






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__UpperCamelCase					:		Dict	  =						logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/image-classification/requirements.txt""")

__UpperCamelCase					:		Dict	  =						list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
__UpperCamelCase					:		Any	  =						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def      a_ (      _A  )			->			Optional[Any]:







				"""simple docstring"""
				with open(_A      ,   'rb'  ) as f:
								snake_case__									=     Image.open(_A  )
								return im.convert('RGB'  )


@dataclass
class   __SCREAMING_SNAKE_CASE:
							_UpperCAmelCase     =       field(
							    default=a_    ,  metadata={
							        "help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."
							    }    ,  )
							_UpperCAmelCase     =       field(
							    default=a_    ,  metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}	)
							_UpperCAmelCase     =       field(default=a_    ,  metadata={"help": "A folder containing the training data."}	)
							_UpperCAmelCase     =       field(default=a_    ,  metadata={"help": "A folder containing the validation data."}	)
							_UpperCAmelCase     =       field(
							    default=0.1_5    ,  metadata={"help": "Percent to split off of train for validation."}	)
							_UpperCAmelCase     =       field(
							    default=a_    ,  metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of training examples to this "
							            "value if set."
							        )
							    }    ,  )
							_UpperCAmelCase     =       field(
							    default=a_    ,  metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
							            "value if set."
							        )
							    }    ,  )
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Dict:
											if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
															raise ValueError(
															    'You must specify either a dataset name from the hub or a train and/or validation directory.'						)


@dataclass
class   __SCREAMING_SNAKE_CASE:
							_UpperCAmelCase     =       field(
							    default="google/vit-base-patch16-224-in21k"    ,  metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}    ,  )
							_UpperCAmelCase     =       field(
							    default=a_    ,  metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(a_	)}    ,  )
							_UpperCAmelCase     =       field(
							    default=a_    ,  metadata={"help": "Pretrained config name or path if not the same as model_name"}	)
							_UpperCAmelCase     =       field(
							    default=a_    ,  metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}	)
							_UpperCAmelCase     =       field(
							    default="main"    ,  metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}    ,  )
							_UpperCAmelCase     =       field(default=a_    ,  metadata={"help": "Name or path of preprocessor config."}	)
							_UpperCAmelCase     =       field(
							    default=a_    ,  metadata={
							        "help": (
							            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
							            "with private models)."
							        )
							    }    ,  )
							_UpperCAmelCase     =       field(
							    default=a_    ,  metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}    ,  )


def      a_ (      _A  )			->			Tuple:







				"""simple docstring"""
				snake_case__									=     torch.stack([example['pixel_values'] for example in examples]  )
				snake_case__									=     torch.tensor([example['labels'] for example in examples]  )
				return {"pixel_values": pixel_values, "labels": labels}
def      a_ (      )			->			Union[str, Any]:







				"""simple docstring"""
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				snake_case__									=     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
				if len(sys.argv  ) == 2 and sys.argv[1].endswith('.json'  ):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								snake_case__							,						snake_case__							,						snake_case__									=     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
				else:
								snake_case__							,						snake_case__							,						snake_case__									=     parser.parse_args_into_dataclasses()

				# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
				# information sent is the one passed as arguments along with your Python/PyTorch versions.
				send_example_telemetry('run_image_classification'      ,   _A      ,   _A  )

				# Setup logging
				logging.basicConfig(
				    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'      ,   datefmt='%m/%d/%Y %H:%M:%S'      ,   handlers=[logging.StreamHandler(sys.stdout  )]      ,   )

				if training_args.should_log:
								# The default of training_args.log_level is passive, so we set log level at info here to have that default.
								transformers.utils.logging.set_verbosity_info()

				snake_case__									=     training_args.get_process_log_level()
				logger.setLevel(_A  )
				transformers.utils.logging.set_verbosity(_A  )
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

				# Log on each process the small summary:
				logger.warning(
				    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
				    + f'''distributed training: {bool(training_args.local_rank != -1  )}, 16-bits training: {training_args.fpaa}'''  )
				logger.info(f'''Training/evaluation parameters {training_args}'''  )

