.push(c),s=f}p?a.push(this.unk_token):a.push(...h)}return a}}class Unigram extends TokenizerModel{constructor(n,a){super(n),this.vocab=new Array(n.vocab.size),this.scores=new Array(n.vocab.size);let u=0;n.vocab.forEach((l,p)=>{this.vocab[u]=p,this.scores[u]=l,++u}),this.unk_token_id=n.unk_id,this.unk_token=this.vocab[n.unk_id],this.tokens_to_ids=new Map(this.vocab.map((l,p)=>[l,p])),this.bosToken=" ",this.bosTokenId=this.tokens_to_ids.get(this.bosToken),this.eosToken=a.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 a=n.sentence,u=a.length;let l=0;for(;l{const _=[...Array.from({length:"~".charCodeAt(0)-"!".charCodeAt(0)+1},(l,p)=>p+"!".charCodeAt(0)),...Array.from({length:"¬".charCodeAt(0)-"¡".charCodeAt(0)+1},(l,p)=>p+"¡".charCodeAt(0)),...Array.from({length:"ÿ".charCodeAt(0)-"®".charCodeAt(0)+1},(l,p)=>p+"®".charCodeAt(0))];let n=_.slice(),a=0;for(let l=0;l<256;++l)_.includes(l)||(_.push(l),n.push(256+a),a+=1);let u=n.map(l=>String.fromCharCode(l));return Object.fromEntries(_.map((l,p)=>[l,u[p]]))})(),UNICODE_TO_BYTES=reverseDictionary(BYTES_TO_UNICODE);class BPE extends TokenizerModel{constructor(n){super(n),this.BPE_SPLIT_TOKEN=" ",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[a,u]of this.tokens_to_ids)this.vocab[u]=a;this.bpe_ranks=Object.fromEntries(n.merges.map((a,u)=>[a,u])),this.merges=n.merges.map(a=>a.split(this.BPE_SPLIT_TOKEN)),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 a=new Set,u=n[0];for(let l=1;l{let r=this.bpe_ranks[t]??1/0,i=this.bpe_ranks[e]??1/0;return r<=i?t:e});if(!(p in this.bpe_ranks))break;let[s,h]=p.split(this.BPE_SPLIT_TOKEN),f=[],c=0,o=-1;for(;c`<0x${s.toString(16).toUpperCase().padStart(2,"0")}>`)):a.push(this.unk_token)}return a}}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 a=createPattern(this.config.pattern);return a===null||(n=n.replaceAll(a,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(a=>Normalizer.fromConfig(a))}normalize(n){return this.normalizers.reduce((a,u)=>u.normalize(a),n)}}class BertNormalizer extends Normalizer{_tokenize_chinese_chars(n){let a=[];for(let u=0;u=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);case"Punctuation":return new PunctuationPreTokenizer(n);case"Digits":return new DigitsPreTokenizer(n);default:throw new Error(`Unknown PreTokenizer type: ${n.type}`)}}pre_tokenize_text(n){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(n){let a=[];return Array.isArray(n)?a=n.map(u=>this.pre_tokenize_text(u)):a=this.pre_tokenize_text(n),a.flat()}_call(n){return this.pre_tokenize(n)}}class BertPreTokenizer extends PreTokenizer{constructor(n){super(),this.pattern=new RegExp(`[^\\s${PUNCTUATION_REGEX}]+|[${PUNCTUATION_REGEX}]`,"gu")}pre_tokenize_text(n){return n.trim().match(this.pattern)||[]}}class ByteLevelPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=/'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+/gu,this.byte_encoder=BYTES_TO_UNICODE,this.text_encoder=new TextEncoder}pre_tokenize_text(n){return(this.use_regex?n.match(this.pattern)||[]:[n]).map(u=>(this.add_prefix_space&&!u.startsWith(" ")&&(u=" "+u),u=Array.from(this.text_encoder.encode(u),l=>this.byte_encoder[l]).join(""),u))}}class SplitPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n,this.pattern=createPattern(this.config.pattern,this.config.invert)}pre_tokenize_text(n){return this.pattern===null?[]:this.config.invert?n.match(this.pattern)||[]:n.split(this.pattern).filter(a=>a)}}class PunctuationPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n,this.pattern=new RegExp(`[^${PUNCTUATION_REGEX}]+|[${PUNCTUATION_REGEX}]+`,"gu")}pre_tokenize_text(n){return n.match(this.pattern)||[]}}class DigitsPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n;const a=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(a,"gu")}pre_tokenize_text(n){return n.match(this.pattern)||[]}}class PostProcessor extends Callable{constructor(n){super(),this.config=n}static fromConfig(n){if(n===null)return null;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,...a){throw Error("post_process should be implemented in subclass.")}_call(n,...a){return this.post_process(n,...a)}}class RobertaProcessing extends PostProcessor{constructor(n){super(n),this.cls=n.cls[0],this.sep=n.sep[0]}post_process(n,a=null){return n=mergeArrays([this.cls],n,[this.sep]),a!==null&&(n=mergeArrays(n,[this.sep],a,[this.sep])),n}}class TemplateProcessing extends PostProcessor{constructor(n){super(n),this.single=n.single,this.pair=n.pair}post_process(n,a=null){let u=a===null?this.single:this.pair,l=[];for(let p of u)"SpecialToken"in p?l.push(p.SpecialToken.id):"Sequence"in p&&(p.Sequence.id==="A"?l=mergeArrays(l,n):p.Sequence.id==="B"&&(l=mergeArrays(l,a)));return l}}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 a=createPattern(this.config.pattern);return a===null?n:n.map(u=>u.replaceAll(a,this.config.content))}}class ByteFallback extends Decoder{constructor(n){super(n),this.text_decoder=new TextDecoder}decode_chain(n){let a=[],u=[];for(let l of n){let p=null;if(l.length===6&&l.startsWith("<0x")&&l.endsWith(">")){let s=parseInt(l.slice(3,5),16);isNaN(s)||(p=s)}if(p!==null)u.push(p);else{if(u.length>0){let s=this.text_decoder.decode(Uint8Array.from(u));a.push(s),u=[]}a.push(l)}}if(u.length>0){let l=this.text_decoder.decode(Uint8Array.from(u));a.push(l),u=[]}return a}}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(a=>{let u=0;for(let p=0;p(u!==0&&(a.startsWith(this.config.prefix)?a=a.replace(this.config.prefix,""):a=" "+a),this.cleanup&&(a=clean_up_tokenization(a)),a))}}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 a=n.join(""),u=new Uint8Array([...a].map(p=>this.byte_decoder[p]));return this.text_decoder.decode(u)}decode_chain(n){let a=[],u=[];for(let l of n)this.added_tokens.includes(l)?