
"""simple docstring"""







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


SCREAMING_SNAKE_CASE_  					= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_  					= {'''vocab_file''': '''vocab.txt'''}

SCREAMING_SNAKE_CASE_  					= {
    '''vocab_file''': {
        '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt''',
        '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt''',
        '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt''',
        '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt''',
    }
}

SCREAMING_SNAKE_CASE_  					= {
    '''tau/splinter-base''': 512,
    '''tau/splinter-base-qass''': 512,
    '''tau/splinter-large''': 512,
    '''tau/splinter-large-qass''': 512,
}

SCREAMING_SNAKE_CASE_  					= {
    '''tau/splinter-base''': {'''do_lower_case''': False},
    '''tau/splinter-base-qass''': {'''do_lower_case''': False},
    '''tau/splinter-large''': {'''do_lower_case''': False},
    '''tau/splinter-large-qass''': {'''do_lower_case''': False},
}






class      __a      (       __a      ):
       _lowerCamelCase				:				Optional[Any]   			=      VOCAB_FILES_NAMES
       _lowerCamelCase				:				Union[str, Any]   			=      PRETRAINED_VOCAB_FILES_MAP
       _lowerCamelCase				:				int   			=      PRETRAINED_INIT_CONFIGURATION
       _lowerCamelCase				:				List[Any]   			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _lowerCamelCase				:				int   			=      SplinterTokenizer







       def __init__(       self				,lowerCamelCase_=None				,lowerCamelCase_=None				,lowerCamelCase_=True				,lowerCamelCase_="[UNK]"				,lowerCamelCase_="[SEP]"				,lowerCamelCase_="[PAD]"				,lowerCamelCase_="[CLS]"				,lowerCamelCase_="[MASK]"				,lowerCamelCase_="[QUESTION]"				,lowerCamelCase_=True				,lowerCamelCase_=None				,**lowerCamelCase_				,):



          '''simple docstring'''

          super().__init__(
              SCREAMING_SNAKE_CASE__				,tokenizer_file=SCREAMING_SNAKE_CASE__				,do_lower_case=SCREAMING_SNAKE_CASE__				,unk_token=SCREAMING_SNAKE_CASE__				,sep_token=SCREAMING_SNAKE_CASE__				,pad_token=SCREAMING_SNAKE_CASE__				,cls_token=SCREAMING_SNAKE_CASE__				,mask_token=SCREAMING_SNAKE_CASE__				,tokenize_chinese_chars=SCREAMING_SNAKE_CASE__				,strip_accents=SCREAMING_SNAKE_CASE__				,additional_special_tokens=(question_token,)				,**SCREAMING_SNAKE_CASE__				,)

          _UpperCAmelCase 						=       json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
          if (
              pre_tok_state.get("lowercase"				,SCREAMING_SNAKE_CASE__		) != do_lower_case
              or pre_tok_state.get("strip_accents"				,SCREAMING_SNAKE_CASE__		) != strip_accents
          ):
             _UpperCAmelCase 						=       getattr(SCREAMING_SNAKE_CASE__				,pre_tok_state.pop("type"		)		)
             _UpperCAmelCase 						=       do_lower_case
             _UpperCAmelCase 						=       strip_accents
             _UpperCAmelCase 						=       pre_tok_class(**SCREAMING_SNAKE_CASE__		)

          _UpperCAmelCase 						=       do_lower_case







       @property
       def  _snake_case      (       self		):



          '''simple docstring'''

          return self.convert_tokens_to_ids(self.question_token		)







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_ = None		):



          '''simple docstring'''

          if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

          _UpperCAmelCase 						=       [self.cls_token_id]
          _UpperCAmelCase 						=       [self.sep_token_id]
          _UpperCAmelCase 						=       [self.question_token_id] + [self.convert_tokens_to_ids("."		)]
          if self.padding_side == "right":
             # Input is question-then-context
             return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
          else:
             # Input is context-then-question
             return cls + token_ids_a + sep + token_ids_a + question_suffix + sep







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_ = None		):



          '''simple docstring'''

          _UpperCAmelCase 						=       [self.sep_token_id]
          _UpperCAmelCase 						=       [self.cls_token_id]
          _UpperCAmelCase 						=       [self.question_token_id] + [self.convert_tokens_to_ids("."		)]
          if token_ids_a is None:
             return len(cls + token_ids_a + sep		) * [0]

          if self.padding_side == "right":
             # Input is question-then-context
             return len(cls + token_ids_a + question_suffix + sep		) * [0] + len(token_ids_a + sep		) * [1]
          else:
             # Input is context-then-question
             return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + question_suffix + sep		) * [1]







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_ = None		):



          '''simple docstring'''

          _UpperCAmelCase 						=       self._tokenizer.model.save(SCREAMING_SNAKE_CASE__				,name=SCREAMING_SNAKE_CASE__		)
          return tuple(SCREAMING_SNAKE_CASE__		)




"""simple docstring"""




import numpy as np
from numpy import floataa
from numpy.typing import NDArray



def 						UpperCAmelCase__ (					A__						,     A__ )   ->   NDArray[floataa]:




   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__						       =      np.shape(A__ )

   lowerCamelCase__						       =      np.zeros((rows, 1)						,     dtype=A__ )
   for row in reversed(range(A__ ) ):
      lowerCamelCase__						       =      0
      for col in range(row + 1						,     A__ ):
         total += coefficients[row, col] * x[col]

      lowerCamelCase__						       =      (vector[row] - total) / coefficients[row, row]

   return x



def 						UpperCAmelCase__ (					A__						,     A__ )   ->   NDArray[floataa]:




   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__						       =      np.shape(A__ )
   if rows != columns:
      return np.array(()						,     dtype=A__ )

   # augmented matrix
   lowerCamelCase__						       =      np.concatenate((coefficients, vector)						,     axis=1 )
   lowerCamelCase__						       =      augmented_mat.astype("float64" )

   # scale the matrix leaving it triangular
   for row in range(rows - 1 ):
      lowerCamelCase__						       =      augmented_mat[row, row]
      for col in range(row + 1						,     A__ ):
         lowerCamelCase__						       =      augmented_mat[col, row] / pivot
         augmented_mat[col, :] -= factor * augmented_mat[row, :]

   lowerCamelCase__						       =      retroactive_resolution(
       augmented_mat[:, 0:columns]						,     augmented_mat[:, columns : columns + 1] )

   return x


if __name__ == "__main__":
     import doctest

     doctest.testmod()




