import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest


class     lowercase     (  UpperCamelCase__  ):
							lowercase__    :							Union[str, Any]										=				(PNDMScheduler,)
							lowercase__    :							str										=				(("""num_inference_steps""", 50),)




							def 						__snake_case(    self :       str						,				**_UpperCamelCase :       Tuple   )       ->					int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							{
												    """num_train_timesteps""": 1_000,
												    """beta_start""": 0.0_0_0_1,
												    """beta_end""": 0.0_2,
												    """beta_schedule""": """linear""",
												}

												config.update(**__snake_case   )
												return config




							def 						__snake_case(    self :       List[Any]						,				_UpperCamelCase :       Union[str, Any]=0						,				**_UpperCamelCase :       Optional[int]   )       ->					List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							dict(self.forward_default_kwargs   )
												SCREAMING_SNAKE_CASE           =							kwargs.pop("num_inference_steps"						,				__snake_case   )
												SCREAMING_SNAKE_CASE           =							self.dummy_sample
												SCREAMING_SNAKE_CASE           =							0.1 * sample
												SCREAMING_SNAKE_CASE           =							[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

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

																	SCREAMING_SNAKE_CASE           =							scheduler.step_prk(__snake_case						,				__snake_case						,				__snake_case						,				**__snake_case   ).prev_sample
																	SCREAMING_SNAKE_CASE           =							new_scheduler.step_prk(__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"

																	SCREAMING_SNAKE_CASE           =							scheduler.step_plms(__snake_case						,				__snake_case						,				__snake_case						,				**__snake_case   ).prev_sample
																	SCREAMING_SNAKE_CASE           =							new_scheduler.step_plms(__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 						__snake_case(    self :       Tuple   )       ->					Optional[int]:



												'''simple docstring'''

												pass




							def 						__snake_case(    self :       Dict						,				_UpperCamelCase :       Optional[int]=0						,				**_UpperCamelCase :       List[Any]   )       ->					Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							dict(self.forward_default_kwargs   )
												SCREAMING_SNAKE_CASE           =							kwargs.pop("num_inference_steps"						,				__snake_case   )
												SCREAMING_SNAKE_CASE           =							self.dummy_sample
												SCREAMING_SNAKE_CASE           =							0.1 * sample
												SCREAMING_SNAKE_CASE           =							[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

												for scheduler_class in self.scheduler_classes:
																	SCREAMING_SNAKE_CASE           =							self.get_scheduler_config()
																	SCREAMING_SNAKE_CASE           =							scheduler_class(**__snake_case   )
																	scheduler.set_timesteps(__snake_case   )

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

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(__snake_case   )
																						SCREAMING_SNAKE_CASE           =							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)
																						SCREAMING_SNAKE_CASE           =							dummy_past_residuals[:]

																	SCREAMING_SNAKE_CASE           =							scheduler.step_prk(__snake_case						,				__snake_case						,				__snake_case						,				**__snake_case   ).prev_sample
																	SCREAMING_SNAKE_CASE           =							new_scheduler.step_prk(__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"

																	SCREAMING_SNAKE_CASE           =							scheduler.step_plms(__snake_case						,				__snake_case						,				__snake_case						,				**__snake_case   ).prev_sample
																	SCREAMING_SNAKE_CASE           =							new_scheduler.step_plms(__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 						__snake_case(    self :       Any						,				**_UpperCamelCase :       int   )       ->					List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE           =							self.get_scheduler_config(**__snake_case   )
												SCREAMING_SNAKE_CASE           =							scheduler_class(**__snake_case   )

												SCREAMING_SNAKE_CASE           =							10
												SCREAMING_SNAKE_CASE           =							self.dummy_model()
												SCREAMING_SNAKE_CASE           =							self.dummy_sample_deter
												scheduler.set_timesteps(__snake_case   )

												for i, t in enumerate(scheduler.prk_timesteps   ):
																	SCREAMING_SNAKE_CASE           =							model(__snake_case						,				__snake_case   )
																	SCREAMING_SNAKE_CASE           =							scheduler.step_prk(__snake_case						,				__snake_case						,				__snake_case   ).prev_sample

												for i, t in enumerate(scheduler.plms_timesteps   ):
																	SCREAMING_SNAKE_CASE           =							model(__snake_case						,				__snake_case   )
																	SCREAMING_SNAKE_CASE           =							scheduler.step_plms(__snake_case						,				__snake_case						,				__snake_case   ).prev_sample

												return sample




							def 						__snake_case(    self :       str   )       ->					List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							dict(self.forward_default_kwargs   )

												SCREAMING_SNAKE_CASE           =							kwargs.pop("num_inference_steps"						,				__snake_case   )

												for scheduler_class in self.scheduler_classes:
																	SCREAMING_SNAKE_CASE           =							self.get_scheduler_config()
																	SCREAMING_SNAKE_CASE           =							scheduler_class(**__snake_case   )

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

																	# copy over dummy past residuals (must be done after set_timesteps)
																	SCREAMING_SNAKE_CASE           =							[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
																	SCREAMING_SNAKE_CASE           =							dummy_past_residuals[:]

																	SCREAMING_SNAKE_CASE           =							scheduler.step_prk(__snake_case						,				0						,				__snake_case						,				**__snake_case   ).prev_sample
																	SCREAMING_SNAKE_CASE           =							scheduler.step_prk(__snake_case						,				1						,				__snake_case						,				**__snake_case   ).prev_sample

