





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__			):
							lowerCamelCase_							=    "MarkupLMFeatureExtractor"
							lowerCamelCase_							=    ("MarkupLMTokenizer", "MarkupLMTokenizerFast")
							lowerCamelCase_							=    True


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



									"""simple docstring"""



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


							def    _snake_case     (							self			:List[Any]							, *__A			:str							, **__A			:str						)      ->      Tuple:



									"""simple docstring"""



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


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self.tokenizer.model_input_names
									return tokenizer_input_names






from __future__ import annotations

import math
import random






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							int    ,      UpperCamelCase__:							int    ,      UpperCamelCase__:							int			):
		SCREAMING_SNAKE_CASE__								=					d * e - 1
		SCREAMING_SNAKE_CASE__								=					0
		SCREAMING_SNAKE_CASE__								=					0
		while p == 0:
				SCREAMING_SNAKE_CASE__								=					random.randint(2    ,      n - 1			)
				SCREAMING_SNAKE_CASE__								=					k
				while True:
						if t % 2 == 0:
								SCREAMING_SNAKE_CASE__								=					t // 2
								SCREAMING_SNAKE_CASE__								=					(g**t) % n
								SCREAMING_SNAKE_CASE__								=					math.gcd(x - 1    ,      UpperCamelCase__			)
								if x > 1 and y > 1:
										SCREAMING_SNAKE_CASE__								=					y
										SCREAMING_SNAKE_CASE__								=					n // y
										break  # find the correct factors
						else:
								break  # t is not divisible by 2, break and choose another g
		return sorted([p, q]			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()