



'''simple docstring'''

import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    List[Any]      							={}

			snake_case:    Optional[Any]      							=job['started_at']
			snake_case:    Dict      							=job['completed_at']

			snake_case:    Tuple      							=date_parser.parse(__UpperCAmelCase					)
			snake_case:    int      							=date_parser.parse(__UpperCAmelCase					)

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

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

			return job_info







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase=None					)				->					Union[str, Any]:





			"""simple docstring"""







			snake_case:    List[Any]      							=None
			if token is not None:
						snake_case:    str      							={'Accept': 'application/vnd.github+json', 'Authorization': f'''Bearer {token}'''}

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

			try:
						job_time.update({job['name']: extract_time_from_single_job(__UpperCAmelCase					) for job in result['jobs']}					)
						snake_case:    str      							=math.ceil((result['total_count'] - 1_00) / 1_00					)

						for i in range(__UpperCAmelCase					):
									snake_case:    Dict      							=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__":
	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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a							   =				{
    'configuration_owlvit': [
        'OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'OwlViTConfig',
        'OwlViTOnnxConfig',
        'OwlViTTextConfig',
        'OwlViTVisionConfig',
    ],
    'processing_owlvit': ['OwlViTProcessor'],
}


try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							   =				['OwlViTFeatureExtractor']
	a							   =				['OwlViTImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							   =				[
	    'OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'OwlViTModel',
	    'OwlViTPreTrainedModel',
	    'OwlViTTextModel',
	    'OwlViTVisionModel',
	    'OwlViTForObjectDetection',
	]

if TYPE_CHECKING:
	from .configuration_owlvit import (
	    OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    OwlViTConfig,
	    OwlViTOnnxConfig,
	    OwlViTTextConfig,
	    OwlViTVisionConfig,
	)
	from .processing_owlvit import OwlViTProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_owlvit import OwlViTFeatureExtractor
		from .image_processing_owlvit import OwlViTImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_owlvit import (
		    OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OwlViTForObjectDetection,
		    OwlViTModel,
		    OwlViTPreTrainedModel,
		    OwlViTTextModel,
		    OwlViTVisionModel,
		)

else:
	import sys

	a							   =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




