"""simple docstring"""




import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
_a   :	Optional[int] 					=     logging.get_logger('transformers.models.speecht5')

_a   :	List[str] 					=     {
    'speech_encoder_prenet.layer_norm': 'speecht5.encoder.prenet.feature_projection.layer_norm',
    'speech_encoder_prenet.post_extract_proj': 'speecht5.encoder.prenet.feature_projection.projection',
    'speech_encoder_prenet.pos_conv.0': 'speecht5.encoder.prenet.pos_conv_embed.conv',
    'speech_encoder_prenet.mask_emb': 'speecht5.encoder.prenet.masked_spec_embed',
}
_a   :	Optional[int] 					=     {
    'text_encoder_prenet.encoder_prenet.0': 'speecht5.encoder.prenet.embed_tokens',
    'text_encoder_prenet.encoder_prenet.1.alpha': 'speecht5.encoder.prenet.encode_positions.alpha',
}
_a   :	Tuple 					=     {
    'speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0': 'speecht5.decoder.prenet.layers.0',
    'speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0': 'speecht5.decoder.prenet.layers.1',
    'speech_decoder_prenet.decoder_prenet.0.1': 'speecht5.decoder.prenet.final_layer',
    'speech_decoder_prenet.decoder_prenet.1.alpha': 'speecht5.decoder.prenet.encode_positions.alpha',
    'speech_decoder_prenet.spkembs_layer.0': 'speecht5.decoder.prenet.speaker_embeds_layer',
}
_a   :	List[str] 					=     {
    'speech_decoder_postnet.feat_out': 'speech_decoder_postnet.feat_out',
    'speech_decoder_postnet.prob_out': 'speech_decoder_postnet.prob_out',
    'speech_decoder_postnet.postnet.postnet.0.0': 'speech_decoder_postnet.layers.0.conv',
    'speech_decoder_postnet.postnet.postnet.0.1': 'speech_decoder_postnet.layers.0.batch_norm',
    'speech_decoder_postnet.postnet.postnet.1.0': 'speech_decoder_postnet.layers.1.conv',
    'speech_decoder_postnet.postnet.postnet.1.1': 'speech_decoder_postnet.layers.1.batch_norm',
    'speech_decoder_postnet.postnet.postnet.2.0': 'speech_decoder_postnet.layers.2.conv',
    'speech_decoder_postnet.postnet.postnet.2.1': 'speech_decoder_postnet.layers.2.batch_norm',
    'speech_decoder_postnet.postnet.postnet.3.0': 'speech_decoder_postnet.layers.3.conv',
    'speech_decoder_postnet.postnet.postnet.3.1': 'speech_decoder_postnet.layers.3.batch_norm',
    'speech_decoder_postnet.postnet.postnet.4.0': 'speech_decoder_postnet.layers.4.conv',
    'speech_decoder_postnet.postnet.postnet.4.1': 'speech_decoder_postnet.layers.4.batch_norm',
}
_a   :	Dict 					=     {
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
_a   :	Any 					=     {
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
_a   :	Dict 					=     {
    'encoder.layers.*.self_attn.k_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj',
    'encoder.layers.*.self_attn.v_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj',
    'encoder.layers.*.self_attn.q_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj',
    'encoder.layers.*.self_attn.out_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj',
    'encoder.layers.*.self_attn_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.layer_norm',
    'encoder.layers.*.fc1': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense',
    'encoder.layers.*.fc2': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense',
    'encoder.layers.*.final_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'speecht5.encoder.wrapped_encoder.layer_norm',
    'encoder.pos_emb.pe_k': 'speecht5.encoder.wrapped_encoder.embed_positions.pe_k',
}
_a   :	Any 					=     {
    'decoder.layers.*.self_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj',
    'decoder.layers.*.self_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj',
    'decoder.layers.*.self_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj',
    'decoder.layers.*.self_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj',
    'decoder.layers.*.self_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm',
    'decoder.layers.*.encoder_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj',
    'decoder.layers.*.encoder_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj',
    'decoder.layers.*.encoder_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj',
    'decoder.layers.*.encoder_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj',
    'decoder.layers.*.encoder_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm',
    'decoder.layers.*.fc1': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense',
    'decoder.layers.*.fc2': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense',
    'decoder.layers.*.final_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm',
}
_a   :	Tuple 					=     {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
_a   :	Tuple 					=     {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_a   :	Tuple 					=     {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_a   :	Optional[Any] 					=     []
_a   :	Any 					=     [
    'encoder.version',
    'encoder.layers.*.norm_k.weight',
    'encoder.layers.*.norm_k.bias',
    'decoder.version',
    'decoder.layers.*.norm_k.weight',
    'decoder.layers.*.norm_k.bias',
    'decoder.pos_emb.pe_k',
    'speech_encoder_prenet.embed_positions._float_tensor',
    'text_decoder_prenet.embed_positions._float_tensor',
]
_a   :	Optional[Any] 					=     IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
_a   :	Union[str, Any] 					=     IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
_a   :	List[Any] 					=     IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Tuple    ,_lowerCamelCase		: Union[str, Any]    ,_lowerCamelCase		: Tuple    ,_lowerCamelCase		: str    ,_lowerCamelCase		: List[Any]			) ->   Optional[int]:
  for attribute in key.split("""."""			):
    _lowerCAmelCase		: Optional[Any]						    =  getattr(_lowerCamelCase    ,_lowerCamelCase			)

