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

# setup for xlayers
from . import super_core


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 (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0

    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(2.0))
        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=0.0, std=1.0, a=-2.0, b=2.0):
    # 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)
    """
    if isinstance(tensor, list):
        return [_no_grad_trunc_normal_(x, mean, std, a, b) for x in tensor]
    else:
        return _no_grad_trunc_normal_(tensor, mean, std, a, b)


def init_transformer(m):
    if isinstance(m, nn.Linear):
        trunc_normal_(m.weight, std=0.02)
        if isinstance(m, nn.Linear) and m.bias is not None:
            nn.init.constant_(m.bias, 0)
    elif isinstance(m, super_core.SuperLinear):
        trunc_normal_(m._super_weight, std=0.02)
        if m._super_bias is not None:
            nn.init.constant_(m._super_bias, 0)
    elif isinstance(m, super_core.SuperLayerNorm1D):
        nn.init.constant_(m.weight, 1.0)
        nn.init.constant_(m.bias, 0)
