






import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		snake_case    =   (EulerDiscreteScheduler,)
		snake_case    =   1_0
		def       A(  self		:       List[str]						,**_SCREAMING_SNAKE_CASE		:       Union[str, Any]		)    ->   List[Any]:


						'''simple docstring'''
						A	 =      {
						    'num_train_timesteps': 1_1_0_0,
						    'beta_start': 0.0001,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						}

						config.update(**_SCREAMING_SNAKE_CASE		)
						return config
		def       A(  self		:       Any		)    ->   List[Any]:


						'''simple docstring'''
						for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
										self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       List[Any]		)    ->   Optional[Any]:


						'''simple docstring'''
						for beta_start, beta_end in zip([0.00001, 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		:       Union[str, Any]		)    ->   List[Any]:


						'''simple docstring'''
						for schedule in ["linear", "scaled_linear"]:
										self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       List[str]		)    ->   Dict:


						'''simple docstring'''
						for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Any		)    ->   Dict:


						'''simple docstring'''
						A	 =      self.scheduler_classes[0]
						A	 =      self.get_scheduler_config()
						A	 =      scheduler_class(**_SCREAMING_SNAKE_CASE		)

						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(_SCREAMING_SNAKE_CASE		)

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

										A	 =      model(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)

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

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

						assert abs(result_sum.item() - 10.0807		) < 1E-2
						assert abs(result_mean.item() - 0.0131		) < 1E-3
		def       A(  self		:       Union[str, Any]		)    ->   List[str]:


						'''simple docstring'''
						A	 =      self.scheduler_classes[0]
						A	 =      self.get_scheduler_config(prediction_type='v_prediction'		)
						A	 =      scheduler_class(**_SCREAMING_SNAKE_CASE		)

						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(_SCREAMING_SNAKE_CASE		)

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

										A	 =      model(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)

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

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

						assert abs(result_sum.item() - 0.0002		) < 1E-2
						assert abs(result_mean.item() - 2.2676E-06		) < 1E-3
		def       A(  self		:       int		)    ->   List[Any]:


						'''simple docstring'''
						A	 =      self.scheduler_classes[0]
						A	 =      self.get_scheduler_config()
						A	 =      scheduler_class(**_SCREAMING_SNAKE_CASE		)

						scheduler.set_timesteps(self.num_inference_steps						,device=_SCREAMING_SNAKE_CASE		)

						A	 =      torch.manual_seed(0		)

						A	 =      self.dummy_model()
						A	 =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						A	 =      sample.to(_SCREAMING_SNAKE_CASE		)

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

										A	 =      model(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)

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

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

						assert abs(result_sum.item() - 10.0807		) < 1E-2
						assert abs(result_mean.item() - 0.0131		) < 1E-3







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


						'''simple docstring'''
						A	 =      self.scheduler_classes[0]
						A	 =      self.get_scheduler_config()
						A	 =      scheduler_class(**_SCREAMING_SNAKE_CASE						,use_karras_sigmas=_SCREAMING_SNAKE_CASE		)

						scheduler.set_timesteps(self.num_inference_steps						,device=_SCREAMING_SNAKE_CASE		)

						A	 =      torch.manual_seed(0		)

						A	 =      self.dummy_model()
						A	 =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						A	 =      sample.to(_SCREAMING_SNAKE_CASE		)

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

										A	 =      model(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)

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

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

						assert abs(result_sum.item() - 124.52299499511719		) < 1E-2
						assert abs(result_mean.item() - 0.16213932633399963		) < 1E-3













from collections.abc import Callable
class      UpperCamelCase :







		"""simple docstring"""



		def __init__(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       Callable | None = None		)    ->   None:


						'''simple docstring'''
						# Stores actual heap items.
						A	 =      []
						# Stores indexes of each item for supporting updates and deletion.
						A	 =      {}
						# Stores current size of heap.
						A	 =      0
						# Stores function used to evaluate the score of an item on which basis ordering
						# will be done.
						A	 =      key or (lambda _SCREAMING_SNAKE_CASE		: x)
		def       A(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       int		)    ->   int | None:


						'''simple docstring'''
						return int((i - 1) / 2		) if i > 0 else None
		def       A(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   int | None:


						'''simple docstring'''
						A	 =      int(2 * i + 1		)
						return left if 0 < left < self.size else None
		def       A(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   int | None:


						'''simple docstring'''
						A	 =      int(2 * i + 2		)
						return right if 0 < right < self.size else None
		def       A(  self		:       List[str]						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       int		)    ->   None:


						'''simple docstring'''
						A ,		A	 =      (
						    self.pos_map[self.arr[j][0]],
						    self.pos_map[self.arr[i][0]],
						)
						# Then swap the items in the list.
						A ,		A	 =      self.arr[j], self.arr[i]
		def       A(  self		:       List[str]						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       int		)    ->   bool:


						'''simple docstring'''
						return self.arr[i][1] < self.arr[j][1]
		def       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       int		)    ->   int:


						'''simple docstring'''
						A	 =      self._left(_SCREAMING_SNAKE_CASE		)
						A	 =      self._right(_SCREAMING_SNAKE_CASE		)
						A	 =      i

						if left is not None and not self._cmp(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      left
						if right is not None and not self._cmp(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      right

						return valid_parent
		def       A(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       int		)    ->   None:


						'''simple docstring'''
						A	 =      self._parent(_SCREAMING_SNAKE_CASE		)
						while parent is not None and not self._cmp(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										self._swap(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
										A ,		A	 =      parent, self._parent(_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   None:


						'''simple docstring'''
						A	 =      self._get_valid_parent(_SCREAMING_SNAKE_CASE		)
						while valid_parent != index:
										self._swap(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
										A ,		A	 =      valid_parent, self._get_valid_parent(_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       int		)    ->   None:


						'''simple docstring'''
						if item not in self.pos_map:
										return
						A	 =      self.pos_map[item]
						A	 =      [item, self.key(_SCREAMING_SNAKE_CASE		)]
						# Make sure heap is right in both up and down direction.
						# Ideally only one of them will make any change.
						self._heapify_up(_SCREAMING_SNAKE_CASE		)
						self._heapify_down(_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       int		)    ->   None:


						'''simple docstring'''
						if item not in self.pos_map:
										return
						A	 =      self.pos_map[item]
						del self.pos_map[item]
						A	 =      self.arr[self.size - 1]
						A	 =      index
						self.size -= 1
						# Make sure heap is right in both up and down direction. Ideally only one
						# of them will make any change- so no performance loss in calling both.
						if self.size > index:
										self._heapify_up(_SCREAMING_SNAKE_CASE		)
										self._heapify_down(_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       int		)    ->   None:


						'''simple docstring'''
						A	 =      len(self.arr		)
						if arr_len == self.size:
										self.arr.append([item, self.key(_SCREAMING_SNAKE_CASE		)]		)
						else:
										A	 =      [item, self.key(_SCREAMING_SNAKE_CASE		)]
						A	 =      self.size
						self.size += 1
						self._heapify_up(self.size - 1		)
		def       A(  self		:       str		)    ->   tuple | None:


						'''simple docstring'''
						return self.arr[0] if self.size else None







		def       A(  self		:       Any		)    ->   tuple | None:


						'''simple docstring'''
						A	 =      self.get_top()
						if top_item_tuple:
										self.delete_item(top_item_tuple[0]		)
						return top_item_tuple







def      snake_case		(							):
				pass


if __name__ == "__main__":
					import doctest

					doctest.testmod()





