


'''simple docstring'''







import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 				__lowerCamelCase				( a_ ):
    """simple docstring"""
    a									=					(EulerAncestralDiscreteScheduler,)
    a									=					10


    def      A					(						self    :							int   ,							**SCREAMING_SNAKE_CASE    :							Union[str, Any]):
           _A  :			Any											=      {
               'num_train_timesteps': 1100,
               'beta_start': 0.0001,
               'beta_end': 0.02,
               'beta_schedule': 'linear',
           }

           config.update(**SCREAMING_SNAKE_CASE)
           return config


    def      A					(						self    :							Dict):
           for timesteps in [10, 50, 100, 1000]:
                  self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Optional[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=SCREAMING_SNAKE_CASE   ,							beta_end=SCREAMING_SNAKE_CASE)


    def      A					(						self    :							int):
           for schedule in ["linear", "scaled_linear"]:
                  self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Optional[Any]):
           for prediction_type in ["epsilon", "v_prediction"]:
                  self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Union[str, Any]):
           _A  :			str											=      self.scheduler_classes[0]
           _A  :			Optional[int]											=      self.get_scheduler_config()
           _A  :			Any											=      scheduler_class(**SCREAMING_SNAKE_CASE)

           scheduler.set_timesteps(self.num_inference_steps)

           _A  :			Tuple											=      torch.manual_seed(0)

           _A  :			Union[str, Any]											=      self.dummy_model()
           _A  :			Tuple											=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
           _A  :			int											=      sample.to(SCREAMING_SNAKE_CASE)

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

                  _A  :			Optional[int]											=      model(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)

                  _A  :			int											=      scheduler.step(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							generator=SCREAMING_SNAKE_CASE)
                  _A  :			int											=      output.prev_sample

           _A  :			Tuple											=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE))
           _A  :			Tuple											=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 152.3192) < 1e-2
           assert abs(result_mean.item() - 0.1983) < 1e-3


    def      A					(						self    :							Optional[int]):
           _A  :			Optional[int]											=      self.scheduler_classes[0]
           _A  :			Optional[Any]											=      self.get_scheduler_config(prediction_type='v_prediction')
           _A  :			Any											=      scheduler_class(**SCREAMING_SNAKE_CASE)

           scheduler.set_timesteps(self.num_inference_steps)

           _A  :			Any											=      torch.manual_seed(0)

           _A  :			Any											=      self.dummy_model()
           _A  :			str											=      self.dummy_sample_deter * scheduler.init_noise_sigma
           _A  :			List[str]											=      sample.to(SCREAMING_SNAKE_CASE)

           for i, t in enumerate(scheduler.timesteps):
                  _A  :			Optional[int]											=      scheduler.scale_model_input(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)

                  _A  :			Optional[Any]											=      model(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)

                  _A  :			Tuple											=      scheduler.step(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							generator=SCREAMING_SNAKE_CASE)
                  _A  :			Union[str, Any]											=      output.prev_sample

           _A  :			int											=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE))
           _A  :			Union[str, Any]											=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 108.4439) < 1e-2
           assert abs(result_mean.item() - 0.1412) < 1e-3


    def      A					(						self    :							Tuple):
           _A  :			Tuple											=      self.scheduler_classes[0]
           _A  :			str											=      self.get_scheduler_config()
           _A  :			List[str]											=      scheduler_class(**SCREAMING_SNAKE_CASE)

           scheduler.set_timesteps(self.num_inference_steps   ,							device=SCREAMING_SNAKE_CASE)
           _A  :			List[str]											=      torch.manual_seed(0)

           _A  :			List[Any]											=      self.dummy_model()
           _A  :			int											=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
           _A  :			Optional[int]											=      sample.to(SCREAMING_SNAKE_CASE)

           for t in scheduler.timesteps:
                  _A  :			List[Any]											=      scheduler.scale_model_input(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)

                  _A  :			Optional[Any]											=      model(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)

                  _A  :			List[str]											=      scheduler.step(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							generator=SCREAMING_SNAKE_CASE)
                  _A  :			List[Any]											=      output.prev_sample

           _A  :			Dict											=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE))
           _A  :			int											=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 152.3192) < 1e-2
           assert abs(result_mean.item() - 0.1983) < 1e-3










'''simple docstring'''







class 				__lowerCamelCase				:
    """simple docstring"""


    def __init__(						self    :							Optional[Any]):
           _A  :			Optional[Any]											=      [0]
           _A  :			Optional[Any]											=      0


    def      A					(						self    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							int):
           _A  :			Optional[Any]											=      self.__heap[i]
           while i // 2 > 0:
                  if self.__heap[i] > self.__heap[i // 2]:
                         _A  :			Dict											=      self.__heap[i // 2]
                         _A  :			Optional[Any]											=      temporary
                  i //= 2


    def      A					(						self    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							int):
           self.__heap.append(SCREAMING_SNAKE_CASE)
           self.__size += 1
           self.__swap_up(self.__size)


    def      A					(						self    :							int   ,							SCREAMING_SNAKE_CASE    :							int):
           while self.__size >= 2 * i:
                  if 2 * i + 1 > self.__size:
                         _A  :			Optional[int]											=      2 * i
                  else:
                         if self.__heap[2 * i] > self.__heap[2 * i + 1]:
                                _A  :			Any											=      2 * i
                         else:
                                _A  :			Optional[Any]											=      2 * i + 1
                  _A  :			List[str]											=      self.__heap[i]
                  if self.__heap[i] < self.__heap[bigger_child]:
                         _A  :			Union[str, Any]											=      self.__heap[bigger_child]
                         _A  :			Optional[int]											=      temporary
                  _A  :			Any											=      bigger_child


    def      A					(						self    :							List[str]):
           _A  :			str											=      self.__heap[1]
           _A  :			Tuple											=      self.__heap[self.__size]
           self.__size -= 1
           self.__heap.pop()
           self.__swap_down(1)
           return max_value


    @property
    def      A					(						self    :							int):
           return self.__heap[1:]


    def __len__(						self    :							List[str]):
           return self.__size


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   # create an instance of BinaryHeap
   A   :  Dict       		=				BinaryHeap()
   binary_heap.insert(6)
   binary_heap.insert(10)
   binary_heap.insert(15)
   binary_heap.insert(12)
   # pop root(max-values because it is max heap)
   print(binary_heap.pop())  # 15
   print(binary_heap.pop())  # 12
   # get the list and size after operations
   print(binary_heap.get_list)
   print(len(binary_heap))






