/**
 * @license
 * Copyright 2023 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
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Ft=function(t){function e(e){var n=t.call(this)||this;return n.DEFAULT_MEAN=0,n.DEFAULT_STDDEV=.05,n.mean=e.mean||n.DEFAULT_MEAN,n.stddev=e.stddev||n.DEFAULT_STDDEV,n.seed=e.seed,n}return o(e,t),e.prototype.apply=function(t,e){if("float32"!==(e=e||"float32")&&"int32"!==e)throw new g("randomNormal does not support dType ".concat(e,"."));return vt(t,this.mean,this.stddev,e,this.seed)},e.prototype.getConfig=function(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}},e}(At);Ft.className="RandomNormal",e.serialization.registerClass(Ft);var Dt=function(t){function n(e){var n=t.call(this)||this;return n.DEFAULT_MEAN=0,n.DEFAULT_STDDEV=.05,n.mean=e.mean||n.DEFAULT_MEAN,n.stddev=e.stddev||n.DEFAULT_STDDEV,n.seed=e.seed,n}return o(n,t),n.prototype.apply=function(t,n){if("float32"!==(n=n||"float32")&&"int32"!==n)throw new g("truncatedNormal does not support dType ".concat(n,"."));return 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n.DEFAULT_GAIN=1,n.ELEMENTS_WARN_SLOW=2e3,n.gain=null==e.gain?n.DEFAULT_GAIN:e.gain,n.seed=e.seed,n}return o(n,t),n.prototype.apply=function(t,n){var r=this;return e.tidy((function(){if(t.length<2)throw new g("Shape must be at least 2D.");if("int32"!==n&&"float32"!==n&&void 0!==n)throw new TypeError("Unsupported data type ".concat(n,"."));var i=e.util.sizeFromShape(t.slice(0,-1)),a=t[t.length-1],o=i*a;o>r.ELEMENTS_WARN_SLOW&&console.warn("Orthogonal initializer is being called on a matrix with more "+"than ".concat(r.ELEMENTS_WARN_SLOW," (").concat(o,") elements: ")+"Slowness may result.");var s=vt([Math.max(a,i),Math.min(a,i)],0,1,n,r.seed),u=e.linalg.qr(s,!1),l=u[0],c=u[1].flatten().stridedSlice([0],[Math.min(a,i)*Math.min(a,i)],[Math.min(a,i)+1]);return l=e.mul(l,c.sign()),i<a&&(l=l.transpose()),e.mul(e.scalar(r.gain),l.reshape(t))}))},n.prototype.getConfig=function(){return{gain:this.gain,seed:this.seed}},n}(At);Ut.className="Orthogonal",e.serialization.registerClass(Ut);var Wt={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function Vt(t,n){return void 0===n&&(n={}),E(t,e.serialization.SerializationMap.getMap().classNameMap,n,"initializer")}function qt(t){return A(t)}function Gt(t){if("string"==typeof t){var e=t in Wt?Wt[t]:t;if("GlorotNormal"===e)return new Ot;if("GlorotUniform"===e)return new Rt;if("HeNormal"===e)return new Mt;if("HeUniform"===e)return new Bt;if("LeCunNormal"===e)return new Pt;if("LeCunUniform"===e)return new jt;var n={};return n.className=e,n.config={},Vt(n)}return t instanceof At?t:Vt(t)}function Kt(t){return Array.isArray(t)&&Array.isArray(t[0])}function Ht(t){return 0===t.length?[]:Array.isArray(t[0])?t:[t]}function Jt(t){var e;if(Array.isArray(t)){if(1!==t.length)throw new d("Expected Tensor length to be 1; got ".concat(t.length));e=t[0]}else e=t;return e}function Zt(t){if(Array.isArray(t)&&Array.isArray(t[0])){if(1===t.length)return t[0];throw new d("Expected exactly 1 Shape; got ".concat(t.length))}return t}function Yt(t){var e,n,r=0;try{for(var i=l(t),a=i.next();!a.done;a=i.next()){var o=a.value;0===o.shape.length?r+=1:r+=o.shape.reduce((function(t,e){return t*e}))}}catch(t){e={error:t}}finally{try{a&&!a.done&&(n=i.return)&&n.call(i)}finally{if(e)throw e.error}}return r}var Xt="Variable",$t=function(){function t(t,e,n,r,a){void 0===e&&(e="float32"),void 0===n&&(n=Xt),void 0===r&&(r=!0),void 0===a&&(a=null),this.dtype=null==e?"float32":e,this.shape=t.shape,this.id=B(),n=null==n?Xt:n,this.originalName=$(n),this.name=Q(this.originalName),this.trainable_=r,this.constraint=a,this.val=i.variable(t,this.trainable_,this.name,this.dtype)}return t.prototype.read=function(){return this.assertNotDisposed(),this.val},t.prototype.write=function(t){return this.assertNotDisposed(),function(t,e){if(t.shape.toString()!==e.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(t.shape)+" vs. "+JSON.stringify(e.shape))}(this.val,t),this.val.id!==t.id&&(this.val.assign(t),null!=this.constraint&&this.val.assign(this.constraint.apply(this.val))),this},t.prototype.dispose=function(){this.assertNotDisposed(),this.val.dispose()},t.prototype.assertNotDisposed=function(){if(this.val.isDisposed)throw new Error("LayersVariable ".concat(this.name," is already disposed."))},Object.defineProperty(t.prototype,"trainable",{get:function(){return this.trainable_},set:function(t){this.trainable_=t,this.val.trainable=t},enumerable:!1,configurable:!0}),t}();function Qt(t){return t.map((function(t){return t.read()}))}function te(t){t.forEach((function(t){t[0].write(t[1])}))}var ee=function(t){this.dtype=t.dtype,this.shape=t.shape,null!=t.shape?this.ndim=t.shape.length:this.ndim=t.ndim,this.maxNDim=t.maxNDim,this.minNDim=t.minNDim,this.axes=t.axes||{}},ne=function(t,e,n,r,i,a,o){this.dtype=t,this.shape=e,this.sourceLayer=n,this.inputs=r,this.callArgs=i,this.outputTensorIndex=o,this.id=B(),null!=a&&(this.originalName=$(a),this.name=Q(this.originalName)),this.rank=e.length},re=0,ie=function(){function t(t,e){var n,r;this.callArgs=e,this.id=re++,this.outboundLayer=t.outboundLayer,this.inboundLayers=t.inboundLayers,this.nodeIndices=t.nodeIndices,this.tensorIndices=t.tensorIndices,this.inputTensors=t.inputTensors,this.outputTensors=t.outputTensors,this.inputMasks=t.inputMasks,this.outputMasks=t.outputMasks,this.inputShapes=t.inputShapes,this.outputShapes=t.outputShapes;try{for(var i=l(t.inboundLayers),a=i.next();!a.done;a=i.next()){var o=a.value;null!=o&&o.outboundNodes.push(this)}}catch(t){n={error:t}}finally{try{a&&!a.done&&(r=i.return)&&r.call(i)}finally{if(n)throw n.error}}t.outboundLayer.inboundNodes.push(this)}return t.prototype.getConfig=function(){var t,e,n=[];try{for(var r=l(this.inboundLayers),i=r.next();!i.done;i=r.next()){var a=i.value;null!=a?n.push(a.name):n.push(null)}}catch(e){t={error:e}}finally{try{i&&!i.done&&(e=r.return)&&e.call(r)}finally{if(t)throw t.error}}return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:n,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}},t}(),ae=0,oe=function(t){function n(e){void 0===e&&(e={});var n=t.call(this)||this;n._callHook=null,n._addedWeightNames=[],n._stateful=!1,n.id=ae++,n.activityRegularizer=null,n.inputSpec=null,n.supportsMasking=!1,n._trainableWeights=[],n._nonTrainableWeights=[],n._losses=[],n._updates=[],n._built=!1,n.inboundNodes=[],n.outboundNodes=[];var r=e.name;if(!r){var i=n.getClassName();r=S(i)+"_"+j(i)}if(n.name=r,n.trainable_=null==e.trainable||e.trainable,null!=e.inputShape||null!=e.batchInputShape){var a=void 0;if(null!=e.batchInputShape)a=e.batchInputShape;else if(null!=e.inputShape){var o=null;null!=e.batchSize&&(o=e.batchSize),a=[o].concat(e.inputShape)}n.batchInputShape=a;var s=e.dtype;null==s&&(s=e.inputDType),null==s&&(s="float32"),n.dtype=s}return null!=e.weights?n.initialWeights=e.weights:n.initialWeights=null,n._refCount=null,n.fastWeightInitDuringBuild=!1,n}return o(n,t),n.nodeKey=function(t,e){return t.name+"_ib-"+e.toString()},n.prototype.getNodeAtIndex=function(t,e){if(0===this.inboundNodes.length)throw new f("The layer has never been called "+"and thus has no defined ".concat(e,"."));if(this.inboundNodes.length<=t)throw new d("Asked to get ".concat(e," at node ").concat(t,", ")+"but the layer has only ".concat(this.inboundNodes.length," inbound nodes."));return this.inboundNodes[t]},n.prototype.getInputAt=function(t){return k(this.getNodeAtIndex(t,"input").inputTensors)},n.prototype.getOutputAt=function(t){return k(this.getNodeAtIndex(t,"output").outputTensors)},Object.defineProperty(n.prototype,"input",{get:function(){if(this.inboundNodes.length>1)throw new p("Layer ".concat(this.name)+' has multiple inbound nodes, hence the notion of "layer input" is ill-defined. 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")+"Input received: ".concat(t));for(var r=0;r<e.length;r++){var i=e[r],a=n[r];if(null!=a){var o=i.rank;if(null!=a.ndim&&o!==a.ndim)throw new d("Input ".concat(r," is incompatible with layer ").concat(this.name,": ")+"expected ndim=".concat(a.ndim,", found ndim=").concat(o));if(null!=a.maxNDim&&o>a.maxNDim)throw new d("Input ".concat(r," is incompatible with layer ").concat(this.name)+": expected max_ndim=".concat(a.maxNDim,", found ndim=").concat(o));if(null!=a.minNDim&&o<a.minNDim)throw new d("Input ".concat(r," is incompatible with layer ").concat(this.name)+": expected min_ndim=".concat(a.minNDim,", found ndim=").concat(o,"."));if(null!=a.dtype&&i.dtype!==a.dtype)throw new d("Input ".concat(r," is incompatible with layer ").concat(this.name," ")+": expected dtype=".concat(a.dtype,", found dtype=").concat(i.dtype,"."));if(a.axes){var s=i.shape;for(var u in a.axes){var l=Number(u),c=a.axes[u],h=l>=0?s[l]:s[s.length+l];if(null!=c&&-1===[c,null].indexOf(h))throw new d("Input ".concat(r," is incompatible with layer ")+"".concat(this.name,": expected axis ").concat(l," of input shape to ")+"have value ".concat(c," but got shape ").concat(s,"."))}}if(null!=a.shape)for(var p=0;p<a.shape.length;++p){var f=a.shape[p],g=i.shape[p];if(null!=f&&null!=g&&f!==g)throw new d("Input ".concat(r," is incompatible with layer ")+"".concat(this.name,": expected shape=").concat(a.shape,", ")+"found shape=".concat(i.shape,"."))}}}}},n.prototype.call=function(t,e){return t},n.prototype.invokeCallHook=function(t,e){null!=this._callHook&&this._callHook(t,e)},n.prototype.setCallHook=function(t){this._callHook=t},n.prototype.clearCallHook=function(){this._callHook=null},n.prototype.apply=function(t,e){var n=this;e=e||{},this.assertNotDisposed();var r=x(t),i=function(t){var e,n,r=!0;try{for(var i=l(x(t)),a=i.next();!a.done;a=i.next()){if(!(a.value instanceof ne)){r=!1;break}}}catch(t){e={error:t}}finally{try{a&&!a.done&&(n=i.return)&&n.call(i)}finally{if(e)throw e.error}}return r}(t),a=function(t){var e,n,r=!0;try{for(var i=l(x(t)),a=i.next();!a.done;a=i.next()){if(a.value instanceof ne){r=!1;break}}}catch(t){e={error:t}}finally{try{a&&!a.done&&(n=i.return)&&n.call(i)}finally{if(e)throw e.error}}return r}(t);if(i===a)throw new d("Arguments to apply() must be all SymbolicTensors or all Tensors");return X(this.name,(function(){var i,o,s,u;if(!n.built){n.assertInputCompatibility(t);var c=[];try{for(var h=l(x(t)),p=h.next();!p.done;p=h.next()){var f=p.value;c.push(f.shape)}}catch(t){i={error:t}}finally{try{p&&!p.done&&(o=h.return)&&o.call(h)}finally{if(i)throw i.error}}n.build(k(c)),n.built=!0,n.initialWeights&&n.setWeights(n.initialWeights),null===n._refCount&&a&&(n._refCount=1)}if(n.assertInputCompatibility(t),a){var d=n.call(t,e);n.supportsMasking&&n.setMaskMetadata(t,d);var v=x(d),y=[];try{for(var m=l(v),b=m.next();!b.done;b=m.next()){var w=b.value;-1!==r.indexOf(w)&&(w=w.clone()),y.push(w)}}catch(t){s={error:t}}finally{try{b&&!b.done&&(u=m.return)&&u.call(m)}finally{if(s)throw s.error}}if(d=k(y),null!=n.activityRegularizer)throw new g("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return d}var S=function(t){var e,n;t=x(t);var r=[];try{for(var i=l(t),a=i.next();!a.done;a=i.next()){var o=a.value;r.push(o.shape)}}catch(t){e={error:t}}finally{try{a&&!a.done&&(n=i.return)&&n.call(i)}finally{if(e)throw e.error}}return k(r)}(t),N=n.computeOutputShape(S),I=(d=void 0,"float32");if(n.warnOnIncompatibleInputShape(Array.isArray(t)?S[0]:S),d=null!=N&&N.length>0&&Array.isArray(N[0])?N.map((function(r,i){return new ne(I,r,n,x(t),e,n.name,i)})):new ne(I,N,n,x(t),e,n.name),n.addInboundNode(t,d,null,null,S,N,e),n._refCount++,null!=n.activityRegularizer)throw new g("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return d}))},n.prototype.warnOnIncompatibleInputShape=function(t){if(null!=this.batchInputShape)if(t.length!==this.batchInputShape.length)console.warn("The rank of the input tensor provided (shape: "+"".concat(JSON.stringify(t),") does not match that of the ")+"batchInputShape (".concat(JSON.stringify(this.batchInputShape),") ")+"of the layer ".concat(this.name));else{var e=!1;this.batchInputShape.forEach((function(n,r){null!=n&&null!=t[r]&&t[r]!==n&&(e=!0)})),e&&console.warn("The shape of the input tensor "+"(".concat(JSON.stringify(t),") does not ")+"match the expectation of layer ".concat(this.name,": ")+"".concat(JSON.stringify(this.batchInputShape)))}},Object.defineProperty(n.prototype,"outputShape",{get:function(){var t,e;if(null==this.inboundNodes||0===this.inboundNodes.length)throw new p("The layer ".concat(this.name," has never been called and thus has no ")+"defined output shape.");var n=[];try{for(var r=l(this.inboundNodes),i=r.next();!i.done;i=r.next()){var a=i.value,o=JSON.stringify(a.outputShapes);-1===n.indexOf(o)&&n.push(o)}}catch(e){t={error:e}}finally{try{i&&!i.done&&(e=r.return)&&e.call(r)}finally{if(t)throw t.error}}if(1===n.length){var s=this.inboundNodes[0].outputShapes;return Array.isArray(s)&&Array.isArray(s[0])&&1===s.length?s[0]:s}throw new p("The layer ".concat(this.name," has multiple inbound nodes with different ")+'output shapes. Hence the notion of "output shape" is ill-defined for the layer.')},enumerable:!1,configurable:!0}),n.prototype.countParams=function(){if(!this.built)throw new f("You tried to call countParams() on ".concat(this.name,", ")+"but the layer is not built yet. Build it first by calling build(batchInputShape).");return Yt(this.weights)},n.prototype.build=function(t){this.built=!0},n.prototype.getWeights=function(t){return void 0===t&&(t=!1),Qt(t?this.trainableWeights:this.weights)},n.prototype.setWeights=function(t){var n=this;e.tidy((function(){var r=n.weights;if(r.length!==t.length)throw new d('You called setWeights(weights) on layer "'.concat(n.name,'" ')+"with a weight list of length ".concat(t.length,", ")+"but the layer was expecting ".concat(r.length," weights. 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0!==this._losses&&null!==this._losses&&(e=this.losses).push.apply(e,h([],c(t),!1)))},n.prototype.computeOutputShape=function(t){return t},n.prototype.computeMask=function(t,e){var n=this;if(!this.supportsMasking){if(null!=e){if(!Array.isArray(e))throw new TypeError("Layer ".concat(this.name," does not support masking, ")+"but was passed an inputMask.");e.forEach((function(t){if(null!=t)throw new TypeError("Layer ".concat(n.name," does not support masking, ")+"but was passed an inputMask.")}))}return null}return e},n.prototype.setMaskMetadata=function(t,e,n){if(this.supportsMasking){var r=this.computeMask(t,n),i=x(e),a=x(r);if(i.length!==a.length)throw new Error("".concat(this.name," outputs ").concat(i.length," tensors ")+"but ".concat(i.length," masks for those tensors"));for(var o=0;o<i.length;o++)i[o].kerasMask=a[o]}},n.prototype.addInboundNode=function(t,e,n,r,i,a,o){var s,u;void 0===o&&(o=null);var c=x(t);e=x(e),n=x(n),r=x(r),i=Ht(i),a=Ht(a);var h=[],p=[],f=[];try{for(var d=l(c),g=d.next();!g.done;g=d.next()){var v=g.value;h.push(v.sourceLayer),p.push(v.nodeIndex),f.push(v.tensorIndex)}}catch(t){s={error:t}}finally{try{g&&!g.done&&(u=d.return)&&u.call(d)}finally{if(s)throw s.error}}new ie({outboundLayer:this,inboundLayers:h,nodeIndices:p,tensorIndices:f,inputTensors:c,outputTensors:e,inputMasks:n,outputMasks:r,inputShapes:i,outputShapes:a},o);for(var y=0;y<e.length;y++)e[y].sourceLayer=this,e[y].nodeIndex=this.inboundNodes.length-1,e[y].tensorIndex=y},n.prototype.getConfig=function(){var t={name:this.name,trainable:this.trainable};return null!=this.batchInputShape&&(t.batchInputShape=this.batchInputShape),null!=this.dtype&&(t.dtype=this.dtype),t},n.prototype.disposeWeights=function(){return this.weights.forEach((function(t){return t.dispose()})),this.weights.length},n.prototype.assertNotDisposed=function(){if(0===this._refCount)throw new Error("Layer '".concat(this.name,"' is already disposed."))},n.prototype.dispose=function(){if(!this.built)throw new Error("Cannot dispose Layer ".concat(this.name," because it has not been ")+"built yet.");if(null===this._refCount)throw new Error("Cannot dispose Layer ".concat(this.name," because it has not been used ")+"yet.");this.assertNotDisposed();var t=0;return 0==--this._refCount&&(t=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:t}},n}(e.serialization.Serializable);function se(t,e,n){var r,i;if((null==e||null!=n&&n>0)&&(e=t.sourceLayer,n=t.nodeIndex),0===e.inboundNodes.length)return[t];var a=e.inboundNodes[n];if(0===a.inboundLayers.length)return a.inputTensors;for(var o=[],s=0;s<a.inboundLayers.length;s++){var u=se(a.inputTensors[s],a.inboundLayers[s],a.nodeIndices[s]);try{for(var c=(r=void 0,l(u)),h=c.next();!h.done;h=c.next()){var p=h.value;-1===o.indexOf(p)&&o.push(p)}}catch(t){r={error:t}}finally{try{h&&!h.done&&(i=c.return)&&i.call(c)}finally{if(r)throw r.error}}}return o}var ue=function(t){function e(e){var n=t.call(this,{dtype:e.dtype,name:null!=e.name?e.name:j("input").toString()})||this;if(null==e.batchSize&&(e.batchSize=null),null==e.sparse&&(e.sparse=!1),n.trainable=!1,n.built=!0,n.sparse=e.sparse,null!=e.inputShape&&null!=e.batchInputShape)throw new d("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");var r=e.batchInputShape;if(null==r){if(null==e.inputShape)throw new d("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");r=[e.batchSize].concat(e.inputShape)}else if(null!=e.batchSize)throw new d("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");var i=e.dtype||"float32";n.batchInputShape=r,n.dtype=i,n.inputSpec=[{shape:r}];var a=new ne(n.dtype,n.batchInputShape,n,[],{},n.name);return a.nodeIndex=0,a.tensorIndex=0,new ie({outboundLayer:n,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[a],outputTensors:[a],inputMasks:[null],outputMasks:[null],inputShapes:[r],outputShapes:[r]}),n}return o(e,t),e.prototype.apply=function(t,e){throw new d("Cannot pass any input to an "+"InputLayer's apply() method. InputLayer name: ".concat(this.name))},e.prototype.dispose=function(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}},e.prototype.getConfig=function(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}},e}(oe);function le(t){if(null==t.batchShape&&null==t.shape)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(null!=t.batchShape&&null!=t.shape)throw new d("Please provide either a `shape` or `batchShape` argument to Input, but not both.");var e=t.batchShape;null!=t.shape&&null==e&&(e=[null].concat(t.shape));var n=t.dtype;return null==n&&(n="float32"),new ue({batchInputShape:e,name:t.name,dtype:n,sparse:t.sparse}).inboundNodes[0].outputTensors[0]}ue.className="InputLayer",e.serialization.registerClass(ue);var ce=function(){function t(e){var n,r;if(this.id2Value={},this.id2Mask={},this.name2Id={},e instanceof t)for(var i in e.id2Value)this.id2Value[i]=e.id2Value[i],i in e.id2Mask&&(this.id2Mask[i]=e.id2Mask[i]);else{if(null==e)return;try{for(var a=l(e),o=a.next();!o.done;o=a.next()){var s=o.value;this.add(s.key,s.value)}}catch(t){n={error:t}}finally{try{o&&!o.done&&(r=a.return)&&r.call(a)}finally{if(n)throw n.error}}}}return t.prototype.add=function(t,n,r){if(null!=this.id2Value[t.id])throw new d("Duplicate key: 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this.id2Mask[t.id]}var e=this.name2Id[t];if(null==e)throw new d("Feed dict has no SymbolicTensor name: ".concat(t));return this.id2Mask[e]},t.prototype.disposeMasks=function(){null!=this.id2Mask&&e.dispose(this.id2Mask)},t}(),he=new y,pe=new y;function fe(t,n,r,i){var a,o,s,u,c=null!=r&&r.training,h=Array.isArray(t),p=h?t:[t],f=p.map((function(t){return t.name})),d=[],g=n.names();try{for(var v=l(f),y=v.next();!y.done;y=v.next()){var m=y.value;-1!==g.indexOf(m)?d.push(n.getValue(m)):d.push(null)}}catch(t){a={error:t}}finally{try{y&&!y.done&&(o=v.return)&&o.call(v)}finally{if(a)throw a.error}}null!=i&&(i.maxNumTensors=-1/0,i.minNumTensors=1/0);var b,w=f.join(",")+"|"+n.names().sort().join(","),k=he.get(w);if(null==k){var S=function(t,n){var r,i,a,o;e.util.assert(null!=t&&t.length>0,(function(){return"Expected at least one fetch, got none"}));var s=[],u={};if(1===t.length){var c=ge(t[0],n);s=c.sorted,u=c.recipientMap}else{var h=new Set;try{for(var p=l(t),f=p.next();!f.done;f=p.next()){var 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R=L.value,O=N.getValue(R),M=N.getMask(R);T.push(O),C.push(M),null!=M&&(D=!0),c||(b[R.name]--,0!==b[R.name]||n.hasKey(R)||-1!==f.indexOf(R.name)||O.isDisposed||!0===R.sourceLayer.stateful||F.push(O))}}catch(t){s={error:t}}finally{try{L&&!L.done&&(u=_.return)&&u.call(_)}finally{if(s)throw s.error}}D&&((r=r||{}).mask=C[0]);var B=x(E.apply(T,r)),P=null;E.supportsMasking&&(P=E.computeMask(T,C));for(var j=ve(z),U=Array.isArray(j)?j:[j],W=0;W<U.length;++W){N.hasKey(U[W])||N.add(U[W],B[W],Array.isArray(P)?P[0]:P);var V=f.indexOf(U[W].name);-1!==V&&(d[V]=B[W])}c||e.dispose(F)}}return N.disposeMasks(),h?d:d[0]}function de(t){var e={};for(var n in t)e[n]=t[n].size;return e}function ge(t,e){var n,r,i,a,o=new Set,s=[],u={};try{for(var c=l(e.names()),h=c.next();!h.done;h=c.next()){var p=h.value;o.add(p)}}catch(t){n={error:t}}finally{try{h&&!h.done&&(r=c.return)&&r.call(c)}finally{if(n)throw n.error}}var f=[],d=[];for(f.push(t);f.length>0;){var g=f[f.length-1];if(o.has(g.name))f.pop();else{var 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Make ")+"sure to await tf.ready() or await tf.setBackend() before calling other methods");if(null==this.backendInstance){var t=this.initializeBackendsAndReturnBest(),e=t.name;if(t.asyncInit)throw new Error("The highest priority backend '".concat(e,"' has not yet been ")+"initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods");this.setBackend(e)}return this.backendInstance},enumerable:!1,configurable:!0}),t.prototype.backendNames=function(){return Object.keys(this.registryFactory)},t.prototype.findBackend=function(t){if(!(t in this.registry)){if(!(t in this.registryFactory))return null;if(this.initializeBackend(t).asyncInit)return null}return this.registry[t]},t.prototype.findBackendFactory=function(t){return t in this.registryFactory?this.registryFactory[t].factory:null},t.prototype.registerBackend=function(t,e,n){return void 0===n&&(n=1),t in this.registryFactory?(Tn("".concat(t," backend was already registered. ")+"Reusing existing backend factory."),!1):(this.registryFactory[t]={factory:e,priority:n},!0)},t.prototype.setBackend=function(t){return s(this,void 0,void 0,(function(){var e,n,r;return u(this,(function(i){switch(i.label){case 0:if(null==this.registryFactory[t])throw new Error("Backend name '".concat(t,"' not found in registry"));return this.backendName=t,null!=this.registry[t]?[3,4]:(this.backendInstance=null,e=this.initializeBackend(t),n=e.success,e.asyncInit?[4,n]:[3,2]);case 1:return r=i.sent(),[3,3];case 2:r=n,i.label=3;case 3:if(!r)return[2,!1];i.label=4;case 4:return this.backendInstance=this.registry[t],this.setupRegisteredKernels(),this.profiler=new yr(this.backendInstance),[2,!0]}}))}))},t.prototype.setupRegisteredKernels=function(){var t=this;Ln(this.backendName).forEach((function(e){null!=e.setupFunc&&e.setupFunc(t.backendInstance)}))},t.prototype.disposeRegisteredKernels=function(t){var e=this;Ln(t).forEach((function(n){null!=n.disposeFunc&&n.disposeFunc(e.registry[t])}))},t.prototype.initializeBackend=function(t){var e=this,n=this.registryFactory[t];if(null==n)throw new Error("Cannot initialize backend ".concat(t,", no registration found."));try{var r=n.factory();if(!r||r instanceof tn||"function"!=typeof r.then)return this.registry[t]=r,{success:!0,asyncInit:!1};var i=++this.pendingBackendInitId,a=r.then((function(n){return!(i<e.pendingBackendInitId)&&(e.registry[t]=n,e.pendingBackendInit=null,!0)})).catch((function(n){return i<e.pendingBackendInitId||(e.pendingBackendInit=null,Tn("Initialization of backend ".concat(t," failed")),Tn(n.stack||n.message)),!1}));return this.pendingBackendInit=a,{success:a,asyncInit:!0}}catch(e){return Tn("Initialization of backend ".concat(t," failed")),Tn(e.stack||e.message),{success:!1,asyncInit:!1}}},t.prototype.removeBackend=function(t){if(!(t in this.registryFactory))throw new Error("".concat(t," backend not found in registry"));this.backendName===t&&null!=this.pendingBackendInit&&this.pendingBackendInitId++,t in this.registry&&(this.disposeRegisteredKernels(t),this.registry[t].dispose(),delete this.registry[t]),delete this.registryFactory[t],this.backendName===t&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)},t.prototype.getSortedBackends=function(){var t=this;if(0===Object.keys(this.registryFactory).length)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((function(e,n){return t.registryFactory[n].priority-t.registryFactory[e].priority}))},t.prototype.initializeBackendsAndReturnBest=function(){for(var t=this.getSortedBackends(),e=0;e<t.length;e++){var n=t[e],r=this.initializeBackend(n),i=r.success,a=r.asyncInit;if(a||i)return{name:n,asyncInit:a}}throw new Error("Could not initialize any backends, all backend initializations failed.")