
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							(		snake_case							):
				lowerCamelCase_       =     DPRContextEncoderTokenizer
				lowerCamelCase_       =     DPRContextEncoderTokenizerFast
				lowerCamelCase_       =     True



@require_tokenizers
class     _SCREAMING_SNAKE_CASE							(		snake_case							):
				lowerCamelCase_       =     DPRQuestionEncoderTokenizer
				lowerCamelCase_       =     DPRQuestionEncoderTokenizerFast
				lowerCamelCase_       =     True



@require_tokenizers
class     _SCREAMING_SNAKE_CASE							(		snake_case							):
				lowerCamelCase_       =     DPRReaderTokenizer
				lowerCamelCase_       =     DPRReaderTokenizerFast
				lowerCamelCase_       =     True





				@slow
				def        _UpperCAmelCase						(		self :		List[str]							):


								"""simple docstring"""

								A     :  Dict   =				self.tokenizer_class.from_pretrained('''bert-base-uncased'''							)

								A     :  Optional[Any]   =				tokenizer.encode('''question sequence'''					,      add_special_tokens=snake_case_							)
								A     :  List[str]   =				tokenizer.encode('''title sequence'''					,      add_special_tokens=snake_case_							)
								A     :  int   =				tokenizer.encode('''text sequence ''' * 4					,      add_special_tokens=snake_case_							)
								A     :  List[Any]   =				[[101] + text_a + [102] + text_a + [102] + text_a]
								A     :  List[Any]   =				BatchEncoding({'''input_ids''': input_ids}							)

								A     :  Any   =				[[0] * len(input_ids[0]							)]
								A     :  Any   =				[[0] * len(input_ids[0]							)]
								A     :  List[Any]   =				[0]
								A     :  Tuple   =				DPRReaderOutput(snake_case_					,      snake_case_					,      snake_case_							)

								A ,       A     :  Union[str, Any]   =				8, 9
								A     :  Tuple   =				10
								A     :  Optional[int]   =				10
								A     :  str   =				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        _UpperCAmelCase						(		self :		str							):


								"""simple docstring"""

								A     :  List[Any]   =				self.tokenizer_class.from_pretrained('''bert-base-uncased'''							)

								A     :  Dict   =				tokenizer.encode('''question sequence'''					,      add_special_tokens=snake_case_							)
								A     :  List[str]   =				tokenizer.encode('''title sequence'''					,      add_special_tokens=snake_case_							)
								A     :  Optional[Any]   =				tokenizer.encode('''text sequence'''					,      add_special_tokens=snake_case_							)
								A     :  Dict   =				[101] + text_a + [102] + text_a + [102] + text_a
								A     :  Union[str, Any]   =				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_							)

UpperCamelCase_  =	{
    "0": 1,
    "1": 1,
    "2": 2,
    "3": 6,
    "4": 24,
    "5": 1_20,
    "6": 7_20,
    "7": 50_40,
    "8": 4_03_20,
    "9": 36_28_80,
}

UpperCamelCase_  =	{1_45: 1_45}

UpperCamelCase_  =	{
    1_45: 0,
    1_69: 3,
    3_63_01: 3,
    14_54: 3,
    8_71: 2,
    4_53_61: 2,
    8_72: 2,
}





def 							_lowerCamelCase			(					lowerCamelCase_:  int		):



				'''simple docstring'''







				if n in CACHE_SUM_DIGIT_FACTORIALS:
								return CACHE_SUM_DIGIT_FACTORIALS[n]
				A     :  Tuple   =				sum(DIGIT_FACTORIALS[let] for let in str(lowerCamelCase_		)		)
				A     :  Dict   =				ret
				return ret





def 							_lowerCamelCase			(					lowerCamelCase_:  int				,	lowerCamelCase_:  set | None = None		):



				'''simple docstring'''







				A     :  Optional[int]   =				previous or set()
				if n in CHAIN_LENGTH_CACHE:
								return CHAIN_LENGTH_CACHE[n]
				A     :  Dict   =				sum_digit_factorials(lowerCamelCase_		)
				if next_number in previous:
								A     :  Optional[int]   =				0
								return 0
				else:
								previous.add(lowerCamelCase_		)
								A     :  str   =				1 + chain_length(lowerCamelCase_				,	lowerCamelCase_		)
								A     :  Any   =				ret
								return ret





def 							_lowerCamelCase			(					lowerCamelCase_:  int = 60				,	lowerCamelCase_:  int = 100_0000		):



				'''simple docstring'''







				return sum(1 for i in range(1				,	lowerCamelCase_		) if chain_length(lowerCamelCase_		) == num_terms		)


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