

import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class    __snake_case	(     lowerCAmelCase__							):
  __lowerCAmelCase  :						str    			=	(KDPMaAncestralDiscreteScheduler,)
  __lowerCAmelCase  :						List[str]    			=	10

  def     lowerCAmelCase__  (       self    ,		**_A):
      SCREAMING_SNAKE_CASE_						=    {
          'num_train_timesteps': 1100,
          'beta_start': 0.0_0_0_1,
          'beta_end': 0.0_2,
          'beta_schedule': 'linear',
      }

      config.update(**_A)
      return config

  def     lowerCAmelCase__  (       self):
      for timesteps in [10, 50, 100, 1000]:
          self.check_over_configs(num_train_timesteps=_A)

  def     lowerCAmelCase__  (       self):
      for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]    ,		[0.0_0_0_2, 0.0_0_2, 0.0_2]):
          self.check_over_configs(beta_start=_A    ,		beta_end=_A)

  def     lowerCAmelCase__  (       self):
      for schedule in ["linear", "scaled_linear"]:
          self.check_over_configs(beta_schedule=_A)

  def     lowerCAmelCase__  (       self):
      if torch_device == "mps":
          return
      SCREAMING_SNAKE_CASE_						=    self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE_						=    self.get_scheduler_config()
      SCREAMING_SNAKE_CASE_						=    scheduler_class(**_A)

      scheduler.set_timesteps(self.num_inference_steps)

      SCREAMING_SNAKE_CASE_						=    torch.manual_seed(0)

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

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

          SCREAMING_SNAKE_CASE_						=    model(_A    ,		_A)

          SCREAMING_SNAKE_CASE_						=    scheduler.step(_A    ,		_A    ,		_A    ,		generator=_A)
          SCREAMING_SNAKE_CASE_						=    output.prev_sample

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

      assert abs(result_sum.item() - 1_3_8_4_9.3_8_7_7) < 1E-2
      assert abs(result_mean.item() - 1_8.0_3_3_1) < 5E-3

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

  def     lowerCAmelCase__  (       self):
      if torch_device == "mps":
          return
      SCREAMING_SNAKE_CASE_						=    self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE_						=    self.get_scheduler_config(prediction_type='v_prediction')
      SCREAMING_SNAKE_CASE_						=    scheduler_class(**_A)

      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(_A)

      SCREAMING_SNAKE_CASE_						=    torch.manual_seed(0)

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

          SCREAMING_SNAKE_CASE_						=    model(_A    ,		_A)

          SCREAMING_SNAKE_CASE_						=    scheduler.step(_A    ,		_A    ,		_A    ,		generator=_A)
          SCREAMING_SNAKE_CASE_						=    output.prev_sample

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

      assert abs(result_sum.item() - 3_2_8.9_9_7_0) < 1E-2
      assert abs(result_mean.item() - 0.4_2_8_4) < 1E-3


  def     lowerCAmelCase__  (       self):
      if torch_device == "mps":
          return
      SCREAMING_SNAKE_CASE_						=    self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE_						=    self.get_scheduler_config()
      SCREAMING_SNAKE_CASE_						=    scheduler_class(**_A)

      scheduler.set_timesteps(self.num_inference_steps    ,		device=_A)
      SCREAMING_SNAKE_CASE_						=    torch.manual_seed(0)

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

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

          SCREAMING_SNAKE_CASE_						=    model(_A    ,		_A)

          SCREAMING_SNAKE_CASE_						=    scheduler.step(_A    ,		_A    ,		_A    ,		generator=_A)
          SCREAMING_SNAKE_CASE_						=    output.prev_sample

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

      assert abs(result_sum.item() - 1_3_8_4_9.3_8_1_8) < 1E-1
      assert abs(result_mean.item() - 1_8.0_3_3_1) < 1E-3





from __future__ import annotations


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list[int]							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    [0] * len(_SCREAMING_SNAKE_CASE							)
    for i in range(1			, len(_SCREAMING_SNAKE_CASE							)							):
        SCREAMING_SNAKE_CASE_						=    duration_times[i - 1] + waiting_times[i - 1]
    return waiting_times


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list[int]			, _SCREAMING_SNAKE_CASE       :		list[int]							):





    """simple docstring"""


    return [
        duration_time + waiting_times[i]
        for i, duration_time in enumerate(_SCREAMING_SNAKE_CASE							)
    ]


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list[int]							):





    """simple docstring"""


    return sum(_SCREAMING_SNAKE_CASE							) / len(_SCREAMING_SNAKE_CASE							)


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list[int]							):





    """simple docstring"""


    return sum(_SCREAMING_SNAKE_CASE							) / len(_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
       # process id's
       UpperCamelCase__	:   List[str]								=     [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
       UpperCamelCase__	:   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
       UpperCamelCase__	:   List[str]								=     calculate_waiting_times(duration_times)
       UpperCamelCase__	:   Union[str, Any]								=     calculate_turnaround_times(duration_times, waiting_times)

       # get the average times
       UpperCamelCase__	:   str								=     calculate_average_waiting_time(waiting_times)
       UpperCamelCase__	:   List[Any]								=     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}')


