






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__     :      List[Any]             =			Args()
							A__     :      List[Any]             =			wandb.init(project='bigbird-natural-questions', config=args.__dict__)
							A__     :      Any             =			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__     :      Any             =			load_dataset('json', data_files=args.tr_data_path)['train']
							A__     :      int             =			load_dataset('json', data_files=args.val_data_path)['train']

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

							if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
														A__     :      Union[str, Any]             =			tr_dataset.shuffle().select(range(8_00_00))
														A__     :      Any             =			val_dataset.shuffle().select(range(80_00))

							print(tr_dataset)
							print(val_dataset)

							A__     :      List[Any]             =			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__     :      List[str]             =			DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

							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__     :      Tuple             =			build_tx(**tx_args)

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












from collections import defaultdict






def   a      (						lowerCamelCase_ = 5    ):






				'''simple docstring'''
				lowercase__       			=      defaultdict(lowerCamelCase_    )
				lowercase__       			=      0

				while True:
								lowercase__       			=      get_digits(lowerCamelCase_    )
								freqs[digits].append(lowerCamelCase_    )

								if len(freqs[digits]    ) == max_base:
												lowercase__       			=      freqs[digits][0] ** 3
												return base

								num += 1






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				return "".join(sorted(str(num**3    )    )    )


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




