



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

		_A					:     Any      =load_dataset('''json''', data_files=args.tr_data_path)['''train''']
		_A					:     List[Any]      =load_dataset('''json''', data_files=args.val_data_path)['''train''']

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

		if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
				_A					:     Optional[int]      =tr_dataset.shuffle().select(range(8_0_0_0_0))
				_A					:     int      =val_dataset.shuffle().select(range(8_0_0_0))

		print(tr_dataset)
		print(val_dataset)

		_A					:     int      =FlaxBigBirdForNaturalQuestions.from_pretrained(
		    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
		)
		_A					:     Dict      =BigBirdTokenizerFast.from_pretrained(args.model_id)
		_A					:     Optional[Any]      =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					:     Union[str, Any]      =build_tx(**tx_args)

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





'''simple docstring'''

from __future__ import annotations

_A					:     Union[str, Any]      ='''Alexander Joslin'''







def     __UpperCamelCase					(				_lowercase       )			->      int:

	if number <= 0:
		_lowercase       :							Optional[Any]  	=		f'''n must be greater than 0. Got n = {number}'''
		raise ValueError(_lowercase       )

	_lowercase       :							List[str]  	=		[number + 1] * (number + 1)

	# starting position
	_lowercase       :							str  	=		0
	for i in range(1,					_lowercase       ):
		_lowercase       :							Union[str, Any]  	=		min(table[i + 1],					table[i] + 1       )
		# check if out of bounds
		if i * 2 <= number:
			_lowercase       :							Optional[Any]  	=		min(table[i * 2],					table[i] + 1       )
		# check if out of bounds
		if i * 3 <= number:
			_lowercase       :							Any  	=		min(table[i * 3],					table[i] + 1       )
	return table[number]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
