from __future__ import division

import math
import warnings

import torch

# These no_grad_* functions are necessary as wrappers around the parts of these
# functions that use `with torch.no_grad()`. The JIT doesn't support context
# managers, so these need to be implemented as builtins. Using these wrappers
# lets us keep those builtins small and re-usable.
def _no_grad_uniform_(tensor, a, b):
    with torch.no_grad():
        return tensor.uniform_(a, b)


def _no_grad_normal_(tensor, mean, std):
    with torch.no_grad():
        return tensor.normal_(mean, std)


def _no_grad_fill_(tensor, val):
    with torch.no_grad():
        return tensor.fill_(val)


def _no_grad_zero_(tensor):
    with torch.no_grad():
        return tensor.zero_()


def calculate_gain(nonlinearity, param=None):
    r"""Return the recommended gain value for the given nonlinearity function.
    The values are as follows:

    ================= ====================================================
    nonlinearity      gain
    ================= ====================================================
    Linear / Identity :math:`1`
    Conv{1,2,3}D      :math:`1`
    Sigmoid           :math:`1`
    Tanh              :math:`\frac{5}{3}`
    ReLU              :math:`\sqrt{2}`
    Leaky Relu        :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}`
    ================= ====================================================

    Args:
        nonlinearity: the non-linear function (`nn.functional` name)
        param: optional parameter for the non-linear function

    Examples:
        >>> gain = nn.init.calculate_gain('leaky_relu', 0.2)  # leaky_relu with negative_slope=0.2
    """
    linear_fns = ['linear', 'conv1d', 'conv2d', 'conv3d', 'conv_transpose1d', 'conv_transpose2d', 'conv_transpose3d']
    if nonlinearity in linear_fns or nonlinearity == 'sigmoid':
        return 1
    elif nonlinearity == 'tanh':
        return 5.0 / 3
    elif nonlinearity == 'relu':
        return math.sqrt(2.0)
    elif nonlinearity == 'leaky_relu':
        if param is None:
            negative_slope = 0.01
        elif not isinstance(param, bool) and isinstance(param, int) or isinstance(param, float):
            # True/False are instances of int, hence check above
            negative_slope = param
        else:
            raise ValueError("negative_slope {} not a valid number".format(param))
        return math.sqrt(2.0 / (1 + negative_slope ** 2))
    else:
        raise ValueError("Unsupported nonlinearity {}".format(nonlinearity))


def uniform_(tensor, a=0., b=1.):
    # type: (Tensor, float, float) -> Tensor
    r"""Fills the input Tensor with values drawn from the uniform
    distribution :math:`\mathcal{U}(a, b)`.

    Args:
        tensor: an n-dimensional `torch.Tensor`
        a: the lower bound of the uniform distribution
        b: the upper bound of the uniform distribution

    Examples:
        >>> w = torch.empty(3, 5)
        >>> nn.init.uniform_(w)
    """
    return _no_grad_uniform_(tensor, a, b)


def normal_(tensor, mean=0., std=1.):
    # type: (Tensor, float, float) -> Tensor
    r"""Fills the input Tensor with values drawn from the normal
    distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`.

    Args:
        tensor: an n-dimensional `torch.Tensor`
        mean: the mean of the normal distribution
        std: the standard deviation of the normal distribution

    Examples:
        >>> w = torch.empty(3, 5)
        >>> nn.init.normal_(w)
    """
    return _no_grad_normal_(tensor, mean, std)


def constant_(tensor, val):
    # type: (Tensor, float) -> Tensor
    r"""Fills the input Tensor with the value :math:`\text{val}`.

    Args:
        tensor: an n-dimensional `torch.Tensor`
        val: the value to fill the tensor with

    Examples:
        >>> w = torch.empty(3, 5)
        >>> nn.init.constant_(w, 0.3)
    """
    return _no_grad_fill_(tensor, val)


def ones_(tensor):
    # type: (Tensor) -> Tensor
    r"""Fills the input Tensor with the scalar value `1`.

