from typing import Any, Dict, Optional

import torch
from torch import Tensor

from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D


class RandomGaussianNoise(IntensityAugmentationBase2D):
    r"""Add gaussian noise to a batch of multi-dimensional images.

    .. image:: _static/img/RandomGaussianNoise.png

    Args:
        mean: The mean of the gaussian distribution.
        std: The standard deviation of the gaussian distribution.
        same_on_batch: apply the same transformation across the batch.
        p: probability of applying the transformation.
        keepdim: whether to keep the output shape the same as input (True) or broadcast it
                 to the batch form (False).
    Examples:
        >>> rng = torch.manual_seed(0)
        >>> img = torch.ones(1, 1, 2, 2)
        >>> RandomGaussianNoise(mean=0., std=1., p=1.)(img)
        tensor([[[[ 2.5410,  0.7066],
                  [-1.1788,  1.5684]]]])

    To apply the exact augmenation again, you may take the advantage of the previous parameter state:
        >>> input = torch.randn(1, 3, 32, 32)
        >>> aug = RandomGaussianNoise(mean=0., std=1., p=1.)
        >>> (aug(input) == aug(input, params=aug._params)).all()
        tensor(True)
    """

    def __init__(
        self,
        mean: float = 0.0,
        std: float = 1.0,
        same_on_batch: bool = False,
        p: float = 0.5,
        keepdim: bool = False,
        return_transform: Optional[bool] = None,
    ) -> None:
        super().__init__(
            p=p, return_transform=return_transform, same_on_batch=same_on_batch, p_batch=1.0, keepdim=keepdim
        )
        self.flags = dict(mean=mean, std=std)

    def generate_parameters(self, shape: torch.Size) -> Dict[str, Tensor]:
        noise = torch.randn(shape)
        return dict(noise=noise)

    def apply_transform(
        self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None
    ) -> Tensor:
        return input + params["noise"].to(input.device) * flags["std"] + flags["mean"]
