/*! For license information please see ort.all.min.js.LICENSE.txt */
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Cl;r=Cr}else{if(this.eq(Lt))return e.eq(_n)||e.eq(Ba)?Lt:e.eq(Lt)?_n:(t=this.shr(1).div(e).shl(1)).eq(nr)?e.isNegative()?_n:Ba:(n=this.sub(e.mul(t)),r=t.add(n.div(e)));if(e.eq(Lt))return this.unsigned?Cr:nr;if(this.isNegative())return e.isNegative()?this.neg().div(e.neg()):this.neg().div(e).neg();if(e.isNegative())return this.div(e.neg()).neg();r=nr}for(n=this;n.gte(e);){t=Math.max(1,Math.floor(n.toNumber()/e.toNumber()));for(var i=Math.ceil(Math.log(t)/Math.LN2),a=i<=48?1:ai(2,i-48),o=jt(t),s=o.mul(e);s.isNegative()||s.gt(n);)s=(o=jt(t-=a,this.unsigned)).mul(e);o.isZero()&&(o=_n),r=r.add(o),n=n.sub(s)}return r},K.div=K.divide,K.modulo=function(e){return Ct(e)||(e=or(e)),qt?Ge((this.unsigned?qt.rem_u:qt.rem_s)(this.low,this.high,e.low,e.high),qt.get_high(),this.unsigned):this.sub(this.div(e).mul(e))},K.mod=K.modulo,K.rem=K.modulo,K.not=function(){return Ge(~this.low,~this.high,this.unsigned)},K.countLeadingZeros=function(){return this.high?Math.clz32(this.high):Math.clz32(this.low)+32},K.clz=K.countLeadingZeros,K.countTrailingZeros=function(){return this.low?_l(this.low):_l(this.high)+32},K.ctz=K.countTrailingZeros,K.and=function(e){return Ct(e)||(e=or(e)),Ge(this.low&e.low,this.high&e.high,this.unsigned)},K.or=function(e){return Ct(e)||(e=or(e)),Ge(this.low|e.low,this.high|e.high,this.unsigned)},K.xor=function(e){return Ct(e)||(e=or(e)),Ge(this.low^e.low,this.high^e.high,this.unsigned)},K.shiftLeft=function(e){return Ct(e)&&(e=e.toInt()),0==(e&=63)?this:e<32?Ge(this.low<<e,this.high<<e|this.low>>>32-e,this.unsigned):Ge(0,this.low<<e-32,this.unsigned)},K.shl=K.shiftLeft,K.shiftRight=function(e){return Ct(e)&&(e=e.toInt()),0==(e&=63)?this:e<32?Ge(this.low>>>e|this.high<<32-e,this.high>>e,this.unsigned):Ge(this.high>>e-32,this.high>=0?0:-1,this.unsigned)},K.shr=K.shiftRight,K.shiftRightUnsigned=function(e){return Ct(e)&&(e=e.toInt()),0==(e&=63)?this:e<32?Ge(this.low>>>e|this.high<<32-e,this.high>>>e,this.unsigned):Ge(32===e?this.high:this.high>>>e-32,0,this.unsigned)},K.shru=K.shiftRightUnsigned,K.shr_u=K.shiftRightUnsigned,K.rotateLeft=function(e){var t;return Ct(e)&&(e=e.toInt()),0==(e&=63)?this:32===e?Ge(this.high,this.low,this.unsigned):e<32?(t=32-e,Ge(this.low<<e|this.high>>>t,this.high<<e|this.low>>>t,this.unsigned)):(t=32-(e-=32),Ge(this.high<<e|this.low>>>t,this.low<<e|this.high>>>t,this.unsigned))},K.rotl=K.rotateLeft,K.rotateRight=function(e){var t;return Ct(e)&&(e=e.toInt()),0==(e&=63)?this:32===e?Ge(this.high,this.low,this.unsigned):e<32?(t=32-e,Ge(this.high<<t|this.low>>>e,this.low<<t|this.high>>>e,this.unsigned)):(t=32-(e-=32),Ge(this.low<<t|this.high>>>e,this.high<<t|this.low>>>e,this.unsigned))},K.rotr=K.rotateRight,K.toSigned=function(){return this.unsigned?Ge(this.low,this.high,!1):this},K.toUnsigned=function(){return this.unsigned?this:Ge(this.low,this.high,!0)},K.toBytes=function(e){return e?this.toBytesLE():this.toBytesBE()},K.toBytesLE=function(){var e=this.high,t=this.low;return[255&t,t>>>8&255,t>>>16&255,t>>>24,255&e,e>>>8&255,e>>>16&255,e>>>24]},K.toBytesBE=function(){var e=this.high,t=this.low;return[e>>>24,e>>>16&255,e>>>8&255,255&e,t>>>24,t>>>16&255,t>>>8&255,255&t]},Qe.fromBytes=function(e,t,n){return n?Qe.fromBytesLE(e,t):Qe.fromBytesBE(e,t)},Qe.fromBytesLE=function(e,t){return new Qe(e[0]|e[1]<<8|e[2]<<16|e[3]<<24,e[4]|e[5]<<8|e[6]<<16|e[7]<<24,t)},Qe.fromBytesBE=function(e,t){return new Qe(e[4]<<24|e[5]<<16|e[6]<<8|e[7],e[0]<<24|e[1]<<16|e[2]<<8|e[3],t)},Pr=Qe})),k,si=D((()=>{k={},k.Table,k.SIZEOF_SHORT=2,k.SIZEOF_INT=4,k.FILE_IDENTIFIER_LENGTH=4,k.SIZE_PREFIX_LENGTH=4,k.Encoding={UTF8_BYTES:1,UTF16_STRING:2},k.int32=new Int32Array(2),k.float32=new Float32Array(k.int32.buffer),k.float64=new Float64Array(k.int32.buffer),k.isLittleEndian=1===new Uint16Array(new 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this.bb},k.Builder.prototype.asUint8Array=function(){return this.bb.bytes().subarray(this.bb.position(),this.bb.position()+this.offset())},k.Builder.prototype.prep=function(e,t){e>this.minalign&&(this.minalign=e);for(var n=1+~(this.bb.capacity()-this.space+t)&e-1;this.space<n+e+t;){var r=this.bb.capacity();this.bb=k.Builder.growByteBuffer(this.bb),this.space+=this.bb.capacity()-r}this.pad(n)},k.Builder.prototype.pad=function(e){for(var t=0;t<e;t++)this.bb.writeInt8(--this.space,0)},k.Builder.prototype.writeInt8=function(e){this.bb.writeInt8(this.space-=1,e)},k.Builder.prototype.writeInt16=function(e){this.bb.writeInt16(this.space-=2,e)},k.Builder.prototype.writeInt32=function(e){this.bb.writeInt32(this.space-=4,e)},k.Builder.prototype.writeInt64=function(e){this.bb.writeInt64(this.space-=8,e)},k.Builder.prototype.writeFloat32=function(e){this.bb.writeFloat32(this.space-=4,e)},k.Builder.prototype.writeFloat64=function(e){this.bb.writeFloat64(this.space-=8,e)},k.Builder.prototype.addInt8=function(e){this.prep(1,0),this.writeInt8(e)},k.Builder.prototype.addInt16=function(e){this.prep(2,0),this.writeInt16(e)},k.Builder.prototype.addInt32=function(e){this.prep(4,0),this.writeInt32(e)},k.Builder.prototype.addInt64=function(e){this.prep(8,0),this.writeInt64(e)},k.Builder.prototype.addFloat32=function(e){this.prep(4,0),this.writeFloat32(e)},k.Builder.prototype.addFloat64=function(e){this.prep(8,0),this.writeFloat64(e)},k.Builder.prototype.addFieldInt8=function(e,t,n){(this.force_defaults||t!=n)&&(this.addInt8(t),this.slot(e))},k.Builder.prototype.addFieldInt16=function(e,t,n){(this.force_defaults||t!=n)&&(this.addInt16(t),this.slot(e))},k.Builder.prototype.addFieldInt32=function(e,t,n){(this.force_defaults||t!=n)&&(this.addInt32(t),this.slot(e))},k.Builder.prototype.addFieldInt64=function(e,t,n){(this.force_defaults||!t.equals(n))&&(this.addInt64(t),this.slot(e))},k.Builder.prototype.addFieldFloat32=function(e,t,n){(this.force_defaults||t!=n)&&(this.addFloat32(t),this.slot(e))},k.Builder.prototype.addFieldFloat64=function(e,t,n){(this.force_defaults||t!=n)&&(this.addFloat64(t),this.slot(e))},k.Builder.prototype.addFieldOffset=function(e,t,n){(this.force_defaults||t!=n)&&(this.addOffset(t),this.slot(e))},k.Builder.prototype.addFieldStruct=function(e,t,n){t!=n&&(this.nested(t),this.slot(e))},k.Builder.prototype.nested=function(e){if(e!=this.offset())throw new Error("FlatBuffers: struct must be serialized inline.")},k.Builder.prototype.notNested=function(){if(this.isNested)throw new Error("FlatBuffers: object serialization must not be nested.")},k.Builder.prototype.slot=function(e){this.vtable[e]=this.offset()},k.Builder.prototype.offset=function(){return this.bb.capacity()-this.space},k.Builder.growByteBuffer=function(e){var t=e.capacity();if(3221225472&t)throw new Error("FlatBuffers: cannot grow buffer beyond 2 gigabytes.");var n=t<<1,r=k.ByteBuffer.allocate(n);return r.setPosition(n-t),r.bytes().set(e.bytes(),n-t),r},k.Builder.prototype.addOffset=function(e){this.prep(k.SIZEOF_INT,0),this.writeInt32(this.offset()-e+k.SIZEOF_INT)},k.Builder.prototype.startObject=function(e){this.notNested(),null==this.vtable&&(this.vtable=[]),this.vtable_in_use=e;for(var t=0;t<e;t++)this.vtable[t]=0;this.isNested=!0,this.object_start=this.offset()},k.Builder.prototype.endObject=function(){if(null==this.vtable||!this.isNested)throw new Error("FlatBuffers: endObject called without startObject");this.addInt32(0);for(var e=this.offset(),t=this.vtable_in_use-1;t>=0&&0==this.vtable[t];t--);for(var n=t+1;t>=0;t--)this.addInt16(0!=this.vtable[t]?e-this.vtable[t]:0);this.addInt16(e-this.object_start);var r=(n+2)*k.SIZEOF_SHORT;this.addInt16(r);var i=0,a=this.space;e:for(t=0;t<this.vtables.length;t++){var o=this.bb.capacity()-this.vtables[t];if(r==this.bb.readInt16(o)){for(var s=k.SIZEOF_SHORT;s<r;s+=k.SIZEOF_SHORT)if(this.bb.readInt16(a+s)!=this.bb.readInt16(o+s))continue e;i=this.vtables[t];break}}return i?(this.space=this.bb.capacity()-e,this.bb.writeInt32(this.space,i-e)):(this.vtables.push(this.offset()),this.bb.writeInt32(this.bb.capacity()-e,this.offset()-e)),this.isNested=!1,e},k.Builder.prototype.finish=function(e,t,n){var r=n?k.SIZE_PREFIX_LENGTH:0;if(t){var i=t;if(this.prep(this.minalign,k.SIZEOF_INT+k.FILE_IDENTIFIER_LENGTH+r),i.length!=k.FILE_IDENTIFIER_LENGTH)throw new Error("FlatBuffers: file identifier must be length "+k.FILE_IDENTIFIER_LENGTH);for(var a=k.FILE_IDENTIFIER_LENGTH-1;a>=0;a--)this.writeInt8(i.charCodeAt(a))}this.prep(this.minalign,k.SIZEOF_INT+r),this.addOffset(e),r&&this.addInt32(this.bb.capacity()-this.space),this.bb.setPosition(this.space)},k.Builder.prototype.finishSizePrefixed=function(e,t){this.finish(e,t,!0)},k.Builder.prototype.requiredField=function(e,t){var n=this.bb.capacity()-e,r=n-this.bb.readInt32(n);if(!(0!=this.bb.readInt16(r+t)))throw new Error("FlatBuffers: field "+t+" must be set")},k.Builder.prototype.startVector=function(e,t,n){this.notNested(),this.vector_num_elems=t,this.prep(k.SIZEOF_INT,e*t),this.prep(n,e*t)},k.Builder.prototype.endVector=function(){return this.writeInt32(this.vector_num_elems),this.offset()},k.Builder.prototype.createString=function(e){if(e instanceof Uint8Array)var t=e;else{t=[];for(var n=0;n<e.length;){var r,i=e.charCodeAt(n++);if(i<55296||i>=56320)r=i;else r=(i<<10)+e.charCodeAt(n++)+-56613888;r<128?t.push(r):(r<2048?t.push(r>>6&31|192):(r<65536?t.push(r>>12&15|224):t.push(r>>18&7|240,r>>12&63|128),t.push(r>>6&63|128)),t.push(63&r|128))}}this.addInt8(0),this.startVector(1,t.length,1),this.bb.setPosition(this.space-=t.length);n=0;for(var a=this.space,o=this.bb.bytes();n<t.length;n++)o[a++]=t[n];return this.endVector()},k.Builder.prototype.createLong=function(e,t){return k.Long.create(e,t)},k.ByteBuffer=function(e){this.bytes_=e,this.position_=0},k.ByteBuffer.allocate=function(e){return new k.ByteBuffer(new Uint8Array(e))},k.ByteBuffer.prototype.clear=function(){this.position_=0},k.ByteBuffer.prototype.bytes=function(){return this.bytes_},k.ByteBuffer.prototype.position=function(){return this.position_},k.ByteBuffer.prototype.setPosition=function(e){this.position_=e},k.ByteBuffer.prototype.capacity=function(){return this.bytes_.length},k.ByteBuffer.prototype.readInt8=function(e){return this.readUint8(e)<<24>>24},k.ByteBuffer.prototype.readUint8=function(e){return this.bytes_[e]},k.ByteBuffer.prototype.readInt16=function(e){return this.readUint16(e)<<16>>16},k.ByteBuffer.prototype.readUint16=function(e){return this.bytes_[e]|this.bytes_[e+1]<<8},k.ByteBuffer.prototype.readInt32=function(e){return this.bytes_[e]|this.bytes_[e+1]<<8|this.bytes_[e+2]<<16|this.bytes_[e+3]<<24},k.ByteBuffer.prototype.readUint32=function(e){return this.readInt32(e)>>>0},k.ByteBuffer.prototype.readInt64=function(e){return new k.Long(this.readInt32(e),this.readInt32(e+4))},k.ByteBuffer.prototype.readUint64=function(e){return new k.Long(this.readUint32(e),this.readUint32(e+4))},k.ByteBuffer.prototype.readFloat32=function(e){return k.int32[0]=this.readInt32(e),k.float32[0]},k.ByteBuffer.prototype.readFloat64=function(e){return k.int32[k.isLittleEndian?0:1]=this.readInt32(e),k.int32[k.isLittleEndian?1:0]=this.readInt32(e+4),k.float64[0]},k.ByteBuffer.prototype.writeInt8=function(e,t){this.bytes_[e]=t},k.ByteBuffer.prototype.writeUint8=function(e,t){this.bytes_[e]=t},k.ByteBuffer.prototype.writeInt16=function(e,t){this.bytes_[e]=t,this.bytes_[e+1]=t>>8},k.ByteBuffer.prototype.writeUint16=function(e,t){this.bytes_[e]=t,this.bytes_[e+1]=t>>8},k.ByteBuffer.prototype.writeInt32=function(e,t){this.bytes_[e]=t,this.bytes_[e+1]=t>>8,this.bytes_[e+2]=t>>16,this.bytes_[e+3]=t>>24},k.ByteBuffer.prototype.writeUint32=function(e,t){this.bytes_[e]=t,this.bytes_[e+1]=t>>8,this.bytes_[e+2]=t>>16,this.bytes_[e+3]=t>>24},k.ByteBuffer.prototype.writeInt64=function(e,t){this.writeInt32(e,t.low),this.writeInt32(e+4,t.high)},k.ByteBuffer.prototype.writeUint64=function(e,t){this.writeUint32(e,t.low),this.writeUint32(e+4,t.high)},k.ByteBuffer.prototype.writeFloat32=function(e,t){k.float32[0]=t,this.writeInt32(e,k.int32[0])},k.ByteBuffer.prototype.writeFloat64=function(e,t){k.float64[0]=t,this.writeInt32(e,k.int32[k.isLittleEndian?0:1]),this.writeInt32(e+4,k.int32[k.isLittleEndian?1:0])},k.ByteBuffer.prototype.getBufferIdentifier=function(){if(this.bytes_.length<this.position_+k.SIZEOF_INT+k.FILE_IDENTIFIER_LENGTH)throw new Error("FlatBuffers: ByteBuffer is too short to contain an identifier.");for(var e="",t=0;t<k.FILE_IDENTIFIER_LENGTH;t++)e+=String.fromCharCode(this.readInt8(this.position_+k.SIZEOF_INT+t));return e},k.ByteBuffer.prototype.__offset=function(e,t){var n=e-this.readInt32(e);return t<this.readInt16(n)?this.readInt16(n+t):0},k.ByteBuffer.prototype.__union=function(e,t){return e.bb_pos=t+this.readInt32(t),e.bb=this,e},k.ByteBuffer.prototype.__string=function(e,t){e+=this.readInt32(e);var n=this.readInt32(e),r="",i=0;if(e+=k.SIZEOF_INT,t===k.Encoding.UTF8_BYTES)return this.bytes_.subarray(e,e+n);for(;i<n;){var a,o=this.readUint8(e+i++);if(o<192)a=o;else{var s=this.readUint8(e+i++);if(o<224)a=(31&o)<<6|63&s;else{var u=this.readUint8(e+i++);if(o<240)a=(15&o)<<12|(63&s)<<6|63&u;else a=(7&o)<<18|(63&s)<<12|(63&u)<<6|63&this.readUint8(e+i++)}}a<65536?r+=String.fromCharCode(a):(a-=65536,r+=String.fromCharCode(55296+(a>>10),56320+(1023&a)))}return 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expected";if(null!=e.t&&e.hasOwnProperty("t")&&(n=l.onnx.TensorProto.verify(e.t)))return"t."+n;if(null!=e.g&&e.hasOwnProperty("g")&&(n=l.onnx.GraphProto.verify(e.g)))return"g."+n;if(null!=e.sparseTensor&&e.hasOwnProperty("sparseTensor")&&(n=l.onnx.SparseTensorProto.verify(e.sparseTensor)))return"sparseTensor."+n;if(null!=e.tp&&e.hasOwnProperty("tp")&&(n=l.onnx.TypeProto.verify(e.tp)))return"tp."+n;if(null!=e.floats&&e.hasOwnProperty("floats")){if(!Array.isArray(e.floats))return"floats: array expected";for(var t=0;t<e.floats.length;++t)if("number"!=typeof e.floats[t])return"floats: number[] expected"}if(null!=e.ints&&e.hasOwnProperty("ints")){if(!Array.isArray(e.ints))return"ints: array expected";for(t=0;t<e.ints.length;++t)if(!(u.isInteger(e.ints[t])||e.ints[t]&&u.isInteger(e.ints[t].low)&&u.isInteger(e.ints[t].high)))return"ints: integer|Long[] expected"}if(null!=e.strings&&e.hasOwnProperty("strings")){if(!Array.isArray(e.strings))return"strings: array expected";for(t=0;t<e.strings.length;++t)if(!(e.strings[t]&&"number"==typeof e.strings[t].length||u.isString(e.strings[t])))return"strings: buffer[] expected"}if(null!=e.tensors&&e.hasOwnProperty("tensors")){if(!Array.isArray(e.tensors))return"tensors: array expected";for(t=0;t<e.tensors.length;++t)if(n=l.onnx.TensorProto.verify(e.tensors[t]))return"tensors."+n}if(null!=e.graphs&&e.hasOwnProperty("graphs")){if(!Array.isArray(e.graphs))return"graphs: array expected";for(t=0;t<e.graphs.length;++t)if(n=l.onnx.GraphProto.verify(e.graphs[t]))return"graphs."+n}if(null!=e.sparseTensors&&e.hasOwnProperty("sparseTensors")){if(!Array.isArray(e.sparseTensors))return"sparseTensors: array expected";for(t=0;t<e.sparseTensors.length;++t)if(n=l.onnx.SparseTensorProto.verify(e.sparseTensors[t]))return"sparseTensors."+n}if(null!=e.typeProtos&&e.hasOwnProperty("typeProtos")){if(!Array.isArray(e.typeProtos))return"typeProtos: array expected";for(t=0;t<e.typeProtos.length;++t){var n;if(n=l.onnx.TypeProto.verify(e.typeProtos[t]))return"typeProtos."+n}}return null},e.fromObject=function(e){if(e instanceof l.onnx.AttributeProto)return e;var t=new l.onnx.AttributeProto;switch(null!=e.name&&(t.name=String(e.name)),null!=e.refAttrName&&(t.refAttrName=String(e.refAttrName)),null!=e.docString&&(t.docString=String(e.docString)),e.type){default:if("number"==typeof e.type){t.type=e.type;break}break;case"UNDEFINED":case 0:t.type=0;break;case"FLOAT":case 1:t.type=1;break;case"INT":case 2:t.type=2;break;case"STRING":case 3:t.type=3;break;case"TENSOR":case 4:t.type=4;break;case"GRAPH":case 5:t.type=5;break;case"SPARSE_TENSOR":case 11:t.type=11;break;case"TYPE_PROTO":case 13:t.type=13;break;case"FLOATS":case 6:t.type=6;break;case"INTS":case 7:t.type=7;break;case"STRINGS":case 8:t.type=8;break;case"TENSORS":case 9:t.type=9;break;case"GRAPHS":case 10:t.type=10;break;case"SPARSE_TENSORS":case 12:t.type=12;break;case"TYPE_PROTOS":case 14:t.type=14}if(null!=e.f&&(t.f=Number(e.f)),null!=e.i&&(u.Long?(t.i=u.Long.fromValue(e.i)).unsigned=!1:"string"==typeof e.i?t.i=parseInt(e.i,10):"number"==typeof e.i?t.i=e.i:"object"==typeof e.i&&(t.i=new u.LongBits(e.i.low>>>0,e.i.high>>>0).toNumber())),null!=e.s&&("string"==typeof e.s?u.base64.decode(e.s,t.s=u.newBuffer(u.base64.length(e.s)),0):e.s.length>=0&&(t.s=e.s)),null!=e.t){if("object"!=typeof e.t)throw TypeError(".onnx.AttributeProto.t: object expected");t.t=l.onnx.TensorProto.fromObject(e.t)}if(null!=e.g){if("object"!=typeof e.g)throw TypeError(".onnx.AttributeProto.g: object expected");t.g=l.onnx.GraphProto.fromObject(e.g)}if(null!=e.sparseTensor){if("object"!=typeof e.sparseTensor)throw TypeError(".onnx.AttributeProto.sparseTensor: object expected");t.sparseTensor=l.onnx.SparseTensorProto.fromObject(e.sparseTensor)}if(null!=e.tp){if("object"!=typeof e.tp)throw TypeError(".onnx.AttributeProto.tp: object expected");t.tp=l.onnx.TypeProto.fromObject(e.tp)}if(e.floats){if(!Array.isArray(e.floats))throw TypeError(".onnx.AttributeProto.floats: array expected");t.floats=[];for(var n=0;n<e.floats.length;++n)t.floats[n]=Number(e.floats[n])}if(e.ints){if(!Array.isArray(e.ints))throw TypeError(".onnx.AttributeProto.ints: array expected");for(t.ints=[],n=0;n<e.ints.length;++n)u.Long?(t.ints[n]=u.Long.fromValue(e.ints[n])).unsigned=!1:"string"==typeof e.ints[n]?t.ints[n]=parseInt(e.ints[n],10):"number"==typeof e.ints[n]?t.ints[n]=e.ints[n]:"object"==typeof e.ints[n]&&(t.ints[n]=new u.LongBits(e.ints[n].low>>>0,e.ints[n].high>>>0).toNumber())}if(e.strings){if(!Array.isArray(e.strings))throw TypeError(".onnx.AttributeProto.strings: array expected");for(t.strings=[],n=0;n<e.strings.length;++n)"string"==typeof e.strings[n]?u.base64.decode(e.strings[n],t.strings[n]=u.newBuffer(u.base64.length(e.strings[n])),0):e.strings[n].length>=0&&(t.strings[n]=e.strings[n])}if(e.tensors){if(!Array.isArray(e.tensors))throw TypeError(".onnx.AttributeProto.tensors: array expected");for(t.tensors=[],n=0;n<e.tensors.length;++n){if("object"!=typeof e.tensors[n])throw TypeError(".onnx.AttributeProto.tensors: object expected");t.tensors[n]=l.onnx.TensorProto.fromObject(e.tensors[n])}}if(e.graphs){if(!Array.isArray(e.graphs))throw TypeError(".onnx.AttributeProto.graphs: array expected");for(t.graphs=[],n=0;n<e.graphs.length;++n){if("object"!=typeof e.graphs[n])throw TypeError(".onnx.AttributeProto.graphs: object expected");t.graphs[n]=l.onnx.GraphProto.fromObject(e.graphs[n])}}if(e.sparseTensors){if(!Array.isArray(e.sparseTensors))throw TypeError(".onnx.AttributeProto.sparseTensors: array expected");for(t.sparseTensors=[],n=0;n<e.sparseTensors.length;++n){if("object"!=typeof e.sparseTensors[n])throw TypeError(".onnx.AttributeProto.sparseTensors: object expected");t.sparseTensors[n]=l.onnx.SparseTensorProto.fromObject(e.sparseTensors[n])}}if(e.typeProtos){if(!Array.isArray(e.typeProtos))throw TypeError(".onnx.AttributeProto.typeProtos: array expected");for(t.typeProtos=[],n=0;n<e.typeProtos.length;++n){if("object"!=typeof e.typeProtos[n])throw TypeError(".onnx.AttributeProto.typeProtos: object expected");t.typeProtos[n]=l.onnx.TypeProto.fromObject(e.typeProtos[n])}}return t},e.toObject=function(e,t){t||(t={});var n={};if((t.arrays||t.defaults)&&(n.floats=[],n.ints=[],n.strings=[],n.tensors=[],n.graphs=[],n.typeProtos=[],n.sparseTensors=[]),t.defaults){if(n.name="",n.f=0,u.Long){var r=new u.Long(0,0,!1);n.i=t.longs===String?r.toString():t.longs===Number?r.toNumber():r}else n.i=t.longs===String?"0":0;t.bytes===String?n.s="":(n.s=[],t.bytes!==Array&&(n.s=u.newBuffer(n.s))),n.t=null,n.g=null,n.docString="",n.tp=null,n.type=t.enums===String?"UNDEFINED":0,n.refAttrName="",n.sparseTensor=null}if(null!=e.name&&e.hasOwnProperty("name")&&(n.name=e.name),null!=e.f&&e.hasOwnProperty("f")&&(n.f=t.json&&!isFinite(e.f)?String(e.f):e.f),null!=e.i&&e.hasOwnProperty("i")&&("number"==typeof e.i?n.i=t.longs===String?String(e.i):e.i:n.i=t.longs===String?u.Long.prototype.toString.call(e.i):t.longs===Number?new u.LongBits(e.i.low>>>0,e.i.high>>>0).toNumber():e.i),null!=e.s&&e.hasOwnProperty("s")&&(n.s=t.bytes===String?u.base64.encode(e.s,0,e.s.length):t.bytes===Array?Array.prototype.slice.call(e.s):e.s),null!=e.t&&e.hasOwnProperty("t")&&(n.t=l.onnx.TensorProto.toObject(e.t,t)),null!=e.g&&e.hasOwnProperty("g")&&(n.g=l.onnx.GraphProto.toObject(e.g,t)),e.floats&&e.floats.length){n.floats=[];for(var i=0;i<e.floats.length;++i)n.floats[i]=t.json&&!isFinite(e.floats[i])?String(e.floats[i]):e.floats[i]}if(e.ints&&e.ints.length)for(n.ints=[],i=0;i<e.ints.length;++i)"number"==typeof e.ints[i]?n.ints[i]=t.longs===String?String(e.ints[i]):e.ints[i]:n.ints[i]=t.longs===String?u.Long.prototype.toString.call(e.ints[i]):t.longs===Number?new u.LongBits(e.ints[i].low>>>0,e.ints[i].high>>>0).toNumber():e.ints[i];if(e.strings&&e.strings.length)for(n.strings=[],i=0;i<e.strings.length;++i)n.strings[i]=t.bytes===String?u.base64.encode(e.strings[i],0,e.strings[i].length):t.bytes===Array?Array.prototype.slice.call(e.strings[i]):e.strings[i];if(e.tensors&&e.tensors.length)for(n.tensors=[],i=0;i<e.tensors.length;++i)n.tensors[i]=l.onnx.TensorProto.toObject(e.tensors[i],t);if(e.graphs&&e.graphs.length)for(n.graphs=[],i=0;i<e.graphs.length;++i)n.graphs[i]=l.onnx.GraphProto.toObject(e.graphs[i],t);if(null!=e.docString&&e.hasOwnProperty("docString")&&(n.docString=e.docString),null!=e.tp&&e.hasOwnProperty("tp")&&(n.tp=l.onnx.TypeProto.toObject(e.tp,t)),e.typeProtos&&e.typeProtos.length)for(n.typeProtos=[],i=0;i<e.typeProtos.length;++i)n.typeProtos[i]=l.onnx.TypeProto.toObject(e.typeProtos[i],t);if(null!=e.type&&e.hasOwnProperty("type")&&(n.type=t.enums===String?void 0===l.onnx.AttributeProto.AttributeType[e.type]?e.type:l.onnx.AttributeProto.AttributeType[e.type]:e.type),null!=e.refAttrName&&e.hasOwnProperty("refAttrName")&&(n.refAttrName=e.refAttrName),null!=e.sparseTensor&&e.hasOwnProperty("sparseTensor")&&(n.sparseTensor=l.onnx.SparseTensorProto.toObject(e.sparseTensor,t)),e.sparseTensors&&e.sparseTensors.length)for(n.sparseTensors=[],i=0;i<e.sparseTensors.length;++i)n.sparseTensors[i]=l.onnx.SparseTensorProto.toObject(e.sparseTensors[i],t);return n},e.prototype.toJSON=function(){return this.constructor.toObject(this,a.util.toJSONOptions)},e.getTypeUrl=function(e){return void 0===e&&(e="type.googleapis.com"),e+"/onnx.AttributeProto"},e.AttributeType=function(){var e={},t=Object.create(e);return t[e[0]="UNDEFINED"]=0,t[e[1]="FLOAT"]=1,t[e[2]="INT"]=2,t[e[3]="STRING"]=3,t[e[4]="TENSOR"]=4,t[e[5]="GRAPH"]=5,t[e[11]="SPARSE_TENSOR"]=11,t[e[13]="TYPE_PROTO"]=13,t[e[6]="FLOATS"]=6,t[e[7]="INTS"]=7,t[e[8]="STRINGS"]=8,t[e[9]="TENSORS"]=9,t[e[10]="GRAPHS"]=10,t[e[12]="SPARSE_TENSORS"]=12,t[e[14]="TYPE_PROTOS"]=14,t}(),e}(),i.ValueInfoProto=function(){function e(e){if(e)for(var t=Object.keys(e),n=0;n<t.length;++n)null!=e[t[n]]&&(this[t[n]]=e[t[n]])}return e.prototype.name="",e.prototype.type=null,e.prototype.docString="",e.create=function(t){return new e(t)},e.encode=function(e,t){return t||(t=s.create()),null!=e.name&&Object.hasOwnProperty.call(e,"name")&&t.uint32(10).string(e.name),null!=e.type&&Object.hasOwnProperty.call(e,"type")&&l.onnx.TypeProto.encode(e.type,t.uint32(18).fork()).ldelim(),null!=e.docString&&Object.hasOwnProperty.call(e,"docString")&&t.uint32(26).string(e.docString),t},e.encodeDelimited=function(e,t){return this.encode(e,t).ldelim()},e.decode=function(e,t){e instanceof o||(e=o.create(e));for(var n=void 0===t?e.len:e.pos+t,r=new l.onnx.ValueInfoProto;e.pos<n;){var i=e.uint32();switch(i>>>3){case 1:r.name=e.string();break;case 2:r.type=l.onnx.TypeProto.decode(e,e.uint32());break;case 3:r.docString=e.string();break;default:e.skipType(7&i)}}return r},e.decodeDelimited=function(e){return e instanceof o||(e=new o(e)),this.decode(e,e.uint32())},e.verify=function(e){if("object"!=typeof e||null===e)return"object expected";if(null!=e.name&&e.hasOwnProperty("name")&&!u.isString(e.name))return"name: string expected";if(null!=e.type&&e.hasOwnProperty("type")){var t=l.onnx.TypeProto.verify(e.type);if(t)return"type."+t}return null!=e.docString&&e.hasOwnProperty("docString")&&!u.isString(e.docString)?"docString: string expected":null},e.fromObject=function(e){if(e instanceof l.onnx.ValueInfoProto)return e;var t=new l.onnx.ValueInfoProto;if(null!=e.name&&(t.name=String(e.name)),null!=e.type){if("object"!=typeof e.type)throw TypeError(".onnx.ValueInfoProto.type: object expected");t.type=l.onnx.TypeProto.fromObject(e.type)}return null!=e.docString&&(t.docString=String(e.docString)),t},e.toObject=function(e,t){t||(t={});var n={};return t.defaults&&(n.name="",n.type=null,n.docString=""),null!=e.name&&e.hasOwnProperty("name")&&(n.name=e.name),null!=e.type&&e.hasOwnProperty("type")&&(n.type=l.onnx.TypeProto.toObject(e.type,t)),null!=e.docString&&e.hasOwnProperty("docString")&&(n.docString=e.docString),n},e.prototype.toJSON=function(){return this.constructor.toObject(this,a.util.toJSONOptions)},e.getTypeUrl=function(e){return void 0===e&&(e="type.googleapis.com"),e+"/onnx.ValueInfoProto"},e}(),i.NodeProto=function(){function e(e){if(this.input=[],this.output=[],this.attribute=[],e)for(var t=Object.keys(e),n=0;n<t.length;++n)null!=e[t[n]]&&(this[t[n]]=e[t[n]])}return e.prototype.input=u.emptyArray,e.prototype.output=u.emptyArray,e.prototype.name="",e.prototype.opType="",e.prototype.domain="",e.prototype.attribute=u.emptyArray,e.prototype.docString="",e.create=function(t){return new e(t)},e.encode=function(e,t){if(t||(t=s.create()),null!=e.input&&e.input.length)for(var n=0;n<e.input.length;++n)t.uint32(10).string(e.input[n]);if(null!=e.output&&e.output.length)for(n=0;n<e.output.length;++n)t.uint32(18).string(e.output[n]);if(null!=e.name&&Object.hasOwnProperty.call(e,"name")&&t.uint32(26).string(e.name),null!=e.opType&&Object.hasOwnProperty.call(e,"opType")&&t.uint32(34).string(e.opType),null!=e.attribute&&e.attribute.length)for(n=0;n<e.attribute.length;++n)l.onnx.AttributeProto.encode(e.attribute[n],t.uint32(42).fork()).ldelim();return null!=e.docString&&Object.hasOwnProperty.call(e,"docString")&&t.uint32(50).string(e.docString),null!=e.domain&&Object.hasOwnProperty.call(e,"domain")&&t.uint32(58).string(e.domain),t},e.encodeDelimited=function(e,t){return this.encode(e,t).ldelim()},e.decode=function(e,t){e instanceof o||(e=o.create(e));for(var n=void 0===t?e.len:e.pos+t,r=new l.onnx.NodeProto;e.pos<n;){var i=e.uint32();switch(i>>>3){case 1:r.input&&r.input.length||(r.input=[]),r.input.push(e.string());break;case 2:r.output&&r.output.length||(r.output=[]),r.output.push(e.string());break;case 3:r.name=e.string();break;case 4:r.opType=e.string();break;case 7:r.domain=e.string();break;case 5:r.attribute&&r.attribute.length||(r.attribute=[]),r.attribute.push(l.onnx.AttributeProto.decode(e,e.uint32()));break;case 6:r.docString=e.string();break;default:e.skipType(7&i)}}return r},e.decodeDelimited=function(e){return e instanceof o||(e=new o(e)),this.decode(e,e.uint32())},e.verify=function(e){if("object"!=typeof e||null===e)return"object expected";if(null!=e.input&&e.hasOwnProperty("input")){if(!Array.isArray(e.input))return"input: array expected";for(var t=0;t<e.input.length;++t)if(!u.isString(e.input[t]))return"input: string[] expected"}if(null!=e.output&&e.hasOwnProperty("output")){if(!Array.isArray(e.output))return"output: array expected";for(t=0;t<e.output.length;++t)if(!u.isString(e.output[t]))return"output: string[] expected"}if(null!=e.name&&e.hasOwnProperty("name")&&!u.isString(e.name))return"name: string expected";if(null!=e.opType&&e.hasOwnProperty("opType")&&!u.isString(e.opType))return"opType: string expected";if(null!=e.domain&&e.hasOwnProperty("domain")&&!u.isString(e.domain))return"domain: string expected";if(null!=e.attribute&&e.hasOwnProperty("attribute")){if(!Array.isArray(e.attribute))return"attribute: array expected";for(t=0;t<e.attribute.length;++t){var n=l.onnx.AttributeProto.verify(e.attribute[t]);if(n)return"attribute."+n}}return null!=e.docString&&e.hasOwnProperty("docString")&&!u.isString(e.docString)?"docString: string expected":null},e.fromObject=function(e){if(e instanceof l.onnx.NodeProto)return e;var t=new l.onnx.NodeProto;if(e.input){if(!Array.isArray(e.input))throw TypeError(".onnx.NodeProto.input: array expected");t.input=[];for(var n=0;n<e.input.length;++n)t.input[n]=String(e.input[n])}if(e.output){if(!Array.isArray(e.output))throw TypeError(".onnx.NodeProto.output: array expected");for(t.output=[],n=0;n<e.output.length;++n)t.output[n]=String(e.output[n])}if(null!=e.name&&(t.name=String(e.name)),null!=e.opType&&(t.opType=String(e.opType)),null!=e.domain&&(t.domain=String(e.domain)),e.attribute){if(!Array.isArray(e.attribute))throw TypeError(".onnx.NodeProto.attribute: array expected");for(t.attribute=[],n=0;n<e.attribute.length;++n){if("object"!=typeof e.attribute[n])throw TypeError(".onnx.NodeProto.attribute: object expected");t.attribute[n]=l.onnx.AttributeProto.fromObject(e.attribute[n])}}return null!=e.docString&&(t.docString=String(e.docString)),t},e.toObject=function(e,t){t||(t={});var n={};if((t.arrays||t.defaults)&&(n.input=[],n.output=[],n.attribute=[]),t.defaults&&(n.name="",n.opType="",n.docString="",n.domain=""),e.input&&e.input.length){n.input=[];for(var r=0;r<e.input.length;++r)n.input[r]=e.input[r]}if(e.output&&e.output.length)for(n.output=[],r=0;r<e.output.length;++r)n.output[r]=e.output[r];if(null!=e.name&&e.hasOwnProperty("name")&&(n.name=e.name),null!=e.opType&&e.hasOwnProperty("opType")&&(n.opType=e.opType),e.attribute&&e.attribute.length)for(n.attribute=[],r=0;r<e.attribute.length;++r)n.attribute[r]=l.onnx.AttributeProto.toObject(e.attribute[r],t);return null!=e.docString&&e.hasOwnProperty("docString")&&(n.docString=e.docString),null!=e.domain&&e.hasOwnProperty("domain")&&(n.domain=e.domain),n},e.prototype.toJSON=function(){return this.constructor.toObject(this,a.util.toJSONOptions)},e.getTypeUrl=function(e){return void 0===e&&(e="type.googleapis.com"),e+"/onnx.NodeProto"},e}(),i.TrainingInfoProto=function(){function e(e){if(this.initializationBinding=[],this.updateBinding=[],e)for(var t=Object.keys(e),n=0;n<t.length;++n)null!=e[t[n]]&&(this[t[n]]=e[t[n]])}return e.prototype.initialization=null,e.prototype.algorithm=null,e.prototype.initializationBinding=u.emptyArray,e.prototype.updateBinding=u.emptyArray,e.create=function(t){return new e(t)},e.encode=function(e,t){if(t||(t=s.create()),null!=e.initialization&&Object.hasOwnProperty.call(e,"initialization")&&l.onnx.GraphProto.encode(e.initialization,t.uint32(10).fork()).ldelim(),null!=e.algorithm&&Object.hasOwnProperty.call(e,"algorithm")&&l.onnx.GraphProto.encode(e.algorithm,t.uint32(18).fork()).ldelim(),null!=e.initializationBinding&&e.initializationBinding.length)for(var n=0;n<e.initializationBinding.length;++n)l.onnx.StringStringEntryProto.encode(e.initializationBinding[n],t.uint32(26).fork()).ldelim();if(null!=e.updateBinding&&e.updateBinding.length)for(n=0;n<e.updateBinding.length;++n)l.onnx.StringStringEntryProto.encode(e.updateBinding[n],t.uint32(34).fork()).ldelim();return t},e.encodeDelimited=function(e,t){return this.encode(e,t).ldelim()},e.decode=function(e,t){e instanceof o||(e=o.create(e));for(var n=void 0===t?e.len:e.pos+t,r=new l.onnx.TrainingInfoProto;e.pos<n;){var i=e.uint32();switch(i>>>3){case 1:r.initialization=l.onnx.GraphProto.decode(e,e.uint32());break;case 2:r.algorithm=l.onnx.GraphProto.decode(e,e.uint32());break;case 3:r.initializationBinding&&r.initializationBinding.length||(r.initializationBinding=[]),r.initializationBinding.push(l.onnx.StringStringEntryProto.decode(e,e.uint32()));break;case 4:r.updateBinding&&r.updateBinding.length||(r.updateBinding=[]),r.updateBinding.push(l.onnx.StringStringEntryProto.decode(e,e.uint32()));break;default:e.skipType(7&i)}}return r},e.decodeDelimited=function(e){return e instanceof o||(e=new o(e)),this.decode(e,e.uint32())},e.verify=function(e){if("object"!=typeof e||null===e)return"object expected";if(null!=e.initialization&&e.hasOwnProperty("initialization")&&(n=l.onnx.GraphProto.verify(e.initialization)))return"initialization."+n;if(null!=e.algorithm&&e.hasOwnProperty("algorithm")&&(n=l.onnx.GraphProto.verify(e.algorithm)))return"algorithm."+n;if(null!=e.initializationBinding&&e.hasOwnProperty("initializationBinding")){if(!Array.isArray(e.initializationBinding))return"initializationBinding: array expected";for(var t=0;t<e.initializationBinding.length;++t)if(n=l.onnx.StringStringEntryProto.verify(e.initializationBinding[t]))return"initializationBinding."+n}if(null!=e.updateBinding&&e.hasOwnProperty("updateBinding")){if(!Array.isArray(e.updateBinding))return"updateBinding: array expected";for(t=0;t<e.updateBinding.length;++t){var n;if(n=l.onnx.StringStringEntryProto.verify(e.updateBinding[t]))return"updateBinding."+n}}return null},e.fromObject=function(e){if(e instanceof l.onnx.TrainingInfoProto)return e;var t=new l.onnx.TrainingInfoProto;if(null!=e.initialization){if("object"!=typeof e.initialization)throw TypeError(".onnx.TrainingInfoProto.initialization: object expected");t.initialization=l.onnx.GraphProto.fromObject(e.initialization)}if(null!=e.algorithm){if("object"!=typeof e.algorithm)throw TypeError(".onnx.TrainingInfoProto.algorithm: object expected");t.algorithm=l.onnx.GraphProto.fromObject(e.algorithm)}if(e.initializationBinding){if(!Array.isArray(e.initializationBinding))throw TypeError(".onnx.TrainingInfoProto.initializationBinding: array expected");t.initializationBinding=[];for(var n=0;n<e.initializationBinding.length;++n){if("object"!=typeof e.initializationBinding[n])throw TypeError(".onnx.TrainingInfoProto.initializationBinding: object expected");t.initializationBinding[n]=l.onnx.StringStringEntryProto.fromObject(e.initializationBinding[n])}}if(e.updateBinding){if(!Array.isArray(e.updateBinding))throw TypeError(".onnx.TrainingInfoProto.updateBinding: array expected");for(t.updateBinding=[],n=0;n<e.updateBinding.length;++n){if("object"!=typeof e.updateBinding[n])throw TypeError(".onnx.TrainingInfoProto.updateBinding: object expected");t.updateBinding[n]=l.onnx.StringStringEntryProto.fromObject(e.updateBinding[n])}}return t},e.toObject=function(e,t){t||(t={});var n={};if((t.arrays||t.defaults)&&(n.initializationBinding=[],n.updateBinding=[]),t.defaults&&(n.initialization=null,n.algorithm=null),null!=e.initialization&&e.hasOwnProperty("initialization")&&(n.initialization=l.onnx.GraphProto.toObject(e.initialization,t)),null!=e.algorithm&&e.hasOwnProperty("algorithm")&&(n.algorithm=l.onnx.GraphProto.toObject(e.algorithm,t)),e.initializationBinding&&e.initializationBinding.length){n.initializationBinding=[];for(var r=0;r<e.initializationBinding.length;++r)n.initializationBinding[r]=l.onnx.StringStringEntryProto.toObject(e.initializationBinding[r],t)}if(e.updateBinding&&e.updateBinding.length)for(n.updateBinding=[],r=0;r<e.updateBinding.length;++r)n.updateBinding[r]=l.onnx.StringStringEntryProto.toObject(e.updateBinding[r],t);return n},e.prototype.toJSON=function(){return this.constructor.toObject(this,a.util.toJSONOptions)},e.getTypeUrl=function(e){return void 0===e&&(e="type.googleapis.com"),e+"/onnx.TrainingInfoProto"},e}(),i.ModelProto=function(){function e(e){if(this.opsetImport=[],this.metadataProps=[],this.trainingInfo=[],this.functions=[],e)for(var t=Object.keys(e),n=0;n<t.length;++n)null!=e[t[n]]&&(this[t[n]]=e[t[n]])}return e.prototype.irVersion=u.Long?u.Long.fromBits(0,0,!1):0,e.prototype.opsetImport=u.emptyArray,e.prototype.producerName="",e.prototype.producerVersion="",e.prototype.domain="",e.prototype.modelVersion=u.Long?u.Long.fromBits(0,0,!1):0,e.prototype.docString="",e.prototype.graph=null,e.prototype.metadataProps=u.emptyArray,e.prototype.trainingInfo=u.emptyArray,e.prototype.functions=u.emptyArray,e.create=function(t){return new 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n=0;n<e.opsetImport.length;++n)l.onnx.OperatorSetIdProto.encode(e.opsetImport[n],t.uint32(66).fork()).ldelim();if(null!=e.metadataProps&&e.metadataProps.length)for(n=0;n<e.metadataProps.length;++n)l.onnx.StringStringEntryProto.encode(e.metadataProps[n],t.uint32(114).fork()).ldelim();if(null!=e.trainingInfo&&e.trainingInfo.length)for(n=0;n<e.trainingInfo.length;++n)l.onnx.TrainingInfoProto.encode(e.trainingInfo[n],t.uint32(162).fork()).ldelim();if(null!=e.functions&&e.functions.length)for(n=0;n<e.functions.length;++n)l.onnx.FunctionProto.encode(e.functions[n],t.uint32(202).fork()).ldelim();return t},e.encodeDelimited=function(e,t){return this.encode(e,t).ldelim()},e.decode=function(e,t){e instanceof o||(e=o.create(e));for(var n=void 0===t?e.len:e.pos+t,r=new l.onnx.ModelProto;e.pos<n;){var i=e.uint32();switch(i>>>3){case 1:r.irVersion=e.int64();break;case 8:r.opsetImport&&r.opsetImport.length||(r.opsetImport=[]),r.opsetImport.push(l.onnx.OperatorSetIdProto.decode(e,e.uint32()));break;case 2:r.producerName=e.string();break;case 3:r.producerVersion=e.string();break;case 4:r.domain=e.string();break;case 5:r.modelVersion=e.int64();break;case 6:r.docString=e.string();break;case 7:r.graph=l.onnx.GraphProto.decode(e,e.uint32());break;case 14:r.metadataProps&&r.metadataProps.length||(r.metadataProps=[]),r.metadataProps.push(l.onnx.StringStringEntryProto.decode(e,e.uint32()));break;case 20:r.trainingInfo&&r.trainingInfo.length||(r.trainingInfo=[]),r.trainingInfo.push(l.onnx.TrainingInfoProto.decode(e,e.uint32()));break;case 25:r.functions&&r.functions.length||(r.functions=[]),r.functions.push(l.onnx.FunctionProto.decode(e,e.uint32()));break;default:e.skipType(7&i)}}return r},e.decodeDelimited=function(e){return e instanceof o||(e=new o(e)),this.decode(e,e.uint32())},e.verify=function(e){if("object"!=typeof e||null===e)return"object 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expected";if(null!=e.modelVersion&&e.hasOwnProperty("modelVersion")&&!u.isInteger(e.modelVersion)&&!(e.modelVersion&&u.isInteger(e.modelVersion.low)&&u.isInteger(e.modelVersion.high)))return"modelVersion: integer|Long expected";if(null!=e.docString&&e.hasOwnProperty("docString")&&!u.isString(e.docString))return"docString: string expected";if(null!=e.graph&&e.hasOwnProperty("graph")&&(n=l.onnx.GraphProto.verify(e.graph)))return"graph."+n;if(null!=e.metadataProps&&e.hasOwnProperty("metadataProps")){if(!Array.isArray(e.metadataProps))return"metadataProps: array expected";for(t=0;t<e.metadataProps.length;++t)if(n=l.onnx.StringStringEntryProto.verify(e.metadataProps[t]))return"metadataProps."+n}if(null!=e.trainingInfo&&e.hasOwnProperty("trainingInfo")){if(!Array.isArray(e.trainingInfo))return"trainingInfo: array expected";for(t=0;t<e.trainingInfo.length;++t)if(n=l.onnx.TrainingInfoProto.verify(e.trainingInfo[t]))return"trainingInfo."+n}if(null!=e.functions&&e.hasOwnProperty("functions")){if(!Array.isArray(e.functions))return"functions: array expected";for(t=0;t<e.functions.length;++t){var n;if(n=l.onnx.FunctionProto.verify(e.functions[t]))return"functions."+n}}return null},e.fromObject=function(e){if(e instanceof l.onnx.ModelProto)return e;var t=new l.onnx.ModelProto;if(null!=e.irVersion&&(u.Long?(t.irVersion=u.Long.fromValue(e.irVersion)).unsigned=!1:"string"==typeof e.irVersion?t.irVersion=parseInt(e.irVersion,10):"number"==typeof e.irVersion?t.irVersion=e.irVersion:"object"==typeof e.irVersion&&(t.irVersion=new u.LongBits(e.irVersion.low>>>0,e.irVersion.high>>>0).toNumber())),e.opsetImport){if(!Array.isArray(e.opsetImport))throw TypeError(".onnx.ModelProto.opsetImport: array expected");t.opsetImport=[];for(var n=0;n<e.opsetImport.length;++n){if("object"!=typeof e.opsetImport[n])throw TypeError(".onnx.ModelProto.opsetImport: object expected");t.opsetImport[n]=l.onnx.OperatorSetIdProto.fromObject(e.opsetImport[n])}}if(null!=e.producerName&&(t.producerName=String(e.producerName)),null!=e.producerVersion&&(t.producerVersion=String(e.producerVersion)),null!=e.domain&&(t.domain=String(e.domain)),null!=e.modelVersion&&(u.Long?(t.modelVersion=u.Long.fromValue(e.modelVersion)).unsigned=!1:"string"==typeof e.modelVersion?t.modelVersion=parseInt(e.modelVersion,10):"number"==typeof e.modelVersion?t.modelVersion=e.modelVersion:"object"==typeof e.modelVersion&&(t.modelVersion=new u.LongBits(e.modelVersion.low>>>0,e.modelVersion.high>>>0).toNumber())),null!=e.docString&&(t.docString=String(e.docString)),null!=e.graph){if("object"!=typeof e.graph)throw TypeError(".onnx.ModelProto.graph: object expected");t.graph=l.onnx.GraphProto.fromObject(e.graph)}if(e.metadataProps){if(!Array.isArray(e.metadataProps))throw TypeError(".onnx.ModelProto.metadataProps: array expected");for(t.metadataProps=[],n=0;n<e.metadataProps.length;++n){if("object"!=typeof e.metadataProps[n])throw TypeError(".onnx.ModelProto.metadataProps: object expected");t.metadataProps[n]=l.onnx.StringStringEntryProto.fromObject(e.metadataProps[n])}}if(e.trainingInfo){if(!Array.isArray(e.trainingInfo))throw TypeError(".onnx.ModelProto.trainingInfo: array expected");for(t.trainingInfo=[],n=0;n<e.trainingInfo.length;++n){if("object"!=typeof e.trainingInfo[n])throw TypeError(".onnx.ModelProto.trainingInfo: object expected");t.trainingInfo[n]=l.onnx.TrainingInfoProto.fromObject(e.trainingInfo[n])}}if(e.functions){if(!Array.isArray(e.functions))throw TypeError(".onnx.ModelProto.functions: array expected");for(t.functions=[],n=0;n<e.functions.length;++n){if("object"!=typeof e.functions[n])throw TypeError(".onnx.ModelProto.functions: object expected");t.functions[n]=l.onnx.FunctionProto.fromObject(e.functions[n])}}return t},e.toObject=function(e,t){t||(t={});var n={};if((t.arrays||t.defaults)&&(n.opsetImport=[],n.metadataProps=[],n.trainingInfo=[],n.functions=[]),t.defaults){if(u.Long){var r=new u.Long(0,0,!1);n.irVersion=t.longs===String?r.toString():t.longs===Number?r.toNumber():r}else n.irVersion=t.longs===String?"0":0;n.producerName="",n.producerVersion="",n.domain="",u.Long?(r=new u.Long(0,0,!1),n.modelVersion=t.longs===String?r.toString():t.longs===Number?r.toNumber():r):n.modelVersion=t.longs===String?"0":0,n.docString="",n.graph=null}if(null!=e.irVersion&&e.hasOwnProperty("irVersion")&&("number"==typeof e.irVersion?n.irVersion=t.longs===String?String(e.irVersion):e.irVersion:n.irVersion=t.longs===String?u.Long.prototype.toString.call(e.irVersion):t.longs===Number?new u.LongBits(e.irVersion.low>>>0,e.irVersion.high>>>0).toNumber():e.irVersion),null!=e.producerName&&e.hasOwnProperty("producerName")&&(n.producerName=e.producerName),null!=e.producerVersion&&e.hasOwnProperty("producerVersion")&&(n.producerVersion=e.producerVersion),null!=e.domain&&e.hasOwnProperty("domain")&&(n.domain=e.domain),null!=e.modelVersion&&e.hasOwnProperty("modelVersion")&&("number"==typeof e.modelVersion?n.modelVersion=t.longs===String?String(e.modelVersion):e.modelVersion:n.modelVersion=t.longs===String?u.Long.prototype.toString.call(e.modelVersion):t.longs===Number?new u.LongBits(e.modelVersion.low>>>0,e.modelVersion.high>>>0).toNumber():e.modelVersion),null!=e.docString&&e.hasOwnProperty("docString")&&(n.docString=e.docString),null!=e.graph&&e.hasOwnProperty("graph")&&(n.graph=l.onnx.GraphProto.toObject(e.graph,t)),e.opsetImport&&e.opsetImport.length){n.opsetImport=[];for(var i=0;i<e.opsetImport.length;++i)n.opsetImport[i]=l.onnx.OperatorSetIdProto.toObject(e.opsetImport[i],t)}if(e.metadataProps&&e.metadataProps.length)for(n.metadataProps=[],i=0;i<e.metadataProps.length;++i)n.metadataProps[i]=l.onnx.StringStringEntryProto.toObject(e.metadataProps[i],t);if(e.trainingInfo&&e.trainingInfo.length)for(n.trainingInfo=[],i=0;i<e.trainingInfo.length;++i)n.trainingInfo[i]=l.onnx.TrainingInfoProto.toObject(e.trainingInfo[i],t);if(e.functions&&e.functions.length)for(n.functions=[],i=0;i<e.functions.length;++i)n.functions[i]=l.onnx.FunctionProto.toObject(e.functions[i],t);return n},e.prototype.toJSON=function(){return this.constructor.toObject(this,a.util.toJSONOptions)},e.getTypeUrl=function(e){return void 0===e&&(e="type.googleapis.com"),e+"/onnx.ModelProto"},e}(),i.StringStringEntryProto=function(){function e(e){if(e)for(var t=Object.keys(e),n=0;n<t.length;++n)null!=e[t[n]]&&(this[t[n]]=e[t[n]])}return e.prototype.key="",e.prototype.value="",e.create=function(t){return new e(t)},e.encode=function(e,t){return t||(t=s.create()),null!=e.key&&Object.hasOwnProperty.call(e,"key")&&t.uint32(10).string(e.key),null!=e.value&&Object.hasOwnProperty.call(e,"value")&&t.uint32(18).string(e.value),t},e.encodeDelimited=function(e,t){return this.encode(e,t).ldelim()},e.decode=function(e,t){e instanceof o||(e=o.create(e));for(var n=void 0===t?e.len:e.pos+t,r=new l.onnx.StringStringEntryProto;e.pos<n;){var i=e.uint32();switch(i>>>3){case 1:r.key=e.string();break;case 2:r.value=e.string();break;default:e.skipType(7&i)}}return r},e.decodeDelimited=function(e){return e instanceof o||(e=new o(e)),this.decode(e,e.uint32())},e.verify=function(e){return"object"!=typeof e||null===e?"object expected":null!=e.key&&e.hasOwnProperty("key")&&!u.isString(e.key)?"key: string expected":null!=e.value&&e.hasOwnProperty("value")&&!u.isString(e.value)?"value: string expected":null},e.fromObject=function(e){if(e instanceof l.onnx.StringStringEntryProto)return e;var t=new l.onnx.StringStringEntryProto;return null!=e.key&&(t.key=String(e.key)),null!=e.value&&(t.value=String(e.value)),t},e.toObject=function(e,t){t||(t={});var n={};return t.defaults&&(n.key="",n.value=""),null!=e.key&&e.hasOwnProperty("key")&&(n.key=e.key),null!=e.value&&e.hasOwnProperty("value")&&(n.value=e.value),n},e.prototype.toJSON=function(){return this.constructor.toObject(this,a.util.toJSONOptions)},e.getTypeUrl=function(e){return void 0===e&&(e="type.googleapis.com"),e+"/onnx.StringStringEntryProto"},e}(),i.TensorAnnotation=function(){function e(e){if(this.quantParameterTensorNames=[],e)for(var t=Object.keys(e),n=0;n<t.length;++n)null!=e[t[n]]&&(this[t[n]]=e[t[n]])}return e.prototype.tensorName="",e.prototype.quantParameterTensorNames=u.emptyArray,e.create=function(t){return new e(t)},e.encode=function(e,t){if(t||(t=s.create()),null!=e.tensorName&&Object.hasOwnProperty.call(e,"tensorName")&&t.uint32(10).string(e.tensorName),null!=e.quantParameterTensorNames&&e.quantParameterTensorNames.length)for(var n=0;n<e.quantParameterTensorNames.length;++n)l.onnx.StringStringEntryProto.encode(e.quantParameterTensorNames[n],t.uint32(18).fork()).ldelim();return t},e.encodeDelimited=function(e,t){return this.encode(e,t).ldelim()},e.decode=function(e,t){e instanceof o||(e=o.create(e));for(var n=void 0===t?e.len:e.pos+t,r=new l.onnx.TensorAnnotation;e.pos<n;){var i=e.uint32();switch(i>>>3){case 1:r.tensorName=e.string();break;case 2:r.quantParameterTensorNames&&r.quantParameterTensorNames.length||(r.quantParameterTensorNames=[]),r.quantParameterTensorNames.push(l.onnx.StringStringEntryProto.decode(e,e.uint32()));break;default:e.skipType(7&i)}}return r},e.decodeDelimited=function(e){return e instanceof o||(e=new o(e)),this.decode(e,e.uint32())},e.verify=function(e){if("object"!=typeof e||null===e)return"object expected";if(null!=e.tensorName&&e.hasOwnProperty("tensorName")&&!u.isString(e.tensorName))return"tensorName: string expected";if(null!=e.quantParameterTensorNames&&e.hasOwnProperty("quantParameterTensorNames")){if(!Array.isArray(e.quantParameterTensorNames))return"quantParameterTensorNames: array expected";for(var t=0;t<e.quantParameterTensorNames.length;++t){var n=l.onnx.StringStringEntryProto.verify(e.quantParameterTensorNames[t]);if(n)return"quantParameterTensorNames."+n}}return null},e.fromObject=function(e){if(e instanceof l.onnx.TensorAnnotation)return e;var t=new l.onnx.TensorAnnotation;if(null!=e.tensorName&&(t.tensorName=String(e.tensorName)),e.quantParameterTensorNames){if(!Array.isArray(e.quantParameterTensorNames))throw TypeError(".onnx.TensorAnnotation.quantParameterTensorNames: array expected");t.quantParameterTensorNames=[];for(var n=0;n<e.quantParameterTensorNames.length;++n){if("object"!=typeof e.quantParameterTensorNames[n])throw TypeError(".onnx.TensorAnnotation.quantParameterTensorNames: object expected");t.quantParameterTensorNames[n]=l.onnx.StringStringEntryProto.fromObject(e.quantParameterTensorNames[n])}}return t},e.toObject=function(e,t){t||(t={});var n={};if((t.arrays||t.defaults)&&(n.quantParameterTensorNames=[]),t.defaults&&(n.tensorName=""),null!=e.tensorName&&e.hasOwnProperty("tensorName")&&(n.tensorName=e.tensorName),e.quantParameterTensorNames&&e.quantParameterTensorNames.length){n.quantParameterTensorNames=[];for(var r=0;r<e.quantParameterTensorNames.length;++r)n.quantParameterTensorNames[r]=l.onnx.StringStringEntryProto.toObject(e.quantParameterTensorNames[r],t)}return n},e.prototype.toJSON=function(){return this.constructor.toObject(this,a.util.toJSONOptions)},e.getTypeUrl=function(e){return void 0===e&&(e="type.googleapis.com"),e+"/onnx.TensorAnnotation"},e}(),i.GraphProto=function(){function e(e){if(this.node=[],this.initializer=[],this.sparseInitializer=[],this.input=[],this.output=[],this.valueInfo=[],this.quantizationAnnotation=[],e)for(var t=Object.keys(e),n=0;n<t.length;++n)null!=e[t[n]]&&(this[t[n]]=e[t[n]])}return e.prototype.node=u.emptyArray,e.prototype.name="",e.prototype.initializer=u.emptyArray,e.prototype.sparseInitializer=u.emptyArray,e.prototype.docString="",e.prototype.input=u.emptyArray,e.prototype.output=u.emptyArray,e.prototype.valueInfo=u.emptyArray,e.prototype.quantizationAnnotation=u.emptyArray,e.create=function(t){return new e(t)},e.encode=function(e,t){if(t||(t=s.create()),null!=e.node&&e.node.length)for(var n=0;n<e.node.length;++n)l.onnx.NodeProto.encode(e.node[n],t.uint32(10).fork()).ldelim();if(null!=e.name&&Object.hasOwnProperty.call(e,"name")&&t.uint32(18).string(e.name),null!=e.initializer&&e.initializer.length)for(n=0;n<e.initializer.length;++n)l.onnx.TensorProto.encode(e.initializer[n],t.uint32(42).fork()).ldelim();if(null!=e.docString&&Object.hasOwnProperty.call(e,"docString")&&t.uint32(82).string(e.docString),null!=e.input&&e.input.length)for(n=0;n<e.input.length;++n)l.onnx.ValueInfoProto.encode(e.input[n],t.uint32(90).fork()).ldelim();if(null!=e.output&&e.output.length)for(n=0;n<e.output.length;++n)l.onnx.ValueInfoProto.encode(e.output[n],t.uint32(98).fork()).ldelim();if(null!=e.valueInfo&&e.valueInfo.length)for(n=0;n<e.valueInfo.length;++n)l.onnx.ValueInfoProto.encode(e.valueInfo[n],t.uint32(106).fork()).ldelim();if(null!=e.quantizationAnnotation&&e.quantizationAnnotation.length)for(n=0;n<e.quantizationAnnotation.length;++n)l.onnx.TensorAnnotation.encode(e.quantizationAnnotation[n],t.uint32(114).fork()).ldelim();if(null!=e.sparseInitializer&&e.sparseInitializer.length)for(n=0;n<e.sparseInitializer.length;++n)l.onnx.SparseTensorProto.encode(e.sparseInitializer[n],t.uint32(122).fork()).ldelim();return t},e.encodeDelimited=function(e,t){return this.encode(e,t).ldelim()},e.decode=function(e,t){e instanceof o||(e=o.create(e));for(var n=void 0===t?e.len:e.pos+t,r=new l.onnx.GraphProto;e.pos<n;){var i=e.uint32();switch(i>>>3){case 1:r.node&&r.node.length||(r.node=[]),r.node.push(l.onnx.NodeProto.decode(e,e.uint32()));break;case 2:r.name=e.string();break;case 5:r.initializer&&r.initializer.length||(r.initializer=[]),r.initializer.push(l.onnx.TensorProto.decode(e,e.uint32()));break;case 15:r.sparseInitializer&&r.sparseInitializer.length||(r.sparseInitializer=[]),r.sparseInitializer.push(l.onnx.SparseTensorProto.decode(e,e.uint32()));break;case 10:r.docString=e.string();break;case 11:r.input&&r.input.length||(r.input=[]),r.input.push(l.onnx.ValueInfoProto.decode(e,e.uint32()));break;case 12:r.output&&r.output.length||(r.output=[]),r.output.push(l.onnx.ValueInfoProto.decode(e,e.uint32()));break;case 13:r.valueInfo&&r.valueInfo.length||(r.valueInfo=[]),r.valueInfo.push(l.onnx.ValueInfoProto.decode(e,e.uint32()));break;case 14:r.quantizationAnnotation&&r.quantizationAnnotation.length||(r.quantizationAnnotation=[]),r.quantizationAnnotation.push(l.onnx.TensorAnnotation.decode(e,e.uint32()));break;default:e.skipType(7&i)}}return r},e.decodeDelimited=function(e){return e instanceof o||(e=new o(e)),this.decode(e,e.uint32())},e.verify=function(e){if("object"!=typeof e||null===e)return"object expected";if(null!=e.node&&e.hasOwnProperty("node")){if(!Array.isArray(e.node))return"node: array expected";for(var t=0;t<e.node.length;++t)if(n=l.onnx.NodeProto.verify(e.node[t]))return"node."+n}if(null!=e.name&&e.hasOwnProperty("name")&&!u.isString(e.name))return"name: string expected";if(null!=e.initializer&&e.hasOwnProperty("initializer")){if(!Array.isArray(e.initializer))return"initializer: array expected";for(t=0;t<e.initializer.length;++t)if(n=l.onnx.TensorProto.verify(e.initializer[t]))return"initializer."+n}if(null!=e.sparseInitializer&&e.hasOwnProperty("sparseInitializer")){if(!Array.isArray(e.sparseInitializer))return"sparseInitializer: array expected";for(t=0;t<e.sparseInitializer.length;++t)if(n=l.onnx.SparseTensorProto.verify(e.sparseInitializer[t]))return"sparseInitializer."+n}if(null!=e.docString&&e.hasOwnProperty("docString")&&!u.isString(e.docString))return"docString: string expected";if(null!=e.input&&e.hasOwnProperty("input")){if(!Array.isArray(e.input))return"input: array expected";for(t=0;t<e.input.length;++t)if(n=l.onnx.ValueInfoProto.verify(e.input[t]))return"input."+n}if(null!=e.output&&e.hasOwnProperty("output")){if(!Array.isArray(e.output))return"output: array expected";for(t=0;t<e.output.length;++t)if(n=l.onnx.ValueInfoProto.verify(e.output[t]))return"output."+n}if(null!=e.valueInfo&&e.hasOwnProperty("valueInfo")){if(!Array.isArray(e.valueInfo))return"valueInfo: array expected";for(t=0;t<e.valueInfo.length;++t)if(n=l.onnx.ValueInfoProto.verify(e.valueInfo[t]))return"valueInfo."+n}if(null!=e.quantizationAnnotation&&e.hasOwnProperty("quantizationAnnotation")){if(!Array.isArray(e.quantizationAnnotation))return"quantizationAnnotation: array expected";for(t=0;t<e.quantizationAnnotation.length;++t){var n;if(n=l.onnx.TensorAnnotation.verify(e.quantizationAnnotation[t]))return"quantizationAnnotation."+n}}return null},e.fromObject=function(e){if(e instanceof l.onnx.GraphProto)return e;var t=new l.onnx.GraphProto;if(e.node){if(!Array.isArray(e.node))throw TypeError(".onnx.GraphProto.node: array expected");t.node=[];for(var n=0;n<e.node.length;++n){if("object"!=typeof e.node[n])throw TypeError(".onnx.GraphProto.node: object 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expected");for(t.input=[],n=0;n<e.input.length;++n){if("object"!=typeof e.input[n])throw TypeError(".onnx.GraphProto.input: object expected");t.input[n]=l.onnx.ValueInfoProto.fromObject(e.input[n])}}if(e.output){if(!Array.isArray(e.output))throw TypeError(".onnx.GraphProto.output: array expected");for(t.output=[],n=0;n<e.output.length;++n){if("object"!=typeof e.output[n])throw TypeError(".onnx.GraphProto.output: object expected");t.output[n]=l.onnx.ValueInfoProto.fromObject(e.output[n])}}if(e.valueInfo){if(!Array.isArray(e.valueInfo))throw TypeError(".onnx.GraphProto.valueInfo: array expected");for(t.valueInfo=[],n=0;n<e.valueInfo.length;++n){if("object"!=typeof e.valueInfo[n])throw TypeError(".onnx.GraphProto.valueInfo: object expected");t.valueInfo[n]=l.onnx.ValueInfoProto.fromObject(e.valueInfo[n])}}if(e.quantizationAnnotation){if(!Array.isArray(e.quantizationAnnotation))throw TypeError(".onnx.GraphProto.quantizationAnnotation: array expected");for(t.quantizationAnnotation=[],n=0;n<e.quantizationAnnotation.length;++n){if("object"!=typeof e.quantizationAnnotation[n])throw TypeError(".onnx.GraphProto.quantizationAnnotation: object expected");t.quantizationAnnotation[n]=l.onnx.TensorAnnotation.fromObject(e.quantizationAnnotation[n])}}return t},e.toObject=function(e,t){t||(t={});var n={};if((t.arrays||t.defaults)&&(n.node=[],n.initializer=[],n.input=[],n.output=[],n.valueInfo=[],n.quantizationAnnotation=[],n.sparseInitializer=[]),t.defaults&&(n.name="",n.docString=""),e.node&&e.node.length){n.node=[];for(var r=0;r<e.node.length;++r)n.node[r]=l.onnx.NodeProto.toObject(e.node[r],t)}if(null!=e.name&&e.hasOwnProperty("name")&&(n.name=e.name),e.initializer&&e.initializer.length)for(n.initializer=[],r=0;r<e.initializer.length;++r)n.initializer[r]=l.onnx.TensorProto.toObject(e.initializer[r],t);if(null!=e.docString&&e.hasOwnProperty("docString")&&(n.docString=e.docString),e.input&&e.input.length)for(n.input=[],r=0;r<e.input.length;++r)n.input[r]=l.onnx.ValueInfoProto.toObject(e.input[r],t);if(e.output&&e.output.length)for(n.output=[],r=0;r<e.output.length;++r)n.output[r]=l.onnx.ValueInfoProto.toObject(e.output[r],t);if(e.valueInfo&&e.valueInfo.length)for(n.valueInfo=[],r=0;r<e.valueInfo.length;++r)n.valueInfo[r]=l.onnx.ValueInfoProto.toObject(e.valueInfo[r],t);if(e.quantizationAnnotation&&e.quantizationAnnotation.length)for(n.quantizationAnnotation=[],r=0;r<e.quantizationAnnotation.length;++r)n.quantizationAnnotation[r]=l.onnx.TensorAnnotation.toObject(e.quantizationAnnotation[r],t);if(e.sparseInitializer&&e.sparseInitializer.length)for(n.sparseInitializer=[],r=0;r<e.sparseInitializer.length;++r)n.sparseInitializer[r]=l.onnx.SparseTensorProto.toObject(e.sparseInitializer[r],t);return n},e.prototype.toJSON=function(){return this.constructor.toObject(this,a.util.toJSONOptions)},e.getTypeUrl=function(e){return void 0===e&&(e="type.googleapis.com"),e+"/onnx.GraphProto"},e}(),i.TensorProto=function(){function e(e){if(this.dims=[],this.floatData=[],this.int32Data=[],this.stringData=[],this.int64Data=[],this.externalData=[],this.doubleData=[],this.uint64Data=[],e)for(var t=Object.keys(e),n=0;n<t.length;++n)null!