






# 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 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		Optional[Any]								=						(
         '''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_				:		Union[str, Any]								=						'''CIDAS/clipseg-rd64-refined'''
     UpperCamelCase_				:		Any								=						'''image_segmenter'''
     UpperCamelCase_				:		int								=						CLIPSegForImageSegmentation

     UpperCamelCase_				:		Optional[Any]								=						['''image''', '''text''']
     UpperCamelCase_				:		int								=						['''image''']


     def __init__(					self							:						str       ,			*UpperCAmelCase_							:						Union[str, Any]       ,			**UpperCAmelCase_							:						Union[str, Any]   ):
       requires_backends(self       ,			["vision"]   )
       super().__init__(*UpperCAmelCase_       ,			**UpperCAmelCase_   )


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


     def 					_A							(					self							:						str       ,			UpperCAmelCase_							:						Optional[Any]   ):
       with torch.no_grad():
         SCREAMING_SNAKE_CASE							:   str						    =     self.model(**UpperCAmelCase_   ).logits
       return logits






     def 					_A							(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						List[str]   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     outputs.cpu().detach().numpy()
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     0
       SCREAMING_SNAKE_CASE							:   str						    =     1
       return Image.fromarray((array * 255).astype(np.uinta   )   )








"""simple docstring"""




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  stooge(_UpperCamelCase			,					0			,					len(_UpperCamelCase    ) - 1    )
  return arr




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  if i >= h:
    return

  # If first element is smaller than the last then swap them
  if arr[i] > arr[h]:
    _lowercase			,					_lowercase:      Optional[Any]   			=  arr[h], arr[i]

  # If there are more than 2 elements in the array
  if h - i + 1 > 2:
    _lowercase:      Tuple   			=  (int)((h - i + 1) / 3    )

    # Recursively sort first 2/3 elements
    stooge(_UpperCamelCase			,					_UpperCamelCase			,					(h - t)    )

    # Recursively sort last 2/3 elements
    stooge(_UpperCamelCase			,					i + t			,					(_UpperCamelCase)    )

    # Recursively sort first 2/3 elements
    stooge(_UpperCamelCase			,					_UpperCamelCase			,					(h - t)    )


if __name__ == "__main__":
  A__ :       Dict				= input('Enter numbers separated by a comma:\n').strip()
  A__ :       Optional[Any]				= [int(item) for item in user_input.split(',')]
  print(stooge_sort(unsorted))
