





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(    lowerCAmelCase_			):
     __A							=							"MarkupLMFeatureExtractor"
     __A							=							("MarkupLMTokenizer", "MarkupLMTokenizerFast")
     __A							=							True

     def __call__(   self,							A=None,							A=None,							A=None,							A=None,							A=None,							A = True,							A = False,							A = None,							A = None,							A = 0,							A = None,							A = None,							A = None,							A = False,							A = False,							A = False,							A = False,							A = True,							A = None,							**A,							):





            """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`'''   )

                   _UpperCamelCase														=    self.feature_extractor(__UpperCamelCase   )
                   _UpperCamelCase														=    features["nodes"]
                   _UpperCamelCase														=    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   ):
                          _UpperCamelCase														=    [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

            _UpperCamelCase														=    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 						_UpperCamelCase  (   self,							*A,							**A   ):





            """simple docstring"""

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

     def 						_UpperCamelCase  (   self,							*A,							**A   ):





            """simple docstring"""

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



     @property
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.tokenizer.model_input_names
            return tokenizer_input_names








"""simple docstring"""




def 				a__			(					lowerCAmelCase						:							str		):






     '''simple docstring'''
     UpperCAmelCase__				:			Optional[Any]							=    str(lowerCAmelCase		).strip()
     if not oct_string:
          raise ValueError("Empty string was passed to the function"		)
     UpperCAmelCase__				:			Tuple							=    oct_string[0] == "-"
     if is_negative:
          UpperCAmelCase__				:			List[Any]							=    oct_string[1:]
     if not oct_string.isdigit() or not all(0 <= int(lowerCAmelCase		) <= 7 for char in oct_string		):
          raise ValueError("Non-octal value was passed to the function"		)
     UpperCAmelCase__				:			Optional[int]							=    0
     for char in oct_string:
          UpperCAmelCase__				:			Tuple							=    8 * decimal_number + int(lowerCAmelCase		)
     if is_negative:
          UpperCAmelCase__				:			int							=    -decimal_number
     return decimal_number


if __name__ == "__main__":
    from doctest import testmod

    testmod()



