code: 
  '''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
  
  
  
style_context: 
  '''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() = }")
  
  
  