
'''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


UpperCAmelCase_       :   Dict   =       4
UpperCAmelCase_       :   List[str]   =       3




class  UpperCAmelCase__						(       A ):
    pass







def 		A_       (	_lowerCAmelCase   :    List[str]							):



      """simple docstring"""




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







def 		A_       (	):



      """simple docstring"""




      _lowerCamelCase				:   str								=   int(os.environ["RANK"]							)
      _lowerCamelCase				:   List[str]								=   int(os.environ["WORLD_SIZE"]							)

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

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

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

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

      _lowerCamelCase				:   Union[str, Any]								=   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'''






def 		A_       (	_lowerCAmelCase   :    int							):



      """simple docstring"""




      if isinstance(_lowerCAmelCase				,	_lowerCAmelCase							):
            raise TypeError("'float' object cannot be interpreted as an integer"							)
      if isinstance(_lowerCAmelCase				,	_lowerCAmelCase							):
            raise TypeError("'str' object cannot be interpreted as an integer"							)

      if num == 0:
            return "0b0"

      _lowerCamelCase				:   Tuple								=   False

      if num < 0:
            _lowerCamelCase				:   List[Any]								=   True
            _lowerCamelCase				:   int								=   -num

      _lowerCamelCase				:   list[int]								=   []
      while num > 0:
            binary.insert(0				,	num % 2							)
            num >>= 1

      if negative:
            return "-0b" + "".join(str(_lowerCAmelCase							) for e in binary							)

      return "0b" + "".join(str(_lowerCAmelCase							) for e in binary							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()