"""Functional interface"""

import warnings
import math
from operator import mul
from functools import reduce

import torch
from torch._C import _infer_size, _add_docstr
from . import _functions
from .modules import utils
from ._functions.padding import ConstantPadNd
from ._functions import vision
from ._functions.thnn.fold import Col2Im, Im2Col
from .modules.utils import _single, _pair, _triple, _list_with_default
from . import grad


class _Reduction:
    # NB: Keep this class in sync with enums in THNN/Reduction.h

    @staticmethod
    def get_enum(reduction):
        if reduction == 'none':
            return 0
        if reduction == 'elementwise_mean':
            return 1
        if reduction == 'sum':
            return 2
        raise ValueError(reduction + " is not a valid value for reduction")

    # In order to support previous versions, accept boolean size_average and reduce
    # and convert them into the new constants for now

    # We use these functions in torch/legacy as well, in which case we'll silence the warning
    @staticmethod
    def legacy_get_string(size_average, reduce, emit_warning=True):
        warning = "size_average and reduce args will be deprecated, please use reduction='{}' instead."

        if size_average is None:
            size_average = True
        if reduce is None:
            reduce = True

        if size_average and reduce:
            ret = 'elementwise_mean'
        elif reduce:
            ret = 'sum'
        else:
            ret = 'none'
        if emit_warning:
            warnings.warn(warning.format(ret))
        return ret

    @staticmethod
    def legacy_get_enum(size_average, reduce, emit_warning=True):
        return _Reduction.get_enum(_Reduction.legacy_get_string(size_average, reduce, emit_warning))


conv1d = _add_docstr(torch.conv1d, r"""
conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor

Applies a 1D convolution over an input signal composed of several input
planes.

See :class:`~torch.nn.Conv1d` for details and output shape.

Args:
    input: input tensor of shape :math:`minibatch \times in\_channels \times iW`
    weight: filters of shape :math:`out\_channels \times \frac{in\_channels}{groups} \times kW`
    bias: optional bias of shape (:math:`out\_channels`). Default: ``None``
    stride: the stride of the convolving kernel. Can be a single number or
      a one-element tuple `(sW,)`. Default: 1
    padding: implicit zero paddings on both sides of the input. Can be a
      single number or a one-element tuple `(padW,)`. Default: 0
    dilation: the spacing between kernel elements. Can be a single number or
      a one-element tuple `(dW,)`. Default: 1
    groups: split input into groups, :math:`in\_channels` should be divisible by
      the number of groups. Default: 1

Examples::

    >>> filters = torch.randn(33, 16, 3)
    >>> inputs = torch.randn(20, 16, 50)
    >>> F.conv1d(inputs, filters)
""")

conv2d = _add_docstr(torch.conv2d, r"""
conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor

Applies a 2D convolution over an input image composed of several input
planes.

See :class:`~torch.nn.Conv2d` for details and output shape.

Args:
    input: input tensor of shape (:math:`minibatch \times in\_channels \times iH \times iW`)
    weight: filters of shape (:math:`out\_channels \times \frac{in\_channels}{groups} \times kH \times kW`)
    bias: optional bias tensor of shape (:math:`out\_channels`). Default: ``None``
    stride: the stride of the convolving kernel. Can be a single number or a
      tuple `(sH, sW)`. Default: 1
    padding: implicit zero paddings on both sides of the input. Can be a
      single number or a tuple `(padH, padW)`. Default: 0
    dilation: the spacing between kernel elements. Can be a single number or
      a tuple `(dH, dW)`. Default: 1
    groups: split input into groups, :math:`in\_channels` should be divisible by the
      number of groups. Default: 1

Examples::

    >>> # With square kernels and equal stride
    >>> filters = torch.randn(8,4,3,3)
    >>> inputs = torch.randn(1,4,5,5)
    >>> F.conv2d(inputs, filters, padding=1)
""")

conv3d = _add_docstr(torch.conv3d, r"""
conv3d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor

Applies a 3D convolution over an input image composed of several input
planes.

See :class:`~torch.nn.Conv3d` for details and output shape.

Args:
    input: input tensor of shape (:math:`minibatch \times in\_channels \times iT \times iH \times iW`)
    weight: filters of shape (:math:`out\_channels \times \frac{in\_channels}{groups} \times kT \times kH \times kW`)
    bias: optional bias tensor of shape (:math:`out\_channels`). Default: None
    stride: the stride of the convolving kernel. Can be a single number or a
      tuple `(sT, sH, sW)`. Default: 1
    padding: implicit zero paddings on both sides of the input. Can be a
      single number or a tuple `(padT, padH, padW)`. Default: 0
    dilation: the spacing between kernel elements. Can be a single number or
      a tuple `(dT, dH, dW)`. Default: 1
    groups: split input into groups, :math:`in\_channels` should be divisible by
      the number of groups. Default: 1

Examples::

    >>> filters = torch.randn(33, 16, 3, 3, 3)
    >>> inputs = torch.randn(20, 16, 50, 10, 20)
    >>> F.conv3d(inputs, filters)
""")

conv_transpose1d = _add_docstr(torch.conv_transpose1d, r"""
conv_transpose1d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor

Applies a 1D transposed convolution operator over an input signal
composed of several input planes, sometimes also called "deconvolution".

See :class:`~torch.nn.ConvTranspose1d` for details and output shape.

Args:
    input: input tensor of shape (:math:`minibatch \times in\_channels \times iW`)
    weight: filters of shape (:math:`in\_channels \times \frac{out\_channels}{groups} \times kW`)
    bias: optional bias of shape (:math:`out\_channels`). Default: None
    stride: the stride of the convolving kernel. Can be a single number or a
      tuple ``(sW,)``. Default: 1
    padding: ``kernel_size - 1 - padding`` zero-padding will be added to both
      sides of each dimension in the input. Can be a single number or a tuple
      ``(padW,)``. Default: 0
    output_padding: additional size added to one side of each dimension in the
      output shape. Can be a single number or a tuple ``(out_padW)``. Default: 0
    groups: split input into groups, :math:`in\_channels` should be divisible by the
      number of groups. Default: 1
    dilation: the spacing between kernel elements. Can be a single number or
      a tuple ``(dW,)``. Default: 1

Examples::

    >>> inputs = torch.randn(20, 16, 50)
    >>> weights = torch.randn(16, 33, 5)
    >>> F.conv_transpose1d(inputs, weights)
""")

conv_transpose2d = _add_docstr(torch.conv_transpose2d, r"""
conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor

Applies a 2D transposed convolution operator over an input image
composed of several input planes, sometimes also called "deconvolution".

See :class:`~torch.nn.ConvTranspose2d` for details and output shape.

Args:
    input: input tensor of shape (:math:`minibatch \times in\_channels \times iH \times iW`)
    weight: filters of shape (:math:`in\_channels \times \frac{out\_channels}{groups} \times kH \times kW`)
    bias: optional bias of shape (:math:`out\_channels`). Default: None
    stride: the stride of the convolving kernel. Can be a single number or a
      tuple ``(sH, sW)``. Default: 1
    padding: ``kernel_size - 1 - padding`` zero-padding will be added to both
      sides of each dimension in the input. Can be a single number or a tuple
      ``(padH, padW)``. Default: 0
    output_padding: additional size added to one side of each dimension in the
      output shape. Can be a single number or a tuple ``(out_padH, out_padW)``.
      Default: 0
    groups: split input into groups, :math:`in\_channels` should be divisible by the
      number of groups. Default: 1
    dilation: the spacing between kernel elements. Can be a single number or
      a tuple ``(dH, dW)``. Default: 1

Examples::

    >>> # With square kernels and equal stride
    >>> inputs = torch.randn(1, 4, 5, 5)
    >>> weights = torch.randn(4, 8, 3, 3)
    >>> F.conv_transpose2d(inputs, weights, padding=1)
""")

conv_transpose3d = _add_docstr(torch.conv_transpose3d, r"""
conv_transpose3d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor

Applies a 3D transposed convolution operator over an input image
composed of several input planes, sometimes also called "deconvolution"

See :class:`~torch.nn.ConvTranspose3d` for details and output shape.

Args:
    input: input tensor of shape (:math:`minibatch \times in\_channels \times iT \times iH \times iW`)
    weight: filters of shape (:math:`in\_channels \times \frac{out\_channels}{groups} \times kT \times kH \times kW`)
    bias: optional bias of shape (:math:`out\_channels`). Default: None
    stride: the stride of the convolving kernel. Can be a single number or a
      tuple ``(sT, sH, sW)``. Default: 1
    padding: ``kernel_size - 1 - padding`` zero-padding will be added to both
      sides of each dimension in the input. Can be a single number or a tuple
      ``(padT, padH, padW)``. Default: 0
    output_padding: additional size added to one side of each dimension in the
      output shape. Can be a single number or a tuple
      ``(out_padT, out_padH, out_padW)``. Default: 0
    groups: split input into groups, :math:`in\_channels` should be divisible by the
      number of groups. Default: 1
    dilation: the spacing between kernel elements. Can be a single number or
      a tuple `(dT, dH, dW)`. Default: 1

Examples::

    >>> inputs = torch.randn(20, 16, 50, 10, 20)
    >>> weights = torch.randn(16, 33, 3, 3, 3)
    >>> F.conv_transpose3d(inputs, weights)
""")


def conv_tbc(input, weight, bias, pad=0):
    r"""Applies a 1-dimensional sequence convolution over an input sequence.
    Input and output dimensions are (Time, Batch, Channels) - hence TBC.

    Args:
        input: input tensor of shape (:math:`\text{sequence length} \times batch \times in\_channels`)
        weight: filter of shape (:math:`\text{kernel width} \times in\_channels \times out\_channels`)
        bias: bias of shape (:math:`out\_channels`)
        pad: number of timesteps to pad
    """
    return input.conv_tbc(weight, bias, pad)


# Pooling
avg_pool1d = _add_docstr(torch.avg_pool1d, r"""
avg_pool1d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True) -> Tensor

Applies a 1D average pooling over an input signal composed of several
input planes.

See :class:`~torch.nn.AvgPool1d` for details and output shape.

Args:
    input: input tensor of shape (:math:`minibatch \times in\_channels \times iW`)
    kernel_size: the size of the window. Can be a single number or a
      tuple `(kW,)`
    stride: the stride of the window. Can be a single number or a tuple
      `(sW,)`. Default: :attr:`kernel_size`
    padding: implicit zero paddings on both sides of the input. Can be a
      single number or a tuple `(padW,)`. Default: 0
    ceil_mode: when True, will use `ceil` instead of `floor` to compute the
        output shape. Default: ``False``
    count_include_pad: when True, will include the zero-padding in the
        averaging calculation. Default: ``True``

Example::
    >>> # pool of square window of size=3, stride=2
    >>> input = torch.tensor([[[1,2,3,4,5,6,7]]])
    >>> F.avg_pool1d(input, kernel_size=3, stride=2)
    tensor([[[ 2.,  4.,  6.]]])
""")


avg_pool2d = _add_docstr(torch._C._nn.avg_pool2d, r"""
avg_pool2d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True) -> Tensor

Applies 2D average-pooling operation in :math:`kH \times kW` regions by step size
:math:`sH \times sW` steps. The number of output features is equal to the number of
input planes.

