




def 					lowerCamelCase__       (						A__       :					list[int]	):





       '''simple docstring'''



       if not numbers:
              return 0

       if not isinstance(_lowerCamelCase  ,			(list, tuple)	) or not all(
           isinstance(_lowerCamelCase  ,			_lowerCamelCase	) for number in numbers	):
              raise ValueError("""numbers must be an iterable of integers"""	)

       __lowerCamelCase   =						numbers[0]

       for i in range(1  ,			len(_lowerCamelCase	)	):
              # update the maximum and minimum subarray products
              __lowerCamelCase   =						numbers[i]
              if number < 0:
                     __lowerCamelCase   =						min_till_now, max_till_now
              __lowerCamelCase   =						max(_lowerCamelCase  ,			max_till_now * number	)
              __lowerCamelCase   =						min(_lowerCamelCase  ,			min_till_now * number	)

              # update the maximum product found till now
              __lowerCamelCase   =						max(_lowerCamelCase  ,			_lowerCamelCase	)

       return max_prod



import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
		lowercase  :					int													=       {
		    'linear': PIL.Image.Resampling.BILINEAR,
		    'bilinear': PIL.Image.Resampling.BILINEAR,
		    'bicubic': PIL.Image.Resampling.BICUBIC,
		    'lanczos': PIL.Image.Resampling.LANCZOS,
		    'nearest': PIL.Image.Resampling.NEAREST,
		}
else:
		lowercase  :					Any													=       {
		    'linear': PIL.Image.LINEAR,
		    'bilinear': PIL.Image.BILINEAR,
		    'bicubic': PIL.Image.BICUBIC,
		    'lanczos': PIL.Image.LANCZOS,
		    'nearest': PIL.Image.NEAREST,
		}




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			int)					->    Any:






							'''simple docstring'''





							__UpperCamelCase	:   Union[str, Any]			   =					(images / 2 + 0.5).clamp(0						,  1)
							__UpperCamelCase	:   int			   =					images.cpu().permute(0						,  2						,  3						,  1).float().numpy()
							__UpperCamelCase	:   Dict			   =					numpy_to_pil(_lowerCamelCase)
							return images




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[Any])					->    Optional[Any]:






							'''simple docstring'''





							if images.ndim == 3:
														__UpperCamelCase	:   Optional[int]			   =					images[None, ...]
							__UpperCamelCase	:   Dict			   =					(images * 255).round().astype("uint8")
							if images.shape[-1] == 1:
														# special case for grayscale (single channel) images
														__UpperCamelCase	:   int			   =					[Image.fromarray(image.squeeze()						,  mode="L") for image in images]
							else:
														__UpperCamelCase	:   Union[str, Any]			   =					[Image.fromarray(_lowerCamelCase) for image in images]

							return pil_images