


from typing import Callable, Optional

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

class       __magic_name__	(     __lowerCAmelCase):


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





      '''simple docstring'''






      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      UpperCAmelCase__	(      self							:     Optional[int]			)  ->      List[Any]:





      '''simple docstring'''






      if self.streaming:
       UpperCamelCase__					:    Union[str, Any]          =   self.builder.as_streaming_dataset(split='''train'''			)
      # Build regular (map-style) dataset
      else:
       UpperCamelCase__					:    Union[str, Any]          =   None
       UpperCamelCase__					:    str          =   None
       UpperCamelCase__					:    Optional[Any]          =   None
       UpperCamelCase__					:    int          =   None

       self.builder.download_and_prepare(
           download_config=lowerCamelCase__							,	download_mode=lowerCamelCase__							,	verification_mode=lowerCamelCase__							,	base_path=lowerCamelCase__							,	num_proc=self.num_proc							,	)
       UpperCamelCase__					:    List[str]          =   self.builder.as_dataset(
           split='''train'''							,	verification_mode=lowerCamelCase__							,	in_memory=self.keep_in_memory			)
      return dataset






from math import factorial, radians

def 			_a  (				SCREAMING_SNAKE_CASE	:				float    , SCREAMING_SNAKE_CASE	:				int = 18    , SCREAMING_SNAKE_CASE	:				int = 10     ):







 """simple docstring"""



 UpperCamelCase__					:    Dict          =   angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

 # Converting from degrees to radians
 UpperCamelCase__					:    int          =   radians(SCREAMING_SNAKE_CASE     )

 UpperCamelCase__					:    Tuple          =   angle_in_radians
 UpperCamelCase__					:    str          =   3
 UpperCamelCase__					:    int          =   -1

 for _ in range(SCREAMING_SNAKE_CASE     ):
  result += (b * (angle_in_radians**a)) / factorial(SCREAMING_SNAKE_CASE     )

  UpperCamelCase__					:    List[Any]          =   -b  # One positive term and the next will be negative and so on...
  a += 2  # Increased by 2 for every term.

 return round(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       __import__("doctest").testmod()