(u.length>0&&(a.push(this.convert_tokens_to_string(u)),u=[]),a.push(l)):u.push(l);return u.length>0&&a.push(this.convert_tokens_to_string(u)),a}}class DecoderSequence extends Decoder{constructor(n){super(n),this.decoders=n.decoders.map(a=>Decoder.fromConfig(a))}decode_chain(n){return this.decoders.reduce((a,u)=>u.decode_chain(a),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 a=[];for(let u of n){let l=u.replaceAll(" ",this.strRep);this.addPrefixSpace&&!l.startsWith(this.replacement)&&(l=this.strRep+l),a.push(l)}return a}}class MetaspaceDecoder extends Decoder{constructor(n){super(n),this.addPrefixSpace=n.add_prefix_space,this.replacement=n.replacement}decode_chain(n){let a=[];for(let u=0;uPreTokenizer.fromConfig(a))}pre_tokenize_text(n){return typeof n=="string"&&(n=[n]),this.tokenizers.reduce((a,u)=>u.pre_tokenize(a),n)}}class WhitespaceSplit extends PreTokenizer{constructor(n){super()}pre_tokenize_text(n){return whitespace_split(n)}}class PreTrainedTokenizer extends Callable{constructor(n,a){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,a),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 u of n.added_tokens){let l=u.id,p=u.content;this.added_tokens.push(p),this.model.tokens_to_ids.set(p,l),this.model.vocab[l]=p,u.special&&(this.special_tokens.push(p),this.all_special_ids.push(l))}this.decoder.added_tokens=this.added_tokens,this.added_tokens_regex=new RegExp("("+this.added_tokens.map(escapeRegExp).join("|")+")"),this.mask_token=this.getToken(a,"mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken(a,"pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken(a,"sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.model_max_length=a.model_max_length,this.remove_space=a.remove_space,this.clean_up_tokenization_spaces=a.clean_up_tokenization_spaces??!0,this.padding_side="right"}getToken(n,...a){for(let u of a){let l=n[u];if(l)if(typeof l=="object"){if(l.__type==="AddedToken")return l.content;throw Error(`Unknown token: ${l}`)}else return l}return null}static async from_pretrained(n,{progress_callback:a=null,config:u=null,cache_dir:l=null,local_files_only:p=!1,revision:s="main"}={}){let h=await loadTokenizer(n,{progress_callback:a,config:u,cache_dir:l,local_files_only:p,revision:s});return new this(...h)}prepare_model_inputs(n){return n}_call(n,{text_pair:a=null,padding:u=!1,truncation:l=null,max_length:p=null,return_tensor:s=!0}={}){let h;if(Array.isArray(n)){if(n.length===0)throw Error("text array must be non-empty");if(a!==null){if(Array.isArray(a)){if(n.length!==a.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,a[e]))}else h=n.map(t=>this.encode(t))}else{if(n===null)throw Error("text may not be null");if(Array.isArray(a))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,a)]}let f=max(h.map(t=>t.length))[0];p===null&&(p=f),p=Math.min(p,this.model_max_length);let c=[];if(u||l)for(let t=0;tp)l&&(h[t]=h[t].slice(0,p)),c.push(new Array(h[t].length).fill(1));else if(u){let e=p-h[t].length;this.padding_side==="right"?(c.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))):(c.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 c.push(new Array(h[t].length).fill(1));else c=h.map(t=>new Array(t.length).fill(1));if(s){if(!(u&&l)&&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),c=new Tensor("int64",BigInt64Array.from(c.flat().map(BigInt)),t)}else Array.isArray(n)||(h=h[0],c=c[0]);let o={input_ids:h,attention_mask:c};return o=this.prepare_model_inputs(o),o}_encode_text(n){return n===null?null:n.split(this.added_tokens_regex).filter(l=>l).map(l=>{if(this.added_tokens.includes(l))return l;{this.remove_space===!0&&(l=l.trim().split(/\s+/).join(" ")),this.normalizer!==null&&(l=this.normalizer(l));let p=this.pre_tokenizer!==null?this.pre_tokenizer(l):[l];return this.model(p)}}).flat()}encode(n,a=null){let u=this._encode_text(n),l=this._encode_text(a),p=this.post_processor!==null?this.post_processor(u,l):mergeArrays(u??[],l??[]);return this.model.convert_tokens_to_ids(p)}batch_decode(n,a={}){return n.map(u=>this.decode(u,a))}decode(n,a={}){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,a)}decode_single(n,{skip_special_tokens:a=!1,clean_up_tokenization_spaces:u=null}){let l=this.model.convert_ids_to_tokens(n);a&&(l=l.filter(s=>!this.special_tokens.includes(s)));let p=this.decoder(l);return this.decoder.end_of_word_suffix&&(p=p.replaceAll(this.decoder.end_of_word_suffix," "),a&&(p=p.trim())),(u??this.clean_up_tokenization_spaces)&&(p=clean_up_tokenization(p)),p}}function add_token_types(_){if(_.input_ids instanceof Tensor)_.token_type_ids=new Tensor("int64",new BigInt64Array(_.input_ids.data.length),_.input_ids.dims);else if(Array.isArray(_.input_ids))Array.isArray(_.input_ids[0])?_.token_type_ids=_.input_ids.map(n=>new Array(n.length).fill(0)):_.token_type_ids=new Array(_.input_ids.length).fill(0);else throw new Error("Input ids must be a Tensor or an Array");return _}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{}class XLMRobertaTokenizer extends PreTrainedTokenizer{}class MPNetTokenizer extends PreTrainedTokenizer{}class FalconTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class GPTNeoXTokenizer extends PreTrainedTokenizer{}class NllbTokenizer extends PreTrainedTokenizer{constructor(n,a){super(n,a),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(u=>this.languageRegex.test(u))}_build_translation_inputs(n,a,u){if(!this.language_codes.includes(u.tgt_lang))throw new Error(`Target language code "${u.tgt_lang}" is not valid. Must be one of: {${this.language_codes.join(", ")}}`);if(u.src_lang!==void 0){if(!this.language_codes.includes(u.src_lang))throw new Error(`Source language code "${u.src_lang}" is not valid. Must be one of: {${this.language_codes.join(", ")}}`);for(let l of this.post_processor.config.single)if("SpecialToken"in l&&this.languageRegex.test(l.SpecialToken.id)){l.SpecialToken.id=u.src_lang;break}}return u.forced_bos_token_id=this.model.convert_tokens_to_ids([u.tgt_lang])[0],this._call(n,a)}}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(([_,n])=>[n,_]),["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:a=!1,return_language:u=!1,time_precision:l=null,force_full_sequences:p=!0}={}){if(l===null)throw Error("Must specify time_precision");let s=null;const h=a==="word";function f(){return{language:s,timestamp:[null,null],text:""}}const c=[];let o=f(),t=0;const e=this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1;let r=[],i=[],d=!1,g=null;const m=new Set(this.all_special_ids);for(let w of n){const v=w.tokens,S=h?w.token_timestamps:null;let A=null,O=e;if("stride"in w){const[N,R,L]=w.stride;if(t-=R,g=N-L,R&&(O=R/l+e),L)for(let $=v.length-1;$>=0;--$){const G=v[$];if(G>=e){if(A!