





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	:					Any			  =					4
__lowerCamelCase	:					Any			  =					3



class  A__       (	__snake_case      ):
      pass



def 		A_    (					_lowerCAmelCase					)						->			str:
 for shard in shards:
  for i in range(_lowerCAmelCase					):
   yield {"i": i, "shard": shard}



def 		A_    (					)						->			Optional[int]:
 UpperCamelCase     : Dict	     =       int(os.environ["RANK"]					)
 UpperCamelCase     : int	     =       int(os.environ["WORLD_SIZE"]					)

 UpperCamelCase     : Tuple	     =       ArgumentParser()
 parser.add_argument("--streaming"							,       type=_lowerCAmelCase					)
 parser.add_argument("--local_rank"							,       type=_lowerCAmelCase					)
 parser.add_argument("--num_workers"							,       type=_lowerCAmelCase							,       default=0					)
 UpperCamelCase     : Union[str, Any]	     =       parser.parse_args()
 UpperCamelCase     : int	     =       args.streaming
 UpperCamelCase     : Tuple	     =       args.num_workers

 UpperCamelCase     : Tuple	     =       {"shards": [F"""shard_{shard_idx}""" for shard_idx in range(_lowerCAmelCase					)]}
 UpperCamelCase     : List[str]	     =       IterableDataset.from_generator(_lowerCAmelCase							,       gen_kwargs=_lowerCAmelCase					)
 if not streaming:
  UpperCamelCase     : List[str]	     =       Dataset.from_list(list(_lowerCAmelCase					)					)

 UpperCamelCase     : List[str]	     =       split_dataset_by_node(_lowerCAmelCase							,       rank=_lowerCAmelCase							,       world_size=_lowerCAmelCase					)
 UpperCamelCase     : Union[str, Any]	     =       torch.utils.data.DataLoader(_lowerCAmelCase							,       num_workers=_lowerCAmelCase					)

 UpperCamelCase     : Union[str, Any]	     =       NUM_SHARDS * NUM_ITEMS_PER_SHARD
 UpperCamelCase     : Optional[int]	     =       full_size // world_size
 expected_local_size += int(rank < (full_size % world_size)					)

 UpperCamelCase     : 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



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			list[str]:
 if partitions <= 0:
  raise ValueError("partitions must be a positive number!"					)
 if partitions > number_of_bytes:
  raise ValueError("partitions can not > number_of_bytes!"					)
 UpperCamelCase     : str	     =       number_of_bytes // partitions
 UpperCamelCase     : List[Any]	     =       []
 for i in range(_lowerCAmelCase					):
  UpperCamelCase     : Optional[Any]	     =       i * bytes_per_partition + 1
  UpperCamelCase     : Any	     =       (
      number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
  )
  allocation_list.append(F"""{start_bytes}-{end_bytes}"""					)
 return allocation_list


if __name__ == "__main__":
    import doctest

    doctest.testmod()
