/**
 * @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 { ENGINE } from '../engine';
import { Conv3D } from '../kernel_names';
import { convertToTensor } from '../tensor_util_env';
import * as util from '../util';
import { eitherStridesOrDilationsAreOne, stridesOrDilationsArePositive } from './conv_util';
import { op } from './operation';
import { reshape } from './reshape';
/**
 * Computes a 3D convolution over the input x.
 *
 * @param x The input tensor, of rank 5 or rank 4, of shape
 *     `[batch, depth, height, width, channels]`. If rank 4,
 * batch of 1 is assumed.
 * @param filter The filter, rank 5, of shape
 *     `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`.
 *      inChannels must match between input and filter.
 * @param strides The strides of the convolution: `[strideDepth, strideHeight,
 * strideWidth]`.
 * @param pad The type of padding algorithm.
 *    - `same` and stride 1: output will be of same size as input,
 *       regardless of filter size.
 *    - `valid`: output will be smaller than input if filter is larger
 *       than 1x1.
 *   - For more info, see this guide:
 *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
 *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
 * @param dataFormat: An optional string from: "NDHWC", "NCDHW". Defaults to
 *     "NDHWC". Specify the data format of the input and output data. With the
 *     default format "NDHWC", the data is stored in the order of: [batch,
 *     depth, height, width, channels]. Only "NDHWC" is currently supported.
 * @param dilations The dilation rates: `[dilationDepth, dilationHeight,
 *     dilationWidth]` in which we sample input values across the height
 *     and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`.
 *     If `dilations` is a single number, then
 *     `dilationDepth == dilationHeight == dilationWidth`. If it is greater
 *     than 1, then all values of `strides` must be 1.
 *
 * @doc {heading: 'Operations', subheading: 'Convolution'}
 */
function conv3d_(x, filter, strides, pad, dataFormat = 'NDHWC', dilations = [1, 1, 1]) {
    const $x = convertToTensor(x, 'x', 'conv3d');
    const $filter = convertToTensor(filter, 'filter', 'conv3d');
    let x5D = $x;
    let reshapedTo5D = false;
    if ($x.rank === 4) {
        reshapedTo5D = true;
        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
    }
    util.assert(x5D.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);
    util.assert($filter.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ` +
        `${$filter.rank}.`);
    util.assert(x5D.shape[4] === $filter.shape[3], () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match ` +
        `input depth for filter ${$filter.shape[3]}.`);
    util.assert(eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv3D: Either strides or dilations must be 1. ' +
        `Got strides ${strides} and dilations '${dilations}'`);
    util.assert(dataFormat === 'NDHWC', () => `Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);
    util.assert(stridesOrDilationsArePositive(dilations), () => 'Error in conv3D: Dilated rates should be larger than 0.');
    util.assert(stridesOrDilationsArePositive(strides), () => 'Error in conv3D: Strides should be larger than 0.');
    const inputs = { x: x5D, filter: $filter };
    const attrs = { strides, pad, dataFormat, dilations };
    // tslint:disable-next-line: no-unnecessary-type-assertion
    const res = ENGINE.runKernel(Conv3D, inputs, attrs);
    if (reshapedTo5D) {
        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
    }
    return res;
}
export const conv3d = /* @__PURE__ */ op({ conv3d_ });
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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 {ENGINE} from '../engine';\nimport {Conv3D, Conv3DAttrs, Conv3DInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor4D, Tensor5D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {eitherStridesOrDilationsAreOne, stridesOrDilationsArePositive} from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes a 3D convolution over the input x.\n *\n * @param x The input tensor, of rank 5 or rank 4, of shape\n *     `[batch, depth, height, width, channels]`. If rank 4,\n * batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`.\n *      inChannels must match between input and filter.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dataFormat: An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationDepth, dilationHeight,\n *     dilationWidth]` in which we sample input values across the height\n *     and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`.\n *     If `dilations` is a single number, then\n *     `dilationDepth == dilationHeight == dilationWidth`. If it is greater\n *     than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3d_<T extends Tensor4D|Tensor5D>(\n    x: T|TensorLike, filter: Tensor5D|TensorLike,\n    strides: [number, number, number]|number, pad: 'valid'|'same',\n    dataFormat: 'NDHWC'|'NCDHW' = 'NDHWC',\n    dilations: [number, number, number]|number = [1, 1, 1]): T {\n  const $x = convertToTensor(x, 'x', 'conv3d');\n  const $filter = convertToTensor(filter, 'filter', 'conv3d');\n\n  let x5D = $x as Tensor5D;\n  let reshapedTo5D = false;\n\n  if ($x.rank === 4) {\n    reshapedTo5D = true;\n    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n  }\n  util.assert(\n      x5D.rank === 5,\n      () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);\n  util.assert(\n      $filter.rank === 5,\n      () => `Error in conv3d: filter must be rank 5, but got rank ` +\n          `${$filter.rank}.`);\n  util.assert(\n      x5D.shape[4] === $filter.shape[3],\n      () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match ` +\n          `input depth for filter ${$filter.shape[3]}.`);\n  util.assert(\n      eitherStridesOrDilationsAreOne(strides, dilations),\n      () => 'Error in conv3D: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n  util.assert(\n      dataFormat === 'NDHWC',\n      () => `Error in conv3d: got dataFormat of ${\n          dataFormat} but only NDHWC is currently supported.`);\n  util.assert(\n      stridesOrDilationsArePositive(dilations),\n      () => 'Error in conv3D: Dilated rates should be larger than 0.');\n  util.assert(\n      stridesOrDilationsArePositive(strides),\n      () => 'Error in conv3D: Strides should be larger than 0.');\n\n  const inputs: Conv3DInputs = {x: x5D, filter: $filter};\n\n  const attrs: Conv3DAttrs = {strides, pad, dataFormat, dilations};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  const res = ENGINE.runKernel(\n                  Conv3D, inputs as unknown as NamedTensorMap,\n                  attrs as unknown as NamedAttrMap) as T;\n\n  if (reshapedTo5D) {\n    return reshape(\n               res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]) as\n        T;\n  }\n  return res;\n}\n\nexport const conv3d = /* @__PURE__ */ op({conv3d_});\n"]}