






'''simple docstring'''
import sys
from collections import defaultdict


class        _UpperCAmelCase						:




    def __init__(			self  :       Optional[int]			):






       '''simple docstring'''






       lowercase_	:    Dict           =     []




    def 				lowerCAmelCase__				(			self  :       Optional[Any]  ,     a  :       str			):






       '''simple docstring'''






       return self.node_position[vertex]




    def 				lowerCAmelCase__				(			self  :       List[str]  ,     a  :       str  ,     a  :       int			):






       '''simple docstring'''






       lowercase_	:    int           =     pos




    def 				lowerCAmelCase__				(			self  :       Any  ,     a  :       List[str]  ,     a  :       Any  ,     a  :       Optional[Any]  ,     a  :       int			):






       '''simple docstring'''






       if start > size // 2 - 1:
          return
       else:
          if 2 * start + 2 >= size:
             lowercase_	:    str           =     2 * start + 1
          else:
             if heap[2 * start + 1] < heap[2 * start + 2]:
                lowercase_	:    int           =     2 * start + 1
             else:
                lowercase_	:    Any           =     2 * start + 2
          if heap[smallest_child] < heap[start]:
             lowercase_    ,		lowercase_	:    int           =     heap[smallest_child], positions[smallest_child]
             lowercase_    ,		lowercase_	:    Union[str, Any]           =     (
                 heap[start],
                 positions[start],
             )
             lowercase_    ,		lowercase_	:    List[Any]           =     temp, tempa

             lowercase_	:    Tuple           =     self.get_position(positions[smallest_child]			)
             self.set_position(
                 positions[smallest_child]  ,     self.get_position(positions[start]			)			)
             self.set_position(positions[start]  ,     a			)

             self.top_to_bottom(a  ,     a  ,     a  ,     a			)




    def 				lowerCAmelCase__				(			self  :       List[str]  ,     a  :       Tuple  ,     a  :       List[Any]  ,     a  :       Tuple  ,     a  :       List[Any]			):






       '''simple docstring'''






       lowercase_	:    List[Any]           =     position[index]

       while index != 0:
          lowercase_	:    List[str]           =     int((index - 2) / 2			) if index % 2 == 0 else int((index - 1) / 2			)

          if val < heap[parent]:
             lowercase_	:    List[Any]           =     heap[parent]
             lowercase_	:    Tuple           =     position[parent]
             self.set_position(position[parent]  ,     a			)
          else:
             lowercase_	:    Union[str, Any]           =     val
             lowercase_	:    str           =     temp
             self.set_position(a  ,     a			)
             break
          lowercase_	:    str           =     parent
       else:
          lowercase_	:    List[str]           =     val
          lowercase_	:    List[Any]           =     temp
          self.set_position(a  ,     0			)




    def 				lowerCAmelCase__				(			self  :       Any  ,     a  :       Any  ,     a  :       Tuple			):






       '''simple docstring'''






       lowercase_	:    Optional[Any]           =     len(a			) // 2 - 1
       for i in range(a  ,     -1  ,     -1			):
          self.top_to_bottom(a  ,     a  ,     len(a			)  ,     a			)





    def 				lowerCAmelCase__				(			self  :       Dict  ,     a  :       Optional[int]  ,     a  :       Tuple			):






       '''simple docstring'''






       lowercase_	:    Dict           =     positions[0]
       lowercase_	:    List[str]           =     sys.maxsize
       self.top_to_bottom(a  ,     0  ,     len(a			)  ,     a			)
       return temp




