# Borrowed from https://github.com/rwightman/pytorch-image-models
import torch
import math
import warnings


def _no_grad_trunc_normal_(tensor, mean, std, a, b):
  # Cut & paste from PyTorch official master until it's in a few official releases - RW
  # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
  def norm_cdf(x):
    # Computes standard normal cumulative distribution function
    return (1. + math.erf(x / math.sqrt(2.))) / 2.

  if (mean < a - 2 * std) or (mean > b + 2 * std):
    warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
                  "The distribution of values may be incorrect.",
                  stacklevel=2)

  with torch.no_grad():
    # Values are generated by using a truncated uniform distribution and
    # then using the inverse CDF for the normal distribution.
    # Get upper and lower cdf values
    l = norm_cdf((a - mean) / std)
    u = norm_cdf((b - mean) / std)

    # Uniformly fill tensor with values from [l, u], then translate to
    # [2l-1, 2u-1].
    tensor.uniform_(2 * l - 1, 2 * u - 1)

    # Use inverse cdf transform for normal distribution to get truncated
    # standard normal
    tensor.erfinv_()

    # Transform to proper mean, std
    tensor.mul_(std * math.sqrt(2.))
    tensor.add_(mean)

    # Clamp to ensure it's in the proper range
    tensor.clamp_(min=a, max=b)
    return tensor


def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
  # type: (Tensor, float, float, float, float) -> Tensor
  r"""Fills the input Tensor with values drawn from a truncated
  normal distribution. The values are effectively drawn from the
  normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
  with values outside :math:`[a, b]` redrawn until they are within
  the bounds. The method used for generating the random values works
  best when :math:`a \leq \text{mean} \leq b`.
  Args:
    tensor: an n-dimensional `torch.Tensor`
    mean: the mean of the normal distribution
    std: the standard deviation of the normal distribution
    a: the minimum cutoff value
    b: the maximum cutoff value
  Examples:
    >>> w = torch.empty(3, 5)
    >>> nn.init.trunc_normal_(w)
  """
  return _no_grad_trunc_normal_(tensor, mean, std, a, b)