"""simple docstring"""
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class 		lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Optional[Any]     					=  (DDIMParallelScheduler,)
				_lowerCAmelCase       :      Optional[int]     					=  (("""eta""", 0.0), ("""num_inference_steps""", 50))

				def 						snake_case	(			self						,      **lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     {
										    'num_train_timesteps': 10_00,
										    'beta_start': 0.00_01,
										    'beta_end': 0.02,
										    'beta_schedule': 'linear',
										    'clip_sample': True,
										}

										config.update(**lowerCAmelCase      )
										return config

				def 						snake_case	(			self						,      **lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     self.scheduler_classes[0]
										snake_case        =     self.get_scheduler_config(**lowerCAmelCase      )
										snake_case        =     scheduler_class(**lowerCAmelCase      )

										snake_case   ,snake_case        =     10, 0.0

										snake_case        =     self.dummy_model()
										snake_case        =     self.dummy_sample_deter

										scheduler.set_timesteps(lowerCAmelCase      )

										for t in scheduler.timesteps:
																snake_case        =     model(lowerCAmelCase						,      lowerCAmelCase      )
																snake_case        =     scheduler.step(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ).prev_sample

										return sample

				def 						snake_case	(			self      ):






										"""simple docstring"""

										for timesteps in [1_00, 5_00, 10_00]:
																self.check_over_configs(num_train_timesteps=lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										for steps_offset in [0, 1]:
																self.check_over_configs(steps_offset=lowerCAmelCase      )

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

				def 						snake_case	(			self      ):






										"""simple docstring"""

										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=lowerCAmelCase						,      beta_end=lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										for schedule in ["linear", "squaredcos_cap_v2"]:
																self.check_over_configs(beta_schedule=lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(prediction_type=lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										for clip_sample in [True, False]:
																self.check_over_configs(clip_sample=lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										for timestep_spacing in ["trailing", "leading"]:
																self.check_over_configs(timestep_spacing=lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										for rescale_betas_zero_snr in [True, False]:
																self.check_over_configs(rescale_betas_zero_snr=lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										self.check_over_configs(thresholding=lowerCAmelCase      )
										for threshold in [0.5, 1.0, 2.0]:
																for prediction_type in ["epsilon", "v_prediction"]:
																						self.check_over_configs(
																						    thresholding=lowerCAmelCase						,      prediction_type=lowerCAmelCase						,      sample_max_value=lowerCAmelCase						,      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										for t in [1, 10, 49]:
																self.check_over_forward(time_step=lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										for t, num_inference_steps in zip([1, 10, 50]						,      [10, 50, 5_00]      ):
																self.check_over_forward(time_step=lowerCAmelCase						,      num_inference_steps=lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

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

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.scheduler_classes[0]
										snake_case        =     self.get_scheduler_config()
										snake_case        =     scheduler_class(**lowerCAmelCase      )

										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 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.scheduler_classes[0]
										snake_case        =     self.get_scheduler_config()
										snake_case        =     scheduler_class(**lowerCAmelCase      )

										snake_case   ,snake_case        =     10, 0.0
										scheduler.set_timesteps(lowerCAmelCase      )

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

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

										snake_case        =     model(samples.flatten(0						,      1      )						,      timesteps.flatten(0						,      1      )      )
										snake_case        =     scheduler.batch_step_no_noise(lowerCAmelCase						,      timesteps.flatten(0						,      1      )						,      samples.flatten(0						,      1      )						,      lowerCAmelCase      )

										snake_case        =     torch.sum(torch.abs(lowerCAmelCase      )      )
										snake_case        =     torch.mean(torch.abs(lowerCAmelCase      )      )

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

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.full_loop()

										snake_case        =     torch.sum(torch.abs(lowerCAmelCase      )      )
										snake_case        =     torch.mean(torch.abs(lowerCAmelCase      )      )

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

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.full_loop(prediction_type='v_prediction'      )

										snake_case        =     torch.sum(torch.abs(lowerCAmelCase      )      )
										snake_case        =     torch.mean(torch.abs(lowerCAmelCase      )      )

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

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.full_loop(set_alpha_to_one=lowerCAmelCase						,      beta_start=0.01      )
										snake_case        =     torch.sum(torch.abs(lowerCAmelCase      )      )
										snake_case        =     torch.mean(torch.abs(lowerCAmelCase      )      )

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

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.full_loop(set_alpha_to_one=lowerCAmelCase						,      beta_start=0.01      )
										snake_case        =     torch.sum(torch.abs(lowerCAmelCase      )      )
										snake_case        =     torch.mean(torch.abs(lowerCAmelCase      )      )

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



"""simple docstring"""
import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Any   )   ->			int:





						"""simple docstring"""






						snake_case        =     {}

						snake_case        =     job['started_at']
						snake_case        =     job['completed_at']

						snake_case        =     date_parser.parse(_UpperCamelCase   )
						snake_case        =     date_parser.parse(_UpperCamelCase   )

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

						snake_case        =     start
						snake_case        =     end
						snake_case        =     duration_in_min

						return job_info





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Dict	,     _UpperCamelCase    :			Any=None   )   ->			Union[str, Any]:





						"""simple docstring"""






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

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

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

												for i in range(_UpperCamelCase   ):
																		snake_case        =     requests.get(url + f"""&page={i + 2}"""	,     headers=_UpperCamelCase   ).json()
																		job_time.update({job['name']: extract_time_from_single_job(_UpperCamelCase   ) 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']}""")


