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"},{name:"101计划",ptimg:"data:image/png;base64,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",swimg:"data:image/png;base64,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"},{name:"国家一流",ptimg:"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAGgAAAAgCAYAAAD+Fz2gAAAAAXNSR0IArs4c6QAAEpRJREFUaEPFWwl4VeWZfs/NSthJwmZIwhYQg6wi4ANEdhBUWqWOGwLFGXVYWhyqKJvsoQK1FpECNkjRtkBFBIskhIQga5SwQ4AEyA65SSCQkJB75vm/fzn/OblJsNOZuQ88996z3XO+93+/7/2WGABwN31zmOc+Yg0YMaZhtDJgwDAMtou/G+w7fRHbne/8OBOAaRhw0ZmA6RLXMOlU2lfXy+VhVxHnsyvyf4Bpqne2xTTp1/h2+sjPY9vpvzyXbTc9ME0P7fewd3aoxwS7Y9MjryWOkeeLd3Zdg36Gn8evzz7L3/Xw59aO5/fDrsfvUe5jT0/btHsVT2c9H5Drgme/YfjMChu9IMtg4NyvqEozYDSD4bIAkSAxfGAHibYQcOwfP4fdCsfDgMcbSNLqdYBkCIOQscUDSVi54SUYFkhsOzei3Ob8zAwqDWY3piEA4tetIuNLI+pGZ/fFAeL79WPk4qkdJH5urSARmNxQLphuw3B1M0pOx22BiX/jRHFBgsTtWDeT6HjJNO39/wYkaUwBpwYSX71ixUrGidVv28eYpLEG8MD0MFZIdlpgyOM4e+wgKZZ7A++fYBLZFJ4vjJJTn+UAaMUNLZnBWWEDiX0naOkozh7u97jrc4Bpc3eae1NO7l/GJIsZtPxqZZLl7nS2kbtT7OMurEYmkQvmK90bm2zgO8HSFopYPZoblD7DcucuINcoTttoUtwQxubAMKN7A0m4Oy8gSbD02FU7SBzk2l66u6MIwlwZfzKJBbmu6kwRD+mIS+o4DQBpUAaSsLq4vgBJAVI7k2wur1pMstio4qe4Nw+5Zwt0xmAyDLtHtuiLTmwwpRDgZPDGJE0wSDf2/wCSFX+EiFAxiT8UNzZnlD0myRWvGcLJEq4gFEjcxf0PQBL3Vi1Wae7OJha8CgcPjOIf1psU3ZVacyg0xSQLJMkSy91Z7vB/xd2R4rJetOq8MomBJBSWlH9K1emskmywAr5SV0xxOmOSUm322OMtJj0wk5TS1GJlNZAMAZCQ0DWDxAAQCk8XBAJYm8r7F8ekqspyXD95BMFh7dE49CEulRn5lRo3aeFzvBiTLNktpbYK6jbpzY6UclnGHckgB0jEpPtKYTnVnQQlM+0g/IMaoEW7aCHDpSrzovocwkG6X7u7M2G4U9eRi7PcnJTU1jbar6S2C+Wlxcg6fVTIbBlLpOyW6o+/C6XB5SVt4mIjont/BDRqRnYV3PQak0xPFbbPn4Sw6D7o+9ybikmUfbDVLphkgSRXZE1MsoQEv4QEyS6fhcYnQ9+95cahL1cj6onRaN2ph1gLPP/heRLPp3789nMUZl/G4Envq9zMKf3lYrEpQS/iQS40w318rcnZwSS2MLbT3TkSVHfWZSSsnQtf/wAYLpaWCgdUdx4Ks6oK9yvuYeSMWIRGdKZkNj/9JK6lfc9Z4OUaeRfSUHarCG0fi9GYYyWw7JTwbv3RvH1XGZw0JnHJrMckexIrk9qa3V1lRTl+2BWH66cOo8ugpxHVfxSKc6+ivLTESpg9wK0b2TiX8g0eGfQsgpqEiISUsyiwQWM0DG5tk+82sARIBLYAnj2hUXhsjQJIqjcSEQIUxS7GIcpEDTCA9n06D4OnzENo24e5XRlQmvS251FsAXCD3sw4iz0fz8aIGbFoHtGZzjmb9DVS/74ezdt3Eb6qLqStagM7oeDyWfR+djK6DHoGLD7xJV6dSXaQvMUky9VJtyXPYddLP7IHJ7/7C7qNeAEFGeeQezFNi4y1fwyP7ovowc95T3JtEl8wUrKz8OgaU5ZyDMPHxqRqIAnQCrMuIfHTBQRQSGRnFF67yA0iy0EyTxKUIJB9fBAa+TAKrpzBd394DyOmx9K5jH/nkncg9es/4aWVX1kE+gl50qaZz6LX2NcIIOnLrXcrma0Wk/SgrIxUnUkU78T+vMunEBLeES6Xj8qDziV9jcwTBzBk8lz4BQZxW3iqUHwjG41CH6pemXDIcG/uTjLJuHnkY1O6Ke7ZLJBMrf4mmcTemZ9lAD35yzkIbdsF2+ZPQFVlRa1LyK9eEMYv2oIbAqCR02MRHNGJ3OqlQ3tweu9fMW7+RmSfOSaiVV0sIggQ3qUPti6YhOjhv0BU3+FWrNMZpHw8eXZZQ9Jqdrqw0IWDCO6eKlSW3YVfYH0BlFW3K8q5gqRNseg+8iW06doPsnSU+WMyTiVuwxPjp6JJq8gaQaotJlEeyAByFkGdMcnJJDcDaN0HiPnl+wiN7IL7FWVcRAiFd2BTLH0eOPE3vFzEvrhc8A+sjxsZ5xSDmkd0orSM/T4VWA1g88xxFKce9PXq6p0KFKnuVL1LXIQXSplRRX2VgSRUhRXEawbpxO7PkXcxDQNe/i8VW0yzCvfu3EJS3HLUbxqKfuPf4nmux0PxzlNVhUNbP0apuwADXn4bAfUa/ASQrOTbuHH49zxRlXFElGxqA4kBtP+PCxEz+X2EtO2sCqbyOokbFtL1Br8+z14Vh4EbGeexd817GDFtOUIjOxF8EiR2F1X37ymhUFbsxpl923H5aAIiewxAz2cmwb9ePRt2Pn6BtGqlSqSUVeRJii3kUqzqMieRN5CsarMuwUuLCnBw80pU3C3FwJdnolHzNrh7qxApf16J24V5JBwYIOWlt3Cv7DYq7twm8JiIqLxXjpYdHsVjT0+qXr/TKx0eDgpPjkX1nD1LwaGPbDLbJgps6s4Ac3lsfxEDaP0ixEx+j+IIe/iqikoFRsrnK+jzgAmzVKvC1z8QLh9fYtDeNXMIoJCITqreJ1sR90oKce3sUVw/eRh56SdRv3Ewyu/cwth3/4AGzVrYwMk+l4q0XZvR/alXECblL4B7ZaW0ir0xSbYdFEhKNcnCqlXt9gsMVLGnovwuUuJicbswFyPeWopS9w0kxS1DQFBDBNRvAH/2HtQA/vUawp99D6yPekENcaf4Jk7t24beYycSUCo2OguuMkezlZY8MPK//50CyOr96NUES4LLmFSUfQVJGxYhZtJsBEd2Rs75VBza8rtavdKAV99GWHRfUnF7P5mLEdOWISSCsY+7tgspu3HleCIKr6WTfI/oOQAd+41ESGQU/rHqbVKJDFS2vLLOHseF5G9IRbXtNQjdRr2IRiGtRU0LiJsx9kE9ZI3HMVHz/AebuWsURmPA5144gXAWa0wTnvuVqCwvg29APUZbilMuXx8YLl9U3a+kuOzvXw/ph/cg/NH+8AsIUn0pZ0tDrwnKRUQlq/yDq1UM0hNWi0myaMrzJLbS3TmXkbxhMQZNfJcYVFF2B6VF+cLYBk7sjKMH7/H0JJTeyEZgwyYIDo9CQP2GxKD4T+Zi+LSlCA1nMpvHqMtH40m6tnm0H1pGdYefnz9KbuYg9asNyDn/AzGyeduHUZRzldxHq07d0ftnU9CkVYQyMmkaj4lLR/byPNOWVvEYI1klsg2ZTKkquPAz1JGJ7DlQ9ZG8tR/yL55EypaVGD39twhs2BjbF03BI4N/ho6PD8Olowk4n/IN7dOluox58nr21gVPnGXDjxLxgpTVpikitK7UpHDgcYWzSDKsMOcKkjcuxsDX3iGAnMAmbVxCpmEqL+HTuWjUPAx9fv4fZJ2bmec5QFOXcBcnGn5CYZABSwpzcTFlNy6k7ELzdl3Q5/k3cDZ+G66k7ke/F6Yi4/h+YlnMlDnVGCBB4hrPKgvp7k7vdMqWrfeYpJVoYBJbkj5bgu6jX0HTh9rhWtpBHNu+DuPe+QQuvwBsWzgJjwxhAA2nBXf+wDcYNeNDq9qgyXXVoXX0nfSaIDX4Cg6sIgZ5KM/krq1mJnGQ3ExafrYEAxhAFEfs5yR/tpQMFzN5Dpg7jF/7PkZOi0XjlhG4mXkO8WvnYdh/LkFoRJRgkAt3igpw9UQKrqYdhDvrCimjHmNeRduegyimHPryI4pL45dsRmnRTexeMQPDpy9Di/bR1d2UCbhkAOY1Bb6KeQCwqgy21nltwoELjMwfknF021o8NXMV6jUORrpMD2avo/0MoOihPyeALh2Jx/mUnRg9Y6VSb1ZX1l7703tI6jMTDeTikleqdoNHiIDaQTJQlJOBpD8tJYAqy+8iP51l1FJmAzkXTpDRWnfuSVuL86+jSYtwtO7cg4JpwqcMoMUIZXkQQGpnx5I3KM5EdOuPtr2fRKv20fC4fJBxPBGpOzZQYsjKPcOmLkGLDl2RtH4RfR81c6X3OOIASao7EaU1sIRLUe1mrXcjmn8cVw8S139AKA+aNJuM/uOuOOSnn8KIqbE037B1wURED32e8rH0I9/h/IGdeOpXq2zq7UFBUpWM/OQPiUFiKkQptdpAYgAlxy3DgAm/wd2SQlw/dcjKd2TU1yKDLApEdHuClFjCuvkY+tYiARBnH5PuTVq0AVN7TDQX52Qg9euNyL98Bp0HjEH3US/iy9kvkrvr2H8kMXNX7HQ8OvIFdBv98gOB5GSSnVF6408wTevO3nEXYOeKqXhs3Oto22sgGT3+kzloGNIKjzH3zRg0/zUCqFO/UUg/8g+cS96JMb9eBVbLK7hylthOocLW+fUy46BVGoy8pN+KPMiqYjN353K2F8iN8ZqaOycTB+KWY+Ars9AsklcD2MtFhVMuxS1FyIupchtzcQnrFmDomwwg7uL4fqDizi1k/piCK6mJcGdlUEnl8effRJOw9vBUVuCLWc+h/0sz0K7PEPqd8/t34Pjf15PfZzmS15eDSapGp9ydlNc6k7R6ngDpVPxfcS5pB56ZvZYWUUnedez5+F30HDOBGM9e2+ZNQNdh4xHVdyQxiJWwxsxcjfLbRfj2o3cw+PW5aNC0hYpJukCoqY9k5OxfYbJ2t2o3MKfkEjlPDTGpKDcDB+JWoOvQ8Wjcoo1q9nF0LIBki0IHoTjvOk7s2oShby5ESDjPg4pyM3F679+QLdRam+jH0aHPULSM6gbD5UNusuDaBexZPQtD/n0BWnXpqbA4ufvPOLnnS3QZPA69np1cA0gmXFp9VUlauVIVWPbJH9lyYMpq5/KpCG7TAX1/MQ2sBcI8CHuW0b9aCZevLxl967wJiB42Hp37jcLFw3sIoLFvf0TPl7hhMUa8tRiBDZtao1faSFe1IRQRQ43sxFhikASJsYSeRTbjZOtBY4Y7NxMpm1Z4N8YDbh3yxkKEhnMGlZWwrPxDqhYwURBQvxEhXZJ3FbfdBbhbfBMXU3ahrLQYzy3YBB9fPzVzx34u9av1FJfadO1b86+bFkhcTFldWjKODST6IgwJ5F86TW45ZuI7aBnVg4QCy9n6vTANbDHJKsXWua9STZC5uOwzR3F46xq06xVD7ptVHMb+ejWVvJwNP+c4l84mI2ffchoa0UFSjTYNJFUOMgwU513F91tWU4m/aet24nAXH0xUvSPeguD1VtGKYK2K7Awc/svv8eSUuQgO66C5QvukEPu9y8cScGz7HxHUOBhNWoZTxaBRy3B+TWZCMRgpiKtcbU0oPfjMnd3dlRbm48LBb9Fj9EtUTL5wcDfdQcd+I0SLnR+fumM9Wj/cGy3ad0VVxT2cjv8b3NmX4OPrjzZdHweLwd7cmtU610pSotJgZO9bTgyipE4Dqi6Q9KoD5UlsiISxzxZ/LKNTOVTJcStWyZglWx7Vp1c5wOz+GMvlpNA/CxIzkHR3P5VJqvKstwu8zNxxBvCaoL2VwAYj5WSqVVKSXVkneMTy7IRlqpIgQbLFJKnw9AYet6pNCHBwuSCglV2t4SeZ5GW+QU6vEsiWaJCLROZnTpBkBVzW8VSDoq4RYw0k+ziXMFot7s4rSNp0kXNWTu8lceBYzd1LS0O2zh2gGlnxS3MMw2glDUqzWKQTrPk42YizVxecak0ApFrn3kCqDqpikGAwTQrx/oQqpPJyED/XG5PU/dUx32BzfTaQeBlIzZRXGzHWJLheDRBAqphBwcTZ8HMySR9DlpVrHu/kbIOoO7FtuUZ2wtItJo3+WgaVA/CWXOYxROZLthxJjzEEqo8YehTD8rW0zi3WcbBVy9w51Sp/Q7x7B8kahCQXRnXDOhTLT2KSGDZxNPw4RpagMFFVQ5yxurK8zCOBtMBXIAkWwcQXRlbi8jBUmWmA2UxJbQEWOSzbpKkXo3Or2oI978ry7d66spT3iPmGauNc+oixw91ZQkXEM6q1WQ0/LlI4KAwk+du1wVTz9KoI2OyC2kAkXUvEGI/o4eilGnKBNbowO0jWX0nYQRIxyR2IwG70OAwkw+OJNU3ESHcn2WKBZGeRPcZwHSUng2RCy9lUB0jKfWnCQTJIV4TaaLJo0Ypx5dpBehAm1Q6SHD4RpVfHHAOXyPa/qqgLJOtPWSSTtPwLyIXp2R9Y5T8rqO+UrP8Gpjgw8TngMx8AAAAASUVORK5CYII=",swimg:"data:image/png;base64,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"},{name:"省级一流",ptimg:"data:image/png;base64,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",swimg:"data:image/png;base64,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"},{name:"精品课程",ptimg:"data:image/png;base64,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",swimg:"data:image/png;base64,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"},{name:"军队一流",ptimg:"data:image/png;base64,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",swimg:"data:image/png;base64,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"},{name:"101计划",ptimg:"data:image/png;base64,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",swimg:"data:image/png;base64,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"},{name:"国家一流",ptimg:"data:image/png;base64,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",swimg:"data:image/png;base64,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"},{name:"省级一流",ptimg:"data:image/png;base64,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",swimg:"data:image/png;base64,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"},{name:"精品课程",ptimg:"data:image/png;base64,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",swimg:"data:image/png;base64,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"},{name:"军队一流",ptimg:"data:image/png;base64,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",swimg:"data:image/png;base64,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i=this.options,a=this.containerData,n=this.canvasData,r=this.cropBoxData,o=i.viewMode,s=n.aspectRatio,h=this.cropped&&r;if(t){var c=Number(i.minCanvasWidth)||0,l=Number(i.minCanvasHeight)||0;o>1?(c=Math.max(c,a.width),l=Math.max(l,a.height),3===o&&(l*s>c?c=l*s:l=c/s)):o>0&&(c?c=Math.max(c,h?r.width:0):l?l=Math.max(l,h?r.height:0):h&&(c=r.width,(l=r.height)*s>c?c=l*s:l=c/s));var p=Jt({aspectRatio:s,width:c,height:l});c=p.width,l=p.height,n.minWidth=c,n.minHeight=l,n.maxWidth=1/0,n.maxHeight=1/0}if(e)if(o>(h?0:1)){var d=a.width-n.width,u=a.height-n.height;n.minLeft=Math.min(0,d),n.minTop=Math.min(0,u),n.maxLeft=Math.max(0,d),n.maxTop=Math.max(0,u),h&&this.limited&&(n.minLeft=Math.min(r.left,r.left+(r.width-n.width)),n.minTop=Math.min(r.top,r.top+(r.height-n.height)),n.maxLeft=r.left,n.maxTop=r.top,2===o&&(n.width>=a.width&&(n.minLeft=Math.min(0,d),n.maxLeft=Math.max(0,d)),n.height>=a.height&&(n.minTop=Math.min(0,u),n.maxTop=Math.max(0,u))))}else 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i=this.options,a=this.containerData,n=this.canvasData,r=this.cropBoxData,o=this.limited,s=i.aspectRatio;if(t){var h=Number(i.minCropBoxWidth)||0,c=Number(i.minCropBoxHeight)||0,l=o?Math.min(a.width,n.width,n.width+n.left,a.width-n.left):a.width,p=o?Math.min(a.height,n.height,n.height+n.top,a.height-n.top):a.height;h=Math.min(h,a.width),c=Math.min(c,a.height),s&&(h&&c?c*s>h?c=h/s:h=c*s:h?c=h/s:c&&(h=c*s),p*s>l?p=l/s:l=p*s),r.minWidth=Math.min(h,l),r.minHeight=Math.min(c,p),r.maxWidth=l,r.maxHeight=p}e&&(o?(r.minLeft=Math.max(0,n.left),r.minTop=Math.max(0,n.top),r.maxLeft=Math.min(a.width,n.left+n.width)-r.width,r.maxTop=Math.min(a.height,n.top+n.height)-r.height):(r.minLeft=0,r.minTop=0,r.maxLeft=a.width-r.width,r.maxTop=a.height-r.height))},renderCropBox:function(){var t=this.options,e=this.containerData,i=this.cropBoxData;(i.width>i.maxWidth||i.widthi.maxHeight||i.height=e.width&&i.height>=e.height?C:w),Et(this.cropBox,Dt({width:i.width,height:i.height},Ft({translateX:i.left,translateY:i.top}))),this.cropped&&this.limited&&this.limitCanvas(!0,!0),this.disabled||this.output()},output:function(){this.preview(),$t(this.element,U,this.getData())}},le={initPreview:function(){var t=this.element,e=this.crossOrigin,i=this.options.preview,a=e?this.crossOriginUrl:this.url,n=t.alt||"The image to preview",r=document.createElement("img");if(e&&(r.crossOrigin=e),r.src=a,r.alt=n,this.viewBox.appendChild(r),this.viewBoxImage=r,i){var o=i;"string"==typeof i?o=t.ownerDocument.querySelectorAll(i):i.querySelector&&(o=[i]),this.previews=o,Mt(o,(function(t){var i=document.createElement("img");Xt(t,L,{width:t.offsetWidth,height:t.offsetHeight,html:t.innerHTML}),e&&(i.crossOrigin=e),i.src=a,i.alt=n,i.style.cssText='display:block;width:100%;height:auto;min-width:0!important;min-height:0!important;max-width:none!important;max-height:none!important;image-orientation:0deg!important;"',t.innerHTML="",t.appendChild(i)}))}},resetPreview:function(){Mt(this.previews,(function(t){var e=Ht(t,L);Et(t,{width:e.width,height:e.height}),t.innerHTML=e.html,Pt(t,L)}))},preview:function(){var t=this.imageData,e=this.canvasData,i=this.cropBoxData,a=i.width,n=i.height,r=t.width,o=t.height,s=i.left-e.left-t.left,h=i.top-e.top-t.top;this.cropped&&!this.disabled&&(Et(this.viewBoxImage,Dt({width:r,height:o},Ft(Dt({translateX:-s,translateY:-h},t)))),Mt(this.previews,(function(e){var 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t=this.element,e=this.options,i=this.cropper;xt(e.cropstart)&&At(t,q,e.cropstart),xt(e.cropmove)&&At(t,Z,e.cropmove),xt(e.cropend)&&At(t,_,e.cropend),xt(e.crop)&&At(t,U,e.crop),xt(e.zoom)&&At(t,nt,e.zoom),At(i,V,this.onCropStart),e.zoomable&&e.zoomOnWheel&&At(i,at,this.onWheel,{passive:!1,capture:!0}),e.toggleDragModeOnDblclick&&At(i,Q,this.onDblclick),At(t.ownerDocument,J,this.onCropMove),At(t.ownerDocument,tt,this.onCropEnd),e.responsive&&At(window,it,this.onResize)}},de={resize:function(){if(!this.disabled){var t,e,i=this.options,a=this.container,n=this.containerData,r=a.offsetWidth/n.width,o=a.offsetHeight/n.height,s=Math.abs(r-1)>Math.abs(o-1)?r:o;1!==s&&(i.restore&&(t=this.getCanvasData(),e=this.getCropBoxData()),this.render(),i.restore&&(this.setCanvasData(Mt(t,(function(e,i){t[i]=e*s}))),this.setCropBoxData(Mt(e,(function(t,i){e[i]=t*s})))))}},dblclick:function(){this.disabled||this.options.dragMode===$||this.setDragMode(jt(this.dragBox,N)?I:A)},wheel:function(t){var e=this,i=Number(this.options.wheelZoomRatio)||.1,a=1;this.disabled||(t.preventDefault(),this.wheeling||(this.wheeling=!0,setTimeout((function(){e.wheeling=!1}),50),t.deltaY?a=t.deltaY>0?1:-1:t.wheelDelta?a=-t.wheelDelta/120:t.detail&&(a=t.detail>0?1:-1),this.zoom(-a*i,t)))},cropStart:function(t){var e=t.buttons,i=t.button;if(!(this.disabled||("mousedown"===t.type||"pointerdown"===t.type&&"mouse"===t.pointerType)&&(ft(e)&&1!==e||ft(i)&&0!==i||t.ctrlKey))){var a,n=this.options,r=this.pointers;t.changedTouches?Mt(t.changedTouches,(function(t){r[t.identifier]=Gt(t)})):r[t.pointerId||0]=Gt(t),a=Object.keys(r).length>1&&n.zoomable&&n.zoomOnTouch?O:Ht(t.target,Y),ot.test(a)&&!1!==$t(this.element,q,{originalEvent:t,action:a})&&(t.preventDefault(),this.action=a,this.cropping=!1,a===x&&(this.cropping=!0,St(this.dragBox,X)))}},cropMove:function(t){var e=this.action;if(!this.disabled&&e){var i=this.pointers;t.preventDefault(),!1!==$t(this.element,Z,{originalEvent:t,action:e})&&(t.changedTouches?Mt(t.changedTouches,(function(t){Dt(i[t.identifier]||{},Gt(t,!0))})):Dt(i[t.pointerId||0]||{},Gt(t,!0)),this.change(t))}},cropEnd:function(t){if(!this.disabled){var e=this.action,i=this.pointers;t.changedTouches?Mt(t.changedTouches,(function(t){delete i[t.identifier]})):delete i[t.pointerId||0],e&&(t.preventDefault(),Object.keys(i).length||(this.action=""),this.cropping&&(this.cropping=!1,zt(this.dragBox,X,this.cropped&&this.options.modal)),$t(this.element,_,{originalEvent:t,action:e}))}}},ue={change:function(t){var e,i=this.options,a=this.canvasData,n=this.containerData,r=this.cropBoxData,o=this.pointers,s=this.action,h=i.aspectRatio,c=r.left,l=r.top,p=r.width,d=r.height,u=c+p,m=l+d,f=0,g=0,v=n.width,b=n.height,y=!0;!h&&t.shiftKey&&(h=p&&d?p/d:1),this.limited&&(f=r.minLeft,g=r.minTop,v=f+Math.min(n.width,a.width,a.left+a.width),b=g+Math.min(n.height,a.height,a.top+a.height));var N=o[Object.keys(o)[0]],z={x:N.endX-N.startX,y:N.endY-N.startY},W=function(t){switch(t){case M:u+z.x>v&&(z.x=v-u);break;case D:c+z.xb&&(z.y=b-m)}};switch(s){case w:c+=z.x,l+=z.y;break;case M:if(z.x>=0&&(u>=v||h&&(l<=g||m>=b))){y=!1;break}W(M),(p+=z.x)<0&&(s=D,c-=p=-p),h&&(d=p/h,l+=(r.height-d)/2);break;case T:if(z.y<=0&&(l<=g||h&&(c<=f||u>=v))){y=!1;break}W(T),d-=z.y,l+=z.y,d<0&&(s=k,l-=d=-d),h&&(p=d*h,c+=(r.width-p)/2);break;case D:if(z.x<=0&&(c<=f||h&&(l<=g||m>=b))){y=!1;break}W(D),p-=z.x,c+=z.x,p<0&&(s=M,c-=p=-p),h&&(d=p/h,l+=(r.height-d)/2);break;case k:if(z.y>=0&&(m>=b||h&&(c<=f||u>=v))){y=!1;break}W(k),(d+=z.y)<0&&(s=T,l-=d=-d),h&&(p=d*h,c+=(r.width-p)/2);break;case B:if(h){if(z.y<=0&&(l<=g||u>=v)){y=!1;break}W(T),d-=z.y,l+=z.y,p=d*h}else W(T),W(M),z.x>=0?ug&&(d-=z.y,l+=z.y):(d-=z.y,l+=z.y);p<0&&d<0?(s=S,l-=d=-d,c-=p=-p):p<0?(s=E,c-=p=-p):d<0&&(s=j,l-=d=-d);break;case E:if(h){if(z.y<=0&&(l<=g||c<=f)){y=!1;break}W(T),d-=z.y,l+=z.y,p=d*h,c+=r.width-p}else W(T),W(D),z.x<=0?c>f?(p-=z.x,c+=z.x):z.y<=0&&l<=g&&(y=!1):(p-=z.x,c+=z.x),z.y<=0?l>g&&(d-=z.y,l+=z.y):(d-=z.y,l+=z.y);p<0&&d<0?(s=j,l-=d=-d,c-=p=-p):p<0?(s=B,c-=p=-p):d<0&&(s=S,l-=d=-d);break;case S:if(h){if(z.x<=0&&(c<=f||m>=b)){y=!1;break}W(D),p-=z.x,c+=z.x,d=p/h}else W(k),W(D),z.x<=0?c>f?(p-=z.x,c+=z.x):z.y>=0&&m>=b&&(y=!1):(p-=z.x,c+=z.x),z.y>=0?m=0&&(u>=v||m>=b)){y=!1;break}W(M),d=(p+=z.x)/h}else W(k),W(M),z.x>=0?u=0&&m>=b&&(y=!1):p+=z.x,z.y>=0?m0?s=z.y>0?j:B:z.x<0&&(c-=p,s=z.y>0?S:E),z.y<0&&(l-=d),this.cropped||(Nt(this.cropBox,R),this.cropped=!0,this.limited&&this.limitCropBox(!0,!0))}y&&(r.width=p,r.height=d,r.left=c,r.top=l,this.action=s,this.renderCropBox()),Mt(o,(function(t){t.startX=t.endX,t.startY=t.endY}))}},me={crop:function(){return!this.ready||this.cropped||this.disabled||(this.cropped=!0,this.limitCropBox(!0,!0),this.options.modal&&St(this.dragBox,X),Nt(this.cropBox,R),this.setCropBoxData(this.initialCropBoxData)),this},reset:function(){return this.ready&&!this.disabled&&(this.imageData=Dt({},this.initialImageData),this.canvasData=Dt({},this.initialCanvasData),this.cropBoxData=Dt({},this.initialCropBoxData),this.renderCanvas(),this.cropped&&this.renderCropBox()),this},clear:function(){return this.cropped&&!this.disabled&&(Dt(this.cropBoxData,{left:0,top:0,width:0,height:0}),this.cropped=!1,this.renderCropBox(),this.limitCanvas(!0,!0),this.renderCanvas(),Nt(this.dragBox,X),St(this.cropBox,R)),this},replace:function(t){var e=arguments.length>1&&void 0!==arguments[1]&&arguments[1];return!this.disabled&&t&&(this.isImg&&(this.element.src=t),e?(this.url=t,this.image.src=t,this.ready&&(this.viewBoxImage.src=t,Mt(this.previews,(function(e){e.getElementsByTagName("img")[0].src=t})))):(this.isImg&&(this.replaced=!0),this.options.data=null,this.uncreate(),this.load(t))),this},enable:function(){return this.ready&&this.disabled&&(this.disabled=!1,Nt(this.cropper,z)),this},disable:function(){return this.ready&&!this.disabled&&(this.disabled=!0,St(this.cropper,z)),this},destroy:function(){var t=this.element;return t[y]?(t[y]=void 0,this.isImg&&this.replaced&&(t.src=this.originalUrl),this.uncreate(),this):this},move:function(t){var e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:t,i=this.canvasData,a=i.left,n=i.top;return this.moveTo(vt(t)?t:a+Number(t),vt(e)?e:n+Number(e))},moveTo:function(t){var e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:t,i=this.canvasData,a=!1;return t=Number(t),e=Number(e),this.ready&&!this.disabled&&this.options.movable&&(ft(t)&&(i.left=t,a=!0),ft(e)&&(i.top=e,a=!0),a&&this.renderCanvas(!0)),this},zoom:function(t,e){var i=this.canvasData;return t=(t=Number(t))<0?1/(1-t):1+t,this.zoomTo(i.width*t/i.naturalWidth,null,e)},zoomTo:function(t,e,i){var a=this.options,n=this.canvasData,r=n.width,o=n.height,s=n.naturalWidth,h=n.naturalHeight;if((t=Number(t))>=0&&this.ready&&!this.disabled&&a.zoomable){var c=s*t,l=h*t;if(!1===$t(this.element,nt,{ratio:t,oldRatio:r/s,originalEvent:i}))return this;if(i){var p=this.pointers,d=Ut(this.cropper),u=p&&Object.keys(p).length?Vt(p):{pageX:i.pageX,pageY:i.pageY};n.left-=(c-r)*((u.pageX-d.left-n.left)/r),n.top-=(l-o)*((u.pageY-d.top-n.top)/o)}else wt(e)&&ft(e.x)&&ft(e.y)?(n.left-=(c-r)*((e.x-n.left)/r),n.top-=(l-o)*((e.y-n.top)/o)):(n.left-=(c-r)/2,n.top-=(l-o)/2);n.width=c,n.height=l,this.renderCanvas(!0)}return this},rotate:function(t){return this.rotateTo((this.imageData.rotate||0)+Number(t))},rotateTo:function(t){return ft(t=Number(t))&&this.ready&&!this.disabled&&this.options.rotatable&&(this.imageData.rotate=t%360,this.renderCanvas(!0,!0)),this},scaleX:function(t){var e=this.imageData.scaleY;return this.scale(t,ft(e)?e:1)},scaleY:function(t){var e=this.imageData.scaleX;return this.scale(ft(e)?e:1,t)},scale:function(t){var e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:t,i=this.imageData,a=!1;return 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r=!1;e.rotatable&&ft(t.rotate)&&t.rotate!==i.rotate&&(i.rotate=t.rotate,r=!0),e.scalable&&(ft(t.scaleX)&&t.scaleX!==i.scaleX&&(i.scaleX=t.scaleX,r=!0),ft(t.scaleY)&&t.scaleY!==i.scaleY&&(i.scaleY=t.scaleY,r=!0)),r&&this.renderCanvas(!0,!0);var o=i.width/i.naturalWidth;ft(t.x)&&(n.left=t.x*o+a.left),ft(t.y)&&(n.top=t.y*o+a.top),ft(t.width)&&(n.width=t.width*o),ft(t.height)&&(n.height=t.height*o),this.setCropBoxData(n)}return this},getContainerData:function(){return this.ready?Dt({},this.containerData):{}},getImageData:function(){return this.sized?Dt({},this.imageData):{}},getCanvasData:function(){var t=this.canvasData,e={};return this.ready&&Mt(["left","top","width","height","naturalWidth","naturalHeight"],(function(i){e[i]=t[i]})),e},setCanvasData:function(t){var e=this.canvasData,i=e.aspectRatio;return this.ready&&!this.disabled&&wt(t)&&(ft(t.left)&&(e.left=t.left),ft(t.top)&&(e.top=t.top),ft(t.width)?(e.width=t.width,e.height=t.width/i):ft(t.height)&&(e.height=t.height,e.width=t.height*i),this.renderCanvas(!0)),this},getCropBoxData:function(){var t,e=this.cropBoxData;return this.ready&&this.cropped&&(t={left:e.left,top:e.top,width:e.width,height:e.height}),t||{}},setCropBoxData:function(t){var e,i,a=this.cropBoxData,n=this.options.aspectRatio;return this.ready&&this.cropped&&!this.disabled&&wt(t)&&(ft(t.left)&&(a.left=t.left),ft(t.top)&&(a.top=t.top),ft(t.width)&&t.width!==a.width&&(e=!0,a.width=t.width),ft(t.height)&&t.height!==a.height&&(i=!0,a.height=t.height),n&&(e?a.height=a.width/n:i&&(a.width=a.height*n)),this.renderCropBox()),this},getCroppedCanvas:function(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};if(!this.ready||!window.HTMLCanvasElement)return null;var e=this.canvasData,i=ee(this.image,this.imageData,e,t);if(!this.cropped)return i;var a=this.getData(t.rounded),n=a.x,r=a.y,o=a.width,h=a.height,c=i.width/Math.floor(e.naturalWidth);1!==c&&(n*=c,r*=c,o*=c,h*=c);var l=o/h,p=Jt({aspectRatio:l,width:t.maxWidth||1/0,height:t.maxHeight||1/0}),d=Jt({aspectRatio:l,width:t.minWidth||0,height:t.minHeight||0},"cover"),u=Jt({aspectRatio:l,width:t.width||(1!==c?i.width:o),height:t.height||(1!==c?i.height:h)}),m=u.width,f=u.height;m=Math.min(p.width,Math.max(d.width,m)),f=Math.min(p.height,Math.max(d.height,f));var g=document.createElement("canvas"),v=g.getContext("2d");g.width=Tt(m),g.height=Tt(f),v.fillStyle=t.fillColor||"transparent",v.fillRect(0,0,m,f);var b=t.imageSmoothingEnabled,y=void 0===b||b,w=t.imageSmoothingQuality;v.imageSmoothingEnabled=y,w&&(v.imageSmoothingQuality=w);var x,C,O,M,D,k,T=i.width,B=i.height,E=n,j=r;E<=-o||E>T?(E=0,x=0,O=0,D=0):E<=0?