

from transformers import (
    DPRContextEncoderTokenizer,
    DPRContextEncoderTokenizerFast,
    DPRQuestionEncoderTokenizer,
    DPRQuestionEncoderTokenizerFast,
    DPRReaderOutput,
    DPRReaderTokenizer,
    DPRReaderTokenizerFast,
)
from transformers.testing_utils import require_tokenizers, slow
from transformers.tokenization_utils_base import BatchEncoding

from ..bert.test_tokenization_bert import BertTokenizationTest




@require_tokenizers
class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	int					     =      DPRContextEncoderTokenizer
	_UpperCAmelCase :	Union[str, Any]					     =      DPRContextEncoderTokenizerFast
	_UpperCAmelCase :	Any					     =      True




@require_tokenizers
class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	Any					     =      DPRQuestionEncoderTokenizer
	_UpperCAmelCase :	Tuple					     =      DPRQuestionEncoderTokenizerFast
	_UpperCAmelCase :	Optional[Any]					     =      True




@require_tokenizers
class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	Optional[Any]					     =      DPRReaderTokenizer
	_UpperCAmelCase :	List[str]					     =      DPRReaderTokenizerFast
	_UpperCAmelCase :	List[Any]					     =      True



	@slow
	def 				A							(						self			:					Optional[Any]			):


				'''simple docstring'''


				_snake_case						=    self.tokenizer_class.from_pretrained('bert-base-uncased'			)

				_snake_case						=    tokenizer.encode('question sequence'    ,						add_special_tokens=lowercase			)
				_snake_case						=    tokenizer.encode('title sequence'    ,						add_special_tokens=lowercase			)
				_snake_case						=    tokenizer.encode('text sequence ' * 4    ,						add_special_tokens=lowercase			)
				_snake_case						=    [[101] + text_a + [102] + text_a + [102] + text_a]
				_snake_case						=    BatchEncoding({'input_ids': input_ids}			)

				_snake_case						=    [[0] * len(input_ids[0]			)]
				_snake_case						=    [[0] * len(input_ids[0]			)]
				_snake_case						=    [0]
				_snake_case						=    DPRReaderOutput(lowercase    ,						lowercase    ,						lowercase			)

				_snake_case			,      _snake_case						=    8, 9
				_snake_case						=    10
				_snake_case						=    10
				_snake_case						=    tokenizer.decode_best_spans(lowercase    ,						lowercase			)
				self.assertEqual(predicted_spans[0].start_index    ,						lowercase			)
				self.assertEqual(predicted_spans[0].end_index    ,						lowercase			)
				self.assertEqual(predicted_spans[0].doc_id    ,						0			)





	@slow
	def 				A							(						self			:					Optional[int]			):


				'''simple docstring'''


				_snake_case						=    self.tokenizer_class.from_pretrained('bert-base-uncased'			)

				_snake_case						=    tokenizer.encode('question sequence'    ,						add_special_tokens=lowercase			)
				_snake_case						=    tokenizer.encode('title sequence'    ,						add_special_tokens=lowercase			)
				_snake_case						=    tokenizer.encode('text sequence'    ,						add_special_tokens=lowercase			)
				_snake_case						=    [101] + text_a + [102] + text_a + [102] + text_a
				_snake_case						=    tokenizer(questions=['question sequence']    ,						titles=['title sequence']    ,						texts=['text sequence']			)
				self.assertIn('input_ids'    ,						lowercase			)
				self.assertIn('attention_mask'    ,						lowercase			)
				self.assertListEqual(encoded_input['input_ids'][0]    ,						lowercase			)

def  a_	( __lowercase     :			int				)				->						bool:
			_snake_case						=    9

			while num > 0:
						if num % 10 != digit:
									return False
						num //= 100
						digit -= 1

			return True






def  a_	( )				->						int:
			_snake_case						=    138_902_663

			while not is_square_form(num * num				):
						if num % 10 == 3:
									num -= 6  # (3 - 6) % 10 = 7
						else:
									num -= 4  # (7 - 4) % 10 = 3

			return num * 10


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