import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class __snake_case    (						__lowerCAmelCase		):
   lowerCAmelCase_														=							(DDIMParallelScheduler,)
   lowerCAmelCase_														=							(('''eta''', 0.0), ('''num_inference_steps''', 50))




   def      __a						(   self      :    Optional[int]	,     **_lowercase      :    str				):



       """simple docstring"""






       SCREAMING_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      __a						(   self      :    Any	,     **_lowercase      :    int				):



       """simple docstring"""






       SCREAMING_SNAKE_CASE__      							=				self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__      							=				self.get_scheduler_config(**lowerCAmelCase_				)
       SCREAMING_SNAKE_CASE__      							=				scheduler_class(**lowerCAmelCase_				)

       SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				10, 0.0

       SCREAMING_SNAKE_CASE__      							=				self.dummy_model()
       SCREAMING_SNAKE_CASE__      							=				self.dummy_sample_deter

       scheduler.set_timesteps(lowerCAmelCase_				)

       for t in scheduler.timesteps:
           SCREAMING_SNAKE_CASE__      							=				model(lowerCAmelCase_	,     lowerCAmelCase_				)
           SCREAMING_SNAKE_CASE__      							=				scheduler.step(lowerCAmelCase_	,     lowerCAmelCase_	,     lowerCAmelCase_	,     lowerCAmelCase_				).prev_sample

       return sample




   def      __a						(   self      :    str				):



       """simple docstring"""






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




   def      __a						(   self      :    Any				):



       """simple docstring"""






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

       SCREAMING_SNAKE_CASE__      							=				self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__      							=				self.get_scheduler_config(steps_offset=1				)
       SCREAMING_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      __a						(   self      :    Tuple				):



       """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      __a						(   self      :    Tuple				):



       """simple docstring"""






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




   def      __a						(   self      :    Optional[Any]				):



       """simple docstring"""






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




   def      __a						(   self      :    Dict				):



       """simple docstring"""






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




   def      __a						(   self      :    Union[str, Any]				):



       """simple docstring"""






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




   def      __a						(   self      :    Optional[int]				):



       """simple docstring"""






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




   def      __a						(   self      :    Optional[Any]				):



       """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      __a						(   self      :    Union[str, Any]				):



       """simple docstring"""






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




   def      __a						(   self      :    Optional[Any]				):



       """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      __a						(   self      :    Tuple				):



       """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      __a						(   self      :    Optional[Any]				):



       """simple docstring"""






       SCREAMING_SNAKE_CASE__      							=				self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__      							=				self.get_scheduler_config()
       SCREAMING_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      __a						(   self      :    str				):



       """simple docstring"""






       SCREAMING_SNAKE_CASE__      							=				self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__      							=				self.get_scheduler_config()
       SCREAMING_SNAKE_CASE__      							=				scheduler_class(**lowerCAmelCase_				)

       SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				10, 0.0
       scheduler.set_timesteps(lowerCAmelCase_				)

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

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

       SCREAMING_SNAKE_CASE__      							=				model(samples.flatten(0	,     1				)	,     timesteps.flatten(0	,     1				)				)
       SCREAMING_SNAKE_CASE__      							=				scheduler.batch_step_no_noise(lowerCAmelCase_	,     timesteps.flatten(0	,     1				)	,     samples.flatten(0	,     1				)	,     lowerCAmelCase_				)

       SCREAMING_SNAKE_CASE__      							=				torch.sum(torch.abs(lowerCAmelCase_				)				)
       SCREAMING_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      __a						(   self      :    Optional[int]				):



       """simple docstring"""






       SCREAMING_SNAKE_CASE__      							=				self.full_loop()

       SCREAMING_SNAKE_CASE__      							=				torch.sum(torch.abs(lowerCAmelCase_				)				)
       SCREAMING_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      __a						(   self      :    Any				):



       """simple docstring"""






       SCREAMING_SNAKE_CASE__      							=				self.full_loop(prediction_type="""v_prediction"""				)

       SCREAMING_SNAKE_CASE__      							=				torch.sum(torch.abs(lowerCAmelCase_				)				)
       SCREAMING_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      __a						(   self      :    Any				):



       """simple docstring"""






       SCREAMING_SNAKE_CASE__      							=				self.full_loop(set_alpha_to_one=lowerCAmelCase_	,     beta_start=0.01				)
       SCREAMING_SNAKE_CASE__      							=				torch.sum(torch.abs(lowerCAmelCase_				)				)
       SCREAMING_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      __a						(   self      :    Dict				):



       """simple docstring"""






       SCREAMING_SNAKE_CASE__      							=				self.full_loop(set_alpha_to_one=lowerCAmelCase_	,     beta_start=0.01				)
       SCREAMING_SNAKE_CASE__      							=				torch.sum(torch.abs(lowerCAmelCase_				)				)
       SCREAMING_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

from __future__ import annotations

__lowerCamelCase      :			Dict							       =		[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__lowerCamelCase      :			Union[str, Any]							       =		[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list[float]     )							-> list[float]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[]
  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     )

  for i in range(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				-1
    for j in range(i + 1			,					__UpperCamelCase     ):
      if arr[i] < arr[j]:
        SCREAMING_SNAKE_CASE__      							=				arr[j]
        break
    result.append(__UpperCamelCase     )
  return result



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list[float]     )							-> list[float]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[]
  for i, outer in enumerate(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				-1
    for inner in arr[i + 1 :]:
      if outer < inner:
        SCREAMING_SNAKE_CASE__      							=				inner
        break
    result.append(__UpperCamelCase     )
  return result



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list[float]     )							-> list[float]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				[]
  SCREAMING_SNAKE_CASE__      							=				[-1] * arr_size

  for index in reversed(range(__UpperCamelCase     )     ):
    if stack:
      while stack[-1] <= arr[index]:
        stack.pop()
        if not stack:
          break
    if stack:
      SCREAMING_SNAKE_CASE__      							=				stack[-1]
    stack.append(arr[index]     )
  return result


if __name__ == "__main__":
   from doctest import testmod
   from timeit import timeit

   testmod()
   print(next_greatest_element_slow(arr))
   print(next_greatest_element_fast(arr))
   print(next_greatest_element(arr))

   __lowerCamelCase      :			List[Any]							       =		(
       '''from __main__ import arr, next_greatest_element_slow, '''
       '''next_greatest_element_fast, next_greatest_element'''
   )
   print(
       '''next_greatest_element_slow():''',
       timeit('''next_greatest_element_slow(arr)''', setup=setup),
   )
   print(
       '''next_greatest_element_fast():''',
       timeit('''next_greatest_element_fast(arr)''', setup=setup),
   )
   print(
       '''     next_greatest_element():''',
       timeit('''next_greatest_element(arr)''', setup=setup),
   )
