



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


lowerCamelCase__			= 4
lowerCamelCase__			= 3






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							pass



def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			List[str]    ):
				"""simple docstring"""







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

def   lowerCAmelCase__						(							):
				"""simple docstring"""







				__a       =		int(os.environ["""RANK"""]    )
				__a       =		int(os.environ["""WORLD_SIZE"""]    )

				__a       =		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    )
				__a       =		parser.parse_args()
				__a       =		args.streaming
				__a       =		args.num_workers

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

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

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

				__a       =		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 Counter
from random import random






class    SCREAMING_SNAKE_CASE			:





							def __init__(   self		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		{}





							def 						UpperCamelCase_					(   self		:		Dict      ,				__lowercase		:		str	):

											'''simple docstring'''
											__a       =		{}





							def 						UpperCamelCase_					(   self		:		List[str]      ,				__lowercase		:		str      ,				__lowercase		:		str      ,				__lowercase		:		float	):

											'''simple docstring'''
											if nodea not in self.connections:
															self.add_node(__lowercase	)
											if nodea not in self.connections:
															self.add_node(__lowercase	)
											__a       =		probability





							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											return list(self.connections	)







							def 						UpperCamelCase_					(   self		:		Tuple      ,				__lowercase		:		str	):

											'''simple docstring'''
											__a       =		0
											__a       =		random()

											for dest in self.connections[node]:
															current_probability += self.connections[node][dest]
															if current_probability > random_value:
																			return dest
											return ""



def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			list[tuple[str, str, float]]				,     _SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				__a       =		MarkovChainGraphUndirectedUnweighted()

				for nodea, nodea, probability in transitions:
								graph.add_transition_probability(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )

				__a       =		Counter(graph.get_nodes()    )
				__a       =		start

				for _ in range(_SCREAMING_SNAKE_CASE    ):
								__a       =		graph.transition(_SCREAMING_SNAKE_CASE    )
								visited[node] += 1

				return visited


if __name__ == "__main__":
						import doctest

						doctest.testmod()
