
'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


lowercase__						=			logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

lowercase__						=			{
    """base""": AutoModel,
    """sequence-classification""": AutoModelForSequenceClassification,
    """question-answering""": AutoModelForQuestionAnswering,
    """pretraining""": AutoModelForPreTraining,
    """token-classification""": AutoModelForTokenClassification,
    """language-modeling""": AutoModelWithLMHead,
    """summarization""": AutoModelForSeqaSeqLM,
    """translation""": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
lowercase__						=			{
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
lowercase__						=			sorted(arg_to_scheduler.keys())
lowercase__						=			"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""






class   snake_case__					(			pl.LightningModule				):







		"""simple docstring"""

		def __init__(			self							: Optional[int]     ,  UpperCamelCase__							: Optional[int]     ,  UpperCamelCase__							: Tuple=None     ,  UpperCamelCase__							: Union[str, Any]="base"     ,  UpperCamelCase__							: Optional[int]=None     ,  UpperCamelCase__							: int=None     ,  UpperCamelCase__							: Tuple=None     ,  **UpperCamelCase__							: Dict     ,  )	-> List[Any]:






				"""simple docstring"""







				super().__init__()
				# TODO: move to self.save_hyperparameters()
				# self.save_hyperparameters()
				# can also expand arguments into trainer signature for easier reading

				self.save_hyperparameters(lowerCAmelCase__			)
				snake_case     :					List[str]     =	0
				snake_case     :					Dict     =	Path(self.hparams.output_dir			)
				snake_case     :					Dict     =	self.hparams.cache_dir if self.hparams.cache_dir else None
				if config is None:
						snake_case     :					List[Any]     =	AutoConfig.from_pretrained(
						    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path     ,  **({'''num_labels''': num_labels} if num_labels is not None else {})     ,  cache_dir=lowerCAmelCase__     ,  **lowerCAmelCase__     ,  )
				else:
						snake_case     :					PretrainedConfig     =	config

				snake_case     :					Union[str, Any]     =	("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
				for p in extra_model_params:
						if getattr(self.hparams     ,  lowerCAmelCase__     ,  lowerCAmelCase__			):
								assert hasattr(self.config     ,  lowerCAmelCase__			), f'model config doesn\'t have a `{p}` attribute'
								setattr(self.config     ,  lowerCAmelCase__     ,  getattr(self.hparams     ,  lowerCAmelCase__			)			)

				if tokenizer is None:
						snake_case     :					Optional[Any]     =	AutoTokenizer.from_pretrained(
						    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path     ,  cache_dir=lowerCAmelCase__     ,  )
				else:
						snake_case     :					PreTrainedTokenizer     =	tokenizer
				snake_case     :					int     =	MODEL_MODES[mode]
				if model is None:
						snake_case     :					List[Any]     =	self.model_type.from_pretrained(
						    self.hparams.model_name_or_path     ,  from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path			)     ,  config=self.config     ,  cache_dir=lowerCAmelCase__     ,  )
				else:
						snake_case     :					Optional[Any]     =	model



		def 			lowerCAmelCase							(			self							: Union[str, Any]     ,  *UpperCamelCase__							: Tuple     ,  **UpperCamelCase__							: Optional[Any]			)	-> List[str]:






				"""simple docstring"""







				snake_case     :					int     =	self.model_type.from_pretrained(*lowerCAmelCase__     ,  **lowerCAmelCase__			)



		def 			lowerCAmelCase							(			self							: Optional[Any]			)	-> List[Any]:






				"""simple docstring"""







				snake_case     :					str     =	arg_to_scheduler[self.hparams.lr_scheduler]
				snake_case     :					Optional[Any]     =	get_schedule_func(
				    self.opt     ,  num_warmup_steps=self.hparams.warmup_steps     ,  num_training_steps=self.total_steps()			)
				snake_case     :					Optional[int]     =	{"scheduler": scheduler, "interval": "step", "frequency": 1}
				return scheduler



		def 			lowerCAmelCase							(			self							: List[str]			)	-> Any:






				"""simple docstring"""







				snake_case     :					List[Any]     =	self.model
				snake_case     :					Any     =	["bias", "LayerNorm.weight"]
				snake_case     :					Dict     =	[
				    {
				        "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay			)],
				        "weight_decay": self.hparams.weight_decay,
				    },
				    {
				        "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay			)],
				        "weight_decay": 0.0,
				    },
				]
				if self.hparams.adafactor:
						snake_case     :					List[Any]     =	Adafactor(
						    lowerCAmelCase__     ,  lr=self.hparams.learning_rate     ,  scale_parameter=lowerCAmelCase__     ,  relative_step=lowerCAmelCase__			)

				else:
						snake_case     :					List[str]     =	AdamW(
						    lowerCAmelCase__     ,  lr=self.hparams.learning_rate     ,  eps=self.hparams.adam_epsilon			)
				snake_case     :					int     =	optimizer

				snake_case     :					Any     =	self.get_lr_scheduler()

				return [optimizer], [scheduler]



		def 			lowerCAmelCase							(			self							: Dict     ,  UpperCamelCase__							: Optional[Any]     ,  UpperCamelCase__							: Any			)	-> Dict:






				"""simple docstring"""







				return self.validation_step(lowerCAmelCase__     ,  lowerCAmelCase__			)



		def 			lowerCAmelCase							(			self							: Optional[Any]     ,  UpperCamelCase__							: Union[str, Any]			)	-> str:






				"""simple docstring"""







				return self.validation_end(lowerCAmelCase__			)



		def 			lowerCAmelCase							(			self							: Optional[int]			)	-> int:






				"""simple docstring"""







				snake_case     :					List[str]     =	max(1     ,  self.hparams.gpus			)  # TODO: consider num_tpu_cores
				snake_case     :					Any     =	self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
				return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs



		def 			lowerCAmelCase							(			self							: Optional[int]     ,  UpperCamelCase__							: str			)	-> Optional[int]:






				"""simple docstring"""







				if mode == "test":
						snake_case     :					str     =	len(self.test_dataloader().dataset			)
				else:
						snake_case     :					Tuple     =	self.get_dataloader('''train'''     ,  self.hparams.train_batch_size     ,  shuffle=lowerCAmelCase__			)
						snake_case     :					List[Any]     =	len(self.train_dataloader().dataset			)



		def 			lowerCAmelCase							(			self							: Tuple     ,  UpperCamelCase__							: Union[str, Any]     ,  UpperCamelCase__							: Optional[int]     ,  UpperCamelCase__							: Any = False			)	-> List[Any]:






				"""simple docstring"""







				raise NotImplementedError('''You must implement this for your task'''			)



		def 			lowerCAmelCase							(			self							: str			)	-> Dict:






				"""simple docstring"""







				return self.train_loader



		def 			lowerCAmelCase							(			self							: List[Any]			)	-> str:






				"""simple docstring"""







				return self.get_dataloader('''dev'''     ,  self.hparams.eval_batch_size     ,  shuffle=lowerCAmelCase__			)



		def 			lowerCAmelCase							(			self							: int			)	-> str:






				"""simple docstring"""







				return self.get_dataloader('''test'''     ,  self.hparams.eval_batch_size     ,  shuffle=lowerCAmelCase__			)



		def 			lowerCAmelCase							(			self							: List[Any]     ,  UpperCamelCase__							: Optional[int]			)	-> List[Any]:






				"""simple docstring"""







				return os.path.join(
				    self.hparams.data_dir     ,  '''cached_{}_{}_{}'''.format(
				        lowerCAmelCase__     ,  list(filter(lowerCAmelCase__     ,  self.hparams.model_name_or_path.split('''/'''			)			)			).pop()     ,  str(self.hparams.max_seq_length			)     ,  )     ,  )



		@pl.utilities.rank_zero_only
		def 			lowerCAmelCase							(			self							: int     ,  UpperCamelCase__							: Union[str, Any]			)	-> None:






				"""simple docstring"""







				snake_case     :					List[str]     =	self.output_dir.joinpath('''best_tfmr'''			)
				snake_case     :					Optional[Any]     =	self.step_count
				self.model.save_pretrained(lowerCAmelCase__			)
				self.tokenizer.save_pretrained(lowerCAmelCase__			)



		@staticmethod
		def 			lowerCAmelCase							(			UpperCamelCase__							: Tuple     ,  UpperCamelCase__							: Any			)	-> Tuple:






				"""simple docstring"""