    Args:
        tensor: an n-dimensional `torch.Tensor`

    Examples:
        >>> w = torch.empty(3, 5)
        >>> nn.init.ones_(w)
    """
    return _no_grad_fill_(tensor, 1.)


def zeros_(tensor):
    # type: (Tensor) -> Tensor
    r"""Fills the input Tensor with the scalar value `0`.

    Args:
        tensor: an n-dimensional `torch.Tensor`

    Examples:
        >>> w = torch.empty(3, 5)
        >>> nn.init.zeros_(w)
    """
    return _no_grad_zero_(tensor)


def eye_(tensor):
    r"""Fills the 2-dimensional input `Tensor` with the identity
    matrix. Preserves the identity of the inputs in `Linear` layers, where as
    many inputs are preserved as possible.

    Args:
        tensor: a 2-dimensional `torch.Tensor`

    Examples:
        >>> w = torch.empty(3, 5)
        >>> nn.init.eye_(w)
    """
    if tensor.ndimension() != 2:
        raise ValueError("Only tensors with 2 dimensions are supported")

    with torch.no_grad():
        torch.eye(*tensor.shape, out=tensor, requires_grad=tensor.requires_grad)
    return tensor


def dirac_(tensor):
    r"""Fills the {3, 4, 5}-dimensional input `Tensor` with the Dirac
    delta function. Preserves the identity of the inputs in `Convolutional`
    layers, where as many input channels are preserved as possible.

    Args:
        tensor: a {3, 4, 5}-dimensional `torch.Tensor`

    Examples:
        >>> w = torch.empty(3, 16, 5, 5)
        >>> nn.init.dirac_(w)
    """
    dimensions = tensor.ndimension()
    if dimensions not in [3, 4, 5]:
        raise ValueError("Only tensors with 3, 4, or 5 dimensions are supported")

    sizes = tensor.size()
    min_dim = min(sizes[0], sizes[1])
    with torch.no_grad():
        tensor.zero_()

        for d in range(min_dim):
            if dimensions == 3:  # Temporal convolution
                tensor[d, d, tensor.size(2) // 2] = 1
            elif dimensions == 4:  # Spatial convolution
                tensor[d, d, tensor.size(2) // 2, tensor.size(3) // 2] = 1
            else:  # Volumetric convolution
                tensor[d, d, tensor.size(2) // 2, tensor.size(3) // 2, tensor.size(4) // 2] = 1
    return tensor


def _calculate_fan_in_and_fan_out(tensor):
    dimensions = tensor.dim()
    if dimensions < 2:
        raise ValueError("Fan in and fan out can not be computed for tensor with fewer than 2 dimensions")

    if dimensions == 2:  # Linear
        fan_in = tensor.size(1)
        fan_out = tensor.size(0)
    else:
        num_input_fmaps = tensor.size(1)
        num_output_fmaps = tensor.size(0)
        receptive_field_size = 1
        if tensor.dim() > 2:
            receptive_field_size = tensor[0][0].numel()
        fan_in = num_input_fmaps * receptive_field_size
        fan_out = num_output_fmaps * receptive_field_size

    return fan_in, fan_out


def xavier_uniform_(tensor, gain=1.):
    # type: (Tensor, float) -> Tensor
    r"""Fills the input `Tensor` with values according to the method
    described in `Understanding the difficulty of training deep feedforward
    neural networks` - Glorot, X. & Bengio, Y. (2010), using a uniform
    distribution. The resulting tensor will have values sampled from
    :math:`\mathcal{U}(-a, a)` where

    .. math::
        a = \text{gain} \times \sqrt{\frac{6}{\text{fan\_in} + \text{fan\_out}}}

    Also known as Glorot initialization.

