





from typing import Optional, Union

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






class  __A   (   lowerCAmelCase			):

			'''simple docstring'''







			lowerCAmelCase_												=						"""MarkupLMFeatureExtractor"""
			lowerCAmelCase_												=						("""MarkupLMTokenizer""", """MarkupLMTokenizerFast""")
			lowerCAmelCase_												=						True





			def __call__(   self	,  __lowerCAmelCase=None	,  __lowerCAmelCase=None	,  __lowerCAmelCase=None	,  __lowerCAmelCase=None	,  __lowerCAmelCase=None	,  __lowerCAmelCase = True	,  __lowerCAmelCase = False	,  __lowerCAmelCase = None	,  __lowerCAmelCase = None	,  __lowerCAmelCase = 0	,  __lowerCAmelCase = None	,  __lowerCAmelCase = None	,  __lowerCAmelCase = None	,  __lowerCAmelCase = False	,  __lowerCAmelCase = False	,  __lowerCAmelCase = False	,  __lowerCAmelCase = False	,  __lowerCAmelCase = True	,  __lowerCAmelCase = None	,  **__lowerCAmelCase	,  ):



				'''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`'''							)

					lowerCamelCase__        =		self.feature_extractor(__lowerCAmelCase							)
					lowerCamelCase__        =		features['''nodes''']
					lowerCamelCase__        =		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							):
						lowerCamelCase__        =		[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

				lowerCamelCase__        =		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 		__lowerCamelCase    (   self	,  *__lowerCAmelCase	,  **__lowerCAmelCase							):



				'''simple docstring'''







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





			def 		__lowerCamelCase    (   self	,  *__lowerCAmelCase	,  **__lowerCAmelCase							):



				'''simple docstring'''







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





			@property
			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				lowerCamelCase__        =		self.tokenizer.model_input_names
				return tokenizer_input_names










from __future__ import annotations

import math
import random





def lowerCAmelCase__(__snake_case	,__snake_case	,__snake_case							)    ->				list[int]:







	'''simple docstring'''







	lowerCamelCase__        =		d * e - 1
	lowerCamelCase__        =		0
	lowerCamelCase__        =		0
	while p == 0:
		lowerCamelCase__        =		random.randint(2	,n - 1							)
		lowerCamelCase__        =		k
		while True:
			if t % 2 == 0:
				lowerCamelCase__        =		t // 2
				lowerCamelCase__        =		(g**t) % n
				lowerCamelCase__        =		math.gcd(x - 1	,__snake_case							)
				if x > 1 and y > 1:
					lowerCamelCase__        =		y
					lowerCamelCase__        =		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()



