/**
 * @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 { backend_util, Cumsum, upcastType, util } from '@tensorflow/tfjs-core';
import { assertNotComplex } from '../cpu_util';
import { transpose } from './Transpose';
export function cumsum(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { axis, exclusive, reverse } = attrs;
    assertNotComplex(x, 'cumsum');
    const permutation = backend_util.getAxesPermutation([axis], x.shape.length);
    let $x = x;
    if (permutation != null) {
        $x = transpose({ inputs: { x }, backend, attrs: { perm: permutation } });
    }
    const permutedAxis = backend_util.getInnerMostAxes(1, x.shape.length)[0];
    if (permutedAxis !== $x.shape.length - 1) {
        throw new Error(`backend.cumsum in CPU expects an inner-most ` +
            `axis=${$x.shape.length - 1} but got axis=${permutedAxis}`);
    }
    const resultDtype = upcastType($x.dtype, 'int32');
    const vals = util.makeZerosTypedArray(util.sizeFromShape($x.shape), resultDtype);
    const aVals = backend.data.get($x.dataId).values;
    const finalDim = $x.shape[$x.shape.length - 1];
    const indexAdjuster = reverse ?
        (i, j) => i + finalDim - j - 1 :
        (i, j) => i + j;
    for (let i = 0; i < aVals.length; i += finalDim) {
        for (let j = 0; j < finalDim; j++) {
            const idx = indexAdjuster(i, j);
            if (j === 0) {
                vals[idx] = exclusive ? 0 : aVals[idx];
            }
            else {
                const prevIdx = indexAdjuster(i, j - 1);
                vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] :
                    aVals[idx] + vals[prevIdx];
            }
        }
    }
    const result = backend.makeTensorInfo($x.shape, resultDtype, vals);
    if (permutation != null) {
        const reversePermutation = backend_util.getUndoAxesPermutation(permutation);
        const reverseTransposedResult = transpose({ inputs: { x: result }, backend, attrs: { perm: reversePermutation } });
        backend.disposeIntermediateTensorInfo(result);
        backend.disposeIntermediateTensorInfo($x);
        return reverseTransposedResult;
    }
    return result;
}
export const cumsumConfig = {
    kernelName: Cumsum,
    backendName: 'cpu',
    kernelFunc: cumsum
};
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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 {backend_util, Cumsum, CumsumAttrs, CumsumInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, upcastType, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\nimport {transpose} from './Transpose';\n\nexport function cumsum(\n    args: {inputs: CumsumInputs, backend: MathBackendCPU, attrs: CumsumAttrs}):\n    TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  const {axis, exclusive, reverse} = attrs;\n\n  assertNotComplex(x, 'cumsum');\n\n  const permutation = backend_util.getAxesPermutation([axis], x.shape.length);\n  let $x = x;\n  if (permutation != null) {\n    $x = transpose({inputs: {x}, backend, attrs: {perm: permutation}});\n  }\n  const permutedAxis = backend_util.getInnerMostAxes(1, x.shape.length)[0];\n\n  if (permutedAxis !== $x.shape.length - 1) {\n    throw new Error(\n        `backend.cumsum in CPU expects an inner-most ` +\n        `axis=${$x.shape.length - 1} but got axis=${permutedAxis}`);\n  }\n\n  const resultDtype = upcastType($x.dtype, 'int32');\n  const vals = util.makeZerosTypedArray(\n                   util.sizeFromShape($x.shape), resultDtype) as TypedArray;\n\n  const aVals = backend.data.get($x.dataId).values as TypedArray;\n  const finalDim = $x.shape[$x.shape.length - 1];\n  const indexAdjuster = reverse ?\n      (i: number, j: number) => i + finalDim - j - 1 :\n      (i: number, j: number) => i + j;\n  for (let i = 0; i < aVals.length; i += finalDim) {\n    for (let j = 0; j < finalDim; j++) {\n      const idx = indexAdjuster(i, j);\n      if (j === 0) {\n        vals[idx] = exclusive ? 0 : aVals[idx];\n      } else {\n        const prevIdx = indexAdjuster(i, j - 1);\n        vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] :\n                                aVals[idx] + vals[prevIdx];\n      }\n    }\n  }\n\n  const result = backend.makeTensorInfo($x.shape, resultDtype, vals);\n\n  if (permutation != null) {\n    const reversePermutation = backend_util.getUndoAxesPermutation(permutation);\n    const reverseTransposedResult = transpose(\n        {inputs: {x: result}, backend, attrs: {perm: reversePermutation}});\n\n    backend.disposeIntermediateTensorInfo(result);\n    backend.disposeIntermediateTensorInfo($x);\n\n    return reverseTransposedResult;\n  }\n\n  return result;\n}\n\nexport const cumsumConfig: KernelConfig = {\n  kernelName: Cumsum,\n  backendName: 'cpu',\n  kernelFunc: cumsum as unknown as KernelFunc\n};\n"]}