


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 			__lowercase	(						_UpperCamelCase     )    ->List[str]:







      """simple docstring"""

      for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
            if class_name in processors:
                  lowercase			:   Tuple					   =		model_type_to_module_name(_UpperCamelCase     )

                  lowercase			:   Optional[Any]					   =		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.
      lowercase			:   Dict					   =		importlib.import_module('''transformers'''     )
      if hasattr(_UpperCamelCase,    _UpperCamelCase     ):
            return getattr(_UpperCamelCase,    _UpperCamelCase     )

      return None







class 				__SCREAMING_SNAKE_CASE   :


      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(SCREAMING_SNAKE_CASE__		)
      def        __lowerCamelCase			(							cls							,						SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		):
            lowercase			:   Dict					   =		kwargs.pop('''config'''							,						SCREAMING_SNAKE_CASE__		)
            lowercase			:   Any					   =		kwargs.pop('''trust_remote_code'''							,						SCREAMING_SNAKE_CASE__		)
            lowercase			:   Dict					   =		True

            lowercase			:   Dict					   =		None
            lowercase			:   List[Any]					   =		None

            # First, let's see if we have a preprocessor config.
            # Filter the kwargs for `get_file_from_repo`.
            lowercase			:   Union[str, Any]					   =		{
                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
            lowercase			:   int					   =		get_file_from_repo(
                SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		)
            if preprocessor_config_file is not None:
                  lowercase   ,			lowercase			:   Optional[int]					   =		ImageProcessingMixin.get_image_processor_dict(SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		)
                  lowercase			:   Dict					   =		config_dict.get('''processor_class'''							,						SCREAMING_SNAKE_CASE__		)
                  if "AutoProcessor" in config_dict.get('''auto_map'''							,						{}		):
                        lowercase			:   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:
                  lowercase   ,			lowercase			:   Dict					   =		FeatureExtractionMixin.get_feature_extractor_dict(SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		)
                  lowercase			:   Optional[int]					   =		config_dict.get('''processor_class'''							,						SCREAMING_SNAKE_CASE__		)
                  if "AutoProcessor" in config_dict.get('''auto_map'''							,						{}		):
                        lowercase			:   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
                  lowercase			:   Optional[Any]					   =		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:
                              lowercase			:   str					   =		json.load(SCREAMING_SNAKE_CASE__		)

                        lowercase			:   List[Any]					   =		config_dict.get('''processor_class'''							,						SCREAMING_SNAKE_CASE__		)
                        if "AutoProcessor" in config_dict.get('''auto_map'''							,						{}		):
                              lowercase			:   Any					   =		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__		):
                        lowercase			:   str					   =		AutoConfig.from_pretrained(
                            SCREAMING_SNAKE_CASE__							,						trust_remote_code=SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		)

                  # And check if the config contains the processor class.
                  lowercase			:   List[Any]					   =		getattr(SCREAMING_SNAKE_CASE__							,						'''processor_class'''							,						SCREAMING_SNAKE_CASE__		)
                  if hasattr(SCREAMING_SNAKE_CASE__							,						'''auto_map'''		) and "AutoProcessor" in config.auto_map:
                        lowercase			:   str					   =		config.auto_map['''AutoProcessor''']

            if processor_class is not None:
                  lowercase			:   List[str]					   =		processor_class_from_name(SCREAMING_SNAKE_CASE__		)

            lowercase			:   Optional[int]					   =		processor_auto_map is not None
            lowercase			:   int					   =		processor_class is not None or type(SCREAMING_SNAKE_CASE__		) in PROCESSOR_MAPPING
            lowercase			:   Tuple					   =		resolve_trust_remote_code(
                SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		)

            if has_remote_code and trust_remote_code:
                  lowercase			:   Tuple					   =		get_class_from_dynamic_module(
                      SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		)
                  lowercase			:   List[str]					   =		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        __lowerCamelCase			(							SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
            PROCESSOR_MAPPING.register(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a   				=      {'''configuration_yoso''': ['''YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''YosoConfig''']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   				=      [
      '''YOSO_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''YosoForMaskedLM''',
      '''YosoForMultipleChoice''',
      '''YosoForQuestionAnswering''',
      '''YosoForSequenceClassification''',
      '''YosoForTokenClassification''',
      '''YosoLayer''',
      '''YosoModel''',
      '''YosoPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_yoso import (
        YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
        YosoForMaskedLM,
        YosoForMultipleChoice,
        YosoForQuestionAnswering,
        YosoForSequenceClassification,
        YosoForTokenClassification,
        YosoLayer,
        YosoModel,
        YosoPreTrainedModel,
    )


else:
  import sys

  __a   				=      _LazyModule(__name__, globals()['''__file__'''], _import_structure)

