





'''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 :			Optional[Any]							   =      logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

A :			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
A :			Dict							   =      {
    '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 :			Optional[Any]							   =      sorted(arg_to_scheduler.keys())
A :			Optional[Any]							   =      '{' + ', '.join(arg_to_scheduler_choices) + '}'







class       lowerCamelCase       (    pl.LightningModule	):


					def __init__(       self						:	Dict							,  __snake_case						:	argparse.Namespace							,  __snake_case						:	Dict=None							,  __snake_case						:	int="base"							,  __snake_case						:	Optional[Any]=None							,  __snake_case						:	List[Any]=None							,  __snake_case						:	List[str]=None							,  **__snake_case						:	int							,  ):





											'''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(lowercase__							)
											_snake_case: str	       =		0
											_snake_case: Union[str, Any]	       =		Path(self.hparams.output_dir							)
											_snake_case: Optional[Any]	       =		self.hparams.cache_dir if self.hparams.cache_dir else None
											if config is None:
																	_snake_case: int	       =		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=lowercase__							,  **lowercase__							,  )
											else:
																	_snake_case: List[str]	       =		config

											_snake_case: Optional[int]	       =		('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
											for p in extra_model_params:
																	if getattr(self.hparams							,  lowercase__							,  lowercase__							):
																							assert hasattr(self.config							,  lowercase__							), f'''model config doesn\'t have a `{p}` attribute'''
																							setattr(self.config							,  lowercase__							,  getattr(self.hparams							,  lowercase__							)							)

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


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							,  *__snake_case						:	Optional[int]							,  **__snake_case						:	int							):





											'''simple docstring'''





											_snake_case: List[Any]	       =		self.model_type.from_pretrained(*lowercase__							,  **lowercase__							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							):





											'''simple docstring'''





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


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							):





											'''simple docstring'''





											_snake_case: str	       =		self.model
											_snake_case: Dict	       =		['bias', 'LayerNorm.weight']
											_snake_case: Tuple	       =		[
											    {
											        '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:
																	_snake_case: Tuple	       =		Adafactor(
																	    lowercase__							,  lr=self.hparams.learning_rate							,  scale_parameter=lowercase__							,  relative_step=lowercase__							)

											else:
																	_snake_case: Optional[Any]	       =		AdamW(
																	    lowercase__							,  lr=self.hparams.learning_rate							,  eps=self.hparams.adam_epsilon							)
											_snake_case: Union[str, Any]	       =		optimizer

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

											return [optimizer], [scheduler]


					def  SCREAMING_SNAKE_CASE_		(       self						:	str							,  __snake_case						:	Union[str, Any]							,  __snake_case						:	Optional[int]							):





											'''simple docstring'''





											return self.validation_step(lowercase__							,  lowercase__							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							,  __snake_case						:	Optional[int]							):





											'''simple docstring'''





											return self.validation_end(lowercase__							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		max(1							,  self.hparams.gpus							)  # TODO: consider num_tpu_cores
											_snake_case: str	       =		self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
											return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							,  __snake_case						:	List[Any]							):





											'''simple docstring'''





											if stage == "test":
																	_snake_case: Any	       =		len(self.test_dataloader().dataset							)
											else:
																	_snake_case: Any	       =		self.get_dataloader('train'							,  self.hparams.train_batch_size							,  shuffle=lowercase__							)
																	_snake_case: List[str]	       =		len(self.train_dataloader().dataset							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							,  __snake_case						:	str							,  __snake_case						:	int							,  __snake_case						:	bool = False							):





											'''simple docstring'''





											raise NotImplementedError('You must implement this for your task'							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							):





											'''simple docstring'''





											return self.train_loader


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''simple docstring'''





											return self.get_dataloader('dev'							,  self.hparams.eval_batch_size							,  shuffle=lowercase__							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											return self.get_dataloader('test'							,  self.hparams.eval_batch_size							,  shuffle=lowercase__							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	str							,  __snake_case						:	Any							):





											'''simple docstring'''





											return os.path.join(
											    self.hparams.data_dir							,  'cached_{}_{}_{}'.format(
											        lowercase__							,  list(filter(lowercase__							,  self.hparams.model_name_or_path.split('/'							)							)							).pop()							,  str(self.hparams.max_seq_length							)							,  )							,  )


