




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       lowercase_		(SCREAMING_SNAKE_CASE__   ):
						lowerCAmelCase__ 			=DPRContextEncoderTokenizer
						lowerCAmelCase__ 			=DPRContextEncoderTokenizerFast
						lowerCAmelCase__ 			=True



@require_tokenizers
class       lowercase_		(SCREAMING_SNAKE_CASE__   ):
						lowerCAmelCase__ 			=DPRQuestionEncoderTokenizer
						lowerCAmelCase__ 			=DPRQuestionEncoderTokenizerFast
						lowerCAmelCase__ 			=True



@require_tokenizers
class       lowercase_		(SCREAMING_SNAKE_CASE__   ):
						lowerCAmelCase__ 			=DPRReaderTokenizer
						lowerCAmelCase__ 			=DPRReaderTokenizerFast
						lowerCAmelCase__ 			=True



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

								"""simple docstring"""

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

								SCREAMING_SNAKE_CASE_							=       tokenizer.encode('question sequence'				,       add_special_tokens=snake_case__     )
								SCREAMING_SNAKE_CASE_							=       tokenizer.encode('title sequence'				,       add_special_tokens=snake_case__     )
								SCREAMING_SNAKE_CASE_							=       tokenizer.encode('text sequence ' * 4				,       add_special_tokens=snake_case__     )
								SCREAMING_SNAKE_CASE_							=       [[1_01] + text_a + [1_02] + text_a + [1_02] + text_a]
								SCREAMING_SNAKE_CASE_							=       BatchEncoding({'input_ids': input_ids}     )

								SCREAMING_SNAKE_CASE_							=       [[0] * len(input_ids[0]     )]
								SCREAMING_SNAKE_CASE_							=       [[0] * len(input_ids[0]     )]
								SCREAMING_SNAKE_CASE_							=       [0]
								SCREAMING_SNAKE_CASE_							=       DPRReaderOutput(snake_case__				,       snake_case__				,       snake_case__     )

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






						@slow
						def   __a				( self  :      int     ):

								"""simple docstring"""

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

								SCREAMING_SNAKE_CASE_							=       tokenizer.encode('question sequence'				,       add_special_tokens=snake_case__     )
								SCREAMING_SNAKE_CASE_							=       tokenizer.encode('title sequence'				,       add_special_tokens=snake_case__     )
								SCREAMING_SNAKE_CASE_							=       tokenizer.encode('text sequence'				,       add_special_tokens=snake_case__     )
								SCREAMING_SNAKE_CASE_							=       [1_01] + text_a + [1_02] + text_a + [1_02] + text_a
								SCREAMING_SNAKE_CASE_							=       tokenizer(questions=['question sequence']				,       titles=['title sequence']				,       texts=['text sequence']     )
								self.assertIn('input_ids'				,       snake_case__     )
								self.assertIn('attention_mask'				,       snake_case__     )
								self.assertListEqual(encoded_input['input_ids'][0]				,       snake_case__     )





from __future__ import annotations


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  int:
		SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       0, len(lowerCAmelCase   ) - 1
		while start_ind <= end_ind:
				if array[start_ind] == search_item:
						return start_ind
				elif array[end_ind] == search_item:
						return end_ind
				else:
						start_ind += 1
						end_ind -= 1
    # returns -1 if search_item is not found in array
		return -1


if __name__ == "__main__":
						print(double_linear_search(list(range(1_0_0)), 4_0))