





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


_UpperCamelCase    :							Union[str, Any]      =       logging.get_logger(__name__)

_UpperCamelCase    :							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"""),
    ]
)

_UpperCamelCase    :							List[Any]      =       _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)
def       __UpperCamelCase  (		snake_case	)  ->						Dict:


   '''simple docstring'''







   for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
      if class_name in processors:
         __A      					=   model_type_to_module_name(SCREAMING_SNAKE_CASE_	)

         __A      					=   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      					=   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	)->					Union[str, 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(_lowercase	)
  def 		SCREAMING_SNAKE_CASE__       (					cls  ,				UpperCAmelCase  ,				**UpperCAmelCase	)->					Any:
     __A      					=   kwargs.pop('''config'''  ,				_lowercase	)
     __A      					=   kwargs.pop('''trust_remote_code'''  ,				_lowercase	)
     __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(_lowercase	).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(
         _lowercase  ,				_lowercase  ,				**_lowercase	)
     if preprocessor_config_file is not None:
        __A					,						__A      					=   ImageProcessingMixin.get_image_processor_dict(_lowercase  ,				**_lowercase	)
        __A      					=   config_dict.get('''processor_class'''  ,				_lowercase	)
        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(_lowercase  ,				**_lowercase	)
        __A      					=   config_dict.get('''processor_class'''  ,				_lowercase	)
        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(
            _lowercase  ,				_lowercase  ,				**_lowercase	)
        if tokenizer_config_file is not None:
           with open(_lowercase  ,				encoding='''utf-8'''	) as reader:
              __A      					=   json.load(_lowercase	)

           __A      					=   config_dict.get('''processor_class'''  ,				_lowercase	)
           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(_lowercase  ,				_lowercase	):
           __A      					=   AutoConfig.from_pretrained(
               _lowercase  ,				trust_remote_code=_lowercase  ,				**_lowercase	)

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

     if processor_class is not None:
        __A      					=   processor_class_from_name(_lowercase	)

     __A      					=   processor_auto_map is not None
     __A      					=   processor_class is not None or type(_lowercase	) in PROCESSOR_MAPPING
     __A      					=   resolve_trust_remote_code(
         _lowercase  ,				_lowercase  ,				_lowercase  ,				_lowercase	)

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

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

        try:
           return AutoFeatureExtractor.from_pretrained(
               _lowercase  ,				trust_remote_code=_lowercase  ,				**_lowercase	)
        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__       (					UpperCAmelCase  ,				UpperCAmelCase	)->					Optional[Any]:
     PROCESSOR_MAPPING.register(_lowercase  ,				_lowercase	)




from typing import Callable




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       )->		List[Any]:



    """simple docstring"""






    def wrapper(SCREAMING_SNAKE_CASE_       ):
        UpperCamelCase_          =		function_with_docstring.__doc__
        return target_function

    return wrapper
