



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 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  List[str]      =			(
			    """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."""
			)
			lowercase__      :  List[str]      =			"""CIDAS/clipseg-rd64-refined"""
			lowercase__      :  Optional[int]      =			"""image_segmenter"""
			lowercase__      :  Any      =			CLIPSegForImageSegmentation

			lowercase__      :  Optional[int]      =			["""image""", """text"""]
			lowercase__      :  Union[str, Any]      =			["""image"""]





			def __init__(				self	:       List[str]			,     *lowercase	:       List[str]			,     **lowercase	:       Union[str, Any]   )						->					List[str]:
									"""simple docstring"""







									requires_backends(self			,     ["""vision"""]   )
									super().__init__(*_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE   )





			def 		snake_case__			(				self	:       str			,     lowercase	:       Dict			,     lowercase	:       Dict   )						->					str:
									"""simple docstring"""







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





			def 		snake_case__			(				self	:       Dict			,     lowercase	:       Tuple   )						->					Tuple:
									"""simple docstring"""







									with torch.no_grad():
															__lowercase					=				self.model(**_SCREAMING_SNAKE_CASE   ).logits
									return logits







			def 		snake_case__			(				self	:       Dict			,     lowercase	:       Dict   )						->					Union[str, Any]:
									"""simple docstring"""







									__lowercase					=				outputs.cpu().detach().numpy()
									__lowercase					=				0
									__lowercase					=				1
									return Image.fromarray((array * 255).astype(np.uinta   )   )










def  UpperCAmelCase__    (    lowercase__ = 100   ) ->      int:
						__lowercase					=				n * (n + 1) * (2 * n + 1) / 6
						__lowercase					=				(n * (n + 1) / 2) ** 2
						return int(square_of_sum - sum_of_squares   )


if __name__ == "__main__":
			print(F"""{solution() = }""")





