"""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     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	: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."
    )
    _UpperCAmelCase	:Any						    = "CIDAS/clipseg-rd64-refined"
    _UpperCAmelCase	:List[str]						    = "image_segmenter"
    _UpperCAmelCase	:Any						    = CLIPSegForImageSegmentation

    _UpperCAmelCase	:str						    = ["image", "text"]
    _UpperCAmelCase	:Tuple						    = ["image"]






    def __init__(   self    ,			*_UpperCAmelCase    ,			**_UpperCAmelCase							):
       requires_backends(self    ,			['''vision''']							)
       super().__init__(*__lowerCamelCase    ,			**__lowerCamelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       return self.pre_processor(text=[label]    ,			images=[image]    ,			padding=__lowerCamelCase    ,			return_tensors='''pt'''							)






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       with torch.no_grad():
          lowercase__:   Dict					=      self.model(**__lowerCamelCase							).logits
       return logits






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   Any					=      outputs.cpu().detach().numpy()
       lowercase__:   int					=      0
       lowercase__:   List[Any]					=      1
       return Image.fromarray((array * 255).astype(np.uinta							)							)




def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[Any]				,							UpperCamelCase__     :  Union[str, Any]						):
       # "extended trapezoidal rule"
       # int(f) = dx/2 * (f1 + 2f2 + ... + fn)
       _A      :       int				    = (boundary[1] - boundary[0]) / steps
       _A      :       Any				    = boundary[0]
       _A      :       List[Any]				    = boundary[1]
       _A      :       str				    = make_points(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__						)
       _A      :       str				    = 0.0
       y += (h / 2.0) * f(UpperCamelCase__						)
       for i in x_i:
              # print(i)
              y += h * f(UpperCamelCase__						)
       y += (h / 2.0) * f(UpperCamelCase__						)
       return y
def 							_UpperCAmelCase				(UpperCamelCase__     :  List[str]				,							UpperCamelCase__     :  Optional[int]				,							UpperCamelCase__     :  Any						):
       _A      :       Optional[int]				    = a + h
       while x < (b - h):
              yield x
              _A      :       Dict				    = x + h
def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[int]						):  # enter your function here
       _A      :       Any				    = (x - 0) * (x - 0)
       return y
def 							_UpperCAmelCase				():
       _A      :       Optional[Any]				    = 0.0  # Lower bound of integration
       _A      :       Optional[int]				    = 1.0  # Upper bound of integration
       _A      :       List[Any]				    = 10.0  # define number of steps or resolution
       _A      :       Any				    = [a, b]  # define boundary of integration
       _A      :       Tuple				    = method_a(UpperCamelCase__				,							UpperCamelCase__						)
       print(f"y = {y}"						)


if __name__ == "__main__":
    main()
