

'''simple docstring'''
def        _lowerCAmelCase					(						_UpperCamelCase						:     Dict				)      ->			List[Any]:
     """simple docstring"""




     return [
         {
             0: [1, 2],
             1: [0, 2],
             2: [0, 1, 3, 5],
             3: [2, 4],
             4: [3],
             5: [2, 6, 8],
             6: [5, 7],
             7: [6, 8],
             8: [5, 7],
         },
         {
             0: [6],
             1: [9],
             2: [4, 5],
             3: [4],
             4: [2, 3],
             5: [2],
             6: [0, 7],
             7: [6],
             8: [],
             9: [1],
         },
         {
             0: [4],
             1: [6],
             2: [],
             3: [5, 6, 7],
             4: [0, 6],
             5: [3, 8, 9],
             6: [1, 3, 4, 7],
             7: [3, 6, 8, 9],
             8: [5, 7],
             9: [5, 7],
         },
         {
             0: [1, 3],
             1: [0, 2, 4],
             2: [1, 3, 4],
             3: [0, 2, 4],
             4: [1, 2, 3],
         },
     ][index]


def        _lowerCAmelCase					(						_UpperCamelCase						:     dict[int, list[int]]				)      ->			list[tuple[int, int]]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =0
     _SCREAMING_SNAKE_CASE							    =len(_UpperCamelCase				)  # No of vertices in graph
     _SCREAMING_SNAKE_CASE							    =[0] * n
     _SCREAMING_SNAKE_CASE							    =[False] * n

     def dfs(_UpperCamelCase						:     int							, _UpperCamelCase						:     Optional[int]							, _UpperCamelCase						:     Optional[int]							, _UpperCamelCase						:     Dict				):
          _SCREAMING_SNAKE_CASE							    =True
          _SCREAMING_SNAKE_CASE							    =id_
          id_ += 1
          for to in graph[at]:
               if to == parent:
                    pass
               elif not visited[to]:
                    dfs(_UpperCamelCase							, _UpperCamelCase							, _UpperCamelCase							, id_				)
                    _SCREAMING_SNAKE_CASE							    =min(low[at]							, low[to]				)
                    if id_ <= low[to]:
                         bridges.append((at, to) if at < to else (to, at)				)
               else:
                    # This edge is a back edge and cannot be a bridge
                    _SCREAMING_SNAKE_CASE							    =min(low[at]							, low[to]				)

     _SCREAMING_SNAKE_CASE							    =[]
     for i in range(_UpperCamelCase				):
          if not visited[i]:
               dfs(_UpperCamelCase							, -1							, _UpperCamelCase							, id_				)
     return bridges


if __name__ == "__main__":
     import doctest

     doctest.testmod()



'''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__    (       A__  ):
  A__		 =				(
      '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__		 =				'CIDAS/clipseg-rd64-refined'
  A__		 =				'image_segmenter'
  A__		 =				CLIPSegForImageSegmentation

  A__		 =				['image', 'text']
  A__		 =				['image']





  def __init__(		self      :	Any							,					*_a      :	Dict							,					**_a      :	str )	->    Any:





       '''simple docstring'''






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





  def 						A  (		self      :	int							,					_a      :	"Image"							,					_a      :	str )	->    Optional[Any]:





       '''simple docstring'''






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





  def 						A  (		self      :	Dict							,					_a      :	Dict )	->    str:





       '''simple docstring'''






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





  def 						A  (		self      :	Any							,					_a      :	str )	->    Union[str, Any]:





       '''simple docstring'''






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