
import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests


def 						UpperCAmelCase      (     a_						)		->     List[Any]:







				"""simple docstring"""



				__A             =			{}

				__A             =			job["started_at"]
				__A             =			job["completed_at"]

				__A             =			date_parser.parse(a_						)
				__A             =			date_parser.parse(a_						)

				__A             =			round((end_datetime - start_datetime).total_seconds() / 60.0						)

				__A             =			start
				__A             =			end
				__A             =			duration_in_min

				return job_info


def 						UpperCAmelCase      (     a_		,       a_=None						)		->     List[Any]:







				"""simple docstring"""



				__A             =			None
				if token is not None:
								__A             =			{"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}

				__A             =			F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
				__A             =			requests.get(a_		,       headers=a_						).json()
				__A             =			{}

				try:
								job_time.update({job["name"]: extract_time_from_single_job(a_						) for job in result["jobs"]}						)
								__A             =			math.ceil((result["total_count"] - 1_0_0) / 1_0_0						)

								for i in range(a_						):
												__A             =			requests.get(url + F'''&page={i + 2}'''		,       headers=a_						).json()
												job_time.update({job["name"]: extract_time_from_single_job(a_						) for job in result["jobs"]}						)

								return job_time
				except Exception:
								print(F'''Unknown error, could not fetch links:\n{traceback.format_exc()}'''						)

				return {}


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Dict										=    argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
					SCREAMING_SNAKE_CASE							:List[str]										=    parser.parse_args()

					SCREAMING_SNAKE_CASE							:Optional[int]										=    get_job_time(args.workflow_run_id)
					SCREAMING_SNAKE_CASE							:int										=    dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))

					for k, v in job_time.items():
										print(f'''{k}: {v["duration"]}''')


from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

SCREAMING_SNAKE_CASE							:Tuple										=    datasets.load_iris()

SCREAMING_SNAKE_CASE							:Dict										=    np.array(data['data'])
SCREAMING_SNAKE_CASE							:Optional[int]										=    np.array(data['target'])
SCREAMING_SNAKE_CASE							:List[str]										=    data['target_names']

SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE							:List[Any]										=    train_test_split(X, y)


def 						UpperCAmelCase      (     a_		,       a_						)		->     Tuple:







				"""simple docstring"""



				return np.linalg.norm(np.array(a_						) - np.array(a_						)						)


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_		,       a_=5						)		->     Dict:







				"""simple docstring"""



				__A             =			zip(a_		,       a_						)
				# List of distances of all points from the point to be classified
				__A             =			[]
				for data_point in data:
								__A             =			euclidean_distance(data_point[0]		,       a_						)
								distances.append((distance, data_point[1])						)
				# Choosing 'k' points with the least distances.
				__A             =			[i[1] for i in sorted(a_						)[:k]]
				# Most commonly occurring class among them
				# is the class into which the point is classified
				__A             =			Counter(a_						).most_common(1						)[0][0]
				return classes[result]


if __name__ == "__main__":
					print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