(O=-E,E=0,D=x=Math.min(T,o+E)):E<=T&&(O=0,D=x=Math.min(o,T-E)),x<=0||j<=-h||j>B?(j=0,C=0,M=0,k=0):j<=0?(M=-j,j=0,k=C=Math.min(B,h+j)):j<=B&&(M=0,k=C=Math.min(h,B-j));var 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i=g.navigator&&/(?:iPad|iPhone|iPod).*?AppleWebKit/i.test(g.navigator.userAgent),a=function(e,i){Dt(t.imageData,{naturalWidth:e,naturalHeight:i,aspectRatio:e/i}),t.initialImageData=Dt({},t.imageData),t.sizing=!1,t.sized=!0,t.build()};if(!e.naturalWidth||i){var n=document.createElement("img"),r=document.body||document.documentElement;this.sizingImage=n,n.onload=function(){a(n.width,n.height),i||r.removeChild(n)},n.src=e.src,i||(n.style.cssText="left:0;max-height:none!important;max-width:none!important;min-height:0!important;min-width:0!important;opacity:0;position:absolute;top:0;z-index:-1;",r.appendChild(n))}else a(e.naturalWidth,e.naturalHeight)}},{key:"stop",value:function(){var t=this.image;t.onload=null,t.onerror=null,t.parentNode.removeChild(t),this.image=null}},{key:"build",value:function(){if(this.sized&&!this.ready){var t=this.element,e=this.options,i=this.image,a=t.parentNode,n=document.createElement("div");n.innerHTML=ut;var r=n.querySelector(".".concat(y,"-container")),o=r.querySelector(".".concat(y,"-canvas")),s=r.querySelector(".".concat(y,"-drag-box")),h=r.querySelector(".".concat(y,"-crop-box")),c=h.querySelector(".".concat(y,"-face"));this.container=a,this.cropper=r,this.canvas=o,this.dragBox=s,this.cropBox=h,this.viewBox=r.querySelector(".".concat(y,"-view-box")),this.face=c,o.appendChild(i),St(t,R),a.insertBefore(r,t.nextSibling),Nt(i,W),this.initPreview(),this.bind(),e.initialAspectRatio=Math.max(0,e.initialAspectRatio)||NaN,e.aspectRatio=Math.max(0,e.aspectRatio)||NaN,e.viewMode=Math.max(0,Math.min(3,Math.round(e.viewMode)))||0,St(h,R),e.guides||St(h.getElementsByClassName("".concat(y,"-dashed")),R),e.center||St(h.getElementsByClassName("".concat(y,"-center")),R),e.background&&St(r,"".concat(y,"-bg")),e.highlight||St(c,H),e.cropBoxMovable&&(St(c,P),Xt(c,Y,w)),e.cropBoxResizable||(St(h.getElementsByClassName("".concat(y,"-line")),R),St(h.getElementsByClassName("".concat(y,"-point")),R)),this.render(),this.ready=!0,this.setDragMode(e.dragMode),e.autoCrop&&this.crop(),this.setData(e.data),xt(e.ready)&&It(t,et,e.ready,{once:!0}),$t(t,et)}}},{key:"unbuild",value:function(){if(this.ready){this.ready=!1,this.unbind(),this.resetPreview();var 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N=o[Object.keys(o)[0]],z={x:N.endX-N.startX,y:N.endY-N.startY},W=function(t){switch(t){case M:u+z.x>v&&(z.x=v-u);break;case D:c+z.xb&&(z.y=b-m)}};switch(s){case w:c+=z.x,l+=z.y;break;case M:if(z.x>=0&&(u>=v||h&&(l<=g||m>=b))){y=!1;break}W(M),(p+=z.x)<0&&(s=D,c-=p=-p),h&&(d=p/h,l+=(r.height-d)/2);break;case T:if(z.y<=0&&(l<=g||h&&(c<=f||u>=v))){y=!1;break}W(T),d-=z.y,l+=z.y,d<0&&(s=k,l-=d=-d),h&&(p=d*h,c+=(r.width-p)/2);break;case D:if(z.x<=0&&(c<=f||h&&(l<=g||m>=b))){y=!1;break}W(D),p-=z.x,c+=z.x,p<0&&(s=M,c-=p=-p),h&&(d=p/h,l+=(r.height-d)/2);break;case k:if(z.y>=0&&(m>=b||h&&(c<=f||u>=v))){y=!1;break}W(k),(d+=z.y)<0&&(s=T,l-=d=-d),h&&(p=d*h,c+=(r.width-p)/2);break;case B:if(h){if(z.y<=0&&(l<=g||u>=v)){y=!1;break}W(T),d-=z.y,l+=z.y,p=d*h}else W(T),W(M),z.x>=0?ug&&(d-=z.y,l+=z.y):(d-=z.y,l+=z.y);p<0&&d<0?(s=S,l-=d=-d,c-=p=-p):p<0?(s=E,c-=p=-p):d<0&&(s=j,l-=d=-d);break;case E:if(h){if(z.y<=0&&(l<=g||c<=f)){y=!1;break}W(T),d-=z.y,l+=z.y,p=d*h,c+=r.width-p}else 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this.ready&&!this.disabled&&(this.imageData=Dt({},this.initialImageData),this.canvasData=Dt({},this.initialCanvasData),this.cropBoxData=Dt({},this.initialCropBoxData),this.renderCanvas(),this.cropped&&this.renderCropBox()),this},clear:function(){return this.cropped&&!this.disabled&&(Dt(this.cropBoxData,{left:0,top:0,width:0,height:0}),this.cropped=!1,this.renderCropBox(),this.limitCanvas(!0,!0),this.renderCanvas(),Nt(this.dragBox,X),St(this.cropBox,R)),this},replace:function(t){var e=arguments.length>1&&void 0!==arguments[1]&&arguments[1];return!this.disabled&&t&&(this.isImg&&(this.element.src=t),e?(this.url=t,this.image.src=t,this.ready&&(this.viewBoxImage.src=t,Mt(this.previews,(function(e){e.getElementsByTagName("img")[0].src=t})))):(this.isImg&&(this.replaced=!0),this.options.data=null,this.uncreate(),this.load(t))),this},enable:function(){return this.ready&&this.disabled&&(this.disabled=!1,Nt(this.cropper,z)),this},disable:function(){return this.ready&&!this.disabled&&(this.disabled=!0,St(this.cropper,z)),this},destroy:function(){var t=this.element;return t[y]?(t[y]=void 0,this.isImg&&this.replaced&&(t.src=this.originalUrl),this.uncreate(),this):this},move:function(t){var e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:t,i=this.canvasData,a=i.left,n=i.top;return this.moveTo(vt(t)?t:a+Number(t),vt(e)?e:n+Number(e))},moveTo:function(t){var e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:t,i=this.canvasData,a=!1;return t=Number(t),e=Number(e),this.ready&&!this.disabled&&this.options.movable&&(ft(t)&&(i.left=t,a=!0),ft(e)&&(i.top=e,a=!0),a&&this.renderCanvas(!0)),this},zoom:function(t,e){var i=this.canvasData;return t=(t=Number(t))<0?1/(1-t):1+t,this.zoomTo(i.width*t/i.naturalWidth,null,e)},zoomTo:function(t,e,i){var a=this.options,n=this.canvasData,r=n.width,o=n.height,s=n.naturalWidth,h=n.naturalHeight;if((t=Number(t))>=0&&this.ready&&!this.disabled&&a.zoomable){var 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r=!1;e.rotatable&&ft(t.rotate)&&t.rotate!==i.rotate&&(i.rotate=t.rotate,r=!0),e.scalable&&(ft(t.scaleX)&&t.scaleX!==i.scaleX&&(i.scaleX=t.scaleX,r=!0),ft(t.scaleY)&&t.scaleY!==i.scaleY&&(i.scaleY=t.scaleY,r=!0)),r&&this.renderCanvas(!0,!0);var o=i.width/i.naturalWidth;ft(t.x)&&(n.left=t.x*o+a.left),ft(t.y)&&(n.top=t.y*o+a.top),ft(t.width)&&(n.width=t.width*o),ft(t.height)&&(n.height=t.height*o),this.setCropBoxData(n)}return this},getContainerData:function(){return this.ready?Dt({},this.containerData):{}},getImageData:function(){return this.sized?Dt({},this.imageData):{}},getCanvasData:function(){var t=this.canvasData,e={};return this.ready&&Mt(["left","top","width","height","naturalWidth","naturalHeight"],(function(i){e[i]=t[i]})),e},setCanvasData:function(t){var e=this.canvasData,i=e.aspectRatio;return this.ready&&!this.disabled&&wt(t)&&(ft(t.left)&&(e.left=t.left),ft(t.top)&&(e.top=t.top),ft(t.width)?(e.width=t.width,e.height=t.width/i):ft(t.height)&&(e.height=t.height,e.width=t.height*i),this.renderCanvas(!0)),this},getCropBoxData:function(){var t,e=this.cropBoxData;return this.ready&&this.cropped&&(t={left:e.left,top:e.top,width:e.width,height:e.height}),t||{}},setCropBoxData:function(t){var e,i,a=this.cropBoxData,n=this.options.aspectRatio;return this.ready&&this.cropped&&!this.disabled&&wt(t)&&(ft(t.left)&&(a.left=t.left),ft(t.top)&&(a.top=t.top),ft(t.width)&&t.width!==a.width&&(e=!0,a.width=t.width),ft(t.height)&&t.height!==a.height&&(i=!0,a.height=t.height),n&&(e?a.height=a.width/n:i&&(a.width=a.height*n)),this.renderCropBox()),this},getCroppedCanvas:function(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};if(!this.ready||!window.HTMLCanvasElement)return null;var e=this.canvasData,i=ee(this.image,this.imageData,e,t);if(!this.cropped)return i;var a=this.getData(t.rounded),n=a.x,r=a.y,o=a.width,h=a.height,c=i.width/Math.floor(e.naturalWidth);1!==c&&(n*=c,r*=c,o*=c,h*=c);var l=o/h,p=Jt({aspectRatio:l,width:t.maxWidth||1/0,height:t.maxHeight||1/0}),d=Jt({aspectRatio:l,width:t.minWidth||0,height:t.minHeight||0},"cover"),u=Jt({aspectRatio:l,width:t.width||(1!==c?i.width:o),height:t.height||(1!==c?i.height:h)}),m=u.width,f=u.height;m=Math.min(p.width,Math.max(d.width,m)),f=Math.min(p.height,Math.max(d.height,f));var g=document.createElement("canvas"),v=g.getContext("2d");g.width=Tt(m),g.height=Tt(f),v.fillStyle=t.fillColor||"transparent",v.fillRect(0,0,m,f);var b=t.imageSmoothingEnabled,y=void 0===b||b,w=t.imageSmoothingQuality;v.imageSmoothingEnabled=y,w&&(v.imageSmoothingQuality=w);var x,C,O,M,D,k,T=i.width,B=i.height,E=n,j=r;E<=-o||E>T?(E=0,x=0,O=0,D=0):E<=0?(O=-E,E=0,D=x=Math.min(T,o+E)):E<=T&&(O=0,D=x=Math.min(o,T-E)),x<=0||j<=-h||j>B?(j=0,C=0,M=0,k=0):j<=0?(M=-j,j=0,k=C=Math.min(B,h+j)):j<=B&&(M=0,k=C=Math.min(h,B-j));var S=[E,j,x,C];if(D>0&&k>0){var N=m/o;S.push(O*N,M*N,D*N,k*N)}return v.drawImage.apply(v,[i].concat(s(S.map((function(t){return Math.floor(Tt(t))}))))),g},setAspectRatio:function(t){var e=this.options;return this.disabled||vt(t)||(e.aspectRatio=Math.max(0,t)||NaN,this.ready&&(this.initCropBox(),this.cropped&&this.renderCropBox())),this},setDragMode:function(t){var e=this.options,i=this.dragBox,a=this.face;if(this.ready&&!this.disabled){var n=t===A,r=e.movable&&t===I;t=n||r?t:$,e.dragMode=t,Xt(i,Y,t),zt(i,N,n),zt(i,P,r),e.cropBoxMovable||(Xt(a,Y,t),zt(a,N,n),zt(a,P,r))}return this}},fe=g.Cropper,ge=function(){function t(e){var i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if(a(this,t),!e||!ct.test(e.tagName))throw new Error("The first argument is required and must be an or element.");this.element=e,this.options=Dt({},dt,wt(i)&&i),this.cropped=!1,this.disabled=!1,this.pointers={},this.ready=!1,this.reloading=!1,this.replaced=!1,this.sized=!1,this.sizing=!1,this.init()}return r(t,[{key:"init",value:function(){var t,e=this.element,i=e.tagName.toLowerCase();if(!e[y]){if(e[y]=this,"img"===i){if(this.isImg=!0,t=e.getAttribute("src")||"",this.originalUrl=t,!t)return;t=e.src}else"canvas"===i&&window.HTMLCanvasElement&&(t=e.toDataURL());this.load(t)}}},{key:"load",value:function(t){var e=this;if(t){this.url=t,this.imageData={};var i=this.element,a=this.options;if(a.rotatable||a.scalable||(a.checkOrientation=!1),a.checkOrientation&&window.ArrayBuffer)if(st.test(t))ht.test(t)?this.read(re(t)):this.clone();else{var n=new XMLHttpRequest,r=this.clone.bind(this);this.reloading=!0,this.xhr=n,n.onabort=r,n.onerror=r,n.ontimeout=r,n.onprogress=function(){n.getResponseHeader("content-type")!==rt&&n.abort()},n.onload=function(){e.read(n.response)},n.onloadend=function(){e.reloading=!1,e.xhr=null},a.checkCrossOrigin&&qt(t)&&i.crossOrigin&&(t=Qt(t)),n.open("GET",t,!0),n.responseType="arraybuffer",n.withCredentials="use-credentials"===i.crossOrigin,n.send()}else this.clone()}}},{key:"read",value:function(t){var e=this.options,i=this.imageData,a=se(t),n=0,r=1,o=1;if(a>1){this.url=oe(t,rt);var s=he(a);n=s.rotate,r=s.scaleX,o=s.scaleY}e.rotatable&&(i.rotate=n),e.scalable&&(i.scaleX=r,i.scaleY=o),this.clone()}},{key:"clone",value:function(){var t=this.element,e=this.url,i=t.crossOrigin,a=e;this.options.checkCrossOrigin&&qt(e)&&(i||(i="anonymous"),a=Qt(e)),this.crossOrigin=i,this.crossOriginUrl=a;var n=document.createElement("img");i&&(n.crossOrigin=i),n.src=a||e,n.alt=t.alt||"The image to crop",this.image=n,n.onload=this.start.bind(this),n.onerror=this.stop.bind(this),St(n,W),t.parentNode.insertBefore(n,t.nextSibling)}},{key:"start",value:function(){var t=this,e=this.image;e.onload=null,e.onerror=null,this.sizing=!0;var i=g.navigator&&/(?:iPad|iPhone|iPod).*?AppleWebKit/i.test(g.navigator.userAgent),a=function(e,i){Dt(t.imageData,{naturalWidth:e,naturalHeight:i,aspectRatio:e/i}),t.initialImageData=Dt({},t.imageData),t.sizing=!1,t.sized=!0,t.build()};if(!e.naturalWidth||i){var n=document.createElement("img"),r=document.body||document.documentElement;this.sizingImage=n,n.onload=function(){a(n.width,n.height),i||r.removeChild(n)},n.src=e.src,i||(n.style.cssText="left:0;max-height:none!important;max-width:none!important;min-height:0!important;min-width:0!important;opacity:0;position:absolute;top:0;z-index:-1;",r.appendChild(n))}else a(e.naturalWidth,e.naturalHeight)}},{key:"stop",value:function(){var t=this.image;t.onload=null,t.onerror=null,t.parentNode.removeChild(t),this.image=null}},{key:"build",value:function(){if(this.sized&&!this.ready){var t=this.element,e=this.options,i=this.image,a=t.parentNode,n=document.createElement("div");n.innerHTML=ut;var r=n.querySelector(".".concat(y,"-container")),o=r.querySelector(".".concat(y,"-canvas")),s=r.querySelector(".".concat(y,"-drag-box")),h=r.querySelector(".".concat(y,"-crop-box")),c=h.querySelector(".".concat(y,"-face"));this.container=a,this.cropper=r,this.canvas=o,this.dragBox=s,this.cropBox=h,this.viewBox=r.querySelector(".".concat(y,"-view-box")),this.face=c,o.appendChild(i),St(t,R),a.insertBefore(r,t.nextSibling),Nt(i,W),this.initPreview(),this.bind(),e.initialAspectRatio=Math.max(0,e.initialAspectRatio)||NaN,e.aspectRatio=Math.max(0,e.aspectRatio)||NaN,e.viewMode=Math.max(0,Math.min(3,Math.round(e.viewMode)))||0,St(h,R),e.guides||St(h.getElementsByClassName("".concat(y,"-dashed")),R),e.center||St(h.getElementsByClassName("".concat(y,"-center")),R),e.background&&St(r,"".concat(y,"-bg")),e.highlight||St(c,H),e.cropBoxMovable&&(St(c,P),Xt(c,Y,w)),e.cropBoxResizable||(St(h.getElementsByClassName("".concat(y,"-line")),R),St(h.getElementsByClassName("".concat(y,"-point")),R)),this.render(),this.ready=!0,this.setDragMode(e.dragMode),e.autoCrop&&this.crop(),this.setData(e.data),xt(e.ready)&&It(t,et,e.ready,{once:!0}),$t(t,et)}}},{key:"unbuild",value:function(){if(this.ready){this.ready=!1,this.unbind(),this.resetPreview();var 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y * (x / y);\n }\n\n int idiv(int a, int b, float sign) {\n int res = a / b;\n int mod = imod(a, b);\n if (sign < 0. && mod != 0) {\n res -= 1;\n }\n return res;\n }\n\n //Based on the work of Dave Hoskins\n //https://www.shadertoy.com/view/4djSRW\n #define HASHSCALE1 443.8975\n float random(float seed){\n vec2 p = resultUV * seed;\n vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);\n p3 += dot(p3, p3.yzx + 19.19);\n return fract((p3.x + p3.y) * p3.z);\n }\n\n "+Do+"\n "+Ao+"\n "+To+"\n "}(l);return e.isPacked?(i=function(t,e){switch(t.length){case 0:return"\n int getOutputCoords() {\n return 0;\n }\n ";case 1:return function(t,e){var n=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];return 1===n[0]?"\n int getOutputCoords() {\n return 2 * int(resultUV.x * "+n[1]+".0);\n }\n ":1===n[1]?"\n int getOutputCoords() {\n return 2 * int(resultUV.y * "+n[0]+".0);\n }\n ":"\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+n[0]+", "+n[1]+"));\n return 2 * (resTexRC.x * "+n[1]+" + resTexRC.y);\n }\n "}(0,e);case 2:return function(t,e){var n=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(A(t,e))return"\n ivec2 getOutputCoords() {\n return 2 * ivec2(resultUV.yx * vec2("+n[0]+", "+n[1]+"));\n }\n ";var r=Math.ceil(t[1]/2);return"\n ivec2 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+n[0]+", "+n[1]+"));\n\n int index = resTexRC.x * "+n[1]+" + resTexRC.y;\n int r = 2 * (index / "+r+");\n int c = imod(index, "+r+") * 2;\n\n return ivec2(r, c);\n }\n "}(t,e);case 3:return n=t,r=e,o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],a=(i=Math.ceil(n[2]/2))*Math.ceil(n[1]/2),"\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+o[0]+", "+o[1]+"));\n int index = resTexRC.x * "+o[1]+" + resTexRC.y;\n\n int b = index / "+a+";\n index -= b * "+a+";\n\n int r = 2 * (index / "+i+");\n int c = imod(index, "+i+") * 2;\n\n return ivec3(b, r, c);\n }\n ";default:return function(t,e){for(var n=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],r=Math.ceil(t[t.length-1]/2),o=r*Math.ceil(t[t.length-2]/2),i=o,a="",s="b, r, c",u=2;u2,(function(){return"Packed arg"+(n.charAt(0).toUpperCase()+n.slice(1))+" supports only inputs with rank above 2."}));var o=t[t.length-1],i=Math.ceil(o/e);this.outputShape=t.slice(0,-1),i>1&&this.outputShape.push(i),r||this.variableNames.push("bestIndicesA");var a,s,u=this.outputShape,c=u.length,l=Oo(c),h=wo("coords",c);if(1===i){var f=Oo(s=c+1);a="\n "+f+" sourceLocR = "+f+"("+h.join()+", 0);\n ++"+h[c-1]+";\n "+f+" sourceLocG = "+f+"("+h.join()+", 0);\n ++"+h[c-2]+";\n "+f+" sourceLocA = "+f+"("+h.join()+", 0);\n --"+h[c-1]+";\n "+f+" sourceLocB = "+f+"("+h.join()+", 0);\n --"+h[c-2]+";"}else s=c,a="\n "+l+" sourceLocR = coords;\n ++"+h[c-1]+";\n "+l+" sourceLocG = coords;\n ++"+h[c-2]+";\n "+l+" sourceLocA = coords;\n --"+h[c-1]+";\n "+l+" sourceLocB = coords;\n --"+h[c-2]+";";var p=["x","y","z","w","u","v"].slice(0,s),d="."+p[s-1],v=p.map((function(t){return"int "+t})),m=wo("sourceLocR",s-1).concat("inIdx.r"),g=wo("sourceLocG",s-1).concat("inIdx.g"),y=wo("sourceLocB",s-1).concat("inIdx.b"),x=wo("sourceLocA",s-1).concat("inIdx.a"),b="max"===n?"greaterThan":"lessThan",w=r?"":"\n inIdx = round(vec4(getBestIndicesAChannel("+m.join()+"),\n getBestIndicesAChannel("+g.join()+"),\n getBestIndicesAChannel("+y.join()+"),\n getBestIndicesAChannel("+x.join()+")));",C="vec4(\n getAChannel("+m.join()+"),\n hasNextCol ? getAChannel("+g.join()+") : 0.,\n hasNextRow ? getAChannel("+y.join()+") : 0.,\n hasNextRow && hasNextCol ? getAChannel("+x.join()+") : 0.)",E=r?"":"\n float getBestIndicesAChannel("+v.join()+") {\n return getChannel(getBestIndicesA("+p.join()+"),\n vec2("+p.slice(-2).join()+"));\n }";this.userCode="\n float getAChannel("+v.join()+") {\n return getChannel(getA("+p.join()+"),\n vec2("+p.slice(-2).join()+"));\n }\n "+E+"\n void main() {\n "+l+" coords = getOutputCoords();\n bool hasNextCol = "+h[c-1]+" < "+(u[c-1]-1)+";\n bool hasNextRow = "+h[c-2]+" < "+(u[c-2]-1)+";\n "+a+"\n ivec4 srcIdx = ivec4(sourceLocR"+d+", sourceLocG"+d+",\n sourceLocB"+d+", sourceLocA"+d+") * "+e+";\n ivec4 inIdx = srcIdx;\n vec4 bestIndex = vec4(inIdx);\n vec4 bestValue = "+C+";\n\n for (int i = 0; i < "+e+"; i++) {\n inIdx = srcIdx;\n "+w+"\n vec4 candidate = "+C+";\n bvec4 nan = isnan(candidate);\n bvec4 replace = bvec4(\n vec4("+b+"(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n bestValue = vec4(replace.x ? candidate.x : bestValue.x,\n replace.y ? candidate.y : bestValue.y,\n replace.z ? candidate.z : bestValue.z,\n replace.w ? candidate.w : bestValue.w);\n bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n srcIdx++;\n }\n setOutput(bestIndex);\n }\n "},Wo=function(t){this.variableNames=["dy"],this.outputShape=t.inShape;var e=t.filterHeight,n=t.filterWidth,r=t.strideHeight,o=t.strideWidth,i=t.dilationHeight,a=t.dilationWidth,s=t.effectiveFilterHeight,u=t.effectiveFilterWidth,c=s-1-t.padInfo.top,l=u-1-t.padInfo.left,h=1/(e*n);this.userCode="\n const ivec2 pads = ivec2("+c+", "+l+");\n const float avgMultiplier = float("+h+");\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < "+s+";\n wR += "+i+") {\n float dyR = float(dyRCorner + wR) / "+r+".0;\n\n if (dyR < 0.0 || dyR >= "+t.outHeight+".0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < "+u+";\n wC+= "+a+") {\n float dyC = float(dyCCorner + wC) / "+o+".0;\n\n if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n "},zo=function(t){this.variableNames=["dy"],this.outputShape=t.inShape;var e=t.filterDepth,n=t.filterHeight,r=t.filterWidth,o=t.strideDepth,i=t.strideHeight,a=t.strideWidth,s=t.dilationDepth,u=t.dilationHeight,c=t.dilationWidth,l=t.effectiveFilterDepth,h=t.effectiveFilterHeight,f=t.effectiveFilterWidth,p=l-1-t.padInfo.front,d=h-1-t.padInfo.top,v=f-1-t.padInfo.left,m=1/(e*n*r);this.userCode="\n const ivec3 pads = ivec3("+p+", "+d+", "+v+");\n const float avgMultiplier = float("+m+");\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < "+l+";\n wD += "+s+") {\n float dyD = float(dyDCorner + wD) / "+o+".0;\n\n if (dyD < 0.0 || dyD >= "+t.outDepth+".0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < "+h+";\n wR += "+u+") {\n float dyR = float(dyRCorner + wR) / "+i+".0;\n\n if (dyR < 0.0 || dyR >= "+t.outHeight+".0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < "+f+";\n wC += "+c+") {\n float dyC = float(dyCCorner + wC) / "+a+".0;\n\n if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n }\n setOutput(dotProd);\n }\n "},Uo=function(t,e,n,r,o,i){this.outputShape=[],this.variableNames=["x","mean","variance"],Lr(t,e),Lr(t,n);var a="0.0";null!=r&&(Lr(t,r),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");var s="1.0";null!=o&&(Lr(t,o),this.variableNames.push("scale"),s="getScaleAtOutCoords()"),this.outputShape=t,this.userCode="\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = "+a+";\n float scale = "+s+";\n float inv = scale * inversesqrt(variance + float("+i+"));\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n }\n "},Vo=function(t,e,n,r,o,i){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],Lr(t,e),Lr(t,n);var a="vec4(0.0)";null!=r&&(Lr(t,r),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");var s="vec4(1.0)";null!