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# Source code for torch.nn.modules.dropout

    from .module import Module
    from .. import functional as F

    from torch import Tensor

    __all__ = ['Dropout', 'Dropout1d', 'Dropout2d', 'Dropout3d', 'AlphaDropout', 'FeatureAlphaDropout']

    class _DropoutNd(Module):
        __constants__ = ['p', 'inplace']
        p: float
        inplace: bool

        def __init__(self, p: float = 0.5, inplace: bool = False) -> None:
            super().__init__()
            if p < 0 or p > 1:
                raise ValueError(f"dropout probability has to be between 0 and 1, but got {p}")
            self.p = p
            self.inplace = inplace

        def extra_repr(self) -> str:
            return f'p={self.p}, inplace={self.inplace}'

    class Dropout(_DropoutNd):
        r"""During training, randomly zeroes some of the elements of the input
        tensor with probability :attr:`p` using samples from a Bernoulli
        distribution. Each channel will be zeroed out independently on every forward
        call.

        This has proven to be an effective technique for regularization and
        preventing the co-adaptation of neurons as described in the paper
        `Improving neural networks by preventing co-adaptation of feature
        detectors`_ .

        Furthermore, the outputs are scaled by a factor of :math:`\frac{1}{1-p}` during
        training. This means that during evaluation the module simply computes an
        identity function.

        Args:
            p: probability of an element to be zeroed. Default: 0.5
            inplace: If set to ``True``, will do this operation in-place. Default: ``False``

        Shape:
            - Input: :math:`(*)`. Input can be of any shape
            - Output: :math:`(*)`. Output is of the same shape as input

        Examples::

            >>> m = nn.Dropout(p=0.2)
            >>> input = torch.randn(20, 16)
            >>> output = m(input)

        .. _Improving neural networks by preventing co-adaptation of feature
            detectors: https://arxiv.org/abs/1207.0580
        """

        def forward(self, input: Tensor) -> Tensor:
            return F.dropout(input, self.p, self.training, self.inplace)

    class Dropout1d(_DropoutNd):
        r"""Randomly zero out entire channels (a channel is a 1D feature map,
        e.g., the :math:`j`-th channel of the :math:`i`-th sample in the
        batched input is a 1D tensor :math:`\text{input}[i, j]`).
        Each channel will be zeroed out independently on every forward call with
        probability :attr:`p` using samples from a Bernoulli distribution.

        Usually the input comes from :class:`nn.Conv1d` modules.

        As described in the paper
        `Efficient Object Localization Using Convolutional Networks`_ ,
        if adjacent pixels within feature maps are strongly correlated
        (as is normally the case in early convolution layers) then i.i.d. dropout
        will not regularize the activations and will otherwise just result
        in an effective learning rate decrease.

        In this case, :func:`nn.Dropout1d` will help promote independence between
        feature maps and should be used instead.

        Args:
            p (float, optional): probability of an element to be zero-ed.
            inplace (bool, optional): If set to ``True``, will do this operation
                in-place

        Shape:
            - Input: :math:`(N, C, L)` or :math:`(C, L)`.
            - Output: :math:`(N, C, L)` or :math:`(C, L)` (same shape as input).

        Examples::

            >>> m = nn.Dropout1d(p=0.2)
            >>> input = torch.randn(20, 16, 32)
            >>> output = m(input)

        .. _Efficient Object Localization Using Convolutional Networks:
           https://arxiv.org/abs/1411.4280
        """

        def forward(self, input: Tensor) -> Tensor:
            return F.dropout1d(input, self.p, self.training, self.inplace)

    class Dropout2d(_DropoutNd):
        r"""Randomly zero out entire channels (a channel is a 2D feature map,
        e.g., the :math:`j`-th channel of the :math:`i`-th sample in the
        batched input is a 2D tensor :math:`\text{input}[i, j]`).
        Each channel will be zeroed out independently on every forward call with
        probability :attr:`p` using samples from a Bernoulli distribution.

        Usually the input comes from :class:`nn.Conv2d` modules.

        As described in the paper
        `Efficient Object Localization Using Convolutional Networks`_ ,
        if adjacent pixels within feature maps are strongly correlated
        (as is normally the case in early convolution layers) then i.i.d. dropout
        will not regularize the activations and will otherwise just result
        in an effective learning rate decrease.

        In this case, :func:`nn.Dropout2d` will help promote independence between
        feature maps and should be used instead.

        Args:
            p (float, optional): probability of an element to be zero-ed.
            inplace (bool, optional): If set to ``True``, will do this operation
                in-place

        .. warning ::
            Due to historical reasons, this class will perform 1D channel-wise dropout
            for 3D inputs (as done by :class:`nn.Dropout1d`). Thus, it currently does NOT
            support inputs without a batch dimension of shape :math:`(C, H, W)`. This
            behavior will change in a future release to interpret 3D inputs as no-batch-dim
            inputs. To maintain the old behavior, switch to :class:`nn.Dropout1d`.

        Shape:
            - Input: :math:`(N, C, H, W)` or :math:`(N, C, L)`.
            - Output: :math:`(N, C, H, W)` or :math:`(N, C, L)` (same shape as input).

