


'''simple docstring'''





import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__lowerCamelCase       				=	logging.getLogger(__name__)




@dataclass
class 							UpperCAmelCase :
 UpperCAmelCase				=				field(
     metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}	)
 UpperCAmelCase				=				field(
     default=_snake_case	,   metadata={"help": "Pretrained config name or path if not the same as model_name"}	)
 UpperCAmelCase				=				field(
     default=_snake_case	,   metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}	)
 UpperCAmelCase				=				field(
     default=_snake_case	,   metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}	,   )




@dataclass
class 							UpperCAmelCase :
 UpperCAmelCase				=				field(
     metadata={"help": "The name of the task to train selected in the list: " + ", ".join(hans_processors.keys()	)}	)
 UpperCAmelCase				=				field(
     metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}	)
 UpperCAmelCase				=				field(
     default=128	,   metadata={
         "help": (
             "The maximum total input sequence length after tokenization. Sequences longer "
             "than this will be truncated, sequences shorter will be padded."
         )
     }	,   )
 UpperCAmelCase				=				field(
     default=_snake_case	,   metadata={"help": "Overwrite the cached training and evaluation sets"}	)


def 					a__    (				UpperCamelCase_	:			List[InputFeatures]	):
   UpperCAmelCase__  :List[Any]            =			default_data_collator(UpperCamelCase_	)
   UpperCAmelCase__  :str            =			batch.pop('''pairID''',      UpperCamelCase_	)
   return batch


def 					a__    (				):
   # See all possible arguments in src/transformers/training_args.py
   # or by passing the --help flag to this script.
   # We now keep distinct sets of args, for a cleaner separation of concerns.

   UpperCAmelCase__  :Union[str, Any]            =			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
   UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__  :Dict            =			parser.parse_args_into_dataclasses()

   if (
       os.path.exists(training_args.output_dir	)
       and os.listdir(training_args.output_dir	)
       and training_args.do_train
       and not training_args.overwrite_output_dir
   ):
      raise ValueError(
          F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
          ''' --overwrite_output_dir to overcome.'''	)

   # Setup logging
   logging.basicConfig(
       format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',      datefmt='''%m/%d/%Y %H:%M:%S''',      level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,      )
   logger.warning(
       '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''',      training_args.local_rank,      training_args.device,      training_args.n_gpu,      bool(training_args.local_rank != -1	),      training_args.fpaa,      )
   # Set the verbosity to info of the Transformers logger (on main process only):
   if is_main_process(training_args.local_rank	):
      transformers.utils.logging.set_verbosity_info()
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()
   logger.info('''Training/evaluation parameters %s''',      UpperCamelCase_	)

   # Set seed
   set_seed(training_args.seed	)

   try:
      UpperCAmelCase__  :Optional[Any]            =			hans_tasks_num_labels[data_args.task_name]
   except KeyError:
      raise ValueError('''Task not found: %s''' % (data_args.task_name)	)

   # Load pretrained model and tokenizer
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.

   UpperCAmelCase__  :Optional[int]            =			AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path,      num_labels=UpperCamelCase_,      finetuning_task=data_args.task_name,      cache_dir=model_args.cache_dir,      )
   UpperCAmelCase__  :List[str]            =			AutoTokenizer.from_pretrained(
       model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,      cache_dir=model_args.cache_dir,      )
   UpperCAmelCase__  :Tuple            =			AutoModelForSequenceClassification.from_pretrained(
       model_args.model_name_or_path,      from_tf=bool('''.ckpt''' in model_args.model_name_or_path	),      config=UpperCamelCase_,      cache_dir=model_args.cache_dir,      )

   # Get datasets
   UpperCAmelCase__  :Optional[Any]            =			(
       HansDataset(
           data_dir=data_args.data_dir,      tokenizer=UpperCamelCase_,      task=data_args.task_name,      max_seq_length=data_args.max_seq_length,      overwrite_cache=data_args.overwrite_cache,      )
       if training_args.do_train
       else None
   )
   UpperCAmelCase__  :Tuple            =			(
       HansDataset(
           data_dir=data_args.data_dir,      tokenizer=UpperCamelCase_,      task=data_args.task_name,      max_seq_length=data_args.max_seq_length,      overwrite_cache=data_args.overwrite_cache,      evaluate=UpperCamelCase_,      )
       if training_args.do_eval
       else None
   )

   # Initialize our Trainer
   UpperCAmelCase__  :Tuple            =			Trainer(
       model=UpperCamelCase_,      args=UpperCamelCase_,      train_dataset=UpperCamelCase_,      eval_dataset=UpperCamelCase_,      data_collator=UpperCamelCase_,      )

   # Training
   if training_args.do_train:
      trainer.train(
          model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path	) else None	)
      trainer.save_model()
      # For convenience, we also re-save the tokenizer to the same directory,
      # so that you can share your model easily on huggingface.co/models =)
      if trainer.is_world_master():
         tokenizer.save_pretrained(training_args.output_dir	)

    # Evaluation
   if training_args.do_eval:
      logger.info('''*** Evaluate ***'''	)

      UpperCAmelCase__  :Optional[Any]            =			trainer.predict(UpperCamelCase_	)
      UpperCAmelCase__  :Union[str, Any]            =			output.predictions
      UpperCAmelCase__  :Any            =			np.argmax(UpperCamelCase_,      axis=1	)

