'''simple docstring'''


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest






class     A ( _lowercase ):
							a_										=   (UniPCMultistepScheduler,)
							a_										=   (('num_inference_steps', 2_5),)





							def    snake_case__    (   self					:				Union[str, Any]  ,		**__a					:				int						)				->						Any:
								__UpperCAmelCase      					=      {
								    '''num_train_timesteps''': 1_0_0_0,
								    '''beta_start''': 0.0_0_0_1,
								    '''beta_end''': 0.0_2,
								    '''beta_schedule''': '''linear''',
								    '''solver_order''': 2,
								    '''solver_type''': '''bh2''',
								}

								config.update(**__SCREAMING_SNAKE_CASE						)
								return config





							def    snake_case__    (   self					:				Dict  ,		__a					:				List[str]=0  ,		**__a					:				List[Any]						)				->						Union[str, Any]:
								__UpperCAmelCase      					=      dict(self.forward_default_kwargs						)
								__UpperCAmelCase      					=      kwargs.pop('''num_inference_steps'''  ,		__SCREAMING_SNAKE_CASE						)
								__UpperCAmelCase      					=      self.dummy_sample
								__UpperCAmelCase      					=      0.1 * sample
								__UpperCAmelCase      					=      [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
									__UpperCAmelCase      					=      self.get_scheduler_config(**__SCREAMING_SNAKE_CASE						)
									__UpperCAmelCase      					=      scheduler_class(**__SCREAMING_SNAKE_CASE						)
									scheduler.set_timesteps(__SCREAMING_SNAKE_CASE						)
									# copy over dummy past residuals
									__UpperCAmelCase      					=      dummy_past_residuals[: scheduler.config.solver_order]

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

									__UpperCAmelCase					,      __UpperCAmelCase      					=      sample, sample
									for t in range(__SCREAMING_SNAKE_CASE  ,		time_step + scheduler.config.solver_order + 1						):
										__UpperCAmelCase      					=      scheduler.step(__SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE  ,		**__SCREAMING_SNAKE_CASE						).prev_sample
										__UpperCAmelCase      					=      new_scheduler.step(__SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE  ,		**__SCREAMING_SNAKE_CASE						).prev_sample

										assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"





							def    snake_case__    (   self					:				int  ,		__a					:				Tuple=0  ,		**__a					:				Any						)				->						Union[str, Any]:
								__UpperCAmelCase      					=      dict(self.forward_default_kwargs						)
								__UpperCAmelCase      					=      kwargs.pop('''num_inference_steps'''  ,		__SCREAMING_SNAKE_CASE						)
								__UpperCAmelCase      					=      self.dummy_sample
								__UpperCAmelCase      					=      0.1 * sample
								__UpperCAmelCase      					=      [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
									__UpperCAmelCase      					=      self.get_scheduler_config()
									__UpperCAmelCase      					=      scheduler_class(**__SCREAMING_SNAKE_CASE						)
									scheduler.set_timesteps(__SCREAMING_SNAKE_CASE						)

									# copy over dummy past residuals (must be after setting timesteps)
									__UpperCAmelCase      					=      dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(__SCREAMING_SNAKE_CASE						)
										__UpperCAmelCase      					=      scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE						)
										# copy over dummy past residuals
										new_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE						)

										# copy over dummy past residual (must be after setting timesteps)
										__UpperCAmelCase      					=      dummy_past_residuals[: new_scheduler.config.solver_order]

									__UpperCAmelCase      					=      scheduler.step(__SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE  ,		**__SCREAMING_SNAKE_CASE						).prev_sample
									__UpperCAmelCase      					=      new_scheduler.step(__SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE  ,		**__SCREAMING_SNAKE_CASE						).prev_sample

									assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"





							def    snake_case__    (   self					:				Optional[int]  ,		__a					:				List[str]=None  ,		**__a					:				str						)				->						List[str]:



								if scheduler is None:
									__UpperCAmelCase      					=      self.scheduler_classes[0]
									__UpperCAmelCase      					=      self.get_scheduler_config(**__SCREAMING_SNAKE_CASE						)
									__UpperCAmelCase      					=      scheduler_class(**__SCREAMING_SNAKE_CASE						)

								__UpperCAmelCase      					=      self.scheduler_classes[0]
								__UpperCAmelCase      					=      self.get_scheduler_config(**__SCREAMING_SNAKE_CASE						)
								__UpperCAmelCase      					=      scheduler_class(**__SCREAMING_SNAKE_CASE						)

