





# 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(						__UpperCAmelCase ):
					__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,       *A,       **A      ):





												"""simple docstring"""

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

					def 	_UpperCamelCase     (      self,       A,       A      ):





												"""simple docstring"""

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

					def 	_UpperCamelCase     (      self,       A      ):





												"""simple docstring"""

												with torch.no_grad():
																			_UpperCamelCase              =						self.model(**__A      ).logits
												return logits



					def 	_UpperCamelCase     (      self,       A      ):





												"""simple docstring"""

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













def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       _UpperCamelCase              =						[]
       if len(lowerCAmelCase					) == 1:
              return [nums.copy()]
       for _ in range(len(lowerCAmelCase					)					):
              _UpperCamelCase              =						nums.pop(0					)
              _UpperCamelCase              =						permute(lowerCAmelCase					)
              for perm in permutations:
                     perm.append(lowerCAmelCase					)
              result.extend(lowerCAmelCase					)
              nums.append(lowerCAmelCase					)
       return result


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):

       def backtrack(lowerCAmelCase					):
              if start == len(lowerCAmelCase					) - 1:
                     output.append(nums[:]					)
              else:
                     for i in range(lowerCAmelCase    ,       len(lowerCAmelCase					)					):
                            _UpperCamelCase       ,		_UpperCamelCase              =						nums[i], nums[start]
                            backtrack(start + 1					)
                            _UpperCamelCase       ,		_UpperCamelCase              =						nums[i], nums[start]  # backtrack

       _UpperCamelCase              =						[]
       backtrack(0					)
       return output


if __name__ == "__main__":
       import doctest

       # use res to print the data in permute2 function
       lowercase    : Optional[Any]    	=  permutea([1, 2, 3])
       print(res)
       doctest.testmod()






