

# 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 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    """dandelin/vilt-b32-finetuned-vqa"""
  _a		     =    (
      """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."""
  )
  _a		     =    """image_qa"""
  _a		     =    AutoProcessor
  _a		     =    AutoModelForVisualQuestionAnswering

  _a		     =    ["""image""", """text"""]
  _a		     =    ["""text"""]




  def __init__(							self				,  *lowerCAmelCase				,  **lowerCAmelCase   )    ->					Dict:
        '''simple docstring'''






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




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase   )    ->					int:
        '''simple docstring'''






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




  def     A__	(							self				,  lowerCAmelCase   )    ->					Optional[Any]:
        '''simple docstring'''






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




  def     A__	(							self				,  lowerCAmelCase   )    ->					Union[str, Any]:
        '''simple docstring'''






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





from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available





@dataclass
class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    42
  _a		     =    42


if is_transformers_available() and is_torch_available():
  from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


