

import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						List[str]	    =    (DDIMParallelScheduler,)
							UpperCAmelCase__       :						Optional[Any]	    =    (("eta", 0.0), ("num_inference_steps", 50))


							def 				__lowercase				(			self       ,							**_a       )    ->    Dict:
												_a	:    Optional[Any]         =		{
												    '''num_train_timesteps''': 1_0_0_0,
												    '''beta_start''': 0.0001,
												    '''beta_end''': 0.02,
												    '''beta_schedule''': '''linear''',
												    '''clip_sample''': True,
												}

												config.update(**_a       )
												return config


							def 				__lowercase				(			self       ,							**_a       )    ->    Tuple:
												_a	:    List[Any]         =		self.scheduler_classes[0]
												_a	:    Dict         =		self.get_scheduler_config(**_a       )
												_a	:    Dict         =		scheduler_class(**_a       )

												_a      ,					_a	:    Any         =		1_0, 0.0

												_a	:    Dict         =		self.dummy_model()
												_a	:    Optional[int]         =		self.dummy_sample_deter

												scheduler.set_timesteps(_a       )

												for t in scheduler.timesteps:
																	_a	:    Tuple         =		model(_a       ,							_a       )
																	_a	:    Union[str, Any]         =		scheduler.step(_a       ,							_a       ,							_a       ,							_a       ).prev_sample

												return sample


							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=_a       )


							def 				__lowercase				(			self       )    ->    Dict:
												for steps_offset in [0, 1]:
																	self.check_over_configs(steps_offset=_a       )

												_a	:    Tuple         =		self.scheduler_classes[0]
												_a	:    Optional[int]         =		self.get_scheduler_config(steps_offset=1       )
												_a	:    int         =		scheduler_class(**_a       )
												scheduler.set_timesteps(5       )
												assert torch.equal(scheduler.timesteps       ,							torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]       )       )


							def 				__lowercase				(			self       )    ->    Tuple:
												for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]       ,							[0.002, 0.02, 0.2, 2]       ):
																	self.check_over_configs(beta_start=_a       ,							beta_end=_a       )


							def 				__lowercase				(			self       )    ->    List[str]:
												for schedule in ["linear", "squaredcos_cap_v2"]:
																	self.check_over_configs(beta_schedule=_a       )


							def 				__lowercase				(			self       )    ->    int:
												for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(prediction_type=_a       )


							def 				__lowercase				(			self       )    ->    List[str]:
												for clip_sample in [True, False]:
																	self.check_over_configs(clip_sample=_a       )


							def 				__lowercase				(			self       )    ->    Dict:
												for timestep_spacing in ["trailing", "leading"]:
																	self.check_over_configs(timestep_spacing=_a       )


							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												for rescale_betas_zero_snr in [True, False]:
																	self.check_over_configs(rescale_betas_zero_snr=_a       )


							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												self.check_over_configs(thresholding=_a       )
												for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "v_prediction"]:
																						self.check_over_configs(
																						    thresholding=_a       ,							prediction_type=_a       ,							sample_max_value=_a       ,							)


							def 				__lowercase				(			self       )    ->    int:
												for t in [1, 1_0, 4_9]:
																	self.check_over_forward(time_step=_a       )


							def 				__lowercase				(			self       )    ->    Dict:
												for t, num_inference_steps in zip([1, 1_0, 5_0]       ,							[1_0, 5_0, 5_0_0]       ):
																	self.check_over_forward(time_step=_a       ,							num_inference_steps=_a       )


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												for t, eta in zip([1, 1_0, 4_9]       ,							[0.0, 0.5, 1.0]       ):
																	self.check_over_forward(time_step=_a       ,							eta=_a       )


							def 				__lowercase				(			self       )    ->    Optional[int]:
												_a	:    List[Any]         =		self.scheduler_classes[0]
												_a	:    Optional[Any]         =		self.get_scheduler_config()
												_a	:    Optional[int]         =		scheduler_class(**_a       )

												assert torch.sum(torch.abs(scheduler._get_variance(0       ,							0       ) - 0.0       )       ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(4_2_0       ,							4_0_0       ) - 0.1_4771       )       ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(9_8_0       ,							9_6_0       ) - 0.3_2460       )       ) < 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_8_7       ,							4_8_6       ) - 0.0_0979       )       ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(9_9_9       ,							9_9_8       ) - 0.02       )       ) < 1e-5


							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												_a	:    Tuple         =		self.scheduler_classes[0]
												_a	:    int         =		self.get_scheduler_config()
												_a	:    List[Any]         =		scheduler_class(**_a       )

												_a      ,					_a	:    Optional[Any]         =		1_0, 0.0
												scheduler.set_timesteps(_a       )

												_a	:    Union[str, Any]         =		self.dummy_model()
												_a	:    int         =		self.dummy_sample_deter
												_a	:    List[Any]         =		self.dummy_sample_deter + 0.1
												_a	:    List[str]         =		self.dummy_sample_deter - 0.1

												_a	:    Dict         =		samplea.shape[0]
												_a	:    str         =		torch.stack([samplea, samplea, samplea]       ,							dim=0       )
												_a	:    List[str]         =		torch.arange(_a       )[0:3, None].repeat(1       ,							_a       )

												_a	:    Dict         =		model(samples.flatten(0       ,							1       )       ,							timesteps.flatten(0       ,							1       )       )
												_a	:    Optional[int]         =		scheduler.batch_step_no_noise(_a       ,							timesteps.flatten(0       ,							1       )       ,							samples.flatten(0       ,							1       )       ,							_a       )

												_a	:    Union[str, Any]         =		torch.sum(torch.abs(_a       )       )
												_a	:    List[str]         =		torch.mean(torch.abs(_a       )       )

												assert abs(result_sum.item() - 1147.7904       ) < 1e-2
												assert abs(result_mean.item() - 0.4982       ) < 1e-3


							def 				__lowercase				(			self       )    ->    str:
												_a	:    str         =		self.full_loop()

