

"""simple docstring"""






import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest







class       snake_case__  ( lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						(CMStochasticIterativeScheduler,)
			SCREAMING_SNAKE_CASE__     				=						10







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  **lowercase			: List[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				{
								    "num_train_timesteps": 2_01,
								    "sigma_min": 0.0_0_2,
								    "sigma_max": 8_0.0,
								}

								config.update(**lowercase  )
								return config







			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   str      =				10

								UpperCAmelCase			:   List[Any]      =				self.get_scheduler_config()
								UpperCAmelCase			:   str      =				self.scheduler_classes[0](**lowercase  )

								scheduler.set_timesteps(lowercase  )

								UpperCAmelCase			:   List[str]      =				scheduler.timesteps[0]
								UpperCAmelCase			:   Any      =				scheduler.timesteps[1]

								UpperCAmelCase			:   Optional[Any]      =				self.dummy_sample
								UpperCAmelCase			:   str      =				0.1 * sample

								UpperCAmelCase			:   str      =				scheduler.step(lowercase			,  lowercase			,  lowercase  ).prev_sample
								UpperCAmelCase			:   int      =				scheduler.step(lowercase			,  lowercase			,  lowercase  ).prev_sample

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







			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								for timesteps in [10, 50, 1_00, 10_00]:
													self.check_over_configs(num_train_timesteps=lowercase  )







			def 			__lowerCAmelCase				(			self			: Tuple  ):





								'''simple docstring'''



								for clip_denoised in [True, False]:
													self.check_over_configs(clip_denoised=lowercase  )







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



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

								UpperCAmelCase			:   List[str]      =				1
								scheduler.set_timesteps(lowercase  )
								UpperCAmelCase			:   int      =				scheduler.timesteps

								UpperCAmelCase			:   Dict      =				torch.manual_seed(0  )

								UpperCAmelCase			:   str      =				self.dummy_model()
								UpperCAmelCase			:   List[str]      =				self.dummy_sample_deter * scheduler.init_noise_sigma

								for i, t in enumerate(lowercase  ):
													# 1. scale model input
													UpperCAmelCase			:   int      =				scheduler.scale_model_input(lowercase			,  lowercase  )

													# 2. predict noise residual
													UpperCAmelCase			:   Optional[int]      =				model(lowercase			,  lowercase  )

													# 3. predict previous sample x_t-1
													UpperCAmelCase			:   Optional[Any]      =				scheduler.step(lowercase			,  lowercase			,  lowercase			,  generator=lowercase  ).prev_sample

													UpperCAmelCase			:   List[Any]      =				pred_prev_sample

								UpperCAmelCase			:   List[Any]      =				torch.sum(torch.abs(lowercase  )  )
								UpperCAmelCase			:   List[str]      =				torch.mean(torch.abs(lowercase  )  )

								assert abs(result_sum.item() - 1_9_2.7_6_1_4  ) < 1E-2
								assert abs(result_mean.item() - 0.2_5_1_0  ) < 1E-3







			def 			__lowerCAmelCase				(			self			: List[str]  ):





								'''simple docstring'''



								UpperCAmelCase			:   int      =				self.scheduler_classes[0]
								UpperCAmelCase			:   Tuple      =				self.get_scheduler_config()
								UpperCAmelCase			:   Optional[int]      =				scheduler_class(**lowercase  )

								UpperCAmelCase			:   str      =				[1_06, 0]
								scheduler.set_timesteps(timesteps=lowercase  )
								UpperCAmelCase			:   str      =				scheduler.timesteps

								UpperCAmelCase			:   Tuple      =				torch.manual_seed(0  )

								UpperCAmelCase			:   Any      =				self.dummy_model()
								UpperCAmelCase			:   Dict      =				self.dummy_sample_deter * scheduler.init_noise_sigma

								for t in timesteps:
													# 1. scale model input
													UpperCAmelCase			:   List[str]      =				scheduler.scale_model_input(lowercase			,  lowercase  )

													# 2. predict noise residual
													UpperCAmelCase			:   Dict      =				model(lowercase			,  lowercase  )

													# 3. predict previous sample x_t-1
													UpperCAmelCase			:   Any      =				scheduler.step(lowercase			,  lowercase			,  lowercase			,  generator=lowercase  ).prev_sample

													UpperCAmelCase			:   int      =				pred_prev_sample

								UpperCAmelCase			:   Dict      =				torch.sum(torch.abs(lowercase  )  )
								UpperCAmelCase			:   List[str]      =				torch.mean(torch.abs(lowercase  )  )

								assert abs(result_sum.item() - 3_4_7.6_3_5_7  ) < 1E-2
								assert abs(result_mean.item() - 0.4_5_2_7  ) < 1E-3







			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Any      =				self.scheduler_classes[0]
								UpperCAmelCase			:   Dict      =				self.get_scheduler_config()
								UpperCAmelCase			:   Any      =				scheduler_class(**lowercase  )

								UpperCAmelCase			:   Dict      =				[39, 30, 12, 15, 0]

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







			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				self.scheduler_classes[0]
								UpperCAmelCase			:   List[str]      =				self.get_scheduler_config()
								UpperCAmelCase			:   str      =				scheduler_class(**lowercase  )

								UpperCAmelCase			:   str      =				[39, 30, 12, 1, 0]
								UpperCAmelCase			:   List[str]      =				len(lowercase  )

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







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				self.scheduler_classes[0]
								UpperCAmelCase			:   Optional[int]      =				self.get_scheduler_config()
								UpperCAmelCase			:   List[str]      =				scheduler_class(**lowercase  )

								UpperCAmelCase			:   List[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  )





"""simple docstring"""






from __future__ import annotations

from collections import Counter
from random import random







class       snake_case__  :







			def __init__(			self			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				{}







			def 			__lowerCAmelCase				(			self			: List[Any]			,  lowercase			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[int]      =				{}







			def 			__lowerCAmelCase				(			self			: Optional[int]			,  lowercase			: str			,  lowercase			: str			,  lowercase			: float  ):





								'''simple docstring'''



								if nodea not in self.connections:
													self.add_node(lowercase  )
								if nodea not in self.connections:
													self.add_node(lowercase  )
								UpperCAmelCase			:   int      =				probability







			def 			__lowerCAmelCase				(			self			: List[Any]  ):





								'''simple docstring'''



								return list(self.connections  )







			def 			__lowerCAmelCase				(			self			: int			,  lowercase			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   int      =				0
								UpperCAmelCase			:   Union[str, Any]      =				random()

								for dest in self.connections[node]:
													current_probability += self.connections[node][dest]
													if current_probability > random_value:
																		return dest
								return ""



def   lowercase_	(    _lowercase       :		str							,      _lowercase       :		list[tuple[str, str, float]]							,      _lowercase       :		int	):




					'''simple docstring'''






					UpperCAmelCase			:   int      =				MarkovChainGraphUndirectedUnweighted()

					for nodea, nodea, probability in transitions:
										graph.add_transition_probability(_lowercase							,      _lowercase							,      _lowercase	)

					UpperCAmelCase			:   List[Any]      =				Counter(graph.get_nodes()	)
					UpperCAmelCase			:   List[Any]      =				start

					for _ in range(_lowercase	):
										UpperCAmelCase			:   Optional[Any]      =				graph.transition(_lowercase	)
										visited[node] += 1

					return visited


if __name__ == "__main__":
	import doctest

	doctest.testmod()