=o&&(Lr(t,o),this.variableNames.push("scale"),s="getScaleAtOutCoords()"),this.outputShape=t,this.userCode="\n void main() {\n vec4 offset = "+a+";\n vec4 scale = "+s+";\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4("+i+"));\n\n setOutput((x - mean) * inv + offset);\n }\n "},Go=function(t,e,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=Lr(e,n),this.userCode="\n float binaryOpComplex(\n float areal, float aimag, float breal, float bimag) {\n "+t+"\n }\n\n void main() {\n float areal = getARealAtOutCoords();\n float aimag = getAImagAtOutCoords();\n float breal = getBRealAtOutCoords();\n float bimag = getBImagAtOutCoords();\n setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n }\n "},Ho="return a + b;",jo="return a - b;",qo="return a * b;",Ko="return (a < 0.) ? b * a : a;",Xo=function(t,e,n){this.variableNames=["A","B"],this.outputShape=Lr(e,n),this.userCode="\n float binaryOperation(float a, float b) {\n "+t+"\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n "},$o="\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n",Yo=function(t,e,n,r){void 0===r&&(r=!1),this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=Lr(e,n);var o=this.outputShape.length,i="";if(r)if(0===o||1===D(this.outputShape))i="\n result.y = 0.;\n result.z = 0.;\n result.w = 0.;\n ";else if(i="\n "+Oo(o)+" coords = getOutputCoords();\n ",1===o)i+="\n result.y = (coords + 1) >= "+this.outputShape[0]+" ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n ";else{var a=wo("coords",o);i+="\n bool nextRowOutOfBounds =\n ("+a[o-2]+" + 1) >= "+this.outputShape[o-2]+";\n bool nextColOutOfBounds =\n ("+a[o-1]+" + 1) >= "+this.outputShape[o-1]+";\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n "}this.userCode="\n vec4 binaryOperation(vec4 a, vec4 b) {\n "+t+"\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n\n vec4 result = binaryOperation(a, b);\n "+i+"\n\n setOutput(result);\n }\n "},Jo=function(){function t(t){this.variableNames=["A"],this.outputShape=t,this.userCode="\n uniform float minVal;\n uniform float maxVal;\n\n void main() {\n float value = getAAtOutCoords();\n if (isnan(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, minVal, maxVal));\n }\n "}return t.prototype.getCustomSetupFunc=function(t,e){var n=this;return function(r,o){null==n.minLoc&&(n.minLoc=r.getUniformLocationNoThrow(o,"minVal"),n.maxLoc=r.getUniformLocationNoThrow(o,"maxVal")),r.gl.uniform1f(n.minLoc,t),r.gl.uniform1f(n.maxLoc,e)}},t}(),Qo=function(){function t(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.userCode="\n uniform float minVal;\n uniform float maxVal;\n\n void main() {\n vec4 value = getAAtOutCoords();\n\n if (any(isnan(value))) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n }\n "}return t.prototype.getCustomSetupFunc=function(t,e){var n=this;return function(r,o){null==n.minLoc&&(n.minLoc=r.getUniformLocationNoThrow(o,"minVal"),n.maxLoc=r.getUniformLocationNoThrow(o,"maxVal")),r.gl.uniform1f(n.minLoc,t),r.gl.uniform1f(n.maxLoc,e)}},t}(),Zo=function(t){this.variableNames=["real","imag"],this.outputShape=t,this.userCode="\n void main() {\n float re = abs(getRealAtOutCoords());\n float im = abs(getImagAtOutCoords());\n float mx = max(re, im);\n\n // sadly the length function in glsl is not underflow-safe\n // (at least not on Intel GPUs). So the safe solution is\n // to ensure underflow-safety in all cases.\n setOutput(\n mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n );\n }\n "},ti=function(t){this.outputShape=[],this.outputShape=rn(t,1),this.variableNames=t.map((function(t,e){return"T"+e}));var e=new Array(t.length-1);e[0]=t[0][1];for(var n=1;n= "+s[u-1]+") {\n return getChannel(\n getT"+u+"("+ni(a,c,p)+"),\n vec2("+ni(l,c,p)+"));\n }"}var d=s.length,v=s[s.length-1];f+="\n return getChannel(\n getT"+d+"("+ni(a,c,v)+"),\n vec2("+ni(l,c,v)+"));",this.userCode="\n float getValue("+a.map((function(t){return"int "+t}))+") {\n "+f+"\n }\n\n void main() {\n "+o+" coords = getOutputCoords();\n vec4 result = vec4(getValue("+i+"), 0., 0., 0.);\n\n "+i[r-1]+" = "+i[r-1]+" + 1;\n if ("+i[r-1]+" < "+n[r-1]+") {\n result.g = getValue("+i+");\n }\n\n "+i[r-2]+" = "+i[r-2]+" + 1;\n if ("+i[r-2]+" < "+n[r-2]+") {\n result.a = getValue("+i+");\n }\n\n "+i[r-1]+" = "+i[r-1]+" - 1;\n if ("+i[r-2]+" < "+n[r-2]+" &&\n "+i[r-1]+" < "+n[r-1]+") {\n result.b = getValue("+i+");\n }\n setOutput(result);\n }\n "};function ni(t,e,n){var r=t.indexOf(e);return t.map((function(t,e){return e===r?t+" - "+n:t})).join()}var ri=function(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;var e=t.strideHeight,n=t.strideWidth,r=t.padInfo.top,o=t.padInfo.left,i="channelsLast"===t.dataFormat;this.userCode="\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < "+t.batchSize+"; b++) {\n for (int yR = 0; yR < "+t.outHeight+"; yR++) {\n int xR = wR + yR * "+e+" - "+r+";\n\n if (xR < 0 || xR >= "+t.inHeight+") {\n continue;\n }\n\n for (int yC = 0; yC < "+t.outWidth+"; yC++) {\n int xC = wC + yC * "+n+" - "+o+";\n\n if (xC < 0 || xC >= "+t.inWidth+") {\n continue;\n }\n\n if ("+i+") {\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n } else {\n float dyValue = getDy(b, d2, yR, yC);\n float xValue = getX(b, d1, xR, xC);\n dotProd += (xValue * dyValue);\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n "},oi=function(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;var e=t.filterHeight,n=t.filterWidth,r=t.strideHeight,o=t.strideWidth,i="channelsLast"===t.dataFormat,a=e-1-t.padInfo.top,s=n-1-t.padInfo.left,u=i?1:2,c=i?2:3,l=i?3:1;this.userCode="\n const ivec2 pads = ivec2("+a+", "+s+");\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords["+l+"];\n\n ivec2 dyCorner = ivec2(coords["+u+"], coords["+c+"]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < "+e+"; wR++) {\n float dyR = float(dyRCorner + wR) / "+r+".0;\n\n if (dyR < 0.0 || dyR >= "+t.outHeight+".0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = "+e+" - 1 - wR;\n\n for (int wC = 0; wC < "+n+"; wC++) {\n float dyC = float(dyCCorner + wC) / "+o+".0;\n\n if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = "+n+" - 1 - wC;\n\n for (int d2 = 0; d2 < "+t.outChannels+"; d2++) {\n\n if ("+i+") {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n } else {\n float xValue = getDy(batch, d2, idyR, idyC);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n "},ii=function(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;var e=t.strideDepth,n=t.strideHeight,r=t.strideWidth,o=t.padInfo.front,i=t.padInfo.top,a=t.padInfo.left;this.userCode="\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < "+t.batchSize+"; b++) {\n for (int yF = 0; yF < "+t.outDepth+"; yF++) {\n int xF = wF + yF * "+e+" - "+o+";\n\n if (xF < 0 || xF >= "+t.inDepth+") {\n continue;\n }\n\n for (int yR = 0; yR < "+t.outHeight+"; yR++) {\n int xR = wR + yR * "+n+" - "+i+";\n\n if (xR < 0 || xR >= "+t.inHeight+") {\n continue;\n }\n\n for (int yC = 0; yC < "+t.outWidth+"; yC++) {\n int xC = wC + yC * "+r+" - "+a+";\n\n if (xC < 0 || xC >= "+t.inWidth+") {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n "},ai=function(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;var e=t.filterDepth,n=t.filterHeight,r=t.filterWidth,o=t.strideDepth,i=t.strideHeight,a=t.strideWidth,s=e-1-t.padInfo.front,u=n-1-t.padInfo.top,c=r-1-t.padInfo.left;this.userCode="\n const ivec3 pads = ivec3("+s+", "+u+", "+c+");\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < "+e+"; wF++) {\n float dyF = float(dyFCorner + wF) / "+o+".0;\n\n if (dyF < 0.0 || dyF >= "+t.outDepth+".0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = "+e+" - 1 - wF;\n\n for (int wR = 0; wR < "+n+"; wR++) {\n float dyR = float(dyRCorner + wR) / "+i+".0;\n\n if (dyR < 0.0 || dyR >= "+t.outHeight+".0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = "+n+" - 1 - wR;\n\n for (int wC = 0; wC < "+r+"; wC++) {\n float dyC = float(dyCCorner + wC) / "+a+".0;\n\n if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = "+r+" - 1 - wC;\n\n for (int d2 = 0; d2 < "+t.outChannels+"; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n "},si=function(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;var e=t.strideHeight,n=t.strideWidth,r=t.padInfo.top,o=t.padInfo.left,i=t.outChannels/t.inChannels;this.userCode="\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * "+i+" + dm;\n\n float dotProd = 0.0;\n\n // TO DO: Vec4 over the batch size\n for (int b = 0; b < "+t.batchSize+"; b++) {\n for (int yR = 0; yR < "+t.outHeight+"; yR++) {\n int xR = wR + yR * "+e+" - "+r+";\n\n if (xR < 0 || xR >= "+t.inHeight+") {\n continue;\n }\n\n for (int yC = 0; yC < "+t.outWidth+"; yC++) {\n int xC = wC + yC * "+n+" - "+o+";\n\n if (xC < 0 || xC >= "+t.inWidth+") {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n "},ui=function(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;var e=t.filterHeight,n=t.filterWidth,r=t.strideHeight,o=t.strideWidth,i=e-1-t.padInfo.top,a=n-1-t.padInfo.left,s=t.outChannels/t.inChannels;this.userCode="\n const ivec2 pads = ivec2("+i+", "+a+");\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < "+e+"; wR++) {\n float dyR = float(dyRCorner + wR) / "+r+".0;\n\n if (dyR < 0.0 || dyR >= "+t.outHeight+".0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = "+e+" - 1 - wR;\n\n for (int wC = 0; wC < "+n+"; wC++) {\n float dyC = float(dyCCorner + wC) / "+o+".0;\n\n if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = "+n+" - 1 - wC;\n\n // TO DO: Vec4 over the channelMul\n for (int dm = 0; dm < "+s+"; dm++) {\n int d2 = d1 * "+s+" + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n "},ci=function(t,e,n,r){void 0===e&&(e=!1),void 0===n&&(n=null),void 0===r&&(r=!1),this.variableNames=["x","W"],this.outputShape=t.outShape;var o=t.padInfo.top,i=t.padInfo.left,a=t.strideHeight,s=t.strideWidth,u=t.dilationHeight,c=t.dilationWidth,l=t.filterHeight,h=t.filterWidth,f=4*Math.floor(t.inChannels/4),p=t.inChannels%4,d="channelsLast"===t.dataFormat,v=d?1:2,m=d?2:3,g=d?3:1,y="",x="";n&&(y=r?"float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n "+n+"\n }":"\n float activation(float x) {\n "+n+"\n }\n ",x="result = activation(result);");var b=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),this.userCode="\n "+y+"\n\n const ivec2 strides = ivec2("+a+", "+s+");\n const ivec2 pads = ivec2("+o+", "+i+");\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords["+g+"];\n\n ivec2 xRCCorner =\n ivec2(coords["+v+"], coords["+m+"]) * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < "+l+"; wR++) {\n int xR = xRCorner + wR * "+u+";\n\n if (xR < 0 || xR >= "+t.inHeight+") {\n continue;\n }\n\n for (int wC = 0; wC < "+h+"; wC++) {\n int xC = xCCorner + wC * "+c+";\n\n if (xC < 0 || xC >= "+t.inWidth+") {\n continue;\n }\n\n for (int d1 = 0; d1 < "+f+"; d1 += 4) {\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n if ("+d+") {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec4 xValues = vec4(\n getX(batch, d1, xR, xC),\n getX(batch, d1 + 1, xR, xC),\n getX(batch, d1 + 2, xR, xC),\n getX(batch, d1 + 3, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n\n if ("+(1===p)+") {\n\n if ("+d+") {\n dotProd +=\n getX(batch, xR, xC, "+f+") *\n getW(wR, wC, "+f+", d2);\n } else {\n dotProd +=\n getX(batch, "+f+", xR, xC) *\n getW(wR, wC, "+f+", d2);\n }\n\n } else if ("+(2===p)+") {\n vec2 wValues = vec2(\n getW(wR, wC, "+f+", d2),\n getW(wR, wC, "+f+" + 1, d2)\n );\n\n if ("+d+") {\n vec2 xValues = vec2(\n getX(batch, xR, xC, "+f+"),\n getX(batch, xR, xC, "+f+" + 1)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec2 xValues = vec2(\n getX(batch, "+f+", xR, xC),\n getX(batch, "+f+" + 1, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n } else if ("+(3===p)+") {\n vec3 wValues = vec3(\n getW(wR, wC, "+f+", d2),\n getW(wR, wC, "+f+" + 1, d2),\n getW(wR, wC, "+f+" + 2, d2)\n );\n\n if ("+d+") {\n vec3 xValues = vec3(\n getX(batch, xR, xC, "+f+"),\n getX(batch, xR, xC, "+f+" + 1),\n getX(batch, xR, xC, "+f+" + 2)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec3 xValues = vec3(\n getX(batch, "+f+", xR, xC),\n getX(batch, "+f+" + 1, xR, xC),\n getX(batch, "+f+" + 2, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n }\n }\n }\n\n float result = dotProd;\n "+b+"\n "+x+"\n setOutput(result);\n }\n "},li=function(t){this.variableNames=["x","W"],this.outputShape=t.outShape;var e=t.padInfo.front,n=t.padInfo.top,r=t.padInfo.left,o=t.strideDepth,i=t.strideHeight,a=t.strideWidth,s=t.dilationDepth,u=t.dilationHeight,c=t.dilationWidth,l=t.filterDepth,h=t.filterHeight,f=t.filterWidth,p=4*Math.floor(t.inChannels/4),d=t.inChannels%4;this.userCode="\n const ivec3 strides = ivec3("+o+", "+i+", "+a+");\n const ivec3 pads = ivec3("+e+", "+n+", "+r+");\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < "+l+"; wF++) {\n int xF = xFCorner + wF * "+s+";\n\n if (xF < 0 || xF >= "+t.inDepth+") {\n continue;\n }\n\n for (int wR = 0; wR < "+h+"; wR++) {\n int xR = xRCorner + wR * "+u+";\n\n if (xR < 0 || xR >= "+t.inHeight+") {\n continue;\n }\n\n for (int wC = 0; wC < "+f+"; wC++) {\n int xC = xCCorner + wC * "+c+";\n\n if (xC < 0 || xC >= "+t.inWidth+") {\n continue;\n }\n\n for (int d1 = 0; d1 < "+p+"; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if ("+(1===d)+") {\n dotProd +=\n getX(batch, xF, xR, xC, "+p+") *\n getW(wF, wR, wC, "+p+", d2);\n } else if ("+(2===d)+") {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, "+p+"),\n getX(batch, xF, xR, xC, "+p+" + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, "+p+", d2),\n getW(wF, wR, wC, "+p+" + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if ("+(3===d)+") {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, "+p+"),\n getX(batch, xF, xR, xC, "+p+" + 1),\n getX(batch, xF, xR, xC, "+p+" + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, "+p+", d2),\n getW(wF, wR, wC, "+p+" + 1, d2),\n getW(wF, wR, wC, "+p+" + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n "},hi=function(t,e,n,r){void 0===e&&(e=!1),void 0===n&&(n=null),void 0===r&&(r=!1),this.variableNames=["x","W"],this.outputShape=t.outShape;var o=t.inHeight,i=t.inWidth,a=t.padInfo.top,s=t.padInfo.left,u=t.strideHeight,c=t.strideWidth,l=t.dilationHeight,h=t.dilationWidth,f=t.filterHeight,p=t.filterWidth,d=t.outChannels/t.inChannels,v="",m="";n&&(v=r?"float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n "+n+"\n }":"\n float activation(float x) {\n "+n+"\n }\n ",m="result = activation(result);");var g=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),this.userCode="\n "+v+"\n\n const ivec2 strides = ivec2("+u+", "+c+");\n const ivec2 pads = ivec2("+a+", "+s+");\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / "+d+";\n int q = d2 - d1 * "+d+";\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < "+f+"; wR++) {\n int xR = xRCorner + wR * "+l+";\n\n if (xR < 0 || xR >= "+o+") {\n continue;\n }\n\n for (int wC = 0; wC < "+p+"; wC++) {\n int xC = xCCorner + wC * "+h+";\n\n if (xC < 0 || xC >= "+i+") {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n\n float result = dotProd;\n "+g+"\n "+m+"\n setOutput(result);\n }\n "},fi=function(t,e,n,r){void 0===e&&(e=!1),void 0===n&&(n=null),void 0===r&&(r=!1),this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.outShape;for(var o=t.inHeight,i=t.inWidth,a=t.padInfo.top,s=t.padInfo.left,u=t.strideHeight,c=t.strideWidth,l=t.dilationHeight,h=t.dilationWidth,f=t.filterHeight,p=t.filterWidth,d=p,v="int xR; int xC; int xCOffset;",m=0;m= 0 && xR < "+o+" && xCOffset >= 0 && xCOffset < "+i+") {\n xTexelR"+m+"C"+g+" = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if(xCOffset + 1 >= "+i+") {\n xTexelR"+m+"C"+g+".zw = vec2(0.);\n }\n } else {\n xTexelR"+m+"C"+g+" = vec4(0.);\n }\n\n xCOffset = xC + 1 - 2;\n if(xR >= 0 && xR < "+o+" && xCOffset >= 0 && xCOffset < "+i+") {\n vec4 previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if(xCOffset + 1 >= "+i+") {\n previous.zw = vec2(0.);\n }\n\n xR"+m+"C"+g+" = vec4(previous.zw, xTexelR"+m+"C"+g+".xy);\n } else {\n xR"+m+"C"+g+" = vec4(0, 0, xTexelR"+m+"C"+g+".xy);\n }\n ":"\n if(xR >= 0 && xR < "+o+" && xC >= 0 && xC < "+i+") {\n xTexelR"+m+"C"+g+" = getX(batch, xR, xC, d1);\n } else {\n xTexelR"+m+"C"+g+" = vec4(0.);\n }\n\n xR"+m+"C"+g+" = xTexelR"+m+"C"+g+";\n ",g+1= 0 && xR < "+o+" &&\n xCOffset >= 0 && xCOffset < "+i+") {\n xTexelR"+m+"C"+(g+2)+" = getX(batch, xR, xCOffset, d1);\n }\n ",h>1&&(v+="\n xCOffset -= 2;\n if(xR >= 0 && xR < "+o+" &&\n xCOffset >= 0 && xCOffset < "+i+") {\n xTexelR"+m+"C"+g+" = getX(batch, xR, xCOffset, d1);\n } else {\n xTexelR"+m+"C"+g+" = vec4(0.);\n }\n "),v+="\n xR"+m+"C"+(g+1)+" = vec4(\n xTexelR"+m+"C"+g+".zw, xTexelR"+m+"C"+(g+2)+".xy);\n "):v+="\n xCOffset = xC + "+x+";\n\n if(xR >= 0 && xR < "+o+" &&\n xCOffset >= 0 && xCOffset < "+i+") {\n xTexelR"+m+"C"+(g+2)+" = getX(batch, xR, xCOffset, d1);\n }\n\n xR"+m+"C"+(g+1)+" = xTexelR"+m+"C"+(g+2)+";\n "}}else g= 0 && xR < "+o+") {\n ",s%2==1?(v+="\n xCOffset = xC + 1 - "+c+";\n if(xCOffset >= 0 && xCOffset < "+i+") {\n xTexelR"+m+"C"+g+" = getX(batch, xR, xCOffset, d1);\n } else {\n xTexelR"+m+"C"+g+" = vec4(0.);\n }\n\n if(xC + 1 >= 0 && xC + 1 < "+i+") {\n xTexelR"+m+"C"+(g+2)+" = getX(batch, xR, xC + 1, d1);\n } else {\n xTexelR"+m+"C"+(g+2)+" = vec4(0.);\n }\n\n xR"+m+"C"+g+" = vec4(\n xTexelR"+m+"C"+g+".zw, xTexelR"+m+"C"+(g+2)+".zw);\n ",g+1= 0 && xCOffset < "+i+") {\n final = getX(batch, xR, xCOffset, d1);\n }\n xR"+m+"C"+(g+1)+" = vec4(xTexelR"+m+"C"+(g+2)+".xy, final.xy);\n ")):(v+="\n if(xC >= 0 && xC < "+i+") {\n xTexelR"+m+"C"+g+" = getX(batch, xR, xC, d1);\n } else {\n xTexelR"+m+"C"+g+" = vec4(0.);\n }\n\n xCOffset = xC + "+c+";\n if(xCOffset >= 0 && xCOffset < "+i+") {\n xTexelR"+m+"C"+(g+2)+" = getX(batch, xR, xCOffset, d1);\n } else {\n xTexelR"+m+"C"+(g+2)+" = vec4(0.);\n }\n\n xR"+m+"C"+g+" = vec4(\n xTexelR"+m+"C"+g+".xy, xTexelR"+m+"C"+(g+2)+".xy);\n ",g+11?[""+(a-1)/(l-1),"(y2-y1) * height_ratio","y1*"+d+" + float(y)*(height_scale)"]:["0.0","0.0","0.5 * (y1+y2) * "+d],g=m[0],y=m[1],x=m[2],b=h>1?[""+(s-1)/(h-1),"(x2-x1) * width_ratio","x1*"+v+" + float(x)*(width_scale)"]:["0.0","0.0","0.5 * (x1+x2) * "+v],w=b[0],C=b[1],E=b[2];this.userCode="\n const float height_ratio = float("+g+");\n const float width_ratio = float("+w+");\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= "+i+") {\n return;\n }\n\n float height_scale = "+y+";\n float width_scale = "+C+";\n\n float in_y = "+x+";\n if( in_y < 0.0 || in_y > "+d+" ) {\n setOutput(float("+o+"));\n return;\n }\n float in_x = "+E+";\n if( in_x < 0.0 || in_x > "+v+" ) {\n setOutput(float("+o+"));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if("+f+" == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n "},di=function(t,e,n){this.variableNames=["x"],this.outputShape=t;var r=t.length,o=t[t.length-1],i=n?"<":">";this.userCode="\n int getIndex(int i) {\n "+(n?"return "+o+" -i - 1;":"return i;")+"\n }\n\n void main() {\n "+Oo(r)+" coords = getOutputCoords();\n int end = "+vi(r,"coords")+";\n float val = 0.0;\n for (int i = "+o+" - 1; i >= 0; i -= 1) {\n int idx = getIndex(i);\n if (idx "+i+" end) {\n continue;\n }\n if (idx == end && "+e+") {\n continue;\n }\n "+vi(r,"coords")+" = idx;\n val += getX("+function(t,e){if(1===t)return""+e;if(2===t)return e+".x, "+e+".y";if(3===t)return e+".x, "+e+".y, "+e+".z";if(4===t)return e+".x, "+e+".y, "+e+".z, "+e+".w";throw Error("Cumulative sum for rank "+t+" is not yet supported")}(r,"coords")+");\n }\n setOutput(val);\n }\n "};function vi(t,e){if(1===t)return""+e;if(2===t)return e+".y";if(3===t)return e+".z";if(4===t)return e+".w";throw Error("Cumulative sum for rank "+t+" is not yet supported")}var mi=function(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Lt.DENSE;var e=qt(t),n=Co();this.outputShape=t,this.userCode="\n ivec3 outCoordsFromFlatIndex(int index) {\n "+Eo(["r","c","d"],t)+"\n return ivec3(r, c, d);\n }\n\n void main() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+e[0]+", "+e[1]+"));\n int index = 4 * (resTexRC.x * "+e[1]+" + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getA(rc.x, rc.y, rc.z);\n }\n\n "+n.output+" = result;\n }\n "},gi=function(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Lt.DENSE;var e=qt(t),n=Co();this.outputShape=t,this.userCode="\n ivec3 outCoordsFromFlatIndex(int index) {\n "+Eo(["r","c","d"],t)+"\n return ivec3(r, c, d);\n }\n\n void main() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+e[0]+", "+e[1]+"));\n int index = 4 * (resTexRC.x * "+e[1]+" + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n }\n\n "+n.output+" = result;\n }\n "},yi=function(){function t(t,e,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=n,this.userCode="\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = "+this.getHeightCoordString()+";\n int w = "+this.getWidthCoordString()+";\n int d = "+this.getDepthCoordString()+";\n\n int in_h = h / "+e+";\n int offset_h = imod(h, "+e+");\n int in_w = w / "+e+";\n int offset_w = imod(w, "+e+");\n int offset_d = (offset_h * "+e+" + offset_w) *\n "+this.getOutputDepthSize()+";\n int in_d = d + offset_d;\n\n float result = "+this.getInputSamplingString()+";\n setOutput(result);\n }\n "}return t.prototype.getHeightCoordString=function(){return"NHWC"===this.dataFormat?"coords[1]":"coords[2]"},t.prototype.getWidthCoordString=function(){return"NHWC"===this.dataFormat?"coords[2]":"coords[3]"},t.prototype.getDepthCoordString=function(){return"NHWC"===this.dataFormat?"coords[3]":"coords[1]"},t.prototype.getOutputDepthSize=function(){return"NHWC"===this.dataFormat?this.outputShape[3]:this.outputShape[1]},t.prototype.getInputSamplingString=function(){return"NHWC"===this.dataFormat?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"},t}(),xi=function(t){this.variableNames=["X"],this.outputShape=[t,t],this.userCode="\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n 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halfCR) / vec2("+i+".0, "+o+".0);\n vec4 values = "+r.texture2D+"(A, uv);\n\n float result;\n\n if(offset == 0) {\n result = values[0];\n } else if(offset == 1) {\n result = values[1];\n } else if(offset == 2) {\n result = values[2];\n } else {\n result = values[3];\n }\n\n "+r.output+" = vec4("+a+", 0., 0., 0.);\n }\n "},Ei=function(t,e,n){void 0===n&&(n=!1),this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;var r=Co(),o=e[0],i=e[1];this.outputShape=t;var a="",s="result";n&&(s="floor(result * 255. + 0.5)");for(var u=0;u<=1;u++)for(var c=0;c<=1;c++){var l=2*u+c;a+="\n localCoords = coords;\n if(localCoords[2] + "+c+" < "+t[2]+") {\n localCoords[2] += "+c+";\n if(localCoords[1] + "+u+" < "+t[1]+") {\n localCoords[1] += "+u+";\n\n flatIndex = getFlatIndex(localCoords);\n offset = imod(flatIndex, 4);\n\n flatIndex = idiv(flatIndex, 4, 1.);\n\n r = flatIndex / "+i+";\n c = imod(flatIndex, "+i+");\n uv = (vec2(c, r) + halfCR) / vec2("+i+".0, "+o+".0);\n values = 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Vi(r.gl,r.debug,e,n,r.textureConfig)}))},t.prototype.downloadPackedMatrixFromBuffer=function(t,e,n,r,o,i){return Gi(this.gl,t,0,0,0,o,i,this.textureConfig)},t.prototype.downloadFloat32MatrixFromBuffer=function(t,e){return Ui(this.gl,t,e)},t.prototype.createBufferFromTexture=function(t,e,n){this.bindTextureToFrameBuffer(t);var r=zi(this.gl,this.debug,e,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r},t.prototype.createAndWaitForFence=function(){var t=this.createFence(this.gl);return this.pollFence(t)},t.prototype.createFence=function(t){var e,n,r=this;if(f().getBool("WEBGL_FENCE_API_ENABLED")){var o=t,i=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);t.flush(),n=function(){var t=o.clientWaitSync(i,0,0);return t===o.ALREADY_SIGNALED||t===o.CONDITION_SATISFIED},e=i}else f().