




"""simple docstring"""



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







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_		)		->	NDArray[floataa]:
 A__,   A__ =       np.shape(lowercase_		)

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

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

 return x







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_		)		->	NDArray[floataa]:
 A__,   A__ =       np.shape(lowercase_		)
 if rows != columns:
  return np.array(()    ,				dtype=lowercase_		)

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

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

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

 return x


if __name__ == "__main__":
      import doctest

      doctest.testmod()











"""simple docstring"""



from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy




class 			UpperCAmelCase_						(		A_							):
    lowercase__		      =   '''MarkupLMFeatureExtractor'''
    lowercase__		      =   ('''MarkupLMTokenizer''', '''MarkupLMTokenizerFast''')
    lowercase__		      =   True




    def __call__(					self		: Dict			,				snake_case_		: Optional[int]=None			,				snake_case_		: Union[str, Any]=None			,				snake_case_		: Tuple=None			,				snake_case_		: str=None			,				snake_case_		: Optional[Any]=None			,				snake_case_		: bool = True			,				snake_case_		: Union[bool, str, PaddingStrategy] = False			,				snake_case_		: Union[bool, str, TruncationStrategy] = None			,				snake_case_		: Optional[int] = None			,				snake_case_		: int = 0			,				snake_case_		: Optional[int] = None			,				snake_case_		: Optional[bool] = None			,				snake_case_		: Optional[bool] = None			,				snake_case_		: bool = False			,				snake_case_		: bool = False			,				snake_case_		: bool = False			,				snake_case_		: bool = False			,				snake_case_		: bool = True			,				snake_case_		: Optional[Union[str, TensorType]] = None			,				**snake_case_		: Dict			,				)   ->							BatchEncoding:




     '''simple docstring'''

     if self.parse_html:
      if html_strings is None:
       raise ValueError("Make sure to pass HTML strings in case `parse_html` is set to `True`"   )

      if nodes is not None or xpaths is not None or node_labels is not None:
       raise ValueError(
           "Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"   )

      A__ =       self.feature_extractor(snake_case_   )
      A__ =       features["nodes"]
      A__ =       features["xpaths"]
     else:
      if html_strings is not None:
       raise ValueError("You have passed HTML strings but `parse_html` is set to `False`."   )
      if nodes is None or xpaths is None:
       raise ValueError("Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"   )

        # # second, apply the tokenizer
     if questions is not None and self.parse_html:
      if isinstance(snake_case_			,				snake_case_   ):
       A__ =       [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

     A__ =       self.tokenizer(
         text=questions if questions is not None else nodes			,				text_pair=nodes if questions is not None else None			,				xpaths=snake_case_			,				node_labels=snake_case_			,				add_special_tokens=snake_case_			,				padding=snake_case_			,				truncation=snake_case_			,				max_length=snake_case_			,				stride=snake_case_			,				pad_to_multiple_of=snake_case_			,				return_token_type_ids=snake_case_			,				return_attention_mask=snake_case_			,				return_overflowing_tokens=snake_case_			,				return_special_tokens_mask=snake_case_			,				return_offsets_mapping=snake_case_			,				return_length=snake_case_			,				verbose=snake_case_			,				return_tensors=snake_case_			,				**snake_case_			,				)

     return encoded_inputs




    def 							__magic_name__							(					self		: Optional[Any]			,				*snake_case_		: Any			,				**snake_case_		: Any   )   ->							int:




     '''simple docstring'''

     return self.tokenizer.batch_decode(*snake_case_			,				**snake_case_   )




    def 							__magic_name__							(					self		: Tuple			,				*snake_case_		: Optional[Any]			,				**snake_case_		: List[Any]   )   ->							Any:




     '''simple docstring'''

     return self.tokenizer.decode(*snake_case_			,				**snake_case_   )




    @property
    def 							__magic_name__							(					self		: List[Any]   )   ->							Optional[int]:




     '''simple docstring'''

     A__ =       self.tokenizer.model_input_names
     return tokenizer_input_names