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    int           =     Heap()

   lowercase_	:    Optional[int]           =     [0] * len(_UpperCamelCase							)
   lowercase_	:    Dict           =     [-1] * len(_UpperCamelCase							)  # Neighboring Tree Vertex of selected vertex
   # Minimum Distance of explored vertex with neighboring vertex of partial tree
   # formed in graph
   lowercase_	:    Optional[Any]           =     []  # Heap of Distance of vertices from their neighboring vertex
   lowercase_	:    List[str]           =     []

   for vertex in range(len(_UpperCamelCase							)							):
      distance_tv.append(sys.maxsize							)
      positions.append(_UpperCamelCase							)
      heap.node_position.append(_UpperCamelCase							)

   lowercase_	:    int           =     []
   lowercase_	:    List[str]           =     1
   lowercase_	:    Dict           =     sys.maxsize
   for neighbor, distance in adjacency_list[0]:
      lowercase_	:    Any           =     0
      lowercase_	:    Union[str, Any]           =     distance
   heap.heapify(_UpperCamelCase						,		_UpperCamelCase							)

   for _ in range(1						,		len(_UpperCamelCase							)							):
      lowercase_	:    Any           =     heap.delete_minimum(_UpperCamelCase						,		_UpperCamelCase							)
      if visited[vertex] == 0:
         tree_edges.append((nbr_tv[vertex], vertex)							)
         lowercase_	:    Union[str, Any]           =     1
         for neighbor, distance in adjacency_list[vertex]:
            if (
                visited[neighbor] == 0
                and distance < distance_tv[heap.get_position(_UpperCamelCase							)]
            ):
               lowercase_	:    Optional[int]           =     distance
               heap.bottom_to_top(
                   _UpperCamelCase						,		heap.get_position(_UpperCamelCase							)						,		_UpperCamelCase						,		_UpperCamelCase							)
               lowercase_	:    Optional[int]           =     vertex
   return tree_edges


if __name__ == "__main__":  # pragma: no cover
 # < --------- Prims Algorithm --------- >
 UpperCamelCase__ =			int(input('Enter number of edges: ').strip())
 UpperCamelCase__ =			defaultdict(list)
 for _ in range(edges_number):
  UpperCamelCase__ =			[int(x) for x in input().strip().split()]
  adjacency_list[edge[0]].append([edge[1], edge[2]])
  adjacency_list[edge[1]].append([edge[0], edge[2]])
 print(prisms_algorithm(adjacency_list))












'''simple docstring'''
import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class        _UpperCAmelCase						(		snake_case				,					unittest.TestCase			):
    __lowerCamelCase:   Dict		       =    KandinskyVaaPriorPipeline
    __lowerCamelCase:   Optional[int]		       =    ['prompt']
    __lowerCamelCase:   Any		       =    ['prompt', 'negative_prompt']
    __lowerCamelCase:   List[Any]		       =    [
        'num_images_per_prompt',
        'generator',
        'num_inference_steps',
        'latents',
        'negative_prompt',
        'guidance_scale',
        'output_type',
        'return_dict',
    ]
    __lowerCamelCase:   List[Any]		       =    False




    @property
    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       return 3_2




    @property
    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       return 3_2




    @property
    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       return self.time_input_dim




    @property
    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       return self.time_input_dim * 4




    @property
    def 				lowerCAmelCase__				(			self  :       Union[str, Any]			):






       '''simple docstring'''






       return 1_0_0




    @property
    def 				lowerCAmelCase__				(			self  :       List[Any]			):






       '''simple docstring'''






       lowercase_	:    Any           =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)
       return tokenizer




    @property
    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       torch.manual_seed(0			)
       lowercase_	:    Tuple           =     CLIPTextConfig(
           bos_token_id=0  ,     eos_token_id=2  ,     hidden_size=self.text_embedder_hidden_size  ,     projection_dim=self.text_embedder_hidden_size  ,     intermediate_size=3_7  ,     layer_norm_eps=1e-05  ,     num_attention_heads=4  ,     num_hidden_layers=5  ,     pad_token_id=1  ,     vocab_size=1_0_0_0  ,     )
       return CLIPTextModelWithProjection(a			)




    @property
    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       torch.manual_seed(0			)

       lowercase_	:    List[str]           =     {
           "num_attention_heads": 2,
           "attention_head_dim": 1_2,
           "embedding_dim": self.text_embedder_hidden_size,
           "num_layers": 1,
       }

       lowercase_	:    Union[str, Any]           =     PriorTransformer(**a			)
       # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
       lowercase_	:    List[Any]           =     nn.Parameter(torch.ones(model.clip_std.shape			)			)
       return model




