/**
 * @license
 * Copyright 2019 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 {NamedTensorInfoMap, registerKernel, TensorInfo} from '../../kernel_registry';

import {MathBackendCPU} from './backend_cpu';
import {assertNotComplex} from './cpu_util';

interface SquareInputs extends NamedTensorInfoMap {
  x: TensorInfo;
}

registerKernel({
  kernelName: 'Square',
  backendName: 'cpu',
  kernelFunc: ({inputs, backend}) => {
    const {x} = inputs as SquareInputs;
    const cpuBackend = backend as MathBackendCPU;
    assertNotComplex(x, 'square');

    const values = cpuBackend.data.get(x.dataId).values as Float32Array;
    const newValues = new Float32Array(values.length);
    for (let i = 0; i < values.length; ++i) {
      const value = values[i];
      newValues[i] = value * value;
    }
    const dataId = cpuBackend.write(newValues, x.shape, x.dtype);
    return {dataId, shape: x.shape, dtype: x.dtype};
  }
});