# 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.
# ==============================================================================
"""Average 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.AveragePooling2D", "keras.layers.AvgPool2D")
class AveragePooling2D(Pooling2D):
    """Average pooling operation for spatial data.

    Downsamples the input along its spatial dimensions (height and width)
    by taking the average 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 `"valid"` padding option has a 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])
    >>> avg_pool_2d = tf.keras.layers.AveragePooling2D(pool_size=(2, 2),
    ...    strides=(1, 1), padding='valid')
    >>> avg_pool_2d(x)
    <tf.Tensor: shape=(1, 2, 2, 1), dtype=float32, numpy=
      array([[[[3.],
               [4.]],
              [[6.],
               [7.]]]], dtype=float32)>

    For example, for `stride=(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])
    >>> avg_pool_2d = tf.keras.layers.AveragePooling2D(pool_size=(2, 2),
    ...    strides=(2, 2), padding='valid')
    >>> avg_pool_2d(x)
    <tf.Tensor: shape=(1, 1, 2, 1), dtype=float32, numpy=
      array([[[[3.5],
               [5.5]]]], dtype=float32)>

    For example, for `strides=(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])
    >>> avg_pool_2d = tf.keras.layers.AveragePooling2D(pool_size=(2, 2),
    ...    strides=(1, 1), padding='same')
    >>> avg_pool_2d(x)
    <tf.Tensor: shape=(1, 3, 3, 1), dtype=float32, numpy=
      array([[[[3.],
               [4.],
               [4.5]],
              [[6.],
               [7.],
               [7.5]],
              [[7.5],
               [8.5],
               [9.]]]], dtype=float32)>

    Args:
      pool_size: integer or tuple of 2 integers,
        factors by which to downscale (vertical, horizontal).
        `(2, 2)` will halve the input in both spatial dimension.
        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.
        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)`.
    """

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


# Alias

AvgPool2D = AveragePooling2D