




"""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


a								=							logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

a								=							{
    '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
a								=							{
    '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
}
a								=							sorted(arg_to_scheduler.keys())
a								=							'{' + ', '.join(arg_to_scheduler_choices) + '}'






class  SCREAMING_SNAKE_CASE__   (   pl.LightningModule   ):


						def __init__(							self			:     Any  ,  lowerCAmelCase			:     argparse.Namespace  ,  lowerCAmelCase			:     Union[str, Any]=None  ,  lowerCAmelCase			:     Union[str, Any]="base"  ,  lowerCAmelCase			:     Optional[Any]=None  ,  lowerCAmelCase			:     List[str]=None  ,  lowerCAmelCase			:     Optional[int]=None  ,  **lowerCAmelCase			:     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   )
										lowerCAmelCase		=			0
										lowerCAmelCase		=			Path(self.hparams.output_dir   )
										lowerCAmelCase		=			self.hparams.cache_dir if self.hparams.cache_dir else None
										if config is None:
														lowerCAmelCase		=			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:
														lowerCAmelCase		=			config

										lowerCAmelCase		=			("""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:
														lowerCAmelCase		=			AutoTokenizer.from_pretrained(
														    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path  ,  cache_dir=lowerCAmelCase  ,  )
										else:
														lowerCAmelCase		=			tokenizer
										lowerCAmelCase		=			MODEL_MODES[mode]
										if model is None:
														lowerCAmelCase		=			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:
														lowerCAmelCase		=			model


						def 					__lowercase	(							self			:     Optional[Any]  ,  *lowerCAmelCase			:     str  ,  **lowerCAmelCase			:     List[str]   ):
										lowerCAmelCase		=			self.model_type.from_pretrained(*lowerCAmelCase  ,  **lowerCAmelCase   )


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


						def 					__lowercase	(							self			:     Tuple   ):
										lowerCAmelCase		=			self.model
										lowerCAmelCase		=			["""bias""", """LayerNorm.weight"""]
										lowerCAmelCase		=			[
										    {
										        """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:
														lowerCAmelCase		=			Adafactor(
														    lowerCAmelCase  ,  lr=self.hparams.learning_rate  ,  scale_parameter=lowerCAmelCase  ,  relative_step=lowerCAmelCase   )

										else:
														lowerCAmelCase		=			AdamW(
														    lowerCAmelCase  ,  lr=self.hparams.learning_rate  ,  eps=self.hparams.adam_epsilon   )
										lowerCAmelCase		=			optimizer

										lowerCAmelCase		=			self.get_lr_scheduler()

										return [optimizer], [scheduler]


						def 					__lowercase	(							self			:     str  ,  lowerCAmelCase			:     Tuple  ,  lowerCAmelCase			:     Union[str, Any]   ):
										return self.validation_step(lowerCAmelCase  ,  lowerCAmelCase   )


						def 					__lowercase	(							self			:     int  ,  lowerCAmelCase			:     Optional[int]   ):
										return self.validation_end(lowerCAmelCase   )


						def 					__lowercase	(							self			:     Optional[int]   ):
										lowerCAmelCase		=			max(1  ,  self.hparams.gpus   )  # TODO: consider num_tpu_cores
										lowerCAmelCase		=			self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
										return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs


						def 					__lowercase	(							self			:     Optional[Any]  ,  lowerCAmelCase			:     Optional[Any]   ):
										if mode == "test":
														lowerCAmelCase		=			len(self.test_dataloader().dataset   )
										else:
														lowerCAmelCase		=			self.get_dataloader("""train"""  ,  self.hparams.train_batch_size  ,  shuffle=lowerCAmelCase   )
														lowerCAmelCase		=			len(self.train_dataloader().dataset   )


						def 					__lowercase	(							self			:     Dict  ,  lowerCAmelCase			:     str  ,  lowerCAmelCase			:     int  ,  lowerCAmelCase			:     bool = False   ):
										raise NotImplementedError("""You must implement this for your task"""   )


						def 					__lowercase	(							self			:     List[str]   ):
										return self.train_loader


						def 					__lowercase	(							self			:     Dict   ):
										return self.get_dataloader("""dev"""  ,  self.hparams.eval_batch_size  ,  shuffle=lowerCAmelCase   )


						def 					__lowercase	(							self			:     Optional[Any]   ):
										return self.get_dataloader("""test"""  ,  self.hparams.eval_batch_size  ,  shuffle=lowerCAmelCase   )


						def 					__lowercase	(							self			:     List[Any]  ,  lowerCAmelCase			:     Dict   ):
										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 					__lowercase	(							self			:     Union[str, Any]  ,  lowerCAmelCase			:     Dict[str, Any]   ):
										lowerCAmelCase		=			self.output_dir.joinpath("""best_tfmr"""   )
										lowerCAmelCase		=			self.step_count
										self.model.save_pretrained(lowerCAmelCase   )
										self.tokenizer.save_pretrained(lowerCAmelCase   )




