# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# 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 copy
import json
import os
import warnings
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
from .dynamic_module_utils import custom_object_save
from .image_processing_utils import (
BatchFeature,
get_size_dict,
)
from .image_processing_utils_fast import BaseImageProcessorFast
from .image_utils import (
ChannelDimension,
SizeDict,
validate_kwargs,
)
from .processing_utils import Unpack, VideosKwargs
from .utils import (
VIDEO_PROCESSOR_NAME,
TensorType,
add_model_info_to_auto_map,
add_model_info_to_custom_pipelines,
add_start_docstrings,
cached_file,
copy_func,
download_url,
is_offline_mode,
is_remote_url,
is_torch_available,
is_torchvision_available,
is_torchvision_v2_available,
is_vision_available,
logging,
)
from .utils.import_utils import requires
from .video_utils import (
VideoInput,
group_videos_by_shape,
load_video,
make_batched_videos,
reorder_videos,
to_channel_dimension_format,
)
if is_vision_available():
from .image_utils import PILImageResampling
if is_torch_available():
import torch
if is_torchvision_available():
from .image_utils import pil_torch_interpolation_mapping
if is_torchvision_v2_available():
from torchvision.transforms.v2 import functional as F
else:
from torchvision.transforms import functional as F
logger = logging.get_logger(__name__)
BASE_VIDEO_PROCESSOR_DOCSTRING = r"""
Args:
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
Whether to resize the video's (height, width) dimensions to the specified `size`. Can be overridden by the
`do_resize` parameter in the `preprocess` method.
size (`dict`, *optional*, defaults to `self.size`):
Size of the output video after resizing. Can be overridden by the `size` parameter in the `preprocess`
method.
size_divisor (`int`, *optional*, defaults to `self.size_divisor`):
The size by which to make sure both the height and width can be divided.
default_to_square (`bool`, *optional*, defaults to `self.default_to_square`):
Whether to default to a square video when resizing, if size is an int.
resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
Resampling filter to use if resizing the video. Only has an effect if `do_resize` is set to `True`. Can be
overridden by the `resample` parameter in the `preprocess` method.
do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`):
Whether to center crop the video to the specified `crop_size`. Can be overridden by `do_center_crop` in the
`preprocess` method.
do_pad (`bool`, *optional*):
Whether to pad the video to the `(max_height, max_width)` of the videos in the batch.
crop_size (`Dict[str, int]` *optional*, defaults to `self.crop_size`):
Size of the output video after applying `center_crop`. Can be overridden by `crop_size` in the `preprocess`
method.
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to rescale the video by the specified scale `rescale_factor`. Can be overridden by the
`do_rescale` parameter in the `preprocess` method.
rescale_factor (`int` or `float`, *optional*, defaults to `self.rescale_factor`):
Scale factor to use if rescaling the video. Only has an effect if `do_rescale` is set to `True`. Can be
overridden by the `rescale_factor` parameter in the `preprocess` method.
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
Whether to normalize the video. Can be overridden by the `do_normalize` parameter in the `preprocess`
method. Can be overridden by the `do_normalize` parameter in the `preprocess` method.
image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
Mean to use if normalizing the video. This is a float or list of floats the length of the number of
channels in the video. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be
overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
Standard deviation to use if normalizing the video. This is a float or list of floats the length of the
number of channels in the video. Can be overridden by the `image_std` parameter in the `preprocess` method.
Can be overridden by the `image_std` parameter in the `preprocess` method.
do_convert_rgb (`bool`, *optional*, defaults to `self.image_std`):
Whether to convert the video to RGB.
return_tensors (`str` or `TensorType`, *optional*):
Returns stacked tensors if set to `pt, otherwise returns a list of tensors.
data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
The channel dimension format for the output video. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: video in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: video in (height, width, num_channels) format.
- Unset: Use the channel dimension format of the input video.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input video. If unset, the channel dimension format is inferred
from the input video. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: video in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: video in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: video in (height, width) format.
device (`torch.device`, *optional*):
The device to process the videos on. If unset, the device is inferred from the input videos."""
