import warnings

from .module import Module
from .. import functional as F
from .. import _reduction as _Reduction
from ..._jit_internal import weak_module, weak_script_method


class _Loss(Module):
    def __init__(self, size_average=None, reduce=None, reduction='mean'):
        super(_Loss, self).__init__()
        if size_average is not None or reduce is not None:
            self.reduction = _Reduction.legacy_get_string(size_average, reduce)
        else:
            self.reduction = reduction


class _WeightedLoss(_Loss):
    def __init__(self, weight=None, size_average=None, reduce=None, reduction='mean'):
        super(_WeightedLoss, self).__init__(size_average, reduce, reduction)
        self.register_buffer('weight', weight)


@weak_module
class L1Loss(_Loss):
    r"""Creates a criterion that measures the mean absolute error (MAE) between each element in
    the input :math:`x` and target :math:`y`.

    The unreduced (i.e. with :attr:`reduction` set to ``'none'``) loss can be described as:

    .. math::
        \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
        l_n = \left| x_n - y_n \right|,

    where :math:`N` is the batch size. If :attr:`reduction` is not ``'none'``
    (default ``'mean'``), then:

    .. math::
        \ell(x, y) =
        \begin{cases}
            \operatorname{mean}(L), & \text{if reduction} = \text{'mean';}\\
            \operatorname{sum}(L),  & \text{if reduction} = \text{'sum'.}
        \end{cases}

    :math:`x` and :math:`y` are tensors of arbitrary shapes with a total
    of :math:`n` elements each.

    The sum operation still operates over all the elements, and divides by :math:`n`.

    The division by :math:`n` can be avoided if one sets ``reduction = 'sum'``.

    Args:
        size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
            the losses are averaged over each loss element in the batch. Note that for
            some losses, there are multiple elements per sample. If the field :attr:`size_average`
            is set to ``False``, the losses are instead summed for each minibatch. Ignored
            when reduce is ``False``. Default: ``True``
        reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
            losses are averaged or summed over observations for each minibatch depending
            on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
            batch element instead and ignores :attr:`size_average`. Default: ``True``
        reduction (string, optional): Specifies the reduction to apply to the output:
            ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
            ``'mean'``: the sum of the output will be divided by the number of
            elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
            and :attr:`reduce` are in the process of being deprecated, and in the meantime,
            specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``

    Shape:
        - Input: :math:`(N, *)` where :math:`*` means, any number of additional
          dimensions
        - Target: :math:`(N, *)`, same shape as the input
        - Output: scalar. If :attr:`reduction` is ``'none'``, then
          :math:`(N, *)`, same shape as the input

    Examples::

        >>> loss = nn.L1Loss()
        >>> input = torch.randn(3, 5, requires_grad=True)
        >>> target = torch.randn(3, 5)
        >>> output = loss(input, target)
        >>> output.backward()
    """
    __constants__ = ['reduction']

    def __init__(self, size_average=None, reduce=None, reduction='mean'):
        super(L1Loss, self).__init__(size_average, reduce, reduction)

    @weak_script_method
    def forward(self, input, target):
        return F.l1_loss(input, target, reduction=self.reduction)


@weak_module
class NLLLoss(_WeightedLoss):
    r"""The negative log likelihood loss. It is useful to train a classification
    problem with `C` classes.

    If provided, the optional argument :attr:`weight` should be a 1D Tensor assigning
    weight to each of the classes. This is particularly useful when you have an
    unbalanced training set.

    The `input` given through a forward call is expected to contain
    log-probabilities of each class. `input` has to be a Tensor of size either
    :math:`(minibatch, C)` or :math:`(minibatch, C, d_1, d_2, ..., d_K)`
    with :math:`K \geq 1` for the `K`-dimensional case (described later).

    Obtaining log-probabilities in a neural network is easily achieved by
    adding a  `LogSoftmax`  layer in the last layer of your network.
    You may use `CrossEntropyLoss` instead, if you prefer not to add an extra
    layer.

    The `target` that this loss expects should be a class index in the range :math:`[0, C-1]`
    where `C = number of classes`; if `ignore_index` is specified, this loss also accepts
    this class index (this index may not necessarily be in the class range).

    The unreduced (i.e. with :attr:`reduction` set to ``'none'``) loss can be described as:

    .. math::
        \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
        l_n = - w_{y_n} x_{n,y_n}, \quad
        w_{c} = \text{weight}[c] \cdot \mathbb{1}\{c \not= \text{ignore\_index}\},

    where :math:`N` is the batch size. If :attr:`reduction` is not ``'none'``
    (default ``'mean'``), then

    .. math::
        \ell(x, y) = \begin{cases}
            \sum_{n=1}^N \frac{1}{\sum_{n=1}^N w_{y_n}} l_n, &
            \text{if reduction} = \text{'mean';}\\
            \sum_{n=1}^N l_n,  &
            \text{if reduction} = \text{'sum'.}
        \end{cases}

    Can also be used for higher dimension inputs, such as 2D images, by providing
    an input of size :math:`(minibatch, C, d_1, d_2, ..., d_K)` with :math:`K \geq 1`,
    where :math:`K` is the number of dimensions, and a target of appropriate shape
    (see below). In the case of images, it computes NLL loss per-pixel.

    Args:
        weight (Tensor, optional): a manual rescaling weight given to each
            class. If given, it has to be a Tensor of size `C`. Otherwise, it is
            treated as if having all ones.
        size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
            the losses are averaged over each loss element in the batch. Note that for
            some losses, there are multiple elements per sample. If the field :attr:`size_average`
            is set to ``False``, the losses are instead summed for each minibatch. Ignored
            when reduce is ``False``. Default: ``True``
        ignore_index (int, optional): Specifies a target value that is ignored
            and does not contribute to the input gradient. When
            :attr:`size_average` is ``True``, the loss is averaged over
            non-ignored targets.
        reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
            losses are averaged or summed over observations for each minibatch depending
            on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
            batch element instead and ignores :attr:`size_average`. Default: ``True``
        reduction (string, optional): Specifies the reduction to apply to the output:
            ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
            ``'mean'``: the sum of the output will be divided by the number of
            elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
            and :attr:`reduce` are in the process of being deprecated, and in the meantime,
            specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``

    Shape:
        - Input: :math:`(N, C)` where `C = number of classes`, or
          :math:`(N, C, d_1, d_2, ..., d_K)` with :math:`K \geq 1`
          in the case of `K`-dimensional loss.
        - Target: :math:`(N)` where each value is :math:`0 \leq \text{targets}[i] \leq C-1`, or
          :math:`(N, d_1, d_2, ..., d_K)` with :math:`K \geq 1` in the case of
          K-dimensional loss.
        - Output: scalar.
          If :attr:`reduction` is ``'none'``, then the same size as the target: :math:`(N)`, or
          :math:`(N, d_1, d_2, ..., d_K)` with :math:`K \geq 1` in the case
          of K-dimensional loss.

