




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


__A						:							List[Any]							=  4
__A						:							List[Any]							=  3




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							pass





def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			for shard in shards:
						for i in range(SCREAMING_SNAKE_CASE   ):
									yield {"i": i, "shard": shard}
def 					lowerCamelCase_    (    ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					int(os.environ["""RANK"""]   )
			SCREAMING_SNAKE_CASE           =					int(os.environ["""WORLD_SIZE"""]   )

			SCREAMING_SNAKE_CASE           =					ArgumentParser()
			parser.add_argument("""--streaming"""					,	type=SCREAMING_SNAKE_CASE   )
			parser.add_argument("""--local_rank"""					,	type=SCREAMING_SNAKE_CASE   )
			parser.add_argument("""--num_workers"""					,	type=SCREAMING_SNAKE_CASE					,	default=0   )
			SCREAMING_SNAKE_CASE           =					parser.parse_args()
			SCREAMING_SNAKE_CASE           =					args.streaming
			SCREAMING_SNAKE_CASE           =					args.num_workers

			SCREAMING_SNAKE_CASE           =					{"""shards""": [f"""shard_{shard_idx}""" for shard_idx in range(SCREAMING_SNAKE_CASE   )]}
			SCREAMING_SNAKE_CASE           =					IterableDataset.from_generator(SCREAMING_SNAKE_CASE					,	gen_kwargs=SCREAMING_SNAKE_CASE   )
			if not streaming:
						SCREAMING_SNAKE_CASE           =					Dataset.from_list(list(SCREAMING_SNAKE_CASE   )   )

			SCREAMING_SNAKE_CASE           =					split_dataset_by_node(SCREAMING_SNAKE_CASE					,	rank=SCREAMING_SNAKE_CASE					,	world_size=SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					torch.utils.data.DataLoader(SCREAMING_SNAKE_CASE					,	num_workers=SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					NUM_SHARDS * NUM_ITEMS_PER_SHARD
			SCREAMING_SNAKE_CASE           =					full_size // world_size
			expected_local_size += int(rank < (full_size % world_size)   )

			SCREAMING_SNAKE_CASE           =					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()












from __future__ import annotations

import math
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''

			if num <= 0:
						SCREAMING_SNAKE_CASE           =					f"""{num}: Invalid input, please enter a positive integer."""
						raise ValueError(SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					[True] * (num + 1)
			SCREAMING_SNAKE_CASE           =					[]
			SCREAMING_SNAKE_CASE           =					2
			SCREAMING_SNAKE_CASE           =					int(math.sqrt(SCREAMING_SNAKE_CASE   )   )

			while start <= end:
						# If start is a prime
						if sieve[start] is True:
									prime.append(SCREAMING_SNAKE_CASE   )

									# Set multiples of start be False
									for i in range(start * start					,	num + 1					,	SCREAMING_SNAKE_CASE   ):
												if sieve[i] is True:
															SCREAMING_SNAKE_CASE           =					False

						start += 1

			for j in range(end + 1					,	num + 1   ):
						if sieve[j] is True:
									prime.append(SCREAMING_SNAKE_CASE   )

			return prime


if __name__ == "__main__":
		print(prime_sieve(int(input("""Enter a positive integer: """).strip())))






