


"""simple docstring"""







from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

lowerCAmelCase__				    =       TypeVar('''T''')
def  a__      (    SCREAMING_SNAKE_CASE   :   int    ):


  '''simple docstring'''







  return (position - 1) // 2
def  a__      (    SCREAMING_SNAKE_CASE   :   int    ):


  '''simple docstring'''







  return (2 * position) + 1
def  a__      (    SCREAMING_SNAKE_CASE   :   int    ):


  '''simple docstring'''







  return (2 * position) + 2


class 				SCREAMING_SNAKE_CASE__    (		Generic[T]    ):
       """simple docstring"""


       def __init__(						self						):






         """simple docstring"""





         lowerCAmelCase					:					list[tuple[T, int]]								=							[]
         lowerCAmelCase					:					dict[T, int]								=							{}
         lowerCAmelCase					:					int								=							0

       def __len__(						self						):






         """simple docstring"""





         return self.elements

       def __repr__(						self						):






         """simple docstring"""





         return str(self.heap						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         return self.elements == 0

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         self.heap.append((elem, weight)						)
         lowerCAmelCase					:					Tuple								=							self.elements
         self.elements += 1
         self._bubble_up(snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





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

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					str								=							self.position_map[elem]
         lowerCAmelCase					:					Optional[int]								=							(elem, weight)
         if position > 0:
           lowerCAmelCase					:					Union[str, Any]								=							get_parent_position(snake_case__						)
           lowerCAmelCase						,  lowerCAmelCase					:					List[Any]								=							self.heap[parent_position]
           if parent_weight > weight:
             self._bubble_up(snake_case__						)
           else:
             self._bubble_down(snake_case__						)
         else:
           self._bubble_down(snake_case__						)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





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

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Any								=							self.position_map[elem]
         lowerCAmelCase						,  lowerCAmelCase					:					List[str]								=							self.heap[curr_pos]
         lowerCAmelCase					:					Optional[Any]								=							get_child_left_position(snake_case__						)
         lowerCAmelCase					:					Union[str, Any]								=							get_child_right_position(snake_case__						)
         if child_left_position < self.elements and child_right_position < self.elements:
           lowerCAmelCase						,  lowerCAmelCase					:					Optional[Any]								=							self.heap[child_left_position]
           lowerCAmelCase						,  lowerCAmelCase					:					Dict								=							self.heap[child_right_position]
           if child_right_weight < child_left_weight and child_right_weight < weight:
             self._swap_nodes(snake_case__		,					snake_case__						)
             return self._bubble_down(snake_case__						)
         if child_left_position < self.elements:
           lowerCAmelCase						,  lowerCAmelCase					:					int								=							self.heap[child_left_position]
           if child_left_weight < weight:
             self._swap_nodes(snake_case__		,					snake_case__						)
             return self._bubble_down(snake_case__						)
         else:
           return None
         if child_right_position < self.elements:
           lowerCAmelCase						,  lowerCAmelCase					:					Optional[int]								=							self.heap[child_right_position]
           if child_right_weight < weight:
             self._swap_nodes(snake_case__		,					snake_case__						)
             return self._bubble_down(snake_case__						)
         return None







       def 	lowercase__							(						self		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							self.heap[nodea_pos][0]
         lowerCAmelCase					:					Optional[Any]								=							self.heap[nodea_pos][0]
         lowerCAmelCase						,  lowerCAmelCase					:					Tuple								=							(
             self.heap[nodea_pos],
             self.heap[nodea_pos],
         )
         lowerCAmelCase					:					int								=							nodea_pos
         lowerCAmelCase					:					str								=							nodea_pos


class 				SCREAMING_SNAKE_CASE__    (		Generic[T]    ):
       """simple docstring"""


       def __init__(						self						):






         """simple docstring"""





         lowerCAmelCase					:					dict[T, dict[T, int]]								=							{}
         lowerCAmelCase					:					int								=							0

       def __repr__(						self						):






         """simple docstring"""





         return str(self.connections						)

       def __len__(						self						):






         """simple docstring"""





         return self.nodes

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         if node not in self.connections:
           lowerCAmelCase					:					List[Any]								=							{}
           self.nodes += 1







       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         self.add_node(snake_case__						)
         self.add_node(snake_case__						)
         lowerCAmelCase					:					List[Any]								=							weight
         lowerCAmelCase					:					Dict								=							weight

def  a__      (    SCREAMING_SNAKE_CASE   :   GraphUndirectedWeighted[T]    ,				):