    Examples::

        >>> m = nn.LogSoftmax(dim=1)
        >>> loss = nn.NLLLoss()
        >>> # input is of size N x C = 3 x 5
        >>> input = torch.randn(3, 5, requires_grad=True)
        >>> # each element in target has to have 0 <= value < C
        >>> target = torch.tensor([1, 0, 4])
        >>> output = loss(m(input), target)
        >>> output.backward()
        >>>
        >>>
        >>> # 2D loss example (used, for example, with image inputs)
        >>> N, C = 5, 4
        >>> loss = nn.NLLLoss()
        >>> # input is of size N x C x height x width
        >>> data = torch.randn(N, 16, 10, 10)
        >>> conv = nn.Conv2d(16, C, (3, 3))
        >>> m = nn.LogSoftmax(dim=1)
        >>> # each element in target has to have 0 <= value < C
        >>> target = torch.empty(N, 8, 8, dtype=torch.long).random_(0, C)
        >>> output = loss(m(conv(data)), target)
        >>> output.backward()
    """
    __constants__ = ['ignore_index', 'weight', 'reduction']

    def __init__(self, weight=None, size_average=None, ignore_index=-100,
                 reduce=None, reduction='mean'):
        super(NLLLoss, self).__init__(weight, size_average, reduce, reduction)
        self.ignore_index = ignore_index

    @weak_script_method
    def forward(self, input, target):
        return F.nll_loss(input, target, weight=self.weight, ignore_index=self.ignore_index, reduction=self.reduction)


@weak_module
class NLLLoss2d(NLLLoss):
    def __init__(self, weight=None, size_average=None, ignore_index=-100,
                 reduce=None, reduction='mean'):
        warnings.warn("NLLLoss2d has been deprecated. "
                      "Please use NLLLoss instead as a drop-in replacement and see "
                      "https://pytorch.org/docs/master/nn.html#torch.nn.NLLLoss for more details.")
        super(NLLLoss2d, self).__init__(weight, size_average, ignore_index, reduce, reduction)


@weak_module
class PoissonNLLLoss(_Loss):
    r"""Negative log likelihood loss with Poisson distribution of target.

    The loss can be described as:

    .. math::
        \text{target} \sim \mathrm{Poisson}(\text{input})

        \text{loss}(\text{input}, \text{target}) = \text{input} - \text{target} * \log(\text{input})
                                    + \log(\text{target!})

    The last term can be omitted or approximated with Stirling formula. The
    approximation is used for target values more than 1. For targets less or
    equal to 1 zeros are added to the loss.

    Args:
        log_input (bool, optional): if ``True`` the loss is computed as
            :math:`\exp(\text{input}) - \text{target}*\text{input}`, if ``False`` the loss is
            :math:`\text{input} - \text{target}*\log(\text{input}+\text{eps})`.
        full (bool, optional): whether to compute full loss, i. e. to add the
            Stirling approximation term

            .. math::
                \text{target}*\log(\text{target}) - \text{target} + 0.5 * \log(2\pi\text{target}).
        size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
            the losses are averaged over each loss element in the batch. Note that for
            some losses, there are multiple elements per sample. If the field :attr:`size_average`
            is set to ``False``, the losses are instead summed for each minibatch. Ignored
            when reduce is ``False``. Default: ``True``
        eps (float, optional): Small value to avoid evaluation of :math:`\log(0)` when
            :attr:`log_input = False`. Default: 1e-8
        reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
            losses are averaged or summed over observations for each minibatch depending
            on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
            batch element instead and ignores :attr:`size_average`. Default: ``True``
        reduction (string, optional): Specifies the reduction to apply to the output:
            ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
            ``'mean'``: the sum of the output will be divided by the number of
            elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
            and :attr:`reduce` are in the process of being deprecated, and in the meantime,
            specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``

    Examples::

        >>> loss = nn.PoissonNLLLoss()
        >>> log_input = torch.randn(5, 2, requires_grad=True)
        >>> target = torch.randn(5, 2)
        >>> output = loss(log_input, target)
        >>> output.backward()

    Shape:
        - Input: :math:`(N, *)` where :math:`*` means, any number of additional
          dimensions
        - Target: :math:`(N, *)`, same shape as the input
        - Output: scalar by default. If :attr:`reduction` is ``'none'``, then :math:`(N, *)`,
          the same shape as the input
    """
    __constants__ = ['log_input', 'full', 'eps', 'reduction']

    def __init__(self, log_input=True, full=False, size_average=None,
                 eps=1e-8, reduce=None, reduction='mean'):
        super(PoissonNLLLoss, self).__init__(size_average, reduce, reduction)
        self.log_input = log_input
        self.full = full
        self.eps = eps

    @weak_script_method
    def forward(self, log_input, target):
        return F.poisson_nll_loss(log_input, target, log_input=self.log_input, full=self.full,
                                  eps=self.eps, reduction=self.reduction)


@weak_module
class KLDivLoss(_Loss):
    r"""The `Kullback-Leibler divergence`_ Loss

    KL divergence is a useful distance measure for continuous distributions
    and is often useful when performing direct regression over the space of
    (discretely sampled) continuous output distributions.

    As with :class:`~torch.nn.NLLLoss`, the `input` given is expected to contain
    *log-probabilities* and is not restricted to a 2D Tensor.
    The targets are given as *probabilities* (i.e. without taking the logarithm).

    This criterion expects a `target` `Tensor` of the same size as the
    `input` `Tensor`.

    The unreduced (i.e. with :attr:`reduction` set to ``'none'``) loss can be described as:

    .. math::
        l(x,y) = L = \{ l_1,\dots,l_N \}, \quad
        l_n = y_n \cdot \left( \log y_n - x_n \right)

    where the index :math:`N` spans all dimensions of ``input`` and :math:`L` has the same
    shape as ``input``. If :attr:`reduction` is not ``'none'`` (default ``'mean'``), then:

    .. math::
        \ell(x, y) = \begin{cases}
            \operatorname{mean}(L), & \text{if reduction} = \text{'mean';} \\
            \operatorname{sum}(L),  & \text{if reduction} = \text{'sum'.}
        \end{cases}

    In default :attr:`reduction` mode ``'mean'``, the losses are averaged for each minibatch over observations
    **as well as** over dimensions. ``'batchmean'`` mode gives the correct KL divergence where losses
    are averaged over batch dimension only. ``'mean'`` mode's behavior will be changed to the same as
    ``'batchmean'`` in the next major release.

