



'''simple docstring'''
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,
)


snake_case_ :	Dict         =  logging.get_logger(__name__)

snake_case_ :	Dict         =  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'),
    ]
)

snake_case_ :	int         =  _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)






def A__    (     UpperCAmelCase_							):
						for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
												if class_name in extractors:
																		_UpperCamelCase					:		Any 				=   model_type_to_module_name(UpperCAmelCase_							)

																		_UpperCamelCase					:		List[str] 				=   importlib.import_module(f'.{module_name}'  ,  'transformers.models'							)
																		try:
																								return getattr(UpperCAmelCase_  ,  UpperCAmelCase_							)
																		except AttributeError:
																								continue

						for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
												if getattr(UpperCAmelCase_  ,  '__name__'  ,  UpperCAmelCase_							) == 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.
						_UpperCamelCase					:		List[Any] 				=   importlib.import_module('transformers'							)
						if hasattr(UpperCAmelCase_  ,  UpperCAmelCase_							):
												return getattr(UpperCAmelCase_  ,  UpperCAmelCase_							)

						return None






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_ = None  ,  UpperCAmelCase_ = False  ,  UpperCAmelCase_ = False  ,  UpperCAmelCase_ = None  ,  UpperCAmelCase_ = None  ,  UpperCAmelCase_ = None  ,  UpperCAmelCase_ = False  ,  **UpperCAmelCase_  ,  ):
						_UpperCamelCase					:		Optional[Any] 				=   get_file_from_repo(
						    UpperCAmelCase_  ,  UpperCAmelCase_  ,  cache_dir=UpperCAmelCase_  ,  force_download=UpperCAmelCase_  ,  resume_download=UpperCAmelCase_  ,  proxies=UpperCAmelCase_  ,  use_auth_token=UpperCAmelCase_  ,  revision=UpperCAmelCase_  ,  local_files_only=UpperCAmelCase_  ,  )
						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(UpperCAmelCase_  ,  encoding='utf-8'							) as reader:
												return json.load(UpperCAmelCase_							)






class 							lowercase__	:





	def __init__( self					:				Tuple ):







							'''simple docstring'''
							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(lowerCamelCase__ )
	def 	UpperCamelCase_  ( cls					:				Tuple ,lowerCamelCase__					:				Tuple ,**lowerCamelCase__					:				Tuple ):







							'''simple docstring'''
							_UpperCamelCase					:		int 				=   kwargs.pop('config' ,lowerCamelCase__ )
							_UpperCamelCase					:		List[Any] 				=   kwargs.pop('trust_remote_code' ,lowerCamelCase__ )
							_UpperCamelCase					:		Tuple 				=   True

							_UpperCamelCase				,			_UpperCamelCase					:		Dict 				=   ImageProcessingMixin.get_image_processor_dict(lowerCamelCase__ ,**lowerCamelCase__ )
							_UpperCamelCase					:		List[Any] 				=   config_dict.get('image_processor_type' ,lowerCamelCase__ )
							_UpperCamelCase					:		Tuple 				=   None
							if "AutoImageProcessor" in config_dict.get('auto_map' ,{} ):
													_UpperCamelCase					:		Optional[int] 				=   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:
													_UpperCamelCase					:		Optional[Any] 				=   config_dict.pop('feature_extractor_type' ,lowerCamelCase__ )
													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.' )
																			_UpperCamelCase					:		int 				=   feature_extractor_class.replace('FeatureExtractor' ,'ImageProcessor' )
													if "AutoFeatureExtractor" in config_dict.get('auto_map' ,{} ):
																			_UpperCamelCase					:		List[Any] 				=   config_dict['auto_map']['AutoFeatureExtractor']
																			_UpperCamelCase					:		List[str] 				=   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(lowerCamelCase__ ,lowerCamelCase__ ):
																			_UpperCamelCase					:		int 				=   AutoConfig.from_pretrained(lowerCamelCase__ ,**lowerCamelCase__ )
													# It could be in `config.image_processor_type``
													_UpperCamelCase					:		List[Any] 				=   getattr(lowerCamelCase__ ,'image_processor_type' ,lowerCamelCase__ )
													if hasattr(lowerCamelCase__ ,'auto_map' ) and "AutoImageProcessor" in config.auto_map:
																			_UpperCamelCase					:		List[str] 				=   config.auto_map['AutoImageProcessor']

							if image_processor_class is not None:
													_UpperCamelCase					:		Union[str, Any] 				=   image_processor_class_from_name(lowerCamelCase__ )

							_UpperCamelCase					:		str 				=   image_processor_auto_map is not None
							_UpperCamelCase					:		Any 				=   image_processor_class is not None or type(lowerCamelCase__ ) in IMAGE_PROCESSOR_MAPPING
							_UpperCamelCase					:		Any 				=   resolve_trust_remote_code(
							    lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )

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

							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 	UpperCamelCase_  ( lowerCamelCase__					:				List[str] ,lowerCamelCase__					:				Union[str, Any] ):







							'''simple docstring'''
							IMAGE_PROCESSOR_MAPPING.register(lowerCamelCase__ ,lowerCamelCase__ )







"""simple docstring"""







import os



def      __UpperCAmelCase      (    )							->      int:
		with open(os.path.dirname(__lowerCamelCase	) + '''/p022_names.txt'''	) as file:
				lowercase__   :    List[Any]				=			str(file.readlines()[0]	)
				lowercase__   :    Dict				=			names.replace('''"'''						,     ''''''	).split(''','''	)

		names.sort()

		lowercase__   :    int				=			0
		lowercase__   :    Optional[Any]				=			0

		for i, name in enumerate(__lowerCamelCase	):
				for letter in name:
						name_score += ord(__lowerCamelCase	) - 64

				total_score += (i + 1) * name_score
				lowercase__   :    List[str]				=			0
		return total_score


if __name__ == "__main__":
							print(solution())