        Examples::

            >>> m = nn.Dropout2d(p=0.2)
            >>> input = torch.randn(20, 16, 32, 32)
            >>> output = m(input)

        .. _Efficient Object Localization Using Convolutional Networks:
           https://arxiv.org/abs/1411.4280
        """

        def forward(self, input: Tensor) -> Tensor:
            return F.dropout2d(input, self.p, self.training, self.inplace)

    class Dropout3d(_DropoutNd):
        r"""Randomly zero out entire channels (a channel is a 3D feature map,
        e.g., the :math:`j`-th channel of the :math:`i`-th sample in the
        batched input is a 3D tensor :math:`\text{input}[i, j]`).
        Each channel will be zeroed out independently on every forward call with
        probability :attr:`p` using samples from a Bernoulli distribution.

        Usually the input comes from :class:`nn.Conv3d` modules.

        As described in the paper
        `Efficient Object Localization Using Convolutional Networks`_ ,
        if adjacent pixels within feature maps are strongly correlated
        (as is normally the case in early convolution layers) then i.i.d. dropout
        will not regularize the activations and will otherwise just result
        in an effective learning rate decrease.

        In this case, :func:`nn.Dropout3d` will help promote independence between
        feature maps and should be used instead.

        Args:
            p (float, optional): probability of an element to be zeroed.
            inplace (bool, optional): If set to ``True``, will do this operation
                in-place

        Shape:
            - Input: :math:`(N, C, D, H, W)` or :math:`(C, D, H, W)`.
            - Output: :math:`(N, C, D, H, W)` or :math:`(C, D, H, W)` (same shape as input).

        Examples::

            >>> m = nn.Dropout3d(p=0.2)
            >>> input = torch.randn(20, 16, 4, 32, 32)
            >>> output = m(input)

        .. _Efficient Object Localization Using Convolutional Networks:
           https://arxiv.org/abs/1411.4280
        """

        def forward(self, input: Tensor) -> Tensor:
            return F.dropout3d(input, self.p, self.training, self.inplace)

    class AlphaDropout(_DropoutNd):
        r"""Applies Alpha Dropout over the input.

        Alpha Dropout is a type of Dropout that maintains the self-normalizing
        property.
        For an input with zero mean and unit standard deviation, the output of
        Alpha Dropout maintains the original mean and standard deviation of the
        input.
        Alpha Dropout goes hand-in-hand with SELU activation function, which ensures
        that the outputs have zero mean and unit standard deviation.

        During training, it randomly masks some of the elements of the input
        tensor with probability *p* using samples from a bernoulli distribution.
        The elements to masked are randomized on every forward call, and scaled
        and shifted to maintain zero mean and unit standard deviation.

        During evaluation the module simply computes an identity function.

        More details can be found in the paper `Self-Normalizing Neural Networks`_ .

        Args:
            p (float): probability of an element to be dropped. Default: 0.5
            inplace (bool, optional): If set to ``True``, will do this operation
                in-place

        Shape:
            - Input: :math:`(*)`. Input can be of any shape
            - Output: :math:`(*)`. Output is of the same shape as input

        Examples::

            >>> m = nn.AlphaDropout(p=0.2)
            >>> input = torch.randn(20, 16)
            >>> output = m(input)

        .. _Self-Normalizing Neural Networks: https://arxiv.org/abs/1706.02515
        """

        def forward(self, input: Tensor) -> Tensor:
            return F.alpha_dropout(input, self.p, self.training)

    class FeatureAlphaDropout(_DropoutNd):
        r"""Randomly masks out entire channels (a channel is a feature map,
        e.g. the :math:`j`-th channel of the :math:`i`-th sample in the batch input
        is a tensor :math:`\text{input}[i, j]`) of the input tensor). Instead of
        setting activations to zero, as in regular Dropout, the activations are set
        to the negative saturation value of the SELU activation function. More details
        can be found in the paper `Self-Normalizing Neural Networks`_ .

        Each element will be masked independently for each sample on every forward
        call with probability :attr:`p` using samples from a Bernoulli distribution.
        The elements to be masked are randomized on every forward call, and scaled
        and shifted to maintain zero mean and unit variance.

        Usually the input comes from :class:`nn.AlphaDropout` modules.

        As described in the paper
        `Efficient Object Localization Using Convolutional Networks`_ ,
        if adjacent pixels within feature maps are strongly correlated
        (as is normally the case in early convolution layers) then i.i.d. dropout
        will not regularize the activations and will otherwise just result
        in an effective learning rate decrease.

        In this case, :func:`nn.AlphaDropout` will help promote independence between
        feature maps and should be used instead.

        Args:
            p (float, optional): probability of an element to be zeroed. Default: 0.5
            inplace (bool, optional): If set to ``True``, will do this operation
                in-place

        Shape:
            - Input: :math:`(N, C, D, H, W)` or :math:`(C, D, H, W)`.
            - Output: :math:`(N, C, D, H, W)` or :math:`(C, D, H, W)` (same shape as input).

        Examples::

            >>> m = nn.FeatureAlphaDropout(p=0.2)
            >>> input = torch.randn(20, 16, 4, 32, 32)
            >>> output = m(input)

        .. _Self-Normalizing Neural Networks: https://arxiv.org/abs/1706.02515
        .. _Efficient Object Localization Using Convolutional Networks:
           https://arxiv.org/abs/1411.4280
        """

        def forward(self, input: Tensor) -> Tensor:
            return F.feature_alpha_dropout(input, self.p, self.training)

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