





"""simple docstring"""






import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests






def      lowercase__ (					lowerCAmelCase__   :			Dict			)    ->			List[str]:



			'''simple docstring'''
			a__  :	Tuple		 =			{}

			a__  :	Optional[int]		 =			job["started_at"]
			a__  :	Any		 =			job["completed_at"]

			a__  :	Optional[int]		 =			date_parser.parse(lowerCAmelCase__			)
			a__  :	List[str]		 =			date_parser.parse(lowerCAmelCase__			)

			a__  :	List[Any]		 =			round((end_datetime - start_datetime).total_seconds() / 60.0			)

			a__  :	int		 =			start
			a__  :	Tuple		 =			end
			a__  :	int		 =			duration_in_min

			return job_info






def      lowercase__ (					lowerCAmelCase__   :			str							,			lowerCAmelCase__   :			List[Any]=None			)    ->			List[Any]:



			'''simple docstring'''
			a__  :	str		 =			None
			if token is not None:
						a__  :	List[str]		 =			{"Accept": "application/vnd.github+json", "Authorization": F"Bearer {token}"}

			a__  :	Optional[int]		 =			F"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"
			a__  :	Union[str, Any]		 =			requests.get(lowerCAmelCase__							,			headers=lowerCAmelCase__			).json()
			a__  :	str		 =			{}

			try:
						job_time.update({job["name"]: extract_time_from_single_job(lowerCAmelCase__			) for job in result["jobs"]}			)
						a__  :	str		 =			math.ceil((result["total_count"] - 1_0_0) / 1_0_0			)

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

		__UpperCAmelCase						=			get_job_time(args.workflow_run_id)
		__UpperCAmelCase						=			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']}")






"""simple docstring"""






import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


__UpperCAmelCase						=			False



class 					__UpperCAmelCase							(						unittest.TestCase  ):
				def       UpperCAmelCase      (		self    :			Tuple	, a_    :			Optional[Any]=32    )	->	Union[str, Any]:



							'''simple docstring'''



							set_seed(0    )
							a__  :	Any		 =			UNetaDModel(sample_size=a_	, in_channels=3	, out_channels=3    )
							a__  :	Union[str, Any]		 =			torch.optim.SGD(model.parameters()	, lr=0.0001    )
							return model, optimizer






				@slow
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Any:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			"cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
							a__  :	Dict		 =			DDPMScheduler(
							    num_train_timesteps=10_00	, beta_start=0.0001	, beta_end=0.02	, beta_schedule="linear"	, clip_sample=a_	, )
							a__  :	Optional[Any]		 =			DDIMScheduler(
							    num_train_timesteps=10_00	, beta_start=0.0001	, beta_end=0.02	, beta_schedule="linear"	, clip_sample=a_	, )

							assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

							# shared batches for DDPM and DDIM
							set_seed(0    )
							a__  :	List[str]		 =			[torch.randn((4, 3, 32, 32)    ).clip(-1	, 1    ).to(a_    ) for _ in range(4    )]
							a__  :	List[str]		 =			[torch.randn((4, 3, 32, 32)    ).to(a_    ) for _ in range(4    )]
							a__  :	Optional[Any]		 =			[torch.randint(0	, 10_00	, (4,)    ).long().to(a_    ) for _ in range(4    )]

							# train with a DDPM scheduler
							a__ ,	a__  :	Union[str, Any]		 =			self.get_model_optimizer(resolution=32    )
							model.train().to(a_    )
							for i in range(4    ):
										optimizer.zero_grad()
										a__  :	Optional[int]		 =			ddpm_scheduler.add_noise(clean_images[i]	, noise[i]	, timesteps[i]    )
										a__  :	List[str]		 =			model(a_	, timesteps[i]    ).sample
										a__  :	Optional[int]		 =			torch.nn.functional.mse_loss(a_	, noise[i]    )
										loss.backward()
										optimizer.step()
							del model, optimizer

							# recreate the model and optimizer, and retry with DDIM
							a__ ,	a__  :	Optional[Any]		 =			self.get_model_optimizer(resolution=32    )
							model.train().to(a_    )
							for i in range(4    ):
										optimizer.zero_grad()
										a__  :	List[str]		 =			ddim_scheduler.add_noise(clean_images[i]	, noise[i]	, timesteps[i]    )
										a__  :	Optional[int]		 =			model(a_	, timesteps[i]    ).sample
										a__  :	Union[str, Any]		 =			torch.nn.functional.mse_loss(a_	, noise[i]    )
										loss.backward()
										optimizer.step()
							del model, optimizer

							self.assertTrue(torch.allclose(a_	, a_	, atol=1E-5    )    )
							self.assertTrue(torch.allclose(a_	, a_	, atol=1E-5    )    )