





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE_						       =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						       =	{
    """shi-labs/dinat-mini-in1k-224""": """https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json""",
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}

class 		UpperCamelCase__			(				lowerCAmelCase_							,				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     str		     =      "dinat"

     __snake_case   :     Any		     =      {
         "num_attention_heads": "num_heads",
         "num_hidden_layers": "num_layers",
     }






     def __init__(							self						:   Dict						,lowerCamelCase__						:   Optional[Any]=4						,lowerCamelCase__						:   Any=3						,lowerCamelCase__						:   int=64						,lowerCamelCase__						:   Union[str, Any]=[3, 4, 6, 5]						,lowerCamelCase__						:   str=[2, 4, 8, 16]						,lowerCamelCase__						:   Tuple=7						,lowerCamelCase__						:   Optional[int]=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]						,lowerCamelCase__						:   str=3.0						,lowerCamelCase__						:   str=True						,lowerCamelCase__						:   Union[str, Any]=0.0						,lowerCamelCase__						:   List[str]=0.0						,lowerCamelCase__						:   str=0.1						,lowerCamelCase__						:   str="gelu"						,lowerCamelCase__						:   Tuple=0.02						,lowerCamelCase__						:   List[Any]=1e-5						,lowerCamelCase__						:   str=0.0						,lowerCamelCase__						:   str=None						,lowerCamelCase__						:   str=None						,**lowerCamelCase__						:   Optional[Any]						,)					->		List[Any]:




           '''simple docstring'''


           super().__init__(**lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						patch_size
           SCREAMING_SNAKE_CASE		=						num_channels
           SCREAMING_SNAKE_CASE		=						embed_dim
           SCREAMING_SNAKE_CASE		=						depths
           SCREAMING_SNAKE_CASE		=						len(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						num_heads
           SCREAMING_SNAKE_CASE		=						kernel_size
           SCREAMING_SNAKE_CASE		=						dilations
           SCREAMING_SNAKE_CASE		=						mlp_ratio
           SCREAMING_SNAKE_CASE		=						qkv_bias
           SCREAMING_SNAKE_CASE		=						hidden_dropout_prob
           SCREAMING_SNAKE_CASE		=						attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE		=						drop_path_rate
           SCREAMING_SNAKE_CASE		=						hidden_act
           SCREAMING_SNAKE_CASE		=						layer_norm_eps
           SCREAMING_SNAKE_CASE		=						initializer_range
           # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
           # this indicates the channel dimension after the last stage of the model
           SCREAMING_SNAKE_CASE		=						int(embed_dim * 2 ** (len(lowerCamelCase__	) - 1)	)
           SCREAMING_SNAKE_CASE		=						layer_scale_init_value
           SCREAMING_SNAKE_CASE		=						["""stem"""] + [F"""stage{idx}""" for idx in range(1						,len(lowerCamelCase__	) + 1	)]
           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						get_aligned_output_features_output_indices(
               out_features=lowerCamelCase__						,out_indices=lowerCamelCase__						,stage_names=self.stage_names	)









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""")


