






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


A_	:	int				=				logging.get_logger(__name__)

A_	:	List[str]				=				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'),
    ]
)

A_	:	Tuple				=				_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)

def 						snake_case      (UpperCAmelCase__						) ->       List[str]:
  for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
    if class_name in processors:
      UpperCamelCase_:      str					   =       model_type_to_module_name(UpperCAmelCase__						)

      UpperCamelCase_:      int					   =       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.
  UpperCamelCase_:      str					   =       importlib.import_module('transformers'						)
  if hasattr(UpperCAmelCase__			,					UpperCAmelCase__						):
    return getattr(UpperCAmelCase__			,					UpperCAmelCase__						)

  return None




class        _lowerCAmelCase:





       """simple docstring"""







       def __init__(      self							):
         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(_lowerCamelCase							)
       def     _a					(      cls						,    _lowerCamelCase						,    **_lowerCamelCase							):
         UpperCamelCase_:      int					   =       kwargs.pop('config'						,    _lowerCamelCase							)
         UpperCamelCase_:      Tuple					   =       kwargs.pop('trust_remote_code'						,    _lowerCamelCase							)
         UpperCamelCase_:      List[Any]					   =       True

         UpperCamelCase_:      int					   =       None
         UpperCamelCase_:      str					   =       None

         # First, let's see if we have a preprocessor config.
         # Filter the kwargs for `get_file_from_repo`.
         UpperCamelCase_:      Tuple					   =       {
             key: kwargs[key] for key in inspect.signature(_lowerCamelCase							).parameters.keys() if key in kwargs
         }
         # Let's start by checking whether the processor class is saved in an image processor
         UpperCamelCase_:      Any					   =       get_file_from_repo(
             _lowerCamelCase						,    _lowerCamelCase						,    **_lowerCamelCase							)
         if preprocessor_config_file is not None:
           UpperCamelCase_:      List[str]					   =       ImageProcessingMixin.get_image_processor_dict(_lowerCamelCase						,    **_lowerCamelCase							)
           UpperCamelCase_:      Optional[Any]					   =       config_dict.get('processor_class'						,    _lowerCamelCase							)
           if "AutoProcessor" in config_dict.get('auto_map'						,    {}							):
             UpperCamelCase_:      int					   =       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:
           UpperCamelCase_:      Optional[Any]					   =       FeatureExtractionMixin.get_feature_extractor_dict(_lowerCamelCase						,    **_lowerCamelCase							)
           UpperCamelCase_:      Tuple					   =       config_dict.get('processor_class'						,    _lowerCamelCase							)
           if "AutoProcessor" in config_dict.get('auto_map'						,    {}							):
             UpperCamelCase_:      Dict					   =       config_dict["""auto_map"""]["""AutoProcessor"""]

         if processor_class is None:
           # Next, let's check whether the processor class is saved in a tokenizer
           UpperCamelCase_:      List[str]					   =       get_file_from_repo(
               _lowerCamelCase						,    _lowerCamelCase						,    **_lowerCamelCase							)
           if tokenizer_config_file is not None:
             with open(_lowerCamelCase						,    encoding='utf-8'							) as reader:
               UpperCamelCase_:      Optional[int]					   =       json.load(_lowerCamelCase							)

             UpperCamelCase_:      Optional[Any]					   =       config_dict.get('processor_class'						,    _lowerCamelCase							)
             if "AutoProcessor" in config_dict.get('auto_map'						,    {}							):
               UpperCamelCase_:      Optional[Any]					   =       config_dict["""auto_map"""]["""AutoProcessor"""]

         if processor_class is None:
           # Otherwise, load config, if it can be loaded.
           if not isinstance(_lowerCamelCase						,    _lowerCamelCase							):
             UpperCamelCase_:      Union[str, Any]					   =       AutoConfig.from_pretrained(
                 _lowerCamelCase						,    trust_remote_code=_lowerCamelCase						,    **_lowerCamelCase							)

           # And check if the config contains the processor class.
           UpperCamelCase_:      str					   =       getattr(_lowerCamelCase						,    'processor_class'						,    _lowerCamelCase							)
           if hasattr(_lowerCamelCase						,    'auto_map'							) and "AutoProcessor" in config.auto_map:
             UpperCamelCase_:      Tuple					   =       config.auto_map["""AutoProcessor"""]

         if processor_class is not None:
           UpperCamelCase_:      Optional[Any]					   =       processor_class_from_name(_lowerCamelCase							)

         UpperCamelCase_:      List[str]					   =       processor_auto_map is not None
         UpperCamelCase_:      int					   =       processor_class is not None or type(_lowerCamelCase							) in PROCESSOR_MAPPING
         UpperCamelCase_:      Any					   =       resolve_trust_remote_code(
             _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase							)

         if has_remote_code and trust_remote_code:
           UpperCamelCase_:      Optional[int]					   =       get_class_from_dynamic_module(
               _lowerCamelCase						,    _lowerCamelCase						,    **_lowerCamelCase							)
           UpperCamelCase_:      str					   =       kwargs.pop('code_revision'						,    _lowerCamelCase							)
           if os.path.isdir(_lowerCamelCase							):
             processor_class.register_for_auto_class()
           return processor_class.from_pretrained(
               _lowerCamelCase						,    trust_remote_code=_lowerCamelCase						,    **_lowerCamelCase							)
         elif processor_class is not None:
           return processor_class.from_pretrained(
               _lowerCamelCase						,    trust_remote_code=_lowerCamelCase						,    **_lowerCamelCase							)
         # Last try: we use the PROCESSOR_MAPPING.
         elif type(_lowerCamelCase							) in PROCESSOR_MAPPING:
           return PROCESSOR_MAPPING[type(_lowerCamelCase							)].from_pretrained(_lowerCamelCase						,    **_lowerCamelCase							)

         # 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(
               _lowerCamelCase						,    trust_remote_code=_lowerCamelCase						,    **_lowerCamelCase							)
         except Exception:
           try:
             return AutoImageProcessor.from_pretrained(
                 _lowerCamelCase						,    trust_remote_code=_lowerCamelCase						,    **_lowerCamelCase							)
           except Exception:
             pass

           try:
             return AutoFeatureExtractor.from_pretrained(
                 _lowerCamelCase						,    trust_remote_code=_lowerCamelCase						,    **_lowerCamelCase							)
           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     _a					(      _lowerCamelCase						,    _lowerCamelCase							):
         PROCESSOR_MAPPING.register(_lowerCamelCase						,    _lowerCamelCase							)







def 						snake_case      (UpperCAmelCase__ = 4_0_0_0_0_0_0						) ->       int:

  if n <= 1:
    return 0
  UpperCamelCase_:      Dict					   =       0
  UpperCamelCase_:      Union[str, Any]					   =       2
  UpperCamelCase_:      int					   =       0
  while 4 * b + a <= n:
    UpperCamelCase_   ,UpperCamelCase_:      List[str]					   =       b, 4 * b + a
    count += a
  return count + b


if __name__ == "__main__":
   print(F'''{solution() = }''')