  '''simple docstring'''







  lowerCAmelCase					:					dict[T, int]								=							{node: maxsize for node in graph.connections}
  lowerCAmelCase					:					dict[T, T | None]								=							{node: None for node in graph.connections}

  lowerCAmelCase					:					MinPriorityQueue[T]								=							MinPriorityQueue()
  for node, weight in dist.items():
    priority_queue.push(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )

  if priority_queue.is_empty():
    return dist, parent

  # initialization
  lowerCAmelCase					:					Optional[Any]								=							priority_queue.extract_min()
  lowerCAmelCase					:					Optional[int]								=							0
  for neighbour in graph.connections[node]:
    if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
      lowerCAmelCase					:					List[str]								=							dist[node] + graph.connections[node][neighbour]
      priority_queue.update_key(SCREAMING_SNAKE_CASE    ,				dist[neighbour]    )
      lowerCAmelCase					:					Optional[Any]								=							node

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









"""simple docstring"""







import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       a  :    Optional[int]       =(DEISMultistepScheduler,)
       a  :    str       =(("num_inference_steps", 25),)

       def 	lowercase__							(						self		,					**snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Tuple								=							{
             "num_train_timesteps": 1_000,
             "beta_start": 0.0001,
             "beta_end": 0.02,
             "beta_schedule": "linear",
             "solver_order": 2,
         }

         config.update(**snake_case__						)
         return config

       def 	lowercase__							(						self		,					snake_case__=0		,					**snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					List[str]								=							dict(self.forward_default_kwargs						)
         lowerCAmelCase					:					List[str]								=							kwargs.pop("num_inference_steps"		,					snake_case__						)
         lowerCAmelCase					:					List[str]								=							self.dummy_sample
         lowerCAmelCase					:					int								=							0.1 * sample
         lowerCAmelCase					:					Tuple								=							[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
           lowerCAmelCase					:					Any								=							self.get_scheduler_config(**snake_case__						)
           lowerCAmelCase					:					List[str]								=							scheduler_class(**snake_case__						)
           scheduler.set_timesteps(snake_case__						)
           # copy over dummy past residuals
           lowerCAmelCase					:					str								=							dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(snake_case__						)
             lowerCAmelCase					:					List[str]								=							scheduler_class.from_pretrained(snake_case__						)
             new_scheduler.set_timesteps(snake_case__						)
             # copy over dummy past residuals
             lowerCAmelCase					:					Optional[int]								=							dummy_past_residuals[: new_scheduler.config.solver_order]

           lowerCAmelCase						,  lowerCAmelCase					:					Union[str, Any]								=							sample, sample
           for t in range(snake_case__		,					time_step + scheduler.config.solver_order + 1						):
             lowerCAmelCase					:					Any								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample
             lowerCAmelCase					:					str								=							new_scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample

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

       def 	lowercase__							(						self						):






         """simple docstring"""





         pass

       def 	lowercase__							(						self		,					snake_case__=0		,					**snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							dict(self.forward_default_kwargs						)
         lowerCAmelCase					:					List[str]								=							kwargs.pop("num_inference_steps"		,					snake_case__						)
         lowerCAmelCase					:					List[str]								=							self.dummy_sample
         lowerCAmelCase					:					Optional[int]								=							0.1 * sample
         lowerCAmelCase					:					List[Any]								=							[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
           lowerCAmelCase					:					int								=							self.get_scheduler_config()
           lowerCAmelCase					:					Any								=							scheduler_class(**snake_case__						)
           scheduler.set_timesteps(snake_case__						)

           # copy over dummy past residuals (must be after setting timesteps)
           lowerCAmelCase					:					Union[str, Any]								=							dummy_past_residuals[: scheduler.config.solver_order]

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

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

           lowerCAmelCase					:					Union[str, Any]								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample
           lowerCAmelCase					:					int								=							new_scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample

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

       def 	lowercase__							(						self		,					snake_case__=None		,					**snake_case__						):






         """simple docstring"""





         if scheduler is None:
           lowerCAmelCase					:					List[str]								=							self.scheduler_classes[0]
           lowerCAmelCase					:					Optional[int]								=							self.get_scheduler_config(**snake_case__						)
           lowerCAmelCase					:					Any								=							scheduler_class(**snake_case__						)

         lowerCAmelCase					:					List[str]								=							self.scheduler_classes[0]
         lowerCAmelCase					:					Tuple								=							self.get_scheduler_config(**snake_case__						)
         lowerCAmelCase					:					List[str]								=							scheduler_class(**snake_case__						)

         lowerCAmelCase					:					int								=							10
         lowerCAmelCase					:					List[str]								=							self.dummy_model()
         lowerCAmelCase					:					List[Any]								=							self.dummy_sample_deter
         scheduler.set_timesteps(snake_case__						)

         for i, t in enumerate(scheduler.timesteps						):
           lowerCAmelCase					:					Any								=							model(snake_case__		,					snake_case__						)
           lowerCAmelCase					:					List[Any]								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__						).prev_sample

         return sample

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							dict(self.forward_default_kwargs						)

         lowerCAmelCase					:					Optional[Any]								=							kwargs.pop("num_inference_steps"		,					snake_case__						)

         for scheduler_class in self.scheduler_classes:
           lowerCAmelCase					:					Dict								=							self.get_scheduler_config()
           lowerCAmelCase					:					List[Any]								=							scheduler_class(**snake_case__						)