@add_start_docstrings(
"Constructs a base VideoProcessor.",
BASE_VIDEO_PROCESSOR_DOCSTRING,
)
@requires(backends=("vision", "torchvision"))
class BaseVideoProcessor(BaseImageProcessorFast):
_auto_class = None
resample = None
image_mean = None
image_std = None
size = None
size_divisor = None
default_to_square = True
crop_size = None
do_resize = None
do_center_crop = None
do_pad = None
do_rescale = None
rescale_factor = 1 / 255
do_normalize = None
do_convert_rgb = None
valid_kwargs = VideosKwargs
model_input_names = ["pixel_values_videos"]
def __init__(self, **kwargs: Unpack[VideosKwargs]) -> None:
super().__init__()
self._processor_class = kwargs.pop("processor_class", None)
# Additional attributes without default values
for key, value in kwargs.items():
try:
setattr(self, key, value)
except AttributeError as err:
logger.error(f"Can't set {key} with value {value} for {self}")
raise err
# Prepare size related keys and turn then into `SizeDict`
size = kwargs.pop("size", self.size)
self.size = (
get_size_dict(size=size, default_to_square=kwargs.pop("default_to_square", self.default_to_square))
if size is not None
else None
)
crop_size = kwargs.pop("crop_size", self.crop_size)
self.crop_size = get_size_dict(crop_size, param_name="crop_size") if crop_size is not None else None
# Save valid kwargs in a list for further processing
self.model_valid_processing_keys = list(self.valid_kwargs.__annotations__.keys())
for key in self.model_valid_processing_keys:
if kwargs.get(key) is not None:
setattr(self, key, kwargs[key])
else:
setattr(self, key, getattr(self, key, None))
def __call__(self, videos, **kwargs) -> BatchFeature:
return self.preprocess(videos, **kwargs)
def convert_to_rgb(
self,
video: "torch.Tensor",
) -> VideoInput:
"""
Converts a video to RGB format.
Args:
video (`"torch.Tensor"`):
The video to convert.
Returns:
`torch.Tensor`: The converted video.
"""
video = F.grayscale_to_rgb(video)
if video.shape[-3] == 3 or not (video[..., 3, :, :] < 255).any():
return video
# There is a transparency layer, blend it with a white background.
# Calculate the alpha proportion for blending.
alpha = video[..., 3, :, :] / 255.0
video = (1 - alpha[..., None, :, :]) * 255 + alpha[..., None, :, :] * video[..., :3, :, :]
return video
def _prepare_input_videos(
self,
videos: VideoInput,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
device: Optional["torch.device"] = None,
) -> List["torch.Tensor"]:
"""
Prepare the input videos for processing.
"""
videos = make_batched_videos(videos)
processed_videos = []
for video in videos:
# `make_batched_videos` always returns a 4D array per video
if isinstance(video, np.ndarray):
video = to_channel_dimension_format(video, ChannelDimension.FIRST, input_data_format)
# not using F.to_tensor as it doesn't handle (C, H, W) numpy arrays
video = torch.from_numpy(video).contiguous()
# Now that we have torch tensors, we can move them to the right device
if device is not None:
video = video.to(device)
processed_videos.append(video)
return processed_videos
@add_start_docstrings(BASE_VIDEO_PROCESSOR_DOCSTRING)
def preprocess(
self,
videos: VideoInput,
**kwargs: Unpack[VideosKwargs],
) -> BatchFeature:
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self.valid_kwargs.__annotations__.keys())
