
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   :					Any    =			logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

_UpperCAmelCase   :					Optional[Any]    =			{
    "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   :					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
}
_UpperCAmelCase   :					Tuple    =			sorted(arg_to_scheduler.keys())
_UpperCAmelCase   :					List[Any]    =			"{" + ", ".join(arg_to_scheduler_choices) + "}"


class 							lowercase   (						pl.LightningModule  ):



		def __init__(					self							, A_							, A_=None							, A_="base"							, A_=None							, A_=None							, A_=None							, **A_							, )    ->   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(A_			)
				UpperCamelCase            =	0
				UpperCamelCase            =	Path(self.hparams.output_dir			)
				UpperCamelCase            =	self.hparams.cache_dir if self.hparams.cache_dir else None
				if config is None:
						UpperCamelCase            =	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=A_							, **A_							, )
				else:
						UpperCamelCase            =	config

				UpperCamelCase            =	('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
				for p in extra_model_params:
						if getattr(self.hparams							, A_							, A_			):
								assert hasattr(self.config							, A_			), F'''model config doesn\'t have a `{p}` attribute'''
								setattr(self.config							, A_							, getattr(self.hparams							, A_			)			)

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



		def __UpperCamelCase					(					self							, *A_							, **A_			)    ->   List[Any]:
				"""simple docstring"""
				UpperCamelCase            =	self.model_type.from_pretrained(*A_							, **A_			)



		def __UpperCamelCase					(					self			)    ->   Optional[int]:
				"""simple docstring"""
				UpperCamelCase            =	arg_to_scheduler[self.hparams.lr_scheduler]
				UpperCamelCase            =	get_schedule_func(
				    self.opt							, num_warmup_steps=self.hparams.warmup_steps							, num_training_steps=self.total_steps()			)
				UpperCamelCase            =	{'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
				return scheduler



		def __UpperCamelCase					(					self			)    ->   Tuple:
				"""simple docstring"""
				UpperCamelCase            =	self.model
				UpperCamelCase            =	['bias', 'LayerNorm.weight']
				UpperCamelCase            =	[
				    {
				        '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            =	Adafactor(
						    A_							, lr=self.hparams.learning_rate							, scale_parameter=A_							, relative_step=A_			)

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

				UpperCamelCase            =	self.get_lr_scheduler()

				return [optimizer], [scheduler]



		def __UpperCamelCase					(					self							, A_							, A_			)    ->   Tuple:
				"""simple docstring"""
				return self.validation_step(A_							, A_			)



		def __UpperCamelCase					(					self							, A_			)    ->   List[Any]:
				"""simple docstring"""
				return self.validation_end(A_			)



		def __UpperCamelCase					(					self			)    ->   int:
				"""simple docstring"""
				UpperCamelCase            =	max(1							, self.hparams.gpus			)  # TODO: consider num_tpu_cores
				UpperCamelCase            =	self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
				return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs



		def __UpperCamelCase					(					self							, A_			)    ->   Optional[Any]:
				"""simple docstring"""
				if mode == "test":
						UpperCamelCase            =	len(self.test_dataloader().dataset			)
				else:
						UpperCamelCase            =	self.get_dataloader('train'							, self.hparams.train_batch_size							, shuffle=A_			)
						UpperCamelCase            =	len(self.train_dataloader().dataset			)



		def __UpperCamelCase					(					self							, A_							, A_							, A_ = False			)    ->   Dict:
				"""simple docstring"""
				raise NotImplementedError('You must implement this for your task'			)



		def __UpperCamelCase					(					self			)    ->   int:
				"""simple docstring"""
				return self.train_loader



		def __UpperCamelCase					(					self			)    ->   Optional[int]:
				"""simple docstring"""
				return self.get_dataloader('dev'							, self.hparams.eval_batch_size							, shuffle=A_			)



		def __UpperCamelCase					(					self			)    ->   Tuple:
				"""simple docstring"""
				return self.get_dataloader('test'							, self.hparams.eval_batch_size							, shuffle=A_			)



		def __UpperCamelCase					(					self							, A_			)    ->   List[Any]:
				"""simple docstring"""
				return os.path.join(
				    self.hparams.data_dir							, 'cached_{}_{}_{}'.format(
				        A_							, list(filter(A_							, self.hparams.model_name_or_path.split('/'			)			)			).pop()							, str(self.hparams.max_seq_length			)							, )							, )



		@pl.utilities.rank_zero_only
		def __UpperCamelCase					(					self							, A_			)    ->   None:
				"""simple docstring"""
				UpperCamelCase            =	self.output_dir.joinpath('best_tfmr'			)
				UpperCamelCase            =	self.step_count
				self.model.save_pretrained(A_			)
				self.tokenizer.save_pretrained(A_			)



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






class 							lowercase   (						pl.Callback  ):



		def __UpperCamelCase					(					self							, A_							, A_			)    ->   Any:
				"""simple docstring"""
				UpperCamelCase            =	trainer.lr_schedulers[0]['scheduler']
				UpperCamelCase            =	{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()			)}
				pl_module.logger.log_metrics(A_			)



		def __UpperCamelCase					(					self							, A_							, A_			)    ->   str:
				"""simple docstring"""
				rank_zero_info('***** Validation results *****'			)
				UpperCamelCase            =	trainer.callback_metrics
				# Log results
				for key in sorted(A_			):
						if key not in ["log", "progress_bar"]:
								rank_zero_info('{} = {}\n'.format(A_							, str(metrics[key]			)			)			)



		def __UpperCamelCase					(					self							, A_							, A_			)    ->   Any:
				"""simple docstring"""
				rank_zero_info('***** Test results *****'			)
				UpperCamelCase            =	trainer.callback_metrics
				# Log and save results to file
				UpperCamelCase            =	os.path.join(pl_module.hparams.output_dir							, 'test_results.txt'			)
				with open(A_							, 'w'			) as writer:
						for key in sorted(A_			):
								if key not in ["log", "progress_bar"]:
										rank_zero_info('{} = {}\n'.format(A_							, str(metrics[key]			)			)			)
										writer.write('{} = {}\n'.format(A_							, str(metrics[key]			)			)			)






def  A			(   lowercase       ,			lowercase					)  ->    None:
		'''simple docstring'''

		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       ,			)  ->    List[str]:
		'''simple docstring'''

		pl.seed_everything(args.seed					)

		# init model
		UpperCamelCase            =	Path(model.hparams.output_dir					)
		odir.mkdir(exist_ok=lowercase					)

		# add custom checkpoints
		if checkpoint_callback is None:
				UpperCamelCase            =	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            =	LoggingCallback()

		UpperCamelCase            =	{}

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

		if args.gpus > 1:
				UpperCamelCase            =	'ddp'

		UpperCamelCase            =	args.accumulate_grad_batches
		UpperCamelCase            =	extra_train_kwargs.get('accelerator'       ,			lowercase					)
		UpperCamelCase            =	extra_train_kwargs.get('profiler'       ,			lowercase					)

		UpperCamelCase            =	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  A			(   lowercase					)  ->    int:
		'''simple docstring'''

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

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

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


def  A			(   lowercase					)  ->    bool:
		'''simple docstring'''

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


def  A			(   lowercase					)  ->    int:
		'''simple docstring'''

		if str(lowercase					).isalpha():
				UpperCamelCase            =	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()
					_UpperCAmelCase   :					List[str]    =			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.''')
