






'''simple docstring'''







from __future__ import annotations

from typing import Any

class _lowercase			:




							def __init__(							self:       Tuple    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       float = 0  ):
														lowerCamelCase__						,	lowerCamelCase__      :			List[Any] 						= row, column
														lowerCamelCase__      :			int 						= [[default_value for c in range(UpperCamelCase__  )] for r in range(UpperCamelCase__  )]




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

														# Make string identifier
														lowerCamelCase__      :			List[str] 						= 0
														for row_vector in self.array:
																					for obj in row_vector:
																												lowerCamelCase__      :			Dict 						= max(UpperCamelCase__    ,							len(str(UpperCamelCase__  )  )  )
														lowerCamelCase__      :			int 						= F'''%{max_element_length}s'''

														# Make string and return
														def single_line(UpperCamelCase__:       list[float]  ) -> str:
																					nonlocal string_format_identifier
																					lowerCamelCase__      :			Dict 						= """["""
																					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:       Any  ):
														return str(self  )




							def 	lowerCamelCase_       (							self:       Any    ,							UpperCamelCase__:       tuple[int, int]  ):
														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:       Dict    ,							UpperCamelCase__:       tuple[int, int]  ):
														assert self.validate_indicies(UpperCamelCase__  )
														return self.array[loc[0]][loc[1]]




							def __setitem__(							self:       int    ,							UpperCamelCase__:       tuple[int, int]    ,							UpperCamelCase__:       float  ):
														assert self.validate_indicies(UpperCamelCase__  )
														lowerCamelCase__      :			Dict 						= value




							def __add__(							self:       Optional[int]    ,							UpperCamelCase__:       Matrix  ):
														assert isinstance(UpperCamelCase__    ,							UpperCamelCase__  )
														assert self.row == another.row and self.column == another.column

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




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




							def __sub__(							self:       List[Any]    ,							UpperCamelCase__:       Matrix  ):
														return self + (-another)




							def __mul__(							self:       Optional[int]    ,							UpperCamelCase__:       int | float | Matrix  ):

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




							def 	lowerCamelCase_       (							self:       int  ):
														lowerCamelCase__      :			Optional[int] 						= Matrix(self.column    ,							self.row  )
														for r in range(self.row  ):
																					for c in range(self.column  ):
																												lowerCamelCase__      :			Any 						= self[r, c]
														return result




							def 	lowerCamelCase_       (							self:       str    ,							UpperCamelCase__:       Matrix    ,							UpperCamelCase__:       Matrix  ):
														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__      :			Optional[Any] 						= v.transpose()
														lowerCamelCase__      :			Optional[Any] 						= (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_ ()   -> None:
										# a^(-1)
										lowerCamelCase__      :			Tuple 						= Matrix(3       ,    3       ,    0   )
										for i in range(3   ):
																	lowerCamelCase__      :			Optional[Any] 						= 1
										print(f'''a^(-1) is {ainv}'''   )
										# u, v
										lowerCamelCase__      :			Dict 						= Matrix(3       ,    1       ,    0   )
										lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__      :			Optional[int] 						= 1, 2, -3
										lowerCamelCase__      :			int 						= Matrix(3       ,    1       ,    0   )
										lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__      :			Dict 						= 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 				SCREAMING_SNAKE_CASE_ ()   -> None:
										import doctest

										doctest.testmod()



			testa()









'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A			:    Dict					  ={
    '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
    '''processing_git''': ['''GitProcessor'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A			:    Union[str, Any]					  =[
			    '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''GitForCausalLM''',
			    '''GitModel''',
			    '''GitPreTrainedModel''',
			    '''GitVisionModel''',
			]

if TYPE_CHECKING:
			from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
			from .processing_git import GitProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_git import (
						    GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GitForCausalLM,
						    GitModel,
						    GitPreTrainedModel,
						    GitVisionModel,
						)

else:
			import sys

			_A			:    Dict					  =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

