





"""simple docstring"""
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class       a							(   a__  ):
 snake_case__             =       (DDIMParallelScheduler,)
 snake_case__             =       (('''eta''', 0.0), ('''num_inference_steps''', 5_0))



 def    UpperCamelCase__  (     self				,       **_snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= {
      'num_train_timesteps': 10_00,
      'beta_start': 0.0_001,
      'beta_end': 0.02,
      'beta_schedule': 'linear',
      'clip_sample': True,
  }

  config.update(**_snake_case     )
  return config



 def    UpperCamelCase__  (     self				,       **_snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= self.scheduler_classes[0]
  lowerCAmelCase    				= self.get_scheduler_config(**_snake_case     )
  lowerCAmelCase    				= scheduler_class(**_snake_case     )

  lowerCAmelCase				,lowerCAmelCase    				= 10, 0.0

  lowerCAmelCase    				= self.dummy_model()
  lowerCAmelCase    				= self.dummy_sample_deter

  scheduler.set_timesteps(_snake_case     )

  for t in scheduler.timesteps:
   lowerCAmelCase    				= model(_snake_case				,       _snake_case     )
   lowerCAmelCase    				= scheduler.step(_snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ).prev_sample

  return sample



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  for timesteps in [1_00, 5_00, 10_00]:
   self.check_over_configs(num_train_timesteps=_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  for steps_offset in [0, 1]:
   self.check_over_configs(steps_offset=_snake_case     )

  lowerCAmelCase    				= self.scheduler_classes[0]
  lowerCAmelCase    				= self.get_scheduler_config(steps_offset=1     )
  lowerCAmelCase    				= scheduler_class(**_snake_case     )
  scheduler.set_timesteps(5     )
  assert torch.equal(scheduler.timesteps				,       torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]     )     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  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=_snake_case				,       beta_end=_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  for clip_sample in [True, False]:
   self.check_over_configs(clip_sample=_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  for timestep_spacing in ["trailing", "leading"]:
   self.check_over_configs(timestep_spacing=_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  for rescale_betas_zero_snr in [True, False]:
   self.check_over_configs(rescale_betas_zero_snr=_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  for t in [1, 10, 49]:
   self.check_over_forward(time_step=_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  for t, num_inference_steps in zip([1, 10, 50]				,       [10, 50, 5_00]     ):
   self.check_over_forward(time_step=_snake_case				,       num_inference_steps=_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  for t, eta in zip([1, 10, 49]				,       [0.0, 0.5, 1.0]     ):
   self.check_over_forward(time_step=_snake_case				,       eta=_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.scheduler_classes[0]
  lowerCAmelCase    				= self.get_scheduler_config()
  lowerCAmelCase    				= scheduler_class(**_snake_case     )

  assert torch.sum(torch.abs(scheduler._get_variance(0				,       0     ) - 0.0     )     ) < 1E-5
  assert torch.sum(torch.abs(scheduler._get_variance(4_20				,       4_00     ) - 0.14_771     )     ) < 1E-5
  assert torch.sum(torch.abs(scheduler._get_variance(9_80				,       9_60     ) - 0.32_460     )     ) < 1E-5
  assert torch.sum(torch.abs(scheduler._get_variance(0				,       0     ) - 0.0     )     ) < 1E-5
  assert torch.sum(torch.abs(scheduler._get_variance(4_87				,       4_86     ) - 0.00_979     )     ) < 1E-5
  assert torch.sum(torch.abs(scheduler._get_variance(9_99				,       9_98     ) - 0.02     )     ) < 1E-5



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.scheduler_classes[0]
  lowerCAmelCase    				= self.get_scheduler_config()
  lowerCAmelCase    				= scheduler_class(**_snake_case     )

  lowerCAmelCase				,lowerCAmelCase    				= 10, 0.0
  scheduler.set_timesteps(_snake_case     )

  lowerCAmelCase    				= self.dummy_model()
  lowerCAmelCase    				= self.dummy_sample_deter
  lowerCAmelCase    				= self.dummy_sample_deter + 0.1
  lowerCAmelCase    				= self.dummy_sample_deter - 0.1

  lowerCAmelCase    				= samplea.shape[0]
  lowerCAmelCase    				= torch.stack([samplea, samplea, samplea]				,       dim=0     )
  lowerCAmelCase    				= torch.arange(_snake_case     )[0:3, None].repeat(1				,       _snake_case     )

  lowerCAmelCase    				= model(samples.flatten(0				,       1     )				,       timesteps.flatten(0				,       1     )     )
  lowerCAmelCase    				= scheduler.batch_step_no_noise(_snake_case				,       timesteps.flatten(0				,       1     )				,       samples.flatten(0				,       1     )				,       _snake_case     )

  lowerCAmelCase    				= torch.sum(torch.abs(_snake_case     )     )
  lowerCAmelCase    				= torch.mean(torch.abs(_snake_case     )     )

  assert abs(result_sum.item() - 1_147.7_904     ) < 1E-2
  assert abs(result_mean.item() - 0.4_982     ) < 1E-3



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.full_loop()

  lowerCAmelCase    				= torch.sum(torch.abs(_snake_case     )     )
  lowerCAmelCase    				= torch.mean(torch.abs(_snake_case     )     )

  assert abs(result_sum.item() - 172.0_067     ) < 1E-2
  assert abs(result_mean.item() - 0.223_967     ) < 1E-3



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.full_loop(prediction_type='v_prediction'     )

  lowerCAmelCase    				= torch.sum(torch.abs(_snake_case     )     )
  lowerCAmelCase    				= torch.mean(torch.abs(_snake_case     )     )

  assert abs(result_sum.item() - 52.5_302     ) < 1E-2
  assert abs(result_mean.item() - 0.0_684     ) < 1E-3



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.full_loop(set_alpha_to_one=_snake_case				,       beta_start=0.01     )
  lowerCAmelCase    				= torch.sum(torch.abs(_snake_case     )     )
  lowerCAmelCase    				= torch.mean(torch.abs(_snake_case     )     )

  assert abs(result_sum.item() - 149.8_295     ) < 1E-2
  assert abs(result_mean.item() - 0.1_951     ) < 1E-3



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.full_loop(set_alpha_to_one=_snake_case				,       beta_start=0.01     )
  lowerCAmelCase    				= torch.sum(torch.abs(_snake_case     )     )
  lowerCAmelCase    				= torch.mean(torch.abs(_snake_case     )     )

  assert abs(result_sum.item() - 149.0_784     ) < 1E-2
  assert abs(result_mean.item() - 0.1_941     ) < 1E-3












"""simple docstring"""
from __future__ import annotations

import os
from collections.abc import Mapping

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







class       a							:



 def __init__(     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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

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

   subgraph.add_edge(_snake_case				,       _snake_case     )

  return subgraph


def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str = "p107_network.txt"			):
 lowerCAmelCase    				= os.path.abspath(os.path.dirname(_UpperCAmelCase			)			)
 lowerCAmelCase    				= os.path.join(_UpperCAmelCase  ,     _UpperCAmelCase			)
 lowerCAmelCase    				= {}
 lowerCAmelCase    				= 42
 lowerCAmelCase    				= 42
 lowerCAmelCase    				= 42

 with open(_UpperCAmelCase			) as f:
  lowerCAmelCase    				= f.read().strip().split('\n'			)

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

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

 lowerCAmelCase    				= Graph(set(range(len(_UpperCAmelCase			)			)			)  ,     _UpperCAmelCase			)

 lowerCAmelCase    				= graph.prims_algorithm()

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

 return initial_total - optimal_total


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





