/**
 * @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 { DepthToSpace, util } from '@tensorflow/tfjs-core';
export function depthToSpace(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { blockSize, dataFormat } = attrs;
    util.assert(dataFormat === 'NHWC', () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`);
    const batchSize = x.shape[0];
    const inputHeight = x.shape[1];
    const inputWidth = x.shape[2];
    const inputDepth = x.shape[3];
    const outputHeight = inputHeight * blockSize;
    const outputWidth = inputWidth * blockSize;
    const outputDepth = inputDepth / (blockSize * blockSize);
    const xValues = backend.data.get(x.dataId).values;
    const result = new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);
    let outputIdx = 0;
    for (let b = 0; b < batchSize; ++b) {
        for (let h = 0; h < outputHeight; ++h) {
            const inH = Math.floor(h / blockSize);
            const offsetH = (h % blockSize);
            for (let w = 0; w < outputWidth; ++w) {
                const inW = Math.floor(w / blockSize);
                const offsetW = (w % blockSize);
                const offsetD = (offsetH * blockSize + offsetW) * outputDepth;
                for (let d = 0; d < outputDepth; ++d) {
                    const inD = d + offsetD;
                    const inputIdx = inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));
                    result[outputIdx++] = xValues[inputIdx];
                }
            }
        }
    }
    return backend.makeTensorInfo([batchSize, outputHeight, outputWidth, outputDepth], x.dtype, result);
}
export const depthToSpaceConfig = {
    kernelName: DepthToSpace,
    backendName: 'cpu',
    kernelFunc: depthToSpace
};
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