    .. _Kullback-Leibler divergence:
        https://en.wikipedia.org/wiki/Kullback-Leibler_divergence

    Args:
        size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
            the losses are averaged over each loss element in the batch. Note that for
            some losses, there are multiple elements per sample. If the field :attr:`size_average`
            is set to ``False``, the losses are instead summed for each minibatch. Ignored
            when reduce is ``False``. Default: ``True``
        reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
            losses are averaged or summed over observations for each minibatch depending
            on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
            batch element instead and ignores :attr:`size_average`. Default: ``True``
        reduction (string, optional): Specifies the reduction to apply to the output:
            ``'none'`` | ``'batchmean'`` | ``'sum'`` | ``'mean'``.
            ``'none'``: no reduction will be applied.
            ``'batchmean'``: the sum of the output will be divided by batchsize.
            ``'sum'``: the output will be summed.
            ``'mean'``: the output will be divided by the number of elements in the output.
            Default: ``'mean'``

    .. note::
        :attr:`size_average` and :attr:`reduce` are in the process of being deprecated,
        and in the meantime, specifying either of those two args will override :attr:`reduction`.

    .. note::
        :attr:`reduction` = ``'mean'`` doesn't return the true kl divergence value, please use
        :attr:`reduction` = ``'batchmean'`` which aligns with KL math definition.
        In the next major release, ``'mean'`` will be changed to be the same as ``'batchmean'``.

    Shape:
        - Input: :math:`(N, *)` where :math:`*` means, any number of additional
          dimensions
        - Target: :math:`(N, *)`, same shape as the input
        - Output: scalar by default. If :attr:``reduction`` is ``'none'``, then :math:`(N, *)`,
          the same shape as the input

    """
    __constants__ = ['reduction']

    def __init__(self, size_average=None, reduce=None, reduction='mean'):
        super(KLDivLoss, self).__init__(size_average, reduce, reduction)

    @weak_script_method
    def forward(self, input, target):
        return F.kl_div(input, target, reduction=self.reduction)


@weak_module
class MSELoss(_Loss):
    r"""Creates a criterion that measures the mean squared error (squared L2 norm) between
    each element in the input :math:`x` and target :math:`y`.

    The unreduced (i.e. with :attr:`reduction` set to ``'none'``) loss can be described as:

    .. math::
        \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
        l_n = \left( x_n - y_n \right)^2,

    where :math:`N` is the batch size. If :attr:`reduction` is not ``'none'``
    (default ``'mean'``), then:

    .. math::
        \ell(x, y) =
        \begin{cases}
            \operatorname{mean}(L), &  \text{if reduction} = \text{'mean';}\\
            \operatorname{sum}(L),  &  \text{if reduction} = \text{'sum'.}
        \end{cases}

    :math:`x` and :math:`y` are tensors of arbitrary shapes with a total
    of :math:`n` elements each.

    The sum operation still operates over all the elements, and divides by :math:`n`.

    The division by :math:`n` can be avoided if one sets ``reduction = 'sum'``.

    Args:
        size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
            the losses are averaged over each loss element in the batch. Note that for
            some losses, there are multiple elements per sample. If the field :attr:`size_average`
            is set to ``False``, the losses are instead summed for each minibatch. Ignored
            when reduce is ``False``. Default: ``True``
        reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
            losses are averaged or summed over observations for each minibatch depending
            on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
            batch element instead and ignores :attr:`size_average`. Default: ``True``
        reduction (string, optional): Specifies the reduction to apply to the output:
            ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
            ``'mean'``: the sum of the output will be divided by the number of
            elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
            and :attr:`reduce` are in the process of being deprecated, and in the meantime,
            specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``

    Shape:
        - Input: :math:`(N, *)` where :math:`*` means, any number of additional
          dimensions
        - Target: :math:`(N, *)`, same shape as the input

    Examples::

        >>> loss = nn.MSELoss()
        >>> input = torch.randn(3, 5, requires_grad=True)
        >>> target = torch.randn(3, 5)
        >>> output = loss(input, target)
        >>> output.backward()
    """
    __constants__ = ['reduction']

    def __init__(self, size_average=None, reduce=None, reduction='mean'):
        super(MSELoss, self).__init__(size_average, reduce, reduction)

    @weak_script_method
    def forward(self, input, target):
        return F.mse_loss(input, target, reduction=self.reduction)


@weak_module
class BCELoss(_WeightedLoss):
    r"""Creates a criterion that measures the Binary Cross Entropy
    between the target and the output:

    The unreduced (i.e. with :attr:`reduction` set to ``'none'``) loss can be described as:

    .. math::
        \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
        l_n = - w_n \left[ y_n \cdot \log x_n + (1 - y_n) \cdot \log (1 - x_n) \right],

    where :math:`N` is the batch size. If :attr:`reduction` is not ``'none'``
    (default ``'mean'``), then

    .. math::
        \ell(x, y) = \begin{cases}
            \operatorname{mean}(L), & \text{if reduction} = \text{'mean';}\\
            \operatorname{sum}(L),  & \text{if reduction} = \text{'sum'.}
        \end{cases}

    This is used for measuring the error of a reconstruction in for example
    an auto-encoder. Note that the targets :math:`y` should be numbers
    between 0 and 1.

    Args:
        weight (Tensor, optional): a manual rescaling weight given to the loss
            of each batch element. If given, has to be a Tensor of size `nbatch`.
        size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
            the losses are averaged over each loss element in the batch. Note that for
            some losses, there are multiple elements per sample. If the field :attr:`size_average`
            is set to ``False``, the losses are instead summed for each minibatch. Ignored
            when reduce is ``False``. Default: ``True``
        reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
            losses are averaged or summed over observations for each minibatch depending
            on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
            batch element instead and ignores :attr:`size_average`. Default: ``True``
        reduction (string, optional): Specifies the reduction to apply to the output:
            ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
            ``'mean'``: the sum of the output will be divided by the number of
            elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
            and :attr:`reduce` are in the process of being deprecated, and in the meantime,
            specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``

    Shape:
        - Input: :math:`(N, *)` where :math:`*` means, any number of additional
          dimensions
        - Target: :math:`(N, *)`, same shape as the input
        - Output: scalar. If :attr:`reduction` is ``'none'``, then :math:`(N, *)`, same
          shape as input.

    Examples::

        >>> m = nn.Sigmoid()
        >>> loss = nn.BCELoss()
        >>> input = torch.randn(3, requires_grad=True)
        >>> target = torch.empty(3).random_(2)
        >>> output = loss(m(input), target)
        >>> output.backward()
    """
    __constants__ = ['reduction', 'weight']

    def __init__(self, weight=None, size_average=None, reduce=None, reduction='mean'):
        super(BCELoss, self).__init__(weight, size_average, reduce, reduction)

    @weak_script_method
    def forward(self, input, target):
        return F.binary_cross_entropy(input, target, weight=self.weight, reduction=self.reduction)


@weak_module
class BCEWithLogitsLoss(_Loss):
    r"""This loss combines a `Sigmoid` layer and the `BCELoss` in one single
    class. This version is more numerically stable than using a plain `Sigmoid`
    followed by a `BCELoss` as, by combining the operations into one layer,
    we take advantage of the log-sum-exp trick for numerical stability.

