


"""simple docstring"""


import re
import string

import numpy as np

import datasets


A     :   Optional[int]			    =			'\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'

A     :   Tuple			    =			'\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results["exact_match"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]\n    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    33.3\n\n'

A     :   str			    =			'\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,_KWARGS_DESCRIPTION		)
class   _UpperCamelCase  (   datasets.Metric		):
							'''simple docstring'''







							def 			snake_case				(     self	):
										return datasets.MetricInfo(
										    description=_DESCRIPTION    ,   citation=_CITATION    ,   inputs_description=_KWARGS_DESCRIPTION    ,   features=datasets.Features(
										        {
										            "predictions": datasets.Value("string"    ,   id="sequence"	),
										            "references": datasets.Value("string"    ,   id="sequence"	),
										        }	)    ,   reference_urls=[]    ,   )



							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a=None    ,   __a=False    ,   __a=False    ,   __a=False    ,   ):
										if regexes_to_ignore is not None:
													for s in regexes_to_ignore:
																__lowerCAmelCase				    =			np.array([re.sub(__a    ,   ""    ,   __a	) for x in predictions]	)
																__lowerCAmelCase				    =			np.array([re.sub(__a    ,   ""    ,   __a	) for x in references]	)
										else:
													__lowerCAmelCase				    =			np.asarray(__a	)
													__lowerCAmelCase				    =			np.asarray(__a	)

										if ignore_case:
													__lowerCAmelCase				    =			np.char.lower(__a	)
													__lowerCAmelCase				    =			np.char.lower(__a	)

										if ignore_punctuation:
													__lowerCAmelCase				    =			string.punctuation.maketrans(""    ,   ""    ,   string.punctuation	)
													__lowerCAmelCase				    =			np.char.translate(__a    ,   table=__a	)
													__lowerCAmelCase				    =			np.char.translate(__a    ,   table=__a	)

										if ignore_numbers:
													__lowerCAmelCase				    =			string.digits.maketrans(""    ,   ""    ,   string.digits	)
													__lowerCAmelCase				    =			np.char.translate(__a    ,   table=__a	)
													__lowerCAmelCase				    =			np.char.translate(__a    ,   table=__a	)

										__lowerCAmelCase				    =			predictions == references

										return {"exact_match": np.mean(__a	) * 1_00}






import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests


def 						UpperCAmelCase			(	a_						)		->	str:







				"""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						)		->	str:







				"""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							:Union[str, Any]							 =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
					SCREAMING_SNAKE_CASE							:Optional[int]							 =				parser.parse_args()

					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				get_job_time(args.workflow_run_id)
					SCREAMING_SNAKE_CASE							:Optional[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"]}''')
