





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

@skip_mps
class      A       (	__a		,      __a		,      unittest.TestCase						):
      _snake_case							     =StableDiffusionPanoramaPipeline
      _snake_case							     =TEXT_TO_IMAGE_PARAMS
      _snake_case							     =TEXT_TO_IMAGE_BATCH_PARAMS
      _snake_case							     =TEXT_TO_IMAGE_IMAGE_PARAMS
      _snake_case							     =TEXT_TO_IMAGE_IMAGE_PARAMS



      def 	lowerCAmelCase__    (	self:						str       )						->    List[str]:
         '''simple docstring'''

         torch.manual_seed(0       )
         UpperCAmelCase_     :   Optional[int]											=UNetaDConditionModel(
             block_out_channels=(32, 64)		,  layers_per_block=1		,  sample_size=32		,  in_channels=4		,  out_channels=4		,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,  up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")		,  cross_attention_dim=32		,  )
         UpperCAmelCase_     :   Any											=DDIMScheduler()
         torch.manual_seed(0       )
         UpperCAmelCase_     :   int											=AutoencoderKL(
             block_out_channels=[32, 64]		,  in_channels=3		,  out_channels=3		,  down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]		,  up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]		,  latent_channels=4		,  )
         torch.manual_seed(0       )
         UpperCAmelCase_     :   Dict											=CLIPTextConfig(
             bos_token_id=0		,  eos_token_id=2		,  hidden_size=32		,  intermediate_size=37		,  layer_norm_eps=1e-05		,  num_attention_heads=4		,  num_hidden_layers=5		,  pad_token_id=1		,  vocab_size=1000		,  )
         UpperCAmelCase_     :   Optional[int]											=CLIPTextModel(A__       )
         UpperCAmelCase_     :   Tuple											=CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

         UpperCAmelCase_     :   Tuple											={
             "unet": unet,
             "scheduler": scheduler,
             "vae": vae,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "safety_checker": None,
             "feature_extractor": None,
         }
         return components



      def 	lowerCAmelCase__    (	self:						Optional[int]		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						Union[str, Any]=0       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_     :   List[Any]											=torch.manual_seed(A__       )
         UpperCAmelCase_     :   List[Any]											={
             "prompt": "a photo of the dolomites",
             "generator": generator,
             # Setting height and width to None to prevent OOMs on CPU.
             "height": None,
             "width": None,
             "num_inference_steps": 1,
             "guidance_scale": 6.0,
             "output_type": "numpy",
         }
         return inputs



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    List[str]:
         '''simple docstring'''

         UpperCAmelCase_     :   Optional[Any]											="cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_     :   Dict											=self.get_dummy_components()
         UpperCAmelCase_     :   str											=StableDiffusionPanoramaPipeline(**A__       )
         UpperCAmelCase_     :   List[str]											=sd_pipe.to(A__       )
         sd_pipe.set_progress_bar_config(disable=A__       )

         UpperCAmelCase_     :   int											=self.get_dummy_inputs(A__       )
         UpperCAmelCase_     :   Union[str, Any]											=sd_pipe(**A__       ).images
         UpperCAmelCase_     :   int											=image[0, -3:, -3:, -1]
         assert image.shape == (1, 64, 64, 3)

         UpperCAmelCase_     :   str											=np.array([0.61_86, 0.53_74, 0.49_15, 0.41_35, 0.41_14, 0.45_63, 0.51_28, 0.49_77, 0.47_57]       )

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



      def 	lowerCAmelCase__    (	self:						int       )						->    List[Any]:
         '''simple docstring'''

         super().test_inference_batch_consistent(batch_sizes=[1, 2]       )



      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    Optional[int]:
         '''simple docstring'''

         super().test_inference_batch_single_identical(batch_size=2		,  expected_max_diff=3.25e-3       )



      def 	lowerCAmelCase__    (	self:						str       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_     :   Any											="cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_     :   Union[str, Any]											=self.get_dummy_components()
         UpperCAmelCase_     :   str											=StableDiffusionPanoramaPipeline(**A__       )
         UpperCAmelCase_     :   int											=sd_pipe.to(A__       )
         sd_pipe.set_progress_bar_config(disable=A__       )

