"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_a   :	Union[str, Any] 					=     {
    'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
    'tokenization_mvp': ['MvpTokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a   :	Any 					=     ['MvpTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a   :	List[Any] 					=     [
         'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MvpForCausalLM',
         'MvpForConditionalGeneration',
         'MvpForQuestionAnswering',
         'MvpForSequenceClassification',
         'MvpModel',
         'MvpPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
     from .tokenization_mvp import MvpTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mvp_fast import MvpTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mvp import (
              MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
              MvpForCausalLM,
              MvpForConditionalGeneration,
              MvpForQuestionAnswering,
              MvpForSequenceClassification,
              MvpModel,
              MvpPreTrainedModel,
          )

else:
     import sys

     _a   :	List[Any] 					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





"""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 		__A							(				unittest.TestCase	):
     _UpperCamelCase							:    str	      =	MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
     _UpperCamelCase							:    Any	      =	TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING






     def 					__A						(	self       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: List[Any]						    =  AudioClassificationPipeline(model=a__       ,  feature_extractor=a__						)

       # test with a raw waveform
       _lowerCAmelCase		: Optional[int]						    =  np.zeros((34000,)						)
       _lowerCAmelCase		: Optional[Any]						    =  np.zeros((14000,)						)
       return audio_classifier, [audioa, audio]






     def 					__A						(	self       ,  a__       ,  a__						):
       _lowerCAmelCase    ,			_lowerCAmelCase		: List[Any]						    =  examples
       _lowerCAmelCase		: List[Any]						    =  audio_classifier(a__						)
       # by default a model is initialized with num_labels=2
       self.assertEqual(
           a__       ,  [
               {"""score""": ANY(a__						), """label""": ANY(a__						)},
               {"""score""": ANY(a__						), """label""": ANY(a__						)},
           ]       ,  )
       _lowerCAmelCase		: Tuple						    =  audio_classifier(a__       ,  top_k=1						)
       self.assertEqual(
           a__       ,  [
               {"""score""": ANY(a__						), """label""": ANY(a__						)},
           ]       ,  )

       self.run_torchaudio(a__						)






     @require_torchaudio
     def 					__A						(	self       ,  a__						):
       import datasets

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






     @require_torch
     def 					__A						(	self						):
       _lowerCAmelCase		: int						    =  """anton-l/wav2vec2-random-tiny-classifier"""

       _lowerCAmelCase		: Optional[Any]						    =  pipeline("""audio-classification"""       ,  model=a__						)

       _lowerCAmelCase		: Any						    =  np.ones((8000,)						)
       _lowerCAmelCase		: List[str]						    =  audio_classifier(a__       ,  top_k=4						)

       _lowerCAmelCase		: List[str]						    =  [
           {"""score""": 0.0_8_4_2, """label""": """no"""},
           {"""score""": 0.0_8_3_8, """label""": """up"""},
           {"""score""": 0.0_8_3_7, """label""": """go"""},
           {"""score""": 0.0_8_3_4, """label""": """right"""},
       ]
       _lowerCAmelCase		: str						    =  [
           {"""score""": 0.0_8_4_5, """label""": """stop"""},
           {"""score""": 0.0_8_4_4, """label""": """on"""},
           {"""score""": 0.0_8_4_1, """label""": """right"""},
           {"""score""": 0.0_8_3_4, """label""": """left"""},
       ]
       self.assertIn(nested_simplify(a__       ,  decimals=4						)       ,  [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]						)

       _lowerCAmelCase		: int						    =  {"""array""": np.ones((8000,)						), """sampling_rate""": audio_classifier.feature_extractor.sampling_rate}
       _lowerCAmelCase		: int						    =  audio_classifier(a__       ,  top_k=4						)
       self.assertIn(nested_simplify(a__       ,  decimals=4						)       ,  [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]						)






     @require_torch
     @slow
     def 					__A						(	self						):
       import datasets

       _lowerCAmelCase		: Optional[Any]						    =  """superb/wav2vec2-base-superb-ks"""

       _lowerCAmelCase		: List[str]						    =  pipeline("""audio-classification"""       ,  model=a__						)
       _lowerCAmelCase		: str						    =  datasets.load_dataset("""anton-l/superb_dummy"""       ,  """ks"""       ,  split="""test"""						)

       _lowerCAmelCase		: Optional[Any]						    =  np.array(dataset[3]["""speech"""]       ,  dtype=np.floataa						)
       _lowerCAmelCase		: List[str]						    =  audio_classifier(a__       ,  top_k=4						)
       self.assertEqual(
           nested_simplify(a__       ,  decimals=3						)       ,  [
               {"""score""": 0.9_8_1, """label""": """go"""},
               {"""score""": 0.0_0_7, """label""": """up"""},
               {"""score""": 0.0_0_6, """label""": """_unknown_"""},
               {"""score""": 0.0_0_1, """label""": """down"""},
           ]       ,  )






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




