


"""simple docstring"""






import os
import sys
import unittest


UpperCamelCase__				:List[Any]			   =	os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


UpperCamelCase__				:Optional[Any]			   =	os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
UpperCamelCase__				:str			   =	os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")






class      A(  unittest.TestCase		):

		"""simple docstring"""





		def   _UpperCamelCase( self   )      ->	List[str]:

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	get_test_to_tester_mapping(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Union[str, Any]					     =	get_test_to_tester_mapping(SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Tuple					     =	{'''BertModelTest''': '''BertModelTester'''}

					_UpperCamelCase					:Union[str, Any]					     =	{
					    '''BlipModelTest''': '''BlipModelTester''',
					    '''BlipTextImageModelTest''': '''BlipTextImageModelsModelTester''',
					    '''BlipTextModelTest''': '''BlipTextModelTester''',
					    '''BlipTextRetrievalModelTest''': '''BlipTextRetrievalModelTester''',
					    '''BlipVQAModelTest''': '''BlipVQAModelTester''',
					    '''BlipVisionModelTest''': '''BlipVisionModelTester''',
					}

					self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE__   )					,				SCREAMING_SNAKE_CASE__   )
					self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE__   )					,				SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	get_model_to_test_mapping(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[Any]					     =	get_model_to_test_mapping(SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Dict					     =	{
					    '''BertForMaskedLM''': ['''BertModelTest'''],
					    '''BertForMultipleChoice''': ['''BertModelTest'''],
					    '''BertForNextSentencePrediction''': ['''BertModelTest'''],
					    '''BertForPreTraining''': ['''BertModelTest'''],
					    '''BertForQuestionAnswering''': ['''BertModelTest'''],
					    '''BertForSequenceClassification''': ['''BertModelTest'''],
					    '''BertForTokenClassification''': ['''BertModelTest'''],
					    '''BertLMHeadModel''': ['''BertModelTest'''],
					    '''BertModel''': ['''BertModelTest'''],
					}

					_UpperCamelCase					:Tuple					     =	{
					    '''BlipForConditionalGeneration''': ['''BlipTextImageModelTest'''],
					    '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTest'''],
					    '''BlipForQuestionAnswering''': ['''BlipVQAModelTest'''],
					    '''BlipModel''': ['''BlipModelTest'''],
					    '''BlipTextModel''': ['''BlipTextModelTest'''],
					    '''BlipVisionModel''': ['''BlipVisionModelTest'''],
					}

					self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE__   )					,				SCREAMING_SNAKE_CASE__   )
					self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE__   )					,				SCREAMING_SNAKE_CASE__   )





		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Tuple					     =	get_model_to_tester_mapping(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Any					     =	get_model_to_tester_mapping(SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Optional[Any]					     =	{
					    '''BertForMaskedLM''': ['''BertModelTester'''],
					    '''BertForMultipleChoice''': ['''BertModelTester'''],
					    '''BertForNextSentencePrediction''': ['''BertModelTester'''],
					    '''BertForPreTraining''': ['''BertModelTester'''],
					    '''BertForQuestionAnswering''': ['''BertModelTester'''],
					    '''BertForSequenceClassification''': ['''BertModelTester'''],
					    '''BertForTokenClassification''': ['''BertModelTester'''],
					    '''BertLMHeadModel''': ['''BertModelTester'''],
					    '''BertModel''': ['''BertModelTester'''],
					}

					_UpperCamelCase					:Union[str, Any]					     =	{
					    '''BlipForConditionalGeneration''': ['''BlipTextImageModelsModelTester'''],
					    '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTester'''],
					    '''BlipForQuestionAnswering''': ['''BlipVQAModelTester'''],
					    '''BlipModel''': ['''BlipModelTester'''],
					    '''BlipTextModel''': ['''BlipTextModelTester'''],
					    '''BlipVisionModel''': ['''BlipVisionModelTester'''],
					}

					self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE__   )					,				SCREAMING_SNAKE_CASE__   )
					self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE__   )					,				SCREAMING_SNAKE_CASE__   )





"""simple docstring"""






import warnings

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






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							["image_processor", "tokenizer"]
		A    =							"ViTImageProcessor"
		A    =							("CLIPTokenizer", "CLIPTokenizerFast")
		def __init__( self					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				**SCREAMING_SNAKE_CASE__   )      ->	List[str]:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	None
					if "feature_extractor" in kwargs:
								warnings.warn(
								    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
								    ''' instead.'''					,				SCREAMING_SNAKE_CASE__					,				)
								_UpperCamelCase					:List[str]					     =	kwargs.pop('''feature_extractor'''   )

					_UpperCamelCase					:Optional[Any]					     =	image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
								raise ValueError('''You need to specify an `image_processor`.'''   )
					if tokenizer is None:
								raise ValueError('''You need to specify a `tokenizer`.'''   )

					super().__init__(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
		def __call__( self					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				**SCREAMING_SNAKE_CASE__   )      ->	Tuple:

					"""simple docstring"""



					if text is None and visual_prompt is None and images is None:
								raise ValueError('''You have to specify either text, visual prompt or images.'''   )

					if text is not None and visual_prompt is not None:
								raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''   )

					if text is not None:
								_UpperCamelCase					:Dict					     =	self.tokenizer(SCREAMING_SNAKE_CASE__					,				return_tensors=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )

					if visual_prompt is not None:
								_UpperCamelCase					:Optional[Any]					     =	self.image_processor(SCREAMING_SNAKE_CASE__					,				return_tensors=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )

					if images is not None:
								_UpperCamelCase					:Union[str, Any]					     =	self.image_processor(SCREAMING_SNAKE_CASE__					,				return_tensors=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )

					if visual_prompt is not None and images is not None:
								_UpperCamelCase					:List[str]					     =	{
								    '''pixel_values''': image_features.pixel_values,
								    '''conditional_pixel_values''': prompt_features.pixel_values,
								}
								return encoding
					elif text is not None and images is not None:
								_UpperCamelCase					:List[Any]					     =	image_features.pixel_values
								return encoding
					elif text is not None:
								return encoding
					elif visual_prompt is not None:
								_UpperCamelCase					:Optional[Any]					     =	{
								    '''conditional_pixel_values''': prompt_features.pixel_values,
								}
								return encoding
					else:
								return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE__   )					,				tensor_type=SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	Any:

					"""simple docstring"""



					return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	Tuple:

					"""simple docstring"""



					return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		@property
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					warnings.warn(
					    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''					,				SCREAMING_SNAKE_CASE__					,				)
					return self.image_processor_class





		@property
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					warnings.warn(
					    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''					,				SCREAMING_SNAKE_CASE__					,				)
					return self.image_processor

