


'''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__  : Optional[int]				=	logging.get_logger(__name__)

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







def _UpperCamelCase	(					__A   )							->		Any:


      '''simple docstring'''



      for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
            if class_name in processors:
                  UpperCamelCase__  =    model_type_to_module_name(__A   )

                  UpperCamelCase__  =    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.
      UpperCamelCase__  =    importlib.import_module("transformers"   )
      if hasattr(__A				,      __A   ):
            return getattr(__A				,      __A   )

      return None



class  lowercase_					:


    def __init__(					self   ):
          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(lowerCAmelCase__   )
    def        __a							(					cls  , a  , **a   ):
          UpperCamelCase__  =    kwargs.pop("config"  , lowerCAmelCase__   )
          UpperCamelCase__  =    kwargs.pop("trust_remote_code"  , lowerCAmelCase__   )
          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(lowerCAmelCase__   ).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(
              lowerCAmelCase__  , lowerCAmelCase__  , **lowerCAmelCase__   )
          if preprocessor_config_file is not None:
                UpperCamelCase__ ,     UpperCamelCase__  =    ImageProcessingMixin.get_image_processor_dict(lowerCAmelCase__  , **lowerCAmelCase__   )
                UpperCamelCase__  =    config_dict.get("processor_class"  , lowerCAmelCase__   )
                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(lowerCAmelCase__  , **lowerCAmelCase__   )
                UpperCamelCase__  =    config_dict.get("processor_class"  , lowerCAmelCase__   )
                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(
                    lowerCAmelCase__  , lowerCAmelCase__  , **lowerCAmelCase__   )
                if tokenizer_config_file is not None:
                      with open(lowerCAmelCase__  , encoding="utf-8"   ) as reader:
                            UpperCamelCase__  =    json.load(lowerCAmelCase__   )

                      UpperCamelCase__  =    config_dict.get("processor_class"  , lowerCAmelCase__   )
                      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(lowerCAmelCase__  , lowerCAmelCase__   ):
                      UpperCamelCase__  =    AutoConfig.from_pretrained(
                          lowerCAmelCase__  , trust_remote_code=lowerCAmelCase__  , **lowerCAmelCase__   )

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

          if processor_class is not None:
                UpperCamelCase__  =    processor_class_from_name(lowerCAmelCase__   )

          UpperCamelCase__  =    processor_auto_map is not None
          UpperCamelCase__  =    processor_class is not None or type(lowerCAmelCase__   ) in PROCESSOR_MAPPING
          UpperCamelCase__  =    resolve_trust_remote_code(
              lowerCAmelCase__  , lowerCAmelCase__  , lowerCAmelCase__  , lowerCAmelCase__   )

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

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

                try:
                      return AutoFeatureExtractor.from_pretrained(
                          lowerCAmelCase__  , trust_remote_code=lowerCAmelCase__  , **lowerCAmelCase__   )
                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							(					a  , a   ):
          PROCESSOR_MAPPING.register(lowerCAmelCase__  , lowerCAmelCase__   )





'''simple docstring'''







a__  : str				=	[
    ['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'be-e6', 'bh-e12', 'cd-e2', 'ce-e4',
     'de-e1', 'df-e8', 'dg-e5', 'dh-e10', 'ef-e3', 'eg-e2', 'fg-e6', 'gh-e6', 'hi-e3'],
    ['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'be-e6', 'cd-e2', 'de-e1', 'df-e8',
     'ef-e3', 'eg-e2', 'fg-e6'],
    ['ab-e1', 'ac-e3', 'bc-e4', 'bd-e2', 'de-e1', 'df-e8', 'dg-e5', 'ef-e3', 'eg-e2',
     'eh-e12', 'fg-e6', 'fh-e10', 'gh-e6'],
    ['ab-e1', 'ac-e3', 'bc-e4', 'bd-e2', 'bh-e12', 'cd-e2', 'df-e8', 'dh-e10'],
    ['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'cd-e2', 'ce-e4', 'de-e1', 'df-e8',
     'dg-e5', 'ef-e3', 'eg-e2', 'fg-e6']
]







def _UpperCamelCase	(					__A   )							->		Tuple:


      '''simple docstring'''



      UpperCamelCase__  =    set()
      for row in edge_array:
            for item in row:
                  distinct_edge.add(item[0]   )
      return list(__A   )







def _UpperCamelCase	(					__A				,      __A   )							->		int:


      '''simple docstring'''



      UpperCamelCase__  =    ["0"] * len(__A   )
      for i, row in enumerate(__A   ):
            for item in row:
                  if distinct_edge in item[0]:
                        UpperCamelCase__  =    "1"
                        break
      return "".join(__A   )







def _UpperCamelCase	(					__A   )							->		Union[str, Any]:


      '''simple docstring'''