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(e=this.beginQuery(),this.endQuery(),n=function(){return r.isQueryAvailable(e,f().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}):n=function(){return!0};return{query:e,isFencePassed:n}},t.prototype.downloadMatrixFromPackedTexture=function(t,e,n){var r=this;return this.downloadMatrixDriver(t,(function(){return Hi(r.gl,r.debug,e,n)}))},t.prototype.createProgram=function(t){this.throwIfDisposed();var e=this.gl,n=te(e,this.debug,t),r=ki(e,this.debug),o=oe(e,this.debug);return $t(e,this.debug,(function(){return e.attachShader(o,r)})),$t(e,this.debug,(function(){return e.attachShader(o,n)})),ie(e,this.debug,o),this.debug&&ae(e,this.debug,o),this.vertexAttrsAreBound||(this.setProgram(o),this.vertexAttrsAreBound=Bi(e,this.debug,this.program,this.vertexBuffer)),o},t.prototype.deleteProgram=function(t){var e=this;this.throwIfDisposed(),t===this.program&&(this.program=null),null!=t&&$t(this.gl,this.debug,(function(){return e.gl.deleteProgram(t)}))},t.prototype.setProgram=function(t){var e=this;this.throwIfDisposed(),this.program=t,null!=this.program&&this.debug&&ae(this.gl,this.debug,this.program),$t(this.gl,this.debug,(function(){return e.gl.useProgram(t)}))},t.prototype.getUniformLocation=function(t,e,n){return void 0===n&&(n=!0),this.throwIfDisposed(),n?de(this.gl,this.debug,t,e):ve(this.gl,t,e)},t.prototype.getAttributeLocation=function(t,e){var n=this;return this.throwIfDisposed(),$t(this.gl,this.debug,(function(){return n.gl.getAttribLocation(t,e)}))},t.prototype.getUniformLocationNoThrow=function(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)},t.prototype.setInputMatrixTexture=function(t,e,n){this.throwIfDisposed(),this.throwIfNoProgram(),me(this.gl,this.debug,this.program,t,e,n)},t.prototype.setOutputMatrixTexture=function(t,e,n){this.setOutputMatrixTextureDriver(t,n,e)},t.prototype.setOutputPackedMatrixTexture=function(t,e,n){this.throwIfDisposed();var r=Kt(e,n),o=r[0],i=r[1];this.setOutputMatrixTextureDriver(t,o,i)},t.prototype.setOutputMatrixWriteRegion=function(t,e,n,r){this.setOutputMatrixWriteRegionDriver(n,t,r,e)},t.prototype.setOutputPackedMatrixWriteRegion=function(t,e,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")},t.prototype.debugValidate=function(){null!=this.program&&ae(this.gl,this.debug,this.program),xe(this.gl)},t.prototype.executeProgram=function(){this.throwIfDisposed(),this.throwIfNoProgram();var t=this.gl;this.debug&&this.debugValidate(),$t(t,this.debug,(function(){return t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0)}))},t.prototype.blockUntilAllProgramsCompleted=function(){var t=this;this.throwIfDisposed(),$t(this.gl,this.debug,(function(){return t.gl.finish()}))},t.prototype.getQueryTimerExtension=function(){return null==this.disjointQueryTimerExtension&&(this.disjointQueryTimerExtension=Qt(this.gl,this.debug,2===f().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension},t.prototype.getQueryTimerExtensionWebGL2=function(){return this.getQueryTimerExtension()},t.prototype.getQueryTimerExtensionWebGL1=function(){return this.getQueryTimerExtension()},t.prototype.beginQuery=function(){if(2===f().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){var t=this.gl,e=this.getQueryTimerExtensionWebGL2(),n=t.createQuery();return t.beginQuery(e.TIME_ELAPSED_EXT,n),n}var r=this.getQueryTimerExtensionWebGL1(),o=r.createQueryEXT();return r.beginQueryEXT(r.TIME_ELAPSED_EXT,o),o},t.prototype.endQuery=function(){if(2!==f().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){var t=this.getQueryTimerExtensionWebGL1();t.endQueryEXT(t.TIME_ELAPSED_EXT)}else{var e=this.gl,n=this.getQueryTimerExtensionWebGL2();e.endQuery(n.TIME_ELAPSED_EXT)}},t.prototype.waitForQueryAndGetTime=function(t){return u(this,void 0,void 0,(function(){var e=this;return c(this,(function(n){switch(n.label){case 0:return[4,O((function(){return e.disposed||e.isQueryAvailable(t,f().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}))];case 1:return n.sent(),[2,this.getQueryTime(t,f().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))]}}))}))},t.prototype.getQueryTime=function(t,e){if(0===e)return null;if(2===e){var n=this.gl;return n.getQueryParameter(t,n.QUERY_RESULT)/1e6}var r=this.getQueryTimerExtensionWebGL1();return r.getQueryObjectEXT(t,r.QUERY_RESULT_EXT)/1e6},t.prototype.isQueryAvailable=function(t,e){if(0===e)return!0;if(2===e){var n=this.gl,r=this.getQueryTimerExtensionWebGL2(),o=n.getQueryParameter(t,n.QUERY_RESULT_AVAILABLE);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint}return o=(r=this.getQueryTimerExtensionWebGL1()).getQueryObjectEXT(t,r.QUERY_RESULT_AVAILABLE_EXT),null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint},t.prototype.pollFence=function(t){var e=this;return new Promise((function(n){e.addItemToPoll((function(){return t.isFencePassed()}),(function(){return n()}))}))},t.prototype.pollItems=function(){for(var t=function(t){for(var e=0;e1||O((function(){return n.pollItems(),0===n.itemsToPoll.length}))},t.prototype.bindTextureToFrameBuffer=function(t){this.throwIfDisposed(),ge(this.gl,this.debug,t,this.framebuffer),this.debug&&xe(this.gl)},t.prototype.unbindTextureToFrameBuffer=function(){null!=this.outputTexture?(ge(this.gl,this.debug,this.outputTexture,this.framebuffer),this.debug&&xe(this.gl)):ye(this.gl,this.debug,this.framebuffer)},t.prototype.downloadMatrixDriver=function(t,e){this.bindTextureToFrameBuffer(t);var n=e();return this.unbindTextureToFrameBuffer(),n},t.prototype.setOutputMatrixTextureDriver=function(t,e,n){this.throwIfDisposed();var r=this.gl;ge(r,this.debug,t,this.framebuffer),this.debug&&xe(r),this.outputTexture=t,$t(r,this.debug,(function(){return r.viewport(0,0,e,n)})),$t(r,this.debug,(function(){return r.scissor(0,0,e,n)}))},t.prototype.setOutputMatrixWriteRegionDriver=function(t,e,n,r){var o=this;this.throwIfDisposed(),$t(this.gl,this.debug,(function(){return o.gl.scissor(t,e,n,r)}))},t.prototype.throwIfDisposed=function(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")},t.prototype.throwIfNoProgram=function(){if(null==this.program)throw new Error("No GPU program is currently set.")},t}();function Ki(t,e){if(t.length!==e.length)throw Error("Binary was compiled with "+t.length+" inputs, but was executed with "+e.length+" inputs");t.forEach((function(t,n){var r=t.logicalShape,o=e[n],i=o.shape;if(!A(r,i))throw Error("Binary was compiled with different shapes than the current args. Shapes "+r+" and "+i+" must match");if(!t.isUniform||!o.isUniform){var a=t.texShape,s=o.isUniform?null:o.texData.texShape;if(!A(a,s))throw Error("Binary was compiled with different texture shapes than the current args. Shape "+a+" and "+s+" must match")}}))}var Xi=function(t,e,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t;for(var r=n.filterWidth,o=n.inChannels,i=n.strideWidth,a=n.strideHeight,s=n.padInfo,u=n.outWidth,c=n.dilationWidth,l=n.dilationHeight,h=n.dataFormat,f=s.left,p=s.top,d=o*r,v=Co(),m="channelsLast"===h,g=m?0:1,y=m?1:2,x="",b=0;b<=1;b++)for(var w=0;w<=1;w++)x+="\n blockIndex = rc.y + "+w+";\n pos = rc.x + "+b+";\n\n if(blockIndex < "+t[1]+" && pos < "+t[0]+") {\n offsetY = int(blockIndex / ("+u+")) * "+a+" - "+p+";\n d0 = offsetY + "+l+" * (pos / "+d+");\n\n if(d0 < "+e[g]+" && d0 >= 0) {\n\n offsetX = int(mod(float(blockIndex), "+u+".) * "+i+". - "+f+".);\n d1 = offsetX + "+c+" * (int(mod(float(pos), "+d+".) / "+o+".));\n\n if(d1 < "+e[y]+" && d1 >= 0) {\n\n ch = int(mod(float(pos), "+o+".));\n\n if ("+m+") {\n innerDims = vec2(d1, ch);\n result["+(2*b+w)+"] = getChannel(\n getA(d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result["+(2*b+w)+"] = getChannel(\n getA(ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n }\n ";this.userCode="\n void main() {\n ivec2 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n\n "+x+"\n\n "+v.output+" = result;\n }\n "},$i=function(t,e,n,r,o){this.variableNames=["x"],this.outputShape=[];var i,a=e,s=t[3]-1;this.outputShape=t;var u="float("+n+") + float("+r+") * sum";i=.5===o?"inversesqrt("+u+")":1===o?"1.0/("+u+")":"exp(log("+u+") * float(-"+o+"));",this.userCode="\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -"+a+"; j <= "+a+"; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= "+s+") {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * "+i+";\n setOutput(val);\n }\n "},Yi=function(t,e,n,r,o){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=n,this.alpha=r,this.beta=o,this.userCode="\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < "+this.depth+"; ++d) {\n int depthBegin = int(max(0.0, float(d - "+e+")));\n int depthEnd = int(min(float("+this.depth+"),\n float(d + "+e+" + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = "+this.depth+";\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float("+r+") * norm + float("+n+");\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float("+r+")\n * float("+o+")\n * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * "+o+");\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n "},Ji=function(t,e,n,r,o){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;var i,a=e,s=t[3]-1;this.outputShape=t;var u="float("+n+") + float("+r+") * sum";i=.5===o?"inversesqrt("+u+")":1===o?"1.0/("+u+")":"exp(log("+u+") * float(-"+o+"));",this.userCode="\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords.x;\n int r = coords.y;\n int c = coords.z;\n int d = coords.w;\n\n bool hasNextCol = d < "+this.outputShape[3]+";\n bool hasNextRow = c < "+this.outputShape[2]+";\n\n vec4 sum = vec4(0.);\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n vec4 xAtOutputCoords = vec4(\n getChannel(xFragAtOutputCoords, vec2(c, d)),\n hasNextCol ?\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n hasNextRow ?\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n (hasNextRow && hasNextCol) ?\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n );\n\n int firstChannel = d - "+a+";\n vec2 cache = vec2(0.);\n if(firstChannel >= 0){\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n if(hasNextRow){\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n }\n }\n\n ivec2 depth = ivec2(d, d + 1);\n for (int j = - "+a+"; j <= "+a+"; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2("+s+"));\n\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n if(depthInRange || depthPlusOneInRange){\n vec4 z = vec4(0.);\n vec4 xFragAtCurrentDepth;\n z.xz = cache.xy;\n if(depthPlusOneInRange && hasNextCol){\n xFragAtCurrentDepth = idx.y != d ?\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n if(hasNextRow){\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n }\n }\n cache.xy = z.yw;\n sum += z * z;\n }\n }\n vec4 result = xAtOutputCoords * "+i+";\n setOutput(result);\n }\n "},Qi=function(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;var e=t.strideHeight,n=t.strideWidth,r=t.dilationHeight,o=t.effectiveFilterHeight,i=t.effectiveFilterWidth,a=o-1-t.padInfo.top,s=i-1-t.padInfo.left,u=o*i-1;this.userCode="\n const ivec2 pads = ivec2("+a+", "+s+");\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < "+o+";\n wR += "+r+") {\n float dyR = float(dyRCorner + wR) / "+e+".0;\n\n if (dyR < 0.0 || dyR >= "+t.outHeight+".0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < "+i+"; wC++) {\n float dyC = float(dyCCorner + wC) / "+n+".0;\n\n if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = "+u+" - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * "+i+" + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n "},Zi=function(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;var e=t.strideDepth,n=t.strideHeight,r=t.strideWidth,o=t.dilationDepth,i=t.dilationHeight,a=t.dilationWidth,s=t.effectiveFilterDepth,u=t.effectiveFilterHeight,c=t.effectiveFilterWidth,l=s-1-t.padInfo.front,h=u-1-t.padInfo.top,f=c-1-t.padInfo.left,p=s*u*c-1;this.userCode="\n const ivec3 pads = ivec3("+l+", "+h+", "+f+");\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < "+s+";\n wD += "+o+") {\n float dyD = float(dyDCorner + wD) / "+e+".0;\n\n if (dyD < 0.0 || dyD >= "+t.outDepth+".0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < "+u+";\n wR += "+i+") {\n float dyR = float(dyRCorner + wR) / "+n+".0;\n\n if (dyR < 0.0 || dyR >= "+t.outHeight+".0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < "+c+";\n wC += "+a+") {\n float dyC = float(dyCCorner + wC) / "+r+".0;\n\n if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n int maxPosValue = "+p+" -\n int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue =\n wD * "+u+" * "+c+" +\n wR * "+c+" + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n }\n setOutput(dotProd);\n }\n "},ta=function(t,e,n,r,o,i,a){void 0===n&&(n=!1),void 0===r&&(r=!1),void 0===o&&(o=!1),void 0===i&&(i=null),void 0===a&&(a=!1),this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;var s=n?t[1]:t[2],u=Math.ceil(s/2),c=n?"i * 2, rc.y":"rc.y, i * 2",l=r?"rc.z, i * 2":"i * 2, rc.z",h=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],p="",d="";i&&(p=a?"vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n "+i+"\n }":"vec4 activation(vec4 x) {\n "+i+"\n }",d="result = activation(result);");var v=o?"result += getBiasAtOutCoords();":"";o&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),this.userCode="\n "+p+"\n\n const float sharedDimension = "+u+".0;\n\n vec4 dot2x2ARowBCol(ivec3 rc) {\n vec4 result = vec4(0);\n for (int i = 0; i < "+u+"; i++) {\n vec4 a = getMatrixA(rc.x, "+c+");\n vec4 b = getMatrixB(rc.x, "+l+");\n\n // These swizzled products need to be separately added.\n // See: https://github.com/tensorflow/tfjs/issues/1735\n result += ("+h[0]+" * "+f[0]+");\n result += ("+h[1]+" * "+f[1]+");\n }\n return result;\n }\n\n void main() {\n ivec3 rc = getOutputCoords();\n vec4 result = dot2x2ARowBCol(rc);\n\n "+v+"\n\n "+d+"\n\n setOutput(result);\n }\n "},ea=function(){function t(t,e,n){this.variableNames=["probs"],this.outputShape=[t,n],this.userCode="\n uniform float seed;\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n\n float r = random(seed);\n float cdf = 0.0;\n\n for (int i = 0; i < "+(e-1)+"; i++) {\n cdf += getProbs(batch, i);\n\n if (r < cdf) {\n setOutput(float(i));\n return;\n }\n }\n\n // If no other event happened, last event happened.\n setOutput(float("+(e-1)+"));\n }\n "}return t.prototype.getCustomSetupFunc=function(t){var e=this;return function(n,r){null==e.seedLoc&&(e.seedLoc=n.getUniformLocation(r,"seed")),n.gl.uniform1f(e.seedLoc,t)}},t}(),na=function(t,e,n,r){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode="\n void main() {\n ivec2 coords = getOutputCoords();\n int index = round(getIndices(coords.x));\n setOutput(mix(float("+r+"), float("+n+"),\n float(index == coords.y)));\n }\n "},ra=function(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=t;var e=t.length;if(0===e)this.userCode="\n void main() {\n setOutput(vec4(getA(), 0., 0., 0.));\n }\n ";else{var n=wo("rc",e),r=Oo(e),o=function(t,e,n){if(1===t)return"rc > "+e[0];for(var r="",o=t-2;o= "+e[o],o= "+e+";\n bool rEdge = rp1 >= "+n+";\n "}(e,t[t.length-1],t[t.length-2],n),a=function(t,e){var n=t.length,r=function(t,e){for(var n=[],r=0;r<=1;r++)for(var o=0;o<=1;o++){for(var i=(0===r?"r":"rp1")+", "+(0===o?"c":"cp1"),a=2;a= "+t[0]+" ? 0. : getA(rc + 1),\n 0, 0":"getA("+r[0]+"),\n cEdge ? 0. : getA("+r[1]+"),\n rEdge ? 0. : getA("+r[2]+"),\n rEdge || cEdge ? 0. : getA("+r[3]+")"}(t,n);this.userCode="\n void main() {\n "+r+" rc = getOutputCoords();\n\n if("+o+") {\n setOutput(vec4(0));\n } else {\n "+i+"\n\n setOutput(vec4("+a+"));\n }\n }\n "}},oa=function(t,e,n){this.variableNames=["x"],this.outputShape=e.map((function(e,n){return e[0]+t[n]+e[1]}));var r=t.length,o=Oo(r),i=e.map((function(t){return t[0]})).join(","),a=e.map((function(e,n){return e[0]+t[n]})).join(","),s=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);this.userCode=1!==r?"\n "+o+" start = "+o+"("+i+");\n "+o+" end = "+o+"("+a+");\n\n void main() {\n "+o+" outC = getOutputCoords();\n if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n setOutput(float("+n+"));\n } else {\n "+o+" coords = outC - start;\n setOutput(getX("+s+"));\n }\n }\n ":"\n int start = "+i+";\n int end = "+a+";\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start || outC >= end) {\n setOutput(float("+n+"));\n } else {\n setOutput(getX(outC - start));\n }\n }\n "},ia=function(t,e,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((function(e,n){return e[0]+t[n]+e[1]}));for(var r=t.length,o=Oo(r),i=e.map((function(t){return t[0]})).join(","),a=e.map((function(e,n){return e[0]+t[n]})).join(","),s=wo("rc",r),u=wo("source",r),c=s[r-1]+" < "+this.outputShape[r-1],l=1===r?"source":"vec2("+u.slice(-2).join()+")",h=[o+" rc = outputLoc;",s[r-1]+" += 1;\n if("+c+") {\n ",1===r?"":"}\n rc = outputLoc;\n "+s[r-2]+" += 1;\n if("+s[r-2]+" < "+this.outputShape[r-2]+") {",1===r?"":" "+s[r-1]+" += 1;\n if("+c+") {"],f=1===r?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",p="",d=0,v=1===r?2:4;d= "+t.inHeight+") {\n continue;\n }\n\n for (int wC = 0; wC < "+c+";\n wC += "+s+") {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= "+t.inWidth+") {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value >= currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = wR * "+c+" + wC;\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n ";else{var d=e+"("+e+"("+e+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"avg"===e&&(d="avgValue / count");var v=4*Math.floor(r/4),m=r%4,g="\n if ("+f+") {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = max(values, minMaxValue);\n }\n ";this.userCode="\n const ivec2 strides = ivec2("+o+", "+i+");\n const ivec2 pads = ivec2("+l+", "+h+");\n const float initializationValue = "+p+";\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= "+t.inWidth+") {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4("+p+");\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < "+u+";\n wR += "+a+") {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= "+t.inHeight+") {\n continue;\n }\n\n for (int wC = 0; wC < "+v+"; wC += 4) {\n int xC = xCCorner + wC * "+s+";\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + "+s+", d),\n getValue(batch, xR, xC + 2 * "+s+", d),\n getValue(batch, xR, xC + 3 * "+s+", d)\n );\n\n "+g+"\n }\n\n int xC = xCCorner + "+v+";\n if ("+(1===m)+") {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n "+g+"\n } else if ("+(2===m)+") {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + "+s+", d),\n initializationValue,\n initializationValue\n );\n\n "+g+"\n } else if ("+(3===m)+") {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + "+s+", d),\n getValue(batch, xR, xC + 2 * "+s+", d),\n initializationValue\n );\n\n "+g+"\n }\n }\n setOutput("+d+");\n }\n "}},sa=function(t,e,n){if(this.variableNames=["x"],"avg"===e&&n)throw new Error("Cannot compute positions for average pool.");var r=t.filterWidth,o=t.strideDepth,i=t.strideHeight,a=t.strideWidth,s=t.dilationDepth,u=t.dilationHeight,c=t.dilationWidth,l=t.effectiveFilterDepth,h=t.effectiveFilterHeight,f=t.effectiveFilterWidth,p=t.padInfo.front,d=t.padInfo.top,v=t.padInfo.left;this.outputShape=t.outShape;var m="avg"===e,g="0.0";if(m||(g="-1.0 / 1e-20"),n)this.userCode="\n const ivec3 strides =\n ivec3("+o+", "+i+", "+a+");\n const ivec3 pads = ivec3("+p+", "+d+", "+v+");\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n\n for (int wD = 0; wD < "+l+";\n wD += "+s+") {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= "+t.inDepth+") {\n continue;\n }\n\n for (int wR = 0; wR < "+h+";\n wR += "+u+") {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= "+t.inHeight+") {\n continue;\n }\n\n for (int wC = 0; wC < "+f+";\n wC += "+c+") {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= "+t.inWidth+") {\n continue;\n }\n\n float value = getX(batch, xD, xR, xC, ch);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value >= currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition =\n wD * "+h+" * "+f+" +\n wR * "+f+" + wC;;\n }\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n ";else{var y=e+"("+e+"("+e+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"avg"===e&&(y="avgValue / count");var x=4*Math.floor(r/4),b=r%4,w="\n if ("+m+") {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = max(values, minMaxValue);\n }\n ";this.userCode="\n const ivec3 strides =\n ivec3("+o+", "+i+", "+a+");\n const ivec3 pads = ivec3("+p+", "+d+", "+v+");\n const float initializationValue = "+g+";\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xD, int xR, int xC, int ch) {\n if (xC < 0 || xC >= "+t.inWidth+") {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xD, xR, xC, ch);\n }\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n // ? = to be determined\n vec4 minMaxValue = vec4("+g+");\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wD = 0; wD < "+l+";\n wD += "+s+") {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= "+t.inDepth+") {\n continue;\n }\n\n for (int wR = 0; wR < "+h+";\n wR += "+u+") {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= "+t.inHeight+") {\n continue;\n }\n\n for (int wC = 0; wC < "+x+"; wC += 4) {\n int xC = xCCorner + wC * "+c+";\n\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + "+c+", ch),\n getValue(batch, xD, xR, xC + 2 * "+c+", ch),\n getValue(batch, xD, xR, xC + 3 * "+c+", ch)\n );\n\n "+w+"\n }\n\n int xC = xCCorner + "+x+";\n if ("+(1===b)+") {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n "+w+"\n } else if ("+(2===b)+") {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + "+c+", ch),\n initializationValue,\n initializationValue\n );\n\n "+w+"\n } else if ("+(3===b)+") {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + "+c+", ch),\n getValue(batch, xD, xR, xC + 2 * "+c+", ch),\n initializationValue\n );\n\n "+w+"\n }\n }\n setOutput("+y+");\n }\n }\n "}},ua=function(t,e){this.variableNames=["x"];var n=t.windowSize,r=t.batchSize,o=t.inSize,i=Math.ceil(o/n);this.outputShape=[r,i];var a="0.0",s="";"prod"===e?a="1.0":"min"===e?