


"""simple docstring"""
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					(				__snake_case							):






		'''simple docstring'''

		lowerCamelCase__           ='MarkupLMFeatureExtractor'
		lowerCamelCase__           =('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
		lowerCamelCase__           =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`'''   )

				__snake_case      :		List[str]         =							self.feature_extractor(a_   )
				__snake_case      :		Tuple         =							features['''nodes''']
				__snake_case      :		Tuple         =							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(a_							,			a_   ):
					__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=a_							,			node_labels=a_							,			add_special_tokens=a_							,			padding=a_							,			truncation=a_							,			max_length=a_							,			stride=a_							,			pad_to_multiple_of=a_							,			return_token_type_ids=a_							,			return_attention_mask=a_							,			return_overflowing_tokens=a_							,			return_special_tokens_mask=a_							,			return_offsets_mapping=a_							,			return_length=a_							,			verbose=a_							,			return_tensors=a_							,			**a_							,			)

			return encoded_inputs



		def 							SCREAMING_SNAKE_CASE    (self							,			*a_							,			**a_   ):





			'''simple docstring'''

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



		def 							SCREAMING_SNAKE_CASE    (self							,			*a_							,			**a_   ):





			'''simple docstring'''

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



		@property
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Tuple         =							self.tokenizer.model_input_names
			return tokenizer_input_names













from __future__ import annotations

import re





def 						A_					(						lowercase_			)				->list[str]:







 """simple docstring"""

 def alphanum_key(lowercase_			):
  return [int(lowercase_			) if s.isdigit() else s.lower() for s in re.split('([0-9]+)'	,  lowercase_			)]

 return sorted(lowercase_	,  key=lowercase_			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()




