





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


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



class    _a     (				lowercase__							):






							"""simple docstring"""

							pass







def       UpperCAmelCase							(     _lowerCamelCase      :  List[str]     ):





						'''simple docstring'''


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







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					int(os.environ["RANK"]     )
						SCREAMING_SNAKE_CASE__     :				int   =					int(os.environ["WORLD_SIZE"]     )

						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					ArgumentParser()
						parser.add_argument("--streaming"			,   type=_lowerCamelCase     )
						parser.add_argument("--local_rank"			,   type=_lowerCamelCase     )
						parser.add_argument("--num_workers"			,   type=_lowerCamelCase			,   default=0     )
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					parser.parse_args()
						SCREAMING_SNAKE_CASE__     :				Tuple   =					args.streaming
						SCREAMING_SNAKE_CASE__     :				Any   =					args.num_workers

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

						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					split_dataset_by_node(_lowerCamelCase			,   rank=_lowerCamelCase			,   world_size=_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				Tuple   =					torch.utils.data.DataLoader(_lowerCamelCase			,   num_workers=_lowerCamelCase     )

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

						SCREAMING_SNAKE_CASE__     :				Dict   =					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()






def       UpperCAmelCase							(     _lowerCamelCase      :  int = 4_000_000     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Any   =					[0, 1]
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					0
						while fib[i] <= n:
												fib.append(fib[i] + fib[i + 1]     )
												if fib[i + 2] > n:
																		break
												i += 1
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					0
						for j in range(len(_lowerCamelCase     ) - 1     ):
												if fib[j] % 2 == 0:
																		total += fib[j]

						return total


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