import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase					      =							logging.get_logger(__name__)

UpperCAmelCase					      =							{"vocab_file": "sentencepiece.model"}

UpperCAmelCase					      =							{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
}

UpperCAmelCase					      =							{
    "google/rembert": 2_5_6,
}



class        snake_case__							(					lowercase_				):
   _SCREAMING_SNAKE_CASE						:			str         =   VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE						:			Tuple         =   PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE						:			Optional[int]         =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



   def __init__(					self : Tuple      ,						A__ : List[str]      ,						A__ : Dict=False      ,						A__ : Union[str, Any]=True      ,						A__ : Tuple=True      ,						A__ : Optional[Any]="[CLS]"      ,						A__ : Tuple="[SEP]"      ,						A__ : Optional[Any]="[UNK]"      ,						A__ : Tuple="[SEP]"      ,						A__ : Any="[PAD]"      ,						A__ : Any="[CLS]"      ,						A__ : List[Any]="[MASK]"      ,						**A__ : Dict      ,						)				->  List[str]:




         '''simple docstring'''




         super().__init__(
             do_lower_case=a__      ,						remove_space=a__      ,						keep_accents=a__      ,						bos_token=a__      ,						eos_token=a__      ,						unk_token=a__      ,						sep_token=a__      ,						pad_token=a__      ,						cls_token=a__      ,						mask_token=a__      ,						**a__      ,						)

         snake_case_  :      List[Any]  	=							do_lower_case
         snake_case_  :      Dict  	=							remove_space
         snake_case_  :      int  	=							keep_accents
         snake_case_  :      Optional[int]  	=							vocab_file

         snake_case_  :      int  	=							spm.SentencePieceProcessor()
         self.sp_model.Load(a__ )



   @property
   def        UpperCAmelCase__							(					self : Optional[int] )				->  Tuple:




         '''simple docstring'''




         return len(self.sp_model )



   def        UpperCAmelCase__							(					self : Optional[Any] )				->  Optional[int]:




         '''simple docstring'''




         snake_case_  :      int  	=							{self.convert_ids_to_tokens(a__ ): i for i in range(self.vocab_size )}
         vocab.update(self.added_tokens_encoder )
         return vocab



   def __getstate__(					self : Union[str, Any] )				->  str:




         '''simple docstring'''




         snake_case_  :      str  	=							self.__dict__.copy()
         snake_case_  :      Dict  	=							None
         return state



   def __setstate__(					self : Union[str, Any]      ,						A__ : Any )				->  str:




         '''simple docstring'''




         snake_case_  :      Dict  	=							d
         snake_case_  :      Dict  	=							spm.SentencePieceProcessor()
         self.sp_model.Load(self.vocab_file )



   def        UpperCAmelCase__							(					self : Union[str, Any]      ,						A__ : str      ,						A__ : int=False )				->  Tuple:




         '''simple docstring'''




         snake_case_  :      Union[str, Any]  	=							self.sp_model.EncodeAsPieces(a__ )
         return pieces



   def        UpperCAmelCase__							(					self : List[Any]      ,						A__ : List[Any] )				->  str:




         '''simple docstring'''




         return self.sp_model.PieceToId(a__ )



   def        UpperCAmelCase__							(					self : Any      ,						A__ : Tuple )				->  Any:




         '''simple docstring'''




         return self.sp_model.IdToPiece(a__ )



   def        UpperCAmelCase__							(					self : List[str]      ,						A__ : str )				->  Union[str, Any]:




         '''simple docstring'''




         snake_case_  :      List[Any]  	=							self.sp_model.decode_pieces(a__ )
         return out_string



   def        UpperCAmelCase__							(					self : Union[str, Any]      ,						A__ : Dict      ,						A__ : Union[str, Any] = None )				->  List[int]:




         '''simple docstring'''




         snake_case_  :      Optional[int]  	=							[self.sep_token_id]
         snake_case_  :      List[Any]  	=							[self.cls_token_id]
         if token_ids_a is None:
               return cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep



   def        UpperCAmelCase__							(					self : Any      ,						A__ : Union[str, Any]      ,						A__ : Optional[int] = None      ,						A__ : Tuple = False )				->  List[int]:




         '''simple docstring'''




         if already_has_special_tokens:
               if token_ids_a is not None:
                     raise ValueError(
                         "You should not supply a second sequence if the provided sequence of "
                         "ids is already formatted with special tokens for the model." )
               return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

         if token_ids_a is not None:
               return [1] + ([0] * len(a__ )) + [1] + ([0] * len(a__ )) + [1]
         return [1] + ([0] * len(a__ )) + [1]



   def        UpperCAmelCase__							(					self : str      ,						A__ : Tuple      ,						A__ : Dict = None )				->  List[int]:




         '''simple docstring'''




         snake_case_  :      Any  	=							[self.sep_token_id]
         snake_case_  :      Any  	=							[self.cls_token_id]

         if token_ids_a is None:
               return len(cls + token_ids_a + sep ) * [0]
         return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]



   def        UpperCAmelCase__							(					self : Optional[int]      ,						A__ : Any      ,						A__ : Tuple = None )				->  Tuple[str]:




         '''simple docstring'''




         if not os.path.isdir(a__ ):
               logger.error("Vocabulary path ({}) should be a directory".format(a__ ) )
               return
         snake_case_  :      Optional[int]  	=							os.path.join(
             a__      ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

         if os.path.abspath(self.vocab_file ) != os.path.abspath(a__ ):
               copyfile(self.vocab_file      ,						a__ )

         return (out_vocab_file,)











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()