				# Detecting last checkpoint.
				snake_case__									=     None
				if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
								snake_case__									=     get_last_checkpoint(training_args.output_dir  )
								if last_checkpoint is None and len(os.listdir(training_args.output_dir  )  ) > 0:
												raise ValueError(
												    f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
												    'Use --overwrite_output_dir to overcome.'  )
								elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
												logger.info(
												    f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
												    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'  )

    # Set seed before initializing model.
				set_seed(training_args.seed  )

				# Initialize our dataset and prepare it for the 'image-classification' task.
				if data_args.dataset_name is not None:
								snake_case__									=     load_dataset(
								    data_args.dataset_name      ,   data_args.dataset_config_name      ,   cache_dir=model_args.cache_dir      ,   task='image-classification'      ,   use_auth_token=True if model_args.use_auth_token else None      ,   )
				else:
								snake_case__									=     {}
								if data_args.train_dir is not None:
												snake_case__									=     os.path.join(data_args.train_dir      ,   '**'  )
								if data_args.validation_dir is not None:
												snake_case__									=     os.path.join(data_args.validation_dir      ,   '**'  )
								snake_case__									=     load_dataset(
								    'imagefolder'      ,   data_files=_A      ,   cache_dir=model_args.cache_dir      ,   task='image-classification'      ,   )

				# If we don't have a validation split, split off a percentage of train as validation.
				snake_case__									=     None if 'validation' in dataset.keys() else data_args.train_val_split
				if isinstance(data_args.train_val_split      ,   _A  ) and data_args.train_val_split > 0.0:
								snake_case__									=     dataset['train'].train_test_split(data_args.train_val_split  )
								snake_case__									=     split['train']
								snake_case__									=     split['test']

				# Prepare label mappings.
				# We'll include these in the model's config to get human readable labels in the Inference API.
				snake_case__									=     dataset['train'].features['labels'].names
				snake_case__							,						snake_case__									=     {}, {}
				for i, label in enumerate(_A  ):
								snake_case__									=     str(_A  )
								snake_case__									=     label

				# Load the accuracy metric from the datasets package
				snake_case__									=     evaluate.load('accuracy'  )

				# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
				# predictions and label_ids field) and has to return a dictionary string to float.
				def compute_metrics(_A  ):
								return metric.compute(predictions=np.argmax(p.predictions      ,   axis=1  )      ,   references=p.label_ids  )

				snake_case__									=     AutoConfig.from_pretrained(
				    model_args.config_name or model_args.model_name_or_path      ,   num_labels=len(_A  )      ,   labelaid=_A      ,   idalabel=_A      ,   finetuning_task='image-classification'      ,   cache_dir=model_args.cache_dir      ,   revision=model_args.model_revision      ,   use_auth_token=True if model_args.use_auth_token else None      ,   )
				snake_case__									=     AutoModelForImageClassification.from_pretrained(
				    model_args.model_name_or_path      ,   from_tf=bool('.ckpt' in model_args.model_name_or_path  )      ,   config=_A      ,   cache_dir=model_args.cache_dir      ,   revision=model_args.model_revision      ,   use_auth_token=True if model_args.use_auth_token else None      ,   ignore_mismatched_sizes=model_args.ignore_mismatched_sizes      ,   )
				snake_case__									=     AutoImageProcessor.from_pretrained(
				    model_args.image_processor_name or model_args.model_name_or_path      ,   cache_dir=model_args.cache_dir      ,   revision=model_args.model_revision      ,   use_auth_token=True if model_args.use_auth_token else None      ,   )

				# Define torchvision transforms to be applied to each image.
				if "shortest_edge" in image_processor.size:
								snake_case__									=     image_processor.size['shortest_edge']
				else:
								snake_case__									=     (image_processor.size['height'], image_processor.size['width'])
				snake_case__									=     Normalize(mean=image_processor.image_mean      ,   std=image_processor.image_std  )
				snake_case__									=     Compose(
				    [
				        RandomResizedCrop(_A  ),
				        RandomHorizontalFlip(),
				        ToTensor(),
				        normalize,
				    ]  )
				snake_case__									=     Compose(
				    [
				        Resize(_A  ),
				        CenterCrop(_A  ),
				        ToTensor(),
				        normalize,
				    ]  )

