




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

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

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

		if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
				__lowerCAmelCase		:    List[str]	      =		tr_dataset.shuffle().select(range(8_0000))
				__lowerCAmelCase		:    List[str]	      =		val_dataset.shuffle().select(range(8000))

		print(tr_dataset)
		print(val_dataset)

		__lowerCAmelCase		:    Dict	      =		FlaxBigBirdForNaturalQuestions.from_pretrained(
		    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
		)
		__lowerCAmelCase		:    Optional[Any]	      =		BigBirdTokenizerFast.from_pretrained(args.model_id)
		__lowerCAmelCase		:    List[str]	      =		DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

		__lowerCAmelCase		:    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,
		}
		__lowerCAmelCase      ,					__lowerCAmelCase		:    Tuple	      =		build_tx(**tx_args)

		__lowerCAmelCase		:    Dict	      =		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,
		)

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












def       __magic_name__    (			A		: float       ):






			'''simple docstring'''







			return -num if num < 0 else num




def       __magic_name__    (			A		: list[int]       ):






			'''simple docstring'''







			if len(A       ) == 0:
						raise ValueError("abs_min() arg is an empty sequence"       )
			a   =      x[0]
			for i in x:
						if abs_val(A       ) < abs_val(A       ):
									a   =      i
			return j




def       __magic_name__    (			A		: list[int]       ):






			'''simple docstring'''







			if len(A       ) == 0:
						raise ValueError("abs_max() arg is an empty sequence"       )
			a   =      x[0]
			for i in x:
						if abs(A       ) > abs(A       ):
									a   =      i
			return j




def       __magic_name__    (			A		: list[int]       ):






			'''simple docstring'''







			if len(A       ) == 0:
						raise ValueError("abs_max_sort() arg is an empty sequence"       )
			return sorted(A,  key=A       )[-1]




def       __magic_name__    (			):






			'''simple docstring'''







			assert abs_val(0       ) == 0
			assert abs_val(34       ) == 34
			assert abs_val(-100000000000       ) == 100000000000

			a   =      [-3, -1, 2, -11]
			assert abs_max(A       ) == -11
			assert abs_max_sort(A       ) == -11
			assert abs_min(A       ) == -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		test_abs_val()
		print(abs_val(-34))  # --> 34






