





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, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast

@require_vision
class 		UpperCamelCase__			(				unittest.TestCase       ):



     '''simple docstring'''

     def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						tempfile.mkdtemp()

           SCREAMING_SNAKE_CASE		=						BlipImageProcessor()
           SCREAMING_SNAKE_CASE		=						BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-BertModel"""	)

           SCREAMING_SNAKE_CASE		=						BlipProcessor(lowerCamelCase__						,lowerCamelCase__	)

           processor.save_pretrained(self.tmpdirname	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]						,**lowerCamelCase__						:   Dict	)					->		Dict:




           '''simple docstring'''


           return AutoProcessor.from_pretrained(self.tmpdirname						,**lowerCamelCase__	).tokenizer






     def     SCREAMING_SNAKE_CASE__  (							self						:   str						,**lowerCamelCase__						:   List[Any]	)					->		Any:




           '''simple docstring'''


           return AutoProcessor.from_pretrained(self.tmpdirname						,**lowerCamelCase__	).image_processor






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		str:




           '''simple docstring'''


           shutil.rmtree(self.tmpdirname	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[np.random.randint(255						,size=(3, 30, 400)						,dtype=np.uinta	)]

           SCREAMING_SNAKE_CASE		=						[Image.fromarray(np.moveaxis(lowerCamelCase__						,0						,-1	)	) for x in image_inputs]

           return image_inputs






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						BlipProcessor(tokenizer=self.get_tokenizer()						,image_processor=self.get_image_processor()	)
           processor.save_pretrained(self.tmpdirname	)

           SCREAMING_SNAKE_CASE		=						self.get_tokenizer(bos_token="""(BOS)"""						,eos_token="""(EOS)"""	)
           SCREAMING_SNAKE_CASE		=						self.get_image_processor(do_normalize=lowerCamelCase__						,padding_value=1.0	)

           SCREAMING_SNAKE_CASE		=						BlipProcessor.from_pretrained(
               self.tmpdirname						,bos_token="""(BOS)"""						,eos_token="""(EOS)"""						,do_normalize=lowerCamelCase__						,padding_value=1.0	)

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

           self.assertEqual(processor.image_processor.to_json_string()						,image_processor_add_kwargs.to_json_string()	)
           self.assertIsInstance(processor.image_processor						,lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.get_image_processor()
           SCREAMING_SNAKE_CASE		=						self.get_tokenizer()

           SCREAMING_SNAKE_CASE		=						BlipProcessor(tokenizer=lowerCamelCase__						,image_processor=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE		=						image_processor(lowerCamelCase__						,return_tensors="""np"""	)
           SCREAMING_SNAKE_CASE		=						processor(images=lowerCamelCase__						,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     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.get_image_processor()
           SCREAMING_SNAKE_CASE		=						self.get_tokenizer()

           SCREAMING_SNAKE_CASE		=						BlipProcessor(tokenizer=lowerCamelCase__						,image_processor=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						"""lower newer"""

           SCREAMING_SNAKE_CASE		=						processor(text=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						tokenizer(lowerCamelCase__						,return_token_type_ids=lowerCamelCase__	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.get_image_processor()
           SCREAMING_SNAKE_CASE		=						self.get_tokenizer()

           SCREAMING_SNAKE_CASE		=						BlipProcessor(tokenizer=lowerCamelCase__						,image_processor=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						"""lower newer"""
           SCREAMING_SNAKE_CASE		=						self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE		=						processor(text=lowerCamelCase__						,images=lowerCamelCase__	)

           self.assertListEqual(list(inputs.keys()	)						,["""pixel_values""", """input_ids""", """attention_mask"""]	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.get_image_processor()
           SCREAMING_SNAKE_CASE		=						self.get_tokenizer()

           SCREAMING_SNAKE_CASE		=						BlipProcessor(tokenizer=lowerCamelCase__						,image_processor=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           SCREAMING_SNAKE_CASE		=						processor.batch_decode(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						tokenizer.batch_decode(lowerCamelCase__	)

           self.assertListEqual(lowerCamelCase__						,lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.get_image_processor()
           SCREAMING_SNAKE_CASE		=						self.get_tokenizer()

           SCREAMING_SNAKE_CASE		=						BlipProcessor(tokenizer=lowerCamelCase__						,image_processor=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						"""lower newer"""
           SCREAMING_SNAKE_CASE		=						self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE		=						processor(text=lowerCamelCase__						,images=lowerCamelCase__	)

           # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
           self.assertListEqual(list(inputs.keys()	)						,["""pixel_values""", """input_ids""", """attention_mask"""]	)









from PIL import Image



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Image:


      '''simple docstring'''


      def brightness(_SCREAMING_SNAKE_CASE     ) -> float:
            return 1_28 + level + (c - 1_28)

      if not -255.0 <= level <= 255.0:
            raise ValueError("""level must be between -255.0 (black) and 255.0 (white)"""     )
      return img.point(_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
      # Load image
      with Image.open("""image_data/lena.jpg""") as img:
            # Change brightness to 100
            SCREAMING_SNAKE_CASE_						       =	change_brightness(img, 1_0_0)
            brigt_img.save("""image_data/lena_brightness.png""", format="""png""")


