





'''simple docstring'''






import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest







class 			lowerCamelCase					(						lowercase__  ):
					_SCREAMING_SNAKE_CASE							   =    (CMStochasticIterativeScheduler,)
					_SCREAMING_SNAKE_CASE							   =    10


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ,     **__snake_case	:  Optional[Any]       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		{
											    '''num_train_timesteps''': 2_01,
											    '''sigma_min''': 0.002,
											    '''sigma_max''': 80.0,
											}

											config.update(**UpperCAmelCase__       )
											return config


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											_snake_case:   str  					=		10

											_snake_case:   Dict  					=		self.get_scheduler_config()
											_snake_case:   Union[str, Any]  					=		self.scheduler_classes[0](**UpperCAmelCase__       )

											scheduler.set_timesteps(UpperCAmelCase__       )

											_snake_case:   Optional[int]  					=		scheduler.timesteps[0]
											_snake_case:   List[Any]  					=		scheduler.timesteps[1]

											_snake_case:   List[Any]  					=		self.dummy_sample
											_snake_case:   Tuple  					=		0.1 * sample

											_snake_case:   Union[str, Any]  					=		scheduler.step(UpperCAmelCase__       ,     UpperCAmelCase__       ,     UpperCAmelCase__       ).prev_sample
											_snake_case:   Optional[int]  					=		scheduler.step(UpperCAmelCase__       ,     UpperCAmelCase__       ,     UpperCAmelCase__       ).prev_sample

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''simple docstring'''





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


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





											'''simple docstring'''





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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ):





											'''simple docstring'''





											_snake_case:   Any  					=		self.scheduler_classes[0]
											_snake_case:   Dict  					=		self.get_scheduler_config()
											_snake_case:   Optional[Any]  					=		scheduler_class(**UpperCAmelCase__       )

											_snake_case:   Optional[int]  					=		1
											scheduler.set_timesteps(UpperCAmelCase__       )
											_snake_case:   Optional[int]  					=		scheduler.timesteps

											_snake_case:   Optional[int]  					=		torch.manual_seed(0       )

											_snake_case:   Union[str, Any]  					=		self.dummy_model()
											_snake_case:   Any  					=		self.dummy_sample_deter * scheduler.init_noise_sigma

											for i, t in enumerate(UpperCAmelCase__       ):
																	# 1. scale model input
																	_snake_case:   Tuple  					=		scheduler.scale_model_input(UpperCAmelCase__       ,     UpperCAmelCase__       )

																	# 2. predict noise residual
																	_snake_case:   Optional[Any]  					=		model(UpperCAmelCase__       ,     UpperCAmelCase__       )

																	# 3. predict previous sample x_t-1
																	_snake_case:   Tuple  					=		scheduler.step(UpperCAmelCase__       ,     UpperCAmelCase__       ,     UpperCAmelCase__       ,     generator=UpperCAmelCase__       ).prev_sample

																	_snake_case:   List[Any]  					=		pred_prev_sample

											_snake_case:   int  					=		torch.sum(torch.abs(UpperCAmelCase__       )       )
											_snake_case:   List[str]  					=		torch.mean(torch.abs(UpperCAmelCase__       )       )

											assert abs(result_sum.item() - 1_92.76_14       ) < 1e-2
											assert abs(result_mean.item() - 0.2_510       ) < 1e-3


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





											'''simple docstring'''





											_snake_case:   str  					=		self.scheduler_classes[0]
											_snake_case:   Optional[Any]  					=		self.get_scheduler_config()
											_snake_case:   str  					=		scheduler_class(**UpperCAmelCase__       )

											_snake_case:   Union[str, Any]  					=		[1_06, 0]
											scheduler.set_timesteps(timesteps=UpperCAmelCase__       )
											_snake_case:   Any  					=		scheduler.timesteps

											_snake_case:   Dict  					=		torch.manual_seed(0       )

											_snake_case:   Union[str, Any]  					=		self.dummy_model()
											_snake_case:   Tuple  					=		self.dummy_sample_deter * scheduler.init_noise_sigma

											for t in timesteps:
																	# 1. scale model input
																	_snake_case:   Optional[Any]  					=		scheduler.scale_model_input(UpperCAmelCase__       ,     UpperCAmelCase__       )

