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#
# 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.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
							from PIL import Image
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			"""dandelin/vilt-b32-finetuned-vqa"""
				lowercase__											=			(
				    """This is a tool that answers a question about an image. It takes an input named `image` which should be the """
				    """image containing the information, as well as a `question` which should be the question in English. It """
				    """returns a text that is the answer to the question."""
				)
				lowercase__											=			"""image_qa"""
				lowercase__											=			AutoProcessor
				lowercase__											=			AutoModelForVisualQuestionAnswering

				lowercase__											=			["""image""", """text"""]
				lowercase__											=			["""text"""]




				def __init__(	self		:       Any,    *lowerCamelCase		:       str,    **lowerCamelCase		:       str    ):

								'''simple docstring'''
								requires_backends(self,    ['''vision''']    )
								super().__init__(*lowerCamelCase,    **lowerCamelCase    )




				def 							lowercase__  (	self		:       Dict,    lowerCamelCase		:       "Image",    lowerCamelCase		:       str    ):

								'''simple docstring'''
								return self.pre_processor(lowerCamelCase,    lowerCamelCase,    return_tensors='''pt'''    )




				def 							lowercase__  (	self		:       Optional[int],    lowerCamelCase		:       Union[str, Any]    ):

								'''simple docstring'''
								with torch.no_grad():
												return self.model(**lowerCamelCase    ).logits




				def 							lowercase__  (	self		:       Tuple,    lowerCamelCase		:       str    ):

								'''simple docstring'''
								lowercase__  					=     outputs.argmax(-1    ).item()
								return self.model.config.idalabel[idx]












from __future__ import annotations

from typing import Any






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				if not postfix_notation:
								return 0

				lowercase__  					=     {'''+''', '''-''', '''*''', '''/'''}
				lowercase__  					=     []

				for token in postfix_notation:
								if token in operations:
												lowercase__					,		lowercase__  					=     stack.pop(), stack.pop()
												if token == "+":
																stack.append(a + b							)
												elif token == "-":
																stack.append(a - b							)
												elif token == "*":
																stack.append(a * b							)
												else:
																if a * b < 0 and a % b != 0:
																				stack.append(a // b + 1							)
																else:
																				stack.append(a // b							)
								else:
												stack.append(int(lowerCamelCase_							)							)

				return stack.pop()


if __name__ == "__main__":
							import doctest

							doctest.testmod()