         UpperCAmelCase_     :   List[str]											=self.get_dummy_inputs(A__       )
         UpperCAmelCase_     :   List[Any]											="french fries"
         UpperCAmelCase_     :   Tuple											=sd_pipe(**A__		,  negative_prompt=A__       )
         UpperCAmelCase_     :   Any											=output.images
         UpperCAmelCase_     :   List[Any]											=image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)

         UpperCAmelCase_     :   List[Any]											=np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57]       )

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



      def 	lowerCAmelCase__    (	self:						str       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_     :   Tuple											="cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_     :   Tuple											=self.get_dummy_components()
         UpperCAmelCase_     :   List[str]											=StableDiffusionPanoramaPipeline(**A__       )
         UpperCAmelCase_     :   Tuple											=sd_pipe.to(A__       )
         sd_pipe.set_progress_bar_config(disable=A__       )

         UpperCAmelCase_     :   Dict											=self.get_dummy_inputs(A__       )
         UpperCAmelCase_     :   int											=sd_pipe(**A__		,  view_batch_size=2       )
         UpperCAmelCase_     :   Any											=output.images
         UpperCAmelCase_     :   Optional[Any]											=image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)

         UpperCAmelCase_     :   Optional[Any]											=np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57]       )

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



      def 	lowerCAmelCase__    (	self:						str       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_     :   Any											="cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_     :   str											=self.get_dummy_components()
         UpperCAmelCase_     :   Dict											=EulerAncestralDiscreteScheduler(
             beta_start=0.0_00_85		,  beta_end=0.0_12		,  beta_schedule="scaled_linear"       )
         UpperCAmelCase_     :   Union[str, Any]											=StableDiffusionPanoramaPipeline(**A__       )
         UpperCAmelCase_     :   List[str]											=sd_pipe.to(A__       )
         sd_pipe.set_progress_bar_config(disable=A__       )

         UpperCAmelCase_     :   Optional[int]											=self.get_dummy_inputs(A__       )
         UpperCAmelCase_     :   str											=sd_pipe(**A__       ).images
         UpperCAmelCase_     :   str											=image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)

         UpperCAmelCase_     :   Tuple											=np.array([0.40_24, 0.65_10, 0.49_01, 0.53_78, 0.58_13, 0.56_22, 0.47_95, 0.44_67, 0.49_52]       )

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






      def 	lowerCAmelCase__    (	self:						Dict       )						->    int:
         '''simple docstring'''

         UpperCAmelCase_     :   List[str]											="cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_     :   Optional[int]											=self.get_dummy_components()
         UpperCAmelCase_     :   Any											=PNDMScheduler(
             beta_start=0.0_00_85		,  beta_end=0.0_12		,  beta_schedule="scaled_linear"		,  skip_prk_steps=A__       )
         UpperCAmelCase_     :   int											=StableDiffusionPanoramaPipeline(**A__       )
         UpperCAmelCase_     :   Tuple											=sd_pipe.to(A__       )
         sd_pipe.set_progress_bar_config(disable=A__       )

         UpperCAmelCase_     :   List[str]											=self.get_dummy_inputs(A__       )
         UpperCAmelCase_     :   Tuple											=sd_pipe(**A__       ).images
         UpperCAmelCase_     :   Any											=image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)

         UpperCAmelCase_     :   Dict											=np.array([0.63_91, 0.62_91, 0.48_61, 0.51_34, 0.55_52, 0.45_78, 0.50_32, 0.50_23, 0.45_39]       )

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

@slow
@require_torch_gpu
class      A       (	unittest.TestCase						):



      def 	lowerCAmelCase__    (	self:						Tuple       )						->    Dict:
         '''simple docstring'''

         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()



      def 	lowerCAmelCase__    (	self:						str		,  _lowerCAmelCase:						List[Any]=0       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_     :   Tuple											=torch.manual_seed(A__       )
         UpperCAmelCase_     :   str											={
             "prompt": "a photo of the dolomites",
             "generator": generator,
             "num_inference_steps": 3,
             "guidance_scale": 7.5,
             "output_type": "numpy",
         }
         return inputs



      def 	lowerCAmelCase__    (	self:						Dict       )						->    Any:
         '''simple docstring'''

