


import heapq as hq
import math
from collections.abc import Iterator
class 	UpperCAmelCase_					:

   '''simple docstring'''




   def __init__(		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				str(id_ )
     lowerCamelCase :					List[Any]    =				None
     lowerCamelCase :					int    =				None
     lowerCamelCase :					Union[str, Any]    =				[]
     lowerCamelCase :					Any    =				{}  # {vertex:distance}






   def __lt__(		self						,				__A ):






     """simple docstring"""





     return self.key < other.key






   def __repr__(		self ):






     """simple docstring"""





     return self.id






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     self.neighbors.append(_a )






   def _snake_case    (		self						,				__A						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				weight






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  graph[a - 1].add_neighbor(graph[b - 1]	)
  graph[b - 1].add_neighbor(graph[a - 1]	)
  # add the edges:
  graph[a - 1].add_edge(graph[b - 1]      ,						__a	)
  graph[b - 1].add_edge(graph[a - 1]      ,						__a	)






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					List[str]    =				[]
  for u in graph:
    lowerCamelCase :					Any    =				math.inf
    lowerCamelCase :					Optional[Any]    =				None
  lowerCamelCase :					Any    =				0
  lowerCamelCase :					Tuple    =				graph[:]
  while q:
    lowerCamelCase :					Any    =				min(__a	)
    q.remove(__a	)
    for v in u.neighbors:
      if (v in q) and (u.edges[v.id] < v.key):
        lowerCamelCase :					List[Any]    =				u
        lowerCamelCase :					Any    =				u.edges[v.id]
  for i in range(1      ,						len(__a	)	):
    a.append((int(graph[i].id	) + 1, int(graph[i].pi.id	) + 1)	)
  return a






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  for u in graph:
    lowerCamelCase :					Any    =				math.inf
    lowerCamelCase :					List[Any]    =				None
  lowerCamelCase :					Union[str, Any]    =				0

  lowerCamelCase :					Tuple    =				list(__a	)
  hq.heapify(__a	)

  while h:
    lowerCamelCase :					Any    =				hq.heappop(__a	)
    for v in u.neighbors:
      if (v in h) and (u.edges[v.id] < v.key):
        lowerCamelCase :					int    =				u
        lowerCamelCase :					str    =				u.edges[v.id]
        hq.heapify(__a	)

  for i in range(1      ,						len(__a	)	):
    yield (int(graph[i].id	) + 1, int(graph[i].pi.id	) + 1)






def 		lowercase_(  ):


  '''simple docstring'''




if __name__ == "__main__":
   import doctest

   doctest.testmod()





'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  @property
  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	int			=   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   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a				:	str			=   self.dummy_uncond_unet
     _a				:	int			=   PNDMScheduler()

     _a				:	str			=   PNDMPipeline(unet=_a							,scheduler=_a       )
     pndm.to(_a       )
     pndm.set_progress_bar_config(disable=_a       )

     _a				:	Optional[int]			=   torch.manual_seed(0       )
     _a				:	Optional[Any]			=   pndm(generator=_a							,num_inference_steps=20							,output_type='numpy'       ).images

     _a				:	List[str]			=   torch.manual_seed(0       )
     _a				:	Any			=   pndm(generator=_a							,num_inference_steps=20							,output_type='numpy'							,return_dict=_a       )[0]

     _a				:	List[Any]			=   image[0, -3:, -3:, -1]
     _a				:	Any			=   image_from_tuple[0, -3:, -3:, -1]

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





  """simple docstring"""

  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	List[str]			=   'google/ddpm-cifar10-32'

     _a				:	str			=   UNetaDModel.from_pretrained(_a       )
     _a				:	Union[str, Any]			=   PNDMScheduler()

     _a				:	Tuple			=   PNDMPipeline(unet=_a							,scheduler=_a       )
     pndm.to(_a       )
     pndm.set_progress_bar_config(disable=_a       )
     _a				:	str			=   torch.manual_seed(0       )
     _a				:	Optional[Any]			=   pndm(generator=_a							,output_type='numpy'       ).images

     _a				:	Dict			=   image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     _a				:	Tuple			=   np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]       )

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