See :class:`~torch.nn.AvgPool2d` for details and output shape.

Args:
    input: input tensor (:math:`minibatch \times in\_channels \times iH \times iW`)
    kernel_size: size of the pooling region. Can be a single number or a
      tuple (:math:`kH \times kW`)
    stride: stride of the pooling operation. Can be a single number or a
      tuple `(sH, sW)`. Default: :attr:`kernel_size`
    padding: implicit zero paddings on both sides of the input. Can be a
      single number or a tuple `(padH, padW)`. Default: 0
    ceil_mode: when True, will use `ceil` instead of `floor` in the formula
        to compute the output shape. Default: ``False``
    count_include_pad: when True, will include the zero-padding in the
        averaging calculation. Default: ``True``
""")

avg_pool3d = _add_docstr(torch._C._nn.avg_pool3d, r"""
avg_pool3d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True) -> Tensor

Applies 3D average-pooling operation in :math:`kT \times kH \times kW` regions by step
size :math:`sT \times sH \times sW` steps. The number of output features is equal to
:math:`\lfloor\frac{\text{input planes}}{sT}\rfloor`.

See :class:`~torch.nn.AvgPool3d` for details and output shape.

Args:
    input: input tensor (:math:`minibatch \times in\_channels \times iT \times iH \times iW`)
    kernel_size: size of the pooling region. Can be a single number or a
      tuple (:math:`kT \times kH \times kW`)
    stride: stride of the pooling operation. Can be a single number or a
      tuple `(sT, sH, sW)`. Default: :attr:`kernel_size`
    padding: implicit zero paddings on both sides of the input. Can be a
      single number or a tuple `(padT, padH, padW)`, Default: 0
    ceil_mode: when True, will use `ceil` instead of `floor` in the formula
        to compute the output shape
    count_include_pad: when True, will include the zero-padding in the
        averaging calculation
""")


def fractional_max_pool2d(input, kernel_size, output_size=None,
                          output_ratio=None, return_indices=False,
                          _random_samples=None):
    r"""Applies 2D fractional max pooling over an input signal composed of several input planes.

    Fractional MaxPooling is described in detail in the paper `Fractional MaxPooling`_ by Ben Graham

    The max-pooling operation is applied in :math:`kH \times kW` regions by a stochastic
    step size determined by the target output size.
    The number of output features is equal to the number of input planes.

    Args:
        kernel_size: the size of the window to take a max over.
                     Can be a single number :math:`k` (for a square kernel of :math:`k \times k`)
                     or a tuple (:math:`kH \times kW`)
        output_size: the target output size of the image of the form :math:`oH \times oW`.
                     Can be a tuple `(oH, oW)` or a single number :math:`oH` for a square image :math:`oH \times oH`
        output_ratio: If one wants to have an output size as a ratio of the input size, this option can be given.
                      This has to be a number or tuple in the range (0, 1)
        return_indices: if ``True``, will return the indices along with the outputs.
                        Useful to pass to `max_unpool2d`.

    Examples::
        >>> input = torch.randn(20, 16, 50, 32)
        >>> # pool of square window of size=3, and target output size 13x12
        >>> F.fractional_max_pool2d(input, 3, output_size=(13, 12))
        >>> # pool of square window and target output size being half of input image size
        >>> F.fractional_max_pool2d(input, 3, output_ratio=(0.5, 0.5))

    .. _Fractional MaxPooling:
        http://arxiv.org/abs/1412.6071
    """
    if output_size is None and output_ratio is None:
        raise ValueError("fractional_max_pool2d requires specifying either "
                         "an output_size, or a output_ratio")
    if output_size is None:
        output_ratio = _pair(output_ratio)
        output_size = (int(input.size(2) * output_ratio[0]),
                       int(input.size(3) * output_ratio[1]))

    if _random_samples is None:
        _random_samples = input.new(input.size(0), input.size(1), 2).uniform_()
    ret = torch._C._nn.fractional_max_pool2d(input, kernel_size, output_size, _random_samples)
    return ret if return_indices else ret[0]


def max_pool1d(input, kernel_size, stride=None, padding=0, dilation=1,
               ceil_mode=False, return_indices=False):
    r"""Applies a 1D max pooling over an input signal composed of several input
    planes.

    See :class:`~torch.nn.MaxPool1d` for details.
    """
    ret = torch.max_pool1d_with_indices(input, kernel_size, stride, padding, dilation, ceil_mode)
    return ret if return_indices else ret[0]


def max_pool2d(input, kernel_size, stride=None, padding=0, dilation=1,
               ceil_mode=False, return_indices=False):
    r"""Applies a 2D max pooling over an input signal composed of several input
    planes.

    See :class:`~torch.nn.MaxPool2d` for details.
    """
    ret = torch._C._nn.max_pool2d_with_indices(input, kernel_size, stride, padding, dilation, ceil_mode)
    return ret if return_indices else ret[0]


def max_pool3d(input, kernel_size, stride=None, padding=0, dilation=1,
               ceil_mode=False, return_indices=False):
    r"""Applies a 3D max pooling over an input signal composed of several input
    planes.

    See :class:`~torch.nn.MaxPool3d` for details.
    """
    ret = torch._C._nn.max_pool3d_with_indices(input, kernel_size, stride, padding, dilation, ceil_mode)
    return ret if return_indices else ret[0]


def _unpool_output_size(input, kernel_size, stride, padding, output_size):
    input_size = input.size()
    default_size = []
    for d in range(len(kernel_size)):
        default_size.append((input_size[d + 2] - 1) * stride[d] +
                            kernel_size[d] - 2 * padding[d])
    if output_size is None:
        return default_size

    output_size = list(output_size)
    if len(output_size) == len(kernel_size) + 2:
        output_size = output_size[2:]
    if len(output_size) != len(kernel_size):
        raise ValueError("output_size should be a sequence containing "
                         "{} or {} elements, but it has a length of '{}'"
                         .format(len(kernel_size), len(kernel_size) + 2,
                                 len(output_size)))
    for d in range(len(kernel_size)):
        min_size = default_size[d] - stride[d]
        max_size = default_size[d] + stride[d]
        if not (min_size < output_size[d] < max_size):
            raise ValueError(
                'invalid output_size "{}" (dim {} must be between {} and {})'
                .format(output_size, d, min_size, max_size))

    return output_size


def max_unpool1d(input, indices, kernel_size, stride=None, padding=0,
                 output_size=None):
    r"""Computes a partial inverse of :class:`MaxPool1d`.

    See :class:`~torch.nn.MaxUnpool1d` for details.
    """
    kernel_size = _single(kernel_size)
    stride = _single(stride or kernel_size)
    padding = _single(padding)
    output_size = _unpool_output_size(input, kernel_size, stride, padding,
                                      output_size)
    return torch._C._nn.max_unpool2d(input.unsqueeze(3), indices.unsqueeze(3), output_size + [1]).squeeze(3)


def max_unpool2d(input, indices, kernel_size, stride=None, padding=0,
                 output_size=None):
    r"""Computes a partial inverse of :class:`MaxPool2d`.

    See :class:`~torch.nn.MaxUnpool2d` for details.
    """
    kernel_size = _pair(kernel_size)
    stride = _pair(stride or kernel_size)
    padding = _pair(padding)
    output_size = _unpool_output_size(input, kernel_size, stride, padding,
                                      output_size)
    return torch._C._nn.max_unpool2d(input, indices, output_size)


def max_unpool3d(input, indices, kernel_size, stride=None, padding=0,
                 output_size=None):
    r"""Computes a partial inverse of :class:`MaxPool3d`.

    See :class:`~torch.nn.MaxUnpool3d` for details.
    """
    kernel_size = _triple(kernel_size)
    stride = _triple(stride or kernel_size)
    padding = _triple(padding)
    output_size = _unpool_output_size(input, kernel_size, stride, padding,
                                      output_size)
    return torch._C._nn.max_unpool3d(input, indices, output_size, stride, padding)


def lp_pool2d(input, norm_type, kernel_size, stride=None, ceil_mode=False):
    r"""Applies a 2D power-average pooling over an input signal composed of
    several input planes. If the sum of all inputs to the power of `p` is
    zero, the gradient is set to zero as well.

    See :class:`~torch.nn.LPPool2d` for details.
    """
    kw, kh = utils._pair(kernel_size)
    out = avg_pool2d(input.pow(norm_type), kernel_size, stride, 0, ceil_mode)
    return (torch.sign(out) * relu(torch.abs(out))).mul(kw * kh).pow(1. / norm_type)


def lp_pool1d(input, norm_type, kernel_size, stride=None, ceil_mode=False):
    r"""Applies a 1D power-average pooling over an input signal composed of
    several input planes. If the sum of all inputs to the power of `p` is
    zero, the gradient is set to zero as well.

    See :class:`~torch.nn.LPPool1d` for details.
    """
    out = avg_pool1d(input.pow(norm_type), kernel_size, stride, 0, ceil_mode)
    return (torch.sign(out) * relu(torch.abs(out))).mul(kernel_size).pow(1. / norm_type)


def adaptive_max_pool1d(input, output_size, return_indices=False):
    r"""Applies a 1D adaptive max pooling over an input signal composed of
    several input planes.

    See :class:`~torch.nn.AdaptiveMaxPool1d` for details and output shape.

    Args:
        output_size: the target output size (single integer)
        return_indices: whether to return pooling indices. Default: ``False``
    """
    ret = torch.adaptive_max_pool1d(input, output_size)
    return ret if return_indices else ret[0]


def adaptive_max_pool2d(input, output_size, return_indices=False):
    r"""Applies a 2D adaptive max pooling over an input signal composed of
    several input planes.

    See :class:`~torch.nn.AdaptiveMaxPool2d` for details and output shape.

    Args:
        output_size: the target output size (single integer or
            double-integer tuple)
        return_indices: whether to return pooling indices. Default: ``False``
    """
    output_size = _list_with_default(output_size, input.size())
    ret = torch._C._nn.adaptive_max_pool2d(input, output_size)
    return ret if return_indices else ret[0]


def adaptive_max_pool3d(input, output_size, return_indices=False):
    r"""Applies a 3D adaptive max pooling over an input signal composed of
    several input planes.

    See :class:`~torch.nn.AdaptiveMaxPool3d` for details and output shape.

