




"""simple docstring"""




from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime

class        UpperCAmelCase_  (	lowerCAmelCase__						):
		def __init__(		self							,		*UpperCamelCase_							,		**UpperCamelCase_   )		->   Union[str, Any]:
					super().__init__(*_A							,		**_A   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_   )		->   Union[str, Any]:
					__lowercase	:       Optional[int]				=   (
					    next_prime(value % self.size_table   )
					    if not is_prime(value % self.size_table   )
					    else value % self.size_table
					)  # gt = bigger than
					return next_prime_gt - (data % next_prime_gt)
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )		->   Optional[int]:
					return (increment * self.__hash_function_a(_A							,		_A   )) % self.size_table
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_=None   )		->   Optional[Any]:
					__lowercase	:       Tuple				=   1
					__lowercase	:       Union[str, Any]				=   self.hash_function(_A   )

					while self.values[new_key] is not None and self.values[new_key] != key:
								__lowercase	:       int				=   (
								    self.__hash_double_function(_A							,		_A							,		_A   )
								    if self.balanced_factor() >= self.lim_charge
								    else None
								)
								if new_key is None:
											break
								else:
											i += 1

					return new_key









"""simple docstring"""




import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
			print('#################### AVAILABLE DEVICES ####################')
			print(jax.devices())
			print('###########################################################')

			# setup for wandb sweep
			a_            =   Args()
			a_            =   wandb.init(project='bigbird-natural-questions', config=args.__dict__)
			a_            =   dict(logger.config)
			del wandb_args["batch_size"]
			a_            =   replace(args, **wandb_args)
			a_            =   args.base_dir + '-' + wandb.run.id
			a_            =   replace(args, base_dir=base_dir)
			print(args)

			a_            =   load_dataset('json', data_files=args.tr_data_path)['train']
			a_            =   load_dataset('json', data_files=args.val_data_path)['train']

			# drop extra batch for now
			a_            =   range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
			a_            =   tr_dataset.shuffle().select(indices)
			a_            =   range(len(val_dataset) - len(val_dataset) % args.batch_size)
			a_            =   val_dataset.shuffle().select(indices)

			if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
						a_            =   tr_dataset.shuffle().select(range(8_0_0_0_0))
						a_            =   val_dataset.shuffle().select(range(8_0_0_0))

			print(tr_dataset)
			print(val_dataset)

			a_            =   FlaxBigBirdForNaturalQuestions.from_pretrained(
			    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
			)
			a_            =   BigBirdTokenizerFast.from_pretrained(args.model_id)
			a_            =   DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

			a_            =   {
			    'lr': args.lr,
			    'init_lr': args.init_lr,
			    'warmup_steps': args.warmup_steps,
			    'num_train_steps': args.max_epochs * (len(tr_dataset) // args.batch_size),
			    'weight_decay': args.weight_decay,
			}
			a_       ,	a_            =   build_tx(**tx_args)

			a_            =   Trainer(
			    args=args,
			    data_collator=data_collator,
			    model_save_fn=model.save_pretrained,
			    train_step_fn=train_step,
			    val_step_fn=val_step,
			    logger=logger,
			    scheduler_fn=lr,
			)

			a_            =   None
			a_            =   trainer.create_state(model, tx, num_train_steps=tx_args['num_train_steps'], ckpt_dir=ckpt_dir)
			try:
						trainer.train(state, tr_dataset, val_dataset)
			except KeyboardInterrupt:
						print('Oooops; TRAINING STOPPED UNFORTUNATELY')

			print('SAVING WEIGHTS IN `final-weights`')
			a_            =   jax_utils.unreplicate(state.params)
			model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)



