




UpperCamelCase__       				=	[
    ['''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			):






  """simple docstring"""


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






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


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






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  get_distinct_edge(__A			)
  a_		    =  {}

  for item in distinct_edge:
    a_		    =  get_bitcode(__A      ,      __A			)
    # print('bit',bit)
    # bt=''.join(bit)
    a_		    =  bit.count('''1'''			)
    a_		    =  [s, bit]
  # Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
  a_		    =  [
      [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			):






  """simple docstring"""


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






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


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






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


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






def 				UpperCAmelCase__  (							):






  """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			):






  """simple docstring"""


  for i in cluster[ca]:
    a_		    =  0
    a_		    =  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:
            a_		    =  [nodes[j]]
          count += 1
      if count == 0:
        a_		    =  ca + 1
      else:
        break






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


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






def 				UpperCAmelCase__  (							_A      ,      _A      ,      _A      ,      _A=None			):






  """simple docstring"""


  a_		    =  (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			):






  """simple docstring"""


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






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


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






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  for i in range(len(__A			)			):
    for j in range(len(edge_array[i]			)			):
      a_		    =  edge_array[i][j].split('''-'''			)
      a_		    =  t


if __name__ == "__main__":
  preprocess(edge_array)
  UpperCamelCase__       				=	get_frequency_table(edge_array)
  UpperCamelCase__       				=	get_nodes(frequency_table)
  UpperCamelCase__       				=	get_cluster(nodes)
  UpperCamelCase__       				=	get_support(cluster)
  UpperCamelCase__       				=	construct_graph(cluster, nodes)
  find_freq_subgraph_given_support(60, cluster, graph)
  UpperCamelCase__       				=	[]
  UpperCamelCase__       				=	freq_subgraphs_edge_list(paths)
  print_all()



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class     __magic_name__     (							_snake_case     ):
 UpperCAmelCase				     =    ["""image_processor""", """tokenizer"""]
 UpperCAmelCase				     =    """LayoutLMv2ImageProcessor"""
 UpperCAmelCase				     =    ("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")


 def __init__(    self		:			Any      ,    lowerCAmelCase__		:			Any=None      ,    lowerCAmelCase__		:			Optional[Any]=None      ,    **lowerCAmelCase__		:			int     )     ->   Union[str, Any]:
   UpperCAmelCase          =	None
   if "feature_extractor" in kwargs:
     warnings.warn(
         "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
         " instead."      ,    lowerCAmelCase__      ,    )
     UpperCAmelCase          =	kwargs.pop("feature_extractor"     )

   UpperCAmelCase          =	image_processor if image_processor is not None else feature_extractor
   if image_processor is None:
     raise ValueError("You need to specify an `image_processor`."     )
   if tokenizer is None:
     raise ValueError("You need to specify a `tokenizer`."     )

   super().__init__(lowerCAmelCase__      ,    lowerCAmelCase__     )


 def __call__(    self		:			int      ,    lowerCAmelCase__		:			Tuple      ,    lowerCAmelCase__		:			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,    lowerCAmelCase__		:			Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None      ,    lowerCAmelCase__		:			Union[List[List[int]], List[List[List[int]]]] = None      ,    lowerCAmelCase__		:			Optional[Union[List[int], List[List[int]]]] = None      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			Union[bool, str, PaddingStrategy] = False      ,    lowerCAmelCase__		:			Union[bool, str, TruncationStrategy] = False      ,    lowerCAmelCase__		:			Optional[int] = None      ,    lowerCAmelCase__		:			int = 0      ,    lowerCAmelCase__		:			Optional[int] = None      ,    lowerCAmelCase__		:			Optional[bool] = None      ,    lowerCAmelCase__		:			Optional[bool] = None      ,    lowerCAmelCase__		:			bool = False      ,    lowerCAmelCase__		:			bool = False      ,    lowerCAmelCase__		:			bool = False      ,    lowerCAmelCase__		:			bool = False      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			Optional[Union[str, TensorType]] = None      ,    **lowerCAmelCase__		:			Any      ,    )     ->   BatchEncoding:
   # verify input
   if self.image_processor.apply_ocr and (boxes is not None):
     raise ValueError(
         "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."     )

   if self.image_processor.apply_ocr and (word_labels is not None):
     raise ValueError(
         "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."     )

   if return_overflowing_tokens is True and return_offsets_mapping is False:
     raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."     )