    Args:
        output_size: the target output size (single integer or
            triple-integer tuple)
        return_indices: whether to return pooling indices. Default: ``False``
    """
    output_size = _list_with_default(output_size, input.size())
    ret = torch._C._nn.adaptive_max_pool3d(input, output_size)
    return ret if return_indices else ret[0]


adaptive_avg_pool1d = _add_docstr(torch.adaptive_avg_pool1d, r"""
adaptive_avg_pool1d(input, output_size) -> Tensor

Applies a 1D adaptive average pooling over an input signal composed of
several input planes.

See :class:`~torch.nn.AdaptiveAvgPool1d` for details and output shape.

Args:
    output_size: the target output size (single integer)
""")


def adaptive_avg_pool2d(input, output_size):
    r"""
    Applies a 2D adaptive average pooling over an input signal composed of
    several input planes.

    See :class:`~torch.nn.AdaptiveAvgPool2d` for details and output shape.

    Args:
        output_size: the target output size (single integer or
            double-integer tuple)
    """
    output_size = _list_with_default(output_size, input.size())
    return torch._C._nn.adaptive_avg_pool2d(input, output_size)


def adaptive_avg_pool3d(input, output_size):
    r"""
    Applies a 3D adaptive average pooling over an input signal composed of
    several input planes.

    See :class:`~torch.nn.AdaptiveAvgPool3d` for details and output shape.

    Args:
        output_size: the target output size (single integer or
            triple-integer tuple)
    """
    output_size = _list_with_default(output_size, input.size())
    return torch._C._nn.adaptive_avg_pool3d(input, output_size)


# Activation functions
def dropout(input, p=0.5, training=False, inplace=False):
    return _functions.dropout.Dropout.apply(input, p, training, inplace)


def alpha_dropout(input, p=0.5, training=False, inplace=False):
    r"""Applies alpha dropout to the input.

    See :class:`~torch.nn.AlphaDropout` for details.
    """
    return _functions.dropout.AlphaDropout.apply(input, p, training, inplace)


def dropout2d(input, p=0.5, training=False, inplace=False):
    return _functions.dropout.FeatureDropout.apply(input, p, training, inplace)


def dropout3d(input, p=0.5, training=False, inplace=False):
    return _functions.dropout.FeatureDropout.apply(input, p, training, inplace)


def feature_alpha_dropout(input, p=0.5, training=False, inplace=False):
    return _functions.dropout.FeatureAlphaDropout.apply(input, p, training, inplace)


def threshold(input, threshold, value, inplace=False):
    r"""Thresholds each element of the input Tensor.

    See :class:`~torch.nn.Threshold` for more details.
    """
    if inplace:
        return torch._C._nn.threshold_(input, threshold, value)
    return torch._C._nn.threshold(input, threshold, value)


threshold_ = _add_docstr(torch._C._nn.threshold_, r"""
threshold_(input, threshold, value) -> Tensor

In-place version of :func:`~threshold`.
""")


def relu(input, inplace=False):
    r"""relu(input, inplace=False) -> Tensor

    Applies the rectified linear unit function element-wise. See
    :class:`~torch.nn.ReLU` for more details.
    """
    if inplace:
        return torch.relu_(input)
    return torch.relu(input)


relu_ = _add_docstr(torch.relu_, r"""
relu_(input) -> Tensor

In-place version of :func:`~relu`.
""")


def glu(input, dim=-1):
    r"""
    glu(input, dim=-1) -> Tensor

    The gated linear unit. Computes:

    .. math ::

        H = A \times \sigma(B)

    where `input` is split in half along `dim` to form `A` and `B`.

    See `Language Modeling with Gated Convolutional Networks <https://arxiv.org/abs/1612.08083>`_.

    Args:
        input (Tensor): input tensor
        dim (int): dimension on which to split the input
    """
    if input.dim() == 0:
        raise RuntimeError("glu does not suppport scalars because halving size must be even")
    return torch._C._nn.glu(input, dim)


def hardtanh(input, min_val=-1., max_val=1., inplace=False):
    r"""
    hardtanh(input, min_val=-1., max_val=1., inplace=False) -> Tensor

    Applies the HardTanh function element-wise. See :class:`~torch.nn.Hardtanh` for more
    details.
    """
    if inplace:
        return torch._C._nn.hardtanh_(input, min_val, max_val)
    return torch._C._nn.hardtanh(input, min_val, max_val)


hardtanh_ = _add_docstr(torch._C._nn.hardtanh_, r"""
hardtanh_(input, min_val=-1., max_val=1.) -> Tensor

In-place version of :func:`~hardtanh`.
""")


def relu6(input, inplace=False):
    r"""relu6(input, inplace=False) -> Tensor

    Applies the element-wise function :math:`\text{ReLU6}(x) = \min(\max(0,x), 6)`.

    See :class:`~torch.nn.ReLU6` for more details.
    """
    return hardtanh(input, 0, 6, inplace)


def elu(input, alpha=1., inplace=False):
    r"""Applies element-wise,
    :math:`\text{ELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x) - 1))`.

    See :class:`~torch.nn.ELU` for more details.
    """
    if inplace:
        return torch._C._nn.elu_(input, alpha)
    return torch._C._nn.elu(input, alpha)


elu_ = _add_docstr(torch._C._nn.elu_, r"""
elu_(input, alpha=1.) -> Tensor

In-place version of :func:`~elu`.
""")


def selu(input, inplace=False):
    r"""selu(input, inplace=False) -> Tensor

    Applies element-wise,
    :math:`\text{SELU}(x) = scale * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1)))`,
    with :math:`\alpha=1.6732632423543772848170429916717` and
    :math:`scale=1.0507009873554804934193349852946`.

    See :class:`~torch.nn.SELU` for more details.
    """
    if inplace:
        return torch.selu_(input)
    return torch.selu(input)

selu_ = _add_docstr(torch.selu_, r"""
selu_(input) -> Tensor

In-place version of :func:`~selu`.
""")


def leaky_relu(input, negative_slope=0.01, inplace=False):
    r"""
    leaky_relu(input, negative_slope=0.01, inplace=False) -> Tensor

    Applies element-wise,
    :math:`\text{LeakyReLU}(x) = \max(0, x) + \text{negative_slope} * \min(0, x)`

    See :class:`~torch.nn.LeakyReLU` for more details.
    """
    if inplace:
        return torch._C._nn.leaky_relu_(input, negative_slope)
    return torch._C._nn.leaky_relu(input, negative_slope)


leaky_relu_ = _add_docstr(torch._C._nn.leaky_relu_, r"""
leaky_relu_(input, negative_slope=0.01) -> Tensor

In-place version of :func:`~leaky_relu`.
""")


prelu = _add_docstr(torch._C._nn.prelu, r"""
prelu(input, weight) -> Tensor

Applies element-wise the function
:math:`\text{PReLU}(x) = \max(0,x) + \text{weight} * \min(0,x)` where weight is a
learnable parameter.

See :class:`~torch.nn.PReLU` for more details.
""")


def rrelu(input, lower=1. / 8, upper=1. / 3, training=False, inplace=False):
    r"""rrelu(input, lower=1./8, upper=1./3, training=False, inplace=False) -> Tensor

    Randomized leaky ReLU.

    See :class:`~torch.nn.RReLU` for more details.
    """
    if inplace:
        return torch.rrelu_(input, lower, upper, training)
    return torch.rrelu(input, lower, upper, training)


rrelu_ = _add_docstr(torch.rrelu_, r"""
rrelu_(input, lower=1./8, upper=1./3, training=False) -> Tensor

In-place version of :func:`~rrelu`.
""")

logsigmoid = _add_docstr(torch._C._nn.log_sigmoid, r"""
logsigmoid(input) -> Tensor

Applies element-wise :math:`\text{LogSigmoid}(x) = \log \left(\frac{1}{1 + \exp(-x_i)}\right)`

See :class:`~torch.nn.LogSigmoid` for more details.
""")


def hardshrink(input, lambd=0.5):
    r"""
    hardshrink(input, lambd=0.5) -> Tensor

    Applies the hard shrinkage function element-wise

    See :class:`~torch.nn.Hardshrink` for more details.
    """
    return torch.hardshrink(input, lambd)


def tanhshrink(input):
    r"""tanhshrink(input) -> Tensor

    Applies element-wise, :math:`\text{Tanhshrink}(x) = x - \text{Tanh}(x)`

    See :class:`~torch.nn.Tanhshrink` for more details.
    """
    return input - input.tanh()


def softsign(input):
    r"""softsign(input) -> Tensor

    Applies element-wise, the function :math:`\text{SoftSign}(x) = \frac{x}{1 + |x|}`

    See :class:`~torch.nn.Softsign` for more details.
    """
    return input / (input.abs() + 1)


softplus = _add_docstr(torch._C._nn.softplus, r"""
softplus(input, beta=1, threshold=20) -> Tensor
""")


def _get_softmax_dim(name, ndim, stacklevel):
    warnings.warn("Implicit dimension choice for " + name + " has been deprecated. "
                  "Change the call to include dim=X as an argument.", stacklevel=stacklevel)
    if ndim == 0 or ndim == 1 or ndim == 3:
        return 0
    else:
        return 1


def softmin(input, dim=None, _stacklevel=3):
    r"""Applies a softmin function.

    Note that :math:`\text{Softmin}(x) = \text{Softmax}(-x)`. See softmax definition for mathematical formula.

    See :class:`~torch.nn.Softmin` for more details.

    Arguments:
        input (Tensor): input
        dim (int): A dimension along which softmin will be computed (so every slice
            along dim will sum to 1).
    """
    if dim is None:
        dim = _get_softmax_dim('softmin', input.dim(), _stacklevel)
    return -input.softmax(dim)


def softmax(input, dim=None, _stacklevel=3):
    r"""Applies a softmax function.

    Softmax is defined as:

    :math:`\text{Softmax}(x_{i}) = \frac{exp(x_i)}{\sum_j exp(x_j)}`

    It is applied to all slices along dim, and will re-scale them so that the elements
    lie in the range `(0, 1)` and sum to 1.

    See :class:`~torch.nn.Softmax` for more details.

    Arguments:
        input (Tensor): input
        dim (int): A dimension along which softmax will be computed.

    .. note::
        This function doesn't work directly with NLLLoss,
        which expects the Log to be computed between the Softmax and itself.
        Use log_softmax instead (it's faster and has better numerical properties).