(a="1.0 / 1e-20",s="min"):"max"===e&&(a="-1.0 / 1e-20",s="max");var u=e+"("+e+"("+e+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"sum"===e?u="sumValue":"prod"===e?u="prodValue":"all"===e?u="allValue":"any"===e&&(u="anyValue");var c=4*Math.floor(n/4),l=n%4,h="\n if ("+("sum"===e)+") {\n sumValue += dot(values, ones);\n } else if ("+("prod"===e)+") {\n vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n prodValue *= tmp[0] * tmp[1];\n } else {\n minMaxValue = "+s+"(values, minMaxValue);\n }\n ",f="vec4";"all"===e?(a="1.0",h="\n bool reducedAllValue = all(values);\n float floatedReducedAllValue = float(reducedAllValue);\n allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n ",f="bvec4"):"any"===e&&(a="0.0",h="\n bool reducedAnyValue = any(values);\n float floatedReducedAnyValue = float(reducedAnyValue);\n anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n ",f="bvec4");var p="";o%n>0&&(p="\n if (inIdx < 0 || inIdx >= "+o+") {\n return initializationValue;\n }\n "),this.userCode="\n const float initializationValue = "+a+";\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n "+p+"\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * "+n+";\n\n vec4 minMaxValue = vec4("+a+");\n float prodValue = 1.0;\n float sumValue = 0.0;\n float allValue = 1.0;\n float anyValue = 0.0;\n\n for (int i = 0; i < "+c+"; i += 4) {\n int inIdx = inOffset + i;\n "+f+" values = "+f+"(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n "+h+"\n }\n\n int inIdx = inOffset + "+c+";\n if ("+(1===l)+") {\n "+f+" values = "+f+"(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n "+h+"\n } else if ("+(2===l)+") {\n "+f+" values = "+f+"(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n "+h+"\n } else if ("+(3===l)+") {\n "+f+" values = "+f+"(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n "+h+"\n }\n setOutput("+u+");\n }\n "},ca=function(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t;for(var n="",r=0;r<4;r++){var o="thisRC = rc;";r%2==1&&(o+="thisRC.z += 1;"),r>1&&(o+="thisRC.y += 1;"),n+="\n "+o+"\n "+(r>0?"if(thisRC.y < rows && thisRC.z < cols){":"")+"\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result["+r+"] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n "+(r>0?"}":"")+"\n "}this.userCode="\n \n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n "+Eo(["r","c","d"],e)+"\n return ivec3(r, c, d);\n }\n \n "+_o(t)+"\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.);\n\n ivec3 thisRC;\n int rows = "+t[1]+";\n int cols = "+t[2]+";\n\n "+n+"\n\n setOutput(result);\n }\n "},la=function(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e.shape;var r=e.shape,o=r[1],i=r[2],a=t.shape,s=a[1],u=a[2],c=[n&&s>1?o-1:o,n&&u>1?i-1:i],l=[n&&s>1?s-1:s,n&&u>1?u-1:u],h=c[0]/l[0],f=c[1]/l[1],p=1/h,d=1/f,v=2*Math.ceil(p)+2,m=2*Math.ceil(d)+2;this.userCode="\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float("+h+");\n const float widthScale = float("+f+");\n\n const float invHeightScale = float("+p+");\n const float invWidthScale = float("+d+");\n\n const int winHeight = int("+v+");\n const int winWidth = int("+m+");\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= "+s+") {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= "+u+") {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), "+(o-1)+".0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), "+(i-1)+".0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n "},ha=function(t,e,n,r){this.variableNames=["A"],this.outputShape=[];var o=t[0],i=t[1],a=t[2],s=t[3];this.outputShape=[o,e,n,s];var u=[r&&e>1?i-1:i,r&&n>1?a-1:a],c=[r&&e>1?e-1:e,r&&n>1?n-1:n];this.userCode="\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n "+u[0]/c[0]+",\n "+u[1]/c[1]+");\n const vec2 inputShapeRC = vec2("+i+".0, "+a+".0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n "},fa=function(t,e,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];var o=t[0],i=t[1],a=t[2],s=t[3];this.outputShape=[o,e,n,s];var u=[r&&e>1?i-1:i,r&&n>1?a-1:a],c=[r&&e>1?e-1:e,r&&n>1?n-1:n];this.userCode="\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n "+u[0]/c[0]+",\n "+u[1]/c[1]+",\n "+u[1]/c[1]+");\n const vec3 inputShapeRC = vec3("+i+".0, "+a+".0,\n "+a+".0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = vec3(yRC) * effectiveInputOverOutputRatioRC;\n\n // Compute the four integer indices.\n ivec3 sourceFloorRC = ivec3(sourceFracIndexRC);\n ivec3 sourceCeilRC = ivec3(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < "+(s-1)+";\n bool hasNextRow = coords.z < "+(n-1)+";\n\n // In parallel, construct four corners for all four components in\n // packed 2x2 cell.\n vec4 topLeft = vec4(\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 bottomLeft = vec4(\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 topRight = vec4(\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec4 bottomRight = vec4(\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n vec4 newValue = mix(top, bottom, fracRC.x);\n\n setOutput(newValue);\n }\n "},pa=function(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e.shape;var r=e.shape,o=r[1],i=r[2],a=t.shape,s=a[1],u=a[2],c=[n&&s>1?o-1:o,n&&u>1?i-1:i],l=[n&&s>1?s-1:s,n&&u>1?u-1:u],h=c[0]/l[0],f=c[1]/l[1],p=1/h,d=1/f,v=2*Math.ceil(p)+2,m=2*Math.ceil(d)+2;this.userCode="\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float("+h+");\n const float widthScale = float("+f+");\n\n const float invHeightScale = float("+p+");\n const float invWidthScale = float("+d+");\n\n const int winHeight = int("+v+");\n const int winWidth = int("+m+");\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= "+s+") {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= "+u+") {\n continue;\n }\n\n float sourceFracRow =\n float("+c[0]+") *\n (float(dyR) / float("+l[0]+"));\n\n float sourceFracCol =\n float("+c[1]+") *\n (float(dyC) / float("+l[1]+"));\n\n int sourceNearestRow = int(min(\n float(int("+o+") - 1),\n "+n+" ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int("+i+") - 1),\n "+n+" ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n "},da=function(t,e,n,r){this.variableNames=["A"],this.outputShape=[];var o=t[0],i=t[1],a=t[2],s=t[3];this.outputShape=[o,e,n,s];var u=[r&&e>1?i-1:i,r&&n>1?a-1:a],c=[r&&e>1?e-1:e,r&&n>1?n-1:n],l=r?"0.5":"0.0";this.userCode="\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n "+u[0]/c[0]+",\n "+u[1]/c[1]+");\n const vec2 inputShapeRC = vec2("+i+".0, "+a+".0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + "+l+")));\n\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n "},va=function(t,e){this.variableNames=["x"];var n=t.length;if(n>4)throw new Error("WebGL backend: Reverse of rank-"+n+" tensor is not yet supported");if(this.outputShape=t,1!==n){var r=t.map((function(n,r){return function(n){return-1!==e.indexOf(n)&&1!==t[n]?t[n]+" - coords["+n+"] - 1":"coords["+n+"]"}(r)})).join(","),o=Oo(n);this.userCode="\n void main() {\n "+o+" coords = getOutputCoords();\n setOutput(getX("+r+"));\n }\n "}else this.userCode="\n void main() {\n int coord = getOutputCoords();\n setOutput(getX("+t[0]+" - coord - 1));\n }\n "},ma=function(t,e){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;var n=t.length;if(n>4)throw new Error("WebGL backend: Reverse of rank-"+n+" tensor is not yet supported");this.outputShape=t;var r=wo("rc",n),o=r[n-1]+" + 1 < "+this.outputShape[n-1],i=r[n-2]+" + 1 < "+this.outputShape[n-2],a=Oo(n);function s(n){var r=t.map((function(r,o){return function(n,r){return-1!==e.indexOf(n)&&1!==t[n]?t[n]+" - "+r[n]+" - 1":""+r[n]}(o,n)}));return"getChannel(getX("+r.join(",")+"), vec2("+r.slice(-2).join(",")+"))"}this.userCode=1===n?"\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX("+t[0]+" - rc - 1),\n "+t[0]+" - rc - 1);\n if("+o+"){\n result.g = getChannel(getX("+t[0]+" - (rc + 1) - 1),\n "+t[0]+" - (rc + 1) - 1);\n }\n setOutput(result);\n }\n ":"\n void main() {\n "+a+" rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = "+function(t){return s(t)}(r.slice())+";\n if("+o+"){\n result.g = "+function(t){return t[n-1]="("+t[n-1]+" + 1)",s(t)}(r.slice())+";\n }\n if("+i+") {\n result.b = "+function(t){return t[n-2]="("+t[n-2]+" + 1)",s(t)}(r.slice())+";\n if("+o+") {\n result.a = "+function(t){return t[n-1]="("+t[n-1]+" + 1)",t[n-2]="("+t[n-2]+" + 1)",s(t)}(r.slice())+";\n }\n }\n setOutput(result);\n }\n "},ga=function(t,e,n,r,o,i,a){void 0===a&&(a=!0),this.variableNames=["updates","indices","defaultValue"],this.outputShape=i;var s=Oo(o.length),u=Oo(i.length),c="";1===n?c="i":2===n&&(c="i, j");var l="getIndices("+c+")",h="";1===r?h="i":2===r&&(h="i, coords[1]");var f="getUpdates("+h+")",p=e>1?"strides[j]":"strides";this.userCode="\n "+s+" strides = "+s+"("+o+");\n\n void main() {\n "+u+" coords = getOutputCoords();\n float sum = 0.0;\n bool found = false;\n for (int i = 0; i < "+t+"; i++) {\n int flattenedIndex = 0;\n for (int j = 0; j < "+e+"; j++) {\n int index = round("+l+");\n flattenedIndex += index * "+p+";\n }\n if (flattenedIndex == coords[0]) {\n sum += "+f+";\n found = true;\n }\n }\n setOutput(mix(getDefaultValue(), sum, float(found)));\n }\n "},ya=function(t,e){this.variableNames=["x","segmentIds"];var n=t.windowSize,r=t.batchSize,o=t.inSize,i=t.numSegments,a=i*Math.ceil(o/n);this.outputShape=[r,a];var s=4*Math.floor(n/4),u=n%4,c="\n sumValue += dot(values, segFilter);\n ",l="";o%n>0&&(l="\n if (inIdx < 0 || inIdx >= "+o+") {\n return initializationValue;\n }\n ");var h="";o%n>0&&(h="\n if (inIdx < 0 || inIdx >= "+o+") {\n return -1.0;\n }\n "),this.userCode="\n const float initializationValue = 0.0;\n\n float getValue(int batch, int inIdx) {\n "+l+"\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n "+h+"\n return getSegmentIds(inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = int(floor(float(outIdx) / float(\n "+i+")) * float("+n+"));\n int currentSeg = int(mod(float(outIdx), float("+i+")));\n\n float sumValue = 0.0;\n\n for (int i = 0; i < "+s+"; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n );\n\n "+c+"\n }\n\n int inIdx = inOffset + "+s+";\n if ("+(1===u)+") {\n vec4 values = vec4(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n 0,\n 0,\n 0\n );\n\n "+c+"\n } else if ("+(2===u)+") {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n 0,\n 0\n );\n\n "+c+"\n } else if ("+(3===u)+") {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n 0\n );\n\n "+c+"\n }\n setOutput(sumValue);\n }\n "},xa=function(t,e,n){var r,o;if(this.variableNames=["c","a","b"],this.outputShape=e,n>4)throw Error("Where for rank "+n+" is not yet supported");if(1===n)o="resRC",r="resRC";else{for(var i=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[],s=[],u=0;u= 1.0) {\n setOutput(getA("+o+"));\n } else {\n setOutput(getB("+o+"));\n }\n }\n "},ba=function(){function t(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;var e,n=Oo(this.rank),r="uniform int start["+this.rank+"];",o=function(t){if(1===t)return"sourceLoc";if(t<=6)return wa.slice(0,t).map((function(t){return"sourceLoc."+t})).join(",");throw Error("Slicing for rank "+t+" is not yet supported")}(this.rank);e="\n "+n+" sourceLoc;\n "+n+" coords = getOutputCoords();\n "+t.map((function(t,e){return"sourceLoc."+wa[e]+" = start["+e+"] + coords."+wa[e]+";"})).join("\n")+"\n ",this.userCode="\n "+r+"\n void main() {\n "+e+"\n setOutput(getSource("+o+"));\n }\n "}return t.prototype.getCustomSetupFunc=function(t){var e=this;if(t.length!==this.rank)throw Error("The rank ("+this.rank+") of the program must match the length of start ("+t.length+")");return function(n,r){null==e.startLoc&&(e.startLoc=n.getUniformLocationNoThrow(r,"start"),null==e.startLoc)||n.gl.uniform1iv(e.startLoc,t)}},t}(),wa=["x","y","z","w","u","v"],Ca=function(){function t(t){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length;var e=Oo(this.rank),n=wo("coords",this.rank),r=wo("sourceLoc",this.rank),o=1===this.rank?"sourceLoc":"vec2("+r.slice(-2).join()+")",i="getChannel(getSource("+r.join()+"), "+o+")",a="\n result.x = "+i+";\n if (++"+n[this.rank-1]+" < "+t[this.rank-1]+") {\n ++"+r[this.rank-1]+";\n result.y = "+i+";\n --"+r[this.rank-1]+";\n }\n ",s=1===this.rank?"":"\n --"+n[this.rank-1]+";\n if (++"+n[this.rank-2]+" < "+t[this.rank-2]+") {\n ++"+r[this.rank-2]+";\n result.z = "+i+";\n if (++"+n[this.rank-1]+" < "+t[this.rank-1]+") {\n ++"+r[this.rank-1]+";\n result.w = "+i+";\n }\n }\n ",u=this.rank<=4?"sourceLoc = coords +\n "+e+"("+t.map((function(t,e){return"start["+e+"]"})).join()+");":t.map((function(t,e){return r[e]+" = "+n[e]+" + start["+e+"];"})).join("\n");this.userCode="\n uniform int start["+this.rank+"];\n void main() {\n "+e+" coords = getOutputCoords();\n "+e+" sourceLoc;\n "+u+"\n vec4 result = vec4(0.);\n "+a+"\n "+s+"\n setOutput(result);\n }\n "}return t.prototype.getCustomSetupFunc=function(t){var e=this;if(t.length!==this.rank)throw Error("The rank ("+this.rank+") of the program must match the length of start ("+t.length+")");return function(n,r){null==e.startLoc&&(e.startLoc=n.getUniformLocationNoThrow(r,"start"),null==e.startLoc)||n.gl.uniform1iv(e.startLoc,t)}},t}(),Ea=function(t,e,n){this.variableNames=["x"],this.outputShape=n;var r=n.length,o=Oo(n.length),i=Oo(n.length),a="";if(1===r)a="coords * strides + begin";else{var s=0;a=n.map((function(t,e){return s++,1===n.length?"coords * strides["+e+"] + begin["+e+"]":"coords["+(s-1)+"] * strides["+e+"] + begin["+e+"]"})).join(",")}this.userCode="\n "+o+" begin = "+o+"("+t+");\n "+o+" strides = "+o+"("+e+");\n\n void main() {\n "+i+" coords = getOutputCoords();\n setOutput(getX("+a+"));\n }\n "},_a=function(){function t(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}return t.prototype.acquireTexture=function(t,e,n){var r,o=Ia(e,n),i=Ra(t,o,n);if(i in this.freeTextures||(this.freeTextures[i]=[]),i in this.usedTextures||(this.usedTextures[i]=[]),this.freeTextures[i].length>0){this.numFreeTextures--,this.numUsedTextures++,this.log();var a=this.freeTextures[i].shift();return this.usedTextures[i].push(a),a}return this.numUsedTextures++,this.log(),o===zt.PACKED_2X2_FLOAT32?r=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===zt.PACKED_2X2_FLOAT16?r=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===zt.UNPACKED_FLOAT32?r=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===zt.UNPACKED_FLOAT16?r=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===zt.PACKED_4X1_UNSIGNED_BYTE&&(r=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[i].push(r),r},t.prototype.releaseTexture=function(t,e,n,r){if(null!=this.freeTextures){var o=Ra(e,Ia(n,r),r);o in this.freeTextures||(this.freeTextures[o]=[]),this.freeTextures[o].push(t),this.numFreeTextures++,this.numUsedTextures--;var i=this.usedTextures[o],a=i.indexOf(t);if(a<0)throw new Error("Cannot release a texture that was never provided by this texture manager");i.splice(a,1),this.log()}},t.prototype.log=function(){if(this.logEnabled){var t=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",this.numFreeTextures+" / "+this.numUsedTextures,"("+t+")")}},t.prototype.getNumUsedTextures=function(){return this.numUsedTextures},t.prototype.getNumFreeTextures=function(){return this.numFreeTextures},t.prototype.dispose=function(){var t=this;if(null!=this.freeTextures){for(var e in this.freeTextures)this.freeTextures[e].forEach((function(e){t.gpgpu.deleteMatrixTexture(e)}));for(var e in this.usedTextures)this.usedTextures[e].forEach((function(e){t.gpgpu.deleteMatrixTexture(e)}));this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0}},t}();function Ia(t,e){if(t===Wt.UPLOAD)return zt.PACKED_2X2_FLOAT32;if(t===Wt.RENDER||null==t)return function(t){return f().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?t?zt.PACKED_2X2_FLOAT32:zt.UNPACKED_FLOAT32:t?zt.PACKED_2X2_FLOAT16:zt.UNPACKED_FLOAT16}(e);if(t===Wt.DOWNLOAD||t===Wt.PIXELS)return zt.PACKED_4X1_UNSIGNED_BYTE;throw new Error("Unknown logical texture type "+t)}function Ra(t,e,n){return t[0]+"_"+t[1]+"_"+e+"_"+n}var Sa=function(t,e){this.variableNames=["A"];for(var n=new Array(t.length),r=0;r5)throw Error("Tile for rank "+e+" is not yet supported");if(1===e)return"imod(resRC, "+t[0]+")";for(var n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[],o=0;o6)throw Error("Transpose for rank "+e+" is not yet supported");for(var n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(e),o=0;o6)throw Error("Packed transpose for rank "+this.rank+" is not yet supported.");var o=Oo(this.rank),i=bo("rc",this.rank),a=new Array(this.rank);for(r=0;r= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n",Wa="return -x;",za="return ceil(x);",Ua="return floor(x);",Va="return exp(x);",Ga="return exp(x) - 1.0;",Ha=Fa+"\n return sin(x);\n",ja=Fa+"\n return cos(x);\n",qa=Fa+"\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n",Ka=Fa+"\n if (abs(x) > 1.) {\n return NAN;\n }\n return acos(x);\n",Xa=Fa+"\n return atan(x);\n",$a=Fa+"return log(x + sqrt(x * x + 1.0));",Ya=Fa+"\n if (x < 1.0) return NAN;\n return log(x + sqrt(x * x - 1.0));",Ja=Fa+"\n if ((x < -1.0) || (x > 1.0)) return NAN;\n return (log(1.0 + x) - log(1.0 - x)) / 2.0;",Qa="return x;",Za="\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n",ts="\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n",es="\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n",ns=function(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.userCode="\n vec4 unaryOperation(vec4 x) {\n "+e+"\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n "},rs=function(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t;var e=t.length,n=wo("rc",e),r=Oo(e),o=function(t,e){if(1===t)return"rc";for(var n="",r=0;r0?[4,Promise.all(o)]:[3,2];case 1:return s=u.sent(),a.kernelMs=_(s),a.getExtraProfileInfo=function(){return s.map((function(t,e){return{name:i[e],ms:t}})).map((function(t){return t.name+": "+t.ms})).join(", ")},[3,3];case 2:a.kernelMs={error:"WebGL query timers are not supported in this environment."},u.label=3;case 3:return this.uploadWaitMs=0,this.downloadWaitMs=0,[2,a]}}))}))},e.prototype.memory=function(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU}},e.prototype.startTimer=function(){return f().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:ot(),endMs:null}},e.prototype.endTimer=function(t){return f().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=ot(),t)},e.prototype.getQueryTime=function(t){return u(this,void 0,void 0,(function(){var e;return c(this,(function(n){return f().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?[2,this.gpgpu.waitForQueryAndGetTime(t)]:[2,(e=t).endMs-e.startMs]}))}))},e.prototype.disposeData=function(t){if(!this.pendingDisposal.has(t)){if(this.pendingRead.has(t))return this.pendingDisposal.add(t),void this.pendingDeletes++;if(this.texData.has(t)){this.releaseGPUData(t);var e=this.texData.get(t).complexTensors;null!=e&&(e.real.dispose(),e.imag.dispose()),this.texData.delete(t)}}},e.prototype.releaseGPUData=function(t){var e=this.texData.get(t),n=e.texture,r=e.dtype,o=e.texShape,i=e.usage,a=e.isPacked,s=e.slice,u=s&&s.origDataId||t,c=this.dataRefCount.get(u);c>1?this.dataRefCount.set(u,c-1):(this.dataRefCount.delete(u),null!=n&&(this.numBytesInGPU-=this.computeBytes(o,r),this.textureManager.releaseTexture(n,o,i,a)));var l=this.texData.get(t);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null},e.prototype.getTexture=function(t){return this.uploadToGPU(t),this.texData.get(t).texture},e.prototype.getDataInfo=function(t){return this.texData.get(t)},e.prototype.getCPUBackend=function(){return f().getBool("WEBGL_CPU_FORWARD")?(null==this.cpuBackend&&(this.cpuBackend=Ot.findBackend("cpu")),this.cpuBackend):null},e.prototype.shouldExecuteOnCPU=function(t,e){var n=this;return void 0===e&&(e=128),null!=this.getCPUBackend()&&t.every((function(t){return null==n.texData.get(t.dataId).texture&&t.sizef().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){var o=Math.floor(t.length/2),i=this.concat(t.slice(0,o),e),a=this.concat(t.slice(o),e);return this.concat([i,a],e)}if(f().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&t[0].rank>1){var s=new ei(t.map((function(t){return t.shape})),e);return this.compileAndRun(s,t)}var u=rn(t.map((function(t){return t.shape})),e),c=t.map((function(t){return t.as2D(-1,D(t.shape.slice(e)))})),l=new ti(c.map((function(t){return t.shape})));return this.compileAndRun(l,c).reshape(u)},e.prototype.neg=function(t){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.neg(t);if(f().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(t,Wa,t.dtype);var e=new Na(t.shape,Wa);return this.compileAndRun(e,[t])},e.prototype.batchMatMul=function(t,e,n,r){var o=n?t.shape[2]:t.shape[1],i=r?e.shape[1]:e.shape[2],a=n?t.shape[1]:t.shape[2],s=t.shape[0];if((1===o||1===i)&&a>1e3){n&&(t=t.transpose([0,2,1])),r&&(e=e.transpose([0,2,1]));var u=1===i?t:t.as3D(s,a,1),c=1===i?2:1,l=1===i?e.as3D(s,1,a):e;return this.multiply(u,l).sum(c,!0)}var h=St(t.dtype,e.dtype),f=new ta(t.shape,[s,o,i],n,r);return this.compileAndRun(f,[t,e],h)},e.prototype.fusedBatchMatMul=function(t){var e=t.a,n=t.b,r=t.transposeA,o=t.transposeB,i=t.bias,a=t.activation,s=t.preluActivationWeights,u=r?e.shape[2]:e.shape[1],c=o?n.shape[1]:n.shape[2],l=e.shape[0],h=St(e.dtype,n.dtype),f=null!=i,p=null!=s,d=a?is(a,!0):null,v=new ta(e.shape,[l,u,c],r,o,f,d,p),m=[e,n];return i&&m.push(i),s&&m.push(s),this.compileAndRun(v,m,h)},e.prototype.multiply=function(t,e){if("complex64"===t.dtype){var n=this.texData.get(t.dataId),r=this.texData.get(e.dataId),o=new Go("return areal * breal - aimag * bimag;",t.shape,e.shape),i=new Go("return areal * bimag + aimag * breal;",t.shape,e.shape),a=[this.makeComplexComponentTensorInfo(t,n.complexTensors.real),this.makeComplexComponentTensorInfo(t,n.complexTensors.imag),this.makeComplexComponentTensorInfo(e,r.complexTensors.real),this.makeComplexComponentTensorInfo(e,r.complexTensors.imag)],s=this.compileAndRun(o,a),u=this.compileAndRun(i,a),c=this.complex(s,u);return s.dispose(),u.dispose(),c}if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.multiply(t,e);if(f().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,qo,t.dtype);var l=new Xo(qo,t.shape,e.shape);return this.compileAndRun(l,[t,e],t.dtype)},e.prototype.batchNormalization=function(t,e,n,r,o,i){var a=[t,e,n],s=null;null!=i&&(s=i.shape,a.push(i));var u=null;if(null!