=e[t[n]]&&(this[t[n]]=e[t[n]])}return e.prototype.dims=u.emptyArray,e.prototype.dataType=0,e.prototype.segment=null,e.prototype.floatData=u.emptyArray,e.prototype.int32Data=u.emptyArray,e.prototype.stringData=u.emptyArray,e.prototype.int64Data=u.emptyArray,e.prototype.name="",e.prototype.docString="",e.prototype.rawData=u.newBuffer([]),e.prototype.externalData=u.emptyArray,e.prototype.dataLocation=0,e.prototype.doubleData=u.emptyArray,e.prototype.uint64Data=u.emptyArray,e.create=function(t){return new e(t)},e.encode=function(e,t){if(t||(t=s.create()),null!=e.dims&&e.dims.length){t.uint32(10).fork();for(var n=0;n<e.dims.length;++n)t.int64(e.dims[n]);t.ldelim()}if(null!=e.dataType&&Object.hasOwnProperty.call(e,"dataType")&&t.uint32(16).int32(e.dataType),null!=e.segment&&Object.hasOwnProperty.call(e,"segment")&&l.onnx.TensorProto.Segment.encode(e.segment,t.uint32(26).fork()).ldelim(),null!=e.floatData&&e.floatData.length){for(t.uint32(34).fork(),n=0;n<e.floatData.length;++n)t.float(e.floatData[n]);t.ldelim()}if(null!=e.int32Data&&e.int32Data.length){for(t.uint32(42).fork(),n=0;n<e.int32Data.length;++n)t.int32(e.int32Data[n]);t.ldelim()}if(null!=e.stringData&&e.stringData.length)for(n=0;n<e.stringData.length;++n)t.uint32(50).bytes(e.stringData[n]);if(null!=e.int64Data&&e.int64Data.length){for(t.uint32(58).fork(),n=0;n<e.int64Data.length;++n)t.int64(e.int64Data[n]);t.ldelim()}if(null!=e.name&&Object.hasOwnProperty.call(e,"name")&&t.uint32(66).string(e.name),null!=e.rawData&&Object.hasOwnProperty.call(e,"rawData")&&t.uint32(74).bytes(e.rawData),null!=e.doubleData&&e.doubleData.length){for(t.uint32(82).fork(),n=0;n<e.doubleData.length;++n)t.double(e.doubleData[n]);t.ldelim()}if(null!=e.uint64Data&&e.uint64Data.length){for(t.uint32(90).fork(),n=0;n<e.uint64Data.length;++n)t.uint64(e.uint64Data[n]);t.ldelim()}if(null!=e.docString&&Object.hasOwnProperty.call(e,"docString")&&t.uint32(98).string(e.docString),null!=e.externalData&&e.externalData.length)for(n=0;n<e.externalData.length;++n)l.onnx.StringStringEntryProto.encode(e.externalData[n],t.uint32(106).fork()).ldelim();return null!=e.dataLocation&&Object.hasOwnProperty.call(e,"dataLocation")&&t.uint32(112).int32(e.dataLocation),t},e.encodeDelimited=function(e,t){return this.encode(e,t).ldelim()},e.decode=function(e,t){e instanceof o||(e=o.create(e));for(var n=void 0===t?e.len:e.pos+t,r=new l.onnx.TensorProto;e.pos<n;){var i=e.uint32();switch(i>>>3){case 1:if(r.dims&&r.dims.length||(r.dims=[]),2==(7&i))for(var a=e.uint32()+e.pos;e.pos<a;)r.dims.push(e.int64());else r.dims.push(e.int64());break;case 2:r.dataType=e.int32();break;case 3:r.segment=l.onnx.TensorProto.Segment.decode(e,e.uint32());break;case 4:if(r.floatData&&r.floatData.length||(r.floatData=[]),2==(7&i))for(a=e.uint32()+e.pos;e.pos<a;)r.floatData.push(e.float());else r.floatData.push(e.float());break;case 5:if(r.int32Data&&r.int32Data.length||(r.int32Data=[]),2==(7&i))for(a=e.uint32()+e.pos;e.pos<a;)r.int32Data.push(e.int32());else r.int32Data.push(e.int32());break;case 6:r.stringData&&r.stringData.length||(r.stringData=[]),r.stringData.push(e.bytes());break;case 7:if(r.int64Data&&r.int64Data.length||(r.int64Data=[]),2==(7&i))for(a=e.uint32()+e.pos;e.pos<a;)r.int64Data.push(e.int64());else r.int64Data.push(e.int64());break;case 8:r.name=e.string();break;case 12:r.docString=e.string();break;case 9:r.rawData=e.bytes();break;case 13:r.externalData&&r.externalData.length||(r.externalData=[]),r.externalData.push(l.onnx.StringStringEntryProto.decode(e,e.uint32()));break;case 14:r.dataLocation=e.int32();break;case 10:if(r.doubleData&&r.doubleData.length||(r.doubleData=[]),2==(7&i))for(a=e.uint32()+e.pos;e.pos<a;)r.doubleData.push(e.double());else r.doubleData.push(e.double());break;case 11:if(r.uint64Data&&r.uint64Data.length||(r.uint64Data=[]),2==(7&i))for(a=e.uint32()+e.pos;e.pos<a;)r.uint64Data.push(e.uint64());else r.uint64Data.push(e.uint64());break;default:e.skipType(7&i)}}return r},e.decodeDelimited=function(e){return e instanceof o||(e=new o(e)),this.decode(e,e.uint32())},e.verify=function(e){if("object"!=typeof e||null===e)return"object expected";if(null!=e.dims&&e.hasOwnProperty("dims")){if(!Array.isArray(e.dims))return"dims: array expected";for(var t=0;t<e.dims.length;++t)if(!(u.isInteger(e.dims[t])||e.dims[t]&&u.isInteger(e.dims[t].low)&&u.isInteger(e.dims[t].high)))return"dims: integer|Long[] expected"}if(null!=e.dataType&&e.hasOwnProperty("dataType")&&!u.isInteger(e.dataType))return"dataType: integer expected";if(null!=e.segment&&e.hasOwnProperty("segment")&&(n=l.onnx.TensorProto.Segment.verify(e.segment)))return"segment."+n;if(null!=e.floatData&&e.hasOwnProperty("floatData")){if(!Array.isArray(e.floatData))return"floatData: array expected";for(t=0;t<e.floatData.length;++t)if("number"!=typeof e.floatData[t])return"floatData: number[] expected"}if(null!=e.int32Data&&e.hasOwnProperty("int32Data")){if(!Array.isArray(e.int32Data))return"int32Data: array expected";for(t=0;t<e.int32Data.length;++t)if(!u.isInteger(e.int32Data[t]))return"int32Data: integer[] expected"}if(null!=e.stringData&&e.hasOwnProperty("stringData")){if(!Array.isArray(e.stringData))return"stringData: array expected";for(t=0;t<e.stringData.length;++t)if(!(e.stringData[t]&&"number"==typeof e.stringData[t].length||u.isString(e.stringData[t])))return"stringData: buffer[] expected"}if(null!=e.int64Data&&e.hasOwnProperty("int64Data")){if(!Array.isArray(e.int64Data))return"int64Data: array expected";for(t=0;t<e.int64Data.length;++t)if(!(u.isInteger(e.int64Data[t])||e.int64Data[t]&&u.isInteger(e.int64Data[t].low)&&u.isInteger(e.int64Data[t].high)))return"int64Data: integer|Long[] expected"}if(null!=e.name&&e.hasOwnProperty("name")&&!u.isString(e.name))return"name: string expected";if(null!=e.docString&&e.hasOwnProperty("docString")&&!u.isString(e.docString))return"docString: string expected";if(null!=e.rawData&&e.hasOwnProperty("rawData")&&!(e.rawData&&"number"==typeof e.rawData.length||u.isString(e.rawData)))return"rawData: buffer expected";if(null!=e.externalData&&e.hasOwnProperty("externalData")){if(!Array.isArray(e.externalData))return"externalData: array expected";for(t=0;t<e.externalData.length;++t){var n;if(n=l.onnx.StringStringEntryProto.verify(e.externalData[t]))return"externalData."+n}}if(null!=e.dataLocation&&e.hasOwnProperty("dataLocation"))switch(e.dataLocation){default:return"dataLocation: enum value expected";case 0:case 1:}if(null!=e.doubleData&&e.hasOwnProperty("doubleData")){if(!Array.isArray(e.doubleData))return"doubleData: array expected";for(t=0;t<e.doubleData.length;++t)if("number"!=typeof e.doubleData[t])return"doubleData: number[] expected"}if(null!=e.uint64Data&&e.hasOwnProperty("uint64Data")){if(!Array.isArray(e.uint64Data))return"uint64Data: array expected";for(t=0;t<e.uint64Data.length;++t)if(!(u.isInteger(e.uint64Data[t])||e.uint64Data[t]&&u.isInteger(e.uint64Data[t].low)&&u.isInteger(e.uint64Data[t].high)))return"uint64Data: integer|Long[] expected"}return null},e.fromObject=function(e){if(e instanceof l.onnx.TensorProto)return e;var t=new l.onnx.TensorProto;if(e.dims){if(!Array.isArray(e.dims))throw TypeError(".onnx.TensorProto.dims: array expected");t.dims=[];for(var n=0;n<e.dims.length;++n)u.Long?(t.dims[n]=u.Long.fromValue(e.dims[n])).unsigned=!1:"string"==typeof e.dims[n]?t.dims[n]=parseInt(e.dims[n],10):"number"==typeof e.dims[n]?t.dims[n]=e.dims[n]:"object"==typeof e.dims[n]&&(t.dims[n]=new u.LongBits(e.dims[n].low>>>0,e.dims[n].high>>>0).toNumber())}if(null!=e.dataType&&(t.dataType=0|e.dataType),null!=e.segment){if("object"!=typeof e.segment)throw TypeError(".onnx.TensorProto.segment: object expected");t.segment=l.onnx.TensorProto.Segment.fromObject(e.segment)}if(e.floatData){if(!Array.isArray(e.floatData))throw TypeError(".onnx.TensorProto.floatData: array expected");for(t.floatData=[],n=0;n<e.floatData.length;++n)t.floatData[n]=Number(e.floatData[n])}if(e.int32Data){if(!Array.isArray(e.int32Data))throw TypeError(".onnx.TensorProto.int32Data: array expected");for(t.int32Data=[],n=0;n<e.int32Data.length;++n)t.int32Data[n]=0|e.int32Data[n]}if(e.stringData){if(!Array.isArray(e.stringData))throw TypeError(".onnx.TensorProto.stringData: array expected");for(t.stringData=[],n=0;n<e.stringData.length;++n)"string"==typeof e.stringData[n]?u.base64.decode(e.stringData[n],t.stringData[n]=u.newBuffer(u.base64.length(e.stringData[n])),0):e.stringData[n].length>=0&&(t.stringData[n]=e.stringData[n])}if(e.int64Data){if(!Array.isArray(e.int64Data))throw TypeError(".onnx.TensorProto.int64Data: array expected");for(t.int64Data=[],n=0;n<e.int64Data.length;++n)u.Long?(t.int64Data[n]=u.Long.fromValue(e.int64Data[n])).unsigned=!1:"string"==typeof e.int64Data[n]?t.int64Data[n]=parseInt(e.int64Data[n],10):"number"==typeof e.int64Data[n]?t.int64Data[n]=e.int64Data[n]:"object"==typeof e.int64Data[n]&&(t.int64Data[n]=new u.LongBits(e.int64Data[n].low>>>0,e.int64Data[n].high>>>0).toNumber())}if(null!=e.name&&(t.name=String(e.name)),null!=e.docString&&(t.docString=String(e.docString)),null!=e.rawData&&("string"==typeof e.rawData?u.base64.decode(e.rawData,t.rawData=u.newBuffer(u.base64.length(e.rawData)),0):e.rawData.length>=0&&(t.rawData=e.rawData)),e.externalData){if(!Array.isArray(e.externalData))throw TypeError(".onnx.TensorProto.externalData: array expected");for(t.externalData=[],n=0;n<e.externalData.length;++n){if("object"!=typeof e.externalData[n])throw TypeError(".onnx.TensorProto.externalData: object expected");t.externalData[n]=l.onnx.StringStringEntryProto.fromObject(e.externalData[n])}}switch(e.dataLocation){default:if("number"==typeof e.dataLocation){t.dataLocation=e.dataLocation;break}break;case"DEFAULT":case 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t},e.toObject=function(e,t){t||(t={});var n={};if((t.arrays||t.defaults)&&(n.dim=[]),e.dim&&e.dim.length){n.dim=[];for(var r=0;r<e.dim.length;++r)n.dim[r]=l.onnx.TensorShapeProto.Dimension.toObject(e.dim[r],t)}return n},e.prototype.toJSON=function(){return this.constructor.toObject(this,a.util.toJSONOptions)},e.getTypeUrl=function(e){return void 0===e&&(e="type.googleapis.com"),e+"/onnx.TensorShapeProto"},e.Dimension=function(){function e(e){if(e)for(var t=Object.keys(e),n=0;n<t.length;++n)null!=e[t[n]]&&(this[t[n]]=e[t[n]])}var t;return e.prototype.dimValue=null,e.prototype.dimParam=null,e.prototype.denotation="",Object.defineProperty(e.prototype,"value",{get:u.oneOfGetter(t=["dimValue","dimParam"]),set:u.oneOfSetter(t)}),e.create=function(t){return new e(t)},e.encode=function(e,t){return t||(t=s.create()),null!=e.dimValue&&Object.hasOwnProperty.call(e,"dimValue")&&t.uint32(8).int64(e.dimValue),null!=e.dimParam&&Object.hasOwnProperty.call(e,"dimParam")&&t.uint32(18).string(e.dimParam),null!=e.denotation&&Object.hasOwnProperty.call(e,"denotation")&&t.uint32(26).string(e.denotation),t},e.encodeDelimited=function(e,t){return this.encode(e,t).ldelim()},e.decode=function(e,t){e instanceof o||(e=o.create(e));for(var n=void 0===t?e.len:e.pos+t,r=new l.onnx.TensorShapeProto.Dimension;e.pos<n;){var i=e.uint32();switch(i>>>3){case 1:r.dimValue=e.int64();break;case 2:r.dimParam=e.string();break;case 3:r.denotation=e.string();break;default:e.skipType(7&i)}}return r},e.decodeDelimited=function(e){return e instanceof o||(e=new o(e)),this.decode(e,e.uint32())},e.verify=function(e){if("object"!=typeof e||null===e)return"object expected";var t={};if(null!=e.dimValue&&e.hasOwnProperty("dimValue")&&(t.value=1,!(u.isInteger(e.dimValue)||e.dimValue&&u.isInteger(e.dimValue.low)&&u.isInteger(e.dimValue.high))))return"dimValue: integer|Long expected";if(null!=e.dimParam&&e.hasOwnProperty("dimParam")){if(1===t.value)return"value: multiple values";if(t.value=1,!u.isString(e.dimParam))return"dimParam: string expected"}return null!=e.denotation&&e.hasOwnProperty("denotation")&&!u.isString(e.denotation)?"denotation: string expected":null},e.fromObject=function(e){if(e instanceof l.onnx.TensorShapeProto.Dimension)return e;var t=new l.onnx.TensorShapeProto.Dimension;return null!=e.dimValue&&(u.Long?(t.dimValue=u.Long.fromValue(e.dimValue)).unsigned=!1:"string"==typeof e.dimValue?t.dimValue=parseInt(e.dimValue,10):"number"==typeof e.dimValue?t.dimValue=e.dimValue:"object"==typeof e.dimValue&&(t.dimValue=new 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instanceof o||(e=o.create(e));for(var n=void 0===t?e.len:e.pos+t,r=new l.onnx.FunctionProto;e.pos<n;){var i=e.uint32();switch(i>>>3){case 1:r.name=e.string();break;case 4:r.input&&r.input.length||(r.input=[]),r.input.push(e.string());break;case 5:r.output&&r.output.length||(r.output=[]),r.output.push(e.string());break;case 6:r.attribute&&r.attribute.length||(r.attribute=[]),r.attribute.push(e.string());break;case 11:r.attributeProto&&r.attributeProto.length||(r.attributeProto=[]),r.attributeProto.push(l.onnx.AttributeProto.decode(e,e.uint32()));break;case 7:r.node&&r.node.length||(r.node=[]),r.node.push(l.onnx.NodeProto.decode(e,e.uint32()));break;case 8:r.docString=e.string();break;case 9:r.opsetImport&&r.opsetImport.length||(r.opsetImport=[]),r.opsetImport.push(l.onnx.OperatorSetIdProto.decode(e,e.uint32()));break;case 10:r.domain=e.string();break;default:e.skipType(7&i)}}return r},e.decodeDelimited=function(e){return e instanceof o||(e=new o(e)),this.decode(e,e.uint32())},e.verify=function(e){if("object"!=typeof e||null===e)return"object expected";if(null!=e.name&&e.hasOwnProperty("name")&&!u.isString(e.name))return"name: string expected";if(null!=e.input&&e.hasOwnProperty("input")){if(!Array.isArray(e.input))return"input: array expected";for(var t=0;t<e.input.length;++t)if(!u.isString(e.input[t]))return"input: string[] expected"}if(null!=e.output&&e.hasOwnProperty("output")){if(!Array.isArray(e.output))return"output: array expected";for(t=0;t<e.output.length;++t)if(!u.isString(e.output[t]))return"output: string[] expected"}if(null!=e.attribute&&e.hasOwnProperty("attribute")){if(!Array.isArray(e.attribute))return"attribute: array expected";for(t=0;t<e.attribute.length;++t)if(!u.isString(e.attribute[t]))return"attribute: string[] expected"}if(null!=e.attributeProto&&e.hasOwnProperty("attributeProto")){if(!Array.isArray(e.attributeProto))return"attributeProto: array 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e.node[n])throw TypeError(".onnx.FunctionProto.node: object expected");t.node[n]=l.onnx.NodeProto.fromObject(e.node[n])}}if(null!=e.docString&&(t.docString=String(e.docString)),e.opsetImport){if(!Array.isArray(e.opsetImport))throw TypeError(".onnx.FunctionProto.opsetImport: array expected");for(t.opsetImport=[],n=0;n<e.opsetImport.length;++n){if("object"!=typeof e.opsetImport[n])throw TypeError(".onnx.FunctionProto.opsetImport: object expected");t.opsetImport[n]=l.onnx.OperatorSetIdProto.fromObject(e.opsetImport[n])}}return null!=e.domain&&(t.domain=String(e.domain)),t},e.toObject=function(e,t){t||(t={});var n={};if((t.arrays||t.defaults)&&(n.input=[],n.output=[],n.attribute=[],n.node=[],n.opsetImport=[],n.attributeProto=[]),t.defaults&&(n.name="",n.docString="",n.domain=""),null!=e.name&&e.hasOwnProperty("name")&&(n.name=e.name),e.input&&e.input.length){n.input=[];for(var r=0;r<e.input.length;++r)n.input[r]=e.input[r]}if(e.output&&e.output.length)for(n.output=[],r=0;r<e.output.length;++r)n.output[r]=e.output[r];if(e.attribute&&e.attribute.length)for(n.attribute=[],r=0;r<e.attribute.length;++r)n.attribute[r]=e.attribute[r];if(e.node&&e.node.length)for(n.node=[],r=0;r<e.node.length;++r)n.node[r]=l.onnx.NodeProto.toObject(e.node[r],t);if(null!=e.docString&&e.hasOwnProperty("docString")&&(n.docString=e.docString),e.opsetImport&&e.opsetImport.length)for(n.opsetImport=[],r=0;r<e.opsetImport.length;++r)n.opsetImport[r]=l.onnx.OperatorSetIdProto.toObject(e.opsetImport[r],t);if(null!=e.domain&&e.hasOwnProperty("domain")&&(n.domain=e.domain),e.attributeProto&&e.attributeProto.length)for(n.attributeProto=[],r=0;r<e.attributeProto.length;++r)n.attributeProto[r]=l.onnx.AttributeProto.toObject(e.attributeProto[r],t);return n},e.prototype.toJSON=function(){return this.constructor.toObject(this,a.util.toJSONOptions)},e.getTypeUrl=function(e){return void 0===e&&(e="type.googleapis.com"),e+"/onnx.FunctionProto"},e}(),i),t.exports=l}));function An(e,t){if(!e)throw new Error("string"==typeof t?t:t())}function co(e){return(new TextDecoder).decode(e)}var tt,nn,Xa,Dt,fi,Ot,Vt,te,lo,on,an,sn,We=D((()=>{si(),za(),tt=Tn(In()),un(),nn=class{static arraysEqual(e,t){if(e.length!==t.length)return!1;for(let n=0;n<e.length;n++)if(e[n]!==t[n])return!1;return!0}},Xa=class{static preprocessInputShapes(e,t){return[1===e.length?[1,e[0]]:e,1===t.length?[t[0],1]:t]}static postprocessOutputShape(e,t,n){1===t&&e.splice(e.length-2,1),1===n&&e.pop()}static calcMatMulShape(e,t){return e[1]!==t[0]?void 0:[e[0],t[1]]}},Dt=class e{static calcShape(e,t,n=!1){let r=e.length,i=t.length;if(0===r)return t;if(0===i)return e;let a=Math.max(e.length,t.length),o=new Array(a);if(n){if(r<2||i<2)return;let n=Xa.calcMatMulShape([e[r-2],e[r-1]],[t[i-2],t[i-1]]);if(void 0===n)return;[o[a-2],o[a-1]]=n}for(let s=n?3:1;s<=a;s++){let n=r-s<0?1:e[r-s],u=i-s<0?1:t[i-s];if(n!==u&&n>1&&u>1)return;o[a-s]=Math.max(n,u)}return o}static index(t,n){let r=new Array(n.length);return e.fillIndex(t,n,r),r}static fillIndex(e,t,n){let r=e.length-t.length;for(let i=0;i<t.length;i++)n[i]=e[r+i]%t[i]}static calc(t,n,r,i,a){let o=e.calcShape(t.dims,n.dims);if(o){if(i&&!te.areEqual(o,t.dims))return;let s=te.size(o),u=i?t:new yt(o,a||t.type);if(0===o.length)u.set([],r(t.get([]),n.get([])));else{let i,a=new Array(o.length),l=new Array(t.dims.length),d=new Array(n.dims.length),p=0,c=0,h=!1,f=!1;0===t.dims.length&&(p=t.get([]),h=!0),0===n.dims.length&&(c=n.get([]),f=!0);for(let m=0;m<s;m++){i=m;for(let e=o.length-1;e>=0;e--)a[e]=i%o[e],i=Math.floor(i/o[e]);h||(e.fillIndex(a,t.dims,l),p=t.get(l)),f||(e.fillIndex(a,n.dims,d),c=n.get(d)),u.set(a,r(p,c))}}return u}}static isValidBroadcast(e,t){let n=e.length,r=t.length;if(n>r)return!1;for(let i=1;i<=n;i++)if(1!==e[n-i]&&e[n-i]!==t[r-i])return!1;return!0}static getBroadcastDims(e,t){let n=e.length,r=[];for(let i=0;i<n;i++){let a=n-1-i,o=e[a]||1;(t[t.length-1-i]||1)>1&&1===o&&r.unshift(a)}return r}},fi=class{static getShapeOfGemmResult(e,t,n,r,i){if(2!==e.length||2!==n.length)throw new Error("shape need to be of size 2");let a,o,s;t?(a=e[1],o=e[0]):(a=e[0],o=e[1]);let u=-1;if(r?(s=n[0],u=1):(s=n[1],u=0),n[u]!==o)throw new Error("dimension mismatch");if(a<=0||s<=0||o<=0)throw new Error("invalid shape specified");if(i&&!Dt.isValidBroadcast(i,[a,s]))throw new Error("gemm: invalid bias shape for broadcast");return[a,s,o]}},Ot=class e{static tensorDataTypeFromProto(e){switch(e){case tt.onnx.TensorProto.DataType.INT8:return"int8";case tt.onnx.TensorProto.DataType.UINT8:return"uint8";case tt.onnx.TensorProto.DataType.BOOL:return"bool";case tt.onnx.TensorProto.DataType.INT16:return"int16";case tt.onnx.TensorProto.DataType.UINT16:return"uint16";case tt.onnx.TensorProto.DataType.INT32:return"int32";case tt.onnx.TensorProto.DataType.UINT32:return"uint32";case tt.onnx.TensorProto.DataType.FLOAT:return"float32";case tt.onnx.TensorProto.DataType.DOUBLE:return"float64";case tt.onnx.TensorProto.DataType.STRING:return"string";case tt.onnx.TensorProto.DataType.INT64:return"int32";case tt.onnx.TensorProto.DataType.UINT64:return"uint32";default:throw new Error(`unsupported data type: ${tt.onnx.TensorProto.DataType[e]}`)}}static tensorDataTypeStringToEnum(e){switch(e){case"int8":return tt.onnx.TensorProto.DataType.INT8;case"uint8":return tt.onnx.TensorProto.DataType.UINT8;case"bool":return tt.onnx.TensorProto.DataType.BOOL;case"int16":return tt.onnx.TensorProto.DataType.INT16;case"uint16":return tt.onnx.TensorProto.DataType.UINT16;case"int32":return tt.onnx.TensorProto.DataType.INT32;case"uint32":return tt.onnx.TensorProto.DataType.UINT32;case"float32":return tt.onnx.TensorProto.DataType.FLOAT;case"float64":return tt.onnx.TensorProto.DataType.DOUBLE;case"string":return tt.onnx.TensorProto.DataType.STRING;case"int64":return tt.onnx.TensorProto.DataType.INT64;case"uint64":return tt.onnx.TensorProto.DataType.UINT64;default:throw new Error(`unsupported data type: ${e}`)}}static tensorDimsFromProto(e){return e.map((e=>Pr.isLong(e)?e.toNumber():e))}static tensorValueTypeFromProto(t){return{tensorType:e.tensorDataTypeFromProto(t.elemType),shape:{dims:e.tensorDimsFromProto(t.shape.dim.map((e=>e.dimValue)))}}}static tensorDimsFromORTFormat(e){let t=[];for(let n=0;n<e.dimsLength();n++)t.push(Vt.longToNumber(e.dims(n)));return t}static tensorAttributesFromORTFormat(e){let t=[];for(let n=0;n<e.attributesLength();n++)t.push(e.attributes(n));return t}},Vt=class{static longToNumber(e,t){return Pr.isLong(e)?e.toNumber():e instanceof k.Long?Pr.fromValue({low:e.low,high:e.high,unsigned:t??!1}).toNumber():e}static isLong(e){return Pr.isLong(e)||e instanceof k.Long}},te=class e{static size(t){return e.getSizeFromDimensionRange(t,0,t.length)}static sizeFromDimension(t,n){if(n<0||n>t.length)throw new Error(`invalid dimension of ${n} for sizeFromDimension as Tensor has ${t.length} dimensions.`);return e.getSizeFromDimensionRange(t,n,t.length)}static sizeToDimension(t,n){if(n<0||n>t.length)throw new Error(`invalid dimension of ${n} for sizeToDimension as Tensor has ${t.length} dimensions.`);return e.getSizeFromDimensionRange(t,0,n)}static getSizeFromDimensionRange(e,t,n){let r=1;for(let i=t;i<n;i++){if(e[i]<=0)throw new Error("cannot get valid size from specified dimension range. Most likely the range contains 0 or negative values in them.");r*=e[i]}return r}static computeStrides(e){let t=e.length;if(0===t)return[];if(1===t)return[1];let n=new Array(t);n[t-1]=1,n[t-2]=e[t-1];for(let r=t-3;r>=0;--r)n[r]=n[r+1]*e[r+1];return n}static transpose(e){return e.slice().reverse()}static indicesToOffset(e,t,n){void 0===n&&(n=e.length);let r=0;for(let i=0;i<n;++i)r+=t[i]*e[i];return r}static offsetToIndices(e,t){let n=t.length;if(0===n)return[];if(1===n)return[e*t[0]];let r=new Array(t.length);for(let n=0;n<r.length-1;++n)r[n]=Math.floor(e/t[n]),e-=r[n]*t[n];return r[r.length-1]=e,r}static normalizeAxis(e,t){if(e<-t&&e>=t)throw new Error("unsupported axis for this operation.");return e<0?e+t:e}static normalizeAxes(e,t){return e.map((e=>this.normalizeAxis(e,t)))}static incrementIndex(e,t,n){if(0===t.length||0===e.length)throw new Error("Index incrementing unsupported for scalar Tensor");if(void 0===n)n=t.length;else if(n<=0||n>t.length)throw new Error("Incorrect axis to increment on");for(let r=n-1;r>=0&&(e[r]++,!(e[r]<t[r]));--r)e[r]=0}static calculateReshapedDims(t,n){if(0===n.length){if(0===t.length||1===e.size(t))return[];throw new Error("cannot reshape to a scalar Tensor")}let r=n.length,i=new Array(r),a=-1,o=1;for(let e=0;e<r;e++){if(n[e]<-1)throw new Error("a dimension in shape hints cannot be less than -1");if(-1===n[e]){if(-1!==a)throw new Error("at most one dimension in shape hints can be -1");a=e}else{if(0===n[e]){if(e>=t.length)throw new Error("the dimension with value zero exceeds the dimension size of the input tensor");i[e]=t[e]}else i[e]=n[e];o*=i[e]}}let s=e.size(t);if(-1!==a){if(s%o!=0)throw new Error(`the input tensor cannot be reshaped to the requested shape. Input shape: [${t}] Output shape: [${n}]`);i[a]=s/o}else if(o!==s)throw new Error("reshapedDims and originalDims don't have matching sizes");return i}static sortBasedOnPerm(e,t){return t?t.map((t=>e[t])):e.slice().reverse()}static padShape(e,t){let n=e.length;return e.map(((e,r)=>e+t[r]+t[r+n]))}static areEqual(e,t){return e.length===t.length&&e.every(((e,n)=>e===t[n]))}static validateDimsAndCalcSize(e){if(e.length>6)throw new TypeError("Only rank 0 to 6 is supported for tensor shape.");let t=1;for(let n of e){if(!Number.isInteger(n))throw new TypeError(`Invalid shape: ${n} is not an integer`);if(n<0||n>2147483647)throw new TypeError(`Invalid shape: length ${n} is not allowed`);t*=n}return t}static flattenShape(e,t){t<0&&(t+=e.length);let n=e.reduce(((e,t)=>e*t),1),r=e.slice(t).reduce(((e,t)=>e*t),1);return[n/r,r]}static squeezeShape(t,n){let r=new Array;n=e.normalizeAxes(n,t.length);for(let e=0;e<t.length;e++){let i=n.indexOf(e)>=0;if(i&&1!==t[e])throw new Error("squeeze an axis of size different than 1");(0===n.length&&t[e]>1||n.length>0&&!i)&&r.push(t[e])}return r}static unsqueezeShape(t,n){let r=new Array(t.length+n.length);r.fill(0);for(let t=0;t<n.length;t++){let i=e.normalizeAxis(n[t],r.length);if(i>=r.length)throw new Error("'axes' has an out of range axis");if(0!==r[i])throw new Error("'axes' has a duplicate axis");r[i]=1}let i=0;for(let e=0;e<r.length;e++)0===r[e]&&(r[e]=t[i++]);if(i!==t.length)throw new Error("the unsqueezed dimension could not be established");return r}},lo=class e{static splitShape(t,n,r,i){if(0===r.length){if(!i)throw new Error("need to know number of outputs when the 'split' attribute is not specified");e.determineSplit(t[n],i,r)}let a=[],o=[0];for(let e=0;e<r.length;++e){0!==e&&o.push(o[e-1]+r[e-1]);let i=t.slice();i[n]=r[e],a.push(i)}return[a,o]}static determineSplit(e,t,n){if(e%t!=0)throw new Error("cannot split tensor to equal sized parts");for(let r=0;r<t;++r)n.push(e/t)}},on=class e{static adjustPoolAttributes(e,t,n,r,i,a){if(!e&&n.length!==t.length-2)throw new Error("length of specified kernel shapes should be 2 less than length of input dimensions");if(e)for(let e=0;e<t.length-2;e++)e>=n.length?n.push(t[e+2]):n[e]=t[e+2];for(let e=0;e<n.length;e++)if(e<r.length){if(r[e]<0)throw new Error("strides should be greater than or equal to 1")}else r.push(1);for(let e=0;e<n.length;e++)if(e<i.length){if(i[e]<0)throw new Error("dilations should be greater than or equal to 1")}else i.push(1);for(let e=0;e<2*n.length;e++)if(e<a.length){if(a[e]<0)throw new Error("pad should be greater than or equal to 1")}else a.push(0);for(let e=0;e<n.length;e++){if(n[e]<=0)throw new Error("kernel shapes need to be greater than 0");if(a[e]>=n[e]||a[e+n.length]>=n[e])throw new Error("pads should be smaller than kernel")}}static adjustPadsBasedOnAutoPad(t,n,r,i,a,o){if(o){if(a.length!==2*(t.length-2))throw new Error("length of pads should be twice the length of data 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u=n*(r-1)+1;if(!s||"NOTSET"===s)return Math.floor((e+i[a]+i[o]-u)/t+1);switch(s){case"VALID":return i[a]=0,i[o]=0,Math.floor((e-u)/t+1);case"SAME_LOWER":case"SAME_UPPER":if(1!==n)throw new Error("Dilation not supported for SAME_UPPER or SAME_LOWER");{let n=((e+t-1)/t-1)*t+r-e;return i[a]=Math.floor("SAME_LOWER"===s?(n+1)/2:n/2),i[o]=n-i[a],Math.floor((e+n-r)/t+1)}default:throw new Error("Unsupported AutoPad type")}}},an=-34028234663852886e22,sn=34028234663852886e22}));function u0(e){switch(e){case"bool":case"int8":case"uint8":return 1;case"int16":case"uint16":return 2;case"int32":case"uint32":case"float32":return 4;case"float64":return 8;default:throw new Error(`cannot calculate sizeof() on type ${e}`)}}function Pd(e){switch(e){case Ee.onnx.TensorProto.DataType.UINT8:case Ee.onnx.TensorProto.DataType.INT8:case Ee.onnx.TensorProto.DataType.BOOL:return 1;case Ee.onnx.TensorProto.DataType.UINT16:case Ee.onnx.TensorProto.DataType.INT16:return 2;case Ee.onnx.TensorProto.DataType.FLOAT:case 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Ee.onnx.TensorProto.DataType.FLOAT:e=t.floatData;break;case Ee.onnx.TensorProto.DataType.INT32:case Ee.onnx.TensorProto.DataType.INT16:case Ee.onnx.TensorProto.DataType.UINT16:case Ee.onnx.TensorProto.DataType.INT8:case Ee.onnx.TensorProto.DataType.UINT8:case Ee.onnx.TensorProto.DataType.BOOL:e=t.int32Data;break;case Ee.onnx.TensorProto.DataType.INT64:e=t.int64Data;break;case Ee.onnx.TensorProto.DataType.DOUBLE:e=t.doubleData;break;case Ee.onnx.TensorProto.DataType.UINT32:case Ee.onnx.TensorProto.DataType.UINT64:e=t.uint64Data;break;default:throw new Error("unspecific error")}if(null==e)throw new Error("failed to populate data from a tensorproto value");let n=i.data;if(n.length!==e.length)throw new Error("array length mismatch");for(let r=0;r<e.length;r++){let i=e[r];Pr.isLong(i)?n[r]=Ja(i,t.dataType):n[r]=i}}return i}static fromData(t,n,r){return new e(n,r,void 0,void 0,t)}static fromOrtTensor(t){if(!t)throw new Error("cannot construct Value from an empty tensor");let n=Ot.tensorDimsFromORTFormat(t),r=Ot.tensorDataTypeFromProto(t.dataType()),i=new e(n,r);if("string"===r)for(let e=0;e<t.stringDataLength();e++)i.data[e]=t.stringData(e);else if(t.rawDataArray()&&"number"==typeof t.rawDataLength()&&t.rawDataLength()>0){let e=i.data,n=new DataView(t.rawDataArray().buffer,t.rawDataArray().byteOffset,t.rawDataLength()),r=Pd(t.dataType()),a=t.rawDataLength()/r;if(t.rawDataLength()%r!=0)throw new Error("invalid buffer length");if(e.length!==a)throw new Error("buffer length mismatch");for(let i=0;i<a;i++){let a=kd(n,t.dataType(),i*r);e[i]=a}}return i}}}));function ce(e){return 1===e?d0:c0}function Rd(e){let t=ce(e);return`${t.version}\n      precision highp float;\n      ${t.attribute} vec3 position;\n      ${t.attribute} vec2 textureCoord;\n\n      ${t.varyingVertex} vec2 TexCoords;\n\n      void main()\n      {\n          gl_Position = vec4(position, 1.0);\n          TexCoords = textureCoord;\n      }`}function zd(e){let t=ce(e);return`${t.version}\n    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es(e){if(0===e.length)return[1,1,1];let t=1;for(let n=0;n<e.length-2;++n)t*=e[n];return[t,e.length>1?e[e.length-2]:1,e[e.length-1]]}function Gd(e,t){let n=!1;return n=0===e.length||0===t.length||(e.length<2||t.length<2?e[e.length-1]===t[t.length-1]:e[e.length-1]===t[t.length-1]&&e[e.length-2]===t[t.length-2]),n}function v0(e){let t=te.computeStrides(e),n=["b","r","c"],r="index";return`\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${t.map(((e,i)=>`${`int ${n[i]} = ${r} / ${e}`}; ${i===t.length-1?`int ${n[i+1]} = ${r} - ${n[i]} * ${e}`:`index -= ${n[i]} * ${e}`};`)).join("")}\n      return ivec3(b, r, c);\n    }\n  `}function w0(e){let t=te.computeStrides(e);return`\n  int getFlattenedIndex(ivec3 coords) {\n    // reverse y, z order\n    return coords.x * ${t[0]} + coords.z * ${t[1]} + coords.y;\n  }\n`}var b0,y0,Ud,Wd=D((()=>{We(),ot(),Ne(),ln(),b0=e=>({name:"Reshape (packed)",inputTypes:[2],inputNames:["A"],cacheHint:`${e}`}),y0=(e,t,n,r)=>{let i=t.dims,a=r,o="";for(let e=0;e<4;e++){let t="";switch(e){case 0:t="outputCoords = rc;";break;case 1:t="outputCoords = ivec3(rc.x, rc.y+1, rc.z);";break;case 2:t="outputCoords = ivec3(rc.x, rc.y, rc.z+1);";break;case 3:t="outputCoords = ivec3(rc.x, rc.y+1, rc.z+1);";break;default:throw new Error}o+=`\n        ${t}\n        ${e>0?"if(outputCoords.y < rows && outputCoords.z < cols){":""}\n          int flattenedIndex = getFlattenedIndex(outputCoords);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flattenedIndex);\n          vec2 innerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${e}] = getChannel(getA(inputRC.x, inputRC.y, inputRC.z), innerDims);\n\n        ${e>0?"}":""}\n      `}let s=ce(e.session.backend.glContext.version),u=`\n      ${v0(i)}\n      ${w0(a)}\n      ${mr()}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.0);\n\n        ivec3 outputCoords;\n        int rows = ${a[2]};\n        int cols = 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Zt("ceil")}function ov(){return Zt("cos")}function iv(e){let t="elu";return{body:`\n  const float alpha = float(${e});\n\n  float ${t}_(float a) {\n    return a >= 0.0 ? a: (exp(a) - 1.0) * alpha;\n  }\n  vec4 ${t}_(vec4 v) {\n    return vec4(${t}_(v.x), ${t}_(v.y), ${t}_(v.z), ${t}_(v.w));\n  }\n  `,name:t,type:0}}function av(){return Zt("exp")}function sv(){return Zt("floor")}function os(e,t){let n="clip";return{body:`\n  const float min = float(${e});\n  const float max = float(${t});\n\n  float ${n}_(float a) {\n    return clamp(a, min, max);\n  }\n  vec4 ${n}_(vec4 v) {\n    return clamp(v, min, max);\n  }\n  `,name:n,type:0}}function uv(){let e="indentity";return{body:`\n  float ${e}_(float a) {\n    return a;\n  }\n  vec4 ${e}_(vec4 v) {\n    return v;\n  }\n  `,name:e,type:0}}function lv(e){let t="leakyRelu";return{body:`\n  const float alpha = float(${e});\n\n  float ${t}_(float a) {\n    return a < 0.0 ? a * alpha : a;\n  }\n  vec4 ${t}_(vec4 v) {\n    return vec4(${t}_(v.x), 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hv(){return Zt("sqrt")}function mv(){return Zt("tan")}function gv(){let e="tanh";return{body:`\n  float ${e}_(float a) {\n    a = clamp(a, -10., 10.);\n    a = exp(2.*a);\n    return (a - 1.) / (a + 1.);\n  }\n  vec4 ${e}_(vec4 v) {\n    v = clamp(v, -10., 10.);\n    v = exp(2.*v);\n    return (v - 1.) / (v + 1.);\n  }\n  `,name:e,type:0}}function Zt(e){return{body:`\n  float ${e}_(float a) {\n    return ${e}(a);\n  }\n  vec4 ${e}_(vec4 v) {\n    return ${e}(v);\n  }\n  `,name:e,type:0}}var bv,mt,Sc,Ic,Ac,Oc,ss,Ec,Cc,yv,Pc,kc,Dc,Bc,Rc,zc,us,Nc,Lc,Mc,Vc,Fc,Uc,Gc,Wc,Hc,qc,jc,ls=D((()=>{St(),We(),kr(),ot(),Ne(),bv=(e,t,n,r)=>{let i=e.session.pack?2:0,a=ce(e.session.backend.glContext.version);return{...t,output:{dims:n.dims,type:n.type,textureType:i},shaderSource:`\n     ${r.body}\n     void main() {\n       vec4 v = ${a.texture2D}(A, TexCoords);\n       v = ${r.name}_(v);\n       ${a.output} = v;\n     }\n     `,hasMain:!0}},mt=(e,t,n,r)=>{let i=e.session.pack?2:0,a={name:n.name,inputTypes:[i],inputNames:["A"],cacheHint:r};return{...a,get:()=>bv(e,a,t,n)}},Sc=(e,t)=>[e.run(mt(e,t[0],Q0()),t)],Ic=(e,t)=>[e.run(mt(e,t[0],ev()),t)],Ac=(e,t)=>[e.run(mt(e,t[0],tv()),t)],Oc=(e,t)=>[e.run(mt(e,t[0],rv()),t)],ss=(e,t,n)=>[e.run(mt(e,t[0],os(n.min,n.max),n.cacheKey),t)],Ec=e=>Ae({min:e.attributes.getFloat("min",an),max:e.attributes.getFloat("max",sn)}),Cc=(e,t)=>{let n=yv(e,t);return ss(e,[t[0]],n)},yv=(e,t)=>{if(t.length>=3&&(!e.session.isInitializer(t[1].dataId)||!e.session.isInitializer(t[2].dataId)))throw new Error("dynamic clip attributes are not allowed");let n=t.length>=3?t[1].numberData[0]:an,r=t.length>=3?t[2].numberData[0]:sn;return 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n=t.name;return{activationFunction:t.body,applyActivation:`value = ${n}_(value);`}}var Pn,dn=D((()=>{We(),ls(),Pn=e=>{let t=e.getString("activation","");if("Clip"===t){let[n,r]=e.getFloats("activation_params",[an,sn]);return{activation:t,clipMax:r,clipMin:n,activationCacheKey:`${t}:${n},${r}`}}return{activation:t,activationCacheKey:t}}})),wv,xv,Kc,Yc=D((()=>{Ht(),ot(),Ne(),wi(),dn(),wv=(e,t)=>({name:"GroupedConv",inputNames:e?["X","W","Bias"]:["X","W"],inputTypes:e?[0,0,0]:[0,0],cacheHint:t}),xv=(e,t,n,r)=>{let i=t.length>2?"value += getBias(output_channel);":"",a=t[0].dims.slice(),o=t[1].dims.slice(),s=o[0]/r.group;qe.verbose("GroupedConv",`autpPad:${r.autoPad}, dilations:${r.dilations}, group:${r.group}, kernelShape:${r.kernelShape}, pads:${r.pads}, strides:${r.strides}`);let u=kn(a,o,r.dilations,r.pads,r.strides),l=ce(e.session.backend.glContext.version),{activationFunction:d,applyActivation:p}=gr(r),c=`\n  const ivec2 strides = ivec2(${r.strides[0]}, ${r.strides[1]});\n  const 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r=t[0].dims,i=t[1].dims,a=Dt.calcShape(r,i,!0);if(!a)throw new Error("Can't use matmul on the given tensors");let o=Bt(a.length),s=sr(),{activationFunction:u,applyActivation:l}=gr(n),d=t.length>2,p=d?"value += getBiasForMatmul();":"",c=d?`${cs(o,s,t[2].dims,a,!1)}`:"",h=a.length,f=r.length,m=i.length,g=`\n    ${u}\n    ${c}\n    float process(int indices[${h}]) {\n        int a[${f}];\n        int b[${m}];\n        bcastMatmulIndices_A(indices, a);\n        bcastMatmulIndices_B(indices, b);\n\n        float value;\n        for (int k=0; k<${r[r.length-1]}; ++k) {\n            a[${f-1}] = k;\n            b[${m-2}] = k;\n            value += _A(a) * _B(b);\n        }\n        ${p}\n        ${l}\n        return value;\n    }`;return{...e,output:{dims:a,type:t[0].type,textureType:0},shaderSource:g}}function ds(e,t){let n=$v(e.length>2,t.activationCacheKey);return{...n,get:()=>Sv(n,e,t)}}function cs(e,t,n,r,i){let a="",o=n.length,s=r.length,u=s-o;a=s<2&&o>0?"coords":n.map(((e,n)=>`coords.${t[n+u]}`)).join(", ");let l=Dt.getBroadcastDims(n,r).map((e=>`coords.${t[e+u]} = 0;`)).join("\n"),d="vec4(outputValue.xx, outputValue.yy)";return 1===te.size(n)&&(d="vec4(outputValue.x)"),i?`\nvec4 getBiasForMatmul() {\n  ${e} coords = getOutputCoords();\n  ${l}\n  vec4 outputValue = getBias(${a});\n  return ${d};\n}`:`\nfloat getBiasForMatmul() {\n  ${e} coords = getOutputCoords();\n  ${l}\n  return getBias(coords.x);\n}`}var Jc,Qc,$v,Iv,xi=D((()=>{We(),Ne(),hr(),dn(),fs(),Jc=(e,t,n)=>(Iv(t),e.session.pack?[e.run(Ti(e,t,n),t)]:[e.run(ds(t,n),t)]),Qc=e=>Pn(e.attributes),$v=(e,t)=>({name:"MatMul",inputNames:e?["A","B","Bias"]:["A","B"],inputTypes:e?[0,0,0]:[0,0],cacheHint:t}),Iv=e=>{if(!e||2!==e.length)throw new Error("MatMul requires 2 inputs.");if(e[0].dims[e[0].dims.length-1]!==e[1].dims[e[1].dims.length-2])throw new Error("shared dimension does not 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${b}\n            void main() {\n              ${l?"":`${m} rc =\n          getOutputCoords(); int lastDim = rc.${y[g-1]}; rc.${y[g-1]} =\n          rc.${y[g-2]}; rc.${y[g-2]} = lastDim;\n      `}\n\n              vec4 value = vec4(0);\n              for (int i = 0; i < ${p}; i++) {\n                vec4 a = ${$};\n                vec4 b = ${_};\n\n                value += (a.rrbb * b.rgrg);\n                value += (a.ggaa * b.baba);\n              }\n              ${a}\n              ${w}\n              ${f.output} = value;\n            }`;return{...t,output:{dims:u,type:n[0].type,textureType:2},shaderSource:T,hasMain:!0}},Ti=(e,t,n)=>{let r=Av(t.length>2,n.activationCacheKey);return{...r,get:()=>Ov(e,r,t,n)}}})),ef,tf=D((()=>{wi(),Zc(),fs(),ef=(e,t,n)=>{let r=t[0].dims,i=t[1].dims,a=kn(r,i,n.dilations,n.pads,n.strides),o=e.run(Xc(e,t[0],t[1],a,n),[t[0]]),s=e.reshapePacked(t[1],[i[0],i[1]*i[2]*i[3]]),u=3===t.length?[s,o,t[2]]:[s,o],l=e.run(Ti(e,u,n),u);return e.reshapePacked(l,a)}})),kv,Dv,rf,ps,hs=D((()=>{Ne(),kv=e=>({name:"Im2Col",inputNames:["X"],inputTypes:[0],cacheHint:e}),Dv=(e,t,n,r,i,a)=>{let o=n.dims,s=r.dims,u=i.length,l=ps(o,s,i,4),d=`\n        const int XC = ${o[1]};\n        const int XH = ${o[2]};\n        const int XW = ${o[3]};\n        const int KH = ${a.kernelShape[0]};\n        const int KW = ${a.kernelShape[1]};\n        const int dilationH = ${a.dilations[0]};\n        const int dilationW = ${a.dilations[1]};\n        const int strideH = ${a.strides[0]};\n        const int strideW = ${a.strides[1]};\n        const int padH = ${a.pads[0]};\n        const int padW = ${a.pads[1]};\n        const int KHKW = KH*KW;\n        const int XCKHKW = XC * KHKW;\n        const int outputChannels = 4;\n        vec4 process(int indices[${u}]) {\n          int b  = indices[0]; // batch size\n          int oh = indices[1] * strideH - padH; //output height\n          int ow = indices[2] * strideW - padW; //output width\n          int p = indices[3] * outputChannels; //patch\n          vec4 value = vec4(0.0);\n          for(int i=0; i < outputChannels; ++i) {\n            if(p < XCKHKW) {\n              int patchC = p / KHKW;\n              int patchH = (p - patchC*KHKW) / KW;\n              int patchW = (p - patchC*KHKW) - patchH * KW;\n              int xh2 = oh + patchH * dilationH;\n              int xw2 = ow + patchW * dilationW;\n              int x[${o.length}];\n              x[0] = b;\n              x[1] = patchC;\n              x[2] = xh2;\n              x[3] = xw2;\n              if(xh2 >= 0 &&\n                  xh2 < XH &&\n                  xw2 >= 0 &&\n                  xw2 < XW) {\n                value[i] = _X(x);\n              }\n            }\n            ++p;\n          }\n          return value;\n        }\n        `;return{...t,output:{dims:l,type:n.type,textureType:4},shaderSource:d}},rf=(e,t,n,r,i)=>{let a=kv(i.cacheKey);return{...a,get:()=>Dv(e,a,t,n,r,i)}},ps=(e,t,n,r=4)=>[n[0],n[2],n[3],Math.ceil(e[1]*t[2]*t[3]/r)]})),Bv,Rv,nf,of=D((()=>{We(),ot(),Ne(),dn(),hs(),Bv=(e,t)=>({name:"ConvDotProduct",inputNames:e?["Im2Col","K","B"]:["Im2Col","K"],inputTypes:e?[0,4,0]:[0,4],cacheKey:t.activationCacheKey}),Rv=(e,t,n,r,i)=>{let a=n[0].dims,o=n[1].dims,s=[o[0],Math.ceil(a[1]*o[2]*o[3]/4)],u=ps(a,o,r),[l,d]=e.calculateTextureWidthAndHeight(s,4),p=te.computeStrides(u),[c,h]=e.calculateTextureWidthAndHeight(u,4),f=r.length,m=n.length<3?"0.0":"_B(b)",g=Math.ceil(a[1]*o[2]*o[3]/4),{activationFunction:y,applyActivation:b}=gr(i),w=ce(e.session.backend.glContext.version),v=`\n${y}\nfloat process(int indices[${f}]) {\n  int b[1];\n  b[0] = indices[1];\n  int im2col[4];\n  im2col[0] = indices[0];\n  im2col[1] = indices[2];\n  im2col[2] = indices[3];\n  int im2colOffset = im2col[0] * ${p[0]} + im2col[1] * ${p[1]} + im2col[2] * ${p[2]};\n  int kernelOffset = indices[1] * ${s[1]};\n  float value = ${m};\n  for (int i = 0; i < ${g}; ++i) {\n    vec2 im2colCoords = offsetToCoords(im2colOffset, ${c}, ${h});\n    vec2 kernelCoords = offsetToCoords(kernelOffset, ${l}, ${d});\n    value += dot(${w.texture2D}(Im2Col, im2colCoords), ${w.texture2D}(K, kernelCoords));\n    ++im2colOffset;\n    ++kernelOffset;\n  }\n  ${b}\n  return value;\n}`;return{...t,output:{dims:r,type:n[0].type,textureType:0},shaderSource:v}},nf=(e,t,n,r)=>{let i=Bv(t.length>2,r);return{...i,get:()=>Rv(e,i,t,n,r)}}})),kn,ms,zv,Nv,Lv,Mv,gs,Vv,wi=D((()=>{St(),We(),Yc(),tf(),of(),dn(),hs(),xi(),kn=(e,t,n,r,i)=>{let a=e[0],o=e.slice(2),s=o.length,u=t[0],l=t.slice(2).map(((e,t)=>e+(e-1)*(n[t]-1))),d=o.map(((e,t)=>e+r[t]+r[t+s])).map(((e,t)=>Math.floor((e-l[t]+i[t])/i[t])));return[a,u].concat(...d)},ms=(e,t,n)=>(Vv(t,n),zv(e,t,n)),zv=(e,t,n)=>{let r=Mv(n,t),i=e.session.pack,a=1===r.kernelShape[0]&&1===r.kernelShape[1];return r.group>1?[e.run(Kc(e,t,r),t)]:a&&i?[Nv(e,t,r)]:i&&4===t[0].dims.length&&1===t[0].dims[0]&&!a?[ef(e,t,r)]:[Lv(e,t,r)]},Nv=(e,t,n)=>{let r=t[0].dims,i=t[1].dims,a=kn(r,i,n.dilations,n.pads,n.strides),o=e.reshapeUnpacked(t[0],[r[1],r[2]*r[3]]),s=e.reshapeUnpacked(t[1],[i[0],i[1]]),u=t.length>2?[s,o,t[2]]:[s,o],l=e.run(ds(u,n),u);return e.reshapeUnpacked(l,a)},Lv=(e,t,n)=>{let r=t[0].dims,i=t[1].dims,a=kn(r,i,n.dilations,n.pads,n.strides),o=e.run(rf(e,t[0],t[1],a,n),[t[0]]),s=3===t.length?[o,t[1],t[2]]:[o,t[1]];return e.run(nf(e,t,a,n),s)},Mv=(e,t)=>{let n=e.kernelShape.slice();if(0===e.kernelShape.length)for(let e=2;e<t[1].dims.length;++e)n.push(t[1].dims[e]);let r=e.pads.slice();on.adjustPadsBasedOnAutoPad(t[0].dims,e.strides,e.dilations,n,r,e.autoPad);let i=Object.assign({},e);return Object.assign(i,{kernelShape:n,pads:r,cacheKey:e.cacheKey}),i},gs=e=>{let t=e.attributes,n=Pn(t),r=t.getString("auto_pad","NOTSET"),i=t.getInts("dilations",[1,1]),a=t.getInt("group",1),o=t.getInts("kernel_shape",[]),s=t.getInts("pads",[0,0,0,0]),u=t.getInts("strides",[1,1]);return Ae({autoPad:r,dilations:i,group:a,kernelShape:o,pads:s,strides:u,...n})},Vv=(e,t)=>{if(!e||2!==e.length&&3!==e.length)throw new Error("Conv requires 2 or 3 inputs");if(4!==e[0].dims.length||4!==e[1].dims.length)throw new Error("currently only support 2-dimensional conv");if(e[0].dims[1]!==e[1].dims[1]*t.group)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(3===e.length&&(1!==e[2].dims.length||e[1].dims[0]!==e[2].dims[0]))throw new Error("invalid bias");let n=e[0].dims.length-2;if(t.dilations.length!==n)throw new Error(`dilations should be ${n}D`);if(t.strides.length!==n)throw new Error(`strides should be ${n}D`);if(t.pads.length!==2*n)throw new Error(`pads should be ${2*n}D`);if(0!==t.kernelShape.length&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape");if("float32"!==e[0].type||"float32"!==e[1].type)throw new Error("Conv input(X,W) should be float tensor");if(3===e.length&&"float32"!==e[2].type)throw new Error("Conv input(bias) should be float tensor")}})),Fv,Uv,Gv,af,Wv,Hv,qv,jv,Kv,Yv,sf,Xv,uf=D((()=>{St(),ot(),Ne(),dn(),Fv=(e,t,n,r,i,a)=>(e-1)*t+n+(r-1)*i+1-a,Uv=(e,t,n,r,i)=>{let a=Math.floor(e/2);"SAME_UPPER"===t?(n[r]=a,n[i]=e-a):"SAME_LOWER"===t&&(n[r]=e-a,n[i]=a)},Gv=(e,t,n,r,i,a,o,s)=>{let u=e.length-2,l=0===s.length;for(let d=0;d<u;++d){let p=l?e[d+2]*a[d]:s[d],c=Fv(e[d+2],a[d],i[d],t[d],n[d],p);Uv(c,r,i,d,d+u),l&&s.push(a[d]*(e[d+2]-1)+o[d]+(t[d]-1)*n[d]+1-i[d]-i[d+u])}},af=(e,t,n)=>(Xv(t,n),Wv(e,t,n)),Wv=(e,t,n)=>{let r=Yv(n,t);return[Kv(e,t,r)]},Hv=(e,t)=>({name:"ConvTranspose",inputNames:e?["X","W","B"]:["X","W"],inputTypes:e?[0,0,0]:[0,0],cacheHint:t}),qv=(e,t,n,r)=>{let i=t.length>2?"getB(output_channel)":"0.0",a=t[0].dims,o=t[1].dims,s=o[1],u=o[0]/r.group,l=[t[0].dims[0],t[1].dims[1]*r.group,...r.outputShape],d=ce(e.session.backend.glContext.version),{activationFunction:p,applyActivation:c}=gr(r),h=`\n  const ivec2 strides = ivec2(${r.strides[0]}, ${r.strides[1]});\n  const ivec2 pads = ivec2(${r.pads[0]}, ${r.pads[1]});\n  ${p}\n  void main() {\n    ivec4 coords = getOutputCoords();\n    int batch = coords.x;\n    int output_channel = coords.y;\n\n    ivec2 loc = coords.zw + pads;\n\n    int group_id = output_channel / ${s};\n    int wOutChannel = output_channel - group_id * ${s};\n\n    float value = ${i};\n    for (int inChannelOffset = 0; inChannelOffset < ${u}; inChannelOffset++) {\n      int input_channel = group_id * ${u} + inChannelOffset;\n      for (int wWOff = 0; wWOff < ${o[2]}; wWOff++) {\n        for (int wHOff = 0; wHOff < ${o[3]}; wHOff++) {\n          ivec2 wOff = ivec2(wWOff * ${r.dilations[0]}, wHOff * ${r.dilations[1]});\n          ivec2 wLoc = loc - wOff;\n          ivec2 wLocIn = wLoc / strides;\n          if (\n            wLocIn * strides == wLoc &&\n            wLocIn.x >= 0 && wLocIn.x < ${a[2]} &&\n            wLocIn.y >= 0 && wLocIn.y < ${a[3]}\n          ) {\n            float xVal = getX(batch, input_channel, wLocIn.y, wLocIn.x);\n            float wVal = getW(input_channel, wOutChannel, wHOff, wWOff);\n            value += xVal * wVal;\n          }\n        }\n      }\n    }\n    ${c}\n    ${d.output} = vec4(value, .0, .0, .0);\n  }\n`;return{...n,output:{dims:l,type:t[0].type,textureType:0},shaderSource:h,hasMain:!0}},jv=(e,t,n)=>{let r=Hv(t.length>2,n.cacheKey);return{...r,get:()=>qv(e,t,r,n)}},Kv=(e,t,n)=>e.run(jv(e,t,n),t),Yv=(e,t)=>{let n=e.kernelShape.slice();if(0===e.kernelShape.length)for(let e=2;e<t[1].dims.length;++e)n.push(t[1].dims[e]);let r=e.pads.slice(),i=e.outputShape.slice(),a=t[0].dims;Gv(a,n,e.dilations,e.autoPad,r,e.strides,e.outputPadding,i);let o=Object.assign({},e);return Object.assign(o,{kernelShape:n,pads:r,outputShape:i,cacheKey:e.cacheKey}),o},sf=e=>{let t=e.attributes,n=Pn(t),r=t.getString("auto_pad","NOTSET"),i=t.getInts("dilations",[1,1]),a=t.getInt("group",1),o=t.getInts("kernel_shape",[]),s=t.getInts("output_padding",[0,0]),u=t.getInts("output_shape",[]),l=t.getInts("pads",[0,0,0,0]),d=t.getInts("strides",[1,1]);return Ae({autoPad:r,dilations:i,group:a,kernelShape:o,outputPadding:s,outputShape:u,pads:l,strides:d,...n})},Xv=(e,t)=>{if(!e||2!==e.length&&3!==e.length)throw new Error("Conv requires 2 or 3 inputs");if(4!==e[0].dims.length||4!==e[1].dims.length)throw new Error("currently only support 2-dimensional conv");if(e[0].dims[1]!==e[1].dims[0])throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let n=e[1].dims[1]*t.group;if(3===e.length&&(1!==e[2].dims.length||e[2].dims[0]!==n))throw new Error("invalid bias");let r=e[0].dims.length-2;if(t.dilations.length!==r)throw new Error(`dilations should be ${r}D`);if(t.strides.length!==r)throw new Error(`strides should be ${r}D`);if(t.pads.length!==2*r)throw new Error(`pads should be ${2*r}D`);if(t.outputPadding.length!==r)throw new Error(`output_padding should be ${r}D`);if(0!==t.kernelShape.length&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape");if(0!==t.outputShape.length&&t.outputShape.length!==e[0].dims.length-2)throw new Error("invalid output shape");if("float32"!==e[0].type||"float32"!==e[1].type)throw new Error("ConvTranspose input(X,W) should be float tensor");if(3===e.length&&"float32"!==e[2].type)throw new Error("ConvTranspose input(bias) should be float tensor")}})),lf,cn,df,Zv,cf,Jv,Qv,ew,_i=D((()=>{St(),We(),Ne(),lf={name:"Transpose",inputNames:["A"],inputTypes:[0]},cn=(e,t,n)=>(ew(t),[e.run({...lf,cacheHint:n.cacheKey,get:()=>Zv(e,t[0],n.perm)},t)]),df=e=>Ae({perm:e.attributes.getInts("perm",[])}),Zv=(e,t,n)=>{let r=t.dims;n=cf(r,n);let i=Jv(r,n),a=r.length,o=`\n      ${Qv("perm",n,a)}\n      float process(int indices[${a}]) {\n        int a[${a}];\n        perm(a, indices);\n        return _A(a);\n      }`;return{...lf,output:{dims:i,type:t.type,textureType:0},shaderSource:o}},cf=(e,t)=>(t&&t.length!==e.length&&(t=[...e.keys()].reverse()),t),Jv=(e,t)=>(t=cf(e,t),te.sortBasedOnPerm(e,t)),Qv=(e,t,n)=>{let r=[];r.push(`void ${e}(out int a[${n}], int src[${n}]) {`);for(let e=0;e<n;++e)r.push(`\ta[${t[e]}]=src[${e}];`);return r.push("\t}"),r.join("\n")},ew=e=>{if(!e||1!==e.length)throw new Error("Transpose requires 1 input.");if("float32"!==e[0].type&&"float64"!==e[0].type)throw new Error("input should be float tensor")}})),ff,pf,tw,hf=D((()=>{_i(),ff=(e,t,n)=>{tw(t);let r=n.blocksize,i=r*r,a="DCR"===n.mode?[0,3,4,1,5,2]:[0,1,4,2,5,3],o="DCR"===n.mode?[t[0].dims[0],r,r,t[0].dims[1]/i,t[0].dims[2],t[0].dims[3]]:[t[0].dims[0],t[0].dims[1]/i,r,r,t[0].dims[2],t[0].dims[3]],s=e.reshapeUnpacked(t[0],o),u={perm:a,cacheKey:`${a}`},[l]=cn(e,[s],u),d=[t[0].dims[0],t[0].dims[1]/i,t[0].dims[2]*r,t[0].dims[3]*r];return[e.reshapeUnpacked(l,d)]},pf=e=>{let t=e.attributes.getInt("blocksize");if(t<1)throw new Error(`blocksize must be >= 1, but got : ${t} for DepthToSpace`);let n=e.attributes.getString("mode","DCR");if("DCR"!==n&&"CRD"!==n)throw new Error(`unrecognized mode: ${n} for DepthToSpace`);return{mode:n,blocksize:t}},tw=e=>{if(1!==e.length)throw new Error(`DepthToSpace expect 1 inputs, but got ${e.length}`);if("string"===e[0].type||4!==e[0].dims.length)throw new TypeError("DepthToSpace input should be a 4-D numeric tensor")}})),mf,gf,rw,bf=D((()=>{We(),mf=(e,t,n)=>{rw(t,n);let r=te.flattenShape(t[0].dims,n);return[e.reshapeUnpacked(t[0],r)]},gf=e=>e.attributes.getInt("axis",1),rw=(e,t)=>{if(!e||1!==e.length)throw new Error("Flatten requires 1 input.");let n=e[0].dims.length;if(0===n)throw new Error("scalar tensor is not supported.");if(t<-n||t>n)throw new Error("Invalid axis");if("string"===e[0].type)throw new Error("string tensor is not supported.")}})),Wr,go=D((()=>{Wr=["float32","float64","int32","int16","int8","uint16","uint32","uint8"]})),yf,vf,nw,ow,iw,aw,wf=D((()=>{St(),go(),We(),Ne(),yf=(e,t,n)=>(aw(t,n.axis),[e.run(iw(e,t,n),t)]),vf=e=>Ae({axis:e.attributes.getInt("axis",0)}),nw={name:"Gather",inputNames:["A","B"],inputTypes:[0,0]},ow=(e,t,n,r)=>{let i=n[0].dims.slice(),a=n[1].dims.slice(),o=new Array(i.length+a.length-1);r=te.normalizeAxis(r,i.length);let s=[];for(let e=0;e<o.length;e++)e<r?(o[e]=i[e],s.push(`inputIdx[${e}] = outputIdx[${e}];`)):e<r+a.length?(o[e]=a[e-r],s.push(`indexDataIdx[${e-r}] = outputIdx[${e}];`)):(o[e]=i[e-a.length+1],s.push(`inputIdx[${e-a.length+1}] = outputIdx[${e}];`));let u=`\n      float process(int outputIdx[${o.length||1}]) {\n        int inputIdx[${i.length}];\n        int indexDataIdx[${a.length||1}];\n        indexDataIdx[0] = 0;\n        ${s.join("\n        ")}\n        int idx = int(_B(indexDataIdx));\n        inputIdx[${r}] = idx < 0 ? idx + ${i[r]} : idx;\n        return _A(inputIdx);\n      }`;return{...t,output:{dims:o,type:n[0].type,textureType:0},shaderSource:u}},iw=(e,t,n)=>{let r={...nw,cacheHint:n.cacheKey};return{...r,get:()=>ow(e,r,t,n.axis)}},aw=(e,t)=>{if(!e||2!==e.length)throw new Error("Gather requires 2 inputs.");let n=e[0].dims.length;if(n<1)throw new Error("Invalid input shape.");if(t<-n||t>n-1)throw new Error("Invalid axis.");if(-1===Wr.indexOf(e[0].type))throw new Error("Invaid input type.");if("int32"!==e[1].type&&"int16"!==e[1].type)throw new Error("Invaid input type.")}})),bs,xf,Tf,_f,sw,uw,lw,$f=D((()=>{St(),We(),Ne(),bs=(e,t,n)=>(lw(t,n),[e.run(sw(t,n),t)]),xf=(e,t)=>{let n=0!==e.attributes.getInt("transA",0),r=0!==e.attributes.getInt("transB",0),i=e.attributes.getFloat("alpha",1),a=e.attributes.getFloat("beta",1);return Ae({transA:n,transB:r,alpha:i,beta:a,isOptionalC:t})},Tf=e=>xf(e,!1),_f=e=>xf(e,!0),sw=(e,t)=>{let n={name:"Gemm",inputNames:3===e.length?["A","B","C"]:["A","B"],inputTypes:3===e.length?[0,0,0]:[0,0],key:t.cacheKey};return{...n,get:()=>uw(n,e,t)}},uw=(e,t,n)=>{let r=t[0].dims.slice(),i=t[1].dims.slice(),[a,o]=fi.getShapeOfGemmResult(r,n.transA,i,n.transB,3===t.length?t[2].dims:void 0),s=[a,o];if(!s)throw new Error("Can't use gemm on the given tensors");let u=r[r.length-1],l="";n.transA&&(u=r[0]),n.transA&&n.transB?l="value += _A_T(a) * _B_T(b);":n.transA&&!n.transB?l="value += _A_T(a) * _B(b);":!n.transA&&n.transB?l="value += _A(a) * _B_T(b);":!n.transA&&!n.transB&&(l="value += _A(a) * _B(b);");let d=s.length,p=`\n      float process(int indices[${d}]) {\n          int a[${d}];\n          int b[${d}];\n          ${3===t.length?`int c[${t[2].dims.length}];`:""}\n\n          copyVec(indices, a);\n          copyVec(indices, b);\n          ${3===t.length?"bcastIndices_C(indices, c);":""}\n\n          float value = 0.0;\n          for (int k=0; k<${u}; ++k) {\n              a[${d-1}] = k;\n              b[${d-2}] = k;\n              ${l}\n          }\n\n          value = value * alpha;\n          ${3===t.length?"value += beta * _C(c);":""}\n          return value;\n      }`;return{...e,output:{dims:s,type:t[0].type,textureType:0},variables:[{name:"alpha",type:"float",data:n.alpha},{name:"beta",type:"float",data:n.beta}],shaderSource:p}},lw=(e,t)=>{if(!e)throw new Error("Input is missing");if(t.isOptionalC&&(e.length<2||e.length>3))throw new Error("Invaid input shape.");if(!t.isOptionalC&&3!==e.length)throw new Error("Gemm requires 3 inputs");if(3===e.length&&1!==e[2].dims.length&&2!==e[2].dims.length)throw new Error("Invalid input shape of C");if("float32"!==e[0].type&&"float64"!==e[0].type||"float32"!==e[1].type&&"float64"!==e[1].type||3===e.length&&"float32"!==e[2].type&&"float64"!==e[2].type)throw new Error("Invalid input type.");if(e[0].type!==e[1].type||3===e.length&&e[0].type!==e[2].type)throw new Error("Input types are mismatched")}})),Sf,If,dw,cw,fw,pw,hw,Af=D((()=>{St(),Ne(),Sf=(e,t,n)=>(hw(t),[e.run(fw(e,t,n),t)]),If=e=>{let t=e.attributes.getFloat("scale"),n=e.attributes.getFloats("bias");return Ae({scale:t,bias:n})},dw={name:"ImageScaler",inputNames:["X"],inputTypes:[0]},cw=(e,t,n,r)=>{let i=n[0].dims.slice(),a=i.length,o=`\n      ${pw(r.bias.length)}\n      float process(int indices[${a}]) {\n        return _X(indices) * scale + getBias(bias, indices[1]);\n      }`;return{...t,output:{dims:i,type:n[0].type,textureType:0},variables:[{name:"bias",type:"float",arrayLength:r.bias.length,data:r.bias},{name:"scale",type:"float",data:r.scale}],shaderSource:o}},fw=(e,t,n)=>{let r={...dw,cacheHint:n.cacheKey};return{...r,get:()=>cw(e,r,t,n)}},pw=e=>{let t=[`float getBias(float bias[${e}], int channel) {`];for(let n=0;n<e;++n)0===n?t.push(`\tif (channel == ${n}) { return bias[${n}]; }`):n===e-1?t.push(`\telse { return bias[${n}]; }`):t.push(`\telse if (channel == ${n}) { return bias[${n}]; }`);return t.push("\t}"),t.join("\n")},hw=e=>{if(!e||1!==e.length)throw new Error("ImageScaler requires 1 input.");if(4!==e[0].dims.length)throw new Error("Invalid input shape.");if("float32"!==e[0].type&&"float64"!==e[0].type)throw new Error("Invalid input type.")}})),Ef,Cf,Of,mw,gw,bw,yw,vw,ww,Pf=D((()=>{ot(),Ne(),Ef=(e,t,n)=>{ww(t);let r=e.run(gw(t[0]),t);return[e.run(vw(e,t[0],n,r.dims),[t[0],r,t[1],t[2]])]},Cf=e=>e.attributes.getFloat("epsilon",1e-5),Of={name:"InstanceNormalization_MeanAndVariance",inputNames:["X"],inputTypes:[0]},mw=(e,t)=>{let n=t.dims.slice(),r=n[1],i=n[2]*n[3],a=[n[0],r],o=`\n      vec4 process(int[2] indices) {\n        vec4 v = vec4(0.0);\n        int a[4];\n        a[0] = indices[0];\n        a[1] = indices[1];\n        float temp = 0.0;\n        for(int a2=0; a2<${n[2]}; a2++) {\n          a[2] = a2;\n          for(int a3=0; a3<${n[3]}; a3++) {\n            a[3] = a3;\n            float x = _X(a);\n            temp += x;\n          }\n        }\n        float mean = temp / float(${i});\n        temp = 0.0;\n        for(int a2=0; a2<${n[2]}; a2++) {\n          a[2] = a2;\n          for(int a3=0; a3<${n[3]}; a3++) {\n            a[3] = a3;\n            float x = _X(a);\n            temp += (x - mean) * (x - mean);\n          }\n        }\n        v.r = mean;\n        v.g = temp / float(${i});\n\n        return v;\n      }`;return{...e,output:{dims:a,type:t.type,textureType:4},shaderSource:o}},gw=e=>({...Of,get:()=>mw(Of,e)}),bw={name:"InstanceNormalization_ComputeOutput",inputNames:["X","MeanAndVariance","Scale","B"],inputTypes:[0,4,0,0]},yw=(e,t,n,r,i)=>{let a=ce(e.session.backend.glContext.version),[o,s]=e.calculateTextureWidthAndHeight(i,4),[u,l]=[o/4,s],d=`\n      vec4 get_MeanAndVariance(int[2] mv) {\n        int offset = indicesToOffset_MeanAndVariance(mv);\n        vec2 coords = offsetToCoords(offset, ${u}, ${l});\n        return ${a.texture2D}(MeanAndVariance, coords);\n      }\n\n      float process(int[4] indices) {\n        int mv[2];\n        mv[0] = indices[0];\n        mv[1] = indices[1];\n        vec4 mean_and_variance = get_MeanAndVariance(mv);\n        float mean = mean_and_variance.r;\n        float variance = mean_and_variance.g;\n\n        int sb[1];\n        sb[0] = indices[1];\n        float scale = _Scale(sb);\n        float b = _B(sb);\n\n        return scale * (_X(indices) - mean) / sqrt(variance + epsilon) + b;\n      }`;return{...t,output:{dims:n.dims,type:n.type,textureType:0},variables:[{name:"epsilon",type:"float",data:r}],shaderSource:d}},vw=(e,t,n,r)=>{let i={...bw,cacheHint:`${n}`};return{...i,get:()=>yw(e,i,t,n,r)}},ww=e=>{if(!e||3!==e.length)throw new Error("InstanceNormalization requires 3 inputs.");let t=e[0],n=e[1],r=e[2];if(t.dims.length<3||1!==n.dims.length||1!