																	# 2. predict noise residual
																	_snake_case:   str  					=		model(UpperCAmelCase__       ,     UpperCAmelCase__       )

																	# 3. predict previous sample x_t-1
																	_snake_case:   Tuple  					=		scheduler.step(UpperCAmelCase__       ,     UpperCAmelCase__       ,     UpperCAmelCase__       ,     generator=UpperCAmelCase__       ).prev_sample

																	_snake_case:   Dict  					=		pred_prev_sample

											_snake_case:   str  					=		torch.sum(torch.abs(UpperCAmelCase__       )       )
											_snake_case:   int  					=		torch.mean(torch.abs(UpperCAmelCase__       )       )

											assert abs(result_sum.item() - 3_47.63_57       ) < 1e-2
											assert abs(result_mean.item() - 0.4_527       ) < 1e-3


					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		self.scheduler_classes[0]
											_snake_case:   Dict  					=		self.get_scheduler_config()
											_snake_case:   str  					=		scheduler_class(**UpperCAmelCase__       )

											_snake_case:   Any  					=		[39, 30, 12, 15, 0]

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		self.scheduler_classes[0]
											_snake_case:   Dict  					=		self.get_scheduler_config()
											_snake_case:   Dict  					=		scheduler_class(**UpperCAmelCase__       )

											_snake_case:   Optional[Any]  					=		[39, 30, 12, 1, 0]
											_snake_case:   str  					=		len(UpperCAmelCase__       )

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




					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''simple docstring'''





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

											_snake_case:   Optional[Any]  					=		[scheduler.config.num_train_timesteps]

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












'''simple docstring'''






from __future__ import annotations

import os
from collections.abc import Mapping

A   : Optional[Any]             =			tuple[int, int]







class 			lowerCamelCase					:


					def __init__(					self	:  Tuple       ,     __snake_case	:  set[int]       ,     __snake_case	:  Mapping[EdgeT, int]       ):





											'''simple docstring'''





											_snake_case:   set[int]  					=		vertices
											_snake_case:   dict[EdgeT, int]  					=		{
											    (min(__snake_case       ), max(__snake_case       )): weight for edge, weight in edges.items()
											}


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ,     __snake_case	:  EdgeT       ,     __snake_case	:  int       ):





											'''simple docstring'''





											self.vertices.add(edge[0]       )
											self.vertices.add(edge[1]       )
											_snake_case:   Dict  					=		weight




					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											_snake_case:   Graph  					=		Graph({min(self.vertices       )}       ,     {}       )
											_snake_case:   EdgeT
											_snake_case:   int
											_snake_case:   EdgeT
											_snake_case:   int

											while len(subgraph.vertices       ) < len(self.vertices       ):
																	_snake_case:   List[str]  					=		max(self.edges.values()       ) + 1
																	for edge, weight in self.edges.items():
																							if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
																													if weight < min_weight:
																																			_snake_case:   Optional[Any]  					=		edge
																																			_snake_case:   Optional[int]  					=		weight

																	subgraph.add_edge(__snake_case       ,     __snake_case       )

											return subgraph





def       lowercase_      (		lowercase__ = "p107_network.txt"						)   ->int:
						_snake_case:   str  					=		os.path.abspath(os.path.dirname(lowercase__						)						)
						_snake_case:   str  					=		os.path.join(lowercase__				,  lowercase__						)
						_snake_case:   dict[EdgeT, int]  					=		{}
						_snake_case:   list[str]
						_snake_case:   int
						_snake_case:   int

						with open(lowercase__						) as f:
												_snake_case:   Tuple  					=		f.read().strip().split('\n'						)

						_snake_case:   Tuple  					=		[line.split(','						) for line in data]

						for edgea in range(1				,  len(lowercase__						)						):
												for edgea in range(lowercase__						):
																		if adjaceny_matrix[edgea][edgea] != "-":
																								_snake_case:   int  					=		int(adjaceny_matrix[edgea][edgea]						)

						_snake_case:   Graph  					=		Graph(set(range(len(lowercase__						)						)						)				,  lowercase__						)

						_snake_case:   Graph  					=		graph.prims_algorithm()

						_snake_case:   int  					=		sum(graph.edges.values()						)
						_snake_case:   int  					=		sum(subgraph.edges.values()						)

						return initial_total - optimal_total


if __name__ == "__main__":
		print(F'{solution() = }')





