





"""simple docstring"""
import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version



def 					__lowerCamelCase       (							*a_					:		Optional[int]			,       a_					:		Optional[Union[Dict, Any]] = None			,       a_					:		Any=True			,       a_					:		Optional[int]=2							)      ->				Any:
      from .. import __version__

      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  take_from
      __SCREAMING_SNAKE_CASE       :str		    =  ()
      if not isinstance(args[0]			,       a_							):
            __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  (args,)

      for attribute, version_name, message in args:
            if version.parse(version.parse(a_							).base_version							) >= version.parse(a_							):
                  raise ValueError(
                      f'''The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''''
                      f''' version {__version__} is >= {version_name}'''							)

            __SCREAMING_SNAKE_CASE       :Any		    =  None
            if isinstance(a_			,       a_							) and attribute in deprecated_kwargs:
                  values += (deprecated_kwargs.pop(a_							),)
                  __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  f'''The `{attribute}` argument is deprecated and will be removed in version {version_name}.'''
            elif hasattr(a_			,       a_							):
                  values += (getattr(a_			,       a_							),)
                  __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  f'''The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'''
            elif deprecated_kwargs is None:
                  __SCREAMING_SNAKE_CASE       :Optional[int]		    =  f'''`{attribute}` is deprecated and will be removed in version {version_name}.'''

            if warning is not None:
                  __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  warning + ''' ''' if standard_warn else ''''''
                  warnings.warn(warning + message			,       a_			,       stacklevel=a_							)

      if isinstance(a_			,       a_							) and len(a_							) > 0:
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  inspect.getouterframes(inspect.currentframe()							)[1]
            __SCREAMING_SNAKE_CASE       :Tuple		    =  call_frame.filename
            __SCREAMING_SNAKE_CASE       :Tuple		    =  call_frame.lineno
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  call_frame.function
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  next(iter(deprecated_kwargs.items()							)							)
            raise TypeError(f'''{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'''							)

      if len(a_							) == 0:
            return
      elif len(a_							) == 1:
            return values[0]
      return values










"""simple docstring"""
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  _SCREAMING_SNAKE_CASE(       unittest.TestCase  ):




       @require_torch
       def    _UpperCamelCase					(						self							)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Dict		    =  pipeline(
                 task='''zero-shot-audio-classification'''		,model='''hf-internal-testing/tiny-clap-htsat-unfused'''							)
             __SCREAMING_SNAKE_CASE       :Any		    =  load_dataset('''ashraq/esc50'''							)
             __SCREAMING_SNAKE_CASE       :int		    =  dataset['''train''']['''audio'''][-1]['''array''']
             __SCREAMING_SNAKE_CASE       :Dict		    =  audio_classifier(SCREAMING_SNAKE_CASE__		,candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']							)
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__							)		,[{'''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    _UpperCamelCase					(						self							)      ->				Optional[int]:





             """simple docstring"""






             pass




       @slow
       @require_torch
       def    _UpperCamelCase					(						self							)      ->				Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  pipeline(
                 task='''zero-shot-audio-classification'''		,model='''laion/clap-htsat-unfused'''		,)
             # This is an audio of a dog
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  load_dataset('''ashraq/esc50'''							)
             __SCREAMING_SNAKE_CASE       :Tuple		    =  dataset['''train''']['''audio'''][-1]['''array''']
             __SCREAMING_SNAKE_CASE       :str		    =  audio_classifier(SCREAMING_SNAKE_CASE__		,candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']							)

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

             __SCREAMING_SNAKE_CASE       :Dict		    =  audio_classifier([audio] * 5		,candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']							)
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__							)		,[
                     [
                         {'''score''': 0.9_9_9, '''label''': '''Sound of a dog'''},
                         {'''score''': 0.0_0_1, '''label''': '''Sound of vaccum cleaner'''},
                     ],
                 ]
                 * 5		,)
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  audio_classifier(
                 [audio] * 5		,candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']		,batch_size=5							)
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__							)		,[
                     [
                         {'''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    _UpperCamelCase					(						self							)      ->				List[str]:





             """simple docstring"""






             pass