






'''simple docstring'''

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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_		(    lowerCamelCase       ):
  lowercase  	=   (
      """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  	=   """CIDAS/clipseg-rd64-refined"""
  lowercase  	=   """image_segmenter"""
  lowercase  	=   CLIPSegForImageSegmentation

  lowercase  	=   ["""image""", """text"""]
  lowercase  	=   ["""image"""]

  def __init__(   self    ,	*_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )						->   List[str]:



        """simple docstring"""






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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   str:



        """simple docstring"""






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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Tuple:



        """simple docstring"""






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


  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Union[str, Any]:



        """simple docstring"""






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








'''simple docstring'''

SCREAMING_SNAKE_CASE__											=     8.31_44_62  # Unit - J mol-1 K-1







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				float:
      if moles < 0 or kelvin < 0 or volume < 0:
            raise ValueError("""Invalid inputs. Enter positive value."""					)
      return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				float:
      if moles < 0 or kelvin < 0 or pressure < 0:
            raise ValueError("""Invalid inputs. Enter positive value."""					)
      return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
     from doctest import testmod

     testmod()
