




'''simple docstring'''





import math
import sys




class 				_snake_case	:




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

										'''simple docstring'''




										snake_case_			      =   0
										snake_case_			      =   []
										snake_case_			      =   {}  # To store the pos of node in array




			def      lowerCAmelCase__       (      self							)       ->       Optional[int]:

										'''simple docstring'''




										return self.cur_size == 0




			def      lowerCAmelCase__       (      self       ,							a__							)       ->       Tuple:

										'''simple docstring'''




										snake_case_			      =   self.left(a__							)
										snake_case_			      =   self.right(a__							)
										if lc < self.cur_size and self.array(a__							)[0] < self.array(a__							)[0]:
																	snake_case_			      =   lc
										else:
																	snake_case_			      =   idx
										if rc < self.cur_size and self.array(a__							)[0] < self.array(a__							)[0]:
																	snake_case_			      =   rc
										if smallest != idx:
																	self.swap(a__       ,							a__							)
																	self.min_heapify(a__							)




			def      lowerCAmelCase__       (      self       ,							a__							)       ->       Tuple:

										'''simple docstring'''




										snake_case_			      =   self.cur_size
										self.cur_size += 1
										self.array.append((sys.maxsize, tup[1])							)
										self.decrease_key((sys.maxsize, tup[1])       ,							tup[0]							)




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

										'''simple docstring'''




										snake_case_			      =   self.array[0][1]
										snake_case_			      =   self.array[self.cur_size - 1]
										self.cur_size -= 1
										self.min_heapify(1							)
										del self.pos[min_node]
										return min_node




			def      lowerCAmelCase__       (      self       ,							a__							)       ->       Dict:

										'''simple docstring'''




										return 2 * i + 1




			def      lowerCAmelCase__       (      self       ,							a__							)       ->       List[str]:

										'''simple docstring'''




										return 2 * i + 2




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

										'''simple docstring'''




										return math.floor(i / 2							)




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__							)       ->       int:

										'''simple docstring'''




										snake_case_			      =   j
										snake_case_			      =   i
										snake_case_			      =   self.array[i]
										snake_case_			      =   self.array[j]
										snake_case_			      =   temp




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__							)       ->       Dict:

										'''simple docstring'''




										snake_case_			      =   self.pos[tup[1]]
										# assuming the new_d is atmost old_d
										snake_case_			      =   (new_d, tup[1])
										while idx > 0 and self.array[self.par(a__							)][0] > self.array[idx][0]:
																	self.swap(a__       ,							self.par(a__							)							)
																	snake_case_			      =   self.par(a__							)




class 				_snake_case	:




			def __init__(      self       ,							a__							)       ->       List[str]:

										'''simple docstring'''




										snake_case_			      =   {}  # To store graph: u -> (v,w)
										snake_case_			      =   num  # Number of nodes in graph
										# To store the distance from source vertex
										snake_case_			      =   [0] * self.num_nodes
										snake_case_			      =   [-1] * self.num_nodes  # To store the path




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__       ,							a__							)       ->       str:

										'''simple docstring'''




										if u in self.adjList:
																	self.adjList[u].append((v, w)							)
										else:
																	snake_case_			      =   [(v, w)]

										# Assuming undirected graph
										if v in self.adjList:
																	self.adjList[v].append((u, w)							)
										else:
																	snake_case_			      =   [(u, w)]




			def      lowerCAmelCase__       (      self							)       ->       int:

										'''simple docstring'''




										for u in self.adjList:
																	print(a__       ,							"->"       ,							" -> ".join(str(F'{v}({w})'							) for v, w in self.adjList[u]							)							)




			def      lowerCAmelCase__       (      self       ,							a__							)       ->       Tuple:

										'''simple docstring'''




										snake_case_			      =   [-1] * self.num_nodes
										# src is the source node
										snake_case_			      =   0
										snake_case_			      =   PriorityQueue()
										q.insert((0, src)							)  # (dist from src, node)
										for u in self.adjList:
																	if u != src:
																								snake_case_			      =   sys.maxsize  # Infinity
																								snake_case_			      =   -1

										while not q.is_empty():
																	snake_case_			      =   q.extract_min()  # Returns node with the min dist from source
																	# Update the distance of all the neighbours of u and
																	# if their prev dist was INFINITY then push them in Q
																	for v, w in self.adjList[u]:
																								snake_case_			      =   self.dist[u] + w
																								if self.dist[v] > new_dist:
																															if self.dist[v] == sys.maxsize:
																																						q.insert((new_dist, v)							)
																															else:
																																						q.decrease_key((self.dist[v], v)       ,							a__							)
																															snake_case_			      =   new_dist
																															snake_case_			      =   u

