




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import (
         AutoProcessor,
         BertTokenizerFast,
         BlipImageProcessor,
         GPTaTokenizer,
         InstructBlipProcessor,
         PreTrainedTokenizerFast,
     )

@require_vision
class     __a(	unittest.TestCase  ):






    """simple docstring"""





    def 				a__ ( self    )					->						Optional[int]:
         UpperCAmelCase_     :				Tuple  =     tempfile.mkdtemp()

         UpperCAmelCase_     :				Tuple  =     BlipImageProcessor()
         UpperCAmelCase_     :				Optional[int]  =     GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''    )
         UpperCAmelCase_     :				Union[str, Any]  =     BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert'''    )

         UpperCAmelCase_     :				Optional[int]  =     InstructBlipProcessor(_lowercase  ,_lowercase  ,_lowercase    )

         processor.save_pretrained(self.tmpdirname    )





    def 				a__ ( self  ,**_SCREAMING_SNAKE_CASE    )					->						str:
         return AutoProcessor.from_pretrained(self.tmpdirname  ,**_lowercase    ).tokenizer





    def 				a__ ( self  ,**_SCREAMING_SNAKE_CASE    )					->						Optional[Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname  ,**_lowercase    ).image_processor





    def 				a__ ( self  ,**_SCREAMING_SNAKE_CASE    )					->						int:
         return AutoProcessor.from_pretrained(self.tmpdirname  ,**_lowercase    ).qformer_tokenizer





    def 				a__ ( self    )					->						Optional[Any]:
         shutil.rmtree(self.tmpdirname    )





    def 				a__ ( self    )					->						Tuple:
         UpperCAmelCase_     :				Optional[Any]  =     [np.random.randint(255  ,size=(3, 30, 400)  ,dtype=np.uinta    )]

         UpperCAmelCase_     :				Union[str, Any]  =     [Image.fromarray(np.moveaxis(_lowercase  ,0  ,-1    )    ) for x in image_inputs]

         return image_inputs





    def 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_     :				int  =     InstructBlipProcessor(
             tokenizer=self.get_tokenizer()  ,image_processor=self.get_image_processor()  ,qformer_tokenizer=self.get_qformer_tokenizer()  ,)
         processor.save_pretrained(self.tmpdirname    )

         UpperCAmelCase_     :				str  =     self.get_tokenizer(bos_token='''(BOS)'''  ,eos_token='''(EOS)'''    )
         UpperCAmelCase_     :				Union[str, Any]  =     self.get_image_processor(do_normalize=_lowercase  ,padding_value=1.0    )

         UpperCAmelCase_     :				Optional[int]  =     InstructBlipProcessor.from_pretrained(
             self.tmpdirname  ,bos_token='''(BOS)'''  ,eos_token='''(EOS)'''  ,do_normalize=_lowercase  ,padding_value=1.0    )

         self.assertEqual(processor.tokenizer.get_vocab()  ,tokenizer_add_kwargs.get_vocab()    )
         self.assertIsInstance(processor.tokenizer  ,_lowercase    )

         self.assertEqual(processor.image_processor.to_json_string()  ,image_processor_add_kwargs.to_json_string()    )
         self.assertIsInstance(processor.image_processor  ,_lowercase    )
         self.assertIsInstance(processor.qformer_tokenizer  ,_lowercase    )





    def 				a__ ( self    )					->						Optional[int]:
         UpperCAmelCase_     :				int  =     self.get_image_processor()
         UpperCAmelCase_     :				int  =     self.get_tokenizer()
         UpperCAmelCase_     :				Any  =     self.get_qformer_tokenizer()

         UpperCAmelCase_     :				int  =     InstructBlipProcessor(
             tokenizer=_lowercase  ,image_processor=_lowercase  ,qformer_tokenizer=_lowercase    )

         UpperCAmelCase_     :				Optional[int]  =     self.prepare_image_inputs()

         UpperCAmelCase_     :				Optional[Any]  =     image_processor(_lowercase  ,return_tensors='''np'''    )
         UpperCAmelCase_     :				int  =     processor(images=_lowercase  ,return_tensors='''np'''    )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()  ,input_processor[key].sum()  ,delta=1e-2    )





    def 				a__ ( self    )					->						Union[str, Any]:
         UpperCAmelCase_     :				List[Any]  =     self.get_image_processor()
         UpperCAmelCase_     :				Tuple  =     self.get_tokenizer()
         UpperCAmelCase_     :				Union[str, Any]  =     self.get_qformer_tokenizer()

         UpperCAmelCase_     :				List[str]  =     InstructBlipProcessor(
             tokenizer=_lowercase  ,image_processor=_lowercase  ,qformer_tokenizer=_lowercase    )

         UpperCAmelCase_     :				int  =     '''lower newer'''

