



"""simple docstring"""






import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests






def 					snake_case__       (		__lowerCamelCase    :   Dict     ):
			"""simple docstring"""
			lowerCamelCase__		:		Union[str, Any]		     ={}

			lowerCamelCase__		:		List[Any]		     =job['''started_at''']
			lowerCamelCase__		:		Tuple		     =job['''completed_at''']

			lowerCamelCase__		:		Dict		     =date_parser.parse(__lowerCamelCase     )
			lowerCamelCase__		:		Union[str, Any]		     =date_parser.parse(__lowerCamelCase     )

			lowerCamelCase__		:		Optional[int]		     =round((end_datetime - start_datetime).total_seconds() / 60.0     )

			lowerCamelCase__		:		Any		     =start
			lowerCamelCase__		:		Optional[int]		     =end
			lowerCamelCase__		:		List[str]		     =duration_in_min

			return job_info






def 					snake_case__       (		__lowerCamelCase    :   str							,		__lowerCamelCase    :   List[Any]=None     ):
			"""simple docstring"""
			lowerCamelCase__		:		List[str]		     =None
			if token is not None:
						lowerCamelCase__		:		List[str]		     ={'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'''Bearer {token}'''}

			lowerCamelCase__		:		Optional[Any]		     =f'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
			lowerCamelCase__		:		int		     =requests.get(__lowerCamelCase							,		headers=__lowerCamelCase     ).json()
			lowerCamelCase__		:		List[Any]		     ={}

			try:
						job_time.update({job['''name''']: extract_time_from_single_job(__lowerCamelCase     ) for job in result['''jobs''']}     )
						lowerCamelCase__		:		Union[str, Any]		     =math.ceil((result['''total_count'''] - 100) / 100     )

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

		_lowercase			:			Optional[Any]										=						get_job_time(args.workflow_run_id)
		_lowercase			:			Dict										=						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"]}')









"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_lowercase			:			Any										=						{
    "configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
    "processing_trocr": ["TrOCRProcessor"],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase			:			List[Any]										=						[
		    "TROCR_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TrOCRForCausalLM",
		    "TrOCRPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
		from .processing_trocr import TrOCRProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
		import sys

		_lowercase			:			Tuple										=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




