





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				):
      _SCREAMING_SNAKE_CASE					=							"""MarkupLMFeatureExtractor"""
      _SCREAMING_SNAKE_CASE					=							("""MarkupLMTokenizer""", """MarkupLMTokenizerFast""")
      _SCREAMING_SNAKE_CASE					=							True



      def __call__(					self     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_ = True     ,  lowercase_ = False     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = 0     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = False     ,  lowercase_ = False     ,  lowercase_ = False     ,  lowercase_ = False     ,  lowercase_ = True     ,  lowercase_ = None     ,  **lowercase_     ,  )      ->   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(lowercase_					)
                    _snake_case      :							Union[str, Any]     				=						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(lowercase_     ,  lowercase_					):
                           _snake_case      :							Dict     				=						[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

             _snake_case      :							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=lowercase_     ,  node_labels=lowercase_     ,  add_special_tokens=lowercase_     ,  padding=lowercase_     ,  truncation=lowercase_     ,  max_length=lowercase_     ,  stride=lowercase_     ,  pad_to_multiple_of=lowercase_     ,  return_token_type_ids=lowercase_     ,  return_attention_mask=lowercase_     ,  return_overflowing_tokens=lowercase_     ,  return_special_tokens_mask=lowercase_     ,  return_offsets_mapping=lowercase_     ,  return_length=lowercase_     ,  verbose=lowercase_     ,  return_tensors=lowercase_     ,  **lowercase_     ,  )

             return encoded_inputs



      def    __a (					self     ,  *lowercase_     ,  **lowercase_					)      ->   Optional[int]:



             '''simple docstring'''

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



      def    __a (					self     ,  *lowercase_     ,  **lowercase_					)      ->   Union[str, Any]:



             '''simple docstring'''

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



      @property
      def    __a (					self					)      ->   str:



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						self.tokenizer.model_input_names
             return tokenizer_input_names













def 						A_			(  lowercase_  )			->						list:
       if len(lowercase_  ) <= 1:
              return data
       else:
              return [
                  *quick_sort([e for e in data[1:] if e <= data[0]]  ),
                  data[0],
                  *quick_sort([e for e in data[1:] if e > data[0]]  ),
              ]


if __name__ == "__main__":
   import doctest

   doctest.testmod()






