





'''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__					:			Dict          =     logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

a__					:			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
a__					:			Union[str, 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
}
a__					:			List[Any]          =     sorted(arg_to_scheduler.keys())
a__					:			Tuple          =     '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''






class 				__snake_case     (					pl.LightningModule							):





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

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





	def   _snake_case   (		self      ,       *UpperCamelCase_      ,       **UpperCamelCase_		)      ->		List[str]:
		snake_case__	       =   self.model_type.from_pretrained(*UpperCamelCase_      ,       **UpperCamelCase_		)





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





	def   _snake_case   (		self		)      ->		List[str]:
		snake_case__	       =   self.model
		snake_case__	       =   ['bias', 'LayerNorm.weight']
		snake_case__	       =   [
		    {
		        'params': [
		            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay		)
		        ],  # check this named paramters
		        '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:
			snake_case__	       =   Adafactor(
			    UpperCamelCase_      ,       lr=self.hparams.learning_rate      ,       scale_parameter=UpperCamelCase_      ,       relative_step=UpperCamelCase_		)

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

		snake_case__	       =   self.get_lr_scheduler()

		return [optimizer], [scheduler]





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Any:
		return self.validation_step(UpperCamelCase_      ,       UpperCamelCase_		)





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		List[Any]:
		return self.validation_end(UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		int:
		snake_case__	       =   max(1      ,       self.hparams.gpus		)  # TODO: consider num_tpu_cores
		snake_case__	       =   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      ,       UpperCamelCase_		)      ->		int:
		if stage == "test":
			snake_case__	       =   len(self.test_dataloader().dataset		)
		else:
			snake_case__	       =   self.get_dataloader('train'      ,       self.hparams.train_batch_size      ,       shuffle=UpperCamelCase_		)
			snake_case__	       =   len(self.train_dataloader().dataset		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_ = False		)      ->		Optional[Any]:
		raise NotImplementedError('You must implement this for your task'		)





	def   _snake_case   (		self		)      ->		str:
		return self.train_loader





	def   _snake_case   (		self		)      ->		str:
		return self.get_dataloader('dev'      ,       self.hparams.eval_batch_size      ,       shuffle=UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		int:
		return self.get_dataloader('test'      ,       self.hparams.eval_batch_size      ,       shuffle=UpperCamelCase_		)





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		List[str]:
		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      ,       UpperCamelCase_		)      ->		None:
		snake_case__	       =   self.output_dir.joinpath('best_tfmr'		)
		snake_case__	       =   self.step_count
		self.model.save_pretrained(UpperCamelCase_		)
		self.tokenizer.save_pretrained(UpperCamelCase_		)





	@staticmethod
	def   _snake_case   (		UpperCamelCase_      ,       UpperCamelCase_		)      ->		List[str]:
		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=str(Path(UpperCamelCase_		).parent / 'test_run' / 'cache'		)      ,       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 				__snake_case     (					pl.Callback							):





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Tuple:
		if (
		    trainer.is_global_zero and trainer.global_rank == 0
		):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
			pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.






class 				__snake_case     (					pl.Callback							):





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Optional[Any]:
		# print(pl_module.model.rag)
		for name, param in pl_module.model.rag.named_parameters():
			if param.grad is None:
				print(UpperCamelCase_		)






class 				__snake_case     (					pl.Callback							):





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Tuple:
		snake_case__	       =   trainer.lr_schedulers[0]['scheduler']
		snake_case__	       =   {F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()		)}
		pl_module.logger.log_metrics(UpperCamelCase_		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Dict:
		rank_zero_info('***** Validation results *****'		)
		snake_case__	       =   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      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		List[Any]:
		rank_zero_info('***** Test results *****'		)
		snake_case__	       =   trainer.callback_metrics
		# Log and save results to file
		snake_case__	       =   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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->None:
	#  To allow all pl args uncomment the following line
	#  parser = pl.Trainer.add_argparse_args(parser)
	parser.add_argument(
	    '--output_dir'					,				default=str(Path(UpperCAmelCase_							).parent / 'test_run' / 'model_checkpoints'							)					,				type=UpperCAmelCase_					,				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=UpperCAmelCase_					,				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=UpperCAmelCase_							)
	parser.add_argument('--max_grad_norm'					,				dest='gradient_clip_val'					,				default=1.0					,				type=UpperCAmelCase_					,				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=UpperCAmelCase_					,				default=1					,				help='Number of updates steps to accumulate before performing a backward/update pass.'					,				)
	parser.add_argument('--seed'					,				type=UpperCAmelCase_					,				default=42					,				help='random seed for initialization'							)
	parser.add_argument(
	    '--data_dir'					,				default=str(Path(UpperCAmelCase_							).parent / 'test_run' / 'dummy-train-data'							)					,				type=UpperCAmelCase_					,				help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'					,				)







def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_=None					,				UpperCAmelCase_=True					,				UpperCAmelCase_=[]					,				UpperCAmelCase_=None					,				UpperCAmelCase_=None					,				**UpperCAmelCase_					,				)   ->List[Any]:
	pl.seed_everything(args.seed							)

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

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

	snake_case__	       =   {}

	if args.fpaa:
		snake_case__	       =   16

	if args.gpus > 1:
		snake_case__	       =   'auto'
		snake_case__	       =   'ddp'

	snake_case__	       =   args.accumulate_grad_batches
	snake_case__	       =   None
	snake_case__	       =   'auto'

	snake_case__	       =   pl.Trainer.from_argparse_args(
	    UpperCAmelCase_					,				weights_summary=UpperCAmelCase_					,				callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]					,				logger=UpperCAmelCase_					,				val_check_interval=1					,				num_sanity_val_steps=2					,				**UpperCAmelCase_					,				)

	if args.do_train:
		trainer.fit(UpperCAmelCase_							)

	else:
		print('RAG modeling tests with new set functions successfuly executed!'							)
	return trainer










'''simple docstring'''




def 						__lowerCamelCase			(	UpperCAmelCase_ = 10_00							)   ->int:
	return sum(e for e in range(3					,				UpperCAmelCase_							) if e % 3 == 0 or e % 5 == 0							)


if __name__ == "__main__":
			print(f"""{solution() = }""")



