






'''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


SCREAMING_SNAKE_CASE_				      =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				      =							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"),
    ]
)

SCREAMING_SNAKE_CASE_				      =							_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):
 for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
  if class_name in processors:
   __a    :	int									=						model_type_to_module_name(SCREAMING_SNAKE_CASE__		)

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

 for processor in PROCESSOR_MAPPING._extra_content.values():
  if getattr(SCREAMING_SNAKE_CASE__       ,		'__name__'       ,		SCREAMING_SNAKE_CASE__		) == 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    :	int									=						importlib.import_module('transformers'		)
 if hasattr(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
  return getattr(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		)

 return None




class 							lowerCAmelCase					:







   """simple docstring"""







   def __init__( self			)    ->   Any:
    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(_A			)
   def    __magic_name__    ( cls   ,   _A   ,   **_A			)    ->   Any:
    __a    :	Optional[Any]									=						kwargs.pop('config'   ,   _A			)
    __a    :	Optional[Any]									=						kwargs.pop('trust_remote_code'   ,   _A			)
    __a    :	str									=						True

    __a    :	int									=						None
    __a    :	Tuple									=						None

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

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

      __a    :	Union[str, Any]									=						config_dict.get('processor_class'   ,   _A			)
      if "AutoProcessor" in config_dict.get('auto_map'   ,   {}			):
       __a    :	Union[str, Any]									=						config_dict['auto_map']['AutoProcessor']

    if processor_class is None:
     # Otherwise, load config, if it can be loaded.
     if not isinstance(_A   ,   _A			):
      __a    :	int									=						AutoConfig.from_pretrained(
          _A   ,   trust_remote_code=_A   ,   **_A			)

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

    if processor_class is not None:
     __a    :	Dict									=						processor_class_from_name(_A			)

    __a    :	Optional[int]									=						processor_auto_map is not None
    __a    :	List[Any]									=						processor_class is not None or type(_A			) in PROCESSOR_MAPPING
    __a    :	Any									=						resolve_trust_remote_code(
        _A   ,   _A   ,   _A   ,   _A			)

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

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

     try:
      return AutoFeatureExtractor.from_pretrained(
          _A   ,   trust_remote_code=_A   ,   **_A			)
     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    __magic_name__    ( _A   ,   _A			)    ->   Union[str, Any]:
    PROCESSOR_MAPPING.register(_A   ,   _A			)












'''simple docstring'''


def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__ = 30		):
 __a    :	Tuple									=						exponent + 2
 __a    :	str									=						(1 + 5**0.5) / 2
 __a    :	Any									=						(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5

 return int(SCREAMING_SNAKE_CASE__		)


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




