






"""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_							(      SCREAMING_SNAKE_CASE_  ):
				UpperCAmelCase__        =				(
				    '''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__        =				'''CIDAS/clipseg-rd64-refined'''
				UpperCAmelCase__        =				'''image_segmenter'''
				UpperCAmelCase__        =				CLIPSegForImageSegmentation

				UpperCAmelCase__        =				['''image''', '''text''']
				UpperCAmelCase__        =				['''image''']







				def __init__(							self							:						Dict						,    *__lowerCamelCase							:						Optional[int]						,    **__lowerCamelCase							:						Tuple	)				->					List[str]:
										requires_backends(self						,    ["vision"]	)
										super().__init__(*UpperCamelCase__						,    **UpperCamelCase__	)







				def     _snake_case					(							self							:						Union[str, Any]						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Optional[Any]	)				->					List[Any]:
										return self.pre_processor(text=[label]						,    images=[image]						,    padding=UpperCamelCase__						,    return_tensors="pt"	)







				def     _snake_case					(							self							:						Union[str, Any]						,    __lowerCamelCase							:						Tuple	)				->					Optional[Any]:
										with torch.no_grad():
																__magic_name__            =     self.model(**UpperCamelCase__	).logits
										return logits







				def     _snake_case					(							self							:						Tuple						,    __lowerCamelCase							:						List[str]	)				->					int:
										__magic_name__            =     outputs.cpu().detach().numpy()
										__magic_name__            =     0
										__magic_name__            =     1
										return Image.fromarray((array * 2_5_5).astype(np.uinta	)	)











"""simple docstring"""

from __future__ import annotations

from random import choice






def _lowerCAmelCase	(  __lowerCamelCase:Optional[int]   ):

						'''simple docstring'''






						return choice(__lowerCamelCase   )






def _lowerCAmelCase	(  __lowerCamelCase:list[int] ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     random_pivot(__lowerCamelCase   )

						# partition based on pivot
						# linear time
						__magic_name__            =     [e for e in lst if e < pivot]
						__magic_name__            =     [e for e in lst if e > pivot]

						# if we get lucky, pivot might be the element we want.
						# we can easily see this:
						# small (elements smaller than k)
						# + pivot (kth element)
						# + big (elements larger than k)
						if len(__lowerCamelCase   ) == k - 1:
												return pivot
						# pivot is in elements bigger than k
						elif len(__lowerCamelCase   ) < k - 1:
												return kth_number(__lowerCamelCase ,						k - len(__lowerCamelCase   ) - 1   )
						# pivot is in elements smaller than k
						else:
												return kth_number(__lowerCamelCase ,						__lowerCamelCase   )


if __name__ == "__main__":
			import doctest

			doctest.testmod()