    """
    if dim is None:
        dim = _get_softmax_dim('softmax', input.dim(), _stacklevel)
    return input.softmax(dim)


def _sample_gumbel(shape, eps=1e-10, out=None):
    """
    Sample from Gumbel(0, 1)

    based on
    https://github.com/ericjang/gumbel-softmax/blob/3c8584924603869e90ca74ac20a6a03d99a91ef9/Categorical%20VAE.ipynb ,
    (MIT license)
    """
    U = out.resize_(shape).uniform_() if out is not None else torch.rand(shape)
    return - torch.log(eps - torch.log(U + eps))


def _gumbel_softmax_sample(logits, tau=1, eps=1e-10):
    """
    Draw a sample from the Gumbel-Softmax distribution

    based on
    https://github.com/ericjang/gumbel-softmax/blob/3c8584924603869e90ca74ac20a6a03d99a91ef9/Categorical%20VAE.ipynb
    (MIT license)
    """
    dims = logits.dim()
    gumbel_noise = _sample_gumbel(logits.size(), eps=eps, out=logits.data.new())
    y = logits + gumbel_noise
    return softmax(y / tau, dims - 1)


def gumbel_softmax(logits, tau=1, hard=False, eps=1e-10):
    r"""
    Sample from the Gumbel-Softmax distribution and optionally discretize.

    Args:
      logits: `[batch_size, num_features]` unnormalized log probabilities
      tau: non-negative scalar temperature
      hard: if ``True``, the returned samples will be discretized as one-hot vectors,
            but will be differentiated as if it is the soft sample in autograd

    Returns:
      Sampled tensor of shape ``batch_size x num_features`` from the Gumbel-Softmax distribution.
      If ``hard=True``, the returned samples will be one-hot, otherwise they will
      be probability distributions that sum to 1 across features

    Constraints:

    - Currently only work on 2D input :attr:`logits` tensor of shape ``batch_size x num_features``

    Based on
    https://github.com/ericjang/gumbel-softmax/blob/3c8584924603869e90ca74ac20a6a03d99a91ef9/Categorical%20VAE.ipynb ,
    (MIT license)
    """
    shape = logits.size()
    assert len(shape) == 2
    y_soft = _gumbel_softmax_sample(logits, tau=tau, eps=eps)
    if hard:
        _, k = y_soft.max(-1)
        # this bit is based on
        # https://discuss.pytorch.org/t/stop-gradients-for-st-gumbel-softmax/530/5
        y_hard = logits.new_zeros(*shape).scatter_(-1, k.view(-1, 1), 1.0)
        # this cool bit of code achieves two things:
        # - makes the output value exactly one-hot (since we add then
        #   subtract y_soft value)
        # - makes the gradient equal to y_soft gradient (since we strip
        #   all other gradients)
        y = y_hard - y_soft.detach() + y_soft
    else:
        y = y_soft
    return y


def log_softmax(input, dim=None, _stacklevel=3):
    r"""Applies a softmax followed by a logarithm.

    While mathematically equivalent to log(softmax(x)), doing these two
    operations separately is slower, and numerically unstable. This function
    uses an alternative formulation to compute the output and gradient correctly.

    See :class:`~torch.nn.LogSoftmax` for more details.

    Arguments:
        input (Tensor): input
        dim (int): A dimension along which log_softmax will be computed.
    """
    if dim is None:
        dim = _get_softmax_dim('log_softmax', input.dim(), _stacklevel)
    return input.log_softmax(dim)


softshrink = _add_docstr(torch._C._nn.softshrink, r"""
softshrink(input, lambd=0.5) -> Tensor

Applies the soft shrinkage function elementwise

See :class:`~torch.nn.Softshrink` for more details.
""")


def tanh(input):
    r"""tanh(input) -> Tensor

    Applies element-wise,
    :math:`\text{Tanh}(x) = \tanh(x) = \frac{\exp(x) - \exp(-x)}{\exp(x) + \exp(-x)}`

    See :class:`~torch.nn.Tanh` for more details.
    """
    warnings.warn("nn.functional.tanh is deprecated. Use torch.tanh instead.")
    return input.tanh()


def sigmoid(input):
    r"""sigmoid(input) -> Tensor

    Applies the element-wise function :math:`\text{Sigmoid}(x) = \frac{1}{1 + \exp(-x)}`

    See :class:`~torch.nn.Sigmoid` for more details.
    """
    warnings.warn("nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.")
    return input.sigmoid()


def linear(input, weight, bias=None):
    r"""
    Applies a linear transformation to the incoming data: :math:`y = xA^T + b`.

    Shape:

        - Input: :math:`(N, *, in\_features)` where `*` means any number of
          additional dimensions
        - Weight: :math:`(out\_features, in\_features)`
        - Bias: :math:`(out\_features)`
        - Output: :math:`(N, *, out\_features)`
    """
    if input.dim() == 2 and bias is not None:
        # fused op is marginally faster
        return torch.addmm(bias, input, weight.t())

    output = input.matmul(weight.t())
    if bias is not None:
        output += bias
    return output


def bilinear(input1, input2, weight, bias=None):
    return torch.bilinear(input1, input2, weight, bias)


def embedding(input, weight, padding_idx=None, max_norm=None, norm_type=2,
              scale_grad_by_freq=False, sparse=False):
    r"""A simple lookup table that looks up embeddings in a fixed dictionary and size.

    This module is often used to retrieve word embeddings using indices.
    The input to the module is a list of indices, and the embedding matrix,
    and the output is the corresponding word embeddings.

    See :class:`torch.nn.Embedding` for more details.

    Args:
        input (LongTensor): Tensor containing indices into the embedding matrix
        weight (Tensor): The embedding matrix
            Number of rows should correspond to the maximum possible index + 1,
            number of columns is the embedding size
        padding_idx (int, optional): If given, pads the output with the embedding vector at :attr:`padding_idx`
                                         (initialized to zeros) whenever it encounters the index.
        max_norm (float, optional): If given, will renormalize the embedding vectors to have a norm lesser than
                                    this before extracting. Note: this will modify :attr:`weight` in-place.
        norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default ``2``.
        scale_grad_by_freq (boolean, optional): if given, this will scale gradients by the inverse of frequency of
                                                the words in the mini-batch. Default ``False``.
        sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` will be a sparse tensor. See Notes under
                                 :class:`torch.nn.Embedding` for more details regarding sparse gradients.

    Shape:
        - Input: LongTensor of arbitrary shape containing the indices to extract
        - Weight: Embedding matrix of floating point type with shape `(V, embedding_dim)`,
                            where V = maximum index + 1 and embedding_dim = the embedding size
        - Output: `(*, embedding_dim)`, where `*` is the input shape

    Examples::

        >>> # a batch of 2 samples of 4 indices each
        >>> input = torch.tensor([[1,2,4,5],[4,3,2,9]])
        >>> # an embedding matrix containing 10 tensors of size 3
        >>> embedding_matrix = torch.rand(10, 3)
        >>> F.embedding(input, embedding_matrix)
        tensor([[[ 0.8490,  0.9625,  0.6753],
                 [ 0.9666,  0.7761,  0.6108],
                 [ 0.6246,  0.9751,  0.3618],
                 [ 0.4161,  0.2419,  0.7383]],

                [[ 0.6246,  0.9751,  0.3618],
                 [ 0.0237,  0.7794,  0.0528],
                 [ 0.9666,  0.7761,  0.6108],
                 [ 0.3385,  0.8612,  0.1867]]])

        >>> # example with padding_idx
        >>> weights = torch.rand(10, 3)
        >>> weights[0, :].zero_()
        >>> embedding_matrix = weights
        >>> input = torch.tensor([[0,2,0,5]])
        >>> F.embedding(input, embedding_matrix, padding_idx=0)
        tensor([[[ 0.0000,  0.0000,  0.0000],
                 [ 0.5609,  0.5384,  0.8720],
                 [ 0.0000,  0.0000,  0.0000],
                 [ 0.6262,  0.2438,  0.7471]]])
    """
    if padding_idx is not None:
        if padding_idx > 0:
            assert padding_idx < weight.size(0), 'Padding_idx must be within num_embeddings'
        elif padding_idx < 0:
            assert padding_idx >= -weight.size(0), 'Padding_idx must be within num_embeddings'
            padding_idx = weight.size(0) + padding_idx
    elif padding_idx is None:
            padding_idx = -1
    if max_norm is not None:
        # `embedding_renorm_` will call .contiguous() on input anyways, so we
        # call it here and take advantage of the improved locality in the
        # `embedding` call below too.
        input = input.contiguous()
        with torch.no_grad():
            torch.embedding_renorm_(weight, input, max_norm, norm_type)
    return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)


def embedding_bag(input, weight, offsets=None, max_norm=None, norm_type=2,
                  scale_grad_by_freq=False, mode='mean', sparse=False):
    r"""Computes sums or means of 'bags' of embeddings, without instantiating the
    intermediate embeddings.

    See :class:`torch.nn.EmbeddingBag` for more details.

    Args:
        input (LongTensor): Tensor containing bags of indices into the embedding matrix
        weight (Tensor): The embedding matrix
            Number of rows should correspond to the maximum possible index + 1,
            number of columns is the embedding size
        offsets (LongTensor, optional): Only used when :attr:`input` is 1D. :attr:`offsets` determines
                             the starting index position of each bag (sequence) in :attr:`input`.
        max_norm (float, optional): If given, will renormalize the embedding vectors to have a norm lesser than
                                    this before extracting. Note: this will modify :attr:`weight` in-place.
        norm_type (float, optional): The ``p`` in the ``p``-norm to compute for the max_norm option. Default ``2``.
        scale_grad_by_freq (boolean, optional): if given, this will scale gradients by the inverse of frequency of
                                                the words in the mini-batch. Default ``False``.
                                                Note: this option is not supported when ``mode="max"``.
        mode (string, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag.
                                 Default: ``"mean"``
        sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` will be a sparse tensor. See Notes under
                                 :class:`torch.nn.Embedding` for more details regarding sparse gradients.
                                 Note: this option is not supported when ``mode="max"``.

    Shape:

        - :attr:`input` (LongTensor) and :attr:`offsets` (LongTensor, optional)

          - If :attr:`input` is 2D of shape ``B x N``,

            it will be treated as ``B`` bags (sequences) each of fixed length ``N``, and
            this will return ``B`` values aggregated in a way depending on the :attr:`mode`.
            :attr:`offsets` is ignored and required to be ``None`` in this case.

          - If :attr:`input` is 1D of shape ``N``,

            it will be treated as a concatenation of multiple bags (sequences).
            :attr:`offsets` is required to be a 1D tensor containing the
            starting index positions of each bag in :attr:`input`. Therefore,
            for :attr:`offsets` of shape ``B``, :attr:`input` will be viewed as
            having ``B`` bags. Empty bags (i.e., having 0-length) will have
            returned vectors filled by zeros.

