

'''simple docstring'''





from PIL import Image





def      __a     (       _UpperCamelCase:   Image ,						_UpperCamelCase:   int   )						->			Image:

     """simple docstring"""




     _snake_case							=				(259 * (level + 255)) / (255 * (259 - level))

     def contrast(_UpperCamelCase:   int   ) -> int:
          return int(128 + factor * (c - 128)   )

     return img.point(_UpperCamelCase   )


if __name__ == "__main__":
   # Load image
   with Image.open('''image_data/lena.jpg''') as img:
      # Change contrast to 170
      UpperCamelCase_						:      Tuple       =						change_contrast(img, 170)
      cont_img.save('''image_data/lena_high_contrast.png''', format='''png''')








'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_						:      int       =						logging.get_logger(__name__)

UpperCamelCase_						:      Tuple       =						{
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   List[str]       =						"""vit_msn"""






     def __init__(			self ,_SCREAMING_SNAKE_CASE=768 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=3_072 ,_SCREAMING_SNAKE_CASE="gelu" ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=0.0_2 ,_SCREAMING_SNAKE_CASE=1e-06 ,_SCREAMING_SNAKE_CASE=224 ,_SCREAMING_SNAKE_CASE=16 ,_SCREAMING_SNAKE_CASE=3 ,_SCREAMING_SNAKE_CASE=True ,**_SCREAMING_SNAKE_CASE ,)							->      int:
          super().__init__(**_SCREAMING_SNAKE_CASE   )

          _snake_case							=				hidden_size
          _snake_case							=				num_hidden_layers
          _snake_case							=				num_attention_heads
          _snake_case							=				intermediate_size
          _snake_case							=				hidden_act
          _snake_case							=				hidden_dropout_prob
          _snake_case							=				attention_probs_dropout_prob
          _snake_case							=				initializer_range
          _snake_case							=				layer_norm_eps
          _snake_case							=				image_size
          _snake_case							=				patch_size
          _snake_case							=				num_channels
          _snake_case							=				qkv_bias