  if weight_type is not None:
    _lowerCAmelCase		: str						    =  getattr(_lowerCamelCase    ,_lowerCamelCase			).shape
  else:
    _lowerCAmelCase		: int						    =  hf_pointer.shape

  if hf_shape != value.shape:
    raise ValueError(
        f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
        f" {value.shape} for {full_name}"			)

  if weight_type == "weight":
    _lowerCAmelCase		: List[str]						    =  value
  elif weight_type == "weight_g":
    _lowerCAmelCase		: Optional[Any]						    =  value
  elif weight_type == "weight_v":
    _lowerCAmelCase		: Optional[Any]						    =  value
  elif weight_type == "bias":
    _lowerCAmelCase		: Tuple						    =  value
  elif weight_type == "running_mean":
    _lowerCAmelCase		: Union[str, Any]						    =  value
  elif weight_type == "running_var":
    _lowerCAmelCase		: str						    =  value
  elif weight_type == "num_batches_tracked":
    _lowerCAmelCase		: Dict						    =  value
  else:
    _lowerCAmelCase		: List[Any]						    =  value

  logger.info(f"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}."			)





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[int]    ,_lowerCamelCase		: Any			) ->   Dict:
  for key in ignore_keys:
    if key.endswith(""".*"""			):
      if name.startswith(key[:-1]			):
        return True
    elif ".*." in key:
      _lowerCAmelCase    ,			_lowerCAmelCase		: Union[str, Any]						    =  key.split(""".*."""			)
      if prefix in name and suffix in name:
        return True
    elif key in name:
      return True
  return False





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: int    ,_lowerCamelCase		: Union[str, Any]			) ->   str:
  _lowerCAmelCase		: str						    =  []

  if task == "s2t":
    _lowerCAmelCase		: Tuple						    =  hf_model.speechta.encoder.prenet.feature_encoder
    _lowerCAmelCase		: str						    =  MAPPING_S2T
    _lowerCAmelCase		: Union[str, Any]						    =  IGNORE_KEYS_S2T
  elif task == "t2s":
    _lowerCAmelCase		: Tuple						    =  None
    _lowerCAmelCase		: Optional[int]						    =  MAPPING_T2S
    _lowerCAmelCase		: Dict						    =  IGNORE_KEYS_T2S
  elif task == "s2s":
    _lowerCAmelCase		: Dict						    =  hf_model.speechta.encoder.prenet.feature_encoder
    _lowerCAmelCase		: str						    =  MAPPING_S2S
    _lowerCAmelCase		: List[str]						    =  IGNORE_KEYS_S2S
  else:
    raise ValueError(f"Unsupported task: {task}"			)

  for name, value in fairseq_dict.items():
    if should_ignore(_lowerCamelCase    ,_lowerCamelCase			):
      logger.info(f"{name} was ignored"			)
      continue

    _lowerCAmelCase		: Union[str, Any]						    =  False
    if "conv_layers" in name:
      load_conv_layer(
          _lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,hf_model.config.feat_extract_norm == """group"""    ,)
      _lowerCAmelCase		: int						    =  True
    else:
      for key, mapped_key in MAPPING.items():
        # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

        if "*" in key:
          _lowerCAmelCase    ,			_lowerCAmelCase		: Optional[Any]						    =  key.split(""".*."""			)
          if prefix in name and suffix in name:
            _lowerCAmelCase		: List[Any]						    =  suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
        if key in name:
          _lowerCAmelCase		: List[str]						    =  True
          if "*" in mapped_key:
            _lowerCAmelCase		: Optional[Any]						    =  name.split(_lowerCamelCase			)[0].split("""."""			)[-2]
            _lowerCAmelCase		: List[Any]						    =  mapped_key.replace("""*"""    ,_lowerCamelCase			)
          if "weight_g" in name:
            _lowerCAmelCase		: Tuple						    =  """weight_g"""
          elif "weight_v" in name:
            _lowerCAmelCase		: Optional[int]						    =  """weight_v"""
          elif "bias" in name:
            _lowerCAmelCase		: Dict						    =  """bias"""
          elif "weight" in name:
            _lowerCAmelCase		: Dict						    =  """weight"""
          elif "running_mean" in name:
            _lowerCAmelCase		: str						    =  """running_mean"""
          elif "running_var" in name:
            _lowerCAmelCase		: int						    =  """running_var"""
          elif "num_batches_tracked" in name:
            _lowerCAmelCase		: Optional[int]						    =  """num_batches_tracked"""
          else:
            _lowerCAmelCase		: Union[str, Any]						    =  None
          set_recursively(_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase			)
        continue
    if not is_used:
      unused_weights.append(_lowerCamelCase			)

  logger.warning(f"Unused weights: {unused_weights}"			)





