



"""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					:					Union[str, Any] 		=		logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

_lowercase					:					Dict 		=		{
    '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					:					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
}
_lowercase					:					List[str] 		=		sorted(arg_to_scheduler.keys())
_lowercase					:					List[str] 		=		'{' + ', '.join(arg_to_scheduler_choices) + '}'

class 			__SCREAMING_SNAKE_CASE      ( pl.LightningModule  ):


					'''simple docstring'''





					def __init__(			self				:    Dict,    lowerCamelCase				:    argparse.Namespace,    lowerCamelCase				:    Dict=None,    lowerCamelCase				:    List[str]="base",    lowerCamelCase				:    Tuple=None,    lowerCamelCase				:    Dict=None,    lowerCamelCase				:    Any=None,    **lowerCamelCase				:    Tuple,    )->   Optional[int]:
								super().__init__()
								# TODO: move to self.save_hyperparameters()
								# self.save_hyperparameters()
								# can also expand arguments into trainer signature for easier reading

								self.save_hyperparameters(__UpperCamelCase	)
								lowerCamelCase__						:						int						      =0
								lowerCamelCase__						:						int						      =Path(self.hparams.output_dir	)
								lowerCamelCase__						:						Tuple						      =self.hparams.cache_dir if self.hparams.cache_dir else None
								if config is None:
											lowerCamelCase__						:						Optional[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=__UpperCamelCase,    **__UpperCamelCase,    )
								else:
											lowerCamelCase__						:						Union[str, Any]						      =config

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

								if tokenizer is None:
											lowerCamelCase__						:						Tuple						      =AutoTokenizer.from_pretrained(
											    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path,    cache_dir=__UpperCamelCase,    )
								else:
											lowerCamelCase__						:						int						      =tokenizer
								lowerCamelCase__						:						str						      =MODEL_MODES[mode]
								if model is None:
											lowerCamelCase__						:						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=__UpperCamelCase,    )
								else:
											lowerCamelCase__						:						List[Any]						      =model



					def 						snake_case			(			self				:    Any,    *lowerCamelCase				:    str,    **lowerCamelCase				:    Tuple	)->   str:
								lowerCamelCase__						:						Optional[int]						      =self.model_type.from_pretrained(*__UpperCamelCase,    **__UpperCamelCase	)



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



					def 						snake_case			(			self				:    List[str]	)->   Dict:
								lowerCamelCase__						:						Union[str, Any]						      =self.model
								lowerCamelCase__						:						Union[str, Any]						      =['''bias''', '''LayerNorm.weight''']
								lowerCamelCase__						:						Union[str, 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:
											lowerCamelCase__						:						Optional[Any]						      =Adafactor(
											    __UpperCamelCase,    lr=self.hparams.learning_rate,    scale_parameter=__UpperCamelCase,    relative_step=__UpperCamelCase	)

								else:
											lowerCamelCase__						:						Optional[int]						      =AdamW(
											    __UpperCamelCase,    lr=self.hparams.learning_rate,    eps=self.hparams.adam_epsilon	)
								lowerCamelCase__						:						str						      =optimizer

								lowerCamelCase__						:						List[str]						      =self.get_lr_scheduler()

								return [optimizer], [scheduler]



					def 						snake_case			(			self				:    List[Any],    lowerCamelCase				:    Optional[int],    lowerCamelCase				:    Tuple	)->   Optional[Any]:
								return self.validation_step(__UpperCamelCase,    __UpperCamelCase	)



					def 						snake_case			(			self				:    List[str],    lowerCamelCase				:    int	)->   Any:
								return self.validation_end(__UpperCamelCase	)



					def 						snake_case			(			self				:    int	)->   Union[str, Any]:
								lowerCamelCase__						:						int						      =max(1,    self.hparams.gpus	)  # TODO: consider num_tpu_cores
								lowerCamelCase__						:						Optional[int]						      =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				:    List[Any],    lowerCamelCase				:    Any	)->   str:
								if mode == "test":
											lowerCamelCase__						:						Tuple						      =len(self.test_dataloader().dataset	)
								else:
											lowerCamelCase__						:						List[Any]						      =self.get_dataloader('''train''',    self.hparams.train_batch_size,    shuffle=__UpperCamelCase	)
											lowerCamelCase__						:						Any						      =len(self.train_dataloader().dataset	)



					def 						snake_case			(			self				:    Optional[Any],    lowerCamelCase				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    bool = False	)->   Optional[int]:
								raise NotImplementedError('''You must implement this for your task'''	)



					def 						snake_case			(			self				:    List[str]	)->   List[Any]:
								return self.train_loader



					def 						snake_case			(			self				:    Optional[int]	)->   List[str]:
								return self.get_dataloader('''dev''',    self.hparams.eval_batch_size,    shuffle=__UpperCamelCase	)



					def 						snake_case			(			self				:    Optional[int]	)->   Optional[int]:
								return self.get_dataloader('''test''',    self.hparams.eval_batch_size,    shuffle=__UpperCamelCase	)



					def 						snake_case			(			self				:    Optional[Any],    lowerCamelCase				:    Union[str, Any]	)->   int:
								return os.path.join(
								    self.hparams.data_dir,    '''cached_{}_{}_{}'''.format(
								        __UpperCamelCase,    list(filter(__UpperCamelCase,    self.hparams.model_name_or_path.split('''/'''	)	)	).pop(),    str(self.hparams.max_seq_length	),    ),    )



