


"""simple docstring"""







from __future__ import annotations

from typing import Any




class  UpperCamelCase_	:

	"""simple docstring"""





	def __init__( self			:				Tuple			,			UpperCAmelCase__			:				int			,			UpperCAmelCase__			:				int			,			UpperCAmelCase__			:				float = 0   )       ->	None:
						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				row, column
						__SCREAMING_SNAKE_CASE						   =				[[default_value for c in range(UpperCAmelCase__   )] for r in range(UpperCAmelCase__   )]




	def __str__( self			:				str   )       ->	str:
						__SCREAMING_SNAKE_CASE						   =				F"""Matrix consist of {self.row} rows and {self.column} columns\n"""

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

						# Make string and return
						def single_line(UpperCAmelCase__			:				list[float]   ) -> str:
											nonlocal string_format_identifier
											__SCREAMING_SNAKE_CASE						   =				"["
											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			:				Union[str, Any]   )       ->	str:
						return str(self   )




	def   UpperCAmelCase_      ( self			:				List[Any]			,			UpperCAmelCase__			:				tuple[int, int]   )       ->	bool:
						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			:				Tuple			,			UpperCAmelCase__			:				tuple[int, int]   )       ->	Any:
						assert self.validate_indicies(UpperCAmelCase__   )
						return self.array[loc[0]][loc[1]]




	def __setitem__( self			:				Optional[int]			,			UpperCAmelCase__			:				tuple[int, int]			,			UpperCAmelCase__			:				float   )       ->	None:
						assert self.validate_indicies(UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				value




	def __add__( self			:				Any			,			UpperCAmelCase__			:				Matrix   )       ->	Matrix:
						assert isinstance(UpperCAmelCase__			,			UpperCAmelCase__   )
						assert self.row == another.row and self.column == another.column

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




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




	def __sub__( self			:				Optional[int]			,			UpperCAmelCase__			:				Matrix   )       ->	Matrix:
						return self + (-another)




	def __mul__( self			:				Optional[int]			,			UpperCAmelCase__			:				int | float | Matrix   )       ->	Matrix:

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




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




	def   UpperCAmelCase_      ( self			:				Dict			,			UpperCAmelCase__			:				Matrix			,			UpperCAmelCase__			:				Matrix   )       ->	Any:
						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
						__SCREAMING_SNAKE_CASE						   =				v.transpose()
						__SCREAMING_SNAKE_CASE						   =				(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__       ():




							'''simple docstring'''


							__SCREAMING_SNAKE_CASE						   =				Matrix(3  ,						3  ,						0   )
							for i in range(3   ):
												__SCREAMING_SNAKE_CASE						   =				1
							print(f"""a^(-1) is {ainv}"""   )
							# u, v
							__SCREAMING_SNAKE_CASE						   =				Matrix(3  ,						1  ,						0   )
							__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				1, 2, -3
							__SCREAMING_SNAKE_CASE						   =				Matrix(3  ,						1  ,						0   )
							__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				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    UpperCAmelCase__       ():




							'''simple docstring'''


							import doctest

							doctest.testmod()



		testa()




"""simple docstring"""







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__				:							int						=     ['''RoFormerTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							int						=     [
		    '''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__				:							Tuple						=     [
		    '''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__				:							int						=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
