




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):
      UpperCAmelCase__      :	str    		=   MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
      UpperCAmelCase__      :	int    		=   TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
      def 			lowerCAmelCase__						(				self: Union[str, Any]    ,       UpperCamelCase_: Tuple    ,       UpperCamelCase_: List[Any]    ,       UpperCamelCase_: List[str]   ):
             __lowerCamelCase   =						AudioClassificationPipeline(model=UpperCamelCase_    ,       feature_extractor=UpperCamelCase_   )

             # test with a raw waveform
             __lowerCamelCase   =						np.zeros((3_40_00,)   )
             __lowerCamelCase   =						np.zeros((1_40_00,)   )
             return audio_classifier, [audioa, audio]
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: Any    ,       UpperCamelCase_: Any   ):
             __lowerCamelCase,   __lowerCamelCase   =						examples
             __lowerCamelCase   =						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_   )},
                 ]    ,       )
             __lowerCamelCase   =						audio_classifier(UpperCamelCase_    ,       top_k=1   )
             self.assertEqual(
                 UpperCamelCase_    ,       [
                     {"""score""": ANY(UpperCamelCase_   ), """label""": ANY(UpperCamelCase_   )},
                 ]    ,       )

             self.run_torchaudio(UpperCamelCase_   )
      @require_torchaudio
      def 			lowerCAmelCase__						(				self: Union[str, Any]    ,       UpperCamelCase_: Union[str, Any]   ):
             import datasets

             # test with a local file
             __lowerCamelCase   =						datasets.load_dataset("""hf-internal-testing/librispeech_asr_dummy"""    ,       """clean"""    ,       split="""validation"""   )
             __lowerCamelCase   =						dataset[0]["""audio"""]["""array"""]
             __lowerCamelCase   =						audio_classifier(UpperCamelCase_   )
             self.assertEqual(
                 UpperCamelCase_    ,       [
                     {"""score""": ANY(UpperCamelCase_   ), """label""": ANY(UpperCamelCase_   )},
                     {"""score""": ANY(UpperCamelCase_   ), """label""": ANY(UpperCamelCase_   )},
                 ]    ,       )
      @require_torch
      def 			lowerCAmelCase__						(				self: Any   ):
             __lowerCamelCase   =						"""anton-l/wav2vec2-random-tiny-classifier"""

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

             __lowerCamelCase   =						np.ones((80_00,)   )
             __lowerCamelCase   =						audio_classifier(UpperCamelCase_    ,       top_k=4   )

             __lowerCamelCase   =						[
                 {"""score""": 0.0842, """label""": """no"""},
                 {"""score""": 0.0838, """label""": """up"""},
                 {"""score""": 0.0837, """label""": """go"""},
                 {"""score""": 0.0834, """label""": """right"""},
             ]
             __lowerCamelCase   =						[
                 {"""score""": 0.0845, """label""": """stop"""},
                 {"""score""": 0.0844, """label""": """on"""},
                 {"""score""": 0.0841, """label""": """right"""},
                 {"""score""": 0.0834, """label""": """left"""},
             ]
             self.assertIn(nested_simplify(UpperCamelCase_    ,       decimals=4   )    ,       [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]   )

             __lowerCamelCase   =						{"""array""": np.ones((80_00,)   ), """sampling_rate""": audio_classifier.feature_extractor.sampling_rate}
             __lowerCamelCase   =						audio_classifier(UpperCamelCase_    ,       top_k=4   )
             self.assertIn(nested_simplify(UpperCamelCase_    ,       decimals=4   )    ,       [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]   )
      @require_torch
      @slow
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             import datasets

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

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

             __lowerCamelCase   =						np.array(dataset[3]["""speech"""]    ,       dtype=np.floataa   )
             __lowerCamelCase   =						audio_classifier(UpperCamelCase_    ,       top_k=4   )
             self.assertEqual(
                 nested_simplify(UpperCamelCase_    ,       decimals=3   )    ,       [
                     {"""score""": 0.981, """label""": """go"""},
                     {"""score""": 0.007, """label""": """up"""},
                     {"""score""": 0.006, """label""": """_unknown_"""},
                     {"""score""": 0.001, """label""": """down"""},
                 ]    ,       )




      @require_tf
      @unittest.skip("""Audio classification is not implemented for TF"""   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             pass







def 				lowerCAmelCase_	(     snake_case_     ):
      if n_term == "":
            return []
      _A							:     list			=						[]
      for temp in range(int(snake_case_     )     ):
            series.append(f'''1/{temp + 1}''' if series else """1"""     )
      return series


if __name__ == "__main__":
  _snake_case										=       input("Enter the last number (nth term) of the Harmonic Series")
  print("Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n")
  print(harmonic_series(nth_term))

