




import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class       A   (		UpperCamelCase__   ):
							lowercase_          =    (DEISMultistepScheduler,)
							lowercase_          =    (("""num_inference_steps""", 25),)







							def        __lowerCAmelCase      (  self	:  Dict							,      **lowerCAmelCase_	:  Union[str, Any] )      ->							int:


														"""simple docstring"""


														_a     					=    {
														    '''num_train_timesteps''': 10_00,
														    '''beta_start''': 0.0_0_0_1,
														    '''beta_end''': 0.0_2,
														    '''beta_schedule''': '''linear''',
														    '''solver_order''': 2,
														}

														config.update(**__a )
														return config







							def        __lowerCAmelCase      (  self	:  Optional[int]							,      lowerCAmelCase_	:  List[Any]=0							,      **lowerCAmelCase_	:  Any )      ->							Any:


														"""simple docstring"""


														_a     					=    dict(self.forward_default_kwargs )
														_a     					=    kwargs.pop('''num_inference_steps'''							,      __a )
														_a     					=    self.dummy_sample
														_a     					=    0.1 * sample
														_a     					=    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

														for scheduler_class in self.scheduler_classes:
																					_a     					=    self.get_scheduler_config(**__a )
																					_a     					=    scheduler_class(**__a )
																					scheduler.set_timesteps(__a )
																					# copy over dummy past residuals
																					_a     					=    dummy_past_residuals[: scheduler.config.solver_order]

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												scheduler.save_config(__a )
																												_a     					=    scheduler_class.from_pretrained(__a )
																												new_scheduler.set_timesteps(__a )
																												# copy over dummy past residuals
																												_a     					=    dummy_past_residuals[: new_scheduler.config.solver_order]

																					_a					,							_a     					=    sample, sample
																					for t in range(__a							,      time_step + scheduler.config.solver_order + 1 ):
																												_a     					=    scheduler.step(__a							,      __a							,      __a							,      **__a ).prev_sample
																												_a     					=    new_scheduler.step(__a							,      __a							,      __a							,      **__a ).prev_sample

																												assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"







							def        __lowerCAmelCase      (  self	:  Any )      ->							Optional[Any]:


														"""simple docstring"""


														pass







							def        __lowerCAmelCase      (  self	:  List[str]							,      lowerCAmelCase_	:  Any=0							,      **lowerCAmelCase_	:  Optional[int] )      ->							List[Any]:


														"""simple docstring"""


														_a     					=    dict(self.forward_default_kwargs )
														_a     					=    kwargs.pop('''num_inference_steps'''							,      __a )
														_a     					=    self.dummy_sample
														_a     					=    0.1 * sample
														_a     					=    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

														for scheduler_class in self.scheduler_classes:
																					_a     					=    self.get_scheduler_config()
																					_a     					=    scheduler_class(**__a )
																					scheduler.set_timesteps(__a )

																					# copy over dummy past residuals (must be after setting timesteps)
																					_a     					=    dummy_past_residuals[: scheduler.config.solver_order]

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												scheduler.save_config(__a )
																												_a     					=    scheduler_class.from_pretrained(__a )
																												# copy over dummy past residuals
																												new_scheduler.set_timesteps(__a )

																												# copy over dummy past residual (must be after setting timesteps)
																												_a     					=    dummy_past_residuals[: new_scheduler.config.solver_order]

																					_a     					=    scheduler.step(__a							,      __a							,      __a							,      **__a ).prev_sample
																					_a     					=    new_scheduler.step(__a							,      __a							,      __a							,      **__a ).prev_sample

																					assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"







							def        __lowerCAmelCase      (  self	:  List[Any]							,      lowerCAmelCase_	:  Optional[Any]=None							,      **lowerCAmelCase_	:  Optional[int] )      ->							Tuple:


														"""simple docstring"""


														if scheduler is None:
																					_a     					=    self.scheduler_classes[0]
																					_a     					=    self.get_scheduler_config(**__a )
																					_a     					=    scheduler_class(**__a )

														_a     					=    self.scheduler_classes[0]
														_a     					=    self.get_scheduler_config(**__a )
														_a     					=    scheduler_class(**__a )

														_a     					=    10
														_a     					=    self.dummy_model()
														_a     					=    self.dummy_sample_deter
														scheduler.set_timesteps(__a )

														for i, t in enumerate(scheduler.timesteps ):
																					_a     					=    model(__a							,      __a )
																					_a     					=    scheduler.step(__a							,      __a							,      __a ).prev_sample

														return sample







							def        __lowerCAmelCase      (  self	:  Union[str, Any] )      ->							int:


														"""simple docstring"""


														_a     					=    dict(self.forward_default_kwargs )

														_a     					=    kwargs.pop('''num_inference_steps'''							,      __a )

														for scheduler_class in self.scheduler_classes:
																					_a     					=    self.get_scheduler_config()
																					_a     					=    scheduler_class(**__a )

																					_a     					=    self.dummy_sample
																					_a     					=    0.1 * sample

																					if num_inference_steps is not None and hasattr(__a							,      '''set_timesteps''' ):
																												scheduler.set_timesteps(__a )
																					elif num_inference_steps is not None and not hasattr(__a							,      '''set_timesteps''' ):
																												_a     					=    num_inference_steps

																					# copy over dummy past residuals (must be done after set_timesteps)
																					_a     					=    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
																					_a     					=    dummy_past_residuals[: scheduler.config.solver_order]

																					_a     					=    scheduler.timesteps[5]
																					_a     					=    scheduler.timesteps[6]

