
"""simple docstring"""






import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
							from PIL import Image




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  (
	    """This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."""
	    """It takes two arguments named `image` which should be the original image, and `label` which should be a text """
	    """describing the elements what should be identified in the segmentation mask. The tool returns the mask."""
	)
	UpperCamelCase				=  """CIDAS/clipseg-rd64-refined"""
	UpperCamelCase				=  """image_segmenter"""
	UpperCamelCase				=  CLIPSegForImageSegmentation

	UpperCamelCase				=  ["""image""", """text"""]
	UpperCamelCase				=  ["""image"""]





	def __init__(						self    :Dict		, *lowerCamelCase_    :int		, **lowerCamelCase_    :Any					)							->			Tuple:







			'''simple docstring'''




			requires_backends(self		, ['''vision''']					)
			super().__init__(*lowerCamelCase_		, **lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Optional[int]		, lowerCamelCase_    :"Image"		, lowerCamelCase_    :str					)							->			Union[str, Any]:







			'''simple docstring'''




			return self.pre_processor(text=[label]		, images=[image]		, padding=lowerCamelCase_		, return_tensors='''pt'''					)





	def   __lowerCAmelCase (						self    :Optional[int]		, lowerCamelCase_    :Dict					)							->			Tuple:







			'''simple docstring'''




			with torch.no_grad():
					SCREAMING_SNAKE_CASE       :     List[str]				= self.model(**lowerCamelCase_					).logits
			return logits







	def   __lowerCAmelCase (						self    :Tuple		, lowerCamelCase_    :Optional[Any]					)							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= outputs.cpu().detach().numpy()
			SCREAMING_SNAKE_CASE       :     str				= 0
			SCREAMING_SNAKE_CASE       :     List[Any]				= 1
			return Image.fromarray((array * 2_55).astype(np.uinta					)					)







"""simple docstring"""






def __A						(   a_ :							int     )->							list[int]:




		'''simple docstring'''






		if num <= 0:
				raise ValueError('''Input must be a positive integer'''     )

		SCREAMING_SNAKE_CASE       :     Optional[int]				= [True] * (num + 1)

		SCREAMING_SNAKE_CASE       :     Optional[Any]				= 2
		while p * p <= num:
				if primes[p]:
						for i in range(p * p		,		num + 1		,		a_     ):
								SCREAMING_SNAKE_CASE       :     Any				= False
				p += 1

		return [prime for prime in range(2		,		num + 1     ) if primes[prime]]


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					lowerCamelCase__ :       str						     =			int(input("Enter a positive integer: ").strip())
					print(prime_sieve_eratosthenes(user_num))