				parser.add_argument(
				    '''--model_name_or_path'''     ,  default=lowerCAmelCase__     ,  type=lowerCAmelCase__     ,  required=lowerCAmelCase__     ,  help='''Path to pretrained model or model identifier from huggingface.co/models'''     ,  )
				parser.add_argument(
				    '''--config_name'''     ,  default=''''''     ,  type=lowerCAmelCase__     ,  help='''Pretrained config name or path if not the same as model_name'''			)
				parser.add_argument(
				    '''--tokenizer_name'''     ,  default=lowerCAmelCase__     ,  type=lowerCAmelCase__     ,  help='''Pretrained tokenizer name or path if not the same as model_name'''     ,  )
				parser.add_argument(
				    '''--cache_dir'''     ,  default=''''''     ,  type=lowerCAmelCase__     ,  help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''     ,  )
				parser.add_argument(
				    '''--encoder_layerdrop'''     ,  type=lowerCAmelCase__     ,  help='''Encoder layer dropout probability (Optional). Goes into model.config'''     ,  )
				parser.add_argument(
				    '''--decoder_layerdrop'''     ,  type=lowerCAmelCase__     ,  help='''Decoder layer dropout probability (Optional). Goes into model.config'''     ,  )
				parser.add_argument(
				    '''--dropout'''     ,  type=lowerCAmelCase__     ,  help='''Dropout probability (Optional). Goes into model.config'''     ,  )
				parser.add_argument(
				    '''--attention_dropout'''     ,  type=lowerCAmelCase__     ,  help='''Attention dropout probability (Optional). Goes into model.config'''     ,  )
				parser.add_argument('''--learning_rate'''     ,  default=5e-5     ,  type=lowerCAmelCase__     ,  help='''The initial learning rate for Adam.'''			)
				parser.add_argument(
				    '''--lr_scheduler'''     ,  default='''linear'''     ,  choices=lowerCAmelCase__     ,  metavar=lowerCAmelCase__     ,  type=lowerCAmelCase__     ,  help='''Learning rate scheduler'''     ,  )
				parser.add_argument('''--weight_decay'''     ,  default=0.0     ,  type=lowerCAmelCase__     ,  help='''Weight decay if we apply some.'''			)
				parser.add_argument('''--adam_epsilon'''     ,  default=1e-8     ,  type=lowerCAmelCase__     ,  help='''Epsilon for Adam optimizer.'''			)
				parser.add_argument('''--warmup_steps'''     ,  default=0     ,  type=lowerCAmelCase__     ,  help='''Linear warmup over warmup_steps.'''			)
				parser.add_argument('''--num_workers'''     ,  default=4     ,  type=lowerCAmelCase__     ,  help='''kwarg passed to DataLoader'''			)
				parser.add_argument('''--num_train_epochs'''     ,  dest='''max_epochs'''     ,  default=3     ,  type=lowerCAmelCase__			)
				parser.add_argument('''--train_batch_size'''     ,  default=32     ,  type=lowerCAmelCase__			)
				parser.add_argument('''--eval_batch_size'''     ,  default=32     ,  type=lowerCAmelCase__			)
				parser.add_argument('''--adafactor'''     ,  action='''store_true'''			)







class   snake_case__					(			pl.Callback				):







		"""simple docstring"""

		def 			lowerCAmelCase							(			self							: List[Any]     ,  UpperCamelCase__							: List[Any]     ,  UpperCamelCase__							: int			)	-> str:






				"""simple docstring"""







				snake_case     :					List[Any]     =	trainer.lr_schedulers[0]["scheduler"]
				snake_case     :					Dict     =	{f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()			)}
				pl_module.logger.log_metrics(lowerCAmelCase__			)



		def 			lowerCAmelCase							(			self							: Tuple     ,  UpperCamelCase__							: Union[str, Any]     ,  UpperCamelCase__							: Optional[Any]			)	-> Any:






				"""simple docstring"""







				rank_zero_info('''***** Validation results *****'''			)
				snake_case     :					int     =	trainer.callback_metrics
				# Log results
				for key in sorted(lowerCAmelCase__			):
						if key not in ["log", "progress_bar"]:
								rank_zero_info('''{} = {}\n'''.format(lowerCAmelCase__     ,  str(metrics[key]			)			)			)



		def 			lowerCAmelCase							(			self							: Optional[int]     ,  UpperCamelCase__							: Union[str, Any]     ,  UpperCamelCase__							: List[str]			)	-> Optional[int]:






				"""simple docstring"""







				rank_zero_info('''***** Test results *****'''			)
				snake_case     :					Dict     =	trainer.callback_metrics
				# Log and save results to file
				snake_case     :					int     =	os.path.join(pl_module.hparams.output_dir     ,  '''test_results.txt'''			)
				with open(lowerCAmelCase__     ,  '''w'''			) as writer:
						for key in sorted(lowerCAmelCase__			):
								if key not in ["log", "progress_bar"]:
										rank_zero_info('''{} = {}\n'''.format(lowerCAmelCase__     ,  str(metrics[key]			)			)			)
										writer.write('''{} = {}\n'''.format(lowerCAmelCase__     ,  str(metrics[key]			)			)			)







def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       List[str]:


		'''simple docstring'''


		parser.add_argument(
		    '''--output_dir'''   ,					default=SCREAMING_SNAKE_CASE__   ,					type=SCREAMING_SNAKE_CASE__   ,					required=SCREAMING_SNAKE_CASE__   ,					help='''The output directory where the model predictions and checkpoints will be written.'''   ,					)
		parser.add_argument(
		    '''--fp16'''   ,					action='''store_true'''   ,					help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''   ,					)

