"""simple docstring"""


import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 					a					(				A_							):
  A_ :				Optional[int] 			=							(EulerAncestralDiscreteScheduler,)
  A_ :				Optional[int] 			=							10



  def        lowerCAmelCase_				(		self   :    int						,  **lowerCamelCase_   :    int     )      -> int:
      __a			      =						{
          """num_train_timesteps""": 11_00,
          """beta_start""": 0.00_01,
          """beta_end""": 0.02,
          """beta_schedule""": """linear""",
      }

      config.update(**lowerCamelCase_     )
      return config



  def        lowerCAmelCase_				(		self   :    List[Any]     )      -> Tuple:
      for timesteps in [10, 50, 1_00, 10_00]:
          self.check_over_configs(num_train_timesteps=lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Optional[Any]     )      -> Union[str, Any]:
      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=lowerCamelCase_						,  beta_end=lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Optional[int]     )      -> str:
      for schedule in ["linear", "scaled_linear"]:
          self.check_over_configs(beta_schedule=lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Any     )      -> str:
      for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    List[str]     )      -> Optional[Any]:
      __a			      =						self.scheduler_classes[0]
      __a			      =						self.get_scheduler_config()
      __a			      =						scheduler_class(**lowerCamelCase_     )

      scheduler.set_timesteps(self.num_inference_steps     )

      __a			      =						torch.manual_seed(0     )

      __a			      =						self.dummy_model()
      __a			      =						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      __a			      =						sample.to(lowerCamelCase_     )

      for i, t in enumerate(scheduler.timesteps     ):
          __a			      =						scheduler.scale_model_input(lowerCamelCase_						,  lowerCamelCase_     )

          __a			      =						model(lowerCamelCase_						,  lowerCamelCase_     )

          __a			      =						scheduler.step(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  generator=lowerCamelCase_     )
          __a			      =						output.prev_sample

      __a			      =						torch.sum(torch.abs(lowerCamelCase_     )     )
      __a			      =						torch.mean(torch.abs(lowerCamelCase_     )     )

      assert abs(result_sum.item() - 1_52.31_92     ) < 1E-2
      assert abs(result_mean.item() - 0.19_83     ) < 1E-3



  def        lowerCAmelCase_				(		self   :    Optional[int]     )      -> Optional[int]:
      __a			      =						self.scheduler_classes[0]
      __a			      =						self.get_scheduler_config(prediction_type="""v_prediction"""     )
      __a			      =						scheduler_class(**lowerCamelCase_     )

      scheduler.set_timesteps(self.num_inference_steps     )

      __a			      =						torch.manual_seed(0     )

      __a			      =						self.dummy_model()
      __a			      =						self.dummy_sample_deter * scheduler.init_noise_sigma
      __a			      =						sample.to(lowerCamelCase_     )

      for i, t in enumerate(scheduler.timesteps     ):
          __a			      =						scheduler.scale_model_input(lowerCamelCase_						,  lowerCamelCase_     )

          __a			      =						model(lowerCamelCase_						,  lowerCamelCase_     )

          __a			      =						scheduler.step(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  generator=lowerCamelCase_     )
          __a			      =						output.prev_sample

      __a			      =						torch.sum(torch.abs(lowerCamelCase_     )     )
      __a			      =						torch.mean(torch.abs(lowerCamelCase_     )     )

      assert abs(result_sum.item() - 1_08.44_39     ) < 1E-2
      assert abs(result_mean.item() - 0.14_12     ) < 1E-3



  def        lowerCAmelCase_				(		self   :    Dict     )      -> Union[str, Any]:
      __a			      =						self.scheduler_classes[0]
      __a			      =						self.get_scheduler_config()
      __a			      =						scheduler_class(**lowerCamelCase_     )

      scheduler.set_timesteps(self.num_inference_steps						,  device=lowerCamelCase_     )
      __a			      =						torch.manual_seed(0     )

      __a			      =						self.dummy_model()
      __a			      =						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      __a			      =						sample.to(lowerCamelCase_     )

      for t in scheduler.timesteps:
          __a			      =						scheduler.scale_model_input(lowerCamelCase_						,  lowerCamelCase_     )

          __a			      =						model(lowerCamelCase_						,  lowerCamelCase_     )

          __a			      =						scheduler.step(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  generator=lowerCamelCase_     )
          __a			      =						output.prev_sample

      __a			      =						torch.sum(torch.abs(lowerCamelCase_     )     )
      __a			      =						torch.mean(torch.abs(lowerCamelCase_     )     )

      assert abs(result_sum.item() - 1_52.31_92     ) < 1E-2
      assert abs(result_mean.item() - 0.19_83     ) < 1E-3

"""simple docstring"""


def 						UpperCamelCase				(	_lowerCAmelCase   :							Dict				, _lowerCAmelCase   :							Tuple						):
    __a			      =						len(_lowerCAmelCase						)
    # Create a stack and push index of fist element to it
    __a			      =						[]
    st.append(0						)

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

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

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

def 						UpperCamelCase				(	_lowerCAmelCase   :							Dict				, _lowerCAmelCase   :							Optional[Any]						):
    for i in range(0				, _lowerCAmelCase						):
        print(arr[i]				, end=""" """						)


# Driver program to test above function
__A       			=      [10, 4, 5, 90, 1_20, 80]
__A       			=      [0 for i in range(len(price) + 1)]

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






print_array(S, len(price))