      UpperCAmelCase__  :Tuple            =			[ex.pairID for ex in eval_dataset]
      UpperCAmelCase__  :Optional[Any]            =			os.path.join(training_args.output_dir,      '''hans_predictions.txt'''	)
      UpperCAmelCase__  :List[str]            =			eval_dataset.get_labels()
      if trainer.is_world_master():
         with open(UpperCamelCase_,      '''w'''	) as writer:
            writer.write('''pairID,gold_label\n'''	)
            for pid, pred in zip(UpperCamelCase_,      UpperCamelCase_	):
               writer.write('''ex''' + str(UpperCamelCase_	) + ''',''' + label_list[int(UpperCamelCase_	)] + '''\n'''	)

      trainer._log(output.metrics	)


def 					a__    (				UpperCamelCase_	:			Optional[int]	):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
    main()







'''simple docstring'''





__lowerCamelCase       				=	[
    ['''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 					a__    (				UpperCamelCase_	:			List[str]	):
   UpperCAmelCase__  :str            =			set()
   for row in edge_array:
      for item in row:
         distinct_edge.add(item[0]	)
   return list(UpperCamelCase_	)


def 					a__    (				UpperCamelCase_	:			Tuple,      UpperCamelCase_	:			List[Any]	):
   UpperCAmelCase__  :int            =			['''0'''] * len(UpperCamelCase_	)
   for i, row in enumerate(UpperCamelCase_	):
      for item in row:
         if distinct_edge in item[0]:
            UpperCAmelCase__  :List[Any]            =			'''1'''
            break
   return "".join(UpperCamelCase_	)


def 					a__    (				UpperCamelCase_	:			Optional[int]	):
   UpperCAmelCase__  :Any            =			get_distinct_edge(UpperCamelCase_	)
   UpperCAmelCase__  :Dict            =			{}

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


def 					a__    (				UpperCamelCase_	:			Optional[int]	):
   UpperCAmelCase__  :int            =			{}
   for _, item in enumerate(UpperCamelCase_	):
      nodes.setdefault(item[2],      []	).append(item[0]	)
   return nodes


def 					a__    (				UpperCamelCase_	:			Any	):
   UpperCAmelCase__  :Any            =			{}
   for key, value in nodes.items():
      UpperCAmelCase__  :Optional[int]            =			value
   return cluster


def 					a__    (				UpperCamelCase_	:			Tuple	):
   return [i * 100 / len(UpperCamelCase_	) for i in cluster]


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


def 					a__    (				UpperCamelCase_	:			Optional[int],      UpperCamelCase_	:			Any,      UpperCamelCase_	:			int,      UpperCamelCase_	:			Dict	):
   for i in cluster[ca]:
      UpperCAmelCase__  :int            =			0
      UpperCAmelCase__  :int            =			ca + 1
      while ca < max(cluster.keys()	):
         for j in cluster[ca]:
            if int(UpperCamelCase_,      2	) & int(UpperCamelCase_,      2	) == int(UpperCamelCase_,      2	):
               if tuple(nodes[i]	) in graph:
                  graph[tuple(nodes[i]	)].append(nodes[j]	)
               else:
                  UpperCAmelCase__  :int            =			[nodes[j]]
               count += 1
         if count == 0:
            UpperCAmelCase__  :List[Any]            =			ca + 1
         else:
            break


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


def 					a__    (				UpperCamelCase_	:			Optional[Any],      UpperCamelCase_	:			int,      UpperCamelCase_	:			Any,      UpperCamelCase_	:			Dict=None	):
   UpperCAmelCase__  :Tuple            =			(path or []) + [start]
   if start == end:
      paths.append(UpperCamelCase_	)
   for node in graph[start]:
      if tuple(UpperCamelCase_	) not in path:
         my_dfs(UpperCamelCase_,      tuple(UpperCamelCase_	),      UpperCamelCase_,      UpperCamelCase_	)


def 					a__    (				UpperCamelCase_	:			Dict,      UpperCamelCase_	:			int,      UpperCamelCase_	:			List[Any]	):
   UpperCAmelCase__  :List[str]            =			int(s / 100 * (len(UpperCamelCase_	) - 1)	)
   for i in cluster[k]:
      my_dfs(UpperCamelCase_,      tuple(cluster[k][i]	),      (['''Header'''],)	)


def 					a__    (				UpperCamelCase_	:			List[Any]	):
   UpperCAmelCase__  :int            =			[]
   for edges in paths:
      UpperCAmelCase__  :List[Any]            =			[]
      for j in range(len(UpperCamelCase_	) - 1	):
         UpperCAmelCase__  :Any            =			list(edges[j]	)
         for e in temp:
            UpperCAmelCase__  :str            =			(e[0], e[1])
            el.append(UpperCamelCase_	)
      freq_sub_el.append(UpperCamelCase_	)
   return freq_sub_el


def 					a__    (				UpperCamelCase_	:			Dict	):
   for i in range(len(UpperCamelCase_	)	):
      for j in range(len(edge_array[i]	)	):
         UpperCAmelCase__  :Dict            =			edge_array[i][j].split('''-'''	)
         UpperCAmelCase__  :Optional[Any]            =			t


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



