'''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      ( UpperCamelCase__	):
    a__       :						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."""
    )
    a__       :						int          =  """CIDAS/clipseg-rd64-refined"""
    a__       :						List[Any]          =  """image_segmenter"""
    a__       :						str          =  CLIPSegForImageSegmentation

    a__       :						List[Any]          =  ["""image""", """text"""]
    a__       :						int          =  ["""image"""]



    def __init__(self    :    Optional[int]   ,			*__a    :    int   ,			**__a    :    Dict ):
         requires_backends(self   ,			["vision"] )
         super().__init__(*__a   ,			**__a )



    def 					_lowercase						(self    :    int   ,			__a    :    "Image"   ,			__a    :    str ):
         return self.pre_processor(text=[label]   ,			images=[image]   ,			padding=__a   ,			return_tensors="pt" )



    def 					_lowercase						(self    :    List[str]   ,			__a    :    Optional[int] ):
         with torch.no_grad():
              UpperCAmelCase_           =   self.model(**__a ).logits
         return logits






    def 					_lowercase						(self    :    int   ,			__a    :    Any ):
         UpperCAmelCase_           =   outputs.cpu().detach().numpy()
         UpperCAmelCase_           =   0
         UpperCAmelCase_           =   1
         return Image.fromarray((array * 255).astype(np.uinta ) )





'''simple docstring'''





import os
from math import logaa





def 	lowerCAmelCase_				(					snake_case_       : str = "base_exp.txt"			)    ->      int:


     '''simple docstring'''




     UpperCAmelCase_           =   0
     UpperCAmelCase_           =   0
     for i, line in enumerate(open(os.path.join(os.path.dirname(snake_case_			)					,    snake_case_			)			)			):
          UpperCAmelCase_    , UpperCAmelCase_           =   list(map(snake_case_					,    line.split(","			)			)			)
          if x * logaa(snake_case_			) > largest:
               UpperCAmelCase_           =   x * logaa(snake_case_			)
               UpperCAmelCase_           =   i + 1
     return result


if __name__ == "__main__":
    print(solution())




