


from typing import Optional, Union

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





class     __a     (       __UpperCamelCase ):
 __lowercase					:				int  	=					'MarkupLMFeatureExtractor'
 __lowercase					:				str  	=					('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
 __lowercase					:				Optional[int]  	=					True
 def __call__(  self							,					lowerCAmelCase__=None							,					lowerCAmelCase__=None							,					lowerCAmelCase__=None							,					lowerCAmelCase__=None							,					lowerCAmelCase__=None							,					lowerCAmelCase__ = True							,					lowerCAmelCase__ = False							,					lowerCAmelCase__ = None							,					lowerCAmelCase__ = None							,					lowerCAmelCase__ = 0							,					lowerCAmelCase__ = None							,					lowerCAmelCase__ = None							,					lowerCAmelCase__ = None							,					lowerCAmelCase__ = False							,					lowerCAmelCase__ = False							,					lowerCAmelCase__ = False							,					lowerCAmelCase__ = False							,					lowerCAmelCase__ = True							,					lowerCAmelCase__ = None							,					**lowerCAmelCase__							,					)   ->							BatchEncoding:




        '''simple docstring'''
        # 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`'					)

               lowercase__:  Tuple         =  self.feature_extractor(lowerCAmelCase__					)
               lowercase__:  Tuple         =  features['nodes']
               lowercase__:  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(lowerCAmelCase__							,					lowerCAmelCase__					):
                      lowercase__:  Optional[Any]         =  [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

        lowercase__:  Optional[int]         =  self.tokenizer(
            text=questions if questions is not None else nodes							,					text_pair=nodes if questions is not None else None							,					xpaths=lowerCAmelCase__							,					node_labels=lowerCAmelCase__							,					add_special_tokens=lowerCAmelCase__							,					padding=lowerCAmelCase__							,					truncation=lowerCAmelCase__							,					max_length=lowerCAmelCase__							,					stride=lowerCAmelCase__							,					pad_to_multiple_of=lowerCAmelCase__							,					return_token_type_ids=lowerCAmelCase__							,					return_attention_mask=lowerCAmelCase__							,					return_overflowing_tokens=lowerCAmelCase__							,					return_special_tokens_mask=lowerCAmelCase__							,					return_offsets_mapping=lowerCAmelCase__							,					return_length=lowerCAmelCase__							,					verbose=lowerCAmelCase__							,					return_tensors=lowerCAmelCase__							,					**lowerCAmelCase__							,					)

        return encoded_inputs
 def 						SCREAMING_SNAKE_CASE__  (  self							,					*lowerCAmelCase__							,					**lowerCAmelCase__					)   ->							Optional[int]:




        '''simple docstring'''
        return self.tokenizer.batch_decode(*lowerCAmelCase__							,					**lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					*lowerCAmelCase__							,					**lowerCAmelCase__					)   ->							Optional[int]:




        '''simple docstring'''
        return self.tokenizer.decode(*lowerCAmelCase__							,					**lowerCAmelCase__					)
 @property
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[Any]:




        '''simple docstring'''
        lowercase__:  Tuple         =  self.tokenizer.model_input_names
        return tokenizer_input_names






from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime





class     __a     (       __UpperCamelCase ):
 def __init__(  self							,					*lowerCAmelCase__							,					**lowerCAmelCase__					)   ->							Any:




        '''simple docstring'''
        super().__init__(*lowerCAmelCase__							,					**lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							Union[str, Any]:




        '''simple docstring'''
        lowercase__:  str         =  (
            next_prime(value % self.size_table					)
            if not is_prime(value % self.size_table					)
            else value % self.size_table
        )  # gt = bigger than
        return next_prime_gt - (data % next_prime_gt)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							Union[str, Any]:




        '''simple docstring'''
        return (increment * self.__hash_function_a(lowerCAmelCase__							,					lowerCAmelCase__					)) % self.size_table
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__=None					)   ->							str:




        '''simple docstring'''
        lowercase__:  Dict         =  1
        lowercase__:  str         =  self.hash_function(lowerCAmelCase__					)

        while self.values[new_key] is not None and self.values[new_key] != key:
               lowercase__:  List[Any]         =  (
                   self.__hash_double_function(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)
                   if self.balanced_factor() >= self.lim_charge
                   else None
               )
               if new_key is None:
                      break
               else:
                      i += 1

        return new_key


