





from datetime import datetime as dt
import os

from github import Github


_lowerCamelCase   				=			[
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''feature request''',
    '''new model''',
    '''wip''',
]






def 				SCREAMING_SNAKE_CASE__							(       ):
  SCREAMING_SNAKE_CASE__			=      Github(os.environ["""GITHUB_TOKEN"""]    )
  SCREAMING_SNAKE_CASE__			=      g.get_repo("""huggingface/transformers"""    )
  SCREAMING_SNAKE_CASE__			=      repo.get_issues(state="""open"""    )

  for issue in open_issues:
    SCREAMING_SNAKE_CASE__			=      sorted([comment for comment in issue.get_comments()]					,   key=lambda UpperCamelCase__    : i.created_at					,   reverse=_lowercase    )
    SCREAMING_SNAKE_CASE__			=      comments[0] if len(_lowercase    ) > 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()    )
    ):
      # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
      issue.edit(state="""closed"""    )
    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()    )
    ):
      # print(f"Would add stale comment to {issue.number}")
      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/transformers/blob/main/CONTRIBUTING.md) """
          """are likely to be ignored."""    )


if __name__ == "__main__":
 main()






import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow




@is_pipeline_test
@require_torch
class   UpperCamelCase_       (     unittest.TestCase    ):


							@require_torch
							def      _snake_case  (							self    :Dict		)   ->   Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      pipeline(
									    task="""zero-shot-audio-classification"""							,				model="""hf-internal-testing/tiny-clap-htsat-unfused"""		)
									SCREAMING_SNAKE_CASE__			=      load_dataset("""ashraq/esc50"""		)
									SCREAMING_SNAKE_CASE__			=      dataset["""train"""]["""audio"""][-1]["""array"""]
									SCREAMING_SNAKE_CASE__			=      audio_classifier(__A							,				candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]		)
									self.assertEqual(
									    nested_simplify(__A		)							,				[{"""score""": 0.5_0_1, """label""": """Sound of a dog"""}, {"""score""": 0.4_9_9, """label""": """Sound of vaccum cleaner"""}]							,				)


							@unittest.skip("""No models are available in TF"""		)
							def      _snake_case  (							self    :Dict		)   ->   List[str]:



									"""simple docstring"""



									pass


							@slow
							@require_torch
							def      _snake_case  (							self    :Any		)   ->   int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      pipeline(
									    task="""zero-shot-audio-classification"""							,				model="""laion/clap-htsat-unfused"""							,				)
									# This is an audio of a dog
									SCREAMING_SNAKE_CASE__			=      load_dataset("""ashraq/esc50"""		)
									SCREAMING_SNAKE_CASE__			=      dataset["""train"""]["""audio"""][-1]["""array"""]
									SCREAMING_SNAKE_CASE__			=      audio_classifier(__A							,				candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]		)

									self.assertEqual(
									    nested_simplify(__A		)							,				[
									        {"""score""": 0.9_9_9, """label""": """Sound of a dog"""},
									        {"""score""": 0.0_0_1, """label""": """Sound of vaccum cleaner"""},
									    ]							,				)

									SCREAMING_SNAKE_CASE__			=      audio_classifier([audio] * 5							,				candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]		)
									self.assertEqual(
									    nested_simplify(__A		)							,				[
									        [
									            {"""score""": 0.9_9_9, """label""": """Sound of a dog"""},
									            {"""score""": 0.0_0_1, """label""": """Sound of vaccum cleaner"""},
									        ],
									    ]
									    * 5							,				)
									SCREAMING_SNAKE_CASE__			=      audio_classifier(
									    [audio] * 5							,				candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]							,				batch_size=5		)
									self.assertEqual(
									    nested_simplify(__A		)							,				[
									        [
									            {"""score""": 0.9_9_9, """label""": """Sound of a dog"""},
									            {"""score""": 0.0_0_1, """label""": """Sound of vaccum cleaner"""},
									        ],
									    ]
									    * 5							,				)


							@unittest.skip("""No models are available in TF"""		)
							def      _snake_case  (							self    :str		)   ->   Optional[int]:



									"""simple docstring"""



									pass