				def train_transforms(_A  ):
								snake_case__									=     [
								    _train_transforms(pil_img.convert('RGB'  )  ) for pil_img in example_batch['image']
								]
								return example_batch

				def val_transforms(_A  ):
								snake_case__									=     [_val_transforms(pil_img.convert('RGB'  )  ) for pil_img in example_batch['image']]
								return example_batch

				if training_args.do_train:
								if "train" not in dataset:
												raise ValueError('--do_train requires a train dataset'  )
								if data_args.max_train_samples is not None:
												snake_case__									=     (
												    dataset['train'].shuffle(seed=training_args.seed  ).select(range(data_args.max_train_samples  )  )
												)
								# Set the training transforms
								dataset["train"].set_transform(_A  )

				if training_args.do_eval:
								if "validation" not in dataset:
												raise ValueError('--do_eval requires a validation dataset'  )
								if data_args.max_eval_samples is not None:
												snake_case__									=     (
												    dataset['validation'].shuffle(seed=training_args.seed  ).select(range(data_args.max_eval_samples  )  )
												)
								# Set the validation transforms
								dataset["validation"].set_transform(_A  )

				# Initalize our trainer
				snake_case__									=     Trainer(
				    model=_A      ,   args=_A      ,   train_dataset=dataset['train'] if training_args.do_train else None      ,   eval_dataset=dataset['validation'] if training_args.do_eval else None      ,   compute_metrics=_A      ,   tokenizer=_A      ,   data_collator=_A      ,   )

				# Training
				if training_args.do_train:
								snake_case__									=     None
								if training_args.resume_from_checkpoint is not None:
												snake_case__									=     training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												snake_case__									=     last_checkpoint
								snake_case__									=     trainer.train(resume_from_checkpoint=_A  )
								trainer.save_model()
								trainer.log_metrics('train'      ,   train_result.metrics  )
								trainer.save_metrics('train'      ,   train_result.metrics  )
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								snake_case__									=     trainer.evaluate()
								trainer.log_metrics('eval'      ,   _A  )
								trainer.save_metrics('eval'      ,   _A  )

				# Write model card and (optionally) push to hub
				snake_case__									=     {
				    'finetuned_from': model_args.model_name_or_path,
				    'tasks': 'image-classification',
				    'dataset': data_args.dataset_name,
				    'tags': ['image-classification', 'vision'],
				}
				if training_args.push_to_hub:
								trainer.push_to_hub(**_A  )
				else:
								trainer.create_model_card(**_A  )


if __name__ == "__main__":
							main()












import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, 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 ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


