
UpperCAmelCase__	:  Optional[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 						lowerCamelCase__							(      a   )   ->     int:
   _A:						int	    = set()
   for row in edge_array:
      for item in row:
         distinct_edge.add(item[0]   )
   return list(lowercase__   )






def 						lowerCamelCase__							(      a	,	a   )   ->     str:
   _A:						Dict	    = ['''0'''] * len(lowercase__   )
   for i, row in enumerate(lowercase__   ):
      for item in row:
         if distinct_edge in item[0]:
            _A:						Any	    = '''1'''
            break
   return "".join(lowercase__   )






def 						lowerCamelCase__							(      a   )   ->     Optional[Any]:
   _A:						Dict	    = get_distinct_edge(lowercase__   )
   _A:						Union[str, Any]	    = {}

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






def 						lowerCamelCase__							(      a   )   ->     str:
   _A:						Tuple	    = {}
   for _, item in enumerate(lowercase__   ):
      nodes.setdefault(item[2]	,	[]   ).append(item[0]   )
   return nodes






def 						lowerCamelCase__							(      a   )   ->     Any:
   _A:						Optional[Any]	    = {}
   for key, value in nodes.items():
      _A:						Tuple	    = value
   return cluster






def 						lowerCamelCase__							(      a   )   ->     Dict:
   return [i * 1_00 / len(lowercase__   ) for i in cluster]






def 						lowerCamelCase__							(      )   ->     Any:
   print('''\nNodes\n'''   )
   for key, value in nodes.items():
      print(lowercase__	,	lowercase__   )
   print('''\nSupport\n'''   )
   print(lowercase__   )
   print('''\n Cluster \n'''   )
   for key, value in sorted(cluster.items()	,	reverse=lowercase__   ):
      print(lowercase__	,	lowercase__   )
   print('''\n Graph\n'''   )
   for key, value in graph.items():
      print(lowercase__	,	lowercase__   )
   print('''\n Edge List of Frequent subgraphs \n'''   )
   for edge_list in freq_subgraph_edge_list:
      print(lowercase__   )






def 						lowerCamelCase__							(      a	,	a	,	a	,	a   )   ->     Optional[int]:
   for i in cluster[ca]:
      _A:						Optional[Any]	    = 0
      _A:						Union[str, Any]	    = ca + 1
      while ca < max(cluster.keys()   ):
         for j in cluster[ca]:
            if int(lowercase__	,	2   ) & int(lowercase__	,	2   ) == int(lowercase__	,	2   ):
               if tuple(nodes[i]   ) in graph:
                  graph[tuple(nodes[i]   )].append(nodes[j]   )
               else:
                  _A:						Union[str, Any]	    = [nodes[j]]
               count += 1
         if count == 0:
            _A:						List[Any]	    = ca + 1
         else:
            break






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






def 						lowerCamelCase__							(      a	,	a	,	a	,	a=None   )   ->     Tuple:
   _A:						int	    = (path or []) + [start]
   if start == end:
      paths.append(lowercase__   )
   for node in graph[start]:
      if tuple(lowercase__   ) not in path:
         my_dfs(lowercase__	,	tuple(lowercase__   )	,	lowercase__	,	lowercase__   )






def 						lowerCamelCase__							(      a	,	a	,	a   )   ->     Optional[int]:
   _A:						Optional[Any]	    = int(s / 1_00 * (len(lowercase__   ) - 1)   )
   for i in cluster[k]:
      my_dfs(lowercase__	,	tuple(cluster[k][i]   )	,	(['''Header'''],)   )






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






def 						lowerCamelCase__							(      a   )   ->     List[Any]:
   for i in range(len(lowercase__   )   ):
      for j in range(len(edge_array[i]   )   ):
         _A:						Union[str, Any]	    = edge_array[i][j].split('''-'''   )
         _A:						str	    = t


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





# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


UpperCAmelCase__	:  Union[str, Any]      =   logging.get_logger(__name__)


if is_torch_available():
   import torch
   from torch import nn
   from torch.utils.data import Dataset

   from transformers import Trainer






   class UpperCAmelCase     (		SCREAMING_SNAKE_CASE__     ):





      '''simple docstring'''


      def __init__(      self  :							List[Any]    ,    lowerCAmelCase_  :							int = 1_0_1  ):

         """simple docstring"""
         _A:						str	    = length


      def __len__(      self  :							List[Any]  ):

         """simple docstring"""
         return self.length


      def __getitem__(      self  :							Any    ,    lowerCAmelCase_  :							Any  ):

         """simple docstring"""
         return i






   class UpperCAmelCase     :





      '''simple docstring'''


      def __call__(      self  :							Any    ,    lowerCAmelCase_  :							Union[str, Any]  ):

         """simple docstring"""
         return {"input_ids": torch.tensor(lowerCAmelCase_  ), "labels": torch.tensor(lowerCAmelCase_  )}






   class UpperCAmelCase     (		nn.Module     ):





      '''simple docstring'''


      def __init__(      self  :							str  ):

         """simple docstring"""
         super().__init__()
         # Add some (unused) params otherwise DDP will complain.
         _A:						Tuple	    = nn.Linear(1_2_0    ,    8_0  )


      def 				__magic_name__       (      self  :							List[Any]    ,    lowerCAmelCase_  :							List[str]    ,    lowerCAmelCase_  :							List[str]=None  ):

         """simple docstring"""
         if labels is not None:
            return torch.tensor(0.0    ,    device=input_ids.device  ), input_ids
         else:
            return input_ids






def 						lowerCamelCase__							(      )   ->     Optional[int]:
   _A:						Any	    = HfArgumentParser((TrainingArguments,)   )
   sys.argv += ["--output_dir", "./examples"]
   _A:						Any	    = parser.parse_args_into_dataclasses()[0]

   logger.warning(
       f"""Process rank: {training_args.local_rank}, device: {training_args.device}, """
       f"""tpu_num_cores: {training_args.tpu_num_cores}"""	,	)

   # Essentially, what we want to verify in the distributed case is
   # that we get all samples back, in the right order.
   # (this is crucial for prediction for instance)
   for dataset_length in [10_01, 2_56, 15]:
      _A:						Any	    = DummyDataset(a   )

      def compute_metrics(a   ) -> Dict:
         _A:						Dict	    = list(range(len(a   )   )   )
         _A:						List[str]	    = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
         return {"success": success}

      _A:						Optional[Any]	    = Trainer(
          model=DummyModel()	,	args=a	,	data_collator=DummyDataCollator()	,	eval_dataset=a	,	compute_metrics=a	,	)
      _A:						List[str]	    = trainer.evaluate()
      logger.info(a   )
      if metrics["eval_success"] is not True:
         logger.error(a   )
         exit(1   )

      _A:						Optional[Any]	    = trainer.predict(a   )
      logger.info(p.metrics   )
      if p.metrics["test_success"] is not True:
         logger.error(p.metrics   )
         exit(1   )

      _A:						str	    = 2

      _A:						Optional[Any]	    = trainer.evaluate()
      logger.info(a   )
      if metrics["eval_success"] is not True:
         logger.error(a   )
         exit(1   )

      _A:						Optional[Any]	    = trainer.predict(a   )
      logger.info(p.metrics   )
      if p.metrics["test_success"] is not True:
         logger.error(p.metrics   )
         exit(1   )

      _A:						str	    = None

   logger.info('''🔥 All distributed tests successful'''   )






def 						lowerCamelCase__							(      a   )   ->     Optional[Any]:
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
   main()



