
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  :					Any						    =							logging.get_logger(__name__)

lowercase  :					Tuple						    =							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  :					Any						    =							_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    List[str]:
       for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
              if class_name in extractors:
                     lowercase							:     Any       	=      model_type_to_module_name(SCREAMING_SNAKE_CASE__				)

                     lowercase							:     Optional[Any]       	=      importlib.import_module(f".{module_name}"					,	"""transformers.models"""				)
                     try:
                            return getattr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
                     except AttributeError:
                            continue

       for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
              if getattr(SCREAMING_SNAKE_CASE__					,	"""__name__"""					,	SCREAMING_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.
       lowercase							:     Optional[int]       	=      importlib.import_module("""transformers"""				)
       if hasattr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
              return getattr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       return None







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__ = None					,	SCREAMING_SNAKE_CASE__ = False					,	SCREAMING_SNAKE_CASE__ = False					,	SCREAMING_SNAKE_CASE__ = None					,	SCREAMING_SNAKE_CASE__ = None					,	SCREAMING_SNAKE_CASE__ = None					,	SCREAMING_SNAKE_CASE__ = False					,	**SCREAMING_SNAKE_CASE__					,	) ->    Optional[Any]:
       lowercase							:     List[Any]       	=      get_file_from_repo(
           SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	cache_dir=SCREAMING_SNAKE_CASE__					,	force_download=SCREAMING_SNAKE_CASE__					,	resume_download=SCREAMING_SNAKE_CASE__					,	proxies=SCREAMING_SNAKE_CASE__					,	use_auth_token=SCREAMING_SNAKE_CASE__					,	revision=SCREAMING_SNAKE_CASE__					,	local_files_only=SCREAMING_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(SCREAMING_SNAKE_CASE__					,	encoding="""utf-8"""				) as reader:
              return json.load(SCREAMING_SNAKE_CASE__				)




class __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(snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  cls		,snake_case		,**snake_case      ):



           '''simple docstring'''







           lowercase							:     int       	=      kwargs.pop("""config"""		,snake_case      )
           lowercase							:     Optional[Any]       	=      kwargs.pop("""trust_remote_code"""		,snake_case      )
           lowercase							:     str       	=      True

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

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

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

           if has_remote_code and trust_remote_code:
                  lowercase							:     int       	=      get_class_from_dynamic_module(
                      snake_case		,snake_case		,**snake_case      )
                  lowercase							:     Dict       	=      kwargs.pop("""code_revision"""		,snake_case      )
                  if os.path.isdir(snake_case      ):
                         image_processor_class.register_for_auto_class()
                  return image_processor_class.from_dict(snake_case		,**snake_case      )
           elif image_processor_class is not None:
                  return image_processor_class.from_dict(snake_case		,**snake_case      )
           # Last try: we use the IMAGE_PROCESSOR_MAPPING.
           elif type(snake_case      ) in IMAGE_PROCESSOR_MAPPING:
                  lowercase							:     List[Any]       	=      IMAGE_PROCESSOR_MAPPING[type(snake_case      )]
                  return image_processor_class.from_dict(snake_case		,**snake_case      )

           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       (  snake_case		,snake_case      ):



           '''simple docstring'''







           IMAGE_PROCESSOR_MAPPING.register(snake_case		,snake_case      )




from __future__ import annotations
def 				__lowerCamelCase						( UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		list[str] | None = None ,  UpperCAmelCase_						:		dict[str, float] | None = None ,  UpperCAmelCase_						:		bool = False ,  ):




      """simple docstring"""





      a						:str 							=       cipher_alphabet or [chr(UpperCAmelCase_						) for i in range(97 ,  123						)]