==r.dims.length)throw new Error("Invalid input shape.");if(n.dims[0]!==t.dims[1]||r.dims[0]!==t.dims[1])throw new Error("Input shapes are mismatched.");if("float32"!==t.type&&"float64"!==t.type||"float32"!==n.type&&"float64"!==n.type||"float32"!==r.type&&"float64"!==r.type)throw new Error("Invalid input type.");if(4!==e[0].dims.length)throw new Error("Only support 4-D input shape.")}}));function xw(e,t){let n=e[0].dims[1],r=e[0].dims.length,i=-Math.floor((t.size-1)/2),a=Math.ceil((t.size-1)/2),o=`float(${t.alpha}) / float(${t.size})`,s=`\n    float process(int indices[${r}]) {\n        int c = indices[1];\n        float x = _X(indices);\n        float square_sum = 0.0;\n\n        for (int i = ${i}; i <= ${a}; i++) {\n          int idx = c + i;\n          if (c >= 0 && c < ${n}) {\n            indices[1] = idx;\n            float j = _X(indices);\n            square_sum += j * j;\n          }\n        }\n        return x / pow(${`float(${t.bias})`} + ${o} * square_sum, ${`float(${t.beta})`});\n    }`;return{...Bf,cacheHint:t.cacheKey,output:{dims:e[0].dims,type:e[0].type,textureType:0},shaderSource:s}}function Tw(e,t){return{...Bf,cacheHint:t.cacheKey,get:()=>xw(e,t)}}var kf,Df,Bf,_w,Rf=D((()=>{St(),Ne(),kf=(e,t,n)=>(_w(t),[e.run(Tw(t,n),t)]),Df=e=>{let t=e.attributes.getFloat("alpha",1e-4),n=e.attributes.getFloat("beta",.75),r=e.attributes.getFloat("bias",1),i=e.attributes.getInt("size");return Ae({alpha:t,beta:n,bias:r,size:i})},Bf={name:"LRN",inputNames:["X"],inputTypes:[0]},_w=e=>{if(!e||1!==e.length)throw new Error("LRN requires 1 input.");if(4!==e[0].dims.length)throw new Error('currently only support LRN for input with "NCHW" format');if("float32"!==e[0].type)throw new Error("input should be float 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}`;return{name:"Pad",inputNames:["A"],inputTypes:[0],output:{dims:r,type:t.type,textureType:0},shaderSource:a}},Aw=e=>{if(!e||1!==e.length)throw new Error("Pad requires 1 input");if("float32"!==e[0].type&&"float64"!==e[0].type)throw new Error("Invalid input type.")},Ow=e=>{if(!e||2!==e.length&&3!==e.length)throw new Error("Pad requires 2 or 3 inputs");if("int32"!==e[1].type)throw new Error("Invalid input type.");if(e.length>=3&&"string"===e[2].type)throw new Error("Invalid input type.")},Ew=(e,t,n)=>{let r=ce(e.session.backend.glContext.version),[i,a]=e.calculateTextureWidthAndHeight(t.dims,0),o=te.computeStrides(t.dims);switch(n.mode){case"constant":return Cw(r,t.dims,o,i,a,n.pads,n.value);case"reflect":return Pw(r,t.dims,o,i,a,n.pads);case"edge":return kw(r,t.dims,o,i,a,n.pads);default:throw new Error("Invalid mode")}},Cw=(e,t,n,r,i,a,o)=>{let s=t.length,u="";for(let e=s-1;e>=0;--e)u+=`\n        k = m[${e}] - ${a[e]};\n        if (k < 0)  return constant;\n        if (k >= ${t[e]}) return constant;\n        offset += k * ${n[e]};\n        `;return`\n      float padA(int m[${s}]) {\n        const float constant = float(${o});\n        int offset = 0;\n        int k = 0;\n        ${u}\n        vec2 coords = offsetToCoords(offset, ${r}, ${i});\n        float value = getColorAsFloat(${e.texture2D}(A, coords));\n        return value;\n      }\n      `},Pw=(e,t,n,r,i,a)=>{let o=t.length,s="";for(let e=o-1;e>=0;--e)s+=`\n        k = m[${e}] - ${a[e]};\n        if (k < 0) { k = -k; }\n        {\n          const int _2n_1 = ${2*(t[e]-1)};\n          k = int( mod( float(k), float(_2n_1) ) ) ;\n          if(k >= ${t[e]}) { k = _2n_1 - k; }\n        }\n        offset += k * ${n[e]};\n        `;return`\n      float padA(int m[${o}]) {\n        int offset = 0;\n        int k = 0;\n        ${s}\n        vec2 coords = offsetToCoords(offset, ${r}, ${i});\n        float value = getColorAsFloat(${e.texture2D}(A, coords));\n        return value;\n      }\n      `},kw=(e,t,n,r,i,a)=>{let o=t.length,s="";for(let e=o-1;e>=0;--e)s+=`\n        k = m[${e}] - ${a[e]};\n        if (k < 0)  k = 0;\n        if (k >= ${t[e]}) k = ${t[e]-1};\n        offset += k * ${n[e]};\n      `;return`\n      float padA(int m[${o}]) {\n        int offset = 0;\n        int k = 0;\n        ${s}\n        vec2 coords = offsetToCoords(offset, ${r}, ${i});\n        float value = getColorAsFloat(${e.texture2D}(A, coords));\n        return value;\n      }\n      `}})),Ff,Uf,Gf,Wf,Hf,qf,jf,Kf,Yf,Dw,Vf,Xf,Si,Zf,$i,Bw,Jf=D((()=>{St(),We(),Ne(),Ff=(e,t,n)=>{Si(t);let r={name:"AveragePool",inputNames:["X"],inputTypes:[0],cacheHint:n.cacheKey};return[e.run({...r,get:()=>Gf(t,r,!1,n)},t)]},Uf=e=>{let t=e.attributes.getString("auto_pad","NOTSET"),n=e.attributes.getInt("ceil_mode",0),r=0!==e.attributes.getInt("count_include_pad",0),i=e.attributes.getInts("kernel_shape"),a=e.attributes.getInts("strides",[]),o=e.attributes.getInts("pads",[]);if(0!==n)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");return Ae({autoPad:t,ceilMode:n,countIncludePad:r,kernelShape:i,strides:a,pads:o})},Gf=(e,t,n,r)=>{let[i,a]=Yf(e,r,n),o=te.size(i.kernelShape),s="";i.countIncludePad?s+=`value /= float(${o});`:s+=`value /= float(${o} - pad);`;let u=`\n        ${Zf(e[0].dims,i,"value += _X(x);",s,"0.0")}\n      `;return{...t,output:{dims:a,type:e[0].type,textureType:0},shaderSource:u}},Wf=(e,t,n)=>{Si(t);let r={name:"GlobalAveragePool",inputNames:["X"],inputTypes:[0],cacheHint:`${n.countIncludePad}`};return[e.run({...r,get:()=>Gf(t,r,!0,n)},t)]},Hf=e=>{let t=0!==e.attributes.getInt("count_include_pad",0);return Ae({autoPad:"",ceilMode:0,countIncludePad:t,kernelShape:[],strides:[],pads:[]})},qf=(e,t,n)=>{Si(t);let r={name:"MaxPool",inputNames:["X"],inputTypes:[0],cacheHint:n.cacheKey};return[e.run({...r,get:()=>Kf(t,r,!1,n)},t)]},jf=e=>{let t=e.attributes.getString("auto_pad","NOTSET"),n=e.attributes.getInt("ceil_mode",0),r=e.attributes.getInts("kernel_shape"),i=e.attributes.getInts("strides",[]),a=e.attributes.getInts("pads",[]),o=e.attributes.getInt("storage_order",0),s=e.attributes.getInts("dilations",[]);if(0!==o)throw new Error("column major storage order is not yet supported for MaxPool");if(0!==n)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");return Ae({autoPad:t,ceilMode:n,countIncludePad:!1,kernelShape:r,strides:i,pads:a,storageOrder:o,dilations:s})},Kf=(e,t,n,r)=>{let[i,a]=Yf(e,r,n),o=`\n      ${Zf(e[0].dims,i,"\n      value = max(_X(x), value);\n    ","","-1e5")}\n    `;return{...t,output:{dims:a,type:e[0].type,textureType:0},shaderSource:o}},Yf=(e,t,n)=>{let r=e[0].dims.slice(),i=Object.hasOwnProperty.call(t,"dilations"),a=t.kernelShape.slice(),o=t.strides.slice(),s=i?t.dilations.slice():[],u=t.pads.slice();on.adjustPoolAttributes(n,r,a,o,s,u);let l=on.computePoolOutputShape(n,r,o,s,a,u,t.autoPad),d=Object.assign({},t);return i?Object.assign(d,{kernelShape:a,strides:o,pads:u,dilations:s,cacheKey:t.cacheKey}):Object.assign(d,{kernelShape:a,strides:o,pads:u,cacheKey:t.cacheKey}),[d,l]},Dw={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[],cacheKey:""},Vf={name:"GlobalMaxPool",inputNames:["X"],inputTypes:[0]},Xf=(e,t)=>(Si(t),[e.run({...Vf,get:()=>Kf(t,Vf,!0,Dw)},t)]),Si=e=>{if(!e||1!==e.length)throw new Error("Pool ops requires 1 input.");if("float32"!==e[0].type&&"float64"!==e[0].type)throw new Error("Invalid input type.")},Zf=(e,t,n,r,i)=>{let a=e.length;if(t.kernelShape.length<=2){let o=t.kernelShape[t.kernelShape.length-1],s=t.strides[t.strides.length-1],u=t.pads[t.pads.length/2-1],l=t.pads[t.pads.length-1],d=e[a-1],p="",c="",h="";if(p=u+l!==0?`\n          for (int i = 0; i < ${o}; i++) {\n            x[${a} - 1] = indices[${a} - 1] * ${s} - ${u} + i;\n            if (x[${a} - 1] < 0 || x[${a} - 1] >= ${d}) {\n              pad++;\n              continue;\n            }\n            ${n}\n          }`:`\n          for (int i = 0; i < ${o}; i++) {\n            x[${a} - 1] = indices[${a} - 1] * ${s} - ${u} + i;\n            ${n}\n          }`,2===t.kernelShape.length){let n=t.kernelShape[t.kernelShape.length-2],r=t.strides[t.strides.length-2],i=t.pads[t.pads.length/2-2],s=t.pads[t.pads.length-2],u=e[a-2];c=i+s!==0?`\n            for (int j = 0; j < ${n}; j++) {\n              x[${a} - 2] = indices[${a} - 2] * ${r} - ${i} + j;\n              if (x[${a} - 2] < 0 || x[${a} - 2] >= ${u}) {\n                pad+= ${o};\n                continue;\n              }\n          `:`\n            for (int j = 0; j < ${n}; j++) {\n              x[${a} - 2] = indices[${a} - 2] * ${r} - ${i} + j;\n            `,h="\n          }\n        "}return`\n        float process(int indices[${a}]) {\n          int x[${a}];\n          copyVec(indices, x);\n\n          float value = ${i};\n          int pad = 0;\n          ${c}\n          ${p}\n          ${h}\n          ${r}\n          return value;\n        }\n      `}{let o=te.size(t.kernelShape),s=te.computeStrides(t.kernelShape),u=s.length,l=t.pads.length,d=Bw(u),p=$i(e,"inputDims"),c=$i(t.pads,"pads"),h=$i(s,"kernelStrides"),f=$i(t.strides,"strides"),m="";return m=t.pads.reduce(((e,t)=>e+t))?`\n            if (x[j] >= inputDims[j] || x[j] < 0) {\n              pad++;\n              isPad = true;\n              break;\n            }\n          }\n          if (!isPad) {\n            ${n}\n          }`:`\n          }\n          ${n}\n        `,`\n        ${d}\n        float process(int indices[${a}]) {\n          int x[${a}];\n          copyVec(indices, x);\n          int offset[${u}];\n          int pads[${l}];\n          int inputDims[${a}];\n          int kernelStrides[${u}];\n          int strides[${u}];\n          ${c}\n          ${p}\n          ${f}\n          ${h}\n\n          float value = ${i};\n          int pad = 0;\n          bool isPad = false;\n          for (int i = 0; i < ${o}; i++) {\n            offsetToIndices(i, kernelStrides, offset);\n            isPad = false;\n            for (int j = ${a} - ${u}; j < ${a}; j++) {\n              x[j] = indices[j] * strides[j - ${a} + ${u}]\n                + offset[j - ${a} + ${u}] - pads[j - 2];\n              ${m}\n          }\n          ${r}\n\n          return value;\n        }\n      `}},$i=(e,t)=>{let n="";for(let r=0;r<e.length;r++)n+=`\n      ${t}[${r}] = ${e[r]};\n    `;return n},Bw=e=>`\n  void offsetToIndices(int offset, int[${e}] strides, out int[${e}] indices) {\n    if (${e} == 0) {\n      return;\n    }\n    for (int i = 0; i < ${e} - 1; ++i) {\n      indices[i] = offset / strides[i];\n      offset -= indices[i] * strides[i];\n    }\n    indices[${e} - 1] = offset;\n  }`})),fn,Hr,Rw,zw,Qf,ep,tp,rp,np,op,ip,ap=D((()=>{St(),go(),We(),Ne(),fn=(e,t,n,r,i)=>{zw(t);let a={name:r,inputNames:["A"],inputTypes:[0]};return[e.run({...a,cacheHint:n.cacheKey,get:()=>Rw(e,t,n,r,i,a)},t)]},Hr=e=>{let t=e.attributes.getInts("axes",[]),n=1===e.attributes.getInt("keepdims",1);return Ae({axes:t,keepDims:n})},Rw=(e,t,n,r,i,a)=>{let o=[],s=t[0].dims.length||1,u=[],l=te.normalizeAxes(n.axes,t[0].dims.length),d=i(t,l),p=d[1];for(let e=0;e<t[0].dims.length;e++)l.indexOf(e)>=0||0===l.length?(n.keepDims&&o.push(1),p=`\n          for(int j${e} = 0; j${e} < ${t[0].dims[e]}; j${e}++) {\n            inputIdx[${e}] = j${e};\n            ${p}\n          }`):(u.push(`inputIdx[${e}] = outputIdx[${o.length}];`),o.push(t[0].dims[e]));let c=`\n      float process(int outputIdx[${o.length||1}]) {\n        float value;                 // final result\n        int inputIdx[${s}];      // addressing input data\n        ${u.join("\n")}\n        ${d[0]}       // init ops for reduce max/min\n        ${p}\n        ${d[2]}       // final computation for reduce mean\n        return value;\n      }`;return{...a,output:{dims:o,type:t[0].type,textureType:0},shaderSource:c}},zw=e=>{if(!e||1!==e.length)throw new Error("Reduce op requires 1 input.");if(-1===Wr.indexOf(e[0].type))throw new Error("Invalid input type.")},Qf=(e,t,n)=>fn(e,t,n,"ReduceSum",(()=>["value = 0.0;","value += _A(inputIdx);",""])),ep=(e,t,n)=>fn(e,t,n,"ReduceMean",((e,t)=>{let n=1;for(let r=0;r<e[0].dims.length;r++)(t.indexOf(r)>=0||0===t.length)&&(n*=e[0].dims[r]);return["value = 0.0;","value += _A(inputIdx);",`value /= ${n}.;`]})),tp=(e,t,n)=>fn(e,t,n,"ReduceMax",((e,t)=>{let n=[];for(let r=0;r<e[0].dims.length;r++)(t.indexOf(r)>=0||0===t.length)&&n.push(`inputIdx[${r}] = 0;`);return[`${n.join("\n")}\nvalue = _A(inputIdx);`,"value = max(value, _A(inputIdx));",""]})),rp=(e,t,n)=>fn(e,t,n,"ReduceMin",((e,t)=>{let n=[];for(let r=0;r<e[0].dims.length;r++)(t.indexOf(r)>=0||0===t.length)&&n.push(`inputIdx[${r}] = 0;`);return[`${n.join("\n")}\nvalue = _A(inputIdx);`,"value = min(value, _A(inputIdx));",""]})),np=(e,t,n)=>fn(e,t,n,"ReduceProd",(()=>["value = 1.0;","value *= _A(inputIdx);",""])),op=(e,t,n)=>fn(e,t,n,"ReduceLogSum",(()=>["value = 0.0;","value += _A(inputIdx);","value = log(value);"])),ip=(e,t,n)=>fn(e,t,n,"ReduceLogSumSquare",(()=>["float t; value = 0.0;","t = _A(inputIdx); value += t * t;",""]))})),sp,up=D((()=>{We(),sp=(e,t)=>{let n=te.calculateReshapedDims(t[0].dims,t[1].integerData);return e.session.pack?[e.reshapePacked(t[0],n)]:[e.reshapeUnpacked(t[0],n)]}})),lp,vs,dp,cp,bo,Nw,ws,Ii,xs=D((()=>{St(),ot(),Ne(),lp={name:"Upsample",inputNames:["X"],inputTypes:[0]},vs=(e,t,n)=>(ws(t,n),[e.run({...lp,cacheHint:n.cacheKey,get:()=>Nw(e,t,n)},t)]),dp=e=>bo(e,7),cp=e=>bo(e,9),bo=(e,t)=>{let n=t>=10,r=e.attributes.getString("mode","nearest");if("nearest"!==r&&"linear"!==r&&(t<11||"cubic"!==r))throw new Error(`unrecognized mode: ${r}`);let i=[];t<9&&(i=e.attributes.getFloats("scales"),Ii(i,r,n));let a=e.attributes.getFloat("extrapolation_value",0),o=t>10?e.attributes.getString("coordinate_transformation_mode","half_pixel"):"asymmetric";if(-1===["asymmetric","pytorch_half_pixel","tf_half_pixel_for_nn","align_corners","tf_crop_and_resize","half_pixel"].indexOf(o))throw new Error(`coordinate_transform_mode '${o}' is not supported`);let s="tf_crop_and_resize"===o,u=s,l="nearest"===r&&t>=11?e.attributes.getString("nearest_mode","round_prefer_floor"):"";if(-1===["round_prefer_floor","round_prefer_ceil","floor","ceil",""].indexOf(l))throw new Error(`nearest_mode '${l}' is not supported`);let d=e.attributes.getFloat("cubic_coeff_a",-.75),p=0!==e.attributes.getInt("exclude_outside",0);if(p&&"cubic"!==r)throw new Error("exclude_outside can be set to 1 only when mode is CUBIC.");let c=t<11||"nearest"===r&&"asymmetric"===o&&"floor"===l,h=0,f=0,m=0;return t>10?e.inputs.length>2?(h=1,f=2,m=3):(f=1,m=2):9===t&&(f=1),Ae({opset:t,isResize:n,mode:r,scales:i,extrapolationValue:a,coordinateTransformMode:o,useExtrapolation:u,needRoiInput:s,nearestMode:l,cubicCoefficientA:d,excludeOutside:p,useNearest2xOptimization:c,roiInputIdx:h,scalesInputIdx:f,sizesInputIdx:m})},Nw=(e,t,n)=>{let r=ce(e.session.backend.glContext.version),[i,a]=e.calculateTextureWidthAndHeight(t[0].dims,0),o=t[0].dims.map(((e,t)=>Math.floor(e*n.scales[t]))),[s,u]=e.calculateTextureWidthAndHeight(o,0),l=o.length,d=new Array(l),p=new Array(l),c=`\n      int output_pitches[${l}];\n      int input_pitches[${l}];\n      `;for(let e=l-1;e>=0;e--)d[e]=e===l-1?1:d[e+1]*o[e+1],p[e]=e===l-1?1:p[e+1]*t[0].dims[e+1],c+=`\n        output_pitches[${e}] = ${d[e]};\n        input_pitches[${e}] = ${p[e]};\n        `;let h=`\n      float getInputFloat(int index) {\n        vec2 coords = offsetToCoords(index, ${i}, ${a});\n        float value = getColorAsFloat(${r.texture2D}(X, coords));\n        return value;\n      }\n      `,f="nearest"===n.mode?`\n    ${h}\n    float process(int indices[${l}]) {\n      int input_index = 0;\n      int output_index = coordsToOffset(TexCoords, ${s}, ${u});\n\n      ${c}\n\n      int d, m;\n      for (int dim = 0; dim < ${l}; ++dim) {\n        d = output_index / output_pitches[dim];\n        m = output_index - d * output_pitches[dim];\n        output_index = m;\n\n        if (scales[dim] != 1 && d > 0) {\n          int d2 = d / scales[dim];\n          m = d - d2 * scales[dim];\n          d = d2;\n        }\n        input_index += input_pitches[dim] * d;\n      }\n\n      return getInputFloat(input_index);\n    }`:4===l?`\n    ${h}\n    float process(int indices[4]) {\n      int input_index = 0;\n      int output_index = coordsToOffset(TexCoords, ${s}, ${u});\n\n      ${c}\n\n      int m;\n      int index_of_dim0, index_of_dim1, index_of_dim2, index_of_dim3;\n      index_of_dim0 = output_index / output_pitches[0];\n      m = output_index - index_of_dim0 * output_pitches[0];\n      index_of_dim1 = m / output_pitches[1];\n      m = m - index_of_dim1 * output_pitches[1];\n      index_of_dim2 = m / output_pitches[2];\n      m = m - index_of_dim2 * output_pitches[2];\n      index_of_dim3 = m;\n\n      int index_of_input_dim2, index_of_input_dim3, x_offset, y_offset;\n      index_of_input_dim2 = index_of_dim2 / scales[2];\n      y_offset = index_of_dim2 - index_of_input_dim2 * scales[2];\n      index_of_input_dim3 = index_of_dim3 / scales[3];\n      x_offset = index_of_dim3 - index_of_input_dim3 * scales[3];\n\n      input_index = index_of_dim0 * input_pitches[0] +\n            index_of_dim1 * input_pitches[1] +\n            index_of_input_dim2 * input_pitches[2] +\n            index_of_input_dim3;\n\n      float x00 = getInputFloat(input_index);\n      float x10, x01, x11;\n\n      bool end_of_dim2 = false;\n      if (index_of_input_dim2 == (${t[0].dims[2]} - 1)) {\n        // It's the end in dimension 2\n        x01 = x00;\n        end_of_dim2 = true;\n      } else {\n        x01 = getInputFloat(input_index + input_pitches[2]);\n      }\n\n      if (index_of_input_dim3 == (input_pitches[2] - 1)) {\n        // It's the end in dimension 3\n        x10 = x00;\n        x11 = x01;\n      }\n      else {\n        x10 = getInputFloat(input_index + 1);\n        x11 = end_of_dim2 ? x10 : getInputFloat(input_index + input_pitches[2] + 1);\n      }\n\n      float y0 = x00 + float(y_offset) * (x01 - x00) / float(scales[2]);\n      float y1 = x10 + float(y_offset) * (x11 - x10) / float(scales[2]);\n      return y0 + float(x_offset) * (y1 - y0) / float(scales[3]);\n    }`:`\n    ${h}\n    float process(int indices[2]) {\n      int input_index = 0;\n      int output_index = coordsToOffset(TexCoords, ${s}, ${u});\n\n      ${c}\n\n      int m;\n      int index_of_dim0, index_of_dim1;\n      index_of_dim0 = output_index / output_pitches[0];\n      m = output_index - index_of_dim0 * output_pitches[0];\n      index_of_dim1 = m;\n\n      int index_of_input_dim0, index_of_input_dim1, x_offset, y_offset;\n      index_of_input_dim0 = index_of_dim0 / scales[0];\n      y_offset = index_of_dim0 - index_of_input_dim0 * scales[0];\n      index_of_input_dim1 = index_of_dim1 / scales[1];\n      x_offset = index_of_dim1 - index_of_input_dim1 * scales[1];\n\n      input_index = index_of_input_dim0 * input_pitches[0] + index_of_input_dim1;\n\n      float x00 = getInputFloat(input_index);\n      float x10, x01, x11;\n\n      bool end_of_dim0 = false;\n      if (index_of_input_dim0 == (${t[0].dims[0]} - 1)) {\n        // It's the end in dimension 0\n        x01 = x00;\n        end_of_dim0 = true;\n      } else {\n        x01 = getInputFloat(input_index + input_pitches[0]);\n      }\n\n      if (index_of_input_dim1 == (input_pitches[0] - 1)) {\n        // It's the end in dimension 1\n        x10 = x00;\n        x11 = x01;\n      }\n      else {\n        x10 = getInputFloat(input_index + 1);\n        x11 = end_of_dim0 ? x10 : getInputFloat(input_index + input_pitches[0] + 1);\n      }\n\n      float y0 = x00 + float(y_offset) * (x01 - x00) / float(scales[0]);\n      float y1 = x10 + float(y_offset) * (x11 - x10) / float(scales[0]);\n      return y0 + float(x_offset) * (y1 - y0) / float(scales[1]);\n    }`;return{...lp,output:{dims:o,type:t[0].type,textureType:0},shaderSource:f,variables:[{name:"scales",type:"int",arrayLength:n.scales.length,data:n.scales.map((e=>Math.ceil(e)))}]}},ws=(e,t)=>{if(!e||t.opset<9&&1!==e.length||t.opset>=9&&t.opset<11&&2!==e.length||t.opset>=11&&e.length<2)throw new Error("invalid inputs.");if(t.scales.length>0&&e[0].dims.length!==t.scales.length)throw new Error("Invalid input shape.");if("string"===e[0].type)throw new Error("Invalid input tensor types.")},Ii=(e,t,n)=>{if(n){for(let t of e)if(t<=0)throw new Error("Scale value should be greater than 0.")}else for(let t of e)if(t<1)throw new Error("Scale value should be greater than or equal to 1.");if(!("linear"!==t&&"cubic"!==t||2===e.length||4===e.length&&1===e[0]&&1===e[1]))throw new Error(`'Linear' mode and 'Cubic' mode only support 2-D inputs ('Bilinear', 'Bicubic')         or 4-D inputs with the corresponding outermost 2 scale values being 1         in the ${n?"Resize":"Upsample"} opeartor.`)}})),Ts,_s,fp,pp,Lw,Mw,Vw,Fw,hp=D((()=>{ot(),Ne(),hr(),ln(),xs(),Ts={name:"Resize",inputNames:["A"],inputTypes:[2]},_s=(e,t,n)=>(ws(t,n),[e.run({...Ts,cacheHint:n.cacheKey,get:()=>Lw(e,t,n)},t)]),fp=e=>bo(e,10),pp=e=>bo(e,11),Lw=(e,t,n)=>{let r=ce(e.session.backend.glContext.version),[i,a]=Mw(t,n);if(i.every((e=>1===e))&&"tf_crop_and_resize"!==n.coordinateTransformMode)return{...Ts,output:{dims:a,type:t[0].type,textureType:2},hasMain:!0,shaderSource:`void main() {\n                    vec4 v = ${r.texture2D}(X, TexCoords);\n                    ${r.output} = v;\n                }`};let o=a.length;if(o<2)throw new Error(`output dimension should be at least 2, but got ${o}`);let s=a[o-2],u=a[o-1],l=t[0].dims;if(o!==l.length)throw new Error(`output dimension should match input ${l.length}, but got ${o}`);let d=l[o-2],p=l[o-1],c=i[o-2],h=i[o-1],f="";if("linear"!==n.mode)throw new Error(`resize (packed) does not support mode: '${n.mode}'`);switch(n.coordinateTransformMode){case"asymmetric":f="\n                    vec4 getSourceFracIndex(ivec4 coords) {\n                        return vec4(coords) / scaleWHWH;\n                    }\n                ";break;case"half_pixel":f="\n                    vec4 getSourceFracIndex(ivec4 coords) {\n                        return (vec4(coords) + 0.5) / scaleWHWH - 0.5;\n                    }\n                ";break;case"pytorch_half_pixel":f=`\n                    vec4 getSourceFracIndex(ivec4 coords) {\n                        vec4 fcoords = vec4(coords);\n                        return vec4(\n                            ${u}.0 > 1.0 ? (fcoords.x + 0.5) / scaleWHWH.x - 0.5 : 0.0,\n                            ${s}.0 > 1.0 ? (fcoords.y + 0.5) / scaleWHWH.y - 0.5 : 0.0,\n                            ${u}.0 > 1.0 ? (fcoords.z + 0.5) / scaleWHWH.z - 0.5 : 0.0,\n                            ${s}.0 > 1.0 ? (fcoords.w + 0.5) / scaleWHWH.w - 0.5 : 0.0\n                          );\n                    }\n                `;break;case"align_corners":f=`\n                    vec4 getSourceFracIndex(ivec4 coords) {\n                        vec4 resized = vec4(${u}.0 - 1.0, ${s}.0 - 1.0, ${u}.0 - 1.0,\n                            ${s}.0 - 1.0);\n                        vec4 original = vec4(${p}.0 - 1.0, ${d}.0 - 1.0, ${p}.0 - 1.0,\n                            ${d}.0 - 1.0);\n                        vec4 new_scale = original / resized;\n                        return vec4(coords) * new_scale;\n                    }\n                `;break;default:throw new Error(`resize (packed) does not support coordinateTransformMode:                                 '${n.coordinateTransformMode}'`)}let m=Bt(o),g=`\n            const vec2 inputWH = vec2(${d}.0, ${p}.0);\n            const vec4 scaleWHWH = vec4(float(${c}), float(${h}), float(${c}), float(${h}));\n            ${mr()}\n            ${f}\n            float getAValue(int x10, int r, int c, int d) {\n                return getChannel(getA(x10, r, c, d), vec2(c, d));\n            }\n            void main() {\n                ${m} rc = getOutputCoords();\n\n                int batch = rc[0];\n                int depth = rc[1];\n\n                // retrieve the 4 coordinates that is used in the 4 packed output values.\n                ivec4 coords = ivec4(rc.wz, rc.w + 1, rc.z + 1);\n\n                // calculate the source index in fraction\n                vec4 sourceFrac = getSourceFracIndex(coords);\n\n                // get the lower and upper bound of the 4 values that will be packed into one texel.\n                ivec4 x00 = ivec4(max(sourceFrac.xy, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.xy)));\n                ivec4 x01 = ivec4(max(sourceFrac.xw, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.xw)));\n                ivec4 x10 = ivec4(max(sourceFrac.zy, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.zy)));\n                ivec4 x11 = ivec4(max(sourceFrac.zw, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.zw)));\n\n                bool hasNextRow = rc.w < ${s-1};\n                bool hasNextCol = rc.z < ${u-1};\n\n                // pack x00, x01, x10, x11's top-left corner into one vec4 structure\n                vec4 topLeft = vec4(\n                    getAValue(batch, depth, x00.x, x00.y),\n                    hasNextCol ? getAValue(batch, depth, x01.x, x01.y) : 0.0,\n                    hasNextRow ? getAValue(batch, depth, x10.x, x10.y) : 0.0,\n                    (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.x, x11.y) : 0.0);\n\n                // pack x00, x01, x10, x11's top-right corner into one vec4 structure\n                vec4 topRight = vec4(\n                    getAValue(batch, depth, x00.x, x00.w),\n                    hasNextCol ? getAValue(batch, depth, x01.x, x01.w) : 0.0,\n                    hasNextRow ? getAValue(batch, depth, x10.x, x10.w) : 0.0,\n                    (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.x, x11.w) : 0.0);\n\n                // pack x00, x01, x10, x11's bottom-left corner into one vec4 structure\n                vec4 bottomLeft = vec4(\n                    getAValue(batch, depth, x00.z, x00.y),\n                    hasNextCol ? getAValue(batch, depth, x01.z, x01.y) : 0.0,\n                    hasNextRow ? getAValue(batch, depth, x10.z, x10.y) : 0.0,\n                    (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.z, x11.y) : 0.0);\n\n                // pack x00, x01, x10, x11's bottom-right corner into one vec4 structure\n                vec4 bottomRight = vec4(\n                    getAValue(batch, depth, x00.z, x00.w),\n                    hasNextCol ? getAValue(batch, depth, x01.z, x01.w) : 0.0,\n                    hasNextRow ? getAValue(batch, depth, x10.z, x10.w) : 0.0,\n                    (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.z, x11.w) : 0.0);\n\n                // calculate the interpolation fraction on u and v direction\n                vec4 frac = vec4(sourceFrac) - floor(sourceFrac);\n                vec4 clampFrac = clamp(frac, vec4(0.0), vec4(1.0));\n\n                vec4 top = mix(topLeft, topRight, clampFrac.ywyw);\n                vec4 bottom = mix(bottomLeft, bottomRight, clampFrac.ywyw);\n                vec4 newValue = mix(top, bottom, clampFrac.xxzz);\n\n                ${r.output} = vec4(newValue);\n            }\n        `;return{...Ts,output:{dims:a,type:t[0].type,textureType:2},hasMain:!0,shaderSource:g}},Mw=(e,t)=>{let n,r=e[0].dims,i=t.scales;if(0===i.length){let a=e[t.scalesInputIdx];if(a&&0!==a.size){if(e[t.sizesInputIdx])throw new Error("Only one of scales or sizes must be provided as input.");i=Vw(a,t.mode,t.isResize)}else{let a=e[t.sizesInputIdx];if(!a||0===a.size)throw new Error("Either scales or sizes MUST be provided as input.");n=Array.from(a.integerData),i=Fw(n,r,t.mode,t.isResize)}}else if(e[t.sizesInputIdx])throw new Error("Only one of scales or sizes must be provided as input.");let a=n||r.map(((e,t)=>Math.floor(e*i[t])));return[i,a]},Vw=(e,t,n)=>{let r=Array.from(e.floatData);return Ii(r,t,n),r},Fw=(e,t,n,r)=>{let i=t.length,a=new Array(i);for(let n=0,r=i;n<r;n++)if(0===t[n]){if(0!==e[n])throw new Error("Input dim is zero but required output dim is non-zero.");a[n]=1}else a[n]=e[n]/t[n];return Ii(a,n,r),a}})),mp,Uw,gp=D((()=>{un(),mp=(e,t)=>(Uw(t),[new yt([t[0].dims.length],"int32",void 0,void 0,new Int32Array(t[0].dims))]),Uw=e=>{if(!e||1!==e.length)throw new Error("Shape requires 1 input.")}})),$s,bp,yp,vp,Gw,wp,Ww,Hw,xp=D((()=>{St(),go(),We(),Ne(),$s={name:"Slice",inputNames:["A"],inputTypes:[0]},bp=(e,t,n)=>(Gw(t),[e.run({...$s,cacheHint:n.cacheKey,get:()=>vp(e,t[0],n)},t)]),yp=e=>{let t=e.attributes.getInts("starts"),n=e.attributes.getInts("ends"),r=e.attributes.getInts("axes",[]);return Ae({starts:t,ends:n,axes:r})},vp=(e,t,n)=>{let r=0===n.axes.length?t.dims.slice(0).map(((e,t)=>t)):n.axes,i=te.normalizeAxes(r,t.dims.length),a=n.starts.map(((e,n)=>e>t.dims[i[n]]-1?t.dims[i[n]]:te.normalizeAxis(e,t.dims[i[n]]))),o=n.ends.map(((e,n)=>e>t.dims[i[n]]-1?t.dims[i[n]]:te.normalizeAxis(e,t.dims[i[n]]))),s=t.dims.slice(),u=[];for(let e=0;e<i.length;e++)s[i[e]]=o[e]-a[e],a[e]>0&&u.push(`outputIdx[${i[e]}] += ${a[e]};`);let l=`\n      float process(int outputIdx[${s.length}]) {\n        ${u.join("\n      ")}\n        return _A(outputIdx);\n      }`;return{...$s,output:{dims:s,type:t.type,textureType:0},shaderSource:l}},Gw=e=>{if(!e||1!==e.length)throw new Error("Slice requires 1 input.");if(-1===Wr.indexOf(e[0].type))throw new Error("Invalid input type.")},wp=(e,t)=>{Hw(t);let 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Yt{constructor(e){super(e)}getFunctions(){return{...this.offsetToCoords(),...this.coordsToOffset(),...this.toVec(),...this.valueFrom(),...this.getCommonUtilFuncs(),...this.getInputsSamplingSnippets(),...this.getOutputSamplingSnippet()}}getCustomTypes(){return{}}offsetToCoords(){return{offsetToCoords:new Q("\n      vec2 offsetToCoords(int offset, int width, int height) {\n        int t = offset / width;\n        int s = offset - t*width;\n        vec2 coords = (vec2(s,t) + vec2(0.5,0.5)) / vec2(width, height);\n        return coords;\n      }\n      ")}}coordsToOffset(){return{coordsToOffset:new Q("\n      int coordsToOffset(vec2 coords, int width, int height) {\n        float s = coords.s * float(width);\n        float t = coords.t * float(height);\n        int offset = int(t) * width + int(s);\n        return offset;\n      }\n      ")}}getOutputSamplingSnippet(){let e=this.context.outputTextureLayout;return e.isPacked?this.getPackedOutputSamplingSnippet(e):this.getUnpackedOutputSamplingSnippet(e)}getPackedOutputSamplingSnippet(e){let t=e.unpackedShape,n=[e.width,e.height],r={},i="getOutputCoords";switch(t.length){case 0:r[i]=this.getOutputScalarCoords();break;case 1:r[i]=this.getOutputPacked1DCoords(t,n);break;case 2:r[i]=this.getOutputPacked2DCoords(t,n);break;case 3:r[i]=this.getOutputPacked3DCoords(t,n);break;default:r[i]=this.getOutputPackedNDCoords(t,n)}let a=`\n      void setOutput(vec4 val) {\n        ${ce(this.context.glContext.version).output} = val;\n      }\n    `;return r.floatTextureSetRGBA=new Q(a),r}getUnpackedOutputSamplingSnippet(e){let t=e.unpackedShape,n=[e.width,e.height],r={},i="getOutputCoords";switch(t.length){case 0:r[i]=this.getOutputScalarCoords();break;case 1:r[i]=this.getOutputUnpacked1DCoords(t,n);break;case 2:r[i]=this.getOutputUnpacked2DCoords(t,n);break;case 3:r[i]=this.getOutputUnpacked3DCoords(t,n);break;case 4:r[i]=this.getOutputUnpacked4DCoords(t,n);break;case 5:r[i]=this.getOutputUnpacked5DCoords(t,n);break;case 6:r[i]=this.getOutputUnpacked6DCoords(t,n);break;default:throw new Error(`Unsupported output dimensionality: ${t.length}`)}let a=`\n        void setOutput(float val) {\n          ${ce(this.context.glContext.version).output} = vec4(val, 0, 0, 0);\n        }\n    `;return r.floatTextureSetR=new Q(a),r}getOutputScalarCoords(){return new Q("\n      int getOutputCoords() {\n        return 0;\n      }\n    ")}getOutputPacked1DCoords(e,t){let n=t,r="";return 1===n[0]?(r=`\n          int getOutputCoords() {\n            return 2 * int(TexCoords.y * ${n[1]}.0);\n          }\n        `,new Q(r)):1===n[1]?(r=`\n          int getOutputCoords() {\n            return 2 * int(TexCoords.x * ${n[0]}.0);\n          }\n        `,new Q(r)):(r=`\n        int getOutputCoords() {\n          ivec2 resTexRC = ivec2(TexCoords.xy *\n                                 vec2(${n[0]}, ${n[1]}));\n          return 2 * (resTexRC.y * ${n[0]} + resTexRC.x);\n        }\n      `,new Q(r))}getOutputPacked2DCoords(e,t){let n="";if(nn.arraysEqual(e,t))return n=`\n        ivec2 getOutputCoords() {\n          return 2 * ivec2(TexCoords.xy * vec2(${t[0]}, ${t[1]}));\n        }\n      `,new Q(n);let r=t,i=Math.ceil(e[1]/2);return n=`\n        ivec2 getOutputCoords() {\n          ivec2 resTexRC = ivec2(TexCoords.xy *\n                                vec2(${r[0]}, ${r[1]}));\n\n          int index = resTexRC.y * ${r[0]} + resTexRC.x;\n\n          // reverse r and c order for packed texture\n          int r = imod(index, ${i}) * 2;\n          int c = 2 * (index / ${i});\n\n          return ivec2(r, c);\n        }\n      `,new Q(n)}getOutputPacked3DCoords(e,t){let n=[t[0],t[1]],r=Math.ceil(e[2]/2),i=r*Math.ceil(e[1]/2);return new Q(`\n        ivec3 getOutputCoords() {\n          ivec2 resTexRC = ivec2(TexCoords.xy *\n                                vec2(${n[0]}, ${n[1]}));\n          int index = resTexRC.y * ${n[0]} + resTexRC.x;\n\n          int b = index / ${i};\n          index -= b * ${i};\n\n          // reverse r and c order for packed texture\n          int r = imod(index, ${r}) * 2;\n          int c = 2 * (index / ${r});\n\n          return ivec3(b, r, c);\n        }\n      `)}getOutputPackedNDCoords(e,t){let n=[t[0],t[1]],r=Math.ceil(e[e.length-1]/2),i=r*Math.ceil(e[e.length-2]/2),a=i,o="",s="b, r, c";for(let t=2;t<e.length-1;t++)a*=e[e.length-t-1],o=`\n      int b${t} = index / ${a};\n      index -= b${t} * ${a};\n    `+o,s=`b${t}, `+s;let u=`\n      ivec${e.length} getOutputCoords() {\n        ivec2 resTexRC = ivec2(TexCoords.xy *\n                              vec2(${n[0]}, ${n[1]}));\n        int index = resTexRC.y * ${n[0]} + resTexRC.x;\n\n        ${o}\n\n        int b = index / ${i};\n        index -= b * ${i};\n\n        // reverse r and c order for packed texture\n        int r = imod(index, ${r}) * 2;\n        int c = 2 * (index / ${r});\n\n        return ivec${e.length}(${s});\n      }\n    `;return new Q(u)}getOutputUnpacked1DCoords(e,t){let n=`\n        int getOutputCoords() {\n          ivec2 resTexRC = ivec2(TexCoords.xy *\n                                vec2(${t[0]}, ${t[1]}));\n          return resTexRC.y * ${t[0]} + resTexRC.x;\n        }\n      `;return new Q(n)}getOutputUnpacked2DCoords(e,t){let n=`\n        ivec2 getOutputCoords() {\n          ivec2 resTexRC = ivec2(TexCoords.xy *\n                                vec2(${t[0]}, ${t[1]}));\n          int index = resTexRC.y * ${t[0]} + resTexRC.x;\n          int r = index / ${e[1]};\n          int c = index - r * ${e[1]};\n          return ivec2(r, c);\n        }\n      `;return new Q(n)}getOutputUnpacked3DCoords(e,t){let n="",r=e.length,i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=e[r-1];for(let t=r-3;t>=0;--t)i[t]=i[t+1]*e[t+1];let a=["r","c","d"],o=i.map(((e,t)=>`${`int ${a[t]} = index / ${e}`}; ${t===i.length-1?`int ${a[t+1]} = index - ${a[t]} * ${e}`:`index -= ${a[t]} * ${e}`};`)).join("");return n=`\n        ivec3 getOutputCoords() {\n          ivec2 resTexRC = ivec2(TexCoords.xy *\n                                vec2(${t[0]}, ${t[1]}));\n          int index = resTexRC.y * ${t[0]} + resTexRC.x;\n          ${o}\n          return ivec3(r, c, d);\n        }\n      `,new Q(n)}getOutputUnpacked4DCoords(e,t){let n="",r=e.length,i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=e[r-1];for(let t=r-3;t>=0;--t)i[t]=i[t+1]*e[t+1];let a=["r","c","d","d2"],o=i.map(((e,t)=>`${`int ${a[t]} = index / ${e}`}; ${t===i.length-1?`int ${a[t+1]} = index - ${a[t]} * ${e}`:`index -= ${a[t]} * ${e}`};`)).join("");return n=`\n      ivec4 getOutputCoords() {\n          ivec2 resTexRC = ivec2(TexCoords.xy *\n                                vec2(${t[0]}, ${t[1]}));\n          int index = resTexRC.y * ${t[0]} + resTexRC.x;\n          ${o}\n          return ivec4(r, c, d, d2);\n        }\n      `,new Q(n)}getOutputUnpacked5DCoords(e,t){let n="",r=e.length,i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=e[r-1];for(let t=r-3;t>=0;--t)i[t]=i[t+1]*e[t+1];let a=["r","c","d","d2","d3"],o=i.map(((e,t)=>`${`int ${a[t]} = index / ${e}`}; ${t===i.length-1?`int ${a[t+1]} = index - ${a[t]} * ${e}`:`index -= ${a[t]} * ${e}`};`)).join("");return n=`\n      ivec5 getOutputCoords() {\n          ivec2 resTexRC = ivec2(TexCoords.xy *\n                                vec2(${t[0]}, ${t[1]}));\n          int index = resTexRC.y * ${t[0]} + resTexRC.x;\n          ${o}\n          return ivec5(r, c, d, d2, d3);\n        }\n      `,new Q(n)}getOutputUnpacked6DCoords(e,t){let n="",r=e.length,i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=e[r-1];for(let t=r-3;t>=0;--t)i[t]=i[t+1]*e[t+1];let a=["r","c","d","d2","d3","d4"],o=i.map(((e,t)=>`${`int ${a[t]} = index / ${e}`}; ${t===i.length-1?`int ${a[t+1]} = index - ${a[t]} * ${e}`:`index -= ${a[t]} * ${e}`};`)).join("");return n=`\n     ivec6 getOutputCoords() {\n         ivec2 resTexRC = ivec2(TexCoords.xy *\n                               vec2(${t[0]}, ${t[1]}));\n         int index = resTexRC.y * ${t[0]} + resTexRC.x;\n         ${o}\n         return ivec6(r, c, d, d2, d3, d4);\n       }\n     `,new Q(n)}getCommonUtilFuncs(){let e={},t="uvFromFlat";e[t]=new Q("\n    vec2 uvFromFlat(int texNumR, int texNumC, int index) {\n      int texC = index / texNumR;\n      int texR = index - texC * texNumR;\n      // TODO: swap texR, texC order in following function so row is corresponding to u and column is corresponding to\n      //       v.\n      return (vec2(texR, texC) + halfCR) / vec2(texNumR, texNumC);\n    }\n    "),t="packedUVfrom1D",e[t]=new Q("\n      vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n        int texelIndex = index / 2;\n        int texR = texelIndex / texNumC;\n        int texC = texelIndex - texR * texNumC;\n        return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n      }\n      "),t="packedUVfrom2D",e[t]=new Q("\n      vec2 packedUVfrom2D(int texNumR, int texNumC, int texelsInLogicalRow, int row, int col) {\n        int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n        int texR = texelIndex / texNumC;\n        int texC = texelIndex - texR * texNumC;\n        return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n      }\n      "),t="packedUVfrom3D",e[t]=new Q("\n      vec2 packedUVfrom3D(int texNumR, int texNumC,\n          int texelsInBatch, int texelsInLogicalRow, int b,\n          int row, int col) {\n        int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n        int texR = index / texNumC;\n        int texC = index - texR * texNumC;\n        return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n      }\n      "),t="sampleTexture";let n=ce(this.context.glContext.version);return e[t]=new Q(`\n        float sampleTexture(sampler2D textureSampler, vec2 uv) {\n            return ${n.texture2D}(textureSampler, uv).r;\n        }`),e}getInputsSamplingSnippets(){let e={},t=this.context.outputTextureLayout;return this.context.programInfo.inputNames.forEach(((n,r)=>{let i=this.context.inputTextureLayouts[r],a=pi(n);i.isPacked?e[a]=this.getPackedSamplerFromInput(a,n,i):e[a]=this.getUnpackedSamplerFromInput(a,n,i);let o=Ld(n);i.unpackedShape.length<=t.unpackedShape.length&&(i.isPacked?e[o]=this.getPackedSamplerAtOutputCoords(o,i,t,n):e[o]=this.getUnpackedSamplerAtOutputCoords(o,i,t,n))})),e}getPackedSamplerAtOutputCoords(e,t,n,r){let i,a=t.unpackedShape,o=n.unpackedShape,s=pi(r),u=a.length,l=o.length,d=Dt.getBroadcastDims(a,o),p=Bt(l),c=l-u,h=sr();i=0===u?"":l<2&&d.length>=1?"coords = 0;":d.map((e=>`coords.${h[e+c]} = 0;`)).join("\n");let f="";f=l<2&&u>0?"coords":a.map(((e,t)=>`coords.${h[t+c]}`)).join(", ");let m="return outputValue;",g=1===te.size(a),y=1===te.size(o);if(1!==u||g||y){if(g&&!y)m=1===l?"\n          return vec4(outputValue.x, outputValue.x, 0., 0.);\n        ":"\n          return vec4(outputValue.x);\n        ";else if(d.length){let e=u-2,t=u-1;d.indexOf(e)>-1&&d.indexOf(t)>-1?m="return vec4(outputValue.x);":d.indexOf(e)>-1?m="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":d.indexOf(t)>-1&&(m="return vec4(outputValue.xx, outputValue.zz);")}}else m="\n        return vec4(outputValue.xy, outputValue.xy);\n      ";let b=`\n        int lastDim = coords.${h[l-1]};\n        coords.${h[l-1]} = coords.${h[l-2]};\n        coords.${h[l-2]} = lastDim;\n      `;return new Q(`\n      vec4 ${e}() {\n        ${p} coords = getOutputCoords();\n        ${b}\n        ${i}\n        vec4 outputValue = ${s}(${f});\n        ${m}\n      }\n    `,["coordinates.getOutputCoords"])}getUnpackedSamplerAtOutputCoords(e,t,n,r){let i=[n.width,n.height],a=[t.width,t.height],o=t.unpackedShape.length,s=n.unpackedShape.length,u=t.unpackedShape,l=n.unpackedShape,d=pi(r);if(o===s&&nn.arraysEqual(a,i)){return new Q(`\n          float ${e}() {\n            return sampleTexture(${r}, TexCoords);\n          }\n        `,["coordinates.sampleTexture"])}let p,c=Bt(s),h=Dt.getBroadcastDims(u,l),f=s-o,m=sr();p=0===o?"":s<2&&h.length>=1?"coords = 0;":h.map((e=>`coords.${m[e+f]} = 0;`)).join("\n");let g="";return g=s<2&&o>0?"coords":t.unpackedShape.map(((e,t)=>`coords.${m[t+f]}`)).join(", "),new Q(`\n        float ${e}() {\n          ${c} coords = getOutputCoords();\n          ${p}\n          return ${d}(${g});\n        }\n      `,["coordinates.getOutputCoords"])}getPackedSamplerFromInput(e,t,n){switch(n.unpackedShape.length){case 0:return this.getPackedSamplerScalar(e,t);case 1:return this.getPackedSampler1D(e,t,n);case 2:return this.getPackedSampler2D(e,t,n);case 3:return this.getPackedSampler3D(e,t,n);default:return this.getPackedSamplerND(e,t,n)}}getUnpackedSamplerFromInput(e,t,n){let r=n.unpackedShape;switch(r.length){case 0:return this.getUnpackedSamplerScalar(e,t,n);case 1:return this.getUnpackedSampler1D(e,t,n);case 2:return this.getUnpackedSampler2D(e,t,n);case 3:return this.getUnpackedSampler3D(e,t,n);case 4:return this.getUnpackedSampler4D(e,t,n);case 5:return this.getUnpackedSampler5D(e,t,n);case 6:return this.getUnpackedSampler6D(e,t,n);default:throw new Error(`Unsupported dimension ${r.length}-D`)}}getPackedSamplerScalar(e,t){let n=`\n          vec4 ${e}() {\n            return ${ce(this.context.glContext.version).texture2D}(${t}, halfCR);\n          }\n        `;return new Q(n)}getPackedSampler1D(e,t,n){let r=[n.width,n.height],i=[r[1],r[0]],a=ce(this.context.glContext.version),o=`vec4 ${e}(int index) {\n      vec2 uv = packedUVfrom1D(\n      ${i[0]}, ${i[1]}, index);\n      return ${a.texture2D}(${t}, uv);\n    }`;return new Q(o,["coordinates.packedUVfrom1D"])}getPackedSampler2D(e,t,n){let r=n.unpackedShape,i=[n.width,n.height],a=ce(this.context.glContext.version),o=i[0],s=i[1];if(null!=i&&nn.arraysEqual(r,i)){let n=`vec4 ${e}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}.0, ${o}.0);\n        return ${a.texture2D}(${t}, uv);\n      }`;return new Q(n)}let u=i,l=Math.ceil(r[1]/2),d=`vec4 ${e}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${u[1]}, ${u[0]}, ${l}, row, col);\n      return ${a.texture2D}(${t}, uv);\n    }`;return new Q(d,["coordinates.packedUVfrom2D"])}getPackedSampler3D(e,t,n){let r=n.unpackedShape,i=[n.width,n.height],a=[i[0],i[1]],o=ce(this.context.glContext.version);if(1===r[0]){let i=r.slice(1),a=[1,2],o=On(r,i),s=["b","row","col"],u=JSON.parse(JSON.stringify(n));u.unpackedShape=o;let l=this.getPackedSamplerFromInput(e,t,u),d=`${l.routineBody}\n      vec4 ${e}(int b, int row, int col) {\n        return ${e}(${En(s,a)});\n      } `;return new Q(d,l.dependencies)}let s=a[0],u=a[1],l=Math.ceil(r[2]/2),d=`vec4 ${e}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${u}, ${s}, ${l*Math.ceil(r[1]/2)}, ${l}, b, row, col);\n      return ${o.texture2D}(${t}, uv);}`;return new Q(d,["coordinates.packedUVfrom3D"])}getPackedSamplerND(e,t,n){let r=n.unpackedShape,i=r.length,a=[n.width,n.height],o=ce(this.context.glContext.version),s=[a[0],a[1]],u=s[1],l=s[0],d=Math.ceil(r[i-1]/2),p=d*Math.ceil(r[i-2]/2),c="int b, int row, int col",h=`b * ${p} + (row / 2) * ${d} + (col / 2)`;for(let e=2;e<i-1;e++)c=`int b${e}, `+c,p*=r[i-e-1],h=`b${e} * ${p} + `+h;let f=`vec4 ${e}(${c}) {\n      int index = ${h};\n      int texR = index / ${l};\n      int texC = index - texR * ${l};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${u});\n      return ${o.texture2D}(${t}, uv);\n    }`;return new Q(f)}getUnpackedSamplerScalar(e,t,n){let[r,i]=[n.width,n.height];if(1===r&&1===i){return new Q(`\n          float ${e}() {\n            return sampleTexture(${t}, halfCR);\n          }\n        `,["coordinates.sampleTexture"])}return new Q(`\n        float ${e}() {\n          int offset_${t} = coordsToOffset(TexCoords, ${r}, ${i});\n          vec2 uv = uvFromFlat(${r}, ${i}, offset_${t});\n          return sampleTexture(${t}, uv);\n        }\n      `,["coordinates.uvFromFlat","coordinates.sampleTexture","coordinates.coordsToOffset"])}getUnpackedSampler1D(e,t,n){let r=n.width,i=n.height;if(1===i&&1===r){return new Q(`\n        float ${e}(int index) {\n          return sampleTexture(${t}, halfCR);\n        }\n      `,["coordinates.sampleTexture"])}if(1===i){return new Q(`\n          float ${e}(int index) {\n            vec2 uv = vec2((float(index) + 0.5) / ${r}.0, 0.5);\n            return sampleTexture(${t}, uv);\n          }\n        `,["coordinates.sampleTexture"])}if(1===r){return new Q(`\n          float ${e}(int index) {\n            vec2 uv = vec2(0.5, (float(index) + 0.5) / ${i}.0);\n            return sampleTexture(${t}, uv);\n          }\n        `,["coordinates.sampleTexture"])}return new Q(`\n        float ${e}(int index) {\n          vec2 uv = uvFromFlat(${r}, ${i}, index);\n          return sampleTexture(${t}, uv);\n        }\n      `,["coordinates.uvFromFlat","coordinates.sampleTexture"])}getUnpackedSampler2D(e,t,n){let r=n.unpackedShape,i=[n.height,n.width];if(null!=i&&nn.arraysEqual(r,i)){return new Q(`\n          float ${e}(int row, int col) {\n            vec2 uv = (vec2(row, col) + halfCR) / vec2(${i[1]}.0, ${i[0]}.0);\n            return sampleTexture(${t}, uv);\n          }\n        `,["coordinates.sampleTexture"])}let{newShape:a,keptDims:o}=Dn(r),s=a;if(s.length<r.length){let i=On(r,s),a=JSON.parse(JSON.stringify(n));a.unpackedShape=i;let u=["col","row"],l=`\n          ${this.getUnpackedSamplerFromInput(e,t,a).routineBody}\n          float ${e}(int row, int col) {\n            return ${e}(${En(u,o)});\n          }\n        `;return new Q(l,["coordinates.sampleTexture"])}let u=i[1],l=i[0];if(1===l){let n=`\n          float ${e}(int row, int col) {\n            int offset_${t} = coordsToOffset(TexCoords, ${u}, ${l});\n            float index = dot(vec3(row, col, offset_${t}), vec3(${r[1]}, 1, 1));\n            vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);\n            return sampleTexture(${t}, uv);\n          }\n        `;return new Q(n,["coordinates.sampleTexture","coordinates.coordsToOffset"])}if(1===u){let n=`\n          float ${e}(int row, int col) {\n            int offset_${t} = coordsToOffset(TexCoords, ${u}, ${l});\n            float index = dot(vec3(row, col, offset_${t}), vec3(${r[1]}, 1, 1));\n            vec2 uv = vec2((index + 0.5) / ${l}.0, 0.5);\n            return sampleTexture(${t}, uv);\n          }\n        `;return new Q(n,["coordinates.sampleTexture","coordinates.coordsToOffset"])}let d=`\n        float ${e}(int row, int col) {\n          int index = col * ${r[1]} + row;\n          vec2 uv = uvFromFlat(${u}, ${l}, index);\n          return sampleTexture(${t}, uv);\n        }\n      `;return new Q(d,["coordinates.uvFromFlat","coordinates.sampleTexture","coordinates.coordsToOffset"])}getUnpackedSampler3D(e,t,n){let r=n.unpackedShape,i=r[1]*r[2],a=r[2],{newShape:o,keptDims:s}=Dn(r),u=o;if(u.length<r.length){let i=On(r,u),a=["batch","col","row"],o=JSON.parse(JSON.stringify(n));o.unpackedShape=i;let l=this.getUnpackedSamplerFromInput(e,t,o),d=s.reverse(),p=`\n          ${l.routineBody}\n          float ${e}(int batch, int row, int col) {\n            return ${e}(${En(a,d)});\n          }\n        `;return new Q(p,l.dependencies)}let l=n.width,d=n.height;return new Q(`\n          float ${e}(int depth, int row, int col) {\n            // Explicitly use integer operations as dot() only works on floats.\n            int index = depth * ${i} + col * ${a} + row;\n            vec2 uv = uvFromFlat(${l}, ${d}, index);\n            return sampleTexture(${t}, uv);\n          }\n      `,["coordinates.uvFromFlat","coordinates.sampleTexture","coordinates.coordsToOffset"])}getUnpackedSampler4D(e,t,n){let r=n.unpackedShape,i=r[3],a=r[2]*i,o=r[1]*a,s=n.width,u=n.height;return new Q(`\n        float ${e}(int row, int col, int depth, int depth2) {\n          int index = row * ${o} + col * ${a} +\n              depth2 * ${i} + depth;\n          vec2 uv = uvFromFlat(${s}, ${u}, index);\n          return sampleTexture(${t}, uv);\n        }\n      `,["coordinates.uvFromFlat","coordinates.sampleTexture"])}getUnpackedSampler5D(e,t,n){let r=n.unpackedShape,i=r[4],a=r[3]*i,o=r[2]*a,s=r[1]*o,{newShape:u,keptDims:l}=Dn(r);if(u.length<r.length){let i=On(r,u),a=["row","col","depth","depth2","depth3"],o=JSON.parse(JSON.stringify(n));o.unpackedShape=i;let s=`\n          ${this.getUnpackedSamplerFromInput(e,t,o).routineBody}\n          float ${e}(int row, int col, int depth, int depth2, int depth3) {\n            return ${e}(${En(a,l)});\n          }\n        `;return new Q(s,["coordinates.sampleTexture","coordinates.uvFromFlat"])}let d=n.width,p=n.height;return new Q(`\n        float ${e}(int row, int col, int depth, int depth2, int depth3) {\n          int index = row * ${s} + col * ${o} + depth * ${a} +\n          depth3 * ${i} + depth2;\n          vec2 uv = uvFromFlat(${d}, ${p}, index);\n          return sampleTexture(${t}, uv);\n        }\n      `,["coordinates.sampleTexture","coordinates.uvFromFlat"])}getUnpackedSampler6D(e,t,n){let r=n.unpackedShape,i=r[5],a=r[4]*i,o=r[3]*a,s=r[2]*o,u=r[1]*s,{newShape:l,keptDims:d}=Dn(r);if(l.length<r.length){let i=On(r,l),a=["row","col","depth","depth2","depth3","depth4"],o=JSON.parse(JSON.stringify(n));o.unpackedShape=i;let s=`\n            ${this.getUnpackedSamplerFromInput(e,t,o).routineBody}\n            float ${e}(int row, int col, int depth,\n              int depth2, int depth3, int depth4) {\n              return ${e}(${En(a,d)});\n            }\n          `;return new Q(s,["coordinates.sampleTexture","coordinates.uvFromFlat"])}let p=n.width,c=n.height;return new Q(`\n          float ${e}(int row, int col, int depth,\n            int depth2, int depth3, int depth4) {\n            int index = row * ${u} + col * ${s} + depth * ${o} +\n            depth2 * ${a} + depth3 * ${i} + depth4;\n            vec2 uv = uvFromFlat(${p}, ${c}, index);\n            return sampleTexture(${t}, uv);\n          }\n        `,["coordinates.uvFromFlat","coordinates.sampleTexture","coordinates.coordsToOffset"])}toVec(){let e=this.context.outputTextureLayout,t=e.shape.length,n=e.strides,r=e.width,i=e.height,a=[];for(let e=0;e<t-1;++e)a.push(`\n        c[${e}] = offset / ${n[e]};`),a.push(`\n        offset -= c[${e}] * ${n[e]};`);a.push(`\n        c[${t-1}] = offset;`);let o=`\n      void toVec(vec2 texCoords, out int c[${t}]) {\n        int offset = coordsToOffset(texCoords, ${r}, ${i});\n        ${a.join("")}\n      }\n      void toVec(int offset, out int c[${t}]) {\n        ${a.join("")}\n      }\n    `;return{toVec:new Q(o,["coordinates.coordsToOffset"])}}valueFrom(){let e={};return this.context.programInfo.inputNames.forEach(((t,n)=>{let r=this.context.inputTextureLayouts[n],i=(r.unpackedShape.length>0?r.unpackedShape:r.shape).length,a=`_${t}`;e[a]=new Q(this.getValueFromSingle(t,i,r.width,r.height,!1),[`shapeUtils.indicesToOffset${a}`,"coordinates.offsetToCoords","fragcolor.getColorAsFloat"]),a+="_T",e[a]=new Q(this.getValueFromSingle(t,i,r.width,r.height,!0),[`shapeUtils.indicesToOffset${a}`,"coordinates.offsetToCoords","fragcolor.getColorAsFloat"])})),e}getValueFromSingle(e,t,n,r,i){let a=`_${e}`;return i&&(a+="_T"),`\n        float ${a}(int m[${t}]) {\n          int offset = indicesToOffset${a}(m);\n          vec2 coords = offsetToCoords(offset, ${n}, ${r});\n          float value = getColorAsFloat(${ce(this.context.glContext.version).texture2D}(${e}, coords));\n          return value;\n        }\n        `}getPackedValueFrom(e,t,n,r,i){let a=`_${e}_Pack`;return i&&(a+="_T"),`\n        vec4 ${a}(int m[${t}]) {\n          int offset = indicesToOffset_${e}(m);\n          vec2 coords = offsetToCoords(offset, ${n}, ${r});\n          return ${ce(this.context.glContext.version).texture2D}(${e}, coords);\n        }\n        `}}})),Ei,Qp=D((()=>{kr(),Ei=class e extends Yt{constructor(e){super(e)}getFunctions(){return{...this.encodeFloat32(),...this.decodeFloat32()}}getCustomTypes(){return{}}encodeFloat32(){return{encode:new Q("highp vec4 encode(highp float f) {\n        return vec4(f, 0.0, 0.0, 0.0);\n      }\n        ")}}decodeFloat32(){return{decode:new Q("highp float decode(highp vec4 rgba) {\n        return rgba.r;\n      }\n        ")}}encodeUint8(){let t=e.isLittleEndian()?"rgba.rgba=rgba.abgr;":"";return{encode:new Q(`\n      highp vec4 encode(highp float f) {\n        highp float F = abs(f);\n        highp float Sign = step(0.0,-f);\n        highp float Exponent = floor(log2(F));\n        highp float Mantissa = (exp2(- Exponent) * F);\n        Exponent = floor(log2(F) + 127.0) + floor(log2(Mantissa));\n        highp vec4 rgba;\n        rgba[0] = 128.0 * Sign  + floor(Exponent*exp2(-1.0));\n        rgba[1] = 128.0 * mod(Exponent,2.0) + mod(floor(Mantissa*128.0),128.0);\n        rgba[2] = floor(mod(floor(Mantissa*exp2(23.0 -8.0)),exp2(8.0)));\n        rgba[3] = floor(exp2(23.0)*mod(Mantissa,exp2(-15.0)));\n        ${t}\n        rgba = rgba / 255.0; // values need to be normalized to [0,1]\n        return rgba;\n    }\n        `)}}decodeUint8(){let t=e.isLittleEndian()?"rgba.rgba=rgba.abgr;":"";return{decode:new Q(`\n        highp float decode(highp vec4 rgba) {\n          rgba = rgba * 255.0; // values need to be de-normalized from [0,1] to [0,255]\n          ${t}\n          highp float Sign = 1.0 - step(128.0,rgba[0])*2.0;\n          highp float Exponent = 2.0 * mod(rgba[0],128.0) + step(128.0,rgba[1]) - 127.0;\n          highp float Mantissa = mod(rgba[1],128.0)*65536.0 + rgba[2]*256.0 +rgba[3] + float(0x800000);\n          highp float Result =  Sign * exp2(Exponent) * (Mantissa * exp2(-23.0 ));\n          return Result;\n      }\n        `)}}static isLittleEndian(){let e=new ArrayBuffer(4),t=new Uint32Array(e),n=new Uint8Array(e);if(t[0]=3735928559,239===n[0])return!0;if(222===n[0])return!1;throw new Error("unknown endianness")}}})),Ci,eh=D((()=>{kr(),ot(),Ci=class extends Yt{constructor(e){super(e)}getFunctions(){return{...this.setFragColor(),...this.getColorAsFloat()}}getCustomTypes(){return{}}setFragColor(){let e=ce(this.context.glContext.version);return{setFragColor:new Q(`\n        void setFragColor(float value) {\n            ${e.output} = encode(value);\n        }\n        `,["encoding.encode"])}}getColorAsFloat(){return{getColorAsFloat:new Q("\n        float getColorAsFloat(vec4 color) {\n            return decode(color);\n        }\n        ",["encoding.decode"])}}}})),Pi,th=D((()=>{kr(),Pi=class e extends Yt{constructor(e){super(e)}getFunctions(){return{...this.bcastIndex(),...this.bcastMatmulIndex(),...this.offsetToIndices(),...this.indicesToOffset(),...this.incrementIndices()}}getCustomTypes(){return{}}bcastIndex(){let e=this.context.outputTextureLayout.shape.length,t={};return this.context.programInfo.inputNames.forEach(((n,r)=>{let i=this.context.inputTextureLayouts[r].unpackedShape;if(i.length<=e){let r=i.length,a=e-r,o=`bcastIndices_${n}`,s="";for(let e=0;e<r;++e)s+=`\n          realIndices[${e}] = int( mod(float(bcastedIndices[${a+e}]), ${i[e]}.0) );\n          `;let u=`\n        void ${o} (int bcastedIndices[${e}], out int realIndices[${r}]) {\n          ${s}\n        }\n        `;t[o]=new Q(u)}})),t}bcastMatmulIndex(){let e=this.context.outputTextureLayout.shape.length,t={};return this.context.programInfo.inputNames.forEach(((n,r)=>{let i=this.context.inputTextureLayouts[r].shape;if(!