

import heapq as hq
import math
from collections.abc import Iterator



class 						_A     :





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		List[Any]        =		str(id_    )
      snake_case							:		Optional[Any]        =		None
      snake_case							:		Tuple        =		None
      snake_case							:		Any        =		[]
      snake_case							:		List[str]        =		{}  # {vertex:distance}





  def __lt__(  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      return self.key < other.key





  def __repr__(  self    ):

      '''simple docstring'''
      return self.id





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      self.neighbors.append(SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Optional[Any]        =		weight







def       lowercase					(	__A :					Dict ,    __A :					Optional[int] ,    __A :					Tuple ,    __A :					List[Any]		)				->			List[str]:





    '''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					(	__A :					Union[str, Any] ,    __A :					str		)				->			int:





    '''simple docstring'''




    snake_case							:		Optional[int]        =		[]
    for u in graph:
        snake_case							:		Tuple        =		math.inf
        snake_case							:		Dict        =		None
    snake_case							:		Union[str, Any]        =		0
    snake_case							:		str        =		graph[:]
    while q:
        snake_case							:		List[str]        =		min(__A		)
        q.remove(__A		)
        for v in u.neighbors:
            if (v in q) and (u.edges[v.id] < v.key):
                snake_case							:		int        =		u
                snake_case							:		List[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					(	__A :					Optional[Any] ,    __A :					Optional[int]		)				->			List[Any]:





    '''simple docstring'''




    for u in graph:
        snake_case							:		str        =		math.inf
        snake_case							:		Optional[Any]        =		None
    snake_case							:		int        =		0

    snake_case							:		List[Any]        =		list(__A		)
    hq.heapify(__A		)

    while h:
        snake_case							:		Optional[Any]        =		hq.heappop(__A		)
        for v in u.neighbors:
            if (v in h) and (u.edges[v.id] < v.key):
                snake_case							:		Optional[Any]        =		u
                snake_case							:		Optional[Any]        =		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					(	)				->			Optional[Any]:





    '''simple docstring'''









if __name__ == "__main__":
     import doctest

     doctest.testmod()








'''simple docstring'''





import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class __A     (    A  ,  A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			List[Any]		=			IFInpaintingSuperResolutionPipeline
 __lowerCamelCase       :			Tuple		=			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
 __lowerCamelCase       :			Optional[Any]		=			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'}		)
 __lowerCamelCase       :			str		=			PipelineTesterMixin.required_optional_params - {'latents'}



 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      return self._get_superresolution_dummy_components()



 def    a__    (self					,  A					,  A=0			)       ->	List[Any]:


      """simple docstring"""



      if str(A			).startswith('''mps'''			):
           _a           =    torch.manual_seed(A			)
      else:
           _a           =    torch.Generator(device=A			).manual_seed(A			)

      _a           =    floats_tensor((1, 3, 16, 16)					,  rng=random.Random(A			)			).to(A			)
      _a           =    floats_tensor((1, 3, 32, 32)					,  rng=random.Random(A			)			).to(A			)
      _a           =    floats_tensor((1, 3, 32, 32)					,  rng=random.Random(A			)			).to(A			)

      _a           =    {
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''image''': image,
          '''original_image''': original_image,
          '''mask_image''': mask_image,
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''output_type''': '''numpy''',
      }

      return inputs



 @unittest.skipIf(
     torch_device != '''cuda''' or not is_xformers_available()					,  reason='''XFormers attention is only available with CUDA and `xformers` installed'''					,  )
 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3			)



 def    a__    (self			)       ->	str:


      """simple docstring"""



      self._test_save_load_optional_components()



 @unittest.skipIf(torch_device != '''cuda'''					,  reason='''float16 requires CUDA'''			)
 def    a__    (self			)       ->	str:


      """simple docstring"""



      super().test_save_load_floataa(expected_max_diff=1E-1			)



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2			)



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      self._test_save_load_local()



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2					,  )



