

from __future__ import annotations







class 				lowercase       :




    def __init__(      self   ,     snake_case ):
           snake_case_           =					TypeError(
               'Matrices must be formed from a list of zero or more lists containing at '
               'least one and the same number of values, each of which must be of type '
               'int or float.' )
           if len(snake_case ) != 0:
                  snake_case_           =					len(rows[0] )
                  if cols == 0:
                         raise error
                  for row in rows:
                         if len(snake_case ) != cols:
                                raise error
                         for value in row:
                                if not isinstance(snake_case   ,     (int, float) ):
                                       raise error
                  snake_case_           =					rows
           else:
                  snake_case_           =					[]




    def 					a							(      self ):
           return [[row[i] for row in self.rows] for i in range(len(self.rows[0] ) )]




    @property
    def 					a							(      self ):
           return len(self.rows )




    @property
    def 					a							(      self ):
           return len(self.rows[0] )




    @property
    def 					a							(      self ):
           return (self.num_rows, self.num_columns)




    @property
    def 					a							(      self ):
           return self.order[0] == self.order[1]




    def 					a							(      self ):
           snake_case_           =					[
               [0 if column_num != row_num else 1 for column_num in range(self.num_rows )]
               for row_num in range(self.num_rows )
           ]
           return Matrix(snake_case )




    def 					a							(      self ):
           if not self.is_square:
                  return 0
           if self.order == (0, 0):
                  return 1
           if self.order == (1, 1):
                  return int(self.rows[0][0] )
           if self.order == (2, 2):
                  return int(
                      (self.rows[0][0] * self.rows[1][1])
                      - (self.rows[0][1] * self.rows[1][0]) )
           else:
                  return sum(
                      self.rows[0][column] * self.cofactors().rows[0][column]
                      for column in range(self.num_columns ) )




    def 					a							(      self ):
           return bool(self.determinant() )




    def 					a							(      self   ,     snake_case   ,     snake_case ):
           snake_case_           =					[
               [
                   self.rows[other_row][other_column]
                   for other_column in range(self.num_columns )
                   if other_column != column
               ]
               for other_row in range(self.num_rows )
               if other_row != row
           ]
           return Matrix(snake_case ).determinant()




    def 					a							(      self   ,     snake_case   ,     snake_case ):
           if (row + column) % 2 == 0:
                  return self.get_minor(snake_case   ,     snake_case )
           return -1 * self.get_minor(snake_case   ,     snake_case )




    def 					a							(      self ):
           return Matrix(
               [
                   [self.get_minor(snake_case   ,     snake_case ) for column in range(self.num_columns )]
                   for row in range(self.num_rows )
               ] )




    def 					a							(      self ):
           return Matrix(
               [
                   [
                       self.minors().rows[row][column]
                       if (row + column) % 2 == 0
                       else self.minors().rows[row][column] * -1
                       for column in range(self.minors().num_columns )
                   ]
                   for row in range(self.minors().num_rows )
               ] )




    def 					a							(      self ):
           snake_case_           =					[
               [self.cofactors().rows[column][row] for column in range(self.num_columns )]
               for row in range(self.num_rows )
           ]
           return Matrix(snake_case )




    def 					a							(      self ):
           snake_case_           =					self.determinant()
           if not determinant:
                  raise TypeError('Only matrices with a non-zero determinant have an inverse' )
           return self.adjugate() * (1 / determinant)




    def __repr__(      self ):
           return str(self.rows )




    def __str__(      self ):
           if self.num_rows == 0:
                  return "[]"
           if self.num_rows == 1:
                  return "[[" + ". ".join(str(self.rows[0] ) ) + "]]"
           return (
               "["
               + "\n ".join(
                   [
                       '[' + '. '.join([str(snake_case ) for value in row] ) + '.]'
                       for row in self.rows
                   ] )
               + "]"
           )




    def 					a							(      self   ,     snake_case   ,     snake_case = None ):
           snake_case_           =					TypeError('Row must be a list containing all ints and/or floats' )
           if not isinstance(snake_case   ,     snake_case ):
                  raise type_error
           for value in row:
                  if not isinstance(snake_case   ,     (int, float) ):
                         raise type_error
           if len(snake_case ) != self.num_columns:
                  raise ValueError(
                      'Row must be equal in length to the other rows in the matrix' )
           if position is None:
                  self.rows.append(snake_case )
           else:
                  snake_case_           =					self.rows[0:position] + [row] + self.rows[position:]




    def 					a							(      self   ,     snake_case   ,     snake_case = None ):
           snake_case_           =					TypeError(
               'Column must be a list containing all ints and/or floats' )
           if not isinstance(snake_case   ,     snake_case ):
                  raise type_error
           for value in column:
                  if not isinstance(snake_case   ,     (int, float) ):
                         raise type_error
           if len(snake_case ) != self.num_rows:
                  raise ValueError(
                      'Column must be equal in length to the other columns in the matrix' )
           if position is None:
                  snake_case_           =					[self.rows[i] + [column[i]] for i in range(self.num_rows )]
           else:
                  snake_case_           =					[
                      self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
                      for i in range(self.num_rows )
                  ]




    def __eq__(      self   ,     snake_case ):
           if not isinstance(snake_case   ,     snake_case ):
                  return NotImplemented
           return self.rows == other.rows




    def __ne__(      self   ,     snake_case ):
           return not self == other




    def __neg__(      self ):
           return self * -1




    def __add__(      self   ,     snake_case ):
           if self.order != other.order:
                  raise ValueError('Addition requires matrices of the same order' )
           return Matrix(
               [
                   [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns )]
                   for i in range(self.num_rows )
               ] )




    def __sub__(      self   ,     snake_case ):
           if self.order != other.order:
                  raise ValueError('Subtraction requires matrices of the same order' )
           return Matrix(
               [
                   [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns )]
                   for i in range(self.num_rows )
               ] )




    def __mul__(      self   ,     snake_case ):
           if isinstance(snake_case   ,     (int, float) ):
                  return Matrix(
                      [[int(element * other ) for element in row] for row in self.rows] )
           elif isinstance(snake_case   ,     snake_case ):
                  if self.num_columns != other.num_rows:
                         raise ValueError(
                             'The number of columns in the first matrix must '
                             'be equal to the number of rows in the second' )
                  return Matrix(
                      [
                          [Matrix.dot_product(snake_case   ,     snake_case ) for column in other.columns()]
                          for row in self.rows
                      ] )
           else:
                  raise TypeError(
                      'A Matrix can only be multiplied by an int, float, or another matrix' )




    def __pow__(      self   ,     snake_case ):
           if not isinstance(snake_case   ,     snake_case ):
                  raise TypeError('A Matrix can only be raised to the power of an int' )
           if not self.is_square:
                  raise ValueError('Only square matrices can be raised to a power' )
           if other == 0:
                  return self.identity()
           if other < 0:
                  if self.is_invertable():
                         return self.inverse() ** (-other)
                  raise ValueError(
                      'Only invertable matrices can be raised to a negative power' )
           snake_case_           =					self
           for _ in range(other - 1 ):
                  result *= self
           return result




    @classmethod
    def 					a							(      cls   ,     snake_case   ,     snake_case ):
           return sum(row[i] * column[i] for i in range(len(snake_case ) ) )


if __name__ == "__main__":
     import doctest

     doctest.testmod()







# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel  # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    """stable diffusion controlnet""",
    """0.22.0""",
    """Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.""",
    standard_warn=False,
    stacklevel=3,
)