    The unreduced (i.e. with :attr:`reduction` set to ``'none'``) loss can be described as:

    .. math::
        \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
        l_n = - w_n \left[ y_n \cdot \log \sigma(x_n)
        + (1 - y_n) \cdot \log (1 - \sigma(x_n)) \right],

    where :math:`N` is the batch size. If :attr:`reduction` is not ``'none'``
    (default ``'mean'``), then

    .. math::
        \ell(x, y) = \begin{cases}
            \operatorname{mean}(L), & \text{if reduction} = \text{'mean';}\\
            \operatorname{sum}(L),  & \text{if reduction} = \text{'sum'.}
        \end{cases}

    This is used for measuring the error of a reconstruction in for example
    an auto-encoder. Note that the targets `t[i]` should be numbers
    between 0 and 1.

    It's possible to trade off recall and precision by adding weights to positive examples.
    In the case of multi-label classification the loss can be described as:

    .. math::
        \ell_c(x, y) = L_c = \{l_{1,c},\dots,l_{N,c}\}^\top, \quad
        l_{n,c} = - w_{n,c} \left[ p_c y_{n,c} \cdot \log \sigma(x_{n,c})
        + (1 - y_{n,c}) \cdot \log (1 - \sigma(x_{n,c})) \right],

    where :math:`c` is the class number (:math:`c > 1` for multi-label binary classification,
    :math:`c = 1` for single-label binary classification),
    :math:`n` is the number of the sample in the batch and
    :math:`p_c` is the weight of the positive answer for the class :math:`c`.

    :math:`p_c > 1` increases the recall, :math:`p_c < 1` increases the precision.

    For example, if a dataset contains 100 positive and 300 negative examples of a single class,
    then `pos_weight` for the class should be equal to :math:`\frac{300}{100}=3`.
    The loss would act as if the dataset contains :math:`3\times 100=300` positive examples.

    Examples::

        >>> target = torch.ones([10, 64], dtype=torch.float32)  # 64 classes, batch size = 10
        >>> output = torch.full([10, 64], 0.999)  # A prediction (logit)
        >>> pos_weight = torch.ones([64])  # All weights are equal to 1
        >>> criterion = torch.nn.BCEWithLogitsLoss(pos_weight=pos_weight)
        >>> criterion(output, target)  # -log(sigmoid(0.999))
        tensor(0.3135)

    Args:
        weight (Tensor, optional): a manual rescaling weight given to the loss
            of each batch element. If given, has to be a Tensor of size `nbatch`.
        size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
            the losses are averaged over each loss element in the batch. Note that for
            some losses, there are multiple elements per sample. If the field :attr:`size_average`
            is set to ``False``, the losses are instead summed for each minibatch. Ignored
            when reduce is ``False``. Default: ``True``
        reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
            losses are averaged or summed over observations for each minibatch depending
            on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
            batch element instead and ignores :attr:`size_average`. Default: ``True``
        reduction (string, optional): Specifies the reduction to apply to the output:
            ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
            ``'mean'``: the sum of the output will be divided by the number of
            elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
            and :attr:`reduce` are in the process of being deprecated, and in the meantime,
            specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``
        pos_weight (Tensor, optional): a weight of positive examples.
                Must be a vector with length equal to the number of classes.

    Shape:
        - Input: :math:`(N, *)` where :math:`*` means, any number of additional dimensions
        - Target: :math:`(N, *)`, same shape as the input
        - Output: scalar. If :attr:`reduction` is ``'none'``, then :math:`(N, *)`, same
          shape as input.

     Examples::

        >>> loss = nn.BCEWithLogitsLoss()
        >>> input = torch.randn(3, requires_grad=True)
        >>> target = torch.empty(3).random_(2)
        >>> output = loss(input, target)
        >>> output.backward()
    """
    __constants__ = ['weight', 'pos_weight', 'reduction']

    def __init__(self, weight=None, size_average=None, reduce=None, reduction='mean', pos_weight=None):
        super(BCEWithLogitsLoss, self).__init__(size_average, reduce, reduction)
        self.register_buffer('weight', weight)
        self.register_buffer('pos_weight', pos_weight)

    @weak_script_method
    def forward(self, input, target):
        return F.binary_cross_entropy_with_logits(input, target,
                                                  self.weight,
                                                  pos_weight=self.pos_weight,
                                                  reduction=self.reduction)


@weak_module
class HingeEmbeddingLoss(_Loss):
    r"""Measures the loss given an input tensor :math:`x` and a labels tensor :math:`y`
    (containing 1 or -1).
    This is usually used for measuring whether two inputs are similar or
    dissimilar, e.g. using the L1 pairwise distance as :math:`x`, and is typically
    used for learning nonlinear embeddings or semi-supervised learning.

    The loss function for :math:`n`-th sample in the mini-batch is

    .. math::
        l_n = \begin{cases}
            x_n, & \text{if}\; y_n = 1,\\
            \max \{0, \Delta - x_n\}, & \text{if}\; y_n = -1,
        \end{cases}

    and the total loss functions is

    .. math::
        \ell(x, y) = \begin{cases}
            \operatorname{mean}(L), & \text{if reduction} = \text{'mean';}\\
            \operatorname{sum}(L),  & \text{if reduction} = \text{'sum'.}
        \end{cases}

    where :math:`L = \{l_1,\dots,l_N\}^\top`.

    Args:
        margin (float, optional): Has a default value of `1`.
        size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
            the losses are averaged over each loss element in the batch. Note that for
            some losses, there are multiple elements per sample. If the field :attr:`size_average`
            is set to ``False``, the losses are instead summed for each minibatch. Ignored
            when reduce is ``False``. Default: ``True``
        reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
            losses are averaged or summed over observations for each minibatch depending
            on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
            batch element instead and ignores :attr:`size_average`. Default: ``True``
        reduction (string, optional): Specifies the reduction to apply to the output:
            ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
            ``'mean'``: the sum of the output will be divided by the number of
            elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
            and :attr:`reduce` are in the process of being deprecated, and in the meantime,
            specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``

    Shape:
        - Input: :math:`(*)` where :math:`*` means, any number of dimensions. The sum operation
          operates over all the elements.
        - Target: :math:`(*)`, same shape as the input
        - Output: scalar. If :attr:`reduction` is ``'none'``, then same shape as the input
    """
    __constants__ = ['margin', 'reduction']

    def __init__(self, margin=1.0, size_average=None, reduce=None, reduction='mean'):
        super(HingeEmbeddingLoss, self).__init__(size_average, reduce, reduction)
        self.margin = margin

    @weak_script_method
    def forward(self, input, target):
        return F.hinge_embedding_loss(input, target, margin=self.margin, reduction=self.reduction)


@weak_module
class MultiLabelMarginLoss(_Loss):
    r"""Creates a criterion that optimizes a multi-class multi-classification
    hinge loss (margin-based loss) between input :math:`x` (a 2D mini-batch `Tensor`)
    and output :math:`y` (which is a 2D `Tensor` of target class indices).
    For each sample in the mini-batch:

    .. math::
        \text{loss}(x, y) = \sum_{ij}\frac{\max(0, 1 - (x[y[j]] - x[i]))}{\text{x.size}(0)}

    where :math:`x \in \left\{0, \; \cdots , \; \text{x.size}(0) - 1\right\}`, \
    :math:`y \in \left\{0, \; \cdots , \; \text{y.size}(0) - 1\right\}`, \
    :math:`0 \leq y[j] \leq \text{x.size}(0)-1`, \
    and :math:`i \neq y[j]` for all :math:`i` and :math:`j`.