        - :attr:`weight` (Tensor): the learnable weights of the module of
          shape ``(num_embeddings x embedding_dim)``

        - :attr:`output`: aggregated embedding values of shape ``B x embedding_dim``

    Examples::

        >>> # an Embedding module containing 10 tensors of size 3
        >>> embedding_matrix = torch.rand(10, 3)
        >>> # a batch of 2 samples of 4 indices each
        >>> input = torch.tensor([1,2,4,5,4,3,2,9])
        >>> offsets = torch.tensor([0,4])
        >>> F.embedding_bag(embedding_matrix, input, offsets)
        tensor([[ 0.3397,  0.3552,  0.5545],
                [ 0.5893,  0.4386,  0.5882]])
    """
    # Check for backward compatibility.
    # Used to be embedding_bag(weight, input, ...)
    # Now is     embedding_bag(input, weight, ...)
    if weight.dtype == torch.long and input.is_floating_point():
        warnings.warn("Argument order of nn.functional.embedding_bag was changed. "
                      "Usage `embedding_bag(weight, input, ...)` is deprecated, "
                      "and should now be `embedding_bag(input, weight, ...)`.")
        weight, input = input, weight

    if input.dim() == 2:
        if offsets is not None:
            raise ValueError("if input is 2D, then offsets has to be None"
                             ", as input is treated is a mini-batch of"
                             " fixed length sequences. However, found "
                             "offsets of type {}".format(type(offsets)))
        else:
            offsets = torch.arange(0, input.numel(), input.size(1),
                                   dtype=torch.long, device=input.device)

            input = input.reshape(-1)
    elif input.dim() == 1:
        if offsets is None:
            raise ValueError("offsets has to be a 1D Tensor but got None")
        if offsets.dim() != 1:
            raise ValueError("offsets has to be a 1D Tensor")
        if offsets[0].item() != 0:
            raise ValueError("offsets[0] has to be 0, i.e., the first sequence "
                             "in the mini-batch has to start from position 0. "
                             "However, got {}".format(offsets[0].item()))
        if offsets[-1].item() > input.size(0):
            raise ValueError("offsets[-1] can not be greater than input's length"
                             " ({}), but got offsets[-1] of {}"
                             .format(input.size(0), offsets[-1].item()))
    else:
        raise ValueError("input has to be 1D or 2D Tensor,"
                         " but got Tensor of dimension {}".format(input.dim()))

    if mode == 'sum':
        mode = 0
    elif mode == 'mean':
        mode = 1
    elif mode == 'max':
        mode = 2

        if scale_grad_by_freq:
            raise ValueError("max mode does not support scaling the gradient by the frequency")

        if sparse:
            raise ValueError("max mode does not support sparse weights")

    else:
        raise ValueError("mode has to be one of sum or mean")

    if max_norm is not None:
        with torch.no_grad():
            torch.embedding_renorm_(weight, input, max_norm, norm_type)

    ret, _, _, _ = torch.embedding_bag(
        weight,
        input,
        offsets,
        scale_grad_by_freq,
        mode,
        sparse)
    return ret


def batch_norm(input, running_mean, running_var, weight=None, bias=None,
               training=False, momentum=0.1, eps=1e-5):
    r"""Applies Batch Normalization for each channel across a batch of data.

    See :class:`~torch.nn.BatchNorm1d`, :class:`~torch.nn.BatchNorm2d`,
    :class:`~torch.nn.BatchNorm3d` for details.
    """
    if training:
        size = list(input.size())
        if reduce(mul, size[2:], size[0]) == 1:
            raise ValueError('Expected more than 1 value per channel when training, got input size {}'.format(size))
    return torch.batch_norm(
        input, weight, bias, running_mean, running_var,
        training, momentum, eps, torch.backends.cudnn.enabled
    )


def instance_norm(input, running_mean=None, running_var=None, weight=None,
                  bias=None, use_input_stats=True, momentum=0.1, eps=1e-5):
    r"""Applies Instance Normalization for each channel in each data sample in a
    batch.

    See :class:`~torch.nn.InstanceNorm1d`, :class:`~torch.nn.InstanceNorm2d`,
    :class:`~torch.nn.InstanceNorm3d` for details.
    """
    if not use_input_stats and (running_mean is None or running_var is None):
        raise ValueError('Expected running_mean and running_var to be not None when use_input_stats=False')

    b, c = input.size(0), input.size(1)
    if weight is not None:
        weight = weight.repeat(b)
    if bias is not None:
        bias = bias.repeat(b)

    import torch.onnx.symbolic

    @torch.onnx.symbolic_override(torch.onnx.symbolic.instance_norm)
    def _instance_norm(input, running_mean=None, running_var=None, weight=None,
                       bias=None, use_input_stats=None, momentum=None, eps=None):
        # Repeat stored stats and affine transform params if necessary
        if running_mean is not None:
            running_mean_orig = running_mean
            running_mean = running_mean_orig.repeat(b)
        if running_var is not None:
            running_var_orig = running_var
            running_var = running_var_orig.repeat(b)

        # Apply instance norm
        input_reshaped = input.contiguous().view(1, b * c, *input.size()[2:])

        out = batch_norm(
            input_reshaped, running_mean, running_var, weight=weight, bias=bias,
            training=use_input_stats, momentum=momentum, eps=eps)

        # Reshape and copy back
        if running_mean is not None:
            running_mean_orig.copy_(running_mean.view(b, c).mean(0, keepdim=False))
        if running_var is not None:
            running_var_orig.copy_(running_var.view(b, c).mean(0, keepdim=False))

        return out.view(b, c, *input.size()[2:])
    return _instance_norm(input, running_mean=running_mean,
                          running_var=running_var, weight=weight, bias=bias,
                          use_input_stats=use_input_stats, momentum=momentum,
                          eps=eps)


def layer_norm(input, normalized_shape, weight=None, bias=None, eps=1e-5):
    r"""Applies Layer Normalization for last certain number of dimensions.

    See :class:`~torch.nn.LayerNorm` for details.
    """
    return torch.layer_norm(input, normalized_shape, weight, bias, eps,
                            torch.backends.cudnn.enabled)


def group_norm(input, num_groups, weight=None, bias=None, eps=1e-5):
    r"""Applies Group Normalization for last certain number of dimensions.

    See :class:`~torch.nn.GroupNorm` for details.
    """
    return torch.group_norm(input, num_groups, weight, bias, eps,
                            torch.backends.cudnn.enabled)


def local_response_norm(input, size, alpha=1e-4, beta=0.75, k=1):
    r"""Applies local response normalization over an input signal composed of
    several input planes, where channels occupy the second dimension.
    Applies normalization across channels.

    See :class:`~torch.nn.LocalResponseNorm` for details.
    """
    dim = input.dim()
    if dim < 3:
        raise ValueError('Expected 3D or higher dimensionality \
                         input (got {} dimensions)'.format(dim))
    div = input.mul(input).unsqueeze(1)
    if dim == 3:
        div = pad(div, (0, 0, size // 2, (size - 1) // 2))
        div = avg_pool2d(div, (size, 1), stride=1).squeeze(1)
    else:
        sizes = input.size()
        div = div.view(sizes[0], 1, sizes[1], sizes[2], -1)
        div = pad(div, (0, 0, 0, 0, size // 2, (size - 1) // 2))
        div = avg_pool3d(div, (size, 1, 1), stride=1).squeeze(1)
        div = div.view(sizes)
    div = div.mul(alpha).add(k).pow(beta)
    return input / div


# loss


def nll_loss(input, target, weight=None, size_average=None, ignore_index=-100,
             reduce=None, reduction='elementwise_mean'):
    r"""The negative log likelihood loss.

    See :class:`~torch.nn.NLLLoss` for details.

    Args:
        input: :math:`(N, C)` where `C = number of classes` or :math:`(N, C, H, W)`
            in case of 2D Loss, or :math:`(N, C, d_1, d_2, ..., d_K)` where :math:`K > 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)` where :math:`K \geq 1` for
            K-dimensional loss.
        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 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. Default: -100
        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' | 'elementwise_mean' | 'sum'. 'none': no reduction will be applied,
            'elementwise_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: 'elementwise_mean'

    Example::

        >>> # 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 = F.nll_loss(F.log_softmax(input), target)
        >>> output.backward()
    """
    if size_average is not None or reduce is not None:
        reduction = _Reduction.legacy_get_string(size_average, reduce)
    dim = input.dim()
    if dim < 2:
        raise ValueError('Expected 2 or more dimensions (got {})'.format(dim))

    if input.size(0) != target.size(0):
        raise ValueError('Expected input batch_size ({}) to match target batch_size ({}).'
                         .format(input.size(0), target.size(0)))
    if dim == 2:
        return torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
    elif dim == 4:
        return torch._C._nn.nll_loss2d(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
    elif dim == 3 or dim > 4:
        n = input.size(0)
        c = input.size(1)
        out_size = (n,) + input.size()[2:]
        if target.size()[1:] != input.size()[2:]:
            raise ValueError('Expected target size {}, got {}'.format(
                out_size, target.size()))
        input = input.contiguous().view(n, c, 1, -1)
        target = target.contiguous().view(n, 1, -1)
        if reduction is not 'none':
            return torch._C._nn.nll_loss2d(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
        out = torch._C._nn.nll_loss2d(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
        return out.view(out_size)


def poisson_nll_loss(input, target, log_input=True, full=False, size_average=None, eps=1e-8,
                     reduce=None, reduction='elementwise_mean'):
    r"""Poisson negative log likelihood loss.

    See :class:`~torch.nn.PoissonNLLLoss` for details.

    Args:
        input: expectation of underlying Poisson distribution.
        target: random sample :math:`target \sim \text{Poisson}(input)`.
        log_input: if ``True`` the loss is computed as
            :math:`\exp(\text{input}) - \text{target} * \text{input}`, if ``False`` then loss is
            :math:`\text{input} - \text{target} * \log(\text{input}+\text{eps})`. Default: ``True``
        full: whether to compute full loss, i. e. to add the Stirling
            approximation term. Default: ``False``
            :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 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' | 'elementwise_mean' | 'sum'. 'none': no reduction will be applied,
            'elementwise_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: 'elementwise_mean'

    """
    if size_average is not None or reduce is not None:
        reduction = _Reduction.legacy_get_string(size_average, reduce)
    if log_input:
        loss = torch.exp(input) - target * input
    else:
        loss = input - target * torch.log(input + eps)
    if full:
        mask = target > 1
        loss[mask] += (target * torch.log(target) - target + 0.5 * torch.log(2 * math.pi * target))[mask]
    if reduction is 'none':
        return loss
    if reduction is 'elementwise_mean':
        return torch.mean(loss)
    return torch.sum(loss)


def kl_div(input, target, size_average=None, reduce=None, reduction='elementwise_mean'):
    r"""The `Kullback-Leibler divergence`_ Loss.

    See :class:`~torch.nn.KLDivLoss` for details.