    @property
    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       torch.manual_seed(0			)
       lowercase_	:    Dict           =     CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size  ,     image_size=2_2_4  ,     projection_dim=self.text_embedder_hidden_size  ,     intermediate_size=3_7  ,     num_attention_heads=4  ,     num_channels=3  ,     num_hidden_layers=5  ,     patch_size=1_4  ,     )

       lowercase_	:    Optional[Any]           =     CLIPVisionModelWithProjection(a			)
       return model




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






       '''simple docstring'''






       lowercase_	:    List[str]           =     CLIPImageProcessor(
           crop_size=2_2_4  ,     do_center_crop=a  ,     do_normalize=a  ,     do_resize=a  ,     image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]  ,     image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]  ,     resample=3  ,     size=2_2_4  ,     )

       return image_processor




    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''simple docstring'''






       lowercase_	:    Any           =     self.dummy_prior
       lowercase_	:    Optional[Any]           =     self.dummy_image_encoder
       lowercase_	:    List[Any]           =     self.dummy_text_encoder
       lowercase_	:    Any           =     self.dummy_tokenizer
       lowercase_	:    Optional[Any]           =     self.dummy_image_processor

       lowercase_	:    List[str]           =     UnCLIPScheduler(
           variance_type="fixed_small_log"  ,     prediction_type="sample"  ,     num_train_timesteps=1_0_0_0  ,     clip_sample=a  ,     clip_sample_range=10.0  ,     )

       lowercase_	:    List[Any]           =     {
           "prior": prior,
           "image_encoder": image_encoder,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "scheduler": scheduler,
           "image_processor": image_processor,
       }

       return components




    def 				lowerCAmelCase__				(			self  :       Any  ,     a  :       Dict  ,     a  :       Dict=0			):






       '''simple docstring'''






       if str(a			).startswith("mps"			):
          lowercase_	:    int           =     torch.manual_seed(a			)
       else:
          lowercase_	:    Optional[Any]           =     torch.Generator(device=a			).manual_seed(a			)
       lowercase_	:    Any           =     {
           "prompt": "horse",
           "generator": generator,
           "guidance_scale": 4.0,
           "num_inference_steps": 2,
           "output_type": "np",
       }
       return inputs




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]			):






       '''simple docstring'''






       lowercase_	:    str           =     "cpu"

       lowercase_	:    Any           =     self.get_dummy_components()

       lowercase_	:    int           =     self.pipeline_class(**a			)
       lowercase_	:    Any           =     pipe.to(a			)

       pipe.set_progress_bar_config(disable=a			)

       lowercase_	:    Any           =     pipe(**self.get_dummy_inputs(a			)			)
       lowercase_	:    List[Any]           =     output.image_embeds

       lowercase_	:    str           =     pipe(
           **self.get_dummy_inputs(a			)  ,     return_dict=a  ,     )[0]

       lowercase_	:    Any           =     image[0, -1_0:]
       lowercase_	:    Dict           =     image_from_tuple[0, -1_0:]

       assert image.shape == (1, 3_2)

       lowercase_	:    int           =     np.array(
           [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]			)

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




    @skip_mps
    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       lowercase_	:    int           =     torch_device == "cpu"
       lowercase_	:    Tuple           =     True
       lowercase_	:    str           =     False

       self._test_inference_batch_single_identical(
           test_max_difference=a  ,     relax_max_difference=a  ,     test_mean_pixel_difference=a  ,     )





    @skip_mps
    def 				lowerCAmelCase__				(			self  :       Union[str, Any]			):






       '''simple docstring'''






       lowercase_	:    Any           =     torch_device == "cpu"
       lowercase_	:    int           =     False

       self._test_attention_slicing_forward_pass(
           test_max_difference=a  ,     test_mean_pixel_difference=a  ,     )




