




import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests




def     __magic_name__       (				A	:      Optional[int]     ):






			'''simple docstring'''







			a					=    {}

			a					=    job["started_at"]
			a					=    job["completed_at"]

			a					=    date_parser.parse(__UpperCamelCase     )
			a					=    date_parser.parse(__UpperCamelCase     )

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

			a					=    start
			a					=    end
			a					=    duration_in_min

			return job_info




def     __magic_name__       (				A	:      List[Any],						A	:      int=None     ):






			'''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(__UpperCamelCase,						headers=__UpperCamelCase     ).json()
			a					=    {}

			try:
						job_time.update({job["name"]: extract_time_from_single_job(__UpperCamelCase     ) for job in result["jobs"]}     )
						a					=    math.ceil((result["total_count"] - 100) / 100     )

						for i in range(__UpperCamelCase     ):
									a					=    requests.get(url + F"""&page={i + 2}""",						headers=__UpperCamelCase     ).json()
									job_time.update({job["name"]: extract_time_from_single_job(__UpperCamelCase     ) 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__":
		__lowerCAmelCase	:       str     =	argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
		__lowerCAmelCase	:       Union[str, Any]     =	parser.parse_args()

		__lowerCAmelCase	:       int     =	get_job_time(args.workflow_run_id)
		__lowerCAmelCase	:       List[Any]     =	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']}''')












import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser




def     __magic_name__       (				A	:      List[str]     ):






			'''simple docstring'''







			a					=    {}
			a					=    tokenizer(example["content"],						truncation=A     )["input_ids"]
			a					=    len(example["content"]     ) / len(output["input_ids"]     )
			return output


__lowerCAmelCase	:       Dict     =	HfArgumentParser(PretokenizationArguments)
__lowerCAmelCase	:       str     =	parser.parse_args()
if args.num_workers is None:
		__lowerCAmelCase	:       List[Any]     =	multiprocessing.cpu_count()
__lowerCAmelCase	:       str     =	AutoTokenizer.from_pretrained(args.tokenizer_dir)

__lowerCAmelCase	:       List[Any]     =	time.time()
__lowerCAmelCase	:       str     =	load_dataset(args.dataset_name, split='train')
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

__lowerCAmelCase	:       int     =	time.time()
__lowerCAmelCase	:       Optional[int]     =	ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        'repo_name',
        'path',
        'copies',
        'size',
        'content',
        'license',
        'hash',
        'line_mean',
        'line_max',
        'alpha_frac',
        'autogenerated',
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

__lowerCAmelCase	:       Tuple     =	time.time()
ds.push_to_hub(args.tokenized_data_repo)






print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')






