

'''simple docstring'''
import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 							A__					( A__						):
  A__					=       (KDPMaAncestralDiscreteScheduler,)
  A__					=       10





  def 		A						(	self   :			Any							,      **_a   :			List[Any]				)			->					Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												={
           'num_train_timesteps': 1100,
           'beta_start': 0.00_01,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
       }

       config.update(**UpperCAmelCase_				)
       return config





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





       '''simple docstring'''






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





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





       '''simple docstring'''






       for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]							,      [0.00_02, 0.0_02, 0.02]				):
            self.check_over_configs(beta_start=UpperCAmelCase_							,      beta_end=UpperCAmelCase_				)





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





       '''simple docstring'''






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





  def 		A						(	self   :			int				)			->					Optional[int]:





       '''simple docstring'''






       if torch_device == "mps":
            return
       _SCREAMING_SNAKE_CASE												=self.scheduler_classes[0]
       _SCREAMING_SNAKE_CASE												=self.get_scheduler_config()
       _SCREAMING_SNAKE_CASE												=scheduler_class(**UpperCAmelCase_				)

       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(UpperCAmelCase_				)

       for i, t in enumerate(scheduler.timesteps				):
            _SCREAMING_SNAKE_CASE												=scheduler.scale_model_input(UpperCAmelCase_							,      UpperCAmelCase_				)

            _SCREAMING_SNAKE_CASE												=model(UpperCAmelCase_							,      UpperCAmelCase_				)

            _SCREAMING_SNAKE_CASE												=scheduler.step(UpperCAmelCase_							,      UpperCAmelCase_							,      UpperCAmelCase_							,      generator=UpperCAmelCase_				)
            _SCREAMING_SNAKE_CASE												=output.prev_sample

       _SCREAMING_SNAKE_CASE												=torch.sum(torch.abs(UpperCAmelCase_				)				)
       _SCREAMING_SNAKE_CASE												=torch.mean(torch.abs(UpperCAmelCase_				)				)

       assert abs(result_sum.item() - 1_3849.3877				) < 1e-2
       assert abs(result_mean.item() - 18.03_31				) < 5e-3





  def 		A						(	self   :			Tuple				)			->					Optional[int]:





       '''simple docstring'''






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





  def 		A						(	self   :			List[str]				)			->					Tuple:





       '''simple docstring'''






       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(**UpperCAmelCase_				)

       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(UpperCAmelCase_				)

       _SCREAMING_SNAKE_CASE												=torch.manual_seed(0				)

       for i, t in enumerate(scheduler.timesteps				):
            _SCREAMING_SNAKE_CASE												=scheduler.scale_model_input(UpperCAmelCase_							,      UpperCAmelCase_				)

            _SCREAMING_SNAKE_CASE												=model(UpperCAmelCase_							,      UpperCAmelCase_				)

            _SCREAMING_SNAKE_CASE												=scheduler.step(UpperCAmelCase_							,      UpperCAmelCase_							,      UpperCAmelCase_							,      generator=UpperCAmelCase_				)
            _SCREAMING_SNAKE_CASE												=output.prev_sample

       _SCREAMING_SNAKE_CASE												=torch.sum(torch.abs(UpperCAmelCase_				)				)
       _SCREAMING_SNAKE_CASE												=torch.mean(torch.abs(UpperCAmelCase_				)				)

       assert abs(result_sum.item() - 328.9970				) < 1e-2
       assert abs(result_mean.item() - 0.42_84				) < 1e-3





  def 		A						(	self   :			Tuple				)			->					Optional[int]:





       '''simple docstring'''






       if torch_device == "mps":
            return
       _SCREAMING_SNAKE_CASE												=self.scheduler_classes[0]
       _SCREAMING_SNAKE_CASE												=self.get_scheduler_config()
       _SCREAMING_SNAKE_CASE												=scheduler_class(**UpperCAmelCase_				)

       scheduler.set_timesteps(self.num_inference_steps							,      device=UpperCAmelCase_				)
       _SCREAMING_SNAKE_CASE												=torch.manual_seed(0				)

       _SCREAMING_SNAKE_CASE												=self.dummy_model()
       _SCREAMING_SNAKE_CASE												=self.dummy_sample_deter.to(UpperCAmelCase_				) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
            _SCREAMING_SNAKE_CASE												=scheduler.scale_model_input(UpperCAmelCase_							,      UpperCAmelCase_				)

            _SCREAMING_SNAKE_CASE												=model(UpperCAmelCase_							,      UpperCAmelCase_				)

            _SCREAMING_SNAKE_CASE												=scheduler.step(UpperCAmelCase_							,      UpperCAmelCase_							,      UpperCAmelCase_							,      generator=UpperCAmelCase_				)
            _SCREAMING_SNAKE_CASE												=output.prev_sample

       _SCREAMING_SNAKE_CASE												=torch.sum(torch.abs(UpperCAmelCase_				)				)
       _SCREAMING_SNAKE_CASE												=torch.mean(torch.abs(UpperCAmelCase_				)				)

       assert abs(result_sum.item() - 1_3849.3818				) < 1e-1
       assert abs(result_mean.item() - 18.03_31				) < 1e-3



'''simple docstring'''
def  _lowerCAmelCase      (		_UpperCamelCase		:  Optional[int]							,				_UpperCamelCase		:  Optional[Any]       )   ->    Tuple:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=len(_UpperCamelCase       )
     # Create a stack and push index of fist element to it
     _SCREAMING_SNAKE_CASE												=[]
     st.append(0       )

     # Span value of first element is always 1
     _SCREAMING_SNAKE_CASE												=1

     # Calculate span values for rest of the elements
     for i in range(1							,				_UpperCamelCase       ):
          # Pop elements from stack while stack is not
          # empty and top of stack is smaller than price[i]
          while len(_UpperCamelCase       ) > 0 and price[st[0]] <= price[i]:
               st.pop()

          # If stack becomes empty, then price[i] is greater
          # than all elements on left of it, i.e. price[0],
          # price[1], ..price[i-1]. Else the price[i]  is
          # greater than elements after top of stack
          _SCREAMING_SNAKE_CASE												=i + 1 if len(_UpperCamelCase       ) <= 0 else (i - st[0])

          # Push this element to stack
          st.append(_UpperCamelCase       )


def  _lowerCAmelCase      (		_UpperCamelCase		:  Any							,				_UpperCamelCase		:  str       )   ->    Any:
     """simple docstring"""




     for i in range(0							,				_UpperCamelCase       ):
          print(arr[i]							,				end=' '       )


# Driver program to test above function
lowerCamelCase				:   str      				=  [1_0, 4, 5, 9_0, 1_2_0, 8_0]
lowerCamelCase				:   str      				=  [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)







print_array(S, len(price))