         UpperCAmelCase_     :   List[str]											="stabilityai/stable-diffusion-2-base"
         UpperCAmelCase_     :   Optional[int]											=DDIMScheduler.from_pretrained(A__		,  subfolder="scheduler"       )
         UpperCAmelCase_     :   List[Any]											=StableDiffusionPanoramaPipeline.from_pretrained(A__		,  scheduler=A__		,  safety_checker=A__       )
         pipe.to(A__       )
         pipe.set_progress_bar_config(disable=A__       )
         pipe.enable_attention_slicing()

         UpperCAmelCase_     :   Tuple											=self.get_inputs()
         UpperCAmelCase_     :   Tuple											=pipe(**A__       ).images
         UpperCAmelCase_     :   Tuple											=image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 2048, 3)

         UpperCAmelCase_     :   Any											=np.array(
             [
                 0.36_96_83_92,
                 0.27_02_53_72,
                 0.32_44_67_66,
                 0.28_37_93_87,
                 0.36_36_32_74,
                 0.30_73_33_47,
                 0.27_10_00_27,
                 0.27_05_41_25,
                 0.25_53_60_96,
             ]       )

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



      def 	lowerCAmelCase__    (	self:						str       )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_     :   int											=StableDiffusionPanoramaPipeline.from_pretrained(
             "stabilityai/stable-diffusion-2-base"		,  safety_checker=A__       )
         UpperCAmelCase_     :   str											=LMSDiscreteScheduler.from_config(pipe.scheduler.config       )
         pipe.to(A__       )
         pipe.set_progress_bar_config(disable=A__       )
         pipe.enable_attention_slicing()

         UpperCAmelCase_     :   Tuple											=self.get_inputs()
         UpperCAmelCase_     :   List[Any]											=pipe(**A__       ).images
         UpperCAmelCase_     :   Any											=image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 2048, 3)

         UpperCAmelCase_     :   Optional[int]											=np.array(
             [
                 [
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                 ]
             ]       )

         assert np.abs(expected_slice - image_slice       ).max() < 1e-3



      def 	lowerCAmelCase__    (	self:						Dict       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_     :   List[str]											=0

         def callback_fn(_lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						Optional[Any]		,  _lowerCAmelCase:						Optional[Any]       ) -> None:
            UpperCAmelCase_     :   str											=True
            nonlocal number_of_steps
            number_of_steps += 1
            if step == 1:
               UpperCAmelCase_     :   List[Any]											=latents.detach().cpu().numpy()
               assert latents.shape == (1, 4, 64, 256)
               UpperCAmelCase_     :   Optional[Any]											=latents[0, -3:, -3:, -1]

               UpperCAmelCase_     :   List[Any]											=np.array(
                   [
                       0.18_68_18_69,
                       0.33_90_78_16,
                       0.5_36_12_76,
                       0.14_43_28_65,
                       -0.02_85_66_11,
                       -0.73_94_11_23,
                       0.23_39_79_87,
                       0.47_32_26_82,
                       -0.37_82_31_64,
                   ]       )
               assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 5e-2
            elif step == 2:
               UpperCAmelCase_     :   Tuple											=latents.detach().cpu().numpy()
               assert latents.shape == (1, 4, 64, 256)
               UpperCAmelCase_     :   Optional[int]											=latents[0, -3:, -3:, -1]

               UpperCAmelCase_     :   List[str]											=np.array(
                   [
                       0.18_53_96_45,
                       0.33_98_72_48,
                       0.5_37_85_59,
                       0.14_43_71_42,
                       -0.02_45_52_61,
                       -0.7_33_83_17,
                       0.23_99_07_55,
                       0.47_35_62_72,
                       -0.3_78_65_05,
                   ]       )

               assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 5e-2

         UpperCAmelCase_     :   List[Any]											=False

         UpperCAmelCase_     :   Any											="stabilityai/stable-diffusion-2-base"
         UpperCAmelCase_     :   Optional[Any]											=DDIMScheduler.from_pretrained(A__		,  subfolder="scheduler"       )
         UpperCAmelCase_     :   Optional[Any]											=StableDiffusionPanoramaPipeline.from_pretrained(A__		,  scheduler=A__		,  safety_checker=A__       )
         UpperCAmelCase_     :   Optional[int]											=pipe.to(A__       )
         pipe.set_progress_bar_config(disable=A__       )
         pipe.enable_attention_slicing()

