


'''simple docstring'''



import sys
from collections import defaultdict


class 						_snake_case    :




					def __init__(					self):
								UpperCAmelCase__     :    Any         =   []




					def  snake_case__  (					self							,							_lowerCamelCase):
								return self.node_position[vertex]




					def  snake_case__  (					self							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__     :    Tuple         =   pos




					def  snake_case__  (					self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								if start > size // 2 - 1:
											return
								else:
											if 2 * start + 2 >= size:
														UpperCAmelCase__     :    List[str]         =   2 * start + 1
											else:
														if heap[2 * start + 1] < heap[2 * start + 2]:
																	UpperCAmelCase__     :    Dict         =   2 * start + 1
														else:
																	UpperCAmelCase__     :    Optional[int]         =   2 * start + 2
											if heap[smallest_child] < heap[start]:
														UpperCAmelCase__     :    List[str]         =   heap[smallest_child], positions[smallest_child]
														UpperCAmelCase__     :    Optional[int]         =   (
														    heap[start],
														    positions[start],
														)
														UpperCAmelCase__     :    List[Any]         =   temp, tempa

														UpperCAmelCase__     :    Optional[Any]         =   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  snake_case__  (					self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__     :    Union[str, Any]         =   position[index]

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

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




					def  snake_case__  (					self							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__     :    str         =   len(__lowerCamelCase) // 2 - 1
								for i in range(__lowerCamelCase							,							-1							,							-1):
											self.top_to_bottom(__lowerCamelCase							,							__lowerCamelCase							,							len(__lowerCamelCase)							,							__lowerCamelCase)




					def  snake_case__  (					self							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__     :    Tuple         =   positions[0]
								UpperCAmelCase__     :    List[str]         =   sys.maxsize
								self.top_to_bottom(__lowerCamelCase							,							0							,							len(__lowerCamelCase)							,							__lowerCamelCase)
								return temp







def 			_UpperCamelCase					(						UpperCamelCase__    ):
			UpperCAmelCase__     :    int         =   Heap()

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

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

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

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


if __name__ == "__main__":  # pragma: no cover
		# < --------- Prims Algorithm --------- >
		__A		=int(input('Enter number of edges: ').strip())
		__A		=defaultdict(list)
		for _ in range(edges_number):
				__A		=[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))



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.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 650, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "pytorch",
            "script": "run_ddp.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "tensorflow",
            "script": "run_tf_dist.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.6, "eval_loss": 0.7},
        },
    ])
class      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


     def  _lowerCamelCase (						self)			->				str:
            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=__lowerCamelCase ,						)
            assert hasattr(self ,						"env")


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Tuple:
            _A      :       Dict				    = F"{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"
            # distributed data settings
            _A      :       Optional[Any]				    = {"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None

            # 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=__lowerCamelCase ,						instance_count=__lowerCamelCase ,						instance_type=self.instance_type ,						debugger_hook_config=__lowerCamelCase ,						hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path} ,						metric_definitions=self.env.metric_definitions ,						distribution=__lowerCamelCase ,						py_version="py36" ,						)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Optional[Any]:
            TrainingJobAnalytics(__lowerCamelCase).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv")


     @parameterized.expand([(2,)])
     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Any:
            # create estimator
            _A      :       Union[str, Any]				    = self.create_estimator(__lowerCamelCase)

            # run training
            estimator.fit()

            # result dataframe
            _A      :       Optional[Any]				    = TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

            # extract kpis
            _A      :       List[Any]				    = list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"])
            _A      :       Dict				    = list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"])
            # get train time from SageMaker job, this includes starting, preprocessing, stopping
            _A      :       Optional[Any]				    = (
                Session().describe_training_job(estimator.latest_training_job.name).get("TrainingTimeInSeconds" ,						9_9_9_9_9_9)
            )

            # 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} ,						__lowerCamelCase)