           lowerCAmelCase					:					int								=							self.dummy_sample
           lowerCAmelCase					:					int								=							0.1 * sample

           if num_inference_steps is not None and hasattr(snake_case__		,					"set_timesteps"						):
             scheduler.set_timesteps(snake_case__						)
           elif num_inference_steps is not None and not hasattr(snake_case__		,					"set_timesteps"						):
             lowerCAmelCase					:					List[str]								=							num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           lowerCAmelCase					:					int								=							[residual + 0.2, residual + 0.15, residual + 0.10]
           lowerCAmelCase					:					Tuple								=							dummy_past_residuals[: scheduler.config.solver_order]

           lowerCAmelCase					:					int								=							scheduler.timesteps[5]
           lowerCAmelCase					:					str								=							scheduler.timesteps[6]

           lowerCAmelCase					:					str								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample
           lowerCAmelCase					:					Union[str, Any]								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample

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

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							DEISMultistepScheduler(**self.get_scheduler_config()						)
         lowerCAmelCase					:					Dict								=							self.full_loop(scheduler=snake_case__						)
         lowerCAmelCase					:					Optional[Any]								=							torch.mean(torch.abs(snake_case__						)						)

         assert abs(result_mean.item() - 0.23916						) < 1e-3

         lowerCAmelCase					:					List[str]								=							DPMSolverSinglestepScheduler.from_config(scheduler.config						)
         lowerCAmelCase					:					Any								=							DPMSolverMultistepScheduler.from_config(scheduler.config						)
         lowerCAmelCase					:					str								=							UniPCMultistepScheduler.from_config(scheduler.config						)
         lowerCAmelCase					:					Union[str, Any]								=							DEISMultistepScheduler.from_config(scheduler.config						)

         lowerCAmelCase					:					Union[str, Any]								=							self.full_loop(scheduler=snake_case__						)
         lowerCAmelCase					:					str								=							torch.mean(torch.abs(snake_case__						)						)

         assert abs(result_mean.item() - 0.23916						) < 1e-3

       def 	lowercase__							(						self						):






         """simple docstring"""





         for timesteps in [25, 50, 100, 999, 1_000]:
           self.check_over_configs(num_train_timesteps=snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         self.check_over_configs(thresholding=snake_case__						)
         for order in [1, 2, 3]:
           for solver_type in ["logrho"]:
             for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     thresholding=snake_case__		,					prediction_type=snake_case__		,					sample_max_value=snake_case__		,					algorithm_type="deis"		,					solver_order=snake_case__		,					solver_type=snake_case__		,					)

       def 	lowercase__							(						self						):






         """simple docstring"""





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

       def 	lowercase__							(						self						):






         """simple docstring"""





         for algorithm_type in ["deis"]:
           for solver_type in ["logrho"]:
             for order in [1, 2, 3]:
               for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     solver_order=snake_case__		,					solver_type=snake_case__		,					prediction_type=snake_case__		,					algorithm_type=snake_case__		,					)
                 lowerCAmelCase					:					Any								=							self.full_loop(
                     solver_order=snake_case__		,					solver_type=snake_case__		,					prediction_type=snake_case__		,					algorithm_type=snake_case__		,					)
                 assert not torch.isnan(snake_case__						).any(), "Samples have nan numbers"

       def 	lowercase__							(						self						):






         """simple docstring"""





         self.check_over_configs(lower_order_final=snake_case__						)
         self.check_over_configs(lower_order_final=snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
           self.check_over_forward(num_inference_steps=snake_case__		,					time_step=0						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					List[str]								=							self.full_loop()
         lowerCAmelCase					:					Dict								=							torch.mean(torch.abs(snake_case__						)						)

         assert abs(result_mean.item() - 0.23916						) < 1e-3

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							self.full_loop(prediction_type="v_prediction"						)
         lowerCAmelCase					:					List[Any]								=							torch.mean(torch.abs(snake_case__						)						)

         assert abs(result_mean.item() - 0.091						) < 1e-3







       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							self.scheduler_classes[0]
         lowerCAmelCase					:					str								=							self.get_scheduler_config(thresholding=snake_case__		,					dynamic_thresholding_ratio=0						)
         lowerCAmelCase					:					Optional[Any]								=							scheduler_class(**snake_case__						)

         lowerCAmelCase					:					Optional[Any]								=							10
         lowerCAmelCase					:					Tuple								=							self.dummy_model()
         lowerCAmelCase					:					List[str]								=							self.dummy_sample_deter.half()
         scheduler.set_timesteps(snake_case__						)

         for i, t in enumerate(scheduler.timesteps						):
           lowerCAmelCase					:					Union[str, Any]								=							model(snake_case__		,					snake_case__						)
           lowerCAmelCase					:					Tuple								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__						).prev_sample

         assert sample.dtype == torch.floataa





