from .module import Module
from .utils import _pair, _quadruple, _ntuple
from .. import functional as F

from torch import Tensor
from ..common_types import _size_2_t, _size_4_t, _size_6_t
from typing import Sequence, Tuple


# TODO: grad_output size asserts in THNN

__all__ = ['ConstantPad1d', 'ConstantPad2d', 'ConstantPad3d', 'ReflectionPad1d', 'ReflectionPad2d',
           'ReflectionPad3d', 'ReplicationPad1d', 'ReplicationPad2d', 'ReplicationPad3d', 'ZeroPad2d']

class _ConstantPadNd(Module):
    __constants__ = ['padding', 'value']
    value: float
    padding: Sequence[int]

    def __init__(self, value: float) -> None:
        super().__init__()
        self.value = value

    def forward(self, input: Tensor) -> Tensor:
        return F.pad(input, self.padding, 'constant', self.value)

    def extra_repr(self) -> str:
        return 'padding={}, value={}'.format(self.padding, self.value)


class ConstantPad1d(_ConstantPadNd):
    r"""Pads the input tensor boundaries with a constant value.

    For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`.

    Args:
        padding (int, tuple): the size of the padding. If is `int`, uses the same
            padding in both boundaries. If a 2-`tuple`, uses
            (:math:`\text{padding\_left}`, :math:`\text{padding\_right}`)

    Shape:
        - Input: :math:`(C, W_{in})` or :math:`(N, C, W_{in})`.
        - Output: :math:`(C, W_{out})` or :math:`(N, C, W_{out})`, where

          :math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}`

    Examples::

        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
        >>> m = nn.ConstantPad1d(2, 3.5)
        >>> input = torch.randn(1, 2, 4)
        >>> input
        tensor([[[-1.0491, -0.7152, -0.0749,  0.8530],
                 [-1.3287,  1.8966,  0.1466, -0.2771]]])
        >>> m(input)
        tensor([[[ 3.5000,  3.5000, -1.0491, -0.7152, -0.0749,  0.8530,  3.5000,
                   3.5000],
                 [ 3.5000,  3.5000, -1.3287,  1.8966,  0.1466, -0.2771,  3.5000,
                   3.5000]]])
        >>> m = nn.ConstantPad1d(2, 3.5)
        >>> input = torch.randn(1, 2, 3)
        >>> input
        tensor([[[ 1.6616,  1.4523, -1.1255],
                 [-3.6372,  0.1182, -1.8652]]])
        >>> m(input)
        tensor([[[ 3.5000,  3.5000,  1.6616,  1.4523, -1.1255,  3.5000,  3.5000],
                 [ 3.5000,  3.5000, -3.6372,  0.1182, -1.8652,  3.5000,  3.5000]]])
        >>> # using different paddings for different sides
        >>> m = nn.ConstantPad1d((3, 1), 3.5)
        >>> m(input)
        tensor([[[ 3.5000,  3.5000,  3.5000,  1.6616,  1.4523, -1.1255,  3.5000],
                 [ 3.5000,  3.5000,  3.5000, -3.6372,  0.1182, -1.8652,  3.5000]]])

    """
    padding: Tuple[int, int]

    def __init__(self, padding: _size_2_t, value: float):
        super().__init__(value)
        self.padding = _pair(padding)


class ConstantPad2d(_ConstantPadNd):
    r"""Pads the input tensor boundaries with a constant value.

    For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`.

    Args:
        padding (int, tuple): the size of the padding. If is `int`, uses the same
            padding in all boundaries. If a 4-`tuple`, uses (:math:`\text{padding\_left}`,
            :math:`\text{padding\_right}`, :math:`\text{padding\_top}`, :math:`\text{padding\_bottom}`)

    Shape:
        - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`.
        - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where

          :math:`H_{out} = H_{in} + \text{padding\_top} + \text{padding\_bottom}`

          :math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}`

    Examples::