},t.prototype.moveData=function(t,e){var n=this.state.tensorInfo.get(e),r=n.backend,i=this.readSync(e),a=r.refCount(e);r.disposeData(e,!0),n.backend=t,t.move(e,i,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++},t.prototype.tidy=function(t,e){var n,r=this,i=null;if(null==e){if("function"!=typeof t)throw new Error("Please provide a function to tidy()");e=t}else{if("string"!=typeof t&&!(t instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if("function"!=typeof e)throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");i=t}return this.scopedRun((function(){return r.startScope(i)}),(function(){return r.endScope(n)}),(function(){return(n=e())instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),n}))},t.prototype.scopedRun=function(t,e,n){t();try{var r=n();return e(),r}catch(t){throw e(),t}},t.prototype.nextTensorId=function(){return t.nextTensorId++},t.prototype.nextVariableId=function(){return t.nextVariableId++},t.prototype.clone=function(t){var e=Vr.runKernel(_e,{x:t}),n={x:t};return this.addTapeNode(this.state.activeScope.name,n,[e],(function(t){return{x:function(){var e={x:t},n={dtype:"float32"};return Vr.runKernel(we,e,n)}}}),[],{}),e},t.prototype.runKernel=function(t,e,n){if(null==this.backendName&&this.backend,!(null!=Dn(t,this.backendName)))throw new Error("Kernel '".concat(t,"' not registered for backend '").concat(this.backendName,"'"));return this.runKernelFunc({kernelName:t,inputs:e,attrs:n})},t.prototype.shouldCheckForMemLeaks=function(){return this.ENV.getBool("IS_TEST")},t.prototype.checkKernelForMemLeak=function(t,e,n){var r=this.backend.numDataIds(),i=0;n.forEach((function(t){i+="complex64"===t.dtype?3:1}));var a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],o=r-e-i-a;if(o>0)throw new Error("Backend '".concat(this.backendName,"' has an internal memory leak ")+"(".concat(o," data ids) after running '").concat(t,"'"))},t.prototype.runKernelFunc=function(t){var e,n,r,i=this,a=[],o=this.isTapeOn(),s=this.state.numBytes,u=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0),null==this.backendName&&this.backend;var l=jr(t)?t.kernelName:null!=this.state.activeScope?this.state.activeScope.name:"";if(jr(t)){var c=t.kernelName,h=t.inputs,p=t.attrs;null==this.backendName&&this.backend;var f=Dn(c,this.backendName);nn(null!=f,(function(){return"Cannot find registered kernel '".concat(c,"' for backend '").concat(i.backendName,"'")})),n=function(){var t=i.backend.numDataIds();r=f.kernelFunc({inputs:h,attrs:p,backend:i.backend});var e=Array.isArray(r)?r:[r];i.shouldCheckForMemLeaks()&&i.checkKernelForMemLeak(c,t,e);var n=e.map((function(t){return null!=t.rank?t:i.makeTensorFromTensorInfo(t)}));if(o){var s=i.getTensorsForGradient(c,h,n);a=i.saveTensorsForBackwardMode(s)}return n}}else{var d=t.forwardFunc,g=function(t){o&&(a=t.map((function(t){return i.keep(i.clone(t))})))};n=function(){var t=i.backend.numDataIds();r=i.tidy((function(){return d(i.backend,g)}));var e=Array.isArray(r)?r:[r];return i.shouldCheckForMemLeaks()&&i.checkKernelForMemLeak(l,t,e),e}}var v,y=t.inputs,m=t.attrs,b=jr(t)?null:t.backwardsFunc;return this.scopedRun((function(){return i.state.kernelDepth++}),(function(){return i.state.kernelDepth--}),(function(){i.ENV.getBool("DEBUG")||i.state.profiling?(v=i.profiler.profileKernel(l,y,(function(){return n()})),i.ENV.getBool("DEBUG")&&i.profiler.logKernelProfile(v),e=v.outputs):e=n()})),o&&this.addTapeNode(l,y,e,b,a,m),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-u,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(y).map((function(t){return null!=y[t]?y[t].shape:null})),outputShapes:e.map((function(t){return t.shape})),kernelTimeMs:v.timeMs,extraInfo:v.extraInfo}),Array.isArray(r)?e:e[0]},t.prototype.saveTensorsForBackwardMode=function(t){var e=this;return t.map((function(t){return e.keep(e.clone(t))}))},t.prototype.getTensorsForGradient=function(t,e,n){var r=_n(t);if(null!=r){var i=r.inputsToSave||[],a=r.outputsToSave||[],o=void 0;r.saveAllInputs?(nn(Array.isArray(e),(function(){return"saveAllInputs is true, expected inputs to be an array."})),o=Object.keys(e).map((function(t){return e[t]}))):o=i.map((function(t){return e[t]}));var s=n.filter((function(t,e){return a[e]}));return o.concat(s)}return[]},t.prototype.makeTensor=function(t,e,n,r){if(null==t)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",r=r||this.backend;var i=t;"string"===n&&cn(t[0])&&(i=t.map((function(t){return e=t,void 0===n&&(n="utf-8"),n=n||"utf-8",Nn().platform.encode(e,n);var e,n})));var a=r.write(i,e,n),o=new Ar(e,n,a,this.nextTensorId());if(this.trackTensor(o,r),"string"===n){var s=this.state.tensorInfo.get(a),u=function(t){if(null==t)return 0;var e=0;return t.forEach((function(t){return e+=t.length})),e}(i);this.state.numBytes+=u-s.bytes,s.bytes=u}return o},t.prototype.makeTensorFromDataId=function(t,e,n,r){var i={dataId:t,shape:e,dtype:n=n||"float32"};return this.makeTensorFromTensorInfo(i,r)},t.prototype.makeTensorFromTensorInfo=function(t,e){var n=t.dataId,r=t.shape,i=t.dtype,a=new Ar(r,i,n,this.nextTensorId());return this.trackTensor(a,e),a},t.prototype.makeVariable=function(t,e,n,r){void 0===e&&(e=!0),n=n||this.nextVariableId().toString(),null!=r&&r!==t.dtype&&(t=t.cast(r));var i=new _r(t,e,n,this.nextTensorId());if(null!=this.state.registeredVariables[i.name])throw new Error("Variable with name ".concat(i.name," was already registered"));return this.state.registeredVariables[i.name]=i,this.incRef(i,this.backend),i},t.prototype.trackTensor=function(t,e){this.state.numTensors++,"string"===t.dtype&&this.state.numStringTensors++;var n=0;"complex64"!==t.dtype&&"string"!==t.dtype&&(n=t.size*ln(t.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(t.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(t.dataId,{backend:e||this.backend,dtype:t.dtype,shape:t.shape,bytes:n})),t instanceof _r||this.track(t)},t.prototype.incRef=function(t,e){this.trackTensor(t,e),this.backend.incRef(t.dataId)},t.prototype.removeDataId=function(t,e){this.state.tensorInfo.has(t)&&this.state.tensorInfo.get(t).backend===e&&(this.state.tensorInfo.delete(t),this.state.numDataBuffers--)},t.prototype.disposeTensor=function(t){if(this.state.tensorInfo.has(t.dataId)){var e=this.state.tensorInfo.get(t.dataId);if(this.state.numTensors--,"string"===t.dtype&&(this.state.numStringTensors--,this.state.numBytes-=e.bytes),"complex64"!==t.dtype&&"string"!==t.dtype){var n=t.size*ln(t.dtype);this.state.numBytes-=n}e.backend.disposeData(t.dataId)&&this.removeDataId(t.dataId,e.backend)}},t.prototype.disposeVariables=function(){for(var t in this.state.registeredVariables){var e=this.state.registeredVariables[t];this.disposeVariable(e)}},t.prototype.disposeVariable=function(t){this.disposeTensor(t),null!=this.state.registeredVariables[t.name]&&delete this.state.registeredVariables[t.name]},t.prototype.memory=function(){var t=this.backend.memory();return t.numTensors=this.state.numTensors,t.numDataBuffers=this.state.numDataBuffers,t.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(t.unreliable=!0,null==t.reasons&&(t.reasons=[]),t.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),t},t.prototype.profile=function(t){return s(this,void 0,void 0,(function(){var e,n,r,i,a,o,s,p,f,d,g;return u(this,(function(u){switch(u.label){case 0:return this.state.profiling=!0,e=this.state.numBytes,n=this.state.numTensors,this.state.activeProfile.kernels=[],r=this.state.activeProfile,[4,t()];case 1:r.result=u.sent(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max.apply(Math,h([],c(this.state.activeProfile.kernels.map((function(t){return t.totalBytesSnapshot}))),!1)),this.state.activeProfile.newBytes=this.state.numBytes-e,this.state.activeProfile.newTensors=this.state.numTensors-n,u.label=2;case 2:u.trys.push([2,8,9,10]),i=l(this.state.activeProfile.kernels),a=i.next(),u.label=3;case 3:return a.done?[3,7]:(o=a.value,s=o,[4,o.kernelTimeMs]);case 4:return s.kernelTimeMs=u.sent(),p=o,[4,o.extraInfo];case 5:p.extraInfo=u.sent(),u.label=6;case 6:return a=i.next(),[3,3];case 7:return[3,10];case 8:return f=u.sent(),d={error:f},[3,10];case 9:try{a&&!a.done&&(g=i.return)&&g.call(i)}finally{if(d)throw d.error}return[7];case 10:return[2,this.state.activeProfile]}}))}))},t.prototype.isTapeOn=function(){return this.state.gradientDepth>0&&0===this.state.kernelDepth},t.prototype.addTapeNode=function(t,e,n,r,i,a){var o=this,s={id:this.state.nextTapeNodeId++,kernelName:t,inputs:e,outputs:n,saved:i},u=_n(t);null!=u&&(r=u.gradFunc),null!=r&&(s.gradient=function(t){return t=t.map((function(t,e){if(null==t){var r=n[e],i=yn(r.size,r.dtype);return o.makeTensor(i,r.shape,r.dtype)}return t})),r(t.length>1?t:t[0],i,a)}),this.state.activeTape.push(s)},t.prototype.keep=function(t){return t.kept=!0,t},t.prototype.startTape=function(){0===this.state.gradientDepth&&(this.state.activeTape=[]),this.state.gradientDepth++},t.prototype.endTape=function(){this.state.gradientDepth--},t.prototype.startScope=function(t){var e={track:[],name:"unnamed scope",id:this.state.nextScopeId++};t&&(e.name=t),this.state.scopeStack.push(e),this.state.activeScope=e},t.prototype.endScope=function(t){for(var e=this,n=Br(t),r=new Set(n.map((function(t){return t.id}))),i=0;i<this.state.activeScope.track.length;i++){var a=this.state.activeScope.track[i];a.kept||r.has(a.id)||a.dispose()}var o=this.state.scopeStack.pop();this.state.activeScope=0===this.state.scopeStack.length?null:this.state.scopeStack[this.state.scopeStack.length-1],n.forEach((function(t){t.kept||t.scopeId!==o.id||e.track(t)}))},t.prototype.gradients=function(t,e,n,r){var i=this;if(void 0===r&&(r=!1),nn(e.length>0,(function(){return"gradients() received an empty list of xs."})),null!=n&&"float32"!==n.dtype)throw new Error("dy must have 'float32' dtype, but has '".concat(n.dtype,"'"));var a=this.scopedRun((function(){return i.startTape()}),(function(){return i.endTape()}),(function(){return i.tidy("forward",t)}));nn(a instanceof Ar,(function(){return"The result y returned by f() must be a tensor."}));var o=function(t,e,n){for(var r={},i={},a=0;a<e.length;a++)r[e[a].id]=!0;for(a=0;a<t.length;a++){var o=(d=t[a]).inputs;for(var s in o){for(var u=o[s],l=!1,c=0;c<e.length;c++)if(r[u.id]){d.outputs.forEach((function(t){return r[t.id]=!0})),l=!0,i[d.id]=!0;break}if(l)break}}var h={};h[n.id]=!0;var p={};for(a=t.length-1;a>=0;a--)for(o=(d=t[a]).inputs,c=0;c<d.outputs.length;c++)if(h[d.outputs[c].id]){for(var s in o)h[o[s].id]=!0,p[d.id]=!0;break}var f=[];for(a=0;a<t.length;a++){var d;if(i[(d=t[a]).id]&&p[d.id]){var g={};for(var s in d.inputs){var v=d.inputs[s];r[v.id]&&(g[s]=v)}var y=Object.assign({},d);y.inputs=g,y.outputs=d.outputs,f.push(y)}}return f}(this.state.activeTape,e,a);if(!r&&0===o.length&&e.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",(function(){var t,r,s={};s[a.id]=null==n?(t=a.shape,r=vn(rn(t),"float32"),Vr.makeTensor(r,t,"float32")):n,function(t,e,n,r){for(var i=function(i){var a=e[i],o=[];if(a.outputs.forEach((function(e){var n=t[e.id];null!=n?o.push(n):o.push(null)})),null==a.gradient)throw new Error("Cannot compute gradient: gradient function not found "+"for ".concat(a.kernelName,"."));var s=a.gradient(o),u=function(e){if(!(e in s))throw new Error("Cannot backprop through input ".concat(e,". ")+"Available gradients found: ".concat(Object.keys(s),"."));var i=n((function(){return s[e]()}));if("float32"!==i.dtype)throw new Error("Error in gradient for op ".concat(a.kernelName,". The gradient of input ")+"".concat(e," must have 'float32' dtype, but has '").concat(i.dtype,"'"));var o=a.inputs[e];if(!an(i.shape,o.shape))throw new Error("Error in gradient for op ".concat(a.kernelName,". The gradient of input ")+"'".concat(e,"' has shape '").concat(i.shape,"', which does not match ")+"the shape of the input '".concat(o.shape,"'"));if(null==t[o.id])t[o.id]=i;else{var u=t[o.id];t[o.id]=r(u,i),u.dispose()}};for(var l in a.inputs)u(l)},a=e.length-1;a>=0;a--)i(a)}(s,o,(function(t){return i.tidy(t)}),qr);var u=e.map((function(t){return s[t.id]}));return 0===i.state.gradientDepth&&(i.state.activeTape.forEach((function(t){var e,n;try{for(var r=l(t.saved),i=r.next();!i.done;i=r.next()){i.value.dispose()}}catch(t){e={error:t}}finally{try{i&&!i.done&&(n=r.return)&&n.call(r)}finally{if(e)throw e.error}}})),i.state.activeTape=null),{value:a,grads:u}}))},t.prototype.customGrad=function(t){var e=this;return nn(pn(t),(function(){return"The f passed in customGrad(f) must be a function."})),function(){for(var n,r=[],i=0;i<arguments.length;i++)r[i]=arguments[i];nn(r.every((function(t){return t instanceof Ar})),(function(){return"The args passed in customGrad(f)(x1, x2,...) must all be tensors"}));var a={};r.forEach((function(t,e){a[e]=t}));var o=function(e,i){return nn((n=t.apply(void 0,h([],c(h(h([],c(r),!1),[i],!1)),!1))).value instanceof Ar,(function(){return"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"})),nn(pn(n.gradFunc),(function(){return"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."})),n.value},s=function(t,e){var i=n.gradFunc(t,e),a=Array.isArray(i)?i:[i];nn(a.length===r.length,(function(){return"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."})),nn(a.every((function(t){return t instanceof Ar})),(function(){return"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors."}));var o={};return a.forEach((function(t,e){o[e]=function(){return t}})),o};return e.runKernelFunc({forwardFunc:o,backwardsFunc:s,inputs:a})}},t.prototype.readSync=function(t){return this.state.tensorInfo.get(t).backend.readSync(t)},t.prototype.read=function(t){return this.state.tensorInfo.get(t).backend.read(t)},t.prototype.readToGPU=function(t,e){return this.state.tensorInfo.get(t).backend.readToGPU(t,e)},t.prototype.time=function(t){return s(this,void 0,void 0,(function(){var e,n;return u(this,(function(r){switch(r.label){case 0:return e=fr(),[4,this.backend.time(t)];case 1:return(n=r.sent()).wallMs=fr()-e,[2,n]}}))}))},t.prototype.track=function(t){return null!=this.state.activeScope&&(t.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(t)),t},Object.defineProperty(t.prototype,"registeredVariables",{get:function(){return this.state.registeredVariables},enumerable:!1,configurable:!0}),t.prototype.reset=function(){for(var t in this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new Ur,this.registry)this.disposeRegisteredKernels(t),this.registry[t].dispose(),delete this.registry[t];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null},t}();Wr.nextTensorId=0,Wr.nextVariableId=0;var Vr=function(){var t=zn();if(null==t._tfengine){var e=new kn(t);t._tfengine=new Wr(e)}return function(t){An=t}(t._tfengine.ENV),Nr=function(){return t._tfengine},t._tfengine}();function qr(t,e){var n={a:t,b:e};return Vr.runKernel(ye,n)}function Gr(t,e){var n=t;if(gr(t))return"string"===e?[]:[t.length];if(Rr(t)){var r=t.channels||"RGBA";return[t.height,t.width*r.length]}if(Or(t))return[t.buffer.size/(null==e?4:ln(e))];if(!Array.isArray(t))return[];for(var i=[];Array.isArray(n)||gr(n)&&"string"!==e;)i.push(n.length),n=n[0];return Array.isArray(t)&&Nn().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&Kr(t,i,[]),i}function Kr(t,e,n){if(n=n||[],Array.isArray(t)||gr(t)){nn(e.length>0,(function(){return"Element arr[".concat(n.join("]["),"] should be a primitive, ")+"but is 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".concat(this.path," failed with status code ")+"".concat(t.status,". Please verify this URL points to ")+"the model JSON of the model to load.");a.label=2;case 2:return a.trys.push([2,4,,5]),[4,t.json()];case 3:return e=a.sent(),[3,5];case 4:throw a.sent(),n="Failed to parse model JSON of response from ".concat(this.path,"."),this.path.endsWith(".pb")?n+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.":n+=" Please make sure the server is serving valid JSON for this request.",new Error(n);case 5:if(r=e.modelTopology,i=e.weightsManifest,null==r&&null==i)throw new Error("The JSON from HTTP path ".concat(this.path," contains neither model ")+"topology or manifest for weights.");return[2,e]}}))}))},t.prototype.load=function(){return s(this,void 0,void 0,(function(){var t=this;return u(this,(function(e){switch(e.label){case 0:return this.loadOptions.streamWeights?[2,this.loadStream()]:[4,this.loadModelJSON()];case 1:return[2,Lo(e.sent(),(function(e){return t.loadWeights(e)}))]}}))}))},t.prototype.loadStream=function(){return s(this,void 0,void 0,(function(){var t,e,n,r,i=this;return u(this,(function(a){switch(a.label){case 0:return[4,this.loadModelJSON()];case 1:return t=a.sent(),[4,this.getWeightUrls(t.weightsManifest)];case 2:return e=a.sent(),n=Oo(t.weightsManifest),r=function(){return function(t,e){var n,r,i=this,a=null==e.fetchFunc?Nn().platform.fetch:e.fetchFunc,o=0;return null===(n=e.onProgress)||void 0===n||n.call(e,0),new ReadableStream({pull:function(n){return s(i,void 0,void 0,(function(){var i,s,l,c,h;return u(this,(function(u){switch(u.label){case 0:return o<t.length?r?[3,2]:[4,a(t[o],e.requestInit,{isBinary:!0})]:[3,4];case 1:s=u.sent().body,r=s.getReader(),u.label=2;case 2:return[4,r.read()];case 3:return l=u.sent(),c=l.done,h=l.value,c?(o++,r=void 0,null===(i=e.onProgress)||void 0===i||i.call(e,o/t.length),[3,0]):(n.enqueue(h),[2]);case 4:return n.close(),[2]}}))}))}})}(e,i.loadOptions)},[2,Object.assign(Object.assign({},t),{weightSpecs:n,getWeightStream:r})]}}))}))},t.prototype.getWeightUrls=function(t){return s(this,void 0,void 0,(function(){var e,n,r,i,a,o,s,p,f,d,g,v,y,m,b,w,k,x,S,N,I;return u(this,(function(u){switch(u.label){case 0:e=Array.isArray(this.path)?this.path[1]:this.path,n=c(function(t){var e=t.lastIndexOf("/"),n=t.lastIndexOf("?"),r=t.substring(0,e),i=n>e?t.substring(n):"";return[r+"/",i]}(e),2),r=n[0],i=n[1],a=this.weightPathPrefix||r,o=[],s=[];try{for(p=l(t),f=p.next();!f.done;f=p.next()){d=f.value;try{for(N=void 0,g=l(d.paths),v=g.next();!v.done;v=g.next())y=v.value,null!=this.weightUrlConverter?s.push(this.weightUrlConverter(y)):o.push(a+y+i)}catch(t){N={error:t}}finally{try{v&&!v.done&&(I=g.return)&&I.call(g)}finally{if(N)throw N.error}}}}catch(t){x={error:t}}finally{try{f&&!f.done&&(S=p.return)&&S.call(p)}finally{if(x)throw x.error}}return this.weightUrlConverter?(b=(m=o.push).apply,w=[o],k=[[]],[4,Promise.all(s)]):[3,2];case 1:b.apply(m,w.concat([h.apply(void 0,k.concat([c.apply(void 0,[u.sent()]),!1]))])),u.label=2;case 2:return[2,o]}}))}))},t.prototype.loadWeights=function(t){return s(this,void 0,void 0,(function(){var e,n,r;return u(this,(function(i){switch(i.label){case 0:return[4,this.getWeightUrls(t)];case 1:return e=i.sent(),n=Oo(t),[4,ts(e,this.loadOptions)];case 2:return r=i.sent(),[2,[n,r]]}}))}))},t}();function ns(t){return null!=t.match(es.URL_SCHEME_REGEX)}es.URL_SCHEME_REGEX=/^https?:\/\//;var rs=function(t,e){if("undefined"==typeof fetch&&(null==e||null==e.fetchFunc))return null;return(Array.isArray(t)?t.every((function(t){return ns(t)})):ns(t))?function(t,e){return new es(t,e)}(t,e):null};function is(t,e,n){if(e.rank<1)throw new Error("tf.scatterND() expects the indices to be rank 1 or higher,"+" but the rank was ".concat(e.rank,"."));if(t.rank<1)throw new Error("tf.scatterND() expects the updates to be rank 1 or higher,"+" but the rank was ".concat(t.rank,"."));if("int32"!==e.dtype)throw new Error("The dtype of 'indices' should be int32, but got dtype: ".concat(e.dtype));if(n.length<1)throw new Error("Output rank must be greater or equal to 1, but got shape: ".concat(n));if(0===n.length){if(0===e.size)throw new Error("Indices specified for empty output. indices shape: ".concat(e.shape));if(0===t.size)throw new Error("Updates specified for empty output. updates shape: ".concat(t.shape))}!function(t,e,n){var r=e.rank>1?e.shape[e.rank-1]:1,i=e.rank>1?e.rank-1:1,a="Must have updates.shape = indices.shape[:batchDim] + "+"shape[sliceDim:], got updates.shape: ".concat(n.shape)+", indices.shape: ".concat(e.shape,", shape: ").concat(t)+", sliceDim: ".concat(r,", and batchDim: ").concat(i,".");if(n.rank<i)throw new Error(a+" update.rank < ".concat(i,". 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S=x;if(null==o[x]){var N=x.split("/");S=N.slice(0,-2).concat([N[N.length-1]]).join("/")}if(null!=o[S])k.push([o[S],t[x]]);else if(e)throw new d("Provided weight data has no target variable: ".concat(x));delete o[S]}if(e){var I=[];for(var x in o)I.push(x);if(I.length>0)throw new d("".concat(I.length," of ").concat(s," weights are not set: ")+"".concat(I))}te(k)},n.prototype.parseWeights=function(t){var e=function(e){var n=e.split("/"),r=["vars","layer_checkpoint_dependencies"],i=n.map((function(t){return t.startsWith("_")?t.slice(1):t})).filter((function(t){return!r.includes(t)})).join("/");i!==e&&(t[i]=t[e],delete t[e])};for(var n in Object.keys(t))e(n)},n.prototype.updatedConfig=function(){var t=this.getConfig(),e={};return e.className=this.getClassName(),e.config=t,e.kerasVersion="tfjs-layers ".concat(al),e.backend="TensorFlow.js",e},n.prototype.toJSON=function(t,e){void 0===e&&(e=!0);var n=il(this.updatedConfig());return e?JSON.stringify(n):n},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){t=x(t);for(var e=new ce,i=0;i<r.inputs.length;++i)e.add(r.inputs[i],t[i]);return fe(r.outputs,e,n)}))},n.prototype.computeMask=function(t,n){var r=this;return e.tidy((function(){var e;return t=x(t),e=null==n?m(null,t.length):x(n),r.runInternalGraph(t,e)[1]}))},n.prototype.computeOutputShape=function(t){var e,n,r,i,a=Ht(t);if(a.length!==this.inputLayers.length)throw new d("Invalid inputShape argument ".concat(t,": ")+"model has ".concat(this.inputLayers.length," tensor inputs."));for(var o={},s=0;s<a.length;s++){var u=this.inputLayers[s],c=a[s];o[_=u.name+"_0_0"]=c}var h=Object.keys(this.nodesByDepth).map((function(t){return parseInt(t,10)})).sort(T);if(h.length>1)try{for(var p=l(h),f=p.next();!f.done;f=p.next()){var g=f.value,v=this.nodesByDepth[g];try{for(var y=(r=void 0,l(v)),m=y.next();!m.done;m=y.next()){var w=m.value;u=w.outboundLayer;if(-1===this.inputLayers.map((function(t){return t.id})).indexOf(u.id)){for(var x=[],S=0;S<w.inboundLayers.length;S++){var N=w.inboundLayers[S],I=w.nodeIndices[S],A=w.tensorIndices[S],z=o[_="".concat(N.name,"_").concat(I,"_").concat(A)];x.push(z)}var E=Ht(u.computeOutputShape(k(x))),C=u.inboundNodes.indexOf(w);for(S=0;S<E.length;S++){o[_="".concat(u.name,"_").concat(C,"_").concat(S)]=E[S]}}}}catch(t){r={error:t}}finally{try{m&&!m.done&&(i=y.return)&&i.call(y)}finally{if(r)throw r.error}}}}catch(t){e={error:t}}finally{try{f&&!f.done&&(n=p.return)&&n.call(p)}finally{if(e)throw e.error}}var F=[],D=[];for(s=0;s<this.outputLayers.length;s++){u=this.outputLayers[s],C=this.outputLayersNodeIndices[s],A=this.outputLayersTensorIndices[s];var _="".concat(u.name,"_").concat(C,"_").concat(A);D.push(_)}for(s=0;s<D.length;s++){var L=D[s];b(L in o),F.push(o[L])}return k(F)},n.prototype.runInternalGraph=function(t,e){var n,r,i,a,o,s,u,h;null==e&&(e=m(null,t.length));for(var p={},f=0;f<this.inputs.length;++f){var d=this.inputs[f],v=t[f],y=e[f];p[d.id]=[v,y]}var w=Object.keys(this.nodesByDepth).map((function(t){return parseInt(t,10)})).sort(T);try{for(var k=l(w),S=k.next();!S.done;S=k.next()){var N=S.value,I=this.nodesByDepth[N];try{for(var A=(i=void 0,l(I)),z=A.next();!z.done;z=A.next()){var E=z.value,C=E.outboundLayer,F=E.inputTensors,D=E.outputTensors,_=new Array;try{for(var L=(o=void 0,l(F)),R=L.next();!R.done;R=L.next()){(d=R.value).id in p&&_.push(p[d.id])}}catch(t){o={error:t}}finally{try{R&&!R.done&&(s=L.return)&&s.call(L)}finally{if(o)throw o.error}}if(_.length===F.length){var O={},M=void 0,B=void 0,P=void 0,j=void 0;if(null!=E.callArgs&&(O=E.callArgs),1===_.length){var U=c(_[0],2),W=U[0],V=U[1];null==O.mask&&(O.mask=V),P=x(C.call(W,O)),j=x(C.computeMask(W,V)),M=[W],B=[V]}else M=_.map((function(t){return t[0]})),B=_.map((function(t){return t[1]})),null==O.mask&&(O.mask=B),P=x(C.call(M,O)),j=x(C.computeMask(M,B));if(C.activityRegularizer)throw new g("LayersModel invocation with concrete Tensor value(s) in the presence of activity 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e.error}}return a},n.prototype.getLayer=function(t,e){var n,r;if(null!