


A				:   int		= [
    ["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 				lowercase_		(		_A      :   int						):

      """simple docstring"""




      lowerCamelCase__		:    Union[str, Any]   =  set()
      for row in edge_array:
            for item in row:
                  distinct_edge.add(item[0]						)
      return list(_A						)

def 				lowercase_		(		_A      :   List[Any]   , _A      :   Optional[int]						):

      """simple docstring"""




      lowerCamelCase__		:    Optional[int]   =  ["0"] * len(_A						)
      for i, row in enumerate(_A						):
            for item in row:
                  if distinct_edge in item[0]:
                        lowerCamelCase__		:    Optional[Any]   =  "1"
                        break
      return "".join(_A						)

def 				lowercase_		(		_A      :   Dict						):

      """simple docstring"""




      lowerCamelCase__		:    Optional[Any]   =  get_distinct_edge(_A						)
      lowerCamelCase__		:    Tuple   =  {}

      for item in distinct_edge:
            lowerCamelCase__		:    Dict   =  get_bitcode(_A   , _A						)
            # print('bit',bit)
            # bt=''.join(bit)
            lowerCamelCase__		:    Any   =  bit.count("1"						)
            lowerCamelCase__		:    Tuple   =  [s, bit]
      # Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
      lowerCamelCase__		:    Any   =  [
          [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 				lowercase_		(		_A      :   Optional[int]						):

      """simple docstring"""




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

def 				lowercase_		(		_A      :   Optional[Any]						):

      """simple docstring"""




      lowerCamelCase__		:    Optional[int]   =  {}
      for key, value in nodes.items():
            lowerCamelCase__		:    Dict   =  value
      return cluster

def 				lowercase_		(		_A      :   Dict						):

      """simple docstring"""




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

def 				lowercase_		(		):

      """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 				lowercase_		(		_A      :   Union[str, Any]   , _A      :   int   , _A      :   Optional[Any]   , _A      :   List[str]						):

      """simple docstring"""




      for i in cluster[ca]:
            lowerCamelCase__		:    Tuple   =  0
            lowerCamelCase__		:    Optional[int]   =  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:
                                    lowerCamelCase__		:    List[Any]   =  [nodes[j]]
                              count += 1
                  if count == 0:
                        lowerCamelCase__		:    List[Any]   =  ca + 1
                  else:
                        break

def 				lowercase_		(		_A      :   Union[str, Any]   , _A      :   List[Any]						):

      """simple docstring"""




      lowerCamelCase__		:    Optional[Any]   =  cluster[max(cluster.keys()						)]
      lowerCamelCase__		:    Optional[Any]   =  "Header"
      lowerCamelCase__		:    int   =  {}
      for i in x:
            if (["Header"],) in graph:
                  graph[(["Header"],)].append(x[i]						)
            else:
                  lowerCamelCase__		:    Union[str, Any]   =  [x[i]]
      for i in x:
            lowerCamelCase__		:    str   =  [["Header"]]
      lowerCamelCase__		:    List[Any]   =  1
      while i < max(_A						) - 1:
            create_edge(_A   , _A   , _A   , _A						)
            lowerCamelCase__		:    Any   =  i + 1
      return graph

def 				lowercase_		(		_A      :   List[Any]   , _A      :   List[Any]   , _A      :   Any   , _A      :   Dict=None						):

      """simple docstring"""




      lowerCamelCase__		:    Union[str, Any]   =  (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 				lowercase_		(		_A      :   Union[str, Any]   , _A      :   Tuple   , _A      :   str						):

      """simple docstring"""




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

def 				lowercase_		(		_A      :   Optional[int]						):

      """simple docstring"""




      lowerCamelCase__		:    Optional[Any]   =  []
      for edges in paths:
            lowerCamelCase__		:    Dict   =  []
            for j in range(len(_A						) - 1						):
                  lowerCamelCase__		:    List[Any]   =  list(edges[j]						)
                  for e in temp:
                        lowerCamelCase__		:    int   =  (e[0], e[1])
                        el.append(_A						)
            freq_sub_el.append(_A						)
      return freq_sub_el

def 				lowercase_		(		_A      :   Optional[int]						):

      """simple docstring"""




      for i in range(len(_A						)						):
            for j in range(len(edge_array[i]						)						):
                  lowerCamelCase__		:    Optional[Any]   =  edge_array[i][j].split("-"						)
                  lowerCamelCase__		:    List[str]   =  t


if __name__ == "__main__":
    preprocess(edge_array)
    A				:   List[str]		= get_frequency_table(edge_array)
    A				:   Optional[Any]		= get_nodes(frequency_table)
    A				:   str		= get_cluster(nodes)
    A				:   Tuple		= get_support(cluster)
    A				:   Any		= construct_graph(cluster, nodes)
    find_freq_subgraph_given_support(60, cluster, graph)
    A				:   list		= []
    A				:   Dict		= freq_subgraphs_edge_list(paths)
    print_all()






from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


A				:   int		= logging.get_logger(__name__)