==null&&(G-e)*l=e){const L=(R-e)*l+t,$=round(L,2);if(A!==null&&R>=A)d=!0;else if(d||r.length>0&&R0?(r.push(x),h&&i.push(I)):r.every(N=>N.length===0)&&(o=f(),r=[],x=[],i=[],I=[])}if(r.length>0){if(p&&a)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. Also make sure WhisperTimeStampLogitsProcessor was used during generation.");const[w,v]=this.findLongestCommonSequence(r,i),S=this.decode(w);o.text=S,h&&(o.words=this.collateWordTimestamps(w,v,s)),c.push(o)}let b=Object.create(null);const y=c.map(w=>w.text).join("");if(a||u){for(let w=0;w0;let h=s?[]:null,f=s?a[0]:null;for(let c=1;c$===N[G]).length,L=R/w+v;R>1&&L>t&&(t=L,e=[S,A,x,I])}const[i,d,g,m]=e,b=Math.floor((d+i)/2),y=Math.floor((m+g)/2);p.push(...u.slice(0,b)),u=o.slice(y),l=u.length,s&&(h.push(...f.slice(0,b)),f=a[c].slice(y))}return p.push(...u),s?(h.push(...f),[p,h]):[p,[]]}collateWordTimestamps(n,a,u){let[l,p,s]=this.combineTokensIntoWords(n,u),h=[];for(let f=0;f=l){let h=(s-l)*u;h=round(h,2),p.push(`<|${h}|>`),p.push([])}else p[p.length-1].push(s);return p=p.map(s=>typeof s=="string"?s:super.decode(s,a)),p.join("")}splitTokensOnUnicode(n){const a=this.decode(n,{decode_with_timestamps:!0}),u="�";let l=[],p=[],s=[],h=[],f=[],c=0;for(let o=0;o=this.model.tokens_to_ids.get("<|endoftext|>"),i=o.startsWith(" "),d=o.trim(),g=f.test(d);if(r||i||g||p.length===0)p.push(o),s.push(t),h.push(e);else{const m=p.length-1;p[m]+=o,s[m].push(...t),h[m].push(...e)}}return[p,s,h]}mergePunctuations(n,a,u,l,p){let s=structuredClone(n),h=structuredClone(a),f=structuredClone(u),c=s.length-2,o=s.length-1;for(;c>=0;)s[c].startsWith(" ")&&l.includes(s[c].trim())?(s[o]=s[c]+s[o],h[o]=mergeArrays(h[c],h[o]),f[o]=mergeArrays(f[c],f[o]),s[c]="",h[c]=[],f[c]=[]):o=c,--c;for(c=0,o=1;ot),h.filter(t=>t.length>0),f.filter(t=>t.length>0)]}get_decoder_prompt_ids({language:n=null,task:a=null,no_timestamps:u=!0}={}){let l=[];if(n){n=n.toLowerCase();let p=WHISPER_TO_LANGUAGE_CODE_MAPPING.get(n);if(p===void 0)if(WHISPER_LANGUAGE_MAPPING.has(n))p=n;else{const f=n.length===2?WHISPER_LANGUAGE_MAPPING.keys():WHISPER_LANGUAGE_MAPPING.values();throw new Error(`Language "${n}" is not supported. Must be one of: ${JSON.stringify(f)}`)}let s=this.model.tokens_to_ids.get(`<|${p}|>`);if(s===void 0)throw new Error(`Unable to find language "${p}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);l.push(s)}else l.push(null);if(a){if(a=a.toLowerCase(),a!=="transcribe"&&a!=="translate")throw new Error(`Task "${a}" is not supported. Must be one of: ["transcribe", "translate"]`);let p=this.model.tokens_to_ids.get(`<|${a}|>`);if(p===void 0)throw new Error(`Unable to find task "${a}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);l.push(p)}else l.push(null);if(u){let p=this.model.tokens_to_ids.get("<|notimestamps|>");if(p===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.');l.push(p)}return l.map((p,s)=>[s+1,p]).filter(p=>p[1]!==null)}}class CodeGenTokenizer extends PreTrainedTokenizer{}class CLIPTokenizer extends PreTrainedTokenizer{}class MarianTokenizer extends PreTrainedTokenizer{constructor(n,a){super(n,a),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(u=>this.languageRegex.test(u)),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[a,...u]=n.trim().split(this.languageRegex);if(u.length===0)return super._encode_text(a);if(u.length===2){let[l,p]=u;return this.supported_language_codes.includes(l)||console.warn(`Unsupported language code "${l}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),mergeArrays([l],super._encode_text(p))}}}class CharTrie{constructor(){this.root=CharTrieNode.default()}extend(n){for(let a of n)this.push(a)}push(n){let a=this.root;for(let u of n){let l=a.children.get(u);l===void 0&&(l=CharTrieNode.default(),a.children.set(u,l)),a=l}a.isLeaf=!0}*commonPrefixSearch(n){let a=this.root,u="";for(let l=0;lf)&&(c=o.clone(),f=t)}if(c!==null)h.prev=c,h.backtraceScore=f;else return[]}++a}const u=[],p=this.beginNodes[n][0].prev;if(p===null)return[];let s=p.clone();for(;s.prev!==null;)u.push(s.clone()),s=s.clone().prev.clone();return u.reverse(),u}piece(n){return this.sentence.slice(n.pos,n.pos+n.length)}tokens(){return this.viterbi().map(a=>this.piece(a))}tokenIds(){return this.viterbi().map(a=>a.tokenId)}}class TokenLatticeNode{constructor(n,a,u,l,p){this.tokenId=n,this.nodeId=a,this.pos=u,this.length=l,this.score=p,this.prev=null,this.backtraceScore=0}clone(){const n=new TokenLatticeNode(this.tokenId,this.nodeId,this.pos,this.length,this.score);return n.prev=this.prev,n.backtraceScore=this.backtraceScore,n}}class AutoTokenizer{static async from_pretrained(n,{quantized:a=!0,progress_callback:u=null,config:l=null,cache_dir:p=null,local_files_only:s=!1,revision:h="main"}={}){let[f,c]=await loadTokenizer(n,{quantized:a,progress_callback:u,config:l,cache_dir:p,local_files_only:s,revision:h}),o=c.tokenizer_class.replace(/Fast$/,""),t=this.TOKENIZER_CLASS_MAPPING[o];return t||(console.warn(`Unknown tokenizer class "${o}", attempting to construct from base class.