




import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       (IPNDMScheduler,)
						UpperCamelCase      =       (("""num_inference_steps""", 50),)

						def      A__    (		self :int     ,			**__snake_case :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										={"""num_train_timesteps""": 10_00}
										config.update(**__snake_case			)
										return config

						def      A__    (		self :int     ,			__snake_case :Any=0     ,			**__snake_case :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=dict(self.forward_default_kwargs			)
										__magic_name__						:						List[str]										=kwargs.pop("""num_inference_steps"""     ,			__snake_case			)
										__magic_name__						:						str										=self.dummy_sample
										__magic_name__						:						Dict										=0.1 * sample
										__magic_name__						:						Any										=[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

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

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

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

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

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										pass

						def      A__    (		self :Optional[int]     ,			__snake_case :Optional[Any]=0     ,			**__snake_case :Dict			):
										'''simple docstring'''

										__magic_name__						:						int										=dict(self.forward_default_kwargs			)
										__magic_name__						:						Dict										=kwargs.pop("""num_inference_steps"""     ,			__snake_case			)
										__magic_name__						:						str										=self.dummy_sample
										__magic_name__						:						Optional[Any]										=0.1 * sample
										__magic_name__						:						Union[str, Any]										=[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
														__magic_name__						:						Dict										=self.get_scheduler_config()
														__magic_name__						:						Optional[Any]										=scheduler_class(**__snake_case			)
														scheduler.set_timesteps(__snake_case			)

														# copy over dummy past residuals (must be after setting timesteps)
														__magic_name__						:						Union[str, Any]										=dummy_past_residuals[:]

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

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

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

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

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

														__magic_name__						:						Tuple										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						Optional[Any]										=new_scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample

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

						def      A__    (		self :Optional[Any]     ,			**__snake_case :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=self.scheduler_classes[0]
										__magic_name__						:						List[str]										=self.get_scheduler_config(**__snake_case			)
										__magic_name__						:						Tuple										=scheduler_class(**__snake_case			)

										__magic_name__						:						List[str]										=10
										__magic_name__						:						Optional[Any]										=self.dummy_model()
										__magic_name__						:						Any										=self.dummy_sample_deter
										scheduler.set_timesteps(__snake_case			)

										for i, t in enumerate(scheduler.timesteps			):
														__magic_name__						:						Union[str, Any]										=model(__snake_case     ,			__snake_case			)
														__magic_name__						:						Union[str, Any]										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case			).prev_sample

										for i, t in enumerate(scheduler.timesteps			):
														__magic_name__						:						Optional[Any]										=model(__snake_case     ,			__snake_case			)
														__magic_name__						:						Optional[Any]										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case			).prev_sample

										return sample

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=dict(self.forward_default_kwargs			)

										__magic_name__						:						str										=kwargs.pop("""num_inference_steps"""     ,			__snake_case			)

										for scheduler_class in self.scheduler_classes:
														__magic_name__						:						Union[str, Any]										=self.get_scheduler_config()
														__magic_name__						:						Optional[int]										=scheduler_class(**__snake_case			)

														__magic_name__						:						Tuple										=self.dummy_sample
														__magic_name__						:						Tuple										=0.1 * sample

														if num_inference_steps is not None and hasattr(__snake_case     ,			"""set_timesteps"""			):
																		scheduler.set_timesteps(__snake_case			)
														elif num_inference_steps is not None and not hasattr(__snake_case     ,			"""set_timesteps"""			):
																		__magic_name__						:						Dict										=num_inference_steps

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

														__magic_name__						:						Optional[Any]										=scheduler.timesteps[5]
														__magic_name__						:						List[str]										=scheduler.timesteps[6]

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

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

														__magic_name__						:						Optional[Any]										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						Union[str, Any]										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample

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

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										for timesteps in [1_00, 10_00]:
														self.check_over_configs(num_train_timesteps=__snake_case     ,			time_step=__snake_case			)

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										for t, num_inference_steps in zip([1, 5, 10]     ,			[10, 50, 1_00]			):
														self.check_over_forward(num_inference_steps=__snake_case     ,			time_step=__snake_case			)


						def      A__    (		self :Any			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=self.full_loop()
										__magic_name__						:						str										=torch.mean(torch.abs(__snake_case			)			)

										assert abs(result_mean.item() - 2_54_05_29			) < 10










import heapq




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						list[list]										=[]

				# for each node and his adjacency list add them and the rank of the node to queue
				# using heapq module the queue will be filled like a Priority Queue
				# heapq works with a min priority queue, so I used -1*len(v) to build it
				for key, value in graph.items():
								# O(log(n))
								heapq.heappush(lowerCamelCase						,  [-1 * len(lowerCamelCase ), (key, value)] )

				# chosen_vertices = set of chosen vertices
				__magic_name__						:						Tuple										=set()

				# while queue isn't empty and there are still edges
				#   (queue[0][0] is the rank of the node with max rank)
				while queue and queue[0][0] != 0:
								# extract vertex with max rank from queue and add it to chosen_vertices
								__magic_name__						:						Tuple										=heapq.heappop(lowerCamelCase )[1][0]
								chosen_vertices.add(lowerCamelCase )

								# Remove all arcs adjacent to argmax
								for elem in queue:
												# if v haven't adjacent node, skip
												if elem[0] == 0:
																continue
												# if argmax is reachable from elem
												# remove argmax from elem's adjacent list and update his rank
												if argmax in elem[1][1]:
																__magic_name__						:						Tuple										=elem[1][1].index(lowerCamelCase )
																del elem[1][1][index]
																elem[0] += 1
        # re-order the queue
								heapq.heapify(lowerCamelCase )
				return chosen_vertices


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							UpperCAmelCase_		:					Optional[int]          =  {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
							print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")




