import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		(DDPMParallelScheduler,)



							def 	UpperCamelCase	(						self				,    **lowercase_    ):
													_snake_case						:					Any    =   {
													    "num_train_timesteps": 1_000,
													    "beta_start": 0.0_001,
													    "beta_end": 0.02,
													    "beta_schedule": "linear",
													    "variance_type": "fixed_small",
													    "clip_sample": True,
													}

													config.update(**lowercase_    )
													return config



							def 	UpperCamelCase	(						self    ):
													for timesteps in [1, 5, 100, 1_000]:
																			self.check_over_configs(num_train_timesteps=lowercase_    )



							def 	UpperCamelCase	(						self    ):
													for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]				,    [0.002, 0.02, 0.2, 2]    ):
																			self.check_over_configs(beta_start=lowercase_				,    beta_end=lowercase_    )



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



							def 	UpperCamelCase	(						self    ):
													for variance in ["fixed_small", "fixed_large", "other"]:
																			self.check_over_configs(variance_type=lowercase_    )



							def 	UpperCamelCase	(						self    ):
													for clip_sample in [True, False]:
																			self.check_over_configs(clip_sample=lowercase_    )



							def 	UpperCamelCase	(						self    ):
													self.check_over_configs(thresholding=lowercase_    )
													for threshold in [0.5, 1.0, 2.0]:
																			for prediction_type in ["epsilon", "sample", "v_prediction"]:
																									self.check_over_configs(
																									    thresholding=lowercase_				,    prediction_type=lowercase_				,    sample_max_value=lowercase_				,    )



							def 	UpperCamelCase	(						self    ):
													for prediction_type in ["epsilon", "sample", "v_prediction"]:
																			self.check_over_configs(prediction_type=lowercase_    )



							def 	UpperCamelCase	(						self    ):
													for t in [0, 500, 999]:
																			self.check_over_forward(time_step=lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Tuple    =   self.scheduler_classes[0]
													_snake_case						:					Optional[Any]    =   self.get_scheduler_config()
													_snake_case						:					Dict    =   scheduler_class(**lowercase_    )

													assert torch.sum(torch.abs(scheduler._get_variance(0    ) - 0.0    )    ) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(487    ) - 0.00_979    )    ) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(999    ) - 0.02    )    ) < 1e-5



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[Any]    =   self.scheduler_classes[0]
													_snake_case						:					Any    =   self.get_scheduler_config()
													_snake_case						:					List[Any]    =   scheduler_class(**lowercase_    )

													_snake_case						:					List[str]    =   len(lowercase_    )

													_snake_case						:					Optional[Any]    =   self.dummy_model()
													_snake_case						:					Optional[int]    =   self.dummy_sample_deter
													_snake_case						:					List[Any]    =   self.dummy_sample_deter + 0.1
													_snake_case						:					str    =   self.dummy_sample_deter - 0.1

													_snake_case						:					Union[str, Any]    =   samplea.shape[0]
													_snake_case						:					Any    =   torch.stack([samplea, samplea, samplea]				,    dim=0    )
													_snake_case						:					List[str]    =   torch.arange(lowercase_    )[0:3, None].repeat(1				,    lowercase_    )

													_snake_case						:					Tuple    =   model(samples.flatten(0				,    1    )				,    timesteps.flatten(0				,    1    )    )
													_snake_case						:					Optional[int]    =   scheduler.batch_step_no_noise(lowercase_				,    timesteps.flatten(0				,    1    )				,    samples.flatten(0				,    1    )    )

													_snake_case						:					List[str]    =   torch.sum(torch.abs(lowercase_    )    )
													_snake_case						:					Dict    =   torch.mean(torch.abs(lowercase_    )    )

													assert abs(result_sum.item() - 1_153.1_833    ) < 1e-2
													assert abs(result_mean.item() - 0.5_005    ) < 1e-3



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[str]    =   self.scheduler_classes[0]
													_snake_case						:					Tuple    =   self.get_scheduler_config()
													_snake_case						:					int    =   scheduler_class(**lowercase_    )

													_snake_case						:					Dict    =   len(lowercase_    )

													_snake_case						:					Any    =   self.dummy_model()
													_snake_case						:					Optional[Any]    =   self.dummy_sample_deter
													_snake_case						:					Tuple    =   torch.manual_seed(0    )

													for t in reversed(range(lowercase_    )    ):
																			# 1. predict noise residual
																			_snake_case						:					str    =   model(lowercase_				,    lowercase_    )

																			# 2. predict previous mean of sample x_t-1
																			_snake_case						:					List[str]    =   scheduler.step(lowercase_				,    lowercase_				,    lowercase_				,    generator=lowercase_    ).prev_sample

																			_snake_case						:					Tuple    =   pred_prev_sample

													_snake_case						:					List[str]    =   torch.sum(torch.abs(lowercase_    )    )
													_snake_case						:					List[Any]    =   torch.mean(torch.abs(lowercase_    )    )

													assert abs(result_sum.item() - 258.9_606    ) < 1e-2
													assert abs(result_mean.item() - 0.3_372    ) < 1e-3



