




'''simple docstring'''


import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class     A					(	_a  ,unittest.TestCase					):
							lowercase_           =       DiTPipeline
							lowercase_           =       CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
							lowercase_           =       PipelineTesterMixin.required_optional_params - {
							    'latents',
							    'num_images_per_prompt',
							    'callback',
							    'callback_steps',
							}
							lowercase_           =       CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
							lowercase_           =       False







							def      __lowerCAmelCase    (						self :						int      )    ->							int:


														"""simple docstring"""


														torch.manual_seed(0      )
														_a			  =       TransformeraDModel(
														    sample_size=16							,    num_layers=2							,    patch_size=4							,    attention_head_dim=8							,    num_attention_heads=2							,    in_channels=4							,    out_channels=8							,    attention_bias=lowerCAmelCase_							,    activation_fn='''gelu-approximate'''							,    num_embeds_ada_norm=10_00							,    norm_type='''ada_norm_zero'''							,    norm_elementwise_affine=lowerCAmelCase_							,    )
														_a			  =       AutoencoderKL()
														_a			  =       DDIMScheduler()
														_a			  =       {'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
														return components







							def      __lowerCAmelCase    (						self :						List[Any]							,    lowerCAmelCase_ :						List[Any]							,    lowerCAmelCase_ :						int=0      )    ->							Optional[Any]:


														"""simple docstring"""


														if str(lowerCAmelCase_      ).startswith('''mps'''      ):
																					_a			  =       torch.manual_seed(lowerCAmelCase_      )
														else:
																					_a			  =       torch.Generator(device=lowerCAmelCase_      ).manual_seed(lowerCAmelCase_      )
														_a			  =       {
														    '''class_labels''': [1],
														    '''generator''': generator,
														    '''num_inference_steps''': 2,
														    '''output_type''': '''numpy''',
														}
														return inputs







							def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							int:


														"""simple docstring"""


														_a			  =       '''cpu'''

														_a			  =       self.get_dummy_components()
														_a			  =       self.pipeline_class(**lowerCAmelCase_      )
														pipe.to(lowerCAmelCase_      )
														pipe.set_progress_bar_config(disable=lowerCAmelCase_      )

														_a			  =       self.get_dummy_inputs(lowerCAmelCase_      )
														_a			  =       pipe(**lowerCAmelCase_      ).images
														_a			  =       image[0, -3:, -3:, -1]

														self.assertEqual(image.shape							,    (1, 16, 16, 3)      )
														_a			  =       np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7]      )
														_a			  =       np.abs(image_slice.flatten() - expected_slice      ).max()
														self.assertLessEqual(lowerCAmelCase_							,    1e-3      )







							def      __lowerCAmelCase    (						self :						Any      )    ->							Dict:


														"""simple docstring"""


														self._test_inference_batch_single_identical(relax_max_difference=lowerCAmelCase_							,    expected_max_diff=1e-3      )







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


														"""simple docstring"""


														self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3      )



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







							def      __lowerCAmelCase    (						self :						str      )    ->							List[str]:


														"""simple docstring"""


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







							def      __lowerCAmelCase    (						self :						List[str]      )    ->							Dict:


														"""simple docstring"""


														_a			  =       torch.manual_seed(0      )

														_a			  =       DiTPipeline.from_pretrained('''facebook/DiT-XL-2-256'''      )
														pipe.to('''cuda'''      )

														_a			  =       ['''vase''', '''umbrella''', '''white shark''', '''white wolf''']
														_a			  =       pipe.get_label_ids(lowerCAmelCase_      )

														_a			  =       pipe(lowerCAmelCase_							,    generator=lowerCAmelCase_							,    num_inference_steps=40							,    output_type='''np'''      ).images

														for word, image in zip(lowerCAmelCase_							,    lowerCAmelCase_      ):
																					_a			  =       load_numpy(
																					    F'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'      )
																					assert np.abs((expected_image - image).max()      ) < 1e-2







							def      __lowerCAmelCase    (						self :						Optional[Any]      )    ->							int:


														"""simple docstring"""


														_a			  =       DiTPipeline.from_pretrained('''facebook/DiT-XL-2-512'''      )
														_a			  =       DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
														pipe.to('''cuda'''      )

														_a			  =       ['''vase''', '''umbrella''']
														_a			  =       pipe.get_label_ids(lowerCAmelCase_      )

														_a			  =       torch.manual_seed(0      )
														_a			  =       pipe(lowerCAmelCase_							,    generator=lowerCAmelCase_							,    num_inference_steps=25							,    output_type='''np'''      ).images

														for word, image in zip(lowerCAmelCase_							,    lowerCAmelCase_      ):
																					_a			  =       load_numpy(
																					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
																					    F'/dit/{word}_512.npy'      )

																					assert np.abs((expected_image - image).max()      ) < 1e-1







'''simple docstring'''


import sys
from collections import defaultdict

class     A					:







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


														"""simple docstring"""


														_a			  =       []







							def      __lowerCAmelCase    (						self :						Union[str, Any]							,    lowerCAmelCase_ :						List[Any]      )    ->							int:


