from typing import Any, Dict, Optional, Union

import torch
from torch import Tensor

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


class RandomInvert(IntensityAugmentationBase2D):
    r"""Invert the tensor images values randomly.

    .. image:: _static/img/RandomInvert.png

    Args:
        max_val: The expected maximum value in the input tensor. The shape has to
          according to the input tensor shape, or at least has to work with broadcasting.
        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).
    .. note::
        This function internally uses :func:`kornia.enhance.invert`.

    Examples:
        >>> rng = torch.manual_seed(0)
        >>> img = torch.rand(1, 1, 5, 5)
        >>> inv = RandomInvert()
        >>> inv(img)
        tensor([[[[0.4963, 0.7682, 0.0885, 0.1320, 0.3074],
                  [0.6341, 0.4901, 0.8964, 0.4556, 0.6323],
                  [0.3489, 0.4017, 0.0223, 0.1689, 0.2939],
                  [0.5185, 0.6977, 0.8000, 0.1610, 0.2823],
                  [0.6816, 0.9152, 0.3971, 0.8742, 0.4194]]]])

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

    def __init__(
        self,
        max_val: Union[float, Tensor] = torch.tensor(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(max_val=max_val)

    def apply_transform(
        self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None
    ) -> Tensor:
        return invert(input, torch.as_tensor(flags["max_val"], device=input.device, dtype=input.dtype))
