'''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				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					Tuple    =				'MarkupLMFeatureExtractor'
       __lowercase					:					int    =				('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
       __lowercase					:					str    =				True

       def __call__(      self    ,     _SCREAMING_SNAKE_CASE=None    ,     _SCREAMING_SNAKE_CASE=None    ,     _SCREAMING_SNAKE_CASE=None    ,     _SCREAMING_SNAKE_CASE=None    ,     _SCREAMING_SNAKE_CASE=None    ,     _SCREAMING_SNAKE_CASE = True    ,     _SCREAMING_SNAKE_CASE = False    ,     _SCREAMING_SNAKE_CASE = None    ,     _SCREAMING_SNAKE_CASE = None    ,     _SCREAMING_SNAKE_CASE = 0    ,     _SCREAMING_SNAKE_CASE = None    ,     _SCREAMING_SNAKE_CASE = None    ,     _SCREAMING_SNAKE_CASE = None    ,     _SCREAMING_SNAKE_CASE = False    ,     _SCREAMING_SNAKE_CASE = False    ,     _SCREAMING_SNAKE_CASE = False    ,     _SCREAMING_SNAKE_CASE = False    ,     _SCREAMING_SNAKE_CASE = True    ,     _SCREAMING_SNAKE_CASE = None    ,     **_SCREAMING_SNAKE_CASE    ,     )		->     BatchEncoding:
           # first, create nodes and xpaths
           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(_SCREAMING_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(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_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=_SCREAMING_SNAKE_CASE    ,     node_labels=_SCREAMING_SNAKE_CASE    ,     add_special_tokens=_SCREAMING_SNAKE_CASE    ,     padding=_SCREAMING_SNAKE_CASE    ,     truncation=_SCREAMING_SNAKE_CASE    ,     max_length=_SCREAMING_SNAKE_CASE    ,     stride=_SCREAMING_SNAKE_CASE    ,     pad_to_multiple_of=_SCREAMING_SNAKE_CASE    ,     return_token_type_ids=_SCREAMING_SNAKE_CASE    ,     return_attention_mask=_SCREAMING_SNAKE_CASE    ,     return_overflowing_tokens=_SCREAMING_SNAKE_CASE    ,     return_special_tokens_mask=_SCREAMING_SNAKE_CASE    ,     return_offsets_mapping=_SCREAMING_SNAKE_CASE    ,     return_length=_SCREAMING_SNAKE_CASE    ,     verbose=_SCREAMING_SNAKE_CASE    ,     return_tensors=_SCREAMING_SNAKE_CASE    ,     **_SCREAMING_SNAKE_CASE    ,     )

           return encoded_inputs

       def 					__A       (      self    ,     *_SCREAMING_SNAKE_CASE    ,     **_SCREAMING_SNAKE_CASE )		->     Tuple:
           return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE    ,     **_SCREAMING_SNAKE_CASE )

       def 					__A       (      self    ,     *_SCREAMING_SNAKE_CASE    ,     **_SCREAMING_SNAKE_CASE )		->     int:
           return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE    ,     **_SCREAMING_SNAKE_CASE )

       @property
       def 					__A       (      self )		->     Tuple:
           A_     =   self.tokenizer.model_input_names
           return tokenizer_input_names










UpperCAmelCase			=    10**8






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  bool:




      """simple docstring"""

      if n % 10 == 0:
            return False
      snake_case_       =      str(SCREAMING_SNAKE_CASE					)
      return s == s[::-1]






def        __lowerCAmelCase    ()->  int:




      """simple docstring"""

      snake_case_       =      set()
      snake_case_       =      1
      snake_case_       =      5
      while sum_squares < LIMIT:
            snake_case_       =      first_square + 1
            while sum_squares < LIMIT:
                  if is_palindrome(SCREAMING_SNAKE_CASE					):
                        answer.add(SCREAMING_SNAKE_CASE					)
                  last_square += 1
                  sum_squares += last_square**2
            first_square += 1
            snake_case_       =      first_square**2 + (first_square + 1) ** 2

      return sum(SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
   print(solution())