



"""simple docstring"""
import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :int						=			{}

						snake_case_ :List[Any]						=			job["""started_at"""]
						snake_case_ :int						=			job["""completed_at"""]

						snake_case_ :str						=			date_parser.parse(_lowercase	)
						snake_case_ :Tuple						=			date_parser.parse(_lowercase	)

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

						snake_case_ :int						=			start
						snake_case_ :Optional[int]						=			end
						snake_case_ :Optional[Any]						=			duration_in_min

						return job_info
def    A_  (			_lowercase,					_lowercase=None	):







						'''simple docstring'''


						snake_case_ :Optional[Any]						=			None
						if token is not None:
												snake_case_ :Optional[Any]						=			{"""Accept""": """application/vnd.github+json""", """Authorization""": f"""Bearer {token}"""}

						snake_case_ :Union[str, Any]						=			f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
						snake_case_ :Optional[int]						=			requests.get(_lowercase,					headers=_lowercase	).json()
						snake_case_ :Optional[Any]						=			{}

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

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

						__a      =				get_job_time(args.workflow_run_id)
						__a      =				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_tokenizers_available,
    is_torch_available,
)


__a      =				{"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				["ReformerTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				["ReformerTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				[
						    "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ReformerAttention",
						    "ReformerForMaskedLM",
						    "ReformerForQuestionAnswering",
						    "ReformerForSequenceClassification",
						    "ReformerLayer",
						    "ReformerModel",
						    "ReformerModelWithLMHead",
						    "ReformerPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_reformer import ReformerTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_reformer_fast import ReformerTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_reformer import (
												    REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ReformerAttention,
												    ReformerForMaskedLM,
												    ReformerForQuestionAnswering,
												    ReformerForSequenceClassification,
												    ReformerLayer,
												    ReformerModel,
												    ReformerModelWithLMHead,
												    ReformerPreTrainedModel,
												)

else:
						import sys

						__a      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
