




'''simple docstring'''



from __future__ import annotations

from typing import Any






class 				_a							:





       '''simple docstring'''






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







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Optional[int]   =		row, column
             SCREAMING_SNAKE_CASE					:	Any   =		[[default_value for c in range(__lowerCamelCase   )] for r in range(__lowerCamelCase   )]







       def __str__(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Tuple   =		F"Matrix consist of {self.row} rows and {self.column} columns\n"

             # Make string identifier
             SCREAMING_SNAKE_CASE					:	Optional[int]   =		0
             for row_vector in self.array:
                   for obj in row_vector:
                         SCREAMING_SNAKE_CASE					:	Any   =		max(__lowerCamelCase,				len(str(__lowerCamelCase   )   )   )
             SCREAMING_SNAKE_CASE					:	Dict   =		F"%{max_element_length}s"

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

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







       def __repr__(	self   ):







             '''simple docstring'''







             return str(self   )







       def     UpperCamelCase_				(	self,				A   ):







             '''simple docstring'''







             if not (isinstance(__lowerCamelCase,				(list, tuple)   ) and len(__lowerCamelCase   ) == 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   ):







             '''simple docstring'''







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







       def __setitem__(	self,				A,				A   ):







             '''simple docstring'''







             assert self.validate_indicies(__lowerCamelCase   )
             SCREAMING_SNAKE_CASE					:	int   =		value







       def __add__(	self,				A   ):







             '''simple docstring'''







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

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







       def __neg__(	self   ):







             '''simple docstring'''







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







       def __sub__(	self,				A   ):







             '''simple docstring'''







             return self + (-another)







       def __mul__(	self,				A   ):







             '''simple docstring'''







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







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







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







       def     UpperCamelCase_				(	self,				A,				A   ):







             '''simple docstring'''







             assert isinstance(__lowerCamelCase,				__lowerCamelCase   ) and isinstance(__lowerCamelCase,				__lowerCamelCase   )
             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					:	Dict   =		v.transpose()
             SCREAMING_SNAKE_CASE					:	Dict   =		(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    lowercase__(       ):

            """simple docstring"""






            SCREAMING_SNAKE_CASE					:	int   =		Matrix(3	,3	,0  )
            for i in range(3  ):
                  SCREAMING_SNAKE_CASE					:	Any   =		1
            print(f"a^(-1) is {ainv}"  )
            # u, v
            SCREAMING_SNAKE_CASE					:	Optional[int]   =		Matrix(3	,1	,0  )
            SCREAMING_SNAKE_CASE					:	List[str]   =		1, 2, -3
            SCREAMING_SNAKE_CASE					:	Optional[int]   =		Matrix(3	,1	,0  )
            SCREAMING_SNAKE_CASE					:	Tuple   =		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(snake_case_	,snake_case_  )}"  )




      def    lowercase__(       ):

            """simple docstring"""






            import doctest

            doctest.testmod()





      testa()









"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_ =			logging.get_logger(__name__)

a_ =			{
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    """bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/config.json""",
    """bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/config.json""",
    """bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/config.json""",
    """bert-base-multilingual-uncased""": """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json""",
    """bert-base-multilingual-cased""": """https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json""",
    """bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/config.json""",
    """bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/config.json""",
    """bert-large-uncased-whole-word-masking""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-base-cased-finetuned-mrpc""": """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json""",
    """bert-base-german-dbmdz-cased""": """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json""",
    """bert-base-german-dbmdz-uncased""": """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese""": """https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-cased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-uncased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"""
    ),
    """wietsedv/bert-base-dutch-cased""": """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json""",
    # See all BERT models at https://huggingface.co/models?filter=bert
}






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     """bert"""

 def __init__(    self					,						__lowerCamelCase=3_0522					,						__lowerCamelCase=768					,						__lowerCamelCase=12					,						__lowerCamelCase=12					,						__lowerCamelCase=3072					,						__lowerCamelCase="gelu"					,						__lowerCamelCase=0.1					,						__lowerCamelCase=0.1					,						__lowerCamelCase=512					,						__lowerCamelCase=2					,						__lowerCamelCase=0.0_2					,						__lowerCamelCase=1e-1_2					,						__lowerCamelCase=0					,						__lowerCamelCase="absolute"					,						__lowerCamelCase=True					,						__lowerCamelCase=None					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     super().__init__(pad_token_id=__lowerCamelCase					,						**__lowerCamelCase  )

     __A    :						Dict						 =     vocab_size
     __A    :						Any						 =     hidden_size
     __A    :						str						 =     num_hidden_layers
     __A    :						int						 =     num_attention_heads
     __A    :						Optional[int]						 =     hidden_act
     __A    :						List[Any]						 =     intermediate_size
     __A    :						Tuple						 =     hidden_dropout_prob
     __A    :						Optional[int]						 =     attention_probs_dropout_prob
     __A    :						Optional[int]						 =     max_position_embeddings
     __A    :						Optional[Any]						 =     type_vocab_size
     __A    :						Optional[Any]						 =     initializer_range
     __A    :						Dict						 =     layer_norm_eps
     __A    :						Any						 =     position_embedding_type
     __A    :						Optional[int]						 =     use_cache
     __A    :						str						 =     classifier_dropout






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     if self.task == "multiple-choice":
         __A    :						str						 =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
         __A    :						Tuple						 =     {0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
             ('''token_type_ids''', dynamic_axis),
         ]  )