								__UpperCAmelCase      					=      1_0
								__UpperCAmelCase      					=      self.dummy_model()
								__UpperCAmelCase      					=      self.dummy_sample_deter
								scheduler.set_timesteps(__SCREAMING_SNAKE_CASE						)

								for i, t in enumerate(scheduler.timesteps						):
									__UpperCAmelCase      					=      model(__SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE						)
									__UpperCAmelCase      					=      scheduler.step(__SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE						).prev_sample

								return sample





							def    snake_case__    (   self					:				Tuple						)				->						Any:
								__UpperCAmelCase      					=      dict(self.forward_default_kwargs						)

								__UpperCAmelCase      					=      kwargs.pop('''num_inference_steps'''  ,		__SCREAMING_SNAKE_CASE						)

								for scheduler_class in self.scheduler_classes:
									__UpperCAmelCase      					=      self.get_scheduler_config()
									__UpperCAmelCase      					=      scheduler_class(**__SCREAMING_SNAKE_CASE						)

									__UpperCAmelCase      					=      self.dummy_sample
									__UpperCAmelCase      					=      0.1 * sample

									if num_inference_steps is not None and hasattr(__SCREAMING_SNAKE_CASE  ,		'''set_timesteps'''						):
										scheduler.set_timesteps(__SCREAMING_SNAKE_CASE						)
									elif num_inference_steps is not None and not hasattr(__SCREAMING_SNAKE_CASE  ,		'''set_timesteps'''						):
										__UpperCAmelCase      					=      num_inference_steps

									# copy over dummy past residuals (must be done after set_timesteps)
									__UpperCAmelCase      					=      [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
									__UpperCAmelCase      					=      dummy_past_residuals[: scheduler.config.solver_order]

									__UpperCAmelCase      					=      scheduler.timesteps[5]
									__UpperCAmelCase      					=      scheduler.timesteps[6]

									__UpperCAmelCase      					=      scheduler.step(__SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE  ,		**__SCREAMING_SNAKE_CASE						).prev_sample
									__UpperCAmelCase      					=      scheduler.step(__SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE  ,		**__SCREAMING_SNAKE_CASE						).prev_sample

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





							def    snake_case__    (   self					:				Optional[Any]						)				->						List[str]:
								__UpperCAmelCase      					=      UniPCMultistepScheduler(**self.get_scheduler_config()						)
								__UpperCAmelCase      					=      self.full_loop(scheduler=__SCREAMING_SNAKE_CASE						)
								__UpperCAmelCase      					=      torch.mean(torch.abs(__SCREAMING_SNAKE_CASE						)						)

								assert abs(result_mean.item() - 0.2_4_6_4						) < 1e-3

								__UpperCAmelCase      					=      DPMSolverSinglestepScheduler.from_config(scheduler.config						)
								__UpperCAmelCase      					=      DEISMultistepScheduler.from_config(scheduler.config						)
								__UpperCAmelCase      					=      DPMSolverMultistepScheduler.from_config(scheduler.config						)
								__UpperCAmelCase      					=      UniPCMultistepScheduler.from_config(scheduler.config						)

								__UpperCAmelCase      					=      self.full_loop(scheduler=__SCREAMING_SNAKE_CASE						)
								__UpperCAmelCase      					=      torch.mean(torch.abs(__SCREAMING_SNAKE_CASE						)						)

								assert abs(result_mean.item() - 0.2_4_6_4						) < 1e-3





							def    snake_case__    (   self					:				Any						)				->						Any:



								for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
									self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE						)





							def    snake_case__    (   self					:				Optional[int]						)				->						Tuple:
								self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE						)
								for order in [1, 2, 3]:
									for solver_type in ["bh1", "bh2"]:
										for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "sample"]:
												self.check_over_configs(
												    thresholding=__SCREAMING_SNAKE_CASE  ,		prediction_type=__SCREAMING_SNAKE_CASE  ,		sample_max_value=__SCREAMING_SNAKE_CASE  ,		solver_order=__SCREAMING_SNAKE_CASE  ,		solver_type=__SCREAMING_SNAKE_CASE  ,		)





