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#
# 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
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# TODO: define specitial functions used in computer vision task 

from ...fluid.dygraph import layers
from .. import functional

__all__ = []


class PixelShuffle(layers.Layer):
    """
    
    PixelShuffle Layer    

    This operator rearranges elements in a tensor of shape [N, C, H, W]
    to a tensor of shape [N, C/upscale_factor**2, H*upscale_factor, W*upscale_factor],
    or from shape [N, H, W, C] to [N, H*upscale_factor, W*upscale_factor, C/upscale_factor**2].
    This is useful for implementing efficient sub-pixel convolution
    with a stride of 1/upscale_factor.
    Please refer to the paper: `Real-Time Single Image and Video Super-Resolution
    Using an Efficient Sub-Pixel Convolutional Neural Network <https://arxiv.org/abs/1609.05158v2>`_ .
    by Shi et. al (2016) for more details.

    Parameters:

        upscale_factor(int): factor to increase spatial resolution.
        data_format (str): The data format of the input and output data. An optional string from: "NCHW", "NHWC". The default is "NCHW". When it is "NCHW", the data is stored in the order of: [batch_size, input_channels, input_height, input_width].
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.

    Shape:
        - x: 4-D tensor with shape: (N, C, H, W) or (N, H, W, C).
        - out: 4-D tensor with shape: (N, C/upscale_factor**2, H*upscale_factor, W*upscale_factor) or (N, H*upscale_factor, W*upscale_factor, C/upscale_factor^2).


    Examples:
        .. code-block:: python
            
            import paddle
            import paddle.nn as nn
            import numpy as np

            x = np.random.randn(2, 9, 4, 4).astype(np.float32)
            x_var = paddle.to_tensor(x)
            pixel_shuffle = nn.PixelShuffle(3)
            out_var = pixel_shuffle(x_var)
            out = out_var.numpy()
            print(out.shape) 
            # (2, 1, 12, 12)

    """

    def __init__(self, upscale_factor, data_format="NCHW", name=None):
        super(PixelShuffle, self).__init__()

        if not isinstance(upscale_factor, int):
            raise TypeError("upscale factor must be int type")

        if data_format not in ["NCHW", "NHWC"]:
            raise ValueError("Data format should be 'NCHW' or 'NHWC'."
                             "But recevie data format: {}".format(data_format))

        self._upscale_factor = upscale_factor
        self._data_format = data_format
        self._name = name

    def forward(self, x):
        return functional.pixel_shuffle(x, self._upscale_factor,
                                        self._data_format, self._name)

    def extra_repr(self):
        main_str = 'upscale_factor={}'.format(self._upscale_factor)
        if self._data_format is not 'NCHW':
            main_str += ', data_format={}'.format(self._data_format)
        if self._name is not None:
            main_str += ', name={}'.format(self._name)
        return main_str
