from typing import Dict, List, Optional, Tuple

import torch
import torch.nn.functional as F
from torch import Tensor
from torchvision import transforms
from torchvision.transforms import InterpolationMode
from torchvision.transforms import functional as F
from torchvision.transforms.autoaugment import _apply_op


class STNAugment(torch.nn.Module):
    r"""RandAugment data augmentation method based on
    `"RandAugment: Practical automated data augmentation with a reduced search space"
    <https://arxiv.org/abs/1909.13719>`_.
    If the image is torch Tensor, it should be of type torch.uint8, and it is expected
    to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
    If img is PIL Image, it is expected to be in mode "L" or "RGB".

    Args:
        num_ops (int): Number of augmentation transformations to apply sequentially.
        magnitude (int): Magnitude for all the transformations.
        num_magnitude_bins (int): The number of different magnitude values.
        interpolation (InterpolationMode): Desired interpolation enum defined by
            :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
            If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
        fill (sequence or number, optional): Pixel fill value for the area outside the transformed
            image. If given a number, the value is used for all bands respectively.
    """

    def __init__(
            self,
            num_ops: int = 2,
            magnitude: int = 9,
            num_magnitude_bins: int = 31,
            interpolation: InterpolationMode = InterpolationMode.NEAREST,
            fill: Optional[List[float]] = None,
    ) -> None:
        super().__init__()
        self.num_ops = num_ops
        self.magnitude = magnitude
        self.num_magnitude_bins = num_magnitude_bins
        self.interpolation = interpolation
        self.fill = fill
        self.aug = transforms.Compose([
            transforms.RandomVerticalFlip(0.2),
        ])

    def _augmentation_space(self, num_bins: int, image_size: Tuple[int, int]) -> Dict[str, Tuple[Tensor, bool]]:
        return {
            # op_name: (magnitudes, signed)
            "Identity": (torch.tensor(0.0), False),
            "ShearX": (torch.linspace(0.0, 0.3, num_bins), True),
            "ShearY": (torch.linspace(0.0, 0.3, num_bins), True),
            "TranslateX": (torch.linspace(0.0, 150.0 / 331.0 * image_size[1], num_bins), True),
            "TranslateY": (torch.linspace(0.0, 150.0 / 331.0 * image_size[0], num_bins), True),
            "Rotate": (torch.linspace(0.0, 30.0, num_bins), True),
        }

    def forward(self, img: List[Tensor]):
        """
            img (PIL Image or Tensor): Image to be transformed.

        Returns:
            PIL Image or Tensor: Transformed image.
        """
        fill = self.fill
        cat_img= torch.cat(img)
        imgs = list(torch.chunk(cat_img,cat_img.shape[0],0))
        channels, height, width = imgs[0].shape
        if isinstance(imgs[0], Tensor):
            if isinstance(fill, (int, float)):
                fill = [float(fill)] * channels
            elif fill is not None:
                fill = [float(f) for f in fill]

        op_meta = self._augmentation_space(self.num_magnitude_bins, (height, width))
        for _ in range(self.num_ops):
            op_index = int(torch.randint(len(op_meta), (1,)).item())
            op_name = list(op_meta.keys())[op_index]
            magnitudes, signed = op_meta[op_name]
            magnitude = float(magnitudes[self.magnitude].item()) if magnitudes.ndim > 0 else 0.0
            if signed and torch.randint(2, (1,)):
                magnitude *= -1.0
            for i in range(len(imgs)):
                imgs[i] = _apply_op(imgs[i], op_name, magnitude, interpolation=self.interpolation, fill=fill)
        return imgs

    def __repr__(self) -> str:
        s = (
            f"{self.__class__.__name__}("
            f"num_ops={self.num_ops}"
            f", magnitude={self.magnitude}"
            f", num_magnitude_bins={self.num_magnitude_bins}"
            f", interpolation={self.interpolation}"
            f", fill={self.fill}"
            f")"
        )
        return s