

"""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
							__UpperCamelCase						:   List[Any]							   =	Args()
							__UpperCamelCase						:   str							   =	wandb.init(project='''bigbird-natural-questions''', config=args.__dict__)
							__UpperCamelCase						:   Optional[Any]							   =	dict(logger.config)
							del wandb_args["batch_size"]
							__UpperCamelCase						:   Dict							   =	replace(args, **wandb_args)
							__UpperCamelCase						:   Any							   =	args.base_dir + '''-''' + wandb.run.id
							__UpperCamelCase						:   str							   =	replace(args, base_dir=base_dir)
							print(args)

							__UpperCamelCase						:   Dict							   =	load_dataset('''json''', data_files=args.tr_data_path)['''train''']
							__UpperCamelCase						:   Dict							   =	load_dataset('''json''', data_files=args.val_data_path)['''train''']

							# drop extra batch for now
							__UpperCamelCase						:   List[str]							   =	range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
							__UpperCamelCase						:   Tuple							   =	tr_dataset.shuffle().select(indices)
							__UpperCamelCase						:   Optional[Any]							   =	range(len(val_dataset) - len(val_dataset) % args.batch_size)
							__UpperCamelCase						:   str							   =	val_dataset.shuffle().select(indices)

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

							print(tr_dataset)
							print(val_dataset)

							__UpperCamelCase						:   str							   =	FlaxBigBirdForNaturalQuestions.from_pretrained(
							    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
							)
							__UpperCamelCase						:   int							   =	BigBirdTokenizerFast.from_pretrained(args.model_id)
							__UpperCamelCase						:   Optional[Any]							   =	DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

							__UpperCamelCase						:   List[str]							   =	{
							    '''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,
							}
							__UpperCamelCase   ,					__UpperCamelCase						:   Union[str, Any]							   =	build_tx(**tx_args)

							__UpperCamelCase						:   int							   =	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,
							)

							__UpperCamelCase						:   List[Any]							   =	None
							__UpperCamelCase						:   Tuple							   =	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`''')
							__UpperCamelCase						:   int							   =	jax_utils.unreplicate(state.params)
							model.save_pretrained(os.path.join(args.base_dir, '''final-weights'''), params=params)







"""simple docstring"""





def 						__SCREAMING_SNAKE_CASE						(   A_				):
	lowerCAmelCase__			:		List[Any]						       =			53_81
	for x in s:
		lowerCAmelCase__			:		Tuple						       =			((hash_value << 5) + hash_value) + ord(A_				)
	return hash_value & 0Xf_f_f_f_f_f_f_f




