


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()



'''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() = }')