# Set default kwargs from self. This ensures that if a kwarg is not provided
# by the user, it gets its default value from the instance, or is set to None.
for kwarg_name in self.valid_kwargs.__annotations__:
kwargs.setdefault(kwarg_name, getattr(self, kwarg_name, None))
input_data_format = kwargs.pop("input_data_format")
device = kwargs.pop("device")
videos = self._prepare_input_videos(videos=videos, input_data_format=input_data_format, device=device)
kwargs = self._further_process_kwargs(**kwargs)
self._validate_preprocess_kwargs(**kwargs)
# torch resize uses interpolation instead of resample
resample = kwargs.pop("resample")
kwargs["interpolation"] = (
pil_torch_interpolation_mapping[resample] if isinstance(resample, (PILImageResampling, int)) else resample
)
# Pop kwargs that are not needed in _preprocess
kwargs.pop("default_to_square")
kwargs.pop("data_format")
return self._preprocess(videos=videos, **kwargs)
def _preprocess(
self,
videos: List["torch.Tensor"],
do_convert_rgb: bool,
do_resize: bool,
size: SizeDict,
size_divisor: Optional[int],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: SizeDict,
do_rescale: bool,
do_pad: bool,
rescale_factor: float,
do_normalize: bool,
image_mean: Optional[Union[float, List[float]]],
image_std: Optional[Union[float, List[float]]],
return_tensors: Optional[Union[str, TensorType]] = None,
) -> BatchFeature:
# Group videos by size for batched resizing
grouped_videos, grouped_videos_index = group_videos_by_shape(videos)
resized_videos_grouped = {}
for shape, stacked_videos in grouped_videos.items():
if do_convert_rgb:
stacked_videos = self.convert_to_rgb(stacked_videos)
if do_resize:
stacked_videos = self.resize(
stacked_videos, size=size, size_divisor=size_divisor, interpolation=interpolation
)
resized_videos_grouped[shape] = stacked_videos
resized_videos = reorder_videos(resized_videos_grouped, grouped_videos_index)
# Group videos by size for further processing
# Needed in case do_resize is False, or resize returns videos with different sizes
grouped_videos, grouped_videos_index = group_videos_by_shape(resized_videos)
processed_videos_grouped = {}
for shape, stacked_videos in grouped_videos.items():
if do_center_crop:
stacked_videos = self.center_crop(stacked_videos, crop_size)
# Fused rescale and normalize
stacked_videos = self.rescale_and_normalize(
stacked_videos, do_rescale, rescale_factor, do_normalize, image_mean, image_std
)
processed_videos_grouped[shape] = stacked_videos
processed_videos = reorder_videos(processed_videos_grouped, grouped_videos_index)
processed_videos = torch.stack(processed_videos, dim=0) if return_tensors else processed_videos
return BatchFeature(data={"pixel_values_videos": processed_videos}, tensor_type=return_tensors)
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: Union[str, os.PathLike],
cache_dir: Optional[Union[str, os.PathLike]] = None,
force_download: bool = False,
local_files_only: bool = False,
token: Optional[Union[str, bool]] = None,
revision: str = "main",
**kwargs,
):
r"""
Instantiate a type of [`~video_processing_utils.VideoProcessorBase`] from an video processor.
Args:
pretrained_model_name_or_path (`str` or `os.PathLike`):
This can be either:
- a string, the *model id* of a pretrained video hosted inside a model repo on
huggingface.co.
- a path to a *directory* containing a video processor file saved using the
[`~video_processing_utils.VideoProcessorBase.save_pretrained`] method, e.g.,
`./my_model_directory/`.
- a path or url to a saved video processor JSON *file*, e.g.,
`./my_model_directory/preprocessor_config.json`.
cache_dir (`str` or `os.PathLike`, *optional*):
Path to a directory in which a downloaded pretrained model video processor should be cached if the
standard cache should not be used.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force to (re-)download the video processor files and override the cached versions if
they exist.
resume_download:
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
token (`str` or `bool`, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, or not specified, will use
the token generated when running `huggingface-cli login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
To test a pull request you made on the Hub, you can pass `revision="refs/pr/"`.
return_unused_kwargs (`bool`, *optional*, defaults to `False`):
If `False`, then this function returns just the final video processor object. If `True`, then this
functions returns a `Tuple(video_processor, unused_kwargs)` where *unused_kwargs* is a dictionary
consisting of the key/value pairs whose keys are not video processor attributes: i.e., the part of
`kwargs` which has not been used to update `video_processor` and is otherwise ignored.
subfolder (`str`, *optional*, defaults to `""`):
In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can
specify the folder name here.
kwargs (`Dict[str, Any]`, *optional*):
The values in kwargs of any keys which are video processor attributes will be used to override the
loaded values. Behavior concerning key/value pairs whose keys are *not* video processor attributes is
controlled by the `return_unused_kwargs` keyword parameter.
Returns:
A video processor of type [`~video_processing_utils.ImagVideoProcessorBase`].
