from __future__ import annotations

from typing import Any



class 			SCREAMING_SNAKE_CASE_			:
  '''simple docstring'''


  def __init__(					self     :       Any   ,						SCREAMING_SNAKE_CASE__     :       Tuple   ,						SCREAMING_SNAKE_CASE__     :       List[str]   ,						SCREAMING_SNAKE_CASE__     :       Dict = 0  )     ->					Optional[Any]:
         A    :  Union[str, Any]								=row, column
         A    :  List[Any]								=[[default_value for c in range(A_  )] for r in range(A_  )]







  def __str__(					self     :       Tuple  )     ->					str:
         A    :  Tuple								=f'Matrix consist of {self.row} rows and {self.column} columns\n'

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

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

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







  def __repr__(					self     :       Union[str, Any]  )     ->					Union[str, Any]:
         return str(self  )







  def 		SCREAMING_SNAKE_CASE_		(					self     :       Any   ,						SCREAMING_SNAKE_CASE__     :       int  )     ->					Optional[Any]:




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







  def __getitem__(					self     :       Dict   ,						SCREAMING_SNAKE_CASE__     :       Optional[int]  )     ->					List[Any]:
         assert self.validate_indicies(A_  )
         return self.array[loc[0]][loc[1]]







  def __setitem__(					self     :       List[Any]   ,						SCREAMING_SNAKE_CASE__     :       List[Any]   ,						SCREAMING_SNAKE_CASE__     :       Union[str, Any]  )     ->					str:
         assert self.validate_indicies(A_  )
         A    :  Optional[Any]								=value







  def __add__(					self     :       str   ,						SCREAMING_SNAKE_CASE__     :       List[Any]  )     ->					Optional[Any]:
         assert isinstance(A_   ,						A_  )
         assert self.row == another.row and self.column == another.column

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







  def __neg__(					self     :       str  )     ->					Optional[int]:
         A    :  Any								=Matrix(self.row   ,						self.column  )
         for r in range(self.row  ):
                for c in range(self.column  ):
                       A    :  Optional[int]								=-self[r, c]
         return result







  def __sub__(					self     :       Optional[int]   ,						SCREAMING_SNAKE_CASE__     :       Any  )     ->					Union[str, Any]:
         return self + (-another)







  def __mul__(					self     :       Optional[Any]   ,						SCREAMING_SNAKE_CASE__     :       Optional[int]  )     ->					Dict:




         if isinstance(A_   ,						(int, float)  ):  # Scalar multiplication
                A    :  Optional[int]								=Matrix(self.row   ,						self.column  )
                for r in range(self.row  ):
                       for c in range(self.column  ):
                              A    :  Tuple								=self[r, c] * another
                return result
         elif isinstance(A_   ,						A_  ):  # Matrix multiplication
                assert self.column == another.row
                A    :  List[Any]								=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    :  Union[str, Any]								=f'Unsupported type given for another ({type(A_  )})'
                raise TypeError(A_  )







  def 		SCREAMING_SNAKE_CASE_		(					self     :       str  )     ->					Optional[int]:
         A    :  Optional[int]								=Matrix(self.column   ,						self.row  )
         for r in range(self.row  ):
                for c in range(self.column  ):
                       A    :  Optional[Any]								=self[r, c]
         return result







  def 		SCREAMING_SNAKE_CASE_		(					self     :       Optional[int]   ,						SCREAMING_SNAKE_CASE__     :       Dict   ,						SCREAMING_SNAKE_CASE__     :       List[Any]  )     ->					str:
         assert isinstance(A_   ,						A_  ) and isinstance(A_   ,						A_  )
         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    :  Dict								=v.transpose()
         A    :  str								=(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 A__	(      )					->		None:
           # a^(-1)
           A    :  List[str]								=Matrix(3,		3,		0       )
           for i in range(3       ):
                  A    :  str								=1
           print(F'a^(-1) is {ainv}'       )
           # u, v
           A    :  Tuple								=Matrix(3,		1,		0       )
           A    :  List[str]								=1, 2, -3
           A    :  List[Any]								=Matrix(3,		1,		0       )
           A    :  int								=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(_lowerCAmelCase,		_lowerCAmelCase       )}'       )



    def A__	(      )					->		None:
           import doctest

           doctest.testmod()
    testa()













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCamelCase			:	List[Any]					    =	{
    """configuration_chinese_clip""": [
        """CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ChineseCLIPConfig""",
        """ChineseCLIPOnnxConfig""",
        """ChineseCLIPTextConfig""",
        """ChineseCLIPVisionConfig""",
    ],
    """processing_chinese_clip""": ["""ChineseCLIPProcessor"""],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase			:	Tuple					    =	["""ChineseCLIPFeatureExtractor"""]
    __lowerCamelCase			:	List[str]					    =	["""ChineseCLIPImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase			:	str					    =	[
        """CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ChineseCLIPModel""",
        """ChineseCLIPPreTrainedModel""",
        """ChineseCLIPTextModel""",
        """ChineseCLIPVisionModel""",
    ]

if TYPE_CHECKING:
    from .configuration_chinese_clip import (
        CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
        ChineseCLIPConfig,
        ChineseCLIPOnnxConfig,
        ChineseCLIPTextConfig,
        ChineseCLIPVisionConfig,
    )
    from .processing_chinese_clip import ChineseCLIPProcessor

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_chinese_clip import (
            CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            ChineseCLIPModel,
            ChineseCLIPPreTrainedModel,
            ChineseCLIPTextModel,
            ChineseCLIPVisionModel,
        )

else:
    import sys

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