    :math:`y` and :math:`x` must have the same size.

    The criterion only considers a contiguous block of non-negative targets that
    starts at the front.

    This allows for different samples to have variable amounts of target classes.

    Args:
        size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
            the losses are averaged over each loss element in the batch. Note that for
            some losses, there are multiple elements per sample. If the field :attr:`size_average`
            is set to ``False``, the losses are instead summed for each minibatch. Ignored
            when reduce is ``False``. Default: ``True``
        reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
            losses are averaged or summed over observations for each minibatch depending
            on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
            batch element instead and ignores :attr:`size_average`. Default: ``True``
        reduction (string, optional): Specifies the reduction to apply to the output:
            ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
            ``'mean'``: the sum of the output will be divided by the number of
            elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
            and :attr:`reduce` are in the process of being deprecated, and in the meantime,
            specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``

    Shape:
        - Input: :math:`(C)` or :math:`(N, C)` where `N` is the batch size and `C`
          is the number of classes.
        - Target: :math:`(C)` or :math:`(N, C)`, label targets padded by -1 ensuring same shape as the input.
        - Output: scalar. If :attr:`reduction` is ``'none'``, then :math:`(N)`.

    Examples::

        >>> loss = nn.MultiLabelMarginLoss()
        >>> x = torch.FloatTensor([[0.1, 0.2, 0.4, 0.8]])
        >>> # for target y, only consider labels 3 and 0, not after label -1
        >>> y = torch.LongTensor([[3, 0, -1, 1]])
        >>> loss(x, y)
        >>> # 0.25 * ((1-(0.1-0.2)) + (1-(0.1-0.4)) + (1-(0.8-0.2)) + (1-(0.8-0.4)))
        tensor(0.8500)

    """
    __constants__ = ['reduction']

    def __init__(self, size_average=None, reduce=None, reduction='mean'):
        super(MultiLabelMarginLoss, self).__init__(size_average, reduce, reduction)

    @weak_script_method
    def forward(self, input, target):
        return F.multilabel_margin_loss(input, target, reduction=self.reduction)


@weak_module
class SmoothL1Loss(_Loss):
    r"""Creates a criterion that uses a squared term if the absolute
    element-wise error falls below 1 and an L1 term otherwise.
    It is less sensitive to outliers than the `MSELoss` and in some cases
    prevents exploding gradients (e.g. see `Fast R-CNN` paper by Ross Girshick).
    Also known as the Huber loss:

    .. math::
        \text{loss}(x, y) = \frac{1}{n} \sum_{i} z_{i}

    where :math:`z_{i}` is given by:

    .. math::
        z_{i} =
        \begin{cases}
        0.5 (x_i - y_i)^2, & \text{if } |x_i - y_i| < 1 \\
        |x_i - y_i| - 0.5, & \text{otherwise }
        \end{cases}

    :math:`x` and :math:`y` arbitrary shapes with a total of :math:`n` elements each
    the sum operation still operates over all the elements, and divides by :math:`n`.

    The division by :math:`n` can be avoided if sets ``reduction = 'sum'``.

    Args:
        size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
            the losses are averaged over each loss element in the batch. Note that for
            some losses, there are multiple elements per sample. If the field :attr:`size_average`
            is set to ``False``, the losses are instead summed for each minibatch. Ignored
            when reduce is ``False``. Default: ``True``
        reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
            losses are averaged or summed over observations for each minibatch depending
            on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
            batch element instead and ignores :attr:`size_average`. Default: ``True``
        reduction (string, optional): Specifies the reduction to apply to the output:
            ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
            ``'mean'``: the sum of the output will be divided by the number of
            elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
            and :attr:`reduce` are in the process of being deprecated, and in the meantime,
            specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``

    Shape:
        - Input: :math:`(N, *)` where :math:`*` means, any number of additional
          dimensions
        - Target: :math:`(N, *)`, same shape as the input
        - Output: scalar. If :attr:`reduction` is ``'none'``, then
          :math:`(N, *)`, same shape as the input

    """
    __constants__ = ['reduction']

    def __init__(self, size_average=None, reduce=None, reduction='mean'):
        super(SmoothL1Loss, self).__init__(size_average, reduce, reduction)

    @weak_script_method
    def forward(self, input, target):
        return F.smooth_l1_loss(input, target, reduction=self.reduction)


@weak_module
class SoftMarginLoss(_Loss):
    r"""Creates a criterion that optimizes a two-class classification
    logistic loss between input tensor :math:`x` and target tensor :math:`y`
    (containing 1 or -1).

    .. math::
        \text{loss}(x, y) = \sum_i \frac{\log(1 + \exp(-y[i]*x[i]))}{\text{x.nelement}()}

    Args:
        size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
            the losses are averaged over each loss element in the batch. Note that for
            some losses, there are multiple elements per sample. If the field :attr:`size_average`
            is set to ``False``, the losses are instead summed for each minibatch. Ignored
            when reduce is ``False``. Default: ``True``
        reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
            losses are averaged or summed over observations for each minibatch depending
            on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
            batch element instead and ignores :attr:`size_average`. Default: ``True``
        reduction (string, optional): Specifies the reduction to apply to the output:
            ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
            ``'mean'``: the sum of the output will be divided by the number of
            elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
            and :attr:`reduce` are in the process of being deprecated, and in the meantime,
            specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``

    Shape:
        - Input: :math:`(*)` where :math:`*` means, any number of additional
          dimensions
        - Target: :math:`(*)`, same shape as the input
        - Output: scalar. If :attr:`reduction` is ``'none'``, then same shape as the input

    """
    __constants__ = ['reduction']

    def __init__(self, size_average=None, reduce=None, reduction='mean'):
        super(SoftMarginLoss, self).__init__(size_average, reduce, reduction)

    @weak_script_method
    def forward(self, input, target):
        return F.soft_margin_loss(input, target, reduction=self.reduction)


@weak_module
class CrossEntropyLoss(_WeightedLoss):
    r"""This criterion combines :func:`nn.LogSoftmax` and :func:`nn.NLLLoss` in one single class.

    It is useful when training a classification problem with `C` classes.
    If provided, the optional argument :attr:`weight` should be a 1D `Tensor`
    assigning weight to each of the classes.
    This is particularly useful when you have an unbalanced training set.

