import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     (DDIMParallelScheduler,)
							UpperCAmelCase				   =     (("eta", 0.0), ("num_inference_steps", 50))







							def  UpperCamelCase_						(   self      :     Dict ,			**_A      :     Any		):
												_UpperCamelCase			     =     {
												    '''num_train_timesteps''': 1000,
												    '''beta_start''': 0.0001,
												    '''beta_end''': 0.02,
												    '''beta_schedule''': '''linear''',
												    '''clip_sample''': True,
												}

												config.update(**_A		)
												return config







							def  UpperCamelCase_						(   self      :     str ,			**_A      :     int		):
												_UpperCamelCase			     =     self.scheduler_classes[0]
												_UpperCamelCase			     =     self.get_scheduler_config(**_A		)
												_UpperCamelCase			     =     scheduler_class(**_A		)

												_UpperCamelCase     ,    _UpperCamelCase			     =     10, 0.0

												_UpperCamelCase			     =     self.dummy_model()
												_UpperCamelCase			     =     self.dummy_sample_deter

												scheduler.set_timesteps(_A		)

												for t in scheduler.timesteps:
																	_UpperCamelCase			     =     model(_A ,			_A		)
																	_UpperCamelCase			     =     scheduler.step(_A ,			_A ,			_A ,			_A		).prev_sample

												return sample







							def  UpperCamelCase_						(   self      :     List[str]		):
												for timesteps in [100, 500, 1000]:
																	self.check_over_configs(num_train_timesteps=_A		)







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												for steps_offset in [0, 1]:
																	self.check_over_configs(steps_offset=_A		)

												_UpperCamelCase			     =     self.scheduler_classes[0]
												_UpperCamelCase			     =     self.get_scheduler_config(steps_offset=1		)
												_UpperCamelCase			     =     scheduler_class(**_A		)
												scheduler.set_timesteps(5		)
												assert torch.equal(scheduler.timesteps ,			torch.LongTensor([801, 601, 401, 201, 1]		)		)







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												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  UpperCamelCase_						(   self      :     Optional[int]		):
												for schedule in ["linear", "squaredcos_cap_v2"]:
																	self.check_over_configs(beta_schedule=_A		)







							def  UpperCamelCase_						(   self      :     Tuple		):
												for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(prediction_type=_A		)







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												for clip_sample in [True, False]:
																	self.check_over_configs(clip_sample=_A		)







							def  UpperCamelCase_						(   self      :     Tuple		):
												for timestep_spacing in ["trailing", "leading"]:
																	self.check_over_configs(timestep_spacing=_A		)







							def  UpperCamelCase_						(   self      :     List[Any]		):
												for rescale_betas_zero_snr in [True, False]:
																	self.check_over_configs(rescale_betas_zero_snr=_A		)







							def  UpperCamelCase_						(   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  UpperCamelCase_						(   self      :     str		):
												for t in [1, 10, 49]:
																	self.check_over_forward(time_step=_A		)







							def  UpperCamelCase_						(   self      :     int		):
												for t, num_inference_steps in zip([1, 10, 50] ,			[10, 50, 500]		):
																	self.check_over_forward(time_step=_A ,			num_inference_steps=_A		)







							def  UpperCamelCase_						(   self      :     Dict		):
												for t, eta in zip([1, 10, 49] ,			[0.0, 0.5, 1.0]		):
																	self.check_over_forward(time_step=_A ,			eta=_A		)







							def  UpperCamelCase_						(   self      :     List[Any]		):
												_UpperCamelCase			     =     self.scheduler_classes[0]
												_UpperCamelCase			     =     self.get_scheduler_config()
												_UpperCamelCase			     =     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(420 ,			400		) - 0.1_4771		)		) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(980 ,			960		) - 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(487 ,			486		) - 0.0_0979		)		) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(999 ,			998		) - 0.02		)		) < 1e-5







							def  UpperCamelCase_						(   self      :     int		):
												_UpperCamelCase			     =     self.scheduler_classes[0]
												_UpperCamelCase			     =     self.get_scheduler_config()
												_UpperCamelCase			     =     scheduler_class(**_A		)

												_UpperCamelCase     ,    _UpperCamelCase			     =     10, 0.0
												scheduler.set_timesteps(_A		)

												_UpperCamelCase			     =     self.dummy_model()
												_UpperCamelCase			     =     self.dummy_sample_deter
												_UpperCamelCase			     =     self.dummy_sample_deter + 0.1
												_UpperCamelCase			     =     self.dummy_sample_deter - 0.1

