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 _lowerCamelCase (		lowercase__							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    List[str]   					=       """MarkupLMFeatureExtractor"""
  		A_			:    Tuple   					=       ("""MarkupLMTokenizer""", """MarkupLMTokenizerFast""")
  		A_			:    Optional[int]   					=       True
  
  
  
  
  
  
  
  		def __call__(      self	:		Any						,  _A	:		str=None						,  _A	:		List[Any]=None						,  _A	:		str=None						,  _A	:		List[str]=None						,  _A	:		Tuple=None						,  _A	:		bool = True						,  _A	:		Union[bool, str, PaddingStrategy] = False						,  _A	:		Union[bool, str, TruncationStrategy] = None						,  _A	:		Optional[int] = None						,  _A	:		int = 0						,  _A	:		Optional[int] = None						,  _A	:		Optional[bool] = None						,  _A	:		Optional[bool] = None						,  _A	:		bool = False						,  _A	:		bool = False						,  _A	:		bool = False						,  _A	:		bool = False						,  _A	:		bool = True						,  _A	:		Optional[Union[str, TensorType]] = None						,  **_A	:		Any						,  )						->				BatchEncoding:
  			# first, create nodes and xpaths
  			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`'    )
  
  				__magic_name__ :					Optional[Any]  			=  self.feature_extractor(_A    )
  				__magic_name__ :					int  			=  features['nodes']
  				__magic_name__ :					Union[str, Any]  			=  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(_A						,  _A    ):
  					__magic_name__ :					Union[str, Any]  			=  [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)
  
  			__magic_name__ :					Any  			=  self.tokenizer(
  			    text=questions if questions is not None else nodes						,  text_pair=nodes if questions is not None else None						,  xpaths=_A						,  node_labels=_A						,  add_special_tokens=_A						,  padding=_A						,  truncation=_A						,  max_length=_A						,  stride=_A						,  pad_to_multiple_of=_A						,  return_token_type_ids=_A						,  return_attention_mask=_A						,  return_overflowing_tokens=_A						,  return_special_tokens_mask=_A						,  return_offsets_mapping=_A						,  return_length=_A						,  verbose=_A						,  return_tensors=_A						,  **_A						,  )
  
  			return encoded_inputs
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Dict						,  *_A	:		Optional[Any]						,  **_A	:		Optional[int]    )						->				int:
  			return self.tokenizer.batch_decode(*_A						,  **_A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[Any]						,  *_A	:		Tuple						,  **_A	:		List[str]    )						->				List[str]:
  			return self.tokenizer.decode(*_A						,  **_A    )
  
  
  
  
  
  
  
  		@property
  		def      __lowerCAmelCase	(      self	:		int    )						->				Any:
  			__magic_name__ :					Any  			=  self.tokenizer.model_input_names
  			return tokenizer_input_names
style_context: 
  
  
  '''simple docstring'''
  
  from .number_theory.prime_numbers import next_prime
  class _lowerCamelCase :
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(      self	:		Dict						,  _A	:		int						,  _A	:		int | None = None						,  _A	:		float | None = None						,  )						->				None:
  			__magic_name__ :					Optional[int]  			=  size_table
  			__magic_name__ :					int  			=  [None] * self.size_table
  			__magic_name__ :					int  			=  0.75 if lim_charge is None else lim_charge
  			__magic_name__ :					int  			=  1 if charge_factor is None else charge_factor
  			__magic_name__ :					list  			=  []
  			__magic_name__ :					dict  			=  {}
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[str]    )						->				str:
  			return self._keys
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Dict    )						->				Optional[Any]:
  			return sum(1 for slot in self.values if slot is not None    ) / (
  			    self.size_table * self.charge_factor
  			)
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		int						,  _A	:		int    )						->				List[Any]:
  			return key % self.size_table
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[Any]						,  _A	:		Tuple    )						->				Optional[int]:
  			print(F'step {step_ord}'    )
  			print(list(range(len(self.values    )    )    )    )
  			print(self.values    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		str						,  _A	:		Optional[Any]    )						->				Optional[int]:
  			__magic_name__ :					int  			=  1
  			__magic_name__ :					Optional[int]  			=  values
  			for value in values:
  				self.insert_data(_A    )
  				self._step_by_step(_A    )
  				i += 1
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[Any]						,  _A	:		List[str]						,  _A	:		List[Any]    )						->				Dict:
  			__magic_name__ :					str  			=  data
  			__magic_name__ :					Optional[int]  			=  data
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]						,  _A	:		List[Any]						,  _A	:		Union[str, Any]=None    )						->				int:
  			__magic_name__ :					int  			=  self.hash_function(key + 1    )
  
  			while self.values[new_key] is not None and self.values[new_key] != key:
  				if self.values.count(_A    ) > 0:
  					__magic_name__ :					Dict  			=  self.hash_function(new_key + 1    )
  				else:
  					__magic_name__ :					Tuple  			=  None
  					break
  
  			return new_key
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[str]    )						->				Tuple:
  			__magic_name__ :					Dict  			=  [value for value in self.values if value is not None]
  			__magic_name__ :					int  			=  next_prime(self.size_table						,  factor=2    )
  			self._keys.clear()
  			__magic_name__ :					Tuple  			=  [None] * self.size_table  # hell's pointers D: don't DRY ;/
  			for value in survivor_values:
  				self.insert_data(_A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Tuple						,  _A	:		Union[str, Any]    )						->				Tuple:
  			__magic_name__ :					Tuple  			=  self.hash_function(_A    )
  
  			if self.values[key] is None:
  				self._set_value(_A						,  _A    )
  
  			elif self.values[key] == data:
  				pass
  
  			else:
  				__magic_name__ :					str  			=  self._collision_resolution(_A						,  _A    )
  				if collision_resolution is not None:
  					self._set_value(_A						,  _A    )
  				else:
  					self.rehashing()
  					self.insert_data(_A    )