





from __future__ import annotations







class 			__A       :




      """simple docstring"""







      def __init__(		self      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     Optional[int]	=		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:
                    _lowerCamelCase							:     str	=		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
                    _lowerCamelCase							:     Optional[int]	=		rows
             else:
                    _lowerCamelCase							:     List[str]	=		[]


      def 				__snake_case			(		self):



             """simple docstring"""





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


      @property
      def 				__snake_case			(		self):



             """simple docstring"""





             return len(self.rows)


      @property
      def 				__snake_case			(		self):



             """simple docstring"""





             return len(self.rows[0])


      @property
      def 				__snake_case			(		self):



             """simple docstring"""





             return (self.num_rows, self.num_columns)


      @property
      def 				__snake_case			(		self):



             """simple docstring"""





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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     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):



             """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 				__snake_case			(		self):



             """simple docstring"""





             return bool(self.determinant())


      def 				__snake_case			(		self      ,							a__      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     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      ,							a__      ,							a__):



             """simple docstring"""





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


      def 				__snake_case			(		self):



             """simple docstring"""





             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):



             """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 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     str	=		[
                 [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):



             """simple docstring"""





             _lowerCamelCase							:     Optional[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):



             """simple docstring"""





             return str(self.rows)


      def __str__(		self):



             """simple docstring"""





             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      ,							a__      ,							a__ = None):



             """simple docstring"""





             _lowerCamelCase							:     List[Any]	=		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:
                    _lowerCamelCase							:     Tuple	=		self.rows[0:position] + [row] + self.rows[position:]


      def 				__snake_case			(		self      ,							a__      ,							a__ = None):



             """simple docstring"""





             _lowerCamelCase							:     Tuple	=		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:
                    _lowerCamelCase							:     Optional[Any]	=		[self.rows[i] + [column[i]] for i in range(self.num_rows)]
             else:
                    _lowerCamelCase							:     Optional[Any]	=		[
                        self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
                        for i in range(self.num_rows)
                    ]


      def __eq__(		self      ,							a__):



             """simple docstring"""





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


      def __ne__(		self      ,							a__):



             """simple docstring"""





             return not self == other


      def __neg__(		self):



             """simple docstring"""





             return self * -1


      def __add__(		self      ,							a__):



             """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      ,							a__):



             """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      ,							a__):



             """simple docstring"""





             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      ,							a__):



             """simple docstring"""





             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''')
             _lowerCamelCase							:     List[str]	=		self
             for _ in range(other - 1):
                    result *= self
             return result



      @classmethod
      def 				__snake_case			(		cls      ,							a__      ,							a__):



             """simple docstring"""





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


if __name__ == "__main__":
       import doctest

       doctest.testmod()








from typing import TYPE_CHECKING

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


_lowerCamelCase			       =      {
    'configuration_roberta_prelayernorm': [
        'ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'RobertaPreLayerNormConfig',
        'RobertaPreLayerNormOnnxConfig',
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
           'RobertaPreLayerNormForCausalLM',
           'RobertaPreLayerNormForMaskedLM',
           'RobertaPreLayerNormForMultipleChoice',
           'RobertaPreLayerNormForQuestionAnswering',
           'RobertaPreLayerNormForSequenceClassification',
           'RobertaPreLayerNormForTokenClassification',
           'RobertaPreLayerNormModel',
           'RobertaPreLayerNormPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFRobertaPreLayerNormForCausalLM',
           'TFRobertaPreLayerNormForMaskedLM',
           'TFRobertaPreLayerNormForMultipleChoice',
           'TFRobertaPreLayerNormForQuestionAnswering',
           'TFRobertaPreLayerNormForSequenceClassification',
           'TFRobertaPreLayerNormForTokenClassification',
           'TFRobertaPreLayerNormMainLayer',
           'TFRobertaPreLayerNormModel',
           'TFRobertaPreLayerNormPreTrainedModel',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'FlaxRobertaPreLayerNormForCausalLM',
           'FlaxRobertaPreLayerNormForMaskedLM',
           'FlaxRobertaPreLayerNormForMultipleChoice',
           'FlaxRobertaPreLayerNormForQuestionAnswering',
           'FlaxRobertaPreLayerNormForSequenceClassification',
           'FlaxRobertaPreLayerNormForTokenClassification',
           'FlaxRobertaPreLayerNormModel',
           'FlaxRobertaPreLayerNormPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_roberta_prelayernorm import (
           ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
           RobertaPreLayerNormConfig,
           RobertaPreLayerNormOnnxConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roberta_prelayernorm import (
                  ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RobertaPreLayerNormForCausalLM,
                  RobertaPreLayerNormForMaskedLM,
                  RobertaPreLayerNormForMultipleChoice,
                  RobertaPreLayerNormForQuestionAnswering,
                  RobertaPreLayerNormForSequenceClassification,
                  RobertaPreLayerNormForTokenClassification,
                  RobertaPreLayerNormModel,
                  RobertaPreLayerNormPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roberta_prelayernorm import (
                  TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRobertaPreLayerNormForCausalLM,
                  TFRobertaPreLayerNormForMaskedLM,
                  TFRobertaPreLayerNormForMultipleChoice,
                  TFRobertaPreLayerNormForQuestionAnswering,
                  TFRobertaPreLayerNormForSequenceClassification,
                  TFRobertaPreLayerNormForTokenClassification,
                  TFRobertaPreLayerNormMainLayer,
                  TFRobertaPreLayerNormModel,
                  TFRobertaPreLayerNormPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roberta_prelayernorm import (
                  FlaxRobertaPreLayerNormForCausalLM,
                  FlaxRobertaPreLayerNormForMaskedLM,
                  FlaxRobertaPreLayerNormForMultipleChoice,
                  FlaxRobertaPreLayerNormForQuestionAnswering,
                  FlaxRobertaPreLayerNormForSequenceClassification,
                  FlaxRobertaPreLayerNormForTokenClassification,
                  FlaxRobertaPreLayerNormModel,
                  FlaxRobertaPreLayerNormPreTrainedModel,
              )

else:
       import sys

       _lowerCamelCase			       =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

