"""simple docstring"""




import operator as op







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   []
		lowerCAmelCase__        =   lambda lowerCamelCase__			,   lowerCamelCase__     : int(x / y     )  # noqa: E731 integer division operation
		lowerCAmelCase__        =   {
		    "^": 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(_lowercase     ))     )

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

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

						stack.append(
						    str(opr[x](int(_lowercase     )			,   int(_lowercase     )     )     )     )  # 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(_lowercase     )			,   sep=""" | """			,   )

		return int(stack[0]     )


if __name__ == "__main__":
			__lowerCAmelCase    :							Optional[Any]													=						input("\n\nEnter a Postfix Equation (space separated) = ").split(" ")
			print("\n\tResult = ", solve(Postfix))





"""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			):
					UpperCamelCase_ :	List[str]			=    (
					    "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_ :	str			=    "CIDAS/clipseg-rd64-refined"
					UpperCamelCase_ :	Any			=    "image_segmenter"
					UpperCamelCase_ :	Optional[Any]			=    CLIPSegForImageSegmentation

					UpperCamelCase_ :	List[str]			=    ["image", "text"]
					UpperCamelCase_ :	int			=    ["image"]




					def __init__(	self						:     Tuple  ,						*snake_case__						:     str  ,						**snake_case__						:     Optional[Any]     ):
							requires_backends(self  ,						["""vision"""]     )
							super().__init__(*snake_case__  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     "Image"  ,						snake_case__						:     str     ):
							return self.pre_processor(text=[label]  ,						images=[image]  ,						padding=snake_case__  ,						return_tensors="""pt"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     Tuple     ):
							with torch.no_grad():
									lowerCAmelCase__        =   self.model(**snake_case__     ).logits
							return logits




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     List[Any]     ):
							lowerCAmelCase__        =   outputs.cpu().detach().numpy()
							lowerCAmelCase__        =   0
							lowerCAmelCase__        =   1
							return Image.fromarray((array * 255).astype(np.uinta     )     )




