






import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests





def 	lowerCAmelCase_		(lowercase__						:       Union[str, Any]       )		->    Optional[int]:


				'''simple docstring'''




				lowerCAmelCase__      			=						{}

				lowerCAmelCase__      			=						job['''started_at''']
				lowerCAmelCase__      			=						job['''completed_at''']

				lowerCAmelCase__      			=						date_parser.parse(lowercase__       )
				lowerCAmelCase__      			=						date_parser.parse(lowercase__       )

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

				lowerCAmelCase__      			=						start
				lowerCAmelCase__      			=						end
				lowerCAmelCase__      			=						duration_in_min

				return job_info





def 	lowerCAmelCase_		(lowercase__						:       Optional[int]     ,					lowercase__						:       str=None       )		->    List[Any]:


				'''simple docstring'''




				lowerCAmelCase__      			=						None
				if token is not None:
								lowerCAmelCase__      			=						{'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'Bearer {token}'}

				lowerCAmelCase__      			=						f'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
				lowerCAmelCase__      			=						requests.get(lowercase__     ,					headers=lowercase__       ).json()
				lowerCAmelCase__      			=						{}

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

								for i in range(lowercase__       ):
												lowerCAmelCase__      			=						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__":
			_UpperCAmelCase			:		Union[str, Any]   				=    argparse.ArgumentParser()
			# Required parameters
			parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
			_UpperCAmelCase			:		Optional[Any]   				=    parser.parse_args()

			_UpperCAmelCase			:		List[Any]   				=    get_job_time(args.workflow_run_id)
			_UpperCAmelCase			:		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"]}''')













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCAmelCase			:		List[Any]   				=    {
    "configuration_mobilenet_v2": [
        "MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileNetV2Config",
        "MobileNetV2OnnxConfig",
    ],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		str   				=    ["MobileNetV2FeatureExtractor"]
			_UpperCAmelCase			:		Union[str, Any]   				=    ["MobileNetV2ImageProcessor"]


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Any   				=    [
			    "MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "MobileNetV2ForImageClassification",
			    "MobileNetV2ForSemanticSegmentation",
			    "MobileNetV2Model",
			    "MobileNetV2PreTrainedModel",
			    "load_tf_weights_in_mobilenet_v2",
			]


if TYPE_CHECKING:
			from .configuration_mobilenet_va import (
			    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    MobileNetVaConfig,
			    MobileNetVaOnnxConfig,
			)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
						from .image_processing_mobilenet_va import MobileNetVaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mobilenet_va import (
						    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MobileNetVaForImageClassification,
						    MobileNetVaForSemanticSegmentation,
						    MobileNetVaModel,
						    MobileNetVaPreTrainedModel,
						    load_tf_weights_in_mobilenet_va,
						)


else:
			import sys

			_UpperCAmelCase			:		Dict   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





