


'''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.
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 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  int      		= "dandelin/vilt-b32-finetuned-vqa"
							lowercase__     :  Optional[int]      		= (
							    "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__     :  Any      		= "image_qa"
							lowercase__     :  Any      		= AutoProcessor
							lowercase__     :  Dict      		= AutoModelForVisualQuestionAnswering

							lowercase__     :  Union[str, Any]      		= ["image", "text"]
							lowercase__     :  List[Any]      		= ["text"]







							def __init__(						self   ,       *lowerCamelCase_   ,       **lowerCamelCase_  )			->				List[Any]:
														requires_backends(self   ,       ['''vision''']  )
														super().__init__(*lowerCamelCase_   ,       **lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				str:
														return self.pre_processor(lowerCamelCase_   ,       lowerCamelCase_   ,       return_tensors='''pt'''  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				Optional[Any]:
														with torch.no_grad():
																					return self.model(**lowerCamelCase_  ).logits







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				Any:
														lowerCAmelCase__			  =      outputs.argmax(-1  ).item()
														return self.model.config.idalabel[idx]



'''simple docstring'''






from __future__ import annotations

from random import choice


def    _snake_case  (	A			)		->       int:
							return choice(A			)


def    _snake_case  (	A					,       A			)		->       int:
							lowerCAmelCase__			  =      random_pivot(A			)

							# partition based on pivot
							# linear time
							lowerCAmelCase__			  =      [e for e in lst if e < pivot]
							lowerCAmelCase__			  =      [e for e in lst if e > pivot]

							# if we get lucky, pivot might be the element we want.
							# we can easily see this:
							# small (elements smaller than k)
							# + pivot (kth element)
							# + big (elements larger than k)
							if len(A			) == k - 1:
														return pivot
							# pivot is in elements bigger than k
							elif len(A			) < k - 1:
														return kth_number(A					,       k - len(A			) - 1			)
							# pivot is in elements smaller than k
							else:
														return kth_number(A					,       A			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()