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/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
import { buffer, slice_util, StridedSlice, util } from '@tensorflow/tfjs-core';
import { stridedSliceImplCPU } from '../kernel_utils/shared';
import { StridedSliceProgram } from '../strided_slice_gpu';
import { reshape } from './Reshape';
import { slice } from './Slice';
export function stridedSlice(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;
    const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);
    let result;
    if (isIdentity) {
        // Optimization #1, slice is a no-op plus reshape
        result = reshape({ inputs: { x }, backend, attrs: { shape: finalShape } });
    }
    else if (sliceDim0 || isSimpleSlice) {
        // Optimization #2, slice is memory contiguous (only occurs in dim 0)
        util.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`);
        const size = slice_util.computeOutShape($begin, $end, $strides);
        // To tolerate begin[0] > end[0] (a 0-output slice), we min(begin, end).
        const sliced = slice({ inputs: { x }, backend, attrs: { begin: $begin, size } });
        result =
            reshape({ inputs: { x: sliced }, backend, attrs: { shape: finalShape } });
        backend.disposeIntermediateTensorInfo(sliced);
    }
    else {
        const shouldExecuteOnCPU = backend.shouldExecuteOnCPU([x]);
        if (shouldExecuteOnCPU) {
            // tslint:disable-next-line: no-unnecessary-type-assertion
            const values = backend.readSync(x.dataId);
            // tslint:disable-next-line: no-unnecessary-type-assertion
            const xBuf = buffer(x.shape, x.dtype, values);
            const resultValues = stridedSliceImplCPU(finalShapeSparse, xBuf, $strides, $begin);
            result = backend.makeTensorInfo(finalShape, x.dtype, resultValues.values);
        }
        else {
            const program = new StridedSliceProgram($begin, $strides, finalShapeSparse);
            result = backend.runWebGLProgram(program, [x], x.dtype);
        }
    }
    const resultReshaped = reshape({ inputs: { x: result }, backend, attrs: { shape: finalShape } });
    backend.disposeIntermediateTensorInfo(result);
    return resultReshaped;
}
export const stridedSliceConfig = {
    kernelName: StridedSlice,
    backendName: 'webgl',
    kernelFunc: stridedSlice
};
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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {buffer, KernelConfig, KernelFunc, Rank, slice_util, StridedSlice, StridedSliceAttrs, StridedSliceInputs, TensorBuffer, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {stridedSliceImplCPU} from '../kernel_utils/shared';\nimport {StridedSliceProgram} from '../strided_slice_gpu';\n\nimport {reshape} from './Reshape';\nimport {slice} from './Slice';\n\nexport function stridedSlice(args: {\n  inputs: StridedSliceInputs,\n  backend: MathBackendWebGL,\n  attrs: StridedSliceAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  const {\n    begin,\n    end,\n    strides,\n    beginMask,\n    endMask,\n    ellipsisMask,\n    newAxisMask,\n    shrinkAxisMask\n  } = attrs;\n\n  const {\n    finalShapeSparse,\n    finalShape,\n    isIdentity,\n    sliceDim0,\n    isSimpleSlice,\n    begin: $begin,\n    end: $end,\n    strides: $strides\n  } =\n      slice_util.sliceInfo(\n          x.shape, begin, end, strides, beginMask, endMask, ellipsisMask,\n          newAxisMask, shrinkAxisMask);\n\n  let result;\n\n  if (isIdentity) {\n    // Optimization #1, slice is a no-op plus reshape\n    result = reshape({inputs: {x}, backend, attrs: {shape: finalShape}});\n  } else if (sliceDim0 || isSimpleSlice) {\n    // Optimization #2, slice is memory contiguous (only occurs in dim 0)\n    util.assert(\n        x.shape.length >= 1,\n        () => `Input must have rank at least 1, got: ${x.shape.length}`);\n\n    const size = slice_util.computeOutShape($begin, $end, $strides);\n    // To tolerate begin[0] > end[0] (a 0-output slice), we min(begin, end).\n    const sliced = slice({inputs: {x}, backend, attrs: {begin: $begin, size}});\n    result =\n        reshape({inputs: {x: sliced}, backend, attrs: {shape: finalShape}});\n    backend.disposeIntermediateTensorInfo(sliced);\n  } else {\n    const shouldExecuteOnCPU = backend.shouldExecuteOnCPU([x]);\n    if (shouldExecuteOnCPU) {\n      // tslint:disable-next-line: no-unnecessary-type-assertion\n      const values = backend.readSync(x.dataId) as TypedArray;\n      // tslint:disable-next-line: no-unnecessary-type-assertion\n      const xBuf = buffer(x.shape, x.dtype, values) as TensorBuffer<Rank>;\n      const resultValues =\n          stridedSliceImplCPU(finalShapeSparse, xBuf, $strides, $begin);\n      result = backend.makeTensorInfo(finalShape, x.dtype, resultValues.values);\n    } else {\n      const program =\n          new StridedSliceProgram($begin, $strides, finalShapeSparse);\n      result = backend.runWebGLProgram(program, [x], x.dtype);\n    }\n  }\n\n  const resultReshaped =\n      reshape({inputs: {x: result}, backend, attrs: {shape: finalShape}});\n\n  backend.disposeIntermediateTensorInfo(result);\n\n  return resultReshaped;\n}\n\nexport const stridedSliceConfig: KernelConfig = {\n  kernelName: StridedSlice,\n  backendName: 'webgl',\n  kernelFunc: stridedSlice as unknown as KernelFunc\n};\n"]}