`),t=PreTrainedTokenizer),new t(f,c)}}Ie(AutoTokenizer,"TOKENIZER_CLASS_MAPPING",{T5Tokenizer,DistilBertTokenizer,BertTokenizer,MobileBertTokenizer,SqueezeBertTokenizer,AlbertTokenizer,GPT2Tokenizer,BartTokenizer,RobertaTokenizer,WhisperTokenizer,CodeGenTokenizer,CLIPTokenizer,MarianTokenizer,BloomTokenizer,NllbTokenizer,LlamaTokenizer,XLMRobertaTokenizer,MPNetTokenizer,FalconTokenizer,GPTNeoXTokenizer,PreTrainedTokenizer});async function loadConfig(_,n){return await getModelJSON(_,"config.json",!0,n)}class PretrainedConfig{constructor(n){this.model_type=null,this.is_encoder_decoder=!1,Object.assign(this,n)}static async from_pretrained(n,{progress_callback:a=null,config:u=null,cache_dir:l=null,local_files_only:p=!1,revision:s="main"}={}){let h=u??await loadConfig(n,{progress_callback:a,config:u,cache_dir:l,local_files_only:p,revision:s});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,a){for(let u of a)this.processors.forEach(l=>l(n,u))}[Symbol.iterator](){return this.processors.values()}}class LogitsProcessor extends Callable{_call(n,a){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,a){let u=this.force_token_map[n.length];return exists(u)&&(a.data.fill(-1/0),a.data[u]=0),a}}class ForcedBOSTokenLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.bos_token_id=n}_call(n,a){return n.length===1&&(a.data.fill(-1/0),a.data[this.bos_token_id]=0),a}}class ForcedEOSTokenLogitsProcessor extends LogitsProcessor{constructor(n,a){super(),this.max_length=n,this.forced_eos_token_id=a}_call(n,a){}}class SuppressTokensAtBeginLogitsProcessor extends LogitsProcessor{constructor(n,a){super(),this.begin_suppress_tokens=n,this.begin_index=a}_call(n,a){if(n.length===this.begin_index)for(let u of this.begin_suppress_tokens)a.data[u]=-1/0;return a}}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,a){if(a.data[this.no_timestamps_token_id]=-1/0,n.length===this.begin_index-1)return a.data.fill(-1/0),a.data[this.timestamp_begin]=0,a;const u=n.slice(this.begin_index),l=u.length>=1&&u[u.length-1]>=this.timestamp_begin,p=u.length<2||u[u.length-2]>=this.timestamp_begin;if(l&&(p?a.data.subarray(this.timestamp_begin).fill(-1/0):a.data.subarray(0,this.eos_token_id).fill(-1/0)),n.length===this.begin_index&&this.max_initial_timestamp_index!==null){const c=this.timestamp_begin+this.max_initial_timestamp_index;a.data.subarray(c+1).fill(-1/0)}const s=log_softmax(a.data),h=Math.log(s.subarray(this.timestamp_begin).map(Math.exp).reduce((c,o)=>c+o)),f=max(s.subarray(0,this.timestamp_begin))[0];return h>f&&a.data.subarray(0,this.timestamp_begin).fill(-1/0),a}}class NoRepeatNGramLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.no_repeat_ngram_size=n}getNgrams(n){const a=n.length,u=[];for(let p=0;p0&&(l=l.map(p=>p/this.generation_config.temperature)),l}randomSelect(n){let a=n.reduce((l,p)=>l+p,0),u=Math.random()*a;for(let l=0;l1)return new BeamSearchSampler(n);if(n.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${n.num_return_sequences}.`);return new GreedySampler(n)}}class GreedySampler extends Sampler{sample(n,a=-1){let u=this.getLogits(n,a);return[[max(u)[1],0]]}}class MultinomialSampler extends Sampler{sample(n,a=-1){let u=n.dims.at(-1);this.generation_config.top_k>0&&(u=Math.min(this.generation_config.top_k,u));const l=this.getLogits(n,a),p=getTopItems(l,u),s=softmax(p.map(h=>h[1]));return Array.from({length:this.generation_config.num_beams},()=>{const h=this.randomSelect(s);return[p[h][0],Math.log(s[h])]})}}class BeamSearchSampler extends Sampler{sample(n,a=-1){let u=n.dims.at(-1);this.generation_config.top_k>0&&(u=Math.min(this.generation_config.top_k,u));const l=this.getLogits(n,a),p=getTopItems(l,u),s=softmax(p.map(h=>h[1]));return Array.from({length:this.generation_config.num_beams},(h,f)=>[p[f][0],Math.log(s[f])])}}const{InferenceSession,Tensor:ONNXTensor}=ONNX;class ModelType{}class EncoderOnlyModelType extends ModelType{}class EncoderDecoderModelType extends ModelType{}class Seq2SeqModelType extends EncoderDecoderModelType{}class DecoderOnlyModelType extends ModelType{}const MODEL_TYPE_MAPPING=new Map,MODEL_CLASS_MAPPING=new Map;async function forward(_,n){return MODEL_TYPE_MAPPING.get(_.constructor.name)===DecoderOnlyModelType?await decoderForward(_,n):await encoderForward(_,n)}async function constructSession(_,n,a){let u=`onnx/${n}${a.quantized?"_quantized":""}.onnx`,l=await getModelFile(_,u,!0,a);try{return await InferenceSession.create(l,{executionProviders})}catch(p){if(executionProviders.length===1&&executionProviders[0]==="wasm")throw p;return console.warn(p),console.warn("Something went wrong during model construction (most likely a missing operation). Using `wasm` as a fallback. "),await InferenceSession.create(l,{executionProviders:["wasm"]})}}async function validateInputs(_,n){const a={},u=[];for(let s of _.inputNames)n[s]===void 0?u.push(s):a[s]=n[s];if(u.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${u.join(", ")}.`);const l=Object.keys(n).length,p=_.inputNames.length;if(l>p){let s=Object.keys(n).filter(h=>!_.inputNames.includes(h));console.warn(`WARNING: Too many inputs were provided (${l} > ${p}). The following inputs will be ignored: "${s.join(", ")}".`)}return a}async function sessionRun(_,n){const a=await validateInputs(_,n);try{let u=await _.run(a);return u=replaceTensors(u),u}catch(u){throw console.error(`An error occurred during model execution: "${u}".`),console.error("Inputs given to model:",a),u}}function replaceTensors(_){for(let n in _)_[n]instanceof ONNXTensor?_[n]=new Tensor(_[n]):typeof _[n]=="object"&&replaceTensors(_[n]);return _}function toI64Tensor(_){if(_ instanceof Tensor)return _;if(_.length===0)throw Error("items must be non-empty");if(Array.isArray(_[0])){if(_.some(n=>n.length!==_[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(_.flat().map(n=>BigInt(n))),[_.length,_[0].length])}else return new Tensor("int64",BigInt64Array.from(_.map(n=>BigInt(n))),[1,_.length])}function prepareAttentionMask(_,n){let a=_.config.pad_token_id??null,u=_.config.eos_token_id??null;isIntegralNumber(u)&&(u=[u]);let l=n.indexOf(a)!==-1,p=u===null||!u.includes(a);if(l&&p){let s=BigInt64Array.from(n.data.map(h=>h!