    The `input` is expected to contain raw, unnormalized scores for each class.

    `input` has to be a Tensor of size either :math:`(minibatch, C)` or
    :math:`(minibatch, C, d_1, d_2, ..., d_K)`
    with :math:`K \geq 1` for the `K`-dimensional case (described later).

    This criterion expects a class index in the range :math:`[0, C-1]` as the
    `target` for each value of a 1D tensor of size `minibatch`; if `ignore_index`
    is specified, this criterion also accepts this class index (this index may not
    necessarily be in the class range).

    The loss can be described as:

    .. math::
        \text{loss}(x, class) = -\log\left(\frac{\exp(x[class])}{\sum_j \exp(x[j])}\right)
                       = -x[class] + \log\left(\sum_j \exp(x[j])\right)

    or in the case of the :attr:`weight` argument being specified:

    .. math::
        \text{loss}(x, class) = weight[class] \left(-x[class] + \log\left(\sum_j \exp(x[j])\right)\right)

    The losses are averaged across observations for each minibatch.

    Can also be used for higher dimension inputs, such as 2D images, by providing
    an input of size :math:`(minibatch, C, d_1, d_2, ..., d_K)` with :math:`K \geq 1`,
    where :math:`K` is the number of dimensions, and a target of appropriate shape
    (see below).


    Args:
        weight (Tensor, optional): a manual rescaling weight given to each class.
            If given, has to be a Tensor of size `C`
        size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
            the losses are averaged over each loss element in the batch. Note that for
            some losses, there are multiple elements per sample. If the field :attr:`size_average`
            is set to ``False``, the losses are instead summed for each minibatch. Ignored
            when reduce is ``False``. Default: ``True``
        ignore_index (int, optional): Specifies a target value that is ignored
            and does not contribute to the input gradient. When :attr:`size_average` is
            ``True``, the loss is averaged over non-ignored targets.
        reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
            losses are averaged or summed over observations for each minibatch depending
            on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
            batch element instead and ignores :attr:`size_average`. Default: ``True``
        reduction (string, optional): Specifies the reduction to apply to the output:
            ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
            ``'mean'``: the sum of the output will be divided by the number of
            elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
            and :attr:`reduce` are in the process of being deprecated, and in the meantime,
            specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``

    Shape:
        - Input: :math:`(N, C)` where `C = number of classes`, or
          :math:`(N, C, d_1, d_2, ..., d_K)` with :math:`K \geq 1`
          in the case of `K`-dimensional loss.
        - Target: :math:`(N)` where each value is :math:`0 \leq \text{targets}[i] \leq C-1`, or
          :math:`(N, d_1, d_2, ..., d_K)` with :math:`K \geq 1` in the case of
          K-dimensional loss.
        - Output: scalar.
          If :attr:`reduction` is ``'none'``, then the same size as the target:
          :math:`(N)`, or
          :math:`(N, d_1, d_2, ..., d_K)` with :math:`K \geq 1` in the case
          of K-dimensional loss.

    Examples::

        >>> loss = nn.CrossEntropyLoss()
        >>> input = torch.randn(3, 5, requires_grad=True)
        >>> target = torch.empty(3, dtype=torch.long).random_(5)
        >>> output = loss(input, target)
        >>> output.backward()
    """
    __constants__ = ['weight', 'ignore_index', 'reduction']

    def __init__(self, weight=None, size_average=None, ignore_index=-100,
                 reduce=None, reduction='mean'):
        super(CrossEntropyLoss, self).__init__(weight, size_average, reduce, reduction)
        self.ignore_index = ignore_index

    @weak_script_method
    def forward(self, input, target):
        return F.cross_entropy(input, target, weight=self.weight,
                               ignore_index=self.ignore_index, reduction=self.reduction)


@weak_module
class MultiLabelSoftMarginLoss(_WeightedLoss):
    r"""Creates a criterion that optimizes a multi-label one-versus-all
    loss based on max-entropy, between input :math:`x` and target :math:`y` of size
    :math:`(N, C)`.
    For each sample in the minibatch:

    .. math::
        loss(x, y) = - \frac{1}{C} * \sum_i y[i] * \log((1 + \exp(-x[i]))^{-1})
                         + (1-y[i]) * \log\left(\frac{\exp(-x[i])}{(1 + \exp(-x[i]))}\right)

    where :math:`i \in \left\{0, \; \cdots , \; \text{x.nElement}() - 1\right\}`,
    :math:`y[i] \in \left\{0, \; 1\right\}`.

    Args:
        weight (Tensor, optional): a manual rescaling weight given to each
            class. If given, it has to be a Tensor of size `C`. Otherwise, it is
            treated as if having all ones.
        size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
            the losses are averaged over each loss element in the batch. Note that for
            some losses, there are multiple elements per sample. If the field :attr:`size_average`
            is set to ``False``, the losses are instead summed for each minibatch. Ignored
            when reduce is ``False``. Default: ``True``
        reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
            losses are averaged or summed over observations for each minibatch depending
            on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
            batch element instead and ignores :attr:`size_average`. Default: ``True``
        reduction (string, optional): Specifies the reduction to apply to the output:
            ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
            ``'mean'``: the sum of the output will be divided by the number of
            elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
            and :attr:`reduce` are in the process of being deprecated, and in the meantime,
            specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``

    Shape:
        - Input: :math:`(N, C)` where `N` is the batch size and `C` is the number of classes.
        - Target: :math:`(N, C)`, label targets padded by -1 ensuring same shape as the input.
        - Output: scalar. If :attr:`reduction` is ``'none'``, then :math:`(N)`.
    """
    __constants__ = ['weight', 'reduction']

    def __init__(self, weight=None, size_average=None, reduce=None, reduction='mean'):
        super(MultiLabelSoftMarginLoss, self).__init__(weight, size_average, reduce, reduction)

    @weak_script_method
    def forward(self, input, target):
        return F.multilabel_soft_margin_loss(input, target, weight=self.weight, reduction=self.reduction)


@weak_module
class CosineEmbeddingLoss(_Loss):
    r"""Creates a criterion that measures the loss given input tensors
    :math:`x_1`, :math:`x_2` and a `Tensor` label :math:`y` with values 1 or -1.
    This is used for measuring whether two inputs are similar or dissimilar,
    using the cosine distance, and is typically used for learning nonlinear
    embeddings or semi-supervised learning.