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: str    ,_lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: Dict    ,_lowerCamelCase		: Union[str, Any]    ,_lowerCamelCase		: Any			) ->   Optional[int]:
  _lowerCAmelCase		: List[str]						    =  full_name.split("""conv_layers."""			)[-1]
  _lowerCAmelCase		: List[Any]						    =  name.split("""."""			)
  _lowerCAmelCase		: str						    =  int(items[0]			)
  _lowerCAmelCase		: int						    =  int(items[1]			)

  if type_id == 0:
    if "bias" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
        raise ValueError(
            f"{full_name} has size {value.shape}, but"
            f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."			)
      _lowerCAmelCase		: List[Any]						    =  value
      logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."			)
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
        raise ValueError(
            f"{full_name} has size {value.shape}, but"
            f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."			)
      _lowerCAmelCase		: Optional[Any]						    =  value
      logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."			)
  elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
    if "bias" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
        raise ValueError(
            f"{full_name} has size {value.shape}, but"
            f" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."			)
      _lowerCAmelCase		: Any						    =  value
      logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."			)
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
        raise ValueError(
            f"{full_name} has size {value.shape}, but"
            f" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."			)
      _lowerCAmelCase		: List[str]						    =  value
      logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."			)
  else:
    unused_weights.append(_lowerCamelCase			)





@torch.no_grad()
def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Tuple    ,_lowerCamelCase		: Union[str, Any]    ,_lowerCamelCase		: List[Any]    ,_lowerCamelCase		: Any=None    ,_lowerCamelCase		: Optional[Any]=None    ,_lowerCamelCase		: Optional[int]=None    ,) ->   Union[str, Any]:
  if config_path is not None:
    _lowerCAmelCase		: Dict						    =  SpeechTaConfig.from_pretrained(_lowerCamelCase			)
  else:
    _lowerCAmelCase		: Optional[Any]						    =  SpeechTaConfig()

  if task == "s2t":
    _lowerCAmelCase		: List[str]						    =  config.max_text_positions
    _lowerCAmelCase		: Tuple						    =  SpeechTaForSpeechToText(_lowerCamelCase			)
  elif task == "t2s":
    _lowerCAmelCase		: Union[str, Any]						    =  1876
    _lowerCAmelCase		: Tuple						    =  600
    _lowerCAmelCase		: Any						    =  config.max_speech_positions
    _lowerCAmelCase		: Optional[int]						    =  SpeechTaForTextToSpeech(_lowerCamelCase			)
  elif task == "s2s":
    _lowerCAmelCase		: int						    =  1876
    _lowerCAmelCase		: Optional[int]						    =  config.max_speech_positions
    _lowerCAmelCase		: Optional[int]						    =  SpeechTaForSpeechToSpeech(_lowerCamelCase			)
  else:
    raise ValueError(f"Unknown task name: {task}"			)

  if vocab_path:
    _lowerCAmelCase		: List[str]						    =  SpeechTaTokenizer(_lowerCamelCase    ,model_max_length=config.max_text_positions			)

    # Mask token behaves like a normal word, i.e. include the space before it
    _lowerCAmelCase		: int						    =  AddedToken("""<mask>"""    ,lstrip=_lowerCamelCase    ,rstrip=_lowerCamelCase			)
    _lowerCAmelCase		: Union[str, Any]						    =  mask_token
    tokenizer.add_special_tokens({"""mask_token""": mask_token}			)
    tokenizer.add_tokens(["""<ctc_blank>"""]			)

  _lowerCAmelCase		: str						    =  SpeechTaFeatureExtractor()
  _lowerCAmelCase		: Optional[int]						    =  SpeechTaProcessor(tokenizer=_lowerCamelCase    ,feature_extractor=_lowerCamelCase			)
  processor.save_pretrained(_lowerCamelCase			)

  _lowerCAmelCase		: List[str]						    =  torch.load(_lowerCamelCase			)
  recursively_load_weights(fairseq_checkpoint["""model"""]    ,_lowerCamelCase    ,_lowerCamelCase			)

  model.save_pretrained(_lowerCamelCase			)

  if repo_id:
    print("""Pushing to the hub..."""			)
    processor.push_to_hub(_lowerCamelCase			)
    model.push_to_hub(_lowerCamelCase			)


if __name__ == "__main__":
     _a   :	Optional[int] 					=     argparse.ArgumentParser()
     parser.add_argument(
         '--task',
         default='s2t',
         type=str,
         help='Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.',
     )
     parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to fairseq checkpoint')
     parser.add_argument('--vocab_path', default=None, type=str, help='Path to SentencePiece model')
     parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
     parser.add_argument(
         '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
     )
     parser.add_argument(
         '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
     )

     _a   :	Optional[int] 					=     parser.parse_args()
     convert_speechta_checkpoint(
         args.task,
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.vocab_path,
         args.push_to_hub,
     )





"""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