=e)return this.findLayer(e);if(null==t)throw new d("Provide either a layer name or layer index");if("number"==typeof t)return this.findLayer(t);try{for(var i=l(this.layers),a=i.next();!a.done;a=i.next()){var o=a.value;if(o.name===t)return o}}catch(t){n={error:t}}finally{try{a&&!a.done&&(r=i.return)&&r.call(i)}finally{if(n)throw n.error}}throw new d("No such layer: ".concat(t))},n.prototype.findLayer=function(t){if(this.layers.length<=t)throw new d("Was asked to retrieve layer at index ".concat(t,", but model only ")+"has ".concat(this.layers.length," layer(s)."));return this.layers[t]},n.prototype.calculateLosses=function(){var t=this;return e.tidy((function(){var e,r,i=[];try{for(var a=l(t.layers),o=a.next();!o.done;o=a.next())for(var s=o.value,u=0;u<s.inboundNodes.length;++u){var p=n.nodeKey(s,u);t.containerNodes.has(p)&&i.push.apply(i,h([],c(s.calculateLosses()),!1))}}catch(t){e={error:t}}finally{try{o&&!o.done&&(r=a.return)&&r.call(a)}finally{if(e)throw e.error}}return i}))},n.prototype.getConfig=function(){var t,e,r={name:this.name},i=this.buildNodeConversionMap(this.layers),a=[];try{for(var o=l(this.layers),s=o.next();!s.done;s=o.next()){for(var u=(S=s.value).getClassName(),c=S.getConfig(),h=[],p=0;p<S.inboundNodes.length;p++){var f=S.inboundNodes[p],d=n.nodeKey(S,p),g={};if(this.containerNodes.has(d)){if(f.callArgs)try{JSON.stringify(f.callArgs),g=f.callArgs}catch(t){console.warn("Layer ".concat(S.name," was passed ")+"non-serializable keyword arguments: "+"".concat(f.callArgs,". 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The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");try{for(var n=l(this.layers),r=n.next();!r.done;r=n.next()){if(r.value.stateful)return!0}}catch(e){t={error:e}}finally{try{r&&!r.done&&(e=n.return)&&e.call(n)}finally{if(t)throw t.error}}return!1},enumerable:!1,configurable:!0}),n.prototype.resetStates=function(){var t=this;e.tidy((function(){t.layers.forEach((function(t){t.stateful&&t.resetStates()}))}))},n}(oe);function sl(t,e){return function(t,e,n){var r=e.length;if(null==t||Array.isArray(t)&&0===t.length)return e.map((function(t){return null}));if(1===r)return Array.isArray(t)&&1===t.length?t:"object"==typeof t&&e[0]in t?[t[e[0]]]:[t];if(Array.isArray(t)){if(t.length!==r)throw new Error("Provided ".concat(n," is an array of ").concat(t.length," ")+"element(s), but the model has ".concat(r," outputs. ")+"Make sure a set of weights is provided for each model output.");return t}if("object"==typeof t&&Object.keys(t).length>0&&"object"==typeof t[Object.keys(t)[0]]){var i=[];return e.forEach((function(e){e in t?i.push(t[e]):i.push(null)})),i}throw new Error("The model has multiple (".concat(r,") outputs, ")+"so ".concat(n," must be either an array with ")+"".concat(r," elements or an object with ").concat(e," keys. ")+"Provided ".concat(n," not understood: ").concat(JSON.stringify(t)))}(t,e,"classWeight")}function ul(t,n,r,i){return s(this,void 0,void 0,(function(){var a,o,s,l,c;return u(this,(function(u){switch(u.label){case 0:if(null!=n||null!=i)throw new Error("Support sampleWeight is not implemented yet");return null==r?[3,2]:(a=e.tidy((function(){if(1===t.shape.length)return e.clone(t);if(2===t.shape.length){if(t.shape[1]>1){return e.argMax(t,1)}if(1===t.shape[1])return e.reshape(t,[t.shape[0]]);throw new Error("Encountered unexpected last-dimension size (".concat(t.shape[1],") ")+"during handling of class weights. The size is expected to be >= 1.")}throw new Error("Unexpected rank of target (y) tensor (".concat(t.rank,") during ")+"handling of class weights. The rank is expected to be 1 or 2.")})),l=(s=Array).from,[4,a.data()]);case 1:return o=l.apply(s,[u.sent()]),e.dispose(a),c=[],o.forEach((function(t){if(null==r[t])throw new Error("classWeight must contain all classes in the training data. "+"The class ".concat(t," exists in the data but not in ")+"classWeight");c.push(r[t])})),[2,e.tensor1d(c,"float32")];case 2:return[2,null]}}))}))}function ll(t,n){return e.mul(t,n)}function cl(t,e){var n,r,a=e;n=a.xs,r=a.ys,i.util.assert(null!=n&&null!=r,(function(){return"A Dataset iterator for fitDataset() is expected to generate objects of the form `{xs: xVal, ys: yVal}`, where the two values may be `tf.Tensor`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates "+"".concat(e)}));var o=hl("input",t.inputNames,n),s=hl("output",t.outputNames,r),u=o[0].shape[0];i.util.assert(o.length===t.inputs.length,(function(){return"LayersModel has ".concat(t.inputs.length," inputs, but the dataset ")+"provides ".concat(o.length," inputs.  (Expected input keys: ")+"".concat(JSON.stringify(t.inputNames),")")})),i.util.assert(s.length===t.outputs.length,(function(){return"LayersModel has ".concat(t.outputs.length," outputs, but the dataset ")+"provides ".concat(s.length," outputs.  (Expected output keys: ")+"".concat(JSON.stringify(t.outputNames),")")}));for(var l=function(e){i.util.assert(o[e].shape[0]===u,(function(){return"Batch size mismatch: input "+"".concat(t.inputNames[e]," has ").concat(o[e].shape[0],"; ")+"expected  ".concat(u," based on input ").concat(t.inputNames[0],".")}))},c=0;c<o.length;c++)l(c);for(var h=function(e){i.util.assert(s[e].shape[0]===u,(function(){return"Batch size mismatch: output "+"".concat(t.outputNames[e]," has ").concat(s[e].shape[0],"; ")+"expected  ".concat(u," based on input ").concat(t.inputNames[0],".")}))},p=0;p<s.length;p++)h(p);return{xs:o,ys:s}}function hl(t,e,n){var r,a;if(n instanceof i.Tensor)return[n];if(Array.isArray(n))return i.util.assert(n.length===e.length,(function(){return"Received an array of ".concat(n.length," Tensors, but expected ").concat(e.length," to match the ").concat(t," keys ").concat(e,".")})),n;var o=[];try{for(var s=l(e),u=s.next();!u.done;u=s.next()){var c=u.value;if(null==n[c])throw new d("The feature data generated by the dataset lacks the required "+"".concat(t," key '").concat(c,"'."));o.push(n[c])}}catch(t){r={error:t}}finally{try{u&&!u.done&&(a=s.return)&&a.call(s)}finally{if(r)throw r.error}}return o}function pl(t,e,n){return s(this,void 0,void 0,(function(){var r,a,o,s,l,c,h,p,f,d,v,y,m,b,w,k,S,N,I,A,z,E,T,C,F,D,_,L,R,O,M,B,P,j;return u(this,(function(u){switch(u.label){case 0:if(r=null!=n.batchesPerEpoch,i.util.assert(null!=t.optimizer,(function(){return"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."})),i.util.assert(null!=n,(function(){return"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."})),i.util.assert(null!=n.epochs&&n.epochs>0&&Number.isInteger(n.epochs),(function(){return"For fitDataset(), config.epochs is expected to be a positive "+"integer, but got ".concat(n.epochs)})),i.util.assert(!r||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),(function(){return"For fitDataset(), config.batchesPerEpoch is expected to be a "+"positive integer if specified, but got ".concat(n.batchesPerEpoch)})),i.util.assert(null==n.validationSplit,(function(){return"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."})),t.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");t.isTraining=!0,u.label=1;case 1:return u.trys.push([1,,26,27]),a=null!=n.validationData,o=void 0,s=void 0,a&&(fl(n.validationData)?i.util.assert(null==n.validationBatches||n.validationBatches>0&&Number.isInteger(n.validationBatches),(function(){return"For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, "+"but got ".concat(n.validationBatches)})):(l=function(t){if(3===t.length)throw new g("Validation with sample weights is not implemented yet.");return{xs:t[0],ys:t[1]}}(n.validationData),o=l.xs,s=l.ys)),c=t.makeTrainFunction(),h=t.getDedupedMetricsNames(),p=void 0,p=a?h.slice().concat(h.map((function(t){return"val_"+t}))):h.slice(),f=Nu(n.callbacks,n.yieldEvery),d=null==n.verbose?1:n.verbose,v=Au(f,d,n.epochs,null,null,function(t,e){var n=null;null!=e.batchesPerEpoch?n=e.batchesPerEpoch:Number.isFinite(t.size)&&(n=t.size);return n}(e,n),null,a,p),y=v.callbackList,m=v.history,y.setModel(t),t.history=m,[4,y.onTrainBegin()];case 2:return u.sent(),t.stopTraining_=!1,b=null==n.initialEpoch?0:n.initialEpoch,[4,e.iterator()];case 3:w=u.sent(),u.label=4;case 4:return b<n.epochs?(k={},[4,y.onEpochBegin(b)]):[3,23];case 5:return u.sent(),S=0,N=0,r?[3,7]:[4,e.iterator()];case 6:w=u.sent(),u.label=7;case 7:return!r||S<n.batchesPerEpoch?[4,w.next()]:[3,21];case 8:return I=u.sent(),r&&I.done?(console.warn("You provided `batchesPerEpoch` as "+"".concat(n.batchesPerEpoch,", ")+"but your dataset iterator ran out of data after "+"".concat(S," batches; ")+"interrupting training. Make sure that your dataset can generate at least `batchesPerEpoch * epochs` batches (in this case, "+"".concat(n.batchesPerEpoch*n.epochs," batches). ")+"You may need to use the repeat() function when building your dataset."),[3,21]):null==I.value?[3,15]:(A=cl(t,I.value),z=A.xs,E=A.ys,(T={}).batch=N,T.size=z[0].shape[0],[4,y.onBatchBegin(N,T)]);case 9:if(u.sent(),C=[],null==n.classWeight)return[3,13];F=sl(n.classWeight,t.outputNames),j=0,u.label=10;case 10:return j<F.length?(_=(D=C).push,[4,ul(E[j],null,F[j])]):[3,13];case 11:_.apply(D,[u.sent()]),u.label=12;case 12:return++j,[3,10];case 13:for(L=z.concat(E).concat(C),R=c(L),i.dispose(L),j=0;j<h.length;++j)O=h[j],M=R[j],T[O]=M,i.keep(M);return[4,y.onBatchEnd(N,T)];case 14:u.sent(),mu(T),N++,S++,u.label=15;case 15:return(r?S>=n.batchesPerEpoch:I.done)?a?(B=void 0,fl(n.validationData)?(P=x,[4,t.evaluateDataset(n.validationData,{batches:n.validationBatches})]):[3,17]):[3,19]:[3,20];case 16:return B=P.apply(void 0,[u.sent()]),[3,18];case 17:B=x(t.evaluate(o,s,{batchSize:null==n.validationBatchSize?32:n.validationBatchSize,verbose:0})),u.label=18;case 18:for(j=0;j<t.metricsNames.length;++j)k["val_".concat(t.metricsNames[j])]=B[j];u.label=19;case 19:return[3,21];case 20:return t.stopTraining_?[3,21]:[3,7];case 21:return[4,y.onEpochEnd(b,k)];case 22:return u.sent(),b++,t.stopTraining_?[3,23]:[3,4];case 23:return[4,y.onTrainEnd()];case 24:return u.sent(),[4,t.history.syncData()];case 25:return u.sent(),[2,t.history];case 26:return t.isTraining=!1,[7];case 27:return[2]}}))}))}function fl(t){return"function"==typeof t.iterator}function dl(t,n,r){return s(this,void 0,void 0,(function(){var a,o,s,l,c,h,p,f,d,v;return u(this,(function(y){switch(y.label){case 0:if(a=null!=(r=r||{}).batches,o=t.testFunction,s=[],r.verbose>0)throw new g("Verbose mode is not implemented yet.");return i.util.assert(!a||r.batches>0&&Number.isInteger(r.batches),(function(){return"Test loop expects `batches` to be a positive integer, but "+"received ".concat(JSON.stringify(r.batches))})),"function"!=typeof n.next?[3,1]:(c=n,[3,3]);case 1:return[4,n.iterator()];case 2:c=y.sent(),y.label=3;case 3:l=c,h=0,p=0,f=function(){var n;return u(this,(function(u){switch(u.label){case 0:return[4,l.next()];case 1:return n=u.sent(),s=i.tidy((function(){if(n.value){var r=cl(t,n.value),a=r.xs,u=r.ys,l=a.concat(u),c=i.tidy((function(){return o(l)}));if(i.dispose(l),0===p)for(var f=0;f<c.length;++f)s.push(e.scalar(0));var d=l[0].shape[0],g=function(t){var e=c[t],n=s[t];s[t]=i.tidy((function(){return i.add(s[t],i.mul(d,e))})),p>0&&i.dispose(n)};for(f=0;f<c.length;++f)g(f);i.dispose(c),h+=d,++p}return s})),n.done?(a&&console.warn("Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least `batches` "+"batches (in this case, ".concat(r.batches," batches). ")+"You may need to use the repeat() function when building your dataset."),[2,"break"]):[2]}}))},y.label=4;case 4:return!a||p<r.batches?[5,f()]:[3,6];case 5:return"break"===y.sent()?[3,6]:[3,4];case 6:for(d=0;d<s.length;++d)v=s[d],s[d]=i.div(s[d],h),i.dispose(v);return[2,k(s)]}}))}))}function gl(t){i.util.assert(t>0&&Number.isInteger(t),(function(){return"batchSize is required to be a positive integer, but got ".concat(t)}))}function vl(t,e,n){return null==t?[null]:Array.isArray(t)?t.map((function(t){return ct(t,e,n-e)})):ct(t,e,n-e)}function yl(t,e){return i.tidy((function(){return null==t?null:Array.isArray(t)?t.map((function(t){return yl(t,e)})):mt(t,"int32"===e.dtype?e:i.cast(e,"int32"))}))}function ml(t,e){for(var n=[],r=0,i=null;r<t;)(i=r+e)>=t&&(i=t),n.push([r,i]),r=i;return n}function bl(t){var n=[];t instanceof e.Tensor&&(t=[t]);for(var r=0;r<t.length;++r){var i=t[r];if(1===i.rank)n.push(lt(i,1));else{if(0===i.rank)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");n.push(i)}}return n}function wl(t,n){if(null!=t){var r=[];if(n instanceof e.Tensor)r.push(n.id);else if(Array.isArray(n))n.forEach((function(t){return r.push(t.id)}));else if(null!=n)for(var i in n){var a=n[i];r.push(a.id)}var o=[];if(t instanceof e.Tensor)-1===r.indexOf(t.id)&&o.push(t);else if(Array.isArray(t))t.forEach((function(t){-1===r.indexOf(t.id)&&o.push(t)}));else if(null!=t)for(var i in t){var s=t[i];-1===r.indexOf(s.id)&&o.push(s)}o.forEach((function(t){t.isDisposed||t.dispose()}))}}function kl(t){return Array.isArray(t)}function xl(t){return!function(t){return t instanceof e.Tensor}(t)&&!kl(t)}function Sl(t,e,n,r,i){var a,o,s;if(void 0===r&&(r=!0),void 0===i&&(i=""),null==e||0===e.length){if(null!=t){var u=!1;if(kl(t)&&t.length>0)u=!0;else if(xl(t)){for(var c in t)if(t.hasOwnProperty(c)){u=!0;break}}else u=!0;if(u)throw new d("Error when checking model ".concat(i," expected no data, ")+"but got ".concat(t))}return[]}if(null==t)return e.map((function(t){return null}));if(xl(t)){s=[];try{for(var h=l(e),p=h.next();!p.done;p=h.next()){var f=p.value;if(null==t[f])throw new d('No data provided for "'.concat(f,'". Need data for each key in: ')+"".concat(e));s.push(t[f])}}catch(t){a={error:t}}finally{try{p&&!p.done&&(o=h.return)&&o.call(h)}finally{if(a)throw a.error}}}else if(kl(t)){if(t.length!==e.length)throw new d("Error when checking model ".concat(i,": the Array of ")+"Tensors that you are passing to your model is not the size the "+"model expected. Expected to see ".concat(e.length," Tensor(s), but ")+"instead got the following list of Tensor(s): ".concat(t));s=t}else{if(e.length>1)throw new d("The model ".concat(i," expects ").concat(e.length," Tensor(s), ")+"but only received one Tensor. Found: Tensor with shape ".concat(t.shape));s=[t]}if(s=bl(s),null!=n)for(var g=0;g<e.length;++g)if(null!=n[g]){var v=s[g];if(v.shape.length!==n[g].length)throw new d("Error when checking ".concat(i,": expected ").concat(e[g]," ")+"to have ".concat(n[g].length," dimension(s). but got array with ")+"shape ".concat(v.shape));for(var y=0;y<n[g].length;++y)if(0!==y||r){var m=v.shape[y],b=n[g][y];if(null!=b&&b>=0&&m!==b)throw new d("".concat(i," expected a batch of elements where each ")+"example has shape [".concat(n[g].slice(1,n[g].length),"] ")+"(i.e.,tensor shape [*,".concat(n[g].slice(1,n[g].length),"])")+" but the ".concat(i," received an input with ").concat(v.shape[0])+" examples, each with shape [".concat(v.shape.slice(1,v.shape.length),"]")+" (tensor shape [".concat(v.shape,"])"))}}return s}function Nl(t,e,n,r,i){var a;if(void 0===r&&(r=!0),void 0===i&&(i=""),Array.isArray(t)){if(t.length!==e.length)throw new d("Error when checking model ".concat(i,": the Array of ")+"Tensors that you are passing to your model is not the size the "+"the model expected. Expected to see ".concat(e.length," Tensor(s),")+" but instead got ".concat(t.length," Tensors(s)."));a=t}else{if(e.length>1)throw new d("The model expects ".concat(e.length," ").concat(i," Tensors, ")+"but only received one Tensor. Found: array with shape "+"".concat(JSON.stringify(t.shape),"."));a=[t]}if(null!=n)for(var o=0;o<e.length;++o)if(null!=n[o]){var s=a[o];if(s.shape.length!==n[o].length)throw new d("Error when checking ".concat(i,": expected ").concat(e[o]," ")+"to have ".concat(n[o].length," dimension(s), but got array with ")+"shape ".concat(JSON.stringify(s.shape)));for(var u=0;u<n[o].length;++u)if(0!==u||r){var l=s.shape[u],c=n[o][u];if(null!=c&&c!==l)throw new d("Error when checking ".concat(i,": expected ")+"".concat(e[o]," to have shape ").concat(JSON.stringify(n[o])," but ")+"got array with shape ".concat(JSON.stringify(s.shape),"."))}}}var Il=function(t){function n(e){var n=t.call(this,e)||this;return n.isTraining=!1,n}return o(n,t),n.prototype.summary=function(t,e,n){if(void 0===n&&(n=console.log),!this.built)throw new d("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");$u(this,t,e,n)},n.prototype.compile=function(t){var n,r,i=this;if(null==t.loss&&(t.loss=[]),this.loss=t.loss,"string"==typeof t.optimizer)this.optimizer_=function(t){var n={Adagrad:function(){return e.train.adagrad(.01)},Adadelta:function(){return e.train.adadelta(1,.95,st())},Adam:function(){return e.train.adam(.001,.9,.999,st())},Adamax:function(){return e.train.adamax(.002,.9,.999,st(),0)},RMSProp:function(){return e.train.rmsprop(.001,.9,0,st())},SGD:function(){return e.train.sgd(.01)}};if(n.adagrad=n.Adagrad,n.adadelta=n.Adadelta,n.adam=n.Adam,n.adamax=n.Adamax,n.rmsprop=n.RMSProp,n.sgd=n.SGD,t in n)return n[t]();throw new d("Unknown Optimizer ".concat(t))}(t.optimizer),this.isOptimizerOwned=!0;else{if(!(t.optimizer instanceof e.Optimizer))throw new d("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=t.optimizer,this.isOptimizerOwned=!1}var a=[];if(Array.isArray(t.loss)||"string"==typeof t.loss||"function"==typeof t.loss)if(Array.isArray(t.loss)){if(t.loss.length!==this.outputs.length)throw new d("When passing an Array as loss, it should have one entry per "+"model output. The model has ".concat(this.outputs.length," output(s), ")+"but you passed loss=".concat(t.loss,"."));var o=t.loss;a=o.map((function(t){return Mu(t)}))}else{var s=Mu(t.loss);this.outputs.forEach((function(t){a.push(s)}))}else{for(var u in t.loss=t.loss,t.loss)if(-1===this.outputNames.indexOf(u))throw new d('Unknown entry in loss dictionary: "'.concat(u,'". ')+"Only expected the following keys: ".concat(this.outputNames));try{for(var c=l(this.outputNames),h=c.next();!h.done;h=c.next()){u=h.value;null==t.loss[u]&&console.warn('Output "'.concat(u,'" is missing from loss dictionary. We assume ')+"this was done on purpose, and we will not be expecting data "+"to be passed to ".concat(u," during training")),a.push(Mu(t.loss[u]))}}catch(t){n={error:t}}finally{try{h&&!h.done&&(r=c.return)&&r.call(c)}finally{if(n)throw n.error}}}this.lossFunctions=a,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(var p=0;p<this.outputs.length;++p){var f=this.internalOutputShapes[p];u=this.outputNames[p];this.feedOutputNames.push(u),this.feedOutputShapes.push(f),this.feedLossFns.push(this.lossFunctions[p])}var g=[];this.metrics=t.metrics,this.metricsNames=["loss"],this.metricsTensors=[],X("loss",(function(){for(var t=0;t<i.outputs.length;++t)if(-1===g.indexOf(t)){var e=i.lossFunctions[t];i.outputs.length>1&&(i.metricsTensors.push([e,t]),i.metricsNames.push(i.outputNames[t]+"_loss"))}}));var v=function(t,e){var n,r,i;if(null==t||Array.isArray(t)&&0===t.length)return e.map((function(t){return[]}));if("string"==typeof t||"function"==typeof t)i=[t];else{if(!Array.isArray(t)&&"object"!=typeof t)throw new TypeError("Type of metrics argument not understood. Expected an string,"+"function, Array, or Object, found: ".concat(t));i=t}if(Array.isArray(i))return e.map((function(t){return i}));var a=[];try{for(var o=l(e),s=o.next();!s.done;s=o.next()){var u=s.value,c=i.hasOwnProperty(u)?i[u]:[];Array.isArray(c)||(c=[c]),a.push(c)}}catch(t){n={error:t}}finally{try{s&&!s.done&&(r=o.return)&&r.call(o)}finally{if(n)throw n.error}}return a}(t.metrics,this.outputNames);X("metric",(function(){for(var t=function(t){if(-1!==g.indexOf(t))return"continue";!function(e){var n,r,a,o,s,u=function(e){if("string"==typeof e&&-1!==["accuracy","acc","crossentropy","ce"].indexOf(e)){var n=i.internalOutputShapes[t];1===n[n.length-1]||i.lossFunctions[t]===Lu?-1!==["accuracy","acc"].indexOf(e)?o=Bu:-1!==["crossentropy","ce"].indexOf(e)&&(o=Vu):i.lossFunctions[t]===_u?-1!==["accuracy","acc"].indexOf(e)?o=qu:-1!==["crossentropy","ce"].indexOf(e)&&(o=Ku):-1!==["accuracy","acc"].indexOf(e)?o=Pu:-1!==["crossentropy","ce"].indexOf(e)&&(o=Gu);var r=void 0;-1!==["accuracy","acc"].indexOf(e)?r="acc":-1!==["crossentropy","ce"].indexOf(e)&&(r="ce"),s=o,a=""+r}else{var u=function(t){if("string"==typeof t&&t in Hu)return Hu[t];if("string"!=typeof t&&null!=t)return t;throw new d("Unknown metric ".concat(t))}(e);s=u,a=""+Ju(e)}var l;X(a,(function(){l=s})),function(t,e,n){i.outputNames.length>1&&(e=i.outputNames[t]+"_"+e),i.metricsNames.push(e),i.metricsTensors.push([n,t])}(t,a,l)};try{for(var c=(n=void 0,l(e)),h=c.next();!h.done;h=c.next()){u(h.value)}}catch(t){n={error:t}}finally{try{h&&!h.done&&(r=c.return)&&r.call(c)}finally{if(n)throw n.error}}}(v[t])},e=0;e<i.outputs.length;++e)t(e)})),this.collectedTrainableWeights=this.trainableWeights},n.prototype.checkTrainableWeightsConsistency=function(){null!=this.collectedTrainableWeights&&this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")},n.prototype.evaluate=function(t,e,n){void 0===n&&(n={});var r=null==n.batchSize?32:n.batchSize;gl(r);var i=this.standardizeUserDataXY(t,e,!0,r);try{var a=i[0].concat(i[1]);this.makeTestFunction();var o=this.testFunction;return k(this.testLoop(o,a,r,n.verbose,n.steps))}finally{wl(i[0],t),wl(i[1],e)}},n.prototype.evaluateDataset=function(t,e){return s(this,void 0,void 0,(function(){return u(this,(function(n){return this.makeTestFunction(),[2,dl(this,t,e)]}))}))},n.prototype.checkNumSamples=function(t,e,n,r){var i;if(void 0===r&&(r="steps"),null!=n){if(i=null,null!=e)throw new d("If ".concat(r," is set, batchSize must be null or undefined.")+"Got batchSize = ".concat(e))}else{if(null==t)throw new d("Either the input data should have a defined shape, or "+"".concat(r," shoud be specified."));i=Array.isArray(t)?t[0].shape[0]:t.shape[0]}return i},n.prototype.execute=function(t,n){var r,i;if(Array.isArray(n)&&0===n.length)throw new d("`outputs` is an empty Array, which is not allowed.");var a=Array.isArray(n),o=a?n:[n],s=this.retrieveSymbolicTensors(o),u=new ce;if(t instanceof e.Tensor&&(t=[t]),Array.isArray(t)){if(t.length!==this.inputs.length)throw new d("The number of inputs provided (".concat(t.length,") ")+"does not match the number of inputs of this model "+"(".concat(this.inputs.length,")."));for(var c=0;c<this.inputs.length;++c)u.add(this.inputs[c],t[c])}else try{for(var h=l(this.inputs),p=h.next();!p.done;p=h.next()){var f=p.value,g=t[f.name];if(null==g)throw new d("No value is provided for the model's input ".concat(f.name));u.add(f,g)}}catch(t){r={error:t}}finally{try{p&&!p.done&&(i=h.return)&&i.call(h)}finally{if(r)throw r.error}}var v=fe(s,u);return a?v:v[0]},n.prototype.retrieveSymbolicTensors=function(t){var e,n,r=m(null,t.length),i=t.length;try{for(var a=l(this.layers),o=a.next();!o.done;o=a.next()){for(var s=o.value,u=Array.isArray(s.output)?s.output:[s.output],c=u.map((function(t){return t.name})),h=0;h<t.length;++h){var p=c.indexOf(t[h]);if(-1!==p&&(r[h]=u[p],i--),0===i)break}if(0===i)break}}catch(t){e={error:t}}finally{try{o&&!o.done&&(n=a.return)&&n.call(a)}finally{if(e)throw e.error}}if(i>0){var f=[];throw r.forEach((function(e,n){null==e&&f.push(t[n])})),new d("Cannot find SymbolicTensors for output name(s): "+"".concat(JSON.stringify(f)))}return r},n.prototype.predictLoop=function(t,e,n){var r=this;return void 0===e&&(e=32),void 0===n&&(n=!1),i.tidy((function(){var a=r.checkNumSamples(t);if(n)throw new g("Verbose predictLoop() is not implemented yet.");for(var o=ml(a,e),s=r.outputs.map((function(t){return[]})),u=function(e){i.tidy((function(){var n=o[e][0],i=o[e][1],a=vl(t,n,i),s=[];if(Array.isArray(a))for(var u=0;u<a.length;++u)s.push({key:r.inputs[u],value:a[u]});else s.push({key:r.inputs[0],value:a});var l=new ce(s);return fe(r.outputs,l)})).forEach((function(t,e){return s[e].push(t)}))},l=0;l<o.length;++l)u(l);return k(s.map((function(t){return i.concat(t,0)})))}))},n.prototype.predict=function(t,e){void 0===e&&(e={});var n=bl(t);Nl(n,this.inputNames,this.feedInputShapes,!1);try{var r=null==e.batchSize?32:e.batchSize;return gl(r),this.predictLoop(n,r)}finally{wl(n,t)}},n.prototype.predictOnBatch=function(t){Nl(t,this.inputNames,this.feedInputShapes,!0);var e=(Array.isArray(t)?t[0]:t).shape[0];return this.predictLoop(t,e)},n.prototype.standardizeUserDataXY=function(t,n,r,i){if(null==this.optimizer_)throw new f("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");for(var a=[],o=0;o<this.feedOutputShapes.length;++o){var s=this.feedOutputShapes[o];this.feedLossFns[o]===_u?a.push(s.slice(0,s.length-1).concat([1])):a.push(s)}if(function(t,n,r){var i=C(t.map((function(t){return t.shape[0]})));i.sort();var a=C(n.map((function(t){return t.shape[0]})));if(a.sort(),i.length>1)throw new d("All input Tensors (x) should have the same number of samples. Got array shapes: "+"".concat(JSON.stringify(t.map((function(t){return t.shape})))));if(a.length>1)throw new d("All target Tensors (y) should have the same number of samples. Got array shapes: "+"".concat(JSON.stringify(n.map((function(t){return t.shape})))));if(i.length>0&&a.length>0&&!e.util.arraysEqual(i,a))throw new d("Input Tensors should have the same number of samples as target "+"Tensors. Found ".concat(i[0]," input sample(s) and ").concat(a[0]," target ")+"sample(s).")}(t=Sl(t,this.feedInputNames,this.feedInputShapes,!1,"input"),n=Sl(n,this.feedOutputNames,a,!1,"target")),function(t,e,n){for(var r=[Tu,Lu,Du],i=0;i<t.length;++i){var a=t[i],o=e[i],s=n[i];if(null!