__UpperCamelCase					:		List[Any]	  =						logging.get_logger(__name__)

__UpperCamelCase					:		str	  =						OrderedDict(
    [
        ("""align""", """EfficientNetImageProcessor"""),
        ("""beit""", """BeitImageProcessor"""),
        ("""bit""", """BitImageProcessor"""),
        ("""blip""", """BlipImageProcessor"""),
        ("""blip-2""", """BlipImageProcessor"""),
        ("""bridgetower""", """BridgeTowerImageProcessor"""),
        ("""chinese_clip""", """ChineseCLIPImageProcessor"""),
        ("""clip""", """CLIPImageProcessor"""),
        ("""clipseg""", """ViTImageProcessor"""),
        ("""conditional_detr""", """ConditionalDetrImageProcessor"""),
        ("""convnext""", """ConvNextImageProcessor"""),
        ("""convnextv2""", """ConvNextImageProcessor"""),
        ("""cvt""", """ConvNextImageProcessor"""),
        ("""data2vec-vision""", """BeitImageProcessor"""),
        ("""deformable_detr""", """DeformableDetrImageProcessor"""),
        ("""deit""", """DeiTImageProcessor"""),
        ("""deta""", """DetaImageProcessor"""),
        ("""detr""", """DetrImageProcessor"""),
        ("""dinat""", """ViTImageProcessor"""),
        ("""donut-swin""", """DonutImageProcessor"""),
        ("""dpt""", """DPTImageProcessor"""),
        ("""efficientformer""", """EfficientFormerImageProcessor"""),
        ("""efficientnet""", """EfficientNetImageProcessor"""),
        ("""flava""", """FlavaImageProcessor"""),
        ("""focalnet""", """BitImageProcessor"""),
        ("""git""", """CLIPImageProcessor"""),
        ("""glpn""", """GLPNImageProcessor"""),
        ("""groupvit""", """CLIPImageProcessor"""),
        ("""imagegpt""", """ImageGPTImageProcessor"""),
        ("""instructblip""", """BlipImageProcessor"""),
        ("""layoutlmv2""", """LayoutLMv2ImageProcessor"""),
        ("""layoutlmv3""", """LayoutLMv3ImageProcessor"""),
        ("""levit""", """LevitImageProcessor"""),
        ("""mask2former""", """Mask2FormerImageProcessor"""),
        ("""maskformer""", """MaskFormerImageProcessor"""),
        ("""mgp-str""", """ViTImageProcessor"""),
        ("""mobilenet_v1""", """MobileNetV1ImageProcessor"""),
        ("""mobilenet_v2""", """MobileNetV2ImageProcessor"""),
        ("""mobilevit""", """MobileViTImageProcessor"""),
        ("""mobilevit""", """MobileViTImageProcessor"""),
        ("""mobilevitv2""", """MobileViTImageProcessor"""),
        ("""nat""", """ViTImageProcessor"""),
        ("""oneformer""", """OneFormerImageProcessor"""),
        ("""owlvit""", """OwlViTImageProcessor"""),
        ("""perceiver""", """PerceiverImageProcessor"""),
        ("""pix2struct""", """Pix2StructImageProcessor"""),
        ("""poolformer""", """PoolFormerImageProcessor"""),
        ("""regnet""", """ConvNextImageProcessor"""),
        ("""resnet""", """ConvNextImageProcessor"""),
        ("""sam""", """SamImageProcessor"""),
        ("""segformer""", """SegformerImageProcessor"""),
        ("""swiftformer""", """ViTImageProcessor"""),
        ("""swin""", """ViTImageProcessor"""),
        ("""swin2sr""", """Swin2SRImageProcessor"""),
        ("""swinv2""", """ViTImageProcessor"""),
        ("""table-transformer""", """DetrImageProcessor"""),
        ("""timesformer""", """VideoMAEImageProcessor"""),
        ("""tvlt""", """TvltImageProcessor"""),
        ("""upernet""", """SegformerImageProcessor"""),
        ("""van""", """ConvNextImageProcessor"""),
        ("""videomae""", """VideoMAEImageProcessor"""),
        ("""vilt""", """ViltImageProcessor"""),
        ("""vit""", """ViTImageProcessor"""),
        ("""vit_hybrid""", """ViTHybridImageProcessor"""),
        ("""vit_mae""", """ViTImageProcessor"""),
        ("""vit_msn""", """ViTImageProcessor"""),
        ("""xclip""", """CLIPImageProcessor"""),
        ("""yolos""", """YolosImageProcessor"""),
    ]
)

__UpperCamelCase					:		Union[str, Any]	  =						_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def      a_ (      _A  )			->			Optional[int]:







				"""simple docstring"""
				for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
								if class_name in extractors:
												snake_case__									=     model_type_to_module_name(_A  )

												snake_case__									=     importlib.import_module(f'''.{module_name}'''      ,   'transformers.models'  )
												try:
																return getattr(_A      ,   _A  )
												except AttributeError:
																continue

				for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
								if getattr(_A      ,   '__name__'      ,   _A  ) == class_name:
												return extractor

    # We did not fine 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.
				snake_case__									=     importlib.import_module('transformers'  )
				if hasattr(_A      ,   _A  ):
								return getattr(_A      ,   _A  )

				return None
def      a_ (      _A      ,   _A = None      ,   _A = False      ,   _A = False      ,   _A = None      ,   _A = None      ,   _A = None      ,   _A = False      ,   **_A      ,   )			->			Optional[Any]:







