# coding=utf-8 # Copyright 2022 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. """AutoImageProcessor class.""" import importlib import json import os import warnings from collections import OrderedDict from typing import TYPE_CHECKING, Dict, Optional, Tuple, Union # Build the list of all image processors from ...configuration_utils import PretrainedConfig from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code from ...image_processing_utils import ImageProcessingMixin from ...image_processing_utils_fast import BaseImageProcessorFast from ...utils import ( CONFIG_NAME, IMAGE_PROCESSOR_NAME, cached_file, is_timm_config_dict, is_timm_local_checkpoint, is_torchvision_available, is_vision_available, logging, ) from .auto_factory import _LazyAutoMapping from .configuration_auto import ( CONFIG_MAPPING_NAMES, AutoConfig, model_type_to_module_name, replace_list_option_in_docstrings, ) logger = logging.get_logger(__name__) if TYPE_CHECKING: # This significantly improves completion suggestion performance when # the transformers package is used with Microsoft's Pylance language server. IMAGE_PROCESSOR_MAPPING_NAMES: OrderedDict[str, Tuple[Optional[str], Optional[str]]] = OrderedDict() else: IMAGE_PROCESSOR_MAPPING_NAMES = OrderedDict( [ ("align", ("EfficientNetImageProcessor",)), ("aria", ("AriaImageProcessor",)), ("beit", ("BeitImageProcessor",)), ("bit", ("BitImageProcessor",)), ("blip", ("BlipImageProcessor", "BlipImageProcessorFast")), ("blip-2", ("BlipImageProcessor", "BlipImageProcessorFast")), ("bridgetower", ("BridgeTowerImageProcessor",)), ("chameleon", ("ChameleonImageProcessor",)), ("chinese_clip", ("ChineseCLIPImageProcessor",)), ("clip", ("CLIPImageProcessor", "CLIPImageProcessorFast")), ("clipseg", ("ViTImageProcessor", "ViTImageProcessorFast")), ("conditional_detr", ("ConditionalDetrImageProcessor",)), ("convnext", ("ConvNextImageProcessor", "ConvNextImageProcessorFast")), ("convnextv2", ("ConvNextImageProcessor", "ConvNextImageProcessorFast")), ("cvt", ("ConvNextImageProcessor", "ConvNextImageProcessorFast")), ("data2vec-vision", ("BeitImageProcessor",)), ("deformable_detr", ("DeformableDetrImageProcessor", "DeformableDetrImageProcessorFast")), ("deit", ("DeiTImageProcessor", "DeiTImageProcessorFast")), ("depth_anything", ("DPTImageProcessor",)), ("depth_pro", ("DepthProImageProcessor", "DepthProImageProcessorFast")), ("deta", ("DetaImageProcessor",)), ("detr", ("DetrImageProcessor", "DetrImageProcessorFast")), ("dinat", ("ViTImageProcessor", "ViTImageProcessorFast")), ("dinov2", ("BitImageProcessor",)), ("donut-swin", ("DonutImageProcessor",)), ("dpt", ("DPTImageProcessor",)), ("efficientformer", ("EfficientFormerImageProcessor",)), ("efficientnet", ("EfficientNetImageProcessor",)), ("flava", ("FlavaImageProcessor",)), ("focalnet", ("BitImageProcessor",)), ("fuyu", ("FuyuImageProcessor",)), ("gemma3", ("Gemma3ImageProcessor", "Gemma3ImageProcessorFast")), ("git", ("CLIPImageProcessor", "CLIPImageProcessorFast")), ("glpn", ("GLPNImageProcessor",)), ("got_ocr2", ("GotOcr2ImageProcessor", "GotOcr2ImageProcessorFast")), ("grounding-dino", ("GroundingDinoImageProcessor",)), ("groupvit", ("CLIPImageProcessor", "CLIPImageProcessorFast")), ("hiera", ("BitImageProcessor",)), ("idefics", ("IdeficsImageProcessor",)), ("idefics2", ("Idefics2ImageProcessor",)), ("idefics3", ("Idefics3ImageProcessor",)), ("ijepa", ("ViTImageProcessor", "ViTImageProcessorFast")), ("imagegpt", ("ImageGPTImageProcessor",)), ("instructblip", ("BlipImageProcessor", "BlipImageProcessorFast")), ("instructblipvideo", ("InstructBlipVideoImageProcessor",)), ("kosmos-2", ("CLIPImageProcessor", "CLIPImageProcessorFast")), ("layoutlmv2", ("LayoutLMv2ImageProcessor",)), ("layoutlmv3", ("LayoutLMv3ImageProcessor",)), ("levit", ("LevitImageProcessor",)), ("llama4", ("Llama4ImageProcessor", "Llama4ImageProcessorFast")), ("llava", ("LlavaImageProcessor", "LlavaImageProcessorFast")), ("llava_next", ("LlavaNextImageProcessor", "LlavaNextImageProcessorFast")), ("llava_next_video", ("LlavaNextVideoImageProcessor",)), ("llava_onevision", ("LlavaOnevisionImageProcessor", "LlavaOnevisionImageProcessorFast")), ("mask2former", ("Mask2FormerImageProcessor",)), ("maskformer", ("MaskFormerImageProcessor",)), ("mgp-str", ("ViTImageProcessor", "ViTImageProcessorFast")), ("mistral3", ("PixtralImageProcessor", "PixtralImageProcessorFast")), ("mllama", ("MllamaImageProcessor",)), ("mobilenet_v1", ("MobileNetV1ImageProcessor",)), ("mobilenet_v2", ("MobileNetV2ImageProcessor",)), ("mobilevit", ("MobileViTImageProcessor",)), ("mobilevitv2", ("MobileViTImageProcessor",)), ("nat", ("ViTImageProcessor", "ViTImageProcessorFast")), ("nougat", ("NougatImageProcessor",)), ("oneformer", ("OneFormerImageProcessor",)), ("owlv2", ("Owlv2ImageProcessor",)), ("owlvit", ("OwlViTImageProcessor",)), ("paligemma", ("SiglipImageProcessor", "SiglipImageProcessorFast")), ("perceiver", ("PerceiverImageProcessor",)), ("phi4_multimodal", "Phi4MultimodalImageProcessorFast"), ("pix2struct", ("Pix2StructImageProcessor",)), ("pixtral", ("PixtralImageProcessor", "PixtralImageProcessorFast")), ("poolformer", ("PoolFormerImageProcessor",)), ("prompt_depth_anything", ("PromptDepthAnythingImageProcessor",)), ("pvt", ("PvtImageProcessor",)), ("pvt_v2", ("PvtImageProcessor",)), ("qwen2_5_vl", ("Qwen2VLImageProcessor", "Qwen2VLImageProcessorFast")), ("qwen2_vl", ("Qwen2VLImageProcessor", "Qwen2VLImageProcessorFast")), ("regnet", ("ConvNextImageProcessor", "ConvNextImageProcessorFast")), ("resnet", ("ConvNextImageProcessor", "ConvNextImageProcessorFast")), ("rt_detr", ("RTDetrImageProcessor", "RTDetrImageProcessorFast")), ("sam", ("SamImageProcessor",)), ("segformer", ("SegformerImageProcessor",)), ("seggpt", ("SegGptImageProcessor",)), ("shieldgemma2", ("Gemma3ImageProcessor", "Gemma3ImageProcessorFast")), ("siglip", ("SiglipImageProcessor", "SiglipImageProcessorFast")), ("siglip2", ("Siglip2ImageProcessor", "Siglip2ImageProcessorFast")), ("superglue", ("SuperGlueImageProcessor",)), ("swiftformer", ("ViTImageProcessor", "ViTImageProcessorFast")), ("swin", ("ViTImageProcessor", "ViTImageProcessorFast")), ("swin2sr", ("Swin2SRImageProcessor",)), ("swinv2", ("ViTImageProcessor", "ViTImageProcessorFast")), ("table-transformer", ("DetrImageProcessor",)), ("timesformer", ("VideoMAEImageProcessor",)), ("timm_wrapper", ("TimmWrapperImageProcessor",)), ("tvlt", ("TvltImageProcessor",)), ("tvp", ("TvpImageProcessor",)), ("udop", ("LayoutLMv3ImageProcessor",)), ("upernet", ("SegformerImageProcessor",)), ("van", ("ConvNextImageProcessor", "ConvNextImageProcessorFast")), ("videomae", ("VideoMAEImageProcessor",)), ("vilt", ("ViltImageProcessor",)), ("vipllava", ("CLIPImageProcessor", "CLIPImageProcessorFast")), ("vit", ("ViTImageProcessor", "ViTImageProcessorFast")), ("vit_hybrid", ("ViTHybridImageProcessor",)), ("vit_mae", ("ViTImageProcessor", "ViTImageProcessorFast")), ("vit_msn", ("ViTImageProcessor", "ViTImageProcessorFast")), ("vitmatte", ("VitMatteImageProcessor",)), ("xclip", ("CLIPImageProcessor", "CLIPImageProcessorFast")), ("yolos", ("YolosImageProcessor",)), ("zoedepth", ("ZoeDepthImageProcessor",)), ] ) for model_type, image_processors in IMAGE_PROCESSOR_MAPPING_NAMES.items(): slow_image_processor_class, *fast_image_processor_class = image_processors if not is_vision_available(): slow_image_processor_class = None # If the fast image processor is not defined, or torchvision is not available, we set it to None if not fast_image_processor_class or fast_image_processor_class[0] is None or not is_torchvision_available(): fast_image_processor_class = None else: fast_image_processor_class = fast_image_processor_class[0] IMAGE_PROCESSOR_MAPPING_NAMES[model_type] = (slow_image_processor_class, fast_image_processor_class) IMAGE_PROCESSOR_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES) def get_image_processor_class_from_name(class_name: str): if class_name == "BaseImageProcessorFast": return BaseImageProcessorFast for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items(): if class_name in extractors: module_name = model_type_to_module_name(module_name) module = importlib.import_module(f".