=a));return new Tensor("int64",s,n.dims)}else return new Tensor("int64",new BigInt64Array(n.data.length).fill(1n),n.dims)}function boolTensor(_){return new Tensor("bool",[_],[1])}async function seq2seqForward(_,n,{add_decoder_pkv:a=!0}={}){let{encoder_outputs:u,past_key_values:l}=n;u||(u=(await encoderForward(_,n)).last_hidden_state);let p={input_ids:n.decoder_input_ids,encoder_hidden_states:u,use_cache_branch:boolTensor(!!l)};_.decoder_merged_session.inputNames.includes("encoder_attention_mask")&&(p.encoder_attention_mask=n.attention_mask),_.addPastKeyValues(p,l,a);const s=await sessionRun(_.decoder_merged_session,p);let h=s.logits;l=_.getPastKeyValues(s,l);const f=_.getAttentions(s);return new Seq2SeqLMOutput({logits:h,past_key_values:l,encoder_outputs:u,...f})}function seq2seqStartBeams(_,n,a,u=!0){let l=[],p=0,s=_.config.decoder_start_token_id;Array.isArray(s)||(s=[s]);for(let h of n){h.dims=[1,...h.dims];let f={inputs:h,encoder_outputs:null,prev_model_outputs:null,output_token_ids:s,done:!1,score:0,id:p++};u&&(f.attention_mask=prepareAttentionMask(_,h)),l.push(f)}return l}async function seq2seqRunBeam(_,n,{input_name:a="input_ids"}={}){var p;let u={[a]:n.inputs,decoder_input_ids:toI64Tensor(n.output_token_ids.slice(-1)),encoder_outputs:n.encoder_outputs,past_key_values:(p=n.prev_model_outputs)==null?void 0:p.past_key_values};n.attention_mask&&(u.attention_mask=n.attention_mask);let l=await _.forward(u);return n.prev_model_outputs=l,n.encoder_outputs=l.encoder_outputs,l}async function encoderForward(_,n){let a={};for(let u of _.session.inputNames)a[u]=n[u];return await sessionRun(_.session,a)}async function decoderForward(_,n){let{input_ids:a,past_key_values:u,attention_mask:l}=n,p={input_ids:a,attention_mask:l??prepareAttentionMask(_,a),use_cache_branch:boolTensor(u!==null)};_.addPastKeyValues(p,u);let s=await sessionRun(_.session,p),h=s.logits;return u=_.getPastKeyValues(s,u),{logits:h,past_key_values:u}}function decoderStartBeams(_,n,a,u){let l=[],p=0;for(let s of n){let h=s.tolist().map(Number);s.dims=[1,...s.dims];let f;u?(f=u[p],f.dims=[1,...f.dims]):f=prepareAttentionMask(_,s);let c={input:s,model_input_ids:s,attention_mask:f,prev_model_outputs:null,output_token_ids:h,num_output_tokens:a,done:!1,score:0,id:p++};l.push(c)}return l}async function decoderRunBeam(_,n){var p;let a=new BigInt64Array(n.output_token_ids.length).fill(1n),u={input_ids:n.model_input_ids,attention_mask:new Tensor("int64",a,[1,a.length]),past_key_values:(p=n.prev_model_outputs)==null?void 0:p.past_key_values},l=await _.forward(u);return n.prev_model_outputs=l,l}function decoderUpdatebeam(_,n){_.output_token_ids=[..._.output_token_ids,n],_.model_input_ids=new Tensor("int64",[BigInt(n)],[1,1])}class PreTrainedModel extends Callable{constructor(n,a){super(),this.config=n,this.session=a}async dispose(){let n=[];for(let a of Object.keys(this)){let u=this[a];u instanceof InferenceSession&&n.push(u.handler.dispose())}return await Promise.all(n)}static async from_pretrained(n,{quantized:a=!0,progress_callback:u=null,config:l=null,cache_dir:p=null,local_files_only:s=!1,revision:h="main"}={}){let f={quantized:a,progress_callback:u,config:l,cache_dir:p,local_files_only:s,revision:h},c=MODEL_TYPE_MAPPING.get(this.name),o;if(c===DecoderOnlyModelType)o=await Promise.all([AutoConfig.from_pretrained(n,f),constructSession(n,"decoder_model_merged",f)]);else if(c===Seq2SeqModelType)o=await Promise.all([AutoConfig.from_pretrained(n,f),constructSession(n,"encoder_model",f),constructSession(n,"decoder_model_merged",f),getModelJSON(n,"generation_config.json",!1,f)]);else if(c===EncoderDecoderModelType)o=await Promise.all([AutoConfig.from_pretrained(n,f),constructSession(n,"encoder_model",f),constructSession(n,"decoder_model_merged",f)]);else if(c===EncoderOnlyModelType)o=await Promise.all([AutoConfig.from_pretrained(n,f),constructSession(n,"model",f)]);else throw console.warn("Malformed class definition.",this),Error(`Unable to load model: ${n}. Please report this bug at https://github.com/xenova/transformers.js/issues/new/choose.`);return new this(...o)}async _call(n){return await this.forward(n)}async forward(n){return await forward(this,n)}_get_logits_processor(n,a,u=null){const l=new LogitsProcessorList;if(n.repetition_penalty!==null&&n.repetition_penalty!==1&&l.push(new RepetitionPenaltyLogitsProcessor(n.repetition_penalty)),n.no_repeat_ngram_size!==null&&n.no_repeat_ngram_size>0&&l.push(new NoRepeatNGramLogitsProcessor(n.no_repeat_ngram_size)),n.forced_bos_token_id!==null&&l.push(new ForcedBOSTokenLogitsProcessor(n.forced_bos_token_id)),n.forced_eos_token_id!==null&&l.push(new ForcedEOSTokenLogitsProcessor(n.max_length,n.forced_eos_token_id)),n.begin_suppress_tokens!==null){let p=a>1||n.forced_bos_token_id===null?a:a+1;n.forced_decoder_ids!==null&&(p+=n.forced_decoder_ids[n.forced_decoder_ids.length-1][0]),l.push(new SuppressTokensAtBeginLogitsProcessor(n.begin_suppress_tokens,p))}return n.forced_decoder_ids!==null&&l.push(new ForceTokensLogitsProcessor(n.forced_decoder_ids)),u!==null&&l.extend(u),l}_get_generation_config(n){let a=new GenerationConfig;return"generation_config"in this&&Object.assign(a,this.generation_config),n!==null&&Object.assign(a,n),a}async generate(n,a=null,u=null,{inputs_attention_mask:l=null}={}){if(!(n instanceof Tensor)&&!isTypedArray(n)&&!Array.isArray(n))throw Error(`\`inputs\` must be a Tensor, TypedArray, or Array, but is "${n.constructor.name}".