

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__     (			_lowercase			):



 """simple docstring"""







 _lowercase  :       List[Any]  			=    '''MarkupLMFeatureExtractor'''
 _lowercase  :       int  			=    ('''MarkupLMTokenizer''', '''MarkupLMTokenizerFast''')
 _lowercase  :       Optional[int]  			=    True

 def __call__(		self			:						int  ,    A_			:						List[str]=None  ,    A_			:						Optional[int]=None  ,    A_			:						Union[str, Any]=None  ,    A_			:						Any=None  ,    A_			:						int=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_			:						Union[str, Any]  ,    ):






      '''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`"							)

           _lowerCAmelCase :       List[str]	   =     self.feature_extractor(snake_case__							)
           _lowerCAmelCase :       Dict	   =     features["""nodes"""]
           _lowerCAmelCase :       int	   =     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__							):
                _lowerCAmelCase :       Union[str, Any]	   =     [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

      _lowerCAmelCase :       Union[str, Any]	   =     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			:						Union[str, Any]  ,    *A_			:						Any  ,    **A_			:						Union[str, Any]							):






      '''simple docstring'''




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

 def  __magic_name__							(		self			:						List[str]  ,    *A_			:						Any  ,    **A_			:						int							):






      '''simple docstring'''




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


 @property
 def  __magic_name__							(		self			:						int							):






      '''simple docstring'''




      _lowerCAmelCase :       List[Any]	   =     self.tokenizer.model_input_names
      return tokenizer_input_names








"""simple docstring"""
from transformers import ProcessorMixin




class  _snake_case	(    _lowercase							):
							SCREAMING_SNAKE_CASE		  =    '''AutoFeatureExtractor'''
							SCREAMING_SNAKE_CASE		  =    '''AutoTokenizer'''
