code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import importlib
  import inspect
  import json
  import os
  from collections import OrderedDict
  
  # Build the list of all feature extractors
  from ...configuration_utils import PretrainedConfig
  from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
  from ...feature_extraction_utils import FeatureExtractionMixin
  from ...image_processing_utils import ImageProcessingMixin
  from ...tokenization_utils import TOKENIZER_CONFIG_FILE
  from ...utils import FEATURE_EXTRACTOR_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,
  )
  from .feature_extraction_auto import AutoFeatureExtractor
  from .image_processing_auto import AutoImageProcessor
  from .tokenization_auto import AutoTokenizer
  
  
  lowerCamelCase_     :     str    		=						logging.get_logger(__name__)
  
  lowerCamelCase_     :     Union[str, Any]    		=						OrderedDict(
      [
          ("""align""", """AlignProcessor"""),
          ("""altclip""", """AltCLIPProcessor"""),
          ("""bark""", """BarkProcessor"""),
          ("""blip""", """BlipProcessor"""),
          ("""blip-2""", """Blip2Processor"""),
          ("""bridgetower""", """BridgeTowerProcessor"""),
          ("""chinese_clip""", """ChineseCLIPProcessor"""),
          ("""clap""", """ClapProcessor"""),
          ("""clip""", """CLIPProcessor"""),
          ("""clipseg""", """CLIPSegProcessor"""),
          ("""flava""", """FlavaProcessor"""),
          ("""git""", """GitProcessor"""),
          ("""groupvit""", """CLIPProcessor"""),
          ("""hubert""", """Wav2Vec2Processor"""),
          ("""instructblip""", """InstructBlipProcessor"""),
          ("""layoutlmv2""", """LayoutLMv2Processor"""),
          ("""layoutlmv3""", """LayoutLMv3Processor"""),
          ("""markuplm""", """MarkupLMProcessor"""),
          ("""mctct""", """MCTCTProcessor"""),
          ("""mgp-str""", """MgpstrProcessor"""),
          ("""oneformer""", """OneFormerProcessor"""),
          ("""owlvit""", """OwlViTProcessor"""),
          ("""pix2struct""", """Pix2StructProcessor"""),
          ("""sam""", """SamProcessor"""),
          ("""sew""", """Wav2Vec2Processor"""),
          ("""sew-d""", """Wav2Vec2Processor"""),
          ("""speech_to_text""", """Speech2TextProcessor"""),
          ("""speech_to_text_2""", """Speech2Text2Processor"""),
          ("""speecht5""", """SpeechT5Processor"""),
          ("""trocr""", """TrOCRProcessor"""),
          ("""tvlt""", """TvltProcessor"""),
          ("""unispeech""", """Wav2Vec2Processor"""),
          ("""unispeech-sat""", """Wav2Vec2Processor"""),
          ("""vilt""", """ViltProcessor"""),
          ("""vision-text-dual-encoder""", """VisionTextDualEncoderProcessor"""),
          ("""wav2vec2""", """Wav2Vec2Processor"""),
          ("""wav2vec2-conformer""", """Wav2Vec2Processor"""),
          ("""wavlm""", """Wav2Vec2Processor"""),
          ("""whisper""", """WhisperProcessor"""),
          ("""xclip""", """XCLIPProcessor"""),
      ]
  )
  
  lowerCamelCase_     :     Tuple    		=						_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
          if class_name in processors:
              a					=model_type_to_module_name(_UpperCAmelCase	)
  
              a					=importlib.import_module(f'''.{module_name}''' ,					'''transformers.models'''	)
              try:
                  return getattr(_UpperCAmelCase ,					_UpperCAmelCase	)
              except AttributeError:
                  continue
  
      for processor in PROCESSOR_MAPPING._extra_content.values():
          if getattr(_UpperCAmelCase ,					'''__name__''' ,					_UpperCAmelCase	) == class_name:
              return processor
  
      # 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					=importlib.import_module('''transformers'''	)
      if hasattr(_UpperCAmelCase ,					_UpperCAmelCase	):
          return getattr(_UpperCAmelCase ,					_UpperCAmelCase	)
  
      return None
  
  
  
  
  
  
  class    __A       :
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def __init__(	self		)      ->  List[str]:
       raise EnvironmentError(
           '''AutoProcessor is designed to be instantiated '''
           '''using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method.'''		)
  
  
   @classmethod
   @replace_list_option_in_docstrings(SCREAMING_SNAKE_CASE_		)
   def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					**__A		)      ->  Tuple:
       a					=kwargs.pop('''config'''				,					SCREAMING_SNAKE_CASE_		)
       a					=kwargs.pop('''trust_remote_code'''				,					SCREAMING_SNAKE_CASE_		)
       a					=True
  
       a					=None
       a					=None
  
       # First, let's see if we have a preprocessor config.
       # Filter the kwargs for `get_file_from_repo`.
       a					={
           key: kwargs[key] for key in inspect.signature(SCREAMING_SNAKE_CASE_		).parameters.keys() if key in kwargs
       }
       # Let's start by checking whether the processor class is saved in an image processor
       a					=get_file_from_repo(
           SCREAMING_SNAKE_CASE_				,					SCREAMING_SNAKE_CASE_				,					**SCREAMING_SNAKE_CASE_		)
       if preprocessor_config_file is not None:
           a	,   a					=ImageProcessingMixin.get_image_processor_dict(SCREAMING_SNAKE_CASE_				,					**SCREAMING_SNAKE_CASE_		)
           a					=config_dict.get('''processor_class'''				,					SCREAMING_SNAKE_CASE_		)
           if "AutoProcessor" in config_dict.get('''auto_map'''				,					{}		):
               a					=config_dict['''auto_map''']['''AutoProcessor''']
  