`);let p;if(this.config.is_encoder_decoder)p=0;else if(p=n instanceof Tensor?n.dims[0]:n.length,p===0)throw Error("Must supply a non-empty array of input token ids.");a=this._get_generation_config(a),u=u??new LogitsProcessorList,u=this._get_logits_processor(a,p,u);let s=1;const h=s+(a.max_new_tokens??1/0),f=Number.isInteger(a.max_length)&&(a.max_new_tokens??null)===null;let c=Sampler.getSampler(a),o=this.getStartBeams(n,s,l);for(;o.some(i=>!i.done)&&s=a.max_length){d.done=!0,i.push(d);continue}let g=await this.runBeam(d);a.output_attentions&&this.addAttentionsToBeam(d,g),a.output_scores;let m=g.logits.slice(null,-1,null);u(d.output_token_ids,m);let b=c(m);for(let[y,w]of b){let v={...d};this.updateBeam(v,y),v.score+=w,y===this.config.eos_token_id&&(v.done=!0),i.push(v)}}++s,i=this.groupBeams(i).map(d=>d.sort((g,m)=>m.score-g.score).slice(0,a.num_beams)),o=i.flat(),a.callback_function&&a.callback_function(o)}const t=this.groupBeams(o),e=i=>t.map(d=>a.num_return_sequences>1?d.slice(0,a.num_return_sequences).map(g=>g[i]):[d[0][i]]).flat(),r=e("output_token_ids");if(a.return_dict_in_generate){const i=e("decoder_attentions"),d=e("cross_attentions");return{sequences:r,decoder_attentions:i,cross_attentions:d}}else return r}addAttentionsToBeam(n,a){if(this.config.is_encoder_decoder){if(!a.cross_attentions||a.cross_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce cross-attentions. This is most likely because the model was not exported with `output_attentions=True`.");n.cross_attentions||(n.cross_attentions=[]),n.cross_attentions.push(a.cross_attentions)}if(!a.decoder_attentions||a.decoder_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce decoder-attentions. This is most likely because the model was not exported with `output_attentions=True`.");n.decoder_attentions||(n.decoder_attentions=[]),n.decoder_attentions.push(a.decoder_attentions)}groupBeams(n){const a=Object.create(null);for(const u of n)a[u.id]===void 0?a[u.id]=[u]:a[u.id].push(u);return Object.values(a)}getPastKeyValues(n,a){const u=Object.create(null);for(const l in n)if(l.startsWith("present")){let p=l.replace("present","past_key_values");a&&l.includes("encoder")?u[p]=a[p]:u[p]=n[l]}return u}getAttentions(n){const a=Object.create(null);for(const u of["cross_attentions","decoder_attentions"]){const l=[];for(const p in n)if(p.startsWith(u)){const s=p.split(".").pop();l[s]=n[p]}a[u]=l}return a}addPastKeyValues(n,a,u=!1){if(a)Object.assign(n,a);else if(u){let l=[1,this.num_encoder_heads,0,this.encoder_dim_kv];for(let s=0;s{let c=Array.from({length:this.config.decoder_layers},(d,g)=>cat(f.map(m=>m[g]),2)),o=stack(a.map(([d,g])=>c[d].slice(null,g)));o=o.transpose(1,0,2,3);let[t,e]=std_mean(o,-2,0,!0),r=o.clone();for(let d=0;do[g+1]-o[g]),r=mergeArrays([1],e).map(d=>!!d),i=[];for(let d=0;d{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this browser.");return new self.OffscreenCanvas(_,n)},loadImageFunction=self.createImageBitmap,ImageDataClass=self.ImageData;else if(fs)loadImageFunction=async _=>{const a=(await _.metadata()).channels;let{data:u,info:l}=await _.raw().toBuffer({resolveWithObject:!0});const p=new RawImage(new Uint8ClampedArray(u),l.width,l.height,l.channels);return a!==void 0&&a!==l.channels&&p.convert(a),p};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,a,u,l){this._update(n,a,u,l)}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 u=await(await getFile(n)).blob();return this.fromBlob(u)}static async fromBlob(n){if(BROWSER_ENV){let a=await loadImageFunction(n);const u=createCanvasFunction(a.width,a.height).getContext("2d");return u.drawImage(a,0,0),new this(u.getImageData(0,0,a.width,a.height).data,a.width,a.height,4)}else{let a=fs(await n.arrayBuffer());return await loadImageFunction(a)}}grayscale(){if(this.channels===1)return this;let n=new Uint8ClampedArray(this.width*this.height*1);switch(this.channels){case 3:case 4:for(let a=0,u=0;a=0?f=u:o=-u,l>=0?c=l:t=-l,h.drawImage(s,f,c,n,a,o,t,n,a),new RawImage(h.getImageData(0,0,n,a).data,n,a,4).convert(p)}else{let p=fs(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}});if(u>=0&&l>=0)p=p.extract({left:Math.floor(u),top:Math.floor(l),width:n,height:a});else if(u<=0&&l<=0){let s=Math.floor(-l),h=Math.floor(-u);p=p.extend({top:s,left:h,right:n-this.width-h,bottom:a-this.height-s})}else{let s=[0,0],h=0;l<0?(s[0]=Math.floor(-l),s[1]=a-this.height-s[0]):h=Math.floor(l);let f=[0,0],c=0;u<0?(f[0]=Math.floor(-u),f[1]=n-this.width-f[0]):c=Math.floor(u),p=p.extend({top:s[0],bottom:s[1],left:f[0],right:f[1]}).extract({left:c,top:h,width:n,height:a})}return await loadImageFunction(p)}}toCanvas(){let n=this.clone().rgba(),a=createCanvasFunction(n.width,n.height),u=new ImageDataClass(n.data,n.width,n.height);return a.getContext("2d").putImageData(u,0,0),a}_update(n,a,u,l=null){return this.data=n,this.width=a,this.height=u,l!==null&&(this.channels=l),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,a="image/png"){if(!env.useFS)throw new Error("Unable to save the image because filesystem is disabled in this environment.");const l=this.toCanvas().toBuffer(a);fs.writeFileSync(n,l)}}async function read_audio(_,n){if(typeof AudioContext>"u")throw Error("Unable to load audio from path/URL since `AudioContext` is not available in your environment. Instead, audio data should be passed directly to the pipeline/processor. For more information and some example code, see https://huggingface.co/docs/transformers.js/tutorials/node-audio-processing.");const a=await(await getFile(_)).arrayBuffer(),u=new AudioContext({sampleRate:n});typeof n>"u"&&console.warn(`No sampling rate provided, using default of ${u.sampleRate}Hz.`);const l=await u.decodeAudioData(a);let p;if(l.numberOfChannels===2){const s=Math.sqrt(2);let h=l.getChannelData(0),f=l.getChannelData(1);p=new Float32Array(h.length);for(let c=0;c=i?e[b]=r*Math.exp(d*(y-i)):e[b]=o+t*y,g[b]=p.map(w=>e[b]-w)}const m=e.slice(1).map((b,y)=>1/(b-e[y]));for(let b=0;bthis.preprocess(l)));return a.forEach(l=>l.pixel_values.dims=[1,...l.pixel_values.dims]),{pixel_values:cat(a.map(l=>l.pixel_values)),original_sizes:a.map(l=>l.original_size),reshaped_input_sizes:a.map(l=>l.reshaped_input_size)}}}class ViTFeatureExtractor extends ImageFeatureExtractor{}class MobileViTFeatureExtractor extends ImageFeatureExtractor{}class DetrFeatureExtractor extends ImageFeatureExtractor{async _call(n){let a=await super._call(n),u=[a.pixel_values.dims[0],64,64];return a.pixel_mask=new Tensor("int64",new BigInt64Array(u.reduce((l,p)=>l*p)).fill(1n),u),a}center_to_corners_format([n,a,u,l]){return[n-u/2,a-l/2,n+u/2,a+l/2]}post_process_object_detection(n,a=.5,u=null){const l=n.logits,p=n.pred_boxes,[s,h,f]=l.dims;if(u!==null&&u.length!==s)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let c=[];for(let o=0;oa){let w=i[d].data;w=this.center_to_corners_format(w),t!