





from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):


			def __init__(   self      :    List[str]      ,					a      :    Callable      ,					a      :    Optional[Features] = None      ,					a      :    str = None      ,					a      :    bool = False      ,					a      :    bool = False      ,					a      :    Optional[dict] = None      ,					a      :    Optional[int] = None      ,					**a      :    str      ,					)->       Tuple:





					"""simple docstring"""







					super().__init__(
					    features=a      ,					cache_dir=a      ,					keep_in_memory=a      ,					streaming=a      ,					num_proc=a      ,					**a      ,					)
					lowercase__           =  Generator(
					    cache_dir=a      ,					features=a      ,					generator=a      ,					gen_kwargs=a      ,					**a      ,					)


			def 	SCREAMING_SNAKE_CASE_							(   self      :    Any	)->       Dict:





					"""simple docstring"""







					if self.streaming:
							lowercase__           =  self.builder.as_streaming_dataset(split='train'	)
					# Build regular (map-style) dataset
					else:
							lowercase__           =  None
							lowercase__           =  None
							lowercase__           =  None
							lowercase__           =  None

							self.builder.download_and_prepare(
							    download_config=a      ,					download_mode=a      ,					verification_mode=a      ,					base_path=a      ,					num_proc=self.num_proc      ,					)
							lowercase__           =  self.builder.as_dataset(
							    split='train'      ,					verification_mode=a      ,					in_memory=self.keep_in_memory	)
					return dataset









from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase				)
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		str			 =	field(default='language-modeling'      ,     metadata={'include_in_asdict_even_if_is_default': True}				)

   _UpperCamelCase   :		ClassVar[Features]			 =	Features({'text': Value('string'				)}				)
   _UpperCamelCase   :		ClassVar[Features]			 =	Features({}				)
   _UpperCamelCase   :		str			 =	"text"


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       Dict[str, str]:





     """simple docstring"""







     return {self.text_column: "text"}


