





'''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,
)


lowercase__   :				Dict      =   logging.get_logger(__name__)

lowercase__   :				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'''),
    ]
)

lowercase__   :				str      =   _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)






def 	_lowerCAmelCase      (  __snake_case  :			str      )					->					Tuple:
     for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
          if class_name in extractors:
               __A						:			Optional[int]								=	model_type_to_module_name(__snake_case      )

               __A						:			Optional[int]								=	importlib.import_module(f'.{module_name}'			,							'transformers.models'      )
               try:
                    return getattr(__snake_case			,							__snake_case      )
               except AttributeError:
                    continue

     for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
          if getattr(__snake_case			,							'__name__'			,							__snake_case      ) == 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.
     __A						:			Union[str, Any]								=	importlib.import_module('transformers'      )
     if hasattr(__snake_case			,							__snake_case      ):
          return getattr(__snake_case			,							__snake_case      )

     return None






def 	_lowerCAmelCase      (  __snake_case  :			Union[str, os.PathLike]			,							__snake_case  :			Optional[Union[str, os.PathLike]] = None			,							__snake_case  :			bool = False			,							__snake_case  :			bool = False			,							__snake_case  :			Optional[Dict[str, str]] = None			,							__snake_case  :			Optional[Union[bool, str]] = None			,							__snake_case  :			Optional[str] = None			,							__snake_case  :			bool = False			,							**__snake_case  :			Union[str, Any]			,							)					->					Tuple:
     __A						:			int								=	get_file_from_repo(
         __snake_case			,							__snake_case			,							cache_dir=__snake_case			,							force_download=__snake_case			,							resume_download=__snake_case			,							proxies=__snake_case			,							use_auth_token=__snake_case			,							revision=__snake_case			,							local_files_only=__snake_case			,							)
     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(__snake_case			,							encoding='utf-8'      ) as reader:
          return json.load(__snake_case      )







class 			SCREAMING_SNAKE_CASE   :


     def __init__(						self):

          '''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(_UpperCAmelCase)
     def 							SCREAMING_SNAKE_CASE  (						cls						,     _UpperCAmelCase						,     **_UpperCAmelCase):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	kwargs.pop('config'						,     _UpperCAmelCase)
          __A						:			List[str]								=	kwargs.pop('trust_remote_code'						,     _UpperCAmelCase)
          __A						:			Dict								=	True

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

          if image_processor_class is not None:
               __A						:			Any								=	image_processor_class_from_name(_UpperCAmelCase)

          __A						:			int								=	image_processor_auto_map is not None
          __A						:			List[str]								=	image_processor_class is not None or type(_UpperCAmelCase) in IMAGE_PROCESSOR_MAPPING
          __A						:			Optional[Any]								=	resolve_trust_remote_code(
              _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase)

          if has_remote_code and trust_remote_code:
               __A						:			Tuple								=	get_class_from_dynamic_module(
                   _UpperCAmelCase						,     _UpperCAmelCase						,     **_UpperCAmelCase)
               __A						:			Optional[int]								=	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:
               __A						:			Any								=	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 							SCREAMING_SNAKE_CASE  (						_UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          IMAGE_PROCESSOR_MAPPING.register(_UpperCAmelCase						,     _UpperCAmelCase)






'''simple docstring'''




def 	_lowerCAmelCase      (  __snake_case  :			str			,							__snake_case  :			str      )					->					float:

     def get_matched_characters(__snake_case  :			str			,							__snake_case  :			str      ) -> str:
          __A						:			Optional[int]								=	[]
          __A						:			Optional[Any]								=	min(len(_stra      )			,							len(_stra      )      ) // 2
          for i, l in enumerate(_stra      ):
               __A						:			str								=	int(max(0			,							i - limit      )      )
               __A						:			str								=	int(min(i + limit + 1			,							len(_stra      )      )      )
               if l in _stra[left:right]:
                    matched.append(__snake_case      )
                    __A						:			Dict								=	f'{_stra[0:_stra.index(__snake_case      )]} {_stra[_stra.index(__snake_case      ) + 1:]}'

          return "".join(__snake_case      )

     # matching characters
     __A						:			Any								=	get_matched_characters(__snake_case			,							__snake_case      )
     __A						:			str								=	get_matched_characters(__snake_case			,							__snake_case      )
     __A						:			str								=	len(__snake_case      )

     # transposition
     __A						:			Tuple								=	(
         len([(ca, ca) for ca, ca in zip(__snake_case			,							__snake_case      ) if ca != ca]      ) // 2
     )

     if not match_count:
          __A						:			int								=	0.0
     else:
          __A						:			Union[str, Any]								=	(
              1
              / 3
              * (
                  match_count / len(__snake_case      )
                  + match_count / len(__snake_case      )
                  + (match_count - transpositions) / match_count
              )
          )

     # common prefix up to 4 characters
     __A						:			Tuple								=	0
     for ca, ca in zip(stra[:4]			,							stra[:4]      ):
          if ca == ca:
               prefix_len += 1
          else:
               break

     return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(jaro_winkler('''hello''', '''world'''))