
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       A__  (		lowerCAmelCase__							):
							lowerCAmelCase__						:		List[Any]            =				DPRContextEncoderTokenizer
							lowerCAmelCase__						:		List[str]            =				DPRContextEncoderTokenizerFast
							lowerCAmelCase__						:		Optional[int]            =				True






@require_tokenizers
class       A__  (		lowerCAmelCase__							):
							lowerCAmelCase__						:		List[str]            =				DPRQuestionEncoderTokenizer
							lowerCAmelCase__						:		Tuple            =				DPRQuestionEncoderTokenizerFast
							lowerCAmelCase__						:		Tuple            =				True






@require_tokenizers
class       A__  (		lowerCAmelCase__							):
							lowerCAmelCase__						:		Union[str, Any]            =				DPRReaderTokenizer
							lowerCAmelCase__						:		Tuple            =				DPRReaderTokenizerFast
							lowerCAmelCase__						:		Optional[Any]            =				True





							@slow
							def      a__					(					self    :  Optional[int]							)		->						Tuple:



											"""simple docstring"""


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

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

											__lowercase								=   [[0] * len(input_ids[0]							)]
											__lowercase								=   [[0] * len(input_ids[0]							)]
											__lowercase								=   [0]
											__lowercase								=   DPRReaderOutput(_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase							)

											__lowercase						,   __lowercase								=   8, 9
											__lowercase								=   10
											__lowercase								=   10
											__lowercase								=   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      a__					(					self    :  Dict							)		->						List[str]:



											"""simple docstring"""


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

											__lowercase								=   tokenizer.encode('question sequence'	,					add_special_tokens=_UpperCAmelCase							)
											__lowercase								=   tokenizer.encode('title sequence'	,					add_special_tokens=_UpperCAmelCase							)
											__lowercase								=   tokenizer.encode('text sequence'	,					add_special_tokens=_UpperCAmelCase							)
											__lowercase								=   [1_01] + text_a + [1_02] + text_a + [1_02] + text_a
											__lowercase								=   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							)







from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
							from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
class       A__  (		lowerCAmelCase__							):
							lowerCAmelCase__						:		Any            =				c_float





							@property
							def      a__					(					self    :  List[str]							)		->						int:



											"""simple docstring"""


											return sizeof(self.value							)






def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: int       ,				SCREAMING_SNAKE_CASE			: int       ,				SCREAMING_SNAKE_CASE			: int = 0     )	->		int:
				# < 0 is possible if using a dynamic axis
				if dimension <= 0:
								__lowercase								=   fixed_dimension

				dimension -= num_token_to_add
				return dimension




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: int       ,				SCREAMING_SNAKE_CASE			: ParameterFormat     )	->		int:
				return num_parameters * dtype.size




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: str     )	->		Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:
				from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

				try:
								return AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE     )
				except (ValueError, OSError, KeyError):
								__lowercase						,   __lowercase								=   None, None
								try:
												__lowercase								=   AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE     )
								except (OSError, KeyError):
												pass
								try:
												__lowercase								=   AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE     )
								except (OSError, KeyError):
												pass

								if tokenizer is not None and feature_extractor is not None:
												raise ValueError(
												    F"""Couldn't auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor."""     )
								elif tokenizer is None and feature_extractor is None:
												return None
								elif tokenizer is not None:
												return tokenizer
								else:
												return feature_extractor