        # Show the shortest distances from src
										self.show_distances(a__							)




			def      lowerCAmelCase__       (      self       ,							a__							)       ->       List[str]:

										'''simple docstring'''




										print(F'Distance from node: {src}'							)
										for u in range(self.num_nodes							):
																	print(F'Node {u} has distance: {self.dist[u]}'							)




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__							)       ->       Any:

										'''simple docstring'''




										snake_case_			      =   []
										snake_case_			      =   0
										snake_case_			      =   dest
										# Backtracking from dest to src
										while self.par[temp] != -1:
																	path.append(a__							)
																	if temp != src:
																								for v, w in self.adjList[temp]:
																															if v == self.par[temp]:
																																						cost += w
																																						break
																	snake_case_			      =   self.par[temp]
										path.append(a__							)
										path.reverse()

										print(F'----Path to reach {dest} from {src}----'							)
										for u in path:
																	print(F'{u}'       ,							end=" "							)
																	if u != dest:
																								print("-> "       ,							end=""							)

										print("\nTotal cost of path: "       ,							a__							)


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:     List[Any]   	=				Graph(9)
		graph.add_edge(0, 1, 4)
		graph.add_edge(0, 7, 8)
		graph.add_edge(1, 2, 8)
		graph.add_edge(1, 7, 11)
		graph.add_edge(2, 3, 7)
		graph.add_edge(2, 8, 2)
		graph.add_edge(2, 5, 4)
		graph.add_edge(3, 4, 9)
		graph.add_edge(3, 5, 14)
		graph.add_edge(4, 5, 10)
		graph.add_edge(5, 6, 2)
		graph.add_edge(6, 7, 1)
		graph.add_edge(6, 8, 6)
		graph.add_edge(7, 8, 7)
		graph.show_graph()
		graph.dijkstra(0)
		graph.show_path(0, 4)

# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path:  21










'''simple docstring'''





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class 				_snake_case	(			lowercase_							):
			lowerCAmelCase_  :   Optional[int]		 =					(DPMSolverMultistepScheduler,)
			lowerCAmelCase_  :   int		 =					(("num_inference_steps", 25),)




			def      lowerCAmelCase__       (      self       ,							**a__							)       ->       Optional[int]:

										'''simple docstring'''




										snake_case_			      =   {
										    "num_train_timesteps": 1_000,
										    "beta_start": 0.0_0_0_1,
										    "beta_end": 0.0_2,
										    "beta_schedule": "linear",
										    "solver_order": 2,
										    "prediction_type": "epsilon",
										    "thresholding": False,
										    "sample_max_value": 1.0,
										    "algorithm_type": "dpmsolver++",
										    "solver_type": "midpoint",
										    "lower_order_final": False,
										    "lambda_min_clipped": -float("inf"							),
										    "variance_type": None,
										}

										config.update(**a__							)
										return config




			def      lowerCAmelCase__       (      self       ,							a__=0       ,							**a__							)       ->       Dict:

										'''simple docstring'''




										snake_case_			      =   dict(self.forward_default_kwargs							)
										snake_case_			      =   kwargs.pop("num_inference_steps"       ,							a__							)
										snake_case_			      =   self.dummy_sample
										snake_case_			      =   0.1 * sample
										snake_case_			      =   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

										for scheduler_class in self.scheduler_classes:
																	snake_case_			      =   self.get_scheduler_config(**a__							)
																	snake_case_			      =   scheduler_class(**a__							)
																	scheduler.set_timesteps(a__							)
																	# copy over dummy past residuals
																	snake_case_			      =   dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																								scheduler.save_config(a__							)
																								snake_case_			      =   scheduler_class.from_pretrained(a__							)
																								new_scheduler.set_timesteps(a__							)
																								# copy over dummy past residuals
																								snake_case_			      =   dummy_past_residuals[: new_scheduler.config.solver_order]

																	snake_case_      ,   snake_case_			      =   sample, sample
																	for t in range(a__       ,							time_step + scheduler.config.solver_order + 1							):
																								snake_case_			      =   scheduler.step(a__       ,							a__       ,							a__       ,							**a__							).prev_sample
																								snake_case_			      =   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      lowerCAmelCase__       (      self							)       ->       Dict:

										'''simple docstring'''




										pass




			def      lowerCAmelCase__       (      self       ,							a__=0       ,							**a__							)       ->       Optional[int]:

										'''simple docstring'''




										snake_case_			      =   dict(self.forward_default_kwargs							)
										snake_case_			      =   kwargs.pop("num_inference_steps"       ,							a__							)
										snake_case_			      =   self.dummy_sample
										snake_case_			      =   0.1 * sample
										snake_case_			      =   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

										for scheduler_class in self.scheduler_classes:
																	snake_case_			      =   self.get_scheduler_config()
																	snake_case_			      =   scheduler_class(**a__							)
																	scheduler.set_timesteps(a__							)

																	# copy over dummy past residuals (must be after setting timesteps)
																	snake_case_			      =   dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																								scheduler.save_config(a__							)
																								snake_case_			      =   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)
																								snake_case_			      =   dummy_past_residuals[: new_scheduler.config.solver_order]

																	snake_case_			      =   scheduler.step(a__       ,							a__       ,							a__       ,							**a__							).prev_sample
																	snake_case_			      =   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      lowerCAmelCase__       (      self       ,							a__=None       ,							**a__							)       ->       Tuple:

										'''simple docstring'''




										if scheduler is None:
																	snake_case_			      =   self.scheduler_classes[0]
																	snake_case_			      =   self.get_scheduler_config(**a__							)
																	snake_case_			      =   scheduler_class(**a__							)

										snake_case_			      =   10
										snake_case_			      =   self.dummy_model()
										snake_case_			      =   self.dummy_sample_deter
										scheduler.set_timesteps(a__							)

										for i, t in enumerate(scheduler.timesteps							):
																	snake_case_			      =   model(a__       ,							a__							)
																	snake_case_			      =   scheduler.step(a__       ,							a__       ,							a__							).prev_sample

										return sample




			def      lowerCAmelCase__       (      self							)       ->       Dict:

										'''simple docstring'''




										snake_case_			      =   dict(self.forward_default_kwargs							)

										snake_case_			      =   kwargs.pop("num_inference_steps"       ,							a__							)

										for scheduler_class in self.scheduler_classes:
																	snake_case_			      =   self.get_scheduler_config()
																	snake_case_			      =   scheduler_class(**a__							)

																	snake_case_			      =   self.dummy_sample
																	snake_case_			      =   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"							):
																								snake_case_			      =   num_inference_steps

																	# copy over dummy past residuals (must be done after set_timesteps)
																	snake_case_			      =   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
																	snake_case_			      =   dummy_past_residuals[: scheduler.config.solver_order]

																	snake_case_			      =   scheduler.timesteps[5]
																	snake_case_			      =   scheduler.timesteps[6]

																	snake_case_			      =   scheduler.step(a__       ,							a__       ,							a__       ,							**a__							).prev_sample
																	snake_case_			      =   scheduler.step(a__       ,							a__       ,							a__       ,							**a__							).prev_sample

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




			def      lowerCAmelCase__       (      self							)       ->       List[str]:

										'''simple docstring'''




										for timesteps in [25, 50, 100, 999, 1_000]:
																	self.check_over_configs(num_train_timesteps=a__							)




			def      lowerCAmelCase__       (      self							)       ->       str:

										'''simple docstring'''




										self.check_over_configs(thresholding=a__							)
										for order in [1, 2, 3]:
																	for solver_type in ["midpoint", "heun"]:
																								for threshold in [0.5, 1.0, 2.0]:
																															for prediction_type in ["epsilon", "sample"]:
																																						self.check_over_configs(
																																						    thresholding=a__       ,							prediction_type=a__       ,							sample_max_value=a__       ,							algorithm_type="dpmsolver++"       ,							solver_order=a__       ,							solver_type=a__       ,							)




			def      lowerCAmelCase__       (      self							)       ->       int:

										'''simple docstring'''




										for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(prediction_type=a__							)




			def      lowerCAmelCase__       (      self							)       ->       Optional[int]:

										'''simple docstring'''




										for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
																	for solver_type in ["midpoint", "heun"]:
																								for order in [1, 2, 3]:
																															for prediction_type in ["epsilon", "sample"]:
																																						if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
																																													if order == 3:
																																																				continue
																																						else:
																																													self.check_over_configs(
																																													    solver_order=a__       ,							solver_type=a__       ,							prediction_type=a__       ,							algorithm_type=a__       ,							)
																																						snake_case_			      =   self.full_loop(
																																						    solver_order=a__       ,							solver_type=a__       ,							prediction_type=a__       ,							algorithm_type=a__       ,							)
																																						assert not torch.isnan(a__							).any(), "Samples have nan numbers"




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

										'''simple docstring'''




										self.check_over_configs(lower_order_final=a__							)
										self.check_over_configs(lower_order_final=a__							)




			def      lowerCAmelCase__       (      self							)       ->       Optional[Any]:

										'''simple docstring'''




										self.check_over_configs(lambda_min_clipped=-float("inf"							)							)
										self.check_over_configs(lambda_min_clipped=-5.1							)




			def      lowerCAmelCase__       (      self							)       ->       Any:

										'''simple docstring'''




										self.check_over_configs(variance_type=a__							)
										self.check_over_configs(variance_type="learned_range"							)




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

										'''simple docstring'''




										for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
																	self.check_over_forward(num_inference_steps=a__       ,							time_step=0							)




			def      lowerCAmelCase__       (      self							)       ->       List[str]:

										'''simple docstring'''




										snake_case_			      =   self.full_loop()
										snake_case_			      =   torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 0.3_3_0_1							) < 1e-3




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

										'''simple docstring'''




										snake_case_			      =   self.full_loop(thresholding=a__       ,							dynamic_thresholding_ratio=0.8_7       ,							sample_max_value=0.5							)
										snake_case_			      =   torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 1.1_3_6_4							) < 1e-3




			def      lowerCAmelCase__       (      self							)       ->       List[str]:

										'''simple docstring'''




										snake_case_			      =   self.full_loop(prediction_type="v_prediction"							)
										snake_case_			      =   torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 0.2_2_5_1							) < 1e-3




			def      lowerCAmelCase__       (      self							)       ->       List[str]:

										'''simple docstring'''




										snake_case_			      =   self.full_loop(prediction_type="v_prediction"       ,							use_karras_sigmas=a__							)
										snake_case_			      =   torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 0.2_0_9_6							) < 1e-3




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

										'''simple docstring'''




										snake_case_			      =   DPMSolverMultistepScheduler(**self.get_scheduler_config()							)
										snake_case_			      =   self.full_loop(scheduler=a__							)
										snake_case_			      =   torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 0.3_3_0_1							) < 1e-3

										snake_case_			      =   DPMSolverSinglestepScheduler.from_config(scheduler.config							)
										snake_case_			      =   UniPCMultistepScheduler.from_config(scheduler.config							)
										snake_case_			      =   DEISMultistepScheduler.from_config(scheduler.config							)
										snake_case_			      =   DPMSolverMultistepScheduler.from_config(scheduler.config							)

										snake_case_			      =   self.full_loop(scheduler=a__							)
										snake_case_			      =   torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 0.3_3_0_1							) < 1e-3




			def      lowerCAmelCase__       (      self							)       ->       int:

										'''simple docstring'''




										snake_case_			      =   self.scheduler_classes[0]
										snake_case_			      =   self.get_scheduler_config(thresholding=a__       ,							dynamic_thresholding_ratio=0							)
										snake_case_			      =   scheduler_class(**a__							)

										snake_case_			      =   10
										snake_case_			      =   self.dummy_model()
										snake_case_			      =   self.dummy_sample_deter.half()
										scheduler.set_timesteps(a__							)

										for i, t in enumerate(scheduler.timesteps							):
																	snake_case_			      =   model(a__       ,							a__							)
																	snake_case_			      =   scheduler.step(a__       ,							a__       ,							a__							).prev_sample

										assert sample.dtype == torch.floataa




			def      lowerCAmelCase__       (      self       ,							**a__							)       ->       Dict:

										'''simple docstring'''




										for scheduler_class in self.scheduler_classes:
																	snake_case_			      =   self.get_scheduler_config(**a__							)
																	snake_case_			      =   scheduler_class(**a__							)

																	scheduler.set_timesteps(scheduler.config.num_train_timesteps							)
																	assert len(scheduler.timesteps.unique()							) == scheduler.num_inference_steps




