





from __future__ import annotations

from typing import Any





class UpperCAmelCase_				:


  def __init__(							self ,__snake_case ,__snake_case ,__snake_case = 0 ):



       """simple docstring"""





       A_   ,    A_          =       row, column
       A_          =       [[default_value for c in range(__snake_case )] for r in range(__snake_case )]


  def __str__(							self ):



       """simple docstring"""





       A_          =       f'Matrix consist of {self.row} rows and {self.column} columns\n'

       # Make string identifier
       A_          =       0
       for row_vector in self.array:
            for obj in row_vector:
                 A_          =       max(__snake_case ,len(str(__snake_case ) ) )
       A_          =       f'%{max_element_length}s'

       # Make string and return
       def single_line(__snake_case ) -> str:
            nonlocal string_format_identifier
            A_          =       '''['''
            line += ", ".join(string_format_identifier % (obj,) for obj in row_vector )
            line += "]"
            return line

       s += "\n".join(single_line(__snake_case ) for row_vector in self.array )
       return s


  def __repr__(							self ):



       """simple docstring"""





       return str(self )


  def 						__UpperCAmelCase     (							self ,__snake_case ):



       """simple docstring"""





       if not (isinstance(__snake_case ,(list, tuple) ) and len(__snake_case ) == 2):
            return False
       elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
            return False
       else:
            return True


  def __getitem__(							self ,__snake_case ):



       """simple docstring"""





       assert self.validate_indicies(__snake_case )
       return self.array[loc[0]][loc[1]]


  def __setitem__(							self ,__snake_case ,__snake_case ):



       """simple docstring"""





       assert self.validate_indicies(__snake_case )
       A_          =       value


  def __add__(							self ,__snake_case ):



       """simple docstring"""





       assert isinstance(__snake_case ,__snake_case )
       assert self.row == another.row and self.column == another.column

       # Add
       A_          =       Matrix(self.row ,self.column )
       for r in range(self.row ):
            for c in range(self.column ):
                 A_          =       self[r, c] + another[r, c]
       return result


  def __neg__(							self ):



       """simple docstring"""





       A_          =       Matrix(self.row ,self.column )
       for r in range(self.row ):
            for c in range(self.column ):
                 A_          =       -self[r, c]
       return result


  def __sub__(							self ,__snake_case ):



       """simple docstring"""





       return self + (-another)


  def __mul__(							self ,__snake_case ):



       """simple docstring"""





       if isinstance(__snake_case ,(int, float) ):  # Scalar multiplication
            A_          =       Matrix(self.row ,self.column )
            for r in range(self.row ):
                 for c in range(self.column ):
                      A_          =       self[r, c] * another
            return result
       elif isinstance(__snake_case ,__snake_case ):  # Matrix multiplication
            assert self.column == another.row
            A_          =       Matrix(self.row ,another.column )
            for r in range(self.row ):
                 for c in range(another.column ):
                      for i in range(self.column ):
                           result[r, c] += self[r, i] * another[i, c]
            return result
       else:
            A_          =       f'Unsupported type given for another ({type(__snake_case )})'
            raise TypeError(__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       Matrix(self.column ,self.row )
       for r in range(self.row ):
            for c in range(self.column ):
                 A_          =       self[r, c]
       return result




  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ):



       """simple docstring"""





       assert isinstance(__snake_case ,__snake_case ) and isinstance(__snake_case ,__snake_case )
       assert self.row == self.column == u.row == v.row  # u, v should be column vector
       assert u.column == v.column == 1  # u, v should be column vector

       # Calculate
       A_          =       v.transpose()
       A_          =       (v_t * self * u)[0, 0] + 1
       if numerator_factor == 0:
            return None  # It's not invertable
       return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":

      def      UpperCAmelCase_    (    )	->				None:
           '''simple docstring'''


           A_          =       Matrix(3  ,						3  ,						0				)
           for i in range(3				):
                A_          =       1
           print(f'a^(-1) is {ainv}'				)
           # u, v
           A_          =       Matrix(3  ,						1  ,						0				)
           A_   ,    A_   ,    A_          =       1, 2, -3
           A_          =       Matrix(3  ,						1  ,						0				)
           A_   ,    A_   ,    A_          =       4, -2, 5
           print(f'u is {u}'				)
           print(f'v is {v}'				)
           print(f'uv^T is {u * v.transpose()}'				)
           # Sherman Morrison
           print(f'(a + uv^T)^(-1) is {ainv.sherman_morrison(_UpperCAmelCase  ,						_UpperCAmelCase				)}'				)

      def      UpperCAmelCase_    (    )	->				None:
           '''simple docstring'''


           import doctest

           doctest.testmod()


      testa()










from typing import TYPE_CHECKING

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


a__      :     str         =		{
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__      :     List[Any]         =		['RoFormerTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__      :     List[str]         =		[
          'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'RoFormerForCausalLM',
          'RoFormerForMaskedLM',
          'RoFormerForMultipleChoice',
          'RoFormerForQuestionAnswering',
          'RoFormerForSequenceClassification',
          'RoFormerForTokenClassification',
          'RoFormerLayer',
          'RoFormerModel',
          'RoFormerPreTrainedModel',
          'load_tf_weights_in_roformer',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__      :     List[Any]         =		[
          'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFRoFormerForCausalLM',
          'TFRoFormerForMaskedLM',
          'TFRoFormerForMultipleChoice',
          'TFRoFormerForQuestionAnswering',
          'TFRoFormerForSequenceClassification',
          'TFRoFormerForTokenClassification',
          'TFRoFormerLayer',
          'TFRoFormerModel',
          'TFRoFormerPreTrainedModel',
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__      :     Dict         =		[
          'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'FlaxRoFormerForMaskedLM',
          'FlaxRoFormerForMultipleChoice',
          'FlaxRoFormerForQuestionAnswering',
          'FlaxRoFormerForSequenceClassification',
          'FlaxRoFormerForTokenClassification',
          'FlaxRoFormerModel',
          'FlaxRoFormerPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
      from .tokenization_roformer import RoFormerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roformer_fast import RoFormerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roformer import (
                ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                RoFormerForCausalLM,
                RoFormerForMaskedLM,
                RoFormerForMultipleChoice,
                RoFormerForQuestionAnswering,
                RoFormerForSequenceClassification,
                RoFormerForTokenClassification,
                RoFormerLayer,
                RoFormerModel,
                RoFormerPreTrainedModel,
                load_tf_weights_in_roformer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roformer import (
                TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRoFormerForCausalLM,
                TFRoFormerForMaskedLM,
                TFRoFormerForMultipleChoice,
                TFRoFormerForQuestionAnswering,
                TFRoFormerForSequenceClassification,
                TFRoFormerForTokenClassification,
                TFRoFormerLayer,
                TFRoFormerModel,
                TFRoFormerPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roformer import (
                FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxRoFormerForMaskedLM,
                FlaxRoFormerForMultipleChoice,
                FlaxRoFormerForQuestionAnswering,
                FlaxRoFormerForSequenceClassification,
                FlaxRoFormerForTokenClassification,
                FlaxRoFormerModel,
                FlaxRoFormerPreTrainedModel,
            )


else:
      import sys

      a__      :     Union[str, Any]         =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



