






'''simple docstring'''

import argparse
import json
import subprocess



def 				snake_case       (  a_				:			Optional[Any]     , a_				:			Optional[int]						)							->					int:





      """simple docstring"""

      UpperCamelCase_					: Tuple            =				[]

      UpperCamelCase_					: int            =				(
          f"curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: Bearer {token}\""
          " https://api.github.com/repos/huggingface/transformers/actions/runners"
      )
      UpperCamelCase_					: Union[str, Any]            =				subprocess.run(_UpperCamelCase     , shell=_UpperCamelCase     , stdout=subprocess.PIPE						)
      UpperCamelCase_					: Tuple            =				output.stdout.decode("""utf-8"""						)
      UpperCamelCase_					: List[Any]            =				json.loads(_UpperCamelCase						)

      UpperCamelCase_					: int            =				status["runners"]
      for runner in runners:
            if runner["name"] in target_runners:
                  if runner["status"] == "offline":
                        offline_runners.append(_UpperCamelCase						)

    # save the result so we can report them on Slack
      with open("""offline_runners.txt"""     , """w"""						) as fp:
            fp.write(json.dumps(_UpperCamelCase						)						)

      if len(_UpperCamelCase						) > 0:
            UpperCamelCase_					: List[Any]            =				"\n".join([x["""name"""] for x in offline_runners]						)
            raise ValueError(f"The following runners are offline:\n{failed}"						)


if __name__ == "__main__":



      def 				snake_case       (  a_				:			List[str]						)							->					int:





            """simple docstring"""

            return values.split(""","""						)

      UpperCamelCase      					=argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--target_runners",
          default=None,
          type=list_str,
          required=True,
          help="Comma-separated list of runners to check status.",
      )

      parser.add_argument(
          "--token", default=None, type=str, required=True, help="A token that has actions:read permission."
      )
      UpperCamelCase      					=parser.parse_args()


      get_runner_status(args.target_runners, args.token)














'''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 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )




    @property
    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  ort.SessionOptions()
       lowercase_						:     Optional[int]							     =  False
       return options





    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/in_paint/overture-creations-5sI6fQgYIuo.png"      )
       lowercase_						:     Tuple							     =  load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"      )
       lowercase_						:     List[str]							     =  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
       lowercase_						:     List[Any]							     =  OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
           "CompVis/stable-diffusion-v1-4"	,  revision="onnx"	,  safety_checker=a	,  feature_extractor=a	,  provider=self.gpu_provider	,  sess_options=self.gpu_options	,  )
       pipe.set_progress_bar_config(disable=a      )

       lowercase_						:     List[str]							     =  "A red cat sitting on a park bench"

       lowercase_						:     int							     =  np.random.RandomState(0      )
       lowercase_						:     int							     =  pipe(
           prompt=a	,  image=a	,  mask_image=a	,  strength=0.75	,  guidance_scale=7.5	,  num_inference_steps=1_5	,  generator=a	,  output_type="np"	,  )

       lowercase_						:     Tuple							     =  output.images[0]

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




