


import operator as op




def  a   (lowerCAmelCase__   ):
   __a					=  []
   __a					=  lambda lowerCAmelCase__		,				lowerCAmelCase__   : int(x / y   )  # noqa: E731 integer division operation
   __a					=  {
       """^""": op.pow,
       """*""": op.mul,
       """/""": div,
       """+""": op.add,
       """-""": op.sub,
   }  # operators & their respective operation

   # print table header
   print("""Symbol""".center(8   )		,				"""Action""".center(12   )		,				"""Stack"""		,				sep=""" | """   )
   print("""-""" * (30 + len(lowerCAmelCase__   ))   )

   for x in post_fix:
      if x.isdigit():  # if x in digit
         stack.append(lowerCAmelCase__   )  # append x to stack
         # output in tabular format
         print(x.rjust(8   )		,				("""push(""" + x + """)""").ljust(12   )		,				""",""".join(lowerCAmelCase__   )		,				sep=""" | """   )
      else:
         __a					=  stack.pop()  # pop stack
         # output in tabular format
         print("""""".rjust(8   )		,				("""pop(""" + b + """)""").ljust(12   )		,				""",""".join(lowerCAmelCase__   )		,				sep=""" | """   )

         __a					=  stack.pop()  # pop stack
         # output in tabular format
         print("""""".rjust(8   )		,				("""pop(""" + a + """)""").ljust(12   )		,				""",""".join(lowerCAmelCase__   )		,				sep=""" | """   )

         stack.append(
             str(opr[x](int(lowerCAmelCase__   )		,				int(lowerCAmelCase__   )   )   )   )  # evaluate the 2 values popped from stack & push result to stack
         # output in tabular format
         print(
             x.rjust(8   )		,				("""push(""" + a + x + b + """)""").ljust(12   )		,				""",""".join(lowerCAmelCase__   )		,				sep=""" | """		,				)

   return int(stack[0]   )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE							    =							input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
  print('\n\tResult = ', solve(Postfix))







# 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							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      (
         """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   =      """CIDAS/clipseg-rd64-refined"""
     _lowerCamelCase   =      """image_segmenter"""
     _lowerCamelCase   =      CLIPSegForImageSegmentation

     _lowerCamelCase   =      ["""image""", """text"""]
     _lowerCamelCase   =      ["""image"""]


     def __init__(	self  ,       *__A  ,       **__A    ):
        requires_backends(self  ,       ["""vision"""]    )
        super().__init__(*__A  ,       **__A    )


     def 	snake_case_  (	self  ,       __A  ,       __A    ):
        return self.pre_processor(text=[label]  ,       images=[image]  ,       padding=__A  ,       return_tensors="""pt"""    )


     def 	snake_case_  (	self  ,       __A    ):
        with torch.no_grad():
           __a					=  self.model(**__A    ).logits
        return logits


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