												_UpperCamelCase			     =     samplea.shape[0]
												_UpperCamelCase			     =     torch.stack([samplea, samplea, samplea] ,			dim=0		)
												_UpperCamelCase			     =     torch.arange(_A		)[0:3, None].repeat(1 ,			_A		)

												_UpperCamelCase			     =     model(samples.flatten(0 ,			1		) ,			timesteps.flatten(0 ,			1		)		)
												_UpperCamelCase			     =     scheduler.batch_step_no_noise(_A ,			timesteps.flatten(0 ,			1		) ,			samples.flatten(0 ,			1		) ,			_A		)

												_UpperCamelCase			     =     torch.sum(torch.abs(_A		)		)
												_UpperCamelCase			     =     torch.mean(torch.abs(_A		)		)

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







							def  UpperCamelCase_						(   self      :     int		):
												_UpperCamelCase			     =     self.full_loop()

												_UpperCamelCase			     =     torch.sum(torch.abs(_A		)		)
												_UpperCamelCase			     =     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  UpperCamelCase_						(   self      :     str		):
												_UpperCamelCase			     =     self.full_loop(prediction_type='''v_prediction'''		)

												_UpperCamelCase			     =     torch.sum(torch.abs(_A		)		)
												_UpperCamelCase			     =     torch.mean(torch.abs(_A		)		)

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







							def  UpperCamelCase_						(   self      :     Any		):
												# We specify different beta, so that the first alpha is 0.99
												_UpperCamelCase			     =     self.full_loop(set_alpha_to_one=_A ,			beta_start=0.01		)
												_UpperCamelCase			     =     torch.sum(torch.abs(_A		)		)
												_UpperCamelCase			     =     torch.mean(torch.abs(_A		)		)

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







							def  UpperCamelCase_						(   self      :     List[Any]		):
												# We specify different beta, so that the first alpha is 0.99
												_UpperCamelCase			     =     self.full_loop(set_alpha_to_one=_A ,			beta_start=0.01		)
												_UpperCamelCase			     =     torch.sum(torch.abs(_A		)		)
												_UpperCamelCase			     =     torch.mean(torch.abs(_A		)		)

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







from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE_   :      Optional[int]			=   TypeVar('''T''')



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:
		return (position - 1) // 2



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:
		return (2 * position) + 1



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:
		return (2 * position) + 2

class 		snake_case_   (							Generic[T]						):







	'''simple docstring'''




	def __init__(						self							:     Any   )       -> None:

			'''simple docstring'''




			__lowercase											=     []
			__lowercase											=     {}
			__lowercase											=     0




	def __len__(						self							:     Optional[Any]   )       -> int:

			'''simple docstring'''




			return self.elements




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

			'''simple docstring'''




			return str(self.heap   )




	def    UpperCAmelCase			(						self							:     Union[str, Any]   )       -> bool:

			'''simple docstring'''




			return self.elements == 0




	def    UpperCAmelCase			(						self							:     str					,      __lowerCamelCase							:     T					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			self.heap.append((elem, weight)   )
			__lowercase											=     self.elements
			self.elements += 1
			self._bubble_up(__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Dict   )       -> T:

			'''simple docstring'''




			if self.elements > 1:
					self._swap_nodes(0					,      self.elements - 1   )
			__lowercase						,      __lowercase											=     self.heap.pop()
			del self.position_map[elem]
			self.elements -= 1
			if self.elements > 0:
					__lowercase						,      __lowercase											=     self.heap[0]
					self._bubble_down(__lowerCamelCase   )
			return elem




	def    UpperCAmelCase			(						self							:     int					,      __lowerCamelCase							:     T					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			__lowercase											=     self.position_map[elem]
			__lowercase											=     (elem, weight)
			if position > 0:
					__lowercase											=     get_parent_position(__lowerCamelCase   )
					__lowercase						,      __lowercase											=     self.heap[parent_position]
					if parent_weight > weight:
							self._bubble_up(__lowerCamelCase   )
					else:
							self._bubble_down(__lowerCamelCase   )
			else:
					self._bubble_down(__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Tuple					,      __lowerCamelCase							:     T   )       -> None:

			'''simple docstring'''




