import torch
import random
import numbers
from torchvision.transforms import RandomCrop, RandomResizedCrop
from PIL import Image
from torchvision.utils import _log_api_usage_once

def _is_tensor_video_clip(clip):
    if not torch.is_tensor(clip):
        raise TypeError("clip should be Tensor. Got %s" % type(clip))

    if not clip.ndimension() == 4:
        raise ValueError("clip should be 4D. Got %dD" % clip.dim())

    return True


def center_crop_arr(pil_image, image_size):
    """
    Center cropping implementation from ADM.
    https://github.com/openai/guided-diffusion/blob/8fb3ad9197f16bbc40620447b2742e13458d2831/guided_diffusion/image_datasets.py#L126
    """
    while min(*pil_image.size) >= 2 * image_size:
        pil_image = pil_image.resize(
            tuple(x // 2 for x in pil_image.size), resample=Image.BOX
        )

    scale = image_size / min(*pil_image.size)
    pil_image = pil_image.resize(
        tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC
    )

    arr = np.array(pil_image)
    crop_y = (arr.shape[0] - image_size) // 2
    crop_x = (arr.shape[1] - image_size) // 2
    return Image.fromarray(arr[crop_y: crop_y + image_size, crop_x: crop_x + image_size])


def crop(clip, i, j, h, w):
    """
    Args:
        clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
    """
    if len(clip.size()) != 4:
        raise ValueError("clip should be a 4D tensor")
    return clip[..., i : i + h, j : j + w]


def resize(clip, target_size, interpolation_mode):
    if len(target_size) != 2:
        raise ValueError(f"target size should be tuple (height, width), instead got {target_size}")
    return torch.nn.functional.interpolate(clip, size=target_size, mode=interpolation_mode, align_corners=False)

def resize_scale(clip, target_size, interpolation_mode):
    if len(target_size) != 2:
        raise ValueError(f"target size should be tuple (height, width), instead got {target_size}")
    H, W = clip.size(-2), clip.size(-1)
    scale_ = target_size[0] / min(H, W)
    return torch.nn.functional.interpolate(clip, scale_factor=scale_, mode=interpolation_mode, align_corners=False)

def resize_with_scale_factor(clip, scale_factor, interpolation_mode):
    return torch.nn.functional.interpolate(clip, scale_factor=scale_factor, mode=interpolation_mode, align_corners=False)

def resize_scale_with_height(clip, target_size, interpolation_mode):
    H, W = clip.size(-2), clip.size(-1)
    scale_ = target_size / H
    return torch.nn.functional.interpolate(clip, scale_factor=scale_, mode=interpolation_mode, align_corners=False)

def resize_scale_with_weight(clip, target_size, interpolation_mode):
    H, W = clip.size(-2), clip.size(-1)
    scale_ = target_size / W
    return torch.nn.functional.interpolate(clip, scale_factor=scale_, mode=interpolation_mode, align_corners=False)


def resized_crop(clip, i, j, h, w, size, interpolation_mode="bilinear"):
    """
    Do spatial cropping and resizing to the video clip
    Args:
        clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
        i (int): i in (i,j) i.e coordinates of the upper left corner.
        j (int): j in (i,j) i.e coordinates of the upper left corner.
        h (int): Height of the cropped region.
        w (int): Width of the cropped region.
        size (tuple(int, int)): height and width of resized clip
    Returns:
        clip (torch.tensor): Resized and cropped clip. Size is (T, C, H, W)
    """
    if not _is_tensor_video_clip(clip):
        raise ValueError("clip should be a 4D torch.tensor")
    clip = crop(clip, i, j, h, w)
    clip = resize(clip, size, interpolation_mode)
    return clip


def center_crop(clip, crop_size):
    if not _is_tensor_video_clip(clip):
        raise ValueError("clip should be a 4D torch.tensor")
    h, w = clip.size(-2), clip.size(-1)
    # print(clip.shape)
    th, tw = crop_size
    if h < th or w < tw:
        # print(h, w)
        raise ValueError("height {} and width {} must be no smaller than crop_size".format(h, w))

