






'''simple docstring'''





from __future__ import annotations
class 				lowercase       :


    '''simple docstring'''

    def __init__(					self     :     str				,     __lowerCamelCase     :     list[list[int]]     )							->							Any:

     '''simple docstring'''




     lowerCamelCase__  =     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(__lowerCamelCase     ) != 0:
      lowerCamelCase__  =     len(rows[0]     )
      if cols == 0:
       raise error
      for row in rows:
       if len(__lowerCamelCase     ) != cols:
        raise error
       for value in row:
        if not isinstance(__lowerCamelCase				,     (int, float)     ):
         raise error
      lowerCamelCase__  =     rows
     else:
      lowerCamelCase__  =     []

    def    a__				(					self     :     Tuple     )							->							list[list[int]]:

     '''simple docstring'''




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

    @property
    def    a__				(					self     :     Any     )							->							int:

     '''simple docstring'''




     return len(self.rows     )

    @property
    def    a__				(					self     :     Optional[Any]     )							->							int:

     '''simple docstring'''




     return len(self.rows[0]     )

    @property
    def    a__				(					self     :     List[str]     )							->							tuple[int, int]:

     '''simple docstring'''




     return (self.num_rows, self.num_columns)

    @property
    def    a__				(					self     :     Optional[Any]     )							->							bool:

     '''simple docstring'''




     return self.order[0] == self.order[1]

    def    a__				(					self     :     Optional[Any]     )							->							Matrix:

     '''simple docstring'''




     lowerCamelCase__  =     [
         [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(__lowerCamelCase     )

    def    a__				(					self     :     str     )							->							int:

     '''simple docstring'''




     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     :     Dict     )							->							bool:

     '''simple docstring'''




     return bool(self.determinant()     )

    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     int     )							->							int:

     '''simple docstring'''




     lowerCamelCase__  =     [
         [
             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(__lowerCamelCase     ).determinant()

    def    a__				(					self     :     List[str]				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     int     )							->							int:

     '''simple docstring'''




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

    def    a__				(					self     :     str     )							->							Matrix:

     '''simple docstring'''




     return Matrix(
         [
             [self.get_minor(__lowerCamelCase				,     __lowerCamelCase     ) for column in range(self.num_columns     )]
             for row in range(self.num_rows     )
         ]     )

    def    a__				(					self     :     Dict     )							->							Matrix:

     '''simple docstring'''




     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     :     Dict     )							->							Matrix:

     '''simple docstring'''




     lowerCamelCase__  =     [
         [self.cofactors().rows[column][row] for column in range(self.num_columns     )]
         for row in range(self.num_rows     )
     ]
     return Matrix(__lowerCamelCase     )

    def    a__				(					self     :     str     )							->							Matrix:

     '''simple docstring'''




     lowerCamelCase__  =     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     :     Dict     )							->							str:

     '''simple docstring'''




     return str(self.rows     )

    def __str__(					self     :     str     )							->							str:

     '''simple docstring'''




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

    def    a__				(					self     :     str				,     __lowerCamelCase     :     list[int]				,     __lowerCamelCase     :     int | None = None     )							->							None:

     '''simple docstring'''




     lowerCamelCase__  =     TypeError("Row must be a list containing all ints and/or floats"     )
     if not isinstance(__lowerCamelCase				,     __lowerCamelCase     ):
      raise type_error
     for value in row:
      if not isinstance(__lowerCamelCase				,     (int, float)     ):
       raise type_error
     if len(__lowerCamelCase     ) != 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(__lowerCamelCase     )
     else:
      lowerCamelCase__  =     self.rows[0:position] + [row] + self.rows[position:]

    def    a__				(					self     :     str				,     __lowerCamelCase     :     list[int]				,     __lowerCamelCase     :     int | None = None     )							->							None:

     '''simple docstring'''




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

    def __eq__(					self     :     Tuple				,     __lowerCamelCase     :     object     )							->							bool:

     '''simple docstring'''




     if not isinstance(__lowerCamelCase				,     __lowerCamelCase     ):
      return NotImplemented
     return self.rows == other.rows

    def __ne__(					self     :     List[Any]				,     __lowerCamelCase     :     object     )							->							bool:

     '''simple docstring'''




     return not self == other

    def __neg__(					self     :     Dict     )							->							Matrix:

     '''simple docstring'''




     return self * -1

    def __add__(					self     :     str				,     __lowerCamelCase     :     Matrix     )							->							Matrix:

     '''simple docstring'''




     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     :     Any				,     __lowerCamelCase     :     Matrix     )							->							Matrix:

     '''simple docstring'''




     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     :     Optional[Any]				,     __lowerCamelCase     :     Matrix | int | float     )							->							Matrix:

     '''simple docstring'''




     if isinstance(__lowerCamelCase				,     (int, float)     ):
      return Matrix(
          [[int(element * other     ) for element in row] for row in self.rows]     )
     elif isinstance(__lowerCamelCase				,     __lowerCamelCase     ):
      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(__lowerCamelCase				,     __lowerCamelCase     ) 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     :     Optional[Any]				,     __lowerCamelCase     :     int     )							->							Matrix:

     '''simple docstring'''




     if not isinstance(__lowerCamelCase				,     __lowerCamelCase     ):
      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"     )
     lowerCamelCase__  =     self
     for _ in range(other - 1     ):
      result *= self
     return result




    @classmethod
    def    a__				(					cls     :     Any				,     __lowerCamelCase     :     list[int]				,     __lowerCamelCase     :     list[int]     )							->							int:

     '''simple docstring'''




     return sum(row[i] * column[i] for i in range(len(__lowerCamelCase     )     )     )


if __name__ == "__main__":
 import doctest

 doctest.testmod()









'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A							:							Tuple  							=		{
    """configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
    """tokenization_deberta""": ["""DebertaTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							int  							=		["""DebertaTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							str  							=		[
     """DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """DebertaForMaskedLM""",
     """DebertaForQuestionAnswering""",
     """DebertaForSequenceClassification""",
     """DebertaForTokenClassification""",
     """DebertaModel""",
     """DebertaPreTrainedModel""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							Union[str, Any]  							=		[
     """TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFDebertaForMaskedLM""",
     """TFDebertaForQuestionAnswering""",
     """TFDebertaForSequenceClassification""",
     """TFDebertaForTokenClassification""",
     """TFDebertaModel""",
     """TFDebertaPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
 from .tokenization_deberta import DebertaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_deberta_fast import DebertaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deberta import (
      DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      DebertaForMaskedLM,
      DebertaForQuestionAnswering,
      DebertaForSequenceClassification,
      DebertaForTokenClassification,
      DebertaModel,
      DebertaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deberta import (
      TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDebertaForMaskedLM,
      TFDebertaForQuestionAnswering,
      TFDebertaForSequenceClassification,
      TFDebertaForTokenClassification,
      TFDebertaModel,
      TFDebertaPreTrainedModel,
  )


else:
 import sys

 __A							:							List[Any]  							=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

