





import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
 snake_case_		:			Tuple											=	{}

 snake_case_		:			Dict											=	job['started_at']
 snake_case_		:			str											=	job['completed_at']

 snake_case_		:			str											=	date_parser.parse(__a   )
 snake_case_		:			int											=	date_parser.parse(__a   )

 snake_case_		:			Optional[Any]											=	round((end_datetime - start_datetime).total_seconds() / 60.0   )

 snake_case_		:			List[Any]											=	start
 snake_case_		:			Tuple											=	end
 snake_case_		:			List[str]											=	duration_in_min

 return job_info


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a=None   ):
 snake_case_		:			Optional[int]											=	None
 if token is not None:
  snake_case_		:			Union[str, Any]											=	{'Accept': 'application/vnd.github+json', 'Authorization': f"""Bearer {token}"""}

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

 try:
  job_time.update({job['name']: extract_time_from_single_job(__a   ) for job in result['jobs']}   )
  snake_case_		:			Any											=	math.ceil((result['total_count'] - 1_00) / 1_00   )

  for i in range(__a   ):
   snake_case_		:			int											=	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__":
      _SCREAMING_SNAKE_CASE      	=		argparse.ArgumentParser()
      # Required parameters
      parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
      _SCREAMING_SNAKE_CASE      	=		parser.parse_args()

      _SCREAMING_SNAKE_CASE      	=		get_job_time(args.workflow_run_id)
      _SCREAMING_SNAKE_CASE      	=		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']}''')








import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						@property
						def UpperCAmelCase_				(      self      :      Any	)							->    Optional[Any]:







							"""simple docstring"""


							torch.manual_seed(0	)
							snake_case_		:			List[Any]											=	UNetaDModel(
							    block_out_channels=(32, 64)		,   layers_per_block=2		,   sample_size=32		,   in_channels=3		,   out_channels=3		,   down_block_types=('DownBlock2D', 'AttnDownBlock2D')		,   up_block_types=('AttnUpBlock2D', 'UpBlock2D')		,   )
							return model






						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    Tuple:







							"""simple docstring"""


							snake_case_		:			List[Any]											=	self.dummy_uncond_unet
							snake_case_		:			Optional[Any]											=	ScoreSdeVeScheduler()

							snake_case_		:			Tuple											=	ScoreSdeVePipeline(unet=_A		,   scheduler=_A	)
							sde_ve.to(_A	)
							sde_ve.set_progress_bar_config(disable=_A	)

							snake_case_		:			Optional[Any]											=	torch.manual_seed(0	)
							snake_case_		:			Tuple											=	sde_ve(num_inference_steps=2		,   output_type='numpy'		,   generator=_A	).images

							snake_case_		:			int											=	torch.manual_seed(0	)
							snake_case_		:			Optional[int]											=	sde_ve(num_inference_steps=2		,   output_type='numpy'		,   generator=_A		,   return_dict=_A	)[
							    0
							]

							snake_case_		:			List[str]											=	image[0, -3:, -3:, -1]
							snake_case_		:			Dict											=	image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_		:			List[Any]											=	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]	)

							assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1E-2
@slow
@require_torch
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def UpperCAmelCase_				(      self      :      Dict	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			Dict											=	'google/ncsnpp-church-256'
							snake_case_		:			List[Any]											=	UNetaDModel.from_pretrained(_A	)

							snake_case_		:			str											=	ScoreSdeVeScheduler.from_pretrained(_A	)

							snake_case_		:			Optional[Any]											=	ScoreSdeVePipeline(unet=_A		,   scheduler=_A	)
							sde_ve.to(_A	)
							sde_ve.set_progress_bar_config(disable=_A	)

							snake_case_		:			Any											=	torch.manual_seed(0	)
							snake_case_		:			Optional[int]											=	sde_ve(num_inference_steps=10		,   output_type='numpy'		,   generator=_A	).images

							snake_case_		:			Union[str, Any]											=	image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)

							snake_case_		:			Dict											=	np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]	)

							assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2