    i = int(round((h - th) / 2.0))
    j = int(round((w - tw) / 2.0))
    return crop(clip, i, j, th, tw), i, j


def center_crop_using_short_edge(clip):
    if not _is_tensor_video_clip(clip):
        raise ValueError("clip should be a 4D torch.tensor")
    h, w = clip.size(-2), clip.size(-1)
    if h < w:
        th, tw = h, h
        i = 0
        j = int(round((w - tw) / 2.0))
    else:
        th, tw = w, w
        i = int(round((h - th) / 2.0))
        j = 0
    return crop(clip, i, j, th, tw)


def random_shift_crop(clip):
    '''
    Slide along the long edge, with the short edge as crop size
    '''
    if not _is_tensor_video_clip(clip):
        raise ValueError("clip should be a 4D torch.tensor")
    h, w = clip.size(-2), clip.size(-1)
    
    if h <= w:
        long_edge = w
        short_edge = h
    else:
        long_edge = h
        short_edge =w

    th, tw = short_edge, short_edge

    i = torch.randint(0, h - th + 1, size=(1,)).item()
    j = torch.randint(0, w - tw + 1, size=(1,)).item()
    return crop(clip, i, j, th, tw), i, j

def random_crop(clip, crop_size):
    if not _is_tensor_video_clip(clip):
        raise ValueError("clip should be a 4D torch.tensor")
    h, w = clip.size(-2), clip.size(-1)
    th, tw = crop_size[-2], crop_size[-1]

    if h < th or w < tw:
        raise ValueError("height {} and width {} must be no smaller than crop_size".format(h, w))
    
    i = torch.randint(0, h - th + 1, size=(1,)).item()
    j = torch.randint(0, w - tw + 1, size=(1,)).item()
    clip_crop = crop(clip, i, j, th, tw)
    return clip_crop, i, j


def to_tensor(clip):
    """
    Convert tensor data type from uint8 to float, divide value by 255.0 and
    permute the dimensions of clip tensor
    Args:
        clip (torch.tensor, dtype=torch.uint8): Size is (T, C, H, W)
    Return:
        clip (torch.tensor, dtype=torch.float): Size is (T, C, H, W)
    """
    _is_tensor_video_clip(clip)
    if not clip.dtype == torch.uint8:
        raise TypeError("clip tensor should have data type uint8. Got %s" % str(clip.dtype))
    # return clip.float().permute(3, 0, 1, 2) / 255.0
    return clip.float() / 255.0


def normalize(clip, mean, std, inplace=False):
    """
    Args:
        clip (torch.tensor): Video clip to be normalized. Size is (T, C, H, W)
        mean (tuple): pixel RGB mean. Size is (3)
        std (tuple): pixel standard deviation. Size is (3)
    Returns:
        normalized clip (torch.tensor): Size is (T, C, H, W)
    """
    if not _is_tensor_video_clip(clip):
        raise ValueError("clip should be a 4D torch.tensor")
    if not inplace:
        clip = clip.clone()
    mean = torch.as_tensor(mean, dtype=clip.dtype, device=clip.device)
    # print(mean)
    std = torch.as_tensor(std, dtype=clip.dtype, device=clip.device)
    clip.sub_(mean[:, None, None, None]).div_(std[:, None, None, None])
    return clip


def hflip(clip):
    """
    Args:
        clip (torch.tensor): Video clip to be normalized. Size is (T, C, H, W)
    Returns:
        flipped clip (torch.tensor): Size is (T, C, H, W)
    """
    if not _is_tensor_video_clip(clip):
        raise ValueError("clip should be a 4D torch.tensor")
    return clip.flip(-1)


class RandomCropVideo:
    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 (T, C, H, W)
        Returns:
            torch.tensor: randomly cropped video clip.
                size is (T, C, OH, OW)
        """
        i, j, h, w = self.get_params(clip)
        return crop(clip, i, j, h, w)
    
    def get_params(self, clip):
        h, w = clip.shape[-2:]
        th, tw = self.size

        if h < th or w < tw:
            raise ValueError(f"Required crop size {(th, tw)} is larger than input image size {(h, w)}")

