code: 
  
  
  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   :Tuple								=    logging.getLogger(__name__)
  @dataclass
  class _lowerCamelCase :
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    str   					=       field(
  		    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}							)
  		A_			:    Optional[str]   					=       field(
  		    default=lowercase__	,  metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}							)
  		A_			:    Optional[str]   					=       field(
  		    default=lowercase__	,  metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}							)
  		A_			:    Optional[str]   					=       field(
  		    default=lowercase__	,  metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}	,  )
  
  
  
  
  
  
  
  @dataclass
  class _lowerCamelCase :
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    str   					=       field(
  		    metadata={"""help""": """The name of the task to train selected in the list: """ + """, """.join(hans_processors.keys()							)}							)
  		A_			:    str   					=       field(
  		    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}							)
  		A_			:    int   					=       field(
  		    default=1_28	,  metadata={
  		        """help""": (
  		            """The maximum total input sequence length after tokenization. Sequences longer """
  		            """than this will be truncated, sequences shorter will be padded."""
  		        )
  		    }	,  )
  		A_			:    bool   					=       field(
  		    default=lowercase__	,  metadata={"""help""": """Overwrite the cached training and evaluation sets"""}							)
  
  
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			List[InputFeatures]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					int  			=  default_data_collator(lowerCAmelCase	)
  	__magic_name__ :					int  			=  batch.pop('pairID'	,	lowerCAmelCase	)
  	return batch
  
  
  
  
  
  def lowerCamelCase    (    ):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					int  			=  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
  	__magic_name__ :					Union[str, Any]  			=  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'	,	lowerCAmelCase	)
  
  	# Set seed
  	set_seed(training_args.seed	)
  
  	try:
  		__magic_name__ :					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.
  
  	__magic_name__ :					Tuple  			=  AutoConfig.from_pretrained(
  	    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,	num_labels=lowerCAmelCase	,	finetuning_task=data_args.task_name	,	cache_dir=model_args.cache_dir	,	)
  	__magic_name__ :					Tuple  			=  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	,	)
  	__magic_name__ :					int  			=  AutoModelForSequenceClassification.from_pretrained(
  	    model_args.model_name_or_path	,	from_tf=bool('.ckpt' in model_args.model_name_or_path	)	,	config=lowerCAmelCase	,	cache_dir=model_args.cache_dir	,	)
  
  	# Get datasets
  	__magic_name__ :					Any  			=  (
  	    HansDataset(
  	        data_dir=data_args.data_dir	,	tokenizer=lowerCAmelCase	,	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
  	)
  	__magic_name__ :					str  			=  (
  	    HansDataset(
  	        data_dir=data_args.data_dir	,	tokenizer=lowerCAmelCase	,	task=data_args.task_name	,	max_seq_length=data_args.max_seq_length	,	overwrite_cache=data_args.overwrite_cache	,	evaluate=lowerCAmelCase	,	)
  	    if training_args.do_eval
  	    else None
  	)
  
  	# Initialize our Trainer
  	__magic_name__ :					Dict  			=  Trainer(
  	    model=lowerCAmelCase	,	args=lowerCAmelCase	,	train_dataset=lowerCAmelCase	,	eval_dataset=lowerCAmelCase	,	data_collator=lowerCAmelCase	,	)
  
  	# 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 ***'	)
  
  		__magic_name__ :					List[str]  			=  trainer.predict(lowerCAmelCase	)
  		__magic_name__ :					str  			=  output.predictions
  		__magic_name__ :					List[str]  			=  np.argmax(lowerCAmelCase	,	axis=1	)
  
  		__magic_name__ :					str  			=  [ex.pairID for ex in eval_dataset]
  		__magic_name__ :					Tuple  			=  os.path.join(training_args.output_dir	,	'hans_predictions.txt'	)
  		__magic_name__ :					Tuple  			=  eval_dataset.get_labels()
  		if trainer.is_world_master():
  			with open(lowerCAmelCase	,	'w'	) as writer:
  				writer.write('pairID,gold_label\n'	)
  				for pid, pred in zip(lowerCAmelCase	,	lowerCAmelCase	):
  					writer.write('ex' + str(lowerCAmelCase	) + ',' + label_list[int(lowerCAmelCase	)] + '\n'	)
  
  		trainer._log(output.metrics	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			str	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	main()
  
  
  if __name__ == "__main__":
  							main()
style_context: 
  
  
  '''simple docstring'''
  
  import itertools
  from pathlib import Path
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			list[str]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					str  			=  [tuple(lowerCAmelCase	) for login in logins]
  
  	__magic_name__ :					int  			=  {char for login in split_logins for char in login}
  
  	for permutation in itertools.permutations(lowerCAmelCase	):
  		__magic_name__ :					List[Any]  			=  True
  		for login in logins:
  			if not (
  			    permutation.index(login[0]	)
  			    < permutation.index(login[1]	)
  			    < permutation.index(login[2]	)
  			):
  				__magic_name__ :					Tuple  			=  False
  				break
  
  		if satisfied:
  			return int(''.join(lowerCAmelCase	)	)
  
  	raise Exception('Unable to find the secret passcode'	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			str = "keylog.txt"	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					List[Any]  			=  Path(lowerCAmelCase	).parent.joinpath(lowerCAmelCase	).read_text().splitlines()
  
  	return find_secret_passcode(lowerCAmelCase	)
  
  
  if __name__ == "__main__":
  							print(F'{solution() = }')