


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


UpperCAmelCase__            =							logging.get_logger(__name__)

UpperCAmelCase__            =							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'''),
    ]
)

UpperCAmelCase__            =							_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)
def     UpperCAmelCase_ (							__snake_case						)					->      int:







 """simple docstring"""


 for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
  if class_name in processors:
   _lowercase											=model_type_to_module_name(__snake_case						)

   _lowercase											=importlib.import_module(F".{module_name}"					,						'''transformers.models'''						)
   try:
    return getattr(__snake_case					,						__snake_case						)
   except AttributeError:
    continue

 for processor in PROCESSOR_MAPPING._extra_content.values():
  if getattr(__snake_case					,						'''__name__'''					,						__snake_case						) == 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(__snake_case					,						__snake_case						):
  return getattr(__snake_case					,						__snake_case						)

 return None







class 				lowerCamelCase__       :





     def __init__(self	)					->       Optional[int]:
      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 					__A (cls		,					UpperCAmelCase		,					**UpperCAmelCase	)					->       int:
      _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 					__A (UpperCAmelCase		,					UpperCAmelCase	)					->       List[str]:
      PROCESSOR_MAPPING.register(UpperCAmelCase		,					UpperCAmelCase	)





from math import sqrt
def     UpperCAmelCase_ (							__snake_case						)					->      list[int]:







 """simple docstring"""


 _lowercase											=[1]
 for i in range(2					,						int(sqrt(__snake_case						)						) + 1					,						1						):
  if number % i == 0:
   values.append(__snake_case						)
   if int(number // i						) != i:
    values.append(int(number // i						)						)
 return sorted(__snake_case						)
def     UpperCAmelCase_ (							__snake_case						)					->      bool:







 """simple docstring"""


 return sum(factors(__snake_case						)						) > n
def     UpperCAmelCase_ (							__snake_case						)					->      bool:







 """simple docstring"""


 _lowercase											=factors(__snake_case						)
 _lowercase											=len(__snake_case						)
 _lowercase											=[[0 for i in range(number + 1						)] for j in range(r + 1						)]
 for i in range(r + 1						):
  _lowercase											=True

 for i in range(1					,						number + 1						):
  _lowercase											=False

 for i in range(1					,						r + 1						):
  for j in range(1					,						number + 1						):
   if j < values[i - 1]:
    _lowercase											=subset[i - 1][j]
   else:
    _lowercase											=subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

 return subset[r][number] != 0
def     UpperCAmelCase_ (							__snake_case						)					->      bool:







 """simple docstring"""


 return abundant(__snake_case						) and not semi_perfect(__snake_case						)


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)
  for number in (69, 70, 71):
    print(f'''{number} is {'' if weird(number) else 'not '}weird.''')