(i.length<2||i.length>e)){let r=i.length,a=e-r,o=`bcastMatmulIndices_${n}`,s="";for(let e=0;e<r-2;++e)s+=`\n          realIndices[${e}] = int( mod(float(bcastedIndices[${a+e}]), ${i[e]}.0) );\n          `;let u=`\n        void ${o}(int bcastedIndices[${e}], out int realIndices[${r}]) {\n          ${s}\n          realIndices[${r-1}] = bcastedIndices[${e-1}];\n          realIndices[${r-2}] = bcastedIndices[${e-2}];\n        }\n        `;t[o]=new Q(u)}})),t}indicesToOffset(){let t={};return this.context.programInfo.inputNames.forEach(((n,r)=>{let i=this.context.inputTextureLayouts[r].shape,a=this.context.inputTextureLayouts[r].strides,o=i.length,s=`indicesToOffset_${n}`;t[s]=new Q(e.indexToOffsetSingle(s,o,a)),s=`indicesToOffset_${n}_T`,t[s]=new Q(e.indexToOffsetSingle(s,o,a.slice().reverse()))})),t}static indexToOffsetSingle(e,t,n){let r="";for(let e=t-1;e>=0;--e)r+=`\n        offset += indices[${e}] * ${n[e]};\n        `;return`\n      int ${e}(int indices[${t}]) {\n        int offset = 0;\n        ${r}\n        return offset;\n      }\n      `}offsetToIndices(){let t={};return this.context.programInfo.inputNames.forEach(((n,r)=>{let i=this.context.inputTextureLayouts[r].shape,a=this.context.inputTextureLayouts[r].strides,o=i.length,s=`offsetToIndices_${n}`;t[s]=new Q(e.offsetToIndicesSingle(s,o,a)),s=`offsetToIndices_${n}_T`,t[s]=new Q(e.offsetToIndicesSingle(s,o,a.slice().reverse()))})),t}static offsetToIndicesSingle(e,t,n){let r=[];for(let e=0;e<t-1;++e)r.push(`\n      indices[${e}] = offset / ${n[e]};`),r.push(`\n        offset -= indices[${e}] * ${n[e]};`);return r.push(`\n      indices[${t-1}] = offset;`),`\n      void ${e}(int offset, out int indices[${t}]) {\n        ${r.join("")}\n      }\n      `}incrementIndices(){let e={};return this.context.programInfo.inputNames.forEach(((t,n)=>{let r=this.context.inputTextureLayouts[n].shape,i=r.length,a=`incrementIndices_${t}`,o="";for(let e=0;e<i;++e)o+=`\n        shape[${e}] = ${r[e]};`;let s=`\n        void ${a}(int axis, out int indices[${i}]) {\n          int shape[${i}];\n          ${o};\n          for(int i = ${i} -1 ; i >= 0; --i) {\n            if(i > axis) continue;\n            indices[i] += 1;\n            if(indices[i] < shape[i]) {\n              break;\n            }\n            indices[i] = 0;\n          }\n        }\n        `;e[a]=new Q(s)})),e}}})),ki,rh=D((()=>{kr(),ki=class extends Yt{constructor(e){super(e)}getCustomTypes(){return{}}getFunctions(){return{...this.binaryVecFunctions(),...this.copyVec(),...this.setVecItem(),...this.getVecItem()}}binaryVecFunctions(){let e=this.context.outputTextureLayout.shape.length,t={add:"+=",sub:"-=",mul:"*=",div:"/="},n={};for(let r in t){let i=`${r}Vec`,a="";for(let n=0;n<e;++n)a+=`\n          dest[${n}] ${t[r]} src[${n}];\n          `;let o=`\n        void ${i}(int src[${e}], out int dest[${e}]) {\n          ${a}\n        }\n        `;n[i]=new Q(o)}return n}copyVec(){let e=this.context.outputTextureLayout.shape.length,t="";for(let n=0;n<e;++n)t+=`\n        dest[${n}] = src[${n}];\n        `;return{copyVec:new Q(`\n      void copyVec(int src[${e}], out int dest[${e}]) {\n        ${t}\n      }\n      `)}}setVecItem(){let e=this.context.outputTextureLayout.shape.length,t=`\n        if(index < 0)\n            index =${e} + index;\n        if (index == 0)\n            m[0] = value;\n        `;for(let n=1;n<e-1;++n)t+=`\n        else if (index == ${n})\n            m[${n}] = value;\n            `;return t+=`\n        else\n            m[${e-1}] = value;\n        `,{setVecItem:new Q(`\n      void setVecItem(out int m[${e}], int index, int value) {\n        ${t}\n      }\n        `)}}getVecItem(){let e=this.context.outputTextureLayout.shape.length,t=`\n        if(index < 0)\n            index = ${e} + index;\n        if (index == 0)\n            return m[0];\n      `;for(let n=1;n<e-1;++n)t+=`\n        else if (index == ${n})\n            return m[${n}];\n      `;return t+=`\n        else\n            return m[${e-1}];\n        `,{getVecItem:new Q(`\n      int getVecItem(int m[${e}], int index) {\n        ${t}\n      }\n    `)}}}})),Os,nh=D((()=>{Jp(),Qp(),eh(),th(),rh(),Os={encoding:Ei,fragcolor:Ci,vec:ki,shapeUtils:Pi,coordinates:Oi}})),Di,oh=D((()=>{kr(),Xp(),nh(),ot(),Di=class{constructor(e,t,n,r){this.libs={},this.glslLibRoutineDependencyGraph={},this.context=new bi(e,t,n,r),Object.keys(Os).forEach((e=>{let t=new Os[e](this.context);this.libs[e]=t}));let i=this.glslLibRoutineDependencyGraph;for(let e in this.libs){let t=this.libs[e].getFunctions();for(let n in t){let r,a=e+"."+n;i[a]?(r=i[a],r.routineBody=t[n].routineBody):(r=new mo(a,t[n].routineBody),i[a]=r);let o=t[n].dependencies;if(o)for(let e=0;e<o.length;++e)if(i[o[e]])r.addDependency(i[o[e]]);else{let t=new mo(o[e]);i[o[e]]=t,r.addDependency(t)}}}}preprocess(){let e=this.context.programInfo,t=e.shaderSource;return this.context.programInfo.hasMain||(t=`${t}\n      ${Nd(this.context.glContext.version,this.context.outputTextureLayout.shape.length)}`),t=Yp(t),`${zd(this.context.glContext.version)}\n    ${this.getUniforms(e.inputNames,e.variables)}\n    ${this.getImports(t)}\n    ${t}`}getImports(e){let t=this.selectGlslLibRoutinesToBeIncluded(e);if(0===t.length)return"";let n="";for(let e=0;e<t.length;++e){if(!t[e].routineBody)throw new Error(`Missing body for the Glsl Library routine: ${t[e].name}`);n+=t[e].routineBody+"\n"}return n}selectGlslLibRoutinesToBeIncluded(e){let t=[];return Object.keys(this.glslLibRoutineDependencyGraph).forEach((n=>{let r=n.split(".")[1];-1!==e.indexOf(r)&&t.push(this.glslLibRoutineDependencyGraph[n])})),yi.returnOrderedNodes(t)}getUniforms(e,t){let n=[];if(e)for(let t of e)n.push(`uniform sampler2D ${t};`);if(t)for(let e of t)n.push(`uniform ${e.type} ${e.name}${e.arrayLength?`[${e.arrayLength}]`:""};`);return n.join("\n")}}})),Bi,ih=D((()=>{Et(),Ht(),oh(),ot(),Bi=class{constructor(e,t,n){this.profiler=e,this.glContext=t,this.textureLayoutStrategy=n,this.repo=new Map,this.attributesBound=!1}getArtifact(e){return this.repo.get(e)}setArtifact(e,t){this.repo.set(e,t)}run(e,t,n){this.profiler.event("op",`ProgramManager.run ${e.programInfo.name??"unknown kernel"}`,(()=>{let r=this.glContext.gl,i=e.program;r.useProgram(i);try{this.bindOutput(n),this.attributesBound||this.bindAttributes(e.attribLocations),this.bindUniforms(e.uniformLocations,e.programInfo.variables??[],t)}catch(t){throw qe.error("ProgramManager",e.programInfo.shaderSource),t}this.profiler.event("backend","GlContext.draw()",(()=>{this.glContext.draw()}))}),this.glContext)}dispose(){this.vertexShader&&this.glContext.deleteShader(this.vertexShader),this.repo.forEach((e=>this.glContext.deleteProgram(e.program)))}build(e,t,n){return this.profiler.event("backend","ProgramManager.build",(()=>{let r=new Di(this.glContext,e,t,n),i=r.preprocess(),a=this.compile(i);return{programInfo:e,program:a,uniformLocations:this.getUniformLocations(a,r.context.programInfo.inputNames,r.context.programInfo.variables),attribLocations:this.getAttribLocations(a)}}))}compile(e){if(!this.vertexShader){qe.verbose("ProrgramManager","Compiling and caching Vertex shader for the first time");let e=Rd(this.glContext.version);this.vertexShader=this.glContext.compileShader(e,this.glContext.gl.VERTEX_SHADER)}be.debug&&qe.verbose("ProrgramManager",`FragShader:\n${e}\n`);let t=this.glContext.compileShader(e,this.glContext.gl.FRAGMENT_SHADER),n=this.glContext.createProgram(this.vertexShader,t);return this.glContext.deleteShader(t),n}bindOutput(e){let t=e.width,n=e.height;qe.verbose("ProrgramManager",`Binding output texture to Framebuffer: w/h=${t}/${n}, shape=${e.shape}, type=${e.tensor.type}`),this.glContext.attachFramebuffer(e.texture,t,n)}bindAttributes(e){let t=e.position,n=e.textureCoord;this.glContext.setVertexAttributes(t,n),this.attributesBound=!0}bindUniforms(e,t,n){let r=this.glContext.gl,i=0;for(let{name:a,type:o,location:s,arrayLength:u}of e){let e=t.find((e=>e.name===a))?.data;if("sampler2D"!==o&&!e)throw new Error(`variable '${a}' does not have data defined in program info`);switch(o){case"sampler2D":this.bindTexture(n[i],s,i),i++;break;case"float":u?r.uniform1fv(s,e):r.uniform1f(s,e);break;case"int":u?r.uniform1iv(s,e):r.uniform1i(s,e);break;default:throw new Error(`Uniform not implemented: ${o}`)}}}bindTexture(e,t,n){this.glContext.bindTextureToUniform(e.texture,n,t)}getAttribLocations(e){return{position:this.getAttribLocation(e,"position"),textureCoord:this.getAttribLocation(e,"textureCoord")}}getUniformLocations(e,t,n){let r=[];if(t)for(let n of t)r.push({name:n,type:"sampler2D",location:this.getUniformLocation(e,n)});if(n)for(let t of n)r.push({...t,location:this.getUniformLocation(e,t.name)});return r}getUniformLocation(e,t){let n=this.glContext.gl.getUniformLocation(e,t);if(null===n)throw new Error(`Uniform ${t} not found.`);return n}getAttribLocation(e,t){return this.glContext.gl.getAttribLocation(e,t)}}})),Ri,ah=D((()=>{Ht(),po(),Ri=class{constructor(e,t,n,r){this.glContext=e,this.layoutStrategy=t,this.profiler=n,this.config=r,this.pendingRead=new Map,r.reuseTextures&&(this.inUseTextures=new Map,this.idleTextures=new Map,this.textureLookup=new Map)}createTextureFromLayout(e,t,n,r){let i=this.toEncoderType(e),a=this.glContext.getEncoder(i,t.channels||1,r);if(t.isPacked&&1===r)throw new Error("not implemented");let o,s,u=t.width,l=t.height;if(this.config.reuseTextures){o=`${u}x${l}_${a.format}_${a.internalFormat}_${a.textureType}`,s=this.inUseTextures.get(o),s||(s=[],this.inUseTextures.set(o,s));let t=this.idleTextures.get(o);if(t&&t.length>0){let i=t.pop();return s.push(i),1===r&&this.glContext.updateTexture(i,u,l,a,this.toTextureData(e,n)),i}}qe.verbose("TextureManager",`Creating new texture of size ${t.width}x${t.height}`);let d=this.glContext.allocateTexture(u,l,a,this.toTextureData(e,n));return this.config.reuseTextures&&(s.push(d),this.textureLookup.set(d,o)),d}readTexture(e,t,n){return n||(n=1),this.profiler.event("backend","TextureManager.readTexture",(()=>{let r=e.shape.reduce(((e,t)=>e*t))*n,i=this.glContext.readTexture(e.texture,e.width,e.height,r,this.toEncoderType(t),n);return this.toTensorData(t,i)}))}async readTextureAsync(e,t,n){let r=e.tensor.dataId;if(n||(n=1),this.pendingRead.has(r)){let e=this.pendingRead.get(r);return new Promise((t=>e?.push(t)))}return this.profiler.event("backend","TextureManager.readTextureAsync",(async()=>{this.pendingRead.set(r,[]);let i=e.shape.reduce(((e,t)=>e*t))*n;await this.glContext.createAndWaitForFence();let a=this.glContext.readTexture(e.texture,e.width,e.height,i,this.toEncoderType(t),n),o=this.toTensorData(t,a),s=this.pendingRead.get(r);return this.pendingRead.delete(r),s?.forEach((e=>e(o))),o}))}readUint8TextureAsFloat(e){return this.profiler.event("backend","TextureManager.readUint8TextureAsFloat",(()=>{let t=e.shape.reduce(((e,t)=>e*t)),n=this.glContext.readTexture(e.texture,e.width,e.height,4*t,"byte",4);return new Float32Array(n.buffer,n.byteOffset,t)}))}releaseTexture(e,t){let n;if(this.config.reuseTextures&&(n=this.textureLookup.get(e.texture),n)){t&&this.textureLookup.delete(n);let r=this.inUseTextures.get(n);if(r){let t=r.indexOf(e.texture);if(-1!==t){r.splice(t,1);let i=this.idleTextures.get(n);i||(i=[],this.idleTextures.set(n,i)),i.push(e.texture)}}}(!n||t)&&(qe.verbose("TextureManager",`Deleting texture of size ${e.width}x${e.height}`),this.glContext.deleteTexture(e.texture))}toTensorData(e,t){switch(e){case"int16":return t instanceof Int16Array?t:Int16Array.from(t);case"int32":return t instanceof Int32Array?t:Int32Array.from(t);case"int8":return t instanceof Int8Array?t:Int8Array.from(t);case"uint16":return t instanceof Uint16Array?t:Uint16Array.from(t);case"uint32":return t instanceof Uint32Array?t:Uint32Array.from(t);case"uint8":case"bool":return t instanceof Uint8Array?t:Uint8Array.from(t);case"float32":return t instanceof Float32Array?t:Float32Array.from(t);case"float64":return t instanceof Float64Array?t:Float64Array.from(t);default:throw new Error(`TensorData type ${e} is not supported`)}}toTextureData(e,t){if(t)return t instanceof Float32Array?t:new Float32Array(t)}toEncoderType(e){return"float"}clearActiveTextures(){this.glContext.clearActiveTextures()}}})),zi,sh=D((()=>{Ht(),xl(),Jd(),jp(),ih(),As(),ah(),zi=class{constructor(e,t){this.backend=e,this.context=t,this.layoutStrategy=new Ai(e.glContext.maxTextureSize),this.programManager=new Bi(this.context.profiler,e.glContext,this.layoutStrategy),this.textureManager=new Ri(e.glContext,this.layoutStrategy,this.context.profiler,{reuseTextures:"full"===e.textureCacheMode}),this.packedTextureDataCache=new Map,this.unpackedTextureDataCache=new Map,this.pack=e.pack,this.pack2unpackMap=new Map,this.unpack2packMap=new Map}createInferenceHandler(){return new gi(this)}onGraphInitialized(e){let t=e.getValues().filter((e=>-1===e.from&&e.tensor)).map((e=>e.tensor.dataId));this.initializers=new Set(t)}isInitializer(e){return!!this.initializers&&this.initializers.has(e)}addInitializer(e){this.initializers.add(e)}getTextureData(e,t){return t?this.packedTextureDataCache.get(e):this.unpackedTextureDataCache.get(e)}setTextureData(e,t,n=!1){qe.verbose("WebGLSessionHandler","Storing Texture data in 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i=this.gl,a=i.createTexture();i.bindTexture(i.TEXTURE_2D,a),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MIN_FILTER,i.NEAREST),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MAG_FILTER,i.NEAREST),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_S,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_T,i.CLAMP_TO_EDGE);let o=r?n.encode(r,e*t):null;return i.texImage2D(i.TEXTURE_2D,0,n.internalFormat,e,t,0,n.format,n.textureType,o),this.checkError(),a}updateTexture(e,t,n,r,i){let a=this.gl;a.bindTexture(a.TEXTURE_2D,e);let o=r.encode(i,t*n);a.texSubImage2D(a.TEXTURE_2D,0,0,0,t,n,r.format,r.textureType,o),this.checkError()}attachFramebuffer(e,t,n){let r=this.gl;r.bindTexture(r.TEXTURE_2D,e),r.bindFramebuffer(r.FRAMEBUFFER,this.framebuffer),r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,e,0),this.checkError(),r.viewport(0,0,t,n),r.scissor(0,0,t,n)}readTexture(e,t,n,r,i,a){let o=this.gl;a||(a=1),this.frameBufferBound||this.attachFramebuffer(e,t,n);let s=this.getEncoder(i,a),u=s.allocate(t*n);return o.bindTexture(o.TEXTURE_2D,e),o.framebufferTexture2D(o.FRAMEBUFFER,o.COLOR_ATTACHMENT0,o.TEXTURE_2D,e,0),o.readPixels(0,0,t,n,o.RGBA,s.textureType,u),this.checkError(),s.decode(u,r)}isFramebufferReady(){return!0}getActiveTexture(){let e=this.gl;return"TEXTURE"+(e.getParameter(this.gl.ACTIVE_TEXTURE)-e.TEXTURE0)}getTextureBinding(){return this.gl.getParameter(this.gl.TEXTURE_BINDING_2D)}getFramebufferBinding(){return this.gl.getParameter(this.gl.FRAMEBUFFER_BINDING)}setVertexAttributes(e,t){let n=this.gl;n.vertexAttribPointer(e,3,n.FLOAT,!1,20,0),n.enableVertexAttribArray(e),-1!==t&&(n.vertexAttribPointer(t,2,n.FLOAT,!1,20,12),n.enableVertexAttribArray(t)),this.checkError()}createProgram(e,t){let n=this.gl,r=n.createProgram();return n.attachShader(r,e),n.attachShader(r,t),n.linkProgram(r),r}compileShader(e,t){let n=this.gl,r=n.createShader(t);if(!r)throw new Error(`createShader() returned null with type ${t}`);if(n.shaderSource(r,e),n.compileShader(r),!1===n.getShaderParameter(r,n.COMPILE_STATUS))throw new Error(`Failed to compile shader: ${n.getShaderInfoLog(r)}\nShader source:\n${e}`);return r}deleteShader(e){this.gl.deleteShader(e)}bindTextureToUniform(e,t,n){let r=this.gl;r.activeTexture(r.TEXTURE0+t),this.checkError(),r.bindTexture(r.TEXTURE_2D,e),this.checkError(),r.uniform1i(n,t),this.checkError()}draw(){this.gl.drawArrays(this.gl.TRIANGLE_STRIP,0,4),this.checkError()}checkError(){if(be.debug){let e=this.gl,t=e.getError(),n="";switch(t){case e.NO_ERROR:return;case e.INVALID_ENUM:n="INVALID_ENUM";break;case e.INVALID_VALUE:n="INVALID_VALUE";break;case e.INVALID_OPERATION:n="INVALID_OPERATION";break;case e.INVALID_FRAMEBUFFER_OPERATION:n="INVALID_FRAMEBUFFER_OPERATION";break;case e.OUT_OF_MEMORY:n="OUT_OF_MEMORY";break;case e.CONTEXT_LOST_WEBGL:n="CONTEXT_LOST_WEBGL";break;default:n=`Unknown WebGL Error: ${t.toString(16)}`}throw new Error(n)}}deleteTexture(e){this.gl.deleteTexture(e)}deleteProgram(e){this.gl.deleteProgram(e)}getEncoder(e,t,n=0){if(2===this.version)return new hi(this.gl,t);switch(e){case"float":return 1===n||this.isRenderFloat32Supported?new fo(this.gl,t):new fo(this.gl,t,this.textureHalfFloatExtension.HALF_FLOAT_OES);case"int":throw new Error("not implemented");case"byte":return new mi(this.gl,t);default:throw new Error(`Invalid dataType: ${e}`)}}clearActiveTextures(){let e=this.gl;for(let t=0;t<this.maxTextureImageUnits;++t)e.activeTexture(e.TEXTURE0+t),e.bindTexture(e.TEXTURE_2D,null)}dispose(){if(this.disposed)return;let e=this.gl;e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteFramebuffer(this.framebuffer),e.bindBuffer(e.ARRAY_BUFFER,null),e.deleteBuffer(this.vertexbuffer),e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null),e.finish(),this.disposed=!0}createDefaultGeometry(){return new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0])}createVertexbuffer(){let e=this.gl,t=e.createBuffer();if(!t)throw new Error("createBuffer() returned null");let n=this.createDefaultGeometry();return e.bindBuffer(e.ARRAY_BUFFER,t),e.bufferData(e.ARRAY_BUFFER,n,e.STATIC_DRAW),this.checkError(),t}createFramebuffer(){let e=this.gl.createFramebuffer();if(!e)throw new Error("createFramebuffer returned null");return e}queryVitalParameters(){let e=this.gl;if(this.isFloatTextureAttachableToFrameBuffer=this.checkFloatTextureAttachableToFrameBuffer(),this.isRenderFloat32Supported=this.checkRenderFloat32(),this.isFloat32DownloadSupported=this.checkFloat32Download(),1===this.version&&!this.textureHalfFloatExtension&&!this.isRenderFloat32Supported)throw new Error("both float32 and float16 TextureType are not supported");this.isBlendSupported=!this.isRenderFloat32Supported||this.checkFloat32Blend(),this.maxTextureSize=e.getParameter(e.MAX_TEXTURE_SIZE),this.maxTextureImageUnits=e.getParameter(e.MAX_TEXTURE_IMAGE_UNITS),this.version}getExtensions(){2===this.version?(this.colorBufferFloatExtension=this.gl.getExtension("EXT_color_buffer_float"),this.disjointTimerQueryWebgl2Extension=this.gl.getExtension("EXT_disjoint_timer_query_webgl2")):(this.textureFloatExtension=this.gl.getExtension("OES_texture_float"),this.textureHalfFloatExtension=this.gl.getExtension("OES_texture_half_float"))}checkFloatTextureAttachableToFrameBuffer(){let e=this.gl,t=e.createTexture();e.bindTexture(e.TEXTURE_2D,t);let n=2===this.version?e.RGBA32F:e.RGBA;e.texImage2D(e.TEXTURE_2D,0,n,1,1,0,e.RGBA,e.FLOAT,null);let r=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,r),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(t),e.deleteFramebuffer(r),i}checkRenderFloat32(){if(2===this.version){if(!this.colorBufferFloatExtension)return!1}else if(!this.textureFloatExtension)return!1;return this.isFloatTextureAttachableToFrameBuffer}checkFloat32Download(){if(2===this.version){if(!this.colorBufferFloatExtension)return!1}else if(!this.textureFloatExtension||!this.gl.getExtension("WEBGL_color_buffer_float"))return!1;return this.isFloatTextureAttachableToFrameBuffer}checkFloat32Blend(){let e,t,n,r,i,a=this.gl;try{e=a.createTexture(),t=a.createFramebuffer(),a.bindTexture(a.TEXTURE_2D,e);let o=2===this.version?a.RGBA32F:a.RGBA;return a.texImage2D(a.TEXTURE_2D,0,o,1,1,0,a.RGBA,a.FLOAT,null),a.bindFramebuffer(a.FRAMEBUFFER,t),a.framebufferTexture2D(a.FRAMEBUFFER,a.COLOR_ATTACHMENT0,a.TEXTURE_2D,e,0),a.enable(a.BLEND),n=a.createShader(a.VERTEX_SHADER),!!(n&&(a.shaderSource(n,"void main(){}"),a.compileShader(n),r=a.createShader(a.FRAGMENT_SHADER),r)&&(a.shaderSource(r,"precision highp float;void main(){gl_FragColor=vec4(0.5);}"),a.compileShader(r),i=a.createProgram(),i))&&(a.attachShader(i,n),a.attachShader(i,r),a.linkProgram(i),a.useProgram(i),a.drawArrays(a.POINTS,0,1),a.getError()===a.NO_ERROR)}finally{a.disable(a.BLEND),i&&a.deleteProgram(i),n&&a.deleteShader(n),r&&a.deleteShader(r),t&&(a.bindFramebuffer(a.FRAMEBUFFER,null),a.deleteFramebuffer(t)),e&&(a.bindTexture(a.TEXTURE_2D,null),a.deleteTexture(e))}}beginTimer(){if(2===this.version&&this.disjointTimerQueryWebgl2Extension){let e=this.gl,t=this.disjointTimerQueryWebgl2Extension,n=e.createQuery();return e.beginQuery(t.TIME_ELAPSED_EXT,n),n}throw new Error("WebGL1 profiling currently not supported.")}endTimer(){if(2!==this.version||!this.disjointTimerQueryWebgl2Extension)throw new Error("WebGL1 profiling currently not supported");{let e=this.gl,t=this.disjointTimerQueryWebgl2Extension;e.endQuery(t.TIME_ELAPSED_EXT)}}isTimerResultAvailable(e){let t=!1,n=!1;if(2!==this.version||!this.disjointTimerQueryWebgl2Extension)throw new Error("WebGL1 profiling currently not supported");{let r=this.gl,i=this.disjointTimerQueryWebgl2Extension;t=r.getQueryParameter(e,r.QUERY_RESULT_AVAILABLE),n=r.getParameter(i.GPU_DISJOINT_EXT)}return t&&!n}getTimerResult(e){let t=0;if(2!==this.version)throw new Error("WebGL1 profiling currently not supported");{let n=this.gl;t=n.getQueryParameter(e,n.QUERY_RESULT),n.deleteQuery(e)}return t/1e6}async waitForQueryAndGetTime(e){return await Qa((()=>this.isTimerResultAvailable(e))),this.getTimerResult(e)}async createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n=e,r=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);return e.flush(),t=null===r?()=>!0:()=>{let e=n.clientWaitSync(r,0,0);return e===n.ALREADY_SIGNALED||e===n.CONDITION_SATISFIED},{query:r,isFencePassed:t}}async pollFence(e){return new Promise((t=>{this.addItemToPoll((()=>e.isFencePassed()),(()=>t()))}))}pollItems(){let e=dx(this.itemsToPoll.map((e=>e.isDoneFn)));for(let t=0;t<=e;++t){let{resolveFn:e}=this.itemsToPoll[t];e()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}async addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&await Qa((()=>(this.pollItems(),0===this.itemsToPoll.length)))}}}));function Es(e){let t;if(e&&"webgl2"!==e||!("webgl2"in Bn)?(!e||"webgl"===e)&&"webgl"in Bn&&(t=Bn.webgl):t=Bn.webgl2,!t)try{t=lh(fx(),e)}catch{t=lh(cx(),e)}e=e||1===t.version?"webgl":"webgl2";let n=t.gl;return Bn[e]=t,n.isContextLost()?(delete Bn[e],Es(e)):(n.disable(n.DEPTH_TEST),n.disable(n.STENCIL_TEST),n.disable(n.BLEND),n.disable(n.DITHER),n.disable(n.POLYGON_OFFSET_FILL),n.disable(n.SAMPLE_COVERAGE),n.enable(n.SCISSOR_TEST),n.enable(n.CULL_FACE),n.cullFace(n.BACK),t)}function lh(e,t){let n,r={alpha:!1,depth:!1,antialias:!1,stencil:!1,preserveDrawingBuffer:!1,premultipliedAlpha:!1,failIfMajorPerformanceCaveat:!1};if((!t||"webgl2"===t)&&(n=e.getContext("webgl2",r),n))try{return new yo(n,2)}catch(e){qe.warning("GlContextFactory",`failed to create WebGLContext using contextId 'webgl2'. Error: ${e}`)}if((!t||"webgl"===t)&&(n=e.getContext("webgl",r)||e.getContext("experimental-webgl",r),n))try{return new yo(n,1)}catch(e){qe.warning("GlContextFactory",`failed to create WebGLContext using contextId 'webgl' or 'experimental-webgl'. Error: ${e}`)}throw new Error("WebGL is not supported")}function cx(){if(typeof document>"u")throw new TypeError("failed to create canvas: document is not supported");let e=document.createElement("canvas");return e.width=1,e.height=1,e}function fx(){if(typeof OffscreenCanvas>"u")throw new TypeError("failed to create offscreen canvas: OffscreenCanvas is not supported");return new OffscreenCanvas(1,1)}var Bn,dh=D((()=>{Ht(),uh(),Bn={}})),Ni,ch=D((()=>{Et(),Ht(),sh(),dh(),Ni=class{get contextId(){return be.webgl.contextId}set contextId(e){be.webgl.contextId=e}get matmulMaxBatchSize(){return be.webgl.matmulMaxBatchSize}set matmulMaxBatchSize(e){be.webgl.matmulMaxBatchSize=e}get textureCacheMode(){return be.webgl.textureCacheMode}set textureCacheMode(e){be.webgl.textureCacheMode=e}get pack(){return be.webgl.pack}set pack(e){be.webgl.pack=e}get async(){return be.webgl.async}set async(e){be.webgl.async=e}initialize(){try{return this.glContext=Es(this.contextId),"number"!=typeof this.matmulMaxBatchSize&&(this.matmulMaxBatchSize=16),"string"!=typeof this.textureCacheMode&&(this.textureCacheMode="full"),"boolean"!=typeof this.pack&&(this.pack=!1),"boolean"!=typeof this.async&&(this.async=!1),qe.setWithEnv(be),be.webgl.context||Object.defineProperty(be.webgl,"context",{value:this.glContext.gl}),qe.verbose("WebGLBackend",`Created WebGLContext: ${typeof this.glContext} with matmulMaxBatchSize: ${this.matmulMaxBatchSize}; textureCacheMode: ${this.textureCacheMode}; pack: ${this.pack}; async: ${this.async}.`),!0}catch(e){return qe.warning("WebGLBackend",`Unable to initialize WebGLBackend. ${e}`),!1}}createSessionHandler(e){return new zi(this,e)}dispose(){this.glContext.dispose()}}}));async function Cs(e){if(!e)return Cs(["webgl"]);{let t="string"==typeof e?[e]:e;for(let e of t){let t=fh.get(e);if(t)return t;let n=await hx(e);if(n)return n}}throw new Error("no available backend to use")}async function hx(e){let t=px;if(typeof t[e]<"u"&&mx(t[e])){let n=t[e],r=n.initialize();if("object"==typeof r&&"then"in r&&(r=await r),r)return fh.set(e,n),n}}function mx(e){let t=e;return"initialize"in t&&"function"==typeof t.initialize&&"createSessionHandler"in t&&"function"==typeof t.createSessionHandler&&"dispose"in t&&"function"==typeof t.dispose}var fh,px,ph=D((()=>{ch(),fh=new Map,px={webgl:new Ni}})),Ps,Li,hh=D((()=>{Ht(),Ps=class{constructor(e,t){this.op=e,this.node=t}},Li=class{constructor(e,t,n){this.graph=e,this.profiler=n,this.initialize(t)}initialize(e){this.profiler.event("session","ExecutionPlan.initialize",(()=>{let t=this.graph.getNodes();if(t.length!==e.length)throw new Error("The size of nodes and OPs do not match.");this._ops=e.map(((e,n)=>new Ps(e,t[n]))),this.reset(),this._starter=[],this._ops.forEach(((e,t)=>{let n=!0;for(let t of e.node.inputs)if(!this._values[t]&&-1===this.graph.getInputIndices().indexOf(t)){n=!1;break}n&&this._starter.push(t)}))}))}reset(){this._values=this.graph.getValues().map((e=>e.tensor))}async execute(e,t){return this.profiler.event("session","ExecutionPlan.execute",(async()=>{this.reset();let n=e.createInferenceHandler(),r=this.graph.getInputIndices();if(t.length!==r.length)throw new Error(`number of input tensors don't match the number of inputs to the model: actual: ${t.length} expected: ${r.length}`);t.forEach(((e,t)=>{let n=r[t];this._values[n]=e}));let i=this._starter.slice(0),a=this.graph.getValues(),o=this.graph.getNodes(),s=0;for(;s<i.length;){let e=i[s++],t=this._ops[e],r=t.node.inputs.map((e=>this._values[e]));if(-1!==r.indexOf(void 0))throw new Error(`unresolved input detected: op: ${t.node}`);let u=r;qe.verbose("ExecPlan",`Runing op:${t.node.name} (${u.map(((e,n)=>`'${t.node.inputs[n]}': ${e.type}[${e.dims.join(",")}]`)).join(", ")})`);let l=await this.profiler.event("node",t.node.name,(async()=>t.op.impl(n,u,t.op.context)));if(l.length!==t.node.outputs.length)throw new Error("the size of output does not match model definition.");l.forEach(((e,n)=>{let r=t.node.outputs[n];if(this._values[r])throw new Error(`output [${r}] already has value: op:${t.node.name}`);this._values[r]=e}));let d=new Set;l.forEach(((e,n)=>{let r=t.node.outputs[n];for(let e of a[r].to){let t=o[e],n=!0;for(let e of t.inputs)if(!this._values[e]){n=!1;break}n&&d.add(e)}})),i.push(...d)}let u=[];for(let e=0;e<this.graph.getOutputIndices().length;e++){let t=this.graph.getOutputIndices()[e],n=this._values[t];if(void 0===n)throw new Error(`required output [${t}] does not have value`);0===t?await n.getData():n.data,u.push(n)}return qe.verbose("ExecPlan","disposing of inferenceHandler"),n.dispose(),u}))}}})),De,Jt,vo,mh=D((()=>{io(),De=Tn(In()),un(),We(),Jt=se.experimental.fbs,vo=class e{constructor(t){if(this._attributes=new Map,null!=t){for(let n of t)n instanceof De.onnx.AttributeProto?this._attributes.set(n.name,[e.getValue(n),e.getType(n)]):n instanceof Jt.Attribute&&this._attributes.set(n.name(),[e.getValue(n),e.getType(n)]);if(this._attributes.size<t.length)throw new Error("duplicated attribute names")}}set(e,t,n){this._attributes.set(e,[n,t])}delete(e){this._attributes.delete(e)}getFloat(e,t){return this.get(e,"float",t)}getInt(e,t){return this.get(e,"int",t)}getString(e,t){return this.get(e,"string",t)}getTensor(e,t){return this.get(e,"tensor",t)}getFloats(e,t){return this.get(e,"floats",t)}getInts(e,t){return this.get(e,"ints",t)}getStrings(e,t){return this.get(e,"strings",t)}getTensors(e,t){return this.get(e,"tensors",t)}get(e,t,n){let r=this._attributes.get(e);if(void 0===r){if(void 0!==n)return n;throw new Error(`required attribute not found: ${e}`)}if(r[1]!==t)throw new Error(`type mismatch: expected ${t} but got ${r[1]}`);return r[0]}static getType(e){let t=e instanceof De.onnx.AttributeProto?e.type:e.type();switch(t){case De.onnx.AttributeProto.AttributeType.FLOAT:return"float";case De.onnx.AttributeProto.AttributeType.INT:return"int";case De.onnx.AttributeProto.AttributeType.STRING:return"string";case De.onnx.AttributeProto.AttributeType.TENSOR:return"tensor";case De.onnx.AttributeProto.AttributeType.FLOATS:return"floats";case De.onnx.AttributeProto.AttributeType.INTS:return"ints";case De.onnx.AttributeProto.AttributeType.STRINGS:return"strings";case De.onnx.AttributeProto.AttributeType.TENSORS:return"tensors";default:throw new Error(`attribute type is not supported yet: ${De.onnx.AttributeProto.AttributeType[t]}`)}}static getValue(e){let t=e instanceof De.onnx.AttributeProto?e.type:e.type();if(t===De.onnx.AttributeProto.AttributeType.GRAPH||t===De.onnx.AttributeProto.AttributeType.GRAPHS)throw new Error("graph attribute is not supported yet");let n=this.getValueNoCheck(e);if(t===De.onnx.AttributeProto.AttributeType.INT&&Vt.isLong(n))return Vt.longToNumber(n);if(t===De.onnx.AttributeProto.AttributeType.INTS){let e=n,t=new Array(e.length);for(let n=0;n<e.length;n++){let r=e[n];t[n]=Vt.longToNumber(r)}return t}if(t===De.onnx.AttributeProto.AttributeType.TENSOR)return e instanceof De.onnx.AttributeProto?yt.fromProto(n):yt.fromOrtTensor(n);if(t===De.onnx.AttributeProto.AttributeType.TENSORS){if(e instanceof De.onnx.AttributeProto)return n.map((e=>yt.fromProto(e)));if(e instanceof Jt.Attribute)return n.map((e=>yt.fromOrtTensor(e)))}return t===De.onnx.AttributeProto.AttributeType.STRING&&e instanceof De.onnx.AttributeProto?co(n):t===De.onnx.AttributeProto.AttributeType.STRINGS&&e instanceof De.onnx.AttributeProto?n.map(co):n}static getValueNoCheck(e){return e instanceof De.onnx.AttributeProto?this.getValueNoCheckFromOnnxFormat(e):this.getValueNoCheckFromOrtFormat(e)}static getValueNoCheckFromOnnxFormat(e){switch(e.type){case De.onnx.AttributeProto.AttributeType.FLOAT:return e.f;case De.onnx.AttributeProto.AttributeType.INT:return e.i;case De.onnx.AttributeProto.AttributeType.STRING:return e.s;case De.onnx.AttributeProto.AttributeType.TENSOR:return e.t;case De.onnx.AttributeProto.AttributeType.GRAPH:return e.g;case De.onnx.AttributeProto.AttributeType.FLOATS:return e.floats;case De.onnx.AttributeProto.AttributeType.INTS:return e.ints;case De.onnx.AttributeProto.AttributeType.STRINGS:return e.strings;case De.onnx.AttributeProto.AttributeType.TENSORS:return e.tensors;case De.onnx.AttributeProto.AttributeType.GRAPHS:return e.graphs;default:throw new Error(`unsupported attribute type: ${De.onnx.AttributeProto.AttributeType[e.type]}`)}}static getValueNoCheckFromOrtFormat(e){switch(e.type()){case Jt.AttributeType.FLOAT:return e.f();case Jt.AttributeType.INT:return e.i();case Jt.AttributeType.STRING:return e.s();case Jt.AttributeType.TENSOR:return e.t();case Jt.AttributeType.GRAPH:return e.g();case Jt.AttributeType.FLOATS:return e.floatsArray();case Jt.AttributeType.INTS:{let t=[];for(let n=0;n<e.intsLength();n++)t.push(e.ints(n));return t}case Jt.AttributeType.STRINGS:{let t=[];for(let n=0;n<e.stringsLength();n++)t.push(e.strings(n));return t}case Jt.AttributeType.TENSORS:{let t=[];for(let n=0;n<e.tensorsLength();n++)t.push(e.tensors(n));return t}default:throw new Error(`unsupported attribute type: ${Jt.AttributeType[e.type()]}`)}}}})),Ds,Mi,Bs,br,Vi,ks,gh=D((()=>{mh(),io(),Ds=Tn(In()),un(),We(),Mi=se.experimental.fbs,Bs={from:(e,t)=>new ks(e,t)},br=class{constructor(e){this._from=void 0,this._to=[],this.tensor=void 0,this.type=void 0,e&&(this.type=Ot.tensorValueTypeFromProto(e.type.tensorType))}get from(){return this._from}get to(){return this._to}},Vi=class{constructor(e,t){e instanceof Ds.onnx.NodeProto?(this.name=e.name,this.opType=e.opType,this.attributes=new vo(e.attribute)):e instanceof Mi.Node&&(this.name=t??e.name(),this.opType=e.opType(),this.attributes=new vo(Ot.tensorAttributesFromORTFormat(e))),this.inputs=[],this.outputs=[],this.executeNode=!0}},ks=class{constructor(e,t){if(!e)throw new TypeError("graph is empty");this.buildGraph(e),this.transformGraph(t),this.checkIsAcyclic()}getInputIndices(){return this._allInputIndices}getInputNames(){return this._allInputNames}getOutputIndices(){return this._allOutputIndices}getOutputNames(){return this._allOutputNames}getValues(){return this._allData}getNodes(){return this._nodes}buildGraph(e){if(e instanceof Ds.onnx.GraphProto)this.buildGraphFromOnnxFormat(e);else{if(!(e instanceof Mi.Graph))throw new TypeError("Graph type is not supported.");this.buildGraphFromOrtFormat(e)}}buildGraphFromOnnxFormat(e){let t=new Map;this._allData=[],this._allInputIndices=[],this._allInputNames=[],this._allOutputIndices=[],this._allOutputNames=[],this._nodes=[];let n=new Map;if(!e.input)throw new Error("missing information in graph: input");let r=[];for(let n of e.input){if(t.has(n.name))throw new Error(`duplicated input name: ${n.name}`);let e=this._allData.push(new br(n))-1;t.set(n.name,e),r.push(n.name)}if(!e.initializer)throw new Error("missing information in graph: initializer");for(let n of e.initializer){let e=t.get(n.name);if(void 0===e){let r=new br;r.type={shape:{dims:Ot.tensorDimsFromProto(n.dims)},tensorType:Ot.tensorDataTypeFromProto(n.dataType)},e=this._allData.push(r)-1,t.set(n.name,e)}this._allData[e]._from=-1,this._allData[e].tensor=yt.fromProto(n)}for(let e=0;e<this._allData.length;e++)this._allData[e].tensor||(this._allInputIndices.push(e),this._allInputNames.push(r[e]));if(!e.output)throw new Error("missing information in graph: output");for(let n of e.output){if(t.has(n.name))throw new Error(`duplicated output name: ${n.name}`);let e=this._allData.push(new br(n))-1;t.set(n.name,e),this._allOutputIndices.push(e),this._allOutputNames.push(n.name)}if(!e.node)throw new Error("missing information in graph: node");for(let t of e.node){if(!t.name)for(let e=0;;e++){let r=`unnamed_${t.opType}_${e}`;if(!n.has(r)){t.name=r;break}}if(n.has(t.name))throw new Error(`duplicated node name: ${t.name}`);let e=this._nodes.push(new Vi(t))-1;n.set(t.name,e)}for(let n=0;n<this._nodes.length;n++){let r=this._nodes[n],i=e.node[n];if(!i.output)throw new Error(`missing output for node: ${i.name}`);for(let e of i.output){let a=t.get(e);if(typeof a>"u"&&(a=this._allData.push(new br)-1,t.set(e,a)),r.outputs.push(a),void 0!==this._allData[a]._from)throw new Error(`multiple nodes output to one data value: ${a}`);if(this._allData[a]._from=n,"Constant"===i.opType){if(!i.attribute||1!==i.attribute.length||!i.attribute[0].t)throw new Error("missing attributes or missing tensor value in attributes for this Constant operator");if(!i.output||1!==i.output.length)throw new Error("missing output or incorrect number of outputs for this Constant operator");r.outputs.pop(),r.executeNode=!1,this._allData[a]._from=-1,this._allData[a].tensor=yt.fromProto(i.attribute[0].t)}}}for(let n=0;n<this._nodes.length;n++){let r=this._nodes[n],i=e.node[n];if(!i.input)throw new Error(`missing input for node: ${i.name}`);for(let e of i.input){let a=t.get(e);if(typeof a>"u"){if(""===e&&(3===i.input.length||4===i.input.length)&&"Resize"===i.opType)continue;throw new Error(`unrecognized input '${e}' for node: ${i.name}`)}r.inputs.push(a),this._allData[a]._to.push(n)}}return!0}buildGraphFromOrtFormat(e){let t=new Map;this._allData=[],this._allInputIndices=[],this._allInputNames=[],this._allOutputIndices=[],this._allOutputNames=[],this._nodes=[];let n=new Map,r=[];for(let n=0;n<e.inputsLength();n++){let i=e.inputs(n);if(t.has(i))throw new Error(`duplicated input name: ${i}`);for(let n=0;n<e.nodeArgsLength();n++)if(e.nodeArgs(n)?.name()===i){let a=new br;if(e.nodeArgs(n)?.type()?.valueType()!==Mi.TypeInfoValue.tensor_type)throw new Error("Unexpected value type for the nodeArg.");let o=e.nodeArgs(n).type().value(new Mi.TensorTypeAndShape),s=Ot.tensorDataTypeFromProto(o.elemType()),u=o.shape(),l=[];for(let e=0;e<u.dimLength();e++)l.push(Vt.longToNumber(u.dim(e).value().dimValue()));a.type={shape:{dims:l},tensorType:s};let d=this._allData.push(a)-1;t.set(i,d),r.push(i)}}for(let n=0;n<e.initializersLength();n++){let r=e.initializers(n),i=t.get(r.name());if(void 0===i){let e=new br,n=Ot.tensorDimsFromORTFormat(r),a=Ot.tensorDataTypeFromProto(r.dataType());e.type={shape:{dims:n},tensorType:a},i=this._allData.push(e)-1,t.set(r.name(),i)}this._allData[i]._from=-1,this._allData[i].tensor=yt.fromOrtTensor(r)}for(let e=0;e<this._allData.length;e++)this._allData[e].tensor||(this._allInputIndices.push(e),this._allInputNames.push(r[e]));for(let n=0;n<e.outputsLength();n++){let r=e.outputs(n);if(t.has(r))throw new Error(`duplicated output name: ${r}`);let i=this._allData.push(new br)-1;t.set(r,i),this._allOutputIndices.push(i),this._allOutputNames.push(r)}if(!e.nodes)throw new Error("missing information in graph: node");for(let t=0;t<e.nodesLength();t++){let r=e.nodes(t),i=r.name();if(!i)for(let e=0;i=`unnamed_${r.opType()}_${e}`,n.has(i);e++);if(n.has(i))throw new Error(`duplicated node name: ${i}`);let a=this._nodes.push(new Vi(r,i))-1;n.set(i,a)}for(let n=0;n<this._nodes.length;n++){let r=this._nodes[n],i=e.nodes(n);if(null==i)throw new Error(`No node exists at index ${n}`);if(0===i?.outputsLength())throw new Error(`missing output for node: ${i.name}`);for(let e=0;e<i?.outputsLength();e++){let a=i?.outputs(e),o=t.get(a);if(typeof o>"u"&&(o=this._allData.push(new br)-1,t.set(a,o)),r.outputs.push(o),void 0!==this._allData[o]._from)throw new Error(`multiple nodes output to one data value: ${o}`);if(this._allData[o]._from=n,"Constant"===i.opType()){if(1!==i.attributesLength()||!i.attributes(0).t())throw new Error("missing attributes or missing tensor value in attributes for this Constant operator");if(1!==i.outputsLength())throw new Error("missing output or incorrect number of outputs for this Constant operator");r.outputs.pop(),r.executeNode=!1,this._allData[o]._from=-1,this._allData[o].tensor=yt.fromOrtTensor(i.attributes(0).t())}}}for(let n=0;n<this._nodes.length;n++){let r=this._nodes[n],i=e.nodes(n);if(0===i.inputsLength())throw new Error(`missing input for node: ${i.name}`);for(let e=0;e<i.inputsLength();e++){let a=i.inputs(e),o=t.get(a);if(typeof o>"u")throw new Error(`unrecognized input '${a}' for node: ${i.name()}`);r.inputs.push(o),this._allData[o]._to.push(n)}}}checkIsAcyclic(){let e=new Set;this._allInputIndices.forEach((t=>{this._allData[t]._to.forEach((t=>{e.add(t)}))}));let t=Array.from(e),n=new Array(this._nodes.length).fill("white");for(;t.length>0;){let e=t.pop();"gray"===n[e]?n[e]="black":(t.push(e),n[e]="gray",this._nodes[e].outputs.forEach((r=>{let i=this._allData[r];if(typeof i.tensor<"u")throw new Error("node outputs should not be initialized");if(i._from!==e)throw new Error("from property of the Value object doesn't match index of Node being processed");i._to.forEach((e=>{if("gray"===n[e])throw new Error("model graph is cyclic");"white"===n[e]&&t.push(e)}))})))}}transformGraph(e){this.removeAllIdentityNodes(),this.removeAllDropoutNodes(),this.fuseConvActivationNodes(),e&&e.transformGraph(this),this.finalizeGraph()}finalizeGraph(){let e=0,t=new Array(this._nodes.length,0),n=0;for(let e=0;e<this._nodes.length;e++)t[e]=n,this._nodes[e].executeNode?(n!==e&&(this._nodes[n]=this._nodes[e]),n++):this._nodes[e].outputs.forEach((e=>{this._allData[e]._from=-2}));this._nodes.splice(n,this._nodes.length-n);for(let e=0;e<this._allData.length;e++){let n=this._allData[e];void 0!==n._from&&-1!==n._from&&-2!==n._from&&(n._from=t[n._from]);for(let e=0;e<n._to.length;e++){if(!(n._to[e]>=0))throw new Error("Trying to update a removed node");n._to[e]=t[n._to[e]]}}e=0;for(let t=0;t<this._allData.length;t++)if(-2!==this._allData[t].from||-1!==this._allOutputIndices.indexOf(t+e)){if(e>0){let n=-1;void 0!==this._allData[t].from&&-1!==this._allData[t].from?(n=this._nodes[this._allData[t].from].outputs.indexOf(t+e),-1!==n&&(this._nodes[this._allData[t].from].outputs[n]=t)):(n=this._allInputIndices.indexOf(t+e),-1!==n&&(this._allInputIndices[n]=t)),this._allData[t].to.forEach((r=>{n=this._nodes[r].inputs.indexOf(t+e),-1!==n&&(this._nodes[r].inputs[n]=t)})),0===this._allData[t].to.length&&(n=this._allOutputIndices.indexOf(t+e),-1!==n&&(this._allOutputIndices[n]=t))}}else e++,this._allData.splice(t,1),t--}deleteNode(e){let t=this._nodes[e];if(t.outputs.length>1)for(let e=1;e<t.outputs.length;e++)if(this._allData[t.outputs[e]].to.length>0)throw new Error("Node deletion with more than one output connected to other nodes is not supported. ");t.executeNode=!1;let n=t.inputs[0],r=t.outputs[0],i=this._allData[r].to;for(let n=0;n<t.inputs.length;n++){let r=this._allData[t.inputs[n]].to.indexOf(e);if(-1===r)throw new Error("The Value object doesn't have the current Node in it's 'to' property ");this._allData[t.inputs[n]].to.splice(r,1)}this._allData[r]._to=[];let a=this._allOutputIndices.indexOf(r);if(-1!==a&&(this._allOutputIndices[a]=n),i&&i.length>0)for(let e of i){let t=this._nodes[e].inputs.indexOf(r);if(-1===t)throw new Error("The Node object doesn't have the output Value in it's 'inputs' property ");this._nodes[e].inputs[t]=n,this._allData[n].to.push(e)}}removeAllDropoutNodes(){let e=0;for(let t of this._nodes){if("Dropout"===t.opType){if(1!==t.inputs.length)throw new Error("Dropout nodes should only contain one input. ");if(1!==t.outputs.length&&2!==t.outputs.length)throw new Error("Dropout nodes should contain either 1 or 2 output(s)");if(2===t.outputs.length&&0!==this._allData[t.outputs[1]]._to.length)throw new Error("Dropout nodes's second output should not be referenced by other nodes");this.deleteNode(e)}e++}}removeAllIdentityNodes(){let e=0;for(let t of this._nodes)"Identity"===t.opType&&this.deleteNode(e),e++}isActivation(e){switch(e.opType){case"Relu":case"Sigmoid":case"Clip":return!0;default:return!1}}fuseConvActivationNodes(){for(let e of this._nodes)if("Conv"===e.opType){let t=this._allData[e.outputs[0]]._to;if(1===t.length&&this.isActivation(this._nodes[t[0]])){let n=this._nodes[t[0]];if("Clip"===n.opType)if(1===n.inputs.length)try{e.attributes.set("activation_params","floats",[n.attributes.getFloat("min"),n.attributes.getFloat("max")])}catch{e.attributes.set("activation_params","floats",[an,sn])}else{if(!(n.inputs.length>=3&&void 0!==this._allData[n.inputs[1]].tensor&&void 0!==this._allData[n.inputs[2]].tensor))continue;e.attributes.set("activation_params","floats",[this._allData[n.inputs[1]].tensor.floatData[0],this._allData[n.inputs[2]].tensor.floatData[0]])}e.attributes.set("activation","string",n.opType),this.deleteNode(t[0])}}}}})),bh,gx,Fi,yh=D((()=>{si(),gh(),io(),bh=Tn(In()),We(),gx=se.experimental.fbs,Fi=class{constructor(){}load(e,t,n){if(!n)try{return void this.loadFromOnnxFormat(e,t)}catch(e){if(void 0!==n)throw e}this.loadFromOrtFormat(e,t)}loadFromOnnxFormat(e,t){let n=bh.onnx.ModelProto.decode(e);if(Vt.longToNumber(n.irVersion)<3)throw new Error("only support ONNX model with IR_VERSION>=3");this._opsets=n.opsetImport.map((e=>({domain:e.domain,version:Vt.longToNumber(e.version)}))),this._graph=Bs.from(n.graph,t)}loadFromOrtFormat(e,t){let n=new k.ByteBuffer(e),r=gx.InferenceSession.getRootAsInferenceSession(n).model();if(Vt.longToNumber(r.irVersion())<3)throw new Error("only support ONNX model with IR_VERSION>=3");this._opsets=[];for(let e=0;e<r.opsetImportLength();e++){let t=r.opsetImport(e);this._opsets.push({domain:t?.domain(),version:Vt.longToNumber(t.version())})}this._graph=Bs.from(r.graph(),t)}get graph(){return this._graph}get opsets(){return this._opsets}}})),Ui,vh=D((()=>{ph(),hh(),Ht(),yh(),Ui=class{constructor(e={}){this._initialized=!1,this.backendHint=e.backendHint,this.profiler=ii.create(e.profiler),this.context={profiler:this.profiler,graphInputTypes:[],graphInputDims:[]}}get inputNames(){return this._model.graph.getInputNames()}get outputNames(){return this._model.graph.getOutputNames()}startProfiling(){this.profiler.start()}endProfiling(){this.profiler.stop()}async loadModel(e,t,n){await this.profiler.event("session","Session.loadModel",(async()=>{let r=await Cs(this.backendHint);if(this.sessionHandler=r.createSessionHandler(this.context),this._model=new Fi,"string"==typeof e){let t=e.endsWith(".ort");if(typeof process<"u"&&process.versions&&process.versions.node){let n=await(void 0)(e);this.initialize(n,t)}else{let n=await(await fetch(e)).arrayBuffer();this.initialize(new Uint8Array(n),t)}}else if(ArrayBuffer.isView(e))this.initialize(e);else{let r=new Uint8Array(e,t||0,n||e.byteLength);this.initialize(r)}}))}initialize(e,t){if(this._initialized)throw new Error("already initialized");this.profiler.event("session","Session.initialize",(()=>{let n=this.sessionHandler.transformGraph?this.sessionHandler:void 0;this._model.load(e,n,t),this.sessionHandler.onGraphInitialized&&this.sessionHandler.onGraphInitialized(this._model.graph),this.initializeOps(this._model.graph),this._executionPlan=new Li(this._model.graph,this._ops,this.profiler)})),this._initialized=!0}async run(e){if(!this._initialized)throw new Error("session not initialized yet");return this.profiler.event("session","Session.run",(async()=>{let t=this.normalizeAndValidateInputs(e),n=await this._executionPlan.execute(this.sessionHandler,t);return this.createOutput(n)}))}normalizeAndValidateInputs(e){let t=this._model.graph.getInputNames();if(Array.isArray(e)){if(e.length!==t.length)throw new Error(`incorrect input array length: expected ${t.length} but got ${e.length}`)}else{if(e.size!==t.length)throw new Error(`incorrect input map size: expected ${t.length} but got ${e.size}`);let n=new Array(e.size),r=0;for(let i=0;i<t.length;++i){let a=e.get(t[i]);if(!a)throw new Error(`missing input tensor for: '${name}'`);n[r++]=a}e=n}if(this.context.graphInputTypes&&0!==this.context.graphInputTypes.length&&this.context.graphInputDims&&0!==this.context.graphInputDims.length)this.validateInputTensorDims(this.context.graphInputDims,e,!1);else{let t=this._model.graph.getInputIndices(),n=this._model.graph.getValues(),r=new Array(t.length);for(let i=0;i<t.length;++i){let a=n[t[i]];r[i]=a.type.shape.dims,this.context.graphInputTypes.push(a.type.tensorType),this.context.graphInputDims.push(e[i].dims)}this.validateInputTensorDims(r,e,!0)}return 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w + local_id.x < uniforms.K) {\n        tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x];\n      }\n      if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) {\n        tileK[TILE_SIZE * local_id.y + local_id.x] = key[kOffset + local_id.y * uniforms.K + w + local_id.x];\n      }\n      workgroupBarrier();\n\n      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {\n        value += tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k];\n      }\n\n      workgroupBarrier();\n    }\n\n    let headOffset = headIdx * uniforms.M * uniforms.N;\n    if (lm < uniforms.M && ln < uniforms.N) {\n      let outputIdx = headOffset + lm * uniforms.N + ln;\n      output[outputIdx] = ${Ut("value",u)} * uniforms.alpha;\n    }\n  }`}},{inputs:f,outputs:[-1]})[0];return u2(e,m,i.batchSize*i.numHeads*i.sequenceLength,i.totalSequenceLength),m},d2=(e,t,n,r)=>{let i=[r.batchSize,r.sequenceLength,r.vHiddenSize],a=12,o={x:Math.ceil(r.vHeadSize/a),y:Math.ceil(r.sequenceLength/a),z:r.batchSize*r.numHeads},s=[{type:"uint32",data:r.sequenceLength},{type:"uint32",data:r.totalSequenceLength},{type:"uint32",data:r.vHeadSize},{type:"uint32",data:r.numHeads},{type:"uint32",data:r.vHiddenSize}];return e.compute({name:"AttentionScore",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:i,dataType:t.dataType,gpuDataType:0}],dispatchGroup:o,programUniforms:s}),getShaderSource:e=>{let r=U("probs",t.dataType,t.dims),o=U("v",n.dataType,n.dims),s=J("output",t.dataType,i);return`\n  const TILE_SIZE = 12u;\n  var<workgroup> tileQ: array<${r.type.value}, 144>;\n  var<workgroup> tileK: array<${r.type.value}, 144>;\n  ${e.registerUniforms([{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"v_hidden_size",type:"u32"}]).declareVariables(r,o,s)}\n  ${e.mainStart([a,a,1])}\n   let headIdx = workgroup_id.z;\n   let m = workgroup_id.y * TILE_SIZE + local_id.y;\n   let n = workgroup_id.x * TILE_SIZE + local_id.x;\n\n   let offsetA = headIdx * (uniforms.M * uniforms.K) + m * uniforms.K;\n   let offsetB = headIdx * (uniforms.N * uniforms.K) + n;\n\n   var value = ${r.type.storage}(0);\n   for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n     if (m < uniforms.M && w + local_id.x < uniforms.K) {\n       tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x];\n     }\n     if (n < uniforms.N && w + local_id.y < uniforms.K) {\n       tileK[TILE_SIZE * local_id.y + local_id.x] = v[offsetB + (w + local_id.y) * uniforms.N];\n     }\n     workgroupBarrier();\n     for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {\n       value += tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * k + local_id.x];\n     }\n     workgroupBarrier();\n   }\n\n   // we need to transpose output from BNSH_v to BSND_v\n   let batchIdx = workgroup_id.z / uniforms.num_heads;\n   let currentBatchHeadNumber = workgroup_id.z % uniforms.num_heads;\n   let headOffset = (batchIdx * uniforms.M * uniforms.num_heads + currentBatchHeadNumber) * uniforms.N;\n   if (m < uniforms.M && n < uniforms.N) {\n     let outputIdx = batchIdx * uniforms.M *uniforms.v_hidden_size + m * uniforms.v_hidden_size\n       + currentBatchHeadNumber * uniforms.N + n;\n     output[outputIdx] = value;\n   }\n  }`}},{inputs:[t,n],outputs:[0]})[0]},ra=(e,t,n,r,i,a,o,s,u,l,d)=>{let p=l2(e,t,n,u,l,d);d2(e,p,r,l)},c2=(e,t)=>{let n=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],r=t.sequenceLength,i=t.inputHiddenSize,a=t.headSize,o=12,s={x:Math.ceil(t.headSize/o),y:Math.ceil(t.sequenceLength/o),z:t.batchSize*t.numHeads},u=[e.inputs[0],e.inputs[1],e.inputs[2]],l=[{type:"uint32",data:r},{type:"uint32",data:i},{type:"uint32",data:a},{type:"uint32",data:t.numHeads},{type:"uint32",data:t.headSize},{type:"uint32",data:t.hiddenSize},{type:"uint32",data:t.hiddenSize+t.hiddenSize+t.vHiddenSize}];return e.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:n,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:n,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:n,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:s,programUniforms:l}),getShaderSource:e=>{let t=J("output_q",u[0].dataType,n),r=J("output_k",u[0].dataType,n),i=J("output_v",u[0].dataType,n),a=U("input",u[0].dataType,u[0].dims),s=U("weight",u[1].dataType,u[1].dims),l=U("bias",u[2].dataType,u[2].dims),d=a.type.storage;return`\n  const TILE_SIZE = 12u;\n  var<workgroup> tileInput: array<${d}, 144>;\n  var<workgroup> tileWeightQ: array<${d}, 144>;\n  var<workgroup> tileWeightK: array<${d}, 144>;\n  var<workgroup> tileWeightV: array<${d}, 144>;\n  ${e.registerUniforms([{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}]).declareVariables(a,s,l,t,r,i)}\n  ${e.mainStart([o,o,1])}\n    let batchIndex = workgroup_id.z / uniforms.num_heads;\n    let headNumber = workgroup_id.z % uniforms.num_heads;\n    let m = workgroup_id.y * TILE_SIZE + local_id.y;\n    let n = workgroup_id.x * TILE_SIZE + local_id.x;\n\n    let inputOffset = batchIndex * (uniforms.M * uniforms.K) + m * uniforms.K;\n    let biasOffsetQ = headNumber * uniforms.head_size;\n    let biasOffsetK = uniforms.hidden_size + biasOffsetQ;\n    let biasOffsetV = uniforms.hidden_size + biasOffsetK;\n\n    var valueQ = ${d}(0);\n    var valueK = ${d}(0);\n    var valueV = ${d}(0);\n    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n      if (m < uniforms.M && w + local_id.x < uniforms.K) {\n        tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];\n      }\n      if (n < uniforms.N && w + local_id.y < uniforms.K) {\n        let offset = n + (w + local_id.y) * uniforms.ldb;\n        tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];\n        tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];\n        tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];\n      }\n      workgroupBarrier();\n      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {\n        let inputTileOffset = TILE_SIZE * local_id.y + k;\n        let weightTileOffset = TILE_SIZE * k + local_id.x;\n        valueQ += tileInput[inputTileOffset] * tileWeightQ[weightTileOffset];\n        valueK += tileInput[inputTileOffset] * tileWeightK[weightTileOffset];\n        valueV += tileInput[inputTileOffset] * tileWeightV[weightTileOffset];\n      }\n\n      workgroupBarrier();\n    }\n\n    let headOffset = (m * uniforms.N + n) % uniforms.head_size;\n    valueQ += bias[headOffset + biasOffsetQ];\n    valueK += bias[headOffset + biasOffsetK];\n    valueV += bias[headOffset + biasOffsetV];\n\n    let offset = workgroup_id.z * uniforms.M * uniforms.N;\n    if (m < uniforms.M && n < uniforms.N) {\n      let outputIdx = offset + m * uniforms.N + n;\n      output_q[outputIdx] = valueQ;\n      output_k[outputIdx] = valueK;\n      output_v[outputIdx] = valueV;\n    }\n  }`}},{inputs:u,outputs:[-1,-1,-1]})},Im=(e,t)=>{let n=s2(e.inputs,t),[r,i,a]=c2(e,n);return ra(e,r,i,a,e.inputs[4],void 0,void 0,void 0,e.inputs[5],n,t)}})),f2,p2,h2,Am,Om=D((()=>{Et(),Re(),Tt(),Be(),f2=(e,t)=>{if(!e||5!==e.length)throw new Error("BatchNormalization requires 5 inputs");let n=(e,t,n)=>{let r=t.length;if(r!==e.length)throw new Error(`${n}: num dimensions != ${r}`);t.forEach(((t,r)=>{if(t!==e[r])throw new Error(`${n}: dim[${r}] do not match`)}))};if(e[0].dims.length>1){let r="NHWC"===t.format?t.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,t.spatial?2:void 0);n(e[1].dims,r,"Invalid input scale"),n(e[2].dims,r,"Invalid input B"),n(e[3].dims,r,"Invalid input mean"),n(e[4].dims,r,"Invalid input var")}else n(e[1].dims,[1],"Invalid input scale"),n(e[2].dims,[1],"Invalid input B"),n(e[3].dims,[1],"Invalid input mean"),n(e[4].dims,[1],"Invalid input 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f=nt(i.length);f&&h.push(...X(i));let m=f?i.length:i,g=J("output",u,m),y=g.indicesGet("indices",r),b=Array.from(Array(o.length).keys()).map((e=>`uniforms.sizeInConcatAxis${e}`)).join(",");return{name:"Concat",shaderCache:{hint:`${t}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:h}),getShaderSource:t=>`\n\n  ${(()=>{t.registerUniform("outputSize","u32");for(let n=0;n<e.length;n++)t.registerUniform(`sizeInConcatAxis${n}`,"u32");return t.declareVariables(...s,g)})()}\n\n  ${I2(o.length,b)}\n\n  ${t.mainStart()}\n    ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n\n    var indices = ${g.offsetToIndices("global_idx")};\n\n    let inputIndex = calculateInputIndex(${y});\n    if (inputIndex != 0u) {\n      let sizeInConcatAxis = array<u32, ${o.length}u>(${b});\n      ${y} -= sizeInConcatAxis[inputIndex - 1u];\n    }\n\n    ${A2(s,g)}\n  }`}},xg=(e,t)=>{S2(e.inputs),e.compute(O2(e.inputs,t.axis))},Tg=e=>Oe({axis:e.axis})})),Sr,ia,jr=D((()=>{Re(),Sr=(e,t)=>{switch(e.activation){case"Relu":return{activationFunction:"",applyActivation:`value = max(value, ${t}(0.0));`};case"Sigmoid":return{activationFunction:"",applyActivation:`value = (${t}(1.0) / (${t}(1.0) + exp(-value)));`};case"Clip":return{activationFunction:`const clip_min_=${t}(${e.clipMin});const clip_max_=${t}(${e.clipMax});`,applyActivation:"value = clamp(value, clip_min_, clip_max_);"};default:return{activationFunction:"",applyActivation:""}}},ia=e=>{let t=e?.activation||"";if("Clip"===t){let[n,r]=e?.activation_params||[Zi,Ji];return{activation:t,clipMax:r,clipMin:n,activationCacheKey:`${t}:${n},${r}`}}return{activation:t,activationCacheKey:t}}})),It,aa,sa=D((()=>{It=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},aa=e=>`\n      ${e?"value = value + getBiasByOutputCoords(coords);":""}\n      `})),ua,ou=D((()=>{ua=e=>`\nfn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {\n  return dot(coords, vec4<i32>(\n      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n}\nfn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {\n  return dot(coords, vec4<i32>(\n    i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));\n}\n`})),E2,C2,$o,$g,P2,So,k2,la,Io=D((()=>{Re(),Be(),jr(),sa(),E2=(e,t)=>e?`\n        mm_Asub[inputRow][inputCol] = mm_readA(batch,\n          kStart + inputRow,\n          globalRowStart / innerElementSize + inputCol${t?", batchIndices":""});\n        `:`\n        mm_Asub[inputRow][inputCol] = mm_readA(batch,\n          globalRow + innerRow,\n          kStart / innerElementSize + inputCol${t?", batchIndices":""});\n        `,C2=(e,t)=>e?`\n        let ACached0 = mm_Asub[k * innerElementSize][localRow];\n        let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];\n        let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];\n        ${3===t?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"}\n        for (var i = 0; i < rowPerThread; i = i + 1) {\n          acc[i] = BCached0 * ACached0[i] + acc[i];\n          acc[i] = BCached1 * ACached1[i] + acc[i];\n          acc[i] = BCached2 * ACached2[i] + acc[i];\n          ${3===t?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"}\n        }`:`\n        for (var i = 0; i < rowPerThread; i = i + 1) {\n          let ACached = mm_Asub[tileRow + i][k];\n          acc[i] = BCached0 * ACached.x + acc[i];\n          acc[i] = BCached1 * ACached.y + acc[i];\n          acc[i] = BCached2 * ACached.z + acc[i];\n          ${3===t?"":"acc[i] = BCached3 * ACached.w + acc[i];"}\n        }`,$o=(e,t,n="f32",r,i=!1,a=32,o=!1,s=32)=>{let u=t[1]*e[1],l=t[0]*e[0],d=i?u:a,p=i?a:u,c=d/t[0],h=a/t[1];if((!i||4!==c||4!==e[1])&&(i||3!==c&&4!==c)||d%t[0]!=0||a%t[1]!=0||4!==e[0])throw new Error(`If transposeA ${i} is true, innerElementSize ${c} and workPerThread[1] ${e[1]} must be 4.\n      Otherwise, innerElementSize ${c} must be 3 or 4.\n  tileAWidth ${d} must be divisible by workgroupSize[0]${t[0]}. tileInner ${a} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return`\nvar<workgroup> mm_Asub: array<array<vec${c}<${n}>, ${d/c}>, ${p}>;\nvar<workgroup> mm_Bsub: array<array<vec4<${n}>, ${l/e[0]}>, ${a}>;\n\nconst rowPerThread = ${e[1]};\nconst colPerThread = ${e[0]};\nconst innerElementSize = ${c};\nconst tileInner = ${a};\n\n@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]})\nfn main(@builtin(local_invocation_id) localId : vec3<u32>,\n        @builtin(global_invocation_id) globalId : vec3<u32>,\n        @builtin(workgroup_id) workgroupId : vec3<u32>) {\n  let localRow = i32(localId.y);\n  let tileRow = localRow * rowPerThread;\n  let tileCol = i32(localId.x);\n\n  let globalRow =i32(globalId.y) * rowPerThread;\n  let globalCol = i32(globalId.x);\n  let batch = ${o?"0":"i32(globalId.z)"};\n  ${r?`let batchIndices = ${r.offsetToIndices("u32(batch)")};`:""}\n  let globalRowStart = i32(workgroupId.y) * ${u};\n\n  let numTiles = ${o?`${Math.ceil(s/a)}`:"(uniforms.dimInner - 1) / tileInner + 1"};\n  var kStart = ${o?