




import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class  lowerCamelCase_							(   _SCREAMING_SNAKE_CASE      ):


 '''simple docstring'''







 lowercase_       					=					(HeunDiscreteScheduler,)
 lowercase_       					=					10



 def    lowerCAmelCase_						(							self						:	Tuple       ,       **_lowerCAmelCase						:	Union[str, Any]		):
       SCREAMING_SNAKE_CASE_					=    {
           'num_train_timesteps': 1_100,
           'beta_start': 0.0001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
       }

       config.update(**_lowerCAmelCase		)
       return config



 def    lowerCAmelCase_						(							self						:	List[Any]		):
       for timesteps in [10, 50, 100, 1_000]:
             self.check_over_configs(num_train_timesteps=_lowerCAmelCase		)



 def    lowerCAmelCase_						(							self						:	Any		):
       for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]       ,       [0.0002, 0.002, 0.02]		):
             self.check_over_configs(beta_start=_lowerCAmelCase       ,       beta_end=_lowerCAmelCase		)



 def    lowerCAmelCase_						(							self						:	Any		):
       for schedule in ["linear", "scaled_linear", "exp"]:
             self.check_over_configs(beta_schedule=_lowerCAmelCase		)



 def    lowerCAmelCase_						(							self						:	Any		):
       for clip_sample_range in [1.0, 2.0, 3.0]:
             self.check_over_configs(clip_sample_range=_lowerCAmelCase       ,       clip_sample=_lowerCAmelCase		)



 def    lowerCAmelCase_						(							self						:	str		):
       for prediction_type in ["epsilon", "v_prediction", "sample"]:
             self.check_over_configs(prediction_type=_lowerCAmelCase		)



 def    lowerCAmelCase_						(							self						:	int		):
       SCREAMING_SNAKE_CASE_					=    self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE_					=    self.get_scheduler_config()
       SCREAMING_SNAKE_CASE_					=    scheduler_class(**_lowerCAmelCase		)

       scheduler.set_timesteps(self.num_inference_steps		)

       SCREAMING_SNAKE_CASE_					=    self.dummy_model()
       SCREAMING_SNAKE_CASE_					=    self.dummy_sample_deter * scheduler.init_noise_sigma
       SCREAMING_SNAKE_CASE_					=    sample.to(_lowerCAmelCase		)

       for i, t in enumerate(scheduler.timesteps		):
             SCREAMING_SNAKE_CASE_					=    scheduler.scale_model_input(_lowerCAmelCase       ,       _lowerCAmelCase		)

             SCREAMING_SNAKE_CASE_					=    model(_lowerCAmelCase       ,       _lowerCAmelCase		)

             SCREAMING_SNAKE_CASE_					=    scheduler.step(_lowerCAmelCase       ,       _lowerCAmelCase       ,       _lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_					=    output.prev_sample

       SCREAMING_SNAKE_CASE_					=    torch.sum(torch.abs(_lowerCAmelCase		)		)
       SCREAMING_SNAKE_CASE_					=    torch.mean(torch.abs(_lowerCAmelCase		)		)

       if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 0.1233		) < 1E-2
             assert abs(result_mean.item() - 0.0002		) < 1E-3
       else:
             # CUDA
             assert abs(result_sum.item() - 0.1233		) < 1E-2
             assert abs(result_mean.item() - 0.0002		) < 1E-3



 def    lowerCAmelCase_						(							self						:	List[Any]		):
       SCREAMING_SNAKE_CASE_					=    self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE_					=    self.get_scheduler_config(prediction_type='v_prediction'		)
       SCREAMING_SNAKE_CASE_					=    scheduler_class(**_lowerCAmelCase		)

       scheduler.set_timesteps(self.num_inference_steps		)

       SCREAMING_SNAKE_CASE_					=    self.dummy_model()
       SCREAMING_SNAKE_CASE_					=    self.dummy_sample_deter * scheduler.init_noise_sigma
       SCREAMING_SNAKE_CASE_					=    sample.to(_lowerCAmelCase		)

       for i, t in enumerate(scheduler.timesteps		):
             SCREAMING_SNAKE_CASE_					=    scheduler.scale_model_input(_lowerCAmelCase       ,       _lowerCAmelCase		)

             SCREAMING_SNAKE_CASE_					=    model(_lowerCAmelCase       ,       _lowerCAmelCase		)

             SCREAMING_SNAKE_CASE_					=    scheduler.step(_lowerCAmelCase       ,       _lowerCAmelCase       ,       _lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_					=    output.prev_sample

       SCREAMING_SNAKE_CASE_					=    torch.sum(torch.abs(_lowerCAmelCase		)		)
       SCREAMING_SNAKE_CASE_					=    torch.mean(torch.abs(_lowerCAmelCase		)		)

       if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 4.6_934E-07		) < 1E-2
             assert abs(result_mean.item() - 6.1_112E-10		) < 1E-3
       else:
             # CUDA
             assert abs(result_sum.item() - 4.693_428_650_170_972E-07		) < 1E-2
             assert abs(result_mean.item() - 0.0002		) < 1E-3



 def    lowerCAmelCase_						(							self						:	Optional[int]		):
       SCREAMING_SNAKE_CASE_					=    self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE_					=    self.get_scheduler_config()
       SCREAMING_SNAKE_CASE_					=    scheduler_class(**_lowerCAmelCase		)

       scheduler.set_timesteps(self.num_inference_steps       ,       device=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    self.dummy_model()
       SCREAMING_SNAKE_CASE_					=    self.dummy_sample_deter.to(_lowerCAmelCase		) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
             SCREAMING_SNAKE_CASE_					=    scheduler.scale_model_input(_lowerCAmelCase       ,       _lowerCAmelCase		)

