




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    				=		logging.get_logger(__name__)

_lowerCamelCase    				=		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    				=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		str   )						->							Tuple:
    for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
        if class_name in processors:
            UpperCAmelCase_											=  model_type_to_module_name(__UpperCamelCase   )

            UpperCAmelCase_											=  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.
    UpperCAmelCase_											=  importlib.import_module('''transformers'''   )
    if hasattr(__UpperCamelCase ,  __UpperCamelCase   ):
        return getattr(__UpperCamelCase ,  __UpperCamelCase   )

    return None



class     a  :

     '''simple docstring'''





     def __init__(			self     :    List[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(__snake_case       )
     def 						lowerCamelCase_     (			cls     :    Union[str, Any]      ,					__snake_case     :    Optional[Any]      ,					**__snake_case     :    str       ):
         UpperCAmelCase_											=  kwargs.pop('''config'''      ,					__snake_case       )
         UpperCAmelCase_											=  kwargs.pop('''trust_remote_code'''      ,					__snake_case       )
         UpperCAmelCase_											=  True

         UpperCAmelCase_											=  None
         UpperCAmelCase_											=  None

         # First, let's see if we have a preprocessor config.
         # Filter the kwargs for `get_file_from_repo`.
         UpperCAmelCase_											=  {
             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
         UpperCAmelCase_											=  get_file_from_repo(
             __snake_case      ,					__snake_case      ,					**__snake_case       )
         if preprocessor_config_file is not None:
             UpperCAmelCase_				,							UpperCAmelCase_											=  ImageProcessingMixin.get_image_processor_dict(__snake_case      ,					**__snake_case       )
             UpperCAmelCase_											=  config_dict.get('''processor_class'''      ,					__snake_case       )
             if "AutoProcessor" in config_dict.get('''auto_map'''      ,					{}       ):
                 UpperCAmelCase_											=  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_											=  FeatureExtractionMixin.get_feature_extractor_dict(__snake_case      ,					**__snake_case       )
             UpperCAmelCase_											=  config_dict.get('''processor_class'''      ,					__snake_case       )
             if "AutoProcessor" in config_dict.get('''auto_map'''      ,					{}       ):
                 UpperCAmelCase_											=  config_dict['''auto_map''']['''AutoProcessor''']

         if processor_class is None:
             # Next, let's check whether the processor class is saved in a tokenizer
             UpperCAmelCase_											=  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:
                     UpperCAmelCase_											=  json.load(__snake_case       )

                 UpperCAmelCase_											=  config_dict.get('''processor_class'''      ,					__snake_case       )
                 if "AutoProcessor" in config_dict.get('''auto_map'''      ,					{}       ):
                     UpperCAmelCase_											=  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       ):
                 UpperCAmelCase_											=  AutoConfig.from_pretrained(
                     __snake_case      ,					trust_remote_code=__snake_case      ,					**__snake_case       )

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

         if processor_class is not None:
             UpperCAmelCase_											=  processor_class_from_name(__snake_case       )

         UpperCAmelCase_											=  processor_auto_map is not None
         UpperCAmelCase_											=  processor_class is not None or type(__snake_case       ) in PROCESSOR_MAPPING
         UpperCAmelCase_											=  resolve_trust_remote_code(
             __snake_case      ,					__snake_case      ,					__snake_case      ,					__snake_case       )

         if has_remote_code and trust_remote_code:
             UpperCAmelCase_											=  get_class_from_dynamic_module(
                 __snake_case      ,					__snake_case      ,					**__snake_case       )
             UpperCAmelCase_											=  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 						lowerCamelCase_     (			__snake_case     :    List[str]      ,					__snake_case     :    Dict       ):
         PROCESSOR_MAPPING.register(__snake_case      ,					__snake_case       )











def       SCREAMING_SNAKE_CASE			( )						->							List[Any]:
    UpperCAmelCase_											=  1

    while True:
        if (
            sorted(str(__UpperCamelCase   )   )
            == sorted(str(2 * i   )   )
            == sorted(str(3 * i   )   )
            == sorted(str(4 * i   )   )
            == sorted(str(5 * i   )   )
            == sorted(str(6 * i   )   )
        ):
            return i

        i += 1


if __name__ == "__main__":
 print(solution())





