


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    lowerCAmelCase__				(			__lowerCamelCase       ):






						"""simple docstring"""
						__UpperCAmelCase   :		List[str]  					=	DPRContextEncoderTokenizer
						__UpperCAmelCase   :		List[str]  					=	DPRContextEncoderTokenizerFast
						__UpperCAmelCase   :		Optional[int]  					=	True





@require_tokenizers
class    lowerCAmelCase__				(			__lowerCamelCase       ):






						"""simple docstring"""
						__UpperCAmelCase   :		List[str]  					=	DPRQuestionEncoderTokenizer
						__UpperCAmelCase   :		str  					=	DPRQuestionEncoderTokenizerFast
						__UpperCAmelCase   :		List[str]  					=	True





@require_tokenizers
class    lowerCAmelCase__				(			__lowerCamelCase       ):






						"""simple docstring"""
						__UpperCAmelCase   :		Union[str, Any]  					=	DPRReaderTokenizer
						__UpperCAmelCase   :		List[str]  					=	DPRReaderTokenizerFast
						__UpperCAmelCase   :		str  					=	True




						@slow
						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Optional[int]      =       self.tokenizer_class.from_pretrained("bert-base-uncased"     )

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

							lowerCamelCase_			:							Dict      =       [[0] * len(input_ids[0]     )]
							lowerCamelCase_			:							List[Any]      =       [[0] * len(input_ids[0]     )]
							lowerCamelCase_			:							List[Any]      =       [0]
							lowerCamelCase_			:							Tuple      =       DPRReaderOutput(a_				,							a_				,							a_     )

							lowerCamelCase_			:							Optional[int]      =       8, 9
							lowerCamelCase_			:							List[str]      =       10
							lowerCamelCase_			:							Any      =       10
							lowerCamelCase_			:							List[Any]      =       tokenizer.decode_best_spans(a_				,							a_     )
							self.assertEqual(predicted_spans[0].start_index				,							a_     )
							self.assertEqual(predicted_spans[0].end_index				,							a_     )
							self.assertEqual(predicted_spans[0].doc_id				,							0     )




						@slow
						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							List[Any]      =       self.tokenizer_class.from_pretrained("bert-base-uncased"     )

							lowerCamelCase_			:							Any      =       tokenizer.encode("question sequence"				,							add_special_tokens=a_     )
							lowerCamelCase_			:							List[str]      =       tokenizer.encode("title sequence"				,							add_special_tokens=a_     )
							lowerCamelCase_			:							Optional[Any]      =       tokenizer.encode("text sequence"				,							add_special_tokens=a_     )
							lowerCamelCase_			:							Union[str, Any]      =       [101] + text_a + [102] + text_a + [102] + text_a
							lowerCamelCase_			:							str      =       tokenizer(questions=["question sequence"]				,							titles=["title sequence"]				,							texts=["text sequence"]     )
							self.assertIn("input_ids"				,							a_     )
							self.assertIn("attention_mask"				,							a_     )
							self.assertListEqual(encoded_input["input_ids"][0]				,							a_     )





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__magic_name__       					=      {'''configuration_levit''': ['''LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LevitConfig''', '''LevitOnnxConfig''']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      ['''LevitFeatureExtractor''']
							__magic_name__       					=      ['''LevitImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      [
							    '''LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LevitForImageClassification''',
							    '''LevitForImageClassificationWithTeacher''',
							    '''LevitModel''',
							    '''LevitPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_levit import LevitFeatureExtractor
														from .image_processing_levit import LevitImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_levit import (
														    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LevitForImageClassification,
														    LevitForImageClassificationWithTeacher,
														    LevitModel,
														    LevitPreTrainedModel,
														)
else:
							import sys

							__magic_name__       					=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

