# 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 2D layer."""


import tensorflow.compat.v2 as tf

from keras.layers.pooling.base_pooling2d import Pooling2D

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


@keras_export("keras.layers.MaxPool2D", "keras.layers.MaxPooling2D")
class MaxPooling2D(Pooling2D):
    """Max pooling operation for 2D spatial data.

    Downsamples the input along its spatial dimensions (height and width)
    by taking the maximum value over an input window
    (of size defined by `pool_size`) for each channel of the input.
    The window is shifted by `strides` along each dimension.

    The resulting output,
    when using the `"valid"` padding option, has a spatial shape
    (number of rows or columns) of:
    `output_shape = math.floor((input_shape - pool_size) / strides) + 1`
    (when `input_shape >= pool_size`)

    The resulting output shape when using the `"same"` padding option is:
    `output_shape = math.floor((input_shape - 1) / strides) + 1`

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

    >>> x = tf.constant([[1., 2., 3.],
    ...                  [4., 5., 6.],
    ...                  [7., 8., 9.]])
    >>> x = tf.reshape(x, [1, 3, 3, 1])
    >>> max_pool_2d = tf.keras.layers.MaxPooling2D(pool_size=(2, 2),
    ...    strides=(1, 1), padding='valid')
    >>> max_pool_2d(x)
    <tf.Tensor: shape=(1, 2, 2, 1), dtype=float32, numpy=
      array([[[[5.],
               [6.]],
              [[8.],
               [9.]]]], dtype=float32)>

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

    >>> x = tf.constant([[1., 2., 3., 4.],
    ...                  [5., 6., 7., 8.],
    ...                  [9., 10., 11., 12.]])
    >>> x = tf.reshape(x, [1, 3, 4, 1])
    >>> max_pool_2d = tf.keras.layers.MaxPooling2D(pool_size=(2, 2),
    ...    strides=(2, 2), padding='valid')
    >>> max_pool_2d(x)
    <tf.Tensor: shape=(1, 1, 2, 1), dtype=float32, numpy=
      array([[[[6.],
               [8.]]]], dtype=float32)>

    Usage Example:

    >>> input_image = tf.constant([[[[1.], [1.], [2.], [4.]],
    ...                            [[2.], [2.], [3.], [2.]],
    ...                            [[4.], [1.], [1.], [1.]],
    ...                            [[2.], [2.], [1.], [4.]]]])
    >>> output = tf.constant([[[[1], [0]],
    ...                       [[0], [1]]]])
    >>> model = tf.keras.models.Sequential()
    >>> model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2),
    ...    input_shape=(4, 4, 1)))
    >>> model.compile('adam', 'mean_squared_error')
    >>> model.predict(input_image, steps=1)
    array([[[[2.],
             [4.]],
            [[4.],
             [4.]]]], dtype=float32)

    For example, for stride=(1, 1) and padding="same":

    >>> x = tf.constant([[1., 2., 3.],
    ...                  [4., 5., 6.],
    ...                  [7., 8., 9.]])
    >>> x = tf.reshape(x, [1, 3, 3, 1])
    >>> max_pool_2d = tf.keras.layers.MaxPooling2D(pool_size=(2, 2),
    ...    strides=(1, 1), padding='same')
    >>> max_pool_2d(x)
    <tf.Tensor: shape=(1, 3, 3, 1), dtype=float32, numpy=
      array([[[[5.],
               [6.],
               [6.]],
              [[8.],
               [9.],
               [9.]],
              [[8.],
               [9.],
               [9.]]]], dtype=float32)>

    Args:
      pool_size: integer or tuple of 2 integers,
        window size over which to take the maximum.
        `(2, 2)` will take the max value over a 2x2 pooling window.
        If only one integer is specified, the same window length
        will be used for both dimensions.
      strides: Integer, tuple of 2 integers, or None.
        Strides values.  Specifies how far 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, height, width, channels)` while `channels_first`
        corresponds to inputs with shape
        `(batch, channels, height, width)`.
        It defaults to the `image_data_format` value found in your
        Keras config file at `~/.keras/keras.json`.
        If you never set it, then it will be "channels_last".

    Input shape:
      - If `data_format='channels_last'`:
        4D tensor with shape `(batch_size, rows, cols, channels)`.
      - If `data_format='channels_first'`:
        4D tensor with shape `(batch_size, channels, rows, cols)`.

    Output shape:
      - If `data_format='channels_last'`:
        4D tensor with shape `(batch_size, pooled_rows, pooled_cols, channels)`.
      - If `data_format='channels_first'`:
        4D tensor with shape `(batch_size, channels, pooled_rows, pooled_cols)`.

    Returns:
      A tensor of rank 4 representing the maximum pooled values.  See above for
      output shape.
    """

    def __init__(
        self,
        pool_size=(2, 2),
        strides=None,
        padding="valid",
        data_format=None,
        **kwargs
    ):
        super().__init__(
            tf.compat.v1.nn.max_pool,
            pool_size=pool_size,
            strides=strides,
            padding=padding,
            data_format=data_format,
            **kwargs
        )


# Alias

MaxPool2D = MaxPooling2D