




"""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 				lowercase_       (     __UpperCamelCase							):



 '''simple docstring'''
 UpperCAmelCase    :     int        =	(
     '''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    :     str        =	'''CIDAS/clipseg-rd64-refined'''
 UpperCAmelCase    :     int        =	'''image_segmenter'''
 UpperCAmelCase    :     List[str]        =	CLIPSegForImageSegmentation

 UpperCAmelCase    :     Optional[Any]        =	['''image''', '''text''']
 UpperCAmelCase    :     Dict        =	['''image''']

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

 def 			lowerCAmelCase_							( self       :       Tuple    ,							_UpperCAmelCase       :       "Image"    ,							_UpperCAmelCase       :       str		):
       return self.pre_processor(text=[label]    ,							images=[image]    ,							padding=_UpperCAmelCase    ,							return_tensors='pt'		)

 def 			lowerCAmelCase_							( self       :       Union[str, Any]    ,							_UpperCAmelCase       :       Dict		):
       with torch.no_grad():
             _A					   =   self.model(**_UpperCAmelCase		).logits
       return logits



 def 			lowerCAmelCase_							( self       :       Dict    ,							_UpperCAmelCase       :       Union[str, Any]		):
       _A					   =   outputs.cpu().detach().numpy()
       _A					   =   0
       _A					   =   1
       return Image.fromarray((array * 255).astype(np.uinta		)		)








def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     set:
    lowerCamelCase__    :      Optional[Any]					 =		set()
    # edges = list of graph's edges
    lowerCamelCase__    :      List[str]					 =		get_edges(_UpperCAmelCase )

    # While there are still elements in edges list, take an arbitrary edge
    # (from_node, to_node) and add his extremity to chosen_vertices and then
    # remove all arcs adjacent to the from_node and to_node
    while edges:
        lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		edges.pop()
        chosen_vertices.add(_UpperCAmelCase )
        chosen_vertices.add(_UpperCAmelCase )
        for edge in edges.copy():
            if from_node in edge or to_node in edge:
                edges.discard(_UpperCAmelCase )
    return chosen_vertices


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     set:
    lowerCamelCase__    :      Union[str, Any]					 =		set()
    for from_node, to_nodes in graph.items():
        for to_node in to_nodes:
            edges.add((from_node, to_node) )
    return edges


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
    # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")