      UpperCamelCase__  =    get_distinct_edge(__A   )
      UpperCamelCase__  =    {}

      for item in distinct_edge:
            UpperCamelCase__  =    get_bitcode(__A				,      __A   )
            # print('bit',bit)
            # bt=''.join(bit)
            UpperCamelCase__  =    bit.count("1"   )
            UpperCamelCase__  =    [s, bit]
      # Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
      UpperCamelCase__  =    [
          [k, v[0], v[1]]
          for k, v in sorted(frequency_table.items()				,      key=lambda __A   : v[1][0]				,      reverse=__A   )
      ]
      return sorted_frequency_table







def _UpperCamelCase	(					__A   )							->		Union[str, Any]:


      '''simple docstring'''



      UpperCamelCase__  =    {}
      for _, item in enumerate(__A   ):
            nodes.setdefault(item[2]				,      []   ).append(item[0]   )
      return nodes







def _UpperCamelCase	(					__A   )							->		Tuple:


      '''simple docstring'''



      UpperCamelCase__  =    {}
      for key, value in nodes.items():
            UpperCamelCase__  =    value
      return cluster







def _UpperCamelCase	(					__A   )							->		str:


      '''simple docstring'''



      return [i * 100 / len(__A   ) for i in cluster]







def _UpperCamelCase	(					)							->		None:


      '''simple docstring'''



      print("\nNodes\n"   )
      for key, value in nodes.items():
            print(__A				,      __A   )
      print("\nSupport\n"   )
      print(__A   )
      print("\n Cluster \n"   )
      for key, value in sorted(cluster.items()				,      reverse=__A   ):
            print(__A				,      __A   )
      print("\n Graph\n"   )
      for key, value in graph.items():
            print(__A				,      __A   )
      print("\n Edge List of Frequent subgraphs \n"   )
      for edge_list in freq_subgraph_edge_list:
            print(__A   )







def _UpperCamelCase	(					__A				,      __A				,      __A				,      __A   )							->		Optional[int]:


      '''simple docstring'''



      for i in cluster[ca]:
            UpperCamelCase__  =    0
            UpperCamelCase__  =    ca + 1
            while ca < max(cluster.keys()   ):
                  for j in cluster[ca]:
                        if int(__A				,      2   ) & int(__A				,      2   ) == int(__A				,      2   ):
                              if tuple(nodes[i]   ) in graph:
                                    graph[tuple(nodes[i]   )].append(nodes[j]   )
                              else:
                                    UpperCamelCase__  =    [nodes[j]]
                              count += 1
                  if count == 0:
                        UpperCamelCase__  =    ca + 1
                  else:
                        break







def _UpperCamelCase	(					__A				,      __A   )							->		List[str]:


      '''simple docstring'''



      UpperCamelCase__  =    cluster[max(cluster.keys()   )]
      UpperCamelCase__  =    "Header"
      UpperCamelCase__  =    {}
      for i in x:
            if (["Header"],) in graph:
                  graph[(["Header"],)].append(x[i]   )
            else:
                  UpperCamelCase__  =    [x[i]]
      for i in x:
            UpperCamelCase__  =    [["Header"]]
      UpperCamelCase__  =    1
      while i < max(__A   ) - 1:
            create_edge(__A				,      __A				,      __A				,      __A   )
            UpperCamelCase__  =    i + 1
      return graph







def _UpperCamelCase	(					__A				,      __A				,      __A				,      __A=None   )							->		str:


      '''simple docstring'''



      UpperCamelCase__  =    (path or []) + [start]
      if start == end:
            paths.append(__A   )
      for node in graph[start]:
            if tuple(__A   ) not in path:
                  my_dfs(__A				,      tuple(__A   )				,      __A				,      __A   )







def _UpperCamelCase	(					__A				,      __A				,      __A   )							->		Union[str, Any]:


      '''simple docstring'''



      UpperCamelCase__  =    int(s / 100 * (len(__A   ) - 1)   )
      for i in cluster[k]:
            my_dfs(__A				,      tuple(cluster[k][i]   )				,      (["Header"],)   )







def _UpperCamelCase	(					__A   )							->		Optional[Any]:


      '''simple docstring'''



      UpperCamelCase__  =    []
      for edges in paths:
            UpperCamelCase__  =    []
            for j in range(len(__A   ) - 1   ):
                  UpperCamelCase__  =    list(edges[j]   )
                  for e in temp:
                        UpperCamelCase__  =    (e[0], e[1])
                        el.append(__A   )
            freq_sub_el.append(__A   )
      return freq_sub_el







def _UpperCamelCase	(					__A   )							->		Optional[int]:


      '''simple docstring'''



      for i in range(len(__A   )   ):
            for j in range(len(edge_array[i]   )   ):
                  UpperCamelCase__  =    edge_array[i][j].split("-"   )
                  UpperCamelCase__  =    t


if __name__ == "__main__":
  preprocess(edge_array)
  a__  : List[Any]				=	get_frequency_table(edge_array)
  a__  : Optional[int]				=	get_nodes(frequency_table)
  a__  : Any				=	get_cluster(nodes)
  a__  : Tuple				=	get_support(cluster)
  a__  : str				=	construct_graph(cluster, nodes)
  find_freq_subgraph_given_support(6_0, cluster, graph)
  a__  : list				=	[]
  a__  : str				=	freq_subgraphs_edge_list(paths)
  print_all()





