




'''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_   (			__SCREAMING_SNAKE_CASE       ):


			"""simple docstring"""

			lowerCamelCase_      =						(
			    '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.'
			)
			lowerCamelCase_      =						'CIDAS/clipseg-rd64-refined'
			lowerCamelCase_      =						'image_segmenter'
			lowerCamelCase_      =						CLIPSegForImageSegmentation

			lowerCamelCase_      =						['image', 'text']
			lowerCamelCase_      =						['image']



			def __init__(   self :     str  ,				*__lowerCamelCase :     str  ,				**__lowerCamelCase :     str   ):

						"""simple docstring"""
						requires_backends(self  ,				["vision"]   )
						super().__init__(*_a  ,				**_a   )



			def 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     Optional[Any]   ):

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



			def 							lowerCAmelCase_      (   self :     Optional[int]  ,				__lowerCamelCase :     Optional[int]   ):

						"""simple docstring"""
						with torch.no_grad():
									_SCREAMING_SNAKE_CASE	=				self.model(**_a   ).logits
						return logits



			def 							lowerCAmelCase_      (   self :     Optional[Any]  ,				__lowerCamelCase :     int   ):

						"""simple docstring"""
						_SCREAMING_SNAKE_CASE	=				outputs.cpu().detach().numpy()
						_SCREAMING_SNAKE_CASE	=				0
						_SCREAMING_SNAKE_CASE	=				1
						return Image.fromarray((array * 2_5_5).astype(np.uinta   )   )







'''simple docstring'''



def 			SCREAMING_SNAKE_CASE_			(							__A    :			int  )			->			int:
   if n == 1 or not isinstance(__A	,					__A  ):
      return 0
   elif n == 2:
      return 1
   else:
      _SCREAMING_SNAKE_CASE	=				[0, 1]
      for i in range(2	,					n + 1  ):
         sequence.append(sequence[i - 1] + sequence[i - 2]  )

      return sequence[n]
def 			SCREAMING_SNAKE_CASE_			(							__A    :			int  )			->			int:
   _SCREAMING_SNAKE_CASE	=				0
   _SCREAMING_SNAKE_CASE	=				2

   while digits < n:
      index += 1
      _SCREAMING_SNAKE_CASE	=				len(str(fibonacci(__A  )  )  )

   return index
def 			SCREAMING_SNAKE_CASE_			(							__A    :			int = 10_00  )			->			int:
   return fibonacci_digits_index(__A  )


if __name__ == "__main__":
 print(solution(int(str(input()).strip())))

