



import os
import sys
import unittest


a_						:     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,
)


a_						:     Dict	     =		os.path.join('tests', 'models', 'bert', 'test_modeling_bert.py')
a_						:     str	     =		os.path.join('tests', 'models', 'blip', 'test_modeling_blip.py')





class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		get_test_to_tester_mapping(__a    )
           lowerCamelCase								=		get_test_to_tester_mapping(__a    )

           lowerCamelCase								=		{"BertModelTest": "BertModelTester"}

           lowerCamelCase								=		{
               "BlipModelTest": "BlipModelTester",
               "BlipTextImageModelTest": "BlipTextImageModelsModelTester",
               "BlipTextModelTest": "BlipTextModelTester",
               "BlipTextRetrievalModelTest": "BlipTextRetrievalModelTester",
               "BlipVQAModelTest": "BlipVQAModelTester",
               "BlipVisionModelTest": "BlipVisionModelTester",
           }

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







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		get_model_to_test_mapping(__a    )
           lowerCamelCase								=		get_model_to_test_mapping(__a    )

           lowerCamelCase								=		{
               "BertForMaskedLM": ["BertModelTest"],
               "BertForMultipleChoice": ["BertModelTest"],
               "BertForNextSentencePrediction": ["BertModelTest"],
               "BertForPreTraining": ["BertModelTest"],
               "BertForQuestionAnswering": ["BertModelTest"],
               "BertForSequenceClassification": ["BertModelTest"],
               "BertForTokenClassification": ["BertModelTest"],
               "BertLMHeadModel": ["BertModelTest"],
               "BertModel": ["BertModelTest"],
           }

           lowerCamelCase								=		{
               "BlipForConditionalGeneration": ["BlipTextImageModelTest"],
               "BlipForImageTextRetrieval": ["BlipTextRetrievalModelTest"],
               "BlipForQuestionAnswering": ["BlipVQAModelTest"],
               "BlipModel": ["BlipModelTest"],
               "BlipTextModel": ["BlipTextModelTest"],
               "BlipVisionModel": ["BlipVisionModelTest"],
           }

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







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		get_model_to_tester_mapping(__a    )
           lowerCamelCase								=		get_model_to_tester_mapping(__a    )

           lowerCamelCase								=		{
               "BertForMaskedLM": ["BertModelTester"],
               "BertForMultipleChoice": ["BertModelTester"],
               "BertForNextSentencePrediction": ["BertModelTester"],
               "BertForPreTraining": ["BertModelTester"],
               "BertForQuestionAnswering": ["BertModelTester"],
               "BertForSequenceClassification": ["BertModelTester"],
               "BertForTokenClassification": ["BertModelTester"],
               "BertLMHeadModel": ["BertModelTester"],
               "BertModel": ["BertModelTester"],
           }

           lowerCamelCase								=		{
               "BlipForConditionalGeneration": ["BlipTextImageModelsModelTester"],
               "BlipForImageTextRetrieval": ["BlipTextRetrievalModelTester"],
               "BlipForQuestionAnswering": ["BlipVQAModelTester"],
               "BlipModel": ["BlipModelTester"],
               "BlipTextModel": ["BlipTextModelTester"],
               "BlipVisionModel": ["BlipVisionModelTester"],
           }

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




import warnings

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





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     _A       =			['image_processor', 'tokenizer']
     _A       =			'CLIPImageProcessor'
     _A       =			('CLIPTokenizer', 'CLIPTokenizerFast')







     def __init__(self				,			__a=None				,			__a=None				,			**__a    ):






           '''simple docstring'''



           lowerCamelCase								=		None
           if "feature_extractor" in kwargs:
                 warnings.warn(
                     "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                     " instead."				,			__a				,			)
                 lowerCamelCase								=		kwargs.pop("feature_extractor"    )

           lowerCamelCase								=		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__(__a				,			__a    )







     def __call__(self				,			__a=None				,			__a=None				,			__a=None				,			**__a    ):






           '''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:
                 lowerCamelCase								=		self.tokenizer(__a				,			return_tensors=__a				,			**__a    )

           if images is not None:
                 lowerCamelCase								=		self.image_processor(__a				,			return_tensors=__a				,			**__a    )

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







     def 						_a      (self				,			*__a				,			**__a    ):






           '''simple docstring'''



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







     def 						_a      (self				,			*__a				,			**__a    ):






           '''simple docstring'''



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







     @property
     def 						_a      (self    ):






           '''simple docstring'''



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







     @property
     def 						_a      (self    ):






           '''simple docstring'''



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







     @property
     def 						_a      (self    ):






           '''simple docstring'''



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