
"""simple docstring"""


from __future__ import annotations

_lowercase							      = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
_lowercase							      = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]






def 					_snake_case (						snake_case__      :   list[float]   ):
	A        =      []
	A        =      len(snake_case__   )

	for i in range(snake_case__   ):
		A        =      -1
		for j in range(i + 1		,					snake_case__   ):
			if arr[i] < arr[j]:
				A        =      arr[j]
				break
		result.append(snake_case__   )
	return result






def 					_snake_case (						snake_case__      :   list[float]   ):
	A        =      []
	for i, outer in enumerate(snake_case__   ):
		A        =      -1
		for inner in arr[i + 1 :]:
			if outer < inner:
				A        =      inner
				break
		result.append(snake_case__   )
	return result






def 					_snake_case (						snake_case__      :   list[float]   ):
	A        =      len(snake_case__   )
	A        =      []
	A        =      [-1] * arr_size

	for index in reversed(range(snake_case__   )   ):
		if stack:
			while stack[-1] <= arr[index]:
				stack.pop()
				if not stack:
					break
		if stack:
			A        =      stack[-1]
		stack.append(arr[index]   )
	return result


if __name__ == "__main__":
		from doctest import testmod
		from timeit import timeit

		testmod()
		print(next_greatest_element_slow(arr))
		print(next_greatest_element_fast(arr))
		print(next_greatest_element(arr))

		_lowercase							      = (
		    '''from __main__ import arr, next_greatest_element_slow, '''
		    '''next_greatest_element_fast, next_greatest_element'''
		)
		print(
		    '''next_greatest_element_slow():''',
		    timeit('''next_greatest_element_slow(arr)''', setup=setup),
		)
		print(
		    '''next_greatest_element_fast():''',
		    timeit('''next_greatest_element_fast(arr)''', setup=setup),
		)
		print(
		    '''     next_greatest_element():''',
		    timeit('''next_greatest_element(arr)''', setup=setup),
		)


'''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__    (       A__  ):
  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      :	Any							,					*_a      :	Dict							,					**_a      :	str )	->    Any:





       '''simple docstring'''






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





  def 						A  (		self      :	int							,					_a      :	"Image"							,					_a      :	str )	->    Optional[Any]:





       '''simple docstring'''






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





  def 						A  (		self      :	Dict							,					_a      :	Dict )	->    str:





       '''simple docstring'''






       with torch.no_grad():
            _SCREAMING_SNAKE_CASE							    =self.model(**_a ).logits
       return logits





  def 						A  (		self      :	Any							,					_a      :	str )	->    Union[str, Any]:





       '''simple docstring'''






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