




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							(		lowerCamelCase  ):
					_lowerCamelCase		  = DPRContextEncoderTokenizer
					_lowerCamelCase		  = DPRContextEncoderTokenizerFast
					_lowerCamelCase		  = True


@require_tokenizers
class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):
					_lowerCamelCase		  = DPRQuestionEncoderTokenizer
					_lowerCamelCase		  = DPRQuestionEncoderTokenizerFast
					_lowerCamelCase		  = True


@require_tokenizers
class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):
					_lowerCamelCase		  = DPRReaderTokenizer
					_lowerCamelCase		  = DPRReaderTokenizerFast
					_lowerCamelCase		  = True

					@slow
					def 	lowercase_							(				self					):







						'''simple docstring'''




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

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

						lowerCamelCase_							:		List[Any]							   =							[[0] * len(input_ids[0]					)]
						lowerCamelCase_							:		Union[str, Any]							   =							[[0] * len(input_ids[0]					)]
						lowerCamelCase_							:		Any							   =							[0]
						lowerCamelCase_							:		Optional[int]							   =							DPRReaderOutput(__lowercase ,       __lowercase ,       __lowercase					)

						lowerCamelCase_   ,			lowerCamelCase_							:		Tuple							   =							8, 9
						lowerCamelCase_							:		List[Any]							   =							10
						lowerCamelCase_							:		Union[str, Any]							   =							10
						lowerCamelCase_							:		Tuple							   =							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 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							   =							self.tokenizer_class.from_pretrained('bert-base-uncased'					)

						lowerCamelCase_							:		Dict							   =							tokenizer.encode('question sequence' ,       add_special_tokens=__lowercase					)
						lowerCamelCase_							:		int							   =							tokenizer.encode('title sequence' ,       add_special_tokens=__lowercase					)
						lowerCamelCase_							:		Dict							   =							tokenizer.encode('text sequence' ,       add_special_tokens=__lowercase					)
						lowerCamelCase_							:		Dict							   =							[101] + text_a + [102] + text_a + [102] + text_a
						lowerCamelCase_							:		Optional[Any]							   =							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 typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


UpperCamelCase__              =						[
    'AutomaticSpeechRecognition',
    'AudioClassification',
    'ImageClassification',
    'LanguageModeling',
    'QuestionAnsweringExtractive',
    'Summarization',
    'TaskTemplate',
    'TextClassification',
]

UpperCamelCase__              =						get_logger(__name__)


UpperCamelCase__              =						{
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}






def 					snake_case					(							A			)							->							Optional[TaskTemplate]:




	'''simple docstring'''


	lowerCamelCase_							:		Any							   =							task_template_dict.get('task'			)
	if task_name is None:
		logger.warning(f"Couldn't find template for task '{task_name}'. Available templates: {list(A			)}"			)
		return None
	lowerCamelCase_							:		Any							   =							NAMEaTEMPLATE.get(A			)
	return template.from_dict(A			)
