





"""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


UpperCamelCase_	 =logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

UpperCamelCase_	 ={
    """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
UpperCamelCase_	 ={
    """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
}
UpperCamelCase_	 =sorted(arg_to_scheduler.keys())
UpperCamelCase_	 ="""{""" + """, """.join(arg_to_scheduler_choices) + """}"""




class 					_a		( pl.LightningModule							):






				def __init__(					self						:       Tuple,					lowerCAmelCase__						:       argparse.Namespace,					lowerCAmelCase__						:       Optional[Any]=None,					lowerCAmelCase__						:       str="base",					lowerCAmelCase__						:       Any=None,					lowerCAmelCase__						:       Any=None,					lowerCAmelCase__						:       List[Any]=None,					**lowerCAmelCase__						:       Any,					)      -> List[str]:




										'''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__				)
										_UpperCamelCase     :   int      =       0
										_UpperCamelCase     :   Tuple      =       Path(self.hparams.output_dir				)
										_UpperCamelCase     :   Optional[Any]      =       self.hparams.cache_dir if self.hparams.cache_dir else None
										if config is None:
																_UpperCamelCase     :   Optional[int]      =       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:
																_UpperCamelCase     :   PretrainedConfig      =       config

										_UpperCamelCase     :   Optional[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:
																_UpperCamelCase     :   Union[str, Any]      =       AutoTokenizer.from_pretrained(
																    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path,					cache_dir=lowerCAmelCase__,					)
										else:
																_UpperCamelCase     :   PreTrainedTokenizer      =       tokenizer
										_UpperCamelCase     :   List[str]      =       MODEL_MODES[mode]
										if model is None:
																_UpperCamelCase     :   Union[str, 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:
																_UpperCamelCase     :   Optional[Any]      =       model






				def 							snake_case      (					self						:       int,					*lowerCAmelCase__						:       List[str],					**lowerCAmelCase__						:       List[str]				)      -> List[str]:




										'''simple docstring'''



										_UpperCamelCase     :   List[str]      =       self.model_type.from_pretrained(*lowerCAmelCase__,					**lowerCAmelCase__				)






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




										'''simple docstring'''



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






				def 							snake_case      (					self						:       Optional[Any]				)      -> Dict:




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       self.model
										_UpperCamelCase     :   List[str]      =       ['''bias''', '''LayerNorm.weight''']
										_UpperCamelCase     :   List[Any]      =       [
										    {
										        '''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:
																_UpperCamelCase     :   Union[str, Any]      =       Adafactor(
																    lowerCAmelCase__,					lr=self.hparams.learning_rate,					scale_parameter=lowerCAmelCase__,					relative_step=lowerCAmelCase__				)

										else:
																_UpperCamelCase     :   Optional[int]      =       AdamW(
																    lowerCAmelCase__,					lr=self.hparams.learning_rate,					eps=self.hparams.adam_epsilon				)
										_UpperCamelCase     :   Dict      =       optimizer

										_UpperCamelCase     :   List[Any]      =       self.get_lr_scheduler()

										return [optimizer], [scheduler]






				def 							snake_case      (					self						:       int,					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       List[Any]				)      -> str:




										'''simple docstring'''



										return self.validation_step(lowerCAmelCase__,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       Dict,					lowerCAmelCase__						:       Optional[int]				)      -> Tuple:




										'''simple docstring'''



										return self.validation_end(lowerCAmelCase__				)






				def 							snake_case      (					self						:       Dict				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   Any      =       max(1,					self.hparams.gpus				)  # TODO: consider num_tpu_cores
										_UpperCamelCase     :   Optional[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 							snake_case      (					self						:       Union[str, Any],					lowerCAmelCase__						:       Dict				)      -> Optional[Any]:




										'''simple docstring'''



										if mode == "test":
																_UpperCamelCase     :   Tuple      =       len(self.test_dataloader().dataset				)
										else:
																_UpperCamelCase     :   Tuple      =       self.get_dataloader('''train''',					self.hparams.train_batch_size,					shuffle=lowerCAmelCase__				)
																_UpperCamelCase     :   Dict      =       len(self.train_dataloader().dataset				)






				def 							snake_case      (					self						:       Tuple,					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       bool = False				)      -> Union[str, Any]:




										'''simple docstring'''



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






				def 							snake_case      (					self						:       Tuple				)      -> Optional[Any]:




										'''simple docstring'''



										return self.train_loader






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




										'''simple docstring'''



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






				def 							snake_case      (					self						:       List[Any]				)      -> Any:




										'''simple docstring'''



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






				def 							snake_case      (					self						:       Dict,					lowerCAmelCase__						:       List[Any]				)      -> Tuple:




										'''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 							snake_case      (					self						:       str,					lowerCAmelCase__						:       Dict[str, Any]				)      -> None:




										'''simple docstring'''



										_UpperCamelCase     :   str      =       self.output_dir.joinpath('''best_tfmr'''				)
										_UpperCamelCase     :   Dict      =       self.step_count
										self.model.save_pretrained(lowerCAmelCase__				)
										self.tokenizer.save_pretrained(lowerCAmelCase__				)






				@staticmethod
				def 							snake_case      (					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       int				)      -> Optional[int]:




										'''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=3_2,					type=lowerCAmelCase__				)
										parser.add_argument('''--eval_batch_size''',					default=3_2,					type=lowerCAmelCase__				)
										parser.add_argument('''--adafactor''',					action='''store_true'''				)




class 					_a		( pl.Callback							):






				def 							snake_case      (					self						:       int,					lowerCAmelCase__						:       Union[str, Any],					lowerCAmelCase__						:       List[Any]				)      -> str:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       trainer.lr_schedulers[0]['''scheduler''']
										_UpperCamelCase     :   Tuple      =       {f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()				)}
										pl_module.logger.log_metrics(lowerCAmelCase__				)






				def 							snake_case      (					self						:       int,					lowerCAmelCase__						:       pl.Trainer,					lowerCAmelCase__						:       pl.LightningModule				)      -> Optional[int]:




										'''simple docstring'''



										rank_zero_info('''***** Validation results *****'''				)
										_UpperCamelCase     :   List[str]      =       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 							snake_case      (					self						:       Dict,					lowerCAmelCase__						:       pl.Trainer,					lowerCAmelCase__						:       pl.LightningModule				)      -> Tuple:




										'''simple docstring'''



										rank_zero_info('''***** Test results *****'''				)
										_UpperCamelCase     :   Optional[Any]      =       trainer.callback_metrics
										# Log and save results to file
										_UpperCamelCase     :   Union[str, Any]      =       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 			a_							(				_lowercase			,		_lowercase  ):
						#  To allow all pl args uncomment the following line
						#  parser = pl.Trainer.add_argparse_args(parser)
						parser.add_argument(
						    '''--output_dir'''			,		default=_lowercase			,		type=_lowercase			,		required=_lowercase			,		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=_lowercase			,		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=_lowercase  )
						parser.add_argument('''--max_grad_norm'''			,		dest='''gradient_clip_val'''			,		default=1.0			,		type=_lowercase			,		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=_lowercase			,		default=1			,		help='''Number of updates steps to accumulate before performing a backward/update pass.'''			,		)
						parser.add_argument('''--seed'''			,		type=_lowercase			,		default=42			,		help='''random seed for initialization'''  )
						parser.add_argument(
						    '''--data_dir'''			,		default=_lowercase			,		type=_lowercase			,		required=_lowercase			,		help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''			,		)






def 			a_							(				_lowercase			,		_lowercase			,		_lowercase=None			,		_lowercase=True			,		_lowercase=[]			,		_lowercase=None			,		_lowercase=None			,		**_lowercase			,		):
						pl.seed_everything(args.seed  )

						# init model
						_UpperCamelCase     :   Optional[Any]      =       Path(model.hparams.output_dir  )
						odir.mkdir(exist_ok=_lowercase  )

						# add custom checkpoints
						if checkpoint_callback is None:
												_UpperCamelCase     :   Union[str, Any]      =       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(_lowercase  )
						if logging_callback is None:
												_UpperCamelCase     :   str      =       LoggingCallback()

						_UpperCamelCase     :   Optional[Any]      =       {}

						# TODO: remove with PyTorch 1.6 since pl uses native amp
						if args.fpaa:
												_UpperCamelCase     :   List[Any]      =       16
												_UpperCamelCase     :   Dict      =       args.fpaa_opt_level

						if args.gpus > 1:
												_UpperCamelCase     :   str      =       '''ddp'''

						_UpperCamelCase     :   List[str]      =       args.accumulate_grad_batches
						_UpperCamelCase     :   int      =       extra_train_kwargs.get('''accelerator'''			,		_lowercase  )
						_UpperCamelCase     :   int      =       extra_train_kwargs.get('''profiler'''			,		_lowercase  )

						_UpperCamelCase     :   Any      =       pl.Trainer.from_argparse_args(
						    _lowercase			,		weights_summary=_lowercase			,		callbacks=[logging_callback] + extra_callbacks			,		logger=_lowercase			,		checkpoint_callback=_lowercase			,		**_lowercase			,		)

						if args.do_train:
												trainer.fit(_lowercase  )

						return trainer










"""simple docstring"""




import os
import sys
import time

from . import transposition_cipher as trans_cipher





def 			a_							(				):
						_UpperCamelCase     :   Dict      =       '''Prehistoric Men.txt'''
						_UpperCamelCase     :   Optional[Any]      =       '''Output.txt'''
						_UpperCamelCase     :   Any      =       int(input('''Enter key: '''  )  )
						_UpperCamelCase     :   Optional[Any]      =       input('''Encrypt/Decrypt [e/d]: '''  )

						if not os.path.exists(_lowercase  ):
												print(F"""File {input_file} does not exist. Quitting..."""  )
												sys.exit()
						if os.path.exists(_lowercase  ):
												print(F"""Overwrite {output_file}? [y/n]"""  )
												_UpperCamelCase     :   Optional[int]      =       input('''> '''  )
												if not response.lower().startswith('''y'''  ):
																		sys.exit()

						_UpperCamelCase     :   List[str]      =       time.time()
						if mode.lower().startswith('''e'''  ):
												with open(_lowercase  ) as f:
																		_UpperCamelCase     :   Optional[int]      =       f.read()
												_UpperCamelCase     :   List[str]      =       trans_cipher.encrypt_message(_lowercase			,		_lowercase  )
						elif mode.lower().startswith('''d'''  ):
												with open(_lowercase  ) as f:
																		_UpperCamelCase     :   Dict      =       f.read()
												_UpperCamelCase     :   Any      =       trans_cipher.decrypt_message(_lowercase			,		_lowercase  )

						with open(_lowercase			,		'''w'''  ) as output_obj:
												output_obj.write(_lowercase  )

						_UpperCamelCase     :   List[str]      =       round(time.time() - start_time			,		2  )
						print(('''Done (''', total_time, '''seconds )''')  )


if __name__ == "__main__":
						main()



