'''simple docstring'''




from __future__ import annotations






class 		__A       :






       def __init__(      self				,     UpperCamelCase_      ):
         __UpperCAmelCase  :				List[Any]       				=		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(__A      ) != 0:
           __UpperCAmelCase  :				Optional[Any]       				=		len(rows[0]      )
           if cols == 0:
             raise error
           for row in rows:
             if len(__A      ) != cols:
               raise error
             for value in row:
               if not isinstance(__A				,     (int, float)      ):
                 raise error
           __UpperCAmelCase  :				Union[str, Any]       				=		rows
         else:
           __UpperCAmelCase  :				List[str]       				=		[]






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






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






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






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






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






       def 			_snake_case						(      self      ):
         __UpperCAmelCase  :				Union[str, Any]       				=		[
             [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(__A      )






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






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_      ):
         __UpperCAmelCase  :				Any       				=		[
             [
                 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(__A      ).determinant()






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






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






       def 			_snake_case						(      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 			_snake_case						(      self      ):
         __UpperCAmelCase  :				Optional[int]       				=		[
             [self.cofactors().rows[column][row] for column in range(self.num_columns      )]
             for row in range(self.num_rows      )
         ]
         return Matrix(__A      )






       def 			_snake_case						(      self      ):
         __UpperCAmelCase  :				Any       				=		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(__A      ) for value in row]      ) + ".]"
                     for row in self.rows
                 ]      )
             + "]"
         )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):
         __UpperCAmelCase  :				Tuple       				=		TypeError("Row must be a list containing all ints and/or floats"      )
         if not isinstance(__A				,     __A      ):
           raise type_error
         for value in row:
           if not isinstance(__A				,     (int, float)      ):
             raise type_error
         if len(__A      ) != 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(__A      )
         else:
           __UpperCAmelCase  :				str       				=		self.rows[0:position] + [row] + self.rows[position:]






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






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






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






       def __neg__(      self      ):
         return self * -1






       def __add__(      self				,     UpperCamelCase_      ):
         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				,     UpperCamelCase_      ):
         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				,     UpperCamelCase_      ):
         if isinstance(__A				,     (int, float)      ):
           return Matrix(
               [[int(element * other      ) for element in row] for row in self.rows]      )
         elif isinstance(__A				,     __A      ):
           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(__A				,     __A      ) 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				,     UpperCamelCase_      ):
         if not isinstance(__A				,     __A      ):
           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"      )
         __UpperCAmelCase  :				Optional[Any]       				=		self
         for _ in range(other - 1      ):
           result *= self
         return result







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


if __name__ == "__main__":
   import doctest

   doctest.testmod()







'''simple docstring'''




from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments

def _lowercase	(      )					->       Dict:





       """simple docstring"""


       __UpperCAmelCase  :				str       				=		HfArgumentParser(lowerCamelCase__						)
       __UpperCAmelCase  :				Optional[Any]       				=		parser.parse_args_into_dataclasses()[0]
       __UpperCAmelCase  :				Any       				=		TensorFlowBenchmark(args=lowerCamelCase__						)
       try:
              __UpperCAmelCase  :				List[Any]       				=		parser.parse_args_into_dataclasses()[0]
       except ValueError as e:
              __UpperCAmelCase  :				str       				=		"Arg --no_{0} is no longer used, please use --no-{0} instead."
              __UpperCAmelCase  :				Tuple       				=		" ".join(str(lowerCamelCase__						).split(" "						)[:-1]						)
              __UpperCAmelCase  :				Any       				=		""
              __UpperCAmelCase  :				List[Any]       				=		eval(str(lowerCamelCase__						).split(" "						)[-1]						)
              __UpperCAmelCase  :				Optional[int]       				=		[]
              for arg in depreciated_args:
                     # arg[2:] removes '--'
                     if arg[2:] in TensorFlowBenchmark.deprecated_args:
                            # arg[5:] removes '--no_'
                            full_error_msg += arg_error_msg.format(arg[5:]						)
                     else:
                            wrong_args.append(lowerCamelCase__						)
              if len(lowerCamelCase__						) > 0:
                     __UpperCAmelCase  :				Union[str, Any]       				=		full_error_msg + begin_error_msg + str(lowerCamelCase__						)
              raise ValueError(lowerCamelCase__						)
       benchmark.run()


if __name__ == "__main__":
   main()






