


class     __UpperCAmelCase							:


     """simple docstring"""


     def __init__(	self  ,       __A    ):
        __a					=  set_counts
        __a					=  max(__A    )
        __a					=  len(__A    )
        __a					=  [1] * num_sets
        __a					=  list(range(__A    )    )


     def 	snake_case_  (	self  ,       __A  ,       __A    ):
        __a					=  self.get_parent(__A    )
        __a					=  self.get_parent(__A    )

        if src_parent == dst_parent:
           return False

        if self.ranks[dst_parent] >= self.ranks[src_parent]:
           self.set_counts[dst_parent] += self.set_counts[src_parent]
           __a					=  0
           __a					=  dst_parent
           if self.ranks[dst_parent] == self.ranks[src_parent]:
              self.ranks[dst_parent] += 1
           __a					=  self.set_counts[dst_parent]
        else:
           self.set_counts[src_parent] += self.set_counts[dst_parent]
           __a					=  0
           __a					=  src_parent
           __a					=  self.set_counts[src_parent]

        __a					=  max(self.max_set  ,       __A    )
        return True


     def 	snake_case_  (	self  ,       __A    ):
        if self.parents[disj_set] == disj_set:
           return disj_set
        __a					=  self.get_parent(self.parents[disj_set]    )
        return self.parents[disj_set]







# 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							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      (
         """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   =      """CIDAS/clipseg-rd64-refined"""
     _lowerCamelCase   =      """image_segmenter"""
     _lowerCamelCase   =      CLIPSegForImageSegmentation

     _lowerCamelCase   =      ["""image""", """text"""]
     _lowerCamelCase   =      ["""image"""]


     def __init__(	self  ,       *__A  ,       **__A    ):
        requires_backends(self  ,       ["""vision"""]    )
        super().__init__(*__A  ,       **__A    )


     def 	snake_case_  (	self  ,       __A  ,       __A    ):
        return self.pre_processor(text=[label]  ,       images=[image]  ,       padding=__A  ,       return_tensors="""pt"""    )


     def 	snake_case_  (	self  ,       __A    ):
        with torch.no_grad():
           __a					=  self.model(**__A    ).logits
        return logits


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  outputs.cpu().detach().numpy()
        __a					=  0
        __a					=  1
        return Image.fromarray((array * 255).astype(np.uinta    )    )



