


'''simple docstring'''







from __future__ import annotations

from decimal import Decimal

from numpy import array


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	int  )  ->			list[list[float]]:
       _a    : List[str]     					=Decimal

       # Check if the provided matrix has 2 rows and 2 columns
       # since this implementation only works for 2x2 matrices
       if len(__a  ) == 2 and len(matrix[0]  ) == 2 and len(matrix[1]  ) == 2:
              # Calculate the determinant of the matrix
              _a    : Optional[int]     					=float(
                  d(matrix[0][0]  ) * d(matrix[1][1]  ) - d(matrix[1][0]  ) * d(matrix[0][1]  )  )
              if determinant == 0:
                     raise ValueError("""This matrix has no inverse."""  )

              # Creates a copy of the matrix with swapped positions of the elements
              _a    : List[Any]     					=[[0.0, 0.0], [0.0, 0.0]]
              _a    : int     					=matrix[1][1], matrix[0][0]
              _a    : Union[str, Any]     					=-matrix[1][0], -matrix[0][1]

              # Calculate the inverse of the matrix
              return [
                  [(float(d(__a  )  ) / determinant) or 0.0 for n in row] for row in swapped_matrix
              ]
       elif (
           len(__a  ) == 3
           and len(matrix[0]  ) == 3
           and len(matrix[1]  ) == 3
           and len(matrix[2]  ) == 3
       ):
              # Calculate the determinant of the matrix using Sarrus rule
              _a    : Tuple     					=float(
                  (
                      (d(matrix[0][0]  ) * d(matrix[1][1]  ) * d(matrix[2][2]  ))
                      + (d(matrix[0][1]  ) * d(matrix[1][2]  ) * d(matrix[2][0]  ))
                      + (d(matrix[0][2]  ) * d(matrix[1][0]  ) * d(matrix[2][1]  ))
                  )
                  - (
                      (d(matrix[0][2]  ) * d(matrix[1][1]  ) * d(matrix[2][0]  ))
                      + (d(matrix[0][1]  ) * d(matrix[1][0]  ) * d(matrix[2][2]  ))
                      + (d(matrix[0][0]  ) * d(matrix[1][2]  ) * d(matrix[2][1]  ))
                  )  )
              if determinant == 0:
                     raise ValueError("""This matrix has no inverse."""  )

              # Creating cofactor matrix
              _a    : Any     					=[
                  [d(0.0  ), d(0.0  ), d(0.0  )],
                  [d(0.0  ), d(0.0  ), d(0.0  )],
                  [d(0.0  ), d(0.0  ), d(0.0  )],
              ]
              _a    : int     					=(d(matrix[1][1]  ) * d(matrix[2][2]  )) - (
                  d(matrix[1][2]  ) * d(matrix[2][1]  )
              )
              _a    : Union[str, Any]     					=-(
                  (d(matrix[1][0]  ) * d(matrix[2][2]  )) - (d(matrix[1][2]  ) * d(matrix[2][0]  ))
              )
              _a    : Tuple     					=(d(matrix[1][0]  ) * d(matrix[2][1]  )) - (
                  d(matrix[1][1]  ) * d(matrix[2][0]  )
              )
              _a    : Any     					=-(
                  (d(matrix[0][1]  ) * d(matrix[2][2]  )) - (d(matrix[0][2]  ) * d(matrix[2][1]  ))
              )
              _a    : Dict     					=(d(matrix[0][0]  ) * d(matrix[2][2]  )) - (
                  d(matrix[0][2]  ) * d(matrix[2][0]  )
              )
              _a    : Tuple     					=-(
                  (d(matrix[0][0]  ) * d(matrix[2][1]  )) - (d(matrix[0][1]  ) * d(matrix[2][0]  ))
              )
              _a    : List[Any]     					=(d(matrix[0][1]  ) * d(matrix[1][2]  )) - (
                  d(matrix[0][2]  ) * d(matrix[1][1]  )
              )
              _a    : str     					=-(
                  (d(matrix[0][0]  ) * d(matrix[1][2]  )) - (d(matrix[0][2]  ) * d(matrix[1][0]  ))
              )
              _a    : Tuple     					=(d(matrix[0][0]  ) * d(matrix[1][1]  )) - (
                  d(matrix[0][1]  ) * d(matrix[1][0]  )
              )

              # Transpose the cofactor matrix (Adjoint matrix)
              _a    : Optional[int]     					=array(__a  )
              for i in range(3  ):
                     for j in range(3  ):
                            _a    : Optional[int]     					=cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
              _a    : str     					=array(__a  )
              for i in range(3  ):
                     for j in range(3  ):
                            inverse_matrix[i][j] /= d(__a  )

        # Calculate the inverse of the matrix
              return [[float(d(__a  )  ) or 0.0 for n in row] for row in inverse_matrix]
       raise ValueError("""Please provide a matrix of size 2x2 or 3x3."""  )






'''simple docstring'''
from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class    lowercase	(							A__					):

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = False    ,   UpperCamelCase_ = False    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = None    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


							super().__init__(
							    features=UpperCamelCase_    ,   cache_dir=UpperCamelCase_    ,   keep_in_memory=UpperCamelCase_    ,   streaming=UpperCamelCase_    ,   num_proc=UpperCamelCase_    ,   **UpperCamelCase_    ,   )
							UpperCamelCase__   :Any												= Generator(
							    cache_dir=UpperCamelCase_    ,   features=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   gen_kwargs=UpperCamelCase_    ,   **UpperCamelCase_    ,   )




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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

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