


import torch

from diffusers import StableDiffusionPipeline


SCREAMING_SNAKE_CASE			: List[str]				   =					"path-to-your-trained-model"
SCREAMING_SNAKE_CASE			: List[Any]				   =					StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")

SCREAMING_SNAKE_CASE			: List[Any]				   =					"A photo of sks dog in a bucket"
SCREAMING_SNAKE_CASE			: Dict				   =					pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("dog-bucket.png")






'''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     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		int     				=    '''dandelin/vilt-b32-finetuned-vqa'''
 __lowercase					:		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.'''
 )
 __lowercase					:		Union[str, Any]     				=    '''image_qa'''
 __lowercase					:		Optional[Any]     				=    AutoProcessor
 __lowercase					:		Tuple     				=    AutoModelForVisualQuestionAnswering

 __lowercase					:		Tuple     				=    ['''image''', '''text''']
 __lowercase					:		List[str]     				=    ['''text''']


 def __init__(   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







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


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







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


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







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







 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		List[str]	  =      outputs.argmax(-1    ).item()
  return self.model.config.idalabel[idx]