    Args:
        tensor: an n-dimensional `torch.Tensor`
        gain: an optional scaling factor

    Examples:
        >>> w = torch.empty(3, 5)
        >>> nn.init.xavier_uniform_(w, gain=nn.init.calculate_gain('relu'))
    """
    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
    std = gain * math.sqrt(2.0 / float(fan_in + fan_out))
    a = math.sqrt(3.0) * std  # Calculate uniform bounds from standard deviation

    return _no_grad_uniform_(tensor, -a, a)


def xavier_normal_(tensor, gain=1.):
    # type: (Tensor, float) -> Tensor
    r"""Fills the input `Tensor` with values according to the method
    described in `Understanding the difficulty of training deep feedforward
    neural networks` - Glorot, X. & Bengio, Y. (2010), using a normal
    distribution. The resulting tensor will have values sampled from
    :math:`\mathcal{N}(0, \text{std}^2)` where

    .. math::
        \text{std} = \text{gain} \times \sqrt{\frac{2}{\text{fan\_in} + \text{fan\_out}}}

    Also known as Glorot initialization.

    Args:
        tensor: an n-dimensional `torch.Tensor`
        gain: an optional scaling factor

    Examples:
        >>> w = torch.empty(3, 5)
        >>> nn.init.xavier_normal_(w)
    """
    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
    std = gain * math.sqrt(2.0 / float(fan_in + fan_out))

    return _no_grad_normal_(tensor, 0., std)


def _calculate_correct_fan(tensor, mode):
    mode = mode.lower()
    valid_modes = ['fan_in', 'fan_out']
    if mode not in valid_modes:
        raise ValueError("Mode {} not supported, please use one of {}".format(mode, valid_modes))

    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
    return fan_in if mode == 'fan_in' else fan_out


def kaiming_uniform_(tensor, a=0, mode='fan_in', nonlinearity='leaky_relu'):
    r"""Fills the input `Tensor` with values according to the method
    described in `Delving deep into rectifiers: Surpassing human-level
    performance on ImageNet classification` - He, K. et al. (2015), using a
    uniform distribution. The resulting tensor will have values sampled from
    :math:`\mathcal{U}(-\text{bound}, \text{bound})` where

    .. math::
        \text{bound} = \sqrt{\frac{6}{(1 + a^2) \times \text{fan\_in}}}

    Also known as He initialization.

    Args:
        tensor: an n-dimensional `torch.Tensor`
        a: the negative slope of the rectifier used after this layer (0 for ReLU
            by default)
        mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'``
            preserves the magnitude of the variance of the weights in the
            forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the
            backwards pass.
        nonlinearity: the non-linear function (`nn.functional` name),
            recommended to use only with ``'relu'`` or ``'leaky_relu'`` (default).

    Examples:
        >>> w = torch.empty(3, 5)
        >>> nn.init.kaiming_uniform_(w, mode='fan_in', nonlinearity='relu')
    """
    fan = _calculate_correct_fan(tensor, mode)
    gain = calculate_gain(nonlinearity, a)
    std = gain / math.sqrt(fan)
    bound = math.sqrt(3.0) * std  # Calculate uniform bounds from standard deviation
    with torch.no_grad():
        return tensor.uniform_(-bound, bound)


def kaiming_normal_(tensor, a=0, mode='fan_in', nonlinearity='leaky_relu'):
    r"""Fills the input `Tensor` with values according to the method
    described in `Delving deep into rectifiers: Surpassing human-level
    performance on ImageNet classification` - He, K. et al. (2015), using a
    normal distribution. The resulting tensor will have values sampled from
    :math:`\mathcal{N}(0, \text{std}^2)` where

    .. math::
        \text{std} = \sqrt{\frac{2}{(1 + a^2) \times \text{fan\_in}}}

    Also known as He initialization.

    Args:
        tensor: an n-dimensional `torch.Tensor`
        a: the negative slope of the rectifier used after this layer (0 for ReLU
            by default)
        mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'``
            preserves the magnitude of the variance of the weights in the
            forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the
            backwards pass.
        nonlinearity: the non-linear function (`nn.functional` name),
            recommended to use only with ``'relu'`` or ``'leaky_relu'`` (default).

