

# 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 __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   "dandelin/vilt-b32-finetuned-vqa"
					__magic_name__        =   (
					    "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."
					)
					__magic_name__        =   "image_qa"
					__magic_name__        =   AutoProcessor
					__magic_name__        =   AutoModelForVisualQuestionAnswering

					__magic_name__        =   ["image", "text"]
					__magic_name__        =   ["text"]





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





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__						):
						return self.pre_processor(lowerCamelCase__				,      lowerCamelCase__				,      return_tensors="pt"						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						with torch.no_grad():
							return self.model(**lowerCamelCase__						).logits





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	str									= outputs.argmax(-1						).item()
						return self.model.config.idalabel[idx]


def 			_A	(      SCREAMING_SNAKE_CASE      ):
	stooge(SCREAMING_SNAKE_CASE	,0	,len(SCREAMING_SNAKE_CASE      ) - 1      )
	return arr



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	if i >= h:
		return

	# If first element is smaller than the last then swap them
	if arr[i] > arr[h]:
		UpperCAmelCase__							,						UpperCAmelCase__:	int									= arr[h], arr[i]

	# If there are more than 2 elements in the array
	if h - i + 1 > 2:
		UpperCAmelCase__:	str									= (int)((h - i + 1) / 3      )

		# Recursively sort first 2/3 elements
		stooge(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,(h - t)      )

		# Recursively sort last 2/3 elements
		stooge(SCREAMING_SNAKE_CASE	,i + t	,(SCREAMING_SNAKE_CASE)      )

		# Recursively sort first 2/3 elements
		stooge(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,(h - t)      )


if __name__ == "__main__":
					_lowerCAmelCase  :      List[str]			 =input("""Enter numbers separated by a comma:\n""").strip()
					_lowerCAmelCase  :      Any			 =[int(item) for item in user_input.split(""",""")]
					print(stooge_sort(unsorted))