=o){if(o===Du&&1===a.shape[a.shape.length-1])throw new d("You are passing a target array of shape ".concat(a.shape," while using ")+"a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].");if(-1!==r.indexOf(o))for(var u=a.shape.slice(1),l=s.slice(1),c=0;c<u.length;++c){var h=u[c],p=l[c];if(null!=p&&h!==p)throw new d("A target Tensor with shape ".concat(a.shape," was passed for an ")+"output of shape ".concat(s,", while using a loss function that ")+"expects targets to have the same shape as the output.")}}}}(n,this.feedLossFns,this.feedOutputShapes),this.stateful&&null!=i&&i>0&&t[0].shape[0]%i!=0)throw new d("In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size "+"".concat(i,". Found: ").concat(t[0].shape[0]," sample(s)."));return[t,n]},n.prototype.standardizeUserData=function(t,e,n,r,i,a){return void 0===i&&(i=!0),s(this,void 0,void 0,(function(){var o,s,l,h,p,f,d,g;return u(this,(function(u){switch(u.label){case 0:if(o=c(this.standardizeUserDataXY(t,e,i,a),2),s=o[0],l=o[1],null!=n)throw new Error("sample weight is not supported yet.");if(h=null,null==r)return[3,4];p=sl(r,this.outputNames),h=[],f=0,u.label=1;case 1:return f<p.length?(g=(d=h).push,[4,ul(l[f],null,p[f])]):[3,4];case 2:g.apply(d,[u.sent()]),u.label=3;case 3:return++f,[3,1];case 4:return[2,[s,l,h]]}}))}))},n.prototype.testLoop=function(t,n,r,a,o){var s=this;return void 0===a&&(a=0),i.tidy((function(){var u=s.checkNumSamples(n,r,o,"steps"),l=[];if(a>0)throw new g("Verbose mode is not implemented yet.");if(null!=o)throw new g("steps mode in testLoop() is not implemented yet");for(var c=ml(u,r),h=e.tensor1d(ot(0,u)),p=0;p<c.length;++p){var f=c[p][0],d=c[p][1],v=ct(h,f,d-f),y=yl(n,v),m=t(y);if(0===p)for(var b=0;b<m.length;++b)l.push(e.scalar(0));for(b=0;b<m.length;++b){var w=m[b];l[b]=i.add(l[b],i.mul(d-f,w))}}for(b=0;b<l.length;++b)l[b]=i.div(l[b],u);return l}))},n.prototype.getDedupedMetricsNames=function(){for(var t=this.metricsNames,e=[],n=0;n<t.length;++n){var r=t[n],i=r;if(w(t,r)>1){var a=w(t.slice(0,n),r);i+="_".concat(a)}e.push(i)}return e},n.prototype.makeTrainFunction=function(){var t=this;return function(e){var n=[],r=e.slice(0,t.inputs.length),a=e.slice(t.inputs.length,t.inputs.length+t.outputs.length),o=e.slice(t.inputs.length+t.outputs.length,t.inputs.length+2*t.outputs.length),s=[],u=t.collectedTrainableWeights.map((function(t){return t.read()}));return[t.optimizer_.minimize((function(){for(var e=[],u=0;u<t.inputs.length;++u)e.push({key:t.inputs[u],value:r[u]});var l,c=new ce(e),h=fe(t.outputs,c,{training:!0});for(u=0;u<t.lossFunctions.length;++u){var p=(0,t.lossFunctions[u])(a[u],h[u]);null!=o[u]&&(p=ll(p,o[u]));var f=i.mean(p);n.push(f),l=0===u?p:i.add(l,p)}for(u=0;u<t.metricsTensors.length;++u){var d=void 0;if(t.outputs.length>1&&u<t.outputs.length)d=n[u];else{var g=t.metricsTensors[u][0],v=t.metricsTensors[u][1];d=i.mean(g(a[v],h[v]))}i.keep(d),s.push(d)}return l=i.mean(l),t.calculateLosses().forEach((function(t){l=i.add(l,t)})),l}),!0,u)].concat(s)}},n.prototype.makeTestFunction=function(){var t=this;this.testFunction=function(e){return i.tidy((function(){for(var n,r=[],a=e.slice(0,t.inputs.length),o=e.slice(t.inputs.length,t.inputs.length+t.outputs.length),s=[],u=0;u<t.inputs.length;++u)s.push({key:t.inputs[u],value:a[u]});var l=new ce(s),c=fe(t.outputs,l);for(u=0;u<t.lossFunctions.length;++u){var h=t.lossFunctions[u],p=i.mean(h(o[u],c[u]));n=0===u?p:i.add(n,p),r.push(n)}for(u=0;u<t.metricsTensors.length;++u){var f=t.metricsTensors[u][0],d=t.metricsTensors[u][1],g=i.mean(f(o[d],c[d]));r.push(g)}return r}))}},n.prototype.fit=function(t,e,n){return void 0===n&&(n={}),s(this,void 0,void 0,(function(){var r,a,o,s,l,c,h,p,f,v,y,m,b,w,k,x,S,N,I,A,z,E;return u(this,(function(u){switch(u.label){case 0:if(this.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");this.isTraining=!0,u.label=1;case 1:return u.trys.push([1,,7,8]),gl(v=null==n.batchSize?32:n.batchSize),!1,[4,this.standardizeUserData(t,e,n.sampleWeight,n.classWeight,false,v)];case 2:if(y=u.sent(),r=y[0],a=y[1],f=y[2],m=!1,b=void 0,!(null!=n.validationData&&n.validationData.length>0))return[3,4];if(m=!0,2!==n.validationData.length)throw 3===n.validationData.length?new g("validationData including sample weights is not supported yet."):new d("When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; "+"".concat(n.validationData," is invalid."));return l=n.validationData[0],c=n.validationData[1],!0,[4,this.standardizeUserData(l,c,null,null,true,v)];case 3:return w=u.sent(),h=w[0],p=w[1],b=h.concat(p),[3,5];case 4:null!=n.validationSplit&&n.validationSplit>0&&n.validationSplit<1?(m=!0,k=Math.floor(r[0].shape[0]*(1-n.validationSplit)),x=r[0].shape[0],h=vl(r,k,x),o=r,r=vl(r,0,k),p=vl(a,k,x),s=a,a=vl(a,0,k),b=h.concat(p)):null!=n.validationSteps&&(m=!0),u.label=5;case 5:return S=r.concat(a).concat(f),this.checkTrainableWeightsConsistency(),N=this.makeTrainFunction(),I=this.getDedupedMetricsNames(),A=void 0,z=void 0,m?(this.makeTestFunction(),A=this.testFunction,z=I.slice().concat(I.map((function(t){return"val_"+t})))):(A=null,b=[],z=I.slice()),E=Nu(n.callbacks,n.yieldEvery),[4,this.fitLoop(N,S,I,v,n.epochs,n.verbose,E,A,b,n.shuffle,z,n.initialEpoch,null,null)];case 6:return[2,u.sent()];case 7:return this.isTraining=!1,wl(r,t),wl(a,e),wl(o,t),wl(s,e),wl(h,l),wl(p,c),null!=f&&i.dispose(f),[7];case 8:return[2]}}))}))},n.prototype.fitLoop=function(t,n,r,a,o,l,c,h,p,f,v,y,m,b){return s(this,void 0,void 0,(function(){var s,w,k,x,S,N,I,A,z,E=this;return u(this,(function(T){switch(T.label){case 0:if(null==a&&(a=32),null==o&&(o=1),null==f&&(f=!0),null==y&&(y=0),s=!1,null!=h&&null!=p&&(s=!0),null!=b&&(s=!0,null==m))throw new d("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");return null!=(w=this.checkNumSamples(n,a,m,"steps_per_epoch"))&&(k=ot(0,w)),null==l&&(l=1),x=Au(c,l,o,y,w,m,a,s,v),S=x.callbackList,N=x.history,S.setModel(this),this.history=N,[4,S.onTrainBegin()];case 1:T.sent(),this.stopTraining_=!1,I=function(o){var l,c,d,v,y;return u(this,(function(b){switch(b.label){case 0:return[4,S.onEpochBegin(o)];case 1:if(b.sent(),l={},null==m)return[3,2];throw new g("stepsPerEpoch mode is not implemented yet.");case 2:if("batch"===f)throw new g("batch shuffling is not implemneted yet");f&&e.util.shuffle(k),c=e.tensor1d(k),d=ml(w,a),v=function(e){var o;return u(this,(function(u){switch(u.label){case 0:return o={},[4,S.onBatchBegin(e,o)];case 1:return u.sent(),i.tidy((function(){var u=d[e][0],f=d[e][1],g=ct(c,u,f-u);o.batch=e,o.size=f-u;for(var v=yl(n,g),y=t(v),m=0;m<r.length;++m){var b=r[m],w=y[m];o[b]=w,i.keep(w)}if(e===d.length-1&&s){var k=E.testLoop(h,p,a);for(m=0;m<r.length;++m){b=r[m],w=k[m];i.keep(w),l["val_"+b]=w}}})),[4,S.onBatchEnd(e,o)];case 2:return u.sent(),mu(o),A.stopTraining_?[2,"break"]:[2]}}))},y=0,b.label=3;case 3:return y<d.length?[5,v(y)]:[3,6];case 4:if("break"===b.sent())return[3,6];b.label=5;case 5:return++y,[3,3];case 6:c.dispose(),b.label=7;case 7:return[4,S.onEpochEnd(o,l)];case 8:return b.sent(),A.stopTraining_?[2,"break"]:[2]}}))},A=this,z=y,T.label=2;case 2:return z<o?[5,I(z)]:[3,5];case 3:if("break"===T.sent())return[3,5];T.label=4;case 4:return++z,[3,2];case 5:return[4,S.onTrainEnd()];case 6:return T.sent(),[4,this.history.syncData()];case 7:return T.sent(),[2,this.history]}}))}))},n.prototype.fitDataset=function(t,e){return s(this,void 0,void 0,(function(){return u(this,(function(n){return[2,pl(this,t,e)]}))}))},n.prototype.trainOnBatch=function(t,e){return s(this,void 0,void 0,(function(){var n,r,a,o,s,c,h,p,f,d,g,v;return u(this,(function(u){switch(u.label){case 0:return[4,this.standardizeUserData(t,e)];case 1:n=u.sent(),r=n[0],a=n[1],o=this.makeTrainFunction(),s=o(r.concat(a)),c=[],u.label=2;case 2:u.trys.push([2,7,8,9]),h=l(s),p=h.next(),u.label=3;case 3:return p.done?[3,6]:[4,p.value.data()];case 4:f=u.sent(),c.push(f[0]),u.label=5;case 5:return p=h.next(),[3,3];case 6:return[3,9];case 7:return d=u.sent(),g={error:d},[3,9];case 8:try{p&&!p.done&&(v=h.return)&&v.call(h)}finally{if(g)throw g.error}return[7];case 9:return i.dispose(s),wl(n[0],t),wl(n[1],e),[2,k(c)]}}))}))},n.prototype.getNamedWeights=function(t){for(var e=[],n=null!=t&&t.trainableOnly,r=n?this.trainableWeights:this.weights,i=this.getWeights(n),a=0;a<r.length;++a)n&&!r[a].trainable||e.push({name:r[a].originalName,tensor:i[a]});return e},Object.defineProperty(n.prototype,"stopTraining",{get:function(){return this.stopTraining_},set:function(t){this.stopTraining_=t},enumerable:!1,configurable:!0}),Object.defineProperty(n.prototype,"optimizer",{get:function(){return this.optimizer_},set:function(t){this.optimizer_!==t&&(this.optimizer_=t,this.isOptimizerOwned=!1)},enumerable:!1,configurable:!0}),n.prototype.dispose=function(){var e=t.prototype.dispose.call(this);if(0===e.refCountAfterDispose&&null!=this.optimizer&&this.isOptimizerOwned){var n=i.memory().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=n-i.memory().numTensors}return e},n.prototype.getLossIdentifiers=function(){var t,e,n,r,i;if("string"==typeof this.loss)i=S(this.loss);else if(Array.isArray(this.loss)){try{for(var a=l(this.loss),o=a.next();!o.done;o=a.next()){if("string"!=typeof o.value)throw new Error("Serialization of non-string loss is not supported.")}}catch(e){t={error:e}}finally{try{o&&!o.done&&(e=a.return)&&e.call(a)}finally{if(t)throw t.error}}i=this.loss.map((function(t){return S(t)}))}else{var s=Object.keys(this.loss);i={};var u=this.loss;try{for(var c=l(s),h=c.next();!h.done;h=c.next()){var p=h.value;if("string"!=typeof u[p])throw new Error("Serialization of non-string loss is not supported.");i[p]=S(u[p])}}catch(t){n={error:t}}finally{try{h&&!h.done&&(r=c.return)&&r.call(c)}finally{if(n)throw n.error}}}return i},n.prototype.getMetricIdentifiers=function(){if("string"==typeof this.metrics||"function"==typeof this.metrics)return[S(Ju(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map((function(t){return S(Ju(t))}));var t={};for(var e in this.metrics)t[e]=S(Ju(this.metrics[e]));return t},n.prototype.getTrainingConfig=function(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}},n.prototype.loadTrainingConfig=function(t){if(null!=t.weighted_metrics)throw new Error("Loading weight_metrics is not supported yet.");if(null!=t.loss_weights)throw new Error("Loading loss_weights is not supported yet.");if(null!=t.sample_weight_mode)throw new Error("Loading sample_weight_mode is not supported yet.");var e,n,r=zu(rl(t.optimizer_config));if("string"==typeof t.loss)e=N(t.loss);else if(Array.isArray(t.loss))e=t.loss.map((function(t){return N(t)}));else if(null!=t.loss)for(var i in e={},t.loss)e[i]=N(t.loss[i]);if(Array.isArray(t.metrics))n=t.metrics.map((function(t){return N(t)}));else if(null!=t.metrics)for(var i in n={},t.metrics)n[i]=N(t.metrics[i]);this.compile({loss:e,metrics:n,optimizer:r})},n.prototype.save=function(t,n){return s(this,void 0,void 0,(function(){var r,i,a,o,s,l,p,f,g,v,y;return u(this,(function(u){switch(u.label){case 0:if("string"==typeof t){if(0===(r=e.io.getSaveHandlers(t)).length)throw new d("Cannot find any save handlers for URL '".concat(t,"'"));if(r.length>1)throw new d("Found more than one (".concat(r.length,") save handlers for ")+"URL '".concat(t,"'"));t=r[0]}if(null==t.save)throw new d("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return[4,e.io.encodeWeights(this.getNamedWeights(n))];case 1:return i=u.sent(),!1,null,a=this.toJSON(null,false),o={modelTopology:a,format:"layers-model",generatedBy:"TensorFlow.js tfjs-layers v".concat(al),convertedBy:null},null!=n&&n.includeOptimizer&&null!=this.optimizer?(o.trainingConfig=this.getTrainingConfig(),s="optimizer",v=(g=e.io).encodeWeights,[4,this.optimizer.getWeights()]):[3,4];case 2:return[4,v.apply(g,[u.sent(),s])];case 3:l=u.sent(),p=l.data,f=l.specs,(y=i.specs).push.apply(y,h([],c(f),!1)),i.data=e.io.concatenateArrayBuffers([i.data,p]),u.label=4;case 4:return null!=this.userDefinedMetadata&&(!0,Yu(this.userDefinedMetadata,this.name,true),o.userDefinedMetadata=this.userDefinedMetadata),o.weightData=i.data,o.weightSpecs=i.specs,[2,t.save(o)]}}))}))},n.prototype.setUserDefinedMetadata=function(t){Yu(t,this.name),this.userDefinedMetadata=t},n.prototype.getUserDefinedMetadata=function(){return this.userDefinedMetadata},n}(ol);Il.className="Model",e.serialization.registerClass(Il);var Al=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return o(e,t),e}(Il);function zl(t,n,r){return s(this,void 0,void 0,(function(){var i,a,o,s,l,c,h,p,f;return u(this,(function(u){switch(u.label){case 0:if(null==r&&(r={}),null==t.load)throw new d("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");return[4,t.load()];case 1:if(i=u.sent(),null!=(a=i.modelTopology).model_config&&(a=a.model_config),o=null==r.strict||r.strict,s=null!=i.weightData&&null!=i.weightSpecs&&o,l=zu(rl(a),n,s),null!=(c=i.trainingConfig)&&l.loadTrainingConfig(c),null!=i.userDefinedMetadata&&l.setUserDefinedMetadata(i.userDefinedMetadata),null==i.weightData)return[3,4];if(null==i.weightSpecs)throw new d("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");return h=function(t,n){var r=e.io.decodeWeights(t,n),i={},a=[];return n.forEach((function(t){"optimizer"===t.group?a.push({name:t.name,tensor:r[t.name]}):i[t.name]=r[t.name]})),{modelWeights:i,optimizerWeights:a}}(i.weightData,i.weightSpecs),p=h.modelWeights,f=h.optimizerWeights,l.loadWeights(p,o),null!=l.optimizer&&f.length>0?[4,l.optimizer.setWeights(f)]:[3,3];case 2:u.sent(),u.label=3;case 3:e.dispose(p),e.dispose(f.map((function(t){return t.tensor}))),u.label=4;case 4:return[2,l]}}))}))}Al.className="Functional",e.serialization.registerClass(Al);var El=function(t){function n(e){var n,r,i=t.call(this,{inputs:[],outputs:[]})||this;if(e=e||{},i.trainable=!0,i.built=!1,i.name=null!=e.name?e.name:j("sequential_"),null!=e.layers)try{for(var a=l(e.layers),o=a.next();!o.done;o=a.next()){var s=o.value;i.add(s)}}catch(t){n={error:t}}finally{try{o&&!o.done&&(r=a.return)&&r.call(a)}finally{if(n)throw n.error}}return i}return o(n,t),n.prototype.checkShape=function(t){if(t.inboundNodes[0].outputTensors[0].shape.some((function(t){return t<0})))throw new d("Negative dimension size caused by adding layer "+"".concat(t.name," with input shape [")+"".concat(t.inboundNodes[0].inputTensors[0].shape,"]"))},n.prototype.add=function(t){var e,r=t instanceof n||t instanceof Il;if(r){if(1!==(e=t).outputs.length)throw new d("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(1!==e.inputs.length)throw new d("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(0===this.outputs.length){if(0===t.inboundNodes.length){if(null==t.batchInputShape)throw new d("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");var i=le({batchShape:t.batchInputShape,dtype:t.dtype,name:t.name+"_input"});t.apply(i)}if(r)this.outputs=e.outputs,this.inputs=e.inputs;else{if(1!==t.inboundNodes.length)throw new d("A layer added to a Sequential model must not already be "+"connected somewhere else. LayersModel received layer ".concat(t.name," ")+"which has ".concat(t.inboundNodes.length," pre-existing inbound ")+"connections.");if(1!==t.inboundNodes[0].outputTensors.length)throw new d("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(t),this.outputs=[t.inboundNodes[0].outputTensors[0]],this.inputs=se(this.outputs[0])}this.inboundNodes=[],new ie({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:m(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map((function(t){return t.shape})),outputShapes:this.outputs[0].shape})}else{var a=t.apply(this.outputs[0]);if(Array.isArray(a))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(t),this.outputs=[a],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(t),this.built=!1},n.prototype.pop=function(){if(0===this.layers.length)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),0===this.layers.length)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{var t=this.layers.length-1;this.layers[t].outboundNodes=[],this.outputs=[this.layers[t].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}},n.prototype.call=function(t,e){return null==this.model&&this.build(),this.model.call(t,e)},n.prototype.build=function(t){if(Zt(t),0===this.inputs.length||0===this.outputs.length)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new Il({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0},n.prototype.countParams=function(){return this.built||this.build(),t.prototype.countParams.call(this)},n.prototype.summary=function(e,n,r){void 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null==this.model&&this.build(),this.model.predictOnBatch(t)},n.prototype.compile=function(t){this.build(),this.model.compile(t),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames},Object.defineProperty(n.prototype,"optimizer",{get:function(){return null==this.model?void 0:this.model.optimizer},set:function(t){this.model.optimizer=t},enumerable:!1,configurable:!0}),n.prototype.fit=function(t,e,n){return void 0===n&&(n={}),s(this,void 0,void 0,(function(){return u(this,(function(r){if(!this.built)throw new f("The model needs to be compiled before being used.");return[2,this.model.fit(t,e,n)]}))}))},n.prototype.fitDataset=function(t,e){return s(this,void 0,void 0,(function(){return u(this,(function(n){if(!this.built)throw new f("The model needs to be compiled before being 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e={alpha:this.alpha},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},n}(oe);nc.className="LeakyReLU",e.serialization.registerClass(nc);var rc=function(t){function n(e){var n=t.call(this,null==e?{}:e)||this;if(n.DEFAULT_ALPHA_INITIALIZER="zeros",null==e&&(e={}),n.supportsMasking=!0,n.alphaInitializer=Gt(e.alphaInitializer||n.DEFAULT_ALPHA_INITIALIZER),n.alphaRegularizer=tc(e.alphaRegularizer),n.alphaConstraint=fu(e.alphaConstraint),null==e.sharedAxes)n.sharedAxes=null;else if(Array.isArray(e.sharedAxes))n.sharedAxes=e.sharedAxes;else{if("number"!=typeof e.sharedAxes)throw new d("Expected sharedAxes to be a number or an array of numbers, "+"but got ".concat(e.sharedAxes));n.sharedAxes=[e.sharedAxes]}return n}return o(n,t),n.prototype.build=function(t){var e,n,r=(t=Zt(t)).slice(1);if(null!=this.sharedAxes)try{for(var i=l(this.sharedAxes),a=i.next();!a.done;a=i.next()){r[(s=a.value)-1]=1}}catch(t){e={error:t}}finally{try{a&&!a.done&&(n=i.return)&&n.call(i)}finally{if(e)throw e.error}}this.alpha=this.addWeight("alpha",r,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);var o={};if(null!=this.sharedAxes)for(var s=1;s<t.length;++s)o[s]=t[s];this.inputSpec=[new ee({ndim:t.length,axes:o})],this.built=!0},n.prototype.call=function(t,n){return t=Jt(t),e.prelu(t,this.alpha.read())},n.prototype.getConfig=function(){var e={alphaInitializer:qt(this.alphaInitializer),alphaRegularizer:$l(this.alphaRegularizer),alphaConstraint:hu(this.alphaConstraint),sharedAxes:this.sharedAxes},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},n}(oe);rc.className="PReLU",e.serialization.registerClass(rc);var ic=function(t){function n(e){var n=t.call(this,null==e?{}:e)||this;if(n.DEFAULT_ALPHA=1,null==e&&(e={}),null!=e.alpha&&e.alpha!==n.DEFAULT_ALPHA)throw new g("Non-default alpha value (".concat(e.alpha,") is not supported by the ")+"ELU layer yet.");return n.alpha=null==e.alpha?n.DEFAULT_ALPHA:e.alpha,n}return o(n,t),n.prototype.call=function(t,n){var r=Jt(t);return e.elu(r)},n.prototype.computeOutputShape=function(t){return t},n.prototype.getConfig=function(){var e={alpha:this.alpha},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},n}(oe);ic.className="ELU",e.serialization.registerClass(ic);var ac=function(t){function n(e){var n=t.call(this,null==e?{}:e)||this;return n.DEFAULT_THETA=1,null==e&&(e={}),n.theta=null==e.theta?n.DEFAULT_THETA:e.theta,n}return o(n,t),n.prototype.call=function(t,n){var r=Jt(t);return e.mul(r,e.cast(e.greater(r,this.theta),"float32"))},n.prototype.computeOutputShape=function(t){return t},n.prototype.getConfig=function(){var e={theta:this.theta},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},n}(oe);ac.className="ThresholdedReLU",e.serialization.registerClass(ac);var oc=function(t){function n(e){var n=t.call(this,null==e?{}:e)||this;return n.DEFAULT_AXIS=1,null==e&&(e={}),n.softmax=(new Ul).apply,n.axis=null==e.axis?n.DEFAULT_AXIS:e.axis,n}return o(n,t),n.prototype.call=function(t,n){var r=this;return e.tidy((function(){var i=Jt(t),a=n.mask;if(null!=a){var o=e.mul(e.sub(e.ones(i.shape),e.cast(a,i.dtype)),e.scalar(-1e9));i=e.add(i,o)}return r.axis instanceof Array?r.axis.length>1?e.exp(e.sub(i,e.logSumExp(i,r.axis,!0))):r.softmax(i,r.axis[0]):r.softmax(i,r.axis)}))},n.prototype.computeOutputShape=function(t){return t},n.prototype.getConfig=function(){var e={axis:this.axis},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},n}(oe);function sc(t,e,n){if("number"==typeof t)return m(t,e);if(t.length!==e)throw new d("The ".concat(n," argument must be an integer or tuple of ").concat(e," integers.")+" Received: ".concat(t.length," elements."));for(var r=0;r<e;++r){var i=t[r];if((a=i)!==parseInt(a.toString(),10))throw new d("The ".concat(n," argument must be an integer or tuple of ").concat(e)+" integers. Received: ".concat(JSON.stringify(t)," including a")+" non-integer number ".concat(i))}return t;var a}function uc(t,e,n,r,i){return void 0===i&&(i=1),null==t?t:(a="same"===n?t:t-(e+(e-1)*(i-1))+1,Math.floor((a+r-1)/r));var a}function lc(t,e,n,r){if(null==t)return null;if("valid"===r)t=t*e+at([n-e,0]);else{if("same"!==r)throw new d("Unsupport padding mode: ".concat(r,"."));t*=e}return t}function cc(t,n){return e.tidy((function(){return H(n),"channelsFirst"===n?i.transpose(t,[0,2,3,1]):t}))}function hc(t,n){return e.tidy((function(){return H(n),"channelsFirst"===n?i.transpose(t,[0,2,3,4,1]):t}))}function pc(t,n,r,a,o,s,u,l){return void 0===a&&(a=[1,1]),void 0===o&&(o="valid"),void 0===l&&(l=null),e.tidy((function(){if(null==s&&(s="channelsLast"),H(s),3!==t.rank&&4!==t.rank)throw new d("conv2dWithBiasActivation expects input to be of rank 3 or 4, "+"but received ".concat(t.rank,"."));if(3!==n.rank&&4!==n.rank)throw new d("conv2dWithBiasActivation expects kernel to be of rank 3 or 4, "+"but received ".concat(t.rank,"."));var e=cc(t,s);if("causal"===o)throw new g("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return e=i.fused.conv2d({x:e,filter:n,strides:a,pad:"same"===o?"same":"valid",dilations:u,dataFormat:"NHWC",bias:r,activation:l}),"channelsFirst"===s&&(e=i.transpose(e,[0,3,1,2])),e}))}oc.className="Softmax",e.serialization.registerClass(oc);var fc=function(t){function e(n,r){var i=t.call(this,r)||this;if(i.bias=null,i.DEFAULT_KERNEL_INITIALIZER="glorotNormal",i.DEFAULT_BIAS_INITIALIZER="zeros",e.verifyArgs(r),i.rank=n,L(i.rank,"rank"),1!==i.rank&&2!==i.rank&&3!==i.rank)throw new g("Convolution layer for rank other than 1, 2, or 3 (".concat(i.rank,") is ")+"not implemented yet.");if(i.kernelSize=sc(r.kernelSize,n,"kernelSize"),i.strides=sc(null==r.strides?1:r.strides,n,"strides"),i.padding=null==r.padding?"valid":r.padding,J(i.padding),i.dataFormat=null==r.dataFormat?"channelsLast":r.dataFormat,H(i.dataFormat),i.activation=Hl(r.activation),i.useBias=null==r.useBias||r.useBias,i.biasInitializer=Gt(r.biasInitializer||i.DEFAULT_BIAS_INITIALIZER),i.biasConstraint=fu(r.biasConstraint),i.biasRegularizer=tc(r.biasRegularizer),i.activityRegularizer=tc(r.activityRegularizer),i.dilationRate=sc(null==r.dilationRate?1:r.dilationRate,n,"dilationRate"),1===i.rank&&Array.isArray(i.dilationRate)&&1!==i.dilationRate.length)throw new d("dilationRate must be a number or an array of a single number for 1D convolution, but received "+"".concat(JSON.stringify(i.dilationRate)));if(2===i.rank){if("number"==typeof i.dilationRate)i.dilationRate=[i.dilationRate,i.dilationRate];else if(2!==i.dilationRate.length)throw new d("dilationRate must be a number or array of two numbers for 2D "+"convolution, but received ".concat(JSON.stringify(i.dilationRate)))}else if(3===i.rank)if("number"==typeof i.dilationRate)i.dilationRate=[i.dilationRate,i.dilationRate,i.dilationRate];else if(3!==i.dilationRate.length)throw new d("dilationRate must be a number or array of three numbers for 3D "+"convolution, but received ".concat(JSON.stringify(i.dilationRate)));return i}return o(e,t),e.verifyArgs=function(t){if(b("kernelSize"in t,"required key 'kernelSize' not in config"),"number"!=typeof t.kernelSize&&!_(t.kernelSize,"number",1,3))throw new d("BaseConv expects config.kernelSize to be number or number[] with "+"length 1, 2, or 3, but received ".concat(JSON.stringify(t.kernelSize),"."))},e.prototype.getConfig=function(){var e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Gl(this.activation),useBias:this.useBias,biasInitializer:qt(this.biasInitializer),biasRegularizer:$l(this.biasRegularizer),activityRegularizer:$l(this.activityRegularizer),biasConstraint:hu(this.biasConstraint)},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e}(oe),dc=function(t){function n(e,r){var i=t.call(this,e,r)||this;return i.kernel=null,n.verifyArgs(r),i.filters=r.filters,L(i.filters,"filters"),i.kernelInitializer=Gt(r.kernelInitializer||i.DEFAULT_KERNEL_INITIALIZER),i.kernelConstraint=fu(r.kernelConstraint),i.kernelRegularizer=tc(r.kernelRegularizer),i}return o(n,t),n.prototype.build=function(t){var e;t=Zt(t);var n="channelsFirst"===this.dataFormat?1:t.length-1;if(null==t[n])throw new d("The channel dimension of the input should be defined. "+"Found ".concat(t[n]));var r=t[n],i=this.kernelSize.concat([r,this.filters]);this.kernel=this.addWeight("kernel",i,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:(e={},e[n]=r,e)}],this.built=!0},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){var n;t=Jt(t);var a,o,s,u,l,c,h,p=null==r.bias?null:r.bias.read(),f=O(r.activation.getClassName());if(null!=f&&2===r.rank)n=pc(t,r.kernel.read(),p,r.strides,r.padding,r.dataFormat,r.dilationRate,f);else{if(1===r.rank)a=t,o=r.kernel.read(),s=p,u=r.strides[0],l=r.padding,c=r.dataFormat,h=r.dilationRate[0],void 0===u&&(u=1),void 0===l&&(l="valid"),void 0===h&&(h=1),n=e.tidy((function(){if(null==c&&(c="channelsLast"),H(c),3!==a.shape.length)throw new d("The input of a conv1dWithBias operation should be 3, but is "+"".concat(a.shape.length," instead."));if(3!==o.shape.length)throw new d("The kernel for a conv1dWithBias operation should be 3, but is "+"".concat(o.shape.length," instead"));if(null!=s&&1!==s.shape.length)throw new d("The bias for a conv1dWithBias operation should be 1, but is "+"".concat(o.shape.length," instead"));if("channelsFirst"===c&&(a=i.transpose(a,[0,2,1])),"causal"===l)throw new g("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");var t=i.conv1d(a,o,u,"same"===l?"same":"valid","NWC",h);return null!=s&&(t=kt(t,s)),t}));else if(2===r.rank)n=pc(t,r.kernel.read(),p,r.strides,r.padding,r.dataFormat,r.dilationRate);else{if(3!==r.rank)throw new g("convolutions greater than 3D are not implemented yet.");n=function(t,n,r,a,o,s,u){return void 0===a&&(a=[1,1,1]),void 0===o&&(o="valid"),e.tidy((function(){if(null==s&&(s="channelsLast"),H(s),4!==t.rank&&5!==t.rank)throw new d("conv3dWithBias expects input to be of rank 4 or 5, but received "+"".concat(t.rank,"."));if(4!==n.rank&&5!==n.rank)throw new d("conv3dWithBias expects kernel to be of rank 4 or 5, but received "+"".concat(t.rank,"."));var e=hc(t,s);if("causal"===o)throw new g("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return e=i.conv3d(e,n,a,"same"===o?"same":"valid","NDHWC",u),null!=r&&(e=kt(e,r)),"channelsFirst"===s&&(e=i.transpose(e,[0,4,1,2,3])),e}))}(t,r.kernel.read(),p,r.strides,r.padding,r.dataFormat,r.dilationRate)}null!=r.activation&&(n=r.activation.apply(n))}return n}))},n.prototype.computeOutputShape=function(t){t=Zt(t);for(var e=[],n="channelsLast"===this.dataFormat?t.slice(1,t.length-1):t.slice(2),r=0;r<n.length;++r){var i=uc(n[r],this.kernelSize[r],this.padding,this.strides[r],"number"==typeof this.dilationRate?this.dilationRate:this.dilationRate[r]);e.push(i)}var a=[t[0]];return"channelsLast"===this.dataFormat?(a=a.concat(e)).push(this.filters):(a.push(this.filters),a=a.concat(e)),a},n.prototype.getConfig=function(){var e={filters:this.filters,kernelInitializer:qt(this.kernelInitializer),kernelRegularizer:$l(this.kernelRegularizer),kernelConstraint:hu(this.kernelConstraint)},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},n.verifyArgs=function(t){if(!("filters"in t)||"number"!=typeof t.filters||t.filters<1)throw new d("Convolution layer expected config.filters to be a 'number' > 0 "+"but got ".concat(JSON.stringify(t.filters)))},n}(fc),gc=function(t){function e(n){var r=t.call(this,2,n)||this;return e.verifyArgs(n),r}return o(e,t),e.prototype.getConfig=function(){var e=t.prototype.getConfig.call(this);return delete e.rank,e},e.verifyArgs=function(t){if("number"!=typeof t.kernelSize&&!_(t.kernelSize,"number",1,2))throw new d("Conv2D expects config.kernelSize to be number or number[] with "+"length 1 or 2, but received ".concat(JSON.stringify(t.kernelSize),"."))},e}(dc);gc.className="Conv2D",e.serialization.registerClass(gc);var vc=function(t){function e(n){var r=t.call(this,3,n)||this;return e.verifyArgs(n),r}return o(e,t),e.prototype.getConfig=function(){var e=t.prototype.getConfig.call(this);return delete e.rank,e},e.verifyArgs=function(t){if("number"!=typeof t.kernelSize&&(!Array.isArray(t.kernelSize)||1!==t.kernelSize.length&&3!==t.kernelSize.length))throw new d("Conv3D expects config.kernelSize to be number or"+" [number, number, number], but received ".concat(JSON.stringify(t.kernelSize),"."))},e}(dc);vc.className="Conv3D",e.serialization.registerClass(vc);var yc=function(t){function e(e){var n=t.call(this,e)||this;if(n.inputSpec=[new ee({ndim:4})],"same"!==n.padding&&"valid"!==n.padding)throw new d("Conv2DTranspose currently supports only padding modes 'same' "+"and 'valid', but received padding mode ".concat(n.padding));return n}return o(e,t),e.prototype.build=function(t){var e;if(4!==(t=Zt(t)).length)throw new d("Input should have rank 4; Received input shape: "+JSON.stringify(t));var n="channelsFirst"===this.dataFormat?1:t.length-1;if(null==t[n])throw new d("The channel dimension of the inputs should be defined. Found `None`.");var r=t[n],i=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",i,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new ee({ndim:4,axes:(e={},e[n]=r,e)})],this.built=!0},e.prototype.call=function(t,e){var n=this;return i.tidy((function(){var e=Jt(t);if(4!==e.shape.length)throw new d("Conv2DTranspose.call() expects input tensor to be rank-4, but "+"received a tensor of rank-".concat(e.shape.length));var r,a,o=e.shape,s=o[0];"channelsFirst"===n.dataFormat?(r=2,a=3):(r=1,a=2);var u=o[r],l=o[a],c=n.kernelSize[0],h=n.kernelSize[1],p=n.strides[0],f=n.strides[1],g=[s,lc(u,p,c,n.padding),lc(l,f,h,n.padding),n.filters];"channelsLast"!==n.dataFormat&&(e=i.transpose(e,[0,2,3,1]));var v=i.conv2dTranspose(e,n.kernel.read(),g,n.strides,n.padding);return"channelsLast"!==n.dataFormat&&(v=i.transpose(v,[0,3,1,2])),null!=n.bias&&(v=kt(v,n.bias.read(),n.dataFormat)),null!=n.activation&&(v=n.activation.apply(v)),v}))},e.prototype.computeOutputShape=function(t){var e,n,r,i=(t=Zt(t)).slice();"channelsFirst"===this.dataFormat?(e=1,n=2,r=3):(e=3,n=1,r=2);var a=this.kernelSize[0],o=this.kernelSize[1],s=this.strides[0],u=this.strides[1];return i[e]=this.filters,i[n]=lc(i[n],s,a,this.padding),i[r]=lc(i[r],u,o,this.padding),i},e.prototype.getConfig=function(){var e=t.prototype.getConfig.call(this);return delete e.dilationRate,e},e}(gc);yc.className="Conv2DTranspose",e.serialization.registerClass(yc);var mc=function(t){function e(e){var n=t.call(this,e)||this;if(n.inputSpec=[new ee({ndim:5})],"same"!==n.padding&&"valid"!==n.padding)throw new d("Conv3DTranspose currently supports only padding modes 'same' "+"and 'valid', but received padding mode ".concat(n.padding));return n}return o(e,t),e.prototype.build=function(t){var e;if(5!==(t=Zt(t)).length)throw new d("Input should have rank 5; Received input shape: "+JSON.stringify(t));var n="channelsFirst"===this.dataFormat?1:t.length-1;if(null==t[n])throw new d("The channel dimension of the inputs should be defined. Found `None`.");var r=t[n],i=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",i,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new ee({ndim:5,axes:(e={},e[n]=r,e)})],this.built=!0},e.prototype.call=function(t,e){var n=this;return i.tidy((function(){var e=Jt(t);if(5!==e.shape.length)throw new d("Conv3DTranspose.call() expects input tensor to be rank-4, but "+"received a tensor of rank-".concat(e.shape.length));var r,a,o,s=e.shape,u=s[0];"channelsFirst"===n.dataFormat?(o=2,r=3,a=4):(o=1,r=2,a=3);var l=s[o],c=s[r],h=s[a],p=n.kernelSize[0],f=n.kernelSize[1],g=n.kernelSize[2],v=n.strides[0],y=n.strides[1],m=n.strides[2],b=[u,lc(l,v,p,n.padding),lc(c,y,f,n.padding),lc(h,m,g,n.padding),n.filters];"channelsLast"!==n.dataFormat&&(e=i.transpose(e,[0,2,3,4,1]));var w=i.conv3dTranspose(e,n.kernel.read(),b,n.strides,n.padding);return"channelsLast"!==n.dataFormat&&(w=i.transpose(w,[0,4,1,2,3])),null!==n.bias&&(w=kt(w,n.bias.read(),n.dataFormat)),null!==n.activation&&(w=n.activation.apply(w)),w}))},e.prototype.computeOutputShape=function(t){var e,n,r,i,a=(t=Zt(t)).slice();"channelsFirst"===this.dataFormat?(e=1,n=2,r=3,i=4):(e=4,n=1,r=2,i=3);var o=this.kernelSize[0],s=this.kernelSize[1],u=this.kernelSize[2],l=this.strides[0],c=this.strides[1],h=this.strides[2];return a[e]=this.filters,a[n]=lc(a[n],l,o,this.padding),a[r]=lc(a[r],c,s,this.padding),a[i]=lc(a[i],h,u,this.padding),a},e.prototype.getConfig=function(){var e=t.prototype.getConfig.call(this);return delete e.dilationRate,e},e}(vc);mc.className="Conv3DTranspose",e.serialization.registerClass(mc);var bc=function(t){function n(e,n){var r=t.call(this,e,n)||this;if(r.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",r.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",r.depthwiseKernel=null,r.pointwiseKernel=null,null==n.filters)throw new d("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(null!=n.kernelInitializer||null!=n.kernelRegularizer||null!=n.kernelConstraint)throw new d("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(null!=n.padding&&"same"!==n.padding&&"valid"!==n.padding)throw new d("SeparableConv".concat(r.rank,"D supports only padding modes: ")+"'same' and 'valid', but received ".concat(JSON.stringify(n.padding)));return r.depthMultiplier=null==n.depthMultiplier?1:n.depthMultiplier,r.depthwiseInitializer=Gt(n.depthwiseInitializer||r.DEFAULT_DEPTHWISE_INITIALIZER),r.depthwiseRegularizer=tc(n.depthwiseRegularizer),r.depthwiseConstraint=fu(n.depthwiseConstraint),r.pointwiseInitializer=Gt(n.depthwiseInitializer||r.DEFAULT_POINTWISE_INITIALIZER),r.pointwiseRegularizer=tc(n.pointwiseRegularizer),r.pointwiseConstraint=fu(n.pointwiseConstraint),r}return o(n,t),n.prototype.build=function(t){var e;if((t=Zt(t)).length<this.rank+2)throw new d("Inputs to SeparableConv".concat(this.rank,"D should have rank ")+"".concat(this.rank+2,", but received input shape: ")+"".concat(JSON.stringify(t)));var n="channelsFirst"===this.dataFormat?1:t.length-1;if(null==t[n]||t[n]<0)throw new d("The channel dimension of the inputs should be defined, "+"but found ".concat(JSON.stringify(t[n])));for(var r=t[n],i=this.kernelSize.concat([r,this.depthMultiplier]),a=[],o=0;o<this.rank;++o)a.push(1);a.push(r*this.depthMultiplier,this.filters);var s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",i,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new ee({ndim:this.rank+2,axes:(e={},e[n]=r,e)})],this.built=!0},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){var e;if(t=Jt(t),1===r.rank)throw new g("1D separable convolution is not implemented yet.");return 2===r.rank&&("channelsFirst"===r.dataFormat&&(t=i.transpose(t,[0,2,3,1])),e=i.separableConv2d(t,r.depthwiseKernel.read(),r.pointwiseKernel.read(),r.strides,r.padding,r.dilationRate,"NHWC")),r.useBias&&(e=kt(e,r.bias.read(),r.dataFormat)),null!=r.activation&&(e=r.activation.apply(e)),"channelsFirst"===r.dataFormat&&(e=i.transpose(e,[0,3,1,2])),e}))},n.prototype.getConfig=function(){var e=t.prototype.getConfig.call(this);return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=qt(this.depthwiseInitializer),e.pointwiseInitializer=qt(this.pointwiseInitializer),e.depthwiseRegularizer=$l(this.depthwiseRegularizer),e.pointwiseRegularizer=$l(this.pointwiseRegularizer),e.depthwiseConstraint=hu(this.depthwiseConstraint),e.pointwiseConstraint=hu(this.pointwiseConstraint),e},n}(dc);bc.className="SeparableConv";var wc=function(t){function e(e){return t.call(this,2,e)||this}return o(e,t),e}(bc);wc.className="SeparableConv2D",e.serialization.registerClass(wc);var kc=function(t){function e(n){var r=t.call(this,1,n)||this;return e.verifyArgs(n),r.inputSpec=[{ndim:3}],r}return o(e,t),e.prototype.getConfig=function(){var e=t.prototype.getConfig.call(this);return delete e.rank,delete e.dataFormat,e},e.verifyArgs=function(t){if("number"!=typeof t.kernelSize&&!_(t.kernelSize,"number",1,1))throw new d("Conv1D expects config.kernelSize to be number or number[] with "+"length 1, but received ".concat(JSON.stringify(t.kernelSize),"."))},e}(dc);kc.className="Conv1D",e.serialization.registerClass(kc);var xc=function(t){function n(e){var n=t.call(this,e)||this;return"number"==typeof e.cropping?n.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:"number"==typeof e.cropping[0]?n.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:n.cropping=e.cropping,n.dataFormat=void 0===e.dataFormat?"channelsLast":e.dataFormat,n.inputSpec=[{ndim:4}],n}return o(n,t),n.prototype.computeOutputShape=function(t){return"channelsFirst"===this.dataFormat?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){if(t=Jt(t),"channelsLast"===r.dataFormat){var e=pt(t,r.cropping[0][0],t.shape[1]-r.cropping[0][0]-r.cropping[0][1],2);return pt(e,r.cropping[1][0],t.shape[2]-r.cropping[1][1]-r.cropping[1][0],3)}e=pt(t,r.cropping[0][0],t.shape[2]-r.cropping[0][0]-r.cropping[0][1],3);return pt(e,r.cropping[1][0],t.shape[3]-r.cropping[1][1]-r.cropping[1][0],4)}))},n.prototype.getConfig=function(){var e={cropping:this.cropping,dataFormat:this.dataFormat},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},n}(oe);xc.className="Cropping2D",e.serialization.registerClass(xc);var Sc=function(t){function e(e){var n,r=t.call(this,e)||this;return r.DEFAULT_SIZE=[2,2],r.inputSpec=[{ndim:4}],r.size=null==e.size?r.DEFAULT_SIZE:e.size,r.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,H(r.dataFormat),r.interpolation=null==e.interpolation?"nearest":e.interpolation,n=r.interpolation,D(W,"InterpolationFormat",n),r}return o(e,t),e.prototype.computeOutputShape=function(t){if("channelsFirst"===this.dataFormat){var e=null==t[2]?null:this.size[0]*t[2],n=null==t[3]?null:this.size[1]*t[3];return[t[0],t[1],e,n]}e=null==t[1]?null:this.size[0]*t[1],n=null==t[2]?null:this.size[1]*t[2];return[t[0],e,n,t[3]]},e.prototype.call=function(t,e){var n=this;return i.tidy((function(){var e=Jt(t),r=e.shape;if("channelsFirst"===n.dataFormat){e=i.transpose(e,[0,2,3,1]);var a=n.size[0]*r[2],o=n.size[1]*r[3],s="nearest"===n.interpolation?i.image.resizeNearestNeighbor(e,[a,o]):i.image.resizeBilinear(e,[a,o]);return i.transpose(s,[0,3,1,2])}a=n.size[0]*r[1],o=n.size[1]*r[2];return"nearest"===n.interpolation?i.image.resizeNearestNeighbor(e,[a,o]):i.image.resizeBilinear(e,[a,o])}))},e.prototype.getConfig=function(){var e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e}(oe);Sc.className="UpSampling2D",e.serialization.registerClass(Sc);var Nc=function(t){function n(e){var n=t.call(this,2,e)||this;return n.depthwiseKernel=null,n.depthMultiplier=null==e.depthMultiplier?1:e.depthMultiplier,n.depthwiseInitializer=Gt(e.depthwiseInitializer||n.DEFAULT_KERNEL_INITIALIZER),n.depthwiseConstraint=fu(e.depthwiseConstraint),n.depthwiseRegularizer=tc(e.depthwiseRegularizer),n}return o(n,t),n.prototype.build=function(t){if((t=Zt(t)).length<4)throw new d("Inputs to DepthwiseConv2D should have rank 4. "+"Received input shape: ".concat(JSON.stringify(t),"."));var e="channelsFirst"===this.dataFormat?1:3;if(null==t[e]||t[e]<0)throw new d("The channel dimension of the inputs to DepthwiseConv2D should "+"be defined, but is not (".concat(t[e],")."));var n=t[e],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){t=Jt(t);var n,a,o,s,u,l,c=(n=t,a=r.depthwiseKernel.read(),o=r.strides,s=r.padding,u=r.dataFormat,l=null,void 0===o&&(o=[1,1]),void 0===s&&(s="valid"),e.tidy((function(){null==u&&(u="channelsLast"),H(u);var t=cc(n,u);if(4!==n.rank)throw new d("Input for depthwiseConv2d is required to be 4-D, but is instead "+"".concat(n.rank,"-D"));if(4!==a.rank)throw new d("depthwiseKernel is required to be 4-D, but is instead "+"".concat(a.rank,"-D"));return t=i.depthwiseConv2d(t,a,o,"same"===s?"same":"valid","NHWC",l),"channelsFirst"===u&&(t=i.transpose(t,[0,3,1,2])),t})));return r.useBias&&(c=kt(c,r.bias.read(),r.dataFormat)),null!=r.activation&&(c=r.activation.apply(c)),c}))},n.prototype.computeOutputShape=function(t){t=Zt(t);var e="channelsFirst"===this.dataFormat?t[2]:t[1],n="channelsFirst"===this.dataFormat?t[3]:t[2],r="channelsFirst"===this.dataFormat?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,i=uc(e,this.kernelSize[0],this.padding,this.strides[0]),a=uc(n,this.kernelSize[1],this.padding,this.strides[1]);return"channelsFirst"===this.dataFormat?[t[0],r,i,a]:[t[0],i,a,r]},n.prototype.getConfig=function(){var e=t.prototype.getConfig.call(this);return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=qt(this.depthwiseInitializer),e.depthwiseRegularizer=$l(this.depthwiseRegularizer),e.depthwiseConstraint=hu(this.depthwiseRegularizer),e},n}(fc);function Ic(t,e,n,r){if(Array.isArray(t)){if(null!=e||null!=n)throw new d("When inputs is an array, neither initialState or constants should be provided");null!=r&&(n=t.slice(t.length-r,t.length),t=t.slice(0,t.length-r)),t.length>1&&(e=t.slice(1,t.length)),t=t[0]}function i(t){return null==t||Array.isArray(t)?t:[t]}return{inputs:t,initialState:e=i(e),constants:n=i(n)}}function Ac(t,e,n,r,a,o,s,u){return void 0===r&&(r=!1),void 0===s&&(s=!1),void 0===u&&(u=!1),i.tidy((function(){var l=e.shape.length;if(l<3)throw new d("Input should be at least 3D, but is ".concat(l,"D."));var c=[1,0].concat(ot(2,l));if(e=i.transpose(e,c),null!=o)throw new g("The rnn() functoin of the deeplearn.js backend does not support constants yet.");s&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),null!=a&&((a=i.cast(i.cast(a,"bool"),"float32")).rank===l-1&&(a=i.expandDims(a,-1)),a=i.transpose(a,c)),r&&(e=i.reverse(e,0),null!=a&&(a=i.reverse(a,0)));var h,p,f=[],v=n,y=e.shape[0],m=i.unstack(e);null!=a&&(p=i.unstack(a));for(var b,w=function(e){var n=m[e],r=i.tidy((function(){return t(n,v)}));if(null==a)h=r[0],v=r[1];else{var o=i.tidy((function(){var t=p[e],n=i.sub(i.onesLike(t),t);return{output:i.add(i.mul(r[0],t),i.mul(v[0],n)),newStates:v.map((function(e,a){return i.add(i.mul(r[1][a],t),i.mul(e,n))}))}}));h=o.output,v=o.newStates}u&&f.push(h)},k=0;k<y;++k)w(k);if(u){b=i.stack(f,1)}return[h,b,v]}))}Nc.className="DepthwiseConv2D",e.serialization.registerClass(Nc);var zc=function(t){function n(e){var n,r=t.call(this,e)||this;if(null==e.cell)throw new d("cell property is missing for the constructor of RNN.");if(null==(n=Array.isArray(e.cell)?new Rc({cells:e.cell}):e.cell).stateSize)throw new d("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");return r.cell=n,r.returnSequences=null!=e.returnSequences&&e.returnSequences,r.returnState=null!=e.returnState&&e.returnState,r.goBackwards=null!=e.goBackwards&&e.goBackwards,r._stateful=null!=e.stateful&&e.stateful,r.unroll=null!=e.unroll&&e.unroll,r.supportsMasking=!0,r.inputSpec=[new ee({ndim:3})],r.stateSpec=null,r.states_=null,r.numConstants=null,r.keptStates=[],r}return o(n,t),n.prototype.getStates=function(){return null==this.states_?ot(0,Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1).map((function(t){return null})):this.states_},n.prototype.setStates=function(t){this.states_=t},n.prototype.computeOutputShape=function(t){var e,n;Kt(t)&&(t=t[0]);var r=this.cell.stateSize;Array.isArray(r)||(r=[r]);var i,a=r[0];if(i=this.returnSequences?[t[0],t[1],a]:[t[0],a],this.returnState){var o=[];try{for(var s=l(r),u=s.next();!u.done;u=s.next()){var c=u.value;o.push([t[0],c])}}catch(t){e={error:t}}finally{try{u&&!u.done&&(n=s.return)&&n.call(s)}finally{if(e)throw e.error}}return[i].concat(o)}return i},n.prototype.computeMask=function(t,e){var n=this;return i.tidy((function(){Array.isArray(e)&&(e=e[0]);var t=n.returnSequences?e:null;if(n.returnState){var r=n.states.map((function(t){return null}));return[t].concat(r)}return t}))},Object.defineProperty(n.prototype,"states",{get:function(){if(null==this.states_){for(var t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[],n=0;n<t;++n)e.push(null);return e}return this.states_},set:function(t){this.states_=t},enumerable:!1,configurable:!0}),n.prototype.build=function(t){if(null!=this.numConstants)throw new g("Constants support is not implemented in RNN yet.");Kt(t)&&(t=t[0]);var n=this.stateful?t[0]:null,r=t.slice(2);this.inputSpec[0]=new ee({shape:h([n,null],c(r),!1)});var i,a=[t[0]].concat(t.slice(2));if(this.cell.build(a),i=Array.isArray(this.cell.stateSize)?this.cell.stateSize:[this.cell.stateSize],null!=this.stateSpec){if(!e.util.arraysEqual(this.stateSpec.map((function(t){return t.shape[t.shape.length-1]})),i))throw new d("An initialState was passed that is not compatible with "+"cell.stateSize. Received stateSpec=".concat(this.stateSpec,"; ")+"However cell.stateSize is ".concat(this.cell.stateSize))}else this.stateSpec=i.map((function(t){return new ee({shape:[null,t]})}));this.stateful&&this.resetStates()},n.prototype.resetStates=function(t,n){var r=this;void 0===n&&(n=!1),e.tidy((function(){if(!r.stateful)throw new p("Cannot call resetStates() on an RNN Layer that is not stateful.");var a=r.inputSpec[0].shape[0];if(null==a)throw new d("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(null==r.states_)Array.isArray(r.cell.stateSize)?r.states_=r.cell.stateSize.map((function(t){return i.zeros([a,t])})):r.states_=[i.zeros([a,r.cell.stateSize])];else if(null==t)i.dispose(r.states_),null!=r.keptStates&&(i.dispose(r.keptStates),r.keptStates=[]),Array.isArray(r.cell.stateSize)?r.states_=r.cell.stateSize.map((function(t){return i.zeros([a,t])})):r.states_[0]=i.zeros([a,r.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==r.states_.length)throw new d("Layer ".concat(r.name," expects ").concat(r.states_.length," state(s), ")+"but it received ".concat(t.length," state value(s). Input ")+"received: ".concat(t));!0===n?r.keptStates.push(r.states_.slice()):i.dispose(r.states_);for(var o=0;o<r.states_.length;++o){var s=t[o],u=Array.isArray(r.cell.stateSize)?r.cell.stateSize[o]:r.cell.stateSize,l=[a,u];if(!e.util.arraysEqual(s.shape,l))throw new d("State ".concat(o," is incompatible with layer ").concat(r.name,": ")+"expected shape=".concat(l,", received shape=").concat(s.shape));r.states_[o]=s}}r.states_=r.states_.map((function(t){return i.keep(t.clone())}))}))},n.prototype.apply=function(e,n){var r,i,a=null==n?null:n.initialState,o=null==n?null:n.constants;null==n&&(n={});var s=Ic(e,a,o,this.numConstants);e=s.inputs,a=s.initialState,o=s.constants;var u=[],c=[];if(null!=a){n.initialState=a,u=u.concat(a),this.stateSpec=[];try{for(var h=l(a),p=h.next();!p.done;p=h.next()){var f=p.value;this.stateSpec.push(new ee({shape:f.shape}))}}catch(t){r={error:t}}finally{try{p&&!p.done&&(i=h.return)&&i.call(h)}finally{if(r)throw r.error}}c=c.concat(this.stateSpec)}if(null!=o&&(n.constants=o,u=u.concat(o),this.numConstants=o.length),u[0]instanceof ne){var d=[e].concat(u),g=this.inputSpec.concat(c),v=this.inputSpec;this.inputSpec=g;var y=t.prototype.apply.call(this,d,n);return this.inputSpec=v,y}return t.prototype.apply.call(this,e,n)},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){var e=null==n?null:n.mask,i=null==n?null:n.training,a=null==n?null:n.initialState;t=Jt(t),null==a&&(a=r.stateful?r.states_:r.getInitialState(t));var o=Array.isArray(r.cell.stateSize)?r.cell.stateSize.length:1;if(a.length!==o)throw new d("RNN Layer has ".concat(o," state(s) but was passed ")+"".concat(a.length," initial state(s)."));r.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");var s={training:i},u=Ac((function(t,e){var n=r.cell.call([t].concat(e),s);return[n[0],n.slice(1)]}),t,a,r.goBackwards,e,null,r.unroll,r.returnSequences),l=u[0],c=u[1],h=u[2];r.stateful&&r.resetStates(h,i);var p=r.returnSequences?c:l;return r.returnState?[p].concat(h):p}))},n.prototype.getInitialState=function(t){var n=this;return e.tidy((function(){var e=i.zeros(t.shape);return e=lt(e=i.sum(e,[1,2])),Array.isArray(n.cell.stateSize)?n.cell.stateSize.map((function(t){return t>1?gt(e,[1,t]):e})):n.cell.stateSize>1?[gt(e,[1,n.cell.stateSize])]:[e]}))},Object.defineProperty(n.prototype,"trainableWeights",{get:function(){return this.trainable?this.cell.trainableWeights:[]},enumerable:!1,configurable:!0}),Object.defineProperty(n.prototype,"nonTrainableWeights",{get:function(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights},enumerable:!1,configurable:!0}),n.prototype.setFastWeightInitDuringBuild=function(e){t.prototype.setFastWeightInitDuringBuild.call(this,e),null!=this.cell&&this.cell.setFastWeightInitDuringBuild(e)},n.prototype.getConfig=function(){var e=t.prototype.getConfig.call(this),r={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};null!=this.numConstants&&(r.numConstants=this.numConstants);var i=this.cell.getConfig();return this.getClassName()===n.className&&(r.cell={className:this.cell.getClassName(),config:i}),Object.assign(Object.assign(Object.assign({},i),e),r)},n.fromConfig=function(t,e,n){void 0===n&&(n={});var r=zu(e.cell,n);return new t(Object.assign(e,{cell:r}))},n}(oe);zc.className="RNN",e.serialization.registerClass(zc);var Ec=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return o(e,t),e}(oe),Tc=function(t){function n(e){var n=t.call(this,e)||this;return n.DEFAULT_ACTIVATION="tanh",n.DEFAULT_KERNEL_INITIALIZER="glorotNormal",n.DEFAULT_RECURRENT_INITIALIZER="orthogonal",n.DEFAULT_BIAS_INITIALIZER="zeros",n.units=e.units,L(n.units,"units"),n.activation=Hl(null==e.activation?n.DEFAULT_ACTIVATION:e.activation),n.useBias=null==e.useBias||e.useBias,n.kernelInitializer=Gt(e.kernelInitializer||n.DEFAULT_KERNEL_INITIALIZER),n.recurrentInitializer=Gt(e.recurrentInitializer||n.DEFAULT_RECURRENT_INITIALIZER),n.biasInitializer=Gt(e.biasInitializer||n.DEFAULT_BIAS_INITIALIZER),n.kernelRegularizer=tc(e.kernelRegularizer),n.recurrentRegularizer=tc(e.recurrentRegularizer),n.biasRegularizer=tc(e.biasRegularizer),n.kernelConstraint=fu(e.kernelConstraint),n.recurrentConstraint=fu(e.recurrentConstraint),n.biasConstraint=fu(e.biasConstraint),n.dropout=it([1,at([0,null==e.dropout?0:e.dropout])]),n.recurrentDropout=it([1,at([0,null==e.recurrentDropout?0:e.recurrentDropout])]),n.dropoutFunc=e.dropoutFunc,n.stateSize=n.units,n.dropoutMask=null,n.recurrentDropoutMask=null,n}return o(n,t),n.prototype.build=function(t){t=Zt(t),this.kernel=this.addWeight("kernel",[t[t.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){if(2!==t.length)throw new d("SimpleRNNCell expects 2 input Tensors, got ".concat(t.length,"."));var e=t[1];t=t[0];var a,o=null!=n.training&&n.training;0<r.dropout&&r.dropout<1&&null==r.dropoutMask&&(r.dropoutMask=Oc({ones:function(){return i.onesLike(t)},rate:r.dropout,training:o,dropoutFunc:r.dropoutFunc})),0<r.recurrentDropout&&r.recurrentDropout<1&&null==r.recurrentDropoutMask&&(r.recurrentDropoutMask=Oc({ones:function(){return i.onesLike(e)},rate:r.recurrentDropout,training:o,dropoutFunc:r.dropoutFunc}));var s=r.dropoutMask,u=r.recurrentDropoutMask;a=yt(null!=s?i.mul(t,s):t,r.kernel.read()),null!=r.bias&&(a=kt(a,r.bias.read())),null!=u&&(e=i.mul(e,u));var l=i.add(a,yt(e,r.recurrentKernel.read()));return null!=r.activation&&(l=r.activation.apply(l)),[l,l]}))},n.prototype.getConfig=function(){var e=t.prototype.getConfig.call(this),n={units:this.units,activation:Gl(this.activation),useBias:this.useBias,kernelInitializer:qt(this.kernelInitializer),recurrentInitializer:qt(this.recurrentInitializer),biasInitializer:qt(this.biasInitializer),kernelRegularizer:$l(this.kernelRegularizer),recurrentRegularizer:$l(this.recurrentRegularizer),biasRegularizer:$l(this.biasRegularizer),activityRegularizer:$l(this.activityRegularizer),kernelConstraint:hu(this.kernelConstraint),recurrentConstraint:hu(this.recurrentConstraint),biasConstraint:hu(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),n)},n}(Ec);Tc.className="SimpleRNNCell",e.serialization.registerClass(Tc);var Cc=function(t){function n(e){return e.cell=new Tc(e),t.call(this,e)||this}return o(n,t),n.prototype.call=function(n,r){var a=this;return e.tidy((function(){null!=a.cell.dropoutMask&&(i.dispose(a.cell.dropoutMask),a.cell.dropoutMask=null),null!=a.cell.recurrentDropoutMask&&(i.dispose(a.cell.recurrentDropoutMask),a.cell.recurrentDropoutMask=null);var e=null==r?null:r.mask,o=null==r?null:r.training,s=null==r?null:r.initialState;return t.prototype.call.call(a,n,{mask:e,training:o,initialState:s})}))},n.fromConfig=function(t,e){return new t(e)},n}(zc);Cc.className="SimpleRNN",e.serialization.registerClass(Cc);var Fc=function(t){function n(e){var n=t.call(this,e)||this;if(n.DEFAULT_ACTIVATION="tanh",n.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",n.DEFAULT_KERNEL_INITIALIZER="glorotNormal",n.DEFAULT_RECURRENT_INITIALIZER="orthogonal",n.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new d("GRUCell does not support reset_after parameter set to true.");return n.units=e.units,L(n.units,"units"),n.activation=Hl(void 0===e.activation?n.DEFAULT_ACTIVATION:e.activation),n.recurrentActivation=Hl(void 0===e.recurrentActivation?n.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),n.useBias=null==e.useBias||e.useBias,n.kernelInitializer=Gt(e.kernelInitializer||n.DEFAULT_KERNEL_INITIALIZER),n.recurrentInitializer=Gt(e.recurrentInitializer||n.DEFAULT_RECURRENT_INITIALIZER),n.biasInitializer=Gt(e.biasInitializer||n.DEFAULT_BIAS_INITIALIZER),n.kernelRegularizer=tc(e.kernelRegularizer),n.recurrentRegularizer=tc(e.recurrentRegularizer),n.biasRegularizer=tc(e.biasRegularizer),n.kernelConstraint=fu(e.kernelConstraint),n.recurrentConstraint=fu(e.recurrentConstraint),n.biasConstraint=fu(e.biasConstraint),n.dropout=it([1,at([0,null==e.dropout?0:e.dropout])]),n.recurrentDropout=it([1,at([0,null==e.recurrentDropout?0:e.recurrentDropout])]),n.dropoutFunc=e.dropoutFunc,n.implementation=e.implementation,n.stateSize=n.units,n.dropoutMask=null,n.recurrentDropoutMask=null,n}return o(n,t),n.prototype.build=function(t){var e=(t=Zt(t))[t.length-1];this.kernel=this.addWeight("kernel",[e,3*this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,3*this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[3*this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){if(2!==t.length)throw new d("GRUCell expects 2 input Tensors (inputs, h, c), got "+"".concat(t.length,"."));var e=null!=n.training&&n.training,a=t[1];t=t[0],0<r.dropout&&r.dropout<1&&null==r.dropoutMask&&(r.dropoutMask=Oc({ones:function(){return i.onesLike(t)},rate:r.dropout,training:e,count:3,dropoutFunc:r.dropoutFunc})),0<r.recurrentDropout&&r.recurrentDropout<1&&null==r.recurrentDropoutMask&&(r.recurrentDropoutMask=Oc({ones:function(){return i.onesLike(a)},rate:r.recurrentDropout,training:e,count:3,dropoutFunc:r.dropoutFunc}));var o,s,u,l=r.dropoutMask,h=r.recurrentDropoutMask;0<r.dropout&&r.dropout<1&&(t=i.mul(t,l[0]));var p=yt(t,r.kernel.read());r.useBias&&(p=kt(p,r.bias.read())),0<r.recurrentDropout&&r.recurrentDropout<1&&(a=i.mul(a,h[0]));var f=r.recurrentKernel.read(),g=c(i.split(f,[2*r.units,r.units],f.rank-1),2),v=g[0],y=g[1],m=yt(a,v),b=c(i.split(p,3,p.rank-1),3),w=b[0],k=b[1],x=b[2],S=c(i.split(m,2,m.rank-1),2),N=S[0],I=S[1];o=r.recurrentActivation.apply(i.add(w,N)),s=r.recurrentActivation.apply(i.add(k,I));var A=yt(i.mul(s,a),y);u=r.activation.apply(i.add(x,A));var z=i.add(i.mul(o,a),i.mul(i.add(1,i.neg(o)),u));return[z,z]}))},n.prototype.getConfig=function(){var e=t.prototype.getConfig.call(this),n={units:this.units,activation:Gl(this.activation),recurrentActivation:Gl(this.recurrentActivation),useBias:this.useBias,kernelInitializer:qt(this.kernelInitializer),recurrentInitializer:qt(this.recurrentInitializer),biasInitializer:qt(this.biasInitializer),kernelRegularizer:$l(this.kernelRegularizer),recurrentRegularizer:$l(this.recurrentRegularizer),biasRegularizer:$l(this.biasRegularizer),activityRegularizer:$l(this.activityRegularizer),kernelConstraint:hu(this.kernelConstraint),recurrentConstraint:hu(this.recurrentConstraint),biasConstraint:hu(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign(Object.assign({},e),n)},n}(Ec);Fc.className="GRUCell",e.serialization.registerClass(Fc);var Dc=function(t){function n(e){return 0===e.implementation&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Fc(e),t.call(this,e)||this}return o(n,t),n.prototype.call=function(n,r){var a=this;return e.tidy((function(){null!=a.cell.dropoutMask&&(i.dispose(a.cell.dropoutMask),a.cell.dropoutMask=null),null!=a.cell.recurrentDropoutMask&&(i.dispose(a.cell.recurrentDropoutMask),a.cell.recurrentDropoutMask=null);var e=null==r?null:r.mask,o=null==r?null:r.training,s=null==r?null:r.initialState;return t.prototype.call.call(a,n,{mask:e,training:o,initialState:s})}))},n.fromConfig=function(t,e){return 0===e.implmentation&&(e.implementation=1),new t(e)},n}(zc);Dc.className="GRU",e.serialization.registerClass(Dc);var _c=function(t){function n(e){var n=t.call(this,e)||this;return n.DEFAULT_ACTIVATION="tanh",n.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",n.DEFAULT_KERNEL_INITIALIZER="glorotNormal",n.DEFAULT_RECURRENT_INITIALIZER="orthogonal",n.DEFAULT_BIAS_INITIALIZER="zeros",n.units=e.units,L(n.units,"units"),n.activation=Hl(void 0===e.activation?n.DEFAULT_ACTIVATION:e.activation),n.recurrentActivation=Hl(void 0===e.recurrentActivation?n.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),n.useBias=null==e.useBias||e.useBias,n.kernelInitializer=Gt(e.kernelInitializer||n.DEFAULT_KERNEL_INITIALIZER),n.recurrentInitializer=Gt(e.recurrentInitializer||n.DEFAULT_RECURRENT_INITIALIZER),n.biasInitializer=Gt(e.biasInitializer||n.DEFAULT_BIAS_INITIALIZER),n.unitForgetBias=e.unitForgetBias,n.kernelRegularizer=tc(e.kernelRegularizer),n.recurrentRegularizer=tc(e.recurrentRegularizer),n.biasRegularizer=tc(e.biasRegularizer),n.kernelConstraint=fu(e.kernelConstraint),n.recurrentConstraint=fu(e.recurrentConstraint),n.biasConstraint=fu(e.biasConstraint),n.dropout=it([1,at([0,null==e.dropout?0:e.dropout])]),n.recurrentDropout=it([1,at([0,null==e.recurrentDropout?0:e.recurrentDropout])]),n.dropoutFunc=e.dropoutFunc,n.implementation=e.implementation,n.stateSize=[n.units,n.units],n.dropoutMask=null,n.recurrentDropoutMask=null,n}return o(n,t),n.prototype.build=function(t){var e,n,r=(t=Zt(t))[t.length-1];if(this.kernel=this.addWeight("kernel",[r,4*this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,4*this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){if(this.unitForgetBias){var i=this.biasInitializer,a=this.units;n=new(e=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return o(e,t),e.prototype.apply=function(t,e){var n=i.apply([a]),r=(new Et).apply([a]),o=i.apply([2*a]);return dt(dt(n,r),o)},e}(At),e.className="CustomInit",e)}else n=this.biasInitializer;this.bias=this.addWeight("bias",[4*this.units],null,n,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){var e=null!=n.training&&n.training;if(3!==t.length)throw new d("LSTMCell expects 3 input Tensors (inputs, h, c), got "+"".concat(t.length,"."));var a=t[1],o=t[2];t=t[0],0<r.dropout&&r.dropout<1&&null==r.dropoutMask&&(r.dropoutMask=Oc({ones:function(){return i.onesLike(t)},rate:r.dropout,training:e,count:4,dropoutFunc:r.dropoutFunc})),0<r.recurrentDropout&&r.recurrentDropout<1&&null==r.recurrentDropoutMask&&(r.recurrentDropoutMask=Oc({ones:function(){return i.onesLike(a)},rate:r.recurrentDropout,training:e,count:4,dropoutFunc:r.dropoutFunc}));var s,u,l,h,p=r.dropoutMask,f=r.recurrentDropoutMask;0<r.dropout&&r.dropout<1&&(t=i.mul(t,p[0]));var g=yt(t,r.kernel.read());0<r.recurrentDropout&&r.recurrentDropout<1&&(a=i.mul(a,f[0])),g=i.add(g,yt(a,r.recurrentKernel.read())),r.useBias&&(g=kt(g,r.bias.read()));var v=c(i.split(g,4,g.rank-1),4),y=v[0],m=v[1],b=v[2],w=v[3];s=r.recurrentActivation.apply(y),u=r.recurrentActivation.apply(m),l=i.add(i.mul(u,o),i.mul(s,r.activation.apply(b))),h=r.recurrentActivation.apply(w);var k=i.mul(h,r.activation.apply(l));return[k,k,l]}))},n.prototype.getConfig=function(){var e=t.prototype.getConfig.call(this),n={units:this.units,activation:Gl(this.activation),recurrentActivation:Gl(this.recurrentActivation),useBias:this.useBias,kernelInitializer:qt(this.kernelInitializer),recurrentInitializer:qt(this.recurrentInitializer),biasInitializer:qt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:$l(this.kernelRegularizer),recurrentRegularizer:$l(this.recurrentRegularizer),biasRegularizer:$l(this.biasRegularizer),activityRegularizer:$l(this.activityRegularizer),kernelConstraint:hu(this.kernelConstraint),recurrentConstraint:hu(this.recurrentConstraint),biasConstraint:hu(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign(Object.assign({},e),n)},n}(Ec);_c.className="LSTMCell",e.serialization.registerClass(_c);var Lc=function(t){function n(e){return 0===e.implementation&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new _c(e),t.call(this,e)||this}return o(n,t),n.prototype.call=function(n,r){var a=this;return e.tidy((function(){null!=a.cell.dropoutMask&&(i.dispose(a.cell.dropoutMask),a.cell.dropoutMask=null),null!=a.cell.recurrentDropoutMask&&(i.dispose(a.cell.recurrentDropoutMask),a.cell.recurrentDropoutMask=null);var e=null==r?null:r.mask,o=null==r?null:r.training,s=null==r?null:r.initialState;return t.prototype.call.call(a,n,{mask:e,training:o,initialState:s})}))},n.fromConfig=function(t,e){return 0===e.implmentation&&(e.implementation=1),new t(e)},n}(zc);Lc.className="LSTM",e.serialization.registerClass(Lc);var Rc=function(t){function n(e){var n=t.call(this,e)||this;return n.cells=e.cells,n}return o(n,t),Object.defineProperty(n.prototype,"stateSize",{get:function(){var t,e,n=[];try{for(var r=l(this.cells.slice().reverse()),i=r.next();!i.done;i=r.next()){var a=i.value;Array.isArray(a.stateSize)?n.push.apply(n,h([],c(a.stateSize),!1)):n.push(a.stateSize)}}catch(e){t={error:e}}finally{try{i&&!i.done&&(e=r.return)&&e.call(r)}finally{if(t)throw t.error}}return n},enumerable:!1,configurable:!0}),n.prototype.call=function(t,n){var r=this;return e.tidy((function(){var e,i,a,o,s=t.slice(1),u=[];try{for(var p=l(r.cells.slice().reverse()),f=p.next();!f.done;f=p.next()){var d=f.value;Array.isArray(d.stateSize)?u.push(s.splice(0,d.stateSize.length)):u.push(s.splice(0,1))}}catch(t){e={error:t}}finally{try{f&&!f.done&&(i=p.return)&&i.call(p)}finally{if(e)throw e.error}}u.reverse();for(var g,v=[],y=0;y<r.cells.length;++y){d=r.cells[y];s=u[y],g=0===y?[t[0]].concat(s):[g[0]].concat(s),g=d.call(g,n),v.push(g.slice(1))}s=[];try{for(var m=l(v.slice().reverse()),b=m.next();!b.done;b=m.next()){var w=b.value;s.push.apply(s,h([],c(w),!1))}}catch(t){a={error:t}}finally{try{b&&!b.done&&(o=m.return)&&o.call(m)}finally{if(a)throw a.error}}return[g[0]].concat(s)}))},n.prototype.build=function(t){var e;Kt(t)&&(t=t[0]),this.cells.forEach((function(n,r){X("RNNCell_".concat(r),(function(){n.build(t),e=Array.isArray(n.stateSize)?n.stateSize[0]:n.stateSize,t=[t[0],e]}))})),this.built=!0},n.prototype.getConfig=function(){var e=t.prototype.getConfig.call(this),n={cells:this.cells.map((function(t){return{className:t.getClassName(),config:t.getConfig()}}))};return Object.assign(Object.assign({},e),n)},n.fromConfig=function(t,e,n){var r,i;void 0===n&&(n={});var a=[];try{for(var o=l(e.cells),s=o.next();!s.done;s=o.next()){var u=s.value;a.push(zu(u,n))}}catch(t){r={error:t}}finally{try{s&&!s.done&&(i=o.return)&&i.call(o)}finally{if(r)throw r.error}}return new t({cells:a})},Object.defineProperty(n.prototype,"trainableWeights",{get:function(){var t,e;if(!this.trainable)return[];var n=[];try{for(var r=l(this.cells),i=r.next();!i.done;i=r.next()){var a=i.value;n.push.apply(n,h([],c(a.trainableWeights),!1))}}catch(e){t={error:e}}finally{try{i&&!i.done&&(e=r.return)&&e.call(r)}finally{if(t)throw t.error}}return n},enumerable:!1,configurable:!0}),Object.defineProperty(n.prototype,"nonTrainableWeights",{get:function(){var t,e,n,r,i=[];try{for(var a=l(this.cells),o=a.next();!o.done;o=a.next()){var s=o.value;i.push.apply(i,h([],c(s.nonTrainableWeights),!1))}}catch(e){t={error:e}}finally{try{o&&!o.done&&(e=a.return)&&e.call(a)}finally{if(t)throw t.error}}if(!this.trainable){var u=[];try{for(var p=l(this.cells),f=p.next();!f.done;f=p.next()){s=f.value;u.push.apply(u,h([],c(s.trainableWeights),!1))}}catch(t){n={error:t}}finally{try{f&&!f.done&&(r=p.return)&&r.call(p)}finally{if(n)throw n.error}}return u.concat(i)}return i},enumerable:!1,configurable:!0}),n.prototype.getWeights=function(){var t,e,n=[];try{for(var r=l(this.cells),i=r.next();!i.done;i=r.next()){var a=i.value;n.push.apply(n,h([],c(a.weights),!1))}}catch(e){t={error:e}}finally{try{i&&!i.done&&(e=r.return)&&e.call(r)}finally{if(t)throw t.error}}return Qt(n)},n.prototype.setWeights=function(t){var e,n,r=[];try{for(var i=l(this.cells),a=i.next();!a.done;a=i.next())for(var o=a.value,s=o.weights.length,u=t.splice(s),c=0;c<o.weights.length;++c)r.push([o.weights[c],u[c]])}catch(t){e={error:t}}finally{try{a&&!a.done&&(n=i.return)&&n.call(i)}finally{if(e)throw e.error}}te(r)},n}(Ec);function Oc(t){var e=t.ones,n=t.rate,r=t.training,a=void 0!==r&&r,o=t.count,s=void 0===o?1:o,u=t.dropoutFunc,l=function(){return null!=u?u(e(),n):xt(e(),n)},c=function(){return St(l,e,a)};return!s||s<=1?i.keep(c().clone()):Array(s).fill(void 0).map(c).map((function(t){return i.keep(t.clone())}))}Rc.className="StackedRNNCells",e.serialization.registerClass(Rc);var Mc=function(t,e){var n={};for(var r in t)Object.prototype.hasOwnProperty.call(t,r)&&e.indexOf(r)<0&&(n[r]=t[r]);if(null!=t&&"function"==typeof Object.getOwnPropertySymbols){var i=0;for(r=Object.getOwnPropertySymbols(t);i<r.length;i++)e.indexOf(r[i])<0&&Object.prototype.propertyIsEnumerable.call(t,r[i])&&(n[r[i]]=t[r[i]])}return n};!function(t){function e(){return null!==t&&t.apply(this,arguments)||this}o(e,t)}(Ec);var Bc=function(t){function n(e){var n=this;if(e.unroll)throw new g("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new g("It is not possible at the moment to stack convolutional cells.");return(n=t.call(this,e)||this).inputSpec=[new ee({ndim:5})],n}return o(n,t),n.prototype.call=function(e,n){var r=this;return i.tidy((function(){if(null!=r.cell.dropoutMask&&(i.dispose(r.cell.dropoutMask),r.cell.dropoutMask=null),null!=r.cell.recurrentDropoutMask&&(i.dispose(r.cell.recurrentDropoutMask),r.cell.recurrentDropoutMask=null),n&&n.constants)throw new d("ConvRNN2D cell does not support constants");var a=null==n?null:n.mask,o=null==n?null:n.training,s=null==n?null:n.initialState;return t.prototype.call.call(r,e,{mask:a,training:o,initialState:s})}))},n.prototype.computeOutputShape=function(t){var e=this.computeSingleOutputShape(t);return this.returnSequences||(e=h([e[0]],c(e.slice(2)),!1)),this.returnState&&(e=h([e],c(Array(2).fill(h([t[0]],c(e.slice(-3)),!1))),!1)),e},n.prototype.getInitialState=function(t){var e=this;return i.tidy((function(){var n=e.cell.stateSize,r=t.shape,a=e.computeSingleOutputShape(r),o=h([a[0]],c(a.slice(2)),!1),s=i.zeros(o);return Array.isArray(n)?Array(n.length).fill(s):[s]}))},n.prototype.resetStates=function(t,n){var r=this;void 0===n&&(n=!1),i.tidy((function(){if(!r.stateful)throw new p("Cannot call resetStates() on an RNN Layer that is not stateful.");var a=r.inputSpec[0].shape,o=r.computeSingleOutputShape(a),s=h([o[0]],c(o.slice(2)),!1);if(null==a[0])throw new d("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(null==r.getStates())Array.isArray(r.cell.stateSize)?r.states_=r.cell.stateSize.map((function(){return i.zeros(s)})):r.states_=[i.zeros(s)];else if(null==t)i.dispose(r.states_),null!=r.keptStates&&(i.dispose(r.keptStates),r.keptStates=[]),Array.isArray(r.cell.stateSize)?r.states_=r.cell.stateSize.map((function(){return i.zeros(s)})):r.states_[0]=i.zeros(s);else{if(Array.isArray(t)||(t=[t]),t.length!==r.states_.length)throw new d("Layer ".concat(r.name," expects ").concat(r.states_.length," state(s), ")+"but it received ".concat(t.length," state value(s). Input ")+"received: ".concat(t));n?r.keptStates.push(r.states_.slice()):i.dispose(r.states_);for(var u=0;u<r.states_.length;++u){var l=t[u],f=s;if(!e.util.arraysEqual(l.shape,f))throw new d("State ".concat(u," is incompatible with layer ").concat(r.name,": ")+"expected shape=".concat(f,", received shape=").concat(l.shape));r.states_[u]=l}}r.states_=r.states_.map((function(t){return i.keep(t.clone())}))}))},n.prototype.computeSingleOutputShape=function(t){var e=this.cell,n=e.dataFormat,r=e.filters,i=e.kernelSize,a=e.padding,o=e.strides,s=e.dilationRate,u="channelsFirst"===n,l=t[u?3:2],p=t[u?4:3],f=uc(l,i[0],a,o[0],s[0]),d=uc(p,i[1],a,o[1],s[1]);return h(h([],c(t.slice(0,2)),!1),c(u?[r,f,d]:[f,d,r]),!1)},n}(zc);Bc.className="ConvRNN2D";var Pc=function(t){function e(e){var n=this,r=e.filters,i=e.kernelSize,a=e.strides,o=e.padding,s=e.dataFormat,u=e.dilationRate;return(n=t.call(this,Object.assign(Object.assign({},e),{units:r}))||this).filters=r,L(n.filters,"filters"),n.kernelSize=sc(i,2,"kernelSize"),n.kernelSize.forEach((function(t){return L(t,"kernelSize")})),n.strides=sc(a||1,2,"strides"),n.strides.forEach((function(t){return L(t,"strides")})),n.padding=o||"valid",J(n.padding),n.dataFormat=s||"channelsLast",H(n.dataFormat),n.dilationRate=sc(u||1,2,"dilationRate"),n.dilationRate.forEach((function(t){return L(t,"dilationRate")})),n}return o(e,t),e.prototype.build=function(t){var e;t=Zt(t);var n="channelsFirst"===this.dataFormat?1:t.length-1;if(null==t[n])throw new d("The channel dimension of the input should be defined. "+"Found ".concat(t[n]));var r=t[n],a=this.kernelSize.concat([r,4*this.filters]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);var s=this.kernelSize.concat([this.filters,4*this.filters]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",s,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){var u=void 0;if(this.unitForgetBias){var l=this.biasInitializer,c=this.filters;u=new(e=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return o(e,t),e.prototype.apply=function(t,e){return ft([l.apply([c]),i.ones([c]),l.apply([2*c])])},e}(At),e.className="CustomInit",e)}else u=this.biasInitializer;this.bias=this.addWeight("bias",[4*this.filters],null,u,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0},e.prototype.call=function(t,e){var n=this;return i.tidy((function(){if(3!==t.length)throw new d("ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got "+"".concat(t.length,"."));var r=e.training||!1,a=t[0],o=t[1],s=t[2];0<n.dropout&&n.dropout<1&&null==n.dropoutMask&&(n.dropoutMask=Oc({ones:function(){return i.onesLike(a)},rate:n.dropout,training:r,count:4,dropoutFunc:n.dropoutFunc}));var u=n.dropoutMask,l=function(t,e,n){return e&&e[n]?i.mul(e[n],t):t},h=l(a,u,0),p=l(a,u,1),f=l(a,u,2),g=l(a,u,3);0<n.recurrentDropout&&n.recurrentDropout<1&&null==n.recurrentDropoutMask&&(n.recurrentDropoutMask=Oc({ones:function(){return i.onesLike(o)},rate:n.recurrentDropout,training:r,count:4,dropoutFunc:n.dropoutFunc}));var v=n.recurrentDropoutMask,y=l(o,v,0),m=l(o,v,1),b=l(o,v,2),w=l(o,v,3),k=c(i.split(n.kernel.read(),4,3),4),x=k[0],S=k[1],N=k[2],I=k[3],A=c(n.useBias?i.split(n.bias.read(),4):[null,null,null,null],4),z=A[0],E=A[1],T=A[2],C=A[3];h=n.inputConv(h,x,z,n.padding),p=n.inputConv(p,S,E,n.padding),f=n.inputConv(f,N,T,n.padding),g=n.inputConv(g,I,C,n.padding);var F=c(i.split(n.recurrentKernel.read(),4,3),4),D=F[0],_=F[1],L=F[2],R=F[3];y=n.recurrentConv(y,D),m=n.recurrentConv(m,_),b=n.recurrentConv(b,L),w=n.recurrentConv(w,R);var O=n.recurrentActivation.apply(i.add(h,y)),M=n.recurrentActivation.apply(i.add(p,m)),B=i.add(i.mul(M,s),i.mul(O,n.activation.apply(i.add(f,b)))),P=i.mul(n.recurrentActivation.apply(i.add(g,w)),n.activation.apply(B));return[P,P,B]}))},e.prototype.getConfig=function(){var e=t.prototype.getConfig.call(this);e.units;var n=Mc(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),r)},e.prototype.inputConv=function(t,e,n,r){var a=i.conv2d(t,e,this.strides,r||"valid","channelsFirst"===this.dataFormat?"NCHW":"NHWC",this.dilationRate);return n?kt(a,n,this.dataFormat):a},e.prototype.recurrentConv=function(t,e){return i.conv2d(t,e,1,"same","channelsFirst"===this.dataFormat?"NCHW":"NHWC")},e}(_c);Pc.className="ConvLSTM2DCell",i.serialization.registerClass(Pc);var jc=function(t){function e(e){var n=new Pc(e);return t.call(this,Object.assign(Object.assign({},e),{cell:n}))||this}return o(e,t),e.fromConfig=function(t,e){return new t(e)},e}(Bc);jc.className="ConvLSTM2D",i.serialization.registerClass(jc);var Uc=function(t){function n(e){var n=t.call(this,e)||this;return n.rate=Math.max(Math.min(e.rate,1),0),n.noiseShape=e.noiseShape,n.seed=e.seed,n.supportsMasking=!0,n}return o(n,t),n.prototype.getNoiseShape=function(t){if(null==this.noiseShape)return this.noiseShape;for(var e=t.shape,n=[],r=0;r<this.noiseShape.length;++r)n.push(null==this.noiseShape[r]?e[r]:this.noiseShape[r]);return n},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){r.invokeCallHook(t,n);var e=Jt(t);if(0<r.rate&&r.rate<1){var i=null!