





"""simple docstring"""


from __future__ import annotations

from typing import Any







class       __a  :



     def __init__(			self   ,	a__   ,	a__   ,	a__ = 0  ):
            _lowerCamelCase      ,      _lowerCamelCase             =   row, column
            _lowerCamelCase             =   [[default_value for c in range(_UpperCamelCase  )] for r in range(_UpperCamelCase  )]



     def __str__(			self  ):
            _lowerCamelCase             =   F'Matrix consist of {self.row} rows and {self.column} columns\n'

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

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

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



     def __repr__(			self  ):
            return str(self  )



     def 		snake_case_     (			self   ,	a__  ):


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



     def __getitem__(			self   ,	a__  ):
            assert self.validate_indicies(_UpperCamelCase  )
            return self.array[loc[0]][loc[1]]



     def __setitem__(			self   ,	a__   ,	a__  ):
            assert self.validate_indicies(_UpperCamelCase  )
            _lowerCamelCase             =   value



     def __add__(			self   ,	a__  ):
            assert isinstance(_UpperCamelCase   ,	_UpperCamelCase  )
            assert self.row == another.row and self.column == another.column

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



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



     def __sub__(			self   ,	a__  ):
            return self + (-another)



     def __mul__(			self   ,	a__  ):


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



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



     def 		snake_case_     (			self   ,	a__   ,	a__  ):
            assert isinstance(_UpperCamelCase   ,	_UpperCamelCase  ) and isinstance(_UpperCamelCase   ,	_UpperCamelCase  )
            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
            _lowerCamelCase             =   v.transpose()
            _lowerCamelCase             =   (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 SCREAMING_SNAKE_CASE_  (			)->						Optional[Any]:
             _lowerCamelCase             =   Matrix(3		,    3		,    0						)
             for i in range(3						):
                    _lowerCamelCase             =   1
             print(f'a^(-1) is {ainv}'						)
             # u, v
             _lowerCamelCase             =   Matrix(3		,    1		,    0						)
             _lowerCamelCase      ,      _lowerCamelCase      ,      _lowerCamelCase             =   1, 2, -3
             _lowerCamelCase             =   Matrix(3		,    1		,    0						)
             _lowerCamelCase      ,      _lowerCamelCase      ,      _lowerCamelCase             =   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(__A		,    __A						)}'						)





      def SCREAMING_SNAKE_CASE_  (			)->						List[str]:
             import doctest

             doctest.testmod()





      testa()









from typing import TYPE_CHECKING

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


UpperCamelCase__		 =     {
    '''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvNextConfig''', '''ConvNextOnnxConfig''']
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		 =     ['''ConvNextFeatureExtractor''']
    UpperCamelCase__		 =     ['''ConvNextImageProcessor''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		 =     [
        '''CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''ConvNextForImageClassification''',
        '''ConvNextModel''',
        '''ConvNextPreTrainedModel''',
        '''ConvNextBackbone''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		 =     [
        '''TFConvNextForImageClassification''',
        '''TFConvNextModel''',
        '''TFConvNextPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_convnext import ConvNextFeatureExtractor
        from .image_processing_convnext import ConvNextImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_convnext import (
            CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
            ConvNextBackbone,
            ConvNextForImageClassification,
            ConvNextModel,
            ConvNextPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
    import sys

    UpperCamelCase__		 =     _LazyModule(__name__, globals()['''__file__'''], _import_structure)

