





"""simple docstring"""


from typing import TypeVar

from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset


A_     :      List[Any]								=TypeVar("""DatasetType""", Dataset, IterableDataset)





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Optional[int]						,  snake_case   :					int						,  snake_case   :					int		)->		Tuple:
       if isinstance(__lowerCamelCase						,  __lowerCamelCase		):
              return _split_by_node_map_style_dataset(__lowerCamelCase						,  rank=__lowerCamelCase						,  world_size=__lowerCamelCase		)
       else:
              return _split_by_node_iterable_dataset(__lowerCamelCase						,  rank=__lowerCamelCase						,  world_size=__lowerCamelCase		)







from dataclasses import dataclass
from typing import Callable, Optional

import datasets


@dataclass
class 						a__							(   datasets.BuilderConfig		):
			A__				:   Optional[Callable]								=			None
			A__				:   Optional[dict]								=			None
			A__				:   Optional[datasets.Features]								=			None



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Any:
								assert self.generator is not None, "generator must be specified"

								if self.gen_kwargs is None:
													__a							=   {}






class 						a__							(   datasets.GeneratorBasedBuilder		):
			A__				:   List[Any]								=			GeneratorConfig



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Tuple:
								return datasets.DatasetInfo(features=self.config.features    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    Optional[int]:
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,						gen_kwargs=self.config.gen_kwargs    )]







			def __SCREAMING_SNAKE_CASE							(						self      ,						**UpperCAmelCase    ) ->    Tuple:
								for idx, ex in enumerate(self.config.generator(**UpperCAmelCase    )    ):
													yield idx, ex