`i32(globalId.z) * ${s}`:"0"};\n\n  var acc: array<vec4<${n}>, rowPerThread>;\n\n  // Loop over shared dimension.\n  let tileRowB = localRow * ${h};\n  for (var t = 0; t < numTiles; t = t + 1) {\n      // Load one tile of A into local memory.\n      for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n          let inputRow = tileRow + innerRow;\n          let inputCol = tileCol;\n          ${E2(i,r)}\n      }\n\n      // Load one tile of B into local memory.\n      for (var innerRow = 0; innerRow < ${h}; innerRow = innerRow + 1) {\n          let inputRow = tileRowB + innerRow;\n          let inputCol = tileCol;\n          mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${r?", batchIndices":""});\n      }\n      kStart = kStart + tileInner;\n      workgroupBarrier();\n\n      // Compute acc values for a single thread.\n      for (var k = 0; k < tileInner / innerElementSize; k = k + 1) {\n          let BCached0 = mm_Bsub[k * innerElementSize][tileCol];\n          let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol];\n          let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol];\n          ${3===c?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"}\n\n          ${C2(i,c)}\n      }\n\n      workgroupBarrier();\n  }\n\n  for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n  }\n}`},$g=(e,t)=>e?`\n            mm_Asub[inputRow][inputCol] = mm_readA(batch,\n              kStart + inputRow,\n              globalRowStart + inputCol${t?", batchIndices":""});\n            `:`\n            mm_Asub[inputRow][inputCol] = mm_readA(batch,\n              globalRowStart + inputRow,\n              kStart + inputCol${t?", batchIndices":""});\n            `,P2=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",So=(e,t,n="f32",r,i=!1,a=32,o=!1,s=32,u=!1)=>{let l=e[1]*t[1],d=e[0]*t[0],p=i?l:a,c=i?a:l;if(c%t[1]!=0||p%t[0]!=0||a%t[1]!=0)throw new Error(`tileAHight ${c} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${p} must be divisible by workgroupSize[0]${t[0]}, tileInner ${a} must be divisible by workgroupSize[1]${t[1]}`);let h=c/t[1],f=p/t[0],m=a/t[1],g=u?`\n    let localRow = i32(localId.y);\n    let localCol = i32(localId.x);\n    let globalRowStart = i32(workgroupId.y) * ${l};\n    let globalColStart = i32(workgroupId.x) * ${d};\n\n    // Loop over shared dimension.\n    for (var t = 0; t < numTiles; t = t + 1) {\n      // Load one tile of A into local memory.\n      for (var inputRow = localRow; inputRow < ${c}; inputRow = inputRow + ${t[1]}) {\n        for (var inputCol = localCol; inputCol < ${p}; inputCol = inputCol + ${t[0]}) {\n          ${$g(i,r)}\n        }\n      }\n      // Load one tile of B into local memory.\n      for (var inputRow = localRow; inputRow < ${a}; inputRow = inputRow + ${t[1]}) {\n            for (var inputCol = localCol; inputCol < ${d}; inputCol = inputCol + ${t[0]}) {\n          mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n            kStart + inputRow,\n            globalColStart + inputCol${r?", batchIndices":""});\n        }\n      }\n      kStart = kStart + tileInner;\n      workgroupBarrier();\n\n      // Compute acc values for a single thread.\n      var BCached : array<${n}, colPerThread>;\n      for (var k = 0; k < tileInner; k = k + 1) {\n        for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n          BCached[inner] = mm_Bsub[k][localCol + inner * ${t[0]}];\n        }\n        for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n          let ACached = ${i?`mm_Asub[k][localRow + innerRow * ${t[1]}];`:`mm_Asub[localRow + innerRow * ${t[1]}][k];`}\n          for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n            acc[innerRow][innerCol] = acc[innerRow][innerCol] +\n                ACached * BCached[innerCol];\n          }\n        }\n      }\n      workgroupBarrier();\n    }\n    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      let gRow = globalRowStart + localRow + innerRow * ${t[1]};\n      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n        let gCol = globalColStart + localCol + innerCol * ${t[0]};\n        mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);\n      }\n    }\n    `:`\nlet tileRow = i32(localId.y) * rowPerThread;\nlet tileCol = i32(localId.x) * colPerThread;\n\nlet globalRow = i32(globalId.y) * rowPerThread;\nlet globalCol = i32(globalId.x) * colPerThread;\nlet globalRowStart = i32(workgroupId.y) * ${l};\n\nlet tileRowA = i32(localId.y) * ${h};\nlet tileColA = i32(localId.x) * ${f};\nlet tileRowB = i32(localId.y) * ${m};\n// Loop over shared dimension.\nfor (var t = 0; t < numTiles; t = t + 1) {\n  // Load one tile of A into local memory.\n  for (var innerRow = 0; innerRow < ${h}; innerRow = innerRow + 1) {\n    for (var innerCol = 0; innerCol < ${f}; innerCol = innerCol + 1) {\n      let inputRow = tileRowA + innerRow;\n      let inputCol = tileColA + innerCol;\n      ${$g(i,r)}\n    }\n  }\n\n  // Load one tile of B into local memory.\n  for (var innerRow = 0; innerRow < ${m}; innerRow = innerRow + 1) {\n    for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n      let inputRow = tileRowB + innerRow;\n      let inputCol = tileCol + innerCol;\n      mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n        kStart + inputRow,\n        globalCol + innerCol${r?", batchIndices":""});\n    }\n  }\n  kStart = kStart + tileInner;\n  workgroupBarrier();\n\n  // Compute acc values for a single thread.\n  var BCached : array<${n}, colPerThread>;\n  for (var k = 0; k < tileInner; k = k + 1) {\n    for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n      BCached[inner] = mm_Bsub[k][tileCol + inner];\n    }\n\n    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      ${P2(i)}\n      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n        acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n      }\n    }\n  }\n\n  workgroupBarrier();\n}\n\nfor (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n  for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n    mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n        acc[innerRow][innerCol]);\n  }\n}\n`;return`\n  var<workgroup> mm_Asub : array<array<${n}, ${p}>, ${c}>;\n  var<workgroup> mm_Bsub : array<array<${n}, ${d}>, ${a}>;\n  const rowPerThread = ${e[1]};\n  const colPerThread = ${e[0]};\n  const tileInner = ${a};\n\n@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]})\nfn main(@builtin(local_invocation_id) localId : vec3<u32>,\n        @builtin(global_invocation_id) globalId : vec3<u32>,\n        @builtin(workgroup_id) workgroupId : vec3<u32>) {\n    let batch = ${o?"0":"i32(globalId.z)"};\n    ${r?`let batchIndices = ${r.offsetToIndices("u32(batch)")};`:""}\n    let numTiles = ${o?`${Math.ceil(s/a)}`:"(uniforms.dimInner - 1) / tileInner + 1"};\n    var kStart = ${o?`i32(globalId.z) * ${s}`:"0"};\n\n    var acc : array<array<${n}, colPerThread>, rowPerThread>;\n\n    // Without this initialization strange values show up in acc.\n    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n        acc[innerRow][innerCol] = 0.0;\n      }\n    }\n    ${g}\n  }\n`},k2=(e,t,n,r,i,a=!1)=>{let[o,s,u]=i,[l,d,p,c]=r,h=zn(o,u),f=zn(s,u),m=vt(r[0].type.tensor);return`\n    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${l.type.indices}) -> ${It(e,m)} {\n      var value = ${It(e,m)}(0.0);\n      let col = colIn * ${e};\n      if(row < uniforms.dimAOuter && col < uniforms.dimInner)\n      {\n        ${(()=>{let e=d.rank,t=l.rank,n=`var aIndices: ${d.type.indices};`;for(let r=e-2-1,i=t-1;r>=0;r--,i--)n+=`\naIndices[${r}] = ${t>1?`batchIndices[${i}]`:"batchIndices"};`;return h.forEach((e=>{n+=`\naIndices[${e}] = 0;`})),n+=`\naIndices[${e-2}] = u32(row);\n                   aIndices[${e-1}] = u32(colIn);`,n})()}\n        value = ${d.getByIndices("aIndices")};\n      }\n      return value;\n    }\n\n    fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${l.type.indices}) -> ${It(e,m)} {\n      var value = ${It(e,m)}(0.0);\n      let col = colIn * ${e};\n      if(row < uniforms.dimInner && col < uniforms.dimBOuter)\n      {\n        ${(()=>{let e=p.rank,t=l.rank,n=`var bIndices: ${p.type.indices};`;for(let r=e-2-1,i=t-1;r>=0;r--,i--)n+=`\nbIndices[${r}] = ${t>1?`batchIndices[${i}]`:"batchIndices"};`;return f.forEach((e=>{n+=`\nbIndices[${e}] = 0;`})),n+=`\nbIndices[${e-2}] = u32(row);\n                   bIndices[${e-1}] = u32(colIn);`,n})()}\n        value = ${p.getByIndices("bIndices")};\n      }\n      return value;\n    }\n\n    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${It(e,m)}) {\n      let col = colIn * ${e};\n      if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n        var value = valueIn;\n        let coords = vec3<i32>(batch, row, colIn);\n        ${t?`value = value + ${a?"bias[colIn]":`${It(e,m)}(bias[row])`};`:""}\n        ${n}\n        ${c.setByIndices("vec3<u32>(coords)","value")}\n      }\n    }\n    `},la=(e,t,n,r,i=!1)=>{let a=e[0].dims,o=e[1].dims,s=a.slice(0,-2),u=o.slice(0,-2),l=r?r.slice(0,-2):n.slice(0,-2),d=nt(l.length),p=d?l.length:l,c=Qi("batchDims",e[0].dataType,p,1),h=G.size(l),f=a[a.length-2],m=a[a.length-1],g=o[o.length-1],y=m%4==0&&g%4==0,b=f<=8?[4,1,1]:[4,4,1],w=[8,8,1],v=[Math.ceil(g/w[0]/b[0]),Math.ceil(f/w[1]/b[1]),Math.ceil(h/w[2]/b[2])],x=vt(e[0].dataType),$=y?4:1,_=[...s,f,m/$],T=nt(_.length),S=T?_.length:_,I=[...u,m,g/$],E=nt(I.length),O=E?I.length:I,A=[h,f,g/$],k=U("a",e[0].dataType,S,$),C=U("b",e[1].dataType,O,$),D=J("result",e[0].dataType,A.length,$),P=[k,C],R=[{type:"int32",data:f},{type:"int32",data:g},{type:"int32",data:m}];d&&R.push(...X(l)),T&&R.push(...X(_)),E&&R.push(...X(I));let B=[];B.push(T?"rank":"dims"),B.push(E?"rank":"dims");let z=e.length>2,{activationFunction:N,applyActivation:M}=Sr(t,D.type.value),F=k2($,z,M,[c,k,C,D],[s,u,l],i);if(z){let t=i?$:1;P.push(U("bias",e[2].dataType,e[2].dims.length,t)),R.push(...X(e[2].dims)),B.push("rank")}R.push(...X(A));return{name:"MatMul",shaderCache:{hint:t.activationCacheKey+`${b}${y}${i}`,inputDependencies:B},getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:v[0],y:v[1],z:v[2]},programUniforms:R}),getShaderSource:e=>`\n  ${e.registerUniform("dimAOuter","i32").registerUniform("dimBOuter","i32").registerUniform("dimInner","i32").registerInternalVariables(c).declareVariables(...P,D)}\n  ${N}\n  ${F}\n  ${y?$o(b,w,x,c):So(b,w,x,c)}\n                   `}}})),D2,Sg,Ig=D((()=>{qr(),Be(),jr(),sa(),ou(),Io(),D2=(e,t,n,r,i=!1,a,o=4,s=4,u=4,l="f32")=>{let d=e?"\n    let coord = vec4<i32>(batch, xRow, xCol, xCh);\n    ":"\n    let coord = vec4<i32>(batch, xCh, xRow, xCol);\n    ",p=e?"\n    let coords = vec4<i32>(\n      batch,\n      row / outWidth,\n      row % outWidth,\n      col);\n    ":"\n    let coords = vec4<i32>(\n      batch,\n      row,\n      col / outWidth,\n      col % outWidth);\n    ",c=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",h=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",f=e?"row":"col",m=e?"col":"row",g=`\n    let inChannels = i32(uniforms.w_shape[2]);\n    let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n    let outRow = ${f} / outWidth;\n    let outCol = ${f} % outWidth;\n\n    let WRow = ${m} / (filterDims[1] * inChannels);\n    let WCol = ${m} / inChannels % filterDims[1];\n    let xRow = outRow * stride[0] + dilation[0] * WRow - pad[0];\n    let xCol = outCol * stride[1] + dilation[1] * WCol - pad[1];\n    let xCh = ${m} % inChannels;\n    var resData = ${It(o,l)}(0.0);\n    // The bounds checking is always needed since we use it to pad zero for\n    // the 'same' padding type.\n    if (xRow >= 0 && xRow < ${c} && xCol >= 0 && xCol < ${h}) {\n      ${d}\n      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));\n      ${(e=>{switch(e){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${l}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${e} is not supported.`)}})(o)}\n    }\n    return resData;`,y=e?t&&r?`\n    let col = colIn * ${o};\n    ${g}`:`\n    let col = colIn * ${o};\n    if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n      ${g}\n    }\n    return ${It(o,l)}(0.0);`:r&&n?`\n    let col = colIn * ${o};\n    ${g}`:`\n    let col = colIn * ${o};\n    if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n      ${g}\n    }\n    return ${It(o,l)}(0.0);`,b=`${(e=>{switch(e){case 1:return"return w[row * i32(uniforms.w_shape[3]) + colIn];";case 4:return"return w[row * i32(uniforms.w_shape[3]) / 4 + colIn];";default:throw new Error(`innerElementSize ${e} is not supported.`)}})(s)}`,w=It(u,l),v=It(e?o:s,l),x=It(e?s:o,l),{activationFunction:$,applyActivation:_}=Sr(a,w);return`\n    ${$}\n    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${v} {\n      ${e?y:b}\n    }\n\n    fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${x} {\n      ${e?b:y}\n    }\n\n    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${w}) {\n      let col = colIn * ${u};\n      if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\n      {\n      var value = valueIn;\n      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n      ${p}\n      ${aa(i)}\n      ${_}\n      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n      }\n    }`},Sg=(e,t,n,r,i,a,o,s)=>{let u="NHWC"===t.format,l=u?e[0].dims[3]:e[0].dims[1],d=n[0],p=u?n[2]:n[3],c=u?n[1]:n[2],h=u?n[3]:n[1],f=u&&(l%4==0||l%3==0)&&h%4==0,m=u?h:p*c,g=u?p*c:h,y=[8,8,1],b=r<=8?[4,1,1]:[4,4,1],w=[Math.ceil(m/y[0]/b[0]),Math.ceil(g/y[1]/b[1]),Math.ceil(d/y[2]/b[2])];it("verbose",(()=>`[conv2d_mm_webgpu] dispatch = ${w}`));let v=f?u&&l%4!=0?3:4:1,x=y[1]*b[1],$=y[0]*b[0],_=Math.max(y[0]*v,y[1]),T=r%x==0,S=i%$==0,I=a%_==0,E=f?[v,4,4]:[1,1,1],O=vt(e[0].dataType),A=f?4:1,k=[{type:"int32",data:r},{type:"int32",data:i},{type:"int32",data:a}],C=[U("x",e[0].dataType,e[0].dims.length,3===v?1:v),U("w",e[1].dataType,e[1].dims.length,A)];k.push(...X(e[0].dims)),k.push(...X(e[1].dims));let D=`\n      fn setOutputAtIndex(flatIndex : i32, value : ${f?`vec4<${O}>`:O}) {\n        result[flatIndex] = ${f?`vec4<${O}>`:O}(value);\n      }\n      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${f?`vec4<${O}>`:O}) {\n        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));\n        setOutputAtIndex(flatIndex ${f?"/ 4":""}, value);\n      }`;if(o){let t=U("bias",e[2].dataType,e[2].dims.length,A);C.push(t),k.push(...X(e[2].dims)),D+=`\n        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${f?`vec4<${O}>`:O} {\n          return bias[coords.${u?"w":"y"}${f?"/ 4":""}];\n        }`}let P=J("result",e[0].dataType,n.length,A);return k.push(...X(n)),{name:"Conv2DMatMul",shaderCache:{hint:t.cacheKey},getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:w[0],y:w[1],z:w[2]},programUniforms:k}),getShaderSource:e=>`\n        ${ua("uniforms.result_strides")}\n        //struct Uniforms { xShape : vec4<i32>, wShape : vec4<i32>, outShape : vec4<i32>,\n        //  outShapeStrides: vec3<i32>, filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>,\n        //  dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32 };\n        ${e.registerUniform("dimAOuter","i32").registerUniform("dimBOuter","i32").registerUniform("dimInner","i32").declareVariables(...C,P)}\n        const filterDims : vec2<i32> = vec2<i32>(${t.kernelShape[0]}, ${t.kernelShape[1]});\n        const pad : vec2<i32> = vec2<i32>(${t.pads[0]}, ${t.pads[1]});\n        const stride : vec2<i32> = vec2<i32>(${t.strides[0]}, ${t.strides[1]});\n        const dilation : vec2<i32> = vec2<i32>(${t.dilations[0]}, ${t.dilations[1]});\n        ${D}\n        ${D2(u,T,S,I,o,t,E[0],E[1],E[2],O)}\n            ${f?$o(b,y,O,void 0,!u,_):So(b,y,O,void 0,!u,_,!1,void 0,s)}`}}})),iu,Ag=D((()=>{Re(),Be(),su(),jr(),iu=(e,t,n)=>{let r=e.length>2,i=r?"value += b[output_channel];":"",a=e[0].dims,o=e[1].dims,s=o[0]/t.group,u="NHWC"===t.format,l=au(a,o,t.dilations,t.pads,t.strides,u),d=G.size(l),p=J("output",e[0].dataType,l),{activationFunction:c,applyActivation:h}=Sr(t,p.type.value),f=U("x",e[0].dataType,a),m=U("w",e[1].dataType,o),g=[f,m];r&&g.push(U("b",e[2].dataType,e[2].dims));return{name:"GroupedConv",shaderCache:{hint:t.cacheKey},getRunData:()=>({outputs:[{dims:n?n(l):l,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)}}),getShaderSource:e=>`\n  const strides: vec2<u32> = vec2(${t.strides[0]}u, ${t.strides[1]}u);\n  const pads: vec2<u32> = vec2(${t.pads[0]}u, ${t.pads[1]}u);\n\n  ${e.declareVariables(...g,p)}\n\n  ${c}\n\n  ${e.mainStart()}\n    ${e.guardAgainstOutOfBoundsWorkgroupSizes(d)}\n\n    let outputIndices = ${p.offsetToIndices("global_idx")};\n    let batch: u32 = outputIndices[0];\n    let output_channel: u32 = outputIndices[${u?3:1}];\n    let xRCCorner: vec2<u32> = vec2<u32>(outputIndices[${u?1:2}], outputIndices[${u?2:3}]) * strides - pads;\n    let group_id: u32 = output_channel / ${s}u;\n\n    var value: ${p.type.value} = ${p.type.value}(0);\n    for (var wInChannel: u32 = 0u; wInChannel < ${o[1]}u; wInChannel++) {\n      let input_channel = group_id * ${o[1]}u + wInChannel;\n      for (var wHeight: u32 = 0u; wHeight < ${o[2]}u; wHeight++) {\n        let xHeight = xRCCorner.x + wHeight * ${t.dilations[0]}u;\n\n        if (xHeight < 0u || xHeight >= ${a[u?1:2]}u) {\n          continue;\n        }\n\n        for (var wWidth: u32 = 0u; wWidth < ${o[3]}u; wWidth++) {\n          let xWidth = xRCCorner.y + wWidth * ${t.dilations[1]}u;\n          if (xWidth < 0u || xWidth >= ${a[u?2:3]}u) {\n            continue;\n          }\n\n          let xVal = ${u?f.get("batch","xHeight","xWidth","input_channel"):f.get("batch","input_channel","xHeight","xWidth")};\n          let wVal = ${m.get("output_channel","wInChannel","wHeight","wWidth")};\n          value += xVal*wVal;\n        }\n      }\n    }\n    ${i}\n    ${h}\n    ${p.setByOffset("global_idx","value")}\n  }`}}})),uu,B2,Og,lu=D((()=>{Re(),Io(),Be(),jr(),uu=(e,t,n,r,i=!1)=>{let a=e[0].dims,o=e[1].dims,s=a[a.length-2],u=o[o.length-1],l=a[a.length-1],d=wt(u),p=wt(l),c=wt(s),h=G.size(n)/d/c,f=e.length>2,m=r?r.slice(0,-2):n.slice(0,-2),g=[G.size(m),s,u],y=[{type:"uint32",data:h},{type:"uint32",data:s},{type:"uint32",data:u},{type:"uint32",data:l},...X(m),...X(a),...X(o)];f&&y.push(...X(e[2].dims)),y.push(...X(g));return{name:"MatMulNaive",shaderCache:{hint:`${t.activationCacheKey}_${d}_${p}_${c}_${i}`,inputDependencies:f?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:y}),getShaderSource:r=>{let s=Qi("batch_dims",e[0].dataType,m.length),u=U("a",e[0].dataType,a.length,p),l=U("b",e[1].dataType,o.length,d),h=J("output",e[0].dataType,g.length,d),{activationFunction:y,applyActivation:b}=Sr(t,h.type.value),w=[u,l],v="";if(f){let t=i?d:1;w.push(U("bias",e[2].dataType,e[2].dims.length,t)),v=""+(i?`value += bias[col / ${t}];`:`value += ${h.type.value}(bias[row + i]);`)}let x=a.slice(0,-2),$=o.slice(0,-2),_=zn(x,m),T=zn($,m),S=(e,t)=>{let n=e.rank,r=e.name;if(2===n)return`var ${r}_indices = ${e.type.indices}(0u, 0u);`;let i=s.rank,a=`var ${r}_indices: ${e.type.indices};`;for(let e=n-2-1,t=i-1;e>=0;e--,t--)a+=`\n${r}_indices[${e}] = ${i>1?`batch_indices[${t}]`:"batch_indices"};`;return t.forEach((e=>{a+=`\n${r}_indices[${e}] = 0;`})),a+=`${r}_indices[${n-2}] = 0u;\n                     ${r}_indices[${n-1}] = 0u;`,a};return`\n  ${r.registerUniform("outputSize","u32").registerUniform("M","u32").registerUniform("N","u32").registerUniform("K","u32").registerInternalVariables(s).declareVariables(...w,h)}\n  ${y}\n  ${r.mainStart()}\n    ${r.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n    let col = (global_idx % (uniforms.N / ${d})) * ${d};\n    var index1 = global_idx / (uniforms.N / ${d});\n    let stride1 = uniforms.M / ${c};\n    let row = (index1 % stride1) * ${c};\n    let batch = index1 / stride1;\n\n    ${2===n.length?"":`let batch_indices = ${s.offsetToIndices("batch")};`}\n    ${S(u,_)}\n    let a_offset = ${u.indicesToOffset("a_indices")};\n    ${S(l,T)}\n    let b_offset = ${l.indicesToOffset("b_indices")};\n    var values: array<${h.type.value}, ${c}>;\n    for (var k: u32 = 0u; k < uniforms.K; k = k + ${p}) {\n      ${(()=>{let e=`var a_data: ${u.type.value};`;for(let t=0;t<p;t++)e+=`\n              let b_data${t} = b[(b_offset + (k + ${t}) * uniforms.N + col) / ${d}];`;for(let t=0;t<c;t++){e+=`a_data = a[(a_offset + (row + ${t}) * uniforms.K + k) / ${p}];`;for(let n=0;n<p;n++)e+=`\n            values[${t}] = fma(${l.type.value}(a_data${1===p?"":`[${n}]`}), b_data${n}, values[${t}]);\n`}return e})()}\n    }\n    for (var i = 0u; i < ${c}u; i++) {\n      var value = values[i];\n      ${v}\n      ${b}\n      let cur_indices = ${h.type.indices}(batch, row + i, col);\n      let offset = ${h.indicesToOffset("cur_indices")};\n      ${h.setByOffset(`offset / ${d}`,"value")};\n    }\n  }\n  `}}},B2=e=>{if(!e||2!==e.length)throw new Error("MatMul requires 2 inputs.");if(e[0].dims[e[0].dims.length-1]!==e[1].dims[e[1].dims.length-2])throw new Error("shared dimension does not match.")},Og=e=>{B2(e.inputs);let t=yr.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!t)throw new Error("Can't use matmul on the given tensors");let n=t[t.length-1],r=e.inputs[0].dims[e.inputs[0].dims.length-1];n<8&&r<8?e.compute(uu(e.inputs,{activation:"",activationCacheKey:""},t)):e.compute(la(e.inputs,{activation:"",activationCacheKey:""},t))}})),au,Eg,R2,Cg,du,z2,N2,cu,su=D((()=>{Re(),Tt(),Ig(),Io(),Ag(),jr(),lu(),Nn(),au=(e,t,n,r,i,a)=>{let o=e[0],s=e.slice(a?1:2,a?3:4),u=s.length,l=t[0],d=t.slice(2).map(((e,t)=>e+(e-1)*(n[t]-1))),p=s.map(((e,t)=>e+r[t]+r[t+u])).map(((e,t)=>Math.floor((e-d[t]+i[t])/i[t])));return p.splice(0,0,o),p.splice(a?3:1,0,l),p},Eg=[2,3,1,0],R2=(e,t)=>{if(!e||2!==e.length&&3!==e.length)throw new Error("Conv requires 2 or 3 inputs");if(4!==e[0].dims.length&&3!==e[0].dims.length)throw new Error("currently only support conv 1D and 2D");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");if(e[0].dims["NHWC"===t.format?e[0].dims.length-1:1]!==e[1].dims[1]*t.group)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(3===e.length&&(1!==e[2].dims.length||e[1].dims[0]!==e[2].dims[0]))throw new Error("invalid bias");let n=e[0].dims.length-2;if(t.dilations.length!==n)throw new Error(`dilations should be ${n}D`);if(t.strides.length!==n)throw new Error(`strides should be ${n}D`);if(t.pads.length!==2*n)throw new Error(`pads should be ${2*n}D`);if(0!==t.kernelShape.length&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},Cg=(e,t)=>{let n=e.kernelShape.slice();for(let e=2;e<t[1].dims.length;++e)0===n[e-2]&&(n[e-2]=t[1].dims[e]);let r=e.pads.slice();pn.adjustPadsBasedOnAutoPad(t[0].dims,e.strides,e.dilations,n,r,"NHWC"===e.format,e.autoPad);let i=Object.assign({},e);return Object.assign(i,{kernelShape:n,pads:r,cacheKey:e.cacheKey}),i},du=e=>{let t=ia(e),n=e.format,r=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],i=e.dilations,a=e.group,o=e.kernel_shape,s=e.pads,u=e.strides,l=e.w_is_const();return Oe({autoPad:r,format:n,dilations:i,group:a,kernelShape:o,pads:s,strides:u,wIsConst:l,...t})},z2=(e,t,n)=>{let r=Cg(n,t),i="NHWC"===n.format;if(1!==n.group)return void e.compute(iu(t,r));let a=3===t.length,o=t[0].dims[i?1:2],s=t[0].dims[i?2:3],u=t[0].dims[i?3:1],l=t[1].dims[2],d=t[1].dims[3],p=au(t[0].dims,t[1].dims,n.dilations,r.pads,n.strides,i),c=p[i?1:2],h=p[i?2:3],f=p[i?3:1],m=i&&l===o&&d===s&&0===n.pads[0]&&0===n.pads[1];if(m||1===l&&1===d&&1===n.dilations[0]&&1===n.dilations[1]&&1===n.strides[0]&&1===n.strides[1]&&0===n.pads[0]&&0===n.pads[1]){let l,d,g,y=p[0],b=[];if(i){let r=e.kernelCustomData.wT??e.compute(lr(t[1],Eg),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];if(n.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=r),m){let e=o*s*u;l=t[0].reshape([1,y,e]),d=r.reshape([1,e,f]),g=[1,y,f]}else l=t[0].reshape([y,o*s,u]),d=r.reshape([1,u,f]),g=[y,c*h,f];b.push(l),b.push(d)}else l=t[0].reshape([y,u,o*s]),d=t[1].reshape([1,f,u]),g=[y,f,c*h],b.push(d),b.push(l);a&&b.push(t[2]);let w=g[2],v=b[0].dims[b[0].dims.length-1];return void(w<8&&v<8?e.compute(uu(b,r,p,g,i),{inputs:b}):e.compute(la(b,r,p,g,i),{inputs:b}))}let g=e.kernelCustomData.wT??e.compute(lr(t[1],Eg),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];n.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=g);let y=[t[0],g];a&&y.push(t[2]);let b=i?c*h:f,w=i?f:c*h,v=l*d*u;e.compute(Sg(y,r,p,b,w,v,a,!0),{inputs:y})},N2=(e,t)=>{let n="NHWC"===t.format,r=[e.inputs[0].reshape(n?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];3===e.inputs.length&&r.push(e.inputs[2]);let i=[0,t.pads[0],0,t.pads[1]],a=[1].concat(t.strides),o=[1].concat(t.dilations),s=[1].concat(t.kernelShape),u=Cg({...t,pads:i,strides:a,dilations:o,kernelShape:s},r);e.compute(iu(r,u,(e=>n?[e[0],e[2],e[3]]:[])))},cu=(e,t)=>{R2(e.inputs,t),3===e.inputs[0].dims.length?N2(e,t):z2(e,e.inputs,t)}})),L2,Pg,kg=D((()=>{qr(),Be(),jr(),sa(),ou(),Io(),L2=(e,t=!1,n,r=4)=>{let i=It(r,"f32"),a=e?"\n    let coords = vec4<i32>(\n      batch,\n      row / outWidth,\n      row % outWidth,\n      col);\n    ":"\n    let coords = vec4<i32>(\n      batch,\n      row,\n      col / outWidth,\n      col % outWidth);\n    ",o=e?"row":"col",s=e?"col":"row",u=`\n      let inChannels = ${e?"outBackprop[3]":"outBackprop[1]"};\n      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n      let outRow = ${o} / outWidth;\n      let outCol = ${o} % outWidth;\n\n      let WRow = ${s} / (filterDims[1] * inChannels);\n      let WCol = ${s} / inChannels % filterDims[1];\n      let xR = f32(outRow - pads[0] + dilation[0] * WRow) / f32(strides[0]);\n      let xC = f32(outCol - pads[1] + dilation[1] * WCol) / f32(strides[1]);\n      if (xR < 0.0 || xR >= f32(${e?"outBackprop[1]":"outBackprop[2]"}) || fract(xR) > 0.0) {\n        return ${i}(0.0);\n      }\n      if (xC < 0.0 || xC >= f32(${e?"outBackprop[2]":"outBackprop[3]"}) || fract(xC) > 0.0) {\n        return ${i}(0.0);\n      }\n      let iXR = i32(xR);\n      let iXC = i32(xC);\n      let xCh = ${s} % inChannels;\n      ${e?"\n      let coord = vec4<i32>(batch, iXR, iXC, xCh);\n      ":"\n      let coord = vec4<i32>(batch, xCh, iXR, iXC);\n      "}\n      return x[getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape))/${r}];`,l=e?`\n      let col = colIn * ${r};\n      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n        ${u}\n      }\n      return ${i}(0.0);`:`\n      let col = colIn * ${r};\n      if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n        ${u}\n      }\n      return ${i}(0.0);`,d=`\n      let col = colIn * ${r};\n      let inChannels = ${e?"outBackprop[3]":"outBackprop[1]"};\n      let coordX = filterDims.x - 1 - row / (filterDims[1] * inChannels);\n      let coordY = filterDims.y - 1 - (row / inChannels) % filterDims[1];\n      if (${e?"row < uniforms.dimInner && col < uniforms.dimBOuter":"row < uniforms.dimInner && col < uniforms.dimAOuter"}  && coordX >= 0 && coordY >= 0) {\n        let rowInner = row % inChannels;\n        let coord = vec4<i32>(coordX, coordY, col, rowInner);\n        ${(e=>{switch(e){case 1:return"return w[getIndexFromCoords4D(coord, vec4<i32>(uniforms.w_shape))];";case 4:return"\n            let coord1 = vec4<i32>(coordX, coordY, col + 1, rowInner);\n            let coord2 = vec4<i32>(coordX, coordY, col + 2, rowInner);\n            let coord3 = vec4<i32>(coordX, coordY, col + 3, rowInner);\n            let v0 = w[getIndexFromCoords4D(coord, vec4<i32>(uniforms.w_shape))];\n            let v1 = w[getIndexFromCoords4D(coord1, vec4<i32>(uniforms.w_shape))];\n            let v2 = w[getIndexFromCoords4D(coord2, vec4<i32>(uniforms.w_shape))];\n            let v3 = w[getIndexFromCoords4D(coord3, vec4<i32>(uniforms.w_shape))];\n            return vec4<f32>(v0, v1, v2, v3);\n            ";default:throw new Error(`innerElementSize ${e} is not supported.`)}})(r)}\n      }\n      return ${i}(0.0);\n      `,{activationFunction:p,applyActivation:c}=Sr(n,i);return`\n      ${p}\n  fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${i} {\n    ${e?l:d}\n  }\n\n  fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${i} {\n    ${e?d:l}\n  }\n\n  fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${i}) {\n    let col = colIn * ${r};\n    if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n      var value = valueInput;\n      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n      ${a}\n      ${aa(t)}\n      ${c}\n      result[getIndexFromCoords4D(coords, vec4<i32>(uniforms.result_shape))/${r}] = value;\n    }\n  }`},Pg=(e,t,n,r,i,a,o,s)=>{let u="NHWC"===t.format,l=u?e[0].dims[3]:e[0].dims[1],d=n[0],p=u?n[2]:n[3],c=u?n[1]:n[2],h=u?n[3]:n[1],f=u?l%4==0&&h%4==0:p%4==0&&h%4==0,m=u?h:p*c,g=u?p*c:h,y=f?[8,8,1]:[m<=4||g<=4?4:16,m>4&&g<=4?4:16,1],b=f?[4,4,1]:[m<=4?1:4,m>4&&g<=4?1:4,1],w=[Math.ceil(m/y[0]/b[0]),Math.ceil(g/y[1]/b[1]),Math.ceil(d/y[2]/b[2])];it("verbose",(()=>`[conv_backprop_mm_webgpu] dispatch = ${w}`));let v=f?4:1,x=Math.max(y[0]*v,y[1]),$=f?4:1,_=[{type:"int32",data:r},{type:"int32",data:i},{type:"int32",data:a}],T=U("x",e[0].dataType,e[0].dims.length,$),S=U("w",e[1].dataType,e[1].dims.length,1),I=J("result",e[0].dataType,n.length,$),E=[T,S];_.push(...X(e[0].dims)),_.push(...X(e[1].dims));let O="";if(o){let t=U("bias",e[2].dataType,e[2].dims.length,$);E.push(t),_.push(...X(e[2].dims)),O+=`\n        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${f?"vec4<f32>":"f32"} {\n          return bias[coords.${u?"w":"y"}${f?"/ 4":""}];\n        }`}return _.push(...X(n)),{name:"Conv2DTransposeMatMul",shaderCache:{hint:t.cacheKey},getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:w[0],y:w[1],z:w[2]},programUniforms:_}),getShaderSource:n=>`\n        ${ua("uniforms.result_strides")}\n        ${n.registerUniform("dimAOuter","i32").registerUniform("dimBOuter","i32").registerUniform("dimInner","i32").declareVariables(...E,I)};\n        const outBackprop : vec4<i32> = vec4<i32>(${e[0].dims.join(",")});\n        const filterDims : vec2<i32> = vec2<i32>(${t.kernelShape[u?1:2]}, ${t.kernelShape[u?2:3]});\n        const effectiveFilterDims : vec2<i32> = filterDims + vec2<i32>(\n              ${t.dilations[0]<=1?0:(t.kernelShape[u?1:2]-1)*(t.dilations[0]-1)},\n              ${t.dilations[1]<=1?0:(t.kernelShape[u?2:3]-1)*(t.dilations[1]-1)});\n        const pads : vec2<i32> = vec2<i32>(i32(effectiveFilterDims[0]) - 1 - (${t.pads[0]+t.pads[2]})/2,\n                                         i32(effectiveFilterDims[1]) - 1 - (${t.pads[1]+t.pads[3]})/2);\n        const strides : vec2<i32> = vec2<i32>(${t.strides[0]}, ${t.strides[1]});\n        const dilation : vec2<i32> = vec2<i32>(${t.dilations[0]}, ${t.dilations[1]});\n        const dimAOuter : i32 = ${r};\n        const dimBOuter : i32 = ${i};\n        const dimInner : i32 = ${a};\n        ${O}\n        ${L2(u,o,t,v)}\n        ${f?$o(b,y,"f32",void 0,!u,x):So(b,y,"f32",void 0,!u,x,!1,void 0,s)}`}}})),M2,fu,Dg=D((()=>{qr(),Re(),Be(),M2=(e,t,n,r,i,a,o=!1,s)=>{let u="NHWC"===n.format,l=u?1:2,d=u?2:3,p=u?3:1,c=G.size(r),h=o?2:1,f=n.group,m=t[1].dims,g=m[0]/f,y=m[1],b=`\n  fn setOutputAtIndex(flatIndex : u32, value : ${o?`vec4<${s}>`:s}) {\n    result[flatIndex] = ${o?`vec4<${s}>`:s}(value);\n  }`;i&&(b+=`\n    fn getBiasByOutputCoords(coords : vec4<u32>) -> ${o?`vec4<${s}>`:s} {\n      return bias[coords.${u?"w":"y"}${o?"/ 4":""}];\n    }`);let w=o?4:1,v=U("W",t[1].dataType,t[1].dims,w),x=U("Dy",t[0].dataType,t[0].dims,w),$=[x,v];i&&$.push(U("bias",t[2].dataType,[r[p]],w));let _=J("result",t[0].dataType,r,w),T=`{\n        let batch: u32 = ${a?"global_id.z":"workgroup_id.z"} / outShape[1];\n        let r = ${a?"global_id.z":"workgroup_id.z"} % outShape[1];\n        let c = ${a?"global_id.y":"workgroup_id.y"} * ${h};\n        let d1: u32 = ${a?"global_id.x":"workgroup_id.x"} * 4;\n\n        let dyCorner = vec2<i32>(i32(r), i32(c)) - vec2<i32>(pads);\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd: array<vec4<${s}>, ${h}>;\n        for (var i = 0; i < ${h}; i++) {\n          dotProd[i] = vec4<${s}>(0.0);\n        }\n        for (var wR: u32 = 0; wR < filterDims[0]; wR = wR + 1) {\n          var dyR = (${s}(dyCorner.x) + ${s}(wR)) / ${s}(strides.x);\n          let wRPerm = filterDims[0] - 1 - wR;\n          if (dyR < 0.0 || dyR >= ${s}(outBackprop[1]) ||\n              fract(dyR) > 0.0 || wRPerm < 0) {\n            continue;\n          }\n          let idyR: u32 = u32(dyR);\n\n          for (var wC: u32 = 0; wC < filterDims[1]; wC = wC + 1) {\n            let dyC = (${s}(dyCorner.y) + ${s}(wC)) / ${s}(strides.y);\n            let dyC2 = (${s}(dyCorner.y) + 1.0 + ${s}(wC)) / ${s}(strides.y);\n            let wCPerm = filterDims[1] - 1 - wC;\n            if (wCPerm < 0) {\n              continue;\n            }\n            var bDyCVal = true;\n            var bDyCVal2 = true;\n            if (dyC < 0.0 || dyC >= ${s}(outBackprop[2]) ||\n                fract(dyC) > 0.0) {\n              bDyCVal = false;\n            }\n            if (dyC2 < 0.0 || dyC2 >= ${s}(outBackprop[2]) ||\n                fract(dyC2) > 0.0) {\n              bDyCVal2 = false;\n            }\n\n            let idyC: u32 = u32(dyC);\n            let idyC2: u32 = u32(dyC2);\n            if (bDyCVal && bDyCVal2) {\n              let d2Length = outBackprop[3];\n              for (var d2 :u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${v.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n                let wValue1 = ${v.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n                let wValue2 = ${v.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n                let wValue3 = ${v.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n                var xValue = ${x.get("batch","idyR","idyC","d2")};\n                let tmpval = vec4<${s}>(dot(xValue, wValue0),\n                                      dot(xValue, wValue1),\n                                      dot(xValue, wValue2),\n                                      dot(xValue, wValue3));\n                dotProd[0] = dotProd[0] + tmpval;\n\n                xValue =  ${x.get("batch","idyR","idyC2","d2")};\n\n                dotProd[1] = dotProd[1] + vec4<${s}>(dot(xValue, wValue0),\n                                                    dot(xValue, wValue1),\n                                                    dot(xValue, wValue2),\n                                                    dot(xValue, wValue3));\n              }\n            } else if (bDyCVal) {\n              let d2Length = outBackprop[${p}];\n              for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${v.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n                let wValue1 = ${v.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n                let wValue2 = ${v.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n                let wValue3 = ${v.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n                var xValue = ${x.get("batch","idyR","idyC","d2")};\n                let tmpval = vec4<${s}>(dot(xValue, wValue0),\n                                      dot(xValue, wValue1),\n                                      dot(xValue, wValue2),\n                                      dot(xValue, wValue3));\n                dotProd[0] = dotProd[0] + tmpval;\n              }\n            } else if (bDyCVal2) {\n              let d2Length = outBackprop[3];\n              for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${v.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n                let wValue1 = ${v.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n                let wValue2 = ${v.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n                let wValue3 = ${v.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n                var xValue = ${x.get("batch","idyR","idyC2","d2")};\n                let tmpval = vec4<${s}>(dot(xValue, wValue0),\n                                      dot(xValue, wValue1),\n                                      dot(xValue, wValue2),\n                                      dot(xValue, wValue3));\n                dotProd[1] = dotProd[1] + tmpval;\n              }\n            }\n          }\n        }\n\n        for (var i: u32 = 0; i < ${h}; i = i + 1) {\n          let value = dotProd[i] + ${i?"bias[c+i]":`vec4<${s}>(0.0)`};\n          ${_.set("batch","r","c + i","d1","value")};\n        }\n      }`,S=`\n          let outputIndices = ${_.offsetToIndices("global_idx")};\n          let batch = ${_.indicesGet("outputIndices",0)};\n          let d1 = ${_.indicesGet("outputIndices",p)};\n          let r = ${_.indicesGet("outputIndices",l)};\n          let c = ${_.indicesGet("outputIndices",d)};\n          let dyCorner = vec2<i32>(i32(r), i32(c)) - pads;\n          let dyRCorner = dyCorner.x;\n          let dyCCorner = dyCorner.y;\n          let groupId = d1 / ${y};\n          let wOutChannel = d1 - groupId * ${y};\n          // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n          // ? = to be determined. : = across all values in that axis.\n          var dotProd = ${s}(0.0);\n          for (var wR: u32 = 0; wR < effectiveFilterDims.x; wR = wR + 1) {\n            if (wR % dilations.x != 0) {\n              continue;\n            }\n            let dyR = (${s}(dyRCorner) + ${s}(wR)) / ${s}(strides[0]);\n            let wRPerm = filterDims.x - 1 - wR / dilations.x;\n            if (dyR < 0.0 || dyR >= ${s}(outBackprop[${l}]) || fract(dyR) > 0.0 ||\n                wRPerm < 0) {\n              continue;\n            }\n            let idyR: u32 = u32(dyR);\n\n            for (var wC: u32 = 0; wC < effectiveFilterDims.y; wC = wC + 1) {\n              if (wC % dilations.y != 0) {\n                continue;\n              }\n              let dyC = (${s}(dyCCorner) + ${s}(wC)) / ${s}(strides.y);\n              let wCPerm = filterDims.y - 1 - wC / dilations.y;\n              if (dyC < 0.0 || dyC >= ${s}(outBackprop[${d}]) ||\n                  fract(dyC) > 0.0 || wCPerm < 0) {\n                continue;\n              }\n              let idyC: u32 = u32(dyC);\n              var inputChannel = groupId * ${g};\n              for (var d2: u32 = 0; d2 < ${g}; d2 = d2 + 1) {\n                let xValue = ${u?x.get("batch","idyR","idyC","inputChannel"):x.get("batch","inputChannel","idyR","idyC")};\n                let wValue = ${v.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")};\n                dotProd = dotProd + xValue * wValue;\n                inputChannel = inputChannel + 1;\n              }\n            }\n          }\n          let value = dotProd + ${i?"bias[d1]":`${s}(0.0)`};\n          ${_.setByOffset("global_idx","value")};\n        `;return`\n  ${e.declareVariables(...$,_)}\n  ${b}\n  const outShape : vec4<u32> = vec4<u32>(${r.join(",")});\n  const outBackprop : vec4<u32> = vec4<u32>(${t[0].dims.join(",")});\n  const strides : vec2<u32> = vec2<u32>(${n.strides[0]}, ${n.strides[1]});\n  const filterDims : vec2<u32> = vec2<u32>(${n.kernelShape[u?1:2]}, ${n.kernelShape[u?2:3]});\n  const dilations : vec2<u32> = vec2<u32>(${n.dilations[0]}, ${n.dilations[1]});\n  const effectiveFilterDims : vec2<u32> = filterDims + vec2<u32>(\n          ${n.dilations[0]<=1?0:(n.kernelShape[u?1:2]-1)*(n.dilations[0]-1)},\n          ${n.dilations[1]<=1?0:(n.kernelShape[u?2:3]-1)*(n.dilations[1]-1)});\n  const pads : vec2<i32> = vec2<i32>(i32(effectiveFilterDims[0]) - 1 - (${n.pads[0]+n.pads[2]})/2,\n                                     i32(effectiveFilterDims[1]) - 1 - (${n.pads[1]+n.pads[3]})/2);\n    ${e.mainStart()}\n    ${e.guardAgainstOutOfBoundsWorkgroupSizes(c)};\n  ${o?T:S}}`},fu=(e,t,n)=>{let r=e.length>2,i=t.outputShape,a=G.size(i),o=[Math.ceil(a/64),1,1];it("verbose",(()=>`[conv2d_backprop_webgpu] dispatch = ${o}`));let s=vt(e[0].dataType);return{name:"ConvTranspose2D",shaderCache:{hint:t.cacheKey},getRunData:()=>({dispatchGroup:{x:o[0],y:o[1],z:o[2]},outputs:[{dims:n?n(i):i,dataType:e[0].dataType}]}),getShaderSource:n=>M2(n,e,t,i,r,1===o[1]&&1===o[2],!1,s)}}})),V2,F2,U2,Bg,Rg,G2,W2,H2,q2,zg,Ng=D((()=>{Tt(),kg(),Dg(),jr(),Nn(),V2=(e,t,n,r,i,a)=>(e-1)*t+n+(r-1)*i+1-a,F2=(e,t,n,r,i)=>{let a=Math.floor(e/2);"SAME_UPPER"===t?(n[r]=a,n[i]=e-a):"SAME_LOWER"===t&&(n[r]=e-a,n[i]=a)},U2=(e,t,n,r,i,a,o,s,u,l)=>{let d=e.length-2,p=0===l.length;if(0===u.length)for(let e=0;e<d;++e)u.push(0);let c=e[0],h=t[s?3:1]*i;for(let i=0,c=e.length-d-(s?1:0);i<d;++i,++c){let s=e[c],h=p?s*o[i]:l[i],f=V2(s,o[i],a[i],t[c],n[i],h);F2(f,r,a,i,i+d),p&&l.push(o[i]*(s-1)+u[i]+(t[c]-1)*n[i]+1-a[i]-a[i+d])}l.splice(0,0,c),l.splice(s?3:1,0,h)},Bg=(e,t)=>{let n=e.kernelShape.slice();if(0===e.kernelShape.length||0===e.kernelShape.reduce(((e,t)=>e*t),1)){n.length=0;for(let e=2;e<t[1].dims.length;++e)n.push(t[1].dims[e])}let r="NHWC"===e.format;n.splice(0,0,t[1].dims[0]),n.splice(r?3:1,0,t[1].dims[1]);let i=e.pads.slice(),a=e.outputShape.slice(),o=e.outputPadding.slice(),s=t[0].dims,u=e.dilations.slice();if(0===u.reduce(((e,t)=>e+t),0)){let e=t[0].dims.length-2;u=new Array(e).fill(1)}let l=e.strides.slice();if(0===l.reduce(((e,t)=>e+t),0)){let e=t[0].dims.length-2;l=new Array(e).fill(1)}U2(s,n,u,e.autoPad,e.group,i,l,r,o,a);let d=Object.assign({},e),p=e.cacheKey+[n.join("n,"),i.join(","),l.join(","),o.join(","),a.join(","),u.join(",")].join("_");return Object.assign(d,{kernelShape:n,pads:i,outputPadding:o,outputShape:a,dilations:u,strides:l,cacheKey:p}),d},Rg=e=>{let t=ia(e),n=e.format,r=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof e.autoPad>"u"?0:e.autoPad],i=e.dilations,a=e.group,o=e.kernelShape,s=e.pads,u=e.strides,l=e.wIsConst(),d=e.outputPadding,p=e.outputShape;return Oe({autoPad:r,format:n,dilations:i,group:a,kernelShape:o,outputPadding:d,outputShape:p,pads:s,strides:u,wIsConst:l,...t})},G2=(e,t)=>{if(!e||2!==e.length&&3!==e.length)throw new Error("Conv requires 2 or 3 inputs");if(4!==e[0].dims.length&&3!==e[0].dims.length)throw new Error("currently only support 2-dimensional conv");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");if(e[0].dims["NHWC"===t.format?e[0].dims.length-1:1]!==e[1].dims[0])throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let n=e[1].dims[1]*t.group;if(3===e.length&&(1!==e[2].dims.length||e[2].dims[0]!==n))throw new Error("invalid bias");let r=e[0].dims.length-2;if(t.dilations.reduce(((e,t)=>e+t),0)>0&&t.dilations.length!==r)throw new Error(`dilations should be ${r}D`);if(t.strides.reduce(((e,t)=>e+t),0)>0&&t.strides.length!==r)throw new Error(`strides should be ${r}D`);if(t.pads.reduce(((e,t)=>e+t),0)>0&&t.pads.length!==2*r)throw new Error(`pads should be ${2*r}D`);if(t.outputPadding.length!==r&&0!==t.outputPadding.length)throw new Error(`output_padding should be ${r}D`);if(t.kernelShape.reduce(((e,t)=>e+t),0)>0&&0!==t.kernelShape.length&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape");if(0!==t.outputShape.length&&t.outputShape.length!==e[0].dims.length-2)throw new Error("invalid output shape")},W2=[2,3,1,0],H2=(e,t,n)=>{let r=Bg(n,t),i="NHWC"===n.format,a=r.outputShape,o=a[i?3:1],s=t[0].dims[i?3:1];if(1!==r.group||1===o&&1===s)return void e.compute(fu(t,r));let u=a[i?1:2],l=a[i?2:3],d=i?u*l:o,p=i?o:u*l,c=t[1].dims[2]*t[1].dims[3]*s,h=e.kernelCustomData.wT??e.compute(lr(t[1],W2),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];n.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=h);let f=[t[0],h],m=3===t.length;m&&(i||1!==t[2].dims.length?f.push(t[2]):f.push(t[2].reshape([t[2].dims[0],1,1]))),e.compute(Pg(f,r,a,d,p,c,m,!0),{inputs:f})},q2=(e,t)=>{let n="NHWC"===t.format,r=[e.inputs[0].reshape(n?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];3===r.length&&r.push(e.inputs[2]);let i=t.kernelShape;(0===i.length||0===i[0])&&(i=[e.inputs[1].dims[2]]);let a=t.dilations;(0===a.length||0===a[0])&&(a=[1]);let o=t.strides;(0===o.length||0===o[0])&&(o=[1]);let s=t.pads;0===s.length&&(s=[0,0]),s=[0,s[0],0,s[1]],o=[1].concat(o),a=[1].concat(a),i=[1].concat(i);let u=Bg({...t,pads:s,strides:o,dilations:a,kernelShape:i},r);e.compute(fu(r,u,(e=>n?[e[0],e[2],e[3]]:[e[0],e[1],e[3]])))},zg=(e,t)=>{G2(e.inputs,t),3===e.inputs[0].dims.length?q2(e,t):H2(e,e.inputs,t)}})),j2,Lg,Mg,Vg=D((()=>{ut(),Re(),Tt(),Be(),j2=(e,t,n,r)=>{let i=G.size(t),a=t.length,o=U("input",e,a),s=J("output",e,a),u=6===n.dataType?n.getInt32Array()[0]:Number(n.getBigInt64Array()[0]),l=G.normalizeAxis(u,a);return{name:"CumSum",shaderCache:{hint:r.cacheKey,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:t,dataType:e}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:[{type:"uint32",data:i},{type:"int32",data:l},...X(t),...X(t)]}),getShaderSource:e=>{let t=` i32(${o.indicesGet("inputIndices","uniforms.axis")}) `,n=Te("uniforms.input_shape","uniforms.axis",a),i=r.reverse?t+(r.exclusive?" + 1":""):"0",u=r.reverse?n:t+(r.exclusive?"":" + 1");return`\n                ${e.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(o,s)}\n                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${e("value",2,"u32")}\n        ${e("value",3,"u32")}\n        ${d.setByOffset("global_idx","value")}\n      `}else n=`\n      let outputIndices = ${d.offsetToIndices("global_idx")};\n      ${c("")};\n      let value = ${s.getByIndices("dataIndices")};\n      ${d.setByOffset("global_idx","value")};\n      `;return`\n      ${t.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(s,l,d)}\n      ${t.mainStart()}\n        ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n        ${n}\n      }`}}},Yg=e=>Oe({axis:e.axis}),Xg=(e,t)=>{let n=e.inputs;e1(n),e.compute(t1(e.inputs,t))}})),r1,n1,Jg,Qg,eb=D((()=>{Re(),Tt(),Be(),r1=e=>{if(!e||2!==e.length)throw new Error("GatherElements requires 2 inputs.");if(e[0].dims.length<1)throw new Error("GatherElements requires that the data input be rank >= 1.");if(e[0].dims.length!==e[1].dims.length)throw new Error("GatherElements requires that the data input and\n           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u=G.size(s),l=[{type:"uint32",data:u},{type:"uint32",data:i},{type:"uint32",data:a},{type:"uint32",data:o},{type:"float32",data:t.alpha},{type:"float32",data:t.beta}],d=["type","type"];3===e.length&&(l.push(...X(e[2].dims)),d.push("rank")),l.push(...X(s));return{name:"Gemm",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:l}),getShaderSource:n=>{let r="";t.transA&&t.transB?r="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":t.transA&&!t.transB?r="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!t.transA&&t.transB?r="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!t.transA&&!t.transB&&(r="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let i=1===t.alpha?"":"value *= 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}`}}},tb=e=>({transA:e.transA,transB:e.transB,alpha:e.alpha,beta:e.beta,cacheKey:`${e.transA};${e.transB};${1===e.alpha}`}),rb=(e,t)=>{o1(e.inputs),e.compute(i1(e.inputs,t))}})),a1,s1,u1,ob,ib=D((()=>{ut(),Re(),Be(),a1=(e,t)=>{let n=e[0].dims,r=n,i=G.sizeToDimension(n,2),a=G.sizeFromDimension(n,2),o=wt(a),s=a/o,u=[n[0],n[1],s],l=[{type:"uint32",data:a},{type:"uint32",data:s}];l.push(...X(u),...X(u));return{name:"InstanceNormalization",shaderCache:{hint:`${t.epsilon};${o}`,inputDependencies:["rank","type","type"]},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:i},programUniforms:l}),getShaderSource:n=>{let r=U("x",e[0].dataType,u.length,o),i=U("scale",e[1].dataType,e[1].dims),a=U("bias",e[2].dataType,e[2].dims),s=J("output",e[0].dataType,u.length,o),l=[r,i,a,s],d=r.type.value,p=1===o?"f32":`vec${o}<f32>`;return`\n  var<workgroup> meanShared : f32;\n  var<workgroup> squaredNormShared : f32;\n  var<workgroup> workgroupShared : array<${p}, 64>;\n  const workgroupSize = 64u;\n  ${n.registerUniforms([{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}]).declareVariables(...l)}\n  ${n.mainStart(64)}\n    let norm = global_idx / workgroupSize;\n    let batch = norm / uniforms.x_shape[1];\n    let channel = norm % uniforms.x_shape[1];\n    let localIndex = local_id.x;\n\n    // initialize workgroup memory\n    var initial = ${p}(0);\n    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n      initial = initial + ${p}(${r.get("batch","channel","h")});\n    }\n    workgroupShared[localIndex] = initial;\n    workgroupBarrier();\n\n    // Calculate the mean of current channel data.\n    for (var currSize = workgroupSize >> 1;  currSize > 0; currSize = currSize >> 1) {\n      if (localIndex < currSize) {\n        workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize];\n      }\n      workgroupBarrier();\n    }\n    if (localIndex == 0) {\n      meanShared = ${Ut("workgroupShared[0]",o)} / f32(uniforms.normSize);\n    }\n    workgroupBarrier();\n\n    // reinitialize workgroup memory.\n    initial = ${p}(0);\n    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n      let deviation =  ${p}(${r.get("batch","channel","h")}) - ${p}(meanShared);\n      initial = initial + deviation * deviation;\n    }\n    workgroupShared[localIndex] = initial;\n    workgroupBarrier();\n\n    // Calculate the sum of square of deviation of current channel data.\n    for (var currSize = workgroupSize >> 1;  currSize > 0; currSize = currSize >> 1) {\n      if (localIndex < currSize) {\n        workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize];\n      }\n      workgroupBarrier();\n    }\n    if (localIndex == 0) {\n      squaredNormShared = ${Ut("workgroupShared[0]",o)};\n    }\n    workgroupBarrier();\n\n    let invStdDev = inverseSqrt(squaredNormShared / f32(uniforms.normSize) + f32(${t.epsilon}));\n    let channelScale = invStdDev * f32(${i.getByOffset("channel")});\n    let channelShift = f32(${a.getByOffset("channel")}) - meanShared * channelScale;\n    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n      let value = ${r.get("batch","channel","h")} * ${d}(${p}(channelScale)) + ${d}(${p}(channelShift));\n      ${s.set("batch","channel","h","value")};\n    }\n  }`}}},s1=(e,t,n,r,i,a,o,s)=>{let u=wt(o),l=64,d=1===u?"vec2f":`mat2x${u}f`,p=1===u?"f32":`vec${u}f`,c=(e,t)=>`${d}(${e}, ${t})`,h=i*o/u,f=[{type:"uint32",data:Math.ceil(a/l)},{type:"uint32",data:a},{type:"uint32",data:Math.floor(o/u)},{type:"uint32",data:Math.floor(a*o/u)}],m=e.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${u}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:[i,o,l,2],dataType:1}],dispatchGroup:{x:i*o/u},programUniforms:f}),getShaderSource:e=>{let n=U("input",t.dataType,t.dims,u);return`\n  ${e.declareVariables(n)}\n  @group(0) 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}`}},{inputs:[t],outputs:[-1]})[0],g=[{type:"uint32",data:h},{type:"uint32",data:a},{type:"uint32",data:Math.floor(o/u)},{type:"uint32",data:Math.floor(l*o/u)}];return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${u};${s}`,inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:[i,o,2],dataType:1}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:g}),getShaderSource:e=>{let t=U("scale",n.dataType,n.dims,u),i=U("bias",r.dataType,r.dims,u);return`\n  @group(0) @binding(0) var<storage, read> input : array<${d}>;\n  @group(0) @binding(1) var<storage, read> scale : array<${t.type.storage}>;\n  @group(0) @binding(2) var<storage, read> bias : array<${i.type.storage}>;\n  @group(0) @binding(3) var<storage, read_write> output : array<${d}>;\n  struct Uniforms {units_of_work : u32, H: u32, C : u32, image_size : u32};\n  @group(0) @binding(4) var<uniform> uniforms: Uniforms;\n\n  ${e.mainStart()}\n    ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.units_of_work")}\n    let currentImageNumber = global_idx / uniforms.C;\n    let currentChannelNumber = global_idx % uniforms.C;\n\n    let offset = currentImageNumber * uniforms.image_size;\n    var sum = ${Pt("f32",u)};\n    var squaredSum = ${Pt("f32",u)};\n    for (var i: u32 = 0; i < 64; i++) {\n        let value = input[offset + i + currentChannelNumber * 64];\n        sum += value[0];\n        squaredSum += value[1];\n    }\n    sum = sum / f32(uniforms.H);\n    squaredSum = squaredSum / f32(uniforms.H);\n    let invStdDev = inverseSqrt(squaredSum - sum * sum + f32(${s}));\n    let channelScale = invStdDev * ${p}(scale[currentChannelNumber]);\n    let channelShift = ${p}(bias[currentChannelNumber]) - sum * channelScale;\n\n    output[global_idx] = ${c("channelScale","channelShift")};\n  }`}},{inputs:[m,n,r],outputs:[-1]})[0]},u1=(e,t,n)=>{let r=t[0].dims,i=r,a=r[0],o=r[r.length-1],s=G.sizeFromDimension(r,1)/o,u=wt(o),l=G.size(i)/u,d=[{type:"uint32",data:s},{type:"uint32",data:Math.floor(o/u)}],p=s1(e,t[0],t[1],t[2],a,s,o,n.epsilon);e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${u}`,inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d}),getShaderSource:e=>{let n=vt(t[0].dataType),r=1===u?"vec2f":`mat2x${u}f`,a=1===u?n:`vec${u}<${n}>`,o=U("input",t[0].dataType,t[0].dims,u),s=J("output",t[0].dataType,i,u);return`\n  @group(0) @binding(0) var<storage, read> input : array<${o.type.storage}>;\n  @group(0) @binding(1) var<storage, read> scaleInput : array<${r}>;\n  @group(0) @binding(2) var<storage, read_write> output : array<${s.type.storage}>;\n  struct Uniforms {H: u32, C : u32};\n  @group(0) @binding(3) var<uniform> uniforms: Uniforms;\n\n  ${e.mainStart()}\n    let currentImageNumber = global_idx / (uniforms.C * uniforms.H);\n    let currentChannelNumber = global_idx % uniforms.C;\n\n    let scaleOffset = currentImageNumber * uniforms.C + currentChannelNumber;\n    let scale = scaleInput[scaleOffset];\n    output[global_idx] = fma(input[global_idx], ${a}(scale[0]), ${a}(scale[1]));\n  }`}},{inputs:[t[0],p]})},ob=(e,t)=>{"NHWC"===t.format?u1(e,e.inputs,t):e.compute(a1(e.inputs,t))}})),l1,d1,ab,sb=D((()=>{ut(),Re(),Be(),l1=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},d1=(e,t,n)=>{let r=e[0].dims,i=e[1],a=e[2],o=r,s=G.normalizeAxis(t.axis,r.length),u=G.sizeToDimension(r,s),l=G.sizeFromDimension(r,s),d=G.size(i.dims),p=a?G.size(a.dims):0;if(d!==l||a&&p!==l)throw new Error(`Size of X.shape()[axis:] == ${l}.\n       Size of scale and bias (if provided) must match this.\n       Got scale size of ${d} and bias size of ${p}`);let c=[];for(let e=0;e<r.length;++e)e<s?c.push(r[e]):c.push(1);let h=wt(l),f=["type","type"],m=[{type:"uint32",data:u},{type:"float32",data:l},{type:"uint32",data:Math.floor(l/h)},{type:"float32",data:t.epsilon}];a&&f.push("type");let g=n>1,y=n>2,b=[{dims:o,dataType:e[0].dataType}];return g&&b.push({dims:c,dataType:1}),y&&b.push({dims:c,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${h};${n}`,inputDependencies:f},getRunData:()=>({outputs:b,dispatchGroup:{x:Math.ceil(u/64)},programUniforms:m}),getShaderSource:t=>{let n=vt(e[0].dataType),r=[U("x",e[0].dataType,e[0].dims,h),U("scale",i.dataType,i.dims,h)];a&&r.push(U("bias",a.dataType,a.dims,h)),r.push(J("output",e[0].dataType,o,h)),g&&r.push(J("mean_data_output",1,c)),y&&r.push(J("inv_std_output",1,c));return`\n  ${t.registerUniforms([{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}]).declareVariables(...r)}\n  ${t.mainStart()}\n    ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}\n    let offset = global_idx * uniforms.norm_size_vectorized;\n    var meanVector = ${Pt("f32",h)};\n    var meanSquareVector = ${Pt("f32",h)};\n\n    for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {\n      let value = ${ur(n,h,"x[h + offset]")};\n      meanVector += value;\n      meanSquareVector += value * value;\n    }\n    let mean = ${Ut("meanVector",h)} / uniforms.norm_size;\n    let invStdDev =\n        inverseSqrt(${Ut("meanSquareVector",h)} / uniforms.norm_size - mean * mean + uniforms.epsilon);\n\n    for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {\n      let f32input = ${ur(n,h,"x[j + offset]")};\n      let f32scale = ${ur(n,h,"scale[j]")};\n      output[j + offset] = ${r[0].type.value}((f32input - mean) * invStdDev * f32scale\n        ${a?`+ ${ur(n,h,"bias[j]")}`:""}\n      );\n    }\n\n    ${g?"mean_data_output[global_idx] = mean":""};\n    ${y?"inv_std_output[global_idx] = invStdDev":""};\n  }`}}},ab=(e,t)=>{l1(e.inputs),e.compute(d1(e.inputs,t,e.outputCount))}})),c1,lb,ub,f1,gu,db,cb=D((()=>{Re(),Tt(),Ki(),ru(),Be(),Nn(),c1=(e,t)=>{let n=e[0],r=e[1],i=e[2],a=e[3],o=e[4],s=e[5],u=e[6],l=e[7];if(3!==n.dims.length&&5!==n.dims.length)throw new Error("Input query is expected to have 3 or 5 dimensions");let d,p=n.dims[0],c=n.dims[1],h=3===n.dims.length?n.dims[2]:t.numHeads*n.dims[4],f=c,m=0,g=0,y=Math.floor(h/t.numHeads);if(u&&l){if(4!==u.dims.length)throw new Error('Input "past_key" is expected to have 4 dimensions');if(4!==l.dims.length)throw new Error('Input "past_value" is expected to have 4 dimensions');m=u.dims[2],g=u.dims[2]}else if(u||l)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');if(r){if(3!==n.dims.length)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(r.dims.length<3||r.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(n.dims[0]!==r.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(3===r.dims.length){if(r.dims[2]!==n.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');d=2,f=r.dims[1]}else if(5===r.dims.length){if(r.dims[2]!==t.numHeads||2!==r.dims[3]||r.dims[4]!==y)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(i)throw new Error('Expect "value" be none when "key" has packed kv format.');d=5,f=r.dims[1]}else{if(r.dims[1]!==t.numHeads||r.dims[3]!==y)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');d=0,f=r.dims[2]}}else{if(3!==n.dims.length&&5!==n.dims.length)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(5===n.dims.length&&(n.dims[2]!==t.numHeads||3!==n.dims[3]))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');d=3}if(a){if(1!==a.dims.length)throw new Error('Input "bias" is expected to have 1 dimension');if(i&&5===n.dims.length&&2===n.dims[3])throw new Error("bias is not allowed for packed kv.")}let b=0;if(o){b=8;let e=o.dims;throw 1===e.length?e[0]===p?b=1:e[0]===3*p+2&&(b=3):2===e.length&&e[0]===p&&e[1]===f&&(b=5),8===b?