
'''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 		lowercase_(   self			:    Tuple       ):



   """simple docstring"""





   return (
       "CUDAExecutionProvider",
       {
           "gpu_mem_limit": "15000000000",  # 15GB
           "arena_extend_strategy": "kSameAsRequested",
       },
   )
 @property
 def 		lowercase_(   self			:    Optional[int]       ):



   """simple docstring"""





   __A		:     str												=		ort.SessionOptions()
   __A		:     Optional[Any]												=		False
   return options






 def 		lowercase_(   self			:    str       ):



   """simple docstring"""





   __A		:     Any												=		load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/overture-creations-5sI6fQgYIuo.png"""       )
   __A		:     Optional[Any]												=		load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""       )
   __A		:     List[Any]												=		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		:     Any												=		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       )

   __A		:     Optional[int]												=		"""A red cat sitting on a park bench"""

   __A		:     Dict												=		np.random.RandomState(0       )
   __A		:     Optional[int]												=		pipe(
       prompt=lowerCamelCase				,   image=lowerCamelCase				,   mask_image=lowerCamelCase				,   strength=0.75				,   guidance_scale=7.5				,   num_inference_steps=15				,   generator=lowerCamelCase				,   output_type="""np"""				,   )

   __A		:     Dict												=		output.images[0]

   assert image.shape == (5_12, 5_12, 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        A_     (						__SCREAMING_SNAKE_CASE						:  Dict )				->       Tuple:





  """simple docstring"""

  __A		:     Dict												=		{}

  __A		:     Optional[Any]												=		job["""started_at"""]
  __A		:     Tuple												=		job["""completed_at"""]

  __A		:     Optional[Any]												=		date_parser.parse(__SCREAMING_SNAKE_CASE )
  __A		:     List[Any]												=		date_parser.parse(__SCREAMING_SNAKE_CASE )

  __A		:     Union[str, Any]												=		round((end_datetime - start_datetime).total_seconds() / 60.0 )

  __A		:     List[Any]												=		start
  __A		:     Tuple												=		end
  __A		:     int												=		duration_in_min

  return job_info







def        A_     (						__SCREAMING_SNAKE_CASE						:  Union[str, Any]			,     __SCREAMING_SNAKE_CASE						:  int=None )				->       Dict:





  """simple docstring"""

  __A		:     List[Any]												=		None
  if token is not None:
    __A		:     Dict												=		{"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}

  __A		:     Tuple												=		F"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"
  __A		:     List[str]												=		requests.get(__SCREAMING_SNAKE_CASE			,     headers=__SCREAMING_SNAKE_CASE ).json()
  __A		:     List[str]												=		{}

  try:
    job_time.update({job["""name"""]: extract_time_from_single_job(__SCREAMING_SNAKE_CASE ) for job in result["""jobs"""]} )
    __A		:     Dict												=		math.ceil((result["""total_count"""] - 100) / 100 )

    for i in range(__SCREAMING_SNAKE_CASE ):
      __A		:     int												=		requests.get(url + F"&page={i + 2}"			,     headers=__SCREAMING_SNAKE_CASE ).json()
      job_time.update({job["""name"""]: extract_time_from_single_job(__SCREAMING_SNAKE_CASE ) 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__   :		Any 		=argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
     A__   :		Any 		=parser.parse_args()

     A__   :		Any 		=get_job_time(args.workflow_run_id)
     A__   :		Dict 		=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']}")



