






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								=						4
__UpperCAmelCase								=						3



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		pass





def      lowercase__      (     __snake_case    :							List[str]  ):


					'''simple docstring'''


					for shard in shards:
										for i in range(__snake_case  ):
															yield {"i": i, "shard": shard}




def      lowercase__      (     ):


					'''simple docstring'''


					UpperCAmelCase_    :      Optional[int]            =						int(os.environ['RANK']  )
					UpperCAmelCase_    :      int            =						int(os.environ['WORLD_SIZE']  )

					UpperCAmelCase_    :      Dict            =						ArgumentParser()
					parser.add_argument('--streaming'     ,       type=__snake_case  )
					parser.add_argument('--local_rank'     ,       type=__snake_case  )
					parser.add_argument('--num_workers'     ,       type=__snake_case     ,       default=0  )
					UpperCAmelCase_    :      Optional[Any]            =						parser.parse_args()
					UpperCAmelCase_    :      Optional[int]            =						args.streaming
					UpperCAmelCase_    :      Optional[Any]            =						args.num_workers

					UpperCAmelCase_    :      Any            =						{'shards': [F"shard_{shard_idx}" for shard_idx in range(__snake_case  )]}
					UpperCAmelCase_    :      List[Any]            =						IterableDataset.from_generator(__snake_case     ,       gen_kwargs=__snake_case  )
					if not streaming:
										UpperCAmelCase_    :      Optional[int]            =						Dataset.from_list(list(__snake_case  )  )

					UpperCAmelCase_    :      List[Any]            =						split_dataset_by_node(__snake_case     ,       rank=__snake_case     ,       world_size=__snake_case  )
					UpperCAmelCase_    :      str            =						torch.utils.data.DataLoader(__snake_case     ,       num_workers=__snake_case  )

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

					UpperCAmelCase_    :      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()














import os

# Precomputes a list of the 100 first triangular numbers
__UpperCAmelCase								=						[int(0.5 * n * (n + 1)) for n in range(1, 101)]




def      lowercase__      (     ):


					'''simple docstring'''


					UpperCAmelCase_    :      Any            =						os.path.dirname(os.path.realpath(__snake_case  )  )
					UpperCAmelCase_    :      Optional[Any]            =						os.path.join(__snake_case     ,       'words.txt'  )

					UpperCAmelCase_    :      Union[str, Any]            =						''
					with open(__snake_case  ) as f:
										UpperCAmelCase_    :      List[Any]            =						f.readline()

					UpperCAmelCase_    :      Optional[int]            =						[word.strip('"'  ) for word in words.strip('\r\n'  ).split(','  )]
					UpperCAmelCase_    :      Optional[int]            =						[
					    word
					    for word in [sum(ord(__snake_case  ) - 64 for x in word  ) for word in words]
					    if word in TRIANGULAR_NUMBERS
					]
					return len(__snake_case  )


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






