


"""simple docstring"""

import unittest

import numpy as np

from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torchaudio,
    slow,
)

from .test_pipelines_common import ANY





@is_pipeline_test
class    lowerCamelCase__						(		unittest.TestCase    ):
      a :     str											=		MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
      a :     List[str]											=		TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]      ,       A_	:	Optional[int]      ,       A_	:	Tuple      ,       A_	:	Dict							):






       '''simple docstring'''


       __lowercase									=	AudioClassificationPipeline(model=UpperCamelCase_      ,       feature_extractor=UpperCamelCase_							)

       # test with a raw waveform
       __lowercase									=	np.zeros((3_4_0_0_0,)							)
       __lowercase									=	np.zeros((1_4_0_0_0,)							)
       return audio_classifier, [audioa, audio]
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]      ,       A_	:	List[Any]      ,       A_	:	Dict							):






       '''simple docstring'''


       __lowercase							,		__lowercase									=	examples
       __lowercase									=	audio_classifier(UpperCamelCase_							)
       # by default a model is initialized with num_labels=2
       self.assertEqual(
           UpperCamelCase_      ,       [
               {"""score""": ANY(UpperCamelCase_							), """label""": ANY(UpperCamelCase_							)},
               {"""score""": ANY(UpperCamelCase_							), """label""": ANY(UpperCamelCase_							)},
           ]      ,       )
       __lowercase									=	audio_classifier(UpperCamelCase_      ,       top_k=1							)
       self.assertEqual(
           UpperCamelCase_      ,       [
               {"""score""": ANY(UpperCamelCase_							), """label""": ANY(UpperCamelCase_							)},
           ]      ,       )

       self.run_torchaudio(UpperCamelCase_							)
      @require_torchaudio
      def SCREAMING_SNAKE_CASE_	(		self	:	int      ,       A_	:	Optional[Any]							):






       '''simple docstring'''


       import datasets

       # test with a local file
       __lowercase									=	datasets.load_dataset("""hf-internal-testing/librispeech_asr_dummy"""      ,       """clean"""      ,       split="""validation"""							)
       __lowercase									=	dataset[0]["""audio"""]["""array"""]
       __lowercase									=	audio_classifier(UpperCamelCase_							)
       self.assertEqual(
           UpperCamelCase_      ,       [
               {"""score""": ANY(UpperCamelCase_							), """label""": ANY(UpperCamelCase_							)},
               {"""score""": ANY(UpperCamelCase_							), """label""": ANY(UpperCamelCase_							)},
           ]      ,       )
      @require_torch
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	"""anton-l/wav2vec2-random-tiny-classifier"""

       __lowercase									=	pipeline("""audio-classification"""      ,       model=UpperCamelCase_							)

       __lowercase									=	np.ones((8_0_0_0,)							)
       __lowercase									=	audio_classifier(UpperCamelCase_      ,       top_k=4							)

       __lowercase									=	[
           {"""score""": 0.08_42, """label""": """no"""},
           {"""score""": 0.08_38, """label""": """up"""},
           {"""score""": 0.08_37, """label""": """go"""},
           {"""score""": 0.08_34, """label""": """right"""},
       ]
       __lowercase									=	[
           {"""score""": 0.08_45, """label""": """stop"""},
           {"""score""": 0.08_44, """label""": """on"""},
           {"""score""": 0.08_41, """label""": """right"""},
           {"""score""": 0.08_34, """label""": """left"""},
       ]
       self.assertIn(nested_simplify(UpperCamelCase_      ,       decimals=4							)      ,       [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]							)

       __lowercase									=	{"""array""": np.ones((8_0_0_0,)							), """sampling_rate""": audio_classifier.feature_extractor.sampling_rate}
       __lowercase									=	audio_classifier(UpperCamelCase_      ,       top_k=4							)
       self.assertIn(nested_simplify(UpperCamelCase_      ,       decimals=4							)      ,       [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]							)
      @require_torch
      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       import datasets

       __lowercase									=	"""superb/wav2vec2-base-superb-ks"""

       __lowercase									=	pipeline("""audio-classification"""      ,       model=UpperCamelCase_							)
       __lowercase									=	datasets.load_dataset("""anton-l/superb_dummy"""      ,       """ks"""      ,       split="""test"""							)

       __lowercase									=	np.array(dataset[3]["""speech"""]      ,       dtype=np.floataa							)
       __lowercase									=	audio_classifier(UpperCamelCase_      ,       top_k=4							)
       self.assertEqual(
           nested_simplify(UpperCamelCase_      ,       decimals=3							)      ,       [
               {"""score""": 0.9_81, """label""": """go"""},
               {"""score""": 0.0_07, """label""": """up"""},
               {"""score""": 0.0_06, """label""": """_unknown_"""},
               {"""score""": 0.0_01, """label""": """down"""},
           ]      ,       )






      @require_tf
      @unittest.skip("""Audio classification is not implemented for TF"""							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple							):






       '''simple docstring'''


       pass




"""simple docstring"""

def lowerCAmelCase_      (				UpperCamelCase__	: str				,   UpperCamelCase__	: str ):






 """simple docstring"""







 def get_matched_characters(UpperCamelCase__	: str				,   UpperCamelCase__	: str ) -> str:
  __lowercase									=	[]
  __lowercase									=	min(len(_stra )				,   len(_stra ) ) // 2
  for i, l in enumerate(_stra ):
   __lowercase									=	int(max(0				,   i - limit ) )
   __lowercase									=	int(min(i + limit + 1				,   len(_stra ) ) )
   if l in _stra[left:right]:
    matched.append(UpperCamelCase__ )
    __lowercase									=	f'''{_stra[0:_stra.index(UpperCamelCase__ )]} {_stra[_stra.index(UpperCamelCase__ ) + 1:]}'''

  return "".join(UpperCamelCase__ )

 # matching characters
 __lowercase									=	get_matched_characters(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	get_matched_characters(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	len(UpperCamelCase__ )

 # transposition
 __lowercase									=	(
     len([(ca, ca) for ca, ca in zip(UpperCamelCase__				,   UpperCamelCase__ ) if ca != ca] ) // 2
 )

 if not match_count:
  __lowercase									=	0.0
 else:
  __lowercase									=	(
      1
      / 3
      * (
          match_count / len(UpperCamelCase__ )
          + match_count / len(UpperCamelCase__ )
          + (match_count - transpositions) / match_count
      )
  )

 # common prefix up to 4 characters
 __lowercase									=	0
 for ca, ca in zip(stra[:4]				,   stra[:4] ):
  if ca == ca:
   prefix_len += 1
  else:
   break

 return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(jaro_winkler("hello", "world"))