        if w == tw and h == th:
            return 0, 0, h, w

        i = torch.randint(0, h - th + 1, size=(1,)).item()
        j = torch.randint(0, w - tw + 1, size=(1,)).item()

        return i, j, th, tw

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}(size={self.size})"
    
class CenterCropResizeVideo:
    '''
    First use the short side for cropping length, 
    center crop video, then resize to the specified size
    '''
    def __init__(
        self,
        size,
        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
       

    def __call__(self, clip):
        """
        Args:
            clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
        Returns:
            torch.tensor: scale resized / center cropped video clip.
                size is (T, C, crop_size, crop_size)
        """
        # print(clip.shape)
        clip_center_crop = center_crop_using_short_edge(clip)
        # print(clip_center_crop.shape) 320 512
        clip_center_crop_resize = resize(clip_center_crop, target_size=self.size, interpolation_mode=self.interpolation_mode)
        return clip_center_crop_resize

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


class SDXL:
    def __init__(
        self,
        size,
        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
       
    def __call__(self, clip):
        """
        Args:
            clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
        Returns:
            torch.tensor: scale resized / center cropped video clip.
                size is (T, C, crop_size, crop_size)
        """
        # add aditional one pixel for avoiding error in center crop 
        ori_h, ori_w = clip.size(-2), clip.size(-1)
        tar_h, tar_w = self.size[0] + 1, self.size[1] + 1

        # if ori_h >= tar_h and ori_w >= tar_w:
        #     clip_tar_crop, i, j =  random_crop(clip=clip, crop_size=self.size)
        # else:
        #     tar_h_div_ori_h = tar_h / ori_h
        #     tar_w_div_ori_w = tar_w / ori_w
        #     if tar_h_div_ori_h > tar_w_div_ori_w:
        #         clip = resize_with_scale_factor(clip=clip, scale_factor=tar_h_div_ori_h, interpolation_mode=self.interpolation_mode)
        #     else:
        #         clip = resize_with_scale_factor(clip=clip, scale_factor=tar_w_div_ori_w, interpolation_mode=self.interpolation_mode)
        #     clip_tar_crop, i, j = random_crop(clip, self.size)
        if ori_h >= tar_h and ori_w >= tar_w:
            tar_h_div_ori_h = tar_h / ori_h
            tar_w_div_ori_w = tar_w / ori_w
            if tar_h_div_ori_h > tar_w_div_ori_w:
                clip = resize_with_scale_factor(clip=clip, scale_factor=tar_h_div_ori_h, interpolation_mode=self.interpolation_mode)
            else:
                clip = resize_with_scale_factor(clip=clip, scale_factor=tar_w_div_ori_w, interpolation_mode=self.interpolation_mode)
            ori_h, ori_w = clip.size(-2), clip.size(-1)
            clip_tar_crop, i, j = random_crop(clip, self.size)
        else:
            tar_h_div_ori_h = tar_h / ori_h
            tar_w_div_ori_w = tar_w / ori_w
            if tar_h_div_ori_h > tar_w_div_ori_w:
                clip = resize_with_scale_factor(clip=clip, scale_factor=tar_h_div_ori_h, interpolation_mode=self.interpolation_mode)
            else:
                clip = resize_with_scale_factor(clip=clip, scale_factor=tar_w_div_ori_w, interpolation_mode=self.interpolation_mode)
            clip_tar_crop, i, j = random_crop(clip, self.size)
        return clip_tar_crop, ori_h, ori_w, i, j
    
    def __repr__(self) -> str:
        return f"{self.__class__.__name__}(size={self.size}, interpolation_mode={self.interpolation_mode}"


class SDXLCenterCrop:
    def __init__(
        self,
        size,
        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
       

