

import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest

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







							UpperCAmelCase__       :						int	    =    (IPNDMScheduler,)
							UpperCAmelCase__       :						Union[str, Any]	    =    (("num_inference_steps", 50),)


							def 				__lowercase				(			self       ,							**_a       )    ->    Dict:
												_a	:    List[str]         =		{'''num_train_timesteps''': 1_0_0_0}
												config.update(**_a       )
												return config


							def 				__lowercase				(			self       ,							_a=0       ,							**_a       )    ->    str:
												_a	:    Optional[int]         =		dict(self.forward_default_kwargs       )
												_a	:    Optional[int]         =		kwargs.pop('''num_inference_steps'''       ,							_a       )
												_a	:    List[Any]         =		self.dummy_sample
												_a	:    List[str]         =		0.1 * sample
												_a	:    List[Any]         =		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

												for scheduler_class in self.scheduler_classes:
																	_a	:    Optional[int]         =		self.get_scheduler_config(**_a       )
																	_a	:    str         =		scheduler_class(**_a       )
																	scheduler.set_timesteps(_a       )
																	# copy over dummy past residuals
																	_a	:    int         =		dummy_past_residuals[:]

																	if time_step is None:
																						_a	:    Dict         =		scheduler.timesteps[len(scheduler.timesteps       ) // 2]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(_a       )
																						_a	:    List[Any]         =		scheduler_class.from_pretrained(_a       )
																						new_scheduler.set_timesteps(_a       )
																						# copy over dummy past residuals
																						_a	:    Tuple         =		dummy_past_residuals[:]

																	_a	:    Optional[int]         =		scheduler.step(_a       ,							_a       ,							_a       ,							**_a       ).prev_sample
																	_a	:    List[str]         =		new_scheduler.step(_a       ,							_a       ,							_a       ,							**_a       ).prev_sample

																	assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"

																	_a	:    List[Any]         =		scheduler.step(_a       ,							_a       ,							_a       ,							**_a       ).prev_sample
																	_a	:    List[Any]         =		new_scheduler.step(_a       ,							_a       ,							_a       ,							**_a       ).prev_sample

																	assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"


							def 				__lowercase				(			self       )    ->    str:
												pass


							def 				__lowercase				(			self       ,							_a=0       ,							**_a       )    ->    Dict:
												_a	:    Tuple         =		dict(self.forward_default_kwargs       )
												_a	:    int         =		kwargs.pop('''num_inference_steps'''       ,							_a       )
												_a	:    Union[str, Any]         =		self.dummy_sample
												_a	:    str         =		0.1 * sample
												_a	:    Dict         =		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

												for scheduler_class in self.scheduler_classes:
																	_a	:    List[str]         =		self.get_scheduler_config()
																	_a	:    List[Any]         =		scheduler_class(**_a       )
																	scheduler.set_timesteps(_a       )

																	# copy over dummy past residuals (must be after setting timesteps)
																	_a	:    Dict         =		dummy_past_residuals[:]

																	if time_step is None:
																						_a	:    Optional[Any]         =		scheduler.timesteps[len(scheduler.timesteps       ) // 2]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(_a       )
																						_a	:    int         =		scheduler_class.from_pretrained(_a       )
																						# copy over dummy past residuals
																						new_scheduler.set_timesteps(_a       )

																						# copy over dummy past residual (must be after setting timesteps)
																						_a	:    str         =		dummy_past_residuals[:]

																	_a	:    Dict         =		scheduler.step(_a       ,							_a       ,							_a       ,							**_a       ).prev_sample
																	_a	:    Dict         =		new_scheduler.step(_a       ,							_a       ,							_a       ,							**_a       ).prev_sample

																	assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"

																	_a	:    Any         =		scheduler.step(_a       ,							_a       ,							_a       ,							**_a       ).prev_sample
																	_a	:    Union[str, Any]         =		new_scheduler.step(_a       ,							_a       ,							_a       ,							**_a       ).prev_sample

																	assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"


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

												_a	:    int         =		1_0
												_a	:    str         =		self.dummy_model()
												_a	:    str         =		self.dummy_sample_deter
												scheduler.set_timesteps(_a       )

												for i, t in enumerate(scheduler.timesteps       ):
																	_a	:    Optional[int]         =		model(_a       ,							_a       )
																	_a	:    Tuple         =		scheduler.step(_a       ,							_a       ,							_a       ).prev_sample

												for i, t in enumerate(scheduler.timesteps       ):
																	_a	:    List[str]         =		model(_a       ,							_a       )
																	_a	:    Optional[int]         =		scheduler.step(_a       ,							_a       ,							_a       ).prev_sample

												return sample


							def 				__lowercase				(			self       )    ->    str:
												_a	:    Any         =		dict(self.forward_default_kwargs       )

												_a	:    Union[str, Any]         =		kwargs.pop('''num_inference_steps'''       ,							_a       )

												for scheduler_class in self.scheduler_classes:
																	_a	:    str         =		self.get_scheduler_config()
																	_a	:    List[Any]         =		scheduler_class(**_a       )

																	_a	:    str         =		self.dummy_sample
																	_a	:    Optional[Any]         =		0.1 * sample

																	if num_inference_steps is not None and hasattr(_a       ,							'''set_timesteps'''       ):
																						scheduler.set_timesteps(_a       )
																	elif num_inference_steps is not None and not hasattr(_a       ,							'''set_timesteps'''       ):
																						_a	:    Dict         =		num_inference_steps

																	# copy over dummy past residuals (must be done after set_timesteps)
																	_a	:    List[str]         =		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
																	_a	:    int         =		dummy_past_residuals[:]

																	_a	:    str         =		scheduler.timesteps[5]
																	_a	:    int         =		scheduler.timesteps[6]

																	_a	:    Tuple         =		scheduler.step(_a       ,							_a       ,							_a       ,							**_a       ).prev_sample
																	_a	:    Union[str, Any]         =		scheduler.step(_a       ,							_a       ,							_a       ,							**_a       ).prev_sample

																	self.assertEqual(output_a.shape       ,							sample.shape       )
																	self.assertEqual(output_a.shape       ,							output_a.shape       )

																	_a	:    Union[str, Any]         =		scheduler.step(_a       ,							_a       ,							_a       ,							**_a       ).prev_sample
																	_a	:    Union[str, Any]         =		scheduler.step(_a       ,							_a       ,							_a       ,							**_a       ).prev_sample

																	self.assertEqual(output_a.shape       ,							sample.shape       )
																	self.assertEqual(output_a.shape       ,							output_a.shape       )


							def 				__lowercase				(			self       )    ->    Dict:
												for timesteps in [1_0_0, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=_a       ,							time_step=_a       )


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




							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												_a	:    List[str]         =		self.full_loop()
												_a	:    int         =		torch.mean(torch.abs(_a       )       )

												assert abs(result_mean.item() - 2_5_4_0_5_2_9       ) < 1_0






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()