																					_a     					=    scheduler.step(__a							,      __a							,      __a							,      **__a ).prev_sample
																					_a     					=    scheduler.step(__a							,      __a							,      __a							,      **__a ).prev_sample

																					self.assertEqual(output_a.shape							,      sample.shape )
																					self.assertEqual(output_a.shape							,      output_a.shape )







							def        __lowerCAmelCase      (  self	:  int )      ->							str:


														"""simple docstring"""


														_a     					=    DEISMultistepScheduler(**self.get_scheduler_config() )
														_a     					=    self.full_loop(scheduler=__a )
														_a     					=    torch.mean(torch.abs(__a ) )

														assert abs(result_mean.item() - 0.2_3_9_1_6 ) < 1e-3

														_a     					=    DPMSolverSinglestepScheduler.from_config(scheduler.config )
														_a     					=    DPMSolverMultistepScheduler.from_config(scheduler.config )
														_a     					=    UniPCMultistepScheduler.from_config(scheduler.config )
														_a     					=    DEISMultistepScheduler.from_config(scheduler.config )

														_a     					=    self.full_loop(scheduler=__a )
														_a     					=    torch.mean(torch.abs(__a ) )

														assert abs(result_mean.item() - 0.2_3_9_1_6 ) < 1e-3







							def        __lowerCAmelCase      (  self	:  Any )      ->							Optional[Any]:


														"""simple docstring"""


														for timesteps in [25, 50, 1_00, 9_99, 10_00]:
																					self.check_over_configs(num_train_timesteps=__a )







							def        __lowerCAmelCase      (  self	:  Optional[int] )      ->							Dict:


														"""simple docstring"""


														self.check_over_configs(thresholding=__a )
														for order in [1, 2, 3]:
																					for solver_type in ["logrho"]:
																												for threshold in [0.5, 1.0, 2.0]:
																																			for prediction_type in ["epsilon", "sample"]:
																																										self.check_over_configs(
																																										    thresholding=__a							,      prediction_type=__a							,      sample_max_value=__a							,      algorithm_type='''deis'''							,      solver_order=__a							,      solver_type=__a							,      )







							def        __lowerCAmelCase      (  self	:  str )      ->							Any:


														"""simple docstring"""


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







							def        __lowerCAmelCase      (  self	:  Optional[int] )      ->							List[Any]:


														"""simple docstring"""


														for algorithm_type in ["deis"]:
																					for solver_type in ["logrho"]:
																												for order in [1, 2, 3]:
																																			for prediction_type in ["epsilon", "sample"]:
																																										self.check_over_configs(
																																										    solver_order=__a							,      solver_type=__a							,      prediction_type=__a							,      algorithm_type=__a							,      )
																																										_a     					=    self.full_loop(
																																										    solver_order=__a							,      solver_type=__a							,      prediction_type=__a							,      algorithm_type=__a							,      )
																																										assert not torch.isnan(__a ).any(), "Samples have nan numbers"







							def        __lowerCAmelCase      (  self	:  Dict )      ->							Tuple:


														"""simple docstring"""


														self.check_over_configs(lower_order_final=__a )
														self.check_over_configs(lower_order_final=__a )







							def        __lowerCAmelCase      (  self	:  Any )      ->							List[str]:


														"""simple docstring"""


														for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
																					self.check_over_forward(num_inference_steps=__a							,      time_step=0 )







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


														"""simple docstring"""


														_a     					=    self.full_loop()
														_a     					=    torch.mean(torch.abs(__a ) )

														assert abs(result_mean.item() - 0.2_3_9_1_6 ) < 1e-3







							def        __lowerCAmelCase      (  self	:  str )      ->							List[Any]:


														"""simple docstring"""


														_a     					=    self.full_loop(prediction_type='''v_prediction''' )
														_a     					=    torch.mean(torch.abs(__a ) )

														assert abs(result_mean.item() - 0.0_9_1 ) < 1e-3







							def        __lowerCAmelCase      (  self	:  Optional[int] )      ->							List[str]:


														"""simple docstring"""


														_a     					=    self.scheduler_classes[0]
														_a     					=    self.get_scheduler_config(thresholding=__a							,      dynamic_thresholding_ratio=0 )
														_a     					=    scheduler_class(**__a )

														_a     					=    10
														_a     					=    self.dummy_model()
														_a     					=    self.dummy_sample_deter.half()
														scheduler.set_timesteps(__a )

														for i, t in enumerate(scheduler.timesteps ):
																					_a     					=    model(__a							,      __a )
																					_a     					=    scheduler.step(__a							,      __a							,      __a ).prev_sample

														assert sample.dtype == torch.floataa







'''simple docstring'''


import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests
def 		snake_case_   (UpperCamelCase	:  Dict				):





							'''simple docstring'''


							_a     					=    {}

							_a     					=    job['''started_at''']
							_a     					=    job['''completed_at''']

							_a     					=    date_parser.parse(UpperCamelCase				)
							_a     					=    date_parser.parse(UpperCamelCase				)

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

							_a     					=    start
							_a     					=    end
							_a     					=    duration_in_min

							return job_info
def 		snake_case_   (UpperCamelCase	:  int			,						UpperCamelCase	:  int=None				):





							'''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(UpperCamelCase			,						headers=UpperCamelCase				).json()
							_a     					=    {}

							try:
														job_time.update({job['''name''']: extract_time_from_single_job(UpperCamelCase				) for job in result['''jobs''']}				)
														_a     					=    math.ceil((result['''total_count'''] - 100) / 100				)

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

				_snake_case				:				int       							=		get_job_time(args.workflow_run_id)
				_snake_case				:				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']}''')

