'''simple docstring'''





from PIL import Image





def       lowerCAmelCase_							(   snake_case_			:					Image     )       -> Image:


     '''simple docstring'''




     UpperCAmelCase_       ,					UpperCAmelCase_			       =  image.size
     UpperCAmelCase_			       =  0
     UpperCAmelCase_			       =  image.load()
     for i in range(snake_case_     ):
          for j in range(snake_case_     ):
               UpperCAmelCase_			       =  pixels[j, i]
               mean += pixel
     mean //= width * height

     for j in range(snake_case_     ):
          for i in range(snake_case_     ):
               UpperCAmelCase_			       =  2_55 if pixels[i, j] > mean else 0
     return image


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:	str    =mean_threshold(Image.open('path_to_image').convert('L'))
    image.save('output_image_path')











from ... import PretrainedConfig


__A							:			Optional[Any]         =			{
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
     __magic_name__   =			'nezha'




     def __init__(    self				,     snake_case_=2_1128				,     snake_case_=768				,     snake_case_=12				,     snake_case_=12				,     snake_case_=3072				,     snake_case_="gelu"				,     snake_case_=0.1				,     snake_case_=0.1				,     snake_case_=512				,     snake_case_=64				,     snake_case_=2				,     snake_case_=0.02				,     snake_case_=1E-12				,     snake_case_=0.1				,     snake_case_=0				,     snake_case_=2				,     snake_case_=3				,     snake_case_=True				,     **snake_case_				,     ):
         super().__init__(pad_token_id=snake_case_				,     bos_token_id=snake_case_				,     eos_token_id=snake_case_				,     **snake_case_							)

         _A     =							vocab_size
         _A     =							hidden_size
         _A     =							num_hidden_layers
         _A     =							num_attention_heads
         _A     =							hidden_act
         _A     =							intermediate_size
         _A     =							hidden_dropout_prob
         _A     =							attention_probs_dropout_prob
         _A     =							max_position_embeddings
         _A     =							max_relative_position
         _A     =							type_vocab_size
         _A     =							initializer_range
         _A     =							layer_norm_eps
         _A     =							classifier_dropout
         _A     =							use_cache