    def __call__(self, clip):
        """
        Args:
            clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
        Returns:
            torch.tensor: scale resized / center cropped video clip.
                size is (T, C, crop_size, crop_size)
        """
        # add aditional one pixel for avoiding error in center crop 
        ori_h, ori_w = clip.size(-2), clip.size(-1)
        tar_h, tar_w = self.size[0] + 1, self.size[1] + 1
        tar_h_div_ori_h = tar_h / ori_h
        tar_w_div_ori_w = tar_w / ori_w
        # print('before resize', clip.shape)
        if tar_h_div_ori_h > tar_w_div_ori_w:
            clip = resize_with_scale_factor(clip=clip, scale_factor=tar_h_div_ori_h, interpolation_mode=self.interpolation_mode)
            # print('after h resize', clip.shape)
        else:
            clip = resize_with_scale_factor(clip=clip, scale_factor=tar_w_div_ori_w, interpolation_mode=self.interpolation_mode)
        # print('after resize', clip.shape)
        # print(clip.shape)
        # clip_tar_crop, i, j = random_crop(clip, self.size)
        clip_tar_crop, i, j = center_crop(clip, self.size)
        # print('after crop', clip_tar_crop.shape)

        return clip_tar_crop, ori_h, ori_w, i, j
    
    def __repr__(self) -> str:
        return f"{self.__class__.__name__}(size={self.size}, interpolation_mode={self.interpolation_mode}"
    

class InternVideo320512:
    def __init__(
        self,
        size,
        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
       

    def __call__(self, clip):
        """
        Args:
            clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
        Returns:
            torch.tensor: scale resized / center cropped video clip.
                size is (T, C, crop_size, crop_size)
        """
        # add aditional one pixel for avoiding error in center crop 
        h, w = clip.size(-2), clip.size(-1)
        # print('before resize', clip.shape)
        if h < 320:
            clip = resize_scale_with_height(clip=clip, target_size=321, interpolation_mode=self.interpolation_mode)
            # print('after h resize', clip.shape)
        if w < 512:
            clip = resize_scale_with_weight(clip=clip, target_size=513, interpolation_mode=self.interpolation_mode)
            # print('after w resize', clip.shape)
        # print(clip.shape)
        clip_center_crop = center_crop(clip, self.size)
        clip_center_crop_no_subtitles = center_crop(clip, (220, 352))
        clip_center_resize = resize(clip_center_crop_no_subtitles, target_size=self.size, interpolation_mode=self.interpolation_mode)
        # print(clip_center_crop.shape)
        return clip_center_resize

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

class CenterCropVideo:
    '''
    First scale to the specified size in equal proportion to the short edge, 
    then center cropping
    '''
    def __init__(
        self,
        size,
        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
       

    def __call__(self, clip):
        """
        Args:
            clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
        Returns:
            torch.tensor: scale resized / center cropped video clip.
                size is (T, C, crop_size, crop_size)
        """
        clip_resize = resize_scale(clip=clip, target_size=self.size, interpolation_mode=self.interpolation_mode)
        clip_center_crop = center_crop(clip_resize, self.size)
        return clip_center_crop

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}(size={self.size}, interpolation_mode={self.interpolation_mode}"
    
class KineticsRandomCropResizeVideo:
    '''
    Slide along the long edge, with the short edge as crop size. And resie to the desired size.
    '''
    def __init__(
            self,
            size,
            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

    def __call__(self, clip):
        clip_random_crop = random_shift_crop(clip)
        clip_resize = resize(clip_random_crop, self.size, self.interpolation_mode)
        return clip_resize

class ResizeVideo():
    '''
    First use the short side for cropping length, 
    center crop video, then resize to the specified size
    '''
    def __init__(
        self,
        size,
        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
       

    def __call__(self, clip):
        """
        Args:
            clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
        Returns:
            torch.tensor: scale resized / center cropped video clip.
                size is (T, C, crop_size, crop_size)
        """
        clip_resize = resize(clip, target_size=self.size, interpolation_mode=self.interpolation_mode)
        return clip_resize

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

class CenterCropVideo:
    def __init__(
        self,
        size,
        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
       

    def __call__(self, clip):
        """
        Args:
            clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
        Returns:
            torch.tensor: center cropped video clip.
                size is (T, C, crop_size, crop_size)
        """
        clip_center_crop = center_crop(clip, self.size)
        return clip_center_crop

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

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 must be normalized. Size is (C, T, H, W)
        """
        return 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, C, H, W)
        Return:
            clip (torch.tensor, dtype=torch.float): Size is (T, C, H, W)
        """
        return 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 (T, C, H, W)
        Return:
            clip (torch.tensor): Size is (T, C, H, W)
        """
        if random.random() < self.p:
            clip = hflip(clip)
        return clip

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}(p={self.p})"
    
class Compose:
    """Composes several transforms together. This transform does not support torchscript.
    Please, see the note below.

    Args:
        transforms (list of ``Transform`` objects): list of transforms to compose.

    Example:
        >>> transforms.Compose([
        >>>     transforms.CenterCrop(10),
        >>>     transforms.PILToTensor(),
        >>>     transforms.ConvertImageDtype(torch.float),
        >>> ])

    .. note::
        In order to script the transformations, please use ``torch.nn.Sequential`` as below.

        >>> transforms = torch.nn.Sequential(
        >>>     transforms.CenterCrop(10),
        >>>     transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
        >>> )
        >>> scripted_transforms = torch.jit.script(transforms)

        Make sure to use only scriptable transformations, i.e. that work with ``torch.Tensor``, does not require
        `lambda` functions or ``PIL.Image``.

    """

    def __init__(self, transforms):
        if not torch.jit.is_scripting() and not torch.jit.is_tracing():
            _log_api_usage_once(self)
        self.transforms = transforms

    def __call__(self, img):
        for t in self.transforms:
            if isinstance(t, SDXLCenterCrop) or isinstance(t, SDXL):
                img, ori_h, ori_w, crops_coords_top, crops_coords_left = t(img)
            else:
                img = t(img)
        return img, ori_h, ori_w, crops_coords_top, crops_coords_left

    def __repr__(self) -> str:
        format_string = self.__class__.__name__ + "("
        for t in self.transforms:
            format_string += "\n"
            format_string += f"    {t}"
        format_string += "\n)"
        return format_string
    
#  ------------------------------------------------------------
#  ---------------------  Sampling  ---------------------------
#  ------------------------------------------------------------
class TemporalRandomCrop(object):
	"""Temporally crop the given frame indices at a random location.

	Args:
		size (int): Desired length of frames will be seen in the model.
	"""

	def __init__(self, size):
		self.size = size

	def __call__(self, total_frames):
		rand_end = max(0, total_frames - self.size - 1)
		begin_index = random.randint(0, rand_end)
		end_index = min(begin_index + self.size, total_frames)
		return begin_index, end_index
    

if __name__ == '__main__':
    from torchvision import transforms
    import torchvision.io as io
    import numpy as np
    from torchvision.utils import save_image
    import os

    vframes, aframes, info = io.read_video(
    filename='./v_Archery_g01_c03.avi',
    pts_unit='sec',
    output_format='TCHW'
    )
 
    trans = transforms.Compose([
        ToTensorVideo(),
        RandomHorizontalFlipVideo(),
        UCFCenterCropVideo(512),
        # NormalizeVideo(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
        transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True)
    ])

    target_video_len = 32
    frame_interval = 1
    total_frames = len(vframes)
    print(total_frames)

    temporal_sample = TemporalRandomCrop(target_video_len * frame_interval)


    # Sampling video frames
    start_frame_ind, end_frame_ind = temporal_sample(total_frames)
    # print(start_frame_ind)
    # print(end_frame_ind)
    assert end_frame_ind - start_frame_ind >= target_video_len
    frame_indice = np.linspace(start_frame_ind, end_frame_ind - 1, target_video_len, dtype=int)
    print(frame_indice)

    select_vframes = vframes[frame_indice]
    print(select_vframes.shape)
    print(select_vframes.dtype)

    select_vframes_trans = trans(select_vframes)
    print(select_vframes_trans.shape)
    print(select_vframes_trans.dtype)

    select_vframes_trans_int = ((select_vframes_trans * 0.5 + 0.5) * 255).to(dtype=torch.uint8)
    print(select_vframes_trans_int.dtype)
    print(select_vframes_trans_int.permute(0, 2, 3, 1).shape)

    io.write_video('./test.avi', select_vframes_trans_int.permute(0, 2, 3, 1), fps=8)
    
    for i in range(target_video_len):
        save_image(select_vframes_trans[i], os.path.join('./test000', '%04d.png' % i), normalize=True, value_range=(-1, 1))