A				:   Any		= {
    "layoutlmv2-base-uncased": "https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json",
    "layoutlmv2-large-uncased": "https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json",
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
    import detectrona







class       _lowercase			(							lowercase__):


       """simple docstring"""




       A__ 						=      "layoutlmv2"





       def __init__(       self							:      Union[str, Any] ,				__lowerCamelCase							:      int=30522 ,				__lowerCamelCase							:      List[Any]=768 ,				__lowerCamelCase							:      Any=12 ,				__lowerCamelCase							:      List[Any]=12 ,				__lowerCamelCase							:      Optional[Any]=3072 ,				__lowerCamelCase							:      List[Any]="gelu" ,				__lowerCamelCase							:      Any=0.1 ,				__lowerCamelCase							:      Union[str, Any]=0.1 ,				__lowerCamelCase							:      int=512 ,				__lowerCamelCase							:      str=2 ,				__lowerCamelCase							:      Any=0.0_2 ,				__lowerCamelCase							:      List[Any]=1E-1_2 ,				__lowerCamelCase							:      List[str]=0 ,				__lowerCamelCase							:      Union[str, Any]=1024 ,				__lowerCamelCase							:      Tuple=128 ,				__lowerCamelCase							:      Tuple=32 ,				__lowerCamelCase							:      Union[str, Any]=True ,				__lowerCamelCase							:      List[Any]=256 ,				__lowerCamelCase							:      Dict=64 ,				__lowerCamelCase							:      List[Any]=True ,				__lowerCamelCase							:      Union[str, Any]=[7, 7, 256] ,				__lowerCamelCase							:      str=128 ,				__lowerCamelCase							:      Dict=128 ,				__lowerCamelCase							:      Dict=True ,				__lowerCamelCase							:      int=True ,				__lowerCamelCase							:      str=False ,				__lowerCamelCase							:      Tuple=None ,				**__lowerCamelCase							:      str ,				):


             '''simple docstring'''



             super().__init__(
                 vocab_size=__lowerCamelCase ,				hidden_size=__lowerCamelCase ,				num_hidden_layers=__lowerCamelCase ,				num_attention_heads=__lowerCamelCase ,				intermediate_size=__lowerCamelCase ,				hidden_act=__lowerCamelCase ,				hidden_dropout_prob=__lowerCamelCase ,				attention_probs_dropout_prob=__lowerCamelCase ,				max_position_embeddings=__lowerCamelCase ,				type_vocab_size=__lowerCamelCase ,				initializer_range=__lowerCamelCase ,				layer_norm_eps=__lowerCamelCase ,				pad_token_id=__lowerCamelCase ,				**__lowerCamelCase ,				)
             lowerCamelCase__		:    Dict   =  max_ad_position_embeddings
             lowerCamelCase__		:    int   =  max_rel_pos
             lowerCamelCase__		:    List[Any]   =  rel_pos_bins
             lowerCamelCase__		:    str   =  fast_qkv
             lowerCamelCase__		:    Optional[Any]   =  max_rel_ad_pos
             lowerCamelCase__		:    List[Any]   =  rel_ad_pos_bins
             lowerCamelCase__		:    Optional[Any]   =  convert_sync_batchnorm
             lowerCamelCase__		:    Any   =  image_feature_pool_shape
             lowerCamelCase__		:    List[Any]   =  coordinate_size
             lowerCamelCase__		:    Any   =  shape_size
             lowerCamelCase__		:    List[str]   =  has_relative_attention_bias
             lowerCamelCase__		:    Optional[int]   =  has_spatial_attention_bias
             lowerCamelCase__		:    Optional[Any]   =  has_visual_segment_embedding
             lowerCamelCase__		:    List[str]   =  (
                 detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
             )





       @classmethod
       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             return {
                 "MODEL.MASK_ON": True,
                 "MODEL.PIXEL_STD": [5_7.3_7_5, 5_7.1_2_0, 5_8.3_9_5],
                 "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
                 "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
                 "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
                 "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
                 "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
                 "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
                 "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
                 "MODEL.POST_NMS_TOPK_TEST": 1000,
                 "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
                 "MODEL.ROI_HEADS.NUM_CLASSES": 5,
                 "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
                 "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
                 "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
                 "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
                 "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
                 "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
                 "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
                 "MODEL.RESNETS.DEPTH": 101,
                 "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
                 "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
                 "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
                 "MODEL.RESNETS.NUM_GROUPS": 32,
                 "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
                 "MODEL.RESNETS.STRIDE_IN_1X1": False,
             }





       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[int]   =  detectrona.config.get_cfg()
             for k, v in self.detectrona_config_args.items():
                   lowerCamelCase__		:    List[Any]   =  k.split("."	)
                   lowerCamelCase__		:    int   =  detectrona_config
                   for attribute in attributes[:-1]:
                         lowerCamelCase__		:    Any   =  getattr(__lowerCamelCase ,				__lowerCamelCase	)
                   setattr(__lowerCamelCase ,				attributes[-1] ,				__lowerCamelCase	)

             return detectrona_config