					@pl.utilities.rank_zero_only
					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							,  __snake_case						:	Dict[str, Any]							):





											'''simple docstring'''





											_snake_case: Dict	       =		self.output_dir.joinpath('best_tfmr'							)
											_snake_case: Optional[int]	       =		self.step_count
											self.model.save_pretrained(lowercase__							)
											self.tokenizer.save_pretrained(lowercase__							)




					@staticmethod
					def  SCREAMING_SNAKE_CASE_		(       __snake_case						:	List[str]							,  __snake_case						:	List[Any]							):





											'''simple docstring'''





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







class       lowerCamelCase       (    pl.Callback	):


					def  SCREAMING_SNAKE_CASE_		(       self						:	str							,  __snake_case						:	int							,  __snake_case						:	Optional[Any]							):





											'''simple docstring'''





											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       lowerCamelCase       (    pl.Callback	):


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							,  __snake_case						:	List[str]							,  __snake_case						:	Union[str, Any]							):





											'''simple docstring'''





											_snake_case: List[Any]	       =		trainer.lr_schedulers[0]['scheduler']
											_snake_case: Optional[Any]	       =		{f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()							)}
											pl_module.logger.log_metrics(lowercase__							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							,  __snake_case						:	pl.Trainer							,  __snake_case						:	pl.LightningModule							):





											'''simple docstring'''





											rank_zero_info('***** Validation results *****'							)
											_snake_case: List[str]	       =		trainer.callback_metrics
											# Log results
											for key in sorted(lowercase__							):
																	if key not in ["log", "progress_bar"]:
																							rank_zero_info('{} = {}\n'.format(lowercase__							,  str(metrics[key]							)							)							)




					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							,  __snake_case						:	pl.Trainer							,  __snake_case						:	pl.LightningModule							):





											'''simple docstring'''





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





def    lowercase_   (		lowercase__					,	lowercase__    ) ->Dict:
						#  To allow all pl args uncomment the following line
						#  parser = pl.Trainer.add_argparse_args(parser)
						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_   (		lowercase__					,	lowercase__					,	lowercase__=None					,	lowercase__=True					,	lowercase__=None					,	lowercase__=[]					,	lowercase__=None					,	lowercase__=None					,	**lowercase__					,	) ->int:
						pl.seed_everything(args.seed    )

						# init model
						_snake_case: Any	       =		Path(model.hparams.output_dir    )
						odir.mkdir(exist_ok=snake_case__    )

						# add custom checkpoints
						if checkpoint_callback is None:
												_snake_case: Dict	       =		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:
												_snake_case: int	       =		LoggingCallback()

						_snake_case: Optional[Any]	       =		{}

						# TODO: remove with PyTorch 1.6 since pl uses native amp
						if args.fpaa:
												_snake_case: Optional[Any]	       =		16
												# train_params["amp_level"] = args.fp16_opt_level

						if args.gpus > 1:
												_snake_case: Tuple	       =		'auto'  # "ddp"
												_snake_case: List[str]	       =		'ddp'

						_snake_case: Dict	       =		args.accumulate_grad_batches
						_snake_case: str	       =		None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
						_snake_case: Tuple	       =		'auto'

						_snake_case: int	       =		pl.Trainer.from_argparse_args(
						    snake_case__					,	weights_summary=snake_case__					,	callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]					,	logger=snake_case__					,	**snake_case__					,	)

						if args.do_train:
												trainer.fit(snake_case__    )

						return trainer













'''simple docstring'''







def     _UpperCamelCase				(  snake_case__ = 1_000				):
      __A		 =   []
      __A		 =   []
      __A		 =   3
      __A		 =   5
      __A		 =   1
      while True:
            __A		 =   z * temp
            if result < n:
                  zmulti.append(snake_case__				)
                  temp += 1
            else:
                  __A		 =   1
                  break
      while True:
            __A		 =   x * temp
            if result < n:
                  xmulti.append(snake_case__				)
                  temp += 1
            else:
                  break
      __A		 =   list(set(xmulti + zmulti				)				)
      return sum(snake_case__				)


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


