


import sys
from collections import defaultdict
class 						_UpperCamelCase						:
							'''simple docstring'''







							def __init__(  self			):
										__lowerCAmelCase       					=    []

							def     snake_case       (  self					,     __a			):
										return self.node_position[vertex]

							def     snake_case       (  self					,     __a					,     __a			):
										__lowerCAmelCase       					=    pos

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

																__lowerCAmelCase       					=    self.get_position(positions[smallest_child]			)
																self.set_position(
																    positions[smallest_child]					,     self.get_position(positions[start]			)			)
																self.set_position(positions[start]					,     UpperCamelCase_			)

																self.top_to_bottom(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_			)

							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    position[index]

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

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

							def     snake_case       (  self					,     __a					,     __a			):
										__lowerCAmelCase       					=    len(UpperCamelCase_			) // 2 - 1
										for i in range(UpperCamelCase_					,     -1					,     -1			):
													self.top_to_bottom(UpperCamelCase_					,     UpperCamelCase_					,     len(UpperCamelCase_			)					,     UpperCamelCase_			)



							def     snake_case       (  self					,     __a					,     __a			):
										__lowerCAmelCase       					=    positions[0]
										__lowerCAmelCase       					=    sys.maxsize
										self.top_to_bottom(UpperCamelCase_					,     0					,     len(UpperCamelCase_			)					,     UpperCamelCase_			)
										return temp


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    Heap()

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

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

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

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


if __name__ == "__main__":  # pragma: no cover
	# < --------- Prims Algorithm --------- >
	A  :     Any    					=    int(input("Enter number of edges: ").strip())
	A  :     int    					=    defaultdict(list)
	for _ in range(edges_number):
		A  :     List[str]    					=    [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 json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
	from sagemaker import Session, TrainingJobAnalytics
	from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
    literal_eval(os.getenv("""TEST_SAGEMAKER""" ,"""False"""	)	) is not True ,reason="""Skipping test because should only be run when releasing minor transformers version""" ,)
@pytest.mark.usefixtures("""sm_env"""	)
@parameterized_class(
    [
        {
            """framework""": """pytorch""",
            """script""": """run_glue_model_parallelism.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 1_6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 1_6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
    ]	)
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							def     snake_case       (  self			):
										if self.framework == "pytorch":
													subprocess.run(
													    f"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split()					,     encoding="utf-8"					,     check=__a					,     )
										assert hasattr(self					,     "env"			)

							def     snake_case       (  self					,     __a			):
										# configuration for running training on smdistributed Model Parallel
										__lowerCAmelCase       					=    {
										    "enabled": True,
										    "processes_per_host": 8,
										}
										__lowerCAmelCase       					=    {
										    "enabled": True,
										    "parameters": {
										        "microbatches": 4,
										        "placement_strategy": "spread",
										        "pipeline": "interleaved",
										        "optimize": "speed",
										        "partitions": 4,
										        "ddp": True,
										    },
										}

										__lowerCAmelCase       					=    {"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

										__lowerCAmelCase       					=    "trainer" if self.script == "run_glue.py" else "smtrainer"
										# creates estimator
										return HuggingFace(
										    entry_point=self.script					,     source_dir=self.env.test_path					,     role=self.env.role					,     image_uri=self.env.image_uri					,     base_job_name=f"{self.env.base_job_name}-{instance_count}-smp-{name_extension}"					,     instance_count=__a					,     instance_type=self.instance_type					,     debugger_hook_config=__a					,     hyperparameters={
										        **self.env.hyperparameters,
										        "model_name_or_path": self.model_name_or_path,
										        "max_steps": 5_00,
										    }					,     metric_definitions=self.env.metric_definitions					,     distribution=__a					,     py_version="py36"					,     )

							def     snake_case       (  self					,     __a			):
										TrainingJobAnalytics(__a			).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv"			)



							@parameterized.expand([(1,)]			)
							def     snake_case       (  self					,     __a			):
										# create estimator
										__lowerCAmelCase       					=    self.create_estimator(__a			)

										# run training
										estimator.fit()

										# result dataframe
										__lowerCAmelCase       					=    TrainingJobAnalytics(estimator.latest_training_job.name			).dataframe()

										# extract kpis
										__lowerCAmelCase       					=    list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]			)
										__lowerCAmelCase       					=    list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]			)
										# get train time from SageMaker job, this includes starting, preprocessing, stopping
										__lowerCAmelCase       					=    (
										    Session().describe_training_job(estimator.latest_training_job.name			).get("TrainingTimeInSeconds"					,     99_99_99			)
										)

										# assert kpis
										assert train_runtime <= self.results["train_runtime"]
										assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy			)
										assert all(t <= self.results["eval_loss"] for t in eval_loss			)

										# dump tests result into json file to share in PR
										with open(f"{estimator.latest_training_job.name}.json"					,     "w"			) as outfile:
													json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}					,     __a			)




