





from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

_SCREAMING_SNAKE_CASE      	=		TypeVar("""T""")


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
 return (position - 1) // 2


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
 return (2 * position) + 1


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
 return (2 * position) + 2
class SCREAMING_SNAKE_CASE_						(					Generic[T]					):






      def __init__(      self      :      int	)							->    None:







       """simple docstring"""


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






      def __len__(      self      :      List[Any]	)							->    int:







       """simple docstring"""


       return self.elements






      def __repr__(      self      :      Optional[Any]	)							->    str:







       """simple docstring"""


       return str(self.heap	)






      def UpperCAmelCase_				(      self      :      Dict	)							->    bool:







       """simple docstring"""


       return self.elements == 0






      def UpperCAmelCase_				(      self      :      List[str]		,   _A      :      T		,   _A      :      int	)							->    None:







       """simple docstring"""


       self.heap.append((elem, weight)	)
       snake_case_		:			Optional[int]											=	self.elements
       self.elements += 1
       self._bubble_up(_A	)






      def UpperCAmelCase_				(      self      :      List[str]	)							->    T:







       """simple docstring"""


       if self.elements > 1:
        self._swap_nodes(0		,   self.elements - 1	)
       snake_case_		:			Optional[int]											=	self.heap.pop()
       del self.position_map[elem]
       self.elements -= 1
       if self.elements > 0:
        snake_case_		:			List[str]											=	self.heap[0]
        self._bubble_down(_A	)
       return elem






      def UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      T		,   _A      :      int	)							->    None:







       """simple docstring"""


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






      def UpperCAmelCase_				(      self      :      Union[str, Any]		,   _A      :      T	)							->    None:







       """simple docstring"""


       snake_case_		:			Tuple											=	self.position_map[elem]
       if curr_pos == 0:
        return None
       snake_case_		:			Dict											=	get_parent_position(_A	)
       snake_case_		:			Tuple											=	self.heap[curr_pos]
       snake_case_		:			Optional[Any]											=	self.heap[parent_position]
       if parent_weight > weight:
        self._swap_nodes(_A		,   _A	)
        return self._bubble_up(_A	)
       return None






      def UpperCAmelCase_				(      self      :      Tuple		,   _A      :      T	)							->    None:







       """simple docstring"""


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






      def UpperCAmelCase_				(      self      :      List[Any]		,   _A      :      int		,   _A      :      int	)							->    None:







       """simple docstring"""


       snake_case_		:			Dict											=	self.heap[nodea_pos][0]
       snake_case_		:			Union[str, Any]											=	self.heap[nodea_pos][0]
       snake_case_		:			Union[str, Any]											=	(
           self.heap[nodea_pos],
           self.heap[nodea_pos],
       )
       snake_case_		:			Dict											=	nodea_pos
       snake_case_		:			Dict											=	nodea_pos
class SCREAMING_SNAKE_CASE_						(					Generic[T]					):






      def __init__(      self      :      List[Any]	)							->    None:







       """simple docstring"""


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






      def __repr__(      self      :      int	)							->    str:







       """simple docstring"""


       return str(self.connections	)






      def __len__(      self      :      Tuple	)							->    int:







       """simple docstring"""


       return self.nodes






      def UpperCAmelCase_				(      self      :      str		,   _A      :      T	)							->    None:







       """simple docstring"""


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






      def UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      T		,   _A      :      T		,   _A      :      int	)							->    None:







       """simple docstring"""


       self.add_node(_A	)
       self.add_node(_A	)
       snake_case_		:			str											=	weight
       snake_case_		:			Tuple											=	weight


def 							SCREAMING_SNAKE_CASE__						(    __a   , ):
 snake_case_		:			dict[T, int]											=	{node: maxsize for node in graph.connections}
 snake_case_		:			dict[T, T | None]											=	{node: None for node in graph.connections}

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

 if priority_queue.is_empty():
  return dist, parent

 # initialization
 snake_case_		:			Tuple											=	priority_queue.extract_min()
 snake_case_		:			Dict											=	0
 for neighbour in graph.connections[node]:
  if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
   snake_case_		:			List[str]											=	dist[node] + graph.connections[node][neighbour]
   priority_queue.update_key(__a   , dist[neighbour]   )
   snake_case_		:			Tuple											=	node

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








import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						@property
						def UpperCAmelCase_				(      self      :      Any	)							->    Optional[Any]:







							"""simple docstring"""


							torch.manual_seed(0	)
							snake_case_		:			List[Any]											=	UNetaDModel(
							    block_out_channels=(32, 64)		,   layers_per_block=2		,   sample_size=32		,   in_channels=3		,   out_channels=3		,   down_block_types=('DownBlock2D', 'AttnDownBlock2D')		,   up_block_types=('AttnUpBlock2D', 'UpBlock2D')		,   )
							return model






						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    Tuple:







							"""simple docstring"""


							snake_case_		:			List[Any]											=	self.dummy_uncond_unet
							snake_case_		:			Optional[Any]											=	ScoreSdeVeScheduler()

							snake_case_		:			Tuple											=	ScoreSdeVePipeline(unet=_A		,   scheduler=_A	)
							sde_ve.to(_A	)
							sde_ve.set_progress_bar_config(disable=_A	)

							snake_case_		:			Optional[Any]											=	torch.manual_seed(0	)
							snake_case_		:			Tuple											=	sde_ve(num_inference_steps=2		,   output_type='numpy'		,   generator=_A	).images

							snake_case_		:			int											=	torch.manual_seed(0	)
							snake_case_		:			Optional[int]											=	sde_ve(num_inference_steps=2		,   output_type='numpy'		,   generator=_A		,   return_dict=_A	)[
							    0
							]

							snake_case_		:			List[str]											=	image[0, -3:, -3:, -1]
							snake_case_		:			Dict											=	image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_		:			List[Any]											=	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]	)

							assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1E-2
@slow
@require_torch
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def UpperCAmelCase_				(      self      :      Dict	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			Dict											=	'google/ncsnpp-church-256'
							snake_case_		:			List[Any]											=	UNetaDModel.from_pretrained(_A	)

							snake_case_		:			str											=	ScoreSdeVeScheduler.from_pretrained(_A	)

							snake_case_		:			Optional[Any]											=	ScoreSdeVePipeline(unet=_A		,   scheduler=_A	)
							sde_ve.to(_A	)
							sde_ve.set_progress_bar_config(disable=_A	)

							snake_case_		:			Any											=	torch.manual_seed(0	)
							snake_case_		:			Optional[int]											=	sde_ve(num_inference_steps=10		,   output_type='numpy'		,   generator=_A	).images

							snake_case_		:			Union[str, Any]											=	image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)

							snake_case_		:			Dict											=	np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]	)

							assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2