         UpperCAmelCase_     :				List[str]  =     processor(text=_lowercase    )

         UpperCAmelCase_     :				Optional[Any]  =     tokenizer(_lowercase  ,return_token_type_ids=_lowercase    )
         UpperCAmelCase_     :				Dict  =     qformer_tokenizer(_lowercase  ,return_token_type_ids=_lowercase    )

         for key in encoded_tokens.keys():
              self.assertListEqual(encoded_tokens[key]  ,encoded_processor[key]    )

         for key in encoded_tokens_qformer.keys():
              self.assertListEqual(encoded_tokens_qformer[key]  ,encoded_processor['''qformer_''' + key]    )





    def 				a__ ( self    )					->						Any:
         UpperCAmelCase_     :				Tuple  =     self.get_image_processor()
         UpperCAmelCase_     :				str  =     self.get_tokenizer()
         UpperCAmelCase_     :				Optional[Any]  =     self.get_qformer_tokenizer()

         UpperCAmelCase_     :				Tuple  =     InstructBlipProcessor(
             tokenizer=_lowercase  ,image_processor=_lowercase  ,qformer_tokenizer=_lowercase    )

         UpperCAmelCase_     :				Optional[Any]  =     '''lower newer'''
         UpperCAmelCase_     :				str  =     self.prepare_image_inputs()

         UpperCAmelCase_     :				str  =     processor(text=_lowercase  ,images=_lowercase    )

         self.assertListEqual(
             list(inputs.keys()    )  ,['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values''']  ,)

         # test if it raises when no input is passed
         with pytest.raises(_lowercase    ):
              processor()





    def 				a__ ( self    )					->						Any:
         UpperCAmelCase_     :				List[Any]  =     self.get_image_processor()
         UpperCAmelCase_     :				List[Any]  =     self.get_tokenizer()
         UpperCAmelCase_     :				str  =     self.get_qformer_tokenizer()

         UpperCAmelCase_     :				List[str]  =     InstructBlipProcessor(
             tokenizer=_lowercase  ,image_processor=_lowercase  ,qformer_tokenizer=_lowercase    )

         UpperCAmelCase_     :				Optional[int]  =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         UpperCAmelCase_     :				List[Any]  =     processor.batch_decode(_lowercase    )
         UpperCAmelCase_     :				Tuple  =     tokenizer.batch_decode(_lowercase    )

         self.assertListEqual(_lowercase  ,_lowercase    )





    def 				a__ ( self    )					->						Dict:
         UpperCAmelCase_     :				Tuple  =     self.get_image_processor()
         UpperCAmelCase_     :				str  =     self.get_tokenizer()
         UpperCAmelCase_     :				Optional[Any]  =     self.get_qformer_tokenizer()

         UpperCAmelCase_     :				Tuple  =     InstructBlipProcessor(
             tokenizer=_lowercase  ,image_processor=_lowercase  ,qformer_tokenizer=_lowercase    )

         UpperCAmelCase_     :				Union[str, Any]  =     '''lower newer'''
         UpperCAmelCase_     :				Union[str, Any]  =     self.prepare_image_inputs()

         UpperCAmelCase_     :				int  =     processor(text=_lowercase  ,images=_lowercase    )

         self.assertListEqual(
             list(inputs.keys()    )  ,['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values''']  ,)


'''simple docstring'''

import warnings

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






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     ["""image_processor""", """tokenizer"""]
				_a    	=     """CLIPImageProcessor"""
				_a    	=     ("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")



				def __init__(    self,     _lowercase=None,     _lowercase=None,     **_lowercase       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				None
											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																		    ' instead.',     _lowercase,     )
																		SCREAMING_SNAKE_CASE_				=				kwargs.pop('feature_extractor'       )

											SCREAMING_SNAKE_CASE_				=				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__(_lowercase,     _lowercase       )



				def __call__(    self,     _lowercase=None,     _lowercase=None,     _lowercase=None,     **_lowercase       )     ->							str:

											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:
																		SCREAMING_SNAKE_CASE_				=				self.tokenizer(_lowercase,     return_tensors=_lowercase,     **_lowercase       )

											if images is not None:
																		SCREAMING_SNAKE_CASE_				=				self.image_processor(_lowercase,     return_tensors=_lowercase,     **_lowercase       )

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



				def   a__					(    self,     *_lowercase,     **_lowercase       )     ->							Optional[int]:
											return self.tokenizer.batch_decode(*_lowercase,     **_lowercase       )



				def   a__					(    self,     *_lowercase,     **_lowercase       )     ->							Optional[Any]:
											return self.tokenizer.decode(*_lowercase,     **_lowercase       )





				@property
				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				self.tokenizer.model_input_names
											SCREAMING_SNAKE_CASE_				=				self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )





