#!/usr/bin/env python3

import numbers
import random
import warnings

from torchvision.transforms import RandomCrop, RandomResizedCrop

from . import _functional_video as F


__all__ = [
    "RandomCropVideo",
    "RandomResizedCropVideo",
    "CenterCropVideo",
    "NormalizeVideo",
    "ToTensorVideo",
    "RandomHorizontalFlipVideo",
]


warnings.warn(
    "The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. "
    "Please use the 'torchvision.transforms' module instead."
)


class RandomCropVideo(RandomCrop):
    def __init__(self, size):
        if isinstance(size, numbers.Number):
            self.size = (int(size), int(size))
        else:
            self.size = size

    def __call__(self, clip):
        """
        Args:
            clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W)
        Returns:
            torch.tensor: randomly cropped/resized video clip.
                size is (C, T, OH, OW)
        """
        i, j, h, w = self.get_params(clip, self.size)
        return F.crop(clip, i, j, h, w)

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}(size={self.size})"


class RandomResizedCropVideo(RandomResizedCrop):
    def __init__(
        self,
        size,
        scale=(0.08, 1.0),
        ratio=(3.0 / 4.0, 4.0 / 3.0),
        interpolation_mode="bilinear",
    ):
        if isinstance(size, tuple):
            if len(size) != 2:
                raise ValueError(f"size should be tuple (height, width), instead got {size}")
            self.size = size
        else:
            self.size = (size, size)

        self.interpolation_mode = interpolation_mode
        self.scale = scale
        self.ratio = ratio

    def __call__(self, clip):
        """
        Args:
            clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W)
        Returns:
            torch.tensor: randomly cropped/resized video clip.
                size is (C, T, H, W)
        """
        i, j, h, w = self.get_params(clip, self.scale, self.ratio)
        return F.resized_crop(clip, i, j, h, w, self.size, self.interpolation_mode)

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}(size={self.size}, interpolation_mode={self.interpolation_mode}, scale={self.scale}, ratio={self.ratio})"


class CenterCropVideo:
    def __init__(self, crop_size):
        if isinstance(crop_size, numbers.Number):
            self.crop_size = (int(crop_size), int(crop_size))
        else:
            self.crop_size = crop_size

    def __call__(self, clip):
        """
        Args:
            clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W)
        Returns:
            torch.tensor: central cropping of video clip. Size is
            (C, T, crop_size, crop_size)
        """
        return F.center_crop(clip, self.crop_size)

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}(crop_size={self.crop_size})"


class NormalizeVideo:
    """
    Normalize the video clip by mean subtraction and division by standard deviation
    Args:
        mean (3-tuple): pixel RGB mean
        std (3-tuple): pixel RGB standard deviation
        inplace (boolean): whether do in-place normalization
    """

    def __init__(self, mean, std, inplace=False):
        self.mean = mean
        self.std = std
        self.inplace = inplace

    def __call__(self, clip):
        """
        Args:
            clip (torch.tensor): video clip to be normalized. Size is (C, T, H, W)
        """
        return F.normalize(clip, self.mean, self.std, self.inplace)

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}(mean={self.mean}, std={self.std}, inplace={self.inplace})"


class ToTensorVideo:
    """
    Convert tensor data type from uint8 to float, divide value by 255.0 and
    permute the dimensions of clip tensor
    """

    def __init__(self):
        pass

    def __call__(self, clip):
        """
        Args:
            clip (torch.tensor, dtype=torch.uint8): Size is (T, H, W, C)
        Return:
            clip (torch.tensor, dtype=torch.float): Size is (C, T, H, W)
        """
        return F.to_tensor(clip)

    def __repr__(self) -> str:
        return self.__class__.__name__


class RandomHorizontalFlipVideo:
    """
    Flip the video clip along the horizontal direction with a given probability
    Args:
        p (float): probability of the clip being flipped. Default value is 0.5
    """

    def __init__(self, p=0.5):
        self.p = p

    def __call__(self, clip):
        """
        Args:
            clip (torch.tensor): Size is (C, T, H, W)
        Return:
            clip (torch.tensor): Size is (C, T, H, W)
        """
        if random.random() < self.p:
            clip = F.hflip(clip)
        return clip

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}(p={self.p})"

#!/usr/bin/env python3

import numbers
import random
import warnings

from torchvision.transforms import RandomCrop, RandomResizedCrop

from . import _functional_video as F


__all__ = [
    "RandomCropVideo",
    "RandomResizedCropVideo",
    "CenterCropVideo",
    "NormalizeVideo",
    "ToTensorVideo",
    "RandomHorizontalFlipVideo",
]


warnings.warn(
    "The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. "
    "Please use the 'torchvision.transforms' module instead."
)


class RandomCropVideo(RandomCrop):
    def __init__(self, size):
        if isinstance(size, numbers.Number):
            self.size = (int(size), int(size))
        else:
            self.size = size

    def __call__(self, clip):
        """
        Args:
            clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W)
        Returns:
            torch.tensor: randomly cropped/resized video clip.
                size is (C, T, OH, OW)
        """
        i, j, h, w = self.get_params(clip, self.size)
        return F.crop(clip, i, j, h, w)

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}(size={self.size})"


class RandomResizedCropVideo(RandomResizedCrop):
    def __init__(
        self,
        size,
        scale=(0.08, 1.0),
        ratio=(3.0 / 4.0, 4.0 / 3.0),
        interpolation_mode="bilinear",
    ):
        if isinstance(size, tuple):
            if len(size) != 2:
                raise ValueError(f"size should be tuple (height, width), instead got {size}")
            self.size = size
        else:
            self.size = (size, size)

        self.interpolation_mode = interpolation_mode
        self.scale = scale
        self.ratio = ratio

    def __call__(self, clip):
        """
        Args:
            clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W)
        Returns:
            torch.tensor: randomly cropped/resized video clip.
                size is (C, T, H, W)
        """
        i, j, h, w = self.get_params(clip, self.scale, self.ratio)
        return F.resized_crop(clip, i, j, h, w, self.size, self.interpolation_mode)

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}(size={self.size}, interpolation_mode={self.interpolation_mode}, scale={self.scale}, ratio={self.ratio})"


class CenterCropVideo:
    def __init__(self, crop_size):
        if isinstance(crop_size, numbers.Number):
            self.crop_size = (int(crop_size), int(crop_size))
        else:
            self.crop_size = crop_size

    def __call__(self, clip):
        """
        Args:
            clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W)
        Returns:
            torch.tensor: central cropping of video clip. Size is
            (C, T, crop_size, crop_size)
        """
        return F.center_crop(clip, self.crop_size)

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}(crop_size={self.crop_size})"


class NormalizeVideo:
    """
    Normalize the video clip by mean subtraction and division by standard deviation
    Args:
        mean (3-tuple): pixel RGB mean
        std (3-tuple): pixel RGB standard deviation
        inplace (boolean): whether do in-place normalization
    """

    def __init__(self, mean, std, inplace=False):
        self.mean = mean
        self.std = std
        self.inplace = inplace

    def __call__(self, clip):
        """
        Args:
            clip (torch.tensor): video clip to be normalized. Size is (C, T, H, W)
        """
        return F.normalize(clip, self.mean, self.std, self.inplace)

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}(mean={self.mean}, std={self.std}, inplace={self.inplace})"


class ToTensorVideo:
    """
    Convert tensor data type from uint8 to float, divide value by 255.0 and
    permute the dimensions of clip tensor
    """

    def __init__(self):
        pass

    def __call__(self, clip):
        """
        Args:
            clip (torch.tensor, dtype=torch.uint8): Size is (T, H, W, C)
        Return:
            clip (torch.tensor, dtype=torch.float): Size is (C, T, H, W)
        """
        return F.to_tensor(clip)

    def __repr__(self) -> str:
        return self.__class__.__name__


class RandomHorizontalFlipVideo:
    """
    Flip the video clip along the horizonal direction with a given probability
    Args:
        p (float): probability of the clip being flipped. Default value is 0.5
    """

    def __init__(self, p=0.5):
        self.p = p

    def __call__(self, clip):
        """
        Args:
            clip (torch.tensor): Size is (C, T, H, W)
        Return:
            clip (torch.tensor): Size is (C, T, H, W)
        """
        if random.random() < self.p:
            clip = F.hflip(clip)
        return clip

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}(p={self.p})"
