





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

							__UpperCAmelCase         =   load_dataset("""json""", data_files=args.tr_data_path)['train']
							__UpperCAmelCase         =   load_dataset("""json""", data_files=args.val_data_path)['train']

							# drop extra batch for now
							__UpperCAmelCase         =   range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
							__UpperCAmelCase         =   tr_dataset.shuffle().select(indices)
							__UpperCAmelCase         =   range(len(val_dataset) - len(val_dataset) % args.batch_size)
							__UpperCAmelCase         =   val_dataset.shuffle().select(indices)

							if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
														__UpperCAmelCase         =   tr_dataset.shuffle().select(range(80000))
														__UpperCAmelCase         =   val_dataset.shuffle().select(range(8000))

							print(tr_dataset)
							print(val_dataset)

							__UpperCAmelCase         =   FlaxBigBirdForNaturalQuestions.from_pretrained(
							    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
							)
							__UpperCAmelCase         =   BigBirdTokenizerFast.from_pretrained(args.model_id)
							__UpperCAmelCase         =   DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

							__UpperCAmelCase         =   {
							    '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,
							}
							__UpperCAmelCase         =   build_tx(**tx_args)

							__UpperCAmelCase         =   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,
							)

							__UpperCAmelCase         =   None
							__UpperCAmelCase         =   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`""")
							__UpperCAmelCase         =   jax_utils.unreplicate(state.params)
							model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)










'''simple docstring'''




from __future__ import annotations

from collections import Counter



def 						__A						(       lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							int						=	[True] * n
		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	False
		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	False
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	True

		for i in range(3	,	int(n**0.5 + 1					)	,	2					):
				SCREAMING_SNAKE_CASE				:							List[Any]						=	i * 2
				while index < n:
						SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	False
						SCREAMING_SNAKE_CASE				:							Optional[Any]						=	index + i

		SCREAMING_SNAKE_CASE				:							Dict						=	[2]

		for i in range(3	,	lowerCamelCase_	,	2					):
				if is_prime[i]:
						primes.append(lowerCamelCase_					)

		return primes



def 						__A						(       lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							List[str]						=	str(lowerCamelCase_					)
		SCREAMING_SNAKE_CASE				:							List[str]						=	[]
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	["""0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9"""]

		for duplicate in Counter(lowerCamelCase_					) - Counter(set(lowerCamelCase_					)					):
				SCREAMING_SNAKE_CASE				:							Tuple						=	[int(number_str.replace(lowerCamelCase_	,	lowerCamelCase_					)					) for digit in digits]
				replacements.append(lowerCamelCase_					)

		return replacements



def 						__A						(       lowerCamelCase_ = 8					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Tuple						=	set()

		# Filter primes with less than 3 replaceable digits
		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	{
		    x for x in set(prime_sieve(1_00_00_00					)					) if len(str(lowerCamelCase_					)					) - len(set(str(lowerCamelCase_					)					)					) >= 3
		}

		for prime in primes:
				if prime in numbers_checked:
						continue

				SCREAMING_SNAKE_CASE				:							int						=	digit_replacements(lowerCamelCase_					)

				for family in replacements:
						numbers_checked.update(lowerCamelCase_					)
						SCREAMING_SNAKE_CASE				:							Dict						=	primes.intersection(lowerCamelCase_					)

						if len(lowerCamelCase_					) != family_length:
								continue

						return min(lowerCamelCase_					)

		return -1


if __name__ == "__main__":
							print(solution())



