# Copyright 2015 The TensorFlow Authors. 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.
# ==============================================================================
"""Max pooling 1D layer."""


import functools

from keras import backend
from keras.layers.pooling.base_pooling1d import Pooling1D

# isort: off
from tensorflow.python.util.tf_export import keras_export


@keras_export("keras.layers.MaxPool1D", "keras.layers.MaxPooling1D")
class MaxPooling1D(Pooling1D):
    """Max pooling operation for 1D temporal data.

    Downsamples the input representation by taking the maximum value over a
    spatial window of size `pool_size`. The window is shifted by `strides`.  The
    resulting output, when using the `"valid"` padding option, has a shape of:
    `output_shape = (input_shape - pool_size + 1) / strides)`

    The resulting output shape when using the `"same"` padding option is:
    `output_shape = input_shape / strides`

    For example, for `strides=1` and `padding="valid"`:

    >>> x = tf.constant([1., 2., 3., 4., 5.])
    >>> x = tf.reshape(x, [1, 5, 1])
    >>> max_pool_1d = tf.keras.layers.MaxPooling1D(pool_size=2,
    ...    strides=1, padding='valid')
    >>> max_pool_1d(x)
    <tf.Tensor: shape=(1, 4, 1), dtype=float32, numpy=
    array([[[2.],
            [3.],
            [4.],
            [5.]]], dtype=float32)>

    For example, for `strides=2` and `padding="valid"`:

    >>> x = tf.constant([1., 2., 3., 4., 5.])
    >>> x = tf.reshape(x, [1, 5, 1])
    >>> max_pool_1d = tf.keras.layers.MaxPooling1D(pool_size=2,
    ...    strides=2, padding='valid')
    >>> max_pool_1d(x)
    <tf.Tensor: shape=(1, 2, 1), dtype=float32, numpy=
    array([[[2.],
            [4.]]], dtype=float32)>

    For example, for `strides=1` and `padding="same"`:

    >>> x = tf.constant([1., 2., 3., 4., 5.])
    >>> x = tf.reshape(x, [1, 5, 1])
    >>> max_pool_1d = tf.keras.layers.MaxPooling1D(pool_size=2,
    ...    strides=1, padding='same')
    >>> max_pool_1d(x)
    <tf.Tensor: shape=(1, 5, 1), dtype=float32, numpy=
    array([[[2.],
            [3.],
            [4.],
            [5.],
            [5.]]], dtype=float32)>

    Args:
      pool_size: Integer, size of the max pooling window.
      strides: Integer, or None. Specifies how much the pooling window moves
        for each pooling step.
        If None, it will default to `pool_size`.
      padding: One of `"valid"` or `"same"` (case-insensitive).
        `"valid"` means no padding. `"same"` results in padding evenly to
        the left/right or up/down of the input such that output has the same
        height/width dimension as the input.
      data_format: A string,
        one of `channels_last` (default) or `channels_first`.
        The ordering of the dimensions in the inputs.
        `channels_last` corresponds to inputs with shape
        `(batch, steps, features)` while `channels_first`
        corresponds to inputs with shape
        `(batch, features, steps)`.

    Input shape:
      - If `data_format='channels_last'`:
        3D tensor with shape `(batch_size, steps, features)`.
      - If `data_format='channels_first'`:
        3D tensor with shape `(batch_size, features, steps)`.

    Output shape:
      - If `data_format='channels_last'`:
        3D tensor with shape `(batch_size, downsampled_steps, features)`.
      - If `data_format='channels_first'`:
        3D tensor with shape `(batch_size, features, downsampled_steps)`.
    """

    def __init__(
        self,
        pool_size=2,
        strides=None,
        padding="valid",
        data_format="channels_last",
        **kwargs
    ):

        super().__init__(
            functools.partial(backend.pool2d, pool_mode="max"),
            pool_size=pool_size,
            strides=strides,
            padding=padding,
            data_format=data_format,
            **kwargs
        )


# Alias

MaxPool1D = MaxPooling1D