
'''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


a__		: List[Any]  							=							logging.get_logger(__name__)

a__		: Optional[Any]  							=							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__		: Tuple  							=							_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)





def 							snake_case				(		UpperCAmelCase						)->      Dict:







      """simple docstring"""
      for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
            if class_name in processors:
                  __A													=      model_type_to_module_name(UpperCAmelCase						)

                  __A													=      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.
      __A													=      importlib.import_module('transformers'						)
      if hasattr(UpperCAmelCase   ,	UpperCAmelCase						):
            return getattr(UpperCAmelCase   ,	UpperCAmelCase						)

      return None


class 			UpperCamelCase__	:






 def __init__(      self	:Optional[Any]      )					->      Tuple:







       '''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(_A      )
 def 						lowercase_    (      cls	:Union[str, Any]						,					_A	:Optional[int]						,					**_A	:int      )					->      Dict:







       '''simple docstring'''



       __A													=      kwargs.pop('config'						,					_A      )
       __A													=      kwargs.pop('trust_remote_code'						,					_A      )
       __A													=      True

       __A													=      None
       __A													=      None

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

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

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

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

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

       if processor_class is not None:
             __A													=      processor_class_from_name(_A      )

       __A													=      processor_auto_map is not None
       __A													=      processor_class is not None or type(_A      ) in PROCESSOR_MAPPING
       __A													=      resolve_trust_remote_code(
           _A						,					_A						,					_A						,					_A      )

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

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

             try:
                   return AutoFeatureExtractor.from_pretrained(
                       _A						,					trust_remote_code=_A						,					**_A      )
             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 						lowercase_    (      _A	:Optional[int]						,					_A	:int      )					->      List[str]:







       '''simple docstring'''



       PROCESSOR_MAPPING.register(_A						,					_A      )












'''simple docstring'''







A					       =[[0, 1, 1], [0, 0, 1], [1, 0, 0]]


class      _a    :
       def __init__(      self		:      Optional[int]  ,							lowercase		:      str							):



           '''simple docstring'''

           UpperCAmelCase     			=						name
           UpperCAmelCase     			=						[]
           UpperCAmelCase     			=						[]
       def        A (      self		:      Union[str, Any]  ,							lowercase		:      int							):



           '''simple docstring'''

           self.inbound.append(lowercase							)
       def        A (      self		:      List[str]  ,							lowercase		:      Optional[Any]							):



           '''simple docstring'''

           self.outbound.append(lowercase							)







       def __repr__(      self		:      Any							):



           '''simple docstring'''

           return f"<node={self.name} inbound={self.inbound} outbound={self.outbound}>"






def 		snake_case_				(_a      :				str       ,  _a      :				str=3       ,  _a      :				Any=0.85	):
    UpperCAmelCase     			=						{}
    for node in nodes:
        UpperCAmelCase     			=						1

    UpperCAmelCase     			=						{}
    for node in nodes:
        UpperCAmelCase     			=						len(node.outbound	)

    for i in range(_a	):
        print(F"======= Iteration {i + 1} ======="	)
        for _, node in enumerate(_a	):
            UpperCAmelCase     			=						(1 - d) + d * sum(
                ranks[ib] / outbounds[ib] for ib in node.inbound	)
        print(_a	)





def 		snake_case_				():
    UpperCAmelCase     			=						list(input('''Enter Names of the Nodes: '''	).split()	)

    UpperCAmelCase     			=						[Node(_a	) for name in names]

    for ri, row in enumerate(_a	):
        for ci, col in enumerate(_a	):
            if col == 1:
                nodes[ci].add_inbound(names[ri]	)
                nodes[ri].add_outbound(names[ci]	)

    print('''======= Nodes ======='''	)
    for node in nodes:
        print(_a	)

    page_rank(_a	)


if __name__ == "__main__":
   main()




