

"""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


SCREAMING_SNAKE_CASE_					:	List[Any]							  =						4
SCREAMING_SNAKE_CASE_					:	Tuple							  =						3



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    pass





def  _snake_case     (    UpperCAmelCase_  :					List[str]			):
     for shard in shards:
          for i in range(UpperCAmelCase_			):
               yield {"i": i, "shard": shard}





def  _snake_case     (    ):
     A__										=  int(os.environ["""RANK"""]			)
     A__										=  int(os.environ["""WORLD_SIZE"""]			)

     A__										=  ArgumentParser()
     parser.add_argument("""--streaming"""					,   type=UpperCAmelCase_			)
     parser.add_argument("""--local_rank"""					,   type=UpperCAmelCase_			)
     parser.add_argument("""--num_workers"""					,   type=UpperCAmelCase_					,   default=0			)
     A__										=  parser.parse_args()
     A__										=  args.streaming
     A__										=  args.num_workers

     A__										=  {"""shards""": [F"""shard_{shard_idx}""" for shard_idx in range(UpperCAmelCase_			)]}
     A__										=  IterableDataset.from_generator(UpperCAmelCase_					,   gen_kwargs=UpperCAmelCase_			)
     if not streaming:
          A__										=  Dataset.from_list(list(UpperCAmelCase_			)			)

     A__										=  split_dataset_by_node(UpperCAmelCase_					,   rank=UpperCAmelCase_					,   world_size=UpperCAmelCase_			)
     A__										=  torch.utils.data.DataLoader(UpperCAmelCase_					,   num_workers=UpperCAmelCase_			)

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

     A__										=  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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_					:	Any							  =						{
    'configuration_jukebox': [
        'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'JukeboxConfig',
        'JukeboxPriorConfig',
        'JukeboxVQVAEConfig',
    ],
    'tokenization_jukebox': ['JukeboxTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						[
     'JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST',
     'JukeboxModel',
     'JukeboxPreTrainedModel',
     'JukeboxVQVAE',
     'JukeboxPrior',
 ]

if TYPE_CHECKING:
 from .configuration_jukebox import (
     JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
     JukeboxConfig,
     JukeboxPriorConfig,
     JukeboxVQVAEConfig,
 )
 from .tokenization_jukebox import JukeboxTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_jukebox import (
      JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
      JukeboxModel,
      JukeboxPreTrainedModel,
      JukeboxPrior,
      JukeboxVQVAE,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






