






import numpy as np

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  np.array					)		->			np.array:






	"""simple docstring"""



	return 1 / (1 + np.exp(-vector					))


if __name__ == "__main__":
	import doctest

	doctest.testmod()








from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream

class     _SCREAMING_SNAKE_CASE       (			_a      ):





	def __init__(		self				:					List[Any] ,  __lowerCamelCase				:					Callable ,  __lowerCamelCase				:					Optional[Features] = None ,  __lowerCamelCase				:					str = None ,  __lowerCamelCase				:					bool = False ,  __lowerCamelCase				:					bool = False ,  __lowerCamelCase				:					Optional[dict] = None ,  __lowerCamelCase				:					Optional[int] = None ,  **__lowerCamelCase				:					List[Any] ,  ):
		super().__init__(
		    features=__lowerCamelCase ,  cache_dir=__lowerCamelCase ,  keep_in_memory=__lowerCamelCase ,  streaming=__lowerCamelCase ,  num_proc=__lowerCamelCase ,  **__lowerCamelCase ,  )
		UpperCamelCase     :Union[str, Any]          =	Generator(
		    cache_dir=__lowerCamelCase ,  features=__lowerCamelCase ,  generator=__lowerCamelCase ,  gen_kwargs=__lowerCamelCase ,  **__lowerCamelCase ,  )





	def 						_A (		self				:					List[str]	):
		# Build iterable dataset
		if self.streaming:
			UpperCamelCase     :Any          =	self.builder.as_streaming_dataset(split="""train"""	)
		# Build regular (map-style) dataset
		else:
			UpperCamelCase     :Tuple          =	None
			UpperCamelCase     :Dict          =	None
			UpperCamelCase     :Dict          =	None
			UpperCamelCase     :List[str]          =	None

			self.builder.download_and_prepare(
			    download_config=__lowerCamelCase ,  download_mode=__lowerCamelCase ,  verification_mode=__lowerCamelCase ,  base_path=__lowerCamelCase ,  num_proc=self.num_proc ,  )
			UpperCamelCase     :Tuple          =	self.builder.as_dataset(
			    split="""train""" ,  verification_mode=__lowerCamelCase ,  in_memory=self.keep_in_memory	)
		return dataset