Examples:
```python
# We can't instantiate directly the base class *VideoProcessorBase* so let's show the examples on a
# derived class: *LlavaOnevisionVideoProcessor*
video_processor = LlavaOnevisionVideoProcessor.from_pretrained(
"llava-hf/llava-onevision-qwen2-0.5b-ov-hf"
) # Download video_processing_config from huggingface.co and cache.
video_processor = LlavaOnevisionVideoProcessor.from_pretrained(
"./test/saved_model/"
) # E.g. video processor (or model) was saved using *save_pretrained('./test/saved_model/')*
video_processor = LlavaOnevisionVideoProcessor.from_pretrained("./test/saved_model/preprocessor_config.json")
video_processor = LlavaOnevisionVideoProcessor.from_pretrained(
"llava-hf/llava-onevision-qwen2-0.5b-ov-hf", do_normalize=False, foo=False
)
assert video_processor.do_normalize is False
video_processor, unused_kwargs = LlavaOnevisionVideoProcessor.from_pretrained(
"llava-hf/llava-onevision-qwen2-0.5b-ov-hf", do_normalize=False, foo=False, return_unused_kwargs=True
)
assert video_processor.do_normalize is False
assert unused_kwargs == {"foo": False}
```"""
kwargs["cache_dir"] = cache_dir
kwargs["force_download"] = force_download
kwargs["local_files_only"] = local_files_only
kwargs["revision"] = revision
use_auth_token = kwargs.pop("use_auth_token", None)
if use_auth_token is not None:
warnings.warn(
"The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.",
FutureWarning,
)
if token is not None:
raise ValueError(
"`token` and `use_auth_token` are both specified. Please set only the argument `token`."
)
token = use_auth_token
if token is not None:
kwargs["token"] = token
video_processor_dict, kwargs = cls.get_video_processor_dict(pretrained_model_name_or_path, **kwargs)
return cls.from_dict(video_processor_dict, **kwargs)
def save_pretrained(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):
"""
Save an video processor object to the directory `save_directory`, so that it can be re-loaded using the
[`~video_processing_utils.VideoProcessorBase.from_pretrained`] class method.
Args:
save_directory (`str` or `os.PathLike`):
Directory where the video processor JSON file will be saved (will be created if it does not exist).
push_to_hub (`bool`, *optional*, defaults to `False`):
Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
namespace).
kwargs (`Dict[str, Any]`, *optional*):
Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
"""
use_auth_token = kwargs.pop("use_auth_token", None)
if use_auth_token is not None:
warnings.warn(
"The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.",
FutureWarning,
)
if kwargs.get("token", None) is not None:
raise ValueError(
"`token` and `use_auth_token` are both specified. Please set only the argument `token`."
)
kwargs["token"] = use_auth_token
if os.path.isfile(save_directory):
raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")
os.makedirs(save_directory, exist_ok=True)
if push_to_hub:
commit_message = kwargs.pop("commit_message", None)
repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
repo_id = self._create_repo(repo_id, **kwargs)
files_timestamps = self._get_files_timestamps(save_directory)
# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
# loaded from the Hub.
if self._auto_class is not None:
custom_object_save(self, save_directory, config=self)
# If we save using the predefined names, we can load using `from_pretrained`
output_video_processor_file = os.path.join(save_directory, VIDEO_PROCESSOR_NAME)
self.to_json_file(output_video_processor_file)
logger.info(f"Video processor saved in {output_video_processor_file}")
if push_to_hub:
self._upload_modified_files(
save_directory,
repo_id,
files_timestamps,
commit_message=commit_message,
token=kwargs.get("token"),
)
return [output_video_processor_file]
@classmethod
def get_video_processor_dict(
cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs
) -> Tuple[Dict[str, Any], Dict[str, Any]]:
"""
From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used for instantiating a
video processor of type [`~video_processing_utils.VideoProcessorBase`] using `from_dict`.
Parameters:
pretrained_model_name_or_path (`str` or `os.PathLike`):
The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.
subfolder (`str`, *optional*, defaults to `""`):
In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can
specify the folder name here.
Returns:
`Tuple[Dict, Dict]`: The dictionary(ies) that will be used to instantiate the video processor object.
"""
cache_dir = kwargs.pop("cache_dir", None)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", None)
proxies = kwargs.pop("proxies", None)
token = kwargs.pop("token", None)
use_auth_token = kwargs.pop("use_auth_token", None)
local_files_only = kwargs.pop("local_files_only", False)
revision = kwargs.pop("revision", None)
subfolder = kwargs.pop("subfolder", "")
from_pipeline = kwargs.pop("_from_pipeline", None)
from_auto_class = kwargs.pop("_from_auto", False)
if use_auth_token is not None:
warnings.warn(
"The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.",
FutureWarning,
)
if token is not None:
raise ValueError(
"`token` and `use_auth_token` are both specified. Please set only the argument `token`."
