






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					:		Tuple	  =						{
    """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					:		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
}
__UpperCamelCase					:		Any	  =						sorted(arg_to_scheduler.keys())
__UpperCamelCase					:		Dict	  =						"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""


class   __SCREAMING_SNAKE_CASE(	pl.LightningModule	):
							def __init__(     self:      Dict       ,	UpperCamelCase:      argparse.Namespace       ,	UpperCamelCase:      str=None       ,	UpperCamelCase:      Union[str, Any]="base"       ,	UpperCamelCase:      List[Any]=None       ,	UpperCamelCase:      Any=None       ,	UpperCamelCase:      Optional[Any]=None       ,	**UpperCamelCase:      Any       ,	)		->		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(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 lowerCAmelCase_ (     self:      Union[str, Any]       ,	*UpperCamelCase:      Dict       ,	**UpperCamelCase:      Union[str, Any]						)		->		Any:
											snake_case__									=     self.model_type.from_pretrained(*UpperCamelCase       ,	**UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Union[str, 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 lowerCAmelCase_ (     self:      str						)		->		Union[str, Any]:
											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 lowerCAmelCase_ (     self:      List[Any]       ,	UpperCamelCase:      Union[str, Any]       ,	UpperCamelCase:      Dict						)		->		Optional[Any]:
											return self.validation_step(UpperCamelCase       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      str       ,	UpperCamelCase:      int						)		->		int:
											return self.validation_end(UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		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 lowerCAmelCase_ (     self:      Optional[int]       ,	UpperCamelCase:      Tuple						)		->		Dict:
											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 lowerCAmelCase_ (     self:      Optional[Any]       ,	UpperCamelCase:      str       ,	UpperCamelCase:      int       ,	UpperCamelCase:      bool = False						)		->		Optional[int]:
											raise NotImplementedError('You must implement this for your task'						)
							def lowerCAmelCase_ (     self:      Dict						)		->		List[Any]:
											return self.train_loader
							def lowerCAmelCase_ (     self:      str						)		->		List[Any]:
											return self.get_dataloader('dev'       ,	self.hparams.eval_batch_size       ,	shuffle=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Tuple						)		->		List[str]:
											return self.get_dataloader('test'       ,	self.hparams.eval_batch_size       ,	shuffle=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      str       ,	UpperCamelCase:      Optional[int]						)		->		Optional[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 lowerCAmelCase_ (     self:      int       ,	UpperCamelCase:      Dict[str, Any]						)		->		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 lowerCAmelCase_ (     UpperCamelCase:      List[Any]       ,	UpperCamelCase:      Optional[Any]						)		->		Optional[int]:
											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   __SCREAMING_SNAKE_CASE(	pl.Callback	):
							def lowerCAmelCase_ (     self:      str       ,	UpperCamelCase:      Optional[int]       ,	UpperCamelCase:      Any						)		->		Union[str, Any]:
											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   __SCREAMING_SNAKE_CASE(	pl.Callback	):
							def lowerCAmelCase_ (     self:      int       ,	UpperCamelCase:      Tuple       ,	UpperCamelCase:      Any						)		->		Tuple:
											# print(pl_module.model.rag)
											for name, param in pl_module.model.rag.named_parameters():
															if param.grad is None:
																			print(UpperCamelCase						)


class   __SCREAMING_SNAKE_CASE(	pl.Callback	):
							def lowerCAmelCase_ (     self:      List[str]       ,	UpperCamelCase:      Union[str, Any]       ,	UpperCamelCase:      Any						)		->		Dict:
											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 lowerCAmelCase_ (     self:      Optional[Any]       ,	UpperCamelCase:      pl.Trainer       ,	UpperCamelCase:      pl.LightningModule						)		->		Tuple:
											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 lowerCAmelCase_ (     self:      List[Any]       ,	UpperCamelCase:      pl.Trainer       ,	UpperCamelCase:      pl.LightningModule						)		->		Optional[int]:
											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      a_ (      _A      ,   _A  )			->			None:







				"""simple docstring"""
				#  To allow all pl args uncomment the following line
				#  parser = pl.Trainer.add_argparse_args(parser)
				parser.add_argument(
				    '--output_dir'      ,   default=str(Path(_A  ).parent / 'test_run' / 'model_checkpoints'  )      ,   type=_A      ,   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=_A      ,   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=_A  )
				parser.add_argument('--max_grad_norm'      ,   dest='gradient_clip_val'      ,   default=1.0      ,   type=_A      ,   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=_A      ,   default=1      ,   help='Number of updates steps to accumulate before performing a backward/update pass.'      ,   )
				parser.add_argument('--seed'      ,   type=_A      ,   default=42      ,   help='random seed for initialization'  )
				parser.add_argument(
				    '--data_dir'      ,   default=str(Path(_A  ).parent / 'test_run' / 'dummy-train-data'  )      ,   type=_A      ,   help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'      ,   )


def      a_ (      _A      ,   _A      ,   _A=None      ,   _A=True      ,   _A=[]      ,   _A=None      ,   _A=None      ,   **_A      ,   )			->			int:







				"""simple docstring"""
				pl.seed_everything(args.seed  )

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

				# 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(_A  )
				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(
				    _A      ,   weights_summary=_A      ,   callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]      ,   logger=_A      ,   val_check_interval=1      ,   num_sanity_val_steps=2      ,   **_A      ,   )

				if args.do_train:
								trainer.fit(_A  )

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












def      a_ (      _A = 4000000  )			->			int:







				"""simple docstring"""
				snake_case__									=     [0, 1]
				snake_case__									=     0
				while fib[i] <= n:
								fib.append(fib[i] + fib[i + 1]  )
								if fib[i + 2] > n:
												break
								i += 1
				snake_case__									=     0
				for j in range(len(_A  ) - 1  ):
								if fib[j] % 2 == 0:
												total += fib[j]

				return total


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




