






"""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


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


class        __UpperCAmelCase(  __lowerCamelCase				):




						"""simple docstring"""




						pass





def 		lowercase__(A		)							->Union[str, Any]:




						"""simple docstring"""



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





def 		lowercase__()							->List[str]:




						"""simple docstring"""



						lowercase__							: Tuple=			int(os.environ["RANK"]		)
						lowercase__							: Optional[int]=			int(os.environ["WORLD_SIZE"]		)

						lowercase__							: List[str]=			ArgumentParser()
						parser.add_argument("--streaming"   ,						type=A		)
						parser.add_argument("--local_rank"   ,						type=A		)
						parser.add_argument("--num_workers"   ,						type=A   ,						default=0		)
						lowercase__							: Optional[int]=			parser.parse_args()
						lowercase__							: Optional[int]=			args.streaming
						lowercase__							: int=			args.num_workers

						lowercase__							: Any=			{"shards": [f'''shard_{shard_idx}''' for shard_idx in range(A		)]}
						lowercase__							: Tuple=			IterableDataset.from_generator(A   ,						gen_kwargs=A		)
						if not streaming:
												lowercase__							: List[Any]=			Dataset.from_list(list(A		)		)

						lowercase__							: Optional[int]=			split_dataset_by_node(A   ,						rank=A   ,						world_size=A		)
						lowercase__							: Optional[int]=			torch.utils.data.DataLoader(A   ,						num_workers=A		)

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

						lowercase__							: Tuple=			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

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass





@dataclass
class     lowerCamelCase      :
	UpperCamelCase_  :					int
	UpperCamelCase_  :					int





class     lowerCamelCase      :







	def __init__(     self      :Dict   ,     lowercase      :int			)		->      Union[str, Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= [[] for _ in range(lowercase			)]
				SCREAMING_SNAKE_CASE     						= size







	def __getitem__(     self      :Union[str, Any]   ,     lowercase      :int			)		->      Iterator[Edge]:







				"""simple docstring"""






				return iter(self._graph[vertex]			)







	@property
	def  snake_case__     (     self      :List[Any]			)		->      str:







				"""simple docstring"""






				return self._size







	def  snake_case__     (     self      :Optional[Any]   ,     lowercase      :int   ,     lowercase      :int   ,     lowercase      :int			)		->      List[Any]:







				"""simple docstring"""






				if weight not in (0, 1):
							raise ValueError('''Edge weight must be either 0 or 1.'''			)

				if to_vertex < 0 or to_vertex >= self.size:
							raise ValueError('''Vertex indexes must be in [0; size).'''			)

				self._graph[from_vertex].append(Edge(lowercase   ,     lowercase			)			)







	def  snake_case__     (     self      :List[str]   ,     lowercase      :int   ,     lowercase      :int			)		->      int | None:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= deque([start_vertex]			)
				SCREAMING_SNAKE_CASE     						= [None] * self.size
				SCREAMING_SNAKE_CASE     						= 0

				while queue:
							SCREAMING_SNAKE_CASE     						= queue.popleft()
							SCREAMING_SNAKE_CASE     						= distances[current_vertex]
							if current_distance is None:
										continue

							for edge in self[current_vertex]:
										SCREAMING_SNAKE_CASE     						= current_distance + edge.weight
										SCREAMING_SNAKE_CASE     						= distances[edge.destination_vertex]
										if (
										    isinstance(lowercase   ,     lowercase			)
										    and new_distance >= dest_vertex_distance
										):
													continue
										SCREAMING_SNAKE_CASE     						= new_distance
										if edge.weight == 0:
													queue.appendleft(edge.destination_vertex			)
										else:
													queue.append(edge.destination_vertex			)

				if distances[finish_vertex] is None:
							raise ValueError('''No path from start_vertex to finish_vertex.'''			)

				return distances[finish_vertex]


if __name__ == "__main__":
							import doctest

							doctest.testmod()