

"""simple docstring"""





import sys

__UpperCamelCase    : Any				 =						(
    '''73167176531330624919225119674426574742355349194934'''
    '''96983520312774506326239578318016984801869478851843'''
    '''85861560789112949495459501737958331952853208805511'''
    '''12540698747158523863050715693290963295227443043557'''
    '''66896648950445244523161731856403098711121722383113'''
    '''62229893423380308135336276614282806444486645238749'''
    '''30358907296290491560440772390713810515859307960866'''
    '''70172427121883998797908792274921901699720888093776'''
    '''65727333001053367881220235421809751254540594752243'''
    '''52584907711670556013604839586446706324415722155397'''
    '''53697817977846174064955149290862569321978468622482'''
    '''83972241375657056057490261407972968652414535100474'''
    '''82166370484403199890008895243450658541227588666881'''
    '''16427171479924442928230863465674813919123162824586'''
    '''17866458359124566529476545682848912883142607690042'''
    '''24219022671055626321111109370544217506941658960408'''
    '''07198403850962455444362981230987879927244284909188'''
    '''84580156166097919133875499200524063689912560717606'''
    '''05886116467109405077541002256983155200055935729725'''
    '''71636269561882670428252483600823257530420752963450'''
)

def     __SCREAMING_SNAKE_CASE    ( A_ = N					):
	lowerCAmelCase__      :		str    							=      -sys.maxsize - 1
	for i in range(len(A_					) - 12					):
		lowerCAmelCase__      :		Any    							=      1
		for j in range(13					):
			product *= int(n[i + j]					)
		if product > largest_product:
			lowerCAmelCase__      :		Tuple    							=      product
	return largest_product


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







"""simple docstring"""





import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__UpperCamelCase    : str				 =						4
__UpperCamelCase    : List[str]				 =						3







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					pass

def     __SCREAMING_SNAKE_CASE    ( A_					):
	for shard in shards:
		for i in range(A_					):
			yield {"i": i, "shard": shard}

def     __SCREAMING_SNAKE_CASE    ( ):
	lowerCAmelCase__      :		List[str]    							=      int(os.environ['''RANK''']					)
	lowerCAmelCase__      :		Union[str, Any]    							=      int(os.environ['''WORLD_SIZE''']					)

	lowerCAmelCase__      :		Tuple    							=      ArgumentParser()
	parser.add_argument('''--streaming'''		,	type=A_					)
	parser.add_argument('''--local_rank'''		,	type=A_					)
	parser.add_argument('''--num_workers'''		,	type=A_		,	default=0					)
	lowerCAmelCase__      :		Tuple    							=      parser.parse_args()
	lowerCAmelCase__      :		Tuple    							=      args.streaming
	lowerCAmelCase__      :		Any    							=      args.num_workers

	lowerCAmelCase__      :		Dict    							=      {'''shards''': [f'shard_{shard_idx}' for shard_idx in range(A_					)]}
	lowerCAmelCase__      :		Optional[int]    							=      IterableDataset.from_generator(A_		,	gen_kwargs=A_					)
	if not streaming:
		lowerCAmelCase__      :		Dict    							=      Dataset.from_list(list(A_					)					)

	lowerCAmelCase__      :		Optional[Any]    							=      split_dataset_by_node(A_		,	rank=A_		,	world_size=A_					)
	lowerCAmelCase__      :		List[Any]    							=      torch.utils.data.DataLoader(A_		,	num_workers=A_					)

	lowerCAmelCase__      :		Optional[int]    							=      NUM_SHARDS * NUM_ITEMS_PER_SHARD
	lowerCAmelCase__      :		List[str]    							=      full_size // world_size
	expected_local_size += int(rank < (full_size % world_size)					)

	lowerCAmelCase__      :		int    							=      sum(1 for _ in dataloader					)
	if local_size != expected_local_size:
		raise FailedTestError(f'local_size {local_size} != expected_local_size {expected_local_size}'					)


if __name__ == "__main__":
							main()




