





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

from sys import maxsize
from typing import Generic, TypeVar

__UpperCamelCase     :						Union[str, Any]						       =  TypeVar('''T''')

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    int			):
 return (position - 1) // 2

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    int			):
 return (2 * position) + 1

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    int			):
 return (2 * position) + 2







class       a							(   Generic[T]  ):



 def __init__(     self     ):







  """simple docstring"""






  lowerCAmelCase    				= []
  lowerCAmelCase    				= {}
  lowerCAmelCase    				= 0



 def __len__(     self     ):







  """simple docstring"""






  return self.elements



 def __repr__(     self     ):







  """simple docstring"""






  return str(self.heap     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return self.elements == 0



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  self.heap.append((elem, weight)     )
  lowerCAmelCase    				= self.elements
  self.elements += 1
  self._bubble_up(_SCREAMING_SNAKE_CASE     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  if self.elements > 1:
   self._swap_nodes(0				,       self.elements - 1     )
  lowerCAmelCase				,lowerCAmelCase    				= self.heap.pop()
  del self.position_map[elem]
  self.elements -= 1
  if self.elements > 0:
   lowerCAmelCase				,lowerCAmelCase    				= self.heap[0]
   self._bubble_down(_SCREAMING_SNAKE_CASE     )
  return elem



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= self.position_map[elem]
  lowerCAmelCase    				= (elem, weight)
  if position > 0:
   lowerCAmelCase    				= get_parent_position(_SCREAMING_SNAKE_CASE     )
   lowerCAmelCase				,lowerCAmelCase    				= self.heap[parent_position]
   if parent_weight > weight:
    self._bubble_up(_SCREAMING_SNAKE_CASE     )
   else:
    self._bubble_down(_SCREAMING_SNAKE_CASE     )
  else:
   self._bubble_down(_SCREAMING_SNAKE_CASE     )



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= self.position_map[elem]
  if curr_pos == 0:
   return None
  lowerCAmelCase    				= get_parent_position(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase				,lowerCAmelCase    				= self.heap[curr_pos]
  lowerCAmelCase				,lowerCAmelCase    				= self.heap[parent_position]
  if parent_weight > weight:
   self._swap_nodes(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE     )
   return self._bubble_up(_SCREAMING_SNAKE_CASE     )
  return None



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= self.position_map[elem]
  lowerCAmelCase				,lowerCAmelCase    				= self.heap[curr_pos]
  lowerCAmelCase    				= get_child_left_position(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase    				= get_child_right_position(_SCREAMING_SNAKE_CASE     )
  if child_left_position < self.elements and child_right_position < self.elements:
   lowerCAmelCase				,lowerCAmelCase    				= self.heap[child_left_position]
   lowerCAmelCase				,lowerCAmelCase    				= self.heap[child_right_position]
   if child_right_weight < child_left_weight and child_right_weight < weight:
    self._swap_nodes(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE     )
    return self._bubble_down(_SCREAMING_SNAKE_CASE     )
  if child_left_position < self.elements:
   lowerCAmelCase				,lowerCAmelCase    				= self.heap[child_left_position]
   if child_left_weight < weight:
    self._swap_nodes(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE     )
    return self._bubble_down(_SCREAMING_SNAKE_CASE     )
  else:
   return None
  if child_right_position < self.elements:
   lowerCAmelCase				,lowerCAmelCase    				= self.heap[child_right_position]
   if child_right_weight < weight:
    self._swap_nodes(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE     )
    return self._bubble_down(_SCREAMING_SNAKE_CASE     )
  return None



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= self.heap[nodea_pos][0]
  lowerCAmelCase    				= self.heap[nodea_pos][0]
  lowerCAmelCase				,lowerCAmelCase    				= (
      self.heap[nodea_pos],
      self.heap[nodea_pos],
  )
  lowerCAmelCase    				= nodea_pos
  lowerCAmelCase    				= nodea_pos







class       a							(   Generic[T]  ):



 def __init__(     self     ):







  """simple docstring"""






  lowerCAmelCase    				= {}
  lowerCAmelCase    				= 0



 def __repr__(     self     ):







  """simple docstring"""






  return str(self.connections     )



 def __len__(     self     ):







  """simple docstring"""






  return self.nodes



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  if node not in self.connections:
   lowerCAmelCase    				= {}
   self.nodes += 1