   # first, apply the image processor
   UpperCAmelCase          =	self.image_processor(images=lowerCAmelCase__      ,    return_tensors=lowerCAmelCase__     )

   # second, apply the tokenizer
   if text is not None and self.image_processor.apply_ocr and text_pair is None:
     if isinstance(lowerCAmelCase__      ,    lowerCAmelCase__     ):
       UpperCAmelCase          =	[text]  # add batch dimension (as the image processor always adds a batch dimension)
     UpperCAmelCase          =	features["words"]

   UpperCAmelCase          =	self.tokenizer(
       text=text if text is not None else features["words"]      ,    text_pair=text_pair if text_pair is not None else None      ,    boxes=boxes if boxes is not None else features["boxes"]      ,    word_labels=lowerCAmelCase__      ,    add_special_tokens=lowerCAmelCase__      ,    padding=lowerCAmelCase__      ,    truncation=lowerCAmelCase__      ,    max_length=lowerCAmelCase__      ,    stride=lowerCAmelCase__      ,    pad_to_multiple_of=lowerCAmelCase__      ,    return_token_type_ids=lowerCAmelCase__      ,    return_attention_mask=lowerCAmelCase__      ,    return_overflowing_tokens=lowerCAmelCase__      ,    return_special_tokens_mask=lowerCAmelCase__      ,    return_offsets_mapping=lowerCAmelCase__      ,    return_length=lowerCAmelCase__      ,    verbose=lowerCAmelCase__      ,    return_tensors=lowerCAmelCase__      ,    **lowerCAmelCase__      ,    )

   # add pixel values
   UpperCAmelCase          =	features.pop("pixel_values"     )
   if return_overflowing_tokens is True:
     UpperCAmelCase          =	self.get_overflowing_images(lowerCAmelCase__      ,    encoded_inputs["overflow_to_sample_mapping"]     )
   UpperCAmelCase          =	images

   return encoded_inputs


 def _UpperCamelCase						(    self		:			str      ,    lowerCAmelCase__		:			Union[str, Any]      ,    lowerCAmelCase__		:			Union[str, Any]     )     ->   int:
   # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
   UpperCAmelCase          =	[]
   for sample_idx in overflow_to_sample_mapping:
     images_with_overflow.append(images[sample_idx]     )

   if len(lowerCAmelCase__     ) != len(lowerCAmelCase__     ):
     raise ValueError(
         "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
         f" {len(lowerCAmelCase__     )} and {len(lowerCAmelCase__     )}"     )

   return images_with_overflow


 def _UpperCamelCase						(    self		:			Optional[Any]      ,    *lowerCAmelCase__		:			int      ,    **lowerCAmelCase__		:			Union[str, Any]     )     ->   List[str]:
   return self.tokenizer.batch_decode(*lowerCAmelCase__      ,    **lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			Union[str, Any]      ,    *lowerCAmelCase__		:			Tuple      ,    **lowerCAmelCase__		:			Union[str, Any]     )     ->   List[Any]:
   return self.tokenizer.decode(*lowerCAmelCase__      ,    **lowerCAmelCase__     )


 @property
 def _UpperCamelCase						(    self		:			int     )     ->   List[str]:
   return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]


 @property
 def _UpperCamelCase						(    self		:			Dict     )     ->   List[str]:
   warnings.warn(
       "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."      ,    lowerCAmelCase__      ,    )
   return self.image_processor_class


 @property
 def _UpperCamelCase						(    self		:			List[Any]     )     ->   int:
   warnings.warn(
       "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."      ,    lowerCAmelCase__      ,    )
   return self.image_processor