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)'):new Error("Mask not supported")}let w=!1,v=h;if(i){if(3!==i.dims.length&&4!==i.dims.length)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(n.dims[0]!==i.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(3===i.dims.length){if(f!==i.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');v=i.dims[2]}else{if(f!==i.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');v=i.dims[1]*i.dims[3],w=!0}}let x=m+f;if(o)throw new Error("Key padding mask is not supported");if(s)throw new Error("extraAddQk is not supported");if(u)throw new Error("pastKey is not supported");if(l)throw new Error("pastValue is not supported");return{batchSize:p,sequenceLength:c,pastSequenceLength:m,kvSequenceLength:f,totalSequenceLength:x,maxSequenceLength:g,inputHiddenSize:0,hiddenSize:h,vHiddenSize:v,headSize:y,vHeadSize:Math.floor(v/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:b,scale:t.scale,broadcastResPosBias:!1,passPastInKv:w,qkvFormat:d}},lb=e=>Oe({...e}),ub=Oe({perm:[0,2,1,3]}),f1=(e,t,n,r,i,a,o)=>{let s=[r,i,a],u=G.size(s),l=[{type:"uint32",data:u},{type:"uint32",data:o},{type:"uint32",data:a}];return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:s,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:l}),getShaderSource:e=>{let r=J("qkv_with_bias",t.dataType,s),i=U("qkv",t.dataType,s),a=U("bias",n.dataType,s);return`\n  ${e.registerUniforms([{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}]).declareVariables(i,a,r)}\n  ${e.mainStart()}\n    ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n    let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;\n\n    qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];\n  }`}},{inputs:[t,n],outputs:[-1]})[0]},gu=(e,t,n,r,i,a,o,s)=>{let u=a;if(o){if(1===r)throw new Error("AddBiasReshape is not implemented. 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x[offset];\n          }\n      `},m1=(e,t,n)=>{let r="";for(let i=t-1;i>=0;--i)r+=`\n                k = i32(${e.indicesGet("indices",i)}) - ${Te("uniforms.pads",i,n)};\n                if (k < 0) {\n                  k = -k;\n                }\n                {\n                  let _2n_1 = 2 * (i32(${Te("uniforms.x_shape",i,t)}) - 1);\n                  k = k % _2n_1;\n                  if(k >= i32(${Te("uniforms.x_shape",i,t)})) {\n                    k = _2n_1 - k;\n                  }\n                }\n                offset += k * i32(${Te("uniforms.x_strides",i,t)});\n            `;return`\n              var offset = 0;\n              var k = 0;\n              ${r}\n              value = x[offset];\n          `},g1=(e,t,n)=>{let r="";for(let i=t-1;i>=0;--i)r+=`\n                k = i32(${e.indicesGet("indices",i)}) - ${Te("uniforms.pads",i,n)};\n                if (k < 0) {\n                  k = 0;\n                }\n                if (k >= 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${i.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n            let indices = ${a.offsetToIndices("global_idx")};\n\n            var value = ${s}(0);\n            ${u}\n            output[global_idx] = value;\n        }`}}},w1=(e,t)=>{if(e.length>1){let n=e[1].getBigInt64Array(),r=e.length>=3&&e[2].data?e[2].getFloat32Array()[0]:0,i=e[0].dims.length,a=new Int32Array(2*i).fill(0);if(e.length>=4){let t=e[3].getBigInt64Array();for(let e=0;e<t.length;e++)a[Number(t[e])]=Number(n[e]),a[Number(t[e])+i]=Number(n[e+t.length])}else n.forEach(((e,t)=>a[Number(t)]=Number(e)));let o=[];return a.forEach((e=>o.push(e))),{mode:t.mode,value:r,pads:o}}return t},fb=(e,t)=>{p1(e.inputs);let n=w1(e.inputs,t);e.compute(v1(e.inputs,n),{inputs:[0]})}})),ca,hb,mb,gb,bb,x1,T1,yb,vb,wb,xb,Tb,_b,$b,Sb,Ib,Ab,Ob,Eb,Cb=D((()=>{Et(),Re(),Be(),ca=e=>{if(be.webgpu.validateInputContent&&(!e||1!==e.length))throw new Error("Pool ops requires 1 input.")},hb=(e,t,n)=>{let r="NHWC"===t.format,i=e.dims.slice();r&&i.splice(1,0,i.pop());let a=Object.hasOwnProperty.call(t,"dilations"),o=t.kernelShape.slice(),s=t.strides.slice(),u=a?t.dilations.slice():[],l=t.pads.slice();pn.adjustPoolAttributes(n,i,o,s,u,l);let d=pn.computePoolOutputShape(n,i,s,u,o,l,t.autoPad),p=Object.assign({},t);a?Object.assign(p,{kernelShape:o,strides:s,pads:l,dilations:u,cacheKey:t.cacheKey}):Object.assign(p,{kernelShape:o,strides:s,pads:l,cacheKey:t.cacheKey});let c=d.slice();return c.push(c.splice(1,1)[0]),[p,r?c:d]},mb=(e,t)=>{let n="NHWC"===t.format,r=[{type:"uint32",data:G.size(e)},{type:"uint32",data:G.size(t.kernelShape)}],i=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let e=t.kernelShape[t.kernelShape.length-1],n=t.strides[t.strides.length-1],a=t.pads[t.pads.length/2-1],o=t.pads[t.pads.length-1],s=!!(a+o);r.push({type:"uint32",data:e},{type:"uint32",data:n},{type:"uint32",data:a},{type:"uint32",data:o}),i.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let u=!1;if(2===t.kernelShape.length){let e=t.kernelShape[t.kernelShape.length-2],n=t.strides[t.strides.length-2],a=t.pads[t.pads.length/2-2],o=t.pads[t.pads.length-2];u=!!(a+o),r.push({type:"uint32",data:e},{type:"uint32",data:n},{type:"uint32",data:a},{type:"uint32",data:o}),i.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[r,i,!0,s,u]}{if(n)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let e=G.computeStrides(t.kernelShape);return r.push({type:"uint32",data:e},{type:"uint32",data:t.pads},{type:"uint32",data:t.strides}),i.push({name:"kernelStrides",type:"u32",length:e.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length}),[r,i,!!t.pads.reduce(((e,t)=>e+t)),!1,!1]}},gb=(e,t,n,r,i,a,o,s,u,l,d,p)=>{let c="NHWC"===i.format,h=t.type.value,f=J("output",t.type.tensor,r);if(i.kernelShape.length<=2){let r="",l="",m="",g=n-(c?2:1);if(r=d?`\n                for (var i: u32 = 0u; i < uniforms.kw; i++) {\n                  xIndices[${g}] = indices[${g}] * uniforms.sw - uniforms.pwStart + i;\n                  if (xIndices[${g}] < 0 || xIndices[${g}]\n                      >= uniforms.x_shape[${g}]) {\n                    pad++;\n                    continue;\n                  }\n                  let x_val = x[${t.indicesToOffset("xIndices")}];\n                  ${a}\n                }`:`\n                for (var i: u32 = 0u; i < uniforms.kw; i++) {\n                  xIndices[${g}] = indices[${g}] * uniforms.sw - uniforms.pwStart + i;\n                  let x_val = x[${t.indicesToOffset("xIndices")}];\n                  ${a}\n                }`,2===i.kernelShape.length){let e=n-(c?3:2);l=p?`\n                for (var j: u32 = 0u; j < uniforms.kh; j++) {\n                  xIndices[${e}] = indices[${e}] * uniforms.sh - uniforms.phStart + j;\n                  if (xIndices[${e}] < 0 || xIndices[${e}] >= uniforms.x_shape[${e}]) {\n                    pad += i32(uniforms.kw);\n                    continue;\n                  }\n              `:`\n                for (var j: u32 = 0u; j < uniforms.kh; j++) {\n                  xIndices[${e}] = indices[${e}] * uniforms.sh - uniforms.phStart + j;\n                `,m="\n              }\n            "}return`\n            ${e.registerUniforms(u).declareVariables(t,f)}\n\n            ${e.mainStart()}\n              ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n\n              let indices = ${f.offsetToIndices("global_idx")};\n              var xIndices = ${f.offsetToIndices("global_idx")};\n\n              var value = ${h}(${s});\n              var pad = 0;\n              ${l}\n              ${r}\n              ${m}\n              ${o}\n\n              output[global_idx] = value;\n            }`}{if(c)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let r=i.kernelShape.length,d=i.pads.length,p="";return p=l?`\n                if (xIndices[j] >= uniforms.x_shape[j]) {\n                  pad++;\n                  isPad = true;\n                  break;\n                }\n              }\n              if (!isPad) {\n                let x_val = x[${t.indicesToOffset("xIndices")}];\n                ${a}\n              }`:`\n              }\n              let x_val = x[${t.indicesToOffset("xIndices")}];\n              ${a}\n            `,`\n            ${e.registerUniforms(u).declareVariables(t,f)}\n\n            ${e.mainStart()}\n              ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n              let indices = ${f.offsetToIndices("global_idx")};\n              var xIndices = ${f.offsetToIndices("global_idx")};\n\n              var offsets: array<u32, ${r}>;\n\n              var value = ${h}(${s});\n              var pad = 0;\n              var isPad = false;\n\n              for (var i: u32 = 0u; i < uniforms.kernelSize; i++) {\n                var offset = i;\n                for (var j = 0u; j < ${r-1}u; j++) {\n                  offsets[j] = offset / ${Te("uniforms.kernelStrides","j",r)};\n                  offset -= offsets[j] * ${Te("uniforms.kernelStrides","j",r)};\n                }\n                offsets[${r-1}] = offset;\n\n                isPad = false;\n                for (var j = ${n-r}u; j < ${n}u; j++) {\n                  xIndices[j] = indices[j] * ${Te("uniforms.strides",`j - ${n-r}u`,r)}\n                    + offsets[j - ${n-r}u] - ${Te("uniforms.pads","j - 2u",d)};\n                  ${p}\n              }\n              ${o}\n\n              output[global_idx] = value;\n            }`}},bb=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,x1=e=>`${bb(e)};${e.countIncludePad}`,T1=e=>`${bb(e)};${e.storageOrder};${e.dilations}`,yb=e=>({format:e.format,autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],ceilMode:e.ceil_mode,kernelShape:e.kernel_shape,strides:e.strides,pads:e.pads}),vb=(e,t,n,r)=>{let[i,a]=hb(t,r,n),o=U("x",t.dataType,t.dims.length),s=o.type.value,u="";i.countIncludePad?u+=`value /= ${s}(uniforms.kernelSize);`:u+=`value /= ${s}(i32(uniforms.kernelSize) - pad);`;let[l,d,p,c,h]=mb(a,i);l.push(...X(t.dims),...X(a));return{name:e,shaderCache:{hint:`${r.cacheKey};${p};${c};${h}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(G.size(a)/64)},programUniforms:l}),getShaderSource:e=>gb(e,o,t.dims.length,a.length,i,"value += x_val;",u,0,d,p,c,h)}},wb=e=>{let t=0!==e.count_include_pad,n=yb(e);if(0!==n.ceilMode)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let r={countIncludePad:t,...n,cacheKey:""};return{...r,cacheKey:x1(r)}},xb=(e,t)=>{ca(e.inputs),e.compute(vb("AveragePool",e.inputs[0],!1,t))},Tb={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},_b=e=>{let t=e.format;return{format:t,...Tb,cacheKey:t}},$b=(e,t)=>{ca(e.inputs),e.compute(vb("GlobalAveragePool",e.inputs[0],!0,t))},Sb=(e,t,n,r)=>{let[i,a]=hb(t,r,n),o=U("x",t.dataType,t.dims.length),[s,u,l,d,p]=mb(a,i);return s.push(...X(t.dims),...X(a)),{name:e,shaderCache:{hint:`${r.cacheKey};${l};${d};${p}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(G.size(a)/64)},programUniforms:s}),getShaderSource:e=>gb(e,o,t.dims.length,a.length,i,"\n      value = max(x_val, value);\n    ","",-1e5,u,l,d,p)}},Ib=(e,t)=>{ca(e.inputs),e.compute(Sb("MaxPool",e.inputs[0],!1,t))},Ab=e=>{let t=e.storage_order,n=e.dilations,r=yb(e);if(0!==t)throw new Error("column major storage order is not yet supported for MaxPool");if(0!==r.ceilMode)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let i={storageOrder:t,dilations:n,...r,cacheKey:""};return{...i,cacheKey:T1(i)}},Ob=e=>{let t=e.format;return{format:t,...Tb,cacheKey:t}},Eb=(e,t)=>{ca(e.inputs),e.compute(Sb("GlobalMaxPool",e.inputs[0],!0,t))}})),$1,S1,Pb,kb=D((()=>{Et(),ut(),Be(),$1=(e,t,n)=>{if(e===t||e<t&&n<0||e>t&&n>0)throw new Error("Range these inputs' contents are 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t=0,n=0,r=0;6===e.inputs[0].dataType?(t=e.inputs[0].getInt32Array()[0],n=e.inputs[1].getInt32Array()[0],r=e.inputs[2].getInt32Array()[0]):1===e.inputs[0].dataType&&(t=e.inputs[0].getFloat32Array()[0],n=e.inputs[1].getFloat32Array()[0],r=e.inputs[2].getFloat32Array()[0]),be.webgpu.validateInputContent&&$1(t,n,r),e.compute(S1(t,n,r,e.inputs[0].dataType),{inputs:[]})}})),I1,A1,O1,E1,C1,P1,k1,D1,B1,R1,z1,Db,N1,L1,M1,V1,F1,Bb,Rb,zb=D((()=>{Re(),Tt(),Be(),I1=(e,t)=>{if(e.every((e=>e>0||(()=>{throw new Error("Resize requires scales input values to be positive")}))),e.length>0)if("linear"===t.mode){if(!(2===e.length||3===e.length||4===e.length&&1===e[0]&&1===e[1]||4===e.length&&1===e[0]&&1===e[3]||5===e.length&&1===e[0]&&1===e[1]))throw new Error("For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and\n            one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1")}else if("cubic"===t.mode&&!(2===e.length||4===e.length&&1===e[0]&&1===e[1]||4===e.length&&1===e[0]&&1===e[3]))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")},A1=(e,t,n)=>{t.every((e=>e>=0&&e<n||(()=>{throw new Error("Resize requires axes input values to be positive and less than rank")})));let r=new Array(n).fill(1);return t.forEach(((t,n)=>r[t]=e[n])),r},O1=(e,t,n,r,i,a)=>{let[o,s,u]=n>10?[1,2,3]:[-1,e.length>1?1:-1,-1],l=e[0].dims.length;if(o>0&&e.length>o&&e[o].dims.length>0)e[o].getFloat32Array().forEach((e=>a.push(e)));else if("tf_crop_and_resize"===t.coordinateTransformMode)throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(s>0&&e.length>s&&e[s].dims.length>0){if(e[s].getFloat32Array().forEach((e=>r.push(e))),0!==r.length&&r.length!==l&&n>=18&&r.length!==t.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");I1(r,t),t.axes.length>0&&A1(r,t.axes,l).forEach(((e,t)=>r[t]=e))}if(u>0&&e.length>u&&(e[u].getBigInt64Array().forEach((e=>i.push(Number(e)))),i.length!==l||n>=18&&i.length===t.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(t.axes.length>0){if(r.length!==t.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(i.length!==t.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof r<"u"&&typeof i<"u"&&r.length>0&&i.length>l)throw new Error("Resize requires only of scales or sizes to be specified")},E1=(e,t)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,\n     lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${t} { `+(()=>{switch(e){case"asymmetric":return`return ${t}(xResized) / ${t}(xScale);`;case"pytorch_half_pixel":return`if (lengthResized > 1) {\n                    return (${t}(xResized) + 0.5) / ${t}(xScale) - 0.5;\n                  } else {\n                    return 0.0;\n                  }`;case"tf_half_pixel_for_nn":return`return (${t}(xResized) + 0.5) / ${t}(xScale);`;case"align_corners":return`if (lengthResized == 1) {\n                    return 0.0;\n                  } else {\n                    // The whole part and the fractional part are calculated separately due to inaccuracy of floating\n                    // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an\n                    // offset-by-one error later in floor().\n                    let whole = ${t}(xResized * (lengthOriginal - 1) / (lengthResized - 1));\n                    let fract =\n                        ${t}(xResized * (lengthOriginal - 1) % (lengthResized - 1)) / ${t}(lengthResized - 1);\n                    return whole + fract;\n                  }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {\n                    return ${t}(roiStart) * ${t}(lengthOriginal - 1) +\n                        (${t}(xResized) * ${t}(roiEnd - roiStart) * ${t}(lengthOriginal - 1)) /\n                        ${t}(lengthResized - 1);\n                  } else {\n                    return 0.5 * ${t}(roiStart + roiEnd) * ${t}(lengthOriginal - 1);\n                  }`;case"half_pixel_symmetric":return`const outputWidth = ${t}xScale * ${t}(lengthResized);\n                  const adjustment = ${t}(lengthResized) / outputWidth;\n                  const center = ${t}(lengthOriginal) / 2;\n                  const offset = center * (1 - adjustment);\n                  return offset + ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;case"half_pixel":return`return ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${e} is not supported`)}})()+"}",C1=(e,t,n)=>`fn getNearestPixelFromOriginal(xOriginal: ${n}, isDownSample: bool) -> ${n} {`+(()=>{switch(e){case"round_prefer_ceil":return"if (fract(xOriginal) == 0.5) {             return ceil(xOriginal);           } else {             return round(xOriginal);           }";case"floor":return"return floor(xOriginal);";case"ceil":return"return ceil(xOriginal);";case"round_prefer_floor":return"if (fract(xOriginal) == 0.5) {                     return floor(xOriginal);                   } else {                     return round(xOriginal);                   }";default:if(t<11)return"if (isDownSample)                     {                       return ceil(xOriginal);                     } else {                       return xOriginal;                     }";throw new Error(`Nearest mode ${e} is not supported`)}})()+"}",P1=(e,t,n)=>{let r=new Array(n).fill(0).concat(new Array(n).fill(1)),i=0===e.length?r:e.slice();return t.length>0?(t.forEach(((e,a)=>{r[e]=i[a],r[a+n]=i[t.length+a]})),r):i},k1=(e,t,n,r)=>{let i=[];if(n.length>0)if(r.length>0){if(e.forEach((e=>i.push(e))),Math.max(...r)>e.length)throw new Error("axes is out of bound");r.forEach(((e,t)=>i[e]=n[t]))}else n.forEach((e=>i.push(e)));else{if(0===t.length)throw new Error("Resize requires either scales or sizes.");i=e.map(((e,n)=>Math.round(e*t[n])))}return i},D1=(e,t,n)=>{let r=(()=>{switch(n.keepAspectRatioPolicy){case"not_larger":return n.axes.length>0?Math.min(...n.axes.map((e=>t[e])),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case"not_smaller":return n.axes.length>0?Math.max(...n.axes.map((e=>t[e])),Number.MIN_VALUE):Math.max(...t,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${n.keepAspectRatioPolicy} is not supported`)}})();t.fill(1,0,t.length);let i=e.slice();return n.axes.length>0?(n.axes.forEach((e=>t[e]=r)),n.axes.forEach((n=>i[n]=Math.round(e[n]*t[n])))):(t.fill(r,0,t.length),i.forEach(((e,n)=>i[n]=Math.round(e*t[n])))),i},B1=(e,t,n,r,i)=>`\n    fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${n.length}> {\n      var original_indices: array<${e.type.value}, ${n.length}>;\n      for (var i:u32 = 0; i < ${n.length}; i++) {\n        var output_index = ${e.indicesGet("output_indices","i")};\n        var scale = ${Te("uniforms.scales","i",r)};\n        var roi_low = ${Te("uniforms.roi","i",i)};\n        var roi_hi = ${Te("uniforms.roi",`i + ${t.length}`,i)};\n        if (scale == 1.0) {\n          original_indices[i] = ${e.type.value}(output_index);\n        } else {\n          var input_shape_i = ${Te("uniforms.input_shape","i",t.length)};\n          var output_shape_i = ${Te("uniforms.output_shape","i",n.length)};\n          original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\n                                                                           input_shape_i, roi_low, roi_hi);\n        }\n      }\n      return original_indices;\n    }`,R1=(e,t,n,r,i,a,o)=>`\n    fn calculateInputIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {\n      var input_indices: ${e.type.indices};\n      for (var i:u32 = 0; i < ${r.length}; i++) {\n        var output_index = ${t.indicesGet("output_indices","i")};\n        var input_index: u32;\n        var scale = ${Te("uniforms.scales","i",i)};\n        if (scale == 1.0) {\n          input_index = output_index;\n        } else {\n          var roi_low = ${Te("uniforms.roi","i",a)};\n          var roi_hi = ${Te("uniforms.roi",`i + ${n.length}`,a)};\n          var input_shape_i = ${Te("uniforms.input_shape","i",n.length)};\n          var output_shape_i = ${Te("uniforms.output_shape","i",r.length)};\n          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\n                                                                        input_shape_i, roi_low, roi_hi);\n          if (!${o} || (original_idx >= 0 && original_idx < ${t.type.value}(input_shape_i))) {\n            if (original_idx < 0) {\n              input_index = 0;\n            } else if (original_idx > ${t.type.value}(input_shape_i - 1)) {\n              input_index = input_shape_i - 1;\n            } else {\n              input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1));\n            }\n          } else {\n            input_index = u32(original_idx);\n          }\n        }\n        ${e.indicesSet("input_indices","i"," input_index")}\n      }\n      return input_indices;\n    }`,z1=(e,t)=>`\n    fn checkInputIndices(input_indices: ${e.type.indices}) -> bool {\n      for (var i:u32 = 0; i < ${t.length}; i++) {\n        var input_index = ${e.indicesGet("input_indices","i")};\n        if (input_index < 0 || input_index >= ${Te("uniforms.input_shape","i",t.length)}) {\n          return false;\n        }\n      }\n      return true;\n    }`,Db=(e,t,n,r)=>e.rank>r?`\n    ${e.indicesSet("input_indices",t,"channel")};\n    ${e.indicesSet("input_indices",n,"batch")};\n`:"",N1=(e,t,n,r,i)=>{let[a,o,s,u]=2===n.length?[-1,0,1,-1]:[0,2,3,1],l=e.type.value;return`\n    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${l} {\n      var input_indices: ${e.type.indices};\n      ${e.indicesSet("input_indices",o,`max(0, min(row, ${n[o]} - 1))`)};\n      ${e.indicesSet("input_indices",s,`max(0, min(col, ${n[s]} - 1))`)};\n      ${Db(e,u,a,2)}\n      return ${e.getByIndices("input_indices")};\n    }\n\n    fn bilinearInterpolation(output_indices: ${t.type.indices}) -> ${l} {\n      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n      var row:${l} = originalIndices[${o}];\n      var col:${l} = originalIndices[${s}];\n      ${r?`if (row < 0 || row > (${n[o]} - 1) || col < 0 || col > (${n[s]} - 1)) {\n        return ${i};\n      }`:""};\n      row = max(0, min(row, ${n[o]} - 1));\n      col = max(0, min(col, ${n[s]} - 1));\n      var row1: u32 = u32(row);\n      var col1: u32 = u32(col);\n      var row2: u32 = u32(row + 1);\n      var col2: u32 = u32(col + 1);\n      var channel: u32 = ${n.length>2?`u32(originalIndices[${u}])`:"0"};\n      var batch: u32 =  ${n.length>2?`u32(originalIndices[${a}])`:"0"};\n      var x11: ${l} = getInputValue(batch, channel, row1, col1);\n      var x12: ${l} = getInputValue(batch, channel, row1, col2);\n      var x21: ${l} = getInputValue(batch, channel, row2, col1);\n      var x22: ${l} = getInputValue(batch, channel, row2, col2);\n      var dx1: ${l} = abs(row - ${l}(row1));\n      var dx2: ${l} = abs(${l}(row2) - row);\n      var dy1: ${l} = abs(col - ${l}(col1));\n      var dy2: ${l} = abs(${l}(col2) - col);\n      if (row1 == row2) {\n        dx1 = 0.5;\n        dx2 = 0.5;\n      }\n      if (col1 == col2) {\n        dy1 = 0.5;\n        dy2 = 0.5;\n      }\n      return (x11 * dx2 * dy2 + x12 * dx2 * dy1 + x21 * dx1 * dy2 + x22 * dx1 * dy1);\n    }`},L1=(e,t,n,r,i,a,o,s,u,l)=>{let d=2===n.length,[p,c]=d?[0,1]:[2,3],h=e.type.value,f=o=>{let d=o===p?"row":"col";return`\n      fn ${d}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${t.type.indices}) -> ${h} {\n        var output_index = ${t.indicesGet("output_indices",o)};\n        var originalIdx: ${h} = getOriginalCoordinateFromResizedCoordinate(output_index, ${i[o]},\n        ${r[o]}, ${n[o]}, ${a[o]}, ${a[o]} + ${n.length});\n        var fractOriginalIdx: ${h} = originalIdx - floor(originalIdx);\n        var coefs = getCubicInterpolationCoefs(fractOriginalIdx);\n\n        if (${s} && (originalIdx < 0 || originalIdx > (${n[o]} - 1))) {\n          return ${u};\n        }\n        var data: array<${h}, 4> = array<${h}, 4>(0.0, 0.0, 0.0, 0.0);\n        for (var i: i32 = -1; i < 3; i++) {\n          var ${d}: ${h} = originalIdx + ${h}(i);\n          if (${d} < 0 || ${d} >= ${n[o]}) {\n            ${l?"coefs[i + 1] = 0.0;\n                        continue;":s?`return ${u};`:`${d} = max(0, min(${d}, ${n[o]} - 1));`};\n          }\n        var input_indices_copy: ${e.type.indices} = input_indices;\n          ${e.indicesSet("input_indices_copy",o,`u32(${d})`)};\n          data[i + 1] = ${o===p?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"};\n        }\n        return cubicInterpolation1D(data, coefs);\n      }`};return`\n    ${f(p)};\n    ${f(c)};\n  fn getCubicInterpolationCoefs(s: ${h}) -> array<${h}, 4> {\n    var absS = abs(s);\n    var coeffs: array<${h}, 4> = array<${h}, 4>(0.0, 0.0, 0.0, 0.0);\n    var oneMinusAbsS: ${h} = 1.0 - absS;\n    var twoMinusAbsS: ${h} = 2.0 - absS;\n    var onePlusAbsS: ${h} = 1.0 + absS;\n    coeffs[0] = ((${o} * onePlusAbsS - 5 * ${o}) * onePlusAbsS + 8 * ${o}) * onePlusAbsS - 4 * ${o};\n    coeffs[1] = ((${o} + 2) * absS - (${o} + 3)) * absS * absS + 1;\n    coeffs[2] = ((${o} + 2) * oneMinusAbsS - (${o} + 3)) * oneMinusAbsS * oneMinusAbsS + 1;\n    coeffs[3] = ((${o} * twoMinusAbsS - 5 * ${o}) * twoMinusAbsS + 8 * ${o}) * twoMinusAbsS - 4 * ${o};\n    return coeffs;\n  }\n\n  fn cubicInterpolation1D(x: array<${h}, 4>, coefs: array<${h}, 4>) -> ${h} {\n    var coefsSum: ${h} = coefs[0] + coefs[1] + coefs[2] + coefs[3];\n    return (x[0] * coefs[0] + x[1] * coefs[1]+ x[2] * coefs[2]+ x[3] * coefs[3]) / coefsSum;\n  }\n\n  fn bicubicInterpolation(output_indices: ${t.type.indices}) -> ${h} {\n    var input_indices: ${e.type.indices} = output_indices;\n    return colCubicInterpolation(input_indices, output_indices);\n  }\n    `},M1=(e,t,n,r,i)=>{let[a,o,s,u,l]=3===n.length?[-1,0,1,2,-1]:[0,2,3,4,1],d=e.type.value;return`\n    fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${d} {\n      var input_indices: ${e.type.indices};\n      ${e.indicesSet("input_indices",o,`max(0, min(depth, ${n[o]} - 1))`)};\n      ${e.indicesSet("input_indices",s,`max(0, min(height, ${n[s]} - 1))`)};\n      ${e.indicesSet("input_indices",u,`max(0, min(width, ${n[u]} - 1))`)};\n      ${Db(e,l,a,3)}\n      return ${e.getByIndices("input_indices")};\n    }\n\n    fn trilinearInterpolation(output_indices: ${t.type.indices}) -> ${d} {\n      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n      var depth:${d} = originalIndices[${o}];\n      var height:${d} = originalIndices[${s}];\n      var width:${d} = originalIndices[${u}];\n      ${r?`if (depth < 0 || depth > (${n[o]} - 1) || height < 0 || height > (${n[s]} - 1) || width < 0 || (width > ${n[u]} - 1)) {\n      return ${i};\n        }`:""};\n\n    depth = max(0, min(depth, ${n[o]} - 1));\n      height = max(0, min(height, ${n[s]} - 1));\n      width = max(0, min(width, ${n[u]} - 1));\n      var depth1: u32 = u32(depth);\n      var height1: u32 = u32(height);\n      var width1: u32 = u32(width);\n      var depth2: u32 = u32(depth + 1);\n      var height2: u32 = u32(height + 1);\n      var width2: u32 = u32(width + 1);\n      var channel: u32 = ${n.length>3?`u32(originalIndices[${l}])`:"0"};\n      var batch: u32 =  ${n.length>3?`u32(originalIndices[${a}])`:"0"};\n\n      var x111: ${d} = getInputValue(batch, channel, depth1, height1, width1);\n      var x112: ${d} = getInputValue(batch, channel, depth1, height1, width2);\n      var x121: ${d} = getInputValue(batch, channel, depth1, height2, width1);\n      var x122: ${d} = getInputValue(batch, channel, depth1, height2, width2);\n      var x211: ${d} = getInputValue(batch, channel, depth2, height1, width1);\n      var x212: ${d} = getInputValue(batch, channel, depth2, height1, width2);\n      var x221: ${d} = getInputValue(batch, channel, depth2, height2, width1);\n      var x222: ${d} = getInputValue(batch, channel, depth2, height2, width2);\n      var dx1: ${d} = abs(depth - ${d}(depth1));\n      var dx2: ${d} = abs(${d}(depth2) - depth);\n      var dy1: ${d} = abs(height - ${d}(height1));\n      var dy2: ${d} = abs(${d}(height2) - height);\n      var dz1: ${d} = abs(width - ${d}(width1));\n      var dz2: ${d} = abs(${d}(width2) - width);\n      if (depth1 == depth2) {\n        dx1 = 0.5;\n        dx2 = 0.5;\n      }\n      if (height1 == height2) {\n        dy1 = 0.5;\n        dy2 = 0.5;\n      }\n      if (width1 == width2) {\n        dz1 = 0.5;\n        dz2 = 0.5;\n      }\n      return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 +\n              x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1);\n    }`},V1=(e,t,n,r,i,a)=>{let o=e.dims,s=P1(a,t.axes,o.length),u=k1(o,r,i,t.axes),l=r.slice();0===r.length&&(l=o.map(((e,t)=>0===e?1:u[t]/e)),"stretch"!==t.keepAspectRatioPolicy&&(u=D1(o,l,t)));let d=J("output",e.dataType,u.length),p=U("input",e.dataType,o.length),c=G.size(u),h=o.length===u.length&&o.every(((e,t)=>e===u[t])),f="tf_crop_and_resize"===t.coordinateTransformMode,m=t.extrapolationValue,g=p.type.value;return{name:"Resize",shaderCache:{hint:`${t.cacheKey}|${n}|${l.length>0?l:""}|${i.length>0?i:""}|${s.length>0?s:""}|${h}|${o}`,inputDependencies:["rank"]},getShaderSource:e=>`\n      ${h?"":`\n      ${E1(t.coordinateTransformMode,g)};\n      ${(()=>{switch(t.mode){case"nearest":return`\n              ${z1(p,o)};\n              ${C1(t.nearestMode,n,g)};\n              ${R1(p,d,o,u,l.length,s.length,f)};\n              `;case"linear":return`\n              ${B1(d,o,u,l.length,s.length)};\n              ${(()=>{if(2===o.length||4===o.length)return`${N1(p,d,o,f,m)}`;if(3===o.length||5===o.length)return`${M1(p,d,o,f,m)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};\n            `;case"cubic":return`\n            ${(()=>{if(2===o.length||4===o.length)return`${L1(p,d,o,u,l,s,t.cubicCoeffA,f,t.extrapolationValue,t.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()};\n            `;default:throw Error("Invalid resize mode")}})()};\n      `}\n      ${e.registerUniform("output_size","u32").registerUniform("scales","f32",l.length).registerUniform("roi","f32",s.length).declareVariables(p,d)}\n      ${e.mainStart()}\n        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n        ${h?"output[global_idx] = input[global_idx];":`\n        let output_indices = ${d.offsetToIndices("global_idx")};\n        var input_indices: ${p.type.indices};\n        ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);\n                if (checkInputIndices(input_indices)) {\n                  output[global_idx] = ${p.getByIndices("input_indices")};\n                } else {\n                  output[global_idx] = ${t.extrapolationValue};\n                }`;case"linear":return`output[global_idx] = ${2===o.length||4===o.length?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${t.mode}`)}})()};\n`}\n      }`,getRunData:()=>({outputs:[{dims:u,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:[{type:"uint32",data:c},{type:"float32",data:l},{type:"float32",data:s},...X(o),...X(u)]})}},F1=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},Bb=(e,t)=>{let n=[],r=[],i=[],a=F1(e);if(0!==t.antialias)throw Error("Only default value (0) for Antialias attribute is supported");O1(e.inputs,t,a,n,r,i),e.compute(V1(e.inputs[0],t,a,n,r,i),{inputs:[0]})},Rb=e=>{let t=e.antialias,n=e.axes,r=e.coordinateTransformMode,i=e.cubicCoeffA,a=0!==e.excludeOutside,o=e.extrapolationValue,s=e.keepAspectRatioPolicy,u=e.mode,l=""===e.nearestMode?"simple":e.nearestMode;return Oe({antialias:t,axes:n,coordinateTransformMode:r,cubicCoeffA:i,excludeOutside:a,extrapolationValue:o,keepAspectRatioPolicy:s,mode:u,nearestMode:l})}})),U1,G1,Nb,Lb,Mb=D((()=>{ut(),Re(),Tt(),Be(),U1=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let t=e[0],n=e[1],r=e[2];if(t.dataType!==n.dataType||t.dataType!==r.dataType)throw new Error("All inputs must have the same data type");if(3!==t.dims.length&&2!==t.dims.length)throw new Error("Input must be 2D or 3D");if(3!==n.dims.length&&2!==n.dims.length)throw new Error("Skip must be 2D or 3D");let i=t.dims[t.dims.length-1],a=t.dims[t.dims.length-2];if(n.dims[n.dims.length-1]!==i)throw new Error("Skip must have the same hidden size as input");if(n.dims[n.dims.length-2]!==a)throw new Error("Skip must have the same sequence length as input");if(1!==r.dims.length)throw new Error("Gamma must be 1D");if(r.dims[r.dims.length-1]!==i)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let t=e[3];if(1!==t.dims.length)throw new Error("Beta must be 1D");if(t.dims[t.dims.length-1]!==i)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let t=e[4];if(1!==t.dims.length)throw new Error("Bias must be 1D");if(t.dims[t.dims.length-1]!==i)throw new Error("Bias must have the same hidden size as input")}},G1=(e,t,n,r)=>{let 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       let offset = global_idx * hiddenSizeVectorized;\n        var sum = ${Pt("f32",f)};\n        var squareSum = ${Pt("f32",f)};\n        for (var i: u32 = 0; i < hiddenSizeVectorized; i++) {\n          let skipValue = skip[offset + i];\n          let biasValue = ${d?"bias[i]":"0.0"};\n          let inputValue = x[offset + i];\n          let value = inputValue + skipValue + biasValue;\n          ${h?"inputSkipBiasSum[offset + i] = value;":""}\n          output[offset + i] = value;\n          let f32Value = ${ur(g,f,"value")};\n          sum += f32Value;\n          squareSum += f32Value * f32Value;\n        }\n        let mean = ${Ut("sum",f)} / hiddenSize;\n        let invStdDev = inverseSqrt(${Ut("squareSum",f)} / hiddenSize - mean * mean + epsilon);\n        ${p?"meanOutput[global_idx] = mean;":""}\n        ${c?"invStdOutput[global_idx] = invStdDev;":""}\n        for (var i: u32 = 0; i < hiddenSizeVectorized; i++) {\n          output[offset + i] = (output[offset + i] - ${g}(mean)) 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(should not happen)");let e=this.kernelCustomData.get(this.currentKernelId);return e||(e={},this.kernelCustomData.set(this.currentKernelId,e)),e}async initialize(e,t){this.env=e;let n=[],r={requiredLimits:{maxComputeWorkgroupStorageSize:t.limits.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:t.limits.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:t.limits.maxStorageBufferBindingSize,maxBufferSize:t.limits.maxBufferSize,maxComputeInvocationsPerWorkgroup:t.limits.maxComputeInvocationsPerWorkgroup,maxComputeWorkgroupSizeX:t.limits.maxComputeWorkgroupSizeX,maxComputeWorkgroupSizeY:t.limits.maxComputeWorkgroupSizeY,maxComputeWorkgroupSizeZ:t.limits.maxComputeWorkgroupSizeZ},requiredFeatures:n};t.features.has("chromium-experimental-timestamp-query-inside-passes")?n.push("chromium-experimental-timestamp-query-inside-passes"):t.features.has("timestamp-query")&&n.push("timestamp-query"),t.features.has("shader-f16")&&n.push("shader-f16"),this.device=await t.requestDevice(r),this.gpuDataManager=Yh(this),this.programManager=new pa(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,Wh(e.logLevel,!!e.debug),this.device.onuncapturederror=e=>{e.error instanceof GPUValidationError&&console.error(`An uncaught WebGPU validation error was raised: ${e.error.message}`)},Object.defineProperty(this.env.webgpu,"device",{value:this.device}),this.setQueryType()}dispose(){typeof this.querySet<"u"&&this.querySet.destroy(),this.gpuDataManager.dispose()}getCommandEncoder(){return this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder(),this.setQueryType(),"none"!==this.queryType&&typeof this.querySet>"u"&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2*this.maxDispatchNumber}),this.queryResolveBuffer=this.device.createBuffer({size:2*this.maxDispatchNumber*8,usage:GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE}))),this.commandEncoder}getComputePassEncoder(){if(!this.computePassEncoder){let e={};"at-passes"===this.queryType&&(e.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:2*this.pendingDispatchNumber,endOfPassWriteIndex:2*this.pendingDispatchNumber+1}),this.computePassEncoder=this.getCommandEncoder().beginComputePass(e)}return this.computePassEncoder}endComputePass(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}flush(){if(!this.commandEncoder)return;let e;zt(),this.endComputePass(),"none"!==this.queryType&&(this.commandEncoder.resolveQuerySet(this.querySet,0,2*this.pendingDispatchNumber,this.queryResolveBuffer,0),e=this.device.createBuffer({size:2*this.pendingDispatchNumber*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(e,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,e,0,2*this.pendingDispatchNumber*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,"none"!==this.queryType&&e.mapAsync(GPUMapMode.READ).then((()=>{let t=new BigUint64Array(e.getMappedRange()),n=this.pendingQueries.get(e);for(let e=0;e<t.length/2;e++){let r=n[e],i=r.kernelId,a=this.kernels.get(i),o=a.kernelType,s=a.kernelName,u=r.programName,l=r.inputTensorViews,d=r.outputTensorViews,p=t[2*e],c=t[2*e+1];typeof this.queryTimeBase>"u"&&(this.queryTimeBase=p);let h=Number(p-this.queryTimeBase),f=Number(c-this.queryTimeBase);if(!Number.isSafeInteger(h)||!Number.isSafeInteger(f))throw new RangeError("incorrect timestamp range");if(this.env.webgpu.profiling?.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:l.map((e=>({dims:e.dims,dataType:Ft(e.dataType)}))),outputsMetadata:d.map((e=>({dims:e.dims,dataType:Ft(e.dataType)}))),kernelId:i,kernelType:o,kernelName:s,programName:u,startTime:h,endTime:f});else{let e="";l.forEach(((t,n)=>{e+=`input[${n}]: [${t.dims}] | ${Ft(t.dataType)}, `}));let t="";d.forEach(((e,n)=>{t+=`output[${n}]: [${e.dims}] | ${Ft(e.dataType)}, `})),console.log(`[profiling] kernel "${i}|${o}|${s}|${u}" ${e}${t}execution time: ${f-h} ns`)}no("GPU",`${u}::${p}::${c}`)}e.unmap(),this.pendingQueries.delete(e)})),Nt()}run(e,t,n,r,i){zt(e.name);let a=[];for(let e=0;e<t.length;++e){let n=this.gpuDataManager.get(t[e].data);if(!n)throw new Error(`no GPU data for input: ${t[e].data}`);a[e]=n}let{outputs:o,dispatchGroup:s,programUniforms:u}=e.getRunData(t),l=0===n.length?o.map(((e,t)=>t)):n;if(l.length!==o.length)throw new Error(`Output size ${l.length} must be equal to ${o.length}.`);let d,p=[],c=[];for(let e=0;e<o.length;++e){if(!Number.isInteger(l[e])||l[e]<-3||l[e]>=o.length)throw new Error(`Invalid output index: ${l[e]}`);if(-3===l[e])continue;let t=-1===l[e],n=-2===l[e],a=t||n?i(o[e].dataType,o[e].dims):r(l[e],o[e].dataType,o[e].dims),s=this.gpuDataManager.get(a.data);if(!s)throw new Error(`no GPU data for output: ${a.data}`);if(t&&this.temporaryData.push(s),n){let e=this.kernelPersistentData.get(this.currentKernelId);e||(e=[],this.kernelPersistentData.set(this.currentKernelId,e)),e.push(s)}p.push(a),c.push(s)}if(u){let e=0,t=[];u.forEach((n=>{let r="number"==typeof n.data?[n.data]:n.data;if(0===r.length)return;let i=r.length<=2?4*r.length:16;e=Math.ceil(e/i)*i,t.push(e),e+=r.length>4?16*Math.ceil(r.length/4):4*r.length}));let n=16;e=Math.ceil(e/n)*n;let r=new ArrayBuffer(e);u.forEach(((e,n)=>{let i=t[n],a="number"==typeof e.data?[e.data]:e.data;"int32"===e.type?new Int32Array(r,i,a.length).set(a):"uint32"===e.type?new Uint32Array(r,i,a.length).set(a):new Float32Array(r,i,a.length).set(a)}));let i=this.gpuDataManager.create(e,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(i.buffer,0,r,0,e),this.gpuDataManager.release(i.id),d={offset:0,size:e,buffer:i.buffer}}let h=this.programManager.normalizeDispatchGroupSize(s),f=1===h[1]&&1===h[2],m=sT(e,t,f),g=this.programManager.getArtifact(m);if(g||(g=this.programManager.build(e,h),this.programManager.setArtifact(m,g),it("info",(()=>`[artifact] key: ${m}, programName: ${e.name}`))),it("info",(()=>`[ProgramManager] run "${e.name}" (key=${m}) with ${h[0]}x${h[1]}x${h[2]}`)),"none"!==this.queryType){let e={kernelId:this.currentKernelId,programName:g.programInfo.name,inputTensorViews:t,outputTensorViews:p};this.pendingKernels.push(e)}return this.programManager.run(g,a,c,h,d),Nt(e.name),p}upload(e,t){this.gpuDataManager.upload(e,t)}memcpy(e,t){this.gpuDataManager.memcpy(e,t)}async download(e,t){await this.gpuDataManager.download(e,t)}alloc(e){return this.gpuDataManager.create(e).id}free(e){return this.gpuDataManager.release(e)}createKernel(e,t,n,r){let i=ty.get(e);if(!i)throw new Error(`kernel not implemented: ${e}`);let a={kernelType:e,kernelName:r,kernelEntry:i[0],attributes:[i[1],n]};this.kernels.set(t,a)}releaseKernel(e){let t=this.kernelPersistentData.get(e);if(t){for(let e of t)this.gpuDataManager.release(e.id);this.kernelPersistentData.delete(e)}this.kernelCustomData.delete(e),this.kernels.delete(e)}computeKernel(e,t,n){let r=this.kernels.get(e);if(!r)throw new Error(`kernel not created: ${e}`);let i=r.kernelType,a=r.kernelName,o=r.kernelEntry,s=r.attributes;if(null!==this.currentKernelId)throw new Error(`kernel "[${i}] ${a}" is not allowed to be called recursively`);this.currentKernelId=e,s[0]&&(s[1]=s[0](s[1]),s[0]=void 0),it("info",(()=>`[WebGPU] Start to run kernel "[${i}] ${a}"...`));let u=this.env.debug;this.temporaryData=[];try{return u&&this.device.pushErrorScope("validation"),o(t,s[1]),0}catch(e){return n.push(Promise.resolve(`[WebGPU] Kernel "[${i}] ${a}" failed. ${e}`)),1}finally{u&&n.push(this.device.popErrorScope().then((e=>e?`GPU validation error for kernel "[${i}] ${a}": ${e.message}`:null)));for(let e of this.temporaryData)this.gpuDataManager.release(e.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(e,t,n,r){let i=this.sessionExternalDataMapping.get(e);i||(i=new Map,this.sessionExternalDataMapping.set(e,i));let a=i.get(t),o=this.gpuDataManager.registerExternalBuffer(n,r,a?.[1]);return i.set(t,[o,n]),o}unregisterBuffers(e){let t=this.sessionExternalDataMapping.get(e);t&&(t.forEach((e=>this.gpuDataManager.unregisterExternalBuffer(e[1]))),this.sessionExternalDataMapping.delete(e))}getBuffer(e){let t=this.gpuDataManager.get(e);if(!t)throw new Error(`no GPU data for buffer: ${e}`);return t.buffer}createDownloader(e,t,n){return async()=>{let r=await Ks(this,e,t);return Hh(r.buffer,n)}}writeTimestamp(e){"inside-passes"===this.queryType&&this.computePassEncoder.writeTimestamp(this.querySet,e)}setQueryType(){this.queryType="none",("default"===this.env.webgpu.profiling?.mode||this.env.wasm.trace)&&(this.device.features.has("chromium-experimental-timestamp-query-inside-passes")?this.queryType="inside-passes":this.device.features.has("timestamp-query")&&(this.queryType="at-passes"))}}})),iy={};Mr(iy,{init:()=>uT});var Ao,bu,uT,ay=D((()=>{ut(),oy(),qr(),Re(),Ao=class e{constructor(e,t,n,r){this.module=e,this.dataType=t,this.data=n,this.dims=r}getFloat32Array(){if(1!==this.dataType)throw new Error("Invalid data type");let e=G.size(this.dims);return 0===e?new Float32Array:new Float32Array(this.module.HEAP8.buffer,this.data,e)}getBigInt64Array(){if(7!==this.dataType)throw new Error("Invalid data type");let e=G.size(this.dims);return 0===e?new BigInt64Array:new BigInt64Array(this.module.HEAP8.buffer,this.data,e)}getInt32Array(){if(6!==this.dataType)throw new Error("Invalid data type");let e=G.size(this.dims);return 0===e?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,e)}reshape(t){if(G.size(t)!==G.size(this.dims))throw new Error("Invalid new shape");return new e(this.module,this.dataType,this.data,t)}},bu=class{constructor(e,t,n){this.module=e,this.backend=t,this.customDataOffset=0,this.customDataSize=0;let r=e.HEAPU32,i=n>>>2;this.opKernelContext=r[i++];let a=r[i++];this.outputCount=r[i++],this.customDataOffset=r[i++],this.customDataSize=r[i++];let o=[];for(let t=0;t<a;t++){let t=r[i++],n=r[i++],a=r[i++],s=[];for(let e=0;e<a;e++)s.push(r[i++]);o.push(new Ao(e,t,n,s))}this.inputs=o}get kernelCustomData(){return this.backend.currentKernelCustomData}get customDataBuffer(){return this.module.HEAPU8.subarray(this.customDataOffset,this.customDataOffset+this.customDataSize)}compute(e,t){let n=t?.inputs?.map((e=>"number"==typeof e?this.inputs[e]:e))??this.inputs,r=t?.outputs??[];return this.backend.run(e,n,r,((e,t,n)=>new Ao(this.module,t,this.output(e,n),n)),((e,t)=>{let n=xo(e);if(!n)throw new Error(`Unsupported data type: ${e}`);let r=n*G.size(t);return new Ao(this.module,e,this.backend.gpuDataManager.create(r).id,t)}))}output(e,t){let n=this.module.stackSave();try{let n=this.module.stackAlloc(4*(1+t.length)),r=n>>2;this.module.HEAPU32[r++]=t.length;for(let e=0;e<t.length;e++)this.module.HEAPU32[r++]=t[e];return this.module._JsepOutput(this.opKernelContext,e,n)}catch(n){throw new Error(`Failed to generate kernel's output[${e}] with dims [${t}]. If you are running with pre-allocated output, please make sure the output type/dims are correct. Error: ${n}`)}finally{this.module.stackRestore(n)}}},uT=async(e,t,n)=>{let r=e.jsepInit;if(!r)throw new Error("Failed to initialize JSEP. The WebAssembly module is not built with JSEP support.");let i=new ha;await i.initialize(t,n),r(i,(e=>i.alloc(e)),(e=>i.free(e)),((t,n,r,a=!1)=>{if(a)it("verbose",(()=>`[WebGPU] jsepCopyGpuToGpu: src=${t}, dst=${n}, size=${r}`)),i.memcpy(t,n);else{it("verbose",(()=>`[WebGPU] jsepCopyCpuToGpu: dataOffset=${t}, gpuDataId=${n}, size=${r}`));let a=e.HEAPU8.subarray(t>>>0,(t>>>0)+r);i.upload(n,a)}}),(async(t,n,r)=>{it("verbose",(()=>`[WebGPU] jsepCopyGpuToCpu: gpuDataId=${t}, dataOffset=${n}, size=${r}`)),await i.download(t,(()=>e.HEAPU8.subarray(n>>>0,(n>>>0)+r)))}),((t,n,r)=>i.createKernel(t,n,r,e.UTF8ToString(e._JsepGetNodeName(n)))),(e=>i.releaseKernel(e)),((t,n,r,a)=>{it("verbose",(()=>`[WebGPU] jsepRun: sessionHandle=${r}, kernel=${t}, contextDataOffset=${n}`));let o=new bu(e,i,n);return i.computeKernel(t,o,a)}))}})),lT,uy,ly,Oo,dT,yu,dy,cy,sy,fy,py,hy,my=D((()=>{Mh(),Fh(),ut(),Rn(),Hi(),qs(),lT=(e,t)=>{0!==ct()._OrtInit(e,t)&&rt("Can't initialize onnxruntime.")},uy=async e=>{lT(e.wasm.numThreads,To(e.logLevel))},ly=async(e,t)=>{if("webgpu"===t){if(typeof navigator>"u"||!navigator.gpu)throw new Error("WebGPU is not supported in current environment");let t=await navigator.gpu.requestAdapter();if(!t)throw new Error('Failed to get GPU adapter. You may need to enable flag "--enable-unsafe-webgpu" if you are using Chrome.');if(!e.wasm.simd)throw new Error("Not supported for WebGPU=ON and SIMD=OFF. Please set `env.wasm.simd` to true when using `webgpu` EP");let n=(ay(),Or(iy)).init;await n(ct(),e,t)}},Oo=new Map,dT=e=>{let t=ct(),n=t.stackSave();try{let n=t.stackAlloc(8);return 0!==t._OrtGetInputOutputCount(e,n,n+4)&&rt("Can't get session input/output count."),[t.HEAP32[n/4],t.HEAP32[n/4+1]]}finally{t.stackRestore(n)}},yu=e=>{let t=ct(),n=t._malloc(e.byteLength);if(0===n)throw new Error(`Can't create a session. failed to allocate a buffer of size ${e.byteLength}.`);return t.HEAPU8.set(e,n),[n,e.byteLength]},dy=async(e,t)=>{let n,r,i=ct();Array.isArray(e)?[n,r]=e:e.buffer===i.HEAPU8.buffer?[n,r]=[e.byteOffset,e.byteLength]:[n,r]=yu(e);let a=0,o=0,s=0,u=[],l=[],d=[];try{if([o,u]=Vh(t),t?.externalData&&i.mountExternalData){let e=[];for(let n of t.externalData){let t="string"==typeof n?n:n.path;e.push(_o("string"==typeof n?n:n.data).then((e=>{i.mountExternalData(t,e)})))}await Promise.all(e)}a=i._OrtCreateSession(n,r,o),0===a&&rt("Can't create a session.");let[e,p]=dT(a),c=[],h=[],f=[];for(let t=0;t<e;t++){let e=i._OrtGetInputName(a,t);0===e&&rt("Can't get an input name."),l.push(e),c.push(i.UTF8ToString(e))}for(let e=0;e<p;e++){let n=i._OrtGetOutputName(a,e);0===n&&rt("Can't get an output name."),d.push(n);let r=i.UTF8ToString(n);h.push(r);{let e="string"==typeof t?.preferredOutputLocation?t.preferredOutputLocation:t?.preferredOutputLocation?.[r]??"cpu";if("cpu"!==e&&"cpu-pinned"!==e&&"gpu-buffer"!==e)throw new Error(`Not supported preferred output location: ${e}.`);f.push(e)}}let m=null;return f.some((e=>"gpu-buffer"===e))&&(s=i._OrtCreateBinding(a),0===s&&rt("Can't create IO binding."),m={handle:s,outputPreferredLocations:f,outputPreferredLocationsEncoded:f.map((e=>Hs(e)))}),Oo.set(a,[a,l,d,m]),[a,c,h]}catch(e){throw l.forEach((e=>i._OrtFree(e))),d.forEach((e=>i._OrtFree(e))),0!==s&&i._OrtReleaseBinding(s),0!==a&&i._OrtReleaseSession(a),e}finally{i._free(n),0!==o&&i._OrtReleaseSessionOptions(o),u.forEach((e=>i._free(e))),i.unmountExternalData?.()}},cy=e=>{let t=ct(),n=Oo.get(e);if(!n)throw new Error(`cannot release session. invalid session id: ${e}`);let[r,i,a,o]=n;o&&t._OrtReleaseBinding(o.handle),t.jsepUnregisterBuffers?.(e),i.forEach((e=>t._OrtFree(e))),a.forEach((e=>t._OrtFree(e))),t._OrtReleaseSession(r),Oo.delete(e)},sy=(e,t,n,r,i)=>{if(!e)return void t.push(0);let a,o,s=ct(),u=e[0],l=e[1],d=e[3];if("string"===u&&"gpu-buffer"===d)throw new Error("String tensor is not supported on GPU.");if("gpu-buffer"===d){let t=e[2].gpuBuffer,n=xo(Ws(u));o=l.reduce(((e,t)=>e*t),1)*n,a=s.jsepRegisterBuffer(r,i,t,o)}else{let t=e[2];if(Array.isArray(t)){o=4*t.length,a=s._malloc(o),n.push(a);let e=a/4;for(let r=0;r<t.length;r++){if("string"!=typeof t[r])throw new TypeError(`tensor data at index ${r} is not a 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type.")},Va=(e,t)=>{Ua(e.inputs);let r=(o,n,s)=>{let u=[];for(let l=0;l<o.rank;l++)(s.indexOf(l)>=0||s.length===0)&&u.push(`input_indices[${l}] = 0;`);return[`${u.join(`\n`)}`,`var value = ${o.getByIndices("input_indices")};\nvar best_index : i32 = 0;`,`if (${o.getByIndices("input_indices")} ${t.selectLastIndex>0?"<=":"<"} value) {\n         value = ${o.getByIndices("input_indices")};\n         best_index = i32(last_index);\n       }`,"",n.setByOffset("global_idx","best_index")]};e.compute(qr("ArgMin",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],r,[t.axis],7,t.keepDims),{inputs:[0]})},Na=(e,t)=>{Ua(e.inputs);let r=(o,n,s)=>{let u=[];for(let l=0;l<o.rank;l++)(s.indexOf(l)>=0||s.length===0)&&u.push(`input_indices[${l}] = 0;`);return[`${u.join(`\n`)}`,`var value = ${o.getByIndices("input_indices")};\nvar best_index : i32 = 0;`,`if (${o.getByIndices("input_indices")} ${t.selectLastIndex>0?">=":">"} value) {\n         value = ${o.getByIndices("input_indices")};\n         best_index = i32(last_index);\n       }`,"",n.setByOffset("global_idx","best_index")]};e.compute(qr("argMax",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],r,[t.axis],7,t.keepDims),{inputs:[0]})},zn=e=>ge(e)});var Ud,Vd,Nd,Wd,Kr,Hd,Ha,Un=j(()=>{"use strict";Ne();Nr();ve();Ud=(e,t)=>{let r=e[0],o=e[1],n=e[2],s=e[3],u=e[4],l=e[5];if(u&&l)throw new Error("Attention cannot have both past and relative_position_bias");if(r.dims.length!==3)throw new Error(\'Input "input" must have 3 dimensions\');let a=r.dims[0],p=r.dims[1],h=r.dims[2];if(n.dims.length!==1)throw new Error(\'Input "bias" is expected to have 1 dimensions\');if(o.dims.length!==2)throw new Error(\'Input "weights" is expected to have 2 dimensions\');if(o.dims[0]!==h)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(n.dims[0]!==o.dims[1])throw new Error(\'Input "bias" dimension 0 should have same length as dimension 1 of input "weights"\');let g=n.dims[0]/3,b=g,w=b;if(t.qkvHiddenSizes.length>0){if(t.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let E of t.qkvHiddenSizes)if(E%t.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");g=t.qkvHiddenSizes[0],b=t.qkvHiddenSizes[1],w=t.qkvHiddenSizes[2]}let y=p;if(g!==b)throw new Error("qkv_hidden_sizes first element should be same as the second");if(n.dims[0]!==g+b+w)throw new Error(\'Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes\');let _=0;if(u){if(b!==w)throw new Error(\'Input "past" expect k_hidden_size == v_hidden_size\');if(u.dims.length!==5)throw new Error(\'Input "past" must have 5 dimensions\');if(u.dims[0]!==2)throw new Error(\'Input "past" first dimension must be 2\');if(u.dims[1]!==a)throw new Error(\'Input "past" second dimension must be batch_size\');if(u.dims[2]!==t.numHeads)throw new Error(\'Input "past" third dimension must be num_heads\');if(u.dims[4]!==b/t.numHeads)throw new Error(\'Input "past" fifth dimension must be k_hidden_size / num_heads\');t.pastPresentShareBuffer||(_=u.dims[3])}let I=y+_,$=-1,x=0;if(s)throw new Error("Mask not supported");if(u)throw new Error("past is not supported");if(l)throw new Error("relativePositionBias is not supported");return{batchSize:a,sequenceLength:p,pastSequenceLength:_,kvSequenceLength:y,totalSequenceLength:I,maxSequenceLength:$,inputHiddenSize:h,hiddenSize:g,vHiddenSize:w,headSize:Math.floor(g/t.numHeads),vHeadSize:Math.floor(w/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:x,scale:t.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},Vd=(e,t,r,o)=>{let n=Fe(o),s=64,u=o/n;u<s?s=1:u/8<64&&(s=Math.ceil(u/8));let l=Math.ceil(o/n/s),p=[{type:Xe(t.dataType),data:1/o},{type:"uint32",data:u},{type:"uint32",data:l}],h=Le(t.dataType,n),g=b=>{let w=F("x",t.dataType,t.dims,n),y="thread_max_vector";n===2?y="max(thread_max_vector.x, thread_max_vector.y)":n===4&&(y="max(max(thread_max_vector.x, thread_max_vector.y), max(thread_max_vector.z, thread_max_vector.w))");let _=lt(t.dataType),I=[{name:"d_inv",type:_},{name:"d_comp",type:"u32"},{name:"elements_per_wg",type:"u32"}];return`\n  var<workgroup> wgMax: array<f32, ${s}>;\n  var<workgroup> wgSum: array<f32, ${s}>;\n  ${b.registerUniforms(I).declareVariables(w)}\n  ${b.mainStart([s,1,1])}\n    let localOffset = local_idx * uniforms.elements_per_wg;\n    let offset: u32 = workgroup_id.x * uniforms.d_comp + localOffset;\n\n    var thread_max_vector = ${Ze("f32",n,"-3.402823e+38f")};\n    for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {\n      thread_max_vector = max(${at(_,n,"x[offset + i]")}, thread_max_vector);\n    }\n    wgMax[local_idx] = ${y};\n    workgroupBarrier();\n\n    var maxValue = -3.402823e+38f;\n    for (var i = 0u; i < ${s}; i++) {\n      maxValue = max(wgMax[i], maxValue);\n    }\n\n    var sumVector = ${Ze("f32",n,"0")};\n    for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {\n      sumVector += exp(${at(_,n,"x[offset + i]")} - maxValue);\n    }\n    wgSum[local_idx] = ${Je("sumVector",n)};\n    workgroupBarrier();\n\n    var sum: f32 = 0;\n    for (var i = 0u; i < ${s}; i++) {\n      sum += wgSum[i];\n    }\n\n    if (sum == 0) {\n      for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {\n        x[offset + i] = ${Ze("f32",n,"uniforms.d_inv")};\n      }\n    } else {\n      for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {\n        let f32input = ${at(_,n,"x[offset + i]")};\n        x[offset + i] = ${w.type.value}(exp(f32input - maxValue) / sum);\n      }\n    }\n  }`};e.compute({name:"AttentionProbsSoftmax",shaderCache:{hint:`${s};${h};${n}`},getShaderSource:g,getRunData:()=>({outputs:[],dispatchGroup:{x:r},programUniforms:p})},{inputs:[t],outputs:[]})},Nd=(e,t,r,o,n,s)=>{let u=[n.batchSize,n.numHeads,n.sequenceLength,n.kvSequenceLength+n.pastSequenceLength],l=s.scale===0?1/Math.sqrt(n.headSize):s.scale,a=Fe(n.headSize),p=n.headSize/a,h=12,g={x:Math.ceil(n.totalSequenceLength/h),y:Math.ceil(n.sequenceLength/h),z:n.batchSize*n.numHeads},b=Xe(t.dataType),w=[{type:"uint32",data:n.sequenceLength},{type:"uint32",data:p},{type:"uint32",data:n.totalSequenceLength},{type:"uint32",data:n.kvSequenceLength},{type:b,data:l}],y=[t,r],_=$=>{let x=M("q",t.dataType,t.dims,a),E=M("key",r.dataType,r.dims,a),A=F("output",t.dataType,u),z=Le(t.dataType),R=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"alpha",type:z}];return`\n  const beta: ${z} = 1.0;\n  const TILE_SIZE = ${h}u;\n\n  var<workgroup> tileQ: array<${x.type.storage}, ${h*h}>;\n  var<workgroup> tileK: array<${x.type.storage}, ${h*h}>;\n  ${$.registerUniforms(R).declareVariables(x,E,A)}\n  ${$.mainStart([h,h,1])}\n    // x holds the N and y holds the M\n    let headIdx = workgroup_id.z;\n    let m = workgroup_id.y * TILE_SIZE;\n    let n = workgroup_id.x * TILE_SIZE;\n    let lm = m + local_id.y;\n    let ln = n + local_id.x;\n\n    let qOffset = uniforms.M * uniforms.K * headIdx + m * uniforms.K;\n    let kOffset = uniforms.kv_sequence_length * uniforms.K * headIdx + n * uniforms.K;\n\n    var value = ${Ze(z,a)};\n    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n      if (m + local_id.y < uniforms.M && w + local_id.x < uniforms.K) {\n        tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x];\n      }\n      if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) {\n        tileK[TILE_SIZE * local_id.y + local_id.x] = key[kOffset + local_id.y * uniforms.K + w + local_id.x];\n      }\n      workgroupBarrier();\n\n      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {\n        value += tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k];\n      }\n\n      workgroupBarrier();\n    }\n\n    let headOffset = headIdx * uniforms.M * uniforms.N;\n    if (lm < uniforms.M && ln < uniforms.N) {\n      let outputIdx = headOffset + lm * uniforms.N + ln;\n      output[outputIdx] = ${Je("value",a)} * uniforms.alpha;\n    }\n  }`},I=e.compute({name:"AttentionProbs",shaderCache:{hint:`${a}`,inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:u,dataType:t.dataType,gpuDataType:0}],dispatchGroup:g,programUniforms:w}),getShaderSource:_},{inputs:y,outputs:[-1]})[0];return Vd(e,I,n.batchSize*n.numHeads*n.sequenceLength,n.totalSequenceLength),I},Wd=(e,t,r,o)=>{let n=[o.batchSize,o.sequenceLength,o.vHiddenSize],s=12,u={x:Math.ceil(o.vHeadSize/s),y:Math.ceil(o.sequenceLength/s),z:o.batchSize*o.numHeads},l=[{type:"uint32",data:o.sequenceLength},{type:"uint32",data:o.totalSequenceLength},{type:"uint32",data:o.vHeadSize},{type:"uint32",data:o.numHeads},{type:"uint32",data:o.vHiddenSize}],a=p=>{let h=M("probs",t.dataType,t.dims),g=M("v",r.dataType,r.dims),b=F("output",t.dataType,n),w=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"v_hidden_size",type:"u32"}];return`\n  const TILE_SIZE = ${s}u;\n  var<workgroup> tileQ: array<${h.type.value}, ${s*s}>;\n  var<workgroup> tileK: array<${h.type.value}, ${s*s}>;\n  ${p.registerUniforms(w).declareVariables(h,g,b)}\n  ${p.mainStart([s,s,1])}\n   let headIdx = workgroup_id.z;\n   let m = workgroup_id.y * TILE_SIZE + local_id.y;\n   let n = workgroup_id.x * TILE_SIZE + local_id.x;\n\n   let offsetA = headIdx * (uniforms.M * uniforms.K) + m * uniforms.K;\n   let offsetB = headIdx * (uniforms.N * uniforms.K) + n;\n\n   var value = ${h.type.storage}(0);\n   for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n     if (m < uniforms.M && w + local_id.x < uniforms.K) {\n       tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x];\n     }\n     if (n < uniforms.N && w + local_id.y < uniforms.K) {\n       tileK[TILE_SIZE * local_id.y + local_id.x] = v[offsetB + (w + local_id.y) * uniforms.N];\n     }\n     workgroupBarrier();\n     for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {\n       value += tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * k + local_id.x];\n     }\n     workgroupBarrier();\n   }\n\n   // we need to transpose output from BNSH_v to BSND_v\n   let batchIdx = workgroup_id.z / uniforms.num_heads;\n   let currentBatchHeadNumber = workgroup_id.z % uniforms.num_heads;\n   let headOffset = (batchIdx * uniforms.M * uniforms.num_heads + currentBatchHeadNumber) * uniforms.N;\n   if (m < uniforms.M && n < uniforms.N) {\n     let outputIdx = batchIdx * uniforms.M *uniforms.v_hidden_size + m * uniforms.v_hidden_size\n       + currentBatchHeadNumber * uniforms.N + n;\n     output[outputIdx] = value;\n   }\n  }`};return e.compute({name:"AttentionScore",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:n,dataType:t.dataType,gpuDataType:0}],dispatchGroup:u,programUniforms:l}),getShaderSource:a},{inputs:[t,r],outputs:[0]})[0]},Kr=(e,t,r,o,n,s,u,l,a,p,h)=>{let g=Nd(e,t,r,a,p,h);Wd(e,g,o,p)},Hd=(e,t)=>{let r=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],o=t.sequenceLength,n=t.inputHiddenSize,s=t.headSize,u=12,l={x:Math.ceil(t.headSize/u),y:Math.ceil(t.sequenceLength/u),z:t.batchSize*t.numHeads},a=[e.inputs[0],e.inputs[1],e.inputs[2]],p=[{type:"uint32",data:o},{type:"uint32",data:n},{type:"uint32",data:s},{type:"uint32",data:t.numHeads},{type:"uint32",data:t.headSize},{type:"uint32",data:t.hiddenSize},{type:"uint32",data:t.hiddenSize+t.hiddenSize+t.vHiddenSize}],h=g=>{let b=F("output_q",a[0].dataType,r),w=F("output_k",a[0].dataType,r),y=F("output_v",a[0].dataType,r),_=M("input",a[0].dataType,a[0].dims),I=M("weight",a[1].dataType,a[1].dims),$=M("bias",a[2].dataType,a[2].dims),x=_.type.storage,E=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}];return`\n  const TILE_SIZE = ${u}u;\n  var<workgroup> tileInput: array<${x}, ${u*u}>;\n  var<workgroup> tileWeightQ: array<${x}, ${u*u}>;\n  var<workgroup> tileWeightK: array<${x}, ${u*u}>;\n  var<workgroup> tileWeightV: array<${x}, ${u*u}>;\n  ${g.registerUniforms(E).declareVariables(_,I,$,b,w,y)}\n  ${g.mainStart([u,u,1])}\n    let batchIndex = workgroup_id.z / uniforms.num_heads;\n    let headNumber = workgroup_id.z % uniforms.num_heads;\n    let m = workgroup_id.y * TILE_SIZE + local_id.y;\n    let n = workgroup_id.x * TILE_SIZE + local_id.x;\n\n    let inputOffset = batchIndex * (uniforms.M * uniforms.K) + m * uniforms.K;\n    let biasOffsetQ = headNumber * uniforms.head_size;\n    let biasOffsetK = uniforms.hidden_size + biasOffsetQ;\n    let biasOffsetV = uniforms.hidden_size + biasOffsetK;\n\n    var valueQ = ${x}(0);\n    var valueK = ${x}(0);\n    var valueV = ${x}(0);\n    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n      if (m < uniforms.M && w + local_id.x < uniforms.K) {\n        tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];\n      }\n      if (n < uniforms.N && w + local_id.y < uniforms.K) {\n        let offset = n + (w + local_id.y) * uniforms.ldb;\n        tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];\n        tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];\n        tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];\n      }\n      workgroupBarrier();\n      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {\n        let inputTileOffset = TILE_SIZE * local_id.y + k;\n        let weightTileOffset = TILE_SIZE * k + local_id.x;\n        valueQ += tileInput[inputTileOffset] * tileWeightQ[weightTileOffset];\n        valueK += tileInput[inputTileOffset] * tileWeightK[weightTileOffset];\n        valueV += tileInput[inputTileOffset] * tileWeightV[weightTileOffset];\n      }\n\n      workgroupBarrier();\n    }\n\n    let headOffset = (m * uniforms.N + n) % uniforms.head_size;\n    valueQ += bias[headOffset + biasOffsetQ];\n    valueK += bias[headOffset + biasOffsetK];\n    valueV += bias[headOffset + biasOffsetV];\n\n    let offset = workgroup_id.z * uniforms.M * uniforms.N;\n    if (m < uniforms.M && n < uniforms.N) {\n      let outputIdx = offset + m * uniforms.N + n;\n      output_q[outputIdx] = valueQ;\n      output_k[outputIdx] = valueK;\n      output_v[outputIdx] = valueV;\n    }\n  }`};return e.