
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		(				lowerCAmelCase					):
    def __init__(  self		,snake_case		,snake_case = None		,snake_case = None		,snake_case = True		,snake_case = None		,snake_case = False		,snake_case = None		,snake_case = True		,snake_case = "arrow"		,**snake_case		,):



           '''simple docstring'''







           super().__init__(
               split=snake_case		,features=snake_case		,cache_dir=snake_case		,keep_in_memory=snake_case		,streaming=snake_case		,**snake_case		,)
           lowercase							:					str	     =				load_from_cache_file
           lowercase							:					Optional[int]	     =				file_format
           lowercase							:					Tuple	     =				Spark(
               df=snake_case		,features=snake_case		,cache_dir=snake_case		,working_dir=snake_case		,**snake_case		,)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           if self.streaming:
                  return self.builder.as_streaming_dataset(split=self.split				)
           lowercase							:					Optional[Any]	     =				None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
           self.builder.download_and_prepare(
               download_mode=snake_case		,file_format=self._file_format		,)
           return self.builder.as_dataset(split=self.split				)


from __future__ import annotations

import numpy as np







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   tuple[np.ndarray, np.ndarray]:
       lowercase     ,      lowercase							:					Dict	     =				np.shape(SCREAMING_SNAKE_CASE__    )
       if rows != columns:
              lowercase							:					str	     =				(
                  """'table' has to be of square shaped array but got a """
                  f"{rows}x{columns} array:\n{table}"
              )
              raise ValueError(SCREAMING_SNAKE_CASE__    )

       lowercase							:					Any	     =				np.zeros((rows, columns)    )
       lowercase							:					int	     =				np.zeros((rows, columns)    )
       for i in range(SCREAMING_SNAKE_CASE__    ):
              for j in range(SCREAMING_SNAKE_CASE__    ):
                     lowercase							:					Optional[int]	     =				sum(lower[i][k] * upper[k][j] for k in range(SCREAMING_SNAKE_CASE__    )    )
                     if upper[j][j] == 0:
                            raise ArithmeticError("""No LU decomposition exists"""    )
                     lowercase							:					str	     =				(table[i][j] - total) / upper[j][j]
              lowercase							:					Optional[Any]	     =				1
              for j in range(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ):
                     lowercase							:					Any	     =				sum(lower[i][k] * upper[k][j] for k in range(SCREAMING_SNAKE_CASE__    )    )
                     lowercase							:					Tuple	     =				table[i][j] - total
       return lower, upper


if __name__ == "__main__":
  import doctest

  doctest.testmod()