{module_name}", "transformers.models") try: return getattr(module, class_name) except AttributeError: continue for _, extractors in IMAGE_PROCESSOR_MAPPING._extra_content.items(): for extractor in extractors: if getattr(extractor, "__name__", None) == class_name: return extractor # We did not find the class, but maybe it's because a dep is missing. In that case, the class will be in the main # init and we return the proper dummy to get an appropriate error message. main_module = importlib.import_module("transformers") if hasattr(main_module, class_name): return getattr(main_module, class_name) return None def get_image_processor_config( pretrained_model_name_or_path: Union[str, os.PathLike], cache_dir: Optional[Union[str, os.PathLike]] = None, force_download: bool = False, resume_download: Optional[bool] = None, proxies: Optional[Dict[str, str]] = None, token: Optional[Union[bool, str]] = None, revision: Optional[str] = None, local_files_only: bool = False, **kwargs, ): """ Loads the image processor configuration from a pretrained model image processor configuration. Args: pretrained_model_name_or_path (`str` or `os.PathLike`): This can be either: - a string, the *model id* of a pretrained model configuration hosted inside a model repo on huggingface.co. - a path to a *directory* containing a configuration file saved using the [`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`. cache_dir (`str` or `os.PathLike`, *optional*): Path to a directory in which a downloaded pretrained model configuration 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 configuration 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`, 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. local_files_only (`bool`, *optional*, defaults to `False`): If `True`, will only try to load the image processor configuration from local files. Passing `token=True` is required when you want to use a private model. Returns: `Dict`: The configuration of the image processor. Examples: ```python # Download configuration from huggingface.co and cache. image_processor_config = get_image_processor_config("google-bert/bert-base-uncased") # This model does not have a image processor config so the result will be an empty dict. image_processor_config = get_image_processor_config("FacebookAI/xlm-roberta-base") # Save a pretrained image processor locally and you can reload its config from transformers import AutoTokenizer image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k") image_processor.save_pretrained("image-processor-test") image_processor_config = get_image_processor_config("image-processor-test") ```""" 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 resolved_config_file = cached_file( pretrained_model_name_or_path, IMAGE_PROCESSOR_NAME, cache_dir=cache_dir, force_download=force_download, resume_download=resume_download, proxies=proxies, token=token, revision=revision, local_files_only=local_files_only, _raise_exceptions_for_gated_repo=False, _raise_exceptions_for_missing_entries=False, _raise_exceptions_for_connection_errors=False, ) if resolved_config_file is None: logger.info( "Could not locate the image processor configuration file, will try to use the model config instead." ) return {} with open(resolved_config_file, encoding="utf-8") as reader: return json.load(reader) def _warning_fast_image_processor_available(fast_class): logger.warning( f"Fast image processor class {fast_class} is available for this model. " "Using slow image processor class. To use the fast image processor class set `use_fast=True`." ) class AutoImageProcessor: r""" This is a generic image processor class that will be instantiated as one of the image processor classes of the library when created with the [`AutoImageProcessor.from_pretrained`] class method. This class cannot be instantiated directly using `__init__()` (throws an error). """ def __init__(self): raise EnvironmentError( "AutoImageProcessor is designed to be instantiated " "using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method." ) @classmethod @replace_list_option_in_docstrings(IMAGE_PROCESSOR_MAPPING_NAMES) def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs): r""" Instantiate one of the image processor classes of the library from a pretrained model vocabulary. The image processor class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it's missing, by falling back to using pattern matching on `pretrained_model_name_or_path`: List options Params: pretrained_model_name_or_path (`str` or `os.PathLike`): This can be either: - a string, the *model id* of a pretrained image_processor hosted inside a model repo on huggingface.co. - a path to a *directory* containing a image processor file saved using the [`~image_processing_utils.ImageProcessingMixin.save_pretrained`] method, e.g., `./my_model_directory/`. - a path or url to a saved image 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 image 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 image 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`, 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. use_fast (`bool`, *optional*, defaults to `False`): Use a fast torchvision-base image processor if it is supported for a given model. If a fast image processor is not available for a given model, a normal numpy-based image processor is returned instead. return_unused_kwargs (`bool`, *optional*, defaults to `False`): If `False`, then this function returns just the final image processor object. If `True`, then this functions returns a `Tuple(image_processor, unused_kwargs)` where *unused_kwargs* is a dictionary consisting of the key/value pairs whose keys are not image processor attributes: i.e., the part of `kwargs` which has not been used to update `image_processor` and is otherwise ignored. trust_remote_code (`bool`, *optional*, defaults to `False`): Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. image_processor_filename (`str`, *optional*, defaults to `"config.json"`): The name of the file in the model directory to use for the image processor config. kwargs (`Dict[str, Any]`, *optional*): The values in kwargs of any keys which are image processor attributes will be used to override the loaded values. Behavior concerning key/value pairs whose keys are *not* image processor attributes is controlled by the `return_unused_kwargs` keyword parameter. Passing `token=True` is required when you want to use a private model. Examples: ```python >>> from transformers import AutoImageProcessor >>> # Download image processor from huggingface.co and cache. >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k") >>> # If image processor files are in a directory (e.g. image processor was saved using *save_pretrained('./test/saved_model/')*) >>> # image_processor = AutoImageProcessor.from_pretrained("./test/saved_model/") ```""" 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 config = kwargs.pop("config", None) # TODO: @yoni, change in v4.48 (use_fast set to True by default) use_fast = kwargs.pop("use_fast", None) trust_remote_code = kwargs.pop("trust_remote_code", None) kwargs["_from_auto"] = True # Resolve the image processor config filename if "image_processor_filename" in kwargs: image_processor_filename = kwargs.pop("image_processor_filename") elif is_timm_local_checkpoint(pretrained_model_name_or_path): image_processor_filename = CONFIG_NAME else: image_processor_filename = IMAGE_PROCESSOR_NAME # Load the image processor config try: # Main path for all transformers models and local TimmWrapper checkpoints config_dict, _ = ImageProcessingMixin.get_image_processor_dict( pretrained_model_name_or_path, image_processor_filename=image_processor_filename, **kwargs ) except Exception as initial_exception: # Fallback path for Hub TimmWrapper checkpoints. Timm models' image processing is saved in `config.json` # instead of `preprocessor_config.json`. Because this is an Auto class and we don't have any information # except the model name, the only way to check if a remote checkpoint is a timm model is to try to # load `config.json` and if it fails with some error, we raise the initial exception. try: config_dict, _ = ImageProcessingMixin.get_image_processor_dict( pretrained_model_name_or_path, image_processor_filename=CONFIG_NAME, **kwargs ) except Exception: raise initial_exception # In case we have a config_dict, but it's not a timm config dict, we raise the initial exception, # because only timm models have image processing in `config.json`. if not is_timm_config_dict(config_dict): raise initial_exception image_processor_type = config_dict.get("image_processor_type", None) image_processor_auto_map = None if "AutoImageProcessor" in config_dict.get("auto_map", {}): image_processor_auto_map = config_dict["auto_map"]["AutoImageProcessor"] # If we still don't have the image processor class, check if we're loading from a previous feature extractor config # and if so, infer the image processor class from there. if image_processor_type is None and image_processor_auto_map is None: feature_extractor_class = config_dict.pop("feature_extractor_type", None) if feature_extractor_class is not None: image_processor_type = feature_extractor_class.replace("FeatureExtractor", "ImageProcessor") if "AutoFeatureExtractor" in config_dict.get("auto_map", {}): feature_extractor_auto_map = config_dict["auto_map"]["AutoFeatureExtractor"] image_processor_auto_map = feature_extractor_auto_map.replace("FeatureExtractor", "ImageProcessor") # If we don't find the image processor class in the image processor config, let's try the model config. if image_processor_type is None and image_processor_auto_map is None: if not isinstance(config, PretrainedConfig): config = AutoConfig.from_pretrained( pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs, ) # It could be in `config.image_processor_type`` image_processor_type = getattr(config, "image_processor_type", None) if hasattr(config, "auto_map") and "AutoImageProcessor" in config.auto_map: image_processor_auto_map = config.auto_map["AutoImageProcessor"] image_processor_class = None # TODO: @yoni, change logic in v4.52 (when use_fast set to True by default) if image_processor_type is not None: # if use_fast is not set and the processor was saved with a fast processor, we use it, otherwise we use the slow processor. if use_fast is None: use_fast = image_processor_type.endswith("Fast") if not use_fast: logger.warning_once( "Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. " "`use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. " "This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`." ) # Update class name to reflect the use_fast option. If class is not found, we fall back to the slow version. if use_fast and not is_torchvision_available(): logger.warning_once( "Using `use_fast=True` but `torchvision` is not available. Falling back to the slow image processor." ) use_fast = False if use_fast: if not image_processor_type.endswith("Fast"): image_processor_type += "Fast" for _, image_processors in IMAGE_PROCESSOR_MAPPING_NAMES.items(): if image_processor_type in image_processors: break else: image_processor_type = image_processor_type[:-4] use_fast = False logger.warning_once( "`use_fast` is set to `True` but the image processor class does not have a fast version. " " Falling back to the slow version." ) image_processor_class = get_image_processor_class_from_name(image_processor_type) else: image_processor_type = ( image_processor_type[:-4] if image_processor_type.endswith("Fast") else image_processor_type ) image_processor_class = get_image_processor_class_from_name(image_processor_type) has_remote_code = image_processor_auto_map is not None has_local_code = image_processor_class is not None or type(config) in IMAGE_PROCESSOR_MAPPING trust_remote_code = resolve_trust_remote_code( trust_remote_code, pretrained_model_name_or_path, has_local_code, has_remote_code ) if image_processor_auto_map is not None and not isinstance(image_processor_auto_map, tuple): # In some configs, only the slow image processor class is stored image_processor_auto_map = (image_processor_auto_map, None) if has_remote_code and trust_remote_code: if not use_fast and image_processor_auto_map[1] is not None: _warning_fast_image_processor_available(image_processor_auto_map[1]) if