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:l,programUniforms:p}),getShaderSource:h},{inputs:a,outputs:[-1,-1,-1]})},Ha=(e,t)=>{let r=Ud(e.inputs,t),[o,n,s]=Hd(e,r);return Kr(e,o,n,s,e.inputs[4],void 0,void 0,void 0,e.inputs[5],r,t)}});var Gd,Ld,Fd,Ga,La=j(()=>{"use strict";Lt();$e();je();ve();Gd=(e,t)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let r=(o,n,s)=>{let u=n.length;if(u!==o.length)throw new Error(`${s}: num dimensions != ${u}`);n.forEach((l,a)=>{if(l!==o[a])throw new Error(`${s}: dim[${a}] do not match`)})};if(e[0].dims.length>1){let o=t.format==="NHWC"?t.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,t.spatial?2:void 0);r(e[1].dims,o,"Invalid input scale"),r(e[2].dims,o,"Invalid input B"),r(e[3].dims,o,"Invalid input mean"),r(e[4].dims,o,"Invalid input var")}else r(e[1].dims,[1],"Invalid input scale"),r(e[2].dims,[1],"Invalid input B"),r(e[3].dims,[1],"Invalid input mean"),r(e[4].dims,[1],"Invalid input 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o.join(`\n`)},al=(e,t)=>{let r=e[0].dims.slice();if(t>=r.length||t<-1*r.length)throw new Error("axis specified for concat doesn\'t match input dimensionality");let o=t<0?r.length+t:t,n=r.slice(0);for(let A=1;A<e.length;A++){let z=e[A].dims.slice();for(let R=0;R<r.length;R++)if(R===o)n[o]+=z[R];else if(r[R]!==z[R])throw new Error("non concat dimensions must match")}let s=U.size(n),u=new Array(e.length),l=new Array(e.length),a=e[0].dataType,p=0,h=[],g=[],b=[],w=[{type:"uint32",data:s}];for(let A=0;A<e.length;++A)p+=e[A].dims[o],u[A]=p,b.push(Re(e[A].dims.length)),g.push(b[A]?e[A].dims.length:e[A].dims),l[A]=M(`input${A}`,a,g[A]),h.push(b[A]?"rank":"dims"),w.push({type:"uint32",data:u[A]});for(let A=0;A<e.length;++A)b[A]&&w.push(...L(e[A].dims));let y=Re(n.length);y&&w.push(...L(n));let _=y?n.length:n,I=F("output",a,_),$=I.indicesGet("indices",o),x=Array.from(Array(u.length).keys()).map(A=>`uniforms.sizeInConcatAxis${A}`).join(","),E=A=>`\n\n  ${(()=>{A.registerUniform("outputSize","u32");for(let z=0;z<e.length;z++)A.registerUniform(`sizeInConcatAxis${z}`,"u32");return A.declareVariables(...l,I)})()}\n\n  ${nl(u.length,x)}\n\n  ${A.mainStart()}\n    ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n\n    var indices = ${I.offsetToIndices("global_idx")};\n\n    let inputIndex = calculateInputIndex(${$});\n    if (inputIndex != 0u) {\n      let sizeInConcatAxis = array<u32, ${u.length}u>(${x});\n      ${$} -= sizeInConcatAxis[inputIndex - 1u];\n    }\n\n    ${ol(l,I)}\n  }`;return{name:"Concat",shaderCache:{hint:`${t}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:w}),getShaderSource:E}},zi=(e,t)=>{rl(e.inputs),e.compute(al(e.inputs,t.axis))},Ui=e=>ge({axis:e.axis})});var gt,Qr,It=j(()=>{"use strict";$e();gt=(e,t)=>{switch(e.activation){case"Relu":return{activationFunction:"",applyActivation:`value = max(value, ${t}(0.0));`};case"Sigmoid":return{activationFunction:"",applyActivation:`value = (${t}(1.0) / (${t}(1.0) + exp(-value)));`};case"Clip":return{activationFunction:`const clip_min_=${t}(${e.clipMin});const clip_max_=${t}(${e.clipMax});`,applyActivation:"value = clamp(value, clip_min_, clip_max_);"};default:return{activationFunction:"",applyActivation:""}}},Qr=e=>{let t=e?.activation||"";if(t==="Clip"){let[r,o]=e?.activation_params||[Gr,Lr];return{activation:t,clipMax:o,clipMin:r,activationCacheKey:`${t}:${r},${o}`}}return{activation:t,activationCacheKey:t}}});var Ke,Xr,Jr=j(()=>{"use strict";Ke=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},Xr=e=>`\n      ${e?"value = value + getBiasByOutputCoords(coords);":""}\n      `});var en,Nn=j(()=>{"use strict";en=e=>`\nfn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {\n  return dot(coords, vec4<i32>(\n      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n}\nfn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {\n  return dot(coords, vec4<i32>(\n    i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));\n}\n`});var il,sl,mr,Ni,ul,fr,dl,tn,hr=j(()=>{"use strict";$e();ve();It();Jr();il=(e,t)=>e?`\n        mm_Asub[inputRow][inputCol] = mm_readA(batch,\n          kStart + inputRow,\n          globalRowStart / innerElementSize + inputCol${t?", batchIndices":""});\n        `:`\n        mm_Asub[inputRow][inputCol] = mm_readA(batch,\n          globalRow + innerRow,\n          kStart / innerElementSize + inputCol${t?", batchIndices":""});\n        `,sl=(e,t)=>e?`\n        let ACached0 = mm_Asub[k * innerElementSize][localRow];\n        let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];\n        let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];\n        ${t===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"}\n        for (var i = 0; i < rowPerThread; i = i + 1) {\n          acc[i] = BCached0 * ACached0[i] + acc[i];\n          acc[i] = BCached1 * ACached1[i] + acc[i];\n          acc[i] = BCached2 * ACached2[i] + acc[i];\n          ${t===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"}\n        }`:`\n        for (var i = 0; i < rowPerThread; i = i + 1) {\n          let ACached = mm_Asub[tileRow + i][k];\n          acc[i] = BCached0 * ACached.x + acc[i];\n          acc[i] = BCached1 * ACached.y + acc[i];\n          acc[i] = BCached2 * ACached.z + acc[i];\n          ${t===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"}\n        }`,mr=(e,t,r="f32",o,n=!1,s=32,u=!1,l=32)=>{let a=t[1]*e[1],p=t[0]*e[0],h=n?a:s,g=n?s:a,b=h/t[0],w=s/t[1];if(!((n&&b===4&&e[1]===4||!n&&(b===3||b===4))&&h%t[0]===0&&s%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${n} is true, innerElementSize ${b} and workPerThread[1] ${e[1]} must be 4.\n      Otherwise, innerElementSize ${b} must be 3 or 4.\n  tileAWidth ${h} must be divisible by workgroupSize[0]${t[0]}. tileInner ${s} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return`\nvar<workgroup> mm_Asub: array<array<vec${b}<${r}>, ${h/b}>, ${g}>;\nvar<workgroup> mm_Bsub: array<array<vec4<${r}>, ${p/e[0]}>, ${s}>;\n\nconst rowPerThread = ${e[1]};\nconst colPerThread = ${e[0]};\nconst innerElementSize = ${b};\nconst tileInner = ${s};\n\n@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]})\nfn main(@builtin(local_invocation_id) localId : vec3<u32>,\n        @builtin(global_invocation_id) globalId : vec3<u32>,\n        @builtin(workgroup_id) workgroupId : vec3<u32>) {\n  let localRow = i32(localId.y);\n  let tileRow = localRow * rowPerThread;\n  let tileCol = i32(localId.x);\n\n  let globalRow =i32(globalId.y) * rowPerThread;\n  let globalCol = i32(globalId.x);\n  let batch = ${u?"0":"i32(globalId.z)"};\n  ${o?`let batchIndices = ${o.offsetToIndices("u32(batch)")};`:""}\n  let globalRowStart = i32(workgroupId.y) * ${a};\n\n  let numTiles = ${u?`${Math.ceil(l/s)}`:"(uniforms.dimInner - 1) / tileInner + 1"};\n  var kStart = ${u?`i32(globalId.z) * ${l}`:"0"};\n\n  var acc: array<vec4<${r}>, rowPerThread>;\n\n  // Loop over shared dimension.\n  let tileRowB = localRow * ${w};\n  for (var t = 0; t < numTiles; t = t + 1) {\n      // Load one tile of A into local memory.\n      for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n          let inputRow = tileRow + innerRow;\n          let inputCol = tileCol;\n          ${il(n,o)}\n      }\n\n      // Load one tile of B into local memory.\n      for (var innerRow = 0; innerRow < ${w}; innerRow = innerRow + 1) {\n          let inputRow = tileRowB + innerRow;\n          let inputCol = tileCol;\n          mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${o?", batchIndices":""});\n      }\n      kStart = kStart + tileInner;\n      workgroupBarrier();\n\n      // Compute acc values for a single thread.\n      for (var k = 0; k < tileInner / innerElementSize; k = k + 1) {\n          let BCached0 = mm_Bsub[k * innerElementSize][tileCol];\n          let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol];\n          let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol];\n          ${b===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"}\n\n          ${sl(n,b)}\n      }\n\n      workgroupBarrier();\n  }\n\n  for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n  }\n}`},Ni=(e,t)=>e?`\n            mm_Asub[inputRow][inputCol] = mm_readA(batch,\n              kStart + inputRow,\n              globalRowStart + inputCol${t?", batchIndices":""});\n            `:`\n            mm_Asub[inputRow][inputCol] = mm_readA(batch,\n              globalRowStart + inputRow,\n              kStart + inputCol${t?", batchIndices":""});\n            `,ul=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",fr=(e,t,r="f32",o,n=!1,s=32,u=!1,l=32,a=!1)=>{let p=e[1]*t[1],h=e[0]*t[0],g=n?p:s,b=n?s:p;if(!(b%t[1]===0&&g%t[0]===0&&s%t[1]===0))throw new Error(`tileAHight ${b} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${g} must be divisible by workgroupSize[0]${t[0]}, tileInner ${s} must be divisible by workgroupSize[1]${t[1]}`);let w=b/t[1],y=g/t[0],_=s/t[1],I=a?`\n    let localRow = i32(localId.y);\n    let localCol = i32(localId.x);\n    let globalRowStart = i32(workgroupId.y) * ${p};\n    let globalColStart = i32(workgroupId.x) * ${h};\n\n    // Loop over shared dimension.\n    for (var t = 0; t < numTiles; t = t + 1) {\n      // Load one tile of A into local memory.\n      for (var inputRow = localRow; inputRow < ${b}; inputRow = inputRow + ${t[1]}) {\n        for (var inputCol = localCol; inputCol < ${g}; inputCol = inputCol + ${t[0]}) {\n          ${Ni(n,o)}\n        }\n      }\n      // Load one tile of B into local memory.\n      for (var inputRow = localRow; inputRow < ${s}; inputRow = inputRow + ${t[1]}) {\n            for (var inputCol = localCol; inputCol < ${h}; inputCol = inputCol + ${t[0]}) {\n          mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n            kStart + inputRow,\n            globalColStart + inputCol${o?", batchIndices":""});\n        }\n      }\n      kStart = kStart + tileInner;\n      workgroupBarrier();\n\n      // Compute acc values for a single thread.\n      var BCached : array<${r}, colPerThread>;\n      for (var k = 0; k < tileInner; k = k + 1) {\n        for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n          BCached[inner] = mm_Bsub[k][localCol + inner * ${t[0]}];\n        }\n        for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n          let ACached = ${n?`mm_Asub[k][localRow + innerRow * ${t[1]}];`:`mm_Asub[localRow + innerRow * ${t[1]}][k];`}\n          for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n            acc[innerRow][innerCol] = acc[innerRow][innerCol] +\n                ACached * BCached[innerCol];\n          }\n        }\n      }\n      workgroupBarrier();\n    }\n    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      let gRow = globalRowStart + localRow + innerRow * ${t[1]};\n      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n        let gCol = globalColStart + localCol + innerCol * ${t[0]};\n        mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);\n      }\n    }\n    `:`\nlet tileRow = i32(localId.y) * rowPerThread;\nlet tileCol = i32(localId.x) * colPerThread;\n\nlet globalRow = i32(globalId.y) * rowPerThread;\nlet globalCol = i32(globalId.x) * colPerThread;\nlet globalRowStart = i32(workgroupId.y) * ${p};\n\nlet tileRowA = i32(localId.y) * ${w};\nlet tileColA = i32(localId.x) * ${y};\nlet tileRowB = i32(localId.y) * ${_};\n// Loop over shared dimension.\nfor (var t = 0; t < numTiles; t = t + 1) {\n  // Load one tile of A into local memory.\n  for (var innerRow = 0; innerRow < ${w}; innerRow = innerRow + 1) {\n    for (var innerCol = 0; innerCol < ${y}; innerCol = innerCol + 1) {\n      let inputRow = tileRowA + innerRow;\n      let inputCol = tileColA + innerCol;\n      ${Ni(n,o)}\n    }\n  }\n\n  // Load one tile of B into local memory.\n  for (var innerRow = 0; innerRow < ${_}; innerRow = innerRow + 1) {\n    for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n      let inputRow = tileRowB + innerRow;\n      let inputCol = tileCol + innerCol;\n      mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n        kStart + inputRow,\n        globalCol + innerCol${o?", batchIndices":""});\n    }\n  }\n  kStart = kStart + tileInner;\n  workgroupBarrier();\n\n  // Compute acc values for a single thread.\n  var BCached : array<${r}, colPerThread>;\n  for (var k = 0; k < tileInner; k = k + 1) {\n    for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n      BCached[inner] = mm_Bsub[k][tileCol + inner];\n    }\n\n    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      ${ul(n)}\n      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n        acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n      }\n    }\n  }\n\n  workgroupBarrier();\n}\n\nfor (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n  for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n    mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n        acc[innerRow][innerCol]);\n  }\n}\n`;return`\n  var<workgroup> mm_Asub : array<array<${r}, ${g}>, ${b}>;\n  var<workgroup> mm_Bsub : array<array<${r}, ${h}>, ${s}>;\n  const rowPerThread = ${e[1]};\n  const colPerThread = ${e[0]};\n  const tileInner = ${s};\n\n@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]})\nfn main(@builtin(local_invocation_id) localId : vec3<u32>,\n        @builtin(global_invocation_id) globalId : vec3<u32>,\n        @builtin(workgroup_id) workgroupId : vec3<u32>) {\n    let batch = ${u?"0":"i32(globalId.z)"};\n    ${o?`let batchIndices = ${o.offsetToIndices("u32(batch)")};`:""}\n    let numTiles = ${u?`${Math.ceil(l/s)}`:"(uniforms.dimInner - 1) / tileInner + 1"};\n    var kStart = ${u?`i32(globalId.z) * ${l}`:"0"};\n\n    var acc : array<array<${r}, colPerThread>, rowPerThread>;\n\n    // Without this initialization strange values show up in acc.\n    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n        acc[innerRow][innerCol] = 0.0;\n      }\n    }\n    ${I}\n  }\n`},dl=(e,t,r,o,n,s=!1)=>{let[u,l,a]=n,[p,h,g,b]=o,w=Ft(u,a),y=Ft(l,a),_=Le(o[0].type.tensor),I=()=>{let E=h.rank,A=p.rank,z=`var aIndices: ${h.type.indices};`;for(let R=E-2-1,V=A-1;R>=0;R--,V--)z+=`\naIndices[${R}] = ${A>1?`batchIndices[${V}]`:"batchIndices"};`;return w.forEach(R=>{z+=`\naIndices[${R}] = 0;`}),z+=`\naIndices[${E-2}] = u32(row);\n                   aIndices[${E-1}] = u32(colIn);`,z},$=()=>{let E=g.rank,A=p.rank,z=`var bIndices: ${g.type.indices};`;for(let R=E-2-1,V=A-1;R>=0;R--,V--)z+=`\nbIndices[${R}] = ${A>1?`batchIndices[${V}]`:"batchIndices"};`;return y.forEach(R=>{z+=`\nbIndices[${R}] = 0;`}),z+=`\nbIndices[${E-2}] = u32(row);\n                   bIndices[${E-1}] = u32(colIn);`,z};return`\n    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${p.type.indices}) -> ${Ke(e,_)} {\n      var value = ${Ke(e,_)}(0.0);\n      let col = colIn * ${e};\n      if(row < uniforms.dimAOuter && col < uniforms.dimInner)\n      {\n        ${I()}\n        value = ${h.getByIndices("aIndices")};\n      }\n      return value;\n    }\n\n    fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${p.type.indices}) -> ${Ke(e,_)} {\n      var value = ${Ke(e,_)}(0.0);\n      let col = colIn * ${e};\n      if(row < uniforms.dimInner && col < uniforms.dimBOuter)\n      {\n        ${$()}\n        value = ${g.getByIndices("bIndices")};\n      }\n      return value;\n    }\n\n    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${Ke(e,_)}) {\n      let col = colIn * ${e};\n      if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n        var value = valueIn;\n        let coords = vec3<i32>(batch, row, colIn);\n        ${t?`value = value + ${s?"bias[colIn]":`${Ke(e,_)}(bias[row])`};`:""}\n        ${r}\n        ${b.setByIndices("vec3<u32>(coords)","value")}\n      }\n    }\n    `},tn=(e,t,r,o,n=!1)=>{let s=e[0].dims,u=e[1].dims,l=s.slice(0,-2),a=u.slice(0,-2),p=o?o.slice(0,-2):r.slice(0,-2),h=Re(p.length),g=h?p.length:p,b=Fr("batchDims",e[0].dataType,g,1),w=U.size(p),y=s[s.length-2],_=s[s.length-1],I=u[u.length-1],$=_%4===0&&I%4===0,x=y<=8?[4,1,1]:[4,4,1],E=[8,8,1],A=[Math.ceil(I/E[0]/x[0]),Math.ceil(y/E[1]/x[1]),Math.ceil(w/E[2]/x[2])],z=Le(e[0].dataType),R=$?4:1,V=[...l,y,_/R],T=Re(V.length),N=T?V.length:V,te=[...a,_,I/R],Y=Re(te.length),K=Y?te.length:te,Q=[w,y,I/R],Z=M("a",e[0].dataType,N,R),Ee=M("b",e[1].dataType,K,R),Pe=F("result",e[0].dataType,Q.length,R),fe=[Z,Ee],Ie=[{type:"int32",data:y},{type:"int32",data:I},{type:"int32",data:_}];h&&Ie.push(...L(p)),T&&Ie.push(...L(V)),Y&&Ie.push(...L(te));let he=[];he.push(T?"rank":"dims"),he.push(Y?"rank":"dims");let ye=e.length>2,{activationFunction:We,applyActivation:De}=gt(t,Pe.type.value),Ge=dl(R,ye,De,[b,Z,Ee,Pe],[l,a,p],n);if(ye){let ee=n?R:1;fe.push(M("bias",e[2].dataType,e[2].dims.length,ee)),Ie.push(...L(e[2].dims)),he.push("rank")}Ie.push(...L(Q));let G=ee=>`\n  ${ee.registerUniform("dimAOuter","i32").registerUniform("dimBOuter","i32").registerUniform("dimInner","i32").registerInternalVariables(b).declareVariables(...fe,Pe)}\n  ${We}\n  ${Ge}\n  ${$?mr(x,E,z,b):fr(x,E,z,b)}\n                   `;return{name:"MatMul",shaderCache:{hint:t.activationCacheKey+`${x}${$}${n}`,inputDependencies:he},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:A[0],y:A[1],z:A[2]},programUniforms:Ie}),getShaderSource:G}}});var ll,Wi,Hi=j(()=>{"use strict";Ct();ve();It();Jr();Nn();hr();ll=(e,t,r,o,n=!1,s,u=4,l=4,a=4,p="f32")=>{let h=Y=>{switch(Y){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${p}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${Y} is not supported.`)}},g=Y=>{switch(Y){case 1:return"return w[row * i32(uniforms.w_shape[3]) + colIn];";case 4:return"return w[row * i32(uniforms.w_shape[3]) / 4 + colIn];";default:throw new Error(`innerElementSize ${Y} is not supported.`)}},b=e?`\n    let coord = vec4<i32>(batch, xRow, xCol, xCh);\n    `:`\n    let coord = vec4<i32>(batch, xCh, xRow, xCol);\n    `,w=e?`\n    let coords = vec4<i32>(\n      batch,\n      row / outWidth,\n      row % outWidth,\n      col);\n    `:`\n    let coords = vec4<i32>(\n      batch,\n      row,\n      col / outWidth,\n      col % outWidth);\n    `,y=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",_=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",I=e?"row":"col",$=e?"col":"row",x=`\n    let inChannels = i32(uniforms.w_shape[2]);\n    let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n    let outRow = ${I} / outWidth;\n    let outCol = ${I} % outWidth;\n\n    let WRow = ${$} / (filterDims[1] * inChannels);\n    let WCol = ${$} / inChannels % filterDims[1];\n    let xRow = outRow * stride[0] + dilation[0] * WRow - pad[0];\n    let xCol = outCol * stride[1] + dilation[1] * WCol - pad[1];\n    let xCh = ${$} % inChannels;\n    var resData = ${Ke(u,p)}(0.0);\n    // The bounds checking is always needed since we use it to pad zero for\n    // the \'same\' padding type.\n    if (xRow >= 0 && xRow < ${y} && xCol >= 0 && xCol < ${_}) {\n      ${b}\n      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));\n      ${h(u)}\n    }\n    return resData;`,E=e?t&&o?`\n    let col = colIn * ${u};\n    ${x}`:`\n    let col = colIn * ${u};\n    if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n      ${x}\n    }\n    return ${Ke(u,p)}(0.0);`:o&&r?`\n    let col = colIn * ${u};\n    ${x}`:`\n    let col = colIn * ${u};\n    if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n      ${x}\n    }\n    return ${Ke(u,p)}(0.0);`,A=`${g(l)}`,z=Ke(a,p),R=e?Ke(u,p):Ke(l,p),V=e?Ke(l,p):Ke(u,p),{activationFunction:T,applyActivation:N}=gt(s,z);return`\n    ${T}\n    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${R} {\n      ${e?E:A}\n    }\n\n    fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${V} {\n      ${e?A:E}\n    }\n\n    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${z}) {\n      let col = colIn * ${a};\n      if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\n      {\n      var value = valueIn;\n      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n      ${w}\n      ${Xr(n)}\n      ${N}\n      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n      }\n    }`},Wi=(e,t,r,o,n,s,u,l)=>{let a=t.format==="NHWC",p=a?e[0].dims[3]:e[0].dims[1],h=r[0],g=a?r[2]:r[3],b=a?r[1]:r[2],w=a?r[3]:r[1],y=a&&(p%4===0||p%3===0)&&w%4===0,_=a?w:g*b,I=a?g*b:w,$=[8,8,1],x=o<=8?[4,1,1]:[4,4,1],E=[Math.ceil(_/$[0]/x[0]),Math.ceil(I/$[1]/x[1]),Math.ceil(h/$[2]/x[2])];Be("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${E}`);let A=y?a&&p%4!==0?3:4:1,z=$[1]*x[1],R=$[0]*x[0],V=Math.max($[0]*A,$[1]),T=o%z===0,N=n%R===0,te=s%V===0,Y=y?[A,4,4]:[1,1,1],K=Le(e[0].dataType),Q=y?4:1,Z=[{type:"int32",data:o},{type:"int32",data:n},{type:"int32",data:s}],Ee=M("x",e[0].dataType,e[0].dims.length,A===3?1:A),Pe=M("w",e[1].dataType,e[1].dims.length,Q),fe=[Ee,Pe];Z.push(...L(e[0].dims)),Z.push(...L(e[1].dims));let Ie=`\n      fn setOutputAtIndex(flatIndex : i32, value : ${y?`vec4<${K}>`:K}) {\n        result[flatIndex] = ${y?`vec4<${K}>`:K}(value);\n      }\n      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${y?`vec4<${K}>`:K}) {\n        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));\n        setOutputAtIndex(flatIndex ${y?"/ 4":""}, value);\n      }`;if(u){let ye=M("bias",e[2].dataType,e[2].dims.length,Q);fe.push(ye),Z.push(...L(e[2].dims)),Ie+=`\n        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${y?`vec4<${K}>`:K} {\n          return bias[coords.${a?"w":"y"}${y?"/ 4":""}];\n        }`}let he=F("result",e[0].dataType,r.length,Q);return Z.push(...L(r)),{name:"Conv2DMatMul",shaderCache:{hint:t.cacheKey},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:Z}),getShaderSource:ye=>`\n        ${en("uniforms.result_strides")}\n        //struct Uniforms { xShape : vec4<i32>, wShape : vec4<i32>, outShape : vec4<i32>,\n        //  outShapeStrides: vec3<i32>, filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>,\n        //  dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32 };\n        ${ye.registerUniform("dimAOuter","i32").registerUniform("dimBOuter","i32").registerUniform("dimInner","i32").declareVariables(...fe,he)}\n        const filterDims : vec2<i32> = vec2<i32>(${t.kernelShape[0]}, ${t.kernelShape[1]});\n        const pad : vec2<i32> = vec2<i32>(${t.pads[0]}, ${t.pads[1]});\n        const stride : vec2<i32> = vec2<i32>(${t.strides[0]}, ${t.strides[1]});\n        const dilation : vec2<i32> = vec2<i32>(${t.dilations[0]}, ${t.dilations[1]});\n        ${Ie}\n        ${ll(a,T,N,te,u,t,Y[0],Y[1],Y[2],K)}\n            ${y?mr(x,$,K,void 0,!a,V):fr(x,$,K,void 0,!a,V,!1,void 0,l)}`}}});var Wn,Gi=j(()=>{"use strict";$e();ve();Gn();It();Wn=(e,t,r)=>{let o=e.length>2,n=o?"value += b[output_channel];":"",s=e[0].dims,u=e[1].dims,l=u[0]/t.group,a=t.format==="NHWC",p=Hn(s,u,t.dilations,t.pads,t.strides,a),h=U.size(p),g=F("output",e[0].dataType,p),{activationFunction:b,applyActivation:w}=gt(t,g.type.value),y=M("x",e[0].dataType,s),_=M("w",e[1].dataType,u),I=[y,_];o&&I.push(M("b",e[2].dataType,e[2].dims));let $=x=>`\n  const strides: vec2<u32> = vec2(${t.strides[0]}u, ${t.strides[1]}u);\n  const pads: vec2<u32> = vec2(${t.pads[0]}u, ${t.pads[1]}u);\n\n  ${x.declareVariables(...I,g)}\n\n  ${b}\n\n  ${x.mainStart()}\n    ${x.guardAgainstOutOfBoundsWorkgroupSizes(h)}\n\n    let outputIndices = ${g.offsetToIndices("global_idx")};\n    let batch: u32 = outputIndices[0];\n    let output_channel: u32 = outputIndices[${a?3:1}];\n    let xRCCorner: vec2<u32> = vec2<u32>(outputIndices[${a?1:2}], outputIndices[${a?2:3}]) * strides - pads;\n    let group_id: u32 = output_channel / ${l}u;\n\n    var value: ${g.type.value} = ${g.type.value}(0);\n    for (var wInChannel: u32 = 0u; wInChannel < ${u[1]}u; wInChannel++) {\n      let input_channel = group_id * ${u[1]}u + wInChannel;\n      for (var wHeight: u32 = 0u; wHeight < ${u[2]}u; wHeight++) {\n        let xHeight = xRCCorner.x + wHeight * ${t.dilations[0]}u;\n\n        if (xHeight < 0u || xHeight >= ${s[a?1:2]}u) {\n          continue;\n        }\n\n        for (var wWidth: u32 = 0u; wWidth < ${u[3]}u; wWidth++) {\n          let xWidth = xRCCorner.y + wWidth * ${t.dilations[1]}u;\n          if (xWidth < 0u || xWidth >= ${s[a?2:3]}u) {\n            continue;\n          }\n\n          let xVal = ${a?y.get("batch","xHeight","xWidth","input_channel"):y.get("batch","input_channel","xHeight","xWidth")};\n          let wVal = ${_.get("output_channel","wInChannel","wHeight","wWidth")};\n          value += xVal*wVal;\n        }\n      }\n    }\n    ${n}\n    ${w}\n    ${g.setByOffset("global_idx","value")}\n  }`;return{name:"GroupedConv",shaderCache:{hint:t.cacheKey},getRunData:()=>({outputs:[{dims:r?r(p):p,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)}}),getShaderSource:$}}});var Ln,cl,Li,Fn=j(()=>{"use strict";$e();hr();ve();It();Ln=(e,t,r,o,n=!1)=>{let s=e[0].dims,u=e[1].dims,l=s[s.length-2],a=u[u.length-1],p=s[s.length-1],h=Fe(a),g=Fe(p),b=Fe(l),w=U.size(r)/h/b,y=e.length>2,_=o?o.slice(0,-2):r.slice(0,-2),$=[U.size(_),l,a],x=[{type:"uint32",data:w},{type:"uint32",data:l},{type:"uint32",data:a},{type:"uint32",data:p},...L(_),...L(s),...L(u)];y&&x.push(...L(e[2].dims)),x.push(...L($));let E=A=>{let z=Fr("batch_dims",e[0].dataType,_.length),R=M("a",e[0].dataType,s.length,g),V=M("b",e[1].dataType,u.length,h),T=F("output",e[0].dataType,$.length,h),{activationFunction:N,applyActivation:te}=gt(t,T.type.value),Y=[R,V],K="";if(y){let he=n?h:1;Y.push(M("bias",e[2].dataType,e[2].dims.length,he)),K=`${n?`value += bias[col / ${he}];`:`value += ${T.type.value}(bias[row + i]);`}`}let Q=s.slice(0,-2),Z=u.slice(0,-2),Ee=Ft(Q,_),Pe=Ft(Z,_),fe=(he,ye)=>{let We=he.rank,De=he.name;if(We===2)return`var ${De}_indices = ${he.type.indices}(0u, 0u);`;let Ge=z.rank,G=`var ${De}_indices: ${he.type.indices};`;for(let ee=We-2-1,be=Ge-1;ee>=0;ee--,be--)G+=`\n${De}_indices[${ee}] = ${Ge>1?`batch_indices[${be}]`:"batch_indices"};`;return ye.forEach(ee=>{G+=`\n${De}_indices[${ee}] = 0;`}),G+=`${De}_indices[${We-2}] = 0u;\n                     ${De}_indices[${We-1}] = 0u;`,G},Ie=()=>{let he=`var a_data: ${R.type.value};`;for(let ye=0;ye<g;ye++)he+=`\n              let b_data${ye} = b[(b_offset + (k + ${ye}) * uniforms.N + col) / ${h}];`;for(let ye=0;ye<b;ye++){he+=`a_data = a[(a_offset + (row + ${ye}) * uniforms.K + k) / ${g}];`;for(let We=0;We<g;We++)he+=`\n            values[${ye}] = fma(${V.type.value}(a_data${g===1?"":`[${We}]`}), b_data${We}, values[${ye}]);\n`}return he};return`\n  ${A.registerUniform("outputSize","u32").registerUniform("M","u32").registerUniform("N","u32").registerUniform("K","u32").registerInternalVariables(z).declareVariables(...Y,T)}\n  ${N}\n  ${A.mainStart()}\n    ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n    let col = (global_idx % (uniforms.N / ${h})) * ${h};\n    var index1 = global_idx / (uniforms.N / ${h});\n    let stride1 = uniforms.M / ${b};\n    let row = (index1 % stride1) * ${b};\n    let batch = index1 / stride1;\n\n    ${r.length===2?"":`let batch_indices = ${z.offsetToIndices("batch")};`}\n    ${fe(R,Ee)}\n    let a_offset = ${R.indicesToOffset("a_indices")};\n    ${fe(V,Pe)}\n    let b_offset = ${V.indicesToOffset("b_indices")};\n    var values: array<${T.type.value}, ${b}>;\n    for (var k: u32 = 0u; k < uniforms.K; k = k + ${g}) {\n      ${Ie()}\n    }\n    for (var i = 0u; i < ${b}u; i++) {\n      var value = values[i];\n      ${K}\n      ${te}\n      let cur_indices = ${T.type.indices}(batch, row + i, col);\n      let offset = ${T.indicesToOffset("cur_indices")};\n      ${T.setByOffset(`offset / ${h}`,"value")};\n    }\n  }\n  `};return{name:"MatMulNaive",shaderCache:{hint:`${t.activationCacheKey}_${h}_${g}_${b}_${n}`,inputDependencies:y?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:x}),getShaderSource:E}},cl=e=>{if(!e||e.length!==2)throw new Error("MatMul requires 2 inputs.");if(e[0].dims[e[0].dims.length-1]!==e[1].dims[e[1].dims.length-2])throw new Error("shared dimension does not match.")},Li=e=>{cl(e.inputs);let t=dt.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!t)throw new Error("Can\'t use matmul on the given tensors");let r=t[t.length-1],o=e.inputs[0].dims[e.inputs[0].dims.length-1];r<8&&o<8?e.compute(Ln(e.inputs,{activation:"",activationCacheKey:""},t)):e.compute(tn(e.inputs,{activation:"",activationCacheKey:""},t))}});var Hn,Fi,pl,ji,jn,ml,fl,qn,Gn=j(()=>{"use strict";$e();je();Hi();hr();Gi();It();Fn();jt();Hn=(e,t,r,o,n,s)=>{let u=e[0],l=e.slice(s?1:2,s?3:4),a=l.length,p=t[0],g=t.slice(2).map((y,_)=>y+(y-1)*(r[_]-1)),w=l.map((y,_)=>y+o[_]+o[_+a]).map((y,_)=>Math.floor((y-g[_]+n[_])/n[_]));return w.splice(0,0,u),w.splice(s?3:1,0,p),w},Fi=[2,3,1,0],pl=(e,t)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length!==4&&e[0].dims.length!==3)throw new Error("currently only support conv 1D and 2D");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let r=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],o=e[1].dims[1]*t.group;if(r!==o)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(e.length===3&&(e[2].dims.length!==1||e[1].dims[0]!==e[2].dims[0]))throw new Error("invalid bias");let n=e[0].dims.length-2;if(t.dilations.length!==n)throw new Error(`dilations should be ${n}D`);if(t.strides.length!==n)throw new Error(`strides should be ${n}D`);if(t.pads.length!==n*2)throw new Error(`pads should be ${n*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},ji=(e,t)=>{let r=e.kernelShape.slice();for(let s=2;s<t[1].dims.length;++s)r[s-2]===0&&(r[s-2]=t[1].dims[s]);let o=e.pads.slice();Bt.adjustPadsBasedOnAutoPad(t[0].dims,e.strides,e.dilations,r,o,e.format==="NHWC",e.autoPad);let n=Object.assign({},e);return Object.assign(n,{kernelShape:r,pads:o,cacheKey:e.cacheKey}),n},jn=e=>{let t=Qr(e),r=e.format,o=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],n=e.dilations,s=e.group,u=e.kernel_shape,l=e.pads,a=e.strides,p=e.w_is_const();return ge({autoPad:o,format:r,dilations:n,group:s,kernelShape:u,pads:l,strides:a,wIsConst:p,...t})},ml=(e,t,r)=>{let o=ji(r,t),n=r.format==="NHWC";if(r.group!==1){e.compute(Wn(t,o));return}let s=t.length===3,u=t[0].dims[n?1:2],l=t[0].dims[n?2:3],a=t[0].dims[n?3:1],p=t[1].dims[2],h=t[1].dims[3],g=Hn(t[0].dims,t[1].dims,r.dilations,o.pads,r.strides,n),b=g[n?1:2],w=g[n?2:3],y=g[n?3:1],_=n&&p===u&&h===l&&r.pads[0]===0&&r.pads[1]===0;if(_||p===1&&h===1&&r.dilations[0]===1&&r.dilations[1]===1&&r.strides[0]===1&&r.strides[1]===1&&r.pads[0]===0&&r.pads[1]===0){let R=g[0],V,T,N,te=[];if(n){let Q=e.kernelCustomData.wT??e.compute(it(t[1],Fi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=Q),_){let Z=u*l*a;V=t[0].reshape([1,R,Z]),T=Q.reshape([1,Z,y]),N=[1,R,y]}else V=t[0].reshape([R,u*l,a]),T=Q.reshape([1,a,y]),N=[R,b*w,y];te.push(V),te.push(T)}else V=t[0].reshape([R,a,u*l]),T=t[1].reshape([1,y,a]),N=[R,y,b*w],te.push(T),te.push(V);s&&te.push(t[2]);let Y=N[2],K=te[0].dims[te[0].dims.length-1];Y<8&&K<8?e.compute(Ln(te,o,g,N,n),{inputs:te}):e.compute(tn(te,o,g,N,n),{inputs:te});return}let I=!0,$=e.kernelCustomData.wT??e.compute(it(t[1],Fi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=$);let x=[t[0],$];s&&x.push(t[2]);let E=n?b*w:y,A=n?y:b*w,z=p*h*a;e.compute(Wi(x,o,g,E,A,z,s,I),{inputs:x})},fl=(e,t)=>{let r=t.format==="NHWC",o=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&o.push(e.inputs[2]);let n=[0,t.pads[0],0,t.pads[1]],s=[1].concat(t.strides),u=[1].concat(t.dilations),l=[1].concat(t.kernelShape),a=ji({...t,pads:n,strides:s,dilations:u,kernelShape:l},o);e.compute(Wn(o,a,p=>r?[p[0],p[2],p[3]]:[]))},qn=(e,t)=>{pl(e.inputs,t),e.inputs[0].dims.length===3?fl(e,t):ml(e,e.inputs,t)}});var hl,qi,Ki=j(()=>{"use strict";Ct();ve();It();Jr();Nn();hr();hl=(e,t=!1,r,o=4)=>{let n=Ke(o,"f32"),s=x=>{switch(x){case 1:return"return w[getIndexFromCoords4D(coord, vec4<i32>(uniforms.w_shape))];";case 4:return`\n            let coord1 = vec4<i32>(coordX, coordY, col + 1, rowInner);\n            let coord2 = vec4<i32>(coordX, coordY, col + 2, rowInner);\n            let coord3 = vec4<i32>(coordX, coordY, col + 3, rowInner);\n            let v0 = w[getIndexFromCoords4D(coord, vec4<i32>(uniforms.w_shape))];\n            let v1 = w[getIndexFromCoords4D(coord1, vec4<i32>(uniforms.w_shape))];\n            let v2 = w[getIndexFromCoords4D(coord2, vec4<i32>(uniforms.w_shape))];\n            let v3 = w[getIndexFromCoords4D(coord3, vec4<i32>(uniforms.w_shape))];\n            return vec4<f32>(v0, v1, v2, v3);\n            `;default:throw new Error(`innerElementSize ${x} is not supported.`)}},u=e?`\n      let coord = vec4<i32>(batch, iXR, iXC, xCh);\n      `:`\n      let coord = vec4<i32>(batch, xCh, iXR, iXC);\n      `,l=e?`\n    let coords = vec4<i32>(\n      batch,\n      row / outWidth,\n      row % outWidth,\n      col);\n    `:`\n    let coords = vec4<i32>(\n      batch,\n      row,\n      col / outWidth,\n      col % outWidth);\n    `,a=e?"outBackprop[1]":"outBackprop[2]",p=e?"outBackprop[2]":"outBackprop[3]",h=e?"row":"col",g=e?"col":"row",b=`\n      let inChannels = ${e?"outBackprop[3]":"outBackprop[1]"};\n      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n      let outRow = ${h} / outWidth;\n      let outCol = ${h} % outWidth;\n\n      let WRow = ${g} / (filterDims[1] * inChannels);\n      let WCol = ${g} / inChannels % filterDims[1];\n      let xR = f32(outRow - pads[0] + dilation[0] * WRow) / f32(strides[0]);\n      let xC = f32(outCol - pads[1] + dilation[1] * WCol) / f32(strides[1]);\n      if (xR < 0.0 || xR >= f32(${a}) || fract(xR) > 0.0) {\n        return ${n}(0.0);\n      }\n      if (xC < 0.0 || xC >= f32(${p}) || fract(xC) > 0.0) {\n        return ${n}(0.0);\n      }\n      let iXR = i32(xR);\n      let iXC = i32(xC);\n      let xCh = ${g} % inChannels;\n      ${u}\n      return x[getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape))/${o}];`,w=e?`\n      let col = colIn * ${o};\n      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n        ${b}\n      }\n      return ${n}(0.0);`:`\n      let col = colIn * ${o};\n      if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n        ${b}\n      }\n      return ${n}(0.0);`,y=`\n      let col = colIn * ${o};\n      let inChannels = ${e?"outBackprop[3]":"outBackprop[1]"};\n      let coordX = filterDims.x - 1 - row / (filterDims[1] * inChannels);\n      let coordY = filterDims.y - 1 - (row / inChannels) % filterDims[1];\n      if (${e?"row < uniforms.dimInner && col < uniforms.dimBOuter":"row < uniforms.dimInner && col < uniforms.dimAOuter"}  && coordX >= 0 && coordY >= 0) {\n        let rowInner = row % inChannels;\n        let coord = vec4<i32>(coordX, coordY, col, rowInner);\n        ${s(o)}\n      }\n      return ${n}(0.0);\n      `,{activationFunction:_,applyActivation:I}=gt(r,n);return`\n      ${_}\n  fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${n} {\n    ${e?w:y}\n  }\n\n  fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${n} {\n    ${e?y:w}\n  }\n\n  fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${n}) {\n    let col = colIn * ${o};\n    if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n      var value = valueInput;\n      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n      ${l}\n      ${Xr(t)}\n      ${I}\n      result[getIndexFromCoords4D(coords, vec4<i32>(uniforms.result_shape))/${o}] = value;\n    }\n  }`},qi=(e,t,r,o,n,s,u,l)=>{let a=t.format==="NHWC",p=a?e[0].dims[3]:e[0].dims[1],h=r[0],g=a?r[2]:r[3],b=a?r[1]:r[2],w=a?r[3]:r[1],y=a?p%4===0&&w%4===0:g%4===0&&w%4===0,_=a?w:g*b,I=a?g*b:w,$=y?[8,8,1]:[_<=4||I<=4?4:16,_>4&&I<=4?4:16,1],x=y?[4,4,1]:[_<=4?1:4,_>4&&I<=4?1:4,1],E=[Math.ceil(_/$[0]/x[0]),Math.ceil(I/$[1]/x[1]),Math.ceil(h/$[2]/x[2])];Be("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${E}`);let A=y?4:1,z=Math.max($[0]*A,$[1]),R=y?4:1,V=[{type:"int32",data:o},{type:"int32",data:n},{type:"int32",data:s}],T=M("x",e[0].dataType,e[0].dims.length,R),N=M("w",e[1].dataType,e[1].dims.length,1),te=F("result",e[0].dataType,r.length,R),Y=[T,N];V.push(...L(e[0].dims)),V.push(...L(e[1].dims));let K="";if(u){let Q=M("bias",e[2].dataType,e[2].dims.length,R);Y.push(Q),V.push(...L(e[2].dims)),K+=`\n        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${y?"vec4<f32>":"f32"} {\n          return bias[coords.${a?"w":"y"}${y?"/ 4":""}];\n        }`}return V.push(...L(r)),{name:"Conv2DTransposeMatMul",shaderCache:{hint:t.cacheKey},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:V}),getShaderSource:Q=>`\n        ${en("uniforms.result_strides")}\n        ${Q.registerUniform("dimAOuter","i32").registerUniform("dimBOuter","i32").registerUniform("dimInner","i32").declareVariables(...Y,te)};\n        const outBackprop : vec4<i32> = vec4<i32>(${e[0].dims.join(",")});\n        const filterDims : vec2<i32> = vec2<i32>(${t.kernelShape[a?1:2]}, ${t.kernelShape[a?2:3]});\n        const effectiveFilterDims : vec2<i32> = filterDims + vec2<i32>(\n              ${t.dilations[0]<=1?0:(t.kernelShape[a?1:2]-1)*(t.dilations[0]-1)},\n              ${t.dilations[1]<=1?0:(t.kernelShape[a?2:3]-1)*(t.dilations[1]-1)});\n        const pads : vec2<i32> = vec2<i32>(i32(effectiveFilterDims[0]) - 1 - (${t.pads[0]+t.pads[2]})/2,\n                                         i32(effectiveFilterDims[1]) - 1 - (${t.pads[1]+t.pads[3]})/2);\n        const strides : vec2<i32> = vec2<i32>(${t.strides[0]}, ${t.strides[1]});\n        const dilation : vec2<i32> = vec2<i32>(${t.dilations[0]}, ${t.dilations[1]});\n        const dimAOuter : i32 = ${o};\n        const dimBOuter : i32 = ${n};\n        const dimInner : i32 = ${s};\n        ${K}\n        ${hl(a,u,t,A)}\n        ${y?mr(x,$,"f32",void 0,!a,z):fr(x,$,"f32",void 0,!a,z,!1,void 0,l)}`}}});var gl,Kn,Yi=j(()=>{"use strict";Ct();$e();ve();gl=(e,t,r,o,n,s,u=!1,l)=>{let a=r.format==="NHWC",p=a?1:2,h=a?2:3,g=a?3:1,b=U.size(o),w=u?2:1,y=r.group,_=t[1].dims,I=_[0]/y,$=_[1],x=`\n  fn setOutputAtIndex(flatIndex : u32, value : ${u?`vec4<${l}>`:l}) {\n    result[flatIndex] = ${u?`vec4<${l}>`:l}(value);\n  }`;n&&(x+=`\n    fn getBiasByOutputCoords(coords : vec4<u32>) -> ${u?`vec4<${l}>`:l} {\n      return bias[coords.${a?"w":"y"}${u?"/ 4":""}];\n    }`);let E=u?4:1,A=M("W",t[1].dataType,t[1].dims,E),z=M("Dy",t[0].dataType,t[0].dims,E),R=[z,A];n&&R.push(M("bias",t[2].dataType,[o[g]],E));let V=F("result",t[0].dataType,o,E),T=`{\n        let batch: u32 = ${s?"global_id.z":"workgroup_id.z"} / outShape[1];\n        let r = ${s?"global_id.z":"workgroup_id.z"} % outShape[1];\n        let c = ${s?"global_id.y":"workgroup_id.y"} * ${w};\n        let d1: u32 = ${s?"global_id.x":"workgroup_id.x"} * 4;\n\n        let dyCorner = vec2<i32>(i32(r), i32(c)) - vec2<i32>(pads);\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd: array<vec4<${l}>, ${w}>;\n        for (var i = 0; i < ${w}; i++) {\n          dotProd[i] = vec4<${l}>(0.0);\n        }\n        for (var wR: u32 = 0; wR < filterDims[0]; wR = wR + 1) {\n          var dyR = (${l}(dyCorner.x) + ${l}(wR)) / ${l}(strides.x);\n          let wRPerm = filterDims[0] - 1 - wR;\n          if (dyR < 0.0 || dyR >= ${l}(outBackprop[1]) ||\n              fract(dyR) > 0.0 || wRPerm < 0) {\n            continue;\n          }\n          let idyR: u32 = u32(dyR);\n\n          for (var wC: u32 = 0; wC < filterDims[1]; wC = wC + 1) {\n            let dyC = (${l}(dyCorner.y) + ${l}(wC)) / ${l}(strides.y);\n            let dyC2 = (${l}(dyCorner.y) + 1.0 + ${l}(wC)) / ${l}(strides.y);\n            let wCPerm = filterDims[1] - 1 - wC;\n            if (wCPerm < 0) {\n              continue;\n            }\n            var bDyCVal = true;\n            var bDyCVal2 = true;\n            if (dyC < 0.0 || dyC >= ${l}(outBackprop[2]) ||\n                fract(dyC) > 0.0) {\n              bDyCVal = false;\n            }\n            if (dyC2 < 0.0 || dyC2 >= ${l}(outBackprop[2]) ||\n                fract(dyC2) > 0.0) {\n              bDyCVal2 = false;\n            }\n\n            let idyC: u32 = u32(dyC);\n            let idyC2: u32 = u32(dyC2);\n            if (bDyCVal && bDyCVal2) {\n              let d2Length = outBackprop[3];\n              for (var d2 :u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n                let wValue1 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n                let wValue2 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n                let wValue3 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n                var xValue = ${z.get("batch","idyR","idyC","d2")};\n                let tmpval = vec4<${l}>(dot(xValue, wValue0),\n                                      dot(xValue, wValue1),\n                                      dot(xValue, wValue2),\n                                      dot(xValue, wValue3));\n                dotProd[0] = dotProd[0] + tmpval;\n\n                xValue =  ${z.get("batch","idyR","idyC2","d2")};\n\n                dotProd[1] = dotProd[1] + vec4<${l}>(dot(xValue, wValue0),\n                                                    dot(xValue, wValue1),\n                                                    dot(xValue, wValue2),\n                                                    dot(xValue, wValue3));\n              }\n            } else if (bDyCVal) {\n              let d2Length = outBackprop[${g}];\n              for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n                let wValue1 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n                let wValue2 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n                let wValue3 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n                var xValue = ${z.get("batch","idyR","idyC","d2")};\n                let tmpval = vec4<${l}>(dot(xValue, wValue0),\n                                      dot(xValue, wValue1),\n                                      dot(xValue, wValue2),\n                                      dot(xValue, wValue3));\n                dotProd[0] = dotProd[0] + tmpval;\n              }\n            } else if (bDyCVal2) {\n              let d2Length = outBackprop[3];\n              for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n                let wValue1 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n                let wValue2 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n                let wValue3 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n                var xValue = ${z.get("batch","idyR","idyC2","d2")};\n                let tmpval = vec4<${l}>(dot(xValue, wValue0),\n                                      dot(xValue, wValue1),\n                                      dot(xValue, wValue2),\n                                      dot(xValue, wValue3));\n                dotProd[1] = dotProd[1] + tmpval;\n              }\n            }\n          }\n        }\n\n        for (var i: u32 = 0; i < ${w}; i = i + 1) {\n          let value = dotProd[i] + ${n?"bias[c+i]":`vec4<${l}>(0.0)`};\n          ${V.set("batch","r","c + i","d1","value")};\n        }\n      }`,N=`\n          let outputIndices = ${V.offsetToIndices("global_idx")};\n          let batch = ${V.indicesGet("outputIndices",0)};\n          let d1 = ${V.indicesGet("outputIndices",g)};\n          let r = ${V.indicesGet("outputIndices",p)};\n          let c = ${V.indicesGet("outputIndices",h)};\n          let dyCorner = vec2<i32>(i32(r), i32(c)) - pads;\n          let dyRCorner = dyCorner.x;\n          let dyCCorner = dyCorner.y;\n          let groupId = d1 / ${$};\n          let wOutChannel = d1 - groupId * ${$};\n          // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n          // ? = to be determined. : = across all values in that axis.\n          var dotProd = ${l}(0.0);\n          for (var wR: u32 = 0; wR < effectiveFilterDims.x; wR = wR + 1) {\n            if (wR % dilations.x != 0) {\n              continue;\n            }\n            let dyR = (${l}(dyRCorner) + ${l}(wR)) / ${l}(strides[0]);\n            let wRPerm = filterDims.x - 1 - wR / dilations.x;\n            if (dyR < 0.0 || dyR >= ${l}(outBackprop[${p}]) || fract(dyR) > 0.0 ||\n                wRPerm < 0) {\n              continue;\n            }\n            let idyR: u32 = u32(dyR);\n\n            for (var wC: u32 = 0; wC < effectiveFilterDims.y; wC = wC + 1) {\n              if (wC % dilations.y != 0) {\n                continue;\n              }\n              let dyC = (${l}(dyCCorner) + ${l}(wC)) / ${l}(strides.y);\n              let wCPerm = filterDims.y - 1 - wC / dilations.y;\n              if (dyC < 0.0 || dyC >= ${l}(outBackprop[${h}]) ||\n                  fract(dyC) > 0.0 || wCPerm < 0) {\n                continue;\n              }\n              let idyC: u32 = u32(dyC);\n              var inputChannel = groupId * ${I};\n              for (var d2: u32 = 0; d2 < ${I}; d2 = d2 + 1) {\n                let xValue = ${a?z.get("batch","idyR","idyC","inputChannel"):z.get("batch","inputChannel","idyR","idyC")};\n                let wValue = ${A.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")};\n                dotProd = dotProd + xValue * wValue;\n                inputChannel = inputChannel + 1;\n              }\n            }\n          }\n          let value = dotProd + ${n?"bias[d1]":`${l}(0.0)`};\n          ${V.setByOffset("global_idx","value")};\n        `;return`\n  ${e.declareVariables(...R,V)}\n  ${x}\n  const outShape : vec4<u32> = vec4<u32>(${o.join(",")});\n  const outBackprop : vec4<u32> = vec4<u32>(${t[0].dims.join(",")});\n  const strides : vec2<u32> = vec2<u32>(${r.strides[0]}, ${r.strides[1]});\n  const filterDims : vec2<u32> = vec2<u32>(${r.kernelShape[a?1:2]}, ${r.kernelShape[a?2:3]});\n  const dilations : vec2<u32> = vec2<u32>(${r.dilations[0]}, ${r.dilations[1]});\n  const effectiveFilterDims : vec2<u32> = filterDims + vec2<u32>(\n          ${r.dilations[0]<=1?0:(r.kernelShape[a?1:2]-1)*(r.dilations[0]-1)},\n          ${r.dilations[1]<=1?0:(r.kernelShape[a?2:3]-1)*(r.dilations[1]-1)});\n  const pads : vec2<i32> = vec2<i32>(i32(effectiveFilterDims[0]) - 1 - (${r.pads[0]+r.pads[2]})/2,\n                                     i32(effectiveFilterDims[1]) - 1 - (${r.pads[1]+r.pads[3]})/2);\n    ${e.mainStart()}\n    ${e.guardAgainstOutOfBoundsWorkgroupSizes(b)};\n  ${u?T:N}}`},Kn=(e,t,r)=>{let o=e.length>2,n=t.outputShape,s=U.size(n),u=[Math.ceil(s/64),1,1];Be("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${u}`);let l=Le(e[0].dataType);return{name:"ConvTranspose2D",shaderCache:{hint:t.cacheKey},getRunData:()=>({dispatchGroup:{x:u[0],y:u[1],z:u[2]},outputs:[{dims:r?r(n):n,dataType:e[0].dataType}]}),getShaderSource:a=>gl(a,e,t,n,o,u[1]===1&&u[2]===1,!1,l)}}});var yl,bl,wl,Zi,Qi,vl,$l,Sl,xl,Xi,Ji=j(()=>{"use strict";je();Ki();Yi();It();jt();yl=(e,t,r,o,n,s)=>(e-1)*t+r+(o-1)*n+1-s,bl=(e,t,r,o,n)=>{let s=Math.floor(e/2);t==="SAME_UPPER"?(r[o]=s,r[n]=e-s):t==="SAME_LOWER"&&(r[o]=e-s,r[n]=s)},wl=(e,t,r,o,n,s,u,l,a,p)=>{let h=e.length-2,g=p.length===0;if(a.length===0)for(let y=0;y<h;++y)a.push(0);let b=e[0],w=t[l?3:1]*n;for(let y=0,_=e.length-h-(l?1:0);y<h;++y,++_){let I=e[_],$=g?I*u[y]:p[y],x=yl(I,u[y],s[y],t[_],r[y],$);bl(x,o,s,y,y+h),g&&p.push(u[y]*(I-1)+a[y]+(t[_]-1)*r[y]+1-s[y]-s[y+h])}p.splice(0,0,b),p.splice(l?3:1,0,w)},Zi=(e,t)=>{let r=e.kernelShape.slice();if(e.kernelShape.length===0||e.kernelShape.reduce((b,w)=>b*w,1)===0){r.length=0;for(let b=2;b<t[1].dims.length;++b)r.push(t[1].dims[b])}let o=e.format==="NHWC";r.splice(0,0,t[1].dims[0]),r.splice(o?3:1,0,t[1].dims[1]);let n=e.pads.slice(),s=e.outputShape.slice(),u=e.outputPadding.slice(),l=t[0].dims,a=e.dilations.slice();if(a.reduce((b,w)=>b+w,0)===0){let b=t[0].dims.length-2;a=new Array(b).fill(1)}let p=e.strides.slice();if(p.reduce((b,w)=>b+w,0)===0){let b=t[0].dims.length-2;p=new Array(b).fill(1)}wl(l,r,a,e.autoPad,e.group,n,p,o,u,s);let h=Object.assign({},e),g=e.cacheKey+[r.join("n,"),n.join(","),p.join(","),u.join(","),s.join(","),a.join(",")].join("_");return Object.assign(h,{kernelShape:r,pads:n,outputPadding:u,outputShape:s,dilations:a,strides:p,cacheKey:g}),h},Qi=e=>{let t=Qr(e),r=e.format,o=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof e.autoPad>"u"?0:e.autoPad],n=e.dilations,s=e.group,u=e.kernelShape,l=e.pads,a=e.strides,p=e.wIsConst(),h=e.outputPadding,g=e.outputShape;return ge({autoPad:o,format:r,dilations:n,group:s,kernelShape:u,outputPadding:h,outputShape:g,pads:l,strides:a,wIsConst:p,...t})},vl=(e,t)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length!==4&&e[0].dims.length!==3)throw new Error("currently only support 2-dimensional conv");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let r=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],o=e[1].dims[0];if(r!==o)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let n=e[1].dims[1]*t.group;if(e.length===3&&(e[2].dims.length!==1||e[2].dims[0]!==n))throw new Error("invalid bias");let s=e[0].dims.length-2;if(t.dilations.reduce((h,g)=>h+g,0)>0&&t.dilations.length!==s)throw new Error(`dilations should be ${s}D`);if(t.strides.reduce((h,g)=>h+g,0)>0&&t.strides.length!==s)throw new Error(`strides should be ${s}D`);if(t.pads.reduce((h,g)=>h+g,0)>0&&t.pads.length!==s*2)throw new Error(`pads should be ${s*2}D`);if(t.outputPadding.length!==s&&t.outputPadding.length!==0)throw new Error(`output_padding should be ${s}D`);if(t.kernelShape.reduce((h,g)=>h+g,0)>0&&t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape");if(t.outputShape.length!==0&&t.outputShape.length!==e[0].dims.length-2)throw new Error("invalid output shape")},$l=[2,3,1,0],Sl=(e,t,r)=>{let o=Zi(r,t),n=r.format==="NHWC",s=o.outputShape,u=s[n?3:1],l=t[0].dims[n?3:1];if(o.group!==1||u===1&&l===1){e.compute(Kn(t,o));return}let a=s[n?1:2],p=s[n?2:3],h=t[1].dims[2],g=t[1].dims[3],b=n?a*p:u,w=n?u:a*p,y=h*g*l,_=!0,I=e.kernelCustomData.wT??e.compute(it(t[1],$l),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=I);let $=[t[0],I],x=t.length===3;x&&(!n&&t[2].dims.length===1?$.push(t[2].reshape([t[2].dims[0],1,1])):$.push(t[2])),e.compute(qi($,o,s,b,w,y,x,_),{inputs:$})},xl=(e,t)=>{let r=t.format==="NHWC",o=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];o.length===3&&o.push(e.inputs[2]);let n=t.kernelShape;(n.length===0||n[0]===0)&&(n=[e.inputs[1].dims[2]]);let s=t.dilations;(s.length===0||s[0]===0)&&(s=[1]);let 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k];":t.transA&&!t.transB?w="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!t.transA&&t.transB?w="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!t.transA&&!t.transB&&(w="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let y=t.alpha===1?"":"value *= uniforms.alpha;",_=M("a",e[0].dataType,e[0].dims),I=M("b",e[1].dataType,e[1].dims),$=_.type.value,x=null,E=[_,I];e.length===3&&(x=M("c",e[2].dataType,e[2].dims.length),E.push(x));let A=F("output",e[0].dataType,l.length);E.push(A);let z=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}];return`\n  ${b.registerUniforms(z).declareVariables(...E)}\n\n  ${b.mainStart()}\n    ${b.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n    let m = global_idx / uniforms.N;\n    let n = global_idx % uniforms.N;\n\n    var value = ${$}(0);\n    for (var k: u32 = 0u; k < uniforms.K; k++) {\n      ${w}\n    }\n\n    ${y}\n    ${(()=>x!=null?`let cOffset = ${x.broadcastedIndicesToOffset("vec2(m, n)",A)}; value += ${$}(uniforms.beta) * ${x.getByOffset("cOffset")};`:"")()}\n    output[global_idx] = value;\n  }`};return{name:"Gemm",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:l,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:p}),getShaderSource:g}},ys=e=>{let t=e.transA,r=e.transB,o=e.alpha,n=e.beta;return{transA:t,transB:r,alpha:o,beta:n,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},bs=(e,t)=>{Dl(e.inputs),e.compute(Ml(e.inputs,t))}});var zl,Ul,Vl,vs,$s=j(()=>{"use strict";Ne();$e();ve();zl=(e,t)=>{let r=e[0].dims,o=r,n=2,s=U.sizeToDimension(r,n),u=U.sizeFromDimension(r,n),l=Fe(u),a=u/l,p=[r[0],r[1],a],h=["rank","type","type"],g=[{type:"uint32",data:u},{type:"uint32",data:a}];g.push(...L(p),...L(p));let b=w=>{let y=M("x",e[0].dataType,p.length,l),_=M("scale",e[1].dataType,e[1].dims),I=M("bias",e[2].dataType,e[2].dims),$=F("output",e[0].dataType,p.length,l),x=[y,_,I,$],E=y.type.value,A=l===1?"f32":`vec${l}<f32>`,z=64,R=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return`\n  var<workgroup> meanShared : f32;\n  var<workgroup> squaredNormShared : f32;\n  var<workgroup> workgroupShared : array<${A}, ${z}>;\n  const workgroupSize = ${z}u;\n  ${w.registerUniforms(R).declareVariables(...x)}\n  ${w.mainStart(z)}\n    let norm = global_idx / workgroupSize;\n    let batch = norm / uniforms.x_shape[1];\n    let channel = norm % uniforms.x_shape[1];\n    let localIndex = local_id.x;\n\n    // initialize workgroup memory\n    var initial = ${A}(0);\n    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n      initial = initial + ${A}(${y.get("batch","channel","h")});\n    }\n    workgroupShared[localIndex] = initial;\n    workgroupBarrier();\n\n    // Calculate the mean of current channel data.\n    for (var currSize = workgroupSize >> 1;  currSize > 0; currSize = currSize >> 1) {\n      if (localIndex < currSize) {\n        workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize];\n      }\n      workgroupBarrier();\n    }\n    if (localIndex == 0) {\n      meanShared = ${Je("workgroupShared[0]",l)} / f32(uniforms.normSize);\n    }\n    workgroupBarrier();\n\n    // reinitialize workgroup memory.\n    initial = ${A}(0);\n    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n      let deviation =  ${A}(${y.get("batch","channel","h")}) - ${A}(meanShared);\n      initial = initial + deviation * deviation;\n    }\n    workgroupShared[localIndex] = initial;\n    workgroupBarrier();\n\n    // Calculate the sum of square of deviation of current channel data.