





"""simple docstring"""






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class 					__UpperCAmelCase							(						_UpperCamelCase  ):
				__lowerCamelCase				:	Dict				     =				(DDPMScheduler,)
				def       UpperCAmelCase      (		self    :			Tuple	, **a_    :			Any    )	->	Dict:



							'''simple docstring'''



							a__  :	List[Any]		 =			{
							    "num_train_timesteps": 10_00,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							    "variance_type": "fixed_small",
							    "clip_sample": True,
							}

							config.update(**a_    )
							return config
				def       UpperCAmelCase      (		self    :			int    )	->	List[Any]:



							'''simple docstring'''



							for timesteps in [1, 5, 1_00, 10_00]:
										self.check_over_configs(num_train_timesteps=a_    )
				def       UpperCAmelCase      (		self    :			List[str]    )	->	List[str]:



							'''simple docstring'''



							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    :			str    )	->	Any:



							'''simple docstring'''



							for schedule in ["linear", "squaredcos_cap_v2"]:
										self.check_over_configs(beta_schedule=a_    )
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	List[Any]:



							'''simple docstring'''



							for variance in ["fixed_small", "fixed_large", "other"]:
										self.check_over_configs(variance_type=a_    )
				def       UpperCAmelCase      (		self    :			Any    )	->	Dict:



							'''simple docstring'''



							for clip_sample in [True, False]:
										self.check_over_configs(clip_sample=a_    )
				def       UpperCAmelCase      (		self    :			int    )	->	Optional[Any]:



							'''simple docstring'''



							self.check_over_configs(thresholding=a_    )
							for threshold in [0.5, 1.0, 2.0]:
										for prediction_type in ["epsilon", "sample", "v_prediction"]:
													self.check_over_configs(
													    thresholding=a_	, prediction_type=a_	, sample_max_value=a_	, )
				def       UpperCAmelCase      (		self    :			Tuple    )	->	Tuple:



							'''simple docstring'''



							for prediction_type in ["epsilon", "sample", "v_prediction"]:
										self.check_over_configs(prediction_type=a_    )
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	List[str]:



							'''simple docstring'''



							for t in [0, 5_00, 9_99]:
										self.check_over_forward(time_step=a_    )
				def       UpperCAmelCase      (		self    :			str    )	->	List[str]:



							'''simple docstring'''



							a__  :	int		 =			self.scheduler_classes[0]
							a__  :	Tuple		 =			self.get_scheduler_config()
							a__  :	Any		 =			scheduler_class(**a_    )

							assert torch.sum(torch.abs(scheduler._get_variance(0    ) - 0.0    )    ) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_87    ) - 0.0_0979    )    ) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_99    ) - 0.02    )    ) < 1E-5
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	Optional[int]:



							'''simple docstring'''



							a__  :	Optional[int]		 =			self.scheduler_classes[0]
							a__  :	Optional[int]		 =			self.get_scheduler_config()
							a__  :	Dict		 =			scheduler_class(**a_    )

							a__  :	List[Any]		 =			len(a_    )

							a__  :	str		 =			self.dummy_model()
							a__  :	Dict		 =			self.dummy_sample_deter
							a__  :	Dict		 =			torch.manual_seed(0    )

							for t in reversed(range(a_    )    ):
										# 1. predict noise residual
										a__  :	List[str]		 =			model(a_	, a_    )

										# 2. predict previous mean of sample x_t-1
										a__  :	Any		 =			scheduler.step(a_	, a_	, a_	, generator=a_    ).prev_sample

										# if t > 0:
										#     noise = self.dummy_sample_deter
										#     variance = scheduler.get_variance(t) ** (0.5) * noise
										#
										# sample = pred_prev_sample + variance
										a__  :	Optional[int]		 =			pred_prev_sample

							a__  :	int		 =			torch.sum(torch.abs(a_    )    )
							a__  :	Tuple		 =			torch.mean(torch.abs(a_    )    )

							assert abs(result_sum.item() - 258.9606    ) < 1E-2
							assert abs(result_mean.item() - 0.3372    ) < 1E-3
				def       UpperCAmelCase      (		self    :			Any    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	Any		 =			self.scheduler_classes[0]
							a__  :	Optional[int]		 =			self.get_scheduler_config(prediction_type="v_prediction"    )
							a__  :	Dict		 =			scheduler_class(**a_    )

							a__  :	Tuple		 =			len(a_    )

							a__  :	Optional[int]		 =			self.dummy_model()
							a__  :	Tuple		 =			self.dummy_sample_deter
							a__  :	Optional[int]		 =			torch.manual_seed(0    )

							for t in reversed(range(a_    )    ):
										# 1. predict noise residual
										a__  :	List[str]		 =			model(a_	, a_    )

										# 2. predict previous mean of sample x_t-1
										a__  :	Optional[Any]		 =			scheduler.step(a_	, a_	, a_	, generator=a_    ).prev_sample

