



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    _A	(     A__   ):
       UpperCamelCase__  :   Dict						 =						(
           "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__  :   List[str]						 =						"CIDAS/clipseg-rd64-refined"
       UpperCamelCase__  :   List[Any]						 =						"image_segmenter"
       UpperCamelCase__  :   Optional[int]						 =						CLIPSegForImageSegmentation

       UpperCamelCase__  :   Tuple						 =						["image", "text"]
       UpperCamelCase__  :   Dict						 =						["image"]



       def __init__(		self							:					Union[str, Any]					,		*__SCREAMING_SNAKE_CASE							:					Tuple					,		**__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Dict):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Tuple):


           '''simple docstring'''

           with torch.no_grad():
               __a							=    self.model(**__A).logits
           return logits





       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					Dict):


           '''simple docstring'''

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










def 					__snake_case     (      _UpperCAmelCase    ):
    if not isinstance(_UpperCAmelCase       ,			_UpperCAmelCase    ):
        raise TypeError('''Input value must be an \'int\' type'''    )

    __a							=    0
    while number:
        position += 1
        number >>= 1

    return position


if __name__ == "__main__":
 import doctest

 doctest.testmod()