      # If the argument is None or the user provided an empty dictionary
      if not frequencies_dict:
            # Frequencies of letters in the english language (how much they show up)
            a						:List[Any] 							=       {
                '''a''': 0.08497,
                '''b''': 0.01492,
                '''c''': 0.02202,
                '''d''': 0.04253,
                '''e''': 0.11162,
                '''f''': 0.02228,
                '''g''': 0.02015,
                '''h''': 0.06094,
                '''i''': 0.07546,
                '''j''': 0.00153,
                '''k''': 0.01292,
                '''l''': 0.04025,
                '''m''': 0.02406,
                '''n''': 0.06749,
                '''o''': 0.07507,
                '''p''': 0.01929,
                '''q''': 0.00095,
                '''r''': 0.07587,
                '''s''': 0.06327,
                '''t''': 0.09356,
                '''u''': 0.02758,
                '''v''': 0.00978,
                '''w''': 0.02560,
                '''x''': 0.00150,
                '''y''': 0.01994,
                '''z''': 0.00077,
            }
      else:
            # Custom frequencies dictionary
            a						:Dict 							=       frequencies_dict

      if not case_sensitive:
            a						:Union[str, Any] 							=       ciphertext.lower()

      # Chi squared statistic values
      a						:dict[int, tuple[float, str]] 							=       {}

      # cycle through all of the shifts
      for shift in range(len(UpperCAmelCase_						)						):
            a						:int 							=       ''''''

            # decrypt the message with the shift
            for letter in ciphertext:
                  try:
                        # Try to index the letter in the alphabet
                        a						:Dict 							=       (alphabet_letters.index(letter.lower()						) - shift) % len(
                            UpperCAmelCase_						)
                        decrypted_with_shift += (
                            alphabet_letters[new_key].upper()
                            if case_sensitive and letter.isupper()
                            else alphabet_letters[new_key]
                        )
                  except ValueError:
                        # Append the character if it isn't in the alphabet
                        decrypted_with_shift += letter

            a						:List[Any] 							=       0.0

            # Loop through each letter in the decoded message with the shift
            for letter in decrypted_with_shift:
                  if case_sensitive:
                        a						:Optional[int] 							=       letter.lower()
                        if letter in frequencies:
                              # Get the amount of times the letter occurs in the message
                              a						:List[Any] 							=       decrypted_with_shift.lower().count(UpperCAmelCase_						)

                              # Get the excepcted amount of times the letter should appear based
                              # on letter frequencies
                              a						:Dict 							=       frequencies[letter] * occurrences

                              # Complete the chi squared statistic formula
                              a						:Any 							=       ((occurrences - expected) ** 2) / expected

                              # Add the margin of error to the total chi squared statistic
                              chi_squared_statistic += chi_letter_value
                  else:
                        if letter.lower() in frequencies:
                              # Get the amount of times the letter occurs in the message
                              a						:int 							=       decrypted_with_shift.count(UpperCAmelCase_						)

                              # Get the excepcted amount of times the letter should appear based
                              # on letter frequencies
                              a						:Tuple 							=       frequencies[letter] * occurrences

                              # Complete the chi squared statistic formula
                              a						:Optional[Any] 							=       ((occurrences - expected) ** 2) / expected

                              # Add the margin of error to the total chi squared statistic
                              chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
            a						:Optional[Any] 							=       (
                chi_squared_statistic,
                decrypted_with_shift,
            )

      # Get the most likely cipher by finding the cipher with the smallest chi squared
      # statistic
      def chi_squared_statistic_values_sorting_key(UpperCAmelCase_						:		int						) -> tuple[float, str]:
            return chi_squared_statistic_values[key]

      a						:int 							=       min(
          UpperCAmelCase_ ,  key=UpperCAmelCase_ ,  )

      # Get all the data from the most likely cipher (key, decoded message)
      (
          (
          a
      )							,				(
          a
      )							,				
      )						:Optional[int] 							=       chi_squared_statistic_values[most_likely_cipher]

      # Return the data on the most likely shift
      return (
          most_likely_cipher,
          most_likely_cipher_chi_squared_value,
          decoded_most_likely_cipher,
      )