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



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





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



								for solver_type in ["bh1", "bh2"]:
									for order in [1, 2, 3]:
										for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(
											    solver_order=__SCREAMING_SNAKE_CASE  ,		solver_type=__SCREAMING_SNAKE_CASE  ,		prediction_type=__SCREAMING_SNAKE_CASE  ,		)
											__UpperCAmelCase      					=      self.full_loop(
											    solver_order=__SCREAMING_SNAKE_CASE  ,		solver_type=__SCREAMING_SNAKE_CASE  ,		prediction_type=__SCREAMING_SNAKE_CASE  ,		)
											assert not torch.isnan(__SCREAMING_SNAKE_CASE						).any(), "Samples have nan numbers"





							def    snake_case__    (   self					:				List[str]						)				->						Union[str, Any]:
								self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE						)
								self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE						)





							def    snake_case__    (   self					:				Optional[Any]						)				->						Any:



								for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
									self.check_over_forward(num_inference_steps=__SCREAMING_SNAKE_CASE  ,		time_step=0						)





							def    snake_case__    (   self					:				Optional[Any]						)				->						Union[str, Any]:
								__UpperCAmelCase      					=      self.full_loop()
								__UpperCAmelCase      					=      torch.mean(torch.abs(__SCREAMING_SNAKE_CASE						)						)

								assert abs(result_mean.item() - 0.2_4_6_4						) < 1e-3





							def    snake_case__    (   self					:				Tuple						)				->						Union[str, Any]:
								__UpperCAmelCase      					=      self.full_loop(prediction_type='''v_prediction'''						)
								__UpperCAmelCase      					=      torch.mean(torch.abs(__SCREAMING_SNAKE_CASE						)						)

								assert abs(result_mean.item() - 0.1_0_1_4						) < 1e-3





							def    snake_case__    (   self					:				List[Any]						)				->						Optional[Any]:
								__UpperCAmelCase      					=      self.scheduler_classes[0]
								__UpperCAmelCase      					=      self.get_scheduler_config(thresholding=__SCREAMING_SNAKE_CASE  ,		dynamic_thresholding_ratio=0						)
								__UpperCAmelCase      					=      scheduler_class(**__SCREAMING_SNAKE_CASE						)

								__UpperCAmelCase      					=      1_0
								__UpperCAmelCase      					=      self.dummy_model()
								__UpperCAmelCase      					=      self.dummy_sample_deter.half()
								scheduler.set_timesteps(__SCREAMING_SNAKE_CASE						)

								for i, t in enumerate(scheduler.timesteps						):
									__UpperCAmelCase      					=      model(__SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE						)
									__UpperCAmelCase      					=      scheduler.step(__SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE						).prev_sample

								assert sample.dtype == torch.floataa






							def    snake_case__    (   self					:				Tuple  ,		**__a					:				List[str]						)				->						Optional[Any]:



								for scheduler_class in self.scheduler_classes:
									__UpperCAmelCase      					=      self.get_scheduler_config(**__SCREAMING_SNAKE_CASE						)
									__UpperCAmelCase      					=      scheduler_class(**__SCREAMING_SNAKE_CASE						)

									scheduler.set_timesteps(scheduler.config.num_train_timesteps						)
									assert len(scheduler.timesteps.unique()						) == scheduler.num_inference_steps


"""simple docstring"""




from __future__ import annotations

import os
from collections.abc import Mapping

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







class     lowercase:



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     None:







         """simple docstring"""



         a__		=			vertices
         a__		=			{
             (min(__SCREAMING_SNAKE_CASE   ), max(__SCREAMING_SNAKE_CASE   )): weight for edge, weight in edges.items()
         }



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     None:







         """simple docstring"""



         self.vertices.add(edge[0]   )
         self.vertices.add(edge[1]   )
         a__		=			weight




     def 			lowercase__  (							self   )		->     Graph:







         """simple docstring"""



         a__		=			Graph({min(self.vertices   )}     ,				{}   )
         a__		=			42
         a__		=			42
         a__		=			42
         a__		=			42

         while len(subgraph.vertices   ) < len(self.vertices   ):
             a__		=			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:
                         a__		=			edge
                         a__		=			weight

             subgraph.add_edge(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         return subgraph




def     __magic_name__		(					UpperCamelCase     :		str = "p107_network.txt"     )     ->						int:
    a__		=			os.path.abspath(os.path.dirname(UpperCamelCase     )     )
    a__		=			os.path.join(UpperCamelCase							,		UpperCamelCase     )
    a__		=			{}
    a__		=			42
    a__		=			42
    a__		=			42

    with open(UpperCamelCase     ) as f:
        a__		=			f.read().strip().split('\n'     )

    a__		=			[line.split(','     ) for line in data]

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

    a__		=			Graph(set(range(len(UpperCamelCase     )     )     )							,		UpperCamelCase     )

    a__		=			graph.prims_algorithm()

    a__		=			sum(graph.edges.values()     )
    a__		=			sum(subgraph.edges.values()     )

    return initial_total - optimal_total


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


