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# http://www.apache.org/licenses/LICENSE-2.0
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from typing import Optional

import torch
import torchvision.transforms.functional as transforms_F

from cosmos_predict2._src.imaginaire.datasets.webdataset.augmentors.augmentor import Augmentor


class HorizontalFlip(Augmentor):
    def __init__(self, input_keys: list, output_keys: Optional[list] = None, args: Optional[dict] = None) -> None:
        super().__init__(input_keys, output_keys, args)

    def __call__(self, data_dict: dict) -> dict:
        r"""Performs horizontal flipping.

        Args:
            data_dict (dict): Input data dict
        Returns:
            data_dict (dict): Output dict where images are center cropped.
        """
        flip_enabled = getattr(self.args, "enabled", True)
        if flip_enabled:
            p = getattr(self.args, "prob", 0.5)
            coin_flip = torch.rand(1).item() > p
            for key in self.input_keys:
                if coin_flip:
                    data_dict[key] = transforms_F.hflip(data_dict[key])

        return data_dict