
'''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 						__UpperCAmelCase      (					_lowerCamelCase						):
							__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    ,   *lowerCAmelCase_    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






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


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_  ):





								"""simple docstring"""






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


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






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




							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






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



'''simple docstring'''







from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
					from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