)
token = use_auth_token
user_agent = {"file_type": "video processor", "from_auto_class": from_auto_class}
if from_pipeline is not None:
user_agent["using_pipeline"] = from_pipeline
if is_offline_mode() and not local_files_only:
logger.info("Offline mode: forcing local_files_only=True")
local_files_only = True
pretrained_model_name_or_path = str(pretrained_model_name_or_path)
is_local = os.path.isdir(pretrained_model_name_or_path)
if os.path.isfile(pretrained_model_name_or_path):
resolved_video_processor_file = pretrained_model_name_or_path
is_local = True
elif is_remote_url(pretrained_model_name_or_path):
video_processor_file = pretrained_model_name_or_path
resolved_video_processor_file = download_url(pretrained_model_name_or_path)
else:
try:
# Try to load with a new config name first and if not successfull try with
# the old file name. In case we can load with old name only, raise a deprecation warning
# Deprecated until v5.0
video_processor_file = VIDEO_PROCESSOR_NAME
resolved_video_processor_file = cached_file(
pretrained_model_name_or_path,
video_processor_file,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
token=token,
user_agent=user_agent,
revision=revision,
subfolder=subfolder,
)
except EnvironmentError:
video_processor_file = "preprocessor_config.json"
resolved_video_processor_file = cached_file(
pretrained_model_name_or_path,
video_processor_file,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
token=token,
user_agent=user_agent,
revision=revision,
subfolder=subfolder,
)
logger.warning_once(
"You have video processor config saved in `preprocessor.json` file which is deprecated. "
"Video processor configs should be saved in their own `video_preprocessor.json` file. You can rename "
"the file or load and save the processor back which renames it automatically. "
"Loading from `preprocessor.json` will be removed in v5.0."
)
except EnvironmentError:
# Raise any environment error raise by `cached_file`. It will have a helpful error message adapted to
# the original exception.
raise
except Exception:
# For any other exception, we throw a generic error.
raise EnvironmentError(
f"Can't load video processor for '{pretrained_model_name_or_path}'. If you were trying to load"
" it from 'https://huggingface.co/models', make sure you don't have a local directory with the"
f" same name. Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a"
f" directory containing a {VIDEO_PROCESSOR_NAME} file"
)
try:
# Load video_processor dict
with open(resolved_video_processor_file, "r", encoding="utf-8") as reader:
text = reader.read()
video_processor_dict = json.loads(text)
except json.JSONDecodeError:
raise EnvironmentError(
f"It looks like the config file at '{resolved_video_processor_file}' is not a valid JSON file."
)
if is_local:
logger.info(f"loading configuration file {resolved_video_processor_file}")
else:
logger.info(
f"loading configuration file {video_processor_file} from cache at {resolved_video_processor_file}"
)
if not is_local:
if "auto_map" in video_processor_dict:
video_processor_dict["auto_map"] = add_model_info_to_auto_map(
video_processor_dict["auto_map"], pretrained_model_name_or_path
)
if "custom_pipelines" in video_processor_dict:
video_processor_dict["custom_pipelines"] = add_model_info_to_custom_pipelines(
video_processor_dict["custom_pipelines"], pretrained_model_name_or_path
)
return video_processor_dict, kwargs
@classmethod
def from_dict(cls, video_processor_dict: Dict[str, Any], **kwargs):
"""
Instantiates a type of [`~video_processing_utils.VideoProcessorBase`] from a Python dictionary of parameters.
Args:
video_processor_dict (`Dict[str, Any]`):
Dictionary that will be used to instantiate the video processor object. Such a dictionary can be
retrieved from a pretrained checkpoint by leveraging the
[`~video_processing_utils.VideoProcessorBase.to_dict`] method.
kwargs (`Dict[str, Any]`):
Additional parameters from which to initialize the video processor object.
Returns:
[`~video_processing_utils.VideoProcessorBase`]: The video processor object instantiated from those
parameters.
