
'''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




'''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()


