


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


__lowerCAmelCase     :			List[str]      =logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

__lowerCAmelCase     :			Optional[int]      ={
    '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
__lowerCAmelCase     :			Optional[Any]      ={
    '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
}
__lowerCAmelCase     :			List[str]      =sorted(arg_to_scheduler.keys())
__lowerCAmelCase     :			List[Any]      ='{' + ', '.join(arg_to_scheduler_choices) + '}'
class 	_lowercase				(      pl.LightningModule	):






				'''simple docstring'''



				def __init__(	self      :List[str]      ,      lowerCAmelCase__      :argparse.Namespace      ,      lowerCAmelCase__      :Tuple=None      ,      lowerCAmelCase__      :List[str]="base"      ,      lowerCAmelCase__      :Tuple=None      ,      lowerCAmelCase__      :List[Any]=None      ,      lowerCAmelCase__      :Any=None      ,      **lowerCAmelCase__      :Optional[int]      ,      )		->      Union[str, Any]:
										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__     )
										__SCREAMING_SNAKE_CASE						:					int		      =		0
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		Path(self.hparams.output_dir     )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.hparams.cache_dir if self.hparams.cache_dir else None
										if config is None:
																__SCREAMING_SNAKE_CASE						:					Dict		      =		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:
																__SCREAMING_SNAKE_CASE						:					PretrainedConfig		      =		config

										__SCREAMING_SNAKE_CASE						:					Dict		      =		('''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:
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		AutoTokenizer.from_pretrained(
																    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path      ,      cache_dir=lowerCAmelCase__      ,      )
										else:
																__SCREAMING_SNAKE_CASE						:					PreTrainedTokenizer		      =		tokenizer
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		MODEL_MODES[mode]
										if model is None:
																__SCREAMING_SNAKE_CASE						:					str		      =		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:
																__SCREAMING_SNAKE_CASE						:					Dict		      =		model


				def 		__magic_name__(	self      :Union[str, Any]      ,      *lowerCAmelCase__      :Dict      ,      **lowerCAmelCase__      :int     )		->      str:
										__SCREAMING_SNAKE_CASE						:					int		      =		self.model_type.from_pretrained(*lowerCAmelCase__      ,      **lowerCAmelCase__     )


				def 		__magic_name__(	self      :Optional[Any]     )		->      Optional[int]:
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		arg_to_scheduler[self.hparams.lr_scheduler]
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		get_schedule_func(
										    self.opt      ,      num_warmup_steps=self.hparams.warmup_steps      ,      num_training_steps=self.total_steps()     )
										__SCREAMING_SNAKE_CASE						:					Dict		      =		{'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
										return scheduler


				def 		__magic_name__(	self      :int     )		->      int:
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.model
										__SCREAMING_SNAKE_CASE						:					Any		      =		['''bias''', '''LayerNorm.weight''']
										__SCREAMING_SNAKE_CASE						:					str		      =		[
										    {
										        '''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:
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		Adafactor(
																    lowerCAmelCase__      ,      lr=self.hparams.learning_rate      ,      scale_parameter=lowerCAmelCase__      ,      relative_step=lowerCAmelCase__     )

										else:
																__SCREAMING_SNAKE_CASE						:					str		      =		AdamW(
																    lowerCAmelCase__      ,      lr=self.hparams.learning_rate      ,      eps=self.hparams.adam_epsilon     )
										__SCREAMING_SNAKE_CASE						:					int		      =		optimizer

										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		self.get_lr_scheduler()

										return [optimizer], [scheduler]


				def 		__magic_name__(	self      :Any      ,      lowerCAmelCase__      :str      ,      lowerCAmelCase__      :List[Any]     )		->      List[str]:
										return self.validation_step(lowerCAmelCase__      ,      lowerCAmelCase__     )


				def 		__magic_name__(	self      :int      ,      lowerCAmelCase__      :Dict     )		->      Optional[int]:
										return self.validation_end(lowerCAmelCase__     )


				def 		__magic_name__(	self      :str     )		->      int:
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		max(1      ,      self.hparams.gpus     )  # TODO: consider num_tpu_cores
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
										return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs


				def 		__magic_name__(	self      :Union[str, Any]      ,      lowerCAmelCase__      :Optional[Any]     )		->      str:
										if mode == "test":
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		len(self.test_dataloader().dataset     )
										else:
																__SCREAMING_SNAKE_CASE						:					Dict		      =		self.get_dataloader('''train'''      ,      self.hparams.train_batch_size      ,      shuffle=lowerCAmelCase__     )
																__SCREAMING_SNAKE_CASE						:					str		      =		len(self.train_dataloader().dataset     )


				def 		__magic_name__(	self      :Dict      ,      lowerCAmelCase__      :str      ,      lowerCAmelCase__      :int      ,      lowerCAmelCase__      :bool = False     )		->      Tuple:
										raise NotImplementedError('''You must implement this for your task'''     )


				def 		__magic_name__(	self      :Optional[int]     )		->      List[Any]:
										return self.train_loader