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  self.add_node(_SCREAMING_SNAKE_CASE     )
  self.add_node(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase    				= weight
  lowerCAmelCase    				= weight


def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    GraphUndirectedWeighted[T]  ,     ):
 lowerCAmelCase    				= {node: maxsize for node in graph.connections}
 lowerCAmelCase    				= {node: None for node in graph.connections}

 lowerCAmelCase    				= MinPriorityQueue()
 for node, weight in dist.items():
  priority_queue.push(_UpperCAmelCase  ,     _UpperCAmelCase			)

 if priority_queue.is_empty():
  return dist, parent

 # initialization
 lowerCAmelCase    				= priority_queue.extract_min()
 lowerCAmelCase    				= 0
 for neighbour in graph.connections[node]:
  if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
   lowerCAmelCase    				= dist[node] + graph.connections[node][neighbour]
   priority_queue.update_key(_UpperCAmelCase  ,     dist[neighbour]			)
   lowerCAmelCase    				= node

    # running prim's algorithm
 while not priority_queue.is_empty():
  lowerCAmelCase    				= priority_queue.extract_min()
  for neighbour in graph.connections[node]:
   if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
    lowerCAmelCase    				= dist[node] + graph.connections[node][neighbour]
    priority_queue.update_key(_UpperCAmelCase  ,     dist[neighbour]			)
    lowerCAmelCase    				= node
 return dist, parent








'''simple docstring'''



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       Dict				=     (DDPMScheduler,)

  def   _SCREAMING_SNAKE_CASE				(				self      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

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

      config.update(**_SCREAMING_SNAKE_CASE	)
      return config

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				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=_SCREAMING_SNAKE_CASE      , beta_end=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for t in [0, 5_00, 9_99]:
          self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

      assert torch.sum(torch.abs(scheduler._get_variance(0	) - 0.0	)	) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_87	) - 0.00_979	)	) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_99	) - 0.02	)	) < 1e-5

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							self.dummy_model()
      lowerCAmelCase 						=							self.dummy_sample_deter
      lowerCAmelCase 						=							torch.manual_seed(0	)

      for t in reversed(range(_SCREAMING_SNAKE_CASE	)	):
          # 1. predict noise residual
          lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

          # 2. predict previous mean of sample x_t-1
          lowerCAmelCase 						=							scheduler.step(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , generator=_SCREAMING_SNAKE_CASE	).prev_sample

          # if t > 0:
          #     noise = self.dummy_sample_deter
          #     variance = scheduler.get_variance(t) ** (0.5) * noise
          #
          # sample = pred_prev_sample + variance
          lowerCAmelCase 						=							pred_prev_sample

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

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config(prediction_type='v_prediction'	)
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							self.dummy_model()
      lowerCAmelCase 						=							self.dummy_sample_deter
      lowerCAmelCase 						=							torch.manual_seed(0	)

      for t in reversed(range(_SCREAMING_SNAKE_CASE	)	):
          # 1. predict noise residual
          lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

          # 2. predict previous mean of sample x_t-1
          lowerCAmelCase 						=							scheduler.step(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , generator=_SCREAMING_SNAKE_CASE	).prev_sample

          # if t > 0:
          #     noise = self.dummy_sample_deter
          #     variance = scheduler.get_variance(t) ** (0.5) * noise
          #
          # sample = pred_prev_sample + variance
          lowerCAmelCase 						=							pred_prev_sample

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

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

      lowerCAmelCase 						=							[1_00, 87, 50, 1, 0]

      scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							scheduler.timesteps

      for i, timestep in enumerate(_SCREAMING_SNAKE_CASE	):
          if i == len(_SCREAMING_SNAKE_CASE	) - 1:
              lowerCAmelCase 						=							-1
          else:
              lowerCAmelCase 						=							timesteps[i + 1]

          lowerCAmelCase 						=							scheduler.previous_timestep(_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase 						=							prev_t.item()

          self.assertEqual(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

      lowerCAmelCase 						=							[1_00, 87, 50, 51, 0]

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

      lowerCAmelCase 						=							[1_00, 87, 50, 1, 0]
      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

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


  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

      lowerCAmelCase 						=							[scheduler.config.num_train_timesteps]

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





