





from typing import Optional, Union

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





class UpperCAmelCase_							(   _SCREAMING_SNAKE_CASE   ):
  __lowerCAmelCase      :	Dict  	=      """MarkupLMFeatureExtractor"""
  __lowerCAmelCase      :	str  	=      ("""MarkupLMTokenizer""", """MarkupLMTokenizerFast""")
  __lowerCAmelCase      :	List[Any]  	=      True


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



       """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`'''   )

            A_					      =					self.feature_extractor(__snake_case   )
            A_					      =					features['''nodes''']
            A_					      =					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   ):
                 A_					      =					[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

       A_					      =					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 	__UpperCAmelCase    (       self   ,*__snake_case   ,**__snake_case   ):



       """simple docstring"""





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


  def 	__UpperCAmelCase    (       self   ,*__snake_case   ,**__snake_case   ):



       """simple docstring"""





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




  @property
  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.tokenizer.model_input_names
       return tokenizer_input_names










from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments

def     UpperCAmelCase_						(						)  ->							Optional[int]:
     '''simple docstring'''


     A_					      =					HfArgumentParser(_UpperCAmelCase							)
     try:
          A_					      =					parser.parse_args_into_dataclasses()[0]
     except ValueError as e:
          A_					      =					'''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
          A_					      =					''' '''.join(str(_UpperCAmelCase							).split(''' '''							)[:-1]							)
          A_					      =					''''''
          A_					      =					eval(str(_UpperCAmelCase							).split(''' '''							)[-1]							)
          A_					      =					[]
          for arg in depreciated_args:
               # arg[2:] removes '--'
               if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
                    # arg[5:] removes '--no_'
                    full_error_msg += arg_error_msg.format(arg[5:]							)
               else:
                    wrong_args.append(_UpperCAmelCase							)
          if len(_UpperCAmelCase							) > 0:
               A_					      =					full_error_msg + begin_error_msg + str(_UpperCAmelCase							)
          raise ValueError(_UpperCAmelCase							)

     A_					      =					PyTorchBenchmark(args=_UpperCAmelCase							)
     benchmark.run()


if __name__ == "__main__":
      main()



