


'''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


lowerCamelCase							:       Dict  						=     logging.get_logger(__name__)

lowerCamelCase							:       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'),
    ]
)

lowerCamelCase							:       List[Any]  						=     _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)

def _SCREAMING_SNAKE_CASE				(A	)	->	List[str]:






       """simple docstring"""







       for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
              if class_name in processors:
                     lowercase__				      =			model_type_to_module_name(A	)

                     lowercase__				      =			importlib.import_module(f".{module_name}"  ,			'''transformers.models'''	)
                     try:
                            return getattr(A  ,			A	)
                     except AttributeError:
                            continue

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

       return None

class   __lowerCAmelCase						:





      '''simple docstring'''






      def __init__(self   :   List[Any]			):

             '''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(UpperCamelCase			)
      def 				UpperCamelCase__		(cls   :   Dict       ,						UpperCamelCase   :   Optional[Any]       ,						**UpperCamelCase   :   str			):

             '''simple docstring'''







             lowercase__				      =			kwargs.pop('''config'''       ,						UpperCamelCase			)
             lowercase__				      =			kwargs.pop('''trust_remote_code'''       ,						UpperCamelCase			)
             lowercase__				      =			True

             lowercase__				      =			None
             lowercase__				      =			None

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

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

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

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

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

             if processor_class is not None:
                    lowercase__				      =			processor_class_from_name(UpperCamelCase			)

             lowercase__				      =			processor_auto_map is not None
             lowercase__				      =			processor_class is not None or type(UpperCamelCase			) in PROCESSOR_MAPPING
             lowercase__				      =			resolve_trust_remote_code(
                 UpperCamelCase       ,						UpperCamelCase       ,						UpperCamelCase       ,						UpperCamelCase			)

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

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

                    try:
                           return AutoFeatureExtractor.from_pretrained(
                               UpperCamelCase       ,						trust_remote_code=UpperCamelCase       ,						**UpperCamelCase			)
                    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__		(UpperCamelCase   :   Optional[int]       ,						UpperCamelCase   :   Any			):

             '''simple docstring'''







             PROCESSOR_MAPPING.register(UpperCamelCase       ,						UpperCamelCase			)








'''simple docstring'''

def _SCREAMING_SNAKE_CASE				(A	)	->	int:






       """simple docstring"""







       return 1 if input_a == 0 else 0

def _SCREAMING_SNAKE_CASE				()	->	None:






       """simple docstring"""







       assert not_gate(0	) == 1
       assert not_gate(1	) == 0


if __name__ == "__main__":
 print(not_gate(0))
 print(not_gate(1))