=o&&(u=o.shape,a.push(o)),f().getBool("WEBGL_PACK_NORMALIZATION")){var c=new Vo(t.shape,e.shape,n.shape,s,u,r);return this.compileAndRun(c,a)}var l=new Uo(t.shape,e.shape,n.shape,s,u,r);return this.compileAndRun(l,a)},e.prototype.localResponseNormalization4D=function(t,e,n,r,o){var i=f().getBool("WEBGL_PACK_NORMALIZATION")?new Ji(t.shape,e,n,r,o):new $i(t.shape,e,n,r,o);return this.compileAndRun(i,[t])},e.prototype.LRNGrad=function(t,e,n,r,o,i,a){var s=new Yi(e.shape,r,o,i,a);return this.compileAndRun(s,[e,n,t])},e.prototype.tile=function(t,e){if("string"===t.dtype){var n=this.readSync(t.dataId).map((function(t){return st(t)}));return po(zn(t.shape,t.dtype,n),e)}var r=new Sa(t.shape,e);return this.compileAndRun(r,[t])},e.prototype.pad=function(t,e,n){var r=f().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ia(t.shape,e,n):new oa(t.shape,e,n);return this.compileAndRun(r,[t])},e.prototype.transpose=function(t,e){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.transpose(t,e);var n=f().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Da(t.shape,e):new ka(t.shape,e);return this.compileAndRun(n,[t])},e.prototype.gather=function(t,e,n){if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.gather(t,e,n);var r=new Ri(t.shape,e.size,n);return this.compileAndRun(r,[t,e])},e.prototype.batchToSpaceND=function(t,e,n){I(t.rank<=4,(function(){return"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet"}));var r=e.reduce((function(t,e){return t*e})),o=pr(t.shape,e,r),i=dr(o.length,e.length),a=vr(t.shape,e,r),s=mr(n,e.length),u=gr(a,n,e.length);return t.reshape(o).transpose(i).reshape(a).slice(s,u)},e.prototype.spaceToBatchND=function(t,e,n){I(t.rank<=4,(function(){return"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet"}));var r=e.reduce((function(t,e){return t*e})),o=[[0,0]];o.push.apply(o,n);for(var i=1+e.length;ie||n===t?r=!0:n=Q(t,n+1);return n}(a,o),u=new ya({windowSize:s,inSize:a,batchSize:i,numSegments:o},e),c=this.compileAndRun(u,[t,n],r);return c.shape[1]===o?c:(n=xn(0,o).tile([a/s]),this.segOpCompute(c,e,n,r,o))},e.prototype.argMinMaxReduce=function(t,e,n){var r=[e];if(Qe("arg"+n.charAt(0).toUpperCase()+n.slice(1),r,t.rank),!f().getBool("WEBGL_PACK_REDUCE")||t.rank<=2){var o=Ye(t.shape,r),i=o[0],a=D(o[1]),s=t.as2D(-1,a);return this.argReduce(s,n).reshape(i)}return this.argReducePacked(t,n)},e.prototype.argMin=function(t,e){return this.argMinMaxReduce(t,e,"min")},e.prototype.argMax=function(t,e){return this.argMinMaxReduce(t,e,"max")},e.prototype.cumsum=function(t,e,n,r){if(e!==t.rank-1)throw new Error("WebGL cumsum shader expects an inner-most axis="+(t.rank-1)+" but got axis="+e);var o=new di(t.shape,n,r);return this.compileAndRun(o,[t])},e.prototype.equal=function(t,e){if(f().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,"\n return vec4(equal(a, b));\n","bool");var n=new Xo("return float(a == b);",t.shape,e.shape);return this.compileAndRun(n,[t,e],"bool")},e.prototype.notEqual=function(t,e){if(f().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,"\n return vec4(notEqual(a, b));\n","bool");var n=new Xo("return float(a != b);",t.shape,e.shape);return this.compileAndRun(n,[t,e],"bool")},e.prototype.less=function(t,e){if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.less(t,e);if(f().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,"\n return vec4(lessThan(a, b));\n","bool");var n=new Xo("return float(a < b);",t.shape,e.shape);return this.compileAndRun(n,[t,e],"bool")},e.prototype.lessEqual=function(t,e){if(f().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,"\n return vec4(lessThanEqual(a, b));\n","bool");var n=new Xo("return float(a <= b);",t.shape,e.shape);return this.compileAndRun(n,[t,e],"bool")},e.prototype.greater=function(t,e){if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.greater(t,e);if(f().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,"\n return vec4(greaterThan(a, b));\n","bool");var n=new Xo("return float(a > b);",t.shape,e.shape);return this.compileAndRun(n,[t,e],"bool")},e.prototype.greaterEqual=function(t,e){if(f().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,"\n return vec4(greaterThanEqual(a, b));\n","bool");var n=new Xo("return float(a >= b);",t.shape,e.shape);return this.compileAndRun(n,[t,e],"bool")},e.prototype.logicalNot=function(t){var e=new Na(t.shape,"return float(!(x >= 1.0));");return this.compileAndRun(e,[t])},e.prototype.logicalAnd=function(t,e){if(f().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,"\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n","bool");var n=new Xo("return float(a >= 1.0 && b >= 1.0);",t.shape,e.shape);return this.compileAndRun(n,[t,e],"bool")},e.prototype.logicalOr=function(t,e){if(f().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,"\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n","bool");var n=new Xo("return float(a >= 1.0 || b >= 1.0);",t.shape,e.shape);return this.compileAndRun(n,[t,e],"bool")},e.prototype.select=function(t,e,n){var r=new xa(t.rank,e.shape,e.rank);return this.compileAndRun(r,[t,e,n],St(e.dtype,n.dtype))},e.prototype.where=function(t){Ge("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");var e=t.dataSync();return mo(t.shape,e)},e.prototype.topk=function(t,e,n){return vo(t.dataSync(),t.shape,t.dtype,e)},e.prototype.min=function(t,e){Qe("min",e,t.rank);var n=Ye(t.shape,e),r=n[0],o=D(n[1]),i=t.as2D(-1,o);return this.reduce(i,"min",i.dtype).reshape(r)},e.prototype.minimum=function(t,e){if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.minimum(t,e);var n=f().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Yo("\n vec4 result = vec4(min(a, b));\n vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n \n result.r = isNaN.r > 0. ? NAN : result.r;\n result.g = isNaN.g > 0. ? NAN : result.g;\n result.b = isNaN.b > 0. ? NAN : result.b;\n result.a = isNaN.a > 0. ? NAN : result.a;\n\n return result;\n",t.shape,e.shape):new Xo("\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n\n return min(a, b);\n",t.shape,e.shape);return this.compileAndRun(n,[t,e])},e.prototype.mod=function(t,e){var n=f().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Yo("\n vec4 result = mod(a, b);\n vec4 isNaN = vec4(equal(b, vec4(0.0)));\n \n result.r = isNaN.r > 0. ? NAN : result.r;\n result.g = isNaN.g > 0. ? NAN : result.g;\n result.b = isNaN.b > 0. ? NAN : result.b;\n result.a = isNaN.a > 0. ? NAN : result.a;\n\n return result;\n",t.shape,e.shape):new Xo("if (b == 0.0) return NAN;\n return mod(a, b);",t.shape,e.shape);return this.compileAndRun(n,[t,e])},e.prototype.max=function(t,e){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.max(t,e);Qe("max",e,t.rank);var n=Ye(t.shape,e),r=n[0],o=D(n[1]),i=t.as2D(-1,o);return this.reduce(i,"max",i.dtype).reshape(r)},e.prototype.maximum=function(t,e){if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.maximum(t,e);var n=f().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Yo("\n vec4 result = vec4(max(a, b));\n vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n \n result.r = isNaN.r > 0. ? NAN : result.r;\n result.g = isNaN.g > 0. ? NAN : result.g;\n result.b = isNaN.b > 0. ? NAN : result.b;\n result.a = isNaN.a > 0. ? NAN : result.a;\n\n return result;\n",t.shape,e.shape):new Xo("\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n\n return max(a, b);\n",t.shape,e.shape);return this.compileAndRun(n,[t,e])},e.prototype.all=function(t,e){Qe("all",e,t.rank);var n=Ye(t.shape,e),r=n[0],o=D(n[1]),i=t.as2D(-1,o);return this.reduce(i,"all",i.dtype).reshape(r)},e.prototype.any=function(t,e){Qe("any",e,t.rank);var n=Ye(t.shape,e),r=n[0],o=D(n[1]),i=t.as2D(-1,o);return this.reduce(i,"any",i.dtype).reshape(r)},e.prototype.realDivide=function(t,e){if(f().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,"\n // vec4 one = vec4(equal(a, b));\n // return one + (vec4(1.0) - one) * a / b;\n vec4 result = a / b;\n if(a.x == b.x) {\n result.x = 1.;\n }\n if(a.y == b.y) {\n result.y = 1.;\n }\n if(a.z == b.z) {\n result.z = 1.;\n }\n if(a.w == b.w) {\n result.w = 1.;\n }\n\n return result;\n","float32",!0);var n=new Xo("\nif (a == b) {\n return 1.0;\n};\nreturn a / b;",t.shape,e.shape);return this.compileAndRun(n,[t,e],"float32")},e.prototype.floorDiv=function(t,e){if(f().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,"\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n bvec4 cond = notEqual(ib, ivec4(0));\n ivec4 result = ivec4(0);\n vec4 s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n result[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n result[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n result[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n result[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(result);\n","int32");var n=new Xo("\n float s = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n if (ib != 0) {\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n return float(idiv(ia, ib, s));\n } else {\n return NAN;\n }\n",t.shape,e.shape);return this.compileAndRun(n,[t,e],"int32")},e.prototype.add=function(t,e){if("complex64"===t.dtype&&"complex64"===e.dtype)return this.complexSeparableBinaryOp(t,e,Ho);if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.add(t,e);var n=St(t.dtype,e.dtype);if(f().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,Ho,n);var r=new Xo(Ho,t.shape,e.shape);return this.compileAndRun(r,[t,e],n)},e.prototype.packedUnaryOp=function(t,e,n){var r=new ns(t.shape,e);return this.compileAndRun(r,[t],n)},e.prototype.packedBinaryOp=function(t,e,n,r,o){void 0===o&&(o=!1);var i=new Yo(n,t.shape,e.shape,o);return this.compileAndRun(i,[t,e],r)},e.prototype.complexSeparableBinaryOp=function(t,e,n){var r=this,o=this.texData.get(t.dataId),i=this.texData.get(e.dataId),a=[[o.complexTensors.real,i.complexTensors.real],[o.complexTensors.imag,i.complexTensors.imag]].map((function(o){var i=o[0],a=o[1],s=r.makeComplexComponentTensorInfo(t,i),u=r.makeComplexComponentTensorInfo(e,a),c=new Xo(n,t.shape,e.shape);return r.compileAndRun(c,[s,u],St(i.dtype,a.dtype))})),s=a[0],u=a[1],c=this.complex(s,u);return s.dispose(),u.dispose(),c},e.prototype.makeComplexComponentTensorInfo=function(t,e){return{dataId:e.dataId,dtype:e.dtype,shape:t.shape}},e.prototype.addN=function(t){if(1===t.length)return t[0];if(t.length>f().get("WEBGL_MAX_TEXTURES_IN_SHADER")){var e=Math.floor(t.length/2),n=this.addN(t.slice(0,e)),r=this.addN(t.slice(e));return this.addN([n,r])}var o=t.map((function(t){return t.dtype})).reduce((function(t,e){return St(t,e)})),i=t.map((function(t){return t.shape})),a=f().getBool("WEBGL_PACK")?new yo(t[0].shape,i):new go(t[0].shape,i);return this.compileAndRun(a,t,o)},e.prototype.subtract=function(t,e){if("complex64"===t.dtype&&"complex64"===e.dtype)return this.complexSeparableBinaryOp(t,e,jo);if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.subtract(t,e);var n=St(t.dtype,e.dtype);if(f().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,jo,t.dtype);var r=new Xo(jo,t.shape,e.shape);return this.compileAndRun(r,[t,e],n)},e.prototype.pow=function(t,e){var n=f().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Yo("\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n bvec4 isExpZero = equal(b, vec4(0.0));\n result.r = isExpZero.r ? 1.0 : result.r;\n result.g = isExpZero.g ? 1.0 : result.g;\n result.b = isExpZero.b ? 1.0 : result.b;\n result.a = isExpZero.a ? 1.0 : result.a;\n\n vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n \n result.r = isNaN.r > 0. ? NAN : result.r;\n result.g = isNaN.g > 0. ? NAN : result.g;\n result.b = isNaN.b > 0. ? NAN : result.b;\n result.a = isNaN.a > 0. ? NAN : result.a;\n\n return result;\n",t.shape,e.shape):new Xo("\nif(a < 0.0 && floor(b) < b){\n return NAN;\n}\nif (b == 0.0) {\n return 1.0;\n}\nreturn (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n",t.shape,e.shape),r=St(t.dtype,e.dtype);return this.compileAndRun(n,[t,e],r)},e.prototype.ceil=function(t){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.ceil(t);if(f().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(t,za,t.dtype);var e=new Na(t.shape,za);return this.compileAndRun(e,[t])},e.prototype.floor=function(t){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.floor(t);if(f().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(t,Ua,t.dtype);var e=new Na(t.shape,Ua);return this.compileAndRun(e,[t])},e.prototype.sign=function(t){var e=new Na(t.shape,"\n if (isnan(x)) { return 0.0; }\n return sign(x);\n");return this.compileAndRun(e,[t])},e.prototype.isNaN=function(t){var e=new Na(t.shape,"return float(isnan(x));");return this.compileAndRun(e,[t],"bool")},e.prototype.isInf=function(t){var e=new Na(t.shape,"return float(isinf(x));");return this.compileAndRun(e,[t],"bool")},e.prototype.isFinite=function(t){var e=new Na(t.shape,"return float(!isnan(x) && !isinf(x));");return this.compileAndRun(e,[t],"bool")},e.prototype.round=function(t){var e=new Na(t.shape,"\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n }\n");return this.compileAndRun(e,[t])},e.prototype.exp=function(t){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.exp(t);if(f().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(t,Va,t.dtype);var e=new Na(t.shape,Va);return this.compileAndRun(e,[t])},e.prototype.expm1=function(t){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.expm1(t);if(f().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(t,Ga,t.dtype);var e=new Na(t.shape,Ga);return this.compileAndRun(e,[t])},e.prototype.softmax=function(t,e){var n=B([e],t.shape),r=this.max(t,n),o=Je(r.shape,n),i=this.subtract(t,r.reshape(o)),a=this.exp(i),s=this.sum(a,n).reshape(o);return this.realDivide(a,s)},e.prototype.log=function(t){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.log(t);if(f().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(t,"\n vec4 result = log(x);\n vec4 isNaN = vec4(lessThan(x, vec4(0.0)));\n result.r = isNaN.r == 1.0 ? NAN : result.r;\n result.g = isNaN.g == 1.0 ? NAN : result.g;\n result.b = isNaN.b == 1.0 ? NAN : result.b;\n result.a = isNaN.a == 1.0 ? NAN : result.a;\n\n return result;\n",t.dtype);var e=new Na(t.shape,"if (x < 0.0) return NAN;\n return log(x);");return this.compileAndRun(e,[t])},e.prototype.log1p=function(t){var e=new Na(t.shape,"return log(1.0 + x);");return this.compileAndRun(e,[t])},e.prototype.sqrt=function(t){var e=new Na(t.shape,"return sqrt(x);");return this.compileAndRun(e,[t])},e.prototype.rsqrt=function(t){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.rsqrt(t);var e=new Na(t.shape,"return inversesqrt(x);");return this.compileAndRun(e,[t])},e.prototype.reciprocal=function(t){var e=new Na(t.shape,"return 1.0 / x;");return this.compileAndRun(e,[t])},e.prototype.relu=function(t){var e;return e=f().getBool("WEBGL_PACK")?new ns(t.shape,Za):new Na(t.shape,Oa),this.compileAndRun(e,[t])},e.prototype.relu6=function(t){var e;return e=f().getBool("WEBGL_PACK")?new ns(t.shape,ts):new Na(t.shape,Pa),this.compileAndRun(e,[t])},e.prototype.prelu=function(t,e){var n=f().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Yo($o,t.shape,e.shape):new Xo(Ko,t.shape,e.shape);return this.compileAndRun(n,[t,e])},e.prototype.elu=function(t){if(f().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(t,es,t.dtype);var e=new Na(t.shape,Ba);return this.compileAndRun(e,[t])},e.prototype.eluDer=function(t,e){var n=f().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Yo("\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n",t.shape,e.shape):new Xo("return (b >= 1.0) ? a : a * (b + 1.0);",t.shape,e.shape);return this.compileAndRun(n,[t,e])},e.prototype.selu=function(t){var e=new Na(t.shape,La);return this.compileAndRun(e,[t])},e.prototype.int=function(t){var e=new Na(t.shape,"return float(int(x));");return this.compileAndRun(e,[t],"int32")},e.prototype.clip=function(t,e,n){var r,o=(r=f().getBool("WEBGL_PACK_CLIP")?new Qo(t.shape):new Jo(t.shape)).getCustomSetupFunc(e,n);return this.compileAndRun(r,[t],null,o)},e.prototype.abs=function(t){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.abs(t);if(f().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(t,Ma,t.dtype);var e=new Na(t.shape,Ma);return this.compileAndRun(e,[t])},e.prototype.complexAbs=function(t){var e=this.texData.get(t.dataId),n=new Zo(t.shape),r=[this.makeComplexComponentTensorInfo(t,e.complexTensors.real),this.makeComplexComponentTensorInfo(t,e.complexTensors.imag)];return this.compileAndRun(n,r)},e.prototype.sigmoid=function(t){var e=new Na(t.shape,"return 1.0 / (1.0 + exp(-1.0 * x));");return this.compileAndRun(e,[t])},e.prototype.softplus=function(t){var e=new Na(t.shape,"\n float epsilon = 1.1920928955078125e-7;\n float threshold = log(epsilon) + 2.0;\n\n bool too_large = x > -threshold;\n bool too_small = x < threshold;\n\n float result;\n float exp_x = exp(x);\n\n if (too_large){\n result = x;\n }\n else if (too_small){\n result = exp_x;\n }\n else{\n result = log(exp_x + 1.0);\n }\n return result;\n");return this.compileAndRun(e,[t])},e.prototype.sin=function(t){var e=new Na(t.shape,Ha);return this.compileAndRun(e,[t])},e.prototype.cos=function(t){var e=new Na(t.shape,ja);return this.compileAndRun(e,[t])},e.prototype.tan=function(t){var e=new Na(t.shape,"return tan(x);");return this.compileAndRun(e,[t])},e.prototype.asin=function(t){var e=new Na(t.shape,qa);return this.compileAndRun(e,[t])},e.prototype.acos=function(t){var e=new Na(t.shape,Ka);return this.compileAndRun(e,[t])},e.prototype.atan=function(t){var e=new Na(t.shape,Xa);return this.compileAndRun(e,[t])},e.prototype.atan2=function(t,e){var n=f().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Yo("\n vec4 result = atan(a, b);\n vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n \n result.r = isNaN.r > 0. ? NAN : result.r;\n result.g = isNaN.g > 0. ? NAN : result.g;\n result.b = isNaN.b > 0. ? NAN : result.b;\n result.a = isNaN.a > 0. ? NAN : result.a;\n\n return result;\n",t.shape,e.shape):new Xo("\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n\n return atan(a, b);\n",t.shape,e.shape);return this.compileAndRun(n,[t,e])},e.prototype.sinh=function(t){var e=new Na(t.shape,"\n float e2x = exp(x);\n return (e2x - 1.0 / e2x) / 2.0;\n");return this.compileAndRun(e,[t])},e.prototype.cosh=function(t){var e=new Na(t.shape,"\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n");return this.compileAndRun(e,[t])},e.prototype.tanh=function(t){var e=new Na(t.shape,"\n float e2x = exp(-2.0 * abs(x));\n return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n");return this.compileAndRun(e,[t])},e.prototype.asinh=function(t){var e=new Na(t.shape,$a);return this.compileAndRun(e,[t])},e.prototype.acosh=function(t){var e=new Na(t.shape,Ya);return this.compileAndRun(e,[t])},e.prototype.atanh=function(t){var e=new Na(t.shape,Ja);return this.compileAndRun(e,[t])},e.prototype.erf=function(t){var e=new Na(t.shape,'\n // Error function is calculated approximately with elementary function.\n // See "Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables", Abramowitz and Stegun.\n float p = 0.3275911;\n float a1 = 0.254829592;\n float a2 = -0.284496736;\n float a3 = 1.421413741;\n float a4 = -1.453152027;\n float a5 = 1.061405429;\n\n float sign = sign(x);\n x = abs(x);\n float t = 1.0 / (1.0 + p * x);\n return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n');return this.compileAndRun(e,[t])},e.prototype.step=function(t,e){var n=new Na(t.shape,function(t){return void 0===t&&(t=0),Fa+"\n return x > 0.0 ? 1.0 : float("+t+");\n "}(e));return this.compileAndRun(n,[t])},e.prototype.conv2dByMatMul=function(t,e,n,r,o,i){var a=t.shape,s=this.texData.get(t.dataId),u=n.inChannels,c=a[0]*a[1]*a[2],l=n.outChannels,h="channelsLast"===n.dataFormat,p=(1===c||1===l)&&u>1e3,d=a[2]%2!=0&&!!s.isPacked;if(p||!f().getBool("WEBGL_LAZILY_UNPACK")||!f().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!d){var v=h?a[0]*a[1]*a[2]:a[0]*a[2]*a[3],m=this.reshape(t,[1,v,n.inChannels]),g=this.reshape(e,[1,n.inChannels,n.outChannels]);return this.reshape(this.fusedBatchMatMul({a:m,b:g,transposeA:!1,transposeB:!1,bias:r,activation:o,preluActivationWeights:i}),n.outShape)}var y=h?a[0]*a[1]*(a[2]+1):a[0]*a[2]*(a[3]+1),x={dataId:t.dataId,shape:[1,y,n.inChannels],dtype:t.dtype},b=s.shape;s.shape=s.shape.slice(),s.shape[s.shape.length-2]++,I(ke(s.shape,x.shape),(function(){return"packed reshape "+s.shape+" to "+x.shape+" isn't free"}));var w=this.reshape(e,[1,n.inChannels,n.outChannels]),C=this.fusedBatchMatMul({a:x,b:w,transposeA:!1,transposeB:!1,bias:r,activation:o,preluActivationWeights:i}),E=this.texData.get(C.dataId);return I(E.isPacked,(function(){return"batchMatMul result is expected to be packed"})),s.shape=b,E.shape=n.outShape,Ot.makeTensorFromDataId(C.dataId,n.outShape,C.dtype)},e.prototype.conv2dWithIm2Row=function(t,e,n,r,o,i){var a=n.filterWidth,s=n.filterHeight,u=n.inChannels,c=n.outWidth,l=n.outHeight,h="channelsLast"===n.dataFormat,f=a*s*u,p=l*c,d=[f,p],v=t.squeeze([0]),m=e.reshape([1,f,-1]),g=new Xi(d,v.shape,n),y=this.compileAndRun(g,[v]).reshape([1,d[0],d[1]]),x=null!=r,b=null!=i,w=o?is(o,!0):null,C=new ta(y.shape,[1,p,n.outChannels],!0,!1,x,w,b),E=[y,m];r&&E.push(r),b&&E.push(i);var _=this.compileAndRun(C,E);return h?_.reshape([1,l,c,n.outChannels]):_.reshape([1,n.outChannels,l,c])},e.prototype.fusedConv2d=function(t){var e=t.input,n=t.filter,r=t.convInfo,o=t.bias,i=t.activation,a=t.preluActivationWeights;if(1===r.filterHeight&&1===r.filterWidth&&1===r.dilationHeight&&1===r.dilationWidth&&1===r.strideHeight&&1===r.strideWidth&&("SAME"===r.padInfo.type||"VALID"===r.padInfo.type))return this.conv2dByMatMul(e,n,r,o,i,a);if(f().getBool("WEBGL_CONV_IM2COL")&&1===e.shape[0])return this.conv2dWithIm2Row(e,n,r,o,i,a);var s=null!=o,u=null!