





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					:				List[str]				=			4
_A					:				int				=			3



class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    pass





def 							__snake_case							(    lowerCAmelCase_	)				->						str:




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


def 							__snake_case							(    )				->						List[str]:
    SCREAMING_SNAKE_CASE__											=			int(os.environ['''RANK''']	)
    SCREAMING_SNAKE_CASE__											=			int(os.environ['''WORLD_SIZE''']	)

    SCREAMING_SNAKE_CASE__											=			ArgumentParser()
    parser.add_argument('''--streaming''' ,							type=_UpperCamelCase	)
    parser.add_argument('''--local_rank''' ,							type=_UpperCamelCase	)
    parser.add_argument('''--num_workers''' ,							type=_UpperCamelCase ,							default=0	)
    SCREAMING_SNAKE_CASE__											=			parser.parse_args()
    SCREAMING_SNAKE_CASE__											=			args.streaming
    SCREAMING_SNAKE_CASE__											=			args.num_workers

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

    SCREAMING_SNAKE_CASE__											=			split_dataset_by_node(_UpperCamelCase ,							rank=_UpperCamelCase ,							world_size=_UpperCamelCase	)
    SCREAMING_SNAKE_CASE__											=			torch.utils.data.DataLoader(_UpperCamelCase ,							num_workers=_UpperCamelCase	)

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

    SCREAMING_SNAKE_CASE__											=			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'''
UpperCamelCase__			=      {
    "joule": 1.0,
    "kilojoule": 1000,
    "megajoule": 1000000,
    "gigajoule": 1000000000,
    "wattsecond": 1.0,
    "watthour": 3600,
    "kilowatthour": 3600000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4186.8,
    "kilocalorie_nutr": 4186800.00,
    "electronvolt": 1.602176634e-19,
    "britishthermalunit_it": 1055.05585,
    "footpound": 1.35_58_18,
}




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
      lowercase_						:     Union[str, Any]							     =  (
          F"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
          F"""Valid values are: {", ".join(_UpperCamelCase				)}"""
      )
      raise ValueError(_UpperCamelCase				)
   return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