"""
video_processor_dict = video_processor_dict.copy()
return_unused_kwargs = kwargs.pop("return_unused_kwargs", False)
# The `size` parameter is a dict and was previously an int or tuple in feature extractors.
# We set `size` here directly to the `video_processor_dict` so that it is converted to the appropriate
# dict within the video processor and isn't overwritten if `size` is passed in as a kwarg.
if "size" in kwargs and "size" in video_processor_dict:
video_processor_dict["size"] = kwargs.pop("size")
if "crop_size" in kwargs and "crop_size" in video_processor_dict:
video_processor_dict["crop_size"] = kwargs.pop("crop_size")
video_processor = cls(**video_processor_dict)
# Update video_processor with kwargs if needed
to_remove = []
for key, value in kwargs.items():
if hasattr(video_processor, key):
setattr(video_processor, key, value)
to_remove.append(key)
for key in to_remove:
kwargs.pop(key, None)
logger.info(f"Video processor {video_processor}")
if return_unused_kwargs:
return video_processor, kwargs
else:
return video_processor
def to_dict(self) -> Dict[str, Any]:
"""
Serializes this instance to a Python dictionary.
Returns:
`Dict[str, Any]`: Dictionary of all the attributes that make up this video processor instance.
"""
output = copy.deepcopy(self.__dict__)
output["video_processor_type"] = self.__class__.__name__
return output
def to_json_string(self) -> str:
"""
Serializes this instance to a JSON string.
Returns:
`str`: String containing all the attributes that make up this feature_extractor instance in JSON format.
"""
dictionary = self.to_dict()
for key, value in dictionary.items():
if isinstance(value, np.ndarray):
dictionary[key] = value.tolist()
# make sure private name "_processor_class" is correctly
# saved as "processor_class"
_processor_class = dictionary.pop("_processor_class", None)
if _processor_class is not None:
dictionary["processor_class"] = _processor_class
return json.dumps(dictionary, indent=2, sort_keys=True) + "\n"
def to_json_file(self, json_file_path: Union[str, os.PathLike]):
"""
Save this instance to a JSON file.
Args:
json_file_path (`str` or `os.PathLike`):
Path to the JSON file in which this image_processor instance's parameters will be saved.
"""
with open(json_file_path, "w", encoding="utf-8") as writer:
writer.write(self.to_json_string())
def __repr__(self):
return f"{self.__class__.__name__} {self.to_json_string()}"
@classmethod
def from_json_file(cls, json_file: Union[str, os.PathLike]):
"""
Instantiates a video processor of type [`~video_processing_utils.VideoProcessorBase`] from the path to a JSON
file of parameters.
Args:
json_file (`str` or `os.PathLike`):
Path to the JSON file containing the parameters.
Returns:
A video processor of type [`~video_processing_utils.VideoProcessorBase`]: The video_processor object
instantiated from that JSON file.
"""
with open(json_file, "r", encoding="utf-8") as reader:
text = reader.read()
video_processor_dict = json.loads(text)
return cls(**video_processor_dict)
@classmethod
def register_for_auto_class(cls, auto_class="AutoVideoProcessor"):
"""
Register this class with a given auto class. This should only be used for custom video processors as the ones
in the library are already mapped with `AutoVideoProcessor `.
This API is experimental and may have some slight breaking changes in the next releases.
Args:
auto_class (`str` or `type`, *optional*, defaults to `"AutoVideoProcessor "`):
The auto class to register this new video processor with.
"""
if not isinstance(auto_class, str):
auto_class = auto_class.__name__
import transformers.models.auto as auto_module
if not hasattr(auto_module, auto_class):
raise ValueError(f"{auto_class} is not a valid auto class.")
cls._auto_class = auto_class
def fetch_videos(self, video_url_or_urls: Union[str, List[str]]):
"""
Convert a single or a list of urls into the corresponding `np.array` objects.
If a single url is passed, the return value will be a single object. If a list is passed a list of objects is
returned.
"""
if isinstance(video_url_or_urls, list):
return [self.fetch_videos(x) for x in video_url_or_urls]
elif isinstance(video_url_or_urls, str):
return load_video(video_url_or_urls)
else:
raise TypeError(f"only a single or a list of entries is supported but got type={type(video_url_or_urls)}")
BaseVideoProcessor.push_to_hub = copy_func(BaseVideoProcessor.push_to_hub)
if BaseVideoProcessor.push_to_hub.__doc__ is not None:
BaseVideoProcessor.push_to_hub.__doc__ = BaseVideoProcessor.push_to_hub.__doc__.format(
object="video processor", object_class="AutoVideoProcessor", object_files="video processor file"
)