										# if t > 0:
										#     noise = self.dummy_sample_deter
										#     variance = scheduler.get_variance(t) ** (0.5) * noise
										#
										# sample = pred_prev_sample + variance
										a__  :	str		 =			pred_prev_sample

							a__  :	Any		 =			torch.sum(torch.abs(a_    )    )
							a__  :	Union[str, Any]		 =			torch.mean(torch.abs(a_    )    )

							assert abs(result_sum.item() - 202.0296    ) < 1E-2
							assert abs(result_mean.item() - 0.2631    ) < 1E-3
				def       UpperCAmelCase      (		self    :			int    )	->	Dict:



							'''simple docstring'''



							a__  :	str		 =			self.scheduler_classes[0]
							a__  :	int		 =			self.get_scheduler_config()
							a__  :	Optional[int]		 =			scheduler_class(**a_    )

							a__  :	Any		 =			[1_00, 87, 50, 1, 0]

							scheduler.set_timesteps(timesteps=a_    )

							a__  :	Any		 =			scheduler.timesteps

							for i, timestep in enumerate(a_    ):
										if i == len(a_    ) - 1:
													a__  :	int		 =			-1
										else:
													a__  :	str		 =			timesteps[i + 1]

										a__  :	Optional[int]		 =			scheduler.previous_timestep(a_    )
										a__  :	Dict		 =			prev_t.item()

										self.assertEqual(a_	, a_    )
				def       UpperCAmelCase      (		self    :			int    )	->	Tuple:



							'''simple docstring'''



							a__  :	int		 =			self.scheduler_classes[0]
							a__  :	Optional[Any]		 =			self.get_scheduler_config()
							a__  :	List[str]		 =			scheduler_class(**a_    )

							a__  :	str		 =			[1_00, 87, 50, 51, 0]

							with self.assertRaises(a_	, msg="`custom_timesteps` must be in descending order."    ):
										scheduler.set_timesteps(timesteps=a_    )
				def       UpperCAmelCase      (		self    :			Dict    )	->	List[Any]:



							'''simple docstring'''



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

							a__  :	List[str]		 =			[1_00, 87, 50, 1, 0]
							a__  :	Union[str, Any]		 =			len(a_    )

							with self.assertRaises(a_	, msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."    ):
										scheduler.set_timesteps(num_inference_steps=a_	, timesteps=a_    )






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



							'''simple docstring'''



							a__  :	int		 =			self.scheduler_classes[0]
							a__  :	int		 =			self.get_scheduler_config()
							a__  :	Dict		 =			scheduler_class(**a_    )

							a__  :	List[Any]		 =			[scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    a_	, msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"	, ):
										scheduler.set_timesteps(timesteps=a_    )






"""simple docstring"""






__UpperCAmelCase						=			[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]






def      lowercase__ (					lowerCAmelCase__   :			Dict							,			lowerCAmelCase__   :			Optional[int]							,			lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			Tuple			)    ->			Dict:



			'''simple docstring'''
			# Return True if there is node that has not iterated.
			a__  :	int		 =			[False] * len(lowerCAmelCase__			)
			a__  :	Optional[Any]		 =			[s]
			a__  :	Optional[int]		 =			True

			while queue:
						a__  :	Any		 =			queue.pop(0			)
						for ind in range(len(graph[u]			)			):
									if visited[ind] is False and graph[u][ind] > 0:
												queue.append(lowerCAmelCase__			)
												a__  :	Any		 =			True
												a__  :	Union[str, Any]		 =			u

			return visited[t]






def      lowercase__ (					lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			Optional[Any]			)    ->			Any:



			'''simple docstring'''
			a__  :	Any		 =			[-1] * (len(lowerCAmelCase__			))
			a__  :	Any		 =			0
			a__  :	List[str]		 =			[]
			a__  :	Tuple		 =			[i[:] for i in graph]  # Record original cut, copy.
			while bfs(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__			):
						a__  :	Any		 =			float("Inf"			)
						a__  :	Optional[int]		 =			sink

						while s != source:
									# Find the minimum value in select path
									a__  :	Union[str, Any]		 =			min(lowerCAmelCase__							,			graph[parent[s]][s]			)
									a__  :	List[str]		 =			parent[s]

						max_flow += path_flow
						a__  :	List[str]		 =			sink

						while v != source:
									a__  :	int		 =			parent[v]
									graph[u][v] -= path_flow
									graph[v][u] += path_flow
									a__  :	List[str]		 =			parent[v]

			for i in range(len(lowerCAmelCase__			)			):
						for j in range(len(graph[0]			)			):
									if graph[i][j] == 0 and temp[i][j] > 0:
												res.append((i, j)			)

			return res


if __name__ == "__main__":
		print(mincut(test_graph, source=0, sink=5))