             SCREAMING_SNAKE_CASE_					=    model(_lowerCAmelCase       ,       _lowerCAmelCase		)

             SCREAMING_SNAKE_CASE_					=    scheduler.step(_lowerCAmelCase       ,       _lowerCAmelCase       ,       _lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_					=    output.prev_sample

       SCREAMING_SNAKE_CASE_					=    torch.sum(torch.abs(_lowerCAmelCase		)		)
       SCREAMING_SNAKE_CASE_					=    torch.mean(torch.abs(_lowerCAmelCase		)		)

       if str(_lowerCAmelCase		).startswith('cpu'		):
             # The following sum varies between 148 and 156 on mps. Why?
             assert abs(result_sum.item() - 0.1233		) < 1E-2
             assert abs(result_mean.item() - 0.0002		) < 1E-3
       elif str(_lowerCAmelCase		).startswith('mps'		):
             # Larger tolerance on mps
             assert abs(result_mean.item() - 0.0002		) < 1E-2
       else:
             # CUDA
             assert abs(result_sum.item() - 0.1233		) < 1E-2
             assert abs(result_mean.item() - 0.0002		) < 1E-3




 def    lowerCAmelCase_						(							self						:	Optional[Any]		):
       SCREAMING_SNAKE_CASE_					=    self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE_					=    self.get_scheduler_config()
       SCREAMING_SNAKE_CASE_					=    scheduler_class(**_lowerCAmelCase       ,       use_karras_sigmas=_lowerCAmelCase		)

       scheduler.set_timesteps(self.num_inference_steps       ,       device=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    self.dummy_model()
       SCREAMING_SNAKE_CASE_					=    self.dummy_sample_deter.to(_lowerCAmelCase		) * scheduler.init_noise_sigma
       SCREAMING_SNAKE_CASE_					=    sample.to(_lowerCAmelCase		)

       for t in scheduler.timesteps:
             SCREAMING_SNAKE_CASE_					=    scheduler.scale_model_input(_lowerCAmelCase       ,       _lowerCAmelCase		)

             SCREAMING_SNAKE_CASE_					=    model(_lowerCAmelCase       ,       _lowerCAmelCase		)

             SCREAMING_SNAKE_CASE_					=    scheduler.step(_lowerCAmelCase       ,       _lowerCAmelCase       ,       _lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_					=    output.prev_sample

       SCREAMING_SNAKE_CASE_					=    torch.sum(torch.abs(_lowerCAmelCase		)		)
       SCREAMING_SNAKE_CASE_					=    torch.mean(torch.abs(_lowerCAmelCase		)		)

       assert abs(result_sum.item() - 0.0_0015		) < 1E-2
       assert abs(result_mean.item() - 1.9_869_554_535_034_695E-07		) < 1E-2





from __future__ import annotations

def       UpperCAmelCase_							(   __UpperCAmelCase		:			list[int]							)      ->				list[int]:
      SCREAMING_SNAKE_CASE_					=    [0] * len(__UpperCAmelCase							)
      for i in range(1    ,    len(__UpperCAmelCase							)							):
            SCREAMING_SNAKE_CASE_					=    duration_times[i - 1] + waiting_times[i - 1]
      return waiting_times

def       UpperCAmelCase_							(   __UpperCAmelCase		:			list[int]    ,    __UpperCAmelCase		:			list[int]							)      ->				list[int]:
      return [
          duration_time + waiting_times[i]
          for i, duration_time in enumerate(__UpperCAmelCase							)
      ]

def       UpperCAmelCase_							(   __UpperCAmelCase		:			list[int]							)      ->				float:
      return sum(__UpperCAmelCase							) / len(__UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			list[int]							)      ->				float:
      return sum(__UpperCAmelCase							) / len(__UpperCAmelCase							)


if __name__ == "__main__":
   # process id's
   lowerCamelCase__				:     Union[str, Any]			       =							[1, 2, 3]

   # ensure that we actually have processes
   if len(processes) == 0:
      print('Zero amount of processes')
      raise SystemExit(0)

   # duration time of all processes
   lowerCamelCase__				:     Optional[Any]			       =							[19, 8, 9]

   # ensure we can match each id to a duration time
   if len(duration_times) != len(processes):
      print('Unable to match all id\'s with their duration time')
      raise SystemExit(0)

   # get the waiting times and the turnaround times
   lowerCamelCase__				:     Dict			       =							calculate_waiting_times(duration_times)
   lowerCamelCase__				:     int			       =							calculate_turnaround_times(duration_times, waiting_times)

   # get the average times
   lowerCamelCase__				:     Any			       =							calculate_average_waiting_time(waiting_times)
   lowerCamelCase__				:     Tuple			       =							calculate_average_turnaround_time(turnaround_times)

   # print all the results
   print('Process ID\tDuration Time\tWaiting Time\tTurnaround Time')
   for i, process in enumerate(processes):
      print(
          f'''{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t'''
          f'''{turnaround_times[i]}'''
      )
   print(f'''Average waiting time = {average_waiting_time}''')
   print(f'''Average turn around time = {average_turnaround_time}''')