==null&&(w=w.map((v,S)=>v*t[(S+1)%2])),e.boxes.push(w),e.classes.push(m),e.scores.push(y)}}c.push(e)}return c}remove_low_and_no_objects(n,a,u,l){let p=[],s=[],h=[];for(let f=0;fu&&(p.push(o),s.push(r),h.push(t))}return[p,s,h]}check_segment_validity(n,a,u,l=.5,p=.8){let s=[],h=0,f=0;for(let o=0;o=l&&++f;let c=h>0&&f>0;return c&&(c=h/f>p),[c,s]}compute_segments(n,a,u,l,p,s=null,h=null){let[f,c]=h??n[0].dims,o=new Tensor("int32",new Int32Array(f*c),[f,c]),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(f)))),s);return{pixel_values:u,original_sizes:l,reshaped_input_sizes:p,input_points:h}}post_process_masks(n,a,u,{mask_threshold:l=0,binarize:p=!0,pad_size:s=null}={}){let h=[];s=s??this.pad_size;let f=[s.height,s.width];for(let c=0;cb>l),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 a;super(n),(a=this.config).mel_filters??(a.mel_filters=getMelFilters(this.config.sampling_rate,this.config.n_fft,this.config.feature_size))}calcOffset(n,a){return Math.abs((n+a)%(2*a)-a)}padReflect(n,a,u){const l=new Float32Array(n.length+a+u),p=n.length-1;for(let s=0;s>1;++v){const S=(v+1-u)**2/2,A=Math.sqrt(m**2+b**2)**S,O=S*Math.atan2(b,m);let x=2*v;c[x]=A*Math.cos(O),c[x+1]=A*Math.sin(O),o[x]=c[x],o[x+1]=-c[x+1]}const y=c.subarray(l,p),w=new FFT(s>>1);w.transform(r,o);for(let v=0;v>1,N=S[I]*a[I];t[O]=N*y[O],t[x]=N*y[x]}w.transform(i,t);for(let O=0;Ol?s-l:0,c=s>1,c=new Float32Array(h*f);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 a=n.slice(0,this.config.n_samples),u=this._extract_fbank_features(a);return{input_features:new Tensor("float32",u.data,[1,...u.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,a){return await this.feature_extractor(n,a)}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:a=null,config:u=null,cache_dir:l=null,local_files_only:p=!1,revision:s="main"}={}){let h=u??await getModelJSON(n,"preprocessor_config.json",!0,{progress_callback:a,config:u,cache_dir:l,local_files_only:p,revision:s}),f=h.feature_extractor_type??h.image_processor_type,c=this.FEATURE_EXTRACTOR_CLASS_MAPPING[f];if(!c)if(h.size!==void 0)console.warn("Feature extractor type not specified, assuming ImageFeatureExtractor due to size parameter in config."),c=ImageFeatureExtractor;else throw new Error(`Unknown Feature Extractor type: ${h.feature_extractor_type}`);let o=this.PROCESSOR_CLASS_MAPPING[h.processor_class]??Processor,t=new c(h);return new o(t)}}Ie(AutoProcessor,"FEATURE_EXTRACTOR_CLASS_MAPPING",{WhisperFeatureExtractor,ViTFeatureExtractor,MobileViTFeatureExtractor,DetrFeatureExtractor,SamImageProcessor}),Ie(AutoProcessor,"PROCESSOR_CLASS_MAPPING",{WhisperProcessor,SamProcessor});async function prepareImages(_){return Array.isArray(_)||(_=[_]),_=await Promise.all(_.map(n=>RawImage.read(n))),_}class Pipeline extends Callable{constructor(n,a,u){super(),this.task=n,this.tokenizer=a,this.model=u}async dispose(){await this.model.dispose()}async _call(n){let a=this.tokenizer(n,{padding:!0,truncation:!0}),u=await this.model(a);return[a,u]}}class TextClassificationPipeline extends Pipeline{async _call(n,{topk:a=1}={}){let[u,l]=await super._call(n),p=this.model.config.id2label,s=[];for(let h of l.logits){let c=getTopItems(softmax(h.data),a).map(function(o){return{label:p[o[0]],score:o[1]}});a===1?s.push(...c):s.push(c)}return Array.isArray(n)||a===1?s:s[0]}}class TokenClassificationPipeline extends Pipeline{async _call(n,{ignore_labels:a=["O"]}={}){let u=Array.isArray(n);u||(n=[n]);let l=this.tokenizer,[p,s]=await super._call(n),h=s.logits,f=this.model.config.id2label,c=[];for(let o=0;o[r,i]).filter(r=>r[1]>c),t=Array.from(softmax(p.end_logits[h].data)).map((r,i)=>[r,i]).filter(r=>r[1]>c),e=product(o,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=[...f];return i[c]=r[0],{score:r[1],token:r[0],token_str:p.model.vocab[r[0]],sequence:p.decode(i,{skip_special_tokens:!0})}}))}return Array.isArray(n)?s:s[0]}}class Text2TextGenerationPipeline extends Pipeline{constructor(){super(...arguments);Ie(this,"_key",null)}async _call(a,u={}){Array.isArray(a)||(a=[a]),this.model.config.prefix&&(a=a.map(c=>this.model.config.prefix+c));let l=this.model.config.task_specific_params;l&&l[this.task]&&l[this.task].prefix&&(a=a.map(c=>l[this.task].prefix+c));let p={padding:!0,truncation:!0},s;this instanceof TranslationPipeline&&"_build_translation_inputs"in this.tokenizer?s=this.tokenizer._build_translation_inputs(a,p,u).input_ids:s=this.tokenizer(a,p).input_ids;let h=await this.model.generate(s,u),f=this.tokenizer.batch_decode(h,{skip_special_tokens:!0});return this._key!==null&&(f=f.map(c=>this._key===null?c:{[this._key]:c})),f}}class SummarizationPipeline extends Text2TextGenerationPipeline{constructor(){super(...arguments);Ie(this,"_key","summary_text")}}class TranslationPipeline extends Text2TextGenerationPipeline{constructor(){super(...arguments);Ie(this,"_key","translation_text")}}class TextGenerationPipeline extends Pipeline{async _call(n,a={}){let u=typeof n=="string"||n instanceof String;u&&(n=[n]),this.tokenizer.padding_side="left";let l=this.tokenizer(n,{padding:!0,truncation:!0}),p=l.input_ids,s=l.attention_mask,h=await this.model.generate(p,a,null,{inputs_attention_mask:s});const f=this.tokenizer.batch_decode(h,{skip_special_tokens:!0}),c=Array.from({length:n.length},o=>[]);for(let o=0;o[l.toLowerCase(),p])),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,a,{hypothesis_template:u="This example is {}.",multi_label:l=!1}={}){let p=Array.isArray(n);p||(n=[n]),Array.isArray(a)||(a=[a]);let s=a.map(c=>u.replace("{}",c)),h=l||a.length===1,f=[];for(let c of n){let o=[];for(let r of s){let i=this.tokenizer(c,{text_pair:r,padding:!0,truncation:!0}),d=await this.model(i);h?o.push([d.logits.data[this.contradiction_id],d.logits.data[this.entailment_id]]):o.push(d.logits.data[this.entailment_id])}let t;h?t=o.map(r=>softmax(r)[1]):t=softmax(o);let e=t.map((r,i)=>[r,i]).sort((r,i)=>i[0]-r[0]);f.push({sequence:c,labels:e.map(r=>a[r[1]]),scores:e.map(r=>r[0])})}return p?f:f[0]}}class FeatureExtractionPipeline extends Pipeline{async _call(n,{pooling:a="none",normalize:u=!1}={}){let[l,p]=await super._call(n),s=p.last_hidden_state??p.logits;if(a!