




"""simple docstring"""




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        UpperCAmelCase_  (	snake_case						):
		UpperCamelCase									=DPRContextEncoderTokenizer
		UpperCamelCase									=DPRContextEncoderTokenizerFast
		UpperCamelCase									=True


@require_tokenizers
class        UpperCAmelCase_  (	snake_case						):
		UpperCamelCase									=DPRQuestionEncoderTokenizer
		UpperCamelCase									=DPRQuestionEncoderTokenizerFast
		UpperCamelCase									=True


@require_tokenizers
class        UpperCAmelCase_  (	snake_case						):
		UpperCamelCase									=DPRReaderTokenizer
		UpperCamelCase									=DPRReaderTokenizerFast
		UpperCamelCase									=True
		@slow
		def 					_lowerCamelCase    (		self   )		->   List[str]:
					__lowercase	:       Tuple				=   self.tokenizer_class.from_pretrained('''bert-base-uncased'''   )

					__lowercase	:       Union[str, Any]				=   tokenizer.encode('''question sequence'''							,		add_special_tokens=UpperCamelCase_   )
					__lowercase	:       Tuple				=   tokenizer.encode('''title sequence'''							,		add_special_tokens=UpperCamelCase_   )
					__lowercase	:       List[Any]				=   tokenizer.encode('''text sequence ''' * 4							,		add_special_tokens=UpperCamelCase_   )
					__lowercase	:       Dict				=   [[1_01] + text_a + [1_02] + text_a + [1_02] + text_a]
					__lowercase	:       int				=   BatchEncoding({'''input_ids''': input_ids}   )

					__lowercase	:       Optional[int]				=   [[0] * len(input_ids[0]   )]
					__lowercase	:       Dict				=   [[0] * len(input_ids[0]   )]
					__lowercase	:       int				=   [0]
					__lowercase	:       List[str]				=   DPRReaderOutput(UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )

					__lowercase	,__lowercase	:       Tuple				=   8, 9
					__lowercase	:       str				=   10
					__lowercase	:       Optional[int]				=   10
					__lowercase	:       List[Any]				=   tokenizer.decode_best_spans(UpperCamelCase_							,		UpperCamelCase_   )
					self.assertEqual(predicted_spans[0].start_index							,		UpperCamelCase_   )
					self.assertEqual(predicted_spans[0].end_index							,		UpperCamelCase_   )
					self.assertEqual(predicted_spans[0].doc_id							,		0   )
		@slow
		def 					_lowerCamelCase    (		self   )		->   Optional[int]:
					__lowercase	:       Union[str, Any]				=   self.tokenizer_class.from_pretrained('''bert-base-uncased'''   )

					__lowercase	:       Dict				=   tokenizer.encode('''question sequence'''							,		add_special_tokens=UpperCamelCase_   )
					__lowercase	:       Tuple				=   tokenizer.encode('''title sequence'''							,		add_special_tokens=UpperCamelCase_   )
					__lowercase	:       int				=   tokenizer.encode('''text sequence'''							,		add_special_tokens=UpperCamelCase_   )
					__lowercase	:       Any				=   [1_01] + text_a + [1_02] + text_a + [1_02] + text_a
					__lowercase	:       List[Any]				=   tokenizer(questions=['''question sequence''']							,		titles=['''title sequence''']							,		texts=['''text sequence''']   )
					self.assertIn('''input_ids'''							,		UpperCamelCase_   )
					self.assertIn('''attention_mask'''							,		UpperCamelCase_   )
					self.assertListEqual(encoded_input['''input_ids'''][0]							,		UpperCamelCase_   )









"""simple docstring"""




from collections import defaultdict







def       __UpperCAmelCase      (				__UpperCamelCase					):
			__lowercase	:       defaultdict[str, int]				=   defaultdict(__UpperCamelCase					)
			# Creating a dictionary containing count of each word
			for word in sentence.split():
						occurrence[word] += 1
			return occurrence


if __name__ == "__main__":
			for word, count in word_occurrence('INPUT STRING').items():
						print(F"{word}: {count}")



