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2025-06-12 7b72ac13a83764a662159d4a49b7fffb90476ecb

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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 { Cast, util } from '@tensorflow/tfjs-core';
import { createSimpleBinaryKernelImpl } from '../utils/binary_impl';
import { zeros } from '../utils/zeros_impl';
import { complex } from './Complex';
import { identity } from './Identity';
import { real } from './Real';
export function castImpl(values, shape, inputType, dtype) {
    if (dtype === 'int32') {
        const resultValues = Int32Array.from(values);
        return [shape, 'int32', resultValues];
    }
    if (dtype === 'bool') {
        // This is essentially the result of notEqual(x, 0). We avoid using
        // kernel notEqual to avoid circular dependency, i.e. binary_utils ->
        // cast -> notEqual -> binary_utils.
        const zero = util.toTypedArray([0], inputType);
        const [resultData, resultShape] = createSimpleBinaryKernelImpl((a, b) => (a !== b) ? 1 : 0)(shape, [], values, zero, 'bool');
        return [resultShape, 'bool', resultData];
    }
    throw new Error(`Error in Cast: failed to cast ${inputType} to ${dtype}`);
}
export function cast(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { dtype } = attrs;
    // Casting to complex64.
    if (dtype === 'complex64') {
        if (x.dtype === 'complex64') {
            return identity({ inputs: { x }, backend });
        }
        const zerosTensorInfo = zeros(backend, x.shape, x.dtype);
        const floatX = cast({ inputs: { x }, backend, attrs: { dtype: 'float32' } });
        const result = complex({ inputs: { real: floatX, imag: zerosTensorInfo }, backend });
        backend.disposeIntermediateTensorInfo(zerosTensorInfo);
        backend.disposeIntermediateTensorInfo(floatX);
        return result;
    }
    // Casting from complex64
    if (x.dtype === 'complex64') {
        const realPart = real({ inputs: { input: x }, backend });
        const result = cast({ inputs: { x: realPart }, backend, attrs: { dtype } });
        backend.disposeIntermediateTensorInfo(realPart);
        return result;
    }
    if (!util.hasEncodingLoss(x.dtype, dtype)) {
        // We don't change the underlying data, since we cast to higher
        // precision.
        const result = identity({ inputs: { x }, backend });
        return { dataId: result.dataId, shape: result.shape, dtype };
    }
    const values = backend.data.get(x.dataId).values;
    const [resultShape, resultType, resultData] = castImpl(values, x.shape, x.dtype, dtype);
    return backend.makeTensorInfo(resultShape, resultType, resultData);
}
export const castConfig = {
    kernelName: Cast,
    backendName: 'cpu',
    kernelFunc: cast
};
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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 */\nimport {Cast, CastAttrs, CastInputs, DataType, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {createSimpleBinaryKernelImpl} from '../utils/binary_impl';\nimport {zeros} from '../utils/zeros_impl';\n\nimport {complex} from './Complex';\nimport {identity} from './Identity';\nimport {real} from './Real';\n\nexport function castImpl(\n    values: TypedArray, shape: number[], inputType: DataType,\n    dtype: DataType): [number[], DataType, TypedArray] {\n  if (dtype === 'int32') {\n    const resultValues = Int32Array.from(values);\n    return [shape, 'int32', resultValues];\n  }\n\n  if (dtype === 'bool') {\n    // This is essentially the result of notEqual(x, 0). We avoid using\n    // kernel notEqual to avoid circular dependency, i.e. binary_utils ->\n    // cast -> notEqual -> binary_utils.\n    const zero = util.toTypedArray([0], inputType);\n\n    const [resultData, resultShape] = createSimpleBinaryKernelImpl(\n        (a, b) => (a !== b) ? 1 : 0)(shape, [], values, zero, 'bool');\n\n    return [resultShape, 'bool', resultData];\n  }\n  throw new Error(`Error in Cast: failed to cast ${inputType} to ${dtype}`);\n}\n\nexport function cast(\n    args: {inputs: CastInputs, backend: MathBackendCPU, attrs: CastAttrs}):\n    TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  const {dtype} = attrs;\n\n  // Casting to complex64.\n  if (dtype === 'complex64') {\n    if (x.dtype === 'complex64') {\n      return identity({inputs: {x}, backend});\n    }\n\n    const zerosTensorInfo = zeros(backend, x.shape, x.dtype);\n    const floatX = cast({inputs: {x}, backend, attrs: {dtype: 'float32'}});\n\n    const result =\n        complex({inputs: {real: floatX, imag: zerosTensorInfo}, backend});\n\n    backend.disposeIntermediateTensorInfo(zerosTensorInfo);\n    backend.disposeIntermediateTensorInfo(floatX);\n\n    return result;\n  }\n\n  // Casting from complex64\n  if (x.dtype === 'complex64') {\n    const realPart = real({inputs: {input: x}, backend});\n    const result = cast({inputs: {x: realPart}, backend, attrs: {dtype}});\n\n    backend.disposeIntermediateTensorInfo(realPart);\n\n    return result;\n  }\n\n  if (!util.hasEncodingLoss(x.dtype, dtype)) {\n    // We don't change the underlying data, since we cast to higher\n    // precision.\n    const result = identity({inputs: {x}, backend});\n    return {dataId: result.dataId, shape: result.shape, dtype};\n  }\n\n  const values = backend.data.get(x.dataId).values as TypedArray;\n  const [resultShape, resultType, resultData] =\n      castImpl(values, x.shape, x.dtype, dtype);\n  return backend.makeTensorInfo(resultShape, resultType, resultData);\n}\n\nexport const castConfig: KernelConfig = {\n  kernelName: Cast,\n  backendName: 'cpu',\n  kernelFunc: cast as unknown as KernelFunc\n};\n"]}