





# 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 			lowercase				(							a_     ):



						"""simple docstring"""

						_UpperCamelCase  :	Optional[Any]								=	"dandelin/vilt-b32-finetuned-vqa"
						_UpperCamelCase  :	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."
						)
						_UpperCamelCase  :	int								=	"image_qa"
						_UpperCamelCase  :	Dict								=	AutoProcessor
						_UpperCamelCase  :	Tuple								=	AutoModelForVisualQuestionAnswering

						_UpperCamelCase  :	str								=	["image", "text"]
						_UpperCamelCase  :	List[Any]								=	["text"]







						def __init__(    self			:		Tuple ,				*lowerCamelCase_			:		Any ,				**lowerCamelCase_			:		Union[str, Any]					):





										'''simple docstring'''







										requires_backends(self ,				['vision']					)
										super().__init__(*lowerCamelCase_ ,				**lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		"Image" ,				lowerCamelCase_			:		str					):





										'''simple docstring'''







										return self.pre_processor(lowerCamelCase_ ,				lowerCamelCase_ ,				return_tensors='pt'					)







						def 		__UpperCAmelCase       (    self			:		str ,				lowerCamelCase_			:		int					):





										'''simple docstring'''







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







						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		str					):





										'''simple docstring'''







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








import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


lowercase_		:				str				=						logging.get_logger(__name__)


class 			lowercase				(							a_     ):



						"""simple docstring"""

						def __init__(    self			:		int ,				*lowerCamelCase_			:		str ,				**lowerCamelCase_			:		Tuple					):





										'''simple docstring'''







										warnings.warn(
										    'The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
										    ' Please use SegformerImageProcessor instead.' ,				lowerCamelCase_ ,				)
										super().__init__(*lowerCamelCase_ ,				**lowerCamelCase_					)






