
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			)

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	int					     =      ["image_processor", "tokenizer"]
	_UpperCAmelCase :	Tuple					     =      "EfficientNetImageProcessor"
	_UpperCAmelCase :	Optional[Any]					     =      ("BertTokenizer", "BertTokenizerFast")



	def __init__(						self			:					Tuple    ,						lowercase			:					List[Any]    ,						lowercase			:					Optional[int]			):


				'''simple docstring'''


				super().__init__(lowercase    ,						lowercase			)



	def __call__(						self			:					Union[str, Any]    ,						lowercase			:					Dict=None    ,						lowercase			:					Optional[int]=None    ,						lowercase			:					List[Any]="max_length"    ,						lowercase			:					Optional[int]=64    ,						lowercase			:					int=None    ,						**lowercase			:					Tuple			):


				'''simple docstring'''


				if text is None and images is None:
							raise ValueError('You have to specify either text or images. Both cannot be none.'			)

				if text is not None:
							_snake_case						=    self.tokenizer(
							    lowercase    ,						padding=lowercase    ,						max_length=lowercase    ,						return_tensors=lowercase    ,						**lowercase			)

				if images is not None:
							_snake_case						=    self.image_processor(lowercase    ,						return_tensors=lowercase    ,						**lowercase			)

				if text is not None and images is not None:
							_snake_case						=    image_features.pixel_values
							return encoding
				elif text is not None:
							return encoding
				else:
							return BatchEncoding(data=dict(**lowercase			)    ,						tensor_type=lowercase			)



	def 				A							(						self			:					Any    ,						*lowercase			:					List[str]    ,						**lowercase			:					Optional[Any]			):


				'''simple docstring'''


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



	def 				A							(						self			:					List[Any]    ,						*lowercase			:					Union[str, Any]    ,						**lowercase			:					Tuple			):


				'''simple docstring'''


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





	@property
	def 				A							(						self			:					List[str]			):


				'''simple docstring'''


				_snake_case						=    self.tokenizer.model_input_names
				_snake_case						=    self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)