






from typing import Optional, Union

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







class 				lowerCamelCase_     (	a_     ):
							SCREAMING_SNAKE_CASE_						     =			'MarkupLMFeatureExtractor'
							SCREAMING_SNAKE_CASE_						     =			('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
							SCREAMING_SNAKE_CASE_						     =			True
							def __call__(						self		:					Union[str, Any]							,__lowerCamelCase		:					Any=None							,__lowerCamelCase		:					Tuple=None							,__lowerCamelCase		:					Tuple=None							,__lowerCamelCase		:					List[str]=None							,__lowerCamelCase		:					Dict=None							,__lowerCamelCase		:					bool = True							,__lowerCamelCase		:					Union[bool, str, PaddingStrategy] = False							,__lowerCamelCase		:					Union[bool, str, TruncationStrategy] = None							,__lowerCamelCase		:					Optional[int] = None							,__lowerCamelCase		:					int = 0							,__lowerCamelCase		:					Optional[int] = None							,__lowerCamelCase		:					Optional[bool] = None							,__lowerCamelCase		:					Optional[bool] = None							,__lowerCamelCase		:					bool = False							,__lowerCamelCase		:					bool = False							,__lowerCamelCase		:					bool = False							,__lowerCamelCase		:					bool = False							,__lowerCamelCase		:					bool = True							,__lowerCamelCase		:					Optional[Union[str, TensorType]] = None							,**__lowerCamelCase		:					List[Any]							,):





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

								return encoded_inputs
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[Any]							,*__lowerCamelCase		:					Optional[Any]							,**__lowerCamelCase		:					List[str]					):





								'''simple docstring'''



								return self.tokenizer.batch_decode(*__lowerCamelCase							,**__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[Any]							,*__lowerCamelCase		:					Optional[int]							,**__lowerCamelCase		:					Optional[Any]					):





								'''simple docstring'''



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




							@property
							def  SCREAMING_SNAKE_CASE_ (						self		:					Dict					):





								'''simple docstring'''



								a		=			self.tokenizer.model_input_names
								return tokenizer_input_names













UpperCamelCase__						:      Dict								=					10**8





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  bool:


	"""simple docstring"""



	if n % 1_0 == 0:
		return False
	a		=			str(snake_case_  )
	return s == s[::-1]





def   SCREAMING_SNAKE_CASE__						(   )   ->  int:


	"""simple docstring"""



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

	return sum(snake_case_  )


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