														"""simple docstring"""


														return self.node_position[vertex]







							def      __lowerCAmelCase    (						self :						List[Any]							,    lowerCAmelCase_ :						Optional[Any]							,    lowerCAmelCase_ :						Optional[Any]      )    ->							Tuple:


														"""simple docstring"""


														_a			  =       pos







							def      __lowerCAmelCase    (						self :						Dict							,    lowerCAmelCase_ :						str							,    lowerCAmelCase_ :						Any							,    lowerCAmelCase_ :						Dict							,    lowerCAmelCase_ :						int      )    ->							Any:


														"""simple docstring"""


														if start > size // 2 - 1:
																					return
														else:
																					if 2 * start + 2 >= size:
																												_a			  =       2 * start + 1
																					else:
																												if heap[2 * start + 1] < heap[2 * start + 2]:
																																			_a			  =       2 * start + 1
																												else:
																																			_a			  =       2 * start + 2
																					if heap[smallest_child] < heap[start]:
																												_a  ,							_a			  =       heap[smallest_child], positions[smallest_child]
																												_a  ,							_a			  =       (
																												    heap[start],
																												    positions[start],
																												)
																												_a  ,							_a			  =       temp, tempa

																												_a			  =       self.get_position(positions[smallest_child]      )
																												self.set_position(
																												    positions[smallest_child]							,    self.get_position(positions[start]      )      )
																												self.set_position(positions[start]							,    lowerCAmelCase_      )

																												self.top_to_bottom(lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Optional[int]							,    lowerCAmelCase_ :						List[str]							,    lowerCAmelCase_ :						List[Any]							,    lowerCAmelCase_ :						Any							,    lowerCAmelCase_ :						Dict      )    ->							Any:


														"""simple docstring"""


														_a			  =       position[index]

														while index != 0:
																					_a			  =       int((index - 2) / 2      ) if index % 2 == 0 else int((index - 1) / 2      )

																					if val < heap[parent]:
																												_a			  =       heap[parent]
																												_a			  =       position[parent]
																												self.set_position(position[parent]							,    lowerCAmelCase_      )
																					else:
																												_a			  =       val
																												_a			  =       temp
																												self.set_position(lowerCAmelCase_							,    lowerCAmelCase_      )
																												break
																					_a			  =       parent
														else:
																					_a			  =       val
																					_a			  =       temp
																					self.set_position(lowerCAmelCase_							,    0      )







							def      __lowerCAmelCase    (						self :						Any							,    lowerCAmelCase_ :						List[Any]							,    lowerCAmelCase_ :						Optional[Any]      )    ->							Optional[int]:


														"""simple docstring"""


														_a			  =       len(lowerCAmelCase_      ) // 2 - 1
														for i in range(lowerCAmelCase_							,    -1							,    -1      ):
																					self.top_to_bottom(lowerCAmelCase_							,    lowerCAmelCase_							,    len(lowerCAmelCase_      )							,    lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						str							,    lowerCAmelCase_ :						str							,    lowerCAmelCase_ :						int      )    ->							List[Any]:


														"""simple docstring"""


														_a			  =       positions[0]
														_a			  =       sys.maxsize
														self.top_to_bottom(lowerCAmelCase_							,    0							,    len(lowerCAmelCase_      )							,    lowerCAmelCase_      )
														return temp



def 	snake_case_					(UpperCamelCase :						Any   ):





							'''simple docstring'''


							_a			  =       Heap()

							_a			  =       [0] * len(UpperCamelCase   )
							_a			  =       [-1] * len(UpperCamelCase   )  # Neighboring Tree Vertex of selected vertex
							# Minimum Distance of explored vertex with neighboring vertex of partial tree
							# formed in graph
							_a			  =       []  # Heap of Distance of vertices from their neighboring vertex
							_a			  =       []

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

							_a			  =       []
							_a			  =       1
							_a			  =       sys.maxsize
							for neighbor, distance in adjacency_list[0]:
														_a			  =       0
														_a			  =       distance
							heap.heapify(UpperCamelCase				,		UpperCamelCase   )

							for _ in range(1				,		len(UpperCamelCase   )   ):
														_a			  =       heap.delete_minimum(UpperCamelCase				,		UpperCamelCase   )
														if visited[vertex] == 0:
																					tree_edges.append((nbr_tv[vertex], vertex)   )
																					_a			  =       1
																					for neighbor, distance in adjacency_list[vertex]:
																												if (
																												    visited[neighbor] == 0
																												    and distance < distance_tv[heap.get_position(UpperCamelCase   )]
																												):
																																			_a			  =       distance
																																			heap.bottom_to_top(
																																			    UpperCamelCase				,		heap.get_position(UpperCamelCase   )				,		UpperCamelCase				,		UpperCamelCase   )
																																			_a			  =       vertex
							return tree_edges


if __name__ == "__main__":  # pragma: no cover
				# < --------- Prims Algorithm --------- >
				_snake_case   :   List[str]     							=	int(input('Enter number of edges: ').strip())
				_snake_case   :   Union[str, Any]     							=	defaultdict(list)
				for _ in range(edges_number):
								_snake_case   :   Tuple     							=	[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))