			__lowercase											=     self.position_map[elem]
			if curr_pos == 0:
					return None
			__lowercase											=     get_parent_position(__lowerCamelCase   )
			__lowercase						,      __lowercase											=     self.heap[curr_pos]
			__lowercase						,      __lowercase											=     self.heap[parent_position]
			if parent_weight > weight:
					self._swap_nodes(__lowerCamelCase					,      __lowerCamelCase   )
					return self._bubble_up(__lowerCamelCase   )
			return None




	def    UpperCAmelCase			(						self							:     List[str]					,      __lowerCamelCase							:     T   )       -> None:

			'''simple docstring'''




			__lowercase											=     self.position_map[elem]
			__lowercase						,      __lowercase											=     self.heap[curr_pos]
			__lowercase											=     get_child_left_position(__lowerCamelCase   )
			__lowercase											=     get_child_right_position(__lowerCamelCase   )
			if child_left_position < self.elements and child_right_position < self.elements:
					__lowercase						,      __lowercase											=     self.heap[child_left_position]
					__lowercase						,      __lowercase											=     self.heap[child_right_position]
					if child_right_weight < child_left_weight and child_right_weight < weight:
							self._swap_nodes(__lowerCamelCase					,      __lowerCamelCase   )
							return self._bubble_down(__lowerCamelCase   )
			if child_left_position < self.elements:
					__lowercase						,      __lowercase											=     self.heap[child_left_position]
					if child_left_weight < weight:
							self._swap_nodes(__lowerCamelCase					,      __lowerCamelCase   )
							return self._bubble_down(__lowerCamelCase   )
			else:
					return None
			if child_right_position < self.elements:
					__lowercase						,      __lowercase											=     self.heap[child_right_position]
					if child_right_weight < weight:
							self._swap_nodes(__lowerCamelCase					,      __lowerCamelCase   )
							return self._bubble_down(__lowerCamelCase   )
			return None






	def    UpperCAmelCase			(						self							:     Optional[int]					,      __lowerCamelCase							:     int					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			__lowercase											=     self.heap[nodea_pos][0]
			__lowercase											=     self.heap[nodea_pos][0]
			__lowercase						,      __lowercase											=     (
			    self.heap[nodea_pos],
			    self.heap[nodea_pos],
			)
			__lowercase											=     nodea_pos
			__lowercase											=     nodea_pos





class 		snake_case_   (							Generic[T]						):







	'''simple docstring'''




	def __init__(						self							:     Optional[Any]   )       -> None:

			'''simple docstring'''




			__lowercase											=     {}
			__lowercase											=     0




	def __repr__(						self							:     Union[str, Any]   )       -> str:

			'''simple docstring'''




			return str(self.connections   )




	def __len__(						self							:     int   )       -> int:

			'''simple docstring'''




			return self.nodes




	def    UpperCAmelCase			(						self							:     Optional[int]					,      __lowerCamelCase							:     T   )       -> None:

			'''simple docstring'''




			if node not in self.connections:
					__lowercase											=     {}
					self.nodes += 1






	def    UpperCAmelCase			(						self							:     List[Any]					,      __lowerCamelCase							:     T					,      __lowerCamelCase							:     T					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			self.add_node(__lowerCamelCase   )
			self.add_node(__lowerCamelCase   )
			__lowercase											=     weight
			__lowercase											=     weight





def  SCREAMING_SNAKE_CASE				(      snake_case						,   )		->    tuple[dict[T, int], dict[T, T | None]]:
		__lowercase											=     {node: maxsize for node in graph.connections}
		__lowercase											=     {node: None for node in graph.connections}

		__lowercase											=     MinPriorityQueue()
		for node, weight in dist.items():
				priority_queue.push(snake_case						,   snake_case							)

		if priority_queue.is_empty():
				return dist, parent

		# initialization
		__lowercase											=     priority_queue.extract_min()
		__lowercase											=     0
		for neighbour in graph.connections[node]:
				if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
						__lowercase											=     dist[node] + graph.connections[node][neighbour]
						priority_queue.update_key(snake_case						,   dist[neighbour]							)
						__lowercase											=     node

    # running prim's algorithm
		while not priority_queue.is_empty():
				__lowercase											=     priority_queue.extract_min()
				for neighbour in graph.connections[node]:
						if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
								__lowercase											=     dist[node] + graph.connections[node][neighbour]
								priority_queue.update_key(snake_case						,   dist[neighbour]							)
								__lowercase											=     node
		return dist, parent