    The loss function for each sample is:

    .. math::
        \text{loss}(x, y) =
        \begin{cases}
        1 - \cos(x_1, x_2), & \text{if } y = 1 \\
        \max(0, \cos(x_1, x_2) - \text{margin}), & \text{if } y = -1
        \end{cases}

    Args:
        margin (float, optional): Should be a number from :math:`-1` to :math:`1`,
            :math:`0` to :math:`0.5` is suggested. If :attr:`margin` is missing, the
            default value is :math:`0`.
        size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
            the losses are averaged over each loss element in the batch. Note that for
            some losses, there are multiple elements per sample. If the field :attr:`size_average`
            is set to ``False``, the losses are instead summed for each minibatch. Ignored
            when reduce is ``False``. Default: ``True``
        reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
            losses are averaged or summed over observations for each minibatch depending
            on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
            batch element instead and ignores :attr:`size_average`. Default: ``True``
        reduction (string, optional): Specifies the reduction to apply to the output:
            ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
            ``'mean'``: the sum of the output will be divided by the number of
            elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
            and :attr:`reduce` are in the process of being deprecated, and in the meantime,
            specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``
    """
    __constants__ = ['margin', 'reduction']

    def __init__(self, margin=0., size_average=None, reduce=None, reduction='mean'):
        super(CosineEmbeddingLoss, self).__init__(size_average, reduce, reduction)
        self.margin = margin

    @weak_script_method
    def forward(self, input1, input2, target):
        return F.cosine_embedding_loss(input1, input2, target, margin=self.margin, reduction=self.reduction)


@weak_module
class MarginRankingLoss(_Loss):
    r"""Creates a criterion that measures the loss given
    inputs :math:`x1`, :math:`x2`, two 1D mini-batch `Tensors`,
    and a label 1D mini-batch tensor :math:`y` (containing 1 or -1).

    If :math:`y = 1` then it assumed the first input should be ranked higher
    (have a larger value) than the second input, and vice-versa for :math:`y = -1`.

    The loss function for each sample in the mini-batch is:

    .. math::
        \text{loss}(x, y) = \max(0, -y * (x1 - x2) + \text{margin})

    Args:
        margin (float, optional): Has a default value of :math:`0`.
        size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
            the losses are averaged over each loss element in the batch. Note that for
            some losses, there are multiple elements per sample. If the field :attr:`size_average`
            is set to ``False``, the losses are instead summed for each minibatch. Ignored
            when reduce is ``False``. Default: ``True``
        reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
            losses are averaged or summed over observations for each minibatch depending
            on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
            batch element instead and ignores :attr:`size_average`. Default: ``True``
        reduction (string, optional): Specifies the reduction to apply to the output:
            ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
            ``'mean'``: the sum of the output will be divided by the number of
            elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
            and :attr:`reduce` are in the process of being deprecated, and in the meantime,
            specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``

    Shape:
        - Input: :math:`(N, D)` where `N` is the batch size and `D` is the size of a sample.
        - Target: :math:`(N)`
        - Output: scalar. If :attr:`reduction` is ``'none'``, then :math:`(N)`.
    """
    __constants__ = ['margin', 'reduction']

    def __init__(self, margin=0., size_average=None, reduce=None, reduction='mean'):
        super(MarginRankingLoss, self).__init__(size_average, reduce, reduction)
        self.margin = margin

    @weak_script_method
    def forward(self, input1, input2, target):
        return F.margin_ranking_loss(input1, input2, target, margin=self.margin, reduction=self.reduction)


@weak_module
class MultiMarginLoss(_WeightedLoss):
    r"""Creates a criterion that optimizes a multi-class classification hinge
    loss (margin-based loss) between input :math:`x` (a 2D mini-batch `Tensor`) and
    output :math:`y` (which is a 1D tensor of target class indices,
    :math:`0 \leq y \leq \text{x.size}(1)-1`):

    For each mini-batch sample, the loss in terms of the 1D input :math:`x` and scalar
    output :math:`y` is:

    .. math::
        \text{loss}(x, y) = \frac{\sum_i \max(0, \text{margin} - x[y] + x[i]))^p}{\text{x.size}(0)}

    where :math:`x \in \left\{0, \; \cdots , \; \text{x.size}(0) - 1\right\}`
    and :math:`i \neq y`.

    Optionally, you can give non-equal weighting on the classes by passing
    a 1D :attr:`weight` tensor into the constructor.

    The loss function then becomes:

    .. math::
        \text{loss}(x, y) = \frac{\sum_i \max(0, w[y] * (\text{margin} - x[y] + x[i]))^p)}{\text{x.size}(0)}

    Args:
        p (int, optional): Has a default value of :math:`1`. :math:`1` and :math:`2`
            are the only supported values.
        margin (float, optional): Has a default value of :math:`1`.
        weight (Tensor, optional): a manual rescaling weight given to each
            class. If given, it has to be a Tensor of size `C`. Otherwise, it is
            treated as if having all ones.
        size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
            the losses are averaged over each loss element in the batch. Note that for
            some losses, there are multiple elements per sample. If the field :attr:`size_average`
            is set to ``False``, the losses are instead summed for each minibatch. Ignored
            when reduce is ``False``. Default: ``True``
        reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
            losses are averaged or summed over observations for each minibatch depending
            on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
            batch element instead and ignores :attr:`size_average`. Default: ``True``
        reduction (string, optional): Specifies the reduction to apply to the output:
            ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
            ``'mean'``: the sum of the output will be divided by the number of
            elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
            and :attr:`reduce` are in the process of being deprecated, and in the meantime,
            specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``
    """
    __constants__ = ['p', 'margin', 'weight', 'reduction']

    def __init__(self, p=1, margin=1., weight=None, size_average=None,
                 reduce=None, reduction='mean'):
        super(MultiMarginLoss, self).__init__(weight, size_average, reduce, reduction)
        if p != 1 and p != 2:
            raise ValueError("only p == 1 and p == 2 supported")
        assert weight is None or weight.dim() == 1
        self.p = p
        self.margin = margin

    @weak_script_method
    def forward(self, input, target):
        return F.multi_margin_loss(input, target, p=self.p, margin=self.margin,
                                   weight=self.weight, reduction=self.reduction)


@weak_module
class TripletMarginLoss(_Loss):
    r"""Creates a criterion that measures the triplet loss given an input
    tensors :math:`x1`, :math:`x2`, :math:`x3` and a margin with a value greater than :math:`0`.
    This is used for measuring a relative similarity between samples. A triplet
    is composed by `a`, `p` and `n` (i.e., `anchor`, `positive examples` and `negative
    examples` respectively). The shapes of all input tensors should be
    :math:`(N, D)`.

    The distance swap is described in detail in the paper `Learning shallow
    convolutional feature descriptors with triplet losses`_ by
    V. Balntas, E. Riba et al.

