






'''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        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   int		       =    (
        '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:   List[Any]		       =    'CIDAS/clipseg-rd64-refined'
    __lowerCamelCase:   List[str]		       =    'image_segmenter'
    __lowerCamelCase:   Dict		       =    CLIPSegForImageSegmentation

    __lowerCamelCase:   Optional[int]		       =    ['image', 'text']
    __lowerCamelCase:   Optional[int]		       =    ['image']




    def __init__(			self  :       List[str]  ,     *a  :       Dict  ,     **a  :       Any			):






       '''simple docstring'''






       requires_backends(self  ,     ["vision"]			)
       super().__init__(*a  ,     **a			)




    def 				lowerCAmelCase__				(			self  :       int  ,     a  :       "Image"  ,     a  :       str			):






       '''simple docstring'''






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




    def 				lowerCAmelCase__				(			self  :       int  ,     a  :       Tuple			):






       '''simple docstring'''






       with torch.no_grad():
          lowercase_	:    str           =     self.model(**a			).logits
       return logits





    def 				lowerCAmelCase__				(			self  :       Optional[Any]  ,     a  :       Tuple			):






       '''simple docstring'''






       lowercase_	:    List[str]           =     outputs.cpu().detach().numpy()
       lowercase_	:    Any           =     0
       lowercase_	:    List[Any]           =     1
       return Image.fromarray((array * 2_5_5).astype(np.uinta			)			)












'''simple docstring'''
from __future__ import annotations

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




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    str           =     []
   lowercase_	:    List[str]           =     len(_UpperCamelCase							)

   for i in range(_UpperCamelCase							):
      lowercase_	:    float           =     -1
      for j in range(i + 1						,		_UpperCamelCase							):
         if arr[i] < arr[j]:
            lowercase_	:    Union[str, Any]           =     arr[j]
            break
      result.append(_UpperCamelCase							)
   return result




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[str]           =     []
   for i, outer in enumerate(_UpperCamelCase							):
      lowercase_	:    float           =     -1
      for inner in arr[i + 1 :]:
         if outer < inner:
            lowercase_	:    Optional[Any]           =     inner
            break
      result.append(_UpperCamelCase							)
   return result




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[str]           =     len(_UpperCamelCase							)
   lowercase_	:    list[float]           =     []
   lowercase_	:    list[float]           =     [-1] * arr_size

   for index in reversed(range(_UpperCamelCase							)							):
      if stack:
         while stack[-1] <= arr[index]:
            stack.pop()
            if not stack:
               break
      if stack:
         lowercase_	:    Optional[Any]           =     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))

 UpperCamelCase__ =			(
     '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),
 )