		parser.add_argument(
		    '''--fp16_opt_level'''   ,					type=SCREAMING_SNAKE_CASE__   ,					default='''O2'''   ,					help=(
		        '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
		        '''See details at https://nvidia.github.io/apex/amp.html'''
		    )   ,					)
		parser.add_argument('''--n_tpu_cores'''   ,					dest='''tpu_cores'''   ,					type=SCREAMING_SNAKE_CASE__ )
		parser.add_argument('''--max_grad_norm'''   ,					dest='''gradient_clip_val'''   ,					default=1.0   ,					type=SCREAMING_SNAKE_CASE__   ,					help='''Max gradient norm''' )
		parser.add_argument('''--do_train'''   ,					action='''store_true'''   ,					help='''Whether to run training.''' )
		parser.add_argument('''--do_predict'''   ,					action='''store_true'''   ,					help='''Whether to run predictions on the test set.''' )
		parser.add_argument(
		    '''--gradient_accumulation_steps'''   ,					dest='''accumulate_grad_batches'''   ,					type=SCREAMING_SNAKE_CASE__   ,					default=1   ,					help='''Number of updates steps to accumulate before performing a backward/update pass.'''   ,					)
		parser.add_argument('''--seed'''   ,					type=SCREAMING_SNAKE_CASE__   ,					default=42   ,					help='''random seed for initialization''' )
		parser.add_argument(
		    '''--data_dir'''   ,					default=SCREAMING_SNAKE_CASE__   ,					type=SCREAMING_SNAKE_CASE__   ,					required=SCREAMING_SNAKE_CASE__   ,					help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''   ,					)




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__=None   ,					SCREAMING_SNAKE_CASE__=True   ,					SCREAMING_SNAKE_CASE__=[]   ,					SCREAMING_SNAKE_CASE__=None   ,					SCREAMING_SNAKE_CASE__=None   ,					**SCREAMING_SNAKE_CASE__   ,					)     ->       Tuple:


		'''simple docstring'''


		pl.seed_everything(args.seed )

		# init model
		snake_case     :					Dict     =	Path(model.hparams.output_dir )
		odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE__ )

		# add custom checkpoints
		if checkpoint_callback is None:
				snake_case     :					Tuple     =	pl.callbacks.ModelCheckpoint(
				    filepath=args.output_dir   ,					prefix='''checkpoint'''   ,					monitor='''val_loss'''   ,					mode='''min'''   ,					save_top_k=1 )
		if early_stopping_callback:
				extra_callbacks.append(SCREAMING_SNAKE_CASE__ )
		if logging_callback is None:
				snake_case     :					str     =	LoggingCallback()

		snake_case     :					Dict     =	{}

		# TODO: remove with PyTorch 1.6 since pl uses native amp
		if args.fpaa:
				snake_case     :					Union[str, Any]     =	16
				snake_case     :					Tuple     =	args.fpaa_opt_level

		if args.gpus > 1:
				snake_case     :					Union[str, Any]     =	"ddp"

		snake_case     :					Dict     =	args.accumulate_grad_batches
		snake_case     :					Any     =	extra_train_kwargs.get('''accelerator'''   ,					SCREAMING_SNAKE_CASE__ )
		snake_case     :					Optional[int]     =	extra_train_kwargs.get('''profiler'''   ,					SCREAMING_SNAKE_CASE__ )

		snake_case     :					int     =	pl.Trainer.from_argparse_args(
		    SCREAMING_SNAKE_CASE__   ,					weights_summary=SCREAMING_SNAKE_CASE__   ,					callbacks=[logging_callback] + extra_callbacks   ,					logger=SCREAMING_SNAKE_CASE__   ,					checkpoint_callback=SCREAMING_SNAKE_CASE__   ,					**SCREAMING_SNAKE_CASE__   ,					)

		if args.do_train:
				trainer.fit(SCREAMING_SNAKE_CASE__ )

		return trainer




'''simple docstring'''
import itertools
from pathlib import Path




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       int:


		'''simple docstring'''


		snake_case     :					Optional[Any]     =	[tuple(SCREAMING_SNAKE_CASE__ ) for login in logins]

		snake_case     :					Dict     =	{char for login in split_logins for char in login}

		for permutation in itertools.permutations(SCREAMING_SNAKE_CASE__ ):
				snake_case     :					Optional[int]     =	True
				for login in logins:
						if not (
						    permutation.index(login[0] )
						    < permutation.index(login[1] )
						    < permutation.index(login[2] )
						):
								snake_case     :					int     =	False
								break

				if satisfied:
						return int(''''''.join(SCREAMING_SNAKE_CASE__ ) )

		raise Exception('''Unable to find the secret passcode''' )




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ = "keylog.txt" )     ->       int:


		'''simple docstring'''


		snake_case     :					Optional[Any]     =	Path(SCREAMING_SNAKE_CASE__ ).parent.joinpath(SCREAMING_SNAKE_CASE__ ).read_text().splitlines()

		return find_secret_passcode(SCREAMING_SNAKE_CASE__ )


if __name__ == "__main__":
						print(f"{solution() = }")


