from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase					      =							logging.get_logger(__name__)

UpperCAmelCase					      =							{
    "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
    "distilbert-base-uncased-distilled-squad": (
        "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
    "distilbert-base-cased-distilled-squad": (
        "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
    "distilbert-base-multilingual-cased": (
        "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
    ),
    "distilbert-base-uncased-finetuned-sst-2-english": (
        "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
    ),
}



class        snake_case__							(					lowercase_				):
   _SCREAMING_SNAKE_CASE						:			List[str]         =   "distilbert"
   _SCREAMING_SNAKE_CASE						:			Tuple         =   {
       "hidden_size": "dim",
       "num_attention_heads": "n_heads",
       "num_hidden_layers": "n_layers",
   }



   def __init__(					self : str      ,						A__ : List[str]=3_05_22      ,						A__ : Dict=5_12      ,						A__ : Tuple=False      ,						A__ : Any=6      ,						A__ : int=12      ,						A__ : Any=7_68      ,						A__ : List[Any]=4 * 7_68      ,						A__ : Optional[Any]=0.1      ,						A__ : Tuple=0.1      ,						A__ : int="gelu"      ,						A__ : int=0.02      ,						A__ : Any=0.1      ,						A__ : str=0.2      ,						A__ : int=0      ,						**A__ : Optional[int]      ,						)				->  str:




         '''simple docstring'''




         snake_case_  :      List[Any]  	=							vocab_size
         snake_case_  :      Dict  	=							max_position_embeddings
         snake_case_  :      Optional[int]  	=							sinusoidal_pos_embds
         snake_case_  :      int  	=							n_layers
         snake_case_  :      Any  	=							n_heads
         snake_case_  :      Dict  	=							dim
         snake_case_  :      str  	=							hidden_dim
         snake_case_  :      Tuple  	=							dropout
         snake_case_  :      Dict  	=							attention_dropout
         snake_case_  :      int  	=							activation
         snake_case_  :      str  	=							initializer_range
         snake_case_  :      Dict  	=							qa_dropout
         snake_case_  :      Dict  	=							seq_classif_dropout
         super().__init__(**a__      ,						pad_token_id=a__ )



class        snake_case__							(					lowercase_				):



   @property
   def        UpperCAmelCase__							(					self : Union[str, Any] )				->  Mapping[str, Mapping[int, str]]:




         '''simple docstring'''




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











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      :Any 							=    np.shape(snake_case__	)
     __snake_case      :List[str] 							=    np.shape(snake_case__	)
     __snake_case      :List[str] 							=    np.shape(snake_case__	)

     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(snake_case__	)

     if shape_b[1] != shape_c[1]:
          __snake_case      :List[str] 							=    (
              """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(snake_case__	)

     __snake_case      :Any 							=    pseudo_inv
     if a_inv is None:
          try:
               __snake_case      :Optional[Any] 							=    np.linalg.inv(snake_case__	)
          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       snake_case__ (				unittest.TestCase):

       '''simple docstring'''



       def        __lowercase    (				self     )			->   None:






            '''simple docstring'''





            __snake_case      :List[str] 							=    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      :Union[str, Any] 							=    np.block([[a, b], [b.T, c]]     )

            __snake_case      :Dict 							=    np.linalg.det(a__     )
            __snake_case      :Tuple 							=    np.linalg.det(a__     )
            __snake_case      :List[Any] 							=    np.linalg.det(a__     )

            self.assertAlmostEqual(a__      ,    det_a * det_s     )



       def        __lowercase    (				self     )			->   None:






            '''simple docstring'''





            __snake_case      :List[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      :Optional[int] 							=    np.array([[2, 1], [6, 3]]     )

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



       def        __lowercase    (				self     )			->   None:






            '''simple docstring'''





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