         UpperCAmelCase_     :   Optional[Any]											=self.get_inputs()
         pipe(**A__		,  callback=A__		,  callback_steps=1       )
         assert callback_fn.has_been_called
         assert number_of_steps == 3






      def 	lowerCAmelCase__    (	self:						Dict       )						->    Union[str, Any]:
         '''simple docstring'''

         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         UpperCAmelCase_     :   Dict											="stabilityai/stable-diffusion-2-base"
         UpperCAmelCase_     :   List[str]											=DDIMScheduler.from_pretrained(A__		,  subfolder="scheduler"       )
         UpperCAmelCase_     :   Union[str, Any]											=StableDiffusionPanoramaPipeline.from_pretrained(A__		,  scheduler=A__		,  safety_checker=A__       )
         UpperCAmelCase_     :   List[Any]											=pipe.to(A__       )
         pipe.set_progress_bar_config(disable=A__       )
         pipe.enable_attention_slicing(1       )
         pipe.enable_sequential_cpu_offload()

         UpperCAmelCase_     :   Dict											=self.get_inputs()
         UpperCAmelCase_     :   Optional[int]											=pipe(**A__       )

         UpperCAmelCase_     :   Any											=torch.cuda.max_memory_allocated()
         # make sure that less than 5.2 GB is allocated
         assert mem_bytes < 5.5 * 10**9











from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

__lowercase  :   Optional[int]						      =TypeVar("""T""")




def     a__ (					lowercase__  ):






   '''simple docstring'''






   return (position - 1) // 2




def     a__ (					lowercase__  ):






   '''simple docstring'''






   return (2 * position) + 1




def     a__ (					lowercase__  ):






   '''simple docstring'''






   return (2 * position) + 2

class      A       (	Generic[T]						):



      def __init__(	self:						List[str]       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											=[]
         UpperCAmelCase_											={}
         UpperCAmelCase_											=0



      def __len__(	self:						Union[str, Any]       )						->    int:
         '''simple docstring'''

         return self.elements



      def __repr__(	self:						Dict       )						->    str:
         '''simple docstring'''

         return str(self.heap       )



      def 	lowerCAmelCase__    (	self:						Any       )						->    bool:
         '''simple docstring'''

         return self.elements == 0



      def 	lowerCAmelCase__    (	self:						Optional[int]		,  _lowerCAmelCase:						T		,  _lowerCAmelCase:						int       )						->    None:
         '''simple docstring'''

         self.heap.append((elem, weight)       )
         UpperCAmelCase_											=self.elements
         self.elements += 1
         self._bubble_up(_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Tuple       )						->    T:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						T		,  _lowerCAmelCase:						int       )						->    None:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Any		,  _lowerCAmelCase:						T       )						->    None:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						T       )						->    None:
         '''simple docstring'''

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






      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						int       )						->    None:
         '''simple docstring'''

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

class      A       (	Generic[T]						):



      def __init__(	self:						Tuple       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											={}
         UpperCAmelCase_											=0



      def __repr__(	self:						List[str]       )						->    str:
         '''simple docstring'''

         return str(self.connections       )



      def __len__(	self:						Optional[Any]       )						->    int:
         '''simple docstring'''

         return self.nodes



      def 	lowerCAmelCase__    (	self:						Union[str, Any]		,  _lowerCAmelCase:						T       )						->    None:
         '''simple docstring'''

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






      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						T		,  _lowerCAmelCase:						T		,  _lowerCAmelCase:						int       )						->    None:
         '''simple docstring'''

         self.add_node(_lowerCAmelCase       )
         self.add_node(_lowerCAmelCase       )
         UpperCAmelCase_											=weight
         UpperCAmelCase_											=weight




def     a__ (					lowercase__  ,    ):






   '''simple docstring'''






   UpperCAmelCase_											={node: maxsize for node in graph.connections}
   UpperCAmelCase_											={node: None for node in graph.connections}

   UpperCAmelCase_											=MinPriorityQueue()
   for node, weight in dist.items():
      priority_queue.push(lowercase__  ,    lowercase__  )

   if priority_queue.is_empty():
      return dist, parent

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

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




