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
  
  
  _A						=     logging.get_logger(__name__)
  
  _A						=     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						=     _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   List[str]:
        for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
              if class_name in processors:
                    UpperCAmelCase__ :       str						=						model_type_to_module_name(lowerCAmelCase						)
  
                    UpperCAmelCase__ :       int						=						importlib.import_module(F""".{module_name}""" ,  """transformers.models"""						)
                    try:
                          return getattr(lowerCAmelCase ,  lowerCAmelCase						)
                    except AttributeError:
                          continue
  
        for processor in PROCESSOR_MAPPING._extra_content.values():
              if getattr(lowerCAmelCase ,  """__name__""" ,  lowerCAmelCase						) == 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(lowerCAmelCase ,  lowerCAmelCase						):
              return getattr(lowerCAmelCase ,  lowerCAmelCase						)
  
        return None
  
  
  
  
  
  class 			lowerCamelCase  :
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self  ):
  
  
          """simple docstring"""
  
          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  ):
  
  
          """simple docstring"""
  
          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__				,		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__				,		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  ):
  
  
          """simple docstring"""
  
          PROCESSOR_MAPPING.register(_lowerCamelCase       ,					_lowerCamelCase  )
  
  
style_context: 
  
  """simple docstring"""
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_keras_nlp_available,
      is_tensorflow_text_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _A						=     {
      """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
      """tokenization_gpt2""": ["""GPT2Tokenizer"""],
  }
  
  try:
      if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""GPT2TokenizerFast"""]
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     [
          """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """GPT2DoubleHeadsModel""",
          """GPT2ForQuestionAnswering""",
          """GPT2ForSequenceClassification""",
          """GPT2ForTokenClassification""",
          """GPT2LMHeadModel""",
          """GPT2Model""",
          """GPT2PreTrainedModel""",
          """load_tf_weights_in_gpt2""",
      ]
  
  try:
      if not is_tf_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     [
          """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFGPT2DoubleHeadsModel""",
          """TFGPT2ForSequenceClassification""",
          """TFGPT2LMHeadModel""",
          """TFGPT2MainLayer""",
          """TFGPT2Model""",
          """TFGPT2PreTrainedModel""",
      ]
  
  try:
      if not is_keras_nlp_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""TFGPT2Tokenizer"""]
  
  try:
      if not is_flax_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]
  
  if TYPE_CHECKING:
      from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
      from .tokenization_gpta import GPTaTokenizer
  
      try:
          if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .tokenization_gpta_fast import GPTaTokenizerFast
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_gpta import (
              GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTaDoubleHeadsModel,
              GPTaForQuestionAnswering,
              GPTaForSequenceClassification,
              GPTaForTokenClassification,
              GPTaLMHeadModel,
              GPTaModel,
              GPTaPreTrainedModel,
              load_tf_weights_in_gpta,
          )
  
      try:
          if not is_tf_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_tf_gpta import (
              TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFGPTaDoubleHeadsModel,
              TFGPTaForSequenceClassification,
              TFGPTaLMHeadModel,
              TFGPTaMainLayer,
              TFGPTaModel,
              TFGPTaPreTrainedModel,
          )
  
      try:
          if not is_keras_nlp_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .tokenization_gpta_tf import TFGPTaTokenizer
  
      try:
          if not is_flax_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel
  
  else:
      import sys
  
      _A						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  