				"""simple docstring"""
				snake_case__									=     get_file_from_repo(
				    _A      ,   _A      ,   cache_dir=_A      ,   force_download=_A      ,   resume_download=_A      ,   proxies=_A      ,   use_auth_token=_A      ,   revision=_A      ,   local_files_only=_A      ,   )
				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(_A      ,   encoding='utf-8'  ) as reader:
								return json.load(_A  )


class   __SCREAMING_SNAKE_CASE:
							def __init__(     self:      Optional[int]						)		->		Union[str, Any]:
											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(UpperCamelCase						)
							def lowerCAmelCase_ (     cls:      int       ,	UpperCamelCase:      int       ,	**UpperCamelCase:      str						)		->		Optional[Any]:
											snake_case__									=     kwargs.pop('config'       ,	UpperCamelCase						)
											snake_case__									=     kwargs.pop('trust_remote_code'       ,	UpperCamelCase						)
											snake_case__									=     True

											snake_case__							,						snake_case__									=     ImageProcessingMixin.get_image_processor_dict(UpperCamelCase       ,	**UpperCamelCase						)
											snake_case__									=     config_dict.get('image_processor_type'       ,	UpperCamelCase						)
											snake_case__									=     None
											if "AutoImageProcessor" in config_dict.get('auto_map'       ,	{}						):
															snake_case__									=     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_class is None and image_processor_auto_map is None:
															snake_case__									=     config_dict.pop('feature_extractor_type'       ,	UpperCamelCase						)
															if feature_extractor_class is not None:
																			logger.warning(
																			    'Could not find image processor class in the image processor config or the model config. Loading'
																			    ' based on pattern matching with the model\'s feature extractor configuration.'						)
																			snake_case__									=     feature_extractor_class.replace('FeatureExtractor'       ,	'ImageProcessor'						)
															if "AutoFeatureExtractor" in config_dict.get('auto_map'       ,	{}						):
																			snake_case__									=     config_dict['auto_map']['AutoFeatureExtractor']
																			snake_case__									=     feature_extractor_auto_map.replace('FeatureExtractor'       ,	'ImageProcessor'						)
																			logger.warning(
																			    'Could not find image processor auto map in the image processor config or the model config.'
																			    ' Loading based on pattern matching with the model\'s feature extractor configuration.'						)

        # If we don't find the image processor class in the image processor config, let's try the model config.
											if image_processor_class is None and image_processor_auto_map is None:
															if not isinstance(UpperCamelCase       ,	UpperCamelCase						):
																			snake_case__									=     AutoConfig.from_pretrained(UpperCamelCase       ,	**UpperCamelCase						)
															# It could be in `config.image_processor_type``
															snake_case__									=     getattr(UpperCamelCase       ,	'image_processor_type'       ,	UpperCamelCase						)
															if hasattr(UpperCamelCase       ,	'auto_map'						) and "AutoImageProcessor" in config.auto_map:
																			snake_case__									=     config.auto_map['AutoImageProcessor']

											if image_processor_class is not None:
															snake_case__									=     image_processor_class_from_name(UpperCamelCase						)

											snake_case__									=     image_processor_auto_map is not None
											snake_case__									=     image_processor_class is not None or type(UpperCamelCase						) in IMAGE_PROCESSOR_MAPPING
											snake_case__									=     resolve_trust_remote_code(
											    UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase						)

											if has_remote_code and trust_remote_code:
															snake_case__									=     get_class_from_dynamic_module(
															    UpperCamelCase       ,	UpperCamelCase       ,	**UpperCamelCase						)
															snake_case__									=     kwargs.pop('code_revision'       ,	UpperCamelCase						)
															if os.path.isdir(UpperCamelCase						):
																			image_processor_class.register_for_auto_class()
															return image_processor_class.from_dict(UpperCamelCase       ,	**UpperCamelCase						)
											elif image_processor_class is not None:
															return image_processor_class.from_dict(UpperCamelCase       ,	**UpperCamelCase						)
											# Last try: we use the IMAGE_PROCESSOR_MAPPING.
											elif type(UpperCamelCase						) in IMAGE_PROCESSOR_MAPPING:
															snake_case__									=     IMAGE_PROCESSOR_MAPPING[type(UpperCamelCase						)]
															return image_processor_class.from_dict(UpperCamelCase       ,	**UpperCamelCase						)

											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 lowerCAmelCase_ (     UpperCamelCase:      Optional[Any]       ,	UpperCamelCase:      int						)		->		Optional[Any]:
											IMAGE_PROCESSOR_MAPPING.register(UpperCamelCase       ,	UpperCamelCase						)




