






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(80000))
				_a   			=						val_dataset.shuffle().select(range(8000))

		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=4096)

		_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   			=						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)













import math
from timeit import timeit


def __A					(   __lowerCAmelCase			)->						int:





						"""simple docstring"""



						_UpperCAmelCase             =    0
						_UpperCAmelCase             =    abs(__lowerCAmelCase			)
						while True:
												_UpperCAmelCase             =    n // 10
												digits += 1
												if n == 0:
																		break
						return digits


def __A					(   __lowerCAmelCase			)->						int:





						"""simple docstring"""



						return 1 if n == 0 else math.floor(math.log(abs(__lowerCAmelCase			)				,	10			) + 1			)


def __A					(   __lowerCAmelCase			)->						int:





						"""simple docstring"""



						return len(str(abs(__lowerCAmelCase			)			)			)


def __A					(   )->						None:





						"""simple docstring"""



						from collections.abc import Callable

						def benchmark_a_function(__lowerCAmelCase				,	__lowerCAmelCase			) -> None:
												_UpperCAmelCase             =    F"""{func.__name__}({value})"""
												_UpperCAmelCase             =    timeit(F"""__main__.{call}"""				,	setup='import __main__'			)
												print(F"""{call}: {func(__lowerCAmelCase			)} -- {timing} seconds"""			)

						for value in (262_144, 1_125_899_906_842_624, 1_267_650_600_228_229_401_496_703_205_376):
												for func in (num_digits, num_digits_fast, num_digits_faster):
																		benchmark_a_function(__lowerCAmelCase				,	__lowerCAmelCase			)
												print()


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		benchmark()
