





import os
from datetime import datetime as dt

from github import Github


_snake_case  :   Any						   =    [
    'good first issue',
    'feature request',
    'wip',
]

def   a_   (			):
   __lowerCAmelCase							     =     Github(os.environ['GITHUB_TOKEN']     )
   __lowerCAmelCase							     =     g.get_repo('huggingface/accelerate'     )
   __lowerCAmelCase							     =     repo.get_issues(state='open'     )

   for issue in open_issues:
      __lowerCAmelCase							     =     sorted([comment for comment in issue.get_comments()],     key=lambda lowerCAmelCase_     : i.created_at,     reverse=__lowerCamelCase     )
      __lowerCAmelCase							     =     comments[0] if len(__lowerCamelCase     ) > 0 else None
      __lowerCAmelCase							     =     dt.utcnow()
      __lowerCAmelCase							     =     (current_time - issue.updated_at).days
      __lowerCAmelCase							     =     (current_time - issue.created_at).days
      if (
          last_comment is not None
          and last_comment.user.login == "github-actions[bot]"
          and days_since_updated > 7
          and days_since_creation >= 30
          and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()     )
      ):
         # Close issue since it has been 7 days of inactivity since bot mention.
         issue.edit(state='closed'     )
      elif (
          days_since_updated > 23
          and days_since_creation >= 30
          and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()     )
      ):
         # Add stale comment
         issue.create_comment(
             'This issue has been automatically marked as stale because it has not had '
             'recent activity. If you think this still needs to be addressed '
             'please comment on this thread.\n\nPlease note that issues that do not follow the '
             '[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) '
             'are likely to be ignored.'     )


if __name__ == "__main__":
     main()








import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
     import onnxruntime as ort



@nightly
@require_onnxruntime
@require_torch_gpu
class     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  @property
  def     lowercase		(    self			:		Union[str, Any]					)							->						Tuple:
     return (
         "CUDAExecutionProvider",
         {
             "gpu_mem_limit": "15000000000",  # 15GB
             "arena_extend_strategy": "kSameAsRequested",
         },
     )






  @property
  def     lowercase		(    self			:		List[Any]					)							->						int:
     __lowerCAmelCase							     =     ort.SessionOptions()
     __lowerCAmelCase							     =     False
     return options






  def     lowercase		(    self			:		Tuple					)							->						List[Any]:
     __lowerCAmelCase							     =     load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo.png'					)
     __lowerCAmelCase							     =     load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'					)
     __lowerCAmelCase							     =     load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'					)

     # using the PNDM scheduler by default
     __lowerCAmelCase							     =     OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
         'CompVis/stable-diffusion-v1-4' ,  revision='onnx' ,  safety_checker=lowerCAmelCase_ ,  feature_extractor=lowerCAmelCase_ ,  provider=self.gpu_provider ,  sess_options=self.gpu_options ,  )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     'A red cat sitting on a park bench'

     __lowerCAmelCase							     =     np.random.RandomState(0					)
     __lowerCAmelCase							     =     pipe(
         prompt=lowerCAmelCase_ ,  image=lowerCAmelCase_ ,  mask_image=lowerCAmelCase_ ,  strength=0.75 ,  guidance_scale=7.5 ,  num_inference_steps=1_5 ,  generator=lowerCAmelCase_ ,  output_type='np' ,  )

     __lowerCAmelCase							     =     output.images[0]

     assert image.shape == (5_1_2, 5_1_2, 3)
     assert np.abs(expected_image - image					).max() < 1e-2