    Examples:
        >>> w = torch.empty(3, 5)
        >>> nn.init.kaiming_normal_(w, mode='fan_out', nonlinearity='relu')
    """
    fan = _calculate_correct_fan(tensor, mode)
    gain = calculate_gain(nonlinearity, a)
    std = gain / math.sqrt(fan)
    with torch.no_grad():
        return tensor.normal_(0, std)


def orthogonal_(tensor, gain=1):
    r"""Fills the input `Tensor` with a (semi) orthogonal matrix, as
    described in `Exact solutions to the nonlinear dynamics of learning in deep
    linear neural networks` - Saxe, A. et al. (2013). The input tensor must have
    at least 2 dimensions, and for tensors with more than 2 dimensions the
    trailing dimensions are flattened.

    Args:
        tensor: an n-dimensional `torch.Tensor`, where :math:`n \geq 2`
        gain: optional scaling factor

    Examples:
        >>> w = torch.empty(3, 5)
        >>> nn.init.orthogonal_(w)
    """
    if tensor.ndimension() < 2:
        raise ValueError("Only tensors with 2 or more dimensions are supported")

    rows = tensor.size(0)
    cols = tensor.numel() // rows
    flattened = tensor.new(rows, cols).normal_(0, 1)

    if rows < cols:
        flattened.t_()

    # Compute the qr factorization
    q, r = torch.qr(flattened)
    # Make Q uniform according to https://arxiv.org/pdf/math-ph/0609050.pdf
    d = torch.diag(r, 0)
    ph = d.sign()
    q *= ph

    if rows < cols:
        q.t_()

    with torch.no_grad():
        tensor.view_as(q).copy_(q)
        tensor.mul_(gain)
    return tensor


def sparse_(tensor, sparsity, std=0.01):
    r"""Fills the 2D input `Tensor` as a sparse matrix, where the
    non-zero elements will be drawn from the normal distribution
    :math:`\mathcal{N}(0, 0.01)`, as described in `Deep learning via
    Hessian-free optimization` - Martens, J. (2010).

    Args:
        tensor: an n-dimensional `torch.Tensor`
        sparsity: The fraction of elements in each column to be set to zero
        std: the standard deviation of the normal distribution used to generate
            the non-zero values

    Examples:
        >>> w = torch.empty(3, 5)
        >>> nn.init.sparse_(w, sparsity=0.1)
    """
    if tensor.ndimension() != 2:
        raise ValueError("Only tensors with 2 dimensions are supported")

    rows, cols = tensor.shape
    num_zeros = int(math.ceil(sparsity * rows))

    with torch.no_grad():
        tensor.normal_(0, std)
        for col_idx in range(cols):
            row_indices = torch.randperm(rows)
            zero_indices = row_indices[:num_zeros]
            tensor[zero_indices, col_idx] = 0
    return tensor


# for backward compatibility
def _make_deprecate(meth):
    new_name = meth.__name__
    old_name = new_name[:-1]

    def deprecated_init(*args, **kwargs):
        warnings.warn("nn.init.{} is now deprecated in favor of nn.init.{}."
                      .format(old_name, new_name), stacklevel=2)
        return meth(*args, **kwargs)

    deprecated_init.__doc__ = r"""
    {old_name}(...)

    .. warning::
        This method is now deprecated in favor of :func:`torch.nn.init.{new_name}`.

    See :func:`~torch.nn.init.{new_name}` for details.""".format(
        old_name=old_name, new_name=new_name)
    deprecated_init.__name__ = old_name
    return deprecated_init


uniform = _make_deprecate(uniform_)
normal = _make_deprecate(normal_)
constant = _make_deprecate(constant_)
eye = _make_deprecate(eye_)
dirac = _make_deprecate(dirac_)
xavier_uniform = _make_deprecate(xavier_uniform_)
xavier_normal = _make_deprecate(xavier_normal_)
kaiming_uniform = _make_deprecate(kaiming_uniform_)
kaiming_normal = _make_deprecate(kaiming_normal_)
orthogonal = _make_deprecate(orthogonal_)
sparse = _make_deprecate(sparse_)