          # If not found, let's check whether the processor class is saved in a feature extractor config
       if preprocessor_config_file is not None and processor_class is None:
           a	,   a					=FeatureExtractionMixin.get_feature_extractor_dict(SCREAMING_SNAKE_CASE_				,					**SCREAMING_SNAKE_CASE_		)
           a					=config_dict.get('''processor_class'''				,					SCREAMING_SNAKE_CASE_		)
           if "AutoProcessor" in config_dict.get('''auto_map'''				,					{}		):
               a					=config_dict['''auto_map''']['''AutoProcessor''']
  
       if processor_class is None:
           # Next, let's check whether the processor class is saved in a tokenizer
           a					=get_file_from_repo(
               SCREAMING_SNAKE_CASE_				,					SCREAMING_SNAKE_CASE_				,					**SCREAMING_SNAKE_CASE_		)
           if tokenizer_config_file is not None:
               with open(SCREAMING_SNAKE_CASE_				,					encoding='''utf-8'''		) as reader:
                   a					=json.load(SCREAMING_SNAKE_CASE_		)
  
               a					=config_dict.get('''processor_class'''				,					SCREAMING_SNAKE_CASE_		)
               if "AutoProcessor" in config_dict.get('''auto_map'''				,					{}		):
                   a					=config_dict['''auto_map''']['''AutoProcessor''']
  
       if processor_class is None:
           # Otherwise, load config, if it can be loaded.
           if not isinstance(SCREAMING_SNAKE_CASE_				,					SCREAMING_SNAKE_CASE_		):
               a					=AutoConfig.from_pretrained(
                   SCREAMING_SNAKE_CASE_				,					trust_remote_code=SCREAMING_SNAKE_CASE_				,					**SCREAMING_SNAKE_CASE_		)
  
           # And check if the config contains the processor class.
           a					=getattr(SCREAMING_SNAKE_CASE_				,					'''processor_class'''				,					SCREAMING_SNAKE_CASE_		)
           if hasattr(SCREAMING_SNAKE_CASE_				,					'''auto_map'''		) and "AutoProcessor" in config.auto_map:
               a					=config.auto_map['''AutoProcessor''']
  
       if processor_class is not None:
           a					=processor_class_from_name(SCREAMING_SNAKE_CASE_		)
  
       a					=processor_auto_map is not None
       a					=processor_class is not None or type(SCREAMING_SNAKE_CASE_		) in PROCESSOR_MAPPING
       a					=resolve_trust_remote_code(
           SCREAMING_SNAKE_CASE_				,					SCREAMING_SNAKE_CASE_				,					SCREAMING_SNAKE_CASE_				,					SCREAMING_SNAKE_CASE_		)
  
       if has_remote_code and trust_remote_code:
           a					=get_class_from_dynamic_module(
               SCREAMING_SNAKE_CASE_				,					SCREAMING_SNAKE_CASE_				,					**SCREAMING_SNAKE_CASE_		)
           a					=kwargs.pop('''code_revision'''				,					SCREAMING_SNAKE_CASE_		)
           if os.path.isdir(SCREAMING_SNAKE_CASE_		):
               processor_class.register_for_auto_class()
           return processor_class.from_pretrained(
               SCREAMING_SNAKE_CASE_				,					trust_remote_code=SCREAMING_SNAKE_CASE_				,					**SCREAMING_SNAKE_CASE_		)
       elif processor_class is not None:
           return processor_class.from_pretrained(
               SCREAMING_SNAKE_CASE_				,					trust_remote_code=SCREAMING_SNAKE_CASE_				,					**SCREAMING_SNAKE_CASE_		)
       # Last try: we use the PROCESSOR_MAPPING.
       elif type(SCREAMING_SNAKE_CASE_		) in PROCESSOR_MAPPING:
           return PROCESSOR_MAPPING[type(SCREAMING_SNAKE_CASE_		)].from_pretrained(SCREAMING_SNAKE_CASE_				,					**SCREAMING_SNAKE_CASE_		)
  
       # At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
       # tokenizer.
       try:
           return AutoTokenizer.from_pretrained(
               SCREAMING_SNAKE_CASE_				,					trust_remote_code=SCREAMING_SNAKE_CASE_				,					**SCREAMING_SNAKE_CASE_		)
       except Exception:
           try:
               return AutoImageProcessor.from_pretrained(
                   SCREAMING_SNAKE_CASE_				,					trust_remote_code=SCREAMING_SNAKE_CASE_				,					**SCREAMING_SNAKE_CASE_		)
           except Exception:
               pass
  
           try:
               return AutoFeatureExtractor.from_pretrained(
                   SCREAMING_SNAKE_CASE_				,					trust_remote_code=SCREAMING_SNAKE_CASE_				,					**SCREAMING_SNAKE_CASE_		)
           except Exception:
               pass
  
       raise ValueError(
           f'''Unrecognized processing class in {pretrained_model_name_or_path}. Can\'t instantiate a processor, a '''
           '''tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains'''
           '''the files of at least one of those processing classes.'''		)
  
  
  
  
  
   @staticmethod
   def    SCREAMING_SNAKE_CASE							(	__A				,					__A		)      ->  Any:
       PROCESSOR_MAPPING.register(SCREAMING_SNAKE_CASE_				,					SCREAMING_SNAKE_CASE_		)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from math import gcd
  def _A		(		lowercase = 1_20_00	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=0
      for d in range(max_d + 1	):
          for n in range(d // 3 + 1 ,					(d + 1) // 2	):
              if gcd(lowercase ,					lowercase	) == 1:
                  fractions_number += 1
      return fractions_number
  
  
  if __name__ == "__main__":
         print(F'{solution() = }')