code: 
  '''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   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	"""MarkupLMFeatureExtractor"""
  		lowerCamelCase           =	("""MarkupLMTokenizer""", """MarkupLMTokenizerFast""")
  		lowerCamelCase           =	True
  
  
  
  		def __call__(			self							: int     ,  UpperCamelCase__							: Tuple=None     ,  UpperCamelCase__							: Optional[Any]=None     ,  UpperCamelCase__							: Optional[Any]=None     ,  UpperCamelCase__							: List[Any]=None     ,  UpperCamelCase__							: str=None     ,  UpperCamelCase__							: bool = True     ,  UpperCamelCase__							: Union[bool, str, PaddingStrategy] = False     ,  UpperCamelCase__							: Union[bool, str, TruncationStrategy] = None     ,  UpperCamelCase__							: Optional[int] = None     ,  UpperCamelCase__							: int = 0     ,  UpperCamelCase__							: Optional[int] = None     ,  UpperCamelCase__							: Optional[bool] = None     ,  UpperCamelCase__							: Optional[bool] = None     ,  UpperCamelCase__							: bool = False     ,  UpperCamelCase__							: bool = False     ,  UpperCamelCase__							: bool = False     ,  UpperCamelCase__							: bool = False     ,  UpperCamelCase__							: bool = True     ,  UpperCamelCase__							: Optional[Union[str, TensorType]] = None     ,  **UpperCamelCase__							: List[Any]     ,  )	-> 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`'''			)
  
  						snake_case     :					int     =	self.feature_extractor(UpperCamelCase__			)
  						snake_case     :					List[str]     =	features['''nodes''']
  						snake_case     :					Dict     =	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(UpperCamelCase__     ,  UpperCamelCase__			):
  								snake_case     :					Dict     =	[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)
  
  				snake_case     :					List[str]     =	self.tokenizer(
  				    text=questions if questions is not None else nodes     ,  text_pair=nodes if questions is not None else None     ,  xpaths=UpperCamelCase__     ,  node_labels=UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__     ,  padding=UpperCamelCase__     ,  truncation=UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  stride=UpperCamelCase__     ,  pad_to_multiple_of=UpperCamelCase__     ,  return_token_type_ids=UpperCamelCase__     ,  return_attention_mask=UpperCamelCase__     ,  return_overflowing_tokens=UpperCamelCase__     ,  return_special_tokens_mask=UpperCamelCase__     ,  return_offsets_mapping=UpperCamelCase__     ,  return_length=UpperCamelCase__     ,  verbose=UpperCamelCase__     ,  return_tensors=UpperCamelCase__     ,  **UpperCamelCase__     ,  )
  
  				return encoded_inputs
  
  
  
  		def 			lowerCAmelCase							(			self							: Union[str, Any]     ,  *UpperCamelCase__							: List[str]     ,  **UpperCamelCase__							: List[Any]			)	-> int:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				return self.tokenizer.batch_decode(*UpperCamelCase__     ,  **UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict     ,  *UpperCamelCase__							: int     ,  **UpperCamelCase__							: Union[str, Any]			)	-> Optional[int]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				return self.tokenizer.decode(*UpperCamelCase__     ,  **UpperCamelCase__			)
  
  
  
  		@property
  		def 			lowerCAmelCase							(			self							: Optional[int]			)	-> List[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					int     =	self.tokenizer.model_input_names
  				return tokenizer_input_names
  
  
  
style_context: 
  '''simple docstring'''
  import numpy as np
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       Optional[Any]:
  
  
  		'''simple docstring'''
  
  
  		snake_case ,snake_case     :					str     =	a.shape
  		snake_case     :					Union[str, Any]     =	min(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
  		snake_case     :					List[Any]     =	np.eye(SCREAMING_SNAKE_CASE__ )
  		snake_case     :					Union[str, Any]     =	a.copy()
  
  		for k in range(t - 1 ):
  				# select a column of modified matrix A':
  				snake_case     :					Union[str, Any]     =	r[k:, [k]]
  				# construct first basis vector
  				snake_case     :					str     =	np.zeros_like(SCREAMING_SNAKE_CASE__ )
  				snake_case     :					List[str]     =	1.0
  				# determine scaling factor
  				snake_case     :					int     =	np.linalg.norm(SCREAMING_SNAKE_CASE__ )
  				# construct vector v for Householder reflection
  				snake_case     :					Optional[int]     =	x + np.sign(x[0] ) * alpha * ea
  				v /= np.linalg.norm(SCREAMING_SNAKE_CASE__ )
  
  				# construct the Householder matrix
  				snake_case     :					Tuple     =	np.eye(m - k ) - 2.0 * v @ v.T
  				# pad with ones and zeros as necessary
  				snake_case     :					int     =	np.block([[np.eye(SCREAMING_SNAKE_CASE__ ), np.zeros((k, m - k) )], [np.zeros((m - k, k) ), q_k]] )
  
  				snake_case     :					int     =	q @ q_k.T
  				snake_case     :					Dict     =	q_k @ r
  
  		return q, r
  
  
  if __name__ == "__main__":
  						import doctest
  
  						doctest.testmod()
  
  
  