

import os
from datetime import datetime as dt

from github import Github


a__												=						[
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''enhancement''',
    '''new pipeline/model''',
    '''new scheduler''',
    '''wip''',
]



def   __UpperCAmelCase					(      )     ->	List[str]:

					"""simple docstring"""

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

					for issue in open_issues:
										_a      :       Optional[int]   =						sorted(issue.get_comments()						,key=lambda __a      : i.created_at						,reverse=__a      )
										_a      :       Union[str, Any]   =						comments[0] if len(__a      ) > 0 else None
										if (
										    last_comment is not None
										    and last_comment.user.login == "github-actions[bot]"
										    and (dt.utcnow() - issue.updated_at).days > 7
										    and (dt.utcnow() - issue.created_at).days >= 30
										    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
										):
															# Closes the issue after 7 days of inactivity since the Stalebot notification.
															issue.edit(state='''closed'''      )
										elif (
										    "stale" in issue.get_labels()
										    and last_comment is not None
										    and last_comment.user.login != "github-actions[bot]"
										):
															# Opens the issue if someone other than Stalebot commented.
															issue.edit(state='''open'''      )
															issue.remove_from_labels('''stale'''      )
										elif (
										    (dt.utcnow() - issue.updated_at).days > 23
										    and (dt.utcnow() - issue.created_at).days >= 30
										    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
										):
															# Post a Stalebot notification after 23 days of inactivity.
															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/diffusers/blob/main/CONTRIBUTING.md) '''
															    '''are likely to be ignored.'''      )
															issue.add_to_labels('''stale'''      )


if __name__ == "__main__":
			main()






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
			from transformers import TvltImageProcessor

if is_speech_available():
			from transformers import TvltFeatureExtractor

from transformers import TvltProcessor

@require_torch
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							def 							__lowercase							(     self			)       ->       int:
												_a      :       Dict   =						'''ZinengTang/tvlt-base'''
												_a      :       List[str]   =						tempfile.mkdtemp()


							def 							__lowercase							(     self			,				**_a			)       ->       int:
												return TvltImageProcessor.from_pretrained(self.checkpoint			,				**_a			)


							def 							__lowercase							(     self			,				**_a			)       ->       List[Any]:
												return TvltFeatureExtractor.from_pretrained(self.checkpoint			,				**_a			)


							def 							__lowercase							(     self			)       ->       Optional[int]:
												shutil.rmtree(self.tmpdirname			)


							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       Union[str, Any]   =						self.get_image_processor()
												_a      :       Dict   =						self.get_feature_extractor()

												_a      :       Optional[int]   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)
												processor.save_pretrained(self.tmpdirname			)
												_a      :       Any   =						TvltProcessor.from_pretrained(self.tmpdirname			)

												self.assertIsInstance(processor.feature_extractor			,				_a			)
												self.assertIsInstance(processor.image_processor			,				_a			)


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       Optional[Any]   =						self.get_image_processor()
												_a      :       Dict   =						self.get_feature_extractor()

												_a      :       Dict   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)

												_a      :       Union[str, Any]   =						np.ones([1_2_0_0_0]			)

												_a      :       Dict   =						feature_extractor(_a			,				return_tensors='''np'''			)
												_a      :       Tuple   =						processor(audio=_a			,				return_tensors='''np'''			)

												for key in audio_dict.keys():
																	self.assertAlmostEqual(audio_dict[key].sum()			,				input_processor[key].sum()			,				delta=1e-2			)


							def 							__lowercase							(     self			)       ->       int:
												_a      :       Optional[Any]   =						self.get_image_processor()
												_a      :       Union[str, Any]   =						self.get_feature_extractor()

												_a      :       Optional[Any]   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)

												_a      :       List[Any]   =						np.ones([3, 2_2_4, 2_2_4]			)

												_a      :       int   =						image_processor(_a			,				return_tensors='''np'''			)
												_a      :       Optional[int]   =						processor(images=_a			,				return_tensors='''np'''			)

												for key in image_dict.keys():
																	self.assertAlmostEqual(image_dict[key].sum()			,				input_processor[key].sum()			,				delta=1e-2			)


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       int   =						self.get_image_processor()
												_a      :       Union[str, Any]   =						self.get_feature_extractor()

												_a      :       Any   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)

												_a      :       List[str]   =						np.ones([1_2_0_0_0]			)
												_a      :       Optional[int]   =						np.ones([3, 2_2_4, 2_2_4]			)

												_a      :       int   =						processor(audio=_a			,				images=_a			)

												self.assertListEqual(list(inputs.keys()			)			,				['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']			)

												# test if it raises when no input is passed
												with pytest.raises(_a			):
																	processor()




							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       str   =						self.get_image_processor()
												_a      :       Union[str, Any]   =						self.get_feature_extractor()

												_a      :       Dict   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)

												self.assertListEqual(
												    processor.model_input_names			,				image_processor.model_input_names + feature_extractor.model_input_names			,				msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''			,				)



