



'''simple docstring'''


from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest

@require_tokenizers
class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  DistilBertTokenizer
    _SCREAMING_SNAKE_CASE	  =  DistilBertTokenizerFast
    _SCREAMING_SNAKE_CASE	  =  True






    @slow
    def  A       (  self      :					List[str] ):


       """simple docstring"""







       UpperCamelCase					=  DistilBertTokenizer.from_pretrained('distilbert-base-uncased' )

       UpperCamelCase					=  tokenizer.encode('sequence builders'  ,						add_special_tokens=UpperCamelCase__ )
       UpperCamelCase					=  tokenizer.encode('multi-sequence build'  ,						add_special_tokens=UpperCamelCase__ )

       UpperCamelCase					=  tokenizer.build_inputs_with_special_tokens(UpperCamelCase__ )
       UpperCamelCase					=  tokenizer.build_inputs_with_special_tokens(UpperCamelCase__  ,						UpperCamelCase__ )

       assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
       assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
           tokenizer.sep_token_id
       ]






'''simple docstring'''


from PIL import Image







def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Image:






   """simple docstring"""

   def brightness(A__    ) -> float:
      return 128 + level + (c - 128)

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


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