=a,c=i?is(i,!1):null,l=new ci(r,s,c,u),h=[e,n];return o&&h.push(o),a&&h.push(a),this.compileAndRun(l,h)},e.prototype.conv2d=function(t,e,n){if(1===n.filterHeight&&1===n.filterWidth&&1===n.dilationHeight&&1===n.dilationWidth&&1===n.strideHeight&&1===n.strideWidth&&("SAME"===n.padInfo.type||"VALID"===n.padInfo.type))return this.conv2dByMatMul(t,e,n);if(f().getBool("WEBGL_CONV_IM2COL")&&1===t.shape[0])return this.conv2dWithIm2Row(t,e,n);var r=new ci(n);return this.compileAndRun(r,[t,e])},e.prototype.conv2dDerInput=function(t,e,n){var r=new oi(n);return this.compileAndRun(r,[t,e])},e.prototype.conv2dDerFilter=function(t,e,n){var r=new ri(n);return this.compileAndRun(r,[t,e])},e.prototype.fusedDepthwiseConv2D=function(t){var e,n=t.input,r=t.filter,o=t.convInfo,i=t.bias,a=t.activation,s=t.preluActivationWeights,u=f().getBool("WEBGL_PACK_DEPTHWISECONV")&&o.strideWidth<=2&&o.outChannels/o.inChannels==1,c=a?is(a,u):null,l=[n,r],h=null!=i,p=null!=s;return h&&l.push(i),p&&l.push(s),u?(e=new fi(o,h,c,p),this.compileAndRun(e,l)):(e=new hi(o,h,c,p),this.compileAndRun(e,l))},e.prototype.depthwiseConv2D=function(t,e,n){var r;return f().getBool("WEBGL_PACK_DEPTHWISECONV")&&n.strideWidth<=2&&n.outChannels/n.inChannels==1?(r=new fi(n),this.compileAndRun(r,[t,e])):(r=new hi(n),this.compileAndRun(r,[t,e]))},e.prototype.depthwiseConv2DDerInput=function(t,e,n){var r=new ui(n);return this.compileAndRun(r,[t,e])},e.prototype.depthwiseConv2DDerFilter=function(t,e,n){var r=new si(n);return this.compileAndRun(r,[t,e])},e.prototype.conv3d=function(t,e,n){var r=new li(n);return this.compileAndRun(r,[t,e])},e.prototype.conv3dDerInput=function(t,e,n){var r=new ai(n);return 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t.constructor.name+"_"+r+"_"+o}(t,l,p),v=this.getAndSaveBinary(d,(function(){return function(t,e,n,r){var o=e.userCode,i=n.map((function(t,n){var r={logicalShape:t.shape,texShape:t.isUniform?null:t.texData.texShape,isUniform:t.isUniform,isPacked:!t.isUniform&&t.texData.isPacked,flatOffset:null};return null!=t.texData&&null!=t.texData.slice&&t.texData.slice.flatOffset>0&&(r.flatOffset=t.texData.slice.flatOffset),{name:e.variableNames[n],shapeInfo:r}})),a=i.map((function(t){return t.shapeInfo})),s={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},u=Ro(i,s,o,e.packedInputs),c=t.createProgram(u),l=null,h=t.getUniformLocation(c,"NAN",!1);1===f().getNumber("WEBGL_VERSION")&&(l=t.getUniformLocation(c,"INFINITY",!1));for(var p={},d=0;d0)return 32}return 16}))),this.floatPrecisionValue},e.prototype.epsilon=function(){return 32===this.floatPrecision()?1e-7:1e-4},e.prototype.uploadToGPU=function(t){var e,n=this.texData.get(t),r=n.shape,o=n.dtype,i=n.values,a=n.texture,s=n.usage,u=n.isPacked;if(null==a){var c,l=null!=this.activeTimers;l&&(c=ot());var h=n.texShape;if(null==h&&(h=Re(r,u),n.texShape=h),null!=i){var f=Ie(r),p=void 0,d=h[1],v=h[0],m=i instanceof Uint8Array;u?(d=(e=Kt(h[0],h[1]))[0],v=e[1],p=new Ei(f,[v,d],m)):p=new Ci(f,[v,d],m);var g=this.makeTensorInfo([v,d],o);this.texData.get(g.dataId).usage=m?Wt.PIXELS:Wt.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),d,v,i);var y=this.runWebGLProgram(p,[g],o,null,!0),x=this.texData.get(y.dataId);n.texture=x.texture,n.texShape=x.texShape,n.isPacked=x.isPacked,n.usage=x.usage,this.disposeData(g.dataId),this.texData.delete(y.dataId),n.values=null,l&&(this.uploadWaitMs+=ot()-c)}else{var b=this.acquireTexture(h,s,o,u);n.texture=b}}},e.prototype.convertAndCacheOnCPU=function(t,e){var n=this.texData.get(t),r=n.dtype;return 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n=qe(t,"a","mul"),r=qe(e,"b","mul");return R(n.shape,r.shape,"Error in multiplyStrict: "),n.mul(r)}}),Ru=on({pow_:function(t,e){var n,r=qe(t,"base","pow"),o=qe(e,"exp","pow");n=Dt(r,o),r=n[0],o=n[1];var i=Lr(r.shape,o.shape),a=[r,o];return Ot.runKernelFunc((function(t,e){var n=t.pow(r,o);return e([r,o,n]),n}),{a:r,b:o},(function(t,e){var n=e[0],r=e[1],o=e[2];return{a:function(){var e=r.toFloat(),o=t.mul(e.mul(n.pow(e.sub(hn(1))))),a=Br(n.shape,i);return a.length>0&&(o=o.sum(a)),o.reshape(n.shape)},b:function(){var e=n.greater(0),a=n.log().where(e,wn(n)),s=t.mul(o.mul(a)),u=Br(r.shape,i);return u.length>0&&(s=s.sum(u)),s.reshape(r.shape)}}}),"Pow",{},a,[!0])}}),Su=on({powStrict_:function(t,e){return R(t.shape,e.shape,"Error in powStrict: "),t.pow(e)}}),ku=on({squaredDifferenceStrict_:function(t,e){var n=qe(t,"a","squaredDifferenceStrict"),r=qe(e,"b","squaredDifferenceStrict");return R(n.shape,r.shape,"Error in squaredDifferenceStrict: 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n,r=qe(t,"a","greater"),o=qe(e,"b","greater");return n=Dt(r,o),r=n[0],o=n[1],Lr(r.shape,o.shape),Ot.runKernelFunc((function(t){return t.greater(r,o)}),{a:r,b:o},null,"Greater")}}),Mu=on({greaterEqual_:function(t,e){var n,r=qe(t,"a","greaterEqual"),o=qe(e,"b","greaterEqual");return n=Dt(r,o),r=n[0],o=n[1],Lr(r.shape,o.shape),Ot.runKernelFunc((function(t,e){var n=t.greaterEqual(r,o);return e([r,o]),n}),{a:r,b:o},(function(t,e){var n=e[0],r=e[1];return{a:function(){return wn(n)},b:function(){return wn(r)}}}),"GreaterEqual")}}),Ou=on({greaterEqualStrict_:function(t,e){var n=qe(t,"a","greaterEqualStrict"),r=qe(e,"b","greaterEqualStrict");return R(n.shape,r.shape,"Error in greaterEqualStrict: "),n.greaterEqual(r)}}),Pu=on({greaterStrict_:function(t,e){var n=qe(t,"a","greaterStrict"),r=qe(e,"b","greaterStrict");return R(n.shape,r.shape,"Error in greaterStrict: "),n.greater(r)}}),Bu=on({less_:function(t,e){var n,r=qe(t,"a","less"),o=qe(e,"b","less");return n=Dt(r,o),r=n[0],o=n[1],Lr(r.shape,o.shape),Ot.runKernelFunc((function(t){return t.less(r,o)}),{a:r,b:o},null,"Less")}}),Lu=on({lessEqual_:function(t,e){var n,r=qe(t,"a","lessEqual"),o=qe(e,"b","lessEqual");return n=Dt(r,o),r=n[0],o=n[1],Lr(r.shape,o.shape),Ot.runKernelFunc((function(t,e){var n=t.lessEqual(r,o);return e([r,o]),n}),{a:r,b:o},null,"LessEqual")}}),Wu=on({lessEqualStrict_:function(t,e){var n=qe(t,"a","lessEqualStrict"),r=qe(e,"b","lessEqualStrict");return R(n.shape,r.shape,"Error in lessEqualStrict: "),n.lessEqual(r)}}),zu=on({lessStrict_:function(t,e){var n=qe(t,"a","lessStrict"),r=qe(e,"b","lessStrict");return R(n.shape,r.shape,"Error in lessStrict: "),n.less(r)}}),Uu=on({notEqual_:function(t,e){var n,r=qe(t,"a","notEqual"),o=qe(e,"b","notEqual");return n=Dt(r,o),r=n[0],o=n[1],Lr(r.shape,o.shape),Ot.runKernelFunc((function(t){return t.notEqual(r,o)}),{a:r,b:o},null,"NotEqual")}}),Vu=on({notEqualStrict_:function(t,e){var n=qe(t,"a","notEqualStrict"),r=qe(e,"b","notEqualStrict");return R(n.shape,r.shape,"Error in notEqualStrict: "),n.notEqual(r)}});function Gu(t,e){for(var n=[],r=t;r=2&&a.rank>=2&&i.rank===a.rank,(function(){return"Error in matMul: inputs must have the same rank of at least 2, got ranks "+i.rank+" and "+a.rank+"."})),I(A(h,f),(function(){return"Error in matMul: outer dimensions ("+h+") and ("+f+") of Tensors with shapes "+i.shape+" and "+a.shape+" must match."})),I(s===u,(function(){return"Error in matMul: inner shapes ("+s+") and ("+u+") of Tensors with shapes "+i.shape+" and "+a.shape+" and transposeA="+n+" and transposeB="+r+" must match."}));var v=i.shape.slice(0,-2).concat([c,l]),m=n?i.as3D(p,s,c):i.as3D(p,c,s),g=r?a.as3D(d,l,u):a.as3D(d,u,l),y={transposeA:n,transposeB:r};return Ot.runKernelFunc((function(t,e){var o=t.batchMatMul(m,g,n,r);return e([m,g]),o}),{a:m,b:g},(function(t,e){var o=e,i=o[0],a=o[1];return n||r?!n&&r?{a:function(){return t.matMul(a,!1,!1)},b:function(){return 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i=qe(t,"labels","huberLoss"),a=qe(e,"predictions","huberLoss"),s=null;null!=n&&(s=qe(n,"weights","huberLoss")),R(i.shape,a.shape,"Error in huberLoss: ");var u=hn(r),c=a.sub(i).abs(),l=bu(c,u),h=c.sub(l),f=hn(.5).mul(l.square()).add(u.mul(h));return gl(f,s,o)}}),wl=on({logLoss_:function(t,e,n,r,o){void 0===r&&(r=1e-7),void 0===o&&(o=ll.SUM_BY_NONZERO_WEIGHTS);var i=qe(t,"labels","logLoss"),a=qe(e,"predictions","logLoss"),s=null;null!=n&&(s=qe(n,"weights","logLoss")),R(i.shape,a.shape,"Error in logLoss: ");var u=hn(1),c=hn(r),l=i.mul(a.add(c).log()).neg().sub(u.sub(i).mul(u.sub(a).add(c).log()));return gl(l,s,o)}}),Cl=on({meanSquaredError_:function(t,e,n,r){void 0===r&&(r=ll.SUM_BY_NONZERO_WEIGHTS);var o=qe(t,"labels","meanSquaredError"),i=qe(e,"predictions","meanSquaredError"),a=null;null!=n&&(a=qe(n,"weights","meanSquaredError")),R(o.shape,i.shape,"Error in meanSquaredError: ");var s=o.squaredDifference(i);return gl(s,a,r)}}),El=on({sigmoidCrossEntropy_:function(t,e,n,r,o){void 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u=hn(r),c=hn(1),l=hn(i.shape[1]);i=i.mul(c.sub(u)).add(u.div(l))}var h=function(t,e,n){if(void 0===n&&(n=-1),-1===n&&(n=e.rank-1),n!==e.rank-1)throw Error("Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank "+e.rank+" and dim was "+n);return Ar((function(t,e,r){var o=e.logSumExp([n],!0),i=e.toFloat().sub(o);return r([t,i]),{value:i.mul(t).neg().sum([n]),gradFunc:function(t,e){var r=e[0],o=e[1],i=Je(t.shape,[n]);return[t.reshape(i).mul(r.toFloat().sub(o.exp())),t.reshape(i).mul(o.exp().sub(r.toFloat()))]}}}))(t,e)}(i,a);return gl(h,s,o)}}),Il=Object.freeze({get Reduction(){return ll},absoluteDifference:ml,computeWeightedLoss:gl,cosineDistance:yl,hingeLoss:xl,huberLoss:bl,logLoss:wl,meanSquaredError:Cl,sigmoidCrossEntropy:El,softmaxCrossEntropy:_l});function Rl(t,e){return void 0===e&&(e=!1),Ot.tidy((function(){if(2!==t.shape.length)throw new Error("qr2d() requires a 2D Tensor, but got a "+t.shape.length+"D Tensor.");for(var n=t.shape[0],r=t.shape[1],o=Xn(n),i=t.clone(),a=pn([[1]],[1,1]),s=a.clone(),u=n>=r?r:n,c=function(t){var e,u=i,c=s,l=o;e=Ot.tidy((function(){var e=i.slice([t,t],[n-t,1]),u=e.norm(),c=i.slice([t,t],[1,1]),l=pn([[-1]]).where(c.greater(0),pn([[1]])),h=c.sub(l.mul(u)),f=e.div(h);s=1===f.shape[0]?a.clone():a.concat(f.slice([1,0],[f.shape[0]-1,f.shape[1]]),0);var p=l.matMul(h).div(u).neg(),d=i.slice([t,0],[n-t,r]),v=p.mul(s);if(0===t)i=d.sub(v.matMul(s.transpose().matMul(d)));else{var m=d.sub(v.matMul(s.transpose().matMul(d)));i=i.slice([0,0],[t,r]).concat(m,0)}var g=o.slice([0,t],[n,o.shape[1]-t]);if(0===t)o=g.sub(g.matMul(s).matMul(v.transpose()));else{var y=g.sub(g.matMul(s).matMul(v.transpose()));o=o.slice([0,0],[n,t]).concat(y,1)}return[s,i,o]})),s=e[0],i=e[1],o=e[2],Ve([u,c,l])},l=0;lr&&(o=o.slice([0,0],[n,r]),i=i.slice([0,0],[r,r])),[o,i]}))}var Sl=on({bandPart_:function(t,e,n){if(e%1!=0)throw new Error("bandPart(): numLower must be an integer, got "+e+".");if(n%1!=0)throw new Error("bandPart(): numUpper must be an integer, got "+n+".");var r=qe(t,"a","bandPart");if(r.rank<2)throw new Error("bandPart(): Rank must be at least 2, got "+r.rank+".");var o=r.shape,i=r.shape.slice(-2),a=i[0],s=i[1];if(!(e<=a))throw new Error("bandPart(): numLower ("+e+") must not be greater than the number of rows ("+a+").");if(!(n<=s))throw new Error("bandPart(): numUpper ("+n+") must not be greater than the number of columns ("+s+").");e<0&&(e=a),n<0&&(n=s);var u=xn(0,a,1,"int32").reshape([-1,1]),c=xn(0,s,1,"int32"),l=Du(u,c),h=ou(l.lessEqual(hn(+e,"int32")),l.greaterEqual(hn(-n,"int32"))),f=gn([a,s],r.dtype);return cr(fr(r.reshape([-1,a,s])).map((function(t){return uu(h,t,f)}))).reshape(o)}}),kl=on({gramSchmidt_:function(t){var e;if(Array.isArray(t)){e=!1,I(null!=t&&t.length>0,(function(){return"Gram-Schmidt process: input must not be null, undefined, or empty"}));for(var n=t[0].shape[0],r=function(e){I(t[e].shape[0]===n,(function(){return"Gram-Schmidt: Non-unique lengths found in the input vectors: ("+t[e].shape[0]+" vs. "+n+")"}))},o=1;o0)for(var n=0;n= 2, but got rank "+t.rank);if(2===t.rank)return Rl(t,e);var n=t.shape.slice(0,t.shape.length-2).reduce((function(t,e){return t*e})),r=fr(t.reshape([n,t.shape[t.shape.length-2],t.shape[t.shape.length-1]]),0),o=[],i=[];return r.forEach((function(t){var n=Rl(t,e),r=n[0],a=n[1];o.push(r),i.push(a)})),[cr(o,0).reshape(t.shape),cr(i,0).reshape(t.shape)]}}),Al=Object.freeze({bandPart:Sl,gramSchmidt:kl,qr:Dl});function Tl(t,e,n,r,o,i){null==r&&(r=.5),null==o&&(o=Number.NEGATIVE_INFINITY),null==i&&(i=0);var a=t.shape[0];return n=Math.min(n,a),I(0<=r&&r<=1,(function(){return"iouThreshold must be in [0, 1], but was '"+r+"'"})),I(2===t.rank,(function(){return"boxes must be a 2D tensor, but was of rank '"+t.rank+"'"})),I(4===t.shape[1],(function(){return"boxes must have 4 columns, but 2nd dimension was "+t.shape[1]})),I(1===e.rank,(function(){return"scores must be a 1D tensor"})),I(e.shape[0]===a,(function(){return"scores has incompatible shape with boxes. Expected "+a+", but was "+e.shape[0]})),I(0<=i&&i<=1,(function(){return"softNmsSigma must be in [0, 1], but was '"+i+"'"})),{maxOutputSize:n,iouThreshold:r,scoreThreshold:o,softNmsSigma:i}}var Nl=on({resizeBilinear_:function(t,e,n){void 0===n&&(n=!1);var r=qe(t,"images","resizeBilinear");I(3===r.rank||4===r.rank,(function(){return"Error in resizeBilinear: x must be rank 3 or 4, but got rank "+r.rank+"."})),I(2===e.length,(function(){return"Error in resizeBilinear: new shape must 2D, but got shape "+e+"."}));var o=r,i=!1;3===r.rank&&(i=!0,o=r.as4D(1,r.shape[0],r.shape[1],r.shape[2]));var a=e[0],s=e[1],u=Ot.runKernelFunc((function(t,e){return e([o]),t.resizeBilinear(o,a,s,n)}),{x:o},(function(t,e){return{x:function(){return Ot.runKernelFunc((function(r){return r.resizeBilinearBackprop(t,e[0],n)}),{})}}}),"ResizeBilinear",{alignCorners:n,newHeight:a,newWidth:s});return i?u.as3D(u.shape[1],u.shape[2],u.shape[3]):u}}),Fl=on({resizeNearestNeighbor_:function(t,e,n){void 0===n&&(n=!1);var r=qe(t,"images","resizeNearestNeighbor");I(3===r.rank||4===r.rank,(function(){return"Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank "+r.rank+"."})),I(2===e.length,(function(){return"Error in resizeNearestNeighbor: new shape must 2D, but got shape "+e+"."})),I("float32"===r.dtype||"int32"===r.dtype,(function(){return"`images` must have `int32` or `float32` as dtype"}));var o=r,i=!1;3===r.rank&&(i=!0,o=r.as4D(1,r.shape[0],r.shape[1],r.shape[2]));var a=e[0],s=e[1],u=Ot.runKernelFunc((function(t,e){return e([o]),t.resizeNearestNeighbor(o,a,s,n)}),{batchImages:o},(function(t,e){return{batchImages:function(){return Ot.runKernelFunc((function(r){return r.resizeNearestNeighborBackprop(t,e[0],n)}),{})}}}));return i?u.as3D(u.shape[1],u.shape[2],u.shape[3]):u}}),Ml=on({nonMaxSuppression_:function(t,e,n,r,o){void 0===r&&(r=.5),void 0===o&&(o=Number.NEGATIVE_INFINITY);var i=qe(t,"boxes","nonMaxSuppression"),a=qe(e,"scores","nonMaxSuppression"),s=Tl(i,a,n,r,o);n=s.maxOutputSize,r=s.iouThreshold,o=s.scoreThreshold;var u={maxOutputSize:n,iouThreshold:r,scoreThreshold:o};return Ot.runKernelFunc((function(t){return t.nonMaxSuppression(i,a,n,r,o)}),{boxes:i,scores:a},null,"NonMaxSuppressionV3",u)}}),Ol=on({nonMaxSuppressionWithScore_:function(t,e,n,r,o,i){void 0===r&&(r=.5),void 0===o&&(o=Number.NEGATIVE_INFINITY),void 0===i&&(i=0);var a=qe(t,"boxes","nonMaxSuppression"),s=qe(e,"scores","nonMaxSuppression"),u=Tl(a,s,n,r,o,i),c={maxOutputSize:n=u.maxOutputSize,iouThreshold:r=u.iouThreshold,scoreThreshold:o=u.scoreThreshold,softNmsSigma:i=u.softNmsSigma},l=Ot.runKernel("NonMaxSuppressionV5",{boxes:a,scores:s},c);return{selectedIndices:l[0],selectedScores:l[1]}}}),Pl=on({cropAndResize_:function(t,e,n,r,o,i){var a=qe(t,"image","cropAndResize"),s=qe(e,"boxes","cropAndResize","float32"),u=qe(n,"boxInd","cropAndResize","int32");o=o||"bilinear",i=i||0;var c=s.shape[0];return I(4===a.rank,(function(){return"Error in cropAndResize: image must be rank 4,but got rank "+a.rank+"."})),I(2===s.rank&&4===s.shape[1],(function(){return"Error in cropAndResize: boxes must be have size ["+c+",4] but had shape "+s.shape+"."})),I(1===u.rank&&u.shape[0]===c,(function(){return"Error in cropAndResize: boxInd must be have size ["+c+"] but had shape "+s.shape+"."})),I(2===r.length,(function(){return"Error in cropAndResize: cropSize must be of length 2, but got length "+r.length+"."})),I(r[0]>=1&&r[1]>=1,(function(){return"cropSize must be atleast [1,1], but was "+r})),I("bilinear"===o||"nearest"===o,(function(){return"method must be bilinear or nearest, but was "+o})),Ot.runKernelFunc((function(t,e){return t.cropAndResize(a,s,u,r,o,i)}),{images:a,boxes:s,boxInd:u},null,"CropAndResize",{method:o,extrapolationValue:i,cropSize:r})}}),Bl=Object.freeze({resizeBilinear:Nl,resizeNearestNeighbor:Fl,nonMaxSuppression:Ml,nonMaxSuppressionAsync:function(t,e,n,r,o){return void 0===r&&(r=.5),void 0===o&&(o=Number.NEGATIVE_INFINITY),u(this,void 0,void 0,(function(){var i,a,s,u,l,h,f;return c(this,(function(c){switch(c.label){case 0:return i=qe(t,"boxes","nonMaxSuppressionAsync"),a=qe(e,"scores","nonMaxSuppressionAsync"),s=Tl(i,a,n,r,o),n=s.maxOutputSize,r=s.iouThreshold,o=s.scoreThreshold,[4,Promise.all([i.data(),a.data()])];case 1:return u=c.sent(),l=u[0],h=u[1],f=ao(l,h,n,r,o),i!==t&&i.dispose(),a!==e&&a.dispose(),[2,f]}}))}))},nonMaxSuppressionWithScore:Ol,nonMaxSuppressionWithScoreAsync:function(t,e,n,r,o,i){return void 0===r&&(r=.5),void 0===o&&(o=Number.NEGATIVE_INFINITY),void 0===i&&(i=0),u(this,void 0,void 0,(function(){var a,s,u,l,h,f,p;return c(this,(function(c){switch(c.label){case 0:return a=qe(t,"boxes","nonMaxSuppressionAsync"),s=qe(e,"scores","nonMaxSuppressionAsync"),u=Tl(a,s,n,r,o,i),n=u.maxOutputSize,r=u.iouThreshold,o=u.scoreThreshold,i=u.softNmsSigma,[4,Promise.all([a.data(),s.data()])];case 1:return l=c.sent(),h=l[0],f=l[1],p=so(h,f,n,r,o,i),a!==t&&a.dispose(),s!==e&&s.dispose(),[2,p]}}))}))},cropAndResize:Pl}),Ll=function(t,e){return!(t>0)||"linear"===e},Wl=function(t,e,n){if(null==n||"linear"===n)return t;if("relu"===n)return t.mul(e.step());throw new Error("Gradient for activation "+n+" has not been implemented yet.")},zl=function(t,e){var n=e,r=Br(t.shape,e.shape);return r.length>0&&(n=n.sum(r)),n.reshape(t.shape)},Ul=function(t,e,n){if("linear"===e)return t;if("relu"===e)return Vc(t);if("elu"===e)return Wc(t);if("relu6"===e)return Gc(t);if("prelu"===e)return Uc(t,n);throw new Error("Unknown fused activation "+e+".")},Vl=on({fusedMatMul_:function(t){var e,n=t.a,r=t.b,o=t.transposeA,i=void 0!==o&&o,a=t.transposeB,s=void 0!==a&&a,u=t.bias,c=t.activation,l=void 0===c?"linear":c,h=t.preluActivationWeights;if(!1===Ll(Ot.state.gradientDepth,l)){var f=sc(n,r,i,s);return null!=u&&(f=lu(f,u)),Ul(f,l,h)}var p=qe(n,"a","fused matMul"),d=qe(r,"b","fused matMul");e=Dt(p,d),p=e[0],d=e[1];var v=i?p.shape[p.rank-2]:p.shape[p.rank-1],m=s?d.shape[d.rank-1]:d.shape[d.rank-2],g=i?p.shape[p.rank-1]:p.shape[p.rank-2],y=s?d.shape[d.rank-2]:d.shape[d.rank-1],x=p.shape.slice(0,-2),b=d.shape.slice(0,-2),w=D(x),C=D(b);I(p.rank>=2&&d.rank>=2&&p.rank===d.rank,(function(){return"Error in fused matMul: inputs must have the same rank of at least 2, got ranks "+p.rank+" and "+d.rank+"."})),I(A(x,b),(function(){return"Error in fused matMul: outer dimensions ("+x+") and ("+b+") of Tensors with shapes "+p.shape+" and "+d.shape+" must match."})),I(v===m,(function(){return"Error in fused matMul: inner shapes ("+v+") and ("+m+") of Tensors with shapes "+p.shape+" and "+d.shape+" and transposeA="+i+" and transposeB="+s+" must match."}));var E,_,R=p.shape.slice(0,-2).concat([g,y]),S=i?p.as3D(w,v,g):p.as3D(w,g,v),k=s?d.as3D(C,y,m):d.as3D(C,m,y);null!=u&&Lr(R,(E=Dt(E=qe(u,"bias","fused matMul"),p)[0]).shape),null!=h&&(_=qe(h,"prelu weights","fused matMul"));var T={a:S,b:k};null!=u&&(T.bias=E),null!=h&&(T.preluActivationWeights=_);var N=[S,k];return Ot.runKernelFunc((function(t,e){var n=t.fusedBatchMatMul({a:S,b:k,transposeA:i,transposeB:s,bias:E,activation:l,preluActivationWeights:_});return e([S,k,n]),n}),T,(function(t,e){var n=e[0],r=e[1],o=e[2],a=Wl(t,o,l),c={};return null!=u&&(c={bias:function(){return zl(E,a)}}),i||s?!i&&s?Object.assign({a:function(){return a.matMul(r,!1,!1)},b:function(){return a.matMul(n,!0,!1)}},c):i&&!s?Object.assign({a:function(){return r.matMul(a,!1,!0)},b:function(){return n.matMul(a,!1,!1)}},c):Object.assign({a:function(){return r.matMul(a,!0,!0)},b:function(){return a.matMul(n,!0,!0)}},c):Object.assign({a:function(){return a.matMul(r,!1,!0)},b:function(){return n.matMul(a,!0,!1)}},c)}),"_FusedMatMul",{transposeA:i,transposeB:s,activation:l},N,[!0]).reshape(R)}}),Gl=on({fusedConv2d_:function(t){var e=t.x,n=t.filter,r=t.strides,o=t.pad,i=t.dataFormat,a=void 0===i?"NHWC":i,s=t.dilations,u=void 0===s?[1,1]:s,c=t.dimRoundingMode,l=t.bias,h=t.activation,f=void 0===h?"linear":h,p=t.preluActivationWeights;if(f=f||"linear",!1===Ll(Ot.state.gradientDepth,f)){var d=Ju(e,n,r,o,a,u,c);return null!=l&&(d=lu(d,l)),Ul(d,f,p)}var v=qe(e,"x","conv2d"),m=qe(n,"filter","conv2d"),g=v,y=!1;3===v.rank&&(y=!0,g=v.as4D(1,v.shape[0],v.shape[1],v.shape[2])),I(4===g.rank,(function(){return"Error in fused conv2d: input must be rank 4, but got rank "+g.rank+"."})),I(4===m.rank,(function(){return"Error in fused conv2d: filter must be rank 4, but got rank "+m.rank+"."})),null!=c&&I(T(o),(function(){return"Error in fused conv2d: pad must be an integer when using, dimRoundingMode "+c+" but got pad "+o+"."})),I(g.shape[3]===m.shape[2],(function(){return"Error in conv2d: depth of input ("+g.shape[3]+") must match input depth for filter "+m.shape[2]+"."})),I($r(r,u),(function(){return"Error in conv2D: Either strides or dilations must be 1. Got strides "+r+" and dilations '"+u+"'"})),I("NHWC"===a,(function(){return"Error in conv2d: got dataFormat of "+a+" but only NHWC is currently supported."}));var x,b,w=Ur(g.shape,m.shape,r,u,o,c);null!=l&&(x=Dt(x=qe(l,"bias","fused conv2d"),v)[0],Lr(w.outShape,x.shape)),null!=p&&(b=qe(p,"prelu weights","fused conv2d"));var C={x:g,filter:m};null!=l&&(C.bias=x),null!=p&&(C.preluActivationWeights=b);var E=[m,g],_=Ot.runKernelFunc((function(t,e){var n=t.fusedConv2d({input:g,filter:m,convInfo:w,bias:x,activation:f,preluActivationWeights:b});return e([m,g,n]),n}),C,(function(t,e){var n=e,i=n[0],a=n[1],s=n[2],c=Wl(t,s,f);I(Xr(u),(function(){return"Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '"+u+"'"}));var h={};return null!=l&&(h={bias:function(){return zl(x,c)}}),Object.assign({x:function(){return tc(a.shape,c,i,r,o)},filter:function(){return Zu(a,c,i.shape,r,o)}},h)}),"FusedConv2D",{convInfo:w,activation:f},E,[!0]);return y?_.as3D(_.shape[1],_.shape[2],_.shape[3]):_}}),Hl=on({fusedDepthwiseConv2d_:function(t){var e=t.x,n=t.filter,r=t.strides,o=t.pad,i=t.dataFormat,a=void 0===i?"NHWC":i,s=t.dilations,u=void 0===s?[1,1]:s,c=t.dimRoundingMode,l=t.bias,h=t.activation,f=void 0===h?"linear":h,p=t.preluActivationWeights;if(!1===Ll(Ot.state.gradientDepth,f)){var d=ec(e,n,r,o,a,u,c);return null!