




'''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     _a						(			unittest.TestCase				):
       @property
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           return (
               "CUDAExecutionProvider",
               {
                   "gpu_mem_limit": "15000000000",  # 15GB
                   "arena_extend_strategy": "kSameAsRequested",
               },
           )
       @property
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase     =	ort.SessionOptions()
           UpperCAmelCase     =	False
           return options







       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

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

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

           UpperCAmelCase     =	np.random.RandomState(0       )
           UpperCAmelCase     =	pipe(
               prompt=lowercase     ,       image=lowercase     ,       mask_image=lowercase     ,       strength=0.75     ,       guidance_scale=7.5     ,       num_inference_steps=15     ,       generator=lowercase     ,       output_type='''np'''     ,       )

           UpperCAmelCase     =	output.images[0]

           assert image.shape == (512, 512, 3)
           assert np.abs(expected_image - image       ).max() < 1E-2










'''simple docstring'''







import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests





def 		snake_case_							(_a			:							List[str]  ):
    UpperCAmelCase     =	{}

    UpperCAmelCase     =	job['''started_at''']
    UpperCAmelCase     =	job['''completed_at''']

    UpperCAmelCase     =	date_parser.parse(_a  )
    UpperCAmelCase     =	date_parser.parse(_a  )

    UpperCAmelCase     =	round((end_datetime - start_datetime).total_seconds() / 60.0  )

    UpperCAmelCase     =	start
    UpperCAmelCase     =	end
    UpperCAmelCase     =	duration_in_min

    return job_info





def 		snake_case_							(_a			:							str					,     _a			:							List[str]=None  ):
    UpperCAmelCase     =	None
    if token is not None:
        UpperCAmelCase     =	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"Bearer {token}"}

    UpperCAmelCase     =	F"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"
    UpperCAmelCase     =	requests.get(_a					,     headers=_a  ).json()
    UpperCAmelCase     =	{}

    try:
        job_time.update({job['''name''']: extract_time_from_single_job(_a  ) for job in result['''jobs''']}  )
        UpperCAmelCase     =	math.ceil((result['''total_count'''] - 1_0_0) / 1_0_0  )

        for i in range(_a  ):
            UpperCAmelCase     =	requests.get(url + F"&page={i + 2}"					,     headers=_a  ).json()
            job_time.update({job['''name''']: extract_time_from_single_job(_a  ) for job in result['''jobs''']}  )

        return job_time
    except Exception:
        print(F"Unknown error, could not fetch links:\n{traceback.format_exc()}"  )

    return {}


if __name__ == "__main__":
   A									=argparse.ArgumentParser()
   # Required parameters
   parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
   A									=parser.parse_args()

   A									=get_job_time(args.workflow_run_id)
   A									=dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))

   for k, v in job_time.items():
      print(f"""{k}: {v["duration"]}""")




