


'''simple docstring'''






from PIL import Image


def       _snake_case     (  A				,					A )		->					int:






							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
				__UpperCAmelCase     :   Tuple					       =							change_brightness(img, 100)
				brigt_img.save('''image_data/lena_brightness.png''', format='''png''')



'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCAmelCase					       =							{
    '''configuration_poolformer''': [
        '''POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''PoolFormerConfig''',
        '''PoolFormerOnnxConfig''',
    ]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase					       =							['''PoolFormerFeatureExtractor''']
		__UpperCAmelCase					       =							['''PoolFormerImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase					       =							[
		    '''POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''PoolFormerForImageClassification''',
		    '''PoolFormerModel''',
		    '''PoolFormerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_poolformer import (
		    POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    PoolFormerConfig,
		    PoolFormerOnnxConfig,
		)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_poolformer import PoolFormerFeatureExtractor
				from .image_processing_poolformer import PoolFormerImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_poolformer import (
				    POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PoolFormerForImageClassification,
				    PoolFormerModel,
				    PoolFormerPreTrainedModel,
				)


else:
		import sys

		__UpperCAmelCase					       =							_LazyModule(__name__, globals()['''__file__'''], _import_structure)