





"""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






"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_       =		logging.get_logger(__name__)

lowerCamelCase_       =		{
    "google/vivit-b-16x2-kinetics400": (
        "https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  '''vivit'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__=2_24		,SCREAMING_SNAKE_CASE__=32		,SCREAMING_SNAKE_CASE__=[2, 16, 16]		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=7_68		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=30_72		,SCREAMING_SNAKE_CASE__="gelu_fast"		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=1E-06		,SCREAMING_SNAKE_CASE__=True		,**SCREAMING_SNAKE_CASE__		,)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  hidden_size
             __SCREAMING_SNAKE_CASE       :List[str]		    =  num_hidden_layers
             __SCREAMING_SNAKE_CASE       :List[str]		    =  num_attention_heads
             __SCREAMING_SNAKE_CASE       :Any		    =  intermediate_size
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  hidden_act
             __SCREAMING_SNAKE_CASE       :int		    =  hidden_dropout_prob
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE       :Dict		    =  initializer_range
             __SCREAMING_SNAKE_CASE       :Dict		    =  layer_norm_eps

             __SCREAMING_SNAKE_CASE       :Any		    =  image_size
             __SCREAMING_SNAKE_CASE       :Any		    =  num_frames
             __SCREAMING_SNAKE_CASE       :Any		    =  tubelet_size
             __SCREAMING_SNAKE_CASE       :Tuple		    =  num_channels
             __SCREAMING_SNAKE_CASE       :List[str]		    =  qkv_bias

             super().__init__(**SCREAMING_SNAKE_CASE__							)