
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     (DDIMParallelScheduler,)
	snake_case_					    =     (("eta", 0.0), ("num_inference_steps", 50))






	def 				UpperCamelCase_  (       self					: Dict							,**A					: List[Any] ):
					__A   			= {
					    "num_train_timesteps": 10_00,
					    "beta_start": 0.00_01,
					    "beta_end": 0.02,
					    "beta_schedule": "linear",
					    "clip_sample": True,
					}

					config.update(**A )
					return config






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,**A					: Union[str, Any] ):
					__A   			= self.scheduler_classes[0]
					__A   			= self.get_scheduler_config(**A )
					__A   			= scheduler_class(**A )

					__A			,							__A   			= 10, 0.0

					__A   			= self.dummy_model()
					__A   			= self.dummy_sample_deter

					scheduler.set_timesteps(A )

					for t in scheduler.timesteps:
									__A   			= model(A							,A )
									__A   			= scheduler.step(A							,A							,A							,A ).prev_sample

					return sample






	def 				UpperCamelCase_  (       self					: Dict ):
					for timesteps in [1_00, 5_00, 10_00]:
									self.check_over_configs(num_train_timesteps=A )






	def 				UpperCamelCase_  (       self					: Optional[int] ):
					for steps_offset in [0, 1]:
									self.check_over_configs(steps_offset=A )

					__A   			= self.scheduler_classes[0]
					__A   			= self.get_scheduler_config(steps_offset=1 )
					__A   			= scheduler_class(**A )
					scheduler.set_timesteps(5 )
					assert torch.equal(scheduler.timesteps							,torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1] ) )






	def 				UpperCamelCase_  (       self					: Dict ):
					for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]							,[0.0_02, 0.02, 0.2, 2] ):
									self.check_over_configs(beta_start=A							,beta_end=A )






	def 				UpperCamelCase_  (       self					: Any ):
					for schedule in ["linear", "squaredcos_cap_v2"]:
									self.check_over_configs(beta_schedule=A )






	def 				UpperCamelCase_  (       self					: Optional[int] ):
					for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=A )






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					for clip_sample in [True, False]:
									self.check_over_configs(clip_sample=A )






	def 				UpperCamelCase_  (       self					: Any ):
					for timestep_spacing in ["trailing", "leading"]:
									self.check_over_configs(timestep_spacing=A )






	def 				UpperCamelCase_  (       self					: Any ):
					for rescale_betas_zero_snr in [True, False]:
									self.check_over_configs(rescale_betas_zero_snr=A )






	def 				UpperCamelCase_  (       self					: List[str] ):
					self.check_over_configs(thresholding=A )
					for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(
													    thresholding=A							,prediction_type=A							,sample_max_value=A							,)






	def 				UpperCamelCase_  (       self					: Any ):
					for t in [1, 10, 49]:
									self.check_over_forward(time_step=A )






	def 				UpperCamelCase_  (       self					: List[Any] ):
					for t, num_inference_steps in zip([1, 10, 50]							,[10, 50, 5_00] ):
									self.check_over_forward(time_step=A							,num_inference_steps=A )






	def 				UpperCamelCase_  (       self					: Dict ):
					for t, eta in zip([1, 10, 49]							,[0.0, 0.5, 1.0] ):
									self.check_over_forward(time_step=A							,eta=A )






	def 				UpperCamelCase_  (       self					: Optional[int] ):
					__A   			= self.scheduler_classes[0]
					__A   			= self.get_scheduler_config()
					__A   			= scheduler_class(**A )

					assert torch.sum(torch.abs(scheduler._get_variance(0							,0 ) - 0.0 ) ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(4_20							,4_00 ) - 0.1_47_71 ) ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(9_80							,9_60 ) - 0.3_24_60 ) ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(0							,0 ) - 0.0 ) ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(4_87							,4_86 ) - 0.0_09_79 ) ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(9_99							,9_98 ) - 0.02 ) ) < 1E-5






	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A   			= self.scheduler_classes[0]
					__A   			= self.get_scheduler_config()
					__A   			= scheduler_class(**A )

					__A			,							__A   			= 10, 0.0
					scheduler.set_timesteps(A )

					__A   			= self.dummy_model()
					__A   			= self.dummy_sample_deter
					__A   			= self.dummy_sample_deter + 0.1
					__A   			= self.dummy_sample_deter - 0.1

					__A   			= samplea.shape[0]
					__A   			= torch.stack([samplea, samplea, samplea]							,dim=0 )
					__A   			= torch.arange(A )[0:3, None].repeat(1							,A )

					__A   			= model(samples.flatten(0							,1 )							,timesteps.flatten(0							,1 ) )
					__A   			= scheduler.batch_step_no_noise(A							,timesteps.flatten(0							,1 )							,samples.flatten(0							,1 )							,A )

					__A   			= torch.sum(torch.abs(A ) )
					__A   			= torch.mean(torch.abs(A ) )

					assert abs(result_sum.item() - 11_47.79_04 ) < 1E-2
					assert abs(result_mean.item() - 0.49_82 ) < 1E-3






	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A   			= self.full_loop()

					__A   			= torch.sum(torch.abs(A ) )
					__A   			= torch.mean(torch.abs(A ) )

					assert abs(result_sum.item() - 1_72.00_67 ) < 1E-2
					assert abs(result_mean.item() - 0.22_39_67 ) < 1E-3






	def 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= self.full_loop(prediction_type="v_prediction" )

					__A   			= torch.sum(torch.abs(A ) )
					__A   			= torch.mean(torch.abs(A ) )

					assert abs(result_sum.item() - 52.53_02 ) < 1E-2
					assert abs(result_mean.item() - 0.06_84 ) < 1E-3






	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					# We specify different beta, so that the first alpha is 0.99
					__A   			= self.full_loop(set_alpha_to_one=A							,beta_start=0.01 )
					__A   			= torch.sum(torch.abs(A ) )
					__A   			= torch.mean(torch.abs(A ) )

					assert abs(result_sum.item() - 1_49.82_95 ) < 1E-2
					assert abs(result_mean.item() - 0.19_51 ) < 1E-3







	def 				UpperCamelCase_  (       self					: Any ):
					# We specify different beta, so that the first alpha is 0.99
					__A   			= self.full_loop(set_alpha_to_one=A							,beta_start=0.01 )
					__A   			= torch.sum(torch.abs(A ) )
					__A   			= torch.mean(torch.abs(A ) )

					assert abs(result_sum.item() - 1_49.07_84 ) < 1E-2
					assert abs(result_mean.item() - 0.19_41 ) < 1E-3


import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests


def 						UpperCAmelCase			(	a_						)		->	str:







				"""simple docstring"""



				__A   			= {}

				__A   			= job["started_at"]
				__A   			= job["completed_at"]

				__A   			= date_parser.parse(a_						)
				__A   			= date_parser.parse(a_						)

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

				__A   			= start
				__A   			= end
				__A   			= duration_in_min

				return job_info


def 						UpperCAmelCase			(	a_		,   a_=None						)		->	str:







				"""simple docstring"""



				__A   			= None
				if token is not None:
								__A   			= {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}

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

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

								for i in range(a_						):
												__A   			= 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							:Union[str, Any]							 =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
					SCREAMING_SNAKE_CASE							:Optional[int]							 =				parser.parse_args()

					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				get_job_time(args.workflow_run_id)
					SCREAMING_SNAKE_CASE							:Optional[int]							 =				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"]}''')
