





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

	_a      :				Tuple											=					load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
	_a      :				List[str]											=					load_dataset("""json""", data_files=args.val_data_path)["""train"""]

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

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

	print(tr_dataset)
	print(val_dataset)

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

	_a      :				int											=					{
	    """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      :				int											=					build_tx(**tx_args)

	_a      :				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,
	)

	_a      :				str											=					None
	_a      :				Dict											=					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      :				List[Any]											=					jax_utils.unreplicate(state.params)
	model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)









'''simple docstring'''







def      _lowerCAmelCase		(		lowercase = 1000			)      ->      int:
			__lowerCAmelCase      		=       []
			__lowerCAmelCase      		=       []
			__lowerCAmelCase      		=       3
			__lowerCAmelCase      		=       5
			__lowerCAmelCase      		=       1
			while True:
						__lowerCAmelCase      		=       z * temp
						if result < n:
									zmulti.append(lowercase			)
									temp += 1
						else:
									__lowerCAmelCase      		=       1
									break
			while True:
						__lowerCAmelCase      		=       x * temp
						if result < n:
									xmulti.append(lowercase			)
									temp += 1
						else:
									break
			__lowerCAmelCase      		=       list(set(xmulti + zmulti			)			)
			return sum(lowercase			)


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

