


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       __magic_name__	(     __lowerCAmelCase):
     A:		Any			=      (
         "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."
     )
     A:		Any			=      "CIDAS/clipseg-rd64-refined"
     A:		Optional[int]			=      "image_segmenter"
     A:		Dict			=      CLIPSegForImageSegmentation

     A:		Union[str, Any]			=      ["image", "text"]
     A:		Any			=      ["image"]


     def __init__(      self							:     int							,	*lowerCamelCase__							:     str							,	**lowerCamelCase__							:     Optional[int]			)  ->      List[str]:





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     int							,	lowerCamelCase__							:     int			)  ->      str:





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     Dict							,	lowerCamelCase__							:     Tuple			)  ->      Tuple:





      '''simple docstring'''






      with torch.no_grad():
       UpperCamelCase__					:    Optional[Any]          =   self.model(**_SCREAMING_SNAKE_CASE			).logits
      return logits


     def      UpperCAmelCase__	(      self							:     Optional[Any]							,	lowerCamelCase__							:     Optional[int]			)  ->      Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Tuple          =   outputs.cpu().detach().numpy()
      UpperCamelCase__					:    Union[str, Any]          =   0
      UpperCamelCase__					:    str          =   1
      return Image.fromarray((array * 255).astype(np.uinta			)			)






from math import factorial, radians

def 			_a  (				SCREAMING_SNAKE_CASE	:				float    , SCREAMING_SNAKE_CASE	:				int = 18    , SCREAMING_SNAKE_CASE	:				int = 10     ):







 """simple docstring"""



 UpperCamelCase__					:    Dict          =   angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

 # Converting from degrees to radians
 UpperCamelCase__					:    int          =   radians(SCREAMING_SNAKE_CASE     )

 UpperCamelCase__					:    Tuple          =   angle_in_radians
 UpperCamelCase__					:    str          =   3
 UpperCamelCase__					:    int          =   -1

 for _ in range(SCREAMING_SNAKE_CASE     ):
  result += (b * (angle_in_radians**a)) / factorial(SCREAMING_SNAKE_CASE     )

  UpperCamelCase__					:    List[Any]          =   -b  # One positive term and the next will be negative and so on...
  a += 2  # Increased by 2 for every term.

 return round(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       __import__("doctest").testmod()