=n.training&&n.training,a=r.getNoiseShape(e);return St((function(){return xt(e,r.rate,a,r.seed)}),(function(){return e}),i)}return t}))},n.prototype.getConfig=function(){var e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},n.prototype.dispose=function(){return t.prototype.dispose.call(this)},n}(oe);Uc.className="Dropout",e.serialization.registerClass(Uc);var Wc=function(t){function e(e){var n=t.call(this,e)||this;return n.inputSpec=[{ndim:3}],n}return o(e,t),e.prototype.getNoiseShape=function(t){var e=t.shape;return[e[0],1,e[2]]},e}(Uc);Wc.className="SpatialDropout1D",e.serialization.registerClass(Wc);var Vc=function(t){function n(e){var n=t.call(this,e)||this;if(n.activation=null,n.useBias=!0,n.kernel=null,n.bias=null,n.DEFAULT_KERNEL_INITIALIZER="glorotNormal",n.DEFAULT_BIAS_INITIALIZER="zeros",null==e.batchInputShape&&null==e.inputShape&&null!=e.inputDim){var r=null;null!=e.batchSize&&(r=e.batchSize),n.batchInputShape=[r,e.inputDim]}return n.units=e.units,L(n.units,"units"),n.activation=Hl(e.activation),null!=e.useBias&&(n.useBias=e.useBias),n.kernelInitializer=Gt(e.kernelInitializer||n.DEFAULT_KERNEL_INITIALIZER),n.biasInitializer=Gt(e.biasInitializer||n.DEFAULT_BIAS_INITIALIZER),n.kernelConstraint=fu(e.kernelConstraint),n.biasConstraint=fu(e.biasConstraint),n.kernelRegularizer=tc(e.kernelRegularizer),n.biasRegularizer=tc(e.biasRegularizer),n.activityRegularizer=tc(e.activityRegularizer),n.supportsMasking=!0,n.inputSpec=[{minNDim:2}],n}return o(n,t),n.prototype.build=function(t){var e,n=(t=Zt(t))[t.length-1];null==this.kernel&&(this.kernel=this.addWeight("kernel",[n,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:(e={},e[-1]=n,e)}],this.built=!0},n.prototype.computeOutputShape=function(t){var e=(t=Zt(t)).slice();return e[e.length-1]=this.units,e},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){r.invokeCallHook(t,n);var e,i=Jt(t),a=O(r.activation.getClassName());return null!=a?e=yt(i,r.kernel.read(),a,r.bias?r.bias.read():null):(e=yt(i,r.kernel.read()),null!=r.bias&&(e=kt(e,r.bias.read())),null!=r.activation&&(e=r.activation.apply(e))),e}))},n.prototype.getConfig=function(){var e={units:this.units,activation:Gl(this.activation),useBias:this.useBias,kernelInitializer:qt(this.kernelInitializer),biasInitializer:qt(this.biasInitializer),kernelRegularizer:$l(this.kernelRegularizer),biasRegularizer:$l(this.biasRegularizer),activityRegularizer:$l(this.activityRegularizer),kernelConstraint:hu(this.kernelConstraint),biasConstraint:hu(this.biasConstraint)},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},n}(oe);Vc.className="Dense",e.serialization.registerClass(Vc);var qc=function(t){function n(e){var n=this;return e=e||{},(n=t.call(this,e)||this).inputSpec=[{minNDim:3}],n.dataFormat=e.dataFormat,n}return o(n,t),n.prototype.computeOutputShape=function(t){var e,n;t=Zt(t);try{for(var r=l(t.slice(1)),i=r.next();!i.done;i=r.next()){if(null==i.value)throw new d('The shape of the input to "Flatten" is not fully defined '+"(got ".concat(t.slice(1),"). Make sure to pass a complete ")+'"input_shape" or "batch_input_shape" argument to the first layer in your model.')}}catch(t){e={error:t}}finally{try{i&&!i.done&&(n=r.return)&&n.call(r)}finally{if(e)throw e.error}}return[t[0],rt(t,1)]},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){r.invokeCallHook(t,n);var a=Jt(t);if("channelsFirst"===r.dataFormat&&a.rank>1){for(var o=[0],s=2;s<a.rank;++s)o.push(s);o.push(1),a=e.transpose(a,o)}return function(t){if(t.rank<=1)throw new d("batchFlatten requires a minimum rank of 2. Got rank: ".concat(t.rank,"."));var e=[t.shape[0],rt(t.shape,1)];return i.reshape(t,e)}(a)}))},n.prototype.getConfig=function(){var e={};null!=this.dataFormat&&(e.dataFormat=this.dataFormat);var n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},n}(oe);qc.className="Flatten",e.serialization.registerClass(qc);var Gc=function(t){function n(e){var n=t.call(this,e)||this;return n.supportsMasking=!0,n.activation=Hl(e.activation),n}return o(n,t),n.prototype.call=function(t,n){var r=this;return e.tidy((function(){r.invokeCallHook(t,n);var e=Jt(t);return r.activation.apply(e)}))},n.prototype.getConfig=function(){var e={activation:Gl(this.activation)},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},n}(oe);Gc.className="Activation",e.serialization.registerClass(Gc);var Kc=function(t){function n(e){var n=t.call(this,e)||this;return n.n=e.n,n.inputSpec=[{ndim:2}],n}return o(n,t),n.prototype.computeOutputShape=function(t){return[t[0],this.n,t[1]]},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){return t=Jt(t),n=t,i=r.n,e.tidy((function(){if(2!==n.shape.length)throw new d("repeat() expects a rank-2 tensor, but received a "+"rank-".concat(n.shape.length," tensor."));return gt(lt(n,1),[1,i,1])}));var n,i}))},n.prototype.getConfig=function(){var e={n:this.n},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},n}(oe);Kc.className="RepeatVector",e.serialization.registerClass(Kc);var Hc=function(t){function n(e){var n=t.call(this,e)||this;n.targetShape=e.targetShape;for(var r=0;r<n.targetShape.length;++r)n.isUnknown(n.targetShape[r])&&(n.targetShape[r]=null);return n}return o(n,t),n.prototype.isUnknown=function(t){return t<0||null==t},n.prototype.fixUnknownDimension=function(t,e){for(var n="Total size of new array must be unchanged.",r=e.slice(),i=1,a=null,o=0;o<r.length;++o){var s=r[o];if(this.isUnknown(s)){if(null!==a)throw new d("Can only specifiy one unknown dimension.");a=o}else i*=s}var u=rt(t);if(null!==a){if(0===i||u%i!=0)throw new d(n);r[a]=u/i}else if(u!==i)throw new d(n);return r},n.prototype.computeOutputShape=function(t){for(var e=!1,n=0;n<t.length;++n)if(this.isUnknown(t[n])){e=!0;break}return e?t.slice(0,1).concat(this.targetShape):t.slice(0,1).concat(this.fixUnknownDimension(t.slice(1),this.targetShape))},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){r.invokeCallHook(t,n);var i=Jt(t),a=i.shape,o=a.slice(0,1).concat(r.fixUnknownDimension(a.slice(1),r.targetShape));return e.reshape(i,o)}))},n.prototype.getConfig=function(){var e={targetShape:this.targetShape},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},n}(oe);Hc.className="Reshape",e.serialization.registerClass(Hc);var Jc=function(t){function n(n){var r=t.call(this,n)||this;if(null==n.dims)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(n.dims))throw new Error("Permute constructor requires `dims` to be an Array, but received "+"".concat(n.dims," instead."));var i=ot(1,n.dims.length+1);if(!e.util.arraysEqual(n.dims.slice().sort(),i))throw new Error("Invalid permutation `dims`: "+JSON.stringify(n.dims)+" `dims` must contain consecutive integers starting from 1.");return r.dims=n.dims,r.dimsIncludingBatch=[0].concat(r.dims),r.inputSpec=[new ee({ndim:r.dims.length+1})],r}return o(n,t),n.prototype.computeOutputShape=function(t){var e=(t=Zt(t)).slice();return this.dims.forEach((function(n,r){e[r+1]=t[n]})),e},n.prototype.call=function(t,n){return e.transpose(Jt(t),this.dimsIncludingBatch)},n.prototype.getConfig=function(){var e={dims:this.dims},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},n}(oe);Jc.className="Permute",e.serialization.registerClass(Jc);var Zc=function(t){function n(e){var n=t.call(this,null==e?{}:e)||this;return n.supportsMasking=!0,n.maskValue=null!=e?null==e.maskValue?0:e.maskValue:0,n}return o(n,t),n.prototype.computeOutputShape=function(t){return t},n.prototype.getConfig=function(){var e=t.prototype.getConfig.call(this),n={maskValue:this.maskValue};return Object.assign(n,e),n},n.prototype.computeMask=function(t,n){var r=Jt(t);return e.any(e.notEqual(r,this.maskValue),-1)},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){r.invokeCallHook(t,n);var i=Jt(t),a=e.any(e.notEqual(i,r.maskValue),-1,!0);return e.mul(i,e.cast(a,i.dtype))}))},n}(oe);Zc.className="Masking",e.serialization.registerClass(Zc);var Yc=function(t){function n(e){var n=t.call(this,e)||this;if(n.embeddings=null,n.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",null==e.batchInputShape&&null==e.inputShape){var r=null;null!=e.batchSize&&(r=e.batchSize),null==e.inputLength?n.batchInputShape=[r,null]:n.batchInputShape=[r].concat(x(e.inputLength))}return n.inputDim=e.inputDim,L(n.inputDim,"inputDim"),n.outputDim=e.outputDim,L(n.outputDim,"outputDim"),n.embeddingsInitializer=Gt(e.embeddingsInitializer||n.DEFAULT_EMBEDDINGS_INITIALIZER),n.embeddingsRegularizer=tc(e.embeddingsRegularizer),n.activityRegularizer=tc(e.activityRegularizer),n.embeddingsConstraint=fu(e.embeddingsConstraint),n.maskZero=e.maskZero,n.supportsMasking=e.maskZero,n.inputLength=e.inputLength,n}return o(n,t),n.prototype.build=function(t){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0},n.prototype.warnOnIncompatibleInputShape=function(t){},n.prototype.computeMask=function(t,n){var r=this;return e.tidy((function(){return r.maskZero?(t=Jt(t),e.notEqual(t,e.zerosLike(t))):null}))},n.prototype.computeOutputShape=function(t){if(t=Zt(t),null==this.inputLength)return h(h([],c(t),!1),[this.outputDim],!1);var e=x(this.inputLength);if(e.length!==t.length-1)throw new d('"inputLength" is '.concat(this.inputLength,", but received ")+"input shape has shape ".concat(t));for(var n=0,r=0;r<e.length;++r){var i=e[r],a=t[r+1];if(null!=i&&null!=a&&i!==a)throw new d('"inputLength" is '.concat(this.inputLength,", but received ")+"input shape has shape ".concat(t));null==i&&(e[n]=a),n++}return h(h([t[0]],c(e),!1),[this.outputDim],!1)},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){r.invokeCallHook(t,n);var i=Jt(t);"int32"!==i.dtype&&(i=ut(i,"int32"));var a=mt(r.embeddings.read(),e.reshape(i,[i.size]));return e.reshape(a,Zt(r.computeOutputShape(i.shape)))}))},n.prototype.getConfig=function(){var e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:qt(this.embeddingsInitializer),embeddingsRegularizer:$l(this.embeddingsRegularizer),activityRegularizer:$l(this.activityRegularizer),embeddingsConstraint:hu(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},n}(oe);Yc.className="Embedding",e.serialization.registerClass(Yc);var Xc=function(t){function n(e){var n=t.call(this,e||{})||this;return n.supportsMasking=!0,n}return o(n,t),n.prototype.mergeFunction=function(t){throw new g},n.prototype.computeElementwiseOpOutputShape=function(t,e){if(null==t||null==e)return null;if(t.length<e.length)return this.computeElementwiseOpOutputShape(e,t);if(0===e.length)return t;for(var n=t.slice(0,t.length-e.length),r=0;r<e.length;++r){var i=t[t.length-e.length+r],a=e[r];if(null==i||null==a||i<0||a<0)n.push(null);else if(1===i)n.push(a);else if(1===a)n.push(i);else{if(i!==a)throw new d("Operands could not be broadcast together with shapes "+JSON.stringify(t)+" "+JSON.stringify(e));n.push(i)}}return n},n.prototype.build=function(t){var e,n;if(Array.isArray(t)&&!Array.isArray(t[0])&&(t=[Zt(t)]),t.length<2)throw new d("A merge layer should be called on an Array of at least 2 inputs."+" Got ".concat(t.length," input(s)."));var r=[];try{for(var i=l(t),a=i.next();!a.done;a=i.next()){null!=(u=a.value)&&null!==u[0]&&r.push(u[0])}}catch(t){e={error:t}}finally{try{a&&!a.done&&(n=i.return)&&n.call(i)}finally{if(e)throw e.error}}if((r=C(r)).length>1)throw new d("Can not merge tensors with different batch sizes. "+"Got tensors with shapes: ".concat(JSON.stringify(t),"."));for(var o=null==t[0]?null:t[0].slice(1),s=1;s<t.length;++s){var u=null==t[s]?null:t[s].slice(1);o=this.computeElementwiseOpOutputShape(o,u)}var c=t.map((function(t){return t.length}));-1===t.indexOf(null)&&1===C(c).length?this.reshapeRequired=!1:this.reshapeRequired=!0},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){var e,n,a,o;if(r.reshapeRequired){var s=[],u=t.map((function(t){return t.rank}));if(-1===u.indexOf(null)){var c=at(u);try{for(var h=l(t),p=h.next();!p.done;p=h.next()){for(var f=(m=p.value).rank,d=0;d<c-f;++d)m=lt(m,1);s.push(m)}}catch(t){e={error:t}}finally{try{p&&!p.done&&(n=h.return)&&n.call(h)}finally{if(e)throw e.error}}return r.mergeFunction(s)}var g=!1;try{for(var v=l(t),y=v.next();!y.done;y=v.next()){var m;if(null==(f=(m=y.value).rank)){var b=m.shape,w=b[0],k=b.slice(1).concat([w]),x=i.reshape(m,[w].concat(rt(b.slice(1))));x=i.transpose(x,[1,0]),x=i.reshape(x,k),s.push(x),g=!0}else if(f>1){var S=ot(1,f).concat([0]);s.push(i.transpose(m,S)),g=!0}else s.push(m)}}catch(t){a={error:t}}finally{try{y&&!y.done&&(o=v.return)&&o.call(v)}finally{if(a)throw a.error}}var N=r.mergeFunction(s),I=N.rank;if(g)if(null==I){var A=N.shape;k=[w=A[A.length-1]].concat(A.slice(0,A.length-1));N=i.reshape(i.transpose(i.reshape(N,[-1,w]),[1,0]),k)}else if(I>1){S=[I-1].concat(ot(0,I-1));N=i.transpose(N,S)}return N}return r.mergeFunction(t)}))},n.prototype.computeOutputShape=function(t){var e,n,r;r=null==t[0]?null:t[0].slice(1);for(var i=1;i<t.length;++i){var a=null==t[i]?null:t[i].slice(1);r=this.computeElementwiseOpOutputShape(r,a)}var o=[];try{for(var s=l(t),u=s.next();!u.done;u=s.next()){null!=(a=u.value)&&null!==a[0]&&o.push(a[0])}}catch(t){e={error:t}}finally{try{u&&!u.done&&(n=s.return)&&n.call(s)}finally{if(e)throw e.error}}return r=1===(o=C(o)).length?o.concat(r):[null].concat(r)},n.prototype.computeMask=function(t,e){return i.tidy((function(){if(null==e)return null;if(!Array.isArray(e))throw new d("`mask` should be an Array");if(!Array.isArray(t))throw new d("`inputs` should be an Array");if(e.length!==t.length)throw new d("The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths "+"(".concat(t.length," vs ").concat(e.length,")"));if(e.every((function(t){return null==t})))return null;for(var n=(e=e.map((function(t){return null==t?t:i.expandDims(t,0)})))[0],r=1;r<e.length-1;++r)n=i.logicalAnd(n,e[r]);return n}))},n}(oe),$c=function(t){function n(e){return t.call(this,e)||this}return o(n,t),n.prototype.mergeFunction=function(t){return e.tidy((function(){for(var e=t[0].clone(),n=1;n<t.length;++n)e=i.add(e,t[n]);return e}))},n}(Xc);$c.className="Add",e.serialization.registerClass($c);var Qc=function(t){function n(e){return t.call(this,e)||this}return o(n,t),n.prototype.mergeFunction=function(t){return e.tidy((function(){for(var e=t[0].clone(),n=1;n<t.length;++n)e=i.mul(e,t[n]);return e}))},n}(Xc);Qc.className="Multiply",e.serialization.registerClass(Qc);var th=function(t){function n(e){return t.call(this,e)||this}return o(n,t),n.prototype.mergeFunction=function(t){return e.tidy((function(){for(var e=t[0].clone(),n=1;n<t.length;++n)e=i.add(e,t[n]);return i.mul(1/t.length,e)}))},n}(Xc);th.className="Average",e.serialization.registerClass(th);var eh=function(t){function n(e){return t.call(this,e)||this}return o(n,t),n.prototype.mergeFunction=function(t){return e.tidy((function(){for(var e=t[0],n=1;n<t.length;++n)e=i.maximum(e,t[n]);return e}))},n}(Xc);eh.className="Maximum",e.serialization.registerClass(eh);var nh=function(t){function n(e){return t.call(this,e)||this}return o(n,t),n.prototype.mergeFunction=function(t){return e.tidy((function(){for(var e=t[0],n=1;n<t.length;++n)e=i.minimum(e,t[n]);return e}))},n}(Xc);nh.className="Minimum",e.serialization.registerClass(nh);var rh=function(t){function n(e){var n=t.call(this,e)||this;return n.DEFAULT_AXIS=-1,null==e&&(e={}),n.axis=null==e.axis?n.DEFAULT_AXIS:e.axis,n.supportsMasking=!0,n.reshapeRequired=!1,n}return o(n,t),n.prototype.build=function(t){var n,r,i,a;if(!Array.isArray(t)||!Array.isArray(t[0])||1===t.length)throw new d("A `Concatenate` layer should be called on a list of at least 2 inputs");var o=!0;try{for(var s=l(t),u=s.next();!u.done;u=s.next()){if(null!=(y=u.value)){o=!1;break}}}catch(t){n={error:t}}finally{try{u&&!u.done&&(r=s.return)&&r.call(s)}finally{if(n)throw n.error}}if(!o){for(var c=[],h=0;h<t.length;++h){var p=t[h].slice();p.splice(this.axis,1);var f=!1;try{for(var g=(i=void 0,l(c)),v=g.next();!v.done;v=g.next()){var y=v.value;if(e.util.arraysEqual(y,p)){f=!0;break}}}catch(t){i={error:t}}finally{try{v&&!v.done&&(a=g.return)&&a.call(g)}finally{if(i)throw i.error}}f||c.push(p)}if(c.length>1)throw new d("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(t))}},n.prototype.mergeFunction=function(t){var n=this;return e.tidy((function(){return ft(t,n.axis)}))},n.prototype.computeOutputShape=function(t){var e,n;if(!Array.isArray(t)||!Array.isArray(t[0]))throw new d("A `Concatenate` layer should be called on a list of inputs.");var r=t,i=r[0].slice(),a=this.axis<0?i.length+this.axis:this.axis;try{for(var o=l(r.slice(1)),s=o.next();!s.done;s=o.next()){var u=s.value;if(null==i[a]||null==u[a]){i[a]=null;break}i[a]+=u[a]}}catch(t){e={error:t}}finally{try{s&&!s.done&&(n=o.return)&&n.call(o)}finally{if(e)throw e.error}}return i},n.prototype.computeMask=function(t,e){var n=this;if(null==e)return null;if(!Array.isArray(e))throw new d("`mask` should be an array for Concatenate");if(!Array.isArray(t))throw new d("`inputs` should be an array for Concatenate");if(e.length!==t.length)throw new d("Mismatch in the length of mask (".concat(e.length,") ")+"and the legnth of inputs (".concat(t.length,")"));return i.tidy((function(){var r=!0;if(e.forEach((function(t){null==t||(r=!1)})),r)return null;for(var a=[],o=0;o<t.length;++o)null==e[o]?a.push(i.cast(i.onesLike(t[o]),"bool")):e[o].rank<t[o].rank?a.push(i.expandDims(e[o],-1)):a.push(e[o]);var s=i.concat(a,n.axis);return i.all(s,-1,!1)}))},n.prototype.getConfig=function(){var e={axis:this.axis},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},n}(Xc);function ih(t,e){for(;t<0;)t+=e;return t}rh.className="Concatenate",e.serialization.registerClass(rh);var ah=function(t){function e(e){var n=t.call(this,e)||this;return n.axes=e.axes,n.normalize=null!=e.normalize&&e.normalize,n.supportsMasking=!0,n.reshapeRequired=!1,n}return o(e,t),e.prototype.build=function(t){i.util.assert(Array.isArray(t)&&2===t.length&&Array.isArray(t[0])&&Array.isArray(t[1]),(function(){return"A `Dot` layer should be called on a list of exactly 2 inputs."}));var e=t[0],n=t[1];if(e.length>3||n.length>3)throw new g("Dot layer does not support tensors of 4D or higher rank yet.");var r=this.interpretAxes(e,n);if(e[r[0]]!==n[r[1]])throw new d("Dimension incompatibility: "+"".concat(e[r[0]]," !== ").concat(n[r[1]]))},e.prototype.mergeFunction=function(t){if(2!==t.length)throw new d("A `Dot` layer must be called on exactly 2 inputs, "+"but received ".concat(t.length," input(s)."));var e,n=t[0],r=t[1];return e=Array.isArray(this.axes)?this.axes.map((function(e,n){return ih(e,t[n].shape.length)})):[ih(this.axes,n.shape.length),ih(this.axes,r.shape.length)],this.normalize&&(n=Eu(n,e[0]),r=Eu(r,e[1])),function(t,e,n){if(t.shape.length>3||e.shape.length>3)throw new g("batchDot is not implemented for tensors of 4D or higher rank yet");if(i.util.assert(t.shape.length>=2,(function(){return"batchDot requires the rank of x to be >= 2, "+"but got ".concat(t.shape.length)})),i.util.assert(t.shape.length>=2,(function(){return"batchDot requires the rank of y to be >= 2, "+"but got ".concat(e.shape.length)})),"number"==typeof n&&(n=[n,n]),"complex64"===t.dtype||"complex64"===e.dtype)throw new g("batchDot is not implemented for complex64-type Tensors yet.");var r=t.shape.length,a=e.shape.length;null==n&&(n=[r-1,a-2]);var o=n;return i.tidy((function(){var n,s;if(r>a){n=r-a;for(var u=[],l=0;l<n;++l)u.push(1);e=i.reshape(e,e.shape.concat(u))}else if(a>r){for(n=a-r,u=[],l=0;l<n;++l)u.push(1);t=i.reshape(t,t.shape.concat(u))}else n=0;if(2===t.shape.length&&2===e.shape.length)s=o[0]===o[1]?i.sum(i.mul(t,e),o[0]):i.sum(i.mul(i.transpose(t,[1,0]),e),o[1]);else{var c=o[0]!==t.shape.length-1,h=o[1]===e.shape.length-1;s=i.matMul(t,e,c,h)}if(n>0){var p=void 0,f=[];for(l=p=r>a?r+a-3:r-1;l<p+n;++l)f.push(l);s=i.squeeze(s,f)}return 1===s.shape.length&&(s=i.expandDims(s,1)),s}))}(n,r,e)},e.prototype.interpretAxes=function(t,e){return Array.isArray(this.axes)?this.axes:[ih(this.axes,t.length),ih(this.axes,e.length)]},e.prototype.computeOutputShape=function(t){i.util.assert(Array.isArray(t)&&2===t.length&&Array.isArray(t[0])&&Array.isArray(t[1]),(function(){return"A `Dot` layer should be called on a list of exactly 2 inputs."}));var e=t[0].slice(),n=t[1].slice();if(e.length>3||n.length>3)throw new g("Dot layer does not support tensors of 4D or higher rank yet.");var r=this.interpretAxes(e,n);e.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);var a=e.concat(n);return 1===a.length&&a.push(1),a},e.prototype.computeMask=function(t,e){return null},e.prototype.getConfig=function(){var e={axes:this.axes,normalize:this.normalize},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e}(Xc);ah.className="Dot",e.serialization.registerClass(ah);var oh=function(t){function n(e){var n=t.call(this,e)||this;return n.supportsMasking=!0,n.stddev=e.stddev,n}return o(n,t),n.prototype.computeOutputShape=function(t){return t},n.prototype.getConfig=function(){var e=t.prototype.getConfig.call(this),n={stddev:this.stddev};return Object.assign(n,e),n},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){r.invokeCallHook(t,n);var i=Jt(t);return St((function(){return e.add(vt(i.shape,0,r.stddev),i)}),(function(){return i}),n.training||!1)}))},n}(oe);oh.className="GaussianNoise",e.serialization.registerClass(oh);var sh=function(t){function n(e){var n=t.call(this,e)||this;return n.supportsMasking=!0,n.rate=e.rate,n}return o(n,t),n.prototype.computeOutputShape=function(t){return t},n.prototype.getConfig=function(){var e=t.prototype.getConfig.call(this),n={rate:this.rate};return Object.assign(n,e),n},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){r.invokeCallHook(t,n);var i=Jt(t);if(r.rate>0&&r.rate<1){return St((function(){var t=Math.sqrt(r.rate/(1-r.rate));return e.mul(i,vt(i.shape,1,t))}),(function(){return i}),n.training||!1)}return i}))},n}(oe);sh.className="GaussianDropout",e.serialization.registerClass(sh);var uh=function(t){function n(e){var n=t.call(this,e)||this;return n.supportsMasking=!0,n.rate=e.rate,n.noiseShape=e.noiseShape,n}return o(n,t),n.prototype._getNoiseShape=function(t){return this.noiseShape||Jt(t).shape},n.prototype.computeOutputShape=function(t){return t},n.prototype.getConfig=function(){var e=t.prototype.getConfig.call(this),n={rate:this.rate};return Object.assign(n,e),n},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){if(r.rate<1&&r.rate>0){var i=r._getNoiseShape(t);return St((function(){var n=Jt(t),a=-1.7580993408473766,o=e.greaterEqual(e.randomUniform(i),r.rate);o=ut(o,"float32");var s=Math.pow((1-r.rate)*(1+r.rate*Math.pow(a,2)),-.5),u=-s*a*r.rate,l=e.add(e.mul(n,o),e.mul(e.add(o,-1),a));return e.add(e.mul(l,s),u)}),(function(){return Jt(t)}),n.training||!1)}return t}))},n}(oe);function lh(t,e,n,r,a,o){var s;if(void 0===o&&(o=.001),2===t.rank)s=i.batchNorm2d(t,e,n,r,a,o);else if(3===t.rank)s=i.batchNorm3d(t,e,n,r,a,o);else{if(4!==t.rank)throw new g("batchNormalization is not implemented for array of rank ".concat(t.rank," ")+"yet");s=i.batchNorm4d(t,e,n,r,a,o)}return s}function ch(t,n,r,a,o){return void 0===o&&(o=.001),e.util.arraysEqual(a.slice().sort(),ot(0,t.rank-1))?function(t,n,r,a,o){return void 0===o&&(o=.001),e.tidy((function(){var e=i.moments(t,a),s=e.mean,u=e.variance;return[lh(t,s,u,r,n,o),s,u]}))}(t,n,r,a,o):function(t,n,r,a,o){return void 0===o&&(o=.001),e.tidy((function(){var s,u,c=i.moments(t,a),h=c.mean,p=c.variance,f=[];try{for(var d=l(ot(0,t.rank)),g=d.next();!g.done;g=d.next()){var v=g.value;-1!==a.indexOf(v)?f.push(1):f.push(t.shape[v])}}catch(t){s={error:t}}finally{try{g&&!g.done&&(u=d.return)&&u.call(d)}finally{if(s)throw s.error}}var y=e.reshape(h,f),m=e.reshape(p,f),b=null==n?null:e.reshape(n,f),w=null==r?null:e.reshape(r,f);return[lh(t,y,m,w,b,o),h,p]}))}(t,n,r,a,o)}uh.className="AlphaDropout",e.serialization.registerClass(uh);var hh=function(t){function n(e){var n=this;return null==e&&(e={}),(n=t.call(this,e)||this).supportsMasking=!0,n.axis=null==e.axis?-1:e.axis,n.momentum=null==e.momentum?.99:e.momentum,n.epsilon=null==e.epsilon?.001:e.epsilon,n.center=null==e.center||e.center,n.scale=null==e.scale||e.scale,n.betaInitializer=Gt(e.betaInitializer||"zeros"),n.gammaInitializer=Gt(e.gammaInitializer||"ones"),n.movingMeanInitializer=Gt(e.movingMeanInitializer||"zeros"),n.movingVarianceInitializer=Gt(e.movingVarianceInitializer||"ones"),n.betaConstraint=fu(e.betaConstraint),n.gammaConstraint=fu(e.gammaConstraint),n.betaRegularizer=tc(e.betaRegularizer),n.gammaRegularizer=tc(e.gammaRegularizer),n}return o(n,t),n.prototype.build=function(t){var e;t=Zt(t);var n=this.axis>=0?this.axis:this.axis+t.length,r=t[n];if(null==r)throw new d("Axis ".concat(n," of input tensor should have a defined dimension but ")+"the layer received an input with shape "+"".concat(JSON.stringify(t),"."));this.inputSpec=[new ee({ndim:t.length,axes:(e={},e[n]=r,e)})];var i=[r];this.scale&&(this.gamma=this.addWeight("gamma",i,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",i,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",i,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",i,null,this.movingVarianceInitializer,null,!1),this.built=!0},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){var a=null!=n.training&&n.training,o=Jt(t),s=o.shape,u=s.length,l=ot(0,u),h=r.axis>=0?r.axis:r.axis+u;l.splice(h,1);var p=m(1,u);p[h]=s[h];var f=l.slice();f.sort();var d=!e.util.arraysEqual(f,ot(0,u).slice(0,u-1));if(!a)return function(){if(d){var t=e.reshape(r.movingMean.read(),p),n=e.reshape(r.movingVariance.read(),p),i=r.center?e.reshape(r.beta.read(),p):null,a=r.scale?e.reshape(r.gamma.read(),p):null;return lh(o,t,n,i,a,r.epsilon)}return lh(o,r.movingMean.read(),r.movingVariance.read(),null==r.beta?null:r.beta.read(),null==r.gamma?null:r.gamma.read(),r.epsilon)}();var g=c(ch(o,r.gamma.read(),r.beta.read(),l,r.epsilon),3),v=g[0],y=g[1],b=g[2],w=function(t,e,n){i.tidy((function(){var r=1-n,a=t.read(),o=i.mul(i.sub(a,e),r);t.write(i.sub(a,o))}))};return w(r.movingMean,y,r.momentum),w(r.movingVariance,b,r.momentum),v}))},n.prototype.getConfig=function(){var e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:qt(this.betaInitializer),gammaInitializer:qt(this.gammaInitializer),movingMeanInitializer:qt(this.movingMeanInitializer),movingVarianceInitializer:qt(this.movingVarianceInitializer),betaRegularizer:$l(this.betaRegularizer),gammaRegularizer:$l(this.gammaRegularizer),betaConstraint:hu(this.betaConstraint),gammaConstraint:hu(this.gammaConstraint)},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},n}(oe);hh.className="BatchNormalization",e.serialization.registerClass(hh);var ph=function(t){function n(e){var n,r,i=this;if(null==e&&(e={}),(i=t.call(this,e)||this).axis=null==e.axis?-1:e.axis,"number"==typeof i.axis){if(!Number.isInteger(i.axis))throw new Error("Expected axis to be an integer, but received ".concat(i.axis))}else{if(!Array.isArray(i.axis))throw new Error("Expected axis to be an integer or an array of integers, "+"but received ".concat(JSON.stringify(i.axis)));try{for(var a=l(i.axis),o=a.next();!o.done;o=a.next()){var s=o.value;if(!Number.isInteger(s))throw new Error("Expected axis to be an array of integers, "+"but received ".concat(JSON.stringify(i.axis)))}}catch(t){n={error:t}}finally{try{o&&!o.done&&(r=a.return)&&r.call(a)}finally{if(n)throw n.error}}}return i.epsilon=null==e.epsilon?.001:e.epsilon,i.center=null==e.center||e.center,i.scale=null==e.scale||e.scale,i.betaInitializer=Gt(e.betaInitializer||"zeros"),i.gammaInitializer=Gt(e.gammaInitializer||"ones"),i.betaRegularizer=tc(e.betaRegularizer),i.gammaRegularizer=tc(e.gammaRegularizer),i.supportsMasking=!0,i}return o(n,t),n.prototype.build=function(t){var e,n,r=(t=Zt(t)).length;"number"==typeof this.axis&&(this.axis=[this.axis]);for(var i=0;i<this.axis.length;++i)this.axis[i]<0&&(this.axis[i]+=r);try{for(var a=l(this.axis),o=a.next();!o.done;o=a.next()){var s=o.value;if(s<0||s>=r)throw new Error("Invalid axis: ".concat(s))}}catch(t){e={error:t}}finally{try{o&&!o.done&&(n=a.return)&&n.call(a)}finally{if(e)throw e.error}}if(this.axis.length!==C(this.axis).length)throw new Error("Found duplicate axes in: ".concat(this.axis));var u=this.axis.map((function(e){return t[e]}));this.scale?this.gamma=this.addWeight("gamma",u,"float32",this.gammaInitializer,this.gammaRegularizer,true):this.gamma=null,this.center?this.beta=this.addWeight("beta",u,"float32",this.betaInitializer,this.betaRegularizer,true):this.beta=null,this.built=!0},n.prototype.call=function(t,n){var r=this,a=Jt(t),o=a.shape,s=o.length;return e.tidy((function(){var t,n,u=e.moments(a,r.axis,!0),c=u.mean,h=u.variance,p=m(1,s);try{for(var f=l(r.axis),d=f.next();!d.done;d=f.next()){var g=d.value;p[g]=o[g]}}catch(e){t={error:e}}finally{try{d&&!d.done&&(n=f.return)&&n.call(f)}finally{if(t)throw t.error}}for(var v=function(t){return null!=t&&t.shape.length!==s?i.reshape(t,p):t},y=r.scale?v(r.gamma.read()):null,b=r.center?v(r.beta.read()):null,w=[],k=[],x=0;x<s;++x)-1!==r.axis.indexOf(x)?(w.push(o[x]),k.push(1)):(w.push(1),k.push(o[x]));return c=i.tile(c,w),h=i.tile(h,w),null!=y&&(y=i.tile(y,k)),null!=b&&(b=i.tile(b,k)),lh(a,c,h,b,y,r.epsilon)}))},n.prototype.getConfig=function(){var e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:qt(this.betaInitializer),gammaInitializer:qt(this.gammaInitializer),betaRegularizer:$l(this.betaRegularizer),gammaRegularizer:$l(this.gammaRegularizer)},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},n}(oe);ph.className="LayerNormalization",e.serialization.registerClass(ph);var fh=function(t){function n(e){var n=this;if(null==e&&(e={}),(n=t.call(this,e)||this).dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,null==e.padding)n.padding=[[1,1],[1,1]];else if("number"==typeof 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e="max"===s?i.maxPool3d(t,n,r,u):i.avgPool3d(t,n,r,u),"channelsFirst"===o&&(e=i.transpose(e,[0,4,1,2,3])),e}))}fh.className="ZeroPadding2D",e.serialization.registerClass(fh);var vh=function(t){function n(e){var n=this;if(null==e.poolSize&&(e.poolSize=2),n=t.call(this,e)||this,"number"==typeof e.poolSize)n.poolSize=[e.poolSize];else{if(!Array.isArray(e.poolSize)||1!==e.poolSize.length||"number"!=typeof e.poolSize[0])throw new d("poolSize for 1D convolutional layer must be a number or an Array of a single number, but received "+"".concat(JSON.stringify(e.poolSize)));n.poolSize=e.poolSize}if(L(n.poolSize,"poolSize"),null==e.strides)n.strides=n.poolSize;else if("number"==typeof e.strides)n.strides=[e.strides];else{if(!Array.isArray(e.strides)||1!==e.strides.length||"number"!=typeof e.strides[0])throw new d("strides for 1D convolutional layer must be a number or an Array of a single number, but received "+"".concat(JSON.stringify(e.strides)));n.strides=e.strides}return L(n.strides,"strides"),n.padding=null==e.padding?"valid":e.padding,J(n.padding),n.inputSpec=[new ee({ndim:3})],n}return o(n,t),n.prototype.computeOutputShape=function(t){var e=uc((t=Zt(t))[1],this.poolSize[0],this.padding,this.strides[0]);return[t[0],e,t[2]]},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){r.invokeCallHook(t,n),t=lt(Jt(t),2);var e=r.poolingFunction(Jt(t),[r.poolSize[0],1],[r.strides[0],1],r.padding,"channelsLast");return i.squeeze(e,[2])}))},n.prototype.getConfig=function(){var e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},n}(oe),yh=function(t){function e(e){return t.call(this,e)||this}return o(e,t),e.prototype.poolingFunction=function(t,e,n,r,i){return H(i),J(r),dh(t,e,n,r,i,"max")},e}(vh);yh.className="MaxPooling1D",e.serialization.registerClass(yh);var mh=function(t){function e(e){return t.call(this,e)||this}return 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e="channelsFirst"===this.dataFormat?t[2]:t[1],n="channelsFirst"===this.dataFormat?t[3]:t[2];return e=uc(e,this.poolSize[0],this.padding,this.strides[0]),n=uc(n,this.poolSize[1],this.padding,this.strides[1]),"channelsFirst"===this.dataFormat?[t[0],t[1],e,n]:[t[0],e,n,t[3]]},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){return r.invokeCallHook(t,n),r.poolingFunction(Jt(t),r.poolSize,r.strides,r.padding,r.dataFormat)}))},n.prototype.getConfig=function(){var e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},n}(oe),wh=function(t){function e(e){return t.call(this,e)||this}return o(e,t),e.prototype.poolingFunction=function(t,e,n,r,i){return H(i),J(r),dh(t,e,n,r,i,"max")},e}(bh);wh.className="MaxPooling2D",e.serialization.registerClass(wh);var kh=function(t){function e(e){return t.call(this,e)||this}return 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e="channelsFirst"===this.dataFormat?t[2]:t[1],n="channelsFirst"===this.dataFormat?t[3]:t[2],r="channelsFirst"===this.dataFormat?t[4]:t[3];return e=uc(e,this.poolSize[0],this.padding,this.strides[0]),n=uc(n,this.poolSize[1],this.padding,this.strides[1]),r=uc(r,this.poolSize[2],this.padding,this.strides[2]),"channelsFirst"===this.dataFormat?[t[0],t[1],e,n,r]:[t[0],e,n,r,t[4]]},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){return r.invokeCallHook(t,n),r.poolingFunction(Jt(t),r.poolSize,r.strides,r.padding,r.dataFormat)}))},n.prototype.getConfig=function(){var e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},n}(oe),Sh=function(t){function e(e){return t.call(this,e)||this}return o(e,t),e.prototype.poolingFunction=function(t,e,n,r,i){return H(i),J(r),gh(t,e,n,r,i,"max")},e}(xh);Sh.className="MaxPooling3D",e.serialization.registerClass(Sh);var Nh=function(t){function e(e){return t.call(this,e)||this}return o(e,t),e.prototype.poolingFunction=function(t,e,n,r,i){return H(i),J(r),gh(t,e,n,r,i,"avg")},e}(xh);Nh.className="AveragePooling3D",e.serialization.registerClass(Nh);var Ih=function(t){function e(e){var n=t.call(this,e)||this;return n.inputSpec=[new ee({ndim:3})],n}return o(e,t),e.prototype.computeOutputShape=function(t){return[t[0],t[2]]},e.prototype.call=function(t,e){throw new g},e}(oe),Ah=function(t){function n(e){return t.call(this,e||{})||this}return o(n,t),n.prototype.call=function(t,n){return e.tidy((function(){var e=Jt(t);return i.mean(e,1)}))},n}(Ih);Ah.className="GlobalAveragePooling1D",e.serialization.registerClass(Ah);var zh=function(t){function n(e){return t.call(this,e||{})||this}return o(n,t),n.prototype.call=function(t,n){return e.tidy((function(){var e=Jt(t);return i.max(e,1)}))},n}(Ih);zh.className="GlobalMaxPooling1D",e.serialization.registerClass(zh);var Eh=function(t){function e(e){var 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n=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(n),this.layer.built=!0),t.prototype.build.call(this,e)},n.prototype.computeOutputShape=function(t){var e=[(t=Zt(t))[0]].concat(t.slice(2)),n=this.layer.computeOutputShape(e),r=t[1];return[n[0],r].concat(n.slice(1))},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){return Ac((function(t,e){return[Jt(r.layer.call(t,n)),[]]}),t=Jt(t),[],!1,null,null,!1,!0)[1]}))},n}(Fh);Dh.className="TimeDistributed",e.serialization.registerClass(Dh);var _h=function(t){function n(e){var n=t.call(this,e)||this,r=e.layer.getConfig(),i={};i.className=e.layer.getClassName(),i.config=r,n.forwardLayer=zu(i),r.goBackwards=!0!==r.goBackwards;var a,o={};if(o.className=e.layer.getClassName(),o.config=r,n.backwardLayer=zu(o),n.forwardLayer.name="forward_"+n.forwardLayer.name,n.backwardLayer.name="backward_"+n.backwardLayer.name,n.mergeMode=void 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n;if(Array.isArray(e)&&(e=e[0]),n=this.returnSequences?null==this.mergeMode?[e,e]:e:null==this.mergeMode?[null,null]:null,this.returnState){var r=this.forwardLayer.states.map((function(t){return null}));return Array.isArray(n)?n.concat(r).concat(r):[n].concat(r).concat(r)}return n},Object.defineProperty(n.prototype,"trainableWeights",{get:function(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)},enumerable:!1,configurable:!0}),Object.defineProperty(n.prototype,"nonTrainableWeights",{get:function(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)},enumerable:!1,configurable:!0}),n.prototype.setFastWeightInitDuringBuild=function(e){t.prototype.setFastWeightInitDuringBuild.call(this,e),null!=this.forwardLayer&&this.forwardLayer.setFastWeightInitDuringBuild(e),null!=this.backwardLayer&&this.backwardLayer.setFastWeightInitDuringBuild(e)},n.prototype.getConfig=function(){var e={mergeMode:this.mergeMode},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},n.fromConfig=function(t,e){var n=zu(e.layer);if(delete e.layer,null!=e.numConstants)throw new g("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");var r=e;return r.layer=n,new t(r)},n}(Fh);_h.className="Bidirectional",e.serialization.registerClass(_h);var Lh=function(t){function n(e){var n=t.call(this,e)||this;return n.scale=e.scale,e.offset?n.offset=e.offset:n.offset=0,n}return o(n,t),n.prototype.getConfig=function(){var e={scale:this.scale,offset:this.offset},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){return"float32"!==(t=Jt(t)).dtype&&(t=ut(t,"float32")),e.add(e.mul(t,r.scale),r.offset)}))},n}(oe);Lh.className="Rescaling",e.serialization.registerClass(Lh);var Rh=e.image.resizeBilinear,Oh=e.image.cropAndResize,Mh=function(t){function n(e){var n=t.call(this,e)||this;return n.height=e.height,n.width=e.width,n}return o(n,t),n.prototype.centerCrop=function(t,n,r,i,a,o,s,u){return e.tidy((function(){var l,c=!1,h=[n/o,r/s,(i+n)/o,(a+r)/s],p=[];3===t.rank?(c=!0,l=e.stack([t])):l=t;for(var f=0;f<l.shape[0];f++)p.push(h);var d=e.tensor(p,[p.length,4]),g=e.range(0,p.length,1,"int32"),v=Oh(l,d,g,[i,a],"nearest");return ut(c?Jt(e.unstack(v)):v,u)}))},n.prototype.upsize=function(t,n,r,i){return e.tidy((function(){return ut(Rh(t,[n,r]),i)}))},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){var e=Jt(t),n=e.dtype,i=e.shape,a=i[i.length-3],o=i[i.length-2],s=0;a!==r.height&&(s=Math.floor((a-r.height)/2));var u=0;return o!==r.width&&0===(u=Math.floor((o-r.width)/2))&&(u=1),s>=0&&u>=0?r.centerCrop(e,s,u,r.height,r.width,a,o,n):r.upsize(t,r.height,r.width,n)}))},n.prototype.getConfig=function(){var e={height:this.height,width:this.width},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},n.prototype.computeOutputShape=function(t){var e=(t=Zt(t)).length-3,n=t.length-2;return t[e]=this.height,t[n]=this.width,t},n}(oe);Mh.className="CenterCrop",e.serialization.registerClass(Mh);var Bh=function(t){function n(e){var n=t.call(this,e)||this;return n.numTokens=e.numTokens,e.outputMode?n.outputMode=e.outputMode:n.outputMode="multiHot",n}return o(n,t),n.prototype.getConfig=function(){var e={numTokens:this.numTokens,outputMode:this.outputMode},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},n.prototype.computeOutputShape=function(t){return null==(t=Zt(t))?[this.numTokens]:"oneHot"===this.outputMode&&1!==t[t.length-1]?(t.push(this.numTokens),t):(t[t.length-1]=this.numTokens,t)},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){var i;if("int32"!==(t=Jt(t)).dtype&&(t=ut(t,"int32")),"undefined"!=typeof n.countWeights){if("count"!==r.outputMode)throw new d("countWeights is not used when outputMode !== count.\n              Received countWeights=".concat(n.countWeights));i=Jt(n.countWeights)}var a=e.max(t),o=e.min(t),s=e.greater(r.numTokens,a).bufferSync().get(0),u=e.greaterEqual(o,0).bufferSync().get(0);if(!s||!u)throw new d("Input values must be between 0 < values <="+" numTokens with numTokens=".concat(r.numTokens));return function(t,n,r,i){var a=Jt(t);if("int32"!==a.dtype&&(a=ut(a,"int32")),"int"===n)return a;var o=a.shape;if(0===a.rank&&(a=e.expandDims(a,-1)),"oneHot"===n&&1!==a.shape[a.shape.length-1]&&(a=e.expandDims(a,-1)),a.rank>2)throw new d("When outputMode is not int, maximum output rank is 2"+" Received outputMode ".concat(n," and input shape ").concat(o)+" which would result in output rank ".concat(a.rank,"."));var s,u=["multiHot","oneHot"].includes(n),l=a;if(s="undefined"!=typeof i&&"count"===n?e.denseBincount(l,i,r,u):e.denseBincount(l,[],r,u),"tfIdf"!==n)return s;if(i)return e.mul(s,i);throw new d("When outputMode is 'tfIdf', weights must be provided.")}(t,r.outputMode,r.numTokens,i)}))},n}(oe);Bh.className="CategoryEncoding",e.serialization.registerClass(Bh);var Ph=new Set(["bilinear","nearest"]),jh=function(t){function n(e){var n=t.call(this,e)||this;if(n.height=e.height,n.width=e.width,e.interpolation){if(!Ph.has(e.interpolation))throw new d("Invalid interpolation parameter: ".concat(e.interpolation," is not implemented"));n.interpolation=e.interpolation}else n.interpolation="bilinear";return n.cropToAspectRatio=Boolean(e.cropToAspectRatio),n}return o(n,t),n.prototype.computeOutputShape=function(t){var e=(t=Zt(t))[2];return[this.height,this.width,e]},n.prototype.getConfig=function(){var e={height:this.height,width:this.width,interpolation:this.interpolation,cropToAspectRatio:this.cropToAspectRatio},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){var n=[r.height,r.width];if("bilinear"===r.interpolation)return e.image.resizeBilinear(t,n,!r.cropToAspectRatio);if("nearest"===r.interpolation)return e.image.resizeNearestNeighbor(t,n,!r.cropToAspectRatio);throw new Error("Interpolation is ".concat(r.interpolation," but only ").concat(h([],c(Ph),!1)," are supported"))}))},n}(oe);jh.className="Resizing",e.serialization.registerClass(jh);var Uh=function(){function t(t){this.seed=t}return t.prototype.next=function(){if(void 0!==this.seed)return this.seed++},t}();Uh.className="RandomSeed";var Wh=function(t){function e(e){var n=t.call(this,e)||this;return n.randomGenerator=new Uh(e.seed),n}return o(e,t),e.prototype.getConfig=function(){var e={seed:this.randomGenerator.seed},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e}(oe);Wh.className="BaseRandomLayer";var Vh=new Set(["bilinear","nearest"]),qh=function(t){function n(e){var n=t.call(this,e)||this,r=e.factor,i=e.interpolation,a=void 0===i?"bilinear":i;if(n.factor=r,Array.isArray(n.factor)&&2===n.factor.length)n.widthLower=n.factor[0],n.widthUpper=n.factor[1];else{if(Array.isArray(n.factor)||!(n.factor>0))throw new d("Invalid factor: ".concat(n.factor,". Must be positive number or tuple of 2 numbers"));n.widthLower=-n.factor,n.widthUpper=n.factor}if(n.widthLower<-1||n.widthUpper<-1)throw new d("factor must have values larger than -1. Got: ".concat(n.factor));if(n.widthUpper<n.widthLower)throw new d("factor cannot have upper bound less than lower bound.\n        Got upper bound: ".concat(n.widthUpper,".\n        Got lower bound: ").concat(n.widthLower,"\n      "));if(a){if(!Vh.has(a))throw new d("Invalid interpolation parameter: ".concat(a," is not implemented"));n.interpolation=a}return n}return o(n,t),n.prototype.getConfig=function(){var e={factor:this.factor,interpolation:this.interpolation},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},n.prototype.computeOutputShape=function(t){var e=(t=Zt(t))[2];return[this.imgHeight,-1,e]},n.prototype.call=function(t,n){var r=this;return e.tidy((function(){var n=Jt(t);r.imgHeight=n.shape[n.shape.length-3];var i=n.shape[n.shape.length-2];r.widthFactor=e.randomUniform([1],1+r.widthLower,1+r.widthUpper,"float32",r.randomGenerator.next());var a=r.widthFactor.dataSync()[0]*i;a=Math.round(a);var o=[r.imgHeight,a];switch(r.interpolation){case"bilinear":return e.image.resizeBilinear(t,o);case"nearest":return e.image.resizeNearestNeighbor(t,o);default:throw new Error("Interpolation is ".concat(r.interpolation,"\n          but only ").concat(h([],c(Vh),!1)," are supported"))}}))},n}(Wh);function Gh(t){return new mh(t)}function Kh(t){return new kh(t)}function Hh(t){return new Nh(t)}function Jh(t){return new zh(t)}function Zh(t){return new Ch(t)}function Yh(t){return new yh(t)}function Xh(t){return new wh(t)}qh.className="RandomWidth",e.serialization.registerClass(qh);var $h={__proto__:null,Layer:oe,RNN:zc,RNNCell:Ec,activation:function(t){return new Gc(t)},add:function(t){return new $c(t)},alphaDropout:function(t){return new uh(t)},average:function(t){return new th(t)},averagePooling1d:Gh,averagePooling2d:Kh,averagePooling3d:Hh,avgPool1d:function(t){return Gh(t)},avgPool2d:function(t){return Kh(t)},avgPool3d:function(t){return Hh(t)},avgPooling1d:function(t){return Gh(t)},avgPooling2d:function(t){return Kh(t)},avgPooling3d:function(t){return Hh(t)},batchNormalization:function(t){return new hh(t)},bidirectional:function(t){return new _h(t)},categoryEncoding:function(t){return new Bh(t)},centerCrop:function(t){return new Mh(t)},concatenate:function(t){return new rh(t)},conv1d:function(t){return new kc(t)},conv2d:function(t){return new gc(t)},conv2dTranspose:function(t){return new yc(t)},conv3d:function(t){return new vc(t)},conv3dTranspose:function(t){return new mc(t)},convLstm2d:function(t){return new jc(t)},convLstm2dCell:function(t){return new Pc(t)},cropping2D:function(t){return new xc(t)},dense:function(t){return new Vc(t)},depthwiseConv2d:function(t){return new Nc(t)},dot:function(t){return new ah(t)},dropout:function(t){return new Uc(t)},elu:function(t){return new ic(t)},embedding:function(t){return new Yc(t)},flatten:function(t){return new qc(t)},gaussianDropout:function(t){return new sh(t)},gaussianNoise:function(t){return new oh(t)},globalAveragePooling1d:function(t){return new Ah(t)},globalAveragePooling2d:function(t){return new Th(t)},globalMaxPool1d:Jh,globalMaxPool2d:Zh,globalMaxPooling1d:Jh,globalMaxPooling2d:Zh,gru:function(t){return new Dc(t)},gruCell:function(t){return new Fc(t)},input:Tl,inputLayer:function(t){return new ue(t)},layerNormalization:function(t){return new ph(t)},leakyReLU:function(t){return new nc(t)},lstm:function(t){return new Lc(t)},lstmCell:function(t){return new _c(t)},masking:function(t){return new Zc(t)},maxPool1d:Yh,maxPool2d:Xh,maxPooling1d:Yh,maxPooling2d:Xh,maxPooling3d:function(t){return new Sh(t)},maximum:function(t){return new eh(t)},minimum:function(t){return new nh(t)},multiply:function(t){return new Qc(t)},permute:function(t){return new Jc(t)},prelu:function(t){return new rc(t)},randomWidth:function(t){return new qh(t)},reLU:function(t){return new ec(t)},repeatVector:function(t){return new Kc(t)},rescaling:function(t){return new Lh(t)},reshape:function(t){return new Hc(t)},resizing:function(t){return new jh(t)},rnn:function(t){return new zc(t)},separableConv2d:function(t){return new wc(t)},simpleRNN:function(t){return new Cc(t)},simpleRNNCell:function(t){return new Tc(t)},softmax:function(t){return new oc(t)},spatialDropout1d:function(t){return new Wc(t)},stackedRNNCells:function(t){return new Rc(t)},thresholdedReLU:function(t){return new ac(t)},timeDistributed:function(t){return new Dh(t)},upSampling2d:function(t){return new Sc(t)},zeroPadding2d:function(t){return new fh(t)}};var Qh={__proto__:null,MAPE:function(t,e){return Fu(t,e)},MSE:function(t,e){return Tu(t,e)},binaryAccuracy:function(t,e){return Bu(t,e)},binaryCrossentropy:function(t,e){return Vu(t,e)},categoricalAccuracy:function(t,e){return Pu(t,e)},categoricalCrossentropy:function(t,e){return Gu(t,e)},cosineProximity:function(t,e){return Ru(t,e)},mape:function(t,e){return Fu(t,e)},meanAbsoluteError:function(t,e){return Cu(t,e)},meanAbsolutePercentageError:function(t,e){return Fu(t,e)},meanSquaredError:function(t,e){return Tu(t,e)},mse:function(t,e){return Tu(t,e)},precision:function(t,e){return Uu(t,e)},recall:function(t,e){return Wu(t,e)},sparseCategoricalAccuracy:function(t,e){return qu(t,e)}},tp={__proto__:null,modelFromJSON:function(t,n){return s(this,void 0,void 0,(function(){var r,i,a,o,s,c,h,p,f,d;return u(this,(function(u){switch(u.label){case 0:return"modelTopology"in t||(t={modelTopology:t}),null!=(r=t.modelTopology).model_config&&(r=r.model_config),i=rl(r),a=zu(i,n),null==t.weightsManifest?[3,2]:[4,e.io.loadWeights(t.weightsManifest,t.pathPrefix,a.weights.map((function(t){return t.originalName})))];case 1:o=u.sent(),s={};try{for(c=l(a.weights),h=c.next();!h.done;h=c.next())p=h.value,s[p.originalName]=o[p.originalName]}catch(t){f={error:t}}finally{try{h&&!h.done&&(d=c.return)&&d.call(c)}finally{if(f)throw f.error}}a.loadWeights(s),e.dispose(o),u.label=2;case 2:return[2,a]}}))}))}};var ep={__proto__:null,l1:function(t){return Jl(e=t),new Yl({l1:null!=e?e.l1:null,l2:0});var e},l1l2:function(t){return new Yl(t)},l2:function(t){return Jl(e=t),new Yl({l2:null!=e?e.l2:null,l1:0});var e}},np=function(t){function e(){var e=t.apply(this,h([],c(arguments),!1))||this;return e.model=null,e}return o(e,t),e.prototype.setModel=function(t){if(!(t instanceof Il))throw new Error("model must be a LayersModel, not some other Container");this.model=t},e}(bu);function rp(t,e){return t<e}function ip(t,e){return t>e}var ap=function(t){function e(e){var n=t.call(this)||this;if(null==e&&(e={}),e.restoreBestWeights)throw new g("restoreBestWeights = True is not implemented in EarlyStopping yet.");return n.monitor=e.monitor||"val_loss",n.minDelta=Math.abs(e.minDelta||0),n.patience=e.patience||0,n.verbose=e.verbose||0,n.mode=e.mode||"auto",n.baseline=e.baseline,-1===["auto","min","max"].indexOf(n.mode)&&(console.warn("EarlyStopping mode '".concat(n.mode,"' is invalid. 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")+"Available metrics are: ".concat(Object.keys(t))),e},e}(np);var op={earlyStopping:function(t){return new ap(t)}};t.Callback=np,t.CallbackList=wu,t.CustomCallback=Su,t.EarlyStopping=ap,t.History=xu,t.InputSpec=ee,t.LayerVariable=$t,t.LayersModel=Il,t.RNN=zc,t.Sequential=El,t.SymbolicTensor=ne,t.callbacks=op,t.constraints=du,t.initializers=vu,t.input=Tl,t.layers=$h,t.loadLayersModel=function(t,n){return s(this,void 0,void 0,(function(){var r;return u(this,(function(i){if(null==n&&(n={}),"string"==typeof t){if(0===(r=e.io.getLoadHandlers(t,n)).length)r.push(e.io.browserHTTPRequest(t,n));else if(r.length>1)throw new d("Found more than one (".concat(r.length,") load handlers for ")+"URL '".concat(t,"'"));t=r[0]}return[2,zl(t,void 0,n)]}))}))},t.metrics=Qh,t.model=function(t){return new Il(t)},t.models=tp,t.registerCallbackConstructor=function(t,e){Iu.registerCallbackConstructor(t,e)},t.regularizers=ep,t.sequential=function(t){return new El(t)},t.version_layers=al}));
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