import warnings
import torch
from torch._six import inf
from typing import Union, Iterable

_tensor_or_tensors = Union[torch.Tensor, Iterable[torch.Tensor]]


def clip_grad_norm_(parameters: _tensor_or_tensors, max_norm: float, norm_type: float = 2.0) -> torch.Tensor:
    r"""Clips gradient norm of an iterable of parameters.

    The norm is computed over all gradients together, as if they were
    concatenated into a single vector. Gradients are modified in-place.

    Args:
        parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
            single Tensor that will have gradients normalized
        max_norm (float or int): max norm of the gradients
        norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
            infinity norm.

    Returns:
        Total norm of the parameters (viewed as a single vector).
    """
    if isinstance(parameters, torch.Tensor):
        parameters = [parameters]
    parameters = [p for p in parameters if p.grad is not None]
    max_norm = float(max_norm)
    norm_type = float(norm_type)
    if len(parameters) == 0:
        return torch.tensor(0.)
    device = parameters[0].grad.device
    if norm_type == inf:
        total_norm = max(p.grad.detach().abs().max().to(device) for p in parameters)
    else:
        total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]), norm_type)
    clip_coef = max_norm / (total_norm + 1e-6)
    if clip_coef < 1:
        for p in parameters:
            p.grad.detach().mul_(clip_coef.to(p.grad.device))
    return total_norm


def clip_grad_norm(parameters: _tensor_or_tensors, max_norm: float, norm_type: float = 2.) -> torch.Tensor:
    r"""Clips gradient norm of an iterable of parameters.

    .. warning::
        This method is now deprecated in favor of
        :func:`torch.nn.utils.clip_grad_norm_`.
    """
    warnings.warn("torch.nn.utils.clip_grad_norm is now deprecated in favor "
                  "of torch.nn.utils.clip_grad_norm_.", stacklevel=2)
    return clip_grad_norm_(parameters, max_norm, norm_type)


def clip_grad_value_(parameters: _tensor_or_tensors, clip_value: float) -> None:
    r"""Clips gradient of an iterable of parameters at specified value.

    Gradients are modified in-place.

    Args:
        parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
            single Tensor that will have gradients normalized
        clip_value (float or int): maximum allowed value of the gradients.
            The gradients are clipped in the range
            :math:`\left[\text{-clip\_value}, \text{clip\_value}\right]`
    """
    if isinstance(parameters, torch.Tensor):
        parameters = [parameters]
    clip_value = float(clip_value)
    for p in filter(lambda p: p.grad is not None, parameters):
        p.grad.data.clamp_(min=-clip_value, max=clip_value)