=="none")if(a==="mean")s=mean_pooling(s,l.attention_mask);else throw Error(`Pooling method '${a}' not supported.`);return u&&(s=s.normalize(2,-1)),s}}class AutomaticSpeechRecognitionPipeline extends Pipeline{constructor(n,a,u,l){super(n,a,u),this.processor=l}async _preprocess(n,a){return isString(n)&&(n=await read_audio(n,a)),n}async _call(n,a={}){let u=a.return_timestamps??!1,l=a.chunk_length_s??0,p=a.stride_length_s??null,s=a.chunk_callback??null,h=a.force_full_sequences??!1;u==="word"&&(a.return_token_timestamps=!0);let f=pop(a,"language",null),c=pop(a,"task",null);if(f||c||u){if(a.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:f,task:c,no_timestamps:!u});i.length>0&&(a.forced_decoder_ids=i)}let o=!Array.isArray(n);o&&(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(l>0){if(p===null)p=l/6;else if(l<=p)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const b=t*l,y=t*p,w=b-2*y;let v=0;for(;v=i.length;d.push({stride:[S.length,O?0:y,x?0:y],input_features:A.input_features,is_last:x}),v+=w}}else d=[{stride:[i.length,0,0],input_features:(await this.processor(i)).input_features,is_last:!0}];for(let b of d){let y=await this.model.generate(b.input_features,a);u==="word"?(b.tokens=y.sequences[0],b.token_timestamps=y.token_timestamps.tolist()[0].map(w=>round(w,2))):b.tokens=y[0],b.stride=b.stride.map(w=>w/t),s!==null&&s(b)}let[g,m]=this.tokenizer._decode_asr(d,{time_precision:e,return_timestamps:u,force_full_sequences:h});r.push({text:g,...m})}return o?r[0]:r}}class ImageToTextPipeline extends Pipeline{constructor(n,a,u,l){super(n,a,u),this.processor=l}async _call(n,a={}){let u=Array.isArray(n);n=await prepareImages(n);let{pixel_values:l}=await this.processor(n),p=[];for(let s of l){s.dims=[1,...s.dims];let h=await this.model.generate(s,a),f=this.tokenizer.batch_decode(h,{skip_special_tokens:!0}).map(c=>({generated_text:c.trim()}));p.push(f)}return u?p:p[0]}}class ImageClassificationPipeline extends Pipeline{constructor(n,a,u){super(n,null,a),this.processor=u}async _call(n,{topk:a=1}={}){let u=Array.isArray(n);n=await prepareImages(n);let{pixel_values:l}=await this.processor(n),p=await this.model({pixel_values:l}),s=this.model.config.id2label,h=[];for(let f of p.logits){let o=getTopItems(softmax(f.data),a).map(function(t){return{label:s[t[0]],score:t[1]}});a===1?h.push(...o):h.push(o)}return u||a===1?h:h[0]}}class ImageSegmentationPipeline extends Pipeline{constructor(n,a,u){super(n,null,a),this.processor=u,this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(n,{threshold:a=.5,mask_threshold:u=.5,overlap_mask_area_threshold:l=.8,label_ids_to_fuse:p=null,target_sizes:s=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 c=n.map(d=>[d.height,d.width]),{pixel_values:o,pixel_mask:t}=await this.processor(n),e=await this.model({pixel_values:o,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,a,u,l,p,s??c)[0],g=d.segmentation,m=this.model.config.id2label;for(let b of d.segments_info){let y=new Uint8ClampedArray(g.data.length);for(let v=0;vu.replace("{}",o)),s=this.tokenizer(p,{padding:!0,truncation:!0}),{pixel_values:h}=await this.processor(n),f=await this.model({...s,pixel_values:h}),c=[];for(let o of f.logits_per_image){let t=softmax(o.data);c.push([...t].map((e,r)=>({score:e,label:a[r]})))}return l?c:c[0]}}class ObjectDetectionPipeline extends Pipeline{constructor(n,a,u){super(n,null,a),this.processor=u}async _call(n,{threshold:a=.9,percentage:u=!1}={}){let l=Array.isArray(n);if(l&&n.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");n=await prepareImages(n);let p=u?null:n.map(e=>[e.height,e.width]),{pixel_values:s,pixel_mask:h}=await this.processor(n),f=await this.model({pixel_values:s,pixel_mask:h}),c=this.processor.feature_extractor.post_process_object_detection(f,a,p),o=this.model.config.id2label;const t=c.map(e=>e.boxes.map((r,i)=>({score:e.scores[i],label:o[e.classes[i]],box:this._get_bounding_box(r,!u)})));return l?t:t[0]}_get_bounding_box(n,a){a&&(n=n.map(h=>h|0));const[u,l,p,s]=n;return{xmin:u,ymin:l,xmax:p,ymax:s}}}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(_,n=null,{quantized:a=!0,progress_callback:u=null,config:l=null,cache_dir:p=null,local_files_only:s=!1,revision:h="main"}={}){_=TASK_ALIASES[_]??_;let f=SUPPORTED_TASKS[_.split("_",1)[0]];if(!f)throw Error(`Unsupported pipeline: ${_}. Must be one of [${Object.keys(SUPPORTED_TASKS)}]`);n||(n=f.default.model,console.log(`No model specified. Using default model: "${n}".`));let c=f.tokenizer,o=f.model,t=f.pipeline,e=f.processor,r=[],i={quantized:a,progress_callback:u,config:l,cache_dir:p,local_files_only:s,revision:h};c&&r.push(c.from_pretrained(n,i)),o&&r.push(o.from_pretrained(n,i)),e&&r.push(e.from_pretrained(n,i));let d=await Promise.all(r);return dispatchCallback(u,{status:"ready",task:_,model:n}),new t(_,...d)}function product(..._){return _.reduce((n,a)=>n.flatMap(u=>a.map(l=>[u,l])))}env.allowLocalModels=!1;class CodeCompletionPipeline{static async getInstance(n=null){return this.instance===null&&(this.instance=pipeline(this.task,this.model,{progress_callback:n})),this.instance}}Ie(CodeCompletionPipeline,"task","text-generation"),Ie(CodeCompletionPipeline,"model",null),Ie(CodeCompletionPipeline,"instance",null),self.addEventListener("message",async _=>{const{model:n,text:a,max_new_tokens:u,temperature:l,top_k:p,do_sample:s}=_.data;CodeCompletionPipeline.model!==n&&(CodeCompletionPipeline.model=n,CodeCompletionPipeline.instance!==null&&((await CodeCompletionPipeline.getInstance()).dispose(),CodeCompletionPipeline.instance=null));let h=await CodeCompletionPipeline.getInstance(c=>{self.postMessage(c)}),f=await h(a,{max_new_tokens:u,temperature:l,top_k:p,do_sample:s,callback_function:c=>{self.postMessage({status:"update",output:h.tokenizer.decode(c[0].output_token_ids,{skip_special_tokens:!0})})}});self.postMessage({status:"complete",output:f})})})();