    The loss function for each sample in the mini-batch is:

    .. math::
        L(a, p, n) = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\}


    where

    .. math::
        d(x_i, y_i) = \left\lVert {\bf x}_i - {\bf y}_i \right\rVert_p

    Args:
        margin (float, optional): Default: :math:`1`.
        p (int, optional): The norm degree for pairwise distance. Default: :math:`2`.
        swap (bool, optional): The distance swap is described in detail in the paper
            `Learning shallow convolutional feature descriptors with triplet losses` by
            V. Balntas, E. Riba et al. Default: ``False``.
        size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
            the losses are averaged over each loss element in the batch. Note that for
            some losses, there are multiple elements per sample. If the field :attr:`size_average`
            is set to ``False``, the losses are instead summed for each minibatch. Ignored
            when reduce is ``False``. Default: ``True``
        reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
            losses are averaged or summed over observations for each minibatch depending
            on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
            batch element instead and ignores :attr:`size_average`. Default: ``True``
        reduction (string, optional): Specifies the reduction to apply to the output:
            ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
            ``'mean'``: the sum of the output will be divided by the number of
            elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
            and :attr:`reduce` are in the process of being deprecated, and in the meantime,
            specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``

    Shape:
        - Input: :math:`(N, D)` where :math:`D` is the vector dimension.
        - Output: scalar. If :attr:`reduction` is ``'none'``, then :math:`(N)`.

    >>> triplet_loss = nn.TripletMarginLoss(margin=1.0, p=2)
    >>> anchor = torch.randn(100, 128, requires_grad=True)
    >>> positive = torch.randn(100, 128, requires_grad=True)
    >>> negative = torch.randn(100, 128, requires_grad=True)
    >>> output = triplet_loss(anchor, positive, negative)
    >>> output.backward()

    .. _Learning shallow convolutional feature descriptors with triplet losses:
        http://www.bmva.org/bmvc/2016/papers/paper119/index.html
    """
    __constants__ = ['margin', 'p', 'eps', 'swap', 'reduction']

    def __init__(self, margin=1.0, p=2., eps=1e-6, swap=False, size_average=None,
                 reduce=None, reduction='mean'):
        super(TripletMarginLoss, self).__init__(size_average, reduce, reduction)
        self.margin = margin
        self.p = p
        self.eps = eps
        self.swap = swap

    @weak_script_method
    def forward(self, anchor, positive, negative):
        return F.triplet_margin_loss(anchor, positive, negative, margin=self.margin, p=self.p,
                                     eps=self.eps, swap=self.swap, reduction=self.reduction)


@weak_module
class CTCLoss(_Loss):
    r"""The Connectionist Temporal Classification loss.

    Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the
    probability of possible alignments of input to target, producing a loss value which is differentiable
    with respect to each input node. The alignment of input to target is assumed to be "many-to-one", which
    limits the length of the target sequence such that it must be :math:`\leq` the input length.

    Args:
        blank (int, optional): blank label. Default :math:`0`.
        reduction (string, optional): Specifies the reduction to apply to the output:
            ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
            ``'mean'``: the output losses will be divided by the target lengths and
            then the mean over the batch is taken. Default: ``'mean'``
        zero_infinity (bool, optional):
            Whether to zero infinite losses and the associated gradients.
            Default: ``False``
            Infinite losses mainly occur when the inputs are too short
            to be aligned to the targets.

    Shape:
        - Log_probs: Tensor of size :math:`(T, N, C)`,
          where :math:`T = \text{input length}`,
          :math:`N = \text{batch size}`, and
          :math:`C = \text{number of classes (including blank)}`.
          The logarithmized probabilities of the outputs (e.g. obtained with
          :func:`torch.nn.functional.log_softmax`).
        - Targets: Tensor of size :math:`(N, S)` or
          :math:`(\operatorname{sum}(\text{target\_lengths}))`,
          where :math:`N = \text{batch size}` and
          :math:`S = \text{max target length, if shape is } (N, S)`.
          It represent the target sequences. Each element in the target
          sequence is a class index. And the target index cannot be blank (default=0).
          In the :math:`(N, S)` form, targets are padded to the
          length of the longest sequence, and stacked.
          In the :math:`(\operatorname{sum}(\text{target\_lengths}))` form,
          the targets are assumed to be un-padded and
          concatenated within 1 dimension.
        - Input_lengths: Tuple or tensor of size :math:`(N)`,
          where :math:`N = \text{batch size}`. It represent the lengths of the
          inputs (must each be :math:`\leq T`). And the lengths are specified
          for each sequence to achieve masking under the assumption that sequences
          are padded to equal lengths.
        - Target_lengths: Tuple or tensor of size :math:`(N)`,
          where :math:`N = \text{batch size}`. It represent lengths of the targets.
          Lengths are specified for each sequence to achieve masking under the
          assumption that sequences are padded to equal lengths. If target shape is
          :math:`(N,S)`, target_lengths are effectively the stop index
          :math:`s_n` for each target sequence, such that ``target_n = targets[n,0:s_n]`` for
          each target in a batch. Lengths must each be :math:`\leq S`
          If the targets are given as a 1d tensor that is the concatenation of individual
          targets, the target_lengths must add up to the total length of the tensor.
        - Output: scalar. If :attr:`reduction` is ``'none'``, then
          :math:`(N)`, where :math:`N = \text{batch size}`.

    Example::

        >>> T = 50      # Input sequence length
        >>> C = 20      # Number of classes (including blank)
        >>> N = 16      # Batch size
        >>> S = 30      # Target sequence length of longest target in batch
        >>> S_min = 10  # Minimum target length, for demonstration purposes
        >>>
        >>> # Initialize random batch of input vectors, for *size = (T,N,C)
        >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_()
        >>>
        >>> # Initialize random batch of targets (0 = blank, 1:C = classes)
        >>> target = torch.randint(low=1, high=C, size=(N, S), dtype=torch.long)
        >>>
        >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long)
        >>> target_lengths = torch.randint(low=S_min, high=S, size=(N,), dtype=torch.long)
        >>> ctc_loss = nn.CTCLoss()
        >>> loss = ctc_loss(input, target, input_lengths, target_lengths)
        >>> loss.backward()

    Reference:
        A. Graves et al.: Connectionist Temporal Classification:
        Labelling Unsegmented Sequence Data with Recurrent Neural Networks:
        https://www.cs.toronto.edu/~graves/icml_2006.pdf

    .. Note::
        In order to use CuDNN, the following must be satisfied: :attr:`targets` must be
        in concatenated format, all :attr:`input_lengths` must be `T`.  :math:`blank=0`,
        :attr:`target_lengths` :math:`\leq 256`, the integer arguments must be of
        dtype :attr:`torch.int32`.

        The regular implementation uses the (more common in PyTorch) `torch.long` dtype.


    .. include:: cudnn_deterministic.rst

    """
    __constants__ = ['blank', 'reduction']

    def __init__(self, blank=0, reduction='mean', zero_infinity=False):
        super(CTCLoss, self).__init__(reduction=reduction)
        self.blank = blank
        self.zero_infinity = zero_infinity

    @weak_script_method
    def forward(self, log_probs, targets, input_lengths, target_lengths):
        return F.ctc_loss(log_probs, targets, input_lengths, target_lengths, self.blank, self.reduction,
                          self.zero_infinity)

# TODO: L1HingeEmbeddingCriterion
# TODO: MSECriterion weight
# TODO: ClassSimplexCriterion