=l&&(d=lu(d,l)),Ul(d,f,p)}var v=qe(e,"x","depthwiseConv2d"),m=qe(n,"filter","depthwiseConv2d"),g=v,y=!1;3===v.rank&&(y=!0,g=v.as4D(1,v.shape[0],v.shape[1],v.shape[2])),I(4===g.rank,(function(){return"Error in fused depthwiseConv2d: input must be rank 4, but got rank "+g.rank+"."})),I(4===m.rank,(function(){return"Error in fused depthwiseConv2d: filter must be rank 4, but got rank "+m.rank+"."})),I(g.shape[3]===m.shape[2],(function(){return"Error in fused depthwiseConv2d: number of input channels ("+g.shape[3]+") must match the inChannels dimension in filter "+m.shape[2]+"."})),null==u&&(u=[1,1]),I($r(r,u),(function(){return"Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides "+r+" and dilations '"+u+"'"})),null!=c&&I(T(o),(function(){return"Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode "+c+" but got pad "+o+"."}));var x,b,w=Ur(g.shape,m.shape,r,u,o,c,!0);null!=l&&(x=Dt(x=qe(l,"bias","fused conv2d"),v)[0],Lr(w.outShape,x.shape)),null!=p&&(b=qe(p,"prelu weights","fused depthwiseConv2d"));var C={x:g,filter:m};null!=l&&(C.bias=x),null!=p&&(C.preluActivationWeights=b);var E=[m,g],_=Ot.runKernelFunc((function(t,e){var n=t.fusedDepthwiseConv2D({input:g,filter:m,convInfo:w,bias:x,activation:f,preluActivationWeights:b});return e([m,g,n]),n}),C,(function(t,e){I(Xr(u),(function(){return"Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '"+u+"'"}));var n=e[0],r=e[1],o=e[2],i=Wl(t,o,f),a={};return null!=l&&(a={bias:function(){return zl(x,i)}}),Object.assign({x:function(){return nc(r.shape,i,n,w)},filter:function(){return rc(r,i,n.shape,w)}},a)}),"FusedDepthwiseConv2D",{convInfo:w,activation:f},E,[!0]);return y?_.as3D(_.shape[1],_.shape[2],_.shape[3]):_}}),jl=Object.freeze({matMul:Vl,conv2d:Gl,depthwiseConv2d:Hl}),ql=Object.freeze({image:Bl,linalg:Al,losses:Il,spectral:ol,fused:jl,signal:vl,square:ss,squaredDifference:cs,conv1d:Yu,conv2d:Ju,conv3d:Qu,depthwiseConv2d:ec,separableConv2d:oc,conv2dTranspose:ic,conv3dTranspose:ac,op:on,batchNormalization2d:Ys,batchNormalization3d:Js,batchNormalization4d:Qs,batchNormalization:Zs,batchNorm:tu,batchNorm2d:eu,batchNorm3d:nu,batchNorm4d:ru,booleanMaskAsync:function(t,e,n){return u(this,void 0,void 0,(function(){var r,o,i,a,s,u,l,h,f,p,d,v,m;return c(this,(function(c){switch(c.label){case 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a},e.prototype.cumsum=function(t,e,n,r){if(Kl(t,"cumsum"),e!==t.rank-1)throw new Error("backend.cumsum in CPU expects an inner-most axis="+(t.rank-1)+" but got axis="+e);for(var o=St(t.dtype,"int32"),i=gn(t.shape,o),a=this.readSync(i.dataId),s=this.readSync(t.dataId),u=t.shape[t.rank-1],c=r?function(t,e){return t+u-e-1}:function(t,e){return t+e},l=0;le?1:0}))},e.prototype.greaterEqual=function(t,e){return Kl([t,e],"greaterEqual"),this.broadcastedBinaryOp(t,e,"bool",(function(t,e){return t>=e?1:0}))},e.prototype.logicalNot=function(t){Kl(t,"logicalNot");for(var e=this.readSync(t.dataId),n=new Uint8Array(e.length),r=0;r1||1===e.rank?1:D(e.shape.slice(1)),l=0;l=0&&e>=0?n:(n+e)%e}))},e.prototype.max=function(t,e){Kl(t,"max"),Qe("max",e,t.rank);for(var n=Ye(t.shape,e),r=n[0],o=n[1],i=gn(r,t.dtype),a=D(o),s=this.readSync(i.dataId),u=this.readSync(t.dataId),c=0;ch&&(h=p)}s[c]=h}return i},e.prototype.maximum=function(t,e){return Kl([t,e],"maximum"),this.broadcastedBinaryOp(t,e,t.dtype,(function(t,e){return Math.max(t,e)}))},e.prototype.all=function(t,e){Kl(t,"all"),Qe("all",e,t.rank);for(var n=Ye(t.shape,e),r=n[0],o=n[1],i=gn(r,t.dtype),a=D(o),s=this.readSync(i.dataId),u=this.readSync(t.dataId),c=0;c0?n[r]=1:n[r]=0;return this.makeOutput(n,t.shape,"float32")},e.prototype.isNaN=function(t){Kl(t,"x");for(var e=this.readSync(t.dataId),n=new Uint8Array(e.length),r=0;r.5?n[r]=Math.ceil(e[r]):n[r]=o%2==0?o:o+1}return this.makeOutput(n,t.shape,"float32")},e.prototype.exp=function(t){Kl(t,"exp");for(var e=this.readSync(t.dataId),n=new Float32Array(e.length),r=0;r=0?o:Math.exp(o)-1}return this.makeOutput(e,t.shape,"float32")},e.prototype.eluDer=function(t,e){Kl([t,e],"eluDer");for(var n=new Float32Array(e.size),r=this.readSync(e.dataId),o=this.readSync(t.dataId),i=0;i=1?o[i]:o[i]*(a+1)}return this.makeOutput(n,e.shape,"float32")},e.prototype.selu=function(t){Kl(t,"selu");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r=0?1.0507009873554805*o:1.7580993408473768*(Math.exp(o)-1)}return this.makeOutput(e,t.shape,"float32")},e.prototype.clip=function(t,e,n){Kl(t,"clip");for(var r=new Float32Array(t.size),o=this.readSync(t.dataId),i=0;in?n:a-e,s=r[o]0?1:e}return this.makeOutput(n,t.shape,"float32")},e.prototype.fusedConv2d=function(t){var e=t.input,n=t.filter,r=t.convInfo,o=t.bias,i=t.activation,a=t.preluActivationWeights,s=this.conv2d(e,n,r);return o&&(s=this.add(s,o)),i&&(s=Xl(this,s,i,a)),s},e.prototype.conv2d=function(t,e,n){Kl([t,e],"conv2d");for(var r=n.filterHeight,o=n.filterWidth,i=n.dilationHeight,a=n.dilationWidth,s=n.padInfo.left,u=n.padInfo.top,c="channelsLast"===n.dataFormat,l=zn(n.outShape,t.dtype),h=t.strides[0],f=c?t.strides[1]:t.strides[2],p=c?t.strides[2]:1,d=c?1:t.strides[1],v=l.strides[0],m=c?l.strides[1]:l.strides[2],g=c?l.strides[2]:1,y=c?1:l.strides[1],x=this.readSync(t.dataId),b=this.readSync(e.dataId),w=l.values,C=0;C=n.inHeight))for(var A=k*e.strides[0],T=E+D*f,N=0;N=n.inWidth))for(var B=T+P*p,L=A+O*e.strides[1],W=0;W=n.inDepth))for(var _=C*e.strides[0],I=g+E*t.strides[1],R=0;R=n.inHeight))for(var T=_+D*e.strides[1],N=I+A*t.strides[2],F=0;F=n.inWidth))for(var L=T+P*e.strides[2],W=N+B*n.inChannels,z=L,U=0;U=n.inHeight))for(var C=b*e.strides[0],E=v+w*t.strides[1],_=0;_=n.inWidth))for(var D=C+S*e.strides[1],A=E+k*n.inChannels,T=I,N=D,F=0;FT?T=B:"avg"===n&&(N+=B,F++)}if(isNaN(T))break}d[R+S*g+w]="avg"===n?N/F:T}return p.toTensor()},e.prototype.maxPool=function(t,e){return this.pool(t,e,"max")},e.prototype.maxPoolPositions=function(t,e){for(var n=zn(e.outShape,"int32"),r=e.strideHeight,o=e.strideWidth,i=e.dilationHeight,a=e.dilationWidth,s=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=e.padInfo.top,l=e.padInfo.left,h=this.bufferSync(t),f=0;fC&&(C=k,E=I*u+S)}n.set(E,f,d,y,p)}}return n.toTensor()},e.prototype.maxPoolBackprop=function(t,e,n,r){Kl([e,n],"maxPoolBackprop");for(var o=this.maxPoolPositions(e,r),i=r.strideHeight,a=r.strideWidth,s=r.dilationHeight,u=r.dilationWidth,c=r.effectiveFilterHeight,l=r.effectiveFilterWidth,h=l-1-r.padInfo.left,f=c-1-r.padInfo.top,p=zn(e.shape,"float32"),d=this.bufferSync(o),v=this.bufferSync(t),m=0;m=r.outHeight||Math.floor(_)!==_))for(var I=0;I=r.outWidth||Math.floor(R)!==R)){var S=c*l-1-d.get(m,_,R,g)===E*l+I?1:0;0!==S&&(C+=v.get(m,_,R,g)*S)}}}p.set(C,m,y,x,g)}return p.toTensor()},e.prototype.avgPoolBackprop=function(t,e,n){Kl([t,e],"avgPoolBackprop");for(var r=n.strideHeight,o=n.strideWidth,i=n.filterHeight,a=n.filterWidth,s=n.dilationHeight,u=n.dilationWidth,c=n.effectiveFilterHeight,l=n.effectiveFilterWidth,h=l-1-n.padInfo.left,f=c-1-n.padInfo.top,p=zn(e.shape,"float32"),d=1/(i*a),v=this.bufferSync(t),m=0;m=n.outHeight||Math.floor(_)!==_))for(var I=0;I=n.outWidth||Math.floor(R)!==R||(C+=v.get(m,_,R,g))}}p.set(C*d,m,y,x,g)}return p.toTensor()},e.prototype.pool3d=function(t,e,n){Kl(t,"pool3d");for(var r=e.strideDepth,o=e.strideHeight,i=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,l=e.effectiveFilterHeight,h=e.effectiveFilterWidth,f=e.padInfo.front,p=e.padInfo.top,d=e.padInfo.left,v="max"===n?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=this.readSync(t.dataId),g=zn(e.outShape,t.dtype),y=g.values,x=e.outShape[1]*e.outShape[2]*e.outShape[3]*e.outShape[4],b=e.outShape[2]*e.outShape[3]*e.outShape[4],w=e.outShape[3]*e.outShape[4],C=e.outShape[4],E=0;EV?V=Y:"avg"===n&&(G+=Y,H++),isNaN(V))break}if(isNaN(V))break}if(isNaN(V))break}y[U+R]="avg"===n?G/H:V}}}return g.toTensor()},e.prototype.avgPool3d=function(t,e){return Kl(t,"avgPool3d"),this.pool3d(t,e,"avg").toFloat()},e.prototype.avgPool3dBackprop=function(t,e,n){Kl([t,e],"avgPool3dBackprop");for(var r=n.strideDepth,o=n.strideHeight,i=n.strideWidth,a=n.filterDepth,s=n.filterHeight,u=n.filterWidth,c=n.dilationDepth,l=n.dilationHeight,h=n.dilationWidth,f=n.effectiveFilterDepth,p=n.effectiveFilterHeight,d=n.effectiveFilterWidth,v=f-1-n.padInfo.front,m=d-1-n.padInfo.left,g=p-1-n.padInfo.top,y=zn(e.shape,"float32"),x=1/(a*s*u),b=this.bufferSync(t),w=0;w=n.outDepth||Math.floor(T)!==T))for(var N=0;N=n.outHeight||Math.floor(F)!==F))for(var M=0;M=n.outWidth||Math.floor(O)!==O||(D+=b.get(w,T,F,O,C))}}}y.set(D*x,w,E,_,I,C)}return y.toTensor()},e.prototype.maxPool3d=function(t,e){return Kl(t,"maxPool3d"),this.pool3d(t,e,"max").toFloat()},e.prototype.maxPool3dPositions=function(t,e){for(var n=zn(e.outShape,"int32"),r=e.strideDepth,o=e.strideHeight,i=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,l=e.effectiveFilterHeight,h=e.effectiveFilterWidth,f=e.padInfo.front,p=e.padInfo.top,d=e.padInfo.left,v=this.bufferSync(t),m=0;m=A&&(A=L,T=F*l*h+O*l+B)}n.set(T,m,y,C,R,g)}}}return n.toTensor()},e.prototype.maxPool3dBackprop=function(t,e,n,r){Kl([e,n],"maxPool3dBackprop");for(var o=this.maxPool3dPositions(e,r),i=r.strideDepth,a=r.strideHeight,s=r.strideWidth,u=r.dilationDepth,c=r.dilationHeight,l=r.dilationWidth,h=r.effectiveFilterDepth,f=r.effectiveFilterHeight,p=r.effectiveFilterWidth,d=h-1-r.padInfo.front,v=p-1-r.padInfo.left,m=f-1-r.padInfo.top,g=zn(e.shape,"float32"),y=this.bufferSync(o),x=this.bufferSync(t),b=0;b=r.outDepth||Math.floor(A)!==A))for(var T=0;T=r.outHeight||Math.floor(N)!==N))for(var F=0;F=r.outWidth||Math.floor(M)!==M)){var O=h*f*p-1-y.get(b,A,N,M,w)===D*f*p+T*p+F?1:0;0!==O&&(k+=x.get(b,A,N,M,w)*O)}}}}g.set(k,b,C,E,_,w)}return g.toTensor()},e.prototype.cast=function(t,e){return Jr(t,e,this)},e.prototype.reshape=function(t,e){return Qr(t,e)},e.prototype.avgPool=function(t,e){return Kl(t,"avgPool"),this.pool(t,e,"avg").toFloat()},e.prototype.resizeBilinear=function(t,e,n,r){Kl(t,"resizeBilinear");for(var o=t.shape,i=o[0],a=o[1],s=o[2],u=o[3],c=this.readSync(t.dataId),l=new Float32Array(D([i,e,n,u])),h=[r&&e>1?a-1:a,r&&n>1?s-1:s],f=[r&&e>1?e-1:e,r&&n>1?n-1:n],p=0,d=h[0]/f[0],v=h[1]/f[1],m=0;m1?i-1:i,n&&l>1?a-1:a],p=[n&&c>1?c-1:c,n&&l>1?l-1:l],d=f[0]/p[0],v=f[1]/p[1],m=this.readSync(t.dataId),g=0,y=0;y1?a-1:a,r&&n>1?s-1:s],f=[r&&e>1?e-1:e,r&&n>1?n-1:n],p=h[0]/f[0],d=h[1]/f[1],v=0,m=0;m1?i-1:i,n&&l>1?a-1:a],d=[n&&c>1?c-1:c,n&&l>1?l-1:l],v=p[0]/d[0],m=p[1]/d[1],g=1/v,y=1/m,x=2*Math.ceil(g)+2,b=2*Math.ceil(y)+2,w=0;w=c)){var O=C+M*t.strides[1],P=M*v;if(E===Math.min(i-1,n?Math.round(P):Math.floor(P)))for(var B=0;B=l)){var W=O+L*t.strides[2],z=L*m;S===Math.min(a-1,n?Math.round(z):Math.floor(z))&&(N+=f[W+T])}}}}h[k+T]=N}return vn(h,e.shape,e.dtype)},e.prototype.batchNormalization=function(t,e,n,r,o,i){Kl([t,e,n,o,i],"batchNorm");for(var a=this.readSync(t.dataId),s=this.readSync(e.dataId),u=this.readSync(n.dataId),c=o?this.readSync(o.dataId):new Float32Array([1]),l=i?this.readSync(i.dataId):new Float32Array([0]),h=new Float32Array(a.length),f=l.length,p=c.length,d=u.length,v=s.length,m=0,g=0,y=0,x=0,b=0;b=f&&(m=0),g>=v&&(g=0),y>=p&&(y=0),x>=d&&(x=0);return vn(h,t.shape)},e.prototype.localResponseNormalization4D=function(t,e,n,r,o){Kl(t,"localResponseNormalization4D");var i=t.shape[3],a=i-1,s=this.readSync(t.dataId),u=t.size,c=new Float32Array(u);function l(t){for(var n=t%i,r=t-n+Math.max(0,n-e),o=t-n+Math.min(n+e,a),u=0;r<=o;r++){var c=s[r];u+=c*c}return u}for(var h=0;h=0&&i[a]1,(function(){return"blockSize should be > 1 for depthToSpace, but was: "+e}));for(var r=t.shape[0],o=t.shape[1],i=t.shape[2],a=t.shape[3],s=o*e,u=i*e,c=a/(e*e),l=this.readSync(t.dataId),h=new Float32Array(r*s*u*c),f=0,p=0;p=s))for(var S=f>1?(_-C)*(u-1)/(f-1):0,k=p>1?(I-E)*(c-1)/(p-1):0,D=0;D1?C*(u-1)+D*S:.5*(C+_)*(u-1);if(A<0||A>u-1)for(var T=0;T1?E*(c-1)+T*k:.5*(E+I)*(c-1))<0||j>c-1)for(N=0;N1?E*(c-1)+T*k:.5*(E+I)*(c-1))<0||j>c-1)for(N=0;N=t.size/s)throw new Error("Invalid indices: "+p+" does not index into "+t.shape);for(var g=0;g=r/o)throw new Error("Invalid indices: "+v+" does not index into "+n);for(var x=0;x0,(function(){return"scheme must not be an empty string."}));var r=t.getInstance();I(null==r.managers[e],(function(){return"A model store manager is already registered for scheme '"+e+"'."})),r.managers[e]=n},t.getManager=function(t){var e=this.getInstance().managers[t];if(null==e)throw new Error("Cannot find model manager for scheme '"+t+"'");return e},t.getSchemes=function(){return Object.keys(this.getInstance().managers)},t}();function xh(t){if(-1===t.indexOf(gh))throw new Error("The url string provided does not contain a scheme. Supported schemes are: "+yh.getSchemes().join(","));return{scheme:t.split(gh)[0],path:t.split(gh)[1]}}function bh(t,e,n){return void 0===n&&(n=!1),u(this,void 0,void 0,(function(){var r,o,i,a,s,u,l,h,f;return c(this,(function(c){switch(c.label){case 0:return I(t!==e,(function(){return"Old path and new path are the same: '"+t+"'"})),I((r=mh.getLoadHandlers(t)).length>0,(function(){return"Copying failed because no load handler is found for source URL "+t+"."})),I(r.length<2,(function(){return"Copying failed because more than one ("+r.length+") load handlers for source URL "+t+"."})),o=r[0],I((i=mh.getSaveHandlers(e)).length>0,(function(){return"Copying failed because no save handler is found for destination URL "+e+"."})),I(i.length<2,(function(){return"Copying failed because more than one ("+r.length+") save handlers for destination URL "+e+"."})),a=i[0],s=xh(t).scheme,u=xh(t).path,l=s===xh(t).scheme,[4,o.load()];case 1:return h=c.sent(),n&&l?[4,yh.getManager(s).removeModel(u)]:[3,3];case 2:c.sent(),c.label=3;case 3:return[4,a.save(h)];case 4:return f=c.sent(),!n||l?[3,6]:[4,yh.getManager(s).removeModel(u)];case 5:c.sent(),c.label=6;case 6:return[2,f.modelArtifactsInfo]}}))}))}var wh="models_store",Ch="model_info_store";function Eh(){if(!f().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");var t=window||self,e=t.indexedDB||t.mozIndexedDB||t.webkitIndexedDB||t.msIndexedDB||t.shimIndexedDB;if(null==e)throw new Error("The current browser does not appear to support IndexedDB.");return e}function _h(t){var e=t.result;e.createObjectStore(wh,{keyPath:"modelPath"}),e.createObjectStore(Ch,{keyPath:"modelPath"})}var Ih=function(){function t(t){if(this.indexedDB=Eh(),null==t||!t)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=t}return t.prototype.save=function(t){return u(this,void 0,void 0,(function(){return c(this,(function(e){if(t.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return[2,this.databaseAction(this.modelPath,t)]}))}))},t.prototype.load=function(){return u(this,void 0,void 0,(function(){return c(this,(function(t){return[2,this.databaseAction(this.modelPath)]}))}))},t.prototype.databaseAction=function(t,e){var n=this;return new Promise((function(t,r){var o=n.indexedDB.open("tensorflowjs",1);o.onupgradeneeded=function(){return _h(o)},o.onsuccess=function(){var i=o.result;if(null==e){var a=i.transaction(wh,"readonly"),s=a.objectStore(wh).get(n.modelPath);s.onsuccess=function(){if(null==s.result)return i.close(),r(new Error("Cannot find model with path '"+n.modelPath+"' in IndexedDB."));t(s.result.modelArtifacts)},s.onerror=function(t){return i.close(),r(s.error)},a.oncomplete=function(){return i.close()}}else{var u,c=vh(e),l=i.transaction(Ch,"readwrite"),h=l.objectStore(Ch),f=h.put({modelPath:n.modelPath,modelArtifactsInfo:c});f.onsuccess=function(){var o=(u=i.transaction(wh,"readwrite")).objectStore(wh).put({modelPath:n.modelPath,modelArtifacts:e,modelArtifactsInfo:c});o.onsuccess=function(){return t({modelArtifactsInfo:c})},o.onerror=function(t){var e=(h=l.objectStore(Ch)).delete(n.modelPath);e.onsuccess=function(){return i.close(),r(o.error)},e.onerror=function(t){return i.close(),r(o.error)}}},f.onerror=function(t){return i.close(),r(f.error)},l.oncomplete=function(){null==u?i.close():u.oncomplete=function(){return i.close()}}}},o.onerror=function(t){return r(o.error)}}))},t.URL_SCHEME="indexeddb://",t}(),Rh=function(t){return f().getBool("IS_BROWSER")&&!Array.isArray(t)&&t.startsWith(Ih.URL_SCHEME)?(e=t.slice(Ih.URL_SCHEME.length),new Ih(e)):null;var e};mh.registerSaveRouter(Rh),mh.registerLoadRouter(Rh);var Sh=function(){function t(){this.indexedDB=Eh()}return t.prototype.listModels=function(){return u(this,void 0,void 0,(function(){var t=this;return c(this,(function(e){return[2,new Promise((function(e,n){var r=t.indexedDB.open("tensorflowjs",1);r.onupgradeneeded=function(){return _h(r)},r.onsuccess=function(){var t=r.result,o=t.transaction(Ch,"readonly"),i=o.objectStore(Ch).getAll();i.onsuccess=function(){for(var t={},n=0,r=i.result;n0,(function(){return"promises must be a none empty array"}))}(t),function(t,e){I(t>=0&&t<=1,(function(){return"Progress fraction must be in range [0, 1], but got startFraction "+t})),I(e>=0&&e<=1,(function(){return"Progress fraction must be in range [0, 1], but got endFraction "+e})),I(e>=t,(function(){return"startFraction must be no more than endFraction, but got startFraction "+t+" and endFraction "+e}))}(n=null==n?0:n,r=null==r?1:r);var o=0;return Promise.all(t.map((function(i){return i.then((function(i){var a=n+ ++o/t.length*(r-n);return e(a),i})),i})))}function Hh(t,e){return u(this,void 0,void 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t?t.quantization.dtype:t.dtype,c=uh[u]*D(t.shape),l=function(){e[n]=!0,null==i[n]&&(i[n]=[]),i[n].push({manifestEntry:t,groupOffset:r,sizeBytes:c})};null!=o?o.forEach((function(e,n){e===t.name&&(l(),a[n]=!0)})):l(),s.push(t.name),r+=c}))})),!a.every((function(t){return t})))throw u=o.filter((function(t,e){return!a[e]})),new Error("Could not find weights in manifest with names: "+u.join(", ")+". \nManifest JSON has weights with names: "+s.join(", ")+".");return l=e.reduce((function(t,e,n){return e&&t.push(n),t}),[]),h=[],l.forEach((function(t){n[t].paths.forEach((function(t){var e=r+(r.endsWith("/")?"":"/")+t;h.push(e)}))})),[4,t(h)];case 1:return f=c.sent(),p={},d=0,l.forEach((function(t){for(var e=n[t].paths.length,r=0,o=0;o0,(function(){return"URL path for http must not be null, undefined or empty."})),Array.isArray(t)&&I(2===t.length,(function(){return"URL paths for http must have a length of 2, (actual length is "+t.length+")."})),this.path=t,null!=e.requestInit&&null!=e.requestInit.body)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=e.requestInit||{}}return t.prototype.save=function(t){return u(this,void 0,void 0,(function(){var e,n,r,o;return c(this,(function(i){switch(i.label){case 0:if(t.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");return(e=Object.assign({method:this.DEFAULT_METHOD},this.requestInit)).body=new FormData,n=[{paths:["./model.weights.bin"],weights:t.weightSpecs}],r={modelTopology:t.modelTopology,format:t.format,generatedBy:t.generatedBy,convertedBy:t.convertedBy,userDefinedMetadata:t.userDefinedMetadata,weightsManifest:n},e.body.append("model.json",new Blob([JSON.stringify(r)],{type:"application/json"}),"model.json"),null!=t.weightData&&e.body.append("model.weights.bin",new Blob([t.weightData],{type:"application/octet-stream"}),"model.weights.bin"),[4,this.fetch(this.path,e)];case 1:if((o=i.sent()).ok)return[2,{modelArtifactsInfo:vh(t),responses:[o]}];throw new Error("BrowserHTTPRequest.save() failed due to HTTP response status "+o.status+".")}}))}))},t.prototype.load=function(){return u(this,void 0,void 0,(function(){var t,e,n,r,o,i,a,s,u,l,h,f;return c(this,(function(c){switch(c.label){case 0:return[4,this.fetch(this.path,this.requestInit)];case 1:if(!(t=c.sent()).ok)throw new Error("Request to "+this.path+" failed with status code "+t.status+". Please verify this URL points to the model JSON of the model to load.");c.label=2;case 2:return c.trys.push([2,4,,5]),[4,t.json()];case 3:return e=c.sent(),[3,5];case 4:throw c.sent(),n="Failed to parse model JSON of response from "+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,o=e.weightsManifest,i=e.generatedBy,a=e.convertedBy,s=e.format,u=e.userDefinedMetadata,null==r&&null==o)throw new Error("The JSON from HTTP path "+this.path+" contains neither model topology or manifest for weights.");return null==o?[3,7]:[4,this.loadWeights(o)];case 6:f=c.sent(),l=f[0],h=f[1],c.label=7;case 7:return[2,{modelTopology:r,weightSpecs:l,weightData:h,userDefinedMetadata:u,generatedBy:i,convertedBy:a,format:s}]}}))}))},t.prototype.loadWeights=function(t){return u(this,void 0,void 0,(function(){var e,n,r,o,i,a,s,u,l,h,f;return c(this,(function(c){switch(c.label){case 0:for(e=Array.isArray(this.path)?this.path[1]:this.path,n=function(t){var e=t.lastIndexOf("/"),n=t.lastIndexOf("?");return[t.substring(0,e)+"/",n>e?t.substring(n):""]}(e),r=n[0],o=n[1],i=this.weightPathPrefix||r,a=[],s=0,u=t;s0&&Number.isInteger(n),(function(){return"If provided, numClasses must be a positive integer, but got "+n})),I(1===r.rank,(function(){return"Expected the rank of labels to be 1, but got "+r.rank})),I(1===o.rank,(function(){return"Expected the rank of predictions to be 1, but got "+o.rank})),I(r.shape[0]===o.shape[0],(function(){return"Mismatch in the number of examples: "+r.shape[0]+" vs. "+o.shape[0]+". Labels and predictions should have the same number of elements."})),I(n>0&&Number.isInteger(n),(function(){return"numClasses is required to be a positive integer, but got "+n}));var i=Yn(r.asType("int32"),n),a=Yn(o.asType("int32"),n);return i.transpose().matMul(a).asType("int32")}}),ef=(Object.freeze({confusionMatrix:tf}),on({fromPixels_:function(t,e){if(void 0===e&&(e=3),e>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(null==t)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");var n=!1,r=!1,o=!1,i=!1,a=!1;if(t.data instanceof Uint8Array)n=!0;else if("undefined"!=typeof ImageData&&t instanceof ImageData)r=!0;else if("undefined"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement)o=!0;else if("undefined"!=typeof HTMLImageElement&&t instanceof HTMLImageElement)i=!0;else{if(null==t.getContext)throw new Error("pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was "+t.constructor.name);a=!0}if(o&&o&&t.readyState<2)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the