



"""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']}""")


import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY


@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class 				lowercase					(		unittest.TestCase   ):
		__lowercase       :       Any	     =				MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     hf_hub_download(
				    repo_id='nateraw/video-demo'				,      filename='archery.mp4'				,      repo_type='dataset'       )
				UpperCamelCase		=     VideoClassificationPipeline(model=A_				,      image_processor=A_				,      top_k=2       )
				UpperCamelCase		=     [
				    example_video_filepath,
				    'https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4',
				]
				return video_classifier, examples



		def __UpperCamelCase ( self				,      A_				,      A_       )  ->					Optional[int]:
				"""simple docstring"""
				for example in examples:
						UpperCamelCase		=     video_classifier(A_       )

						self.assertEqual(
						    A_				,      [
						        {'score': ANY(A_       ), 'label': ANY(A_       )},
						        {'score': ANY(A_       ), 'label': ANY(A_       )},
						    ]				,      )



		@require_torch
		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     'hf-internal-testing/tiny-random-VideoMAEForVideoClassification'
				UpperCamelCase		=     VideoMAEFeatureExtractor(
				    size={'shortest_edge': 10}				,      crop_size={'height': 10, 'width': 10}       )
				UpperCamelCase		=     pipeline(
				    'video-classification'				,      model=A_				,      feature_extractor=A_				,      frame_sampling_rate=4       )

				UpperCamelCase		=     hf_hub_download(repo_id='nateraw/video-demo'				,      filename='archery.mp4'				,      repo_type='dataset'       )
				UpperCamelCase		=     video_classifier(A_				,      top_k=2       )
				self.assertEqual(
				    nested_simplify(A_				,      decimals=4       )				,      [{'score': 0.5199, 'label': 'LABEL_0'}, {'score': 0.4801, 'label': 'LABEL_1'}]				,      )

				UpperCamelCase		=     video_classifier(
				    [
				        video_file_path,
				        video_file_path,
				    ]				,      top_k=2				,      )
				self.assertEqual(
				    nested_simplify(A_				,      decimals=4       )				,      [
				        [{'score': 0.5199, 'label': 'LABEL_0'}, {'score': 0.4801, 'label': 'LABEL_1'}],
				        [{'score': 0.5199, 'label': 'LABEL_0'}, {'score': 0.4801, 'label': 'LABEL_1'}],
				    ]				,      )



		@require_tf
		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				pass
