class __snake_case    :




   def __init__(   self      :    Dict	,     _lowercase      :    List[Any]	,     _lowercase      :    str	,     _lowercase      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				name
     SCREAMING_SNAKE_CASE__      							=				value
     SCREAMING_SNAKE_CASE__      							=				weight




   def __repr__(   self      :    List[str]				):



     """simple docstring"""






     return f"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     return self.value




   def      __a						(   self      :    Tuple				):



     """simple docstring"""






     return self.name




   def      __a						(   self      :    Tuple				):



     """simple docstring"""






     return self.weight




   def      __a						(   self      :    Tuple				):



     """simple docstring"""






     return self.value / self.weight





def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Any			,					__UpperCamelCase   :	Optional[Any]			,					__UpperCamelCase   :	Dict     )							-> Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[]
  for i in range(len(__UpperCamelCase     )     ):
    menu.append(Things(name[i]			,					value[i]			,					weight[i]     )     )
  return menu



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	str			,					__UpperCamelCase   :	Any			,					__UpperCamelCase   :	Any     )							-> List[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				sorted(__UpperCamelCase			,					key=__UpperCamelCase			,					reverse=__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				[]
  SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				0.0, 0.0
  for i in range(len(__UpperCamelCase     )     ):
    if (total_cost + items_copy[i].get_weight()) <= max_cost:
      result.append(items_copy[i]     )
      total_cost += items_copy[i].get_weight()
      total_value += items_copy[i].get_value()
  return (result, total_value)



def      __SCREAMING_SNAKE_CASE  ( )							-> Any:







  """simple docstring"""







if __name__ == "__main__":
   import doctest

   doctest.testmod()

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      :			int							       =		4
__lowerCamelCase      :			Dict							       =		3







class __snake_case    (						lowerCamelCase_		):
   pass





def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[str]     )							-> List[str]:







  """simple docstring"""





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



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







  """simple docstring"""





  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()
