






"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = {
    '''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}







class      UpperCamelCase_			(__A							,		__A	):
	__magic_name__       =							'''bit'''
	__magic_name__       =							['''preactivation''', '''bottleneck''']
	__magic_name__       =							['''SAME''', '''VALID''']





	def __init__( self   :					str      ,      lowerCAmelCase_   :					int=3      ,      lowerCAmelCase_   :					Any=64      ,      lowerCAmelCase_   :					Optional[Any]=[256, 512, 1_024, 2_048]      ,      lowerCAmelCase_   :					Tuple=[3, 4, 6, 3]      ,      lowerCAmelCase_   :					Optional[Any]="preactivation"      ,      lowerCAmelCase_   :					Optional[int]="relu"      ,      lowerCAmelCase_   :					Tuple=None      ,      lowerCAmelCase_   :					List[str]=32      ,      lowerCAmelCase_   :					Tuple=0.0      ,      lowerCAmelCase_   :					Dict=False      ,      lowerCAmelCase_   :					Any=32      ,      lowerCAmelCase_   :					List[Any]=1      ,      lowerCAmelCase_   :					Optional[Any]=None      ,      lowerCAmelCase_   :					Optional[int]=None      ,      **lowerCAmelCase_   :					List[Any]      ,      )		->     Optional[Any]:
				super().__init__(**lowerCAmelCase_		)
				if layer_type not in self.layer_types:
							raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types		)}"""		)
				if global_padding is not None:
							if global_padding.upper() in self.supported_padding:
										UpperCAmelCase_	:					Optional[int]     	=   global_padding.upper()
							else:
										raise ValueError(f"""Padding strategy {global_padding} not supported"""		)
				UpperCAmelCase_	:					Optional[Any]     	=   num_channels
				UpperCAmelCase_	:					Optional[int]     	=   embedding_size
				UpperCAmelCase_	:					Optional[Any]     	=   hidden_sizes
				UpperCAmelCase_	:					Dict     	=   depths
				UpperCAmelCase_	:					Dict     	=   layer_type
				UpperCAmelCase_	:					Any     	=   hidden_act
				UpperCAmelCase_	:					Optional[Any]     	=   global_padding
				UpperCAmelCase_	:					List[str]     	=   num_groups
				UpperCAmelCase_	:					Any     	=   drop_path_rate
				UpperCAmelCase_	:					Optional[Any]     	=   embedding_dynamic_padding
				UpperCAmelCase_	:					str     	=   output_stride
				UpperCAmelCase_	:					List[str]     	=   width_factor

				UpperCAmelCase_	:					Tuple     	=   ["stem"] + [f"""stage{idx}""" for idx in range(1      ,      len(lowerCAmelCase_		) + 1		)]
				UpperCAmelCase_	,      UpperCAmelCase_	:					Union[str, Any]     	=   get_aligned_output_features_output_indices(
				    out_features=lowerCAmelCase_      ,      out_indices=lowerCAmelCase_      ,      stage_names=self.stage_names		)









'''simple docstring'''

from PIL import Image


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   Image:

					'''simple docstring'''

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

					if not -2_5_5.0 <= level <= 2_5_5.0:
										raise ValueError('''level must be between -255.0 (black) and 255.0 (white)'''      )
					return img.point(__UpperCAmelCase      )


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