        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
        >>> m = nn.ConstantPad2d(2, 3.5)
        >>> input = torch.randn(1, 2, 2)
        >>> input
        tensor([[[ 1.6585,  0.4320],
                 [-0.8701, -0.4649]]])
        >>> m(input)
        tensor([[[ 3.5000,  3.5000,  3.5000,  3.5000,  3.5000,  3.5000],
                 [ 3.5000,  3.5000,  3.5000,  3.5000,  3.5000,  3.5000],
                 [ 3.5000,  3.5000,  1.6585,  0.4320,  3.5000,  3.5000],
                 [ 3.5000,  3.5000, -0.8701, -0.4649,  3.5000,  3.5000],
                 [ 3.5000,  3.5000,  3.5000,  3.5000,  3.5000,  3.5000],
                 [ 3.5000,  3.5000,  3.5000,  3.5000,  3.5000,  3.5000]]])
        >>> # using different paddings for different sides
        >>> m = nn.ConstantPad2d((3, 0, 2, 1), 3.5)
        >>> m(input)
        tensor([[[ 3.5000,  3.5000,  3.5000,  3.5000,  3.5000],
                 [ 3.5000,  3.5000,  3.5000,  3.5000,  3.5000],
                 [ 3.5000,  3.5000,  3.5000,  1.6585,  0.4320],
                 [ 3.5000,  3.5000,  3.5000, -0.8701, -0.4649],
                 [ 3.5000,  3.5000,  3.5000,  3.5000,  3.5000]]])

    """
    __constants__ = ['padding', 'value']
    padding: Tuple[int, int, int, int]

    def __init__(self, padding: _size_4_t, value: float) -> None:
        super().__init__(value)
        self.padding = _quadruple(padding)


class ConstantPad3d(_ConstantPadNd):
    r"""Pads the input tensor boundaries with a constant value.

    For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`.

    Args:
        padding (int, tuple): the size of the padding. If is `int`, uses the same
            padding in all boundaries. If a 6-`tuple`, uses
            (:math:`\text{padding\_left}`, :math:`\text{padding\_right}`,
            :math:`\text{padding\_top}`, :math:`\text{padding\_bottom}`,
            :math:`\text{padding\_front}`, :math:`\text{padding\_back}`)

    Shape:
        - Input: :math:`(N, C, D_{in}, H_{in}, W_{in})` or :math:`(C, D_{in}, H_{in}, W_{in})`.
        - Output: :math:`(N, C, D_{out}, H_{out}, W_{out})` or
          :math:`(C, D_{out}, H_{out}, W_{out})`, where

          :math:`D_{out} = D_{in} + \text{padding\_front} + \text{padding\_back}`

          :math:`H_{out} = H_{in} + \text{padding\_top} + \text{padding\_bottom}`

          :math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}`

    Examples::

        >>> m = nn.ConstantPad3d(3, 3.5)
        >>> input = torch.randn(16, 3, 10, 20, 30)
        >>> output = m(input)
        >>> # using different paddings for different sides
        >>> m = nn.ConstantPad3d((3, 3, 6, 6, 0, 1), 3.5)
        >>> output = m(input)

    """
    padding: Tuple[int, int, int, int, int, int]

    def __init__(self, padding: _size_6_t, value: float) -> None:
        super().__init__(value)
        self.padding = _ntuple(6)(padding)


class _ReflectionPadNd(Module):
    __constants__ = ['padding']
    padding: Sequence[int]

    def forward(self, input: Tensor) -> Tensor:
        return F.pad(input, self.padding, 'reflect')

    def extra_repr(self) -> str:
        return '{}'.format(self.padding)


class ReflectionPad1d(_ReflectionPadNd):
    r"""Pads the input tensor using the reflection of the input boundary.

    For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`.

    Args:
        padding (int, tuple): the size of the padding. If is `int`, uses the same
            padding in all boundaries. If a 2-`tuple`, uses
            (:math:`\text{padding\_left}`, :math:`\text{padding\_right}`)

    Shape:
        - Input: :math:`(C, W_{in})` or :math:`(N, C, W_{in})`.
        - Output: :math:`(C, W_{out})` or :math:`(N, C, W_{out})`, where

          :math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}`

    Examples::

        >>> m = nn.ReflectionPad1d(2)
        >>> # xdoctest: +IGNORE_WANT("other tests seem to modify printing styles")
        >>> input = torch.arange(8, dtype=torch.float).reshape(1, 2, 4)
        >>> input
        tensor([[[0., 1., 2., 3.],
                 [4., 5., 6., 7.]]])
        >>> m(input)
        tensor([[[2., 1., 0., 1., 2., 3., 2., 1.],
                 [6., 5., 4., 5., 6., 7., 6., 5.]]])
        >>> # using different paddings for different sides
        >>> m = nn.ReflectionPad1d((3, 1))
        >>> m(input)
        tensor([[[3., 2., 1., 0., 1., 2., 3., 2.],
                 [7., 6., 5., 4., 5., 6., 7., 6.]]])

    """
    padding: Tuple[int, int]

    def __init__(self, padding: _size_2_t) -> None:
        super().__init__()
        self.padding = _pair(padding)


class ReflectionPad2d(_ReflectionPadNd):
    r"""Pads the input tensor using the reflection of the input boundary.