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Any    =   self.scheduler_classes[0]
													_snake_case						:					List[str]    =   self.get_scheduler_config(prediction_type="v_prediction"    )
													_snake_case						:					Tuple    =   scheduler_class(**lowercase_    )

													_snake_case						:					Any    =   len(lowercase_    )

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

													for t in reversed(range(lowercase_    )    ):
																			# 1. predict noise residual
																			_snake_case						:					Union[str, Any]    =   model(lowercase_				,    lowercase_    )

																			# 2. predict previous mean of sample x_t-1
																			_snake_case						:					Tuple    =   scheduler.step(lowercase_				,    lowercase_				,    lowercase_				,    generator=lowercase_    ).prev_sample

																			_snake_case						:					Union[str, Any]    =   pred_prev_sample

													_snake_case						:					str    =   torch.sum(torch.abs(lowercase_    )    )
													_snake_case						:					Union[str, Any]    =   torch.mean(torch.abs(lowercase_    )    )

													assert abs(result_sum.item() - 202.0_296    ) < 1e-2
													assert abs(result_mean.item() - 0.2_631    ) < 1e-3



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Union[str, Any]    =   self.scheduler_classes[0]
													_snake_case						:					Optional[Any]    =   self.get_scheduler_config()
													_snake_case						:					Optional[Any]    =   scheduler_class(**lowercase_    )

													_snake_case						:					List[str]    =   [100, 87, 50, 1, 0]

													scheduler.set_timesteps(timesteps=lowercase_    )

													_snake_case						:					Dict    =   scheduler.timesteps

													for i, timestep in enumerate(lowercase_    ):
																			if i == len(lowercase_    ) - 1:
																									_snake_case						:					Union[str, Any]    =   -1
																			else:
																									_snake_case						:					Dict    =   timesteps[i + 1]

																			_snake_case						:					Tuple    =   scheduler.previous_timestep(lowercase_    )
																			_snake_case						:					str    =   prev_t.item()

																			self.assertEqual(lowercase_				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Union[str, Any]    =   self.scheduler_classes[0]
													_snake_case						:					Union[str, Any]    =   self.get_scheduler_config()
													_snake_case						:					Dict    =   scheduler_class(**lowercase_    )

													_snake_case						:					Union[str, Any]    =   [100, 87, 50, 51, 0]

													with self.assertRaises(lowercase_				,    msg="`custom_timesteps` must be in descending order."    ):
																			scheduler.set_timesteps(timesteps=lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Tuple    =   self.scheduler_classes[0]
													_snake_case						:					Tuple    =   self.get_scheduler_config()
													_snake_case						:					List[Any]    =   scheduler_class(**lowercase_    )

													_snake_case						:					Any    =   [100, 87, 50, 1, 0]
													_snake_case						:					str    =   len(lowercase_    )

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



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Any    =   self.scheduler_classes[0]
													_snake_case						:					List[str]    =   self.get_scheduler_config()
													_snake_case						:					List[Any]    =   scheduler_class(**lowercase_    )

													_snake_case						:					Union[str, Any]    =   [scheduler.config.num_train_timesteps]

													with self.assertRaises(
													    lowercase_				,    msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"				,    ):
																			scheduler.set_timesteps(timesteps=lowercase_    )
from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass



@dataclass
class        lowercase_   :
							_lowerCamelCase						=		42
							_lowerCamelCase						=		42




class        lowercase_   :



							def __init__(						self				,    lowercase_    ):
													_snake_case						:					list[list[Edge]]    =   [[] for _ in range(lowercase_    )]
													_snake_case						:					Union[str, Any]    =   size



							def __getitem__(						self				,    lowercase_    ):
													return iter(self._graph[vertex]    )



							@property
							def 	UpperCamelCase	(						self    ):
													return self._size



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_    ):
													if weight not in (0, 1):
																			raise ValueError("Edge weight must be either 0 or 1."    )

													if to_vertex < 0 or to_vertex >= self.size:
																			raise ValueError("Vertex indexes must be in [0; size)."    )

													self._graph[from_vertex].append(Edge(lowercase_				,    lowercase_    )    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Optional[int]    =   deque([start_vertex]    )
													_snake_case						:					list[int | None]    =   [None] * self.size
													_snake_case						:					Tuple    =   0

													while queue:
																			_snake_case						:					List[Any]    =   queue.popleft()
																			_snake_case						:					Tuple    =   distances[current_vertex]
																			if current_distance is None:
																									continue

																			for edge in self[current_vertex]:
																									_snake_case						:					Dict    =   current_distance + edge.weight
																									_snake_case						:					str    =   distances[edge.destination_vertex]
																									if (
																									    isinstance(lowercase_				,    lowercase_    )
																									    and new_distance >= dest_vertex_distance
																									):
																															continue
																									_snake_case						:					List[Any]    =   new_distance
																									if edge.weight == 0:
																															queue.appendleft(edge.destination_vertex    )
																									else:
																															queue.append(edge.destination_vertex    )

													if distances[finish_vertex] is None:
																			raise ValueError("No path from start_vertex to finish_vertex."    )

													return distances[finish_vertex]


if __name__ == "__main__":
				import doctest

				doctest.testmod()