





import os
from datetime import datetime as dt

from github import Github


lowerCAmelCase_    =				[
    '''good first issue''',
    '''feature request''',
    '''wip''',
]






def      lowerCamelCase_						(				)		->						List[Any]:






				"""simple docstring"""


				snake_case_						:    List[str]				=  Github(os.environ['''GITHUB_TOKEN'''] )
				snake_case_						:    Any				=  g.get_repo('''huggingface/accelerate''' )
				snake_case_						:    Tuple				=  repo.get_issues(state='''open''' )

				for issue in open_issues:
								snake_case_						:    Tuple				=  sorted([comment for comment in issue.get_comments()] ,				key=lambda _UpperCamelCase : i.created_at ,				reverse=_UpperCamelCase )
								snake_case_						:    Any				=  comments[0] if len(_UpperCamelCase ) > 0 else None
								snake_case_						:    List[str]				=  dt.utcnow()
								snake_case_						:    Optional[int]				=  (current_time - issue.updated_at).days
								snake_case_						:    Optional[Any]				=  (current_time - issue.created_at).days
								if (
								    last_comment is not None
								    and last_comment.user.login == "github-actions[bot]"
								    and days_since_updated > 7
								    and days_since_creation >= 30
								    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
								):
												# Close issue since it has been 7 days of inactivity since bot mention.
												issue.edit(state='''closed''' )
								elif (
								    days_since_updated > 23
								    and days_since_creation >= 30
								    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
								):
												# Add stale comment
												issue.create_comment(
												    '''This issue has been automatically marked as stale because it has not had '''
												    '''recent activity. If you think this still needs to be addressed '''
												    '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
												    '''[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) '''
												    '''are likely to be ignored.''' )


if __name__ == "__main__":
	main()







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 					__lowerCAmelCase       (			unittest.TestCase     ):
			lowerCamelCase_     :     Any       			=      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    Any				=  hf_hub_download(
							    repo_id='''nateraw/video-demo'''					,							filename='''archery.mp4'''					,							repo_type='''dataset'''    )
							snake_case_						:    List[Any]				=  VideoClassificationPipeline(model=__magic_name__					,							image_processor=__magic_name__					,							top_k=2    )
							snake_case_						:    str				=  [
							    example_video_filepath,
							    '''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
							]
							return video_classifier, examples






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__    )     ->   Any:


							'''simple docstring'''



							for example in examples:
											snake_case_						:    Union[str, Any]				=  video_classifier(__magic_name__    )

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






			@require_torch
			def 			lowerCamelCase  (self    )     ->   str:


							'''simple docstring'''



							snake_case_						:    Any				=  '''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
							snake_case_						:    str				=  VideoMAEFeatureExtractor(
							    size={'''shortest_edge''': 10}					,							crop_size={'''height''': 10, '''width''': 10}    )
							snake_case_						:    int				=  pipeline(
							    '''video-classification'''					,							model=__magic_name__					,							feature_extractor=__magic_name__					,							frame_sampling_rate=4    )

							snake_case_						:    List[str]				=  hf_hub_download(repo_id='''nateraw/video-demo'''					,							filename='''archery.mp4'''					,							repo_type='''dataset'''    )
							snake_case_						:    Union[str, Any]				=  video_classifier(__magic_name__					,							top_k=2    )
							self.assertEqual(
							    nested_simplify(__magic_name__					,							decimals=4    )					,							[{'''score''': 0.5_199, '''label''': '''LABEL_0'''}, {'''score''': 0.4_801, '''label''': '''LABEL_1'''}]					,							)

							snake_case_						:    int				=  video_classifier(
							    [
							        video_file_path,
							        video_file_path,
							    ]					,							top_k=2					,							)
							self.assertEqual(
							    nested_simplify(__magic_name__					,							decimals=4    )					,							[
							        [{'''score''': 0.5_199, '''label''': '''LABEL_0'''}, {'''score''': 0.4_801, '''label''': '''LABEL_1'''}],
							        [{'''score''': 0.5_199, '''label''': '''LABEL_0'''}, {'''score''': 0.4_801, '''label''': '''LABEL_1'''}],
							    ]					,							)






			@require_tf
			def 			lowerCamelCase  (self    )     ->   Optional[int]:


							'''simple docstring'''



							pass
