



'''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							   =				4
a							   =				3






class 						a_		(     snake_case_    ):
							pass







def a_     (    __UpperCAmelCase					)				->					Union[str, Any]:





			"""simple docstring"""







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







def a_     (    )				->					str:





			"""simple docstring"""







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

			snake_case:    Any      							=ArgumentParser()
			parser.add_argument('--streaming'				,							type=__UpperCAmelCase					)
			parser.add_argument('--local_rank'				,							type=__UpperCAmelCase					)
			parser.add_argument('--num_workers'				,							type=__UpperCAmelCase				,							default=0					)
			snake_case:    str      							=parser.parse_args()
			snake_case:    List[Any]      							=args.streaming
			snake_case:    str      							=args.num_workers

			snake_case:    str      							={'shards': [f'''shard_{shard_idx}''' for shard_idx in range(__UpperCAmelCase					)]}
			snake_case:    Union[str, Any]      							=IterableDataset.from_generator(__UpperCAmelCase				,							gen_kwargs=__UpperCAmelCase					)
			if not streaming:
						snake_case:    Dict      							=Dataset.from_list(list(__UpperCAmelCase					)					)

			snake_case:    List[str]      							=split_dataset_by_node(__UpperCAmelCase				,							rank=__UpperCAmelCase				,							world_size=__UpperCAmelCase					)
			snake_case:    Dict      							=torch.utils.data.DataLoader(__UpperCAmelCase				,							num_workers=__UpperCAmelCase					)

			snake_case:    Tuple      							=NUM_SHARDS * NUM_ITEMS_PER_SHARD
			snake_case:    int      							=full_size // world_size
			expected_local_size += int(rank < (full_size % world_size)					)

			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()











'''simple docstring'''






from __future__ import annotations

import numpy as np
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase	,				_lowerCAmelCase					=				np.shape(lowerCAmelCase    )
      if rows != columns:
            _lowerCAmelCase					=				(
                """'table' has to be of square shaped array but got a """
                f"{rows}x{columns} array:\n{table}"
            )
            raise ValueError(lowerCAmelCase    )

      _lowerCAmelCase					=				np.zeros((rows, columns)    )
      _lowerCAmelCase					=				np.zeros((rows, columns)    )
      for i in range(lowerCAmelCase    ):
            for j in range(lowerCAmelCase    ):
                  _lowerCAmelCase					=				sum(lower[i][k] * upper[k][j] for k in range(lowerCAmelCase    )    )
                  if upper[j][j] == 0:
                        raise ArithmeticError("""No LU decomposition exists"""    )
                  _lowerCAmelCase					=				(table[i][j] - total) / upper[j][j]
            _lowerCAmelCase					=				1
            for j in range(lowerCAmelCase				,      lowerCAmelCase    ):
                  _lowerCAmelCase					=				sum(lower[i][k] * upper[k][j] for k in range(lowerCAmelCase    )    )
                  _lowerCAmelCase					=				table[i][j] - total
      return lower, upper


if __name__ == "__main__":
      import doctest

      doctest.testmod()