				def 		__magic_name__(	self      :Optional[Any]     )		->      Optional[int]:
										return self.get_dataloader('''dev'''      ,      self.hparams.eval_batch_size      ,      shuffle=lowerCAmelCase__     )


				def 		__magic_name__(	self      :Union[str, Any]     )		->      List[str]:
										return self.get_dataloader('''test'''      ,      self.hparams.eval_batch_size      ,      shuffle=lowerCAmelCase__     )


				def 		__magic_name__(	self      :str      ,      lowerCAmelCase__      :List[str]     )		->      Optional[Any]:
										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 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :Dict[str, Any]     )		->      None:
										__SCREAMING_SNAKE_CASE						:					int		      =		self.output_dir.joinpath('''best_tfmr'''     )
										__SCREAMING_SNAKE_CASE						:					Dict		      =		self.step_count
										self.model.save_pretrained(lowerCAmelCase__     )
										self.tokenizer.save_pretrained(lowerCAmelCase__     )



				@staticmethod
				def 		__magic_name__(	lowerCAmelCase__      :Tuple      ,      lowerCAmelCase__      :List[Any]     )		->      List[Any]:
										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 	_lowercase				(      pl.Callback	):






				'''simple docstring'''



				def 		__magic_name__(	self      :Union[str, Any]      ,      lowerCAmelCase__      :Optional[int]      ,      lowerCAmelCase__      :Union[str, Any]     )		->      Optional[Any]:
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		trainer.lr_schedulers[0]['''scheduler''']
										__SCREAMING_SNAKE_CASE						:					Any		      =		{f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()     )}
										pl_module.logger.log_metrics(lowerCAmelCase__     )


				def 		__magic_name__(	self      :Union[str, Any]      ,      lowerCAmelCase__      :pl.Trainer      ,      lowerCAmelCase__      :pl.LightningModule     )		->      str:
										rank_zero_info('''***** Validation results *****'''     )
										__SCREAMING_SNAKE_CASE						:					Any		      =		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 		__magic_name__(	self      :int      ,      lowerCAmelCase__      :pl.Trainer      ,      lowerCAmelCase__      :pl.LightningModule     )		->      List[Any]:
										rank_zero_info('''***** Test results *****'''     )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		trainer.callback_metrics
										# Log and save results to file
										__SCREAMING_SNAKE_CASE						:					Optional[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							(				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 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__=None   ,	lowercase__=True   ,	lowercase__=[]   ,	lowercase__=None   ,	lowercase__=None   ,	**lowercase__   ,	):
						pl.seed_everything(args.seed				)

						# init model
						__SCREAMING_SNAKE_CASE						:					str		      =		Path(model.hparams.output_dir				)
						odir.mkdir(exist_ok=lowercase__				)

						# add custom checkpoints
						if checkpoint_callback is None:
												__SCREAMING_SNAKE_CASE						:					int		      =		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:
												__SCREAMING_SNAKE_CASE						:					Tuple		      =		LoggingCallback()

						__SCREAMING_SNAKE_CASE						:					int		      =		{}

						# TODO: remove with PyTorch 1.6 since pl uses native amp
						if args.fpaa:
												__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		16
												__SCREAMING_SNAKE_CASE						:					Dict		      =		args.fpaa_opt_level

						if args.gpus > 1:
												__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		'''ddp'''

						__SCREAMING_SNAKE_CASE						:					int		      =		args.accumulate_grad_batches
						__SCREAMING_SNAKE_CASE						:					Any		      =		extra_train_kwargs.get('''accelerator'''   ,	lowercase__				)
						__SCREAMING_SNAKE_CASE						:					List[str]		      =		extra_train_kwargs.get('''profiler'''   ,	lowercase__				)

						__SCREAMING_SNAKE_CASE						:					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




def 	_UpperCamelCase							(				lowercase__				):
						barcode //= 10  # exclude the last digit
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		False
						__SCREAMING_SNAKE_CASE						:					Dict		      =		0

						# extract and check each digit
						while barcode != 0:
												__SCREAMING_SNAKE_CASE						:					int		      =		1 if checker else 3
												s += mult * (barcode % 10)
												barcode //= 10
												__SCREAMING_SNAKE_CASE						:					Dict		      =		not checker

						return (10 - (s % 10)) % 10


def 	_UpperCamelCase							(				lowercase__				):
						return len(str(lowercase__				)				) == 13 and get_check_digit(lowercase__				) == barcode % 10


def 	_UpperCamelCase							(				lowercase__				):
						if str(lowercase__				).isalpha():
												__SCREAMING_SNAKE_CASE						:					str		      =		F'''Barcode \'{barcode}\' has alphabetic characters.'''
												raise ValueError(lowercase__				)
						elif int(lowercase__				) < 0:
												raise ValueError('''The entered barcode has a negative value. Try again.'''				)
						else:
												return int(lowercase__				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						__lowerCAmelCase     :			Union[str, Any]      =get_barcode(input('Barcode: ').strip())

						if is_valid(barcode):
												print(f"""'{barcode}' is a valid barcode.""")
						else:
												print(f"""'{barcode}' is NOT a valid barcode.""")