						@staticmethod
						def 					__lowercase	(							lowerCAmelCase			:     str  ,  lowerCAmelCase			:     str   ):
										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  SCREAMING_SNAKE_CASE__   (   pl.Callback   ):


						def 					__lowercase	(							self			:     Union[str, Any]  ,  lowerCAmelCase			:     Tuple  ,  lowerCAmelCase			:     Union[str, Any]   ):
										lowerCAmelCase		=			trainer.lr_schedulers[0]["""scheduler"""]
										lowerCAmelCase		=			{f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()   )}
										pl_module.logger.log_metrics(lowerCAmelCase   )


						def 					__lowercase	(							self			:     Optional[int]  ,  lowerCAmelCase			:     pl.Trainer  ,  lowerCAmelCase			:     pl.LightningModule   ):
										rank_zero_info("""***** Validation results *****"""   )
										lowerCAmelCase		=			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 					__lowercase	(							self			:     str  ,  lowerCAmelCase			:     pl.Trainer  ,  lowerCAmelCase			:     pl.LightningModule   ):
										rank_zero_info("""***** Test results *****"""   )
										lowerCAmelCase		=			trainer.callback_metrics
										# Log and save results to file
										lowerCAmelCase		=			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 							lowercase   (snake_case__				:	Optional[int] ,    snake_case__				:	Any	)       ->					None:


				'''simple docstring'''






				parser.add_argument(
				    """--output_dir""" ,    default=snake_case__ ,    type=snake_case__ ,    required=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=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=snake_case__	)
				parser.add_argument("""--max_grad_norm""" ,    dest="""gradient_clip_val""" ,    default=1.0 ,    type=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=snake_case__ ,    default=1 ,    help="""Number of updates steps to accumulate before performing a backward/update pass.""" ,    )
				parser.add_argument("""--seed""" ,    type=snake_case__ ,    default=42 ,    help="""random seed for initialization"""	)
				parser.add_argument(
				    """--data_dir""" ,    default=snake_case__ ,    type=snake_case__ ,    required=snake_case__ ,    help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task.""" ,    )






def 							lowercase   (snake_case__				:	BaseTransformer ,    snake_case__				:	argparse.Namespace ,    snake_case__				:	Optional[int]=None ,    snake_case__				:	Any=True ,    snake_case__				:	Union[str, Any]=[] ,    snake_case__				:	Tuple=None ,    snake_case__				:	Tuple=None ,    **snake_case__				:	Any ,    )       ->					Dict:


				'''simple docstring'''






				pl.seed_everything(args.seed	)

				# init model
				lowerCAmelCase		=			Path(model.hparams.output_dir	)
				odir.mkdir(exist_ok=snake_case__	)

				# add custom checkpoints
				if checkpoint_callback is None:
								lowerCAmelCase		=			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(snake_case__	)
				if logging_callback is None:
								lowerCAmelCase		=			LoggingCallback()

				lowerCAmelCase		=			{}

				# TODO: remove with PyTorch 1.6 since pl uses native amp
				if args.fpaa:
								lowerCAmelCase		=			16
								lowerCAmelCase		=			args.fpaa_opt_level

				if args.gpus > 1:
								lowerCAmelCase		=			"""ddp"""

				lowerCAmelCase		=			args.accumulate_grad_batches
				lowerCAmelCase		=			extra_train_kwargs.get("""accelerator""" ,    snake_case__	)
				lowerCAmelCase		=			extra_train_kwargs.get("""profiler""" ,    snake_case__	)

				lowerCAmelCase		=			pl.Trainer.from_argparse_args(
				    snake_case__ ,    weights_summary=snake_case__ ,    callbacks=[logging_callback] + extra_callbacks ,    logger=snake_case__ ,    checkpoint_callback=snake_case__ ,    **snake_case__ ,    )

				if args.do_train:
								trainer.fit(snake_case__	)

				return trainer











"""simple docstring"""







def 							lowercase   (snake_case__				:	int	)       ->					int:


				'''simple docstring'''






				barcode //= 10  # exclude the last digit
				lowerCAmelCase		=			False
				lowerCAmelCase		=			0

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

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





def 							lowercase   (snake_case__				:	int	)       ->					bool:


				'''simple docstring'''






				return len(str(snake_case__	)	) == 13 and get_check_digit(snake_case__	) == barcode % 10





def 							lowercase   (snake_case__				:	str	)       ->					int:


				'''simple docstring'''






				if str(snake_case__	).isalpha():
								lowerCAmelCase		=			f'''Barcode \'{barcode}\' has alphabetic characters.'''
								raise ValueError(snake_case__	)
				elif int(snake_case__	) < 0:
								raise ValueError("""The entered barcode has a negative value. Try again."""	)
				else:
								return int(snake_case__	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		a								=							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.""")