use_fast and image_processor_auto_map[1] is not None: class_ref = image_processor_auto_map[1] else: class_ref = image_processor_auto_map[0] image_processor_class = get_class_from_dynamic_module(class_ref, pretrained_model_name_or_path, **kwargs) _ = kwargs.pop("code_revision", None) if os.path.isdir(pretrained_model_name_or_path): image_processor_class.register_for_auto_class() return image_processor_class.from_dict(config_dict, **kwargs) elif image_processor_class is not None: return image_processor_class.from_dict(config_dict, **kwargs) # Last try: we use the IMAGE_PROCESSOR_MAPPING. elif type(config) in IMAGE_PROCESSOR_MAPPING: image_processor_tuple = IMAGE_PROCESSOR_MAPPING[type(config)] image_processor_class_py, image_processor_class_fast = image_processor_tuple if not use_fast and image_processor_class_fast is not None: _warning_fast_image_processor_available(image_processor_class_fast) if image_processor_class_fast and (use_fast or image_processor_class_py is None): return image_processor_class_fast.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) else: if image_processor_class_py is not None: return image_processor_class_py.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) else: raise ValueError( "This image processor cannot be instantiated. Please make sure you have `Pillow` installed." ) raise ValueError( f"Unrecognized image processor in {pretrained_model_name_or_path}. Should have a " f"`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following " f"`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys())}" ) @staticmethod def register( config_class, image_processor_class=None, slow_image_processor_class=None, fast_image_processor_class=None, exist_ok=False, ): """ Register a new image processor for this class. Args: config_class ([`PretrainedConfig`]): The configuration corresponding to the model to register. image_processor_class ([`ImageProcessingMixin`]): The image processor to register. """ if image_processor_class is not None: if slow_image_processor_class is not None: raise ValueError("Cannot specify both image_processor_class and slow_image_processor_class") warnings.warn( "The image_processor_class argument is deprecated and will be removed in v4.42. Please use `slow_image_processor_class`, or `fast_image_processor_class` instead", FutureWarning, ) slow_image_processor_class = image_processor_class if slow_image_processor_class is None and fast_image_processor_class is None: raise ValueError("You need to specify either slow_image_processor_class or fast_image_processor_class") if slow_image_processor_class is not None and issubclass(slow_image_processor_class, BaseImageProcessorFast): raise ValueError("You passed a fast image processor in as the `slow_image_processor_class`.") if fast_image_processor_class is not None and not issubclass( fast_image_processor_class, BaseImageProcessorFast ): raise ValueError("The `fast_image_processor_class` should inherit from `BaseImageProcessorFast`.") if ( slow_image_processor_class is not None and fast_image_processor_class is not None and issubclass(fast_image_processor_class, BaseImageProcessorFast) and fast_image_processor_class.slow_image_processor_class != slow_image_processor_class ): raise ValueError( "The fast processor class you are passing has a `slow_image_processor_class` attribute that is not " "consistent with the slow processor class you passed (fast tokenizer has " f"{fast_image_processor_class.slow_image_processor_class} and you passed {slow_image_processor_class}. Fix one of those " "so they match!" ) # Avoid resetting a set slow/fast image processor if we are passing just the other ones. if config_class in IMAGE_PROCESSOR_MAPPING._extra_content: existing_slow, existing_fast = IMAGE_PROCESSOR_MAPPING[config_class] if slow_image_processor_class is None: slow_image_processor_class = existing_slow if fast_image_processor_class is None: fast_image_processor_class = existing_fast IMAGE_PROCESSOR_MAPPING.register( config_class, (slow_image_processor_class, fast_image_processor_class), exist_ok=exist_ok )