



'''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 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    Tuple   				=					(
							    '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.'
							)
							lowerCAmelCase       :    List[str]   				=					'CIDAS/clipseg-rd64-refined'
							lowerCAmelCase       :    Optional[int]   				=					'image_segmenter'
							lowerCAmelCase       :    Union[str, Any]   				=					CLIPSegForImageSegmentation

							lowerCAmelCase       :    str   				=					['image', 'text']
							lowerCAmelCase       :    int   				=					['image']







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







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







							def 							__lowercase			(							self					:       Optional[int]	,_UpperCAmelCase					:       Optional[Any]		):
									with torch.no_grad():
											_a     :      int        =							self.model(**_UpperCAmelCase		).logits
									return logits







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











from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst





def      lowerCAmelCase_ (       )     ->		Optional[int]:







  """simple docstring"""





  lowerCamelCase__  ,					lowerCamelCase__:      int			  =9, 14  # noqa: F841
  lowerCamelCase__:      List[Any]			  =[
      [0, 1, 4],
      [0, 7, 8],
      [1, 2, 8],
      [7, 8, 7],
      [7, 6, 1],
      [2, 8, 2],
      [8, 6, 6],
      [2, 3, 7],
      [2, 5, 4],
      [6, 5, 2],
      [3, 5, 14],
      [3, 4, 9],
      [5, 4, 10],
      [1, 7, 11],
  ]

  lowerCamelCase__:      List[str]			  =defaultdict(__a		)
  for nodea, nodea, cost in edges:
    adjancency[nodea].append([nodea, cost]		)
    adjancency[nodea].append([nodea, cost]		)

  lowerCamelCase__:      List[str]			  =mst(__a		)

  lowerCamelCase__:      Union[str, Any]			  =[
      [7, 6, 1],
      [2, 8, 2],
      [6, 5, 2],
      [0, 1, 4],
      [2, 5, 4],
      [2, 3, 7],
      [0, 7, 8],
      [3, 4, 9],
  ]

  for answer in expected:
    lowerCamelCase__:      Optional[int]			  =tuple(answer[:2]		)
    lowerCamelCase__:      List[Any]			  =tuple(edge[::-1]		)
    assert edge in result or reverse in result

