

from decimal import Decimal, getcontext
from math import ceil, factorial


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):

						if not isinstance(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
												raise TypeError('''Undefined for non-integers'''       )
						elif precision < 1:
												raise ValueError('''Undefined for non-natural numbers'''       )

						snake_case_   =	precision
						snake_case_   =	ceil(precision / 14       )
						snake_case_   =	426880 * Decimal(10005       ).sqrt()
						snake_case_   =	1
						snake_case_   =	13591409
						snake_case_   =	Decimal(SCREAMING_SNAKE_CASE__       )
						for k in range(1  ,		SCREAMING_SNAKE_CASE__       ):
												snake_case_   =	factorial(6 * k       ) // (factorial(3 * k       ) * factorial(SCREAMING_SNAKE_CASE__       ) ** 3)
												linear_term += 545140134
												exponential_term *= -262537412640768000
												partial_sum += Decimal(multinomial_term * linear_term       ) / exponential_term
						return str(constant_term / partial_sum       )[:-1]


if __name__ == "__main__":
					lowerCAmelCase_       	=	50
					print(f"""The first {n} digits of pi is: {pi(n)}""")


from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader





class 	snake_case_   (		__A					):







			'''simple docstring'''

			def __init__(     self :				int							,   _UpperCamelCase :				pyspark.sql.DataFrame							,   _UpperCamelCase :				Optional[NamedSplit] = None							,   _UpperCamelCase :				Optional[Features] = None							,   _UpperCamelCase :				bool = True							,   _UpperCamelCase :				str = None							,   _UpperCamelCase :				bool = False							,   _UpperCamelCase :				str = None							,   _UpperCamelCase :				bool = True							,   _UpperCamelCase :				str = "arrow"							,   **_UpperCamelCase :				Tuple							,   )      ->str:
									super().__init__(
									    split=_UpperCamelCase							,   features=_UpperCamelCase							,   cache_dir=_UpperCamelCase							,   keep_in_memory=_UpperCamelCase							,   streaming=_UpperCamelCase							,   **_UpperCamelCase							,   )
									snake_case_   =	load_from_cache_file
									snake_case_   =	file_format
									snake_case_   =	Spark(
									    df=_UpperCamelCase							,   features=_UpperCamelCase							,   cache_dir=_UpperCamelCase							,   working_dir=_UpperCamelCase							,   **_UpperCamelCase							,   )







			def 				snake_case__(     self :				int							)      ->Tuple:
									if self.streaming:
															return self.builder.as_streaming_dataset(split=self.split							)
									snake_case_   =	None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
									self.builder.download_and_prepare(
									    download_mode=_UpperCamelCase							,   file_format=self._file_format							,   )
									return self.builder.as_dataset(split=self.split							)