												_a	:    str         =		torch.sum(torch.abs(_a       )       )
												_a	:    List[Any]         =		torch.mean(torch.abs(_a       )       )

												assert abs(result_sum.item() - 172.0067       ) < 1e-2
												assert abs(result_mean.item() - 0.22_3967       ) < 1e-3


							def 				__lowercase				(			self       )    ->    Dict:
												_a	:    Tuple         =		self.full_loop(prediction_type='''v_prediction'''       )

												_a	:    Tuple         =		torch.sum(torch.abs(_a       )       )
												_a	:    Union[str, Any]         =		torch.mean(torch.abs(_a       )       )

												assert abs(result_sum.item() - 52.5302       ) < 1e-2
												assert abs(result_mean.item() - 0.0684       ) < 1e-3


							def 				__lowercase				(			self       )    ->    int:
												# We specify different beta, so that the first alpha is 0.99
												_a	:    Optional[Any]         =		self.full_loop(set_alpha_to_one=_a       ,							beta_start=0.01       )
												_a	:    int         =		torch.sum(torch.abs(_a       )       )
												_a	:    List[str]         =		torch.mean(torch.abs(_a       )       )

												assert abs(result_sum.item() - 149.8295       ) < 1e-2
												assert abs(result_mean.item() - 0.1951       ) < 1e-3




							def 				__lowercase				(			self       )    ->    Optional[Any]:
												# We specify different beta, so that the first alpha is 0.99
												_a	:    Optional[Any]         =		self.full_loop(set_alpha_to_one=_a       ,							beta_start=0.01       )
												_a	:    List[Any]         =		torch.sum(torch.abs(_a       )       )
												_a	:    Tuple         =		torch.mean(torch.abs(_a       )       )

												assert abs(result_sum.item() - 149.0784       ) < 1e-2
												assert abs(result_mean.item() - 0.1941       ) < 1e-3






from collections.abc import Callable

class 				UpperCAmelCase_		:
							"""simple docstring"""







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


							def 				__lowercase				(			self       ,							_a       )    ->    int | None:
												return int((i - 1) / 2       ) if i > 0 else None


							def 				__lowercase				(			self       ,							_a       )    ->    int | None:
												_a	:    Optional[int]         =		int(2 * i + 1       )
												return left if 0 < left < self.size else None


							def 				__lowercase				(			self       ,							_a       )    ->    int | None:
												_a	:    int         =		int(2 * i + 2       )
												return right if 0 < right < self.size else None


							def 				__lowercase				(			self       ,							_a       ,							_a       )    ->    None:
												_a      ,					_a	:    Union[str, Any]         =		(
												    self.pos_map[self.arr[j][0]],
												    self.pos_map[self.arr[i][0]],
												)
												# Then swap the items in the list.
												_a      ,					_a	:    List[Any]         =		self.arr[j], self.arr[i]


							def 				__lowercase				(			self       ,							_a       ,							_a       )    ->    bool:
												return self.arr[i][1] < self.arr[j][1]


							def 				__lowercase				(			self       ,							_a       )    ->    int:
												_a	:    Dict         =		self._left(_a       )
												_a	:    str         =		self._right(_a       )
												_a	:    str         =		i

												if left is not None and not self._cmp(_a       ,							_a       ):
																	_a	:    Optional[Any]         =		left
												if right is not None and not self._cmp(_a       ,							_a       ):
																	_a	:    Any         =		right

												return valid_parent


							def 				__lowercase				(			self       ,							_a       )    ->    None:
												_a	:    List[str]         =		self._parent(_a       )
												while parent is not None and not self._cmp(_a       ,							_a       ):
																	self._swap(_a       ,							_a       )
																	_a      ,					_a	:    Any         =		parent, self._parent(_a       )


							def 				__lowercase				(			self       ,							_a       )    ->    None:
												_a	:    List[Any]         =		self._get_valid_parent(_a       )
												while valid_parent != index:
																	self._swap(_a       ,							_a       )
																	_a      ,					_a	:    int         =		valid_parent, self._get_valid_parent(_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a       )    ->    None:
												if item not in self.pos_map:
																	return
												_a	:    str         =		self.pos_map[item]
												_a	:    List[Any]         =		[item, self.key(_a       )]
												# Make sure heap is right in both up and down direction.
												# Ideally only one of them will make any change.
												self._heapify_up(_a       )
												self._heapify_down(_a       )


							def 				__lowercase				(			self       ,							_a       )    ->    None:
												if item not in self.pos_map:
																	return
												_a	:    Tuple         =		self.pos_map[item]
												del self.pos_map[item]
												_a	:    Tuple         =		self.arr[self.size - 1]
												_a	:    str         =		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(_a       )
																	self._heapify_down(_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a       )    ->    None:
												_a	:    Union[str, Any]         =		len(self.arr       )
												if arr_len == self.size:
																	self.arr.append([item, self.key(_a       )]       )
												else:
																	_a	:    Optional[int]         =		[item, self.key(_a       )]
												_a	:    Tuple         =		self.size
												self.size += 1
												self._heapify_up(self.size - 1       )


							def 				__lowercase				(			self       )    ->    tuple | None:
												return self.arr[0] if self.size else None




							def 				__lowercase				(			self       )    ->    tuple | None:
												_a	:    Tuple         =		self.get_top()
												if top_item_tuple:
																	self.delete_item(top_item_tuple[0]       )
												return top_item_tuple



def    __UpperCAmelCase (	)			->     None:

					"""simple docstring"""



if __name__ == "__main__":
			import doctest

			doctest.testmod()