\n    for (var currSize = workgroupSize >> 1;  currSize > 0; currSize = currSize >> 1) {\n      if (localIndex < currSize) {\n        workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize];\n      }\n      workgroupBarrier();\n    }\n    if (localIndex == 0) {\n      squaredNormShared = ${Je("workgroupShared[0]",l)};\n    }\n    workgroupBarrier();\n\n    let invStdDev = inverseSqrt(squaredNormShared / f32(uniforms.normSize) + f32(${t.epsilon}));\n    let channelScale = invStdDev * f32(${_.getByOffset("channel")});\n    let channelShift = f32(${I.getByOffset("channel")}) - meanShared * channelScale;\n    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n      let value = ${y.get("batch","channel","h")} * ${E}(${A}(channelScale)) + ${E}(${A}(channelShift));\n      ${$.set("batch","channel","h","value")};\n    }\n  }`};return{name:"InstanceNormalization",shaderCache:{hint:`${t.epsilon};${l}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:s},programUniforms:g}),getShaderSource:b}},Ul=(e,t,r,o,n,s,u,l)=>{let a=Fe(u),p=64,h=a===1?"vec2f":`mat2x${a}f`,g=a===1?"f32":`vec${a}f`,b=(R,V)=>`${h}(${R}, ${V})`,w=n*u/a,y=Math.ceil(s/p),_=["type"],I=[{type:"uint32",data:y},{type:"uint32",data:s},{type:"uint32",data:Math.floor(u/a)},{type:"uint32",data:Math.floor(s*u/a)}],$=R=>{let V=M("input",t.dataType,t.dims,a);return`\n  ${R.declareVariables(V)}\n  @group(0) @binding(1) var<storage, read_write> output : array<${h}>;\n  struct Uniforms {wg_size:u32, H:u32, C:u32, image_size:u32};\n  @group(0) @binding(2) var<uniform> uniforms: Uniforms;\n\n  ${R.mainStart(p)}\n    let currentImageNumber = global_idx / ${p} / uniforms.C;\n    let currentChannelNumber = (global_idx / ${p}) % uniforms.C;\n    let wgId = global_idx % ${p};\n    let wgOffset = wgId * uniforms.wg_size;\n    if (wgOffset >= uniforms.H) {\n        return;\n    }\n    let wgMax = min(wgOffset + uniforms.wg_size, uniforms.H);\n\n    let offset = currentImageNumber * uniforms.image_size + currentChannelNumber;\n    var sum = ${Ze("f32",a)};\n    var squaredSum = ${Ze("f32",a)};\n    for (var i: u32 = wgOffset; i < wgMax; i++) {\n        let value = ${g}(input[offset + i * uniforms.C]);\n        sum += value;\n        squaredSum += value * value;\n    }\n    output[global_idx] = ${b("sum","squaredSum")};\n  }`},x=e.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${a}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:[n,u,p,2],dataType:1}],dispatchGroup:{x:n*u/a},programUniforms:I}),getShaderSource:$},{inputs:[t],outputs:[-1]})[0],E=[{type:"uint32",data:w},{type:"uint32",data:s},{type:"uint32",data:Math.floor(u/a)},{type:"uint32",data:Math.floor(p*u/a)}],A=["type","type","type"],z=R=>{let V=M("scale",r.dataType,r.dims,a),T=M("bias",o.dataType,o.dims,a);return`\n  @group(0) @binding(0) var<storage, read> input : array<${h}>;\n  @group(0) @binding(1) var<storage, read> scale : array<${V.type.storage}>;\n  @group(0) @binding(2) var<storage, read> bias : array<${T.type.storage}>;\n  @group(0) @binding(3) var<storage, read_write> output : array<${h}>;\n  struct Uniforms {units_of_work : u32, H: u32, C : u32, image_size : u32};\n  @group(0) @binding(4) var<uniform> uniforms: Uniforms;\n\n  ${R.mainStart()}\n    ${R.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.units_of_work")}\n    let currentImageNumber = global_idx / uniforms.C;\n    let currentChannelNumber = global_idx % uniforms.C;\n\n    let offset = currentImageNumber * uniforms.image_size;\n    var sum = ${Ze("f32",a)};\n    var squaredSum = ${Ze("f32",a)};\n    for (var i: u32 = 0; i < ${p}; i++) {\n        let value = input[offset + i + currentChannelNumber * ${p}];\n        sum += value[0];\n        squaredSum += value[1];\n    }\n    sum = sum / f32(uniforms.H);\n    squaredSum = squaredSum / f32(uniforms.H);\n    let invStdDev = inverseSqrt(squaredSum - sum * sum + f32(${l}));\n    let channelScale = invStdDev * ${g}(scale[currentChannelNumber]);\n    let channelShift = ${g}(bias[currentChannelNumber]) - sum * channelScale;\n\n    output[global_idx] = ${b("channelScale","channelShift")};\n  }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${a};${l}`,inputDependencies:A},getRunData:()=>({outputs:[{dims:[n,u,2],dataType:1}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:E}),getShaderSource:z},{inputs:[x,r,o],outputs:[-1]})[0]},Vl=(e,t,r)=>{let o=t[0].dims,n=o,s=o[0],u=o[o.length-1],l=U.sizeFromDimension(o,1)/u,a=Fe(u),p=U.size(n)/a,h=[{type:"uint32",data:l},{type:"uint32",data:Math.floor(u/a)}],g=["type","type"],b=Ul(e,t[0],t[1],t[2],s,l,u,r.epsilon),w=y=>{let _=Le(t[0].dataType),I=a===1?"vec2f":`mat2x${a}f`,$=a===1?_:`vec${a}<${_}>`,x=M("input",t[0].dataType,t[0].dims,a),E=F("output",t[0].dataType,n,a);return`\n  @group(0) @binding(0) var<storage, read> input : array<${x.type.storage}>;\n  @group(0) @binding(1) var<storage, read> scaleInput : array<${I}>;\n  @group(0) @binding(2) var<storage, read_write> output : array<${E.type.storage}>;\n  struct Uniforms {H: u32, C : u32};\n  @group(0) @binding(3) var<uniform> uniforms: Uniforms;\n\n  ${y.mainStart()}\n    let currentImageNumber = global_idx / (uniforms.C * uniforms.H);\n    let currentChannelNumber = global_idx % uniforms.C;\n\n    let scaleOffset = currentImageNumber * uniforms.C + currentChannelNumber;\n    let scale = scaleInput[scaleOffset];\n    output[global_idx] = fma(input[global_idx], ${$}(scale[0]), ${$}(scale[1]));\n  }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${a}`,inputDependencies:g},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:h}),getShaderSource:w},{inputs:[t[0],b]})},vs=(e,t)=>{t.format==="NHWC"?Vl(e,e.inputs,t):e.compute(zl(e.inputs,t))}});var Nl,Wl,Ss,xs=j(()=>{"use strict";Ne();$e();ve();Nl=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},Wl=(e,t,r)=>{let o=e[0].dims,n=e[1],s=e[2],u=o,l=U.normalizeAxis(t.axis,o.length),a=U.sizeToDimension(o,l),p=U.sizeFromDimension(o,l),h=U.size(n.dims),g=s?U.size(s.dims):0;if(h!==p||s&&g!==p)throw new Error(`Size of X.shape()[axis:] == ${p}.\n       Size of scale and bias (if provided) must match this.\n       Got scale size of ${h} and bias size of ${g}`);let b=[];for(let A=0;A<o.length;++A)A<l?b.push(o[A]):b.push(1);let w=Fe(p),y=["type","type"],_=[{type:"uint32",data:a},{type:"float32",data:p},{type:"uint32",data:Math.floor(p/w)},{type:"float32",data:t.epsilon}];s&&y.push("type");let I=r>1,$=r>2,x=A=>{let z=Le(e[0].dataType),R=[M("x",e[0].dataType,e[0].dims,w),M("scale",n.dataType,n.dims,w)];s&&R.push(M("bias",s.dataType,s.dims,w)),R.push(F("output",e[0].dataType,u,w)),I&&R.push(F("mean_data_output",1,b)),$&&R.push(F("inv_std_output",1,b));let V=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`\n  ${A.registerUniforms(V).declareVariables(...R)}\n  ${A.mainStart()}\n    ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}\n    let offset = global_idx * uniforms.norm_size_vectorized;\n    var meanVector = ${Ze("f32",w)};\n    var meanSquareVector = ${Ze("f32",w)};\n\n    for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {\n      let value = ${at(z,w,"x[h + offset]")};\n      meanVector += value;\n      meanSquareVector += value * value;\n    }\n    let mean = ${Je("meanVector",w)} / uniforms.norm_size;\n    let invStdDev =\n        inverseSqrt(${Je("meanSquareVector",w)} / uniforms.norm_size - mean * mean + uniforms.epsilon);\n\n    for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {\n      let f32input = ${at(z,w,"x[j + offset]")};\n      let f32scale = ${at(z,w,"scale[j]")};\n      output[j + offset] = ${R[0].type.value}((f32input - mean) * invStdDev * f32scale\n        ${s?`+ ${at(z,w,"bias[j]")}`:""}\n      );\n    }\n\n    ${I?"mean_data_output[global_idx] = mean":""};\n    ${$?"inv_std_output[global_idx] = invStdDev":""};\n  }`},E=[{dims:u,dataType:e[0].dataType}];return I&&E.push({dims:b,dataType:1}),$&&E.push({dims:b,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${w};${r}`,inputDependencies:y},getRunData:()=>({outputs:E,dispatchGroup:{x:Math.ceil(a/64)},programUniforms:_}),getShaderSource:x}},Ss=(e,t)=>{Nl(e.inputs),e.compute(Wl(e.inputs,t,e.outputCount))}});var Hl,Cs,_s,Gl,Xn,Is,As=j(()=>{"use strict";$e();je();Nr();Un();ve();jt();Hl=(e,t)=>{let r=e[0],o=e[1],n=e[2],s=e[3],u=e[4],l=e[5],a=e[6],p=e[7];if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let h=!1,g=r.dims[0],b=r.dims[1],w=r.dims.length===3?h?r.dims[2]/3:r.dims[2]:t.numHeads*r.dims[4],y=b,_=0,I=0,$=Math.floor(w/t.numHeads);if(a&&p){if(a.dims.length!==4)throw new Error(\'Input "past_key" is expected to have 4 dimensions\');if(p.dims.length!==4)throw new Error(\'Input "past_value" is expected to have 4 dimensions\');_=a.dims[2],I=a.dims[2]}else if(a||p)throw new Error(\'Input "past_key" and "past_value" shall be both present or both absent\');let x;if(o){if(r.dims.length!==3)throw new Error(\'Input "query" is expected to have 3 dimensions when key is given\');if(o.dims.length<3||o.dims.length>5)throw new Error(\'Input "key" is expected to have 3, 4, or 5 dimensions\');if(r.dims[0]!==o.dims[0])throw new Error(\'Input "query" and "key" shall have same dim 0 (batch size)\');if(o.dims.length===3){if(o.dims[2]!==r.dims[2])throw new Error(\'Input "query" and "key" shall have same dim 2 (hidden_size)\');x=2,y=o.dims[1]}else if(o.dims.length===5){if(o.dims[2]!==t.numHeads||o.dims[3]!==2||o.dims[4]!==$)throw new Error(\'Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv\');if(n)throw new Error(\'Expect "value" be none when "key" has packed kv format.\');x=5,y=o.dims[1]}else{if(o.dims[1]!==t.numHeads||o.dims[3]!==$)throw new Error(\'Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key\');x=0,y=o.dims[2]}}else{if(r.dims.length!==3&&r.dims.length!==5)throw new Error(\'Input "query" is expected to have 3 or 5 dimensions when key is empty\');if(r.dims.length===5&&(r.dims[2]!==t.numHeads||r.dims[3]!==3))throw new Error(\'Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv\');x=3}if(s){if(s.dims.length!==1)throw new Error(\'Input "bias" is expected to have 1 dimension\');if(n&&r.dims.length===5&&r.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let E=0;if(u){E=8;let T=u.dims;throw T.length===1?T[0]===g?E=1:T[0]===3*g+2&&(E=3):T.length===2&&T[0]===g&&T[1]===y&&(E=5),E===8?new Error(\'Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)\'):new Error("Mask not supported")}let A=!1,z=w;if(n){if(n.dims.length!==3&&n.dims.length!==4)throw new Error(\'Input "value" is expected to have 3 or 4 dimensions\');if(r.dims[0]!==n.dims[0])throw new Error(\'Input "query" and "value" shall have same dim 0 (batch_size)\');if(n.dims.length===3){if(y!==n.dims[1])throw new Error(\'Input "key" and "value" shall have the same dim 1 (kv_sequence_length)\');z=n.dims[2]}else{if(y!==n.dims[2])throw new Error(\'Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)\');z=n.dims[1]*n.dims[3],A=!0}}let R=_+y,V=!1;if(u)throw new Error("Key padding mask is not supported");if(l)throw new Error("extraAddQk is not supported");if(a)throw new Error("pastKey is not supported");if(p)throw new Error("pastValue is not supported");return{batchSize:g,sequenceLength:b,pastSequenceLength:_,kvSequenceLength:y,totalSequenceLength:R,maxSequenceLength:I,inputHiddenSize:0,hiddenSize:w,vHiddenSize:z,headSize:$,vHeadSize:Math.floor(z/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:E,scale:t.scale,broadcastResPosBias:V,passPastInKv:A,qkvFormat:x}},Cs=e=>ge({...e}),_s=ge({perm:[0,2,1,3]}),Gl=(e,t,r,o,n,s,u)=>{let l=[o,n,s],a=U.size(l),p=[{type:"uint32",data:a},{type:"uint32",data:u},{type:"uint32",data:s}],h=g=>{let b=F("qkv_with_bias",t.dataType,l),w=M("qkv",t.dataType,l),y=M("bias",r.dataType,l),_=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`\n  ${g.registerUniforms(_).declareVariables(w,y,b)}\n  ${g.mainStart()}\n    ${g.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n    let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;\n\n    qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];\n  }`};return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:l,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:p}),getShaderSource:h},{inputs:[t,r],outputs:[-1]})[0]},Xn=(e,t,r,o,n,s,u,l)=>{let a=s;if(u){if(o===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return a=Gl(e,s,u,t,o,r*n,l),a=a.reshape([t,o,r,n]),e.compute(it(a,_s.perm),{inputs:[a],outputs:[-1]})[0]}else return s.dims.length===3&&(a=s.reshape([t,o,r,n])),e.compute(it(a,_s.perm),{inputs:[a],outputs:[-1]})[0]},Is=(e,t)=>{let r=Hl(e.inputs,t);if(e.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(e.inputs[1]?.dims.length===5)throw new Error("Packed KV is not implemented");let o=e.inputs[1]&&e.inputs[2]&&e.inputs[1].dims.length===4&&e.inputs[2].dims.length===4,n=Xn(e,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,e.inputs[0],e.inputs[3],0);if(o)return Kr(e,n,e.inputs[1],e.inputs[2],e.inputs[4],void 0,void 0,void 0,e.inputs[5],r,t);let s=Xn(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,e.inputs[1],e.inputs[3],r.hiddenSize),u=Xn(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,e.inputs[2],e.inputs[3],2*r.hiddenSize);Kr(e,n,s,u,e.inputs[4],void 0,e.inputs[6],e.inputs[7],e.inputs[5],r,t)}});var Ll,Fl,jl,ql,Kl,Yl,Zl,Ql,Ts,Es=j(()=>{"use strict";Ne();$e();ve();Ll=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1)throw new Error("Input type must be float.");if(e.length>=2){let t=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(t=e[3].dims[0]*2===e[1].dims[0]),!t)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},Fl=(e,t,r)=>{let o="";for(let n=t-1;n>=0;--n)o+=`\n            k = i32(${e.indicesGet("indices",n)}) - ${ce("uniforms.pads",n,r)};\n            if (k < 0) {\n              break;\n            }\n            if (k >= i32(${ce("uniforms.x_shape",n,t)})) {\n              break;\n            }\n            offset += k * i32(${ce("uniforms.x_strides",n,t)});\n        `;return`\n          value = ${e.type.value}(uniforms.constant_value);\n          for (var i = 0; i < 1; i++) {\n            var offset = 0;\n            var k = 0;\n            ${o}\n            value = x[offset];\n          }\n      `},jl=(e,t,r)=>{let o="";for(let n=t-1;n>=0;--n)o+=`\n                k = i32(${e.indicesGet("indices",n)}) - ${ce("uniforms.pads",n,r)};\n                if (k < 0) {\n                  k = -k;\n                }\n                {\n                  let _2n_1 = 2 * (i32(${ce("uniforms.x_shape",n,t)}) - 1);\n                  k = k % _2n_1;\n                  if(k >= i32(${ce("uniforms.x_shape",n,t)})) {\n                    k = _2n_1 - k;\n                  }\n                }\n                offset += k * i32(${ce("uniforms.x_strides",n,t)});\n            `;return`\n              var offset = 0;\n              var k = 0;\n              ${o}\n              value = x[offset];\n          `},ql=(e,t,r)=>{let o="";for(let n=t-1;n>=0;--n)o+=`\n                k = i32(${e.indicesGet("indices",n)}) - ${ce("uniforms.pads",n,r)};\n                if (k < 0) {\n                  k = 0;\n                }\n                if (k >= i32(${ce("uniforms.x_shape",n,t)})) {\n                  k = i32(${ce("uniforms.x_shape",n,t)}) - 1;\n                }\n                offset += k * i32(${ce("uniforms.x_strides",n,t)});\n            `;return`\n              var offset = 0;\n              var k = 0;\n              ${o}\n              value = x[offset];\n          `},Kl=(e,t,r)=>{let o="";for(let n=t-1;n>=0;--n)o+=`\n                k = i32(${e.indicesGet("indices",n)}) - ${ce("uniforms.pads",n,r)};\n                if (k < 0)  {\n                  k += i32(${ce("uniforms.x_shape",n,t)}]);\n                }\n                if (k >= i32(${ce("uniforms.x_shape",n,t)})) {\n                  k -= i32(${ce("uniforms.x_shape",n,t)});\n                }\n                offset += k * i32(${ce("uniforms.x_strides",n,t)});\n            `;return`\n              var offset = 0;\n              var k = 0;\n              ${o}\n              value = x[offset];\n          `},Yl=(e,t,r)=>{switch(r.mode){case 0:return Fl(e,t,r.pads.length);case 1:return jl(e,t,r.pads.length);case 2:return ql(e,t,r.pads.length);case 3:return Kl(e,t,r.pads.length);default:throw new Error("Invalid mode")}},Zl=(e,t)=>{let r=U.padShape(e[0].dims.slice(),t.pads),o=e[0].dims,s=[{type:"uint32",data:U.size(r)},{type:"uint32",data:t.pads}];if(t.mode===0){let a=Xe(e[0].dataType);s.push({type:a,data:t.value})}s.push(...L(e[0].dims),...L(r));let u=["rank"],l=a=>{let p=F("output",e[0].dataType,r.length),h=M("x",e[0].dataType,o.length),g=h.type.value,b=Yl(p,o.length,t),w=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:t.pads.length}];return t.mode===0&&w.push({name:"constant_value",type:g}),`\n            ${a.registerUniforms(w).declareVariables(h,p)}\n            ${a.mainStart()}\n            ${a.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n            let indices = ${p.offsetToIndices("global_idx")};\n\n            var value = ${g}(0);\n            ${b}\n            output[global_idx] = value;\n        }`};return{name:"Pad",shaderCache:{hint:`${t.mode}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(U.size(r)/64)},programUniforms:s}),getShaderSource:l}},Ql=(e,t)=>{if(e.length>1){let r=e[1].getBigInt64Array(),o=e.length>=3&&e[2].data?e[2].getFloat32Array()[0]:0,n=e[0].dims.length,s=new Int32Array(2*n).fill(0);if(e.length>=4){let l=e[3].getBigInt64Array();for(let a=0;a<l.length;a++)s[Number(l[a])]=Number(r[a]),s[Number(l[a])+n]=Number(r[a+l.length])}else r.forEach((l,a)=>s[Number(a)]=Number(l));let u=[];return s.forEach(l=>u.push(l)),{mode:t.mode,value:o,pads:u}}else return t},Ts=(e,t)=>{Ll(e.inputs);let r=Ql(e.inputs,t);e.compute(Zl(e.inputs,r),{inputs:[0]})}});var nn,Os,Ps,ks,Rs,Xl,Jl,Bs,Ds,Ms,zs,Us,Vs,Ns,Ws,Hs,Gs,Ls,Fs,js=j(()=>{"use strict";Lt();$e();ve();nn=e=>{if(Gt.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},Os=(e,t,r)=>{let o=t.format==="NHWC",n=e.dims.slice();o&&n.splice(1,0,n.pop());let s=Object.hasOwnProperty.call(t,"dilations"),u=t.kernelShape.slice(),l=t.strides.slice(),a=s?t.dilations.slice():[],p=t.pads.slice();Bt.adjustPoolAttributes(r,n,u,l,a,p);let h=Bt.computePoolOutputShape(r,n,l,a,u,p,t.autoPad),g=Object.assign({},t);s?Object.assign(g,{kernelShape:u,strides:l,pads:p,dilations:a,cacheKey:t.cacheKey}):Object.assign(g,{kernelShape:u,strides:l,pads:p,cacheKey:t.cacheKey});let b=h.slice();return b.push(b.splice(1,1)[0]),[g,o?b:h]},Ps=(e,t)=>{let r=t.format==="NHWC",o=U.size(e),n=U.size(t.kernelShape),s=[{type:"uint32",data:o},{type:"uint32",data:n}],u=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let l=t.kernelShape[t.kernelShape.length-1],a=t.strides[t.strides.length-1],p=t.pads[t.pads.length/2-1],h=t.pads[t.pads.length-1],g=!!(p+h);s.push({type:"uint32",data:l},{type:"uint32",data:a},{type:"uint32",data:p},{type:"uint32",data:h}),u.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let b=!1;if(t.kernelShape.length===2){let w=t.kernelShape[t.kernelShape.length-2],y=t.strides[t.strides.length-2],_=t.pads[t.pads.length/2-2],I=t.pads[t.pads.length-2];b=!!(_+I),s.push({type:"uint32",data:w},{type:"uint32",data:y},{type:"uint32",data:_},{type:"uint32",data:I}),u.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[s,u,!0,g,b]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let l=U.computeStrides(t.kernelShape);s.push({type:"uint32",data:l},{type:"uint32",data:t.pads},{type:"uint32",data:t.strides}),u.push({name:"kernelStrides",type:"u32",length:l.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length});let a=t.pads.reduce((p,h)=>p+h);return[s,u,!!a,!1,!1]}},ks=(e,t,r,o,n,s,u,l,a,p,h,g)=>{let b=n.format==="NHWC",w=t.type.value,y=F("output",t.type.tensor,o);if(n.kernelShape.length<=2){let _="",I="",$="",x=r-(b?2:1);if(h?_=`\n                for (var i: u32 = 0u; i < uniforms.kw; i++) {\n                  xIndices[${x}] = indices[${x}] * uniforms.sw - uniforms.pwStart + i;\n                  if (xIndices[${x}] < 0 || xIndices[${x}]\n                      >= uniforms.x_shape[${x}]) {\n                    pad++;\n                    continue;\n                  }\n                  let x_val = x[${t.indicesToOffset("xIndices")}];\n                  ${s}\n                }`:_=`\n                for (var i: u32 = 0u; i < uniforms.kw; i++) {\n                  xIndices[${x}] = indices[${x}] * uniforms.sw - uniforms.pwStart + i;\n                  let x_val = x[${t.indicesToOffset("xIndices")}];\n                  ${s}\n                }`,n.kernelShape.length===2){let A=r-(b?3:2);g?I=`\n                for (var j: u32 = 0u; j < uniforms.kh; j++) {\n                  xIndices[${A}] = indices[${A}] * uniforms.sh - uniforms.phStart + j;\n                  if (xIndices[${A}] < 0 || xIndices[${A}] >= uniforms.x_shape[${A}]) {\n                    pad += i32(uniforms.kw);\n                    continue;\n                  }\n              `:I=`\n                for (var j: u32 = 0u; j < uniforms.kh; j++) {\n                  xIndices[${A}] = indices[${A}] * uniforms.sh - uniforms.phStart + j;\n                `,$=`\n              }\n            `}return`\n            ${e.registerUniforms(a).declareVariables(t,y)}\n\n            ${e.mainStart()}\n              ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n\n              let indices = ${y.offsetToIndices("global_idx")};\n              var xIndices = ${y.offsetToIndices("global_idx")};\n\n              var value = ${w}(${l});\n              var pad = 0;\n              ${I}\n              ${_}\n              ${$}\n              ${u}\n\n              output[global_idx] = value;\n            }`}else{if(b)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let _=n.kernelShape.length,I=n.pads.length,$="";return p?$=`\n                if (xIndices[j] >= uniforms.x_shape[j]) {\n                  pad++;\n                  isPad = true;\n                  break;\n                }\n              }\n              if (!isPad) {\n                let x_val = x[${t.indicesToOffset("xIndices")}];\n                ${s}\n              }`:$=`\n              }\n              let x_val = x[${t.indicesToOffset("xIndices")}];\n              ${s}\n            `,`\n            ${e.registerUniforms(a).declareVariables(t,y)}\n\n            ${e.mainStart()}\n              ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n              let indices = ${y.offsetToIndices("global_idx")};\n              var xIndices = ${y.offsetToIndices("global_idx")};\n\n              var offsets: array<u32, ${_}>;\n\n              var value = ${w}(${l});\n              var pad = 0;\n              var isPad = false;\n\n              for (var i: u32 = 0u; i < uniforms.kernelSize; i++) {\n                var offset = i;\n                for (var j = 0u; j < ${_-1}u; j++) {\n                  offsets[j] = offset / ${ce("uniforms.kernelStrides","j",_)};\n                  offset -= offsets[j] * ${ce("uniforms.kernelStrides","j",_)};\n                }\n                offsets[${_-1}] = offset;\n\n                isPad = false;\n                for (var j = ${r-_}u; j < ${r}u; j++) {\n                  xIndices[j] = indices[j] * ${ce("uniforms.strides",`j - ${r-_}u`,_)}\n                    + offsets[j - ${r-_}u] - ${ce("uniforms.pads","j - 2u",I)};\n                  ${$}\n              }\n              ${u}\n\n              output[global_idx] = value;\n            }`}},Rs=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,Xl=e=>`${Rs(e)};${e.countIncludePad}`,Jl=e=>`${Rs(e)};${e.storageOrder};${e.dilations}`,Bs=e=>({format:e.format,autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],ceilMode:e.ceil_mode,kernelShape:e.kernel_shape,strides:e.strides,pads:e.pads}),Ds=(e,t,r,o)=>{let[n,s]=Os(t,o,r),u=M("x",t.dataType,t.dims.length),l=u.type.value,a="value += x_val;",p="";n.countIncludePad?p+=`value /= ${l}(uniforms.kernelSize);`:p+=`value /= ${l}(i32(uniforms.kernelSize) - pad);`;let[h,g,b,w,y]=Ps(s,n);h.push(...L(t.dims),...L(s));let _=["rank"];return{name:e,shaderCache:{hint:`${o.cacheKey};${b};${w};${y}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:s,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(U.size(s)/64)},programUniforms:h}),getShaderSource:I=>ks(I,u,t.dims.length,s.length,n,a,p,0,g,b,w,y)}},Ms=e=>{let t=e.count_include_pad!==0,r=Bs(e);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let o={countIncludePad:t,...r,cacheKey:""};return{...o,cacheKey:Xl(o)}},zs=(e,t)=>{nn(e.inputs),e.compute(Ds("AveragePool",e.inputs[0],!1,t))},Us={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},Vs=e=>{let t=e.format;return{format:t,...Us,cacheKey:t}},Ns=(e,t)=>{nn(e.inputs),e.compute(Ds("GlobalAveragePool",e.inputs[0],!0,t))},Ws=(e,t,r,o)=>{let[n,s]=Os(t,o,r),u=`\n      value = max(x_val, value);\n    `,l="",a=M("x",t.dataType,t.dims.length),p=["rank"],[h,g,b,w,y]=Ps(s,n);return h.push(...L(t.dims),...L(s)),{name:e,shaderCache:{hint:`${o.cacheKey};${b};${w};${y}`,inputDependencies:p},getRunData:()=>({outputs:[{dims:s,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(U.size(s)/64)},programUniforms:h}),getShaderSource:_=>ks(_,a,t.dims.length,s.length,n,u,l,-1e5,g,b,w,y)}},Hs=(e,t)=>{nn(e.inputs),e.compute(Ws("MaxPool",e.inputs[0],!1,t))},Gs=e=>{let t=e.storage_order,r=e.dilations,o=Bs(e);if(t!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(o.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let n={storageOrder:t,dilations:r,...o,cacheKey:""};return{...n,cacheKey:Jl(n)}},Ls=e=>{let t=e.format;return{format:t,...Us,cacheKey:t}},Fs=(e,t)=>{nn(e.inputs),e.compute(Ws("GlobalMaxPool",e.inputs[0],!0,t))}});var tc,rc,qs,Ks=j(()=>{"use strict";Lt();Ne();ve();tc=(e,t,r)=>{let o=e===t,n=e<t&&r<0,s=e>t&&r>0;if(o||n||s)throw new Error("Range these inputs\' contents are invalid.")},rc=(e,t,r,o)=>{let n=Math.abs(Math.ceil((t-e)/r)),s=[n],u=n,l=Xe(o),a=[{type:"uint32",data:u},{type:l,data:e},{type:l,data:r},...L(s)],p=h=>{let g=F("output",o,s.length),b=g.type.value,w=[{name:"outputSize",type:"u32"},{name:"start",type:b},{name:"delta",type:b}];return`\n        ${h.registerUniforms(w).declareVariables(g)}\n        ${h.mainStart()}\n        ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n        output[global_idx] = uniforms.start + ${b}(global_idx) * uniforms.delta;\n      }`};return{name:"Range",shaderCache:{hint:`${o}`},getShaderSource:p,getRunData:()=>({outputs:[{dims:s,dataType:o}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:a})}},qs=e=>{let t=0,r=0,o=0;e.inputs[0].dataType===6?(t=e.inputs[0].getInt32Array()[0],r=e.inputs[1].getInt32Array()[0],o=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(t=e.inputs[0].getFloat32Array()[0],r=e.inputs[1].getFloat32Array()[0],o=e.inputs[2].getFloat32Array()[0]),Gt.webgpu.validateInputContent&&tc(t,r,o),e.compute(rc(t,r,o,e.inputs[0].dataType),{inputs:[]})}});var nc,oc,ac,ic,sc,uc,dc,lc,cc,pc,mc,Ys,fc,hc,gc,yc,bc,Zs,Qs,Xs=j(()=>{"use strict";$e();je();ve();nc=(e,t)=>{if(e.every(r=>r>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),e.length>0){if(t.mode==="linear"){if(!(e.length===2||e.length===3||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1||e.length===5&&e[0]===1&&e[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and\n            one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(t.mode==="cubic"&&!(e.length===2||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},oc=(e,t,r)=>{t.every(n=>n>=0&&n<r||(()=>{throw new Error("Resize requires axes input values to be positive and less than rank")}));let o=new Array(r).fill(1);return t.forEach((n,s)=>o[n]=e[s]),o},ac=(e,t,r,o,n,s)=>{let[u,l,a]=r>10?[1,2,3]:[-1,e.length>1?1:-1,-1],p=e[0].dims.length;if(u>0&&e.length>u&&e[u].dims.length>0)e[u].getFloat32Array().forEach(h=>s.push(h));else if(t.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(l>0&&e.length>l&&e[l].dims.length>0){if(e[l].getFloat32Array().forEach(h=>o.push(h)),o.length!==0&&o.length!==p&&r>=18&&o.length!==t.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");nc(o,t),t.axes.length>0&&oc(o,t.axes,p).forEach((h,g)=>o[g]=h)}if(a>0&&e.length>a&&(e[a].getBigInt64Array().forEach(h=>n.push(Number(h))),n.length!==p||r>=18&&n.length===t.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(t.axes.length>0){if(o.length!==t.axes.length)throw new Error(\'Resize requires "scales" input size to be of axes rank when axes attributes is specified\');if(n.length!==t.axes.length)throw new Error(\'Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified\')}if(typeof o<"u"&&typeof n<"u"&&o.length>0&&n.length>p)throw new Error("Resize requires only of scales or sizes to be specified")},ic=(e,t)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,\n     lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${t} { `+(()=>{switch(e){case"asymmetric":return`return ${t}(xResized) / ${t}(xScale);`;case"pytorch_half_pixel":return`if (lengthResized > 1) {\n                    return (${t}(xResized) + 0.5) / ${t}(xScale) - 0.5;\n                  } else {\n                    return 0.0;\n                  }`;case"tf_half_pixel_for_nn":return`return (${t}(xResized) + 0.5) / ${t}(xScale);`;case"align_corners":return`if (lengthResized == 1) {\n                    return 0.0;\n                  } else {\n                    // The whole part and the fractional part are calculated separately due to inaccuracy of floating\n                    // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an\n                    // offset-by-one error later in floor().\n                    let whole = ${t}(xResized * (lengthOriginal - 1) / (lengthResized - 1));\n                    let fract =\n                        ${t}(xResized * (lengthOriginal - 1) % (lengthResized - 1)) / ${t}(lengthResized - 1);\n                    return whole + fract;\n                  }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {\n                    return ${t}(roiStart) * ${t}(lengthOriginal - 1) +\n                        (${t}(xResized) * ${t}(roiEnd - roiStart) * ${t}(lengthOriginal - 1)) /\n                        ${t}(lengthResized - 1);\n                  } else {\n                    return 0.5 * ${t}(roiStart + roiEnd) * ${t}(lengthOriginal - 1);\n                  }`;case"half_pixel_symmetric":return`const outputWidth = ${t}xScale * ${t}(lengthResized);\n                  const adjustment = ${t}(lengthResized) / outputWidth;\n                  const center = ${t}(lengthOriginal) / 2;\n                  const offset = center * (1 - adjustment);\n                  return offset + ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;case"half_pixel":return`return ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${e} is not supported`)}})()+"}",sc=(e,t,r)=>`fn getNearestPixelFromOriginal(xOriginal: ${r}, isDownSample: bool) -> ${r} {`+(()=>{switch(e){case"round_prefer_ceil":return"if (fract(xOriginal) == 0.5) {             return ceil(xOriginal);           } else {             return round(xOriginal);           }";case"floor":return"return floor(xOriginal);";case"ceil":return"return ceil(xOriginal);";case"round_prefer_floor":return"if (fract(xOriginal) == 0.5) {                     return floor(xOriginal);                   } else {                     return round(xOriginal);                   }";case"simple":default:if(t<11)return"if (isDownSample)                     {                       return ceil(xOriginal);                     } else {                       return xOriginal;                     }";throw new Error(`Nearest mode ${e} is not supported`)}})()+"}",uc=(e,t,r)=>{let o=new Array(r).fill(0).concat(new Array(r).fill(1)),n=e.length===0?o:e.slice();return t.length>0?(t.forEach((s,u)=>{o[s]=n[u],o[u+r]=n[t.length+u]}),o):n},dc=(e,t,r,o)=>{let n=[];if(r.length>0)if(o.length>0){if(e.forEach(s=>n.push(s)),Math.max(...o)>e.length)throw new Error("axes is out of bound");o.forEach((s,u)=>n[s]=r[u])}else r.forEach(s=>n.push(s));else{if(t.length===0)throw new Error("Resize requires either scales or sizes.");n=e.map((s,u)=>Math.round(s*t[u]))}return n},lc=(e,t,r)=>{let o=(()=>{switch(r.keepAspectRatioPolicy){case"not_larger":return r.axes.length>0?Math.min(...r.axes.map(s=>t[s]),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case"not_smaller":return r.axes.length>0?Math.max(...r.axes.map(s=>t[s]),Number.MIN_VALUE):Math.max(...t,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${r.keepAspectRatioPolicy} is not supported`)}})();t.fill(1,0,t.length);let n=e.slice();return r.axes.length>0?(r.axes.forEach(s=>t[s]=o),r.axes.forEach(s=>n[s]=Math.round(e[s]*t[s]))):(t.fill(o,0,t.length),n.forEach((s,u)=>n[u]=Math.round(s*t[u]))),n},cc=(e,t,r,o,n)=>`\n    fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${r.length}> {\n      var original_indices: array<${e.type.value}, ${r.length}>;\n      for (var i:u32 = 0; i < ${r.length}; i++) {\n        var output_index = ${e.indicesGet("output_indices","i")};\n        var scale = ${ce("uniforms.scales","i",o)};\n        var roi_low = ${ce("uniforms.roi","i",n)};\n        var roi_hi = ${ce("uniforms.roi",`i + ${t.length}`,n)};\n        if (scale == 1.0) {\n          original_indices[i] = ${e.type.value}(output_index);\n        } else {\n          var input_shape_i = ${ce("uniforms.input_shape","i",t.length)};\n          var output_shape_i = ${ce("uniforms.output_shape","i",r.length)};\n          original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\n                                                                           input_shape_i, roi_low, roi_hi);\n        }\n      }\n      return original_indices;\n    }`,pc=(e,t,r,o,n,s,u)=>`\n    fn calculateInputIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {\n      var input_indices: ${e.type.indices};\n      for (var i:u32 = 0; i < ${o.length}; i++) {\n        var output_index = ${t.indicesGet("output_indices","i")};\n        var input_index: u32;\n        var scale = ${ce("uniforms.scales","i",n)};\n        if (scale == 1.0) {\n          input_index = output_index;\n        } else {\n          var roi_low = ${ce("uniforms.roi","i",s)};\n          var roi_hi = ${ce("uniforms.roi",`i + ${r.length}`,s)};\n          var input_shape_i = ${ce("uniforms.input_shape","i",r.length)};\n          var output_shape_i = ${ce("uniforms.output_shape","i",o.length)};\n          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\n                                                                        input_shape_i, roi_low, roi_hi);\n          if (!${u} || (original_idx >= 0 && original_idx < ${t.type.value}(input_shape_i))) {\n            if (original_idx < 0) {\n              input_index = 0;\n            } else if (original_idx > ${t.type.value}(input_shape_i - 1)) {\n              input_index = input_shape_i - 1;\n            } else {\n              input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1));\n            }\n          } else {\n            input_index = u32(original_idx);\n          }\n        }\n        ${e.indicesSet("input_indices","i"," input_index")}\n      }\n      return input_indices;\n    }`,mc=(e,t)=>`\n    fn checkInputIndices(input_indices: ${e.type.indices}) -> bool {\n      for (var i:u32 = 0; i < ${t.length}; i++) {\n        var input_index = ${e.indicesGet("input_indices","i")};\n        if (input_index < 0 || input_index >= ${ce("uniforms.input_shape","i",t.length)}) {\n          return false;\n        }\n      }\n      return true;\n    }`,Ys=(e,t,r,o)=>e.rank>o?`\n    ${e.indicesSet("input_indices",t,"channel")};\n    ${e.indicesSet("input_indices",r,"batch")};\n`:"",fc=(e,t,r,o,n)=>{let[u,l,a,p]=r.length===2?[-1,0,1,-1]:[0,2,3,1],h=e.type.value;return`\n    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${h} {\n      var input_indices: ${e.type.indices};\n      ${e.indicesSet("input_indices",l,`max(0, min(row, ${r[l]} - 1))`)};\n      ${e.indicesSet("input_indices",a,`max(0, min(col, ${r[a]} - 1))`)};\n      ${Ys(e,p,u,2)}\n      return ${e.getByIndices("input_indices")};\n    }\n\n    fn bilinearInterpolation(output_indices: ${t.type.indices}) -> ${h} {\n      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n      var row:${h} = originalIndices[${l}];\n      var col:${h} = originalIndices[${a}];\n      ${o?`if (row < 0 || row > (${r[l]} - 1) || col < 0 || col > (${r[a]} - 1)) {\n        return ${n};\n      }`:""};\n      row = max(0, min(row, ${r[l]} - 1));\n      col = max(0, min(col, ${r[a]} - 1));\n      var row1: u32 = u32(row);\n      var col1: u32 = u32(col);\n      var row2: u32 = u32(row + 1);\n      var col2: u32 = u32(col + 1);\n      var channel: u32 = ${r.length>2?`u32(originalIndices[${p}])`:"0"};\n      var batch: u32 =  ${r.length>2?`u32(originalIndices[${u}])`:"0"};\n      var x11: ${h} = getInputValue(batch, channel, row1, col1);\n      var x12: ${h} = getInputValue(batch, channel, row1, col2);\n      var x21: ${h} = getInputValue(batch, channel, row2, col1);\n      var x22: ${h} = getInputValue(batch, channel, row2, col2);\n      var dx1: ${h} = abs(row - ${h}(row1));\n      var dx2: ${h} = abs(${h}(row2) - row);\n      var dy1: ${h} = abs(col - ${h}(col1));\n      var dy2: ${h} = abs(${h}(col2) - col);\n      if (row1 == row2) {\n        dx1 = 0.5;\n        dx2 = 0.5;\n      }\n      if (col1 == col2) {\n        dy1 = 0.5;\n        dy2 = 0.5;\n      }\n      return (x11 * dx2 * dy2 + x12 * dx2 * dy1 + x21 * dx1 * dy2 + x22 * dx1 * dy1);\n    }`},hc=(e,t,r,o,n,s,u,l,a,p)=>{let h=r.length===2,g=!0,[b,w]=h?[0,1]:g?[2,3]:[1,2],y=e.type.value,_=I=>{let $=I===b?"row":"col";return`\n      fn ${$}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${t.type.indices}) -> ${y} {\n        var output_index = ${t.indicesGet("output_indices",I)};\n        var originalIdx: ${y} = getOriginalCoordinateFromResizedCoordinate(output_index, ${n[I]},\n        ${o[I]}, ${r[I]}, ${s[I]}, ${s[I]} + ${r.length});\n        var fractOriginalIdx: ${y} = originalIdx - floor(originalIdx);\n        var coefs = getCubicInterpolationCoefs(fractOriginalIdx);\n\n        if (${l} && (originalIdx < 0 || originalIdx > (${r[I]} - 1))) {\n          return ${a};\n        }\n        var data: array<${y}, 4> = array<${y}, 4>(0.0, 0.0, 0.0, 0.0);\n        for (var i: i32 = -1; i < 3; i++) {\n          var ${$}: ${y} = originalIdx + ${y}(i);\n          if (${$} < 0 || ${$} >= ${r[I]}) {\n            ${(()=>p?`coefs[i + 1] = 0.0;\n                        continue;`:l?`return ${a};`:`${$} = max(0, min(${$}, ${r[I]} - 1));`)()};\n          }\n        var input_indices_copy: ${e.type.indices} = input_indices;\n          ${e.indicesSet("input_indices_copy",I,`u32(${$})`)};\n          data[i + 1] = ${I===b?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"};\n        }\n        return cubicInterpolation1D(data, coefs);\n      }`};return`\n    ${_(b)};\n    ${_(w)};\n  fn getCubicInterpolationCoefs(s: ${y}) -> array<${y}, 4> {\n    var absS = abs(s);\n    var coeffs: array<${y}, 4> = array<${y}, 4>(0.0, 0.0, 0.0, 0.0);\n    var oneMinusAbsS: ${y} = 1.0 - absS;\n    var twoMinusAbsS: ${y} = 2.0 - absS;\n    var onePlusAbsS: ${y} = 1.0 + absS;\n    coeffs[0] = ((${u} * onePlusAbsS - 5 * ${u}) * onePlusAbsS + 8 * ${u}) * onePlusAbsS - 4 * ${u};\n    coeffs[1] = ((${u} + 2) * absS - (${u} + 3)) * absS * absS + 1;\n    coeffs[2] = ((${u} + 2) * oneMinusAbsS - (${u} + 3)) * oneMinusAbsS * oneMinusAbsS + 1;\n    coeffs[3] = ((${u} * twoMinusAbsS - 5 * ${u}) * twoMinusAbsS + 8 * ${u}) * twoMinusAbsS - 4 * ${u};\n    return coeffs;\n  }\n\n  fn cubicInterpolation1D(x: array<${y}, 4>, coefs: array<${y}, 4>) -> ${y} {\n    var coefsSum: ${y} = coefs[0] + coefs[1] + coefs[2] + coefs[3];\n    return (x[0] * coefs[0] + x[1] * coefs[1]+ x[2] * coefs[2]+ x[3] * coefs[3]) / coefsSum;\n  }\n\n  fn bicubicInterpolation(output_indices: ${t.type.indices}) -> ${y} {\n    var input_indices: ${e.type.indices} = output_indices;\n    return colCubicInterpolation(input_indices, output_indices);\n  }\n    `},gc=(e,t,r,o,n)=>{let[u,l,a,p,h]=r.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],g=e.type.value;return`\n    fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${g} {\n      var input_indices: ${e.type.indices};\n      ${e.indicesSet("input_indices",l,`max(0, min(depth, ${r[l]} - 1))`)};\n      ${e.indicesSet("input_indices",a,`max(0, min(height, ${r[a]} - 1))`)};\n      ${e.indicesSet("input_indices",p,`max(0, min(width, ${r[p]} - 1))`)};\n      ${Ys(e,h,u,3)}\n      return ${e.getByIndices("input_indices")};\n    }\n\n    fn trilinearInterpolation(output_indices: ${t.type.indices}) -> ${g} {\n      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n      var depth:${g} = originalIndices[${l}];\n      var height:${g} = originalIndices[${a}];\n      var width:${g} = originalIndices[${p}];\n      ${o?`if (depth < 0 || depth > (${r[l]} - 1) || height < 0 || height > (${r[a]} - 1) || width < 0 || (width > ${r[p]} - 1)) {\n      return ${n};\n        }`:""};\n\n    depth = max(0, min(depth, ${r[l]} - 1));\n      height = max(0, min(height, ${r[a]} - 1));\n      width = max(0, min(width, ${r[p]} - 1));\n      var depth1: u32 = u32(depth);\n      var height1: u32 = u32(height);\n      var width1: u32 = u32(width);\n      var depth2: u32 = u32(depth + 1);\n      var height2: u32 = u32(height + 1);\n      var width2: u32 = u32(width + 1);\n      var channel: u32 = ${r.length>3?`u32(originalIndices[${h}])`:"0"};\n      var batch: u32 =  ${r.length>3?`u32(originalIndices[${u}])`:"0"};\n\n      var x111: ${g} = getInputValue(batch, channel, depth1, height1, width1);\n      var x112: ${g} = getInputValue(batch, channel, depth1, height1, width2);\n      var x121: ${g} = getInputValue(batch, channel, depth1, height2, width1);\n      var x122: ${g} = getInputValue(batch, channel, depth1, height2, width2);\n      var x211: ${g} = getInputValue(batch, channel, depth2, height1, width1);\n      var x212: ${g} = getInputValue(batch, channel, depth2, height1, width2);\n      var x221: ${g} = getInputValue(batch, channel, depth2, height2, width1);\n      var x222: ${g} = getInputValue(batch, channel, depth2, height2, width2);\n      var dx1: ${g} = abs(depth - ${g}(depth1));\n      var dx2: ${g} = abs(${g}(depth2) - depth);\n      var dy1: ${g} = abs(height - ${g}(height1));\n      var dy2: ${g} = abs(${g}(height2) - height);\n      var dz1: ${g} = abs(width - ${g}(width1));\n      var dz2: ${g} = abs(${g}(width2) - width);\n      if (depth1 == depth2) {\n        dx1 = 0.5;\n        dx2 = 0.5;\n      }\n      if (height1 == height2) {\n        dy1 = 0.5;\n        dy2 = 0.5;\n      }\n      if (width1 == width2) {\n        dz1 = 0.5;\n        dz2 = 0.5;\n      }\n      return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 +\n              x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1);\n    }`},yc=(e,t,r,o,n,s)=>{let u=e.dims,l=uc(s,t.axes,u.length),a=dc(u,o,n,t.axes),p=o.slice();o.length===0&&(p=u.map((x,E)=>x===0?1:a[E]/x),t.keepAspectRatioPolicy!=="stretch"&&(a=lc(u,p,t)));let h=F("output",e.dataType,a.length),g=M("input",e.dataType,u.length),b=U.size(a),w=u.length===a.length&&u.every((x,E)=>x===a[E]),y=t.coordinateTransformMode==="tf_crop_and_resize",_=t.extrapolationValue,I=g.type.value,$=x=>`\n      ${w?"":`\n      ${ic(t.coordinateTransformMode,I)};\n      ${(()=>{switch(t.mode){case"nearest":return`\n              ${mc(g,u)};\n              ${sc(t.nearestMode,r,I)};\n              ${pc(g,h,u,a,p.length,l.length,y)};\n              `;case"linear":return`\n              ${cc(h,u,a,p.length,l.length)};\n              ${(()=>{if(u.length===2||u.length===4)return`${fc(g,h,u,y,_)}`;if(u.length===3||u.length===5)return`${gc(g,h,u,y,_)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};\n            `;case"cubic":return`\n            ${(()=>{if(u.length===2||u.length===4)return`${hc(g,h,u,a,p,l,t.cubicCoeffA,y,t.extrapolationValue,t.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()};\n            `;default:throw Error("Invalid resize mode")}})()};\n      `}\n      ${x.registerUniform("output_size","u32").registerUniform("scales","f32",p.length).registerUniform("roi","f32",l.length).declareVariables(g,h)}\n      ${x.mainStart()}\n        ${x.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n        ${w?"output[global_idx] = input[global_idx];":`\n        let output_indices = ${h.offsetToIndices("global_idx")};\n        var input_indices: ${g.type.indices};\n        ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);\n                if (checkInputIndices(input_indices)) {\n                  output[global_idx] = ${g.getByIndices("input_indices")};\n                } else {\n                  output[global_idx] = ${t.extrapolationValue};\n                }`;case"linear":return`output[global_idx] = ${u.length===2||u.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${t.mode}`)}})()};\n`}\n      }`;return{name:"Resize",shaderCache:{hint:`${t.cacheKey}|${r}|${p.length>0?p:""}|${n.length>0?n:""}|${l.length>0?l:""}|${w}|${u}`,inputDependencies:["rank"]},getShaderSource:$,getRunData:()=>({outputs:[{dims:a,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(b/64)},programUniforms:[{type:"uint32",data:b},{type:"float32",data:p},{type:"float32",data:l},...L(u),...L(a)]})}},bc=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},Zs=(e,t)=>{let r=[],o=[],n=[],s=bc(e);if(t.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");ac(e.inputs,t,s,r,o,n),e.compute(yc(e.inputs[0],t,s,r,o,n),{inputs:[0]})},Qs=e=>{let t=e.antialias,r=e.axes,o=e.coordinateTransformMode,n=e.cubicCoeffA,s=e.excludeOutside!==0,u=e.extrapolationValue,l=e.keepAspectRatioPolicy,a=e.mode,p=e.nearestMode===""?"simple":e.nearestMode;return ge({antialias:t,axes:r,coordinateTransformMode:o,cubicCoeffA:n,excludeOutside:s,extrapolationValue:u,keepAspectRatioPolicy:l,mode:a,nearestMode:p})}});var wc,vc,Js,eu,tu=j(()=>{"use strict";Ne();$e();je();ve();wc=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let t=e[0],r=e[1],o=e[2];if(t.dataType!==r.dataType||t.dataType!==o.dataType)throw new Error("All inputs must have the same data type");if(t.dims.length!==3&&t.dims.length!==2)throw new Error("Input must be 2D or 3D");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Skip must be 2D or 3D");let n=t.dims[t.dims.length-1],s=t.dims[t.dims.length-2];if(r.dims[r.dims.length-1]!==n)throw new Error("Skip must have the same hidden size as input");if(r.dims[r.dims.length-2]!==s)throw new Error("Skip must have the same sequence length as input");if(o.dims.length!==1)throw new Error("Gamma must be 1D");if(o.dims[o.dims.length-1]!==n)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let u=e[3];if(u.dims.length!==1)throw new Error("Beta must be 1D");if(u.dims[u.dims.length-1]!==n)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let u=e[4];if(u.dims.length!==1)throw new Error("Bias must be 1D");if(u.dims[u.dims.length-1]!==n)throw new Error("Bias must have the same hidden size as input")}},vc=(e,t,r,o)=>{let n=e[0].dims,s=U.size(n),u=n,l=s,a=n.slice(-1)[0],p=o?n.slice(0,-1).concat(1):[],h=e.length>3,g=e.length>4,b=o&&r>1,w=o&&r>2,y=r>3,_=Fe(a),I=[M("x",e[0].dataType,e[0].dims,_),M("skip",e[1].dataType,e[1].dims,_),M("gamma",e[2].dataType,e[2].dims,_)];h&&I.push(M("beta",e[3].dataType,e[3].dims,_)),g&&I.push(M("bias",e[4].dataType,e[4].dims,_)),I.push(F("output",e[0].dataType,u,_)),b&&I.push(F("meanOutput",1,p)),w&&I.push(F("invStdOutput",1,p)),y&&I.push(F("inputSkipBiasSum",e[0].dataType,u,_));let $=Le(e[0].dataType),x=A=>`\n      const hiddenSize: f32 = ${a};\n      const hiddenSizeVectorized: u32 = ${a/_};\n      const epsilon: f32 = ${t.epsilon};\n\n      ${A.declareVariables(...I)}\n\n      ${A.mainStart()}\n        ${A.guardAgainstOutOfBoundsWorkgroupSizes(l/a)}\n        let offset = global_idx * hiddenSizeVectorized;\n        var sum = ${Ze("f32",_)};\n        var squareSum = ${Ze("f32",_)};\n        for (var i: u32 = 0; i < hiddenSizeVectorized; i++) {\n          let skipValue = skip[offset + i];\n          let biasValue = ${g?"bias[i]":"0.0"};\n          let inputValue = x[offset + i];\n          let value = inputValue + skipValue + biasValue;\n          ${y?"inputSkipBiasSum[offset + i] = value;":""}\n          output[offset + i] = value;\n          let f32Value = ${at($,_,"value")};\n          sum += f32Value;\n          squareSum += f32Value * f32Value;\n        }\n        let mean = ${Je("sum",_)} / hiddenSize;\n        let invStdDev = inverseSqrt(${Je("squareSum",_)} / hiddenSize - mean * mean + epsilon);\n        ${b?"meanOutput[global_idx] = mean;":""}\n        ${w?"invStdOutput[global_idx] = invStdDev;":""}\n        for (var i: u32 = 0; i < hiddenSizeVectorized; i++) {\n          output[offset + i] = (output[offset + i] - ${$}(mean)) * ${$}(invStdDev) * gamma[i]\n           + ${h?"beta[i]":"0.0"};\n        }\n      }`,E=[{dims:u,dataType:e[0].dataType}];return r>1&&E.push({dims:p,dataType:1}),r>2&&E.push({dims:p,dataType:1}),r>3&&E.push({dims:n,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:t.cacheKey},getShaderSource:x,getRunData:()=>({outputs:E,dispatchGroup:{x:Math.ceil(l/a/64)}})}},Js=(e,t)=>{wc(e.inputs);let o=[0];e.outputCount>1&&o.push(-3),e.outputCount>2&&o.push(-3),e.outputCount>3&&o.push(3),e.compute(vc(e.inputs,t,e.outputCount,!1),{outputs:o})},eu=e=>{let t=e.epsilon;return ge({epsilon:t})}});var $c,on,Sc,ru,xc,_c,nu,ou,au=j(()=>{"use strict";Ne();$e();je();ve();$c=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");if(t.axes.length!==0){if(t.axes.length!==t.starts.length||t.axes.length!==t.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(t.starts.length!==t.ends.length)throw new Error("starts and ends must have the same length");e.slice(1).forEach((r,o)=>{if(e[o+1].dataType!==6&&e[o+1].dataType!==7)throw new Error(`Input ${o} must be an array of int32 or int64`)})},on=(e,t)=>{let r=[];if(e.length>t)if(e[t].dataType===7)e[t].getBigInt64Array().forEach(o=>r.push(Number(o)));else if(e[t].dataType===6)e[t].getInt32Array().forEach(o=>r.push(Number(o)));else throw new Error(`Input ${t} must be an array of int32 or int64`);return r},Sc=(e,t)=>{if(e.length>1){let r=on(e,1),o=on(e,2),n=on(e,3);return n.length===0&&(n=[...Array(e[0].dims.length).keys()]),ge({starts:r,ends:o,axes:n})}else return t},ru=(e,t,r,o,n)=>{let s=e;return e<0&&(s+=r[o[t]]),n[t]<0?Math.max(0,Math.min(s,r[o[t]]-1)):Math.max(0,Math.min(s,r[o[t]]))},xc=(e,t,r)=>`fn calculateInputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {\n          var input_indices: ${e.type.indices};\n          var carry = 0u;\n          for (var i = ${r.length}; i >= 0; i--) {\n            let input_shape_i = ${ce("uniforms.input_shape","i",r.length)};\n            let steps_i = ${ce("uniforms.steps","i",r.length)};\n            let signs_i = ${ce("uniforms.signs","i",r.length)};\n   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elements");if(n.length!==r.length)for(let $=0;$<r.length;++$)n.includes($)||(u.splice($,0,0),l.splice($,0,r[$]),s.splice($,0,1));let a=s.map($=>Math.sign($));s.forEach(($,x,E)=>{if($<0){let A=(l[x]-u[x])/$,z=u[x],R=z+A*s[x];u[x]=R,l[x]=z,E[x]=-$}});let p=r.slice(0);n.forEach(($,x)=>{p[$]=Math.ceil((l[$]-u[$])/s[$])});let h={dims:p,dataType:e[0].dataType},g=F("output",e[0].dataType,p.length),b=M("input",e[0].dataType,e[0].dims.length),w=U.size(p),y=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:u.length},{name:"signs",type:"i32",length:a.length},{name:"steps",type:"u32",length:s.length}],_=[{type:"uint32",data:w},{type:"uint32",data:u},{type:"int32",data:a},{type:"uint32",data:s},...L(e[0].dims),...L(p)],I=$=>`\n      ${$.registerUniforms(y).declareVariables(b,g)}\n        ${xc(b,g,r)}\n        ${$.mainStart()}\n          ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n          let output_indices = ${g.offsetToIndices("global_idx")};\n          let input_indices = calculateInputIndices(output_indices);\n          ${g.setByOffset("global_idx",b.getByIndices("input_indices"))}\n      }`;return{name:"Slice",shaderCache:{hint:`${a.length}_${u.length}_${s.length}`,inputDependencies:["rank"]},getShaderSource:I,getRunData:()=>({outputs:[h],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:_})}},nu=(e,t)=>{$c(e.inputs,t);let r=Sc(e.inputs,t);e.compute(_c(e.inputs,r),{inputs:[0]})},ou=e=>{let t=e.starts,r=e.ends,o=e.axes;return ge({starts:t,ends:r,axes:o})}});var Cc,Ic,iu,su,uu=j(()=>{"use strict";$e();je();ve();Cc=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},Ic=(e,t)=>{let r=e.dims,o=U.size(r),n=64,s=t.axis;if(s<0&&(s=r.length+s),s<r.length-1)throw new Error("softmax only supports last axis for now.");let u=r[s],l=o/u,a=Fe(u),p=u/a,h=(I,$)=>$===4?`max(max(${I}.x, ${I}.y), max(${I}.z, ${I}.w))`:$===2?`max(${I}.x, ${I}.y)`:$===3?`max(max(${I}.x, ${I}.y), ${I}.z)`:I,g=M("x",e.dataType,e.dims,a),b=F("result",e.dataType,e.dims,a),w=g.type.value,y=Le(e.dataType)==="f32"?`var threadMax = ${w}(-3.402823e+38f);`:`var threadMax = ${w}(-65504.0h);`,_=I=>`\n      var<workgroup> rowMaxShared : ${w};\n      var<workgroup> rowSumShared : ${w};\n      var<workgroup> threadShared : array<${w}, ${n}>;\n\n      fn getValue(row: i32, col: i32, row_stride: i32) -> ${w} {\n        let index = row * row_stride + col;\n        return x[index];\n      }\n\n      fn setValue(row: i32, col: i32, row_stride: i32, value: ${w}) {\n        let index = row * row_stride + col;\n        result[index] = value;\n      }\n      ${I.registerUniform("packedCols","i32").declareVariables(g,b)}\n      ${I.mainStart()}\n        let gindex = i32(global_idx);\n        let lindex = i32(local_idx);\n        const wg = ${n};\n        let row = gindex / wg;\n        let cols = uniforms.packedCols;\n        let row_stride : i32 = uniforms.packedCols;\n\n        // find the rows max\n        ${y}\n        for (var col = lindex; col < cols; col += wg) {\n          let value = getValue(row, col, row_stride);\n          threadMax = max(threadMax, value);\n        }\n        if (lindex < cols) {\n          threadShared[lindex] = threadMax;\n        }\n        workgroupBarrier();\n\n        var reduceSize = min(cols, wg);\n        for (var currSize = reduceSize >> 1;  currSize > 0; currSize = reduceSize >> 1) {\n          reduceSize = currSize + (reduceSize & 1);\n          if (lindex < currSize) {\n            threadShared[lindex] = max(threadShared[lindex], threadShared[lindex + reduceSize]);\n          }\n          workgroupBarrier();\n        }\n        if (lindex == 0) {\n          rowMaxShared = ${w}(${h("threadShared[0]",a)});\n        }\n        workgroupBarrier();\n\n        // find the rows sum\n        var threadSum = ${w}(0.0);\n        for (var col = lindex; col < cols; col += wg) {\n          let subExp = exp(getValue(row, col, row_stride) - rowMaxShared);\n          threadSum += subExp;\n        }\n        threadShared[lindex] = threadSum;\n        workgroupBarrier();\n\n        for (var currSize = wg >> 1;  currSize > 0; currSize = currSize >> 1) {\n          if (lindex < currSize) {\n            threadShared[lindex] = threadShared[lindex] + threadShared[lindex + currSize];\n          }\n          workgroupBarrier();\n        }\n        if (lindex == 0) {\n          rowSumShared = ${w}(${Je("threadShared[0]",a)});\n        }\n        workgroupBarrier();\n\n        // calculate final value for each element in the row\n        for (var col = lindex; col < cols; col += wg) {\n          let value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared;\n          setValue(row, col, row_stride, value);\n        }\n      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= 0; i < ${t.length}; i++) {\n        let input_dim_i = ${u.indicesGet("uniforms.input_shape","i")};\n        let input_dim_value = ${l.indicesGet("output_indices","i")}  % input_dim_i;\n\n        ${u.indicesSet("input_indices","i","input_dim_value")}\n      }\n      ${l.setByOffset("global_idx",u.getByIndices("input_indices"))}\n    }`;return{name:"Tile",shaderCache:{hint:`${r}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:[{type:"uint32",data:n},...L(e[0].dims),...L(o)]}),getShaderSource:a}},mu=e=>{kc(e.inputs),e.compute(Bc(e.inputs),{inputs:[0]})}});var Dc,Mc,hu,gu=j(()=>{"use strict";Ne();$e();ve();Dc=(e,t,r,o,n)=>{let s=F("output_data",n,r.length,4),u=M("a_data",t[1].dataType,t[1].dims.length,4),l=M("b_data",t[2].dataType,t[2].dims.length,4),a=M("c_data",t[0].dataType,t[0].dims.length,4),p,h=(g,b,w)=>`select(${b}, ${g}, 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an,wu=j(()=>{"use strict";Lt();Ct();ve();an=class{constructor(t){this.backend=t;this.repo=new Map,this.attributesBound=!1}getArtifact(t){return this.repo.get(t)}setArtifact(t,r){this.repo.set(t,r)}run(t,r,o,n,s){kt(t.programInfo.name);let u=this.backend.device,l=this.backend.getComputePassEncoder();this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2),l.setPipeline(t.computePipeline);let a=[];for(let h of r)a.push({binding:a.length,resource:{buffer:h.buffer}});for(let h of o)a.push({binding:a.length,resource:{buffer:h.buffer}});s&&a.push({binding:a.length,resource:s});let 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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the "License");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an "AS IS" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the "License");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an "AS IS" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the "License");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an "AS IS" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n'})),hn,Qt,Eo,ga,ba,vu,wu,Ln,Mn,pT,ma,by,yy,vy,wy,xy,Ty,_y,xu=D((()=>{Et(),my(),Rn(),hn=()=>!!be.wasm.proxy&&typeof document<"u",Eo=!1,ga=!1,ba=!1,wu=new Map,Ln=(e,t)=>{let n=wu.get(e);n?n.push(t):wu.set(e,[t])},Mn=()=>{if(Eo||!ga||ba||!Qt)throw new Error("worker not 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