# Copyright 2025 The HuggingFace Team. All rights reserved.

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# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

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# http://www.apache.org/licenses/LICENSE-2.0

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# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

import warnings

from typing import List, Optional, Tuple, Union

import numpy as np

import PIL

import torch

import torch.nn.functional as F

from .image_processor import VaeImageProcessor, is_valid_image, is_valid_image_imagelist

class VideoProcessor(VaeImageProcessor):

r"""Simple video processor."""

def preprocess_video(self, video, height: Optional[int] = None, width: Optional[int] = None) -> torch.Tensor:

r"""

Preprocesses input video(s).

Args:

video (`List[PIL.Image]`, `List[List[PIL.Image]]`, `torch.Tensor`, `np.array`, `List[torch.Tensor]`, `List[np.array]`):

The input video. It can be one of the following:

* List of the PIL images.

* List of list of PIL images.

* 4D Torch tensors (expected shape for each tensor `(num_frames, num_channels, height, width)`).

* 4D NumPy arrays (expected shape for each array `(num_frames, height, width, num_channels)`).

* List of 4D Torch tensors (expected shape for each tensor `(num_frames, num_channels, height,

width)`).

* List of 4D NumPy arrays (expected shape for each array `(num_frames, height, width, num_channels)`).

* 5D NumPy arrays: expected shape for each array `(batch_size, num_frames, height, width,

num_channels)`.

* 5D Torch tensors: expected shape for each array `(batch_size, num_frames, num_channels, height,

width)`.

height (`int`, *optional*, defaults to `None`):

The height in preprocessed frames of the video. If `None`, will use the `get_default_height_width()` to

get default height.

width (`int`, *optional*`, defaults to `None`):

The width in preprocessed frames of the video. If `None`, will use get_default_height_width()` to get

the default width.

"""

if isinstance(video, list) and isinstance(video[0], np.ndarray) and video[0].ndim == 5:

warnings.warn(

"Passing `video` as a list of 5d np.ndarray is deprecated."

"Please concatenate the list along the batch dimension and pass it as a single 5d np.ndarray",

FutureWarning,

)

video = np.concatenate(video, axis=0)

if isinstance(video, list) and isinstance(video[0], torch.Tensor) and video[0].ndim == 5:

warnings.warn(

"Passing `video` as a list of 5d torch.Tensor is deprecated."

"Please concatenate the list along the batch dimension and pass it as a single 5d torch.Tensor",

FutureWarning,

)

video = torch.cat(video, axis=0)

# ensure the input is a list of videos:

# - if it is a batch of videos (5d torch.Tensor or np.ndarray), it is converted to a list of videos (a list of 4d torch.Tensor or np.ndarray)

# - if it is a single video, it is converted to a list of one video.

if isinstance(video, (np.ndarray, torch.Tensor)) and video.ndim == 5:

video = list(video)

elif isinstance(video, list) and is_valid_image(video[0]) or is_valid_image_imagelist(video):

video = [video]

elif isinstance(video, list) and is_valid_image_imagelist(video[0]):

video = video

else:

raise ValueError(

"Input is in incorrect format. Currently, we only support numpy.ndarray, torch.Tensor, PIL.Image.Image"

)

video = torch.stack([self.preprocess(img, height=height, width=width) for img in video], dim=0)

# move the number of channels before the number of frames.

video = video.permute(0, 2, 1, 3, 4)

return video

def postprocess_video(

self, video: torch.Tensor, output_type: str = "np"

) -> Union[np.ndarray, torch.Tensor, List[PIL.Image.Image]]:

r"""

Converts a video tensor to a list of frames for export.

Args:

video (`torch.Tensor`): The video as a tensor.

output_type (`str`, defaults to `"np"`): Output type of the postprocessed `video` tensor.

"""

batch_size = video.shape[0]

outputs = []

for batch_idx in range(batch_size):

batch_vid = video[batch_idx].permute(1, 0, 2, 3)

batch_output = self.postprocess(batch_vid, output_type)

outputs.append(batch_output)

if output_type == "np":

outputs = np.stack(outputs)

elif output_type == "pt":

outputs = torch.stack(outputs)

elif not output_type == "pil":

raise ValueError(f"{output_type} does not exist. Please choose one of ['np', 'pt', 'pil']")

return outputs

@staticmethod

def classify_height_width_bin(height: int, width: int, ratios: dict) -> Tuple[int, int]:

r"""

Returns the binned height and width based on the aspect ratio.

Args:

height (`int`): The height of the image.

width (`int`): The width of the image.

ratios (`dict`): A dictionary where keys are aspect ratios and values are tuples of (height, width).

Returns:

`Tuple[int, int]`: The closest binned height and width.

"""

ar = float(height / width)

closest_ratio = min(ratios.keys(), key=lambda ratio: abs(float(ratio) - ar))

default_hw = ratios[closest_ratio]

return int(default_hw[0]), int(default_hw[1])

@staticmethod

def resize_and_crop_tensor(samples: torch.Tensor, new_width: int, new_height: int) -> torch.Tensor:

r"""

Resizes and crops a tensor of videos to the specified dimensions.

Args:

samples (`torch.Tensor`):

A tensor of shape (N, C, T, H, W) where N is the batch size, C is the number of channels, T is the

number of frames, H is the height, and W is the width.

new_width (`int`): The desired width of the output videos.

new_height (`int`): The desired height of the output videos.

Returns:

`torch.Tensor`: A tensor containing the resized and cropped videos.

"""

orig_height, orig_width = samples.shape[3], samples.shape[4]

# Check if resizing is needed

if orig_height != new_height or orig_width != new_width:

ratio = max(new_height / orig_height, new_width / orig_width)

resized_width = int(orig_width * ratio)

resized_height = int(orig_height * ratio)

# Reshape to (N*T, C, H, W) for interpolation

n, c, t, h, w = samples.shape

samples = samples.permute(0, 2, 1, 3, 4).reshape(n * t, c, h, w)

# Resize

samples = F.interpolate(

samples, size=(resized_height, resized_width), mode="bilinear", align_corners=False

)

# Center Crop

start_x = (resized_width - new_width) // 2

end_x = start_x + new_width

start_y = (resized_height - new_height) // 2

end_y = start_y + new_height

samples = samples[:, :, start_y:end_y, start_x:end_x]

# Reshape back to (N, C, T, H, W)

samples = samples.reshape(n, t, c, new_height, new_width).permute(0, 2, 1, 3, 4)

return samples