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


snake_case__						      =			logging.get_logger(__name__)

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'),
    ]
)

snake_case__						      =			_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)
def    __magic_name__(	__UpperCAmelCase		)    ->     Optional[Any]:



      '''simple docstring'''

      for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
            if class_name in processors:
                  _lowerCamelCase           =					model_type_to_module_name(lowerCamelCase_		)

                  _lowerCamelCase           =					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.
      _lowerCamelCase           =					importlib.import_module('''transformers'''		)
      if hasattr(lowerCamelCase_			,    lowerCamelCase_		):
            return getattr(lowerCamelCase_			,    lowerCamelCase_		)

      return None




class 			UpperCamelCase					:

     '''simple docstring'''

     def __init__(       self    )			->					Dict:






           """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(snake_case__    )
     def 		UpperCamelCase_    (       cls					,					A_					,					**A_    )			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase           =					kwargs.pop('''config'''					,					snake_case__    )
           _lowerCamelCase           =					kwargs.pop('''trust_remote_code'''					,					snake_case__    )
           _lowerCamelCase           =					True

           _lowerCamelCase           =					None
           _lowerCamelCase           =					None

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

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

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

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

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

           if processor_class is not None:
                 _lowerCamelCase           =					processor_class_from_name(snake_case__    )

           _lowerCamelCase           =					processor_auto_map is not None
           _lowerCamelCase           =					processor_class is not None or type(snake_case__    ) in PROCESSOR_MAPPING
           _lowerCamelCase           =					resolve_trust_remote_code(
               snake_case__					,					snake_case__					,					snake_case__					,					snake_case__    )

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

                 try:
                       return AutoFeatureExtractor.from_pretrained(
                           snake_case__					,					trust_remote_code=snake_case__					,					**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 		UpperCamelCase_    (       A_					,					A_    )			->					int:






           """simple docstring"""
           PROCESSOR_MAPPING.register(snake_case__					,					snake_case__    )

"""simple docstring"""
from transformers import ProcessorMixin




class  _snake_case	(    _lowercase							):
							SCREAMING_SNAKE_CASE		  =    '''AutoFeatureExtractor'''
							SCREAMING_SNAKE_CASE		  =    '''AutoTokenizer'''
