





# 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    ):
		lowerCamelCase__     :int   				=		'dandelin/vilt-b32-finetuned-vqa'
		lowerCamelCase__     :List[Any]   				=		(
		    '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.'
		)
		lowerCamelCase__     :Optional[Any]   				=		'image_qa'
		lowerCamelCase__     :Union[str, Any]   				=		AutoProcessor
		lowerCamelCase__     :List[Any]   				=		AutoModelForVisualQuestionAnswering

		lowerCamelCase__     :int   				=		['image', 'text']
		lowerCamelCase__     :Union[str, Any]   				=		['text']







		def __init__(    self ,						*__snake_case ,						**__snake_case				)      ->      Any:


							'''simple docstring'''

							requires_backends(self ,						["vision"]				)
							super().__init__(*__snake_case ,						**__snake_case				)







		def        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      List[Any]:


							'''simple docstring'''

							return self.pre_processor(__snake_case ,						__snake_case ,						return_tensors="pt"				)







		def        lowercase       (    self ,						__snake_case				)      ->      List[str]:


							'''simple docstring'''

							with torch.no_grad():
												return self.model(**__snake_case				).logits







		def        lowercase       (    self ,						__snake_case				)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   outputs.argmax(-1				).item()
							return self.model.config.idalabel[idx]






import math

from numpy import inf
from scipy.integrate import quad







def    A				(							UpperCAmelCase				):
					if num <= 0:
										raise ValueError("math domain error"				)

					return quad(UpperCAmelCase  ,  0  ,  UpperCAmelCase  ,  args=(UpperCAmelCase)				)[0]







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return math.pow(UpperCAmelCase  ,  z - 1				) * math.exp(-x				)


if __name__ == "__main__":
							from doctest import testmod

							testmod()