from typing import Optional, Union

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


class   lowercase			(   a   ):
							lowercase__  :				int									=							"""MarkupLMFeatureExtractor"""
							lowercase__  :				int									=							("""MarkupLMTokenizer""", """MarkupLMTokenizerFast""")
							lowercase__  :				List[Any]									=							True




							def __call__(  self      :    Optional[Any]		,							_UpperCamelCase      :    List[Any]=None		,							_UpperCamelCase      :    List[Any]=None		,							_UpperCamelCase      :    Union[str, Any]=None		,							_UpperCamelCase      :    List[str]=None		,							_UpperCamelCase      :    int=None		,							_UpperCamelCase      :    bool = True		,							_UpperCamelCase      :    Union[bool, str, PaddingStrategy] = False		,							_UpperCamelCase      :    Union[bool, str, TruncationStrategy] = None		,							_UpperCamelCase      :    Optional[int] = None		,							_UpperCamelCase      :    int = 0		,							_UpperCamelCase      :    Optional[int] = None		,							_UpperCamelCase      :    Optional[bool] = None		,							_UpperCamelCase      :    Optional[bool] = None		,							_UpperCamelCase      :    bool = False		,							_UpperCamelCase      :    bool = False		,							_UpperCamelCase      :    bool = False		,							_UpperCamelCase      :    bool = False		,							_UpperCamelCase      :    bool = True		,							_UpperCamelCase      :    Optional[Union[str, TensorType]] = None		,							**_UpperCamelCase      :    Any		,							)    -> BatchEncoding:



												'''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`"					)

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

												SCREAMING_SNAKE_CASE				=				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 		__snake_case(  self      :    Optional[int]		,							*_UpperCamelCase      :    Any		,							**_UpperCamelCase      :    Tuple					)    -> List[Any]:



												'''simple docstring'''

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




							def 		__snake_case(  self      :    Optional[int]		,							*_UpperCamelCase      :    int		,							**_UpperCamelCase      :    str					)    -> Any:



												'''simple docstring'''

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





							@property
							def 		__snake_case(  self      :    Optional[int]					)    -> Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.tokenizer.model_input_names
												return tokenizer_input_names





import math





def 		__lowerCamelCase							(UpperCAmelCase__   :							int     ):

					if 1 < number < 4:
										# 2 and 3 are primes
										return True
					elif number < 2 or number % 2 == 0 or number % 3 == 0:
										# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
										return False

					# All primes number are in format of 6k +/- 1
					for i in range(5					,       int(math.sqrt(UpperCAmelCase__     ) + 1     )					,       6     ):
										if number % i == 0 or number % (i + 2) == 0:
															return False
					return True





def 		__lowerCamelCase							(UpperCAmelCase__   :							int = 2_0_0_0_0_0_0     ):
					return sum(num for num in range(3					,       UpperCAmelCase__					,       2     ) if is_prime(UpperCAmelCase__     )     ) + 2 if n > 2 else 0


if __name__ == "__main__":
							print(f"""{solution() = }""")