    For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`.

    Args:
        padding (int, tuple): the size of the padding. If is `int`, uses the same
            padding in all boundaries. If a 4-`tuple`, uses (:math:`\text{padding\_left}`,
            :math:`\text{padding\_right}`, :math:`\text{padding\_top}`, :math:`\text{padding\_bottom}`)

    Shape:
        - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`.
        - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})` where

          :math:`H_{out} = H_{in} + \text{padding\_top} + \text{padding\_bottom}`

          :math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}`

    Examples::

        >>> # xdoctest: +IGNORE_WANT("not sure why xdoctest is choking on this")
        >>> m = nn.ReflectionPad2d(2)
        >>> input = torch.arange(9, dtype=torch.float).reshape(1, 1, 3, 3)
        >>> input
        tensor([[[[0., 1., 2.],
                  [3., 4., 5.],
                  [6., 7., 8.]]]])
        >>> m(input)
        tensor([[[[8., 7., 6., 7., 8., 7., 6.],
                  [5., 4., 3., 4., 5., 4., 3.],
                  [2., 1., 0., 1., 2., 1., 0.],
                  [5., 4., 3., 4., 5., 4., 3.],
                  [8., 7., 6., 7., 8., 7., 6.],
                  [5., 4., 3., 4., 5., 4., 3.],
                  [2., 1., 0., 1., 2., 1., 0.]]]])
        >>> # using different paddings for different sides
        >>> m = nn.ReflectionPad2d((1, 1, 2, 0))
        >>> m(input)
        tensor([[[[7., 6., 7., 8., 7.],
                  [4., 3., 4., 5., 4.],
                  [1., 0., 1., 2., 1.],
                  [4., 3., 4., 5., 4.],
                  [7., 6., 7., 8., 7.]]]])

    """
    padding: Tuple[int, int, int, int]

    def __init__(self, padding: _size_4_t) -> None:
        super().__init__()
        self.padding = _quadruple(padding)


class ReflectionPad3d(_ReflectionPadNd):
    r"""Pads the input tensor using the reflection of the input boundary.

    For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`.

    Args:
        padding (int, tuple): the size of the padding. If is `int`, uses the same
            padding in all boundaries. If a 6-`tuple`, uses
            (:math:`\text{padding\_left}`, :math:`\text{padding\_right}`,
            :math:`\text{padding\_top}`, :math:`\text{padding\_bottom}`,
            :math:`\text{padding\_front}`, :math:`\text{padding\_back}`)

    Shape:
        - Input: :math:`(N, C, D_{in}, H_{in}, W_{in})` or :math:`(C, D_{in}, H_{in}, W_{in})`.
        - Output: :math:`(N, C, D_{out}, H_{out}, W_{out})` or :math:`(C, D_{out}, H_{out}, W_{out})`,
          where

          :math:`D_{out} = D_{in} + \text{padding\_front} + \text{padding\_back}`

          :math:`H_{out} = H_{in} + \text{padding\_top} + \text{padding\_bottom}`

          :math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}`

    Examples::

        >>> # xdoctest: +IGNORE_WANT("not sure why xdoctest is choking on this")
        >>> m = nn.ReflectionPad3d(1)
        >>> input = torch.arange(8, dtype=torch.float).reshape(1, 1, 2, 2, 2)
        >>> m(input)
        tensor([[[[[7., 6., 7., 6.],
                   [5., 4., 5., 4.],
                   [7., 6., 7., 6.],
                   [5., 4., 5., 4.]],
                  [[3., 2., 3., 2.],
                   [1., 0., 1., 0.],
                   [3., 2., 3., 2.],
                   [1., 0., 1., 0.]],
                  [[7., 6., 7., 6.],
                   [5., 4., 5., 4.],
                   [7., 6., 7., 6.],
                   [5., 4., 5., 4.]],
                  [[3., 2., 3., 2.],
                   [1., 0., 1., 0.],
                   [3., 2., 3., 2.],
                   [1., 0., 1., 0.]]]]])
    """
    padding: Tuple[int, int, int, int, int, int]

    def __init__(self, padding: _size_6_t) -> None:
        super().__init__()
        self.padding = _ntuple(6)(padding)


class _ReplicationPadNd(Module):
    __constants__ = ['padding']
    padding: Sequence[int]

    def forward(self, input: Tensor) -> Tensor:
        return F.pad(input, self.padding, 'replicate')

    def extra_repr(self) -> str:
        return '{}'.format(self.padding)


class ReplicationPad1d(_ReplicationPadNd):
    r"""Pads the input tensor using replication of the input boundary.

    For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`.