


def 		_A		(		_lowercase )					-> Optional[int]:


     """simple docstring"""


     __UpperCamelCase =			len(_lowercase )
     __UpperCamelCase =			len(matrix[0] )
     __UpperCamelCase =			min(_lowercase					,  _lowercase )

     for row in range(_lowercase ):
          # Check if diagonal element is not zero
          if matrix[row][row] != 0:
               # Eliminate all the elements below the diagonal
               for col in range(row + 1					,  _lowercase ):
                    __UpperCamelCase =			matrix[col][row] / matrix[row][row]
                    for i in range(_lowercase					,  _lowercase ):
                         matrix[col][i] -= multiplier * matrix[row][i]
          else:
               # Find a non-zero diagonal element to swap rows
               __UpperCamelCase =			True
               for i in range(row + 1					,  _lowercase ):
                    if matrix[i][row] != 0:
                         __UpperCamelCase =			matrix[i], matrix[row]
                         __UpperCamelCase =			False
                         break
               if reduce:
                    rank -= 1
                    for i in range(_lowercase ):
                         __UpperCamelCase =			matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
               row -= 1

     return rank


if __name__ == "__main__":
    import doctest

    doctest.testmod()









"""simple docstring"""


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 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""







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






									'''simple docstring'''







									super().__init__(
									    split=_A						,   features=_A						,   cache_dir=_A						,   keep_in_memory=_A						,   streaming=_A						,   **_A						,   )
									UpperCamelCase				:		Optional[Any]				   =			load_from_cache_file
									UpperCamelCase				:		List[str]				   =			file_format
									UpperCamelCase				:		Optional[Any]				   =			Spark(
									    df=_A						,   features=_A						,   cache_dir=_A						,   working_dir=_A						,   **_A						,   )







			def   _a      (      self    ):






									'''simple docstring'''







									if self.streaming:
															return self.builder.as_streaming_dataset(split=self.split    )
									UpperCamelCase				:		Dict				   =			None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
									self.builder.download_and_prepare(
									    download_mode=_A						,   file_format=self._file_format						,   )
									return self.builder.as_dataset(split=self.split    )



