

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_							:					Optional[int]		 =   4
UpperCAmelCase_							:					List[str]		 =   3






class      lowerCamelCase_      (	_lowercase	):
	pass






def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[str]       )					->				Optional[Any]:
	for shard in shards:
		for i in range(a__       ):
			yield {"i": i, "shard": shard}
def 							__SCREAMING_SNAKE_CASE     (   )					->				Union[str, Any]:
	__A :			Any		    =			int(os.environ["""RANK"""]       )
	__A :			str		    =			int(os.environ["""WORLD_SIZE"""]       )

	__A :			Any		    =			ArgumentParser()
	parser.add_argument("""--streaming"""			,type=a__       )
	parser.add_argument("""--local_rank"""			,type=a__       )
	parser.add_argument("""--num_workers"""			,type=a__			,default=0       )
	__A :			Optional[int]		    =			parser.parse_args()
	__A :			Optional[Any]		    =			args.streaming
	__A :			Optional[Any]		    =			args.num_workers

	__A :			Tuple		    =			{"""shards""": [f"""shard_{shard_idx}""" for shard_idx in range(a__       )]}
	__A :			Optional[int]		    =			IterableDataset.from_generator(a__			,gen_kwargs=a__       )
	if not streaming:
		__A :			List[str]		    =			Dataset.from_list(list(a__       )       )

	__A :			Optional[int]		    =			split_dataset_by_node(a__			,rank=a__			,world_size=a__       )
	__A :			List[Any]		    =			torch.utils.data.DataLoader(a__			,num_workers=a__       )

	__A :			List[Any]		    =			NUM_SHARDS * NUM_ITEMS_PER_SHARD
	__A :			int		    =			full_size // world_size
	expected_local_size += int(rank < (full_size % world_size)       )

	__A :			Optional[Any]		    =			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()








UpperCAmelCase_							:					dict[tuple[int, int, int], int]		 =   {}
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int			,a__    :   int			,a__    :   int       )					->				int:

	# if we are absent twice, or late 3 consecutive days,
	# no further prize strings are possible
	if late == 3 or absent == 2:
		return 0

	# if we have no days left, and have not failed any other rules,
	# we have a prize string
	if days == 0:
		return 1

	# No easy solution, so now we need to do the recursive calculation

	# First, check if the combination is already in the cache, and
	# if yes, return the stored value from there since we already
	# know the number of possible prize strings from this point on
	__A :			List[Any]		    =			(days, absent, late)
	if key in cache:
		return cache[key]

	# now we calculate the three possible ways that can unfold from
	# this point on, depending on our attendance today

	# 1) if we are late (but not absent), the "absent" counter stays as
	# it is, but the "late" counter increases by one
	__A :			Dict		    =			_calculate(days - 1			,a__			,late + 1       )

	# 2) if we are absent, the "absent" counter increases by 1, and the
	# "late" counter resets to 0
	__A :			List[str]		    =			_calculate(days - 1			,absent + 1			,0       )

	# 3) if we are on time, this resets the "late" counter and keeps the
	# absent counter
	__A :			int		    =			_calculate(days - 1			,a__			,0       )

	__A :			Optional[int]		    =			state_late + state_absent + state_ontime

	__A :			Tuple		    =			prizestrings
	return prizestrings
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int = 30       )					->				int:
	return _calculate(a__			,absent=0			,late=0       )


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





