




"""simple docstring"""


import unittest

import numpy as np


def 					UpperCAmelCase__       (snake_case__							:					np.ndarray		,   snake_case__							:					np.ndarray		,   snake_case__							:					np.ndarray		,   snake_case__							:					np.ndarray | None = None		,   ):
  """simple docstring"""

  _snake_case							:		int								=  np.shape(_UpperCAmelCase    )
  _snake_case							:		Optional[Any]								=  np.shape(_UpperCAmelCase    )
  _snake_case							:		Optional[Any]								=  np.shape(_UpperCAmelCase    )

  if shape_a[0] != shape_b[0]:
    _snake_case							:		int								=  (
        """Expected the same number of rows for A and B. """
        F"Instead found A of size {shape_a} and B of size {shape_b}"
    )
    raise ValueError(_UpperCAmelCase    )

  if shape_b[1] != shape_c[1]:
    _snake_case							:		List[Any]								=  (
        """Expected the same number of columns for B and C. """
        F"Instead found B of size {shape_b} and C of size {shape_c}"
    )
    raise ValueError(_UpperCAmelCase    )

  _snake_case							:		Tuple								=  pseudo_inv
  if a_inv is None:
    try:
      _snake_case							:		str								=  np.linalg.inv(_UpperCAmelCase    )
    except np.linalg.LinAlgError:
      raise ValueError(
          """Input matrix A is not invertible. Cannot compute Schur complement."""    )

  return mat_c - mat_b.T @ a_inv @ mat_b






class 					lowercase(     unittest.TestCase ):







 '''simple docstring'''





 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]		)
   _snake_case							:		Union[str, Any]								=  np.array([[0, 3], [3, 0], [2, 3]]		)
   _snake_case							:		List[Any]								=  np.array([[2, 1], [6, 3]]		)

   _snake_case							:		int								=  schur_complement(a_,      a_,      a_		)

   _snake_case							:		Tuple								=  np.block([[a, b], [b.T, c]]		)

   _snake_case							:		Tuple								=  np.linalg.det(a_		)
   _snake_case							:		Dict								=  np.linalg.det(a_		)
   _snake_case							:		List[str]								=  np.linalg.det(a_		)

   self.assertAlmostEqual(a_,      det_a * det_s		)


 def  UpperCamelCase_   (				self:		Union[str, Any]		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]		)
   _snake_case							:		Union[str, Any]								=  np.array([[0, 3], [3, 0], [2, 3]]		)
   _snake_case							:		List[str]								=  np.array([[2, 1], [6, 3]]		)

   with self.assertRaises(a_		):
     schur_complement(a_,      a_,      a_		)


 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   _snake_case							:		str								=  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]		)
   _snake_case							:		int								=  np.array([[0, 3], [3, 0], [2, 3]]		)
   _snake_case							:		Tuple								=  np.array([[2, 1, 3], [6, 3, 5]]		)

   with self.assertRaises(a_		):
     schur_complement(a_,      a_,      a_		)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     unittest.main()






'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__:    Dict					    =       logging.get_logger(__name__)

A__:    Tuple					    =       {
    '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}







class A__       (						UpperCAmelCase__      ):
      __UpperCamelCase       :  Tuple	       =							"roc_bert"






      def __init__( self			:Optional[int]     ,						SCREAMING_SNAKE_CASE			:Tuple=3_0_5_2_2     ,						SCREAMING_SNAKE_CASE			:List[str]=7_6_8     ,						SCREAMING_SNAKE_CASE			:Dict=1_2     ,						SCREAMING_SNAKE_CASE			:List[str]=1_2     ,						SCREAMING_SNAKE_CASE			:Tuple=3_0_7_2     ,						SCREAMING_SNAKE_CASE			:List[Any]="gelu"     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=0.1     ,						SCREAMING_SNAKE_CASE			:List[Any]=0.1     ,						SCREAMING_SNAKE_CASE			:int=5_1_2     ,						SCREAMING_SNAKE_CASE			:Optional[Any]=2     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=0.02     ,						SCREAMING_SNAKE_CASE			:Optional[Any]=1e-12     ,						SCREAMING_SNAKE_CASE			:Any=True     ,						SCREAMING_SNAKE_CASE			:List[Any]=0     ,						SCREAMING_SNAKE_CASE			:Optional[int]="absolute"     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=None     ,						SCREAMING_SNAKE_CASE			:List[Any]=True     ,						SCREAMING_SNAKE_CASE			:int=True     ,						SCREAMING_SNAKE_CASE			:Optional[int]=7_6_8     ,						SCREAMING_SNAKE_CASE			:Optional[Any]=9_1_0     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=5_1_2     ,						SCREAMING_SNAKE_CASE			:str=2_4_8_5_8     ,						SCREAMING_SNAKE_CASE			:List[Any]=True     ,						**SCREAMING_SNAKE_CASE			:Tuple     ,						)       ->					Optional[int]:




             '''simple docstring'''
             _a    : List[str]     					=vocab_size
             _a    : List[str]     					=max_position_embeddings
             _a    : Optional[Any]     					=hidden_size
             _a    : List[Any]     					=num_hidden_layers
             _a    : List[str]     					=num_attention_heads
             _a    : int     					=intermediate_size
             _a    : Any     					=hidden_act
             _a    : Dict     					=hidden_dropout_prob
             _a    : int     					=attention_probs_dropout_prob
             _a    : str     					=initializer_range
             _a    : Optional[int]     					=type_vocab_size
             _a    : Any     					=layer_norm_eps
             _a    : Any     					=use_cache
             _a    : Optional[int]     					=enable_pronunciation
             _a    : Optional[Any]     					=enable_shape
             _a    : Optional[Any]     					=pronunciation_embed_dim
             _a    : Tuple     					=pronunciation_vocab_size
             _a    : Union[str, Any]     					=shape_embed_dim
             _a    : Any     					=shape_vocab_size
             _a    : Tuple     					=concat_input
             _a    : List[str]     					=position_embedding_type
             _a    : List[str]     					=classifier_dropout
             super().__init__(pad_token_id=SCREAMING_SNAKE_CASE     ,						**SCREAMING_SNAKE_CASE	)


