"""simple docstring"""




from datetime import datetime as dt
import os

from github import Github


__lowerCamelCase											=  [
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """feature request""",
    """new model""",
    """wip""",
]






def   UpperCAmelCase					(	):






							"""simple docstring"""





							A__	     =							Github(os.environ['GITHUB_TOKEN']				)
							A__	     =							g.get_repo('huggingface/transformers'				)
							A__	     =							repo.get_issues(state='open'				)

							for issue in open_issues:
														A__	     =							sorted([comment for comment in issue.get_comments()]       ,    key=lambda UpperCamelCase__				: i.created_at       ,    reverse=UpperCamelCase__				)
														A__	     =							comments[0] if len(UpperCamelCase__				) > 0 else None
														if (
														    last_comment is not None
														    and last_comment.user.login == "github-actions[bot]"
														    and (dt.utcnow() - issue.updated_at).days > 7
														    and (dt.utcnow() - issue.created_at).days >= 30
														    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()				)
														):
																					# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
																					issue.edit(state='closed'				)
														elif (
														    (dt.utcnow() - issue.updated_at).days > 23
														    and (dt.utcnow() - issue.created_at).days >= 30
														    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()				)
														):
																					# print(f"Would add stale comment to {issue.number}")
																					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/transformers/blob/main/CONTRIBUTING.md) '
																					    'are likely to be ignored.'				)


if __name__ == "__main__":
				main()






"""simple docstring"""




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 ):





						@property
						def 				snake_case__	(       self     )					->				int:
													return (
													    "CUDAExecutionProvider",
													    {
													        "gpu_mem_limit": "15000000000",  # 15GB
													        "arena_extend_strategy": "kSameAsRequested",
													    },
													)





						@property
						def 				snake_case__	(       self     )					->				Union[str, Any]:
													A__	     =							ort.SessionOptions()
													A__	     =							False
													return options





						def 				snake_case__	(       self     )					->				str:
													A__	     =							load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/in_paint/overture-creations-5sI6fQgYIuo.png'     )
													A__	     =							load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'     )
													A__	     =							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
													A__	     =							OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
													    'CompVis/stable-diffusion-v1-4'		,revision='onnx'		,safety_checker=__UpperCAmelCase		,feature_extractor=__UpperCAmelCase		,provider=self.gpu_provider		,sess_options=self.gpu_options		,)
													pipe.set_progress_bar_config(disable=__UpperCAmelCase     )

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

													A__	     =							np.random.RandomState(0     )
													A__	     =							pipe(
													    prompt=__UpperCAmelCase		,image=__UpperCAmelCase		,mask_image=__UpperCAmelCase		,strength=0.7_5		,guidance_scale=7.5		,num_inference_steps=15		,generator=__UpperCAmelCase		,output_type='np'		,)

													A__	     =							output.images[0]

													assert image.shape == (5_12, 5_12, 3)
													assert np.abs(expected_image - image     ).max() < 1e-2