    Args:
        input: Tensor of arbitrary shape
        target: Tensor of the same shape as input
        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 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' | 'elementwise_mean' | 'sum'. 'none': no reduction will be applied,
            'elementwise_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: 'elementwise_mean'
    """
    if size_average is not None or reduce is not None:
        reduction = _Reduction.legacy_get_enum(size_average, reduce)
    else:
        reduction = _Reduction.get_enum(reduction)
    return torch._C._nn.kl_div(input, target, reduction)


def cross_entropy(input, target, weight=None, size_average=None, ignore_index=-100,
                  reduce=None, reduction='elementwise_mean'):
    r"""This criterion combines `log_softmax` and `nll_loss` in a single
    function.

    See :class:`~torch.nn.CrossEntropyLoss` for details.

    Args:
        input (Tensor) : :math:`(N, C)` where `C = number of classes` or :math:`(N, C, H, W)`
            in case of 2D Loss, or :math:`(N, C, d_1, d_2, ..., d_K)` where :math:`K > 1`
            in the case of K-dimensional loss.
        target (Tensor) : :math:`(N)` where each value is :math:`0 \leq \text{targets}[i] \leq C-1`,
            or :math:`(N, d_1, d_2, ..., d_K)` where :math:`K \geq 1` for
            K-dimensional loss.
        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 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. Default: -100
        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' | 'elementwise_mean' | 'sum'. 'none': no reduction will be applied,
            'elementwise_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: 'elementwise_mean'

    Examples::

        >>> input = torch.randn(3, 5, requires_grad=True)
        >>> target = torch.randint(5, (3,), dtype=torch.int64)
        >>> loss = F.cross_entropy(input, target)
        >>> loss.backward()
    """
    if size_average is not None or reduce is not None:
        reduction = _Reduction.legacy_get_string(size_average, reduce)
    return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)


def binary_cross_entropy(input, target, weight=None, size_average=None,
                         reduce=None, reduction='elementwise_mean'):
    r"""Function that measures the Binary Cross Entropy
    between the target and the output.

    See :class:`~torch.nn.BCELoss` for details.

    Args:
        input: Tensor of arbitrary shape
        target: Tensor of the same shape as input
        weight (Tensor, optional): a manual rescaling weight
                if provided it's repeated to match input tensor shape
        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 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' | 'elementwise_mean' | 'sum'. 'none': no reduction will be applied,
            'elementwise_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: 'elementwise_mean'

    Examples::

        >>> input = torch.randn((3, 2), requires_grad=True)
        >>> target = torch.rand((3, 2), requires_grad=False)
        >>> loss = F.binary_cross_entropy(F.sigmoid(input), target)
        >>> loss.backward()
    """
    if size_average is not None or reduce is not None:
        reduction = _Reduction.legacy_get_enum(size_average, reduce)
    else:
        reduction = _Reduction.get_enum(reduction)
    if not (target.size() == input.size()):
        warnings.warn("Using a target size ({}) that is different to the input size ({}) is deprecated. "
                      "Please ensure they have the same size.".format(target.size(), input.size()))
    if input.nelement() != target.nelement():
        raise ValueError("Target and input must have the same number of elements. target nelement ({}) "
                         "!= input nelement ({})".format(target.nelement(), input.nelement()))

    if weight is not None:
        new_size = _infer_size(target.size(), weight.size())
        weight = weight.expand(new_size)

    return torch._C._nn.binary_cross_entropy(input, target, weight, reduction)


def binary_cross_entropy_with_logits(input, target, weight=None, size_average=None,
                                     reduce=None, reduction='elementwise_mean', pos_weight=None):
    r"""Function that measures Binary Cross Entropy between target and output
    logits.

    See :class:`~torch.nn.BCEWithLogitsLoss` for details.

    Args:
        input: Tensor of arbitrary shape
        target: Tensor of the same shape as input
        weight (Tensor, optional): a manual rescaling weight
            if provided it's repeated to match input tensor shape
        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 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' | 'elementwise_mean' | 'sum'. 'none': no reduction will be applied,
            'elementwise_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: 'elementwise_mean'
        pos_weight (Tensor, optional): a weight of positive examples.
                Must be a vector with length equal to the number of classes.

    Examples::

         >>> input = torch.randn(3, requires_grad=True)
         >>> target = torch.empty(3).random_(2)
         >>> loss = F.binary_cross_entropy_with_logits(input, target)
         >>> loss.backward()
    """
    if size_average is not None or reduce is not None:
        reduction = _Reduction.legacy_get_string(size_average, reduce)
    if not (target.size() == input.size()):
        raise ValueError("Target size ({}) must be the same as input size ({})".format(target.size(), input.size()))

    max_val = (-input).clamp(min=0)

    if pos_weight is None:
        loss = input - input * target + max_val + ((-max_val).exp() + (-input - max_val).exp()).log()
    else:
        log_weight = 1 + (pos_weight - 1) * target
        loss = input - input * target + log_weight * (max_val + ((-max_val).exp() + (-input - max_val).exp()).log())

    if weight is not None:
        loss = loss * weight

    if reduction == 'none':
        return loss
    elif reduction == 'elementwise_mean':
        return loss.mean()
    else:
        return loss.sum()


def _pointwise_loss(lambd, lambd_optimized, input, target, reduction='elementwise_mean'):
    if target.requires_grad:
        d = lambd(input, target)
        if reduction == 'none':
            return d
        return torch.mean(d) if reduction == 'elementwise_mean' else torch.sum(d)
    else:
        return lambd_optimized(input, target, reduction)


def smooth_l1_loss(input, target, size_average=None, reduce=None, reduction='elementwise_mean'):
    r"""Function that uses a squared term if the absolute
    element-wise error falls below 1 and an L1 term otherwise.

    See :class:`~torch.nn.SmoothL1Loss` for details.
    """
    if size_average is not None or reduce is not None:
        reduction = _Reduction.legacy_get_enum(size_average, reduce)
    else:
        reduction = _Reduction.get_enum(reduction)
    return torch._C._nn.smooth_l1_loss(input, target, reduction)


def l1_loss(input, target, size_average=None, reduce=None, reduction='elementwise_mean'):
    r"""l1_loss(input, target, size_average=None, reduce=None, reduction='elementwise_mean') -> Tensor

    Function that takes the mean element-wise absolute value difference.

    See :class:`~torch.nn.L1Loss` for details.
    """
    if size_average is not None or reduce is not None:
        reduction = _Reduction.legacy_get_enum(size_average, reduce)
    else:
        reduction = _Reduction.get_enum(reduction)
    return _pointwise_loss(lambda a, b: torch.abs(a - b), torch._C._nn.l1_loss,
                           input, target, reduction)


def mse_loss(input, target, size_average=None, reduce=None, reduction='elementwise_mean'):
    r"""mse_loss(input, target, size_average=None, reduce=None, reduction='elementwise_mean') -> Tensor

    Measures the element-wise mean squared error.

    See :class:`~torch.nn.MSELoss` for details.
    """
    if size_average is not None or reduce is not None:
        reduction = _Reduction.legacy_get_enum(size_average, reduce)
    else:
        reduction = _Reduction.get_enum(reduction)
    return _pointwise_loss(lambda a, b: (a - b) ** 2, torch._C._nn.mse_loss, input, target, reduction)


def margin_ranking_loss(input1, input2, target, margin=0, size_average=None,
                        reduce=None, reduction='elementwise_mean'):
    r"""margin_ranking_loss(input1, input2, target, margin=0, size_average=None, reduce=None, reduction='elementwise_mean') -> Tensor

    See :class:`~torch.nn.MarginRankingLoss` for details.
    """  # noqa
    if size_average is not None or reduce is not None:
        reduction = _Reduction.legacy_get_enum(size_average, reduce)
    else:
        reduction = _Reduction.get_enum(reduction)
    if input1.dim() == 0 or input2.dim() == 0 or target.dim() == 0:
        raise RuntimeError(("margin_ranking_loss does not support scalars, got sizes: "
                            "input1: {}, input2: {}, target: {} ".format(input1.size(), input2.size(), target.size())))
    return torch.margin_ranking_loss(input1, input2, target, margin, reduction)


def hinge_embedding_loss(input, target, margin=1.0, size_average=None,
                         reduce=None, reduction='elementwise_mean'):
    r"""hinge_embedding_loss(input, target, margin=1.0, size_average=None, reduce=None, reduction='elementwise_mean') -> Tensor

    See :class:`~torch.nn.HingeEmbeddingLoss` for details.
    """  # noqa
    if size_average is not None or reduce is not None:
        reduction = _Reduction.legacy_get_enum(size_average, reduce)
    else:
        reduction = _Reduction.get_enum(reduction)
    return torch.hinge_embedding_loss(input, target, margin, reduction)


def multilabel_margin_loss(input, target, size_average=None, reduce=None, reduction='elementwise_mean'):
    r"""multilabel_margin_loss(input, target, size_average=None, reduce=None, reduction='elementwise_mean') -> Tensor

    See :class:`~torch.nn.MultiLabelMarginLoss` for details.
    """
    if size_average is not None or reduce is not None:
        reduction = _Reduction.legacy_get_enum(size_average, reduce)
    else:
        reduction = _Reduction.get_enum(reduction)
    return torch._C._nn.multilabel_margin_loss(input, target, reduction)


def soft_margin_loss(input, target, size_average=None, reduce=None, reduction='elementwise_mean'):
    r"""soft_margin_loss(input, target, size_average=None, reduce=None, reduction='elementwise_mean') -> Tensor

    See :class:`~torch.nn.SoftMarginLoss` for details.
    """
    if size_average is not None or reduce is not None:
        reduction = _Reduction.legacy_get_enum(size_average, reduce)
    else:
        reduction = _Reduction.get_enum(reduction)
    return torch._C._nn.soft_margin_loss(input, target, reduction)


def multilabel_soft_margin_loss(input, target, weight=None, size_average=None,
                                reduce=None, reduction='elementwise_mean'):
    r"""multilabel_soft_margin_loss(input, target, weight=None, size_average=None) -> Tensor

    See :class:`~torch.nn.MultiLabelSoftMarginLoss` for details.
    """
    if size_average is not None or reduce is not None:
        reduction = _Reduction.legacy_get_string(size_average, reduce)
    input = torch.sigmoid(input)
    return binary_cross_entropy(input, target, weight, None, None, reduction)


def cosine_embedding_loss(input1, input2, target, margin=0, size_average=None,
                          reduce=None, reduction='elementwise_mean'):
    r"""cosine_embedding_loss(input1, input2, target, margin=0, size_average=None, reduce=None, reduction='elementwise_mean') -> Tensor

    See :class:`~torch.nn.CosineEmbeddingLoss` for details.
    """  # noqa
    if size_average is not None or reduce is not None:
        reduction = _Reduction.legacy_get_enum(size_average, reduce)
    else:
        reduction = _Reduction.get_enum(reduction)
    return torch.cosine_embedding_loss(input1, input2, target, margin, reduction)


def multi_margin_loss(input, target, p=1, margin=1, weight=None, size_average=None,
                      reduce=None, reduction='elementwise_mean'):
    r"""multi_margin_loss(input, target, p=1, margin=1, weight=None, size_average=None,
                          reduce=None, reduction='elementwise_mean') -> Tensor

    See :class:`~torch.nn.MultiMarginLoss` for details.
    """
    if size_average is not None or reduce is not None:
        reduction = _Reduction.legacy_get_enum(size_average, reduce)
    else:
        reduction = _Reduction.get_enum(reduction)
    if p != 1 and p != 2:
        raise ValueError('only p == 1 and p == 2 supported')
    if weight is not None and weight.dim() != 1:
        raise ValueError('weight must be one-dimensional')

    return torch._C._nn.multi_margin_loss(input, target, p, margin, weight, reduction)


def pixel_shuffle(input, upscale_factor):
    r"""Rearranges elements in a tensor of shape :math:`[*, C*r^2, H, W]` to a
    tensor of shape :math:`[C, H*r, W*r]`.

    See :class:`~torch.nn.PixelShuffle` for details.

    Args:
        input (Tensor): Input
        upscale_factor (int): factor to increase spatial resolution by

    Examples::

        >>> ps = nn.PixelShuffle(3)
        >>> input = torch.empty(1, 9, 4, 4)
        >>> output = ps(input)
        >>> print(output.size())
        torch.Size([1, 1, 12, 12])
    """
    batch_size, channels, in_height, in_width = input.size()
    channels //= upscale_factor ** 2

    out_height = in_height * upscale_factor
    out_width = in_width * upscale_factor

    input_view = input.contiguous().view(
        batch_size, channels, upscale_factor, upscale_factor,
        in_height, in_width)

    shuffle_out = input_view.permute(0, 1, 4, 2, 5, 3).contiguous()
    return shuffle_out.view(batch_size, channels, out_height, out_width)


def upsample(input, size=None, scale_factor=None, mode='nearest', align_corners=None):
    r"""Upsamples the input to either the given :attr:`size` or the given
    :attr:`scale_factor`

    .. warning::
        This function is deprecated in favor of :func:`torch.nn.functional.interpolate`.
        This is equivalent with ``nn.functional.interpolate(...)``.


    The algorithm used for upsampling is determined by :attr:`mode`.

    Currently temporal, spatial and volumetric upsampling are supported, i.e.
    expected inputs are 3-D, 4-D or 5-D in shape.

    The input dimensions are interpreted in the form:
    `mini-batch x channels x [optional depth] x [optional height] x width`.

    The modes available for upsampling are: `nearest`, `linear` (3D-only),
    `bilinear` (4D-only), `trilinear` (5D-only)

    Args:
        input (Tensor): the input tensor
        size (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int]):
            output spatial size.
        scale_factor (int): multiplier for spatial size. Has to be an integer.
        mode (string): algorithm used for upsampling:
            'nearest' | 'linear' | 'bilinear' | 'trilinear'. Default: 'nearest'
        align_corners (bool, optional): if True, the corner pixels of the input
            and output tensors are aligned, and thus preserving the values at
            those pixels. This only has effect when :attr:`mode` is `linear`,
            `bilinear`, or `trilinear`. Default: False

    .. warning::
        With ``align_corners = True``, the linearly interpolating modes
        (`linear`, `bilinear`, and `trilinear`) don't proportionally align the
        output and input pixels, and thus the output values can depend on the
        input size. This was the default behavior for these modes up to version
        0.3.1. Since then, the default behavior is ``align_corners = False``.
        See :class:`~torch.nn.Upsample` for concrete examples on how this
        affects the outputs.

    """
    warnings.warn("nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.")
    return interpolate(input, size, scale_factor, mode, align_corners)


def interpolate(input, size=None, scale_factor=None, mode='nearest', align_corners=None):
    r"""Down/up samples the input to either the given :attr:`size` or the given
    :attr:`scale_factor`

    The algorithm used for interpolation is determined by :attr:`mode`.

    Currently temporal, spatial and volumetric sampling are supported, i.e.
    expected inputs are 3-D, 4-D or 5-D in shape.

    The input dimensions are interpreted in the form:
    `mini-batch x channels x [optional depth] x [optional height] x width`.

    The modes available for resizing are: `nearest`, `linear` (3D-only),
    `bilinear` (4D-only), `trilinear` (5D-only), `area`

    Args:
        input (Tensor): the input tensor
        size (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int]):
            output spatial size.
        scale_factor (float or Tuple[float]): multiplier for spatial size. Has to match input size if it is a tuple.
        mode (string): algorithm used for upsampling:
            'nearest' | 'linear' | 'bilinear' | 'trilinear' | 'area'. Default: 'nearest'
        align_corners (bool, optional): if True, the corner pixels of the input
            and output tensors are aligned, and thus preserving the values at
            those pixels. This only has effect when :attr:`mode` is `linear`,
            `bilinear`, or `trilinear`. Default: False

    .. warning::
        With ``align_corners = True``, the linearly interpolating modes
        (`linear`, `bilinear`, and `trilinear`) don't proportionally align the
        output and input pixels, and thus the output values can depend on the
        input size. This was the default behavior for these modes up to version
        0.3.1. Since then, the default behavior is ``align_corners = False``.
        See :class:`~torch.nn.Upsample` for concrete examples on how this
        affects the outputs.

    """
    from numbers import Integral
    from .modules.utils import _ntuple

    def _check_size_scale_factor(dim):
        if size is None and scale_factor is None:
            raise ValueError('either size or scale_factor should be defined')
        if size is not None and scale_factor is not None:
            raise ValueError('only one of size or scale_factor should be defined')
        if scale_factor is not None and isinstance(scale_factor, tuple)\
                and len(scale_factor) != dim:
            raise ValueError('scale_factor shape must match input shape. '
                             'Input is {}D, scale_factor size is {}'.format(dim, len(scale_factor)))

    def _output_size(dim):
        _check_size_scale_factor(dim)
        if size is not None:
            return size
        scale_factors = _ntuple(dim)(scale_factor)
        # math.floor might return float in py2.7
        return [int(math.floor(input.size(i + 2) * scale_factors[i])) for i in range(dim)]

    if mode in ('nearest', 'area'):
        if align_corners is not None:
            raise ValueError("align_corners option can only be set with the "
                             "interpolating modes: linear | bilinear | trilinear")
    else:
        if align_corners is None:
            warnings.warn("Default upsampling behavior when mode={} is changed "
                          "to align_corners=False since 0.4.0. Please specify "
                          "align_corners=True if the old behavior is desired. "
                          "See the documentation of nn.Upsample for details.".format(mode))
            align_corners = False

    if input.dim() == 3 and mode == 'nearest':
        return torch._C._nn.upsample_nearest1d(input, _output_size(1))
    elif input.dim() == 4 and mode == 'nearest':
        return torch._C._nn.upsample_nearest2d(input, _output_size(2))
    elif input.dim() == 5 and mode == 'nearest':
        return torch._C._nn.upsample_nearest3d(input, _output_size(3))
    elif input.dim() == 3 and mode == 'area':
        return adaptive_avg_pool1d(input, _output_size(1))
    elif input.dim() == 4 and mode == 'area':
        return adaptive_avg_pool2d(input, _output_size(2))
    elif input.dim() == 5 and mode == 'area':
        return adaptive_avg_pool3d(input, _output_size(3))
    elif input.dim() == 3 and mode == 'linear':
        return torch._C._nn.upsample_linear1d(input, _output_size(1), align_corners)
    elif input.dim() == 3 and mode == 'bilinear':
        raise NotImplementedError("Got 3D input, but bilinear mode needs 4D input")
    elif input.dim() == 3 and mode == 'trilinear':
        raise NotImplementedError("Got 3D input, but trilinear mode needs 5D input")
    elif input.dim() == 4 and mode == 'linear':
        raise NotImplementedError("Got 4D input, but linear mode needs 3D input")
    elif input.dim() == 4 and mode == 'bilinear':
        return torch._C._nn.upsample_bilinear2d(input, _output_size(2), align_corners)
    elif input.dim() == 4 and mode == 'trilinear':
        raise NotImplementedError("Got 4D input, but trilinear mode needs 5D input")
    elif input.dim() == 5 and mode == 'linear':
        raise NotImplementedError("Got 5D input, but linear mode needs 3D input")
    elif input.dim() == 5 and mode == 'bilinear':
        raise NotImplementedError("Got 5D input, but bilinear mode needs 4D input")
    elif input.dim() == 5 and mode == 'trilinear':
        return torch._C._nn.upsample_trilinear3d(input, _output_size(3), align_corners)
    else:
        raise NotImplementedError("Input Error: Only 3D, 4D and 5D input Tensors supported"
                                  " (got {}D) for the modes: nearest | linear | bilinear | trilinear"
                                  " (got {})".format(input.dim(), mode))


def upsample_nearest(input, size=None, scale_factor=None):
    r"""Upsamples the input, using nearest neighbours' pixel values.

    .. warning::
        This function is deprecated in favor of :func:`torch.nn.functional.interpolate`.
        This is equivalent with ``nn.functional.interpolate(..., mode='nearest')``.

    Currently spatial and volumetric upsampling are supported (i.e. expected
    inputs are 4 or 5 dimensional).

    Args:
        input (Tensor): input
        size (int or Tuple[int, int] or Tuple[int, int, int]): output spatia
            size.
        scale_factor (int): multiplier for spatial size. Has to be an integer.
    """
    # DeprecationWarning is ignored by default
    warnings.warn("nn.functional.upsample_nearest is deprecated. Use nn.functional.interpolate instead.")
    return interpolate(input, size, scale_factor, mode='nearest')


def upsample_bilinear(input, size=None, scale_factor=None):
    r"""Upsamples the input, using bilinear upsampling.

    .. warning::
        This function is deprecated in favor of :func:`torch.nn.functional.interpolate`.
        This is equivalent with
        ``nn.functional.interpolate(..., mode='bilinear', align_corners=True)``.

    Expected inputs are spatial (4 dimensional). Use `upsample_trilinear` fo
    volumetric (5 dimensional) inputs.

    Args:
        input (Tensor): input
        size (int or Tuple[int, int]): output spatial size.
        scale_factor (int or Tuple[int, int]): multiplier for spatial size
    """
    # DeprecationWarning is ignored by default
    warnings.warn("nn.functional.upsample_bilinear is deprecated. Use nn.functional.interpolate instead.")
    return interpolate(input, size, scale_factor, mode='bilinear', align_corners=True)


GRID_SAMPLE_MODE_ZEROS = 0
GRID_SAMPLE_MODE_BORDER = 1


def grid_sample(input, grid, mode='bilinear', padding_mode='zeros'):
    r"""Given an :attr:`input` and a flow-field :attr:`grid`, computes the
    `output` using input pixel locations from the grid.

    Uses bilinear interpolation to sample the input pixels.
    Currently, only spatial (4 dimensional) and volumetric (5 dimensional)
    inputs are supported.

    For each output location, :attr:`grid` has `x`, `y`
    input pixel locations which are used to compute output.
    In the case of 5D inputs, :attr:`grid` has `x`, `y`, `z` pixel locations.

    .. Note::
        To avoid confusion in notation, let's note that `x` corresponds to the `width` dimension `IW`,
        `y` corresponds to the height dimension `IH` and `z` corresponds to the `depth` dimension `ID`.

    :attr:`grid` has values in the range of `[-1, 1]`. This is because the
    pixel locations are normalized by the input height and width.

    For example, values: x: -1, y: -1 is the left-top pixel of the input, and
    values: x: 1, y: 1 is the right-bottom pixel of the input.

    If :attr:`grid` has values outside the range of `[-1, 1]`, those locations
    are handled as defined by `padding_mode`. Options are `zeros` or `border`,
    defining those locations to use 0 or image border values as contribution
    to the bilinear interpolation.

    .. Note:: This function is used in building Spatial Transformer Networks

    Args:
        input (Tensor): input batch (N x C x IH x IW) or (N x C x ID x IH x IW)
        grid (Tensor): flow-field of size (N x OH x OW x 2) or (N x OD x OH x OW x 3)
        padding_mode (str): padding mode for outside grid values
            'zeros' | 'border'. Default: 'zeros'

    Returns:
        output (Tensor): output Tensor

    """
    if mode != 'bilinear':
        raise NotImplementedError("nn.functional.grid_sample got unsupported mode: '{}'".format(mode))
    if padding_mode == 'zeros':
        padding_mode = GRID_SAMPLE_MODE_ZEROS
    elif padding_mode == 'border':
        padding_mode = GRID_SAMPLE_MODE_BORDER
    else:
        raise ValueError("padding_mode needs to be 'zeros' or 'border', but got {}".format(padding_mode))
    return torch.grid_sampler(input, grid, padding_mode)


def affine_grid(theta, size):
    r"""Generates a 2d flow field, given a batch of affine matrices :attr:`theta`
    Generally used in conjunction with :func:`grid_sample` to
    implement Spatial Transformer Networks.

    Args:
        theta (Tensor): input batch of affine matrices (:math:`N \times 2 \times 3`)
        size (torch.Size): the target output image size (:math:`N \times C \times H \times W`)
                           Example: torch.Size((32, 3, 24, 24))

    Returns:
        output (Tensor): output Tensor of size (:math:`N \times H \times W \times 2`)
    """
    return vision.affine_grid_generator(theta, size)


def pad(input, pad, mode='constant', value=0):
    r"""Pads tensor.

    `Nd` constant padding:  The number of dimensions to pad is
        :math:`\left\lfloor\frac{len(padding)}{2}\right\rfloor` and the dimensions that get padded begins with the
        last dimension and moves forward. See below for examples.

    `1D`, `2D` and `3D` "reflect" / "replicate" padding:
        for 1D:
                3D input tensor with padding of the form `(padLeft, padRight)`
        for 2D:
                4D input tensor with padding of the form `(padLeft, padRight, padTop, padBottom)`.
        for 3D:
                5D input tensor with padding of the form
                `(padLeft, padRight, padTop, padBottom, padFront, padBack)`. No "reflect" implementation.

    See :class:`torch.nn.ConstantPad2d`, :class:`torch.nn.ReflectionPad2d`, and
    :class:`torch.nn.ReplicationPad2d` for concrete examples on how each of the
    padding modes works.

    Args:
        input (Tensor): `Nd` tensor
        pad (tuple): m-elem tuple, where :math:`\frac{m}{2} \leq` input dimensions and :math:`m` is even.
        mode: 'constant', 'reflect' or 'replicate'. Default: 'constant'
        value: fill value for 'constant' padding. Default: 0

    Examples::

        >>> t4d = torch.empty(3, 3, 4, 2)
        >>> p1d = (1, 1) # pad last dim by 1 on each side
        >>> out = F.pad(t4d, p1d, "constant", 0)  # effectively zero padding
        >>> print(out.data.size())
        torch.Size([3, 3, 4, 4])
        >>> p2d = (1, 1, 2, 2) # pad last dim by (1, 1) and 2nd to last by (2, 2)
        >>> out = F.pad(t4d, p2d, "constant", 0)
        >>> print(out.data.size())
        torch.Size([3, 3, 8, 4])
        >>> t4d = torch.empty(3, 3, 4, 2)
        >>> p3d = (0, 1, 2, 1, 3, 3) # pad by (0, 1), (2, 1), and (3, 3)
        >>> out = F.pad(t4d, p3d, "constant", 0)
        >>> print(out.data.size())
        torch.Size([3, 9, 7, 3])

    """
    assert len(pad) % 2 == 0, 'Padding length must be divisible by 2'
    assert len(pad) // 2 <= input.dim(), 'Padding length too large'
    if mode == 'constant':
        return ConstantPadNd.apply(input, pad, value)
    else:
        assert value == 0, 'Padding mode "{}"" doesn\'t take in value argument'.format(mode)
        if input.dim() == 3:
            assert len(pad) == 2, '3D tensors expect 2 values for padding'
            if mode == 'reflect':
                return torch._C._nn.reflection_pad1d(input, pad)
            elif mode == 'replicate':
                return torch._C._nn.replication_pad1d(input, pad)
        elif input.dim() == 4:
            assert len(pad) == 4, '4D tensors expect 4 values for padding'
            if mode == 'reflect':
                return torch._C._nn.reflection_pad2d(input, pad)
            elif mode == 'replicate':
                return torch._C._nn.replication_pad2d(input, pad)
        elif input.dim() == 5:
            assert len(pad) == 6, '5D tensors expect 6 values for padding'
            if mode == 'reflect':
                raise NotImplementedError
            elif mode == 'replicate':
                return torch._C._nn.replication_pad3d(input, pad)
        else:
            raise NotImplementedError("Only 3D, 4D, 5D padding with non-constant padding are supported for now")


# distance

def pairwise_distance(x1, x2, p=2, eps=1e-6, keepdim=False):
    r"""
    See :class:`torch.nn.PairwiseDistance` for details
    """
    return torch.pairwise_distance(x1, x2, p, eps, keepdim)


def cosine_similarity(x1, x2, dim=1, eps=1e-8):
    r"""Returns cosine similarity between x1 and x2, computed along dim.

    .. math ::
        \text{similarity} = \dfrac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}

    Args:
        x1 (Tensor): First input.
        x2 (Tensor): Second input (of size matching x1).
        dim (int, optional): Dimension of vectors. Default: 1
        eps (float, optional): Small value to avoid division by zero.
            Default: 1e-8

    Shape:
        - Input: :math:`(\ast_1, D, \ast_2)` where D is at position `dim`.
        - Output: :math:`(\ast_1, \ast_2)` where 1 is at position `dim`.

    Example::

        >>> input1 = torch.randn(100, 128)
        >>> input2 = torch.randn(100, 128)
        >>> output = F.cosine_similarity(input1, input2)
        >>> print(output)
    """
    w12 = torch.sum(x1 * x2, dim)
    w1 = torch.norm(x1, 2, dim)
    w2 = torch.norm(x2, 2, dim)
    return w12 / (w1 * w2).clamp(min=eps)


def triplet_margin_loss(anchor, positive, negative, margin=1.0, p=2, eps=1e-6, swap=False, size_average=None,
                        reduce=None, reduction="elementwise_mean"):
    r"""
    See :class:`~torch.nn.TripletMarginLoss` for details
    """
    if size_average is not None or reduce is not None:
        reduction = _Reduction.legacy_get_enum(size_average, reduce)
    else:
        reduction = _Reduction.get_enum(reduction)
    return torch.triplet_margin_loss(anchor, positive, negative, margin, p, eps,
                                     swap, reduction)


def normalize(input, p=2, dim=1, eps=1e-12):
    r"""Performs :math:`L_p` normalization of inputs over specified dimension.

    Does:

    .. math::
        v = \frac{v}{\max(\lVert v \rVert_p, \epsilon)}

    for each subtensor v over dimension dim of input. Each subtensor is
    flattened into a vector, i.e. :math:`\lVert v \rVert_p` is not a matrix
    norm.

    With default arguments normalizes over the second dimension with Euclidean
    norm.

    Args:
        input: input tensor of any shape
        p (float): the exponent value in the norm formulation. Default: 2
        dim (int): the dimension to reduce. Default: 1
        eps (float): small value to avoid division by zero. Default: 1e-12
    """
    return input / input.norm(p, dim, True).clamp(min=eps).expand_as(input)


def assert_int_or_pair(arg, arg_name, message):
    assert isinstance(arg, int) or len(arg) == 2, message.format(arg_name)


def unfold(input, kernel_size, dilation=1, padding=0, stride=1):
    r"""Extracts sliding local blocks from an batched input tensor.

    .. warning::
        Currently, only 4-D input tensors (batched image-like tensors) are
        supported.

    See :class:`torch.nn.Unfold` for details
    """

    if input.dim() == 4:
        msg = '{} must be int or 2-tuple for 4D input'
        assert_int_or_pair(kernel_size, 'kernel_size', msg)
        assert_int_or_pair(dilation, 'dilation', msg)
        assert_int_or_pair(padding, 'padding', msg)
        assert_int_or_pair(stride, 'stride', msg)

        return Im2Col.apply(input, _pair(kernel_size),
                            _pair(dilation), _pair(padding), _pair(stride))
    else:
        raise NotImplementedError("Input Error: Only 4D input Tensors are supported (got {}D)".format(input.dim()))


def fold(input, output_size, kernel_size, dilation=1, padding=0, stride=1):
    r"""Combines an array of sliding local blocks into a large containing
    tensor.

    .. warning::
        Currently, only 4-D output tensors (batched image-like tensors) are
        supported.

    See :class:`torch.nn.Fold` for details
    """
    if input.dim() == 3:
        msg = '{} must be int or 2-tuple for 3D input'
        assert_int_or_pair(output_size, 'output_size', msg)
        assert_int_or_pair(kernel_size, 'kernel_size', msg)
        assert_int_or_pair(dilation, 'dilation', msg)
        assert_int_or_pair(padding, 'padding', msg)
        assert_int_or_pair(stride, 'stride', msg)

        return Col2Im.apply(input, _pair(output_size), _pair(kernel_size),
                            _pair(dilation), _pair(padding), _pair(stride))
    else:
        raise NotImplementedError("Input Error: Only 3D input Tensors are supported (got {}D)".format(input.dim()))