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

																	SCREAMING_SNAKE_CASE           =							scheduler.step_plms(__snake_case						,				0						,				__snake_case						,				**__snake_case   ).prev_sample
																	SCREAMING_SNAKE_CASE           =							scheduler.step_plms(__snake_case						,				1						,				__snake_case						,				**__snake_case   ).prev_sample

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




							def 						__snake_case(    self :       Tuple   )       ->					int:



												'''simple docstring'''

												for timesteps in [100, 1_000]:
																	self.check_over_configs(num_train_timesteps=__snake_case   )




							def 						__snake_case(    self :       int   )       ->					int:



												'''simple docstring'''

												for steps_offset in [0, 1]:
																	self.check_over_configs(steps_offset=__snake_case   )

												SCREAMING_SNAKE_CASE           =							self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE           =							self.get_scheduler_config(steps_offset=1   )
												SCREAMING_SNAKE_CASE           =							scheduler_class(**__snake_case   )
												scheduler.set_timesteps(10   )
												assert torch.equal(
												    scheduler.timesteps						,				torch.LongTensor(
												        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]   )						,				)




							def 						__snake_case(    self :       Optional[int]   )       ->					Tuple:



												'''simple docstring'''

												for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]						,				[0.0_0_2, 0.0_2]   ):
																	self.check_over_configs(beta_start=__snake_case						,				beta_end=__snake_case   )




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



												'''simple docstring'''

												for schedule in ["linear", "squaredcos_cap_v2"]:
																	self.check_over_configs(beta_schedule=__snake_case   )




							def 						__snake_case(    self :       str   )       ->					List[Any]:



												'''simple docstring'''

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




							def 						__snake_case(    self :       Tuple   )       ->					List[Any]:



												'''simple docstring'''

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




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



												'''simple docstring'''

												for t, num_inference_steps in zip([1, 5, 10]						,				[10, 50, 100]   ):
																	self.check_over_forward(num_inference_steps=__snake_case   )




							def 						__snake_case(    self :       Optional[int]   )       ->					List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							27

												for scheduler_class in self.scheduler_classes:
																	SCREAMING_SNAKE_CASE           =							self.dummy_sample
																	SCREAMING_SNAKE_CASE           =							0.1 * sample

																	SCREAMING_SNAKE_CASE           =							self.get_scheduler_config()
																	SCREAMING_SNAKE_CASE           =							scheduler_class(**__snake_case   )

																	scheduler.set_timesteps(__snake_case   )

																	# before power of 3 fix, would error on first step, so we only need to do two
																	for i, t in enumerate(scheduler.prk_timesteps[:2]   ):
																						SCREAMING_SNAKE_CASE           =							scheduler.step_prk(__snake_case						,				__snake_case						,				__snake_case   ).prev_sample




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



												'''simple docstring'''

												with self.assertRaises(__snake_case   ):
																	SCREAMING_SNAKE_CASE           =							self.scheduler_classes[0]
																	SCREAMING_SNAKE_CASE           =							self.get_scheduler_config()
																	SCREAMING_SNAKE_CASE           =							scheduler_class(**__snake_case   )

																	scheduler.step_plms(self.dummy_sample						,				1						,				self.dummy_sample   ).prev_sample




							def 						__snake_case(    self :       Tuple   )       ->					Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							self.full_loop()
												SCREAMING_SNAKE_CASE           =							torch.sum(torch.abs(__snake_case   )   )
												SCREAMING_SNAKE_CASE           =							torch.mean(torch.abs(__snake_case   )   )

												assert abs(result_sum.item() - 1_9_8.1_3_1_8   ) < 1e-2
												assert abs(result_mean.item() - 0.2_5_8_0   ) < 1e-3




							def 						__snake_case(    self :       List[str]   )       ->					Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							self.full_loop(prediction_type="v_prediction"   )
												SCREAMING_SNAKE_CASE           =							torch.sum(torch.abs(__snake_case   )   )
												SCREAMING_SNAKE_CASE           =							torch.mean(torch.abs(__snake_case   )   )

												assert abs(result_sum.item() - 6_7.3_9_8_6   ) < 1e-2
												assert abs(result_mean.item() - 0.0_8_7_8   ) < 1e-3




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							self.full_loop(set_alpha_to_one=__snake_case						,				beta_start=0.0_1   )
												SCREAMING_SNAKE_CASE           =							torch.sum(torch.abs(__snake_case   )   )
												SCREAMING_SNAKE_CASE           =							torch.mean(torch.abs(__snake_case   )   )

												assert abs(result_sum.item() - 2_3_0.0_3_9_9   ) < 1e-2
												assert abs(result_mean.item() - 0.2_9_9_5   ) < 1e-3





							def 						__snake_case(    self :       Dict   )       ->					Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							self.full_loop(set_alpha_to_one=__snake_case						,				beta_start=0.0_1   )
												SCREAMING_SNAKE_CASE           =							torch.sum(torch.abs(__snake_case   )   )
												SCREAMING_SNAKE_CASE           =							torch.mean(torch.abs(__snake_case   )   )

												assert abs(result_sum.item() - 1_8_6.9_4_8_2   ) < 1e-2
												assert abs(result_mean.item() - 0.2_4_3_4   ) < 1e-3










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)}""")