					@pl.utilities.rank_zero_only
					def 						snake_case			(			self				:    int,    lowerCamelCase				:    Dict[str, Any]	)->   List[Any]:
								lowerCamelCase__						:						Tuple						      =self.output_dir.joinpath('''best_tfmr'''	)
								lowerCamelCase__						:						List[Any]						      =self.step_count
								self.model.save_pretrained(__UpperCamelCase	)
								self.tokenizer.save_pretrained(__UpperCamelCase	)




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





class 			__SCREAMING_SNAKE_CASE      ( pl.Callback  ):


					'''simple docstring'''





					def 						snake_case			(			self				:    List[Any],    lowerCamelCase				:    Any,    lowerCamelCase				:    Dict	)->   List[Any]:
								lowerCamelCase__						:						Union[str, Any]						      =trainer.lr_schedulers[0]['''scheduler''']
								lowerCamelCase__						:						Any						      ={F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
								pl_module.logger.log_metrics(__UpperCamelCase	)



					def 						snake_case			(			self				:    List[str],    lowerCamelCase				:    pl.Trainer,    lowerCamelCase				:    pl.LightningModule	)->   Any:
								rank_zero_info('''***** Validation results *****'''	)
								lowerCamelCase__						:						List[Any]						      =trainer.callback_metrics
								# Log results
								for key in sorted(__UpperCamelCase	):
											if key not in ["log", "progress_bar"]:
														rank_zero_info('''{} = {}\n'''.format(__UpperCamelCase,    str(metrics[key]	)	)	)




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






def 	snake_case__  (						__lowerCamelCase							:       List[Any]				,						__lowerCamelCase							:       List[Any]      ):
			"""simple docstring"""
			#  To allow all pl args uncomment the following line
			#  parser = pl.Trainer.add_argparse_args(parser)
			parser.add_argument(
			    '''--output_dir'''				,						default=__lowerCamelCase				,						type=__lowerCamelCase				,						required=__lowerCamelCase				,						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=__lowerCamelCase				,						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=__lowerCamelCase      )
			parser.add_argument('''--max_grad_norm'''				,						dest='''gradient_clip_val'''				,						default=1.0				,						type=__lowerCamelCase				,						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=__lowerCamelCase				,						default=1				,						help='''Number of updates steps to accumulate before performing a backward/update pass.'''				,						)
			parser.add_argument('''--seed'''				,						type=__lowerCamelCase				,						default=42				,						help='''random seed for initialization'''      )
			parser.add_argument(
			    '''--data_dir'''				,						default=__lowerCamelCase				,						type=__lowerCamelCase				,						required=__lowerCamelCase				,						help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''				,						)






def 	snake_case__  (						__lowerCamelCase							:       Dict				,						__lowerCamelCase							:       Optional[int]				,						__lowerCamelCase							:       List[Any]=None				,						__lowerCamelCase							:       Any=True				,						__lowerCamelCase							:       Any=[]				,						__lowerCamelCase							:       str=None				,						__lowerCamelCase							:       int=None				,						**__lowerCamelCase							:       int				,						):
			"""simple docstring"""
			pl.seed_everything(args.seed      )

			# init model
			lowerCamelCase__						:						int						      =Path(model.hparams.output_dir      )
			odir.mkdir(exist_ok=__lowerCamelCase      )

			# add custom checkpoints
			if checkpoint_callback is None:
						lowerCamelCase__						:						str						      =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(__lowerCamelCase      )
			if logging_callback is None:
						lowerCamelCase__						:						int						      =LoggingCallback()

			lowerCamelCase__						:						int						      ={}

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

			if args.gpus > 1:
						lowerCamelCase__						:						Optional[Any]						      ='''ddp'''

			lowerCamelCase__						:						List[str]						      =args.accumulate_grad_batches
			lowerCamelCase__						:						List[str]						      =extra_train_kwargs.get('''accelerator'''				,						__lowerCamelCase      )
			lowerCamelCase__						:						Optional[Any]						      =extra_train_kwargs.get('''profiler'''				,						__lowerCamelCase      )

			lowerCamelCase__						:						List[str]						      =pl.Trainer.from_argparse_args(
			    __lowerCamelCase				,						weights_summary=__lowerCamelCase				,						callbacks=[logging_callback] + extra_callbacks				,						logger=__lowerCamelCase				,						checkpoint_callback=__lowerCamelCase				,						**__lowerCamelCase				,						)

			if args.do_train:
						trainer.fit(__lowerCamelCase      )

			return trainer











"""simple docstring"""
from __future__ import annotations






def    A__							(     UpperCamelCase			):
							A						=    42
							A						=    list(UpperCamelCase			)
							A,      A						=    _vector[:-1], int(_vector[-1]			)
							A						=    [int(UpperCamelCase			) for digit in __vector]

							vector.reverse()
							for i, digit in enumerate(UpperCamelCase			):
														if i & 1 == 0:
																					A						=    digit * 2
																					if doubled > 9:
																												doubled -= 9
																					check_digit += doubled
														else:
																					check_digit += digit

							return check_digit % 10 == 0


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		assert is_luhn('79